esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Outlook  
In this work, a surrogate ANN model was proposed and optimized for prediction of the LBC of CFSS members. The ANN model was trained and validated against experimental data. A parametric study was performed to calibrate the parameters of the model, including the number of neurons, activation function, cost function and training algorithm, respectively. Statistical analysis based on  
Figure 13: (**a-d**) Effect of eccentric loading on the LBC of compressive CFSS members in different scenarios. The same limits are applied on the x- and y-axes.  
Monte Carlo random sampling techniques was conducted to track the variability of the input space. The main conclusions of this study are as follows:  
* For the considered problem, the optimal ANN model includes 15 neurons, rectilin activation function, mean squared error cost function and LM training algorithm;
* The proposed ANN model offers reliable prediction in terms of various performance indicators: R\({}^{2}\) = 0.975, Slope = 0.975, RMSE = 294.424 kN and MAE = 191.878 kN, respectively;
* By performing sensitivity analysis based on the ICE technique, the geometric parameters of the cross section (B and \(\delta\)), together with the compressive strength of the concrete (f\({}^{\prime}\)c), have been found to be the most influential variables on the LBC;
* The effect of eccentric loading on the LBC of CFSS members is explored, within the ranges of the input variables adopted in this study;
* The proposed ANN model is able to assist the initial phase of research and design of CFSS members before any experiments are carried out.  
However, there are several limitations of the current model, including the lack of data points in several classes of values of input variables. Updated data must be collected through further studies in order to enhance the model's performance. For comparison and validation purposes, the effect of loading eccentricities should be compared with mathematical models and/or experimental results in further works. In addition, in terms of practical application, a Graphical User Interface, especially one based on Microsoft Excel, should be developed and provided for researchers and engineers, and to support the teaching and interpretation of the compressive behavior of CFSS members.  
Conceptualization, T.-T.L.; methodology, T.-T.L.; software, T.-T.L.; validation, T.-T.L.; investigation, T.-T.L.; writing--original draft preparation, T.-T.L.; writing--review and editing, T.-T.L.; funding acquisition, T.-T.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare that there is no conflict of interest.  
The raw/processed data required to reproduce these findings will be made available on request.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 2 Underwater Acoustic Channel Model  
In this section, we first introduce the attenuation of underwater acoustic signal in Section 2.1, and then present the impacts of the ambient noise in Section 2.2.
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 4 Simulation Results and Discussion
### Spectral Efficiency Enhancement
However, the total spectral efficiency is still increased by using 64 QAM when compared with 16 QAM. Figure 11: Spectral efficiency for 16 QAM-1/2 versus noise power with different values of \(\gamma\). Figure 10: Spectral efficiency for QPSK-1/2 versus noise power with different values of \(\gamma\).
# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
## 3 Construction of a BN  
### Risk Transmission for Overload-Induced Early Cable Fires  
In general, the process of risk transmission occurs in four phases: (i) risk incubation, (ii) diffusion, (iii) outbreak, and (iv) extinction [39]. This study focuses on the processes before fire spreading, namely risk incubation and diffusion phases, which correspond to the early evolution and formation stages of fire elements.  
The evolution of overload-induced cable fires needs to be explored to prevent risk generation and transmission, which is of great significance for cable fire prevention and early warning. To investigate the overload-induced cable fires' evolution characteristics, three categories of factors are usually considered: (i) basic factors, (ii) intermediate factors, and (iii) fire risk factors. The basic factors are the root causes of cable overheating, which usually include electric currents, cable damage, aging, and environment temperature. These factors directly or indirectly affect the overload fire risk factors through the second category of intermediate factors. The intermediate factor considered in this study, the cable temperature rise, is the bridge connecting the basic factors and the third category of the fire risk factors, which consists of the insulation breakdown, cable heat resource, and material pyrolysis risks.  
During the early fire risk evolution, regardless of external factors, the overcurrent is the main culprit of overheating, increasing the cable temperature. In addition, cable aging, mechanical damage, and high ambient temperature are also important factors that increase the cable temperature. In particular, the cable insulation material, PVC, suffers from thermal aging, which irreversibly increases the cable insulation breakdown risk [40]. In addition, the loss of the plasticizer causes the formation of voids in the material, which reduces the insulation breakdown strength [41]. Based on the above considerations, the risk transmission process of overload-induced cable fires in this study is presented in Figure 1. In the cable fire formation stage, electric sparks, high-temperature heat sources, and flammable substances are caused by three risk factors, as shown in Figure 1.
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 1 Introduction
In this study, two of the most commonly used docking tools--AutoDock (version 4.2.6) and Vina (AutoDock Vina)--were evaluated for different types of targets and ligands, using an unbiased reference validation set--Directory of Useful Decoys-Enhanced (DUD-E) [31]. Both docking programs are widely used to this day, for a large diversity of targets and problems [32; 33; 34; 35; 36; 37; 38; 39]. AutoDock 4 is a well-known docking program developed by [PERSON] and co-workers [40; 41; 42; 43] at the Scripps Research Institute. Its free availability to academic users, together with the good accuracy and high versatility shown, had made it a very popular first choice for new users and have contributed to a widespread use of AutoDock, well portrayed in its impressively high number of citations. AutoDock 4 offers a variety of search algorithms and a scoring function that is based on a linear regression analysis, the Assisted Model Building with Energy Refinement (AMBER) force field, and a large set of diverse protein-ligand complexes with known inhibition constants. The program could be used with a visual interface called AutoDock Tools (ADT) which ensures an efficient analysis of the docking results. AutoDock Vina [44; 45; 46] is a docking program developed by [PERSON] and [PERSON] also at the Scripps Research Institute, La Jolla, California, following the success of previous AutoDock versions. Vina is freely accessible to a large number of users, as it is open source. AutoDock Vina inherits some of the ideas and approaches of AutoDock 4, but it is designed in a conceptually different way. It offers significant improvements in the average accuracy of the binding mode predictions, while also being up to two orders of magnitude faster than AutoDock 4. It also features new search algorithm and a hybrid scoring function, combining empirical and knowledge-based scoring function. Its multi-core capability, high performance and enhanced accuracy, ease-of-use and free availability have contributed to an extremely fast dissemination through the docking community, well-portrayed in the high number of citations of the original paper. Its high computational efficiency and ability to use multiple CPUs or CPU cores also makes this program a competitive alternative for virtual screening. The Directory of Useful Decoys-Enhanced (DUD-E) [31] holds a collection of decoys and ligands for benchmarking virtual screening, containing 22,432 active compounds and their affinities against 102 targets set by [PERSON] et al. For each of the active compounds (i.e., the ligands), this database contains a set of 50 \"decoys\", i.e., molecules with similar 1-D physico-chemical properties to remove bias (e.g., molecular weight, calculated LogP), but dissimilar 2-D topology to be likely non-binders, i.e., inactives. These characteristics make DUD-E a challenging dataset to test scoring functions and protein-ligand docking algorithms. Ideally, the perfect scoring function would rank the active molecules higher than the decoys, but that is not often the case.
# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
## 1 Introduction
[28] proposed a \(P\) probability RRT algorithm based on goal bias strategy. [PERSON] et al. [29] proposed a variable step size trunk fast exploration random tree (VT-RRT) algorithm. By transforming the search space of random nodes in the RRT algorithm and adaptively adjusting the step size according to the target position, the search efficiency is effectively improved, and the path planning time is reduced. [PERSON] et al. [30] proposed a new node expansion method that is biased towards the target point. The method combines the target point gravity, obstacle repulsion, and random point gravity, and adds an adaptive function related to the obstacle distance. A pruning optimization method is proposed to optimize the path length. [PERSON] et al. [31] used an ant colony algorithm to optimize RRT. Experiments show that the combination of an ant colony algorithm and RRT algorithm effectively reduces the number of nodes and the average computing time. [PERSON] [32] proposed a dynamic path planning method for robot autonomous obstacle avoidance based on an improved RRT algorithm, namely smooth RRT (S-RRT). This method takes the directional node as the goal, which greatly improves the sampling speed and efficiency of RRT. In the field of path planning for dual-arm robots, [PERSON] [33] proposed a dual-arm path planning method based on closed-chain kinematics to meet the motion constraints of the manipulator. [PERSON] [34] proposed a dimension reduction RRT method, which reduced the dimension of high-dimensional path planning space according to the task requirements of the dual-arm robot. To ensure that the RRT algorithm has higher efficiency, [PERSON] [35] proposed a cooperative path planning method for a dual-arm robot based on gravity adaptive step length RRT. The simulation results show that the gravity adaptive step size RRT method can effectively constrain the step size in the workspace to ensure the effectiveness of the collision detection algorithm. In this paper, the GA_RRT path planning algorithm is proposed based on the traditional RRT algorithm by introducing the goal probability bias strategy and A* cost function. In addition, two manipulators use the bounding sphere collision detection method. The main arm considers static obstacles, and the slave arm considers static obstacles and dynamic main arm obstacles. Finally, MATLAB software is used to simulate the obstacle avoidance path planning for the dual-arm robot. It is finally proved that the algorithm is effective and superior for the path planning of dual-arm robot.
## Appendix A Portable X-ray Fluorescence Calibration Procedures  
In this study, we used a Bruker Tracer III-SD portable X-ray Fluorescence (pXRF) analyser. The instrument employs an X-ray tube with an Rh target and a 10 mm\({}^{2}\) silicon drift detector (SDD) with a typical resolution of 145 eV at 100,000 cps. For analysis of copper-based alloys, we found that operating the X-ray tube with a setting of 40 keV at 5.0 uA in an air-path and through a window composed of 12 mil Al and 1 mil Ti filters([PERSON]'s Yellow filter) provided a good count rate for the elements of interest. Assays of artefacts were taken for 60 s each, which was sufficient time to obtain stable results and was in line with other researchers using similar instruments [5; 31; 32]. Nine modern copper-based alloy reference standards were used for the calibration of these data (Table A1). Each reference standard was analysed twice, and the results were averaged.  
Given the relatively small number of standards available, the calibration procedure was kept as simple as possible. Linear regressions on the net characteristic element peaks (normalised to counts-per-second) were employed, and corrections for interference peaks were included in the regression formulas where necessary (Table A2, Figure A1). For example, the escape K\(\alpha\) peak of Cu (6.306 keV) overlaps with the characteristic K\(\alpha\) peak of Fe (6.405 keV) and introduces error if not accounted for in the regression equation.  
In the case of three elements (Ni, Sb, and Pb), slightly negative values were occasionally obtained from the calibration. Examination of typical spectra indicates that, at low concentrations, the peaks for these elements are swamped by the tails of the much larger Cu, K\(\alpha\), and K\(\beta\) peaks at low concentrations (Figure A2).  
Figure A1: Scatterplots of given versus calibrated values for the reference standards. The dashed black lines show the ideal 1:1 \(x\)-\(y\) line. As and Pb are shown with logarithmic axes.
# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 3 Outdoor Activity Recognition System
### Semantic Location Extraction
#### 3.2.3 Activity Recognition through Semantic Location Mapping  
After the POIs visited by the users have been identified, and the semantic location extraction process has extracted the semantic meaning of each of those places, a mapping is performed from the OSM category taxonomy to activities. Therefore, based on that taxonomy it is possible to recognize the following activities based on location data:  
1. **Related to the instrumental activities of daily living:** Go to the shop, Go to clothing store, Go to the department store, Go to the shopping mall, Go to shoe store, Go to the home goods store, Go to jewelry store, Go to the hardware store, Go to flower store, Go to laundry shop, Go to Sports shop, Cooking, Go to the pharmacy, Go to the drugstore, Doing housework, Doing laundry at home, Driving or using public, transportation, Take plane, Take train, International travel, Use taxi, Going to work, Go to the accountant, Go to the ATM, Go to the Bank.
2. **Related to eating habits:** Going to the restaurant, Going to the food shop, Going to the bar, Going to the cafe, Going to the liquor store, Order food in person, Go to the bakery, Buy food, Buy fish, Buy frozen food, Buy ice cream, Go to the butcher, Go to the deli
3. **Related to physical activity:** Go to the gym, Walking more than a mile, Walking several blocks, Walking one block, Still time, Swimming, Dancing, Golf.
4. **Related to socialization:** Going to the restaurant, Visiting Family, Visiting Friends, Go to the bar, Go out in public.
5. **Related to hobbies:** Rent a movie, Go to the cinema, Go to the aquarium, Go to the amusement park, Go to the bowling alley, Go to the gambling shop, Go camping, Go to the zoo, Go to the stadium, Go to the nightclub, Go to tourist attraction, Go to the beach, Nature, Go to the Spa/Sauna, Go to the beauty salon, Go to the hairdresser, Go to cosmetics store.
6. **Doctor related activities:** Go to the doctor, Go to the dentist, Go to the physiotherapist, Got to the hospital, Go to the gym.
7. **Related to cognition:** Spirituality, Education, Go to cinema, Go to museum, Go to the art gallery, Go to bookshop, Go to library.
8. **Other:** Lodging, Take children to school, Take children to the playground, Go to the kindergarten, Buy baby products.  
Additionally, the home of the user is also inferred clustering with DBSCAN the GPS coordinates between 1 a.m. and 6 a.m. and selecting as home the largest cluster. Using this method, it is possible to detect days where the user has slept away from home or even if the user has moved. Providing more insight about user's habits.
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 4 Experiments and Discussion  
To evaluate the compression power of BYOT on ST-GCN, and the extracted feature quality, action classification is adopted to evaluate the HAR accuracy and feature clusters. The proposed method is tested on NTU-RGB+D [18], a dataset built in 2016 and captured by three Kinect V2 cameras. It contains 60 action classes and 56,880 video samples. RGB videos, depth map sequences, 3D skeletons and infrared (IR) videos for each sample are provided. Each skeleton has 25 body joints. The first 50 actions are performed by a single  
Figure 5: The compression algorithm framework, drawn based on [4]. The ST Block is one block of the ST-GCN algorithm. There are three ST Blocks in total. The FC is the fully connection layer. The number of star-shaped symbols for computing denotes the relative magnitude of computing resources required.  
subject, while the last ten are interacted actions. Here, only the 3D skeletons are picked as the input.  
The NTU dataset is divided into two datasets; one is NTU xsub, the other is NTU xview. The NTU xsub dataset evaluates cross-subjects, with 20 subjects as the training set and the remaining 20 subjects as the test set. The NTU xview dataset evaluates cross-views, with three views as the training set and the remaining two views as the test set. The NTU xview is easier to use than the NTU xsub for action recognition [15].  
The evaluation was carried out on three backbones, which differed in the number of layers: ST-GCN 8, ST-GCN 12 and ST-GCN 20, respectively (Figure 4). Furthermore, the influence of input features is tested, which are divided as three types and noted as _pos_, _mov and mov + pos_ for the skeleton coordinates, one frame displacements and both, respectively. The method of building these features is described in Section 3.2.  
The performance is measured by top-1 accuracy,  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}, \tag{12}\]  
where \(TP\) is the true positive, \(FP\) is the false positive, \(TN\) is the true negative and \(FN\) is the false negative.  
Next, the main observations that can be drawn from the results are stated.
## 7 Concluding Remarks  
In this paper, we have derived feedback power control strategies and evaluated their impacts on the performance of block-faded multiple access schemes with JCD. In all cases, the use of feedback power control is expedient to set the network operational point in the feasible SNR region. First, we have derived a classical power control strategy which tries to equalize the link SNRs at the AP. Then, we have derived an innovative optimized feedback power control strategy, which allows the system operational point to lie in the feasible SNR region at the lowest overall transmit energy cost. In this case, the SNRs are typically unbalanced. Our results show that both feedback power control strategies significantly improve the performance, with respect to schemes without power control. In particular, the unbalanced SNR feedback power control strategy guarantees a performance better than that with the balanced SNR power control strategy, and this is more pronounced when a proper channel code is used (namely, a properly designed SCCC). We have then analyzed the robustness of the proposed power control strategies in the presence of non-idealities, in terms of residual multiple access interference and noisy feedback channels. Our results show that even in non-ideal scenarios the best feedback power control strategy is to unbalance the target SNRs. Finally, we have applied the proposed feedback power control strategies to a limiting case of the considered multiple access scheme, obtaining a CEO scenario where the \(n\) sensors make noisy observations of a common binary source. In this case, we have derived a proper fusion rule, at the AP, to be applied after power control. We have then shown that, for increasing SNR at the sensors, a limiting probability of decision error (i.e., a floor) is asymptotically reached, regardless of the channel code and feedback power control strategy under use. The feedback power control strategy, however, has an impact on the \"speed,\" i.e., the minimum SNR, at which this floor is reached.
## 4 Discussion
### Engineering Suggestions on Expansive Soil Slope in Seasonal Freezing Area  
When using a finite element method to analyze expansive soil slopes in seasonal frozen zones or to deal with actual projects in this area, attention should be paid to the influence of slope ratio on slope stability, which is not only different from the calculation of the slope safety factor in the traditional sense. Due to the crack characteristics of expansive soils, the width and depth of cracks on slopes with larger slope ratios are larger, which is particularly important in finite element analysis because the path of external moisture entering the slope is increased. At the same time, it can be found that compared with the small slope ratio, the large slope is more temperature sensitive in the expansive soil slope, and the moisture content and temperature inside the slope change more dramatically. Therefore, in both modeling and practical engineering, additional safety factors should be added to the slope ratio. The absence of these changes may cause the calculation of slope support structure to be smaller. Finally, there is potential displacement creep for expansive soil slope in the freeze-thaw cycle, and there is a downward trend for expansive soil slope at all slope ratios.  
The freeze-thaw cycle process of the slope is the freezing process, the cold front gradually invades the soil, and then gradually leaves the slope in the melting stage. The physical and mechanical properties of expansive soil in the area where the cold front is  
Figure 13: Slope displacement variation after freezing and thawing. (**a**) Slope bottom displacement; (**b**) slope top displacement.  
active have changed greatly, but there will be no significant change in the area where the cold front is not invaded. The intrusion of a cold front will lead to a loose slope structure and creep to the lower part of the slope, increase the risk of landslide and reduce the bearing capacity of the slope. Under the actual engineering conditions, there is replenishment and evaporation of external water sources in different seasons. Due to the uncertainty of rainfall and evaporation by air humidity and wind speed, the change of expansive soil slope in a freeze-thaw cycle under an open system should be more complex. However, as a freeze-thaw cycle, the sensors required for in situ tests are complex, and the cycle time takes one year. Many unpredictable risks are easy to occur in such a long-time dimension. Combining this test with the field expansive soil engineering, it is necessary to gradually increase the volume and internal stress of the slope, and the external complex environment should also be controlled, and a single variable should be discussed step by step. It is necessary to use some finite element simulations for the problem of expansive soil slopes in seasonally frozen areas and continue to study the indoor model test.