esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Approach
### RL-Based Optimization Method
#### 2.2.2 Policy Deployment  
The reinforcement learning approach for policy generation is summarized in Algorithm 1. Once the training process described in Section 2.2.1 is complete, the best policy (\(\pi_{\theta}^{*}\)) is saved, and, afterward, can be deployed for global optimization of functions. The steps involved in the policy deployment stage are summarized in Algorithm 1. As in the policy generation phase, the positions of the particles are progressively updated. In contrast to policy generation, the policy is frozen during this process; i.e., the reward structure described in Equations (8)-(10) is not used to update the policy during deployment. Furthermore, the best particle at each design iteration is kept frozen. This provides an anchor for the entire swarm and prevents all the particles from drifting off from promising regions of the design space.  
```
0: number of dimensions, \(D\); number of agents, \(N\); maximum number of iterations, \(K\)
0: objective function, \(f\); exploration function, \(\psi\)
1: Define: particle index, \(i\); dimension index, \(j\); iteration number, \(k\); index of globally best particle, \(g\)
2: Initialize: \(\mathbf{x}_{i}^{(0)},\mathbf{x}_{i}^{(0)},\ldots,\mathbf{x}_{N}^{(0)}\) (randomly initialize position vector of particles)
3: \(\bar{\mathbf{x}}_{i}=\mathbf{x}_{i}\) for \(i=1,2,\ldots,N\)
4:for\(k=1\) to \(K\)do
5: \(g=argmax_{i\in N}\{f(\mathbf{x}_{i}^{(k)})\}_{i}^{N}\)
6:for\(i=1,2,\ldots,N\) with \(i\
eq g\)do
7: \(n=\text{rand}\{1,2,\ldots,N\}\
eq i\)
8:for\(j=1\) to \(D\)do
9: \(\xi_{i,j}=[\bar{x}_{i,j}^{(k)}-\bar{x}_{i,j},x_{ij}^{(k)}-\bar{x}_{n,j}^{(k)},x _{ij}^{(k)}-x_{pi,j}^{(k)},x_{ij}^{(k)}-x_{pi,j}^{*(k)},x_{ij}^{(k)}-x_{i,j}^{* (k)}]\)
10: \(\Delta x_{i,j}(k)\leftarrow\mathcal{N}_{\pi_{\theta}}\left(\xi_{i,j^{\prime}} \psi(x_{i,j}^{(k)}-x_{g,j}^{(k)})\right)\)
11: \(x_{i,j}^{(k+1)}\leftarrow x_{i,j}^{(k)}+\Delta x_{i,j}^{(k)}\)
12:endfor
13:if\(f(\mathbf{x}_{i}^{(k+1)})>f(\bar{\mathbf{x}}_{i})\)then
14: \(\bar{\mathbf{x}}_{i}=\mathbf{x}_{i}^{(k+1)}\)
15:endif
16:endfor
17:endfor
```  
**Algorithm 1** Policy deployment phase of Deephive.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm
### Genetic Algorithms
#### 6.1.1 Selection and Parent Sampling  
The selection scheme assigns to each individual of the population an expected number of descendants that is proportional to the probability of that individual to be selected. In the simple GA method, it is used propositional selection as sampling algorithm and stochastic sampling with replacement [62] or roulette wheel [63]. The selection probability assigned to each chromosome \(i\) is given by:  
\[\pi_{i}^{t}=\frac{f_{i}^{t}}{\sum_{i=1}^{p}f_{i}^{t}},i=1,...,m. \tag{16}\]  
where \(p\) is the population number, \(t\) states the generation number, and \(f_{i}^{t}\) indicates the quality of chromosome \(i\) at generation \(t\). The cumulative probability of each chromosome is computed according to the following equation:  
\[\Pi_{i}^{t}=\sum_{j=1}^{i}\pi_{i}^{t},i=1,...,m. \tag{17}\]  
Finally, the selection process is performed based on the \(\Pi_{i}^{t}\) values of each chromosome, which represents a random choice in a roulette wheel, where each mark corresponds to each chromosome, but in which the separation between marks is proportional to the quality of each individual.
## 4 GDOP and WGDOP Calculation with Matrix Multiplication
### Proposed WGDOP Calculation with Matrix Multiplication
#### 4.2.2 Five or More Measurement Units  
Many scholars have proposed WGDOP calculation methods only using in the case of exactly four measurement units. Most of the existing literatures and patents only discuss the derivations of the formula in the situation of selecting four measuring devices. When the number of measurements increases, WGDOP is decreasing. More than four measurements are selected because more measurements can obtain a higher positioning accuracy. Here, we expand the WGDOP computational formulas, so that it can be successfully applied in the case of more than four measurements. This calculation device greatly enhances WGDOP computing applications in practice. If the number of measurement units is \(N\), the dimension of measurement matrix \(M\) is \(N\times N\). When more than four measurements are available for location purposes, Equations (9) and (10) can still be adopted to describe the calculation forms of \(p_{1}(\lambda)\), \(p_{2}(\lambda)\), \(p_{3}(\lambda)\), \(p_{4}(\lambda)\) and WGDOP.  
In this method, the rest formula for \(N(N\geq 5)\) may be deduced by analogy. Reduction by Newton's identities simplifies WGDOP formula and satisfies the needs of obtaining various parameters in the formula. Finally, we can obtain the WGDOP value for positioning. From Table 2, the matrix multiplication scheme with five measurements needs only 221 additions, 326 multiplications, one division, and one square root. Simultaneously, the computational complexity of the proposed WGDOP criteria can be reduced by a matrix multiplication scheme for measurement units \(N(N\geq 5)\). These results show that the WGDOP methods proposed in this paper have low computational complexity and are easy to implement in the hardware equipment because of the simplicity of its operation rules. The proposed WGDOP formula with matrix multiplication does not limit the number of measurement units selected.
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
## 3 Experiments and Results
### Datasets and Performance Evaluations
#### 3.1.3 Performance Evaluations  
We evaluated the recognition performance based on PR curve (precision-recall rate curve), matching time, and F1-score. The PR curve was used in the comparison experiment, and the matching time and F1-score were used in the ablation study. For each dataset, ground truth is the frame-level correspondence, and we set a tolerance of one frame. For each query, if the matched reference image was close enough to the correct reference image, it will be considered as a true positive match. For example, if the correct reference image is the \(k\)th image, then the \((k-1)\)th, \(k\)th, and \((k+1)\) th reference image are all considered to be the true positive match to the query.
###### Abstract  
The recent development of quantum cascade lasers, with room-temperature emission in the mid-infrared range, opened new opportunities for the implementation of ultra-wideband communication systems. Specifically, the mid-infrared atmospheric transparency windows, comprising wavelengths between 3-5 \(\upmu\)m and 8-14 \(\upmu\)m, have great potential for free-space communications, as they provide a wide unregulated spectrum with low Mie and Rayleigh scattering and reduced background noise. Despite the great efforts devoted to the development of mid-infrared sources and detectors, little attention is dedicated to the management of polarization for signal processing. In this work, we used Ge-rich SiGe alloys to build a wideband and polarization-insensitive mid-infrared photonic platform. We showed that the gradual index change in the SiGe alloys enabled the design of waveguides with remarkably low birefringence, below 2 \(\times\) 10\({}^{-4}\), over ultra-wide wavelength ranges within both atmospheric transparency windows, near wavelengths of 3.5 \(\upmu\)m and 9 \(\upmu\)m. We also report on the design of a polarization-independent multimode interference device achieving efficient power splitting in an unprecedented 4.5-\(\upmu\)m bandwidth at around 10-\(\upmu\)m wavelength. The ultra-wideband polarization-insensitive building blocks presented here pave the way for the development of high-performance on-chip photonic circuits for next-generation mid-infrared free-space communication systems.  
Keywords:photonic integrated circuits; mid-infrared; polarization-insensitive +
Footnote †: journal: Applied Physics Letters
## 3 Satellite Geometries
### Downlink Satellite Geometry for DoA Estimation  
In the downlink signal model shown in Figure 3, the angle between two points, represented by their respective coordinates, is calculated using the inverse tangent function. More precisely, when the transmitting satellite SP is located at coordinates \((\mathrm{X_{Tx}},\mathrm{Y_{Tx}})\) and the receiving ES is at coordinates \((\mathrm{X_{Rx}},\mathrm{Y_{Rx}})\) in the Earth-centered inertial (ECI) coordinate system, the angle between them can be computed as follows:  
\[\theta=\arctan[(\mathrm{Y_{Rx}}-\mathrm{Y_{Tx}})/(\mathrm{X_{Rx}}-\mathrm{X_ {Tx}}))]. \tag{6}\]  
Therefore, \(\theta\) is the downlink DoA angle at the ES, i.e., above the Earth's surface around the horizon line. Thus, for simplicity, the DoA angle is represented as \(\theta\) for both uplink and downlink scenarios.  
Figure 2: Uplink satellite geometry for DoA estimation.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 1 Introduction
Different from the open and obstacle-free scenarios, the forest site involves densely- and irregularly-distributed trees, shrubs and vegetation, all of which will not only significantly degrade the performance of wireless links, but also make it more difficult to model the link dynamics. Second, although researchers have realized the unique challenge of low-power wireless links in complex environments and several works have been proposed to evaluate the performance of low-power radio signals, they either consider only the physical-layer characteristics or study the link behaviors just from one or a few aspects. The physical-layer results are helpful to understand the path loss ratio of the wireless link and to optimize the design of the transceiver and modulation schemes, but they hardly provide adequate insights into improving the whole low-power wireless network because at runtime, the program cannot obtain the precise in situ path loss information to carry out the real-time adjustment of protocol parameters. Some prior works analyze the link characteristics in terms of link reliability, the signal strength or other link metrics. Nevertheless, the observations and analyses are mostly achieved in open-field scenarios or indoor environments with less obstacles. The performance of low-power links in forests and their effects on upper-layer protocols remain uncertain or unknown to some extent. The remainder of paper is organized as follows. Section 2 describes the experimental sites and configurations. Section 3 presents the evaluation results of a single low-power link, and Section 4 evaluates the links of a small-scale wireless sensor network. Section 5 summarizes the experimental observations of this paper with insightful remarks. Section 6 introduces major works related to ours. Finally, Section 7 concludes this paper.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 2 Related Work
### TB-CSPN: Addressing Architectural Deficits Through Formal Foundations  
Our TB-CSPN framework directly addresses these architectural limitations by:  
* _Separating Semantic and Coordination Logic:_ LLMs handle semantic processing (topic extraction) while Petri Net semantics manage coordination deterministically.
* _Enabling Formal Verification:_ Built on Colored Petri Net foundations, TB-CSPN enables mathematical verification of coordination properties.
* _Supporting Distributed Coordination:_ Topic-based communication eliminates centralized orchestration bottlenecks while maintaining semantic coherence.
* _Integrating Human Agents:_ Explicit support for human participants as first-class agents in hybrid workflows.  
While recent advances in multimodal retrieval-augmented generation (e.g., Uni-RAG [20]) and cognitive sentiment analysis (e.g., cognitive ABSA [21]) tackle related coordination challenges at the level of model fusion and affective reasoning, TB-CSPN addresses a different frontier: intra-agent semantic mediation in single-modal, token-driven workflows. By focusing on topic-based token propagation, our approach emphasizes scalable coordination across agents without relying on tightly coupled model ensembles or specialized embeddings.  
[PERSON] et al. [22] extend CPN Tools [23] with ontology-based context models to support context-adaptive business processes. Their approach uses OWL ([[https://www.w3.org/TR/owl-ref/](https://www.w3.org/TR/owl-ref/)]([https://www.w3.org/TR/owl-ref/](https://www.w3.org/TR/owl-ref/)), accessed on 29 July 2025) ontologies to inject runtime context into Petri net workflows, enabling dynamic behavior. In contrast, the TB-CSPN approach focuses on semantic coordination across heterogeneous agents using topic-based tokens as a shared semantic interface.  
In principle, however, the definition of topics can rely on some domain-specific ontology, if the need arises.  
This positions TB-CSPN not as another framework variation, but as a _fundamental architecture advance_ that reclaims the formal foundations of traditional multi-agent systems while leveraging modern AI capabilities strategically rather than ubiquitously.
## 3 Methodology for Recommendation Systems
### HVAC Systems  
The conceived algorithm for the RS developed in the second use case utilizes the SRI data and is meant to give suggestions related to the HVAC systems of a building. One benefit of utilizing SRI data is that as the smart-ready services undergo enhancements, the SRI assessment files will also be informed, consequently impacting the RS suggestions. However, the main question to be answered when developing an RS utilizing SRI data is 'how to use the SRI data'. This is because an SRI assessment is rich in information about building systems. As mentioned in the literature review portion of the SRI, the assessment is based on a smart service catalogue which contains a list of over 50 smart-ready services with their corresponding functionality levels (level of smartness). It is important to understand that while the final SRI score of a building depicts the level of smart readiness of a building, it does not give much useful information for the development of an RS. The crucial piece of information comes from the smart service catalogue, as it contains the list of all possible services. This can be used to train ML algorithms.  
Figure 2: User flow (first use case).  
The main concept for the SRI-based algorithm for RS is depicted in Figure 3. The RS takes the impact-domain matrix from the SRI assessment along with some building information like building type and year of construction. The reason for using the impact-domain matrix from the SRI information is that it tells us about the area in the systems of the buildings that needs the most improvement. The lowest percentage in the impact-domain matrix is identified, and then the services contributing to this low percentage are listed.  
These services are then fed to an algorithm that looks for buildings with similar construction years and types in the dataset. The services with their corresponding functionality levels belonging to buildings with similar construction years and types are then suggested to the user. The reason for selecting building construction year as an input variable to the ML algorithms is because [39] indicates that there is a relationship between the year of construction and the SRI score. The newer the building is, the higher the score.  
The algorithm is designed to consider a possible link between buildings, specifically prioritizing newer structures with similar construction years in its dataset. Consequently, it may suggest smart-ready services implemented in these buildings to the user. The SRI data currently come in an Excel file. Thus, the data require pre-processing before being used for the ML algorithm. The data collection, pre-processing, and ML algorithms are discussed in the following subsections.
## 3 Results and Discussions
### Supply Current Values  
During both the SEE and SESD experiments, the supply current of this SRAM showed small periodic fluctuations due to factors such as leakage current and power leakage [13; 14; 15] in the circuit. The comparison between the number of errors and the supply current (Figure 10) indicated that when a memory cell is flipped, the supply current drops significantly.  
Figure 9: Schematic diagram of SRAM structure with SEE and SESD.  
Combined with the structural characteristics of SRAM, the entire read/write processes require pre-charging of the bit lines, which increases the consumption of power. As long as the SRAM functions properly, the circuit performs a read-only operation, and the current drops uniformly. According to the predefined experimental mechanism, if an error occurs, the device automatically enters a refresh state to re-write the correct data to the incorrect address and identify it. In other words, once an error has been detected, the circuit immediately performs a write operation. At that time, the power consumption and leakage current of the read/write state appear superimposed, and the current appears to decrease at a higher magnitude than before. This means that the current characteristics can be used to measure the appearance of errors, and the greater the current decline, the greater the number of errors produced.  
As shown in Figure 11, the minimum value of the collected current at the Vcc terminal is plotted as a line graph with laser energy and discharge voltage. The overall trend indicates that the higher the external sources of stimulation, the greater the decrease in the value of the current at the Vcc end. This could be explained by the fact that as the discharge voltage increases, so do the number of errors and the number of multi-bit upsets, and thus the number of bits that need to be refreshed. As a result, the read/write current consumption and leakage current in the circuit increase, causing the lowest current value at the Vcc terminal to display a decreasing trend over time.  
Figure 10: The comparison of error number and current.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 5 Simulation
### Analysis of the Test Signal
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Signal**} & **Oscillation Parameter** & **S\({}_{\text{IFO-1}}\)** & **S\({}_{\text{IFO-2}}\)** & **S\({}_{\text{SSO-1}}\)** & **S\({}_{\text{SSO-2}}\)** & **S\({}_{\text{SurSO}}\)** \\ \cline{2-7}  & Frequency\(/\)Hz & 0.68 & 1.93 & 26.58 & 21.94 & 93.42 \\ \cline{2-7}  & Amplitude/MW & 1.46 & 2.00 & 0.49 & 0.22 & 0.32 \\ \hline \multirow{4}{*}{**IA-VMD-Prony**} & Frequency\(/\)Hz & 0.68 & 1.90 & 26.82 & 22.04 & 93.00 \\  & Error\(/\)\% & 0 & \(-\)1.55 & 0.90 & 0.46 & 0.45 \\ \cline{2-7}  & Amplitude/MW & 1.37 & 2.0 & 0.49 & 0.21 & 0.32 \\  & Error\(/\)\% & \(-\)6.16 & 0 & 2.04 & \(-\)9.09 & 0 \\ \hline \multirow{4}{*}{**EMD-Prony**} & Frequency\(/\)Hz & 0.68 & 1.95 & 26.37 & 51.0 & - \\  & Error\(/\)\% & 0 & 1.04 & \(-\)0.79 & 132.45 & - \\ \cline{2-7}  & Amplitude/MW & 1.25 & 2.0 & 0.60 & 0.25 & - \\ \cline{2-7}  & Error\(/\)\% & \(-\)14.38 & 0 & 22.45 & 13.64 & - \\ \hline \multirow{4}{*}{**MF-Prony**} & Frequency\(/\)Hz & 0.67 & 2.0 & 27.0 & - & - \\  & Error\(/\)\% & \(-\)1.47 & 3.63 & 1.58 & - & - \\ \cline{1-1} \cline{2-7}  & Amplitude/MW & 1.20 & 2.0 & 0.49 & - & - \\ \cline{1-1}  & Error\(/\)\% & \(-\)17.81 & 0 & 2.04 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fitting errors of the different methods. Figure 4: All modes of the signal with noise after IA-VMD. amplitude identification is large, but it is still in existence. The simulation results show that the proposed method has improved effectiveness in broadband oscillation identification. Table 1 shows that the EMD-Prony method can accurately identify the two modes of LFO; the fitting errors of the oscillation frequencies are 0 and 1.04%. However, this method has a poor identification effect on subsynchronous oscillations; the fitting errors of the oscillation frequencies are 22.45% and 13.64%. In addition, supersynchronous oscillation cannot be identified by this method. The EMD-Prony method is obviously less effective. Table 1 shows that the MF-Prony method has a very poor identification effect. The maximum fitting error of the oscillation amplitude is 17.81%, which is higher than that of other identification methods. This method cannot even identify subsynchronous oscillation and supersynchronous oscillation. Especially when the test signal contains high-frequency and low-amplitude modes, the traditional Prony algorithm is very sensitive to noise. Therefore, in general, the signal cannot be directly identified by the Prony algorithm.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 1 Introduction
Similarly, [PERSON] et al. [73] analysed and summarised the SAR image-based road segmentation methods. They introduced the traditional edge detection and deep learning-based road segmentation methods and predicted that new segmentation methods of deep neural networks based on the self-attention mechanism and capsule paradigm as the future development trends. [PERSON] and [PERSON] [74] summarised the road extraction techniques based on LiDAR. In this report, road clusters were defined using the classification framework and algorithms for LiDAR point data-based road identification. Furthermore, techniques for generating road networks, including road classification refinement and centreline extraction, were summarised as well. Several other similar reviews [75; 76; 77] that provide scientific references for road extraction have been reported in recent years. To the best of our understanding, the existing reviews on road extraction methods are commonly based on only a single data source; hence, they fail to provide a comprehensive view that can be derived using different data sources. However, a comprehensive road extraction review based on high-resolution imaging, hyperspectral imaging, SAR imaging, and LiDAR technologies is crucial to bridge the gap between potential applications and available technologies of road extraction. Thus, the aim of this study was to achieve this goal by combining the road extraction techniques based on diverse data sources, including high-resolution images, hyperspectral images, SAR images, and LiDAR data. In Section 2, we provide an overview of the four techniques and summarise the typically used sensors. In Section 3, we introduce and analyse the main road extraction methods using various data sources as well as summarise the road extraction status and prospects for different data sources. Finally, different combinations of the road extraction techniques are presentedin Section 4. To the best of our knowledge, in this paper, we present the first comprehensive review of road extraction, including high-resolution images, hyperspectral images, SAR images and LiDAR data sources.