esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methodology
### Annual Energy Production and Capacity Factor Calculations  
The ultimate goal of the wind resource estimation method presented in this paper is to get the Annual Energy Productions (AEP) and Capacity Factors (CF) of various possible installations. This is meant to help decide the characteristics of the installation, such as the nominal power per absorbed square meter, which is the characteristic studied here. The AEP also serves to get an estimate of the economic viability and the carbon emission savings of an installation.  
These two variables were calculated for a total of 36 scenarios that arise from the combination of different possibilities regarding different aspects of the installation:  
* The rated potency per 1 m\({}^{2}\) of transferal area, which can be 100, 150, 200, or 250 W, as explained in Section 2.5.
* The correction of wind speed, which can be increased by 1.28, 2.34, or 3.50, as explained in Section 2.4.1.
* The effect of the PAGV, which gives three possible cases named C1, C2, and C3, as explained in Section 2.5.1.  
To calculate the AEP, first, every wind speed value in which corresponding wind direction does not fall into the absorption range of the selected facade is set to zero. In the case of the measured wind speed series, the measured wind direction is used for this, whereas, for the calibrated wind speed series, the predicted wind direction is used. Then, after applying the corresponding augmenting factor, each wind speed value is used to calculate the generated power for each power curve. In this study, since we are using three cases regarding the effect of the PAGV and 4 possible rated powers, we use a total of 12 power curves. All these calculations are performed using hourly wind speed time series, so the value of the generated power expressed in \(kW\) for each data point is equal to the value of the generated energy in \(kWh\). Finally, the generated energy for each hour is summed and then normalized to get the AEP. The CF is calculated by dividing the AEP by the energy the turbine would generate if it was working at the rated power for a whole year.  
First, only the AEP is calculated with the measured and predicted wind speeds and directions of the testing data set. This will help select the type of quantile mapping that gets a energy production estimation closer to that calculated with the measurements. Then, the AEP and CF will be calculated with the reanalysis data covering 10 years, using the wind speed time series from the calibration that proves to be best and the predicted wind direction. This will serve to select the best rated power and to get an energy production estimation.
## 2 Literature Review
### Recent Advancements  
Several recent methods have achieved real-time or near-real-time surface reconstruction using learning-based techniques, yet they remain unsuitable for dynamic excavation environments. NeuralRecon reconstructs 3D geometry from monocular video by fusing depth across sequential frames, but it assumes a static scene and smooth camera motion--conditions rarely met during excavation, where the geometry changes abruptly [24]. Co-SLAM combines neural representations with traditional SLAM for high-fidelity RGB-D mapping, but it also depends on temporal consistency and static backgrounds, making it vulnerable to occlusions and terrain changes typical of excavation [25]. Meanwhile, Neural Surface Reconstruction of Dynamic Scenes models non-rigid deformation from RGB-D input, but its computational cost and sensitivity to topological changes limit its ability to capture the fast, discontinuous transformations of soil and debris [26]. These methods, though advanced, are not designed for single-shot, real-time volumetric reconstruction in highly dynamic, unstructured outdoor settings.
## 4 Discussion
### Limitations  
First, the sample was limited to active participants aged 19 to 30 who engaged in university sports. Future research should confirm the concurrent validity of VOT-NIRS data in other populations, such as elite athletes and various sports, as well as older adults or those with chronic diseases, which could significantly affect resting SmO\({}_{2}\) and HRV data. Additionally, this cross-sectional study requires more exhaustive fatigue monitoring, such as using electromyography or tensionyography markers, to better understand accumulated fatigue over time. To allow VOT-NIRS to identify chronic fatigue and overtraining, it is essential to monitor acute fatigue phases resulting from training over several weeks and associate them with physical performance markers. Another limitation is the variation in VOT-NIRS protocols, ranging from 30 s to 5 min, with 5 min being the most commonly used [3, 63]. We recognize the importance of achieving an ischemic period to activate vasodilatory mechanisms in the muscle and observe the metabolic response. However, similar to other studies, we prefer a 3 min protocol, which is highly correlated with the 5 min period (r = 0.90) [63]. The shorter occlusion time reduces discomfort and the burden on participants from suprasystolic pressures (>220 mmHg) in the vastus lateralis muscle [5]. Another limitation is that NIRS can be sensitive to skin pigmentation, and the data can vary depending on adiposity. These technological errors can be minimized with periodic evaluations of the subjects. Finally, although feedback was provided to participants regarding maintaining habits during measurements, factors such as sleep monitoring, hydration status, and psychological stress--each directly influenced by fatigue--should be addressed more rigorously.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Space
#### 3.1.1 Space Protocols  
To ensure reliable communication in space exploration, space protocols encompass a range of specialized protocols for interplanetary, satellite, and space-based systems. These protocols are designed to address the unique challenges of space environments, including long communication delays, intermittent connectivity, and high error rates. Some important protocols for space-related applications are listed below:  
* _Bundle Protocol (BP)_ and derivatives: One of the most popular and widely implemented protocols, also highlighted in Section 2, which influenced subsequent protocols, e.g., _(B-)DTN7_[15; 16]--a BP7 implementation in Golang and _IBR-DTN_[17; 18] based on BP6.
* _Licklider Transmission Protocol (LTP)_[19]: Operates asynchronously by focusing on reliable transmission through segmentation, storage, reassembly, and congestion control of data packets in DTN environments [20; 21; 22].
* _Advanced Orbiting Systems (AOSs) Space Data Link Protocol_[23]: Includes mechanisms for error detection and correction and link synchronization between spacecrafts and GS.
* _ION-DTN_[24]: A DTN protocol suite/infrastructure suitable for interplanetary flight mission systems. For instance, ION CCSDS File Delivery Protocol (CFDP) [25], which focuses on file transmission among DTN nodes.
* _QUILC Protocol_ and derivatives (e.g., _QUILC_[26]): Although primarily designed for use over traditional internet connections to provide low-latency communication over unreliable networks [27], it gained attention in the satellite domain.
* _Delay-Tolerant Payload Conditioning (DTPC) Protocol_[28]: Adding an application-independent protocol layer to provide end-to-end transport services to the ION-DTN implementation.
* _Deep Space Transport Protocol (DS-TP)_[29]: Includes novel proactive transmission and retransmission scheduling rules.  
* _Space Packet Protocol (SPP)_[30]: Provide unidirectional space data transfer service containing an application process identifier to identify packet streams.
* _Encapsulation Packet Protocol (EPP)_[31]: Encapsulate higher-layer protocol data units using Space Data Link protocols without authorized packet version Numbers over space links.
* Routing Protocols: _Spray and Wait_[32; 33], _Contact Graph_[34; 35], Epidemic, Probabilistic, Social-Based.
# Energy Management Integrated Circuit for Multi-Source Energy Harvesters in WBAN Applications
## 3 Energy Management Integrated Circuit (EMIC) Structure
### Energy Management with a Single Harvester
By adopting dual output paths, a wide range of input energy can be handled.
## 4 Application--Results
### Energy Mix by 2030 and 2050  
By 2030 and 2050, considering the developments and assumptions mentioned above, the final energy mix could be formed as depicted in Figure 6.  
The estimated nominal output of hydro-pumped storage units reaches 1500 MW for the reference year 2030. The capacity factor of pumped-storage units, expressed by the  
Figure 6: Energy share by technology for reference years 2030 and 2050.  
rated power of the turbines, reaches 19%. This value of capacity factor is below the lower acceptable limit which renders the investment economically viable (a benchmark of the capacity factor could be 25% [6]). In this study, this value is considered acceptable, since, according to current data for Greece, large hydro units' capacity factor varies to similar levels (15-17%); therefore, this value could also be considered for reverse hydro-pumped storage projects. The capacity factor of hydropower units may be lower than benchmark, because they are used as a safety net, for cases of emergency, so they are over-dimensioned in order to be able to support the system when a deficit occurs.  
The energy contribution of wind and PV installations reaches 49%, and the energy contribution of conventional units is lowered to 31%. The electricity sector is characterized by a higher renewable share, and electricity production is partially decarbonized, compared to the current situation. Hydro-pumped storage units contribute with 6% to the annual electricity demand, by exploiting 70% of curtailed energy.  
In 2050, the installed capacity of wind and photovoltaic installations is considered to be 24 GW in total, which results in a reduction of the conventional power plants energy contribution to 25%. Renewable energy sources possess the highest share of production, accounting for approximately 60%. The energy surplus increases due to the higher integration of renewables, and the hydro-pumped storage capacity is estimated at 2700 MW. The share of hydro-pumped storage projects reaches 7% of the total energy produced. The capacity factor of hydro-pumped storage units is estimated at 23%. This higher capacity factor could be attributed to the higher integration of RES, which results in the increase of curtailed power. Consequently, the higher integration of RES contributes to the enhancement of the economically viable operation of hydro-pumped storage units.
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 5 Case Study
### Decomposition Results by EEMD  
In order to improve the overall forecasting performance, this paper firstly adopted EEMD to decompose the original wind speed series of the three cases separately, and the decomposition results are listed in Figure 6. It is obvious that each wind speed series is decomposed into 13 components which are respectively denoted by IMF\({}_{1}\), IMF\({}_{2}\),..., IMF\({}_{12}\) and R from top to bottom.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Forecasting Models** & **Parameters** & **Number or Type** \\ \hline \multirow{3}{*}{BPNN model} & Number of neurons in the hidden layer & 20 \\  & Learning rate of training & 0.001 \\  & Training target & 0.00001 \\ \hline \multirow{3}{*}{SVM model} & Type of svm & epsilon-SVR \\  & Type of kernel function & linear kernel function \\ \hline \multirow{3}{*}{LSTMNN model} & Number of neurons in the LSTM layer & 20 \\  & Type of activation function of the output layer & tanh \\ \cline{1-1}  & Optimizer & adam \\ \cline{1-1}  & Learning rate & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings of wind speed forecasting model.  
Figure 6: _Cont._Figure 6: The decomposition results of wind speed series: (a) Case A; (b) Case B; (c) Case C.
## 5 Discussion
### Late Attention Layer Analysis  
The first two rows in Figure 8 highlight the late Conv layers, where feature maps become highly selective and abstract. These layers capture distinct, high-level representations that embody the unique sound signatures of each instrument. The network has refined its focus to specific frequency and amplitude regions, isolating instrument-specific characteristics like the spectral textures unique to trumpet and bass. The sparse yet sharply defined patterns in these feature maps indicate that the model has distilled the input into concentrated features necessary for final classification. This layer's focus on highly abstract, distinguishing features reflects the network's advanced ability to identify each instrument's unique sound signature.  
In the height and width attention maps of the late layers (third and fourth rows), we see a strong, focused emphasis on select frequency and time segments. The height attention maps isolate frequency regions that contribute most to the tonal identities of trumpet and bass, likely capturing Chroma components that characterize the harmonic profile of each instrument. The width attention maps display precise temporal focus, essential for capturing articulation and rhythmic qualities that differentiate the instruments. This advanced focus on time and frequency allows the model to highlight key sound features, capturing the unique temporal dynamics and harmonic content crucial for final instrument classification.  
In the final row, the channel attention maps show the model's ability to focus on abstracted, high-level channel features. These maps reveal selective emphasis on specific channels, which are likely to correspond to spectral regions that capture the most informative cues for instrument identification. By isolating and concentrating on these channels, the network leverages the most relevant aspects of the combined Log-Mel and scaled CST features. This channel attention helps synthesize frequency and temporal information into a cohesive representation, enabling the model to accurately distinguish between complex instrumental sounds, completing the classification process with a robust, high-level understanding of each instrument.  
Figure 8: Late feature and attention maps for a trumpet and bass sample, illustrating high-level abstracted representations formed by deeper layers. The Log-Mel spectrogram and scaled CST features at 1/4 size contribute to an enriched final classification layer, enhancing the recognition of instrument-specific patterns through focused, high-contrast attention maps. The grayscale shades represent values in the feature maps and attention maps, where darker shades indicate lower values, and lighter shades indicate higher values, providing a visual representation of the extracted features.
# Effect of Polycarboxylate Admixture on the Performance of Fluorogypsum-Based Self-Leveling Material
###### Abstract  
The study explores the influence of polycarboxylate admixture (PCE) on fluorogypsum-based self-leveling material (FSLM) performance. We conducted an array of tests to assess workability and mechanical properties, and utilized XRD, TG-DSC, SEM, and MIP techniques for microscopic analysis. The fresh state results showed that PCE enables FSLM to achieve good workability at lower water demand. It was found that PCE partially inhibited plaster hydration from anhydrite to dihydrate, as observed by X-ray diffraction analysis and thermogravimetric analysis, and with the increasing in PCE, the mass loss of samples reached 15.66% at 28 days. The mechanical properties and the microstructure studies proved that the optimal PCE doping level is 0.14%. At this doping level, there is an enhancement in the denseness of the hardened structure, a reduction in porosity--especially when the pores are more than 200 nm, optimization of pore size distribution, an increase in crystal aspect ratio, an enhancement in effective intergranular overlap, and a significant improvement in the 28-day flexural and compressive strength to 7.2 MPa and 36 MPa, respectively. The FSLM prepared under these conditions demonstrates good performance and meets the primary performance index requirements of the Chinese Industry Standard JC/T 1023-2021 (gypsum-based self-leveling floor compound), thereby promoting the comprehensive utilization of fluorogypsum.  
fluorogypsum; polycarboxylate admixture; self-leveling material; hydration mechanism +
Footnote †: journal: Applied Sciences
# Kernel Density Derivative Estimation of Euler Solutions
## 3 Results
### Model Studies
#### 3.1.2 Sensitivity of 3-D KDE to Separations  
To assess the performance of the proposed algorithm in distinguishing adjacent anomaly sources, we used two similar causative sources with separations of L = 4.0 km, 2.5 km, and 1.0 km to form three models. These separations of L allowed two causative bodies to simultaneously approach each other in the \(x\)- and \(y\)- directions. The parameters of the two causative bodies were listed in Table 2. The rectangular survey grid was divided into 40,000 = 200 \(\times\) 200 observation points with an interval of 100 m. The 3-D KDE was used in the current investigation to analyze the subset \(\{x_{\mathrm{o}},y_{\mathrm{o}},z_{\mathrm{o}}\}\) derived from the FTG data contaminated by 3% Gaussian noise based on Equation (17).  
As the separation L decreased, it became more difficult to distinguish the two adjacent cubes using the Euler solutions in Figure 8d-f. Furthermore, in Figure 8g-i, the FCM algorithm, whose performance depended on the preset number of clusters, effectively distinguished adjacent anomaly sources, and did not eliminate spurious solutions. In contrast, when the distance between adjacent anomaly sources was considerable, the density-based spatial clustering of applications with noise (DBSCAN) could determine some spurious solutions, such as the Euler solutions in green in Figure 8j,k. Nevertheless, if the two anomaly sources were close (Figure 8l), this method could not effectively separate them or discriminate spurious solutions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Separations** & **Centroid of Left Cube** & **Centroid of Right Cube** \\ \hline
4000 & (\(-4000,4000,2500\)) & (4000, \(-4000,2500\)) \\
2500 & (\(-2500,2500,2500\)) & (2500, \(-2500,2500\)) \\
1000 & (\(-1000,1000,2500\)) & (1000, \(-1000,2500\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The geometric parameters for the three synthetic models with various separations.  
In addition, [PERSON] et al. [34] reported that Euler deconvolution yields many similar or duplicate solutions, which may tightly cluster in the vicinity of real sources. Therefore, the density cluster is essential for classifying Euler solutions [65,89]. This main feature could discriminate spurious solutions to indicate adjacent anomalous sources. Therefore, we used the multivariate KDDE, which has the advantages of asymptotic unbiasedness, square consistency, and uniform convergence [75,76], to overcome this difficulty.  
Figure 9 illustrates the partitioning of geological bodies using the 3-D KDDE to estimate the subset \(\{x_{\alpha},y_{\alpha},z_{\alpha}\}\). Compared to other techniques for separating adjacent anomalous sources, as shown in Figure 8, two probability density peaks directly discriminated the two geological bodies with varied separations.
## 1 Introduction
It is considered a noninvasive sampling device that extracts biomarkers for the early diagnosis of advanced or chronic diseases or for reporting impurity in food samples to assess food quality [14]. That study also reported that SPME is 10-50 times more efficient than any static headspace sampling. Moreover, the use of SPME is able to extract volatile organic compounds (VOCs) from a biological sample both ex vivo and in vitro for analysis using gas chromatography-mass spectrometry (GC-MS). This approach of sample extraction can reduce sample preparation steps and can extract the chemicals without modifying their original form. The use of SPME fibers has only recently been reported for marking fluid extraction and identification for _Panthera tigris_ subspecies [8]. To our knowledge, there is no research published on odorous chemicals emitted by urine and fecal samples of domestic cat species using SPME-GC-MS-Olfactometry (SPME-GC-MS-Ol) chemical and sensory analysis, especially in the context of smell development over time. Simultaneous chemical and sensory analyses can facilitate linking conventional chemical speciation with specific odors. Chemical identification of odor-causing chemicals can be aided by odor databases [15; 16]. This current study was designed to find out the temporal behavior of odorous compounds emitted from cat urine and feces in a noninvasive way for the betterment of the human environment. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Species** & **Sample Type** & **Sample Preparation** & **Chemical Analysis** & **Sensory Analysis** & **Identified Compounds** & **Study Purpose** & **Reference** \\ \hline Domestic Cat (_Fels atus_) & Urine & Solvent extraction and derivatization & Paper chromatography & Not conducted & Feinine and amino acids & To separate felinine and & [PERSON] 1953 [3] \\ Domestic Cat (_Fels atus_) & Urine & Solvent extraction and membrane concentration & HPLC, GC-MS-backspace & Not conducted & Carbonylesteras & To identify hydrolyzed & To identify hydrolyzed & [PERSON] et al. 2006 [4] \\  & & Solvent extraction and temperature & HPLC, GC-MS-backspace & Not conducted & Feinine derivatives & To quantify felinine and & [PERSON] et al. 2006 [4] \\  & & & & & & & & \\  & & & & & & & & \\  & &  
The current study's objective is to use SPME fiber extraction to identify odorous VOCs emitted from fresh and aged urine and feces of domestic cat species. This SPME-GC-MS-O will simplify the chemical and sensory characterization of odorous compounds emitted from cat urine and feces. It may also answer the following question: Is it possible to predict odor intensity using GC-MS chemical analysis? This study might also help formulate cleaners and other remediation techniques to reduce cat urine and feces odor problems in the long term.