esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 2 Transmission Line Monitoring Network Architecture in Mountainous Areas  
According to the analysis of the fault rate of transmission lines in mountainous areas [35], we defined three monitoring states: low-risk state, high-risk state, and fault maintenance state. The failure rate of the transmission line showed low or high under low-risk condition or high-risk state, respectively. Under the fault maintenance state, the transmission line fault has occurred and is waiting for location maintenance. Obviously, the communication quality requirements of the three monitoring states are different, and a monitoring system with a single working mode cannot solve the data transmission problem of the system.  
As shown in Figure 1, this paper first proposed an online monitoring system of a transmission line in a mountainous area. Then, we established the delay model, energy model, and path loss model of the monitoring system. The optimal solution to balance the delay and the energy can be obtained through an optimization algorithm. Combining the LoRa transmission model established by the path loss model with the optimal solution of the optimization algorithm, the LoRa dynamic packet model can be obtained. Finally, three working modes can be achieved according to the LoRa dynamic grouping model, which includes the monitoring mode, early warning mode, and maintenance mode.  
Figure 2 shows the proposed three-layer monitoring network structure of a transmission line in a mountainous area. Considering the network cost, LoRa was adopted in the first layer to collect massive sensor data and then transmit it to the sink node. In order to satisfy the delay time requirement, a cellular mobile network was employed in the second layer to transmit the data of the sink node to the monitoring center. The third layer was composed of the monitoring center, which can collect data transmitted through wired and cellular mobile networks.  
In order to make the proposed monitoring network structure suitable for a mountainous environment, this paper proposed a LoRa dynamic networking model based on the multi-objective optimization algorithm and path loss model, which can change the networking mode under the different conditions of delay and energy consumption. In the network grouping mode of low-risk state, the energy consumption constraint of each terminal node is given priority while the delay consumption constraint is given priority in the fault maintenance state. In the high-risk state, the terminal node and the sink node consider the delay and energy consumption constraints at the same time.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology
### Hybrid Early Warning System
#### 4.8.1 Hardware Components  
Figure 7 illustrates the proposed system block diagram, and it defines the relationships of the hardware components and their features. It receives input through weather sensors and cameras, and its output is displayed through an optical panel and the electric horn.  
Figure 7: Hybrid early warning system block diagram.  
A minicomputer (Raspberry Pi v3) was used to perform device computations, which appear in the central part of this graph. The minicomputer was fitted with USB ports, digital ports, and analogue ports. This single-board machine enables sensors and other devices to be connected. The left part of this diagram shows a temperature sensor and a rain gage. The temperature sensor is used to measure surrounding air temperature and generate a digital signal every two seconds (0.5 Hz sampling rate). The rain gauge is a tipping-bucket rain scale used with a resolution of 0.1 mm per tip to measure instantaneous rainfall. The one bucket tip produces one electrical signal (pulse).  
There are four devices in the right part: the light warning screen, the relay module, the electric horn, and the WIFI module. The light warning panel is a 24 \(\times\) 24 cm frame with an RGB LED matrix with high light strength. Suppose each color depends on the particular degree of hazard: this panel shows the warning light alert in three different colors (green, black, and red). The relay module consists of a photoelectric coupler with anti-interference insulating capacity. It supports the Raspberry Pi by general purpose input/output (GPIO) pins to drive the electric horn and the optical screen.  
The bottom section of this graph displays the power system used during the day to maintain electrical power. It consists of a solar panel, a battery pack, and an intelligent solar charge controller. The solar panel transforms photo power into electrical energy. During hours of darkness, the battery pack is a backup power source for the device. The intelligent solar charge controller was used to supply the device and refresh the tank.
## 2 Related Works
### Application Layer  
The application layer provides the user with the interfaces they need in order to engage with the real world through the WSNs. The application layer is also responsible for transforming data into a readable format for the purpose of detecting actual information. Assigning a priority at the application layer guarantees that all nodes are aware of the priority when delivering the packet, allowing for quicker transmission of the critical events.  
When developing a new protocol, prioritization is an effective strategy for improving network performance. Identifying events of interest in sensor networks is a very effective method of exploiting priorities in sensor networks. A strategy to prioritize sensor-based applications in smart cities is presented in [40], which can be differentiated based on their monitoring goals, certain contextual prioritization characteristics and the detection of critical events. Data packets generated by sensor nodes comprise critical and non-critical events across the range of applications. Critical events must be sent to the closest emergency responders with the shortest possible delay and must be prioritized above non-critical or less urgent events [41]. FROG-MAC [42] introduced a new fragmentation strategy for heterogeneous traffic in WSNs that enables high-priority data packets to interrupt any unimportant ongoing transmissions.  
QoS refers to the ability to prioritize data packets based on the user's needs. QoS prioritizes emergency situations such as fire threats, volcanic eruptions, medical emergencies, leaks, and hazardous gas detection [43; 44; 45]. In contrast to less urgent events, higher priority ones often need better QoS, which means less delays and higher packet success rates. The authors in [46] proposed a QoS-aware cross-layer configuration that aims to establish a network configuration which satisfies application-specific process requirements while also adhering to user-defined QoS restrictions on event-transport reliability. Because of the wide range of possible uses for WSNs, the system requirements may differ significantly from one application to another. Table 1 summarizes the QoS needs for various applications.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel
### Code Design for Gaussian Interference
#### 3.4.2. Raptor-Like LDPC Code Design  
Protograph-based Raptor-like LDPC codes [42] belong to the class of rate-compatible LDPC codes. The base matrix has the following structure:  
\[\textbf{B}=\left[\begin{array}{cc}\textbf{B}_{\text{pre}}&\textbf{0}\\ \textbf{B}_{\text{LT}}&\textbf{I}\end{array}\right]\]  
where in analogy to Raptor codes, \(\textbf{B}_{\text{pre}}\) and \(\textbf{B}_{\text{LT}}\) represent the base matrices of the precode and Luby transform (LT) code, respectively. Further, **I** is an identity matrix. Owing to their excellent performance, protograph-based Raptor-like LDPC codes are adapted in the context of the 5G standardization for enhanced mobile broadband (eMBB). During the standardization, several proposals for base matrices have been made with very similar performance. We consider one of these 5G proposals for short blocks [43]  
\[\textbf{B}_{\text{5G}}=\left[\begin{array}{cccccccccccccccccccc}1&0&0&0&1&1&1& 0&1&1&1&1&0&0&0&0&0&0&0&0&0&0&0&0\\ 0&1&0&1&1&1&0&1&0&0&0&1&1&0&0&0&0&0&0&0&0\\ 1&0&1&0&0&1&0&1&0&0&1&0&1&1&0&0&0&0&0&0&0\\ 1&1&1&0&1&0&0&0&1&1&0&0&0&1&1&0&0&0&0&0&0\\ 1&1&1&0&1&0&0&0&1&1&0&0&0&0&1&1&0&0&0&0&0\\ \hline 1&1&0&0&0&0&0&0&0&0&0&0&0&0&0&1&0&0&0&0&0\\ 1&1&0&0&0&0&1&1&0&0&1&0&0&0&0&0&0&1&0&0&0&0\\ 0&1&0&0&0&1&1&1&0&0&0&0&0&0&0&0&1&0&0&0\\ 1&0&1&0&0&0&1&1&0&0&0&0&0&0&0&0&0&1&0&0\\ 1&0&0&0&0&0&1&0&1&0&0&0&0&1&0&0&0&0&0&1&0\\ 1&1&0&0&0&0&0&1&0&1&0&0&0&0&1&0&0&0&0&0&1&0\\ 1&1&0&0&0&0&0&1&0&0&0&0&1&0&0&0&0&0&0&1\end{array}\right] \tag{8}\]  
where the first two VNs are punctured. The motivation for this choice is to illustrate that 5G eMBB codes might be suitable candidates for RA applications.  
The base matrix in (8) is not tailored to the specific interference patterns that occur for our asynchronous RA protocol. Recall that for our code design, we consider interference that is confined to the beginning or end of a codeword. To improve the iterative decoding threshold for the base matrix in (8), we may permute its columns such that the gain function in (7) is maximized. We evaluate the gain function for two different \(\alpha\), i.e., \(\alpha^{(0)}=12/20\) and \(\alpha^{(1)}=16/20\).  
Note that there exist 20! permutations for the base matrix \(\textbf{B}_{\text{5G}}\) (ignoring the punctured columns). To reduce the number of permutations to test, we group VNs together based on their degrees with the intuition that placing a VN from a certain group at a position in the protograph will yield a comparable value of the gain function. Therefore, the total number of different permutations to be tested reduces to 4200. The permutation vector yielding the highest value of the gain function is:  
\[\boldsymbol{\pi}=\left[0\;1\;16\;4\;6\;8\;10\;12\;20\;2\;14\;18\;19\;15\;3\;21 \;13\;11\;9\;7\;5\;17\right].\]  
We denote the resulting permuted base matrix by \(\textbf{B}_{\text{5G}}^{\pi}\).
## 3 The Methodology
### Simulation Study
#### 4.1.2 Impact of Energy Consumption  
The ratio of the total amount of energy used during information transfer is known as the energy-consumption ratio (ECR). The remaining power of a node is calculated using this value. The ECR may be written down as  
\[ECR=\frac{AE}{AD} \tag{22}\]  
where \(AE\) and \(AD\) denote the average energy consumption and average information transfer rate, respectively. Further, the number of messages in the buffer is estimated. This parameter is considered if a node has a large number of messages; then, all those data are dropped. This metric is defined as the number of messages that are waiting in the buffer of the node. When the number of simulation runs increases, the energy-consumption rate increases. Minimizing the energy consumption of IoMT devices requires knowledge about the specific traffic in the network and also associated end-to-end communications. Routing packets requires a certain amount of energy to store them in the buffer. Most researchers have found intelligent algorithms for routing packets that improve the network performance in the network layer. In this paper, a study of the energy-consumption usage of wireless devices is presented. The optimization of routing using a simple design in the IoMT environment addresses the energy-consumption problem. Also, IoMT devices and sensors enable not only interoperability among devices but also control in the environment.  
The simulation results of energy usage per node are shown in Figure 7. The suggested work reduces power usage by eliminating the faulty data before sending them to the target node, as shown by the simulation results. Further, this is illustrated in Figure 8 based on the simulation rounds.  
Figure 6: Many nodes vs. throughput.
## 4 Proposed System
### Trajectory-based Message Delivery
#### 4.4.1 Message Distribution to RSUs  
Let \(msg\) be a content of location-aware service provided by \(sRSU_{j}\). In this phase, \(sRSU_{j}\) compose a message package \(M_{jd}\) for \(V_{d}\) and distributes \(M_{jd}\) to \(V_{d}\)'s contact point \(RSU_{i}\) as follows:  
1. Extract \(pid_{di}\) corresponding to \(loc_{i}\) from \(VList\).
2. Set a message \(M_{jd}=\{pid_{di},sRSU_{j},msg,ts^{\prime},S_{j},ttl\}\) where \(S_{j}\gets idSig(rsk_{j},pid_{di}|sRSU_{j}|msg|ts^{\prime})\) is \(sRSU_{j}\)'s signature and \(ttl\) is the message lifetime.
3. Distribute \(M_{jd}\) to \(RSU_{i}\) nearby \(loc_{i}\).  
When we assume that RSUs are inter-connected, a message \(M_{jd}\) can be easily distributed to other RSUs. During the message delivery, a message bundle has a certain lifetime specified as \(ttl\) so that an expired message bundle is discarded. This is for RSUs or carrying vehicles to avoid consuming their storage excessively for a long time even if a target vehicle \(V_{d}\) is not met on the roads.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
###### Abstract
Initially, the raw data obtained by the system's measurements is filtered out to reduce the inherent measurement noise [12] by smoothing the signals using exponentially weighted moving averages (EWMAs) and exponentially weighted moving standard deviation (EWMS) quantities of the original measure. Subsequently, with the enriched dataset, a detailed feature selection is performed by using an additive features attribution method called SHAP (SHapley Additive exPlanation), which quantifies how much each individual feature contributes to the model's overall performance on a dataset [13]. The outcome of the SHAP method is a subset of the enriched dataset, which contains only the most impactful features for the model output, which in the case herein is the SOC estimation. After selecting the relevant features, the subsequent step involves optimizing the hyperparameters of the proposed model. The selected model is a bidirectional LSTM, as in [14], due to its inherent capacity to solve the issue of vanishing gradients for time-dependent and sequential data series. A random search process is used to optimize the hyperparameters for the bidirectional LSTM model. The most relevant hyperparameters taken into consideration are the number of hidden layers, neurons per layer, learning rate,and timesteps which determine the time interval that the network will buffer at at any processing step. Finally, the model is generated and trained, using cross-validation techniques, until it reaches an acceptable mean square error (MSE) loss value and the model is demonstrated with smallest estimated risk [15]. The model is then saved for posterior usage and the offline processing stage is accomplished. The generated model, referred to herein as data-driven PHM, is then compared with the state-of-the-art Kalman filters methods: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The EKF uses the entire observable system and aims to linearize the state and observation models by a first-order Taylor-series expansion [8]; whereas UKF samples the observable system generating the so-called sigma points which are then propagated through the nonlinear observation model, and system variables are then estimated. UKF is expected to have better performance for higher-order nonlinear differential systems, which can well represent the battery behavior. This paper is structured as follows: Section 3 details the Kalman filters' theory and concepts, for both extended and unscented versions, while Section 4 presents the main contribution of this work--a novel SOC estimator using data-driven PHM approach--including the proposed framework. Section 5 provides the methodology, dataset, and setup used herein, Section 6 presents the main results and its explanation, and finally Section 8 expands the study's findings and outlines future work.
## 3 Numerical Results
### Temperature Variation Inside the Building
This trend indicates that the active lower ventilation helps partially dissipate hot gases to the outside, reducing immediate thermal accumulation. However, after 120 s, an accelerated temperature rise is observed, reaching a significant peak above 700 \({}^{\circ}\)C--the highest among the three scenarios. This suggests that, in the short term, forced exhaust limits combustion intensity, but as the hot gas layer develops, the effect reverses: the introduced air stimulates combustion, and ventilation becomes an additional oxygen source. Figure 7: Temperature distribution in the slice at Y = 5.75 m at time T = 360 s.  
In scenario S1, the door opening at 120 s leads to a slight temperature increase up to approximately 620 \({}^{\circ}\)C, followed by a short decline until around t = 170 s, when a sharp rise occurs, reaching 700 \({}^{\circ}\)C. The upper pressurization system supplies oxygen directly to the fire source, accelerating combustion. In scenario S0, the temperature increases steadily but without significant oscillations or high peaks. This reflects a more stable combustion regime, though with moderate thermal efficiency, characteristic of organized natural ventilation. Although the peak temperature is lower, the persistent heat accumulation compromises long-term evacuation safety. The simulations clearly demonstrate the critical role of door opening timing and ventilation system configuration on the localized thermal regime at the fire source. Scenario S2's lower ventilation has a dual effect: initially low thermal growth, but it later promotes intensified combustion and the development of a high thermal regime. Scenario S1, lacking a gas exhaust path, triggers a rapid and aggressive reaction upon door opening. Meanwhile, scenario S0 offers a more predictable evolution but lacks active control over fire development. Therefore, the combined control of door opening timing, fan placement, and airflow direction is essential to limit fire growth in the early phase and protect evacuation routes. In conclusion, the simulation results obtained with PyroSim demonstrate the effectiveness of the mechanical stairwell pressurization system presented in scenario S1. This system generates a differential pressure that prevents heat accumulation along evacuation routes, thereby ensuring their accessibility for both emergency responders and occupants during evacuation. This underscores the critical importance of employing mechanical pressurization in fire scenarios to maintain safety and efficiency in evacuation management. Scenario S1, which combines stairwell pressurization with delayed door opening, provides the most favorable thermal safety conditions by maintaining low temperatures vertically and ensuring a protected evacuation path. In contrast, scenario S0 facilitates heat propagation within the stairwell, while scenario S2, although more effective at the fire source level, partially compromises the thermal protection of the evacuation route.
# Deep Neural Network-Based Guidance Law Using Supervised Learning
## 4 DNN-Based Guidance (DNNG) Law
### Data Collection  
The dataset used for supervised learning (SL) is generated from the simulation data of the missile system with PNG for the missile-target engagement scenario. This missile system with PNG was constructed with MATLAB/Simulink. The DNNG model is trained to learn about the relationship between state and action from the PNG-based generated dataset. The dataset consists of \((x^{*},\ u^{*})\) pairs, the state variables \(x^{*}\) are the LOS rate \(\Omega\) and the speed of the missile \(V_{m}\), and the action variable \(u^{*}\) is the lateral acceleration of the missile \(a_{m}\). The state-action pairs are clean data without considering noise. The purpose is to create a DNN-based guidance law using information received from the seeker. Two pieces of information are provided from the seeker: the LOS rate and the speed of the missile. The initial conditions used for data collection were randomly generated within the bounds shown in Table 1. That is, the initialization area \(A\) is defined as \(x_{0}\in A\), which is an area that contains all the values within the initial bounds in Table 1. The training data consisted of a total of \(89,025\) trajectories and \(1,108,587,789\) state-control pairs, which were sampled at 0.01 s intervals. Figure 6 shows the initial position distribution of the missile. It appears that its position is uniformly distributed. In addition, the initial heading angle of the missile is uniformly distributed within the range \(-\frac{\pi}{2}-\frac{\pi}{2}\), based on the look angle. This shows that the training data used to learn the DNN model are trained in various initial conditions, and that at the same time the initial conditions have a sufficiently uniform distribution. The constant value used for learning the DNNG is as follows: the navigation constant \(N\) is 3, and the speed of the missile \(V_{m}\) has an arbitrary value between Mach 0.5 and 0.95, respectively.  
Figure 5: Learning process of supervised learning.  
Figure 6: Distribution of the initial position and heading angle of the training dataset.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 3 Method
### WloU Loss Function
#### 3.3.3 Wise-IoUv3  
The Wise-IoUv3 loss function directly replaces the IoU value with the outlier degree to describe the mass of the anchor frame. The outlier degree is calculated by Equation (7).  
\[\beta=\frac{\mathcal{L}_{IoU}^{*}}{\mathcal{L}_{IoU}},\ \beta\in[0,+\infty) \tag{7}\]  
As observed in the given formula, the mass of the anchor frame increases as the outlier becomes smaller, and a non-monotonic focusing coefficient, \(\beta\), is constructed. This coefficient, when multiplied by Wise-IoUv1, yields Wise-IoUv3 (Equation (8)). The introduced coefficient allows the regression of bounding frames to focus on anchor frames with ordinary mass. By assigning smaller gradient gains to anchors with larger outliers, the model can effectively prevent harmful gradients from low-quality samples. This approach helps in improving the overall performance and robustness of the model during the training process.  
\[\mathcal{L}_{WIoUv3}=\theta\mathcal{L}_{WIoUv1},\theta=\frac{\beta}{\delta \alpha^{\beta-\delta}} \tag{8}\]  
In Equation (8), \(\alpha\) and \(\delta\) are hyperparameters, which need to be selected according to specific experiments. Since \(\mathcal{L}_{IoU}\) is in a dynamic process, the quality division criteria of the anchor frame are also dynamic, which enables the Wise-IoUv3 loss function to create the gradient gain allocation strategy that best meets the current situation during each training process.