esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Battery Experiments
### Identification Results  
The identified model was validated by the validation test profile. Here, we defined the original model as lacking hysteresis terms, whereas the improved model as considering hysteresis and temperature effect. The quantitative evaluation of goodness-of-fit of the model, MAE (Mean Absolute Error), and RMSE (Root Mean Squared Error) were introduced by the following equations:  
\[\text{MAE}=\left(\frac{\int_{k=0}^{n}\lvert\hat{S}(k)-S(k)\rvert}{n}\right) \times 100\% \tag{11}\]  
\[\text{RMSE}=\left(\sqrt{\frac{\sum_{k=0}^{n}(\hat{S}(k)-S(k))}{n}}\right) \times 100\% \tag{12}\]  
Figure 7: The OCV and hysteresis level under different temperatures: (**a**) the OCV boundaries of discharge and charge process at different temperatures; (**b**) the hysteresis voltage level.  
where \(S(k)\) is the experimental data and \(\hat{S}(k)\) is the estimated value at step k.  
Again, taking 0 \({}^{\circ}\)C for example, the simulated voltage by the original model and improved model are shown in Figure 8a and the errors of the two models are depicted in Figure 8b. For the profile at 0 \({}^{\circ}\)C, the MAE of the original model and improved model were 47.9 mV and 24.0 mV, respectively; and the RMSE of the original model and improved model were 59.8 mV and 29.9 mV, respectively. The MAE and RMSE at all temperatures are illustrated in Figure 9. Generally, the MAE and RMSE of both the original model and improved model decreased as the temperatures increased. However, as the temperature moves farther away from 30 \({}^{\circ}\)C, the performance of the proposed model becomes better than the original model. Unlike the proposed model, the original model's parameters were identified at 0 \({}^{\circ}\)C, thus the lack of parameters' correction in terms of the temperatures led the above results.  
Figure 8: Comparison between measured data and model results at 0 \({}^{\circ}\)C: (**a**) voltage of experimental, original model and improved model; (**b**) estimated error of the original and improved model.
## 4 Simulation Results
### Simulation Setup
The slanting rays emerging from the vehicle highlight the position of the altitude sensors w.r.t. the body of the vehicle. The dynamics of the vehicle is assumed to be governed by a PI-based velocity control system given by  
\[\mathbf{u}=\mathbf{K}_{p}\tilde{\mathbf{v}}+\mathbf{K}_{i}\int_{t_{0}}^{t}\tilde{\mathbf{v}}dt, \tag{17}\]  
where \(\tilde{\mathbf{v}}\) is the velocity tracking error (excluding the \(p\) angular velocity), and \(\mathbf{K}_{p}\) and \(\mathbf{K}_{i}\) are the proportional and integral gain matrices, respectively. The controller is initialized with gains \(\mathbf{K}_{p}=diag([750,780,780,380,100])\) and \(\mathbf{K}_{i}=diag([2280,2310,2310,2000,90])\). For the simulation, we also assume the availability of sensors (e.g., a forward looking sonar) that are able to compute distances to obstacles when in close proximity with the vehicle for providing feedback to the obstacle avoidance tasks. Moreover, we also assume acoustic communications (e.g., Ultra Short Base Line (USBL) unit with integrated acoustic modem) with the docking station for long range homing and visual markers on the docking station that are recognizable with an on-board camera for terminal docking. We further assume that the vehicle can measure its pose and velocity using on-board sensors (e.g., Inertial Measurement Unit (IMU) and Doppler Velocity Log (DVL)). In case of a moving docking station, its motion is constrained to a straight line motion with a constant heading, and to allow rendezvous, the docking station will be moving with a velocity lower than that of the maximum velocity of the vehicle. In order to achieve realistic simulations, obstacles are added into the simulation environment. For simplicity, the obstacles are modeled as spheres and the paper is not concerned regarding a fine modeling of the obstacles. As is commonly done, we envelope all the area around the docking place into sphere obstacles for repulsing the vehicle from colliding into structural elements of the station. We can also use these spheres to envelope no-fly zones to represent places or areas where the vehicle should not travel for other reasons than collision risks. For example, to dock to a submarine [31] or an ASV [33], a no fly zone can be defined by placing several spheres around the propeller area of the submarine or ASV so that the vehicle avoids entering into turbulent waters generated by the propulsion system of the submarine or ASV. Clearly when defined w.r.t. the dock location, the spheres will be attached to the dock position body coordinates and would move at the same rate as that of the dock.
## 4 Discussion
### Limitations and Strengths  
The limitations of the study are the following: (i) not making a correlation by sex, which was not possible due to the low number in the sample; (ii) not comparing body composition and physical performance according to playing position by sex, where previous studies [60; 61] have reported that there are differences in both body composition as well as physical performance according to playing position in different sports; and (iii) not controlling the hydration status and nutritional aspects that may have influenced body composition in professional basketball players. Among the strengths we find the following: (i) differentiating body composition and physical performance by sex in basketball players at a professional level, which can help in making decisions to program various training, maximizing performance; (ii) performing all measurements under the same conditions for both groups; (iii) having carried out the study on Chilean professional basketball players, due to the small number of studies related to the topic.
## 4 Results
### Risk Coupling Value Generation  
As discussed in the previous section, the risk coupling probabilities for different risk types across different ACs were calculated as shown in Figures 8-11. Based on the N-K model and mutual information, the risk coupling values for coupling scenarios involving two to five risk types in both PA and SA were calculated according Equations (6), (9), (11), and (13).  
The top three and bottom three risk coupling scenarios in PA and SA are listed in Table 5. Overall, the lowest risk coupling value occurred in the two-factor coupling type (\(T_{2,2}(H,R)\)), while the risk coupling value for the five-factor coupling type was the highest (\(T_{5}(H,V,R,W,M)\)). An interesting phenomenon was observed in the two-factor coupling type, where the human-road coupling scenario \(T_{2,2}(H,R)\) exhibited the lowest coupling value of \(0.000052\). However, as discussed in the previous section, the human-road risk coupling probability \(P(h,r)\) (\(h=1\) and \(r=1\)) was relatively high. This indicates that although human and road factors frequently occur together, accidents are not primarily caused by the coupling of these two factors alone. Instead, the involvement of additional risk factors may exacerbate the progression of risks. For example, in the three-factor coupling type, when human-road-weather coupling occurs (\(T_{3,4}(H,R,W)\)), the risk coupling value increased to \(0.0538\).  
Unlike in PA, the highest risk coupling value occurred in the four-factor coupling type (\(T_{4,1}(H,V,R,W)\)). Similarly, the risk coupling values for the two-factor coupling type were the lowest (\(T_{2,10}(W,M)\)). It is worth noting that, in the two-factor coupling type, the road-weather coupling scenario \(T_{2,8}(R,W)\) had the higher risk coupling value of \(0.032453\)  
Figure 11: Risk coupling probabilities of four risk types in different accident categories: (**a**) coupling probabilities of four risks in PA; (**b**) coupling probabilities of four risks in SA. Risk coupling probability values greater than \(0.35\) are highlighted with a black border.  
As discussed in the previous section, the coupling probability for the road-weather scenario \(P(r,w)\) (\(r=1\) and \(w=1\)) was the lowest. This suggests that road-related and weather-related factors rarely coexist, but when they do, they exacerbate the evolution of PA, leading to SA.
## 1 Introduction
The proposed approach satisfies the requirements of high accuracy, low latency, and strong generalization in rail surface condition identification (test set accuracy \(\geq\) 90%, model inference time \(\leq\) 0.04 s, and the number of parameters \(\leq\) 5 M). Additionally, it provides a more accurate threshold for the maximum adhesion coefficient, thereby facilitating optimal adhesion control. The rest of this paper is organized as follows. Section 2 describes the overall framework of the method, including the multi-source data input module, lightweight rail surface condition identification module, and fuzzy fusion estimation module. Section 3 shows the experimental results of the corresponding modules and analyzes them. Section 4 summarizes the research results and discusses the future research direction.
# Spatial Variations in Microfiber Transport in a Transnational River Basin
###### Abstract  
Five countries share the catchment of the Tisza River (Central Europe). In most households electricity and water are available, and by washing synthetic clothes they can produce a large number of microfibers. However, in many sub-catchments of the river, the wastewater treatment is insufficient; therefore, microplastics (MP), especially plastic microfiber emissions into rivers, represent a problem. Our goal was to analyze the suspended sediment and microfiber transport at the low stage, making repeated (2021 and 2022) measurements in the Tisza River (946 km) at 26 sites across three countries. Water sampling was performed by pumping 1 m\({}^{3}\) of water through sieves (90-200 \(\upmu\)m). The mean MP transport in 2021 was 19 \(\pm\) 13.6 items/m\({}^{3}\), but it increased by 17% in 2022 (22.4 \(\pm\) 14.8 items/m\({}^{3}\)). The most polluted sections were the Upper Tisza (Ukraine, Hungary) and the Lower Tisza (Serbia), where wastewater treatment is not satisfactory, whereas the Middle Tisza (Hungary) was less polluted. The tributaries increased the sediment and MP budget of the main river. Microfibers dominate (84-97%) the suspended MP transport, and thus it can be determined that they originated from wastewater. The MP transport was influenced by the availability of wastewater treatment plants, dams, tributaries, and mobilization of bottom sediments. At the low stage, no connection was found between the suspended sediment and MP particle transport.  
microplastic transport; reservoir; impoundment; tributary; bottom sediment; suspended sediment; wastewater management Article
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 3 Deep Learning Model
### Transformer
#### 3.2.1 Self Attention  
The most important structures in Transformer are the self-attention mechanism and the multi-head attention mechanism. The schematic diagram of finding one of the outputs \(\mathbf{b_{1}}\) is shown in Figure 5.  
First, we assume that the input is a sequence of four vectors \(\mathbf{a_{1}}\), \(\mathbf{a_{2}}\), \(\mathbf{a_{3}}\), \(\mathbf{a_{4}}\), and then multiply these four vectors by three transformation matrices \(\mathbf{W^{Q}}\), \(\mathbf{W^{K}}\) and \(\mathbf{W^{W}}\) to get each \(\mathbf{q^{i}}\), \(\mathbf{k^{i}}\) and \(\mathbf{v^{i}}\) corresponding to each input vector, that is:  
\[\mathbf{q^{i}} =\mathbf{W^{Q}}\mathbf{a^{i}} \tag{1}\] \[\mathbf{k^{i}} =\mathbf{W^{k}}\mathbf{a^{i}}\] (2) \[\mathbf{v^{i}} =\mathbf{W^{n}}\mathbf{a^{i}} \tag{3}\]  
where \(i=1\), \(2\), \(3\), \(4\).  
After getting these three elements, we can start attention, as shown in Figure 5. Here, we take the output \(\mathbf{b_{1}}\) as an example.  
First, we perform Scaled Dot Product on \(\mathbf{q^{1}}\) with \(\mathbf{k^{1}}\), \(\mathbf{k^{2}}\), \(\mathbf{k^{3}}\) and \(\mathbf{k^{4}}\), and we can get \(\mathbf{\alpha_{1,1}}\), \(\mathbf{\alpha_{1,2}}\), \(\mathbf{\alpha_{1,3}}\) and \(\mathbf{\alpha_{1,4}}\).  
Then, we perform Softmax on \(\mathbf{\alpha_{1,1}}\), \(\mathbf{\alpha_{1,2}}\), \(\mathbf{\alpha_{1,3}}\) and \(\mathbf{\alpha_{1,4}}\), we can get \(\mathbf{\alpha^{\prime}_{1,1}}\), \(\mathbf{\alpha^{\prime}_{1,2}}\), \(\mathbf{\alpha^{\prime}_{1,3}}\) and \(\mathbf{\alpha^{\prime}_{1,4}}\), and then \(\mathbf{\alpha^{\prime}_{1,1}}\), \(\mathbf{\alpha^{\prime}_{1,2}}\).  
\[\mathbf{\alpha_{1,1}} =\mathbf{q^{1}}\cdot\mathbf{k^{1}} \tag{4}\] \[\mathbf{\alpha_{1,2}} =\mathbf{q^{1}}\cdot\mathbf{k^{2}}\] (5) \[\mathbf{\alpha_{1,3}} =\mathbf{q^{1}}\cdot\mathbf{k^{3}}\] (6) \[\mathbf{\alpha_{1,4}} =\mathbf{q^{1}}\cdot\mathbf{k^{4}} \tag{7}\]  
\(\mathbf{\alpha^{\prime}_{1,3}}\) and \(\mathbf{\alpha^{\prime}_{1,4}}\) are multiplied by \(\mathbf{v^{1}}\), \(\mathbf{v^{2}}\), \(\mathbf{v^{3}}\) and \(\mathbf{v^{4}}\), respectively, and finally the four results are added to obtain the output \(\mathbf{b_{1}}\), that is:  
\[\mathbf{b_{1}}=\sum_{i=1}^{4}\mathbf{\alpha^{\prime}_{1}}\mathbf{v^{i}}=\sum_{i=1}^{4} \mathit{Softmax}(\mathbf{\alpha_{1,i}})\mathbf{v^{i}} \tag{8}\]  
As for \(\mathbf{b_{2}}\), \(\mathbf{b_{3}}\) and \(\mathbf{b_{4}}\), we can refer to Formula (8) and express it as the following formula:  
\[\mathbf{b_{2}}=\sum_{i=1}^{4}\mathbf{\alpha^{\prime}_{2,i}}\mathbf{v^{i}} \tag{9}\]  
\[\mathbf{b_{3}}=\sum_{i=1}^{4}\mathbf{\alpha^{\prime}_{3,i}}\mathbf{v^{i}} \tag{10}\]  
\[\mathbf{b_{4}}=\sum_{i=1}^{4}\mathbf{\alpha^{\prime}_{4,i}}\mathbf{v^{i}} \tag{11}\]  
Figure 5: The schematic diagram of finding one of the outputs \(\mathbf{b_{1}}\).
# Study on Stiffness Parameters of the Hardening Soil Model in Sandy Gravel Stratum
## 2 Large-Scale Experiments of Sandy Gravel
### Standard Consolidation Test  
There is little research on the standard consolidation test of coarse-grained soil in China, and most scholars focus on isobaric consolidation. However, the standard consolidation test is one of the necessary tests to study the HS model. In this paper, a large-scaled consolidator (Figure 7) with a 300 mm diameter is used to measure the relationship between the deformation and pressure of a sandy gravel soil sample by a standard consolidation test. The stress-strain curve under lateral limit conditions is obtained, and the oedometric loading Young's modulus \(E_{\text{oed}}\) can be calculated.  
Figure 6: Loading-unloading-reloading experimental curve of sandy gravel.  
The soil sample was subjected to five incremental axial loads of 50, 100, 200, 400, and 800 kPa, respectively, after saturation. A consolidation time of 24 h was allocated for each loading level. The deformation was considered to have reached completion when the hourly axial displacement was less than 0.01 mm/min under each loading level, at which point the next load level was applied until the completion of the test. The resulting stress-strain relationship was documented in Figure 8.  
The axial strain of soil under lateral confinement exhibits an exponential increase in response to the applied stress, primarily due to the limited ability of soil volume to decrease as soil porosity decreases during the compression process. The oedometric loading Young's modulus can be determined by the slope of the tangent at a specific point on the stress-strain curve, with the axial stress being 100 kPa. In this test, the obtained modulus value was found to be 424 kPa. The results of the triaxial drained shear test, loading-unloading-reloading test, and standard consolidation test were analyzed to determine the proportional relationship between three stiffness parameters of the HS model. The findings indicate that the proportion is \(E_{\text{oed}}\):\(E_{\text{50}}\):\(E_{\text{ur}}\) = 1:1:4.3.  
Figure 8: Curve of standard consolidation test of sandy gravel soil sample.  
Figure 7: Large-scaled consolidator.
# Walking in the Absence of Anterior Cruciate Ligament: The Role of the Quadriceps and Hamstrings
## 4 Discussion
### Knee Kinematics  
Our previous work [10] showed that, during walking, the ACL is recruited in the load acceptance phase and at the late stance-early swing phase when the internal moment produced by the muscles is an extensor.  
In the absence of the ACL, during the load acceptance phase, the tibia reaches a maximum anterior translation exactly at the same instant in which the ACL should express its maximum tension.  
In the late stance-early swing phase, the forward traction transmitted to the tibia by the quadriceps would be compensated in an intact knee by the tension expressed by the ACL. In the absence of an ACL, the anterior translation of the tibia was increased and reached a maximum just before the mid-swing, at approximately the same time identified by [PERSON] et al. [1]. Then, as confirmed by these authors, the tibia recovered the same physiological translation in the second half of the swing phase as soon as the hamstring muscles were activated and exerted their posterior pulling action. Specifically, our results showed that the hamstring activity in an ACL-deficient knee prevented the anterior translation of the tibia from increasing further away from the physiological ranges, avoiding an average increase of 9 mm in the second half of the swing phase.  
All these findings confirmed the hamstrings' ability to compensate for the ligament deficit, demonstrating the importance of training these muscles during the rehabilitation program of ACL-deficient knee subjects. This aspect has been investigated and conceptually confirmed by [26], but thanks to the dynamic simulation, this effect was quantified in our study.  
Concerning the quadriceps, in our simulation, the inactivation of this muscle in an ACL-deficient knee resulted in an average reduction in the anterior translation of the tibia by 4 mm during load acceptance and by 9.5 mm during the large knee flexion compared with the knee with the ACL sacrificed and quadriceps active. This behavior supports the well-known tendency to atrophy of the quadriceps that arises in patients with an anterior cruciate rupture [27].
# Optimization of Cellular Automata Model for Moving Bottlenecks in Urban Roads
## 3 Simulation Setup and Result Analysis  
### Setting of Cellular Parameters  
This study utilizes a self-built simulation environment in Python 3.10, which includes a cellular automaton model, a parameter configuration file, a method for visualizing simulation results, and a main simulation execution program. The cellular automaton model is used to define the road environment, vehicle update rules, and other environmental factors, as well as to record simulation data. The parameter configuration file is used to set various parameters for the cellular simulation. The method for visualizing simulation results is used to display the simulation outcomes visually. The main simulation execution program is responsible for invoking the aforementioned files to run the simulation and output the results.  
Figure 3: Schematic diagram of intersection.  
The simulation environment consists of an arterial road and several access roads, all of which are two-way four-lane urban roads. Each road is modeled with a length of 1000 cells, with each cell measuring 5 m in length. Small cars have a length of 1 cell (5 m), while heavy-duty vehicles occupy a length of 2 cells (10 m). The maximum speed for small cars is 5 cells (25 m/s), with an initial speed of 3 cells (15 m/s). For heavy-duty vehicles, the maximum speed is 3 cells (15 m/s), and the initial speed is 2 cells (10 m/s). Among them, the signal-controlled intersection has a total of two phases. The arterial road runs in the east-west direction, while the access road runs in the north-south direction. Right-turning vehicles are not controlled by signals. In the first phase, the green light duration for straight and left-turn movements in the east-west direction is 38 s. In the second phase, the green light duration for straight and left-turn movements in the north-south direction is 17 s. The duration of the yellow light is 4 s, and the all-red time is 2 s. The probabilities for vehicles to travel straight and turn right in each direction are 0.8 and 0.2, respectively, while the random deceleration probability for vehicles is 0.02. To facilitate the control of the traffic flow density, the simulation adopts periodic boundary conditions, meaning that vehicles exiting from the end of the road will re-enter the lane from the beginning of the road. For each vehicle type configuration and each traffic flow density, the simulation runs for 1000 time steps. During the simulation process, the average values of various traffic flow parameters are calculated every 20 time steps.
# Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach
## 3 Innovative Method  
### The Method  
The method considers the road pavement as a filter of the acoustic and seismic waves (see Figure 1) [39; 40; 41]. These latter are generated by a mechanical source (i.e., the vehicular traffic), travel into the medium (i.e., the road pavement), and finally are detected by a receiver (i.e., a microphone isolated from the airborne noise, attached on the road surface in a nondestructive way, connected to an external soundcard, in turn connected to a laptop running the Matlab code used to record sounds). Consequently, the related data set consists of the acoustic responses (ARs) of the road pavements to the loads induced by the vehicles. It is expected that the propagation of the above-mentioned signals will change if the structural health status (SHS) of the medium gets worse. As is well known, the seismic wave propagation depends on different parameters, such as the elastic modulus that in turns is affected by the temperature, the distance source-receiver (geometric damping), the material damping (related to the properties of the material and the vibration amplitude), the boundary conditions, and the occurrence of concealed distresses [42; 43]. Figure 1 shows the tasks carried out in this study, from 1 (experimental investigation) to 5 (comparison of the results obtained by the ML algorithms, in terms of model accuracy, to find the best ML algorithm for the application of the proposed monitoring method).  
Importantly, in the last years, a considerable effort was paid to set up the method. In particular, different experimental configurations and data analysis approaches were used to analyze the data gathered applying the proposed method. In particular, the analysis in time, frequency, and time-frequency domains, features extraction, hierarchical clustering [44], and Finite Element Models (FEM) [45] were used for the purposes mentioned above, while, in this study, different ML models were used. Based on the potentialities of the ML classifiers that were selected for this study and are presented in Section 3.3, good performance in term of automatic crack detection and monitoring was expected.