esa-sceva/satcom-chunk-collection huggingface dataset:
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 2 Related Research
### Overview of the Application of Neural Networks in Task Planning Algorithms  
The existing research, which is mainly conducted through the establishment of mathematical programming models, using various algorithms to directly solve the satellite task planning problem, has less reference to historical task planning results. Some researchers have tried to learn relevant empirical knowledge from historical task planning data using machine learning methods to solve satellite task planning problems to improve the efficiency of problem solving.  
For example, [PERSON] et al. [32] mapped the satellite task planning problem into a dynamic random knapsack problem. Asynchronous advantage actor-critic (A3C) reinforcement learning algorithm was used to determine whether each observation task should be inserted into the observation scheme in real time. Experiments show that this method is superior to the first-come-first-serve greedy algorithm in terms of optimization. [PERSON] et al. [33] combined the supervised learning method with the heuristic search algorithm and used the neural network to calculate the scheduling priority of each task. Then, the heuristic search algorithm sequentially inserts the observation task into the observation scheme, according to the scheduling priority of the task, which enhances the search ability of heuristic search algorithms. [PERSON] et al. [34] proposed an improved genetic algorithm based on memory learning to solve the resource conflict in the evolutionary search process of a genetic algorithm by learning knowledge rules from historical planning results, which effectively improved the search efficiency and stability of the algorithm. [PERSON] et al. [35] designed the feature expression model based on the satellite observation constraint model and the objective function, which effectively accelerated the convergence speed. [PERSON] et al. [36] introduced the intensive learning method into the multi-satellite collaborative task assignment problem and improved the task negotiation distribution efficiency of multiple satellites by an iteratively \"trial and error\" search for the best negotiation strategy.  
It can be seen, from the above research, that the use of machine learning methods has played a positive role in improving the efficiency and optimization of algorithms. This is also the theoretical basis of this paper.
## 4 Stretchable MSP Antenna-Based Strain Sensor
### Optimization
\[S(x,f_{i},\varepsilon)+\gamma\leq 0, \tag{20}\]1 \(\mathrm{R_{f}}\) comparison of initial, deformed, non-deformed optimal antenna, and deformed optimal antenna. (**a**) 2.3454 GHz; (**b**) 2.6539 GHz; (**c**) 4.9130 GHz; (**d**) 5.1070 GHz. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Displacement of 0 mm**} & \multicolumn{4}{c}{**Displacement of 12 mm**} \\ \cline{2-9}
**Estimated Design** & \multicolumn{2}{c}{**Resonance Freq.**} & \multicolumn{2}{c}{**Return Loss**} & \multicolumn{2}{c}{**Resonance Freq.**} & \multicolumn{2}{c}{**Return Loss**} \\ \cline{2-9}  & **at** & **at** & **at** & **at** & **at** & **at** & **at** & **at** & **at** \\  & **2.5 GHz** & **5.0 GHz** & **2.5 GHz** & **5.0 GHz** & **2.5 GHz** & **5.0 GHz** & **2.5 GHz** & **5.0 GHz** \\ \hline Initial MSP antenna & 2.5 & 5.05 & \(-\)14.83 & \(-\)12.73 & 2.73 & 5.23 & \(-\)10.74 & \(-\)8.68 \\ Initial MSP antenna (with optimization) & 2.45 & 5.02 & \(-\)14.97 & \(-\)13.64 & 2.59 & 5.09 & \(-\)11.36 & \(-\)11.20 \\ Initial MSP antenna (with DR) & 2.81 & 5.20 & \(-\)14.11 & \(-\)13.83 & 2.69 & 5.22 & \(-\)11.07 & \(-\)8.90 \\ Initial MSP antenna (with DR and optimization) & 2.48 & 4.74 & \(-\)14.34 & \(-\)14.11 & 2.65 & 5.10 & \(-\)13.86 & \(-\)13.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of resonance frequency (GHz) and return loss (dB) under strain test.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 2 Background  
When working with BLE, developers have the option to configure various parameters of their systems, such as transmit strength, duration between transmissions, and duration of their transmissions, as well as size of the data transmitted. It is important to note that different manufacturers and operating systems may dictate the amount of configurability available to the developer. For example, Apple, Inc. has specific guidelines for connection parameters that accessory designers must follow when developing devices to pair with their products [23].  
When developing a wireless sensor network, it is crucial to understand how BLE parameter selection influences the system performance in terms of power consumption, latency, and supported network size (i.e., maximum number of connections). For a real-time EMG-powered prosthesis controller, latency is a primary concern. Subjects have reported that they felt as though they were operating their prosthesis \"in molasses\" when delays longer than 50-100 ms were purposely imposed on their controller [6]. In addition, frequent charging of their device is inconvenient and impractical, so it is intended that a user get a full day (12 h or more) of use before they must recharge their device. To achieve this requirement, the average power consumption must be kept low, ideally to a few mA. Finally, for many EMG-driven applications, multiple channels of EMG or other sensor modalities are acquired. Thus, we also assessed the maximum number of sustained peripherals. In this work, the nRF52840 System-on-Chip (SoC) from Nordic Semiconductor was used to implement a multi-node wireless system to measure the average and maximum power consumption, latency, and maximum network size for different BLE configurations.  
In a wireless prosthesis/orthosis controller, the peripheral devices would typically be placed around the remnant muscle, and the central device would be placed within the prosthetic device's control system as shown in Figure 1. In some cases, the sensors may be placed around a joint or other location on the body, depending on the task at hand. In a traditional wired system, the sensors are physically connected via cabling between the sensors and control system, which requires housing (socket) to contain them. In this proposed wireless system, an enclosure would no longer be required, which is ideal for socket-less solutions such as prosthetic liners and osseointegrated devices.  
Figure 1: Illustration of the intended upper limb prosthetic control application. The peripheral devices (IMU and EMG sensors) are placed at their sensing location, the remnant muscle for the EMG  
sensor and the shoulder for the IMU sensor, and wirelessly connected to a central device placed inside of the prosthetic device's control module.
## 2 Methodology
### Experimental Methodology
#### 2.4.3 Mode 3: Simultaneous Charging and Discharging  
The experiments in Mode 3 were performed when both the HTF and refrigerant circuits were switched on, hence allowing direct heat exchange between the condensing unit and the water bath through the RPW-HEX. Two types of experiments were performed in this mode, depending on the initial state of the RPW-HEX. That is, for the first case the RPW-HEX was initially completely discharged while for the second case it was initially completely charged.  
In Mode 3a, the PCM was initially at 12 \({}^{\circ}\)C, which is the temperature at which the RPW-HEX is considered totally discharged. The HTF flow rate was set to either 50 L\(\cdot\)h\({}^{-1}\), 100 L\(\cdot\)h\({}^{-1}\) or 150 L\(\cdot\)h\({}^{-1}\), while the water bath temperature was maintained at 12 \({}^{\circ}\)C, which is the typical HTF temperature at evaporator inlet in cooling applications in buildings. When all the sensors within the RPW-HEX were at 12 \(\pm\) 0.5 \({}^{\circ}\)C, the condensing unit and the HTF pump were switched on to initiate the experiment. The experimental plan in Mode 3a is shown in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial PCM Temperature [\({}^{\circ}\)C]** & **HTF Inlet Temperature [\({}^{\circ}\)C]** & **HTF Flow Rate [L-h\({}^{-1}\)]** \\ \hline \multirow{2}{*}{\(-4\)} & 09 & \multirow{2}{*}{150} \\  & 12 & \\  & 15 & \\ \hline \multirow{2}{*}{\(-4\)} & 09 & \multirow{2}{*}{100} \\  & 12 & \\  & 15 & \\ \hline \multirow{2}{*}{\(-4\)} & 09 & \multirow{2}{*}{50} \\  & 12 & \\ \hline \multirow{2}{*}{\(-4\)} & 09 & \multirow{2}{*}{50} \\  & 12 & \\ \cline{1-1}  & 15 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Set of experiments in Mode 2.  
The experiments in Mode 3b are similar to those in Mode 3a with the exception that the initial temperature of the PCM was set at \(-4\)\({}^{\circ}\)C, at which the RPW-HEX is considered completely charged. The experimental plan in Mode 3b is shown in Table 7.
# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 3 Preliminaries
### Notation
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Agent** & **Skill 1** & **Skill 2** & **Skill 3** \\ \hline A1 & Intrusion Detection & Agent Computing & Distributed Data Mining \\ \hline A2 & Agent Computing & Speech Acts & - \\ \hline A3 & Multi-Agent Systems & Social Dynamics & Security \\ \hline A4 & Verification & Model Checking & Agent Computing \\ \hline A5 & Advice Exchange & Multi-Agent Systems & Machine Learning \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Agents and their skills. performing a single-point crossover:  
\[Offspring_{1}=\{A_{1},A_{2},B_{3},B_{4}\},\]  
\[Offspring_{2}=\{B_{1},B_{2},A_{3},A_{4}\}.\]  
We select the offspring that has the minimum communication cost. **Definition 5**. : _[_19_]_ _[Total Communication Cost (TC) of a Team] Let a team comprise the agents \(A_{1},\ldots,A_{m}\), \(m>1\). Let the communication graph for the team be \(G=(V,E)\), where \(V=\{A_{1},\ldots,A_{m}\}\), and \(CC_{i,j}\) is the weight of the edge \((A_{i},A_{j})\). Then, TC is the sum of the weights of all the edges in \(G\):_  
\[TC=\sum_{i}^{m}\sum_{i=j+1}^{m}CC_{i,j}. \tag{2}\]  
Consider a task that requires specific expertise, denoted as \(T=\{\)security, machine learning, agent computing, model checking\(\}\). We have identified a set of eligible agents \(\{A_{1},A_{2},A_{3},A_{4},A_{5}\}\) who can perform this task. The required skills for the task and the corresponding agents are outlined in Table 3. For example, the'security' task is performed by agent \(A_{3}\), the 'agent computing' task by agents \(A_{1}\), \(A_{2}\), and \(A_{4}\), the'model checking' task by agent \(A_{4}\), and the'machine learning' task by agent \(A_{5}\). Using this set of agents, we can form different possible teams, such as \(T_{1}=\{A_{3},A_{1},A_{4},A_{5}\}\), \(T_{2}=\{A_{3},A_{2},A_{4},A_{5}\}\), and \(T_{3}=\{A_{3},A_{4},A_{5}\}\). We find out the communication cost between agent \((A_{1},A_{3})\) using Definition 3 as follows:  
\[CC_{(A_{1},A_{3})}=1-\frac{|A_{1}\cap A_{3}|}{|A_{1}\cup A_{3}|}=1-\frac{|0|}{ |3|}=1.\]  
Similarly, we calculate the communication cost between other agents. For team \(T_{1}\), the edges are \((A_{1},A_{3})\), \((A_{4},A_{3})\), \((A_{5},A_{3})\), \((A_{4},A_{1})\), \((A_{5},A_{1})\), and \((A_{4},A_{5})\), with respective weights of 1, 1, 0.8, 0.8, 1, and 1. Therefore, for \(T_{1}=1+1+0.8+0.8+1.1\), which is 5.6. Similarly, the total communication costs calculated for \(T_{2}\) and \(T_{3}\) are 5.55 and 2.8, respectively. If \(T_{1}\) is deemed the best global solution, a single-point crossover can be executed between \(T_{1}\) and \(T_{2}\) to create solutions \(T_{12}\) and \(T_{21}\), as illustrated in Figure 2. Likewise, performing a single-point crossover with \(T_{3}\) generates solutions \(T_{13}\) and \(T_{31}\), with total  
Figure 1: Agent skill-based team formation. communication costs of 2.8, 5.55, and 5.6, respectively. Team \(T_{13}\) is preferred since it has the minimum communication cost 2.8.
## 5 Proposed Solutions and Policy Recommendations
### Demystifying Controversy between Principles of Equitable Access and Non-Appropriation
#### 5.3.2 Defining and Enforcing Limitations for Equitable Outer Space Access  
The issue can also be dealt with based on the principle of equitable access by placing a limit on the number of orbital slots per satellite based on a minimum requirement, to ensure that operators efficiently operate the satellite constellation. The ITU can also factor in the operator's ability to perform mitigation measures to lower the interference rates--for instance, SpaceX's introduction of a \"darksat\" prototype--to curb astronomical interferences.  
Every right, therefore, has a limitation, and, in order to fully enjoy outer space eq-uitably, these limitations must be clearly delineated to avoid misunderstandings and to also ensure equitable access for all. Thus, by enforcing these measures, it is ensured that mega-constellation operators have access to outer space, as well as other actors, while also ensuring that the appropriation of outer space is not condoned by the granting of licenses and approval for specific amounts of time and employing the milestone-based approach to avoid the warehousing of orbital slots.
# Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient
## 2 Statistical Speech Recognition Using a DNN-Based Acoustic Model
### Statistical Speech Recognition  
Statistical speech recognition is a process which finds a word sequence \(\mathbf{W}^{*}\) that produces the highest probability for a given acoustic feature vector sequence \(\mathbf{X}=\mathbf{x}_{1},\mathbf{x}_{2},...,\mathbf{x}_{T}\) as follows [16; 17]:  
\[\mathbf{W}^{*}\approx argmax_{\mathbf{W}}P(\mathbf{W}|\mathbf{X}) \tag{1}\]  
In practice, it is divided into the following components by applying [PERSON] rule:  
\[\begin{split} argmax_{\mathbf{W}}P(\mathbf{W}|\mathbf{X})& =argmax_{\mathbf{W}}\frac{P(\mathbf{X}|\mathbf{W})P(\mathbf{W})} {P(\mathbf{X})}\\ &=argmax_{\mathbf{W}}P(\mathbf{X}|\mathbf{W})P(\mathbf{W})\end{split} \tag{2}\]  
where \(P(\mathbf{X})\) is removed in recognition stage because the \(argmax\) operator does not depend on \(\mathbf{X}\). It is hard to model \(P(\mathbf{X}|\mathbf{W})\) directly. So, words are represented by subword units \(\mathbf{Q}\)[18]  
\[\begin{split} argmax_{\mathbf{W}}P(\mathbf{X}|\mathbf{W})P( \mathbf{W})&=argmax_{\mathbf{W}}\sum_{\mathbf{Q}}P(\mathbf{X}| \mathbf{Q},\mathbf{W})P(\mathbf{Q},\mathbf{W})\\ &\approx argmax_{\mathbf{W}}\sum_{\mathbf{Q}}P(\mathbf{X}| \mathbf{Q})P(\mathbf{Q}|\mathbf{W})P(\mathbf{W})\\ &\approx argmax_{\mathbf{W}}max_{\mathbf{Q}}P(\mathbf{X}| \mathbf{Q})P(\mathbf{Q}|\mathbf{W})P(\mathbf{W})\end{split} \tag{3}\]  
where \(P(\mathbf{W})\) is a language model, \(P(\mathbf{Q}|\mathbf{W})\) is a pronunciation model and \(P(\mathbf{X}|\mathbf{Q})\) is an acoustic model. Figure 1 depicts the role of the probabilistic models for ASR systems to recognize a given sentence \"This is \(\cdots\)\". A language model, \(P(\mathbf{W})\), represents a priori probability of a word sequence. \(N\)-gram language model is commonly used by assuming that the word sequence depends only on the previous \(N-1\) words. Figure 1 shows a 2-gram example. A pronunciation model, \(P(\mathbf{Q}|\mathbf{W})\), plays a role to map words to their corresponding pronunciation or subword units. Figure 1 shows that the words, \"This\" and \"is\", are mapped to phoneme sequence \"dh ih s\", and \"ih z\" respectively. An acoustic model, \(P(\mathbf{X}|\mathbf{Q})\), represents the probabilistic relationship between an input acoustic feature and the phonemes or other subword units.  
Among these probabilistic models, we focus on acoustic model, especially training in a semi-supervised manner.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Secure and Resilient Communication Channels  
Developing secure and resilient communication channels in smart grids can defend against the majority of malicious cyber attacks, which reflects security concern logical interface category 22 (the interface between security management consoles, networks,and systems) in NISTIR [5; 28]. The confidentiality, integrity, and availability impact levels are all marked as high in category 22 [5]. In [32], the author listed four main communication technical challenges in the smart grid: (1) short latency and high reliability, (2) high-density random access, (3) reliable coverage, and (4) coexistence of H2H (human to human) and M2M (machine to machine) traffic.  
To guarantee the security and resilience of the communication channel, there are three basic requirements: information security, communication reliability and scalability, and transmission latency [33].
## 4 Slp-macgt Model Design
### Secure Data Transmission Based on Fountain Codes
#### 4.4.2 Data Decoding  
After receiving an encoded packet, the receiver finds a check code \(t_{n}\) with a degree of 1. If there is no such check code, decoding terminates, and the source files cannot be recovered. Next, let \(s_{k}=t_{n}\), carry out XOR operations between \(s_{k}\) and all \(t_{n}\) that are connected to \(s_{k}\), and delete all connections with \(s_{k}\). The above procedure is repeated until all \(s_{k}\) are  
Figure 3: Data encoding process.  
determined. The decoding process of LT code is shown in Figure 4. First, an encoded packet with a degree of 1 is found, which is used as a check code; \(s_{1},s_{2},s_{3}\) with degrees of 1 are found as shown in the figure; and \(s_{1},s_{2},s_{3}\) are determined to be 110, 101, and 001, respectively. Since \(s_{1},s_{2},s_{3}\) are also related to \(s_{4},s_{6}\), XOR is performed between \(s_{1},s_{2}\) and \(s_{4},s_{6}\), respectively; hence, \(s_{4},s_{6}\) are 111 and 010, and all links with \(s_{1},s_{2},s_{3}\) are disconnected at the same time. Then, the search for a check code of degree 1 is continued, \(s_{6}\) is found, and the above decoding process is repeated until all \(s_{k}\) values are determined.  
The reliability of data transmission can be improved by the introduction of redundant data, the security of data transmission can be increased through using the random coding method, and the risk of information leakage and tampering during data transmission is reduced by the forward error correction feature of the LT code. The secure data transmission method based on fountain codes adapts to a complex transmission environment, provides effective support for the secure transmission of underwater acoustic sensor networks and effectively resists active attacks during passive attacks.
## 2 Optimized Hybrid Energy System for High-Speed Rail Integrating Carbon-REC Trading
### Models of System Components
#### 2.2.2 Load Model
The power \(P\) (kW) of the high-speed train under cruising conditions is derived from the calculation Equation (5) [39]  
\[P=\frac{\left[\frac{M\left(3.09+0.03621v+0.001098v^{2}\right)}{10^{3}}+m(1+\gamma )a\right]v}{3.6} \tag{5}\]  
where \(v\) represents the speed of the high-speed train when traveling at a constant velocity, with the unit of km/h; \(\gamma\) represents the rotational inertia coefficient, taken as 0.06 in the work; according to the specification that a high-speed train traveling at a speed of 350 km/h on a straight railway still has a residual acceleration of 0.05 m/s\({}^{2}\), a represents the residual acceleration, taken as 0.05 m/s\({}^{2}\). In the work, during braking, the movement of the high-speed train over a unit time interval is considered as a point mass. By taking the data at the start and end of the time interval, including the magnitude of the braking force and the velocity, and then calculating the average of the data, the calculation Equation (6) for the power of the RBE generated can be derived from a kinematic perspective, as shown below. \[P_{B_{r}}=\frac{B_{r}(V_{t})+B_{r}(V_{t-1})}{2}\times\frac{V_{t}+V_{t-1}}{2} \tag{6}\]  
where \(B_{r}(v)\) represents the braking force, with the unit of KN, \(B_{r}(V_{t})\) and \(B_{r}(V_{t-1})\) denote the braking forces at time \(t\) and \(t-1\), respectively; \(V_{t}\) and \(V_{t-1}\) represent the velocities of the high-speed train at time \(t\) and \(t-1\), with the unit of km/h; \(P_{B_{r}}\) indicates the power of RBE produced, with the unit of kW. After substituting the relevant data into the equation, the following results were obtained: the load during the traction condition is average 10,400 kW; the load during the cruising condition is average 6100 kW; under braking conditions with 0 load demand, the RBE exhibits a power output peaking at 10,260 kW initially before gradual decay. In this work, the load variation of the high-speed train along the route every minute is shown in the Figure 4, which includes the load conditions for one trip and return trip. Figure 4 with minute-level temporal resolution on the x-axis (capturing round-trip load variations) reveals that train speed predominantly governs electrical load demand in high-speed rail systems when other parameters are maintained to be constant. Specifically, an increase in speed significantly enhances aerodynamic resistance, leading to higher traction power requirements and increased grid electricity procurement costs. Conversely, reducing the speed effectively decreases system load and associated power costs. This fundamental relationship between train speed and load demand provides the theoretical foundation for analyzing system economics under different speed constraints.