esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 2 Related Works
Image-based methods employ a range of vision sensors, including depth cameras, stereo cameras, and single cameras. Conversely, non-image-based methods typically utilize wearable sensors such as gloves and bands [24], which often require sophisticated sensing devices and can be costly. Vision sensors such as depth and stereo cameras also incur costs. However, image-based methods are generally regarded as more comfortable compared to non-image-based methods because the tools are less cumbersome to wear or install. Therefore, we propose an image-based HGR method. The most common method of utilizing visual data is to extract 2D or 3D keypoints of the hands from an image to assess the hand posture and shape, which can be used to recognize gestures [10]. These require additional computation to model the hand shape before recognizing the gestures. On the other hand, there are approaches to inferring gestures in images by detecting the hand positions and estimating their shapes without any modeling information about the human hand [28]. These methods commonly aim to find the exact location and area of the hands by considering all aspects other than the hands as the background. Although HGR utilizing keypoints employs extra modules to estimate the hand pose, many lightweight hand pose estimation methods [29; 30] have been introduced. Furthermore, the extraction of hand keypoints is more robust in capturing sophisticated hand gestures from different viewpoints. In this paper, we present a vision-based HGR method designed for drone control. Initially, we define a set of hand gestures, considering both a natural user interface and drone flight patterns. Subsequently, we propose a method of classifying these gestures. Our approach is cost-effectively implemented using a simple RGB camera capable of edge device operation.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 4 Detailed Description of the Proposed Algorithm
### Task Allocation
#### 4.3.3 Exploration-Exploitation Policy  
The exploration-exploitation policy has a direct effect on learning efficiency in Q-learning [22]. Most of the existing research uses the greedy policy, and the exploration probability \(\rho\) is given by  
\[\rho=\min(\rho_{\max},\rho_{\min}+k*(S_{\max}-S)/S_{\max}), \tag{16}\]  
where \(\rho_{\max}\) and \(\rho_{\min}\) are the upper and lower boundaries for the exploration factor, respectively; \(k\in[0,1]\) is a fixed constant; \(S_{\max}\) is the maximum number of states; and \(S\) is the current number of states already known. At each time step, random action is selected with the exploration probability \(\rho\) and the optimal action is selected with the exploitation probability \(1-\rho\). However, this strategy cannot provide meaningful guidance for the agent to choose actions based on the real-time allocation of sensor resources in the network. To manage this issue, we propose, with the use of target priority information, a novel exploration probability \(\rho\):  
\[\rho=\min(\rho_{\max},\rho_{\min}+(P_{\max}-P_{\min})/P_{\max}), \tag{17}\]  
where \(P_{\max}\) and \(P_{\min}\) are the maximum and minimum values of the target priority. Note that if we obtain a more reasonable task allocation, then different targets obtain more balanced priorities in real-time. In the early learning process, the tasks are unevenly allocated, and the maximum differences in target priorities are larger, so the agent is encouraged to perform more exploratory actions. With the iterative learning process, the resource allocation scheme learned by the agent gradually becomes more balanced, thus \(\rho\) should be decreased to take more greedy actions to complete the convergence of the final Q-table.  
The pseudocode of the proposed multi-target-tracking task allocation mechanism is shown in Algorithm 1.  
In general, the operation process of our proposed improved Q-learning-based sensor-scheduling algorithm is as follows: Firstly, in subsection A, based on distance and energy information, sensors use a delay-based broadcast mechanism to select CH. CH can provide sensor nodes, i.e., intelligent agents, with the environment information and computation of rewards necessary for the Q-learning model. In subsection B, we propose four indicators according to target property and network status, and use EWM to calculate the tracking priority of targets. In Section 4.3, we detail the process of utilizing Q-learning models for task allocation. The reward function and the exploration-exploitation policy are designed to reasonably schedule nodes, thus achieving a balanced allocation of tracking tasks for targets. The overall flow chart of the proposed sensor-scheduling algorithm MTT-SS is shown in Figure 4.  
Figure 4: Flow chart of the proposed MTT-SS method.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 3 Background  
Zero-day threats refer to cyberattacks that exploit previously unknown vulnerabilities in software, hardware, or firmware, often before the vendor or security community becomes aware of them. These types of attacks are particularly dangerous because traditional detection systems based on known signatures or heuristics are unable to identify them in time [8,32]. In cloud computing environments, the risk is amplified due to the dynamic and distributed nature of resources, the presence of multiple tenants, and the complexity of data access policies [4,5].  
To address these challenges, the Zero Trust Architecture (ZTA) has been proposed as a modern security model. ZTA operates on the principle of \"never trust, always verify\", enforcing strict identity verification and access control across all users, devices, and applications [1,19]. It eliminates the assumption of trust within the internal network and instead treats every access request as potentially malicious, applying continuous authentication and policy enforcement.  
In parallel, advances in artificial intelligence, especially in deep learning, have opened new paths for proactive threat detection. Unlike traditional machine learning, which often requires manual feature engineering, deep learning models are capable of extracting complex representations directly from raw input data [12,14]. This is particularly relevant in malware detection, where binary files can be transformed into visual representations such as byteplot images, allowing models originally designed for computer vision to be repurposed for cybersecurity tasks [14,22].  
Transformers, first introduced in the context of natural language processing, have recently been adapted for image classification through architectures like vision transformer (ViT) (Figure 2) the asterisk (*) in the image indicates that the Transformer Encoding module includes a class token, a special vector added at the beginning of the input sequence that does not represent specific malware data but serves as a global summary. This token enables the model to generate an aggregated representation of the entire input, which is subsequently used for malware classification and detection. Then, Patch and Position Embeddings are applied to segment and position the data. The Transformer Layers process these representations with self-attention, enabling the identification of patterns.. These models leverage self-attention mechanisms to capture global dependencies in data and have demonstrated strong performance in malware detection scenarios when trained on large datasets [12,33]. Hybrid models combining convolutional and transformer-based layers have also shown promise in reducing false positives and improving detection of zero-day variants [24,34].  
The intersection of Zero Trust principles with deep learning-based detection systems represents a powerful strategy for defending cloud environments against emerging and stealthy malware attacks. This study builds on these foundations to propose a transformer-based model optimized for image-based malware classification.
# Research on Equalization Strategy of Lithium Battery Pack Based on Multi-Layer Circuit
## 3 Multi-Layer Equilibrium System Model  
Figure 1 shows the multi-layer battery equalization structure adopted in this study, similar to a binary tree. For a battery pack with 2\({}^{\text{n}}\) battery units, there are n-layer equalizers, in which the m (1 \(\leq\) m \(\leq\) n) layer has 2\({}^{\text{m}-1}\) equalizers e\({}_{\text{m}\text{i}\text{n}}\) (1 \(\leq\) i\({}_{\text{m}}\)\(\leq\) 2\({}^{\text{m}-1}\)). Each pair of batteries is connected to its common node by the n-th layer equalizer, which controls the equalization current to complete the equalization of adjacent battery cells at the bottom layer. The upper-level equalizer is connected to the lower-level equalizer with its common node for equalization at the same time, and so on, until the multi-layer equalizer controls the complete group of batteries for equalization all at once. The Buck-Boost circuit is used in each layer's equalizer, and its working mechanism is shown in Figure 2. Buck-Boost Continuous Conduction Mode (CCM), Boundary Conduction Mode (BCM), and Discontinuous Conduction Mode (DCM) are the three working modes. To ensure that all the stored energy in the inductor is transferred, the working model of the Buck-Boost circuit is set to BCM and DCM. The work presented in this study mainly relates to the design of the equalization control method and equalization strategy of the multi-layer equalization circuit. The proposed method can ensure the equalization speed and the charge-discharge current limit well.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equalization Strategy** & **Equalization Circuit** & \begin{tabular}{c} **Equilibrium** \\ **Speed** \\ \end{tabular} & \begin{tabular}{c} **Current Exceeds** \\ **Limit** \\ \end{tabular} & \begin{tabular}{c} **Implementation** \\ **Diffulty** \\ \end{tabular} \\ \hline Minimum energy loss [28] & Multi-layer inductor & Low/Medium & No & Difficult \\ \hline Two-level equalization strategy [29] & Two-layer equalization circuit & Medium & Yes & Medium \\ \hline Optimal path strategy [30] & Two-layer equalization circuit & Low & Yes & Difficult \\ \hline Multi-mode uniform strategy [31] & \begin{tabular}{c} Multi-switch \\ equalization circuit \\ \end{tabular} & Low/Medium & Yes & Difficult \\ \hline SOC difference control strategy [32] & \
\begin{tabular}{c} Adjacent inductance \\ equalization \\ \end{tabular} & High & Yes & Simple \\ \hline Fuzzy control strategy [35] &
\begin{tabular}{c} Adjacent inductance \\ equalization \\ \end{tabular} & High & Yes & Medium \\ \hline MPC [37] & Two-layer equalization circuit & Low/Medium & Yes & Difficult \\ \hline This article strategy & Multi-layer inductor & High & No & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different balancing strategies.
## 3 Methodology
### BATMAN Algorithm
#### 3.1.1 Path Transmission Quality  
Path transmission quality is computed from source to destination with \(1\) or more intermediate nodes. The value of path transmission quality is measured with the incoming \(TQ\) in the OGMs and inducing it with the local \(TQ\) calculated towards that node and forwarded in the OGM to its neighbors. Since local \(TQ\) is the ratio of transmitted packets with the number of packets received through re-broadcast or echoes towards the sending node, it can be presented as a percentage of the quality of the node towards another node. We must consider the scenario presented in Figure 3, where node \(1\) transmits its \(TQ\) via OGM to node \(2\) assuming the best \(TQ\) of \(100\%\).  
Figure 2: Flowchart for BATMAN algorithm.  
For the simplicity of the metric computations, all other \(T\)Qs towards their neighbors are considered as 75%. When node 2 receives the OGM at time \(t\), it will compute the path transmission quality [17] as follows:  
\[TQ_{path}=TQ_{received}\times TQ_{local} \tag{2}\]  
where \(TQ_{received}\) is extracted from the OGM received from node 1. The \(TQ\) that will be forwarded to another neighbor in the direction of the path will be \(TQ_{path}\), which when received by node 3 will be termed as simply \(TQ_{received}\), while \(TQ_{local}\) is the same as calculated in the previous section. Node 3 at time interval \(t+2\) will compute the new \(TQ_{path}\) using (2) and transmit the calculated \(TQ\) when sending OGM to other neighboring in the path. The same procedure will be repeated for all other consecutive nodes in the path until the destination is reached.  
In such scenarios, where multiple paths lead to the destination, the best path metric would be identified by comparing the \(TQ_{path}\) obtained from Equation (2) of two nodes. A similar case is presented in Figure 4, where node 1 and node 3 transmit their \(TQ\) to node 2, and node 2 \(TQ_{path}\) results as 75% and 81 for the paths from 1 and 3, respectively.  
Figure 3: Path transmission quality computation and forwarding mechanism.  
Since 81% offers a better quality of the link than 75% of node 1, node 2 will use the best value of 81% in the forwarding OGM(s). The compound path \(TQ\) can be found using all the node(s) of \(TQ\) using the below equation, where \(p\) is the total number of nodes in a selected path towards the destination [17].  
\[TQ_{path}=\prod_{p}TQ_{local} \tag{3}\]
# Missed Detection of Entanglement in Two-Mode Squeezed States Based on the Inseparability Criterion
In this paper, we investigate the vulnerability of missed entanglement detections when applying the inseparability criterion to asymmetric two-mode squeezed states. We undertake a comparative analysis with the PPT criterion, known for its superior entanglement detection capabilities, theoretically and experimentally examining how entanglement mode asymmetry modulates the effectiveness of these criteria. Our methodology illuminates the mechanism behind undetected entanglement by the inseparability criterion. By artificially inducing controlled asymmetric optical losses in the distribution of two-mode squeezed states, we simulate experimental asymmetry. Our results reveal a nuanced picture: in symmetric settings, the inseparability criterion aligns with the PPT criterion, fulfilling both sufficiency and necessity for entanglement measurement. Conversely, in asymmetric environments, it reduces to a sufficient condition, incapable of fully and accurately capturing the extent of entanglement. This underscores the crucial importance of judiciously selecting entanglement criteria tailored to specific quantum information processing applications to ensure accurate and reliable entanglement detection. Our contribution aims to propel the advancement of more efficient entanglement verification strategies, indispensable for the robust implementation of real-world quantum systems.
## 6 Simulation Results and Analysis
### Discussion  
Although the proposed AIFG algorithm has demonstrated substantial improvements in reducing queue delay, increasing throughput, and enhancing the multiplexing efficiency, several limitations and open challenges remain:  
* Simulation-Based Validation: The current evaluation is based solely on a simulation platform using NS-3 (version 3.33) and PyTorch (version 2.2.2). Real-world satellite communication environments introduce complexities, such as hardware constraints, channel fading, and protocol interoperability, which are not modeled here.
* Static Traffic Model: The number of service streams and their classifications were fixed during the simulations. In practical deployments, service types and loads are often time-varying and mission-dependent, which may impact the algorithm's adaptability.
* Energy Efficiency Not Considered: This study focuses on QoS metrics without evaluating energy consumption, which is a critical constraint in space systems. Future work should explore low-power optimization and computational efficiency.  
* Algorithm Scalability and Convergence: Although PPO provides stable learning, its convergence speed and computational cost may become significant in larger-scale AOS. Exploring more lightweight or distributed RL techniques would be beneficial.
* Cross-Layer Optimization: Integrating AIFG with transport-layer protocols or application-layer protocols, or co-designing with physical-layer resource adaptation, could further enhance end-to-end QoS.  
These limitations provide a roadmap for future research and guide the further refinement of the AIFG framework toward deployment in practical aerospace communication systems.
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Data Collection  
The first phase of our work consists of compiling as much information as possible from different stakeholders in order to have the most accurate view of daily deliveries in the pedestrian plateau. We began with a merchant audit that identified the transporters involved in delivery on the plateau; then, we met them to identify their practices. In parallel to these two audits, data were recompiled to achieve a better mapping of user flows. Finally, the data are stored in a relational database. The relational database is the most popular choice of database worldwide. In a relational database, data are stored in tables, which are related to each other through primary and foreign keys. These tables, which are transporters, merchants, products, zones, categories, and deliveries, can be accessed and manipulated using a Structured Query Language (SQL).
## 1 Introduction
To push the technological boundaries of ISL miniaturization, a novel and adaptive communication network concept, suitable for low-onboard resource missions, is proposed in this article, which includes possible network topologies for short and medium distances, multiple medium access techniques and channel coding, and an organization of the functional layers. To demonstrate and verify the ISL feasibility, Technische Universitat Berlin (TUB) has developed the innovative mission (S-NET) of four nanosatellites with the S Band (2.0-2.3 GHz) transceiver (SLINK) radio as the main payload [2]. The SLINK, developed at TUB, is the core element of this mission, with 100 kbps crosslink and 1 Mbps downlink rates which make it suitable for accommodation in nanosatellites. On the system level, the ISL data rate mainly depends on the distance, available transmission power, antenna gain, and pointing and is, thus, adaptable. So, higher order of magnitudes can be achieved with more performant platforms. The description of the implemented network architecture and system design of this mission is the main intention of this article. The novelty of this work is the optimization and organization of the functional communication techniques and protocol layers suitable for resource-limited nanosatellites.
## 4 Classification and Features of Smartphone Apps for Solar PV Energy
### Environmental and Economic Assessment of PV Systems
Some apps also provide feasibility analysis suitable for roof and/or ground PV systems [58; 59; 75; 88; 90]. Furthermore, a couple of apps allows virtually placing PV arrays on the roof through high-resolution satellite images [59; 88] or on the ground through a map [58], as summarized in Table 7. The most representative app that includes all features mentioned above (i.e., assessment of all three aspects, applicable to roof PV, and PV array design) is the Mapdwell Solar System app [88], which reveals the PV potentialof a building rooftop by intuitively designing the type and layout of PV array on the satellite image considering the rooftop shape, local weather, and shading from nearby structures and vegetation (Figure 5). It also helps decision making via comprehensive cost-benefit analysis.
_applied_  
_sciences_  
_Article_  
Development of a Generalized Photothermal Measurement  
Model for the Layer Thickness Determination of Multi-Layered Coating Systems  
[PERSON]\({}^{\dagger}\)\({}^{\copyright}\) and [PERSON]\({}^{\ast,\dagger}\)\({}^{\copyright}\)  
Department of Mathematics, Saarland University, 66123 Saarbrucken, Germany; [EMAIL_ADDRESS]  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +49-681-302-57425  
\(\dagger\) These authors contributed equally to this work.  
**Featured Application: The results in this article might contribute to the construction of a photothermal measurement device to determine the thicknesses of layers in multi-layered coating systems as they appear, e.g., in the automotive industry.**  
In this article, a general model for 1D thermal wave interference is derived for multi-layered coating systems (with \(n\in\mathbb{N}\) coating layers) applied on a thermally thick substrate. Such a model means the first step to building a non-contact photothermal measurement device that is able to determine the coating thickness of each layer. Test objects are to be illuminated on the surface using planar, sinusoidal excitation waves with fixed frequencies leading to the generation of thermal waves inside the object. Due to the multi-layered structure, each of these thermal waves is reflected and transmitted at layer interfaces. This process leads to infinitely many wave trains that need to be tracked to formulate the final surface temperature as a superposition of all waves. A mathematical and physical formulation of thermal wave interference is needed to model this process and relate the dependencies of the layer thicknesses, the materials, and the frequencies to the phase angle data, which then can be measured using, e.g., an infrared camera. In practice, the thermal properties of the layers might be unknown, which makes the process even more difficult. This article presents a concept to determine the thermal properties in advance. Finally, numerical experiments are presented that demonstrate the feasibility of the introduced layer thickness determination process.  
**Keywords:** photothermal measurements; infrared thermography; thermal wave interference; parameter estimation; layer thickness determination; multi-layered coating systems; thermal properties  
**MSC:** 35R30