esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Verification of Transferring Pressure Across Goal Effect  
### Model Establishment and Simulation Method  
#### 4.1.1 Model Establishment  
To analyze the pressure transfer mechanism from working face 1511 mining to BHR 1513, a UDEC numerical model (Figure 4) was constructed based on the mine's geological conditions. The model spans 337 m horizontally and 120.6 m vertically, with BHR 1513 dimensions defined as 4.0 m (width) \(\times\) 3.4 m (height). Critical components include the following: strip working face 1513 (60 m width), working face 1511 (200 m width), and dual coal pillars A/B (5 m each). The 3 up coal seam exhibits a 3.4 m thickness at a 507 m burial depth, positioned 102 m below the model's upper boundary. A vertical stress gradient of 0.025 MPa/m was implemented, resulting in 10 MPa/m vertical load imposed on the upper boundary. Displacement constraints were applied as follows: vertical fixation at the base, and horizontal fixation at lateral boundaries. Coal seam occurrence characteristics and mechanical parameters (Table 1) were incorporated into the model, with simulations executed using the Mohr-Coulomb yield criterion. In the discrete element model, the mechanical parameters of block and interface jointly determine the mechanical properties of rock mass. Block parameters include density, bulk modulus, and shear modulus. Interface parameters include normal stiffness, tangential stiffness, cohesion, and friction angle [20]. The bulk modulus \(K\) and the shear modulus \(G\) in the model are determined by the elastic modulus \(E\) and Poisson's ratio \(v\), and the specific conversion relationship can be found in Formulas (1) and (2). \[K=\frac{E}{3(1-2v)} \tag{1}\]  
\[G==\frac{E}{2(1+v)} \tag{2}\]  
The mechanical parameters of the rock mass obtained from the engineering site are obtained by uniaxial compressive and tensile tests in the laboratory. However, the parameters applied in the discrete element UDEC 6.0 software are the parameters of the rock mass, and it is necessary to convert the rock mechanical parameters obtained in the laboratory. The elastic modulus of block is converted to the elastic modulus of the rock mass by Formula (3); the compressive strength of rock is converted to the compressive strength of the rock mass by Formula (4); and the tensile strength of the rock mass is determined by Formula (5). After the mechanical parameters of rock are transformed into the mechanical parameters of the rock mass, the numerical model is verified. After several parameter verifications and adjustments, the mechanical parameters of each rock strata in the model are determined as shown in Table 1.  
Figure 4: Numerical simulation model.
# Design and Multi-Objective Optimization of Fiber-Reinforced Polymer Composite Flywheel Rotors
## Manufacturing Process
### Machine Costs  
A basic manufacturing system is herein considered. It consists of a single four-axis filament winding machine and a fiber tensioner that can be loaded with up to six creels [21]. Further, a resin bath, a resin mixing system, a lathe, a press, and a curing oven are part of the manufacturing system. The required space to calculate room costs along with estimated purchase prices are presented in Table 1.  
In the optimization model, the machine costs are considered by calculating the machine hour rate (MHR). The total time a machine operates within one year is \(t_{\text{operation}}\). In accordance with [21], 252 days at 8 h per day are assumed, equaling 2016 h per year. \(K_{\text{A}}\) are the cost-accounting depreciations. This study considers a linear depreciation over 10 years. After 10 years the machine is replaced by a newer version. \(K_{\text{I}}\) are the cost-accounting interests which are not an actual expense but reflect the opportunity costs. These costs are not included in the presented study. \(K_{\text{M}}\) are the maintenance costs  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name** & **Acquisition Price [CAD]** & **Space [m\({}^{2}\)]** & **Maintenance [CAD/year]** \\ \hline
**FW-machine four-axis** & 145,000 & 16 & 7250 \\
**Resin mix-system** & 125,000 & 5 & 6250 \\
**Heated resin bath** & 8000 & 3 & 400 \\
**Fiber tensioner with 4 creels** & 71,000 & 10 & 3200 \\
**Curing oven** & 50,000 & 30 & 2500 \\
**Press** & 50,000 & 5 & 2500 \\
**Lathe** & 50,000 & 5 & 2500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prices, space and maintenance costs of manufacturing machines (based on [21]).  
Figure 2: Examples of rotor rim geometries to facilitate press fitting. Adapted from [15].  
which are assumed to be 5% of the acquisition price per year. \(K_{\text{R}}\) and \(K_{\text{E}}\) are room costs and energy costs, respectively. Energy costs are neglected in this study and the room costs are assumed to be 20 CAD/m\({}^{2}\). An example calculation of the MHR for the four-axis filament winding system consisting of a four-axis filament winding machine, a resin mixing system, a resin bath and a fiber tensioner equipped with six creels is shown in Table 2. Similarly, the MHR of the oven, the lathe and the press are calculated. The MHRs of all machines are depicted in Table 3. Please note that MHR are facility and enterprise dependent, and values used in this study should therefore be considered an example.
## 3 Results and Discussion
### Antimicrobial Activity  
#### 3.5.1 Disc Diffusion Test  
The disc inhibition assay was used to examine the antimicrobial potential of P1E, P2E, and P3E against five pathogenic microorganisms. Table 1 shows the DIZ values of our three extracts. The DIZ results varied from 15.00 \(\pm\) 1.00 mm for _E. coli_ CIP 53126 to 10.00 \(\pm\) 2.00 mm for _C. albicans_. The _K. pneumoniae_ strain is resistant to P3E and the _C. albicans_ strain is resistant to P1E and P3E samples. The difference between the DIZ values of the three samples against the strains _E. coli_ ATCC 25922, _S. aureus_, and _K. pneumoniae_ is not significant (_p_ > 0.05), whereas against the _E. coli_ CIP 53126 strain, this difference is significant between P2E and P3E with the P1E. The antibiotic tetracycline shows higher DIZ values against all bacterial strains compared to our extracts, whereas streptomycin shows a lower DIZ of 09.61 \(\pm\) 0.20 mm against the _S. aureus_ strain compared to our samples. Fluconazole causes a DIZ of 21.20 \(\pm\) 04.20 mm against _C. albicans_. The difference in DIZ values between extracts and antibiotics is significant (_p_ < 0.05). In comparing the DIZ results obtained from the three samples with previous studies, in a recent Moroccan study, the majority of the strains studied (_C. albicans_, _E. coli_ ATCC, _K. pneumoniae_, _E. coli_ CIP, and _S. aureus_) were sensitive to extracts of four organs of _P. rhoes_. The DIZ values ranged from 8.33 mm against _K. pneumoniae_ to 13.66 mm against _S. aureus_[33].  
Figure 5: TAC values of PE1, PE2, and PE3; AAE: ascorbic acid equivalents; a: the difference between the three samples was not significant (_p_ > 0.05).
## 2 PHM: Reference Methodologies, Frameworks, and Architectures
### Data Processing Function
In other words, they require the data to be associated with a label that corresponds to the health status of the machinery and which guides the search for relevant features. On the contrary, sparse representation of the signals, together with dictionary learning, can be directly applied to raw signals; also, they are adaptive methods, as they do not require any expertise from the mechanical point of view and do not require any target variables [36]. However, both sparse coding and dictionary learning are computationally expensive and not suitable for online monitoring of machinery conditions [33]. The DP function also includes the HI extraction. It is a feature that reveals the degradation process of a certain component or system. While features for the fault diagnosis correspond to normal and fault conditions, the HI is extracted during the whole life of the component/system, from a normal condition to the failure occurrence. To be considered a good HI, it has to respect precise evaluation criteria, such as monotonicity, failure consistency, trendability, correlation with the RUL, Mean Absolute Error (MAE), and accuracy [37]. The HI can be extracted directly from raw signals or constructed from features extracted through the techniques described above. In the first case, the HI can be computed as the difference between signals under degraded and normal conditions [38]. In the second case, the HI can be computed simply from previously extracted features through a proper formula [39], through Genetic Programming (GP) algorithms [37; 38; 39; 40; 41], or Deep Learning models [42; 43]. In general, the HI construction strongly depends on the component under analysis [44]. In addition, the health condition's labels and the value of the Failure Threshold are required. On the contrary, both Deep Learning models and GP represent a great opportunity for building HIs that are less dependent on expert knowledge and the availability of labels. The issues of these models are related to the computational time for training and the input parameter setting.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### Simulations
#### 4.3.1 Route Update Time and Throughput  
It is critical that drone systems react to topology changes promptly, particularly for real-time applications. Thus, we evaluated GCS-routing performance when topology changes using simulations to investigate route update time and throughput. Similar to the experiment in Section 4.2.1, we configured a network composed of drones, and then added a new drone to the network, using DSDV with various HELLO intervals and GCS-routing. Figure 13(a) shows the time until all drones updated the optimal route. This result is consistent with the previous experimental outcomes (Figure 11(a), Section 4.2.1). Route update times for DSDV were much longer than those of GCS-routing. DSDV could not react to topology changes promptly, whereas GCS-routing reacted immediately due to utilizing GCS and low GCS-routing complexity.  
Figure 13(b) shows that average DSDV throughput decreased as the HELLO interval shortened, due to frequent HELLO message transmission consuming available throughput, hence limiting availability for data transmission. Shorter intervals decreased the time for route update, but degraded throughput. Therefore, GCS-routing is more suitable for drone networks, providing significantly superior throughput and route update time.
## 4 Darts vs. Ship Detection
### Testing Results and Analysis  
To test the performance of Darts, we implement comprehensive evaluations on the dataset HRSID [9], where 90% of SAR images are randomly selected for the architecture search and neural network training, and the remaining 10% of SAR images are left for performance validation. _IoU_ (intersection over union) is set to 0.3, and the threshold of confidence is set to 0.15. We perform a neural architecture search multiple times and select the best three of them (named HArch_1, HArch_2, and HArch_3) to show the results. Cell structures are depicted in Appendix A, Figure 11. According to the Darts settings, 12 normal cells and 2 reduction cells are stacked to construct the backbone (see Figure 3).  
Table 2 demonstrates that the detection results are not good: for all three neural networks generated, F1 is below 75, recall is below 69, and precision is around 80. Note that for all the results, there is a big gap between the validation and training results, which is typical overfitting phenomena. To increase the detection accuracy while preventing overfitting, we perform two straightforward methods. One is early-stopping: cutting down the number of training epochs. However, we observe that overfitting appears in the early epochs. When overfitting first appears, the detection results are even worse than those given in Table 2. The second way is to simplify the neural network. What we did was to reduce the number of stacked normal cells. Experimental results are given in Table 3. We can see that stacking 8 or 4 cells to construct the backbone will obtain better results than stacking 14 cells. Surprisingly, even if the backbone is only composed of 2 reductions cells (by suppressing all normal cells), the results are still competitive. Unfortunately, although we cut down the number of cells, overfitting is still significant. We also perform similar experiments on SSDD [10] (Table 4; cell structures are depicted in Appendix A. Figure 11). There are similar observations: cutting down the number of cells can obtain benefits, however, overfitting still exists.  
According to these experimental results, we have two observations. One is that simplifying the neural network generated by Darts is potentially good for ship detection. On the other hand, performing an architecture search first and then roughly decreasing the number of stacked cells is far from enough. Hence, it is necessary to customize a new model for ship detection.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Archs**} & \multirow{2}{*}{**Cells**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**Precision**} \\ \cline{3-8}  & & **Validation** & **Training** & **Validation** & **Training** & **Validation** & **Training** \\ \hline HArch\_1 & 14 & 74.6 & 90.0 & 68.4 & 84.5 & 82.1 & 96.2 \\ \hline HArch\_2 & 14 & 71.0 & 86.8 & 63.8 & 81.2 & 80.1 & 93.3 \\ \hline HArch\_3 & 14 & 68.6 & 86.4 & 62.1 & 80.8 & 76.7 & 92.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Applying Darts directly for ship detection on HRSID.  
Figure 6: The algorithm steps of Darts.
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 5 Experiment  
### Experimental Settings  
#### 5.1.1 Datasets  
We select and evaluate the proposed model on three public datasets from location-based social platforms: Foursquare-NYC, Foursquare-TKY, and Gowalla-CA. These datasets are commonly used in the next POI recommendation [22; 23]. We provide the data statistical description in Table 1. Specifically, the datasets Foursquare-NYC and Foursquare-TKY cover the period from April 2012 to February 2013, with Foursquare-NYC focusing on New York City and Foursquare-TKY on Tokyo. The Gowalla-CA dataset, sourced from the public Gowalla dataset, includes data from California and Nevada, making it cover a wider range. Each check-in record includes the user, the POI, its category, geographical coordinates, and the time of visit. The NYC and TKY do not include friendship, which can test the effectiveness of our meta-path-based method. In order to use the same model, we do not use friendship in the CA dataset. The friendship data in CA can promote further analysis on our model.  
All three datasets are preprocessed as follows: First, users and POIs with fewer than 10 visits are removed. Then, the visit records of each user are divided into several sequences within a 24 h interval. It means, if the interval between two check-ins is larger than 24 h and the two check-ins belong to the same user, then the two check-ins will be divided into two sequences. Sequences containing only one check-in are excluded. The entire dataset is then sorted by time and divided into training, validation, and test sets with the ratio 8:1:1. Additionally, the validation and test sets exclude users and POIs that are not present in the training set. The check-in data from the training set is used to construct the graph required for heterogeneous graph contrastive learning, and the historical check-in sequences are also from training set. For each sequence in the training set, this paper predicts all check-ins except the first one during training. For both the validation and test sets, this paper only predicts the last check-in of each sequence.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Datasets** & **\#User** & **\#POI** & **\#Category** & **\#Check-In** \\ \hline NYC & 1048 & 4981 & 318 & 103,941 \\ \hline TKY & 2282 & 7833 & 290 & 405,000 \\ \hline CA & 3957 & 9690 & 296 & 238,269 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of the experiment datasets.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 3 CubeSat Power Supply Model  
### PV Input Power Model  
The PV panels are body-mounted on the CubeSat, and the power generation by PV panels on a satellite is given by the contribution of each one of the six sides \(k\) of a standard CubeSat, as follows [25]:  
\[v_{t}=\sum_{k=1}^{6}v_{k,t}=\sum_{k=1}^{6}\eta A_{k}l_{sun}F_{k,t\to sun} \psi_{t} \tag{1}\]  
where \(v_{k,t}\) is the PV input power of the CubeSat in the period \(t\) on the side \(k\); \(\eta\) is the efficiency of the PV panels; \(A_{k}\) is the layout area of the PV panels on the side \(k\); \(l_{sun}\) is the solar irradiance; \(F_{k,t\to sun}\) is the view factor of the surface \(k\) in relation to the sun; \(\psi_{t}\) is a 0-1  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constants** & **Definition** \\ \hline Sets & \\ \(\mathcal{J}\) & Set of tasks to be scheduled. \\ \(\mathcal{T}\) & Set of units of time. \\ \(\mathcal{V}\) & Set of PV input power. \\ Indexes & \\ \(j\) & Task or job index, \(j\in\mathcal{J}\). \\ \(t\) & Time index, \(t\in\mathcal{T}\). \\ \(v_{t}\) & PV input power index in period \(t\), \(v_{t}\in\mathcal{V}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sets and descriptions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constants** & **Definition** \\ \hline \(J\) & The number of tasks/jobs. \\ \(T(\min)\) & The time of the orbital cycle. \\ \(q_{j}(\mathrm{W})\) & The rated power consumed by executing job \(j\). \\ \(u_{j}\) & The priority of job \(j\). \\ \(t_{j}^{min}(\min)\) & The minimum time for the job \(j\) to be executed continuously. \\ \(t_{j}^{max}(\min)\) & The maximum time for the job \(j\) to be executed continuously. \\ \(y_{j}^{min}\) & The minimum number of start-ups of job \(j\). \\ \(y_{j}^{max}\) & The maximum number of start-ups of job \(j\). \\ \(p_{j}^{min}(\min)\) & The minimum period of job \(j\). \\ \(p_{j}^{max}(\min)\) & The maximum period of job \(j\). \\ \(w_{j}^{min}\) & The start times of the available time window for the job \(j\). \\ \(w_{j}^{max}\) & The finish times of the available time window for the job \(j\). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constants and descriptions.  
variable, determined by the position of the CubeSat in the period \(t\), where taking 1 means it is in the illuminated area, and taking 0 means it is in the shadow area.
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 5 Case Analysis
### Performance Evaluation for the Cooperation between Dry Ports and Shanghai Port  
The increasingly fierce port competition and limited development space of the ports themselves promote more and more ports to expand their economic hinterlands towards the inland. The same is true for Shanghai Port. Shanghai Port has established cooperative relations with many inland dry ports and hopes to continue to cooperate with potential dry ports. In this study, on the basis of selecting Suzhou, Ningbo, Zhengzhou, and Xi'an [20], we further selected Xiaoshan, Jinhua, Shaoxing, and Lishui in the economic hinterland of Shanghai Port as the cooperation objectives of Shanghai Port. Thus, the cooperation performance of Suzhou (1), Ningbo (2), Xiaoshan (3), Jinhua (4), Shaoxing (5), Lishui (6), Zhengzhou (7), and Xi'an (8) with Shanghai Port are evaluated separately.  
In Section 4, the cooperation performance evaluation system was established. Based on that evaluation system, this section evaluates the cooperation between the eight dry ports and Shanghai Port. The cooperation indicators are scored quantitatively and qualitatively; namely, some indicators are scored through actual data search and some are scored by experts.  
(1) Data pre-processing  
According to Equations (8)-(10), the scores in Table 2 are normalized (a = 0.1 and b = 0.7), and the normalization results are shown in Table 3.  
Figure 1: Training process of SVM.  
(2) Parameter selection  
It is not difficult for SVM to handle nonlinear sample problems, which can be simplified by appropriate functions. Similarly, the algorithm has a significant influence on the kernel function. When the kernel function is stable, the influence of kernel parameters on the model should also be considered. In this study, considering the actual characteristics of dimensionality and sample number, we adopt the radial basis function (RBF).  
During the debugging of RBF, the penalty coefficient C, kernel width \(\sigma\), and insensitivity coefficient \(\varepsilon\) are the important influencing factors. It is found that if different parameters are selected, the results would have different degrees of fluctuation. Hence, it is necessary to constantly debug RBF in order to achieve an ideal state.  
We input the training samples and labels, and set the initialization range for the three parameters as C = [0, 100], \(\sigma\) = [0.01, 10]  
Figure 4: Variation of insensitivity coefficient with cycle number.  
Figure 5: Variation of mean squared error with cycle number.  
Figure 3: Variation of RBF core parameter with cycle number.  
Figure 2: Variation of penalty coefficient with cycle number.  
When the parameters and mean squared error all tend to be stable, the optimal state is reached. At this point, the coefficient values are as follows: the penalty coefficient C = 96, the kernel width \(\sigma\) = 0.95, the insensitivity coefficient \(\varepsilon\) = 0.0012, and the minimum mean squared error MSE = 0.00128.
# A Focused Event Crawler with Temporal Intent
## 5 Experiments and Discussion
### Experimental Setup
#### 5.1.2 Data Preparation  
According to the definition of a focused crawler, topical keywords must be identified prior to the crawling experiment. In this paper, a time-related event \"Typhoon Haiyan\" was chosen to test the effectiveness of our focused crawler. Specifically, \"Typhoon Haiyan\" was submitted as a query to Baidu News Search to identify the topical keywords. Based on the search results, 100 pages related to \"Typhoon Haiyan\" were manually selected. The content of the 100 relevant web pages was then segmented into different keywords using Pangu Chinese word segmentation, and these keywords were counted by word frequency. The keywords were then ranked according to their frequency, and 12 keywords with high frequency and practical importance were selected as topical keywords. It should be noted that synonyms were combined in this process, e.g., \"typhoon\" and \"hurricane\". The weight of the topical keywords was then calculated using the document-inverse document word frequency method. The final set of topical keywords and their weight for the event \"Typhoon Haiyan\" are shown in Equation (18).  
\[V_{\text{Tk}}=\left\{\begin{array}{l}(typhoon,1),(Haiyan,0.92),(Philippines,0.57),\\ (Guangxi,0.45),(rescue,0.32),(Sanya,0.26),\\ (heavyrain,0.24),(death,0.2),(Hainan,0.2),\\ (Vietnam,0.2),(disaster,0.19),(landfall,0.16)\end{array}\right\} \tag{18}\]  
The initial URLs for the crawling experiment were selected manually and automatically based on search engines. First, 12 web pages about Typhoon Haiyan from major portals were manually selected. Then, \"Typhoon Haiyan\" was submitted as a query to the Baidu News search engine and 360 News search engines, and 100 non-repeating URLs were obtained. Finally, 112 URLs were used as the initial URLs for this experiment.