esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Control Application Overview  
In the simulations, a commonplace control system design is used to keep the results as general as possible. The model of the controlled physical system is given below, which may represent an inverted pendulum system, a common benchmark problem in the control field [38]:  
\[G(s)=\frac{4.546s}{s^{3}+0.182s^{2}-31.182s-4.454}\]  
The controllers use the PID control law, the most popular control law in practical control applications, with the following parameters: \(K_{P}=120\), \(K_{I}=1000\), and \(K_{D}=5\). The PID control algorithm is implemented in the actuator as follows [14]:  
\[e(k)=r(k)-y(k)\] \[P(k)=K_{P}e(k)\] \[I(k)=I(k-1)+K_{I}h(e(k)+e(k-1))/2\] \[D(k)=K_{D}(e(k)-e(k-1))/h\] \[u(k)=P(k)+I(k)+D(k)\]  
where _r(k)_ is the desired system output (i.e., reference input or set-point), _y(k)_ is the sensed value of the system output (i.e., measurement), \(h\) is the sampling period of the sensor. In the simulations, \(h\) is set to 20 ms.  
As the major perturbations on the controlled system, the reference input changes over time as a square wave with a period of 2s. To measure the performance of the control application, the integral of absolute error (IAE), one of the widely used control performance metric defined as \(J(t)=\int_{0}^{t}\left|\,r(\tau)-y(\tau)\,\right|\,d\tau\), is recorded. The bigger the IAE value the worse the control performance.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 5 Conclusions
### Future Research Directions  
While this research has focused on short-term load forecasting integration, several promising avenues for future work emerge from our findings. These include the following: (1) an integration with extreme-weather-event prediction models; (2) the application of explainable AI techniques to increase transparency; (3) the development of adaptive weighting schemes with continuous learning capabilities; (4) an extension to incorporate emerging distributed energy resource data; and (5) an integration with edge computing architectures for more resilient implementation.  
In conclusion, our weighted fusion methodology addresses a critical gap in the load forecasting literature by focusing on the integration challenge rather than individual forecasting techniques. By systematically evaluating and dynamically combining regional forecasts based on their demonstrated reliability and significance, our approach provides a practical solution for enhancing grid-level load forecasting accuracy in modern power systems characterized by increasing complexity and renewable energy penetration.  
Conceptualization, H.Y. and K.Z.; methodology, X.T. and K.Z.; software, B.S.; validation, M.Z.; formal analysis, G.H.; investigation, H.Y.; resources, X.T.; data curation, B.S.; writing--original draft preparation, K.Z.; writing--review and editing, M.Z.; visualization, G.H.; supervision, H.Y.; project administration, K.Z.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Project of East China Branch of State Grid under Grant No. 529924240008.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to involving national security protection.  
We would like to extend our sincere gratitude to all project collaborators and contributors for their invaluable insights and support throughout the research.  
Authors [PERSON], [PERSON], [PERSON] were employed by the company East China Branch of State Grid Corporation of China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 2 Models and Restrictions
### Resource Reservation Protocol (RSVP)  
The quality control of packet transmission in an MANET is realized with a Resource Reservation Protocol (RSVP) [34]. The RSVP protocol allows applications to request the  
Figure 3: The OLSR routing protocol flowchart.  
desired level of the quality of service. It operates over the Internet Protocol (IP) and initiates resource reservations from the receiver's side.  
The main characteristics of RSVP can be highlighted as follows:  
1. RSVP can work with various protocol stacks, but it is primarily designed for TCP/IP networks where data are transmitted as datagrams without establishing connections.
2. The main purpose of the protocol is to provide QoS for selected data streams whose transmission has unacceptable latency or bandwidth requirements.
3. RSVP runs on top of IPv4 or IPv6. RSVP is more of an Internet control protocol whose implementation runs in the background rather than in the data forwarding path.
4. RSVP is a receiver-oriented signaling protocol. The receiver initiates and maintains the resource reservation.
5. The protocol is used for both unicast and multicast transmissions.
6. RSVP supports dynamic automatic adaptation to changes in the network.  
RSVP requests resources for simplex flows, meaning it requests resources in only one direction. The transmission process begins when the data source (application) sends a PATH command to potential receivers. The command includes a stream identifier (which may contain the source address and other fields from the TCP/IP header) that allows the router to associate packets with a specific RSVP session. The PATH command also describes the expected information flow, such as specifying its volume. Each router processing the PATH command remembers the stream identifier and the incoming channel for that stream. This allows for the creation of tables of data flow paths and ensures that all routers are ready to allocate resources.  
If a potential receiver, upon receiving the PATH command, wants to receive the specified data, it sends a RESV command. Using the same stream identifier, the RESV command follows the reverse path of the PATH command. It informs the routers on its path of the required QoS level. A router that receives RESV commands from multiple receivers combines them into a single command. If a router can provide the requested QoS level, it adds the reservation to the so-called flow table; otherwise, it denies resource reservation. Routers use flow tables to determine whether incoming datagrams belong to a particular RSVP session.  
Since the resulting multicast topology may change over time, the RSVP design assumes that the RSVP state and traffic control state should be dynamically created and torn down in routers and hosts. To achieve this, RSVP establishes a \"soft\" state, meaning it sends periodic refresh messages to maintain the state along the reserved path(s). In the absence of refresh messages, the state is automatically deactivated and removed.
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 2 Artificial Neural Network
### Principles and Basic Architecture  
The BPNN can learn and store a large number of input-output pattern mapping relationships without revealing mathematical equations describing such mapping relationships in advance. The topological structure of the BPNN model has three layers, which include an input layer, hidden layer, and output layer. Its network architecture is shown in Figure 1. The circle in Figure 1 represents the artificial neuron. Note that \(x_{i},x_{j}\) and \(x_{n}\) represents the inputs of the artificial neural network and they are the components of the vector \(x\); \(y\) represents the actual output of the artificial neural network; and \(z\) represents the expected output of the artificial neural network. The integers \(i,j,n\) are the data pre-processing units of the neural network, and \(f_{i},f_{j},f_{n}\) are the activation functions, which are the components of the vector \(f\) as the adder or the multiplier. \(v\) is the connection weight vector or threshold vector between the input layer and the hidden layer, and \(w\) is the connection weight vector or threshold vector between the hidden layer and the output layer, respectively. There is no connection between the same layer of the network and full unidirectional interconnection between different layers. If the layers of input and the output were proposed, the relationship between the input and the output will be:  
\[y=f(vx+w) \tag{1}\]The activation functions must be a quasi-linear one which is continuous, derivable, and monotonously incremental. It can be chosen as following Sigmoid function:  
\[f(x)=\frac{1}{1+e^{-x}} \tag{2}\]  
A sigmoid function is a bounded, differentiable, real function that is defined for all real input values and has a non-negative derivative at each point [23] and exactly one inflexion point.
## 3 Proposed System Model
### Selection and Application Methods of Machine Learning Algorithms
#### 3.3.3 K-Nearest Neighbors (KNNs) Algorithm  
K-Nearest Neighbors (KNNs) is a versatile algorithm used for classification and regression tasks [57]. It classifies new data points by measuring distances to the K-Nearest Neighbors in the feature space and assigning the majority class for classification or the average for regression [58]. The algorithm is based on the principle of \"like attracts like\", where a point's class is determined by its closest neighbors [48].  
KNNs' performance relies on the choice of distance metric, such as Euclidean, Manhattan, or Minkowski distances. Euclidean distance is the most common for continuous variables, while Manhattan is used for grid-like data, and Minkowski can generalize to both [59]. The choice of metric can significantly impact classification accuracy depending on the dataset structure [60]. KNNs' effectiveness is influenced by the value of K and the dataset size, with optimal choices leading to high accuracy and flexibility [61].
# Method of Feature Reduction in Short Text Classification Based on Feature Clustering
## 1 Introduction
Feature clustering selects a sub-feature set in the original feature set and replaces the original feature set with a sub-feature set for correlation calculations [15]. For example, \"[PERSON]\", \"Iron Man\", and \"[PERSON]\" tend not to appear in the same article, but they fall into the same category of \"Marvel characters\" and they have a high degree of semantic similarity. If such words are clustered, the semantic representation capacity of a single vocabulary can be precisely and appropriately extended, and the feature dimension can be declined. However, there are two critical problems when employing this method: enhancing the efficiency of building similar feature clusters and upgrading the effectiveness of similar feature clusters. Many studies have proven the validity of the feature reduction method. In this paper, the TSFC (two-stage feature clustering) algorithm is proposed, which employs cosine semantics as a mechanism to establish the relationship between the features and create many compact feature sets. Firstly, an improved feature clustering algorithm is used to construct similar feature subsets, and a deep relationship between the features is then established by embedding words. Finally, semantic extension of short texts is achieved. Our work focuses on how to address the above two troublesome problems in feature clustering. The enormous external knowledge is unnecessary in the approach that we proposed, which reduces a considerable amount of data requirements when compared with methods that require millions of external data, and feature dimension is significantly reduced by feature replacement. Our main contributions are summarized, as follows:  
* A two-stage feature clustering (TSFC) method is proposed, which takes into account the efficiency and accuracy of feature clustering. * The original feature was replaced by similar feature clusters to express feature vectorization. * The method that is presented in this paper can effectively reduce feature dimension. * We conducted vast experiments by utilizing data of different types and lengths: A China Mobile SMS (Short Message Service) dataset and a Fudan News dataset. The rest of this paper is organized, as follows: Section 2 introduces the related works of text semantic extension. Section 3 presents the proposed method, including the TSFC algorithm and the intra-cluster feature selection rules. Section 4 verifies the validity of the method with experiments with classification of short Chinese texts. We draw conclusions in Section 5.
## 3 Results and Discussion
### NS2 Simulation Results
Figures 12 and 13 show the sustainability of the proposed model as more vehicles are introduced, the PDR and PLR are not significantly affected compared to the model with the absence of interference power control. Figure 12: Packet delivery ratio (PRD) (%) versus the number of vehicles. Figure 14 depicts the E2E reported against the number of vehicles. When no interference power control is applied, the E2E significantly decreases as the number of vehicles increases until the number of vehicles reaches 150, then the decrease is almost linear. The steep decline in the E2E is due to the increased number of vehicles getting away from the range of the interference nodes, allowing packets to be delivered with minimal delay and retransmissions. However, when the proposed model is applied, the vehicles are already experiencing minimal interference, allowing the packets to be delivered with minimal delay. It is worth mentioning that decreasing the interference power decreases the required retransmission time as the channel performance is enhanced, thus decreasing the E2E. Clearly, to enhance the system performance and achieve the preferable QoS as clarified in this paper, optimum required interference transmission power (\(P_{I}\)) should be indicated based on the system conditions such as the outage probability, \(P_{out}\), vehicle transmission power (\(P_{V}\)), and required signal-interference-plus-noise-ratio (\(SINR_{th}\)). The results of this proposed model can help decide how and when the connection between V-X should be employed to have a proper connection that improves system performance and enhances the reception of road traffic information and increases road safety. Figure 14: Average end-to-end delay (E2E) (msec) versus the number of vehicles. Figure 13: Packet loss rate (PLR) versus the number of vehicles.
# Utilizing Network Structure to Accelerate Markov Chain Monte Carlo Algorithms
## 2 Aggregation in Markov Chains
Informally, a Markov chain is called _quasi-lumpable_ or _\(\epsilon\)-quasi-lumpable_ or simply _\(\epsilon\)-lumpable_, if it may not be perfectly lumpable, but it is \"not too far\" from that. This \"\(\epsilon\)-closeness\" is defined in [10; 11] in a way that the transition matrix can be decomposed as \(P=P^{-}+P^{\epsilon}\). Here \(P^{-}\) is a component-wise non-negative lower bound for the original transition matrix \(P\), such that \(P^{-}\) satisfies the necessary and sufficient condition of Theorem 1. The other matrix, \(P^{\epsilon}\), represents a _perturbation_. It is an arbitrary non-negative matrix in which each entry is bounded by \(\epsilon\). This definition is not very easy to visualize, therefore, we use the following simpler but equivalent definition. Definition 2: (\(\epsilon\)-lumpability) Let \(\epsilon\geq 0\). A Markov chain \(\mathcal{M}=(S,P)\) is called \(\epsilon\)-lumpable with respect to a partition \(\mathcal{Q}=\{A_{1},\ldots,A_{m}\}\) of \(S\) if  
\[\big{|}p(x,A_{j})-p(y,A_{j})\big{|}\leq\epsilon\]  
holds for any \(x,y\in A_{i}\) and for any \(i,j\in\{1,\ldots,m\}\). Note that if we take \(\epsilon=0\), then we get back the ordinary concept of lumpability. Thus, quasi-lumpability is indeed a relaxation of the original concept. It can also be interpreted in the following way. If \(\epsilon>0\), then the original definition of lumpability may not hold. This means, the aggregated process may not remain Markov. i.e., it does not satisfy (2). On the other hand, if \(\epsilon\) is small, then the aggregated process will be, in a sense, \"close\" to being Markov, that is, to satisfying (2). What we are interested in is the convergence analysis of quasi-lumpable Markov chains, typically for a small value of \(\epsilon\). For the analysis we need to introduce another definition. Definition 3: (Lower and upper transition matrices) Let \(\mathcal{M}=(S,P)\) be a Markov chain which is \(\epsilon\)-lumpable with respect to a partition \(\mathcal{Q}=\{A_{1},\ldots,A_{m}\}\). The lower and upper transition matrices \(L=[l_{ij}]\) and \(U=[u_{ij}]\) are defined as \(m\times m\) matrices with entries  
\[l_{ij}=\min_{x\in A_{i}}p(x,A_{j})\quad\text{ and }\quad u_{ij}=\max_{x\in A_{i}}p(x,A_{ j})\]  
respectively, for \(i,j=1,\ldots,m\). Note that it always holds (component-wise) that \(L\leq U\). If the chain is lumpable, then these matrices coincide, so then \(L=U=\bar{P}\), where \(\bar{P}\) is the transition matrix of the lumped chain. If the chain is \(\epsilon\)-lumpable, then \(L\) and \(U\) differ at most by \(\epsilon\) in each entry. Generally, \(L\) and \(U\) are not necessarily stochastic matrices (A vector is called stochastic if each coordinate is non-negative and their sum is 1. A matrix is called stochastic if each row vector of it is stochastic. ), as their rows may not sum up to 1.
## 4 Numerical Results and Analysis
### Evaluation of UAV Trajectory planning  
Based on the environmental information and user trajectories determined above, trajectory planning for the UAV base station has been achieved through the iterative optimization algorithm for trajectory sub-block problem, with parameters in the other two sub-block problems fixed.  
As shown in Figure 7, the proposed trajectory planning method achieves the fastest convergence. Compared to several path planning methods including A* and genetic algorithm (GA), our optimized path planning strategy performs best in the discussed environment.  
Visualization of the planned UAV trajectory is given in Figure 8, with trajectories of 10 moving users also displayed.  
In each sub-graph, we release the aerial base station from different initial points and test the algorithm with different user distributions, finding that it maintains stability and moves synchronously with the trajectories of users toward the exit. In Figure 8a,b, there are two different-sized obstacle buildings in the environment, with the exit located at the center-right of the map. The UAVs are released from different initial positions, demonstrating their adaptability to different initial release positions. In Figure 8c,d, the exit is set at the bottom-right corner of the map. The scenarios with six dispersed obstacles and one central obstacle in the map are tested, thus validating the effectiveness of the algorithm in different scenarios.  
Meanwhile, the number of inflection points on the convergence curve matches the number of users specified in the simulation, lending reasonable support to the optimization's correctness. Therefore, the effectiveness and robustness of the proposed trajectory planning algorithm are validated.  
Figure 6: **Software Illustration of ground users’ moving trajectory simulation. As time passes, the number of evacuation people updates and provides a real-time demonstration of user evacuation movement based on CA simulation.**  
Figure 8: **Trajectory planning illustration.** Different initial positions for UAV trajectory optimization via ISATR are tested to verify the effectiveness and robustness of our algorithm. It can be seen that the trajectory of UAV is always consistent with users. (**a**) ISATR opted path 1. (**b**) ISATR opted path 2. (**c**) ISATR opted path 3. (**d**) ISATR opted path 4.  
Figure 7: **Throughput optimized with UAV trajectory in different methods.** It can be found that our ISATR opt method outperforms the traditional A* algorithm and the algorithm with fixed UAV trajectory.
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 4 Knowledge Interpolated Conditional Variational Auto-Encoder (KIV)
### Context and Knowledge Encoders  
The context and knowledge latent variables \(\mathbf{z}_{c},\mathbf{z}_{k}\) are derived from two variational neural modules that share the same architecture but utilize different parameters. Taking the knowledge latent variable \(\mathbf{z}_{k}\) as an example, its variational neural module, \(f^{k}\), is composed of a posterior network \(f^{k}_{pst}\) and a prior network \(f^{k}_{pri}\). The prior distribution is a factorized normal distribution \(p_{\theta}(\mathbf{z}_{k}|\mathbf{k})\sim\mathcal{N}(\mathbf{p}^{k}_{pri},\mathbf{\sigma}^{k} _{pri})\), which is parameterized by the prior network \(f^{k}_{pri}\) as follows:  
\[[\mathbf{p}^{k}_{pri},\log\mathbf{\sigma}^{k}_{pri}]=f^{k}_{pri}(\mathbf{k}). \tag{4}\]  
In the training phase of our model, \(\mathbf{k}\) and \(\mathbf{y}\) jointly define the posterior distribution \(q_{\mathbf{\phi}}\sim\mathcal{N}(\mathbf{p}^{k}_{pst},\mathbf{\sigma}^{k}_{pst})\) by  
\[[\mathbf{p}^{k}_{pst},\log\mathbf{\sigma}^{k}_{pst}]=f^{k}_{pst}([\mathbf{k},\mathbf{y}]), \tag{5}\]  
where \(f^{k}_{pst}\) and \(f^{k}_{pri}\) are multi-layer perceptrons with tanh activation function. The knowledge representation \(\mathbf{k}\) and response representation \(\mathbf{y}\) are derived by extracting the final hidden state from two bidirectional GRU encoders.  
Likewise, we can obtain the parameterized prior distribution \(p_{\theta}(\mathbf{z}_{c}|\mathbf{c})\) and posterior distribution \(q_{\mathbf{\phi}}(\mathbf{z}_{c}|\mathbf{c},\mathbf{y})\) of the context latent variable \(\mathbf{z}_{c}\) via its variational neural module \(f^{c}\). The context representation \(\mathbf{c}\) is obtained by extracting the final hidden state from a hierarchical GRU encoder [3]. This method uses a word-level GRU network for each utterance and then feeds the outputs of the word-level GRU's last hidden state into an utterance-level GRU network.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Data
#### 2.2.3 Ground Sample Data Collection  
Sufficient samples of five types of classes, including forest, rubber, farmland, water, and building, were obtained according to three field investigations conducted from January to April 2020 and according to the visual interpretation of high-resolution Google historical images using BIGEMAP software. We obtained a total of 787 point-geometry samples, of which approximately 2/3 were randomly selected as training samples to build the classification model and the remaining 1/3 were used as verification samples to validate the classification model (shown in Table 2).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Orbit} & International Space & \multirow{3}{*}{Orbit Altitude} & 400 km \\  & Station’s orbit & & 475 km \\  & Sun-synchronous orbit & & \\ \hline \multirow{3}{*}{Orbit inclination} & 52\({}^{\circ}\) & \multirow{3}{*}{Sensor type} & Bayer filter \\  & 98\({}^{\circ}\) & & charge-coupled \\ \cline{1-1}  & & & device (CCD) camera \\ \hline Spatial resolution & 3–4 m & \multirow{3}{*}{Breadth} & 24.6 km \(\times\) 16.4 km \\ \hline \multirow{6}{*}{Spectral band} & Band 1: blue (455–515 nm) & \multirow{6}{*}{} \\  & Band 2: green (500–590 nm) & \multirow{6}{*}{} \\ \cline{1-1}  & Band 3: red (590–670 nm) & \multirow{6}{*}{} \\ \cline{1-1}  & Band 4: near-infrared & \multirow{6}{*}{} \\ \cline{1-1}  & (780–860 nm) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the PlanetScope satellites.