esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study Analysis  
The research focused on analyzing historical data from the database of a comprehensive energy system in a northern industrial park in China, including electricity, gas, cooling, heating, and hydrogen. The conventional load curves were obtained using clustering analysis. The industrial park comprises WT (Penglai Dajin Offshore Heavy Industry Co., Qingdao, China), CCHP systems (Liebherr, Qingdao, China), EC, GB, (Moon Environment Technology Co., Qingdao, China) and P2G-CCS equipment (Cockerill Jingli Hydrogen; Anhui CO\({}_{2}\) CAP&CONV Technology Co., LTD., Qingdao, China). The system's structure diagram is depicted in Figure 1, the primary equipment parameters are presented in Table 1, and the parameters of the energy storage devices are listed in Table 2. The analysis is performed using a 24 h scheduling cycle, taking into account seven operating scenarios to verify the efficiency and logic of the scheduling model. Moreover, the size and capacity of the equipment are uniform across all scenarios.  
Scenario 1: Gray hydrogen is the only hydrogen source, excluding demand response and CCS.  
Scenario 2: Gray hydrogen is the hydrogen source, considering price-responsive demand, without considering CCS.  
Scenario 3: Gray hydrogen is the hydrogen source, considering incentive-driven demand response, without considering CCS.  
Scenario 4: Gray hydrogen is the hydrogen source, considering both price-responsive and incentive-driven demand response, without considering CCS.  
Scenario 5: Gray hydrogen is the hydrogen source, considering both price-responsive and incentive-driven demand response, along with CCS.  
Scenario 6: Green hydrogen produced solely through P2G is the hydrogen source, considering both price-responsive and incentive-driven demand response, along with CCS.  
Scenario 7: Both gray and green hydrogen are the hydrogen sources, considering both price-responsive and incentive-driven demand response, along with CCS.
# Biochar from Agro-Forest Residue: Application Perspective Based on Decision Support Analysis
## 3 Results and Discussion  
### Biochar Yield  
The relative mass yield of the biochar, obtained in the present work, and the relevant literature data are summarized in Table 2.  
The results expressed in % were obtained using Equation (5):  
\[Y_{m}=\frac{m_{biochar,fin}}{m_{biomass,init}}\cdot 100\% \tag{5}\]  
where \(Y_{m}\) is the relative biochar yield, \(m_{biochar,fin}\) is the measured mass of the biochar, and \(m_{biomass,init}\) is the initial mass of the biomass residue prior to the carbonization process.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of** & **Colza** & **Cherry Stones** & **Peach Stones** & **Grape Pomace** & **Softwood with** & **Sunflower** \\
**Biomass** & & & & & & **Bark** & **Husks** \\ \hline \multicolumn{6}{c}{Biochar derived from different feedstock} \\ \hline Biomass (g) & 1.05 & 1.06 & 1.05 & 1.06 & 1.05 & 1.03 \\ \hline Biochar (g) & 0.30 & 0.31 & 0.34 & 0.37 & 0.28 & 0.33 \\ \hline Biochar yield (\%), present work & 28.66 \(\pm\) 0.14 & 29.24 \(\pm\) 0.73 & 32.26 \(\pm\) 1.32 & 35.07 \(\pm\) 0.40 & 26.35 \(\pm\) 1.09 & 31.48 \(\pm\) 0.77 \\ \hline Biochar yield (\%), & 28–30 [36] & 27.50 [14] & 33.56 [11] & 39.68 [37] & 27.00 [38] & 31.59 [39] \\ \hline \multicolumn{6}{c}{EDS analysis} \\ \hline Carbon (\%) & 78.67 & 76.71 & 76.17 & 73.94 & 82.62 & 65.42 \\ \hline Oxygen (5) & 15.75 & 14.14 & 19.41 & 17.08 & 16.66 & 19.17 \\ \hline Ash (\%) & 5.58 & 9.14 & 13.32 & 8.99 & 0.73 & 15.41 \\ \hline \multicolumn{6}{c}{K (10.05) \(>\) Si} \\ \multicolumn{6}{c}{\begin{tabular}{c} K (2.88) \(>\) Ca \\ (2.35) \(>\) Na \\ order of ash \\ (0.16) \(>\) Mg \\ component (\%) \\ \end{tabular} } & \begin{tabular}{c} K (4.30) \(>\) P \\ (0.16) \(>\) Mg \\ (0.15) \(>\) S (0.03) \\ \(>\) P (0.01) \\ \end{tabular} & \begin{tabular}{c} K (4.30) \(>\) P \\ (0.45) \(>\) Al \\ (0.99) \(>\) Si (0.01) \\ (0.02) \\ \end{tabular} & \begin{tabular}{c} K (4.23) \(>\) Ca \\ (0.43) \(>\) Al \\ (0.97) \(>\) P (0.80) \\ \(>\) Fe (0.40) \(>\) Mg (0.32) \(>\) Si \\ (0.28) \(>\) Al \\ (0.04) \(>\) Na \\ (0.02) \\ \end{tabular} & \
\begin{tabular}{c} Ca (8.20) \(>\) K \\ (0.27) \(>\) Mg \\ (0.32) \(>\) Si \\ (0.28) \(>\) Al \\ (0.16) \\ \end{tabular} &
\begin{tabular}{c} Ca (8.20) \(>\) K \\ (0.27) \(>\) K \\ (0.43) \(>\) S (0.42) \\ (0.04) \(>\) Na \\ (0.04) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Yield and EDS analysis of biochar derived from different feedstock.  
The grape pomace showed the highest biochar yield with 35.07 \(\pm\) 0.40%, while the lowest one was derived from the softwood, 26.35 \(\pm\) 1.09%. Similar results were reported in [55; 56]. The presently obtained biochar yield, derived from sunflower husks, softwood, peach and cherry stones, and colza is in agreement with the results from other experiments [18; 21; 53; 56; 57] (Table 2). An observable difference was found only for the grape pomace samples, which was dedicated to the variations in the biomass composition (seeds/skin/stems).
## 4 Results and Discussion
### Model Training and Results  
The ANN training was performed multiple times to check the effectiveness of various combinations of features extracted from the PCG signal. As mentioned, there are a total of 164 features. The training was performed based on harmonics and percussive features. Harmonic features of a PCG are related to the sound of the heartbeat itself, such as the rate and rhythm. Percussive features are related to the timing and intensity of the heartbeat, for example, the timing of systole and diastole. The hypothesis was tested with both the harmonic and percussive features, and it was found that with the current ANN setup, the harmonic-based features performed well with an accuracy between 92.00% and 94.00%. This work tried using PCA and ICA techniques, but the results were not encouraging. Therefore, the approach was changed to analyze the features individually based on their harmonic and percussive components separately. Thus, a manual \"sequential search method\" was used, where features were added or removed from the set one at a time based on the model performance until the optimal set of features was found. Therefore, no specific feature selection method is mentioned in the manuscript.  
Also, Table 9 presents the set of records demonstrated in the confusion matrix for harmonic features (Figure 13).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature Set (Count)** & \begin{tabular}{c} **True** \\ **Positives** \\ \end{tabular} & \begin{tabular}{c} **True** \\ **Negatives** \\ \end{tabular} & \
\begin{tabular}{c} **False** \\ **Positives** \\ \end{tabular} &
\begin{tabular}{c} **False** \\ **Negatives** \\ \end{tabular} \\ \hline All Features & 2776 & 588 & 126 & 95 \\ Chroma & 2760 & 590 & 142 & 93 \\ Chroma CENS & 2830 & 47 & 72 & 636 \\ MFCC & 2780 & 538 & 122 & 145 \\ Statistical & 2747 & 577 & 155 & 106 \\ Chroma and Statistical & 2767 & 562 & 135 & 121 \\ Chroma CENS and Statistical & 2759 & 552 & 143 & 131 \\ MFCC and Statistical & 2774 & 577 & 128 & 106 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results: model validation results of harmonic features.  
Figure 13: Confusion matrix of harmonic features. (**a**) all features, (**b**) chroma, (**c**) Chroma CENS, (**d**) MFCC, (**e**) Statistical, (**f**) chroma and statistical, (**g**) Chroma CENS and statistical, and (**h**) MFCC and statistical.
# PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm
## 4 Evaluation
### Simulation Results  
Figure 3 presents the simulation results of subflow throughput, wherein a comparison is made among the MPTCP-RLDS, MPTCP-round-robin, MPTCP-Fastest-RTT, MPTCP-PDAA3C, and MPTCP-PDASTSGAT algorithms. In the figure, the subflow throughput of the MPTCP-PDAASTSGAT algorithm outperforms the MPTCP-PDAA3C algorithm by 8.6%, outperforms the MPTCP-RLDS algorithm by 8.9%, surpasses the MPTCP-round-robin algorithm by 49.6%, exceeds the MPTCP-Fastest-RTT algorithm by 52.6%, and approaches the theoretically optimal value of throughput. In the multipath transmission simulation experiment, PDASTSGAT selects the optimal path by combining packet information and the overall spatiotemporal characteristics of the links when transmitting data packets. Therefore, MPTCP-PDASTSGAT achieves higher throughput. We utilize the Jain fairness index to compare the fairness of the links [12]. Figure 4 presents a comparison of subflow fairness values among MPTCP-RLDS, MPTCP-round-robin, MPTCP-Fastest-RTT, MPTCP-PDAA3C, and MPTCP-PDASTSGAT. Due to the consideration of spatiotemporal data relationships during data transmission, MPTCP-PDASTSGAT exhibits slightly better subflow fairness compared to the other approaches.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(Path\)**1** & \(Path\)**2** & \(Path\)**3** & \(Path\)**4** \\ \hline Bandwidth & 2 Mbps & 2 Mbps & 4 Mbps & 2 Mbps \\ Delay & 10 ms & 10 ms & 10 ms & 10 ms \\ Packet Loss Rate & & 3\% & & \\ Accept cache & & 128,000 Bytes & & \\ Send cache & & 128,000 Bytes & & \\ Packet size & & 1 kb, 5 kb, 10 kb & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Link performance parameters.  
Figure 3: Comparison of the subflow throughput results of the five algorithms. In our simulation setup, the throughput is sampled at regular intervals of 100 ms, resulting in a subflow selection process occurring at both the sender and receiver sides every 100 ms. The specific interval for capturing subflow throughput can be flexibly configured within the NS3 framework.
## 6 Discussion
### Limitations  
The expert panel for this validation phase comprised five professionals. While a larger sample might provide broader insights, a panel of five experts is often considered acceptable for qualitative feedback and identifying major usability or conceptual issues in formative evaluations of new tools or methodologies, particularly when in-depth interaction and detailed feedback are sought [8; 9]. The aim of this expert evaluation was not to achieve statistical generalization but to gather rich, qualitative data to validate the VR tool's core functionalities and its potential for accessibility assessment. The findings from this panel were intended to inform further iterative development and larger-scale user studies. In addition to previously mentioned limitations, the composition of the expert panel--consisting primarily of academics and technical professionals--represents a gap. While expert validation is crucial for assessing technical feasibility and ensuring that the tool meets fundamental usability and functionality requirements, it lacks direct input from end-users with disabilities. Such direct engagement is essential for capturing the nuanced experiences and specific accessibility needs that only actual users can provide. The current study, therefore, represents a necessary initial phase of validation, establishing foundational effectiveness before more extensive and inclusive user-centered evaluations.  
The study faced both software and hardware limitations. Unreal Engine is optimized for general first-person applications, but documentation on using VR for accessibility evaluations is sparse. Most practical knowledge comes from independent developers. On the hardware side, while basic setups suffice for simple experiences, increasing visual and interactive complexity demands high-end components, making cost a potential barrier. Another limitation was the exclusive focus on physical disability. Although UAH models are designed for all types of disability or reduced mobility, this study did not address cognitive, sensory, or psychosocial disabilities. More comprehensive frameworks are needed to accommodate diverse user needs.  
Finally, the simulation did not address tactile feedback or object-level product design, both of which would enhance immersion. Haptic sensations were limited, reducing physical realism. However, with additional development and time investment, these aspects could be improved, leading to a significantly more realistic and immersive VR experience.
## 4 Mobile Localization Algorithms
### Summary  
As shown in Table 2, we compare the mobile localization algorithms from following aspects:  
* Anchor type: In mobile localization algorithms, almost all anchor nodes are mobile except the stationary surface buoys used in MFALM and SLMP.
* Ranging method: Compared with one-way ToA, two-way ToA and TDoA alleviate the need for time synchronization. However, more message exchange is required and higher energy is consumed.
* Message communication: Unknown nodes stay \"silent\" in DNR, MS-DNR, AUV and MS-AUV to save energy.
* Time synchronization: In one-way ToA, accurate time synchronization is needed to estimate distances between anchor nodes and unknown nodes. Therefore, most algorithms require time synchronization except 3 DUT, AUV and MS-AUV.
* Localization coverage: Most mobile localization algorithms have large localization coverage. However, localization coverage of APS and CLS are limited by the special network model.  
* Localization time: In mobile UWSNs, when tracking unknown nodes or targets (APS, CLS and 3 DUT), localization time should be as short as possible. Longer time used in EERS, MASL and DNR always introduce more energy consumption.
* Localization accuracy: In mobile UWSNs, localization accuracy of most algorithms is not high enough.
* Computational complexity: Computational complexity of mobile localization algorithms are relatively low.
* Energy consumption: Almost all mobile localization algorithms have high energy consumption for target tracking or hierarchical localization (MS-DNR, MS-AUV).
## 3 System Model
### Computation Model
#### 3.3.2 Mobile Edge Cloud Computing  
If mobile device \(n\) choose to offload task \(k\) to the edge cloud, the process contains task transmission and task execution. The total completion time has two parts: (i) task transmission time \(T_{n,k}^{tra}\) (ii) task process time \(T_{n,k}^{pro}\).  
The total latency of \(u_{n,k}\) for mobile-edge computing is obtained by:  
\[T_{n,k}^{c}=T_{n,k}^{tra}+T_{n,k}^{pro}=\frac{s_{k}}{r_{n}}+\frac{w_{k}}{f_{n,k }^{c}} \tag{5}\]  
where \(f_{n,k}^{c}\) denotes the computation resource of the edge cloud assigned to \(u_{n,k}\). Meanwhile, the energy consumption of mobile device \(n\) can be defined as \(E_{n,k}^{c}\), which only consists of the transmission energy consumption while transferring tasks to the edge cloud. The energy consumption of mobile device as shown below:  
\[E_{n,k}^{c}=P_{n}T_{n,k}^{tra}=P_{n}\frac{s_{k}}{r_{n}} \tag{6}\]
## 2 Materials and Methods
### Model Performance
#### 2.2.3 Stage 3: Computing FHC/DAE Measurement  
The task of determining the distance between the FHC and the DAE can be expressed as a mathematical problem. The FHC is represented as a center point, \((x_{c},y_{c})\), while the acetabular contour is defined as a set of points, \(P=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\}\), where each point \((x_{i},y_{i})\) corresponds to a vertex on the acetabular contour, \(C\).  
To calculate the distance, the algorithm identifies the intersection of a horizontal line passing through the center point \((x_{c},y_{c})\) with the acetabular contour, \(C\). This is achieved by iterating through all consecutive pairs of contour points \((x_{i},y_{i})\) and \((x_{i+1},y_{i+1})\) and determining whether \(y=y_{c}\) intersects the segment connecting the points \((x_{i},y_{i})\) and \((x_{i+1},y_{i+1})(x_{\text{intersect}})\). For any segment where this condition is satisfied, the x-coordinate of the intersection point \((x_{\text{intersect}})\) is calculated using linear interpolation (see illustration of the procedure bellow in Figure 5):  
\[x_{\text{intersect}}=x_{i}+\frac{y_{c}-y_{i}}{m} \tag{5}\]  
The slope of the line segment is then computed as:  
\[m=\frac{y_{i+1}-y_{i}}{x_{i+1}-x_{i}} \tag{6}\]  
Figure 5: Illustration of the procedure to compute the distance between the femoral head center (FHC) and the dorsal acetabular edge (DAE).  
Finally, the signed distance between the FHC and the DAE is determined using the following equation:  
\[Signed_{\text{distance}}(mm)=side\cdot(x_{\text{intersect}}-x_{c})\cdot pixels spacing_{x} \tag{7}\]  
where the parameter _side_ indicates the direction of the distance relative to the FHC (Figure 6).
## 2 Materials and Methods
### Specim Preparation  
Samples of each composite were made using a thermo-formed polyurethane mould, replicating the negative shape of a disc. The mould was filled with one uncured composite at a time and, using a transparent strip (Have Neos Dental, Bioggio, Switzerland) pressed on top of the mould, the surface was flattened and uniformed; in addition, microscope glass slides were pressed over and under the mould.  
A light-curing unit (LCU) (LED starlight lamp) was employed, whose power density was measured by a curing radiometer (Model 100, Demetron Research Corp., Danbury, CT, USA, Serial no. 129540) and then set at 400 mW/cm\({}^{2}\).  
The orthodontic adhesive composites have been light-activated as suggested by the producers, applying the LCU at the top and bottom surfaces, with the light tip positioned in contact with the glass plate at a distance of 1.0 mm from the samples.  
Twelve test discs (10.0 mm in diameter and 4.0 mm in thick) obtained from each orthodontic composite were produced for a total of 36 samples (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Orthodontic Composite Resin** & **Manufacturer** & **Composition** & **Acronym** \\ \hline \multirow{3}{*}{Bisco Ortho Bracket Paste LC} & \multirow{3}{*}{Bisco, Schaumburg, IL, USA} & UDMA (5–10\%), TEGDMA (5–10\%), & \\  & & molten silicon (50–75\%). The substances & \\  & & contained in the remaining part of the & \\  & & adhesive are not specified by the supplier & \\ \hline \multirow{3}{*}{Light-Cure Orthodontic Paste} & \multirow{3}{*}{Leone s.p.a., Sesto Fiorentino, FI, Italy} & Bis-GMA, UDMA, TEGDMA, Silica and other inert fillers, catalysts and stabilizers (unknown percentages since not provided by the manufacturer) & \multirow{3}{*}{Light} \\  & & & \\ \hline \multirow{3}{*}{Transbond XTM Light Cure Adhesive} & \multirow{3}{*}{3M Unitek, Monrovia, CA, USA} & Bis-GMA (5–10\%), bis-EMA (10–20\%), & \\  & & TEGDMA (5–10\%), reaction products with quartz (70–80\%), reaction products with dichlorodimethylsinane with silica (\(<\)2\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical compositions of the three light-cured orthodontic adhesive composites considered for the study.  
Figure 1: The test discs obtained from each orthodontic composite resin: (**a**) Light-Cure Orthodontic Paste (Light); (**b**) Transbond XTM Light Cure Adhesive (Trans); (**c**) Bisco Ortho Bracket Paste LC (Bisco).  
To replicate a condition as close as possible to the clinical situations, no finishing and polishing were carried out [21].
## 1 Introduction
Calcium aluminates are cheap and nontoxic compounds; they are basic components of cements called calcium aluminate cements (CAC) [26] and often related to construction materials [27; 28]. They generally provide suitable mechanical properties to the concrete and play a fundamental role in the hydration process of the cements, releasing a high amount of heat during the product hydration. In cement technology, the release of heat is an issue that needs to be avoided, although, for other applications, such as energy storage, this aspect could be a key factor, if properly exploited. From this point of view, the main compound responsible for heat release is the most thermodynamically stable tricalcium aluminate Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\), when it forms the corresponding hexahydrate specie with a standard reaction enthalpy (\(\Delta\)H\({}^{0}\)) equal to \(-\)258.6 kJ/mol [29; 30]. Tricalcium aluminate hexahydrate, Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\):6H\({}_{2}\)O, known as katoite mineral, belongs to the hydrogarnet group [31]. Studies on thermal decomposition [32] reported a two-step process in which the first dehydration takes place at 240-300 \({}^{\circ}\)C and the 1.5-hydrate product is formed. The reaction is reversible; indeed, Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\):1.5H\({}_{2}\)O is used as drying agent due to the high tendency to adsorb water vapor from the surrounding atmosphere (Equation (2)) [33]. At higher temperature, Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\):1.5H\({}_{2}\)O transforms into mayenite (Ca\({}_{12}\)Al\({}_{14}\)O\({}_{33}\)) [34]. \[\text{Ca}_{3}\text{Al}_{2}\text{O}_{6}\cdot\text{6H}_{2}\text{O}_{(\text{s})} \rightleftharpoons\text{Ca}_{3}\text{Al}_{2}\text{O}_{6}\cdot\text{1.5H}_{2} \text{O}_{(\text{s})}+4.5\text{H}_{2}\text{O}_{(\text{g})} \tag{2}\]  
In this work, katoite was prepared via hydration starting from synthetic Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\). In order to achieve, selectively, the Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\) formation, several preparation methods have been investigated, such as co-precipitation and solid-solid reaction. Morphological and structural characterization were necessary to underline the differences between the samples. Therefore, the characterization was carried out on the anhydrous and hydrated batches. Moreover, the materials were analyzed by means of differential scanning calorimetry and thermogravimetry. The coupling of these techniques, in fact, allowed us to determine operating temperature and endothermic heat associated with the dehydration process. These studies were useful to highlight how the preparation process affects the features of the thermochemical storage material. Finally, the stability during low-term operating cycles was studied through consecutive dehydration/hydration reactions.
## 1 Introduction
[PERSON] and [PERSON] [14] proposed a classification model using extreme gradient boosting (XGBoost) as the classifier for predicting second primary cancers in women with breast cancer. MARS, SVM, ELM, RF, and XGBoost methods have achieved good performances for constructing effective predictive models of cancer. In addition, gene expression classifiers are currently being used in therapeutic intervention. [PERSON] et al. [15] developed ColoPrint prediction method for improving prognostic accuracy independent of microsatellite status. [PERSON] et al. [16] developed cancer hallmark-based gene signature sets (CSS sets) for prognostic predictions and for facilitating the identification of patients with stage II colorectal cancer. Since they are based on different concepts, involving nonparametric statistics, statistical learning, a neural network, and a decision tree, to make effective algorithms with different characteristics for cancer data analysis, they are used in this study for analyzing SPCs in colorectal cancer. Over the last two decades, cancer registration databases have been used to store records related to the treatment of colorectal cancer patients. Indeed, a vast network of useful information is hidden in these collected datasets. Although traditional data query and statistical functions can be utilized, such as prediction of primary colorectal cancer by National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) data [17]. Statistic methods such as the chi-square test and Student's t-test were used in univariate statistical analysis, and the Kaplan-Meier method and log-rank test are used for continued variables in traditional medical articles [18; 19]. It is not easy to find unknown information features in practice, and information about their potential value cannot be directly observed from the dataset. As such, how to explore hidden, unknown, and valuable information from SPC databases through specific procedures and methods is an important research topic that aims to improve prevention and treatment strategies for colorectal cancer survivors. In this study, we used five machine learning techniques to develop a predictive model for the SPC among colorectal cancer patients. The model can be used to identify various analyzable risk factors and clinical features within primary tumors, providing decision support for intensive treatments such as adjuvant chemotherapy, or aggressive following up procedures, such as positron emission tomography (PET).