esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Attribute Collaborative Optimization for Multimodal Transportation Based on User Preferences
## 4 Methodology
### Multi-Attribute Decision Making  
The decision-making indicators for transportation schemes encompass cost, carbon emissions, time, and risk. To reconcile the tension between personal preferences and objective weights, an uncertain multi-attribute decision-making method [32] is employed. This method harmonizes users' subjective preferences for indicators with their objective weights, leveraging the complementary strengths of both, to produce a more scientifically sound and rational decision-making outcome. The steps for scheme decision making are as follows:  
Figure 7: Diagram of random mutation strategy.  
Step 1: List the decision indicators of all alternative schemes in a matrix form \(A=(a_{ij}),i\in N,j\in M\), where \(N\) denotes the number of schemes and \(M\) represents the number of evaluation criteria.  
Step 2: To eliminate dimensional differences, normalize the matrix by Equation (24).  
\[R=(r_{ij})=\frac{\min\limits_{i}(a_{ij})}{a_{ij}},i\in N,j\in M \tag{24}\]  
Step 3: To unify decision-making information, incorporate the objective weights \(W=(\omega_{1},\omega_{2},\cdots,\omega_{k})\) into the normalized matrix \(R\), and construct the objective complementary judgment matrix \(\overline{B}=(\overline{b}_{ij})_{i\times i}\), where \(\overline{b}_{ij}=\frac{1}{2}\bigg{[}1+\sum\limits_{k=1}^{k}r_{ik}\omega_{k} -\sum\limits_{k=1}^{k}r_{jk}\omega_{k}\bigg{]},i,j\in N\).  
Step 4: Based on decision makers' subjective preferences, construct the subjective complementary judgment matrix \(B=(b_{ij})_{i\times i}\) using the 0.1-0.9 complementary scale, where \(b_{ij}+b_{ji}=\overline{b}_{ij}+\overline{b}_{ji}=1\), \(b_{ii}=\overline{b}_{ii}=0.5\), \(b_{ii}\geq 0,\overline{b}_{ii}\geq 0,i,j\in N\).  
Step 5: Integrate subjective and objective weights by defining the deviation function \(f_{ij}=b_{ij}-\overline{b}_{ij},i,j\in N\) and then formulate the single-objective optimization model by Equation (25).  
\[\left\{\begin{array}{l}\min\limits F(\omega)=\sum\limits_{i=1}^{n}\sum \limits_{j=1}^{n}{f_{ij}}^{2}\\ s.t.\omega_{j}\geq 0,j\in M,\sum\limits_{j=1}^{m}\omega_{j}=1\end{array}\right. \tag{25}\]  
Step 6: Solve the above optimization problem to obtain the integrated weights \(\omega=(\omega_{1},\omega_{2},\cdots,\omega_{k})\), calculate the comprehensive attribute value of each scheme using \(z_{i}(\omega)=\sum\limits_{j=1}^{m}{\tau_{ij}\omega_{j}},i\in N,j\in M\), and rank the schemes to determine the optimal alternative.
# Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
###### Abstract  
Today, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually taken as an optimization problem and be solved with different optimization methods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied and have the advantages of strong robustness and excellent global search ability. Due to the high complexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve the problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied to hardware/software partitioning. In order to improve the performance of the BSO, we analyzed its optimization process when solving the hardware/software partitioning problem and found the disadvantages in terms of the clustering method and the updating strategy. Then we proposed the improved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting the cluster points and improved the updating strategy by decreasing the number of updated individuals in each iteration. Based on the simulation methods which are usually used to evaluate the performance of the hardware/software partitioning algorithms, we generated eight benchmarks which represent tasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and two improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the highest quality within the shortest running time among these algorithms.  
Keywords:brainstorm optimization; hardware/software partitioning; swarm intelligence +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion
### Quantification of Aroma Compounds in Baijiu Samples and Trace Compounds OAV Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Odor Threshold**} & \multicolumn{2}{c}{**OAV**} & \multicolumn{2}{c}{**G-15/G-0**} \\ \cline{3-6}  & & & **(\#g/L)** & **G-15** & **G-0** & **Ratio \({}^{\text{j}}\)** \\ \hline
1 & 4-Methyl-2-methoxyphenol & 314.56 \({}^{\text{b}}\) & 7.3 & 0.8 & 9.59 \\
2 & Heptan-1-ololololol & 26,600 \({}^{\text{c}}\) & 0.1 & \textless{}0.1 & 6.48 \\
3 & 4-Ethyl-2-methoxyphenol & 122.74 \({}^{\text{b}}\) & 16.8 & 3.6 & 4.67 \\
4 & Heptanoic acid & 13,821.32 \({}^{\text{b}}\) & 2.6 & 0.6 & 4.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: OAVs and their ratios of trace compounds in aged and young Baijiu. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Compound** & \begin{tabular}{c} **Odor Threshold** \\ **(ug/L)** \\ \end{tabular}  & \multicolumn{2}{c}{**OAV**} & \multicolumn{2}{c}{**G-15/G-0**} \\ \cline{3-6}  & & **G-15** & **G-0** & **Ratio \({}^{\dagger}\)** \\ \hline
5 & \(\beta\)-damascenone & 5  
Eighteen compounds were identified with Odor Activity Value (OAV) ratios exceeding 2 between aged (G-15) and young (G-0) Baijiu, indicating at least a twofold increase in their concentrations. Conversely, nine compounds exhibited a reduction in concentrations by at least half. These aroma compounds, characterized by pronounced disparities in OAV values, were hypothesized to contribute significantly to the divergent aroma profiles between aged and young Baijiu samples.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.14. PALSAR-Derived AGB Map of Cambodia  
Based on field biomass and PALSAR data, [PERSON] et al. [75] generated Cambodia's AGB using a multilinear regression model. Data uncertainties were mainly from the field data, allometric equations and the saturation of the PALSAR data. The R\({}^{2}\) and RMSE between the field-based biomass data and the predicted biomass map were 0.61 and 63 Mg/ha, respectively.
## 3 Results and Discussion
### Effect of Biochar and Humic Acid on the Content, Characterization and Component of DOM in Soil
#### 3.2.3 PARAFAC Analysis of Soil DOM  
The composition information of biochar-derived DOM (BC-DOM) and biochar + HA-derived DOM (BC + H-DOM) was obtained by three-dimensional fluorescence scanning. Subsequently, these fluorescence spectra were decomposed using parallel factor analysis (PARAFAC), with the specific component distributions illustrated in Figure 4A,B. From BC-DOM, four fluorescence components were identified based on their excitation (Ex) and emission (Em) wavelengths: C1 (Ex/Em = 240-260/405 nm), C2 (Ex/Em = 350/465 nm), C3 (Ex/Em = 260/460 nm), and C4 (Ex/Em = 270/320 nm). Likewise, four fluorescent components were isolated from BC + H-DOM as C1 (Ex/Em = 250/405 nm), C2 (Ex/Em = 270/450 nm), C3 (Ex/Em = 200/400 nm), and C4 (Ex/Em = 240/340 nm). Notably, C1 was identified as a fulvic acid-like substance, which appears in the conventional A-peak region (230-260/380-420 nm). Fulvic acids are low-molecular-weight compounds that are water-soluble, making them easily transported and absorbed within both environmental and biological systems [17]. C2 and C3 are humic acid-like substances or their compounds (Ex/Em = \(<\)250-350/400-465 nm). These substances correspond to the traditional humic acid-like A and C peaks, whose absorption peaks are typically associated with aromatic compounds and unsaturated structures [32; 51]. C4 was the only protein-like component, with peaks located in the region of the protein-like T-peak (270-280/320-340), and was considered to be a tryptophan-like substance [52]. This component is closely  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **SUVA254** & **SUVA260** & **FI** & **BIX** & **HIX** \\ \hline Y & 9.96 \(\pm\) 1.05 c & 8.80 \(\pm\) 0.67 c & 2.31 & 0.81 & 3.64 \\ YB3 & 11.49 \(\pm\) 0.46 c & 10.65 \(\pm\) 0.44 c & 2.46 & 0.80 & 4.50 \\ YB6 & 12.28 \(\pm\) 0.55 c & 11.35 \(\pm\) 0.5 c & 2.31 & 0.76 & 4.75 \\ YB3H & 86.11 \(\pm\) 2.51 a & 82.3 \(\pm\) 2.5 a & 2.21 & 0.67 & 10.25 \\ YB6H & 28.71 \(\pm\) 3.88 b & 28.48 \(\pm\) 1.97 b & 2.06 & 0.68 & 11.25 \\ \hline \end{tabular}
\end{table}
Table 1: Soil DOM UV-visible and spectral parameters. Different letters indicate significant differences between treatments.  
related to microbial activities and organic matter degradation. The Ex and Em of the above components reveal that C3 exhibited a redshift relative to C2. This observation suggested that C2 possesses a higher molecular weight, a greater degree of humification, and typically resides in a more intricate environment [32]. In addition, C2 and C3 also showed a redshift in BC + H-DOM compared with their counterparts in BC-DOM, which indicated that the addition of HA improved the complexity and compactness of DOM molecules.  
Figure 4: \(Cont\).  
the combined use of biochar and HA can increase the humus-like composition of DOM by nearly 5% compared with the addition of biochar alone, which may be the main reason for the more complex structure of BC + HA-DOM [53; 54].
# A Theoretical Method and Model for TBM Tunnelling Trajectory Adjustment
## 5 Discussion
### Limitation and Further Researches  
This research proposed a theoretical method of TBM trajectory adjustment, and it is modified by the field data to be suitable for actual projects. However, this research still has some limitations which require further research to be corrected. The main limitations are summarized as follows.  
Figure 15: Combination of the bias angle and distance suitable for the proposed model.  
In the proposed method, rational rotated angles of the main beam are used to deal with different initial TBM bias conditions. However, the rotated angle cannot be directly controlled by the TBM drivers and is instead indirectly controlled by controlling the stroke of the main thrust cylinder system. Therefore, the results of this research are only a reference for TBM tunnelling and cannot be directly used in the field. To address this limitation, the relationship between the thrust cylinder strokes and the rotated angle of main beam should be studied, and this will be an important part of our further studies.
2. Considering the complexity of TBM trajectory adjustment, the tunnelling route in this paper is assumed to be straight line, which is the most common situation in actual projects. However, there are still some conditions in which the TBM is tunnelling along curves. For curve trajectories, the proposed method is not suitable, and a more perfect theoretical method suitable for curve trajectories should be further researched.
## 3 Preliminaries
### Assumptions of the Protocol
* **Cryptographic** primitive idealness: The chosen cryptographic hash function \(H(\cdot)\) (e.g., SHA-256) behaves as a random oracle (or at least meets standard security properties like collision resistance). The symmetric encryption scheme \(E_{K}(\cdot)/D_{K}(\cdot)\) (e.g., AES-128 in CBC mode) is IND-CPA secure. The key derivation function (KDF) (e.g., PBKDF2) is a secure pseudorandom function. * **Secure** local OD-drone link (for relay): The communication link between the OD and the drone \(D_{i}\) for relaying authentication messages \(M_{1}\) and \(M_{2}\) is assumed to be reasonably secure (e.g., encrypted Bluetooth, local Wi-Fi with WPA2/3) or its exposure is limited due to short range. * **Loosely** synchronized clocks: The OD and the GCS maintain loosely synchronized clocks, allowing for effective timestamp-based replay attack detection within a predefined tolerance window \(\Delta_{t}\)[8]. * **GCS** database protection: While the protocol aims to mitigate the impact of a GCS database compromise (e.g., by not storing \(P_{ID_{O}}\) directly), standard security practices are assumed to be in place to protect the GCS and its database of \((RPMD_{ID_{O}},Salt_{1},LLT_{O})\).
## 4 Incorporation of Wearable Sensors to Measure OA Risks
### Foot Pressure Measurement Insole  
Foot-ground impact recordings from pressure sensitive insoles (e.g., Pedar, F-scan) can be used to predict gait-related injury risks associated with foot deformities (e.g., hallux valgus, hammer toe, craw toe), inversion ankle sprains and knee joint conditions [64]. A battery pack is required for continuous data acquisition [65], but footwear-integrated insoles have the potential to estimate foot-ground contact pressures and modulate joint stresses by augmenting an active knee orthosis control system.  
Wearable sensors can, therefore, supply real-time gait-related data that can be useful for OA risk assessment and monitoring. Sensor data can be stored in the cloud and made available to clinicians or exercise prescribers for more effective personalised interventions. In addition to data management applications, wearable sensor signals can be used to refine active orthosis actuation to enable protective mechanisms by setting torque motor thresholds (e.g., joint angle, acceleration, force, pressure) as considered in the next section.
# Enhancement of Metabolite Production in High-Altitude Microalgal Strains by Optimized C/N/P Ratio
## 3 Results
### Production of Lipids under Different C/N/P  
To produce lipids, the use of sodium carbonate (Sa) as a carbon source shows that none of the analyzed variables significantly (\(p\) = 0.05) affects the final content of lipids (Figure 3a). The surface response (Figure 3b) shows that medium concentrations of K2 HPO4 and KH2 PO4 (0.0375 and 0.0875 g/L, respectively) and the carbon source (3 g/L of Na2 CO3) might increase the total content of lipids. In the case of sodium bicarbonate (Sb), NaNO3, and the interaction between the phosphate buffer and the carbon source significantly affect the synthesis of lipids (Figure 3c). The surface response (Figure 3d) shows that high concentrations of K2 HPO4 and KH2 PO4 (>0.075 and 0.175 g/L, respectively), and low concentrations of the carbon source (<0.7 g/L of NaHCO3) positively affect the synthesis of lipids up to 30% \(w/w\) of the total biomass. Finally, the Pareto chart of sodium acetate (Sa) (Figure 3e) shows that sodium nitrate affects the synthesis of lipids. The surface response (Figure 3f) shows that low NaNO3 (<0.075 g/L) and medium concentrations of the carbon source (0.68-1.7 g/L of C2H3 NaO2) might increase the total content of lipids in _Chlorella_ sp. UFPS019.  
Figure 3: Pareto charts and surface response of lipid production using Na2 CO3 (**a**,**b**), NaHCO3 (**c**,**d**), and C2H3 NaO2 (**e**,**f**).
## 2 External Load Monitoring  
The external load can be monitored by quantifying the parameters performed during the training session, such as distance covered, sprints performed, power output, number of repetitions, and/or weight lifted [2; 3]. Quantifying individual sessions or the cumulative load across sessions allows coaches to assess the external load applied to the individual, whether in a training program for performance or for fitness [3]. However, some of these methods for quantifying external load monitoring may involve calculations and require technological instruments, ranging from simpler devices like pedometers to more complex ones like accelerometers or global positioning systems (GPS). In addition, the choice of methods may vary depending on the type of sport (e.g., team, individual, endurance, strength, or power) [2; 3]. Moreover, utilizing two or more methods can be beneficial for monitoring external load and identifying variables that influence performance [2; 5]. For example, monitoring the swimming velocity and stroke rate can support coaches regarding metabolic pace and swimming efficiency, informing about the swimmer's skill and conditioning levels [11]. In this way, GPS and accelerometers have been considered tools with accuracy in assessing swimming velocity and stroke rate due to the correlation to data from kinematics analysis [12] For example, stroke counting with accelerometer and video analysis correlated in breaststroke (r > 0.98) and butterfly (r > 0.99), as well as the measurements of velocity with GPS, and video analysis showed an acceptable standard error in freestyle (0.13 m/s) and breaststroke (0.12 m/s) swimming. Hence, in other sports in which displacement velocity and motion rate are important variables to infer the level and progress in conditioning and performance (e.g., running and cycling velocity, step rate and length in running, and rotation per minute in cycling), the information from different variables of external loads might reduce mistakes while planning and evaluating training [5; 13].
## 4 Traffic Filtering-Based Techniques
### Quick Removing (QR)  
#### 4.1.1 QR Concepts  
The purpose of QR [8; 9; 10; 11] is to remove duplicated and circulated frames from a ring when all the nodes have received one copy of the frame and begin to receive the second copy. QR is suited for ring or connected ring topologies and can be applied for any traffic, such as unicast, multicast and broadcast.
### Stage II: Regression and Interpretable Analysis
Compared with the importance of the base models in stage 1, features such as M stage, Tumor size, Grade 2 and T stage 1, surgery of cancer, and other features related to tumor and surgery are key factors in the deceased RCC patients. Different from stage 1, old age is not the top important factor, which suggests that age is not a key factor in the prognosis of death. Surgical and tumor-related information factors are more important in both stage 1 and stage 2. Therefore, in the prognosis \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** \\ \hline ADA & 52.64 & 37.39 \\ ET & 46.63 & 32.00 \\ GB & 50.64 & 35.25 \\ RF & 50.36 & 34.99 \\ XGB & 51.39 & 35.64 \\ Voting & 50.54 & 35.14 \\ Stacking & 50.40 & 34.98 \\ Blending & 50.35 & 34.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Survival time prediction metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Metrics** & **1 Year** & **2 Year** & **3 Year** & **4 Year** & **5 Year** \\ \hline \multirow{4}{*}{ADA} & Accuracy & 66.42 & 68.52 & 70.05 & 69.79 & 70.42 \\  & Precision & 69.06 & 73.27 & 72.71 & 73.36 & 72.64 \\  & Recall & 66.42 & 68.52 & 70.05 & 69.79 & 70.42 \\  & F1 score & 63.09 & 68.11 & 70.56 & 70.68 & 71.24 \\  & Accuracy & 68.67 & 72.81 & 74.38 & 74.60 & 75.23 \\  & Precision & 71.19 & 74.99 & 76.67 & 76.02 & 73.62 \\  & Recall & 68.67 & 72.81 & 74.38 & 74.60 & 75.23 \\  & F1 score & 65.77 & 72.76 & 74.76 & 75.07 & 74.00 \\  & Accuracy & 68.29 & 71.90 & 73.56 & 73.14 & 74.38 \\  & Precision & 70.92 & 74.52 & 76.20 & 74.64 & 73.75 \\  & Recall & 68.19 & 71.90 & 73.56 & 73.14 & 74.38 \\  & F1 score & 65.46 & 71.84 & 73.97 & 73.64 & 74.01 \\  & Accuracy & 67.57 & 71.18 & 72.71 & 73.80 & 75.02 \\  & Precision & 70.60 & 74.12 & 75.69 & 75.40 & 74.53 \\  & Recall & 67.57 & 71.18 & 72.71 & 73.80 & 75.02 \\  & F1 score & 64.61 & 71.17 & 73.22 & 74.34 & 74.75 \\  & Accuracy & 68.33 & 71.68 & 72.90 & 73.10 & 74.47 \\  & Precision & 71.20 & 74.34 & 75.63 & 74.55 & 73.51 \\  & Recall & 68.33 & 71.68 & 72.90 & 73.10 & 74.47 \\  & F1 score & 65.46 & 71.66 & 73.37 & 73.60 & 73.88 \\  & Accuracy & 67.56 & 70.86 & 72.84 & 73.75 & 75.14 \\  & Precision & 71.07 & 74.29 & 75.49 & 75.42 & 74.38 \\  & Recall & 67.56 & 70.86 & 72.84 & 73.75 & 75.14 \\  & F1 score & 64.42 & 70.81 & 73.34 & 74.30 & 74.69 \\  & Accuracy & 68.56 & 71.87 & 73.28 & 73.42 & 74.45 \\  & Precision & 71.28 & 74.52 & 76.00 & 74.89 & 73.45 \\  & Recall & 68.56 & 71.87 & 73.28 & 73.42 & 74.45 \\  & F1 score & 65.83 & 71.83 & 73.72 & 73.91 & 73.82 \\  & Accuracy & 68.98 & 71.65 & 72.82 & 73.61 & 74.25 \\  & Precision & 71.17 & 74.24 & 75.67 & 75.30 & 73.27 \\  & Recall & 68.98 & 71.65 & 72.82 & 73.61 & 74.25 \\  & F1 score & 66.58 & 71.61 & 73.27 & 74.16 & 73.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Survival status prognosis metrics of 1–5 years. Figure 14: Feature importance of GB in stage 2. Figure 13: Feature importance of linear regression of Blending.
## 2 Study Area and Data
### Study Data  
This study utilized four primary data types: traditional data, geographic data, network data, and image data. Traditional data comprised statistical yearbooks, development bulletins, and city master plans from Weinan City's government units. Geographic data were mainly sourced from the National Geographic Information Resources Catalogue Service System (www.webmap.cn, accessed on 12 July 2022), while network data included point of interest (POI) data from www.amap.com and OSM road network data from Open Street Map (www.openstreetmap.com, accessed on 12 July 2022). Image data consisted of street view photos from Linwei District. For efficiency, these data were organized,  
Figure 1: The district of Weinan City.  
calibrated, and integrated into Arc GIS 10.4, with the final coordinate system unified to WGS_1984_UTM_Zone_53N.  
POI data were the most important data for this study, as it encapsulated the vital locations that defined the functionality and vibrancy of urban life within the Linwei District. These data were critical because it provided insights into the distribution and diversity of services, amenities, and infrastructures. The acquisition of POI data was executed through advanced Python scripts utilizing the requests and json libraries, which enabled the dynamic collection of data from Amap's location service APIs for the periods of 2023. Linwei District POI data contained 44,034 points (Figure 2). There were eight categories, of which administration had a total of 6399 data, business had a total of 22,886 data, education had a total of 2088 data, medical had a total of 2465 data, office had a total of 3609 data, residential had a total of 2256 data, tourism had a total of 192 data, and traffic had a total of 4139 data. All data were disseminated through the website.
## 5 Case Study
### Effectiveness Analysis of the Proposed Method
In summary, the HCMLCS algorithm demonstrates superior adaptability and optimization capabilities in multi-resource, high-disturbance production environments, effectively enhancing production efficiency and scheduling stability, thereby proving its practical value in real-world manufacturing scenarios. However, current research on human-machine-logistics collaborative scheduling in multi-resource, high-dynamic environments remains limited, with almost no publicly available code resources, which hinders broader comparative studies and slows research progress in this field. Therefore, this study not only fills a research gap but also provides a novel approach and methodology for future scheduling optimization research.