esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methodology
#### 3.3.2 EQ Measurement  
To accurately represent the ecological environment status of lake-ring urban agglomerations, this study employs the RSEI model to measure the spatiotemporal distribution of EQ. Within the Pressure-State-Response conceptual framework, the RSEI index selects NDVI, WET, NDBSI, and LST as EQ indicators from the dimensions of greenness, humidity, dryness, and thermal factors, respectively. Compared to evaluations using a single remote sensing indicator, RSEI more comprehensively captures the response of the ecological environment to human activities, making it more advantageous. The RSEI calculation process does not require manual intervention, yielding objective and reliable results. Due to space limitations, detailed calculation methods for each indicator can be found in the references [34; 35; 36]. Principal Component Analysis is conducted on the four normalized indicators to extract the first principal component. The initial RSEI undergoes normalization to calculate the final RSEI. The calculation formulas for EQ are as follows:  
\[NI_{i}=(I_{i}-I_{min})/(I_{max}-I_{min}) \tag{2}\]  
\[RSEI_{0}=1-PC1[f(NDVI,LST,WET,NDBSI)] \tag{3}\]  
\[RSEI=(RSEI_{0}-RSEI_{0-min})/(RSEI_{0-max}-RSEI_{0-min}) \tag{4}\]  
where \(NI_{i}\) represents the normalized value of the \(i\)th indicator; \(NI_{i}\) is the value of the \(i\)th pixel; \(I_{min}\) is the indicator's minimum value; \(I_{max}\) the indicator's maximum value. To facilitate statistical analysis, this study categorizes EQ into five levels: very poor (0-0.2), poor (0.2-0.4), moderate (0.4-0.6), good (0.6-0.8), and excellent (0.8-1.0).
## 2 Related Work
### High-Quality P2P Video Streaming Systems
From a computational perspective, mesh and hybrid topologies (e.g., SmoothCache, ALIVE, Celerity) require each node to maintain multiple neighbor states, perform complex scheduling, and handle frequent signaling, leading to \(O(n)\) or higher per-node overhead as the network scales. WebRTC-based systems, while offering NAT traversal and real-time media, incur additional CPU/memory costs for ICE negotiation, media encoding, and multi-peer synchronization. In contrast, the chain-based approach reduces per-node state to a single upstream and downstream connection, with failure detection and recovery logic that is both lightweight and deterministic. This design not only lowers computational and memory requirements but also enhances predictability and scalability for large deployments.
# Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
## 3 Bidirectional Cached Rapidly Exploring Random Tree Star
### Path-Caching Strategy
\[p_{goal}+p_{path}+p_{rand}=l \tag{8}\]  
The determination of \(p_{goal}\) and \(p_{path}\) represents a pivotal aspect among the three probabilities, wherein comprehensive simulation experiments are imperative to ascertain based on distinct contextual contingencies. In order to debug, \(p_{goal}=0.3\) and \(p_{path}=0.6\) serve as the preliminary parameters. This section primarily concerns the enhancement of the node selection function utilized in the RRT* algorithm for selecting the new node, which involves a stochastic process. Specifically, the aforementioned operation entails the selection of an integer value obtained from a pseudo-random number generator in the form of a uniform distribution \(i\), with \(p\) being uniformly distributed over the interval \([0,1]\), while \(i\) is uniformly selected from the interval \([1,N]\). Here, \(N\) indicates the cardinality of the path cache set. If \(p<p_{goal}\), an additional vertex is created as a result of the algorithmic process towards the desired objective point. If \(p_{goal}<p<p_{goal}+p_{path}\), a novel vertex is produced in the direction of the \(i\)-th point on the stored trajectory; if not, in the absence of this condition a new node is produced towards a randomly selected location. The corresponding sampling formula is thus derived as follows:  
\[x_{rand}=\left\{ \begin{array}{rl}&x_{goal}\quad p<p_{goal}\\ &x_{path}\quad p_{goal}<p<p_{goal}+p_{path}\\ &x_{free}\quad p>p_{goal}+p_{path}\end{array} \right. \tag{9}\]  
Among them, \(x_{goal}\) denotes the positional values of the aimed destination, whereas \(x_{path,i}\) denotes the coordinate of the i-th node in the stored trajectory. Additionally, \(x_{free}\) denotes the positional values of a point randomly selected from the sampled space. Upon examination of Equation (8), it is evident that the summation of the probabilities yields a constant value. Hence, the probability of random sampling is affected by the target offset probability and the path cache offset probability. The significant reduction of irrelevant node exploration during tree traversal hinges on guiding its growth towards more effective paths. This is achieved by incorporating directional cues such as pre-planned path nodes and target points in random tree variations, ultimately diminishing random sampling while eliminating unnecessary node computation. The algorithm proposed in this paper is depicted in the overall flowchart illustrated in Figure 3.  
Figure 3: Schematic diagram of two-way extended RRT* algorithm. \(T_{i}\) and \(T_{S}\) mean two random trees. \(q_{i}\) and \(q_{S}\) represent the node of random sampling expansion. The bolded boxes highlight points of divergence between the methodology proposed in this paper and other existing approaches.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 3 Energy Management of an HRES Based on Deep Q-Network
### Methodology of the DQN-Based EMS  
In this part, the DQN algorithm is described. Its objective is to find an optimal policy that maximizes the expected total rewards from a starting state. Figure 3 shows a graph of DQN-based EMS. The optimal policy is formulated as [42]:  
\[V^{\pi^{*}}(s)=maxE_{\pi}\left[\sum_{t=0}^{T}\gamma^{t}r_{t+1}|s_{0}=s\right] \tag{28}\]  
where \(\pi^{*}\in\Pi\) is the optimal policy in response to a given state and action. It is a strategy which is applied by the agent to decide the next action based on the current state. \(0<\gamma<1\) is the discount factor used to define the importance of future reward. \(E_{\pi}\) denotes the expected value of reward according to the policy the agent follows.  
In the DQN formulation, the optimal policy is represented by the optimal action-value function:  
\[V^{\pi^{*}}(s)=maxQ^{\pi^{*}}(s,a) \tag{29}\]  
where \(V^{\pi^{*}}(s)\) is the optimal state-value function of an MDP. It is the expected return starting from state \"\(s\)\" following optimal \(\pi^{*};Q^{\pi^{*}}(s,a)\) is the optimal action-value function. It is the expected return starting from state \"\(s\)\", following optimal policy \(\pi^{*}\), taking action \"\(a\)\". It focuses on the particular action at the particular state.  
It is expressed as follows [42]:  
\[Q^{\pi^{*}}(s,a)=E_{\pi^{*}}\left[\sum_{k=1}^{\infty}\gamma^{k-1}r_{t+k}|s_{t }=s,a_{t}=a\right]=E_{\pi^{*}}\Big{[}r_{t}+\gamma maxQ^{\pi^{*}}(s_{t+1},a_{t +1})|s_{t}=s,a_{t}=a\Big{]} \tag{30}\]Following the optimal action-value function, the optimal policy can be determined by [42]:  
\[\pi^{*}(s)=argmaxQ^{\pi^{*}}(s,a) \tag{31}\]  
In the DQN algorithm as shown in Figure 4, a deep neural network is used to calculate \(Q^{\pi^{*}}(s,a)\). It is expressed as \(Q(s,a|\theta)\) network, where \(\theta\) is the weight vector of the neural networks. As shown in the pseudo code in Figure 4, two separate \(Q\)-networks are used. \(Q(s,a|\theta)\) represents the prediction network, while \(Q(s,a|\theta^{\prime})\) represents the target network [42]. To train the \(Q\)-network, a gradient descent is applied to minimize the loss function of the target and prediction networks. In every time step of the training process, the prediction \(Q\) network is updated by back-propagation method. In contrast, the target network is frozen. After a period of C time steps (C steps in the algorithm), its weights are updated by simply copying the weights from the current prediction \(Q\) network. Freezing the target \(Q\) network for a period of time helps stabilize the training process. In general, the Deep \(Q\) Network must be trained through the process in Figure 4 to ensure that EMS controller always chooses the best action. Then, EMS uses its trained Deep \(Q\) Network to calculate the \(Q\) value based on the current state information, and the next action is chosen following that \(Q\) value.  
Figure 4: DQN algorithm.  
Figure 3: A graph of DQN-based EMS.
# A Simulation of Tire Hydroplaning Based on Laser Scanning of Road Surfaces
## 4 Result Analysis
### Influence of Road Surface Texture Uniformity on Anti-Slip Performance  
Although it can be seen from the measurement results of the road anti-slip index in the previous text that the swing value and MTD of the SMA-13 and the OGFC-13 roads are not much different, the swing value and MTD of the two roads differ by 8.3% and 6.7% on average, but the maximum dynamic water pressure in the same situation differs by 13.8%.  
Through the re-analysis of the actual road surface, it is evident that in multiple SMA rutting plates, there are areas with uneven construction distribution as shown in the Figure 20a,c. At location A marked on the map, due to the close packing of stone materials and the accumulation of asphalt paste, the structure has become shallower or even disappeared. In contrast, at location B marked in Figure 20a,c, due to the lack of stone materials, the structure has become deeper and larger. The interaction between these two factors results in an average construction depth that is not small, yet the overall uniformity of the SMA pavement structure is poor. In comparison, as shown in Figure 20b,d the construction uniformity of the OGFC is better, with a more even distribution across the surface. The unevenness in construction leads to significant differences in actual anti-skid performance despite similar values for slip resistance and MTD between the two different gradations.  
Figure 19: Relationship between critical water sliding speed and water film thickness of different pavement types.  
Research shows that after the initial paving of [42] and SMA, due to the presence of a large amount of asphalt and mineral powder in the SMA mix, it is prone to issues such as oil spots, leading to uneven pavement structure. Additionally, excessive asphalt coating prevents the aggregate from coming into contact with tires, resulting in poorer anti-skid performance. After a certain period of operation, the anti-skid performance of SMA pavements improves due to the redistribution of the asphalt and exposure of the aggregate.  
It can be seen that MDP and swing friction value cannot completely evaluate the anti-slip condition of the road surface, and the evaluation of the road surface texture should also consider the uniform distribution of the road surface texture.
# LDPC Coded Massive MIMO Systems
## 5 Numerical Results
### Convergence Speed of JDD Strategy  
We show the benefit of using the proposed JDD strategy in view of the threshold with respect to the amount of computational complexity. In Figure 6, we plot the threshold \((E_{b}/N_{0})^{*}\) obtained by the density evolution algorithm given in Algorithm 3 for the proposed JDD strategy and GIO JDD over \(16\times 16\) channel with various numbers of multiplications. It was observed that the proposed JDD strategy converged to the lowest threshold faster than GIO JDD. It was also observed that the proposed JDD strategy resulted in a lower threshold than GIO JDD for a given amount of computational complexity.
# Formal Matters on the Topic of Risk Mitigation: A Mathematical Perspective
## 3 Formalizing Risk Mitigation  
When discussing risk mitigation, we should consider the \(h,e,v\) (and _a fortiori_\(r\)) functions to be _fixed_: they are the (mathematical or numerical) models that describe how to compute the hazard, exposure, and vulnerability (and risk) given the appropriate input data. For example, the values of \(h\) may be obtained using deterministic physical-mathematical models of the phenomenon, and \(e\) may be computed from well-established criteria that assign value to human resources present in the area.  
Given the unchanging nature of \(h,e,v\), to mitigate the risk, we must operate on the _input data_ to these functions. Thus, while Equation (8) for risk may not seem particularly innovative, the explication of the dependency on the choice of \(T,P,B,Q\) in their respective sets is essential for the formalization of risk mitigation: these are the model inputs on which decision-makers have influence, i.e., the _decision variables_[8].  
To see how the choice of these functions maps to risk mitigation efforts, consider, for example, that to reduce risk, we could strengthen the buildings to make them less vulnerable to earthquakes [16], which corresponds to choosing a different \(Q\), raise the river banks to reduce flooding hazard [17] (resulting in a different \(T\)), and displace population [18; 19; 20] or reconsider land-use patterns [21] so that high-exposure elements are moved to low-hazard areas (equivalent to a different choice of \(P\), with a possible indirect effect on \(B\) and \(Q\)).  
More formally, assume we have a given \(T_{0}\in\mathcal{T}\), \(P_{0}\in\mathcal{P}\), \(B_{0}\in\mathcal{B}(P_{0})\), \(Q_{0}\in\mathcal{Q}(P_{0},B_{0})\) and a corresponding (pointwise) risk \(r(T_{0},P_{0},B_{0},Q_{0},x,y)\) and total risk \(R(\Omega,T_{0},P_{0},B_{0},Q_{0})=\int_{\Omega}r(T_{0},P_{0},B_{0},Q_{0},x,y) \mathrm{d}x\mathrm{d}y\). To mitigate risk, we need to find \(T_{1}\in\mathcal{T}\), \(P_{1}\in\mathcal{P}\), \(B_{1}\in\mathcal{B}(P_{1})\)  
Figure 1: The functional dependencies between risk, vulnerability, exposure, hazard, and their respective input variables. Arrows point towards the dependent (i.e., \(X\to Y\) indicates that \(Y\) depends on \(X\)).  
\(Q_{1}\in\mathcal{Q}(P_{1},B_{1})\) such that the total risk \(R(\Omega,T_{1},P_{1},B_{1},Q_{1})=\int_{\Omega}r(T_{1},P_{1},B_{1},Q_{1},x,y) \mathrm{d}x\mathrm{d}y\) satisfies:  
\[R(\Omega,T_{1},P_{1},B_{1},Q_{1})<R(\Omega,T_{0},P_{0},B_{0},Q_{0}).\]  
In practice, the function quartet \(C_{0}=(T_{0},P_{0},B_{0},Q_{0})\) is a functional representation of the current situation, and the function quartet \(C_{1}=(T_{1},P_{1},B_{1},Q_{1})\) would be the functional representation of a distribution of resources that leads to a lower overall risk than the current situation.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Resource Accumulation
#### 4.1.2 M2 ASR  
In 2016, Tsinghua University, Xinjiang University, and Northwest Minzu University kicked off a joint project called Multilingual Minorlingual Automatic Speech Recognition (M2 ASR). The goal of this project was to conduct research on speech recognition technologies for five minority languages, including Tibetan, Mongolia, Uyghur, Kazakh, and Kyrgyz [113]. At the end of this project in 2021, the project team released all the resources they constructed. The resources included speech databases (277 h in Uyghur, 250 h in Kazakh, and 166 h in Kyrgyz), text corpora (88 M in Uyghur, 100 M in Kazakh, and 6 M in Kyrgyz), and pronunciation dictionaries (45,000 in Uyghur, 100,000 in Kazakh, and 15,000 in Kyrgyz). This is the most-comprehensive and -complete resource for Uyghur, Kazakh, and Kyrgyz speech recognition.
## 4 Design of TLQoS
### Queuing Manager  
The queuing manager in TLQoS exploits a multi-queue priority policy where higher priority is given to the delay constrained traffic (\(Cr\) and \(Dc\)) than the non-delay constrained (\(Rc\) and \(Rg\)) traffic types. To achieve this, this module maintains two separate queues, namely DCQ and RQ for storing delay constrained packets, and non-delay constrained packets respectively. The DCQ has the higher priority over RQ. It implies that the packets from RQ will not be sent until the DCQ becomes empty. However, it might cause starvation problem where the lower priority traffic could be indefinitely blocked by higher priority traffic. To avoid the problem we adopt the similar mechanism as described in [13], in which a time-out based policy have been employed. In this policy, a packet will be moved to a higher priority queue (irrespective of its type) if it waits in a lower priority queue until a time-out occurs. The multi-queue priority policy addresses the priority contention between packets in the same node. However, to address the priority contention between neighboring nodes, the MAC protocol could be modified which is beyond the scope of the paper.
## 1 Introduction  
Handball is a sport characterized by high-intensity physical activities, rapid directional changes, and explosive movements, all of which emphasize the critical role of motor capacity in athletic performance [1]. In this context, attributes such as speed, agility, and explosive power directly influence a player's effectiveness on the court [2,3]. However, the relationship between these fundamental motor qualities and individual variables--particularly anthropometric characteristics, chronological age, and training age--remains insufficiently clarified in the current literature.  
Anthropometric structure plays a crucial role in determining athletes' physical performance capacities [4]. Anthropometric parameters such as body composition, limb proportions, and segment lengths can significantly influence physical abilities, particularly speed and agility [5, 6, 7, 8]. Nonetheless, physical structure alone is not a comprehensive predictor. Time-related factors such as chronological age and training age also exert substantial effects on performance [6, 9]. Chronological age represents physiological maturity and natural development, while training age reflects the athlete's sport-specific adaptations and accumulated motor skill experience [10,11]. The combined effects of these two factors on athletic performance have rarely been examined holistically.  
[PERSON] et al. [12] reported that physical performance metrics such as sprinting and countermovement jump exhibit a steady but nonlinear increase with chronological age, plateauing after approximately age 16. They also noted that maximal aerobic speed significantly increases until around age 14.5, after which it stabilizes. Furthermore, [PERSON] et al. [6] found that athletes in the U17 and U18 age groups outperformed their younger counterparts (U14-U16) in all physical tests, identifying age as the strongest predictor of sprint and jump performance.  
Despite these findings, the specific impact of training age (i.e., athletic experience) has not been delineated. [PERSON] et al. [13] found that lower performance in sport-specific skills among handball players was primarily due to a lack of training, suggesting that training age may directly influence performance. In light of this evidence, both chronological and training age emerge as influential parameters in athletic development.  
Given the current body of literature, there is a clear need for studies that concurrently examine anthropometric structure, chronological age, and training age. In sports such as handball, where both youth and adult athletes compete, understanding the relationship between these variables and key performance indicators like speed, agility, and explosive power is essential for talent identification, the individualization of training programs, and long-term athlete development. Therefore, this study aims to investigate the relationships among anthropometric characteristics, chronological age, and training age with speed, agility, and explosive power in handball players, thereby elucidating their effects on motor performance through empirical evidence.
## 1 Introduction  
RC walls are usually used in tall buildings to withstand lateral loads for their effectiveness in limiting drifts [1]. RC short-limb shear walls refer to specific RC walls with the ratio of the cross-section height to width between 5 and 8 [2]. A short-limb shear wall structure has a flexible layout and good building function [3; 4]. It has been very common to use short-limb shear walls with a rectangular section to optimize economy and design. Numerous studies have been conducted to investigate the seismic behavior of rectangular shear walls [5; 6; 7; 8]. Previous experimental studies have shown that rectangular shear walls usually showed poor seismic performance [9], especially for rectangular short-limb shear walls [10]. For reasons of functionality, it is also a common practice to combine rectangular short-limb shear walls to form T-shaped or L-shaped short-limb shear walls [11].  
Previous studies have been restricted to T-shaped [12; 13; 14], L-shaped [15; 16; 17], and rectangular walls [18; 19] with ordinary-strength rebars and concrete. Very few documents have investigated the seismic performance of L-shaped short-limb shear walls with high-strength rebar and high-strength concrete. Previous experimental results also indicated that the web was the weakest part of conventional flanged walls, which generally failed with premature crushing in the free web boundary because of insufficient restraint [20]. In order to enhance the seismic performances of short-limb shear walls with an L-shaped section, we designed L-shaped short-limb shear walls with high-strength rebar and high-strength concrete. The high-strength longitudinal rebars serve to enhance the load-carrying capacity, especially when web is under tension. The high-strength horizontal rebars serve to confine concrete and postpone the bucking of longitudinal rebars, while increasing ductility simultaneously. The high-strength concrete serves to improve the load-carrying capacity when the web is under compression.  
In this paper, six L-shaped short-limb shear walls constructed with high-strength rebar and high-strength concrete (in short HPLW) were loaded to destruction under lateral cyclic loading. The effects of axial compression ratio, aspect ratio, and horizontal rebar spacing on the seismic performance of an HPLW were critically investigated. Experimental results are discussed and compared with an L-shaped short-limb shear wall with high-strength horizontal rebar (in short LW), and key issues related to seismic design are discussed in detail.