esa-sceva/satcom-chunk-collection huggingface dataset:
# Battery Management System Hardware Concepts: An Overview
###### Abstract  
This paper focuses on the hardware aspects of battery management systems (BMS) for electric vehicle and stationary applications. The purpose is giving an overview on existing concepts in state-of-the-art systems and enabling the reader to estimate what has to be considered when designing a BMS for a given application. After a short analysis of general requirements, several possible topologies for battery packs and their consequences for the BMS' complexity are examined. Four battery packs that were taken from commercially available electric vehicles are shown as examples. Later, implementation aspects regarding measurement of needed physical variables (voltage, current, temperature, etc.) are discussed, as well as balancing issues and strategies. Finally, safety considerations and reliability aspects are investigated.  
battery management systems; BMS requirements; battery pack topologies; electric vehicles; BMS-ASIC; balancing; insulation measurement +
Footnote †: journal: Applied Sciences
# Grey-Box Fuzzing Based on Reinforcement Learning for XSS Vulnerabilities
## 3 Background
### Reinforcement Learning  
Reinforcement learning is a type of feedback-based learning [26]. As shown in Figure 1, an agent can observe the state and rewards of the environment and generate and execute an action based on the state. After the environment is affected by the action of the agent, it will switch to the next state and give the agent a new reward. The agent's goal is to maximize long-term rewards and expectations in constant interactions with the environment.  
A basic reinforcement learning model is modeled as Markov decision processes (MDPs) [27]. An MDP is represented by a five-tuple \(\langle S,A,R,P,\gamma\rangle\), where \(S\) is the state set of the agent, \(A\) is the action set of the agent, \(P\) is the state transition matrix \(P_{ss^{\prime}}^{a}=\mathrm{P}[S_{t+1}=s^{\prime}|S_{t}=s,\)\(A_{t}=a]\), \(R\) is the reward function, \(R(s,\ a)=E[R_{t+1}|S_{t}=s,\ A_{t}=a]\), \(\gamma\) is the reward discount factor, \(\gamma\in(0,1)\). In MDPs, state changes and rewards depend on the current state and action, not the previous state and action. The agent needs to learn the policy \(\pi\) to determine the action chosen for the current state, which represents a mapping relationship from state to action. The goal of reinforcement learning is to learn a policy to describe all actions chosen by the agent. In addition to policies, the MDP contains two value functions, which are set state function \(V\) and action state function \(Q\).  
The value state function starts from state \(s\), and the policy reward obtained by using policy \(\pi\) is:  
\[v_{\pi}(s)=E_{\pi}[R_{t+1}+\gamma v_{\pi}(S_{t+1})|S_{t}=s] \tag{1}\]  
The state action value function starts from state \(s\), executes action \(a\), and then uses policy \(\pi\) to obtain the expected reward value:  
\[q_{\pi}(s,a)=E_{\pi}[R_{t+1}+\gamma q_{\pi}(S_{t+1},A_{t+1})|S_{t}=s,A_{t}=a] \tag{2}\]  
Reinforcement learning hopes to find the largest value function of all policies, that is, the optimal value function, including the optimal \(V\) function and the optimal \(Q\) function.  
\[v_{*}(s)=max_{\pi}v_{\pi}(s) \tag{3}\]  
\[q_{*}(s,\ a)=max_{\pi}q_{\pi}(s,a) \tag{4}\]  
The optimal value function represents the best performance that can be achieved in an MDP, and when the optimal value function is found, it represents the current MDP  
Figure 1: Graphical representation of reinforcement learning.  
problem to be solved. To find the optimal value function, we use the recurrence relationship between the optimal \(Q\) function and the optimal \(V\) function:  
\[v_{*}(s)=v_{\pi_{*}}(s)=\sum_{a\in A}\pi_{*}(a|s)q_{\pi_{*}}(s,a)=max_{a}q_{\pi_{* }}(s,\ a)=max_{a}q_{*}(s,\ a) \tag{5}\]  
Through continuous iteration, the optimal \(Q\) function and the optimal \(V\) function are finally obtained.
## 3 Impact Factors Analysis
### The Microseismic Importance Factor  
In the process of underground mining, due to the complex and changeable ore body shape and mining conditions, the microseismic monitoring range is often of irregular geometry. The macroscopic requirement of network optimization is that the spatial geometry formed by the stations has good properties, that is, the effective monitoring range and the designed monitoring range are highly consistent [25]. To construct the monitoring range index of the station network from a quantitative point of view, it is necessary to define the effective monitoring range. The \(D\)-value method is used to calculate the theoretical positioning error under the network, and it is considered that the three-dimensional space with an error less than e (the value of e is set by the mine according to the needs of safety production, such as 50 m) is an effective monitoring area [17]. The design monitoring range is recorded as \(V_{0}\), the effective monitoring range of the station network is \(V_{1}\), and the overlapping area of the design monitoring range and the effective monitoring range is \(V^{\prime}\). The microseismic importance factor \(F_{V}\) is introduced, and its mathematical definition is shown in Equation (5):  
\[F_{V}=\frac{V^{\prime}}{V_{0}} \tag{5}\]  
Ideally, the effective monitoring range of the station network completely coincides with the designed monitoring range, that is, \(F_{V}=1\), and it is stipulated that \(F_{V}\leq 1\).
## 2 Literature Review
Another problem is hacking. Hacking can be done by irresponsible people which can damage the system as well as the real-life traffic. The operator must take control of the system as the intelligent system cannot do it by itself and this may take a while [25; 26; 27; 28; 29]. Another circumstance are Smart Environmental Sensors systems, which could benefit businesses and organizations. These sensors can detect the presence of people and cars. For businesses, employers may install these systems to detect the presence of their employees whether they are around in the building or area of working or not. They also can be used to check attendance, and the systems can detect at what time they come to and leave the office, etc. Smart Environmental sensors systems also help some organizations/businesses to tell their customers where to find available parking in their area. The system will analyze and calculate this and then give a result. If the parking is not full, the system will tell the customer with a green light with how many parking spaces are available and when it is full, the system will tell the customer this with a red light [6; 23]. From the cloud intelligent systems technical point of view, this study implemented a combination between the high-possibilistic c-means (HOPCM) clustering algorithm and the scheduling FDHEFT algorithm, introduced by [15], to find the optimal solution in minimizing monetary cost and execution time (Figure 2). To analyzed the workflow load and activity, [PERSON] et al., implemented a workflow structure modeled with a direct acyclic graph (DAG) G = (T, D), where T = {T1, T2,..., Tn} is the set of tasks and  
Figure 2: Cloud intelligent system scheduling processes. \(\rm D=\{(Ti,T)\}|\rm(Ti,Tj)\in T\}\) is the data dependencies. The tasks may involve predecessors (Pre), successors (Succ), without predecessor (Tentry), and without successor (Texit) that is expressed by:  
\[\rm Pre\;(Ti)=[Tj](Tj,Ti)\in D] \tag{1}\]  
\[\rm Succ\;(Ti)=[Tj](Tj,Tj)\in D] \tag{2}\]  
\[\rm Pre\;(Tentry)=0 \tag{3}\]  
\[\rm Succ\;(Texit)=0 \tag{4}\]  
Here, T1 is (Pre) of T2, T3, and T4, and those three tasks, T2, T3, T4, are (Pre) of T5, T6, and T7, vice-versa as successor. On the other hand, T1 is (Tentry), and T7 is (Texit), within the overall scheduling task (Figure 3). In other hand, [20] created a scheduling algorithm to minimize monetary cost and workflow execution time in the cloud environment with two objectives called a multi-objective optimization problem (MOP). This MOP main function minimizes conflicts between tasks. Here, \"X\" is stated as the solution space, \(F_{r}(x)\) is \(r^{th}\); here the main focus is MOP where the monetary cost and execution time contradict each other. The approach is state by the following equation:  
\[\rm F(x)\;=\;(F_{1}(x),\;F_{2}(x),\;F_{3}(x),\ldots F_{r}(x))^{T},\;where\;x\; \in X \tag{5}\]
## 1 Introduction
To accurately predict the failure mode of layered composites subject to low velocity impacts and simultaneously calibrate interface constitutive laws, a coupled finite element (FE) Kalman filter approach is here proposed. Upon space discretization (through displacement-based finite elements) and time integration (through the explicit \(\alpha-\)method, in order to damp spurious high frequency oscillations in the fully discretized solution), Kalman filtering is adopted to fully track the structural state in a stochastic frame [4; 5; 6], exploiting system evolution to continuously improve estimates. Because of the nonlinearities induced by inter-laminar softening, the customarily adopted extended Kalman filter (EKF) is not able to provide robust state tracking and accurate estimations of inter-laminar properties like e.g. strength and toughness [7]. We therefore attack the problem with the newly proposed sigma-point Kalman filter (SPKF) [8; 9; 10], which avoids the occurrence of unstable evolution of the estimates (see also [11]). While the EKF linearizes the system evolution equations (through a Taylor series expansion of the relevant mapping, thought to be analytic everywhere, arrested at first order) and accordingly updates the statistics of the structural state, the SPKF samples the probability distribution of the state via an appropriately chosen set of sigma-points, which are then evolved according to the actual nonlinear system dynamics to obtain the updated statistics of the state itself. Among alternative approaches to system identification and, specifically, to model calibration, it is worth quoting here [12]. In this work the capability of the proposed FE-SPKF approach is assessed through comparison with experimental data available in the literature and concerning failure of laminated plates subject to plane impacts [13; 14]; in such tests, the composites are mainly stressed in the region surrounding the impact location by dilatational waves propagating in the through-the-thickness direction and causing mode I delamination. Outcomes show that the failure mode is always correctly captured, and estimated values of inter-laminar strength and toughness well agree with available data. Plane impact tests do not allow a complete calibration of the interface constitutive law, since only model parameters relevant to mode I failure can be estimated. Anyway, forthcoming results show that under very adverse conditions, as due to fast completion of the whole debonding process, the SPKF proves to be robust and accurate; on the contrary, under similar conditions the EKF was shown in [7; 15] to either diverge or provide biased estimates. An extensive assessment of the SPKF performance in the presence of diffused damage/delamination patterns is beyond the scope of this paper and will be presented in future works. As far as notation is concerned, a matrix one will be adopted throughout, with uppercase and lowercase bold symbols respectively denoting matrices and vectors. A superscript \({}^{\rm T}\) will stand for transpose, \(|\Box|\) will denote the norm of vector \(\Box\), and a superposed dot will represent time rates.
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 1 Introduction
### New Contribution  
This paper tackles the aforementioned problems by proposing the design and implementation of a non-contact and non-intrusive add-on wireless Internet-of-Things (IoT) device for remote elevator control. This tool has been designed as an external button panel attached above the original elevator car buttons. The physical action of pressing the buttons of the panel is performed by a set of low-cost servomotors, and the device is remotely accessed through the wireless network of the building. The IoT device has been designed as a battery-operated non-intrusive equipment in order to avoid any manipulation or additional wiring connection that may compromise any security standard or regulation. The design of the IoT device allows direct machine-to-machine (M2M) interaction through an Internet-based communication protocol. The internal structure of this IoT device has been designed to ensure its compatibility with the stock button panel, which must remain usable in case the battery runs out. This was achieved by including pressure-sensitive buttons that transfer the pressure to the original button panel of the elevator. The device specificallyavoids the shared use of buttons by providing a smartphone-based web interface to control the elevator. This web interface can be accessed from a visual quick response (QR) code that simplifies access to the web interface of the elevator and reduces the number of actions that need to be performed by the user to call the elevator, which is called automatically after scanning the QR code.  
This contribution was inspired by the proposals of [PERSON] et al. [4] and [PERSON] et al. [5], who developed several devices with a similar objective, and by the conclusion of [PERSON] et al. [6], who recommended the use of add-on devices for elevator control. However, until now, the devices proposed for remote elevator control required intrusive manipulation of the original infrastructure [4; 5; 7]. This is a strong limitation because intrusive intervention in elevators may not be feasible in most cases because of its cost, the closed design of the elevator, and the particularities of strict regulations regarding elevators.  
The IoT device has been deployed in a real elevator and has been experimentally tested by users accessing the elevator with their smartphones and by an autonomous mobile robot.
## 4 Materials and Methods
### Method Validation  
To validate the data-acquisition method, repeatability, reproducibility, linearity and limits of detection (LOD) and quantification (LOQ), the chromatographic standards \(\alpha\)-Pinene and p-Cymene from Honeywell Fluka(tm) (Morristown, NJ, USA) and, \(\beta\)-Pinene (99%) and 2-Undecanone (99%) from Sigma-Aldrich (St. Louis, MO, USA) were used. Validation parameters were performed in a pooled-wine (PW) matrix to prevent matrix interferences. Concentration range of \(\alpha\)-Pinene and \(\beta\)-Pinene was 1.56 (L1) to 25.00 (L5) ng/L with 1:1 factor, while p-Cymene and 2-Undecanone was 0.31 (L1) to 5.00 ng/L (L5) with the same factor. Repeatability was determined using a five-level curve by triplicate on day one. Reproducibility was calculated with a three-level curve (L1, L3 and L5), also in triplicate, on the second day of work. Mean, standard deviation (SD) and relative standard deviation (%RSD) were calculated for each level to determine repeatability and reproducibility. Linearity was determined by the correlation coefficient (\(r\)) of five-level standard curves and PW as a blank sample (matrix sample without standards). LOD and LOQ were determined from ten injections of L1 in three different days (Supplementary Materials, Table S1) and calculated using Agilent MassHunter WorkStation Quantitative Analysis version 10.0 (Agilent Technologies, Santa Clara, CA, USA).
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 4 Simulation Experiment and Analysis
### Scenario 1: Multi-UAV Search without Association Rules  
In modern operations, ships perform various combat missions and adopt various battle formations. For example, a ship formation can consist of destroyers, frigates, and nuclear submarines, which are usually used in enemy attack and air defense. In this experiment, multi-UAVs are mainly for the search of underwater targets (nuclear submarines). The prior information on the targets is shown in Figure 4. Targets on water (warships) are in a high-probability area, so the search for underwater targets (nuclear submarines) is based on the location of warships. The initial position and movement speed of the water target are shown in Table 3, and the movement direction of all targets is unknown.  
In scenario 1, four algorithms--RI-MAC, MAC [15], ABC [19], and PSO [12]--are used to search. Figure 5 shows the trajectories of search targets solved by multi-UAVs using four algorithms without considering association rules. After UAV searches for water targets (marked in light blue in the figure). The probability is updated according to the Bayesian probability update formula until all underwater targets (marked in dark blue in the figure) are searched and the search task ends.  
Table 4 shows the target-discovery benefits of the four search algorithms with no association rules. The Monte Carlo method was used for 500 experiments, with the average number of targets found under different iteration steps. In the initial stage, the differences in the number of targets searched by the four search algorithms are small. Comparing the target-discovery gains of the four algorithms, the MAC algorithm has the highest target-discovery gains at the 20 th and 40 th times, whereas the RI-MAC algorithm has slightly lower target-discovery gains than the MAC algorithm. Because the RI-MAC algorithm uses a grid that conforms to the association rules as the heuristic information, when the association rules are not used, the heuristic information is invalid, and there are many parameters to be initialized in the initial stage compared with the MAC algorithm, resulting in a slow convergence speed. However, with an increase in the number of iterations, the sea area information mastered by the UAVs continues to increase, and the advantages of the RI-MAC algorithm in the pheromone update method improve the performance of the algorithm. These results can be further verified by the convergence curves shown in Figure 5. According to the results of target-discovery revenue, the PSO algorithm requires the most iteration steps, with the worst revenue, and the target-discover revenue of ABC is slightly lower than that of RI-MAC and MAC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Label** & **Starting Coordinate (km)** & **Velocity (km/h)** \\ \hline
1 & (6, 8) & 3 \\
2 & (14, 13) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Initial target information.  
Figure 5: Search track of UAVs in Scenario 1: (**a**) RI-MAC, (**b**) MAC, (**c**) ABC, (**d**) PSO.
## 4 Results
### Simulation Results
#### 4.2.4 Comparison of Analytical and Numerical Results  
In order to understand the impact of thicknesses that are close to the periodic penetration depth, numerical solutions for the equations (Equations (10), (11) and (14)) are presented. Only the case of gypsum was analysed. The Matlab pdepe-solver were used with a space resolution of around 0.1 mm.  
From the results it can be seem that the semi-infinite analytical solution is verified by the numerical simulation and that it also works well for diurnal variations as long as the thickness is 20 mm or more (Table 10). For thinner material thicknesses the moisture buffering effect can be over or under estimated while the time lag becomes overestimated. It can also be observed that there is a maximum buffering effect for material thickness similar to the periodic penetration depth, which is result well known from the heat transfer area investigating effect of thermal mass on energy demand [24].  
Figure 11: Comparison between measured and modified simulated moisture uptake for Gypsum and triangular 12 h/12 h cycles.  
For 72/72 variations the semi-infinite analytical solution works well as long as the thickness is 40 mm or more (Table 11). For thinner material thicknesses the moisture buffering effect can be over or under estimated while the time lag becomes overestimated. The moisture buffering goes up with 14% for the 72/72 simulation with the thickness 20 mm instead of infinite thickness. This reduces the difference between experimental and simulation result.  
The time lags is reduced between with 3.8 and 5.0 hours, when thickness is reduced down to 20 mm for infinity. The match between this simulation and the measurement becomes better for the top peak time delay but there is a larger lag for the bottom peak. Overall, it can be stated that either in the experimental and simulations temperature is a significant factor that delays the response of materials to buffer humidity. This is further justified in [8], in which the response of materials to constant and variable temperature were compared, showing that variable temperatures generate a significant lag compared to the case at constant temperature.
## 5 Conclusions  
To understand the role of landslide disasters in small watersheds more comprehensively, a landslide susceptibility assessment that considers the amount of landslides is necessary. The applied analysis of the VR model, RIRA method, and ArcGIS within the framework of the present study achieved more comprehension for disaster prevention and land planning with respect to the location and volume of the past landslides. The relationship among landslide occurrence, landslide volume, and nine landslide conditioning factors such as slope, elevation, NDVI, land use, lithology, distance to road, distance to river, profile curvature, and rainfall are evaluated using the above models. Our analysis demonstrated that the sub-classes in the same conditioning factors had different VR values, as indicated that sub-regions with homogenous properties contributed differently to the landslide scales. In addition, the results showed that the various parameters of landslides had different sensitive values to the landslide volume and occurrence. Among those factors, profile curvature and distance to river were the two most highly sensitive, followed by distance to road, rainfall, elevation, slope, and NDVI. The lithology and land-use were the two lowest sensitive factors. The sensitive values are 0.3616, 0.2567, 0.0908, 0.0628, 0.0523, 0.0345, 0.0203, 0.0099, and 0.0097, respectively. Also, the landslide susceptibility map considering landslide volume classified the study area into five zones, with susceptibility degrees of very high, high, medium, low, and very low, which showed that about 60% of the landslides occurred in a high and very high susceptibility area, accounting for 87% of the total volume of landslides. Landslides such as slides and falls are important contents of geological disaster prevention and control, and this phenomenon is also the main form of soil erosion. Small landslide erosion is more likely to cause sediment yield. Thus, the spatial distribution of the existing landslide susceptibility can be analyzed in order to identify the components that are located in large landslide scale areas and small landslide scale areas, so as to set the priorities to prevent and control landslide disasters and soil erosion. Engineers, decision-makers, and environmental managers may implement the analysis that we implemented in the present study during new or existing planning projects and produce maps that will make possible the adoption of policies and strategies aimed at multi-hazard mitigation.  
Writing--original draft preparation, H.G.; writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript. This research was funded and supported by the National Natural Science Foundation of China (Grant No: 52009103, 42177346) and the Innovation Capability Support Program of Shaanxi (2019 TD-040). Not applicable.  
**Acknowledgments:** We are grateful to the anonymous reviewers for their valuable suggestions in improving the manuscript.