esa-sceva/satcom-chunk-collection huggingface dataset:
# On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
## 1 Introduction
Note that this is actually a joint-source channel coding problem where the source is \(\mathbf{x}\in\{0,1\}^{M}\), the channel is Gaussian and can be used \(n\) times, and the distortion measure is Hamming distortion. It is often desirable to use update efficient schemes. In such schemes changing one bit in the input vector \(\mathbf{x}\), should correspond to changing the content of a small number of memory cells (see, e.g., [23]). When the encoding scheme is \(\mathbf{s}=A\mathbf{x}\), an update in one coordinate of \(\mathbf{x}\), say \(x_{i}\), corresponds to adding (removing) the \(i\)th column of \(A\) to (from) \(\mathbf{s}\). If each column has a small number of nonzero entries, the update involves changing the stored value in a small number of cells. Thus, using a matrix \(A\) with sparse columns is highly desirable. * **Group testing. ** In group testing, the goal is to detect a set of at most \(k\) defective items from \(M\) possible items. To this end, we designate by \(\mathbf{x}\in\{0,1\}^{M}\) the vector whose nonzero entries are defective. We have \(n\) measurements of \(\mathbf{x}\), each corresponding to a different \"pool\". Each pool is a subset of \([n]\), and the corresponding measurement is obtained by passing the number of defective items in the pool, denoted by \(\ell\), through some noisy channel \(P_{\mathbf{y}|L}(\mathbf{y}|\ell)\) (see, Definitions 3.1 and 3.3 in [24]). The typical case is that the channel depends on the number of defective items \(\ell\) only through the indicator on the event \(\{\ell>0\}\), but the general model allows the measurement to be distributed as \(\ell+\sigma z\), where \(z\sim\mathcal{N}(0,1)\). Thus, with this model the design of the group testing scheme corresponds to designing a binary sensing matrix \(A\in\{0,1\}^{n\times M}\), and the measurements are \(\mathbf{y}=A\mathbf{x}+\sigma\mathbf{z}\). Using pools, corresponding to the rows of \(\mathbf{A}\), with small Hamming weight, results in simpler tests. For example, the original application for which the group testing framework was developed was detection of syphilis among a large group of patients, using a small number of tests. Using pools with small Hamming weight means that we need to mix samples from fewer patients in each pool, which results in less work for the lab technician. In Section 2, we formalize the problem of compresses sensing of binary signals and present our suggested construction for the sparse sensing matrix and our MCMC-based recovery algorithm. Some theoretical analysis and justification for our suggested method is also given. In Section 3, we evaluate the performance of the proposed scheme numerically and compare it to other state-of-the art schemes for the compressed sensing of binary signals problem. We also evaluate the performance of an end-to-end communication scheme for the unsourced random access channel with a small amount of feedback, which uses the proposed compressed sensing of binary signals scheme as an important ingredient. Section 4 is devoted to conclusion and additional discussion.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
###### Abstract  
Delay Tolerant Networking (DTN) is well suited to challenging environments, defined by the lack of reliable end-to-end communication paths to the destination. However, the available energy is not considered in the majority of existing DTN routing protocols when they make forwarding decisions. This limits both delivery probabilities and the network lifetimes in energy-constrained applications. This paper investigates energy-aware routing protocols for wildlife tracking application to transmit data from attached sensors on the animal's back to data collection base stations. We propose three new network protocol strategies to extend common DTN routing protocols, and consider the available energy to achieve efficient utilization of the node's energy in transmission and sensing. These strategies enhance packet delivery rates up to 13% by carefully using the limited energy resources. We simulate two different animal tracking scenarios and show that the new strategies provide significant performance improvement under different scenarios.  
delay tolerant network; routing protocols; energy-aware routing protocol; forwarding algorithm; the ONE simulator; wireless sensor networks; animal tracking +
Footnote †: journal: Sensor and Actuator Networks
# Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
## 3 Proposed Method
### Implicit Translation Layer  
Reusing the encoder layer trained in the source language and only learning the word embeddings in the target language can be seen as finding a token-level mapping between the two languages. However, this is suboptimal for two reasons. First, such mapping is most likely to be impossible due to ambiguity such as homographs. Second, linguistic differences beyond token-level, such as word order, cannot be learned with this method.  
To overcome these shortcomings, we propose the Implicit Translation Layer (ITL) to find this mapping at a sequence level. The ITL takes a sequence of vectors as input and outputs contextualized vectors of equal length. To maximize compatibility, we utilize the same architecture used as the encoder layer. Two ITLs are added to the language model, one before the first encoder layer (input-to-encoder) and another one after the last encoder layer (encoder-to-output). These ITLs can be seen as implicitly translating from \(L_{T}\) to \(L_{S}\) and \(L_{S}\) to \(L_{T}\), respectively. With the addition of ITLs, the computation by a LM in \(L_{T}\) becomes  
\[\mathbf{E}_{L_{T}}=[\mathbf{e}_{1}^{T};\mathbf{e}_{2}^{T};\cdots;\mathbf{e}_{ V}^{T}],\mathbf{e}_{i}^{T}\in\mathbb{R}^{d} \tag{7}\]  
\[\mathcal{H}_{L_{T}}=Embedding(\mathcal{T},\mathbf{E}_{L_{T}}) \tag{8}\]  
\[\mathcal{H}_{0}=ITL_{in}(\mathcal{H}_{L_{T}},\theta_{ITL_{in}}) \tag{9}\]  
\[LM(\mathcal{T},\Theta^{\prime})=ITL_{out}(\mathcal{H}_{C},\theta_{ITL_{out}}), \tag{10}\]  
where \(\Theta^{\prime}=[\mathbf{E}_{L_{T}},\theta_{ITL_{in}},\theta_{ITL_{out}}, \theta_{1},\theta_{2},\cdots,\theta_{C}]\). \(\mathcal{H}_{C}\) is computed using Equation (3), and the token vectors in Equations (5) and (6) are also replaced with respective vectors from \(\mathbf{E}_{L_{T}}\). This configuration allows a more flexible mapping compared to modules that operate on a token level such as Adapters. It is a great advantage as multilingual contextualized representations are known to be highly sensitive to word order [37].
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 4 Experiment and Evaluation
### Performance Test
#### 4.2.1 Main Process Performance Evaluation
This connection is essential for real-time content streaming, as it directly impacts the responsiveness and fluidity of the streaming experience. * Seoul (364.4 ms) and Tokyo (457.2 ms) demonstrate low P2P connection latencies, showcasing VidBlock's strong performance in regions with robust network infrastructure and close geographical proximity. The ability to establish low-latency P2P connections in these areas highlights VidBlock's effectiveness in delivering a responsive and seamless streaming experience, even without the reliance on centralized servers. In more distant regions, such as California (653.6 ms) and Frankfurt (809.4 ms), the P2P connection latency is higher due to the challenges of maintaining real-time connectivity over long distances. Despite this increase, VidBlock's decentralized model remains operational and resilient, demonstrating the flexibility to accommodate geographically dispersed users. This robustness showcases VidBlock's strength in providing global reach, offering a decentralized streaming solution that functions reliably even in remote locations.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 7 Prospects of TE and Recommendation
Each actor would optimize its individual objective functions using different game theory tactics and different pricing strategies to optimize profit. The objective of each player is to determine the optimal quantity and price at which it wants to trade energy to maximize profit. * The platform would implement auction theory (double auction) and matching game from game theory to match a potential buyer to a seller. * Communication amongst buyers and sellers would be implemented by using distributed algorithm based on graph theory subject to some defined objective functions and some constraints, e.g., transmission link capacity and actor data processing capacity. * The platform would determine the optimal path for the energy transmission using a distributed algorithm and would also determine how the seller will be reimbursed.
# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 2 Methods  
GKGP-DC first identifies the geographic entities from a geospatial knowledge graph, and then extracts coordinates from these entities. Then, a spatial clustering method named density peak clustering is applied to obtained spatial clusters. Next, nodes within each spatial cluster are merged into super-nodes to ensure that spatially adjacent nodes can be assigned to the same partition. Subsequently, the graph clustering method named Leiden is used, further consolidating the aggregated graph into a weighted graph. The weighted graph not only accounts for geospatial correlation but also reflects the connectivity between entities in the graph. Finally, the partitions are produced by performing LDG on the weighted graph to balance the computational load of each partition. The overall process of the proposed method is shown in Figure 2.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 9 Conclusions  
Based on the Paillier cryptosystem, a secure homomorphic K-means clustering protocol is proposed to address the urgent need for privacy-preserving clustering techniques in sensitive fields such as healthcare and finance. The protocol consists of three main components: secure computation distance (SCD) protocol, secure cluster assignment (SCA) protocol, and secure cluster center update (SUCC) protocol. Each of these components plays a crucial role in maintaining the security and privacy of the clustering process. Through analysis and experimental verification, the effectiveness and practicability of the protocol are proved. Experimental results show that the proposed method provides robust privacy guarantees while maintaining computational efficiency and is suitable for real-world applications where data privacy is critical. This work provides a practical solution for secure clustering based on homomorphic encryption and contributes to the research in the field of privacy-preserving data mining. Although this protocol solves the key problems of secure distance computation, cluster assignment and centroid update, there are still areas for further research. These include optimizing the computational efficiency of the protocol, exploring other homomorphic encryption schemes that may provide better performance, and extending the protocol to handle more complex clustering algorithms.  
Conceptualization, Z.X. and J.Y.; methodology, Z.Z. and Z.X.; software, Z.Z.; validation, Z.Z., Z.X., and J.Y.; formal analysis, J.Y.; investigation, Z.X.; resources, Z.Z.; data curation, Z.X.; writing--original draft preparation, Z.Z. and Z.X.; writing--review and editing, Z.X. and J.Y.; visualization, Z.Z.; supervision, J.Y.; project administration, Z.X.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by Key Laboratory Of Data Science And Intelligence Education (Hainan Normal University), instiry of Education (DSIE202202), the Scientific Research of Shanwei Institute of Technology (SKQD2021B-010), and the Haikou Science and Technology Special Fund (No. 2024-017).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 6 Conclusions  
(1) With the combination of a numerical analysis and an orthogonal test, the factors affecting the loose zone range of the surrounding rock were studied. According to CCR model with fuzzy restrictions on the weight in statistics, the order of factors influencing the weight value from large to small is \(B>\text{GSI}>K>\lambda>H\).  
(2) The loose boundary zone expression of a deeply buried tunnel was determined to be \(y=ax^{2}+bx+c\), and a calculation method for the undetermined coefficients of the boundary expression was proposed. The correlations of typical geometric quantities \(\mathbf{L}\) and \(\mathbf{H}_{1}\), representing the loose zone range, were established based on the unitary regression equations between the factors affecting the loose zone and typical geometric quantities.  
(3) Based on the boundary function of the loose zone and the upper bound limit analysis method, a limit analysis model of the loose pressure in the surrounding rock mass under a non-linear failure criterion was established via the tangent method and virtual power principle, and the correlations of the loose pressure were deduced. The calculated values of the loose pressure were consistent with the pressure-monitoring results in the different sections of the large-span section, verifying the correctness of the proposed  
Figure 16: Installation of the earth pressure cell. (**a**) Arch crown; (**b**) haunch.  
Figure 17: Comparison between the calculated and measured values.  
calculation method. The research results can provide new ideas for the selection of a supporting structure for similar super-section tunnel projects.
## 2 Background Theory and Methods
### Numerical Verification  
The three-dimensional unsteady heat conduction equation, shown in Equations (18)-(20), was set as a governing equation. It was analyzed in a three-dimensional orthogonal coordinate system as shown in Figure 5, using COMSOL Multiphysics 5.2, a commercial numerical analysis program. The specimen in the numerical analysis model was 25 mm long and 25 mm wide, containing the thin light absorption film with a thickness of 20 \(\upmu\)m and a measurement target material with a thickness of 250 \(\upmu\)m. The numerical analysis was conducted by using the boundary conditions mentioned in Equations (12)-(14) as the input.  
Table 2 presents the results of the mesh dependency evaluation. The value of the phase delay was calculated at the coordinates \(x=0\), \(y=0\), and \(z=-150\)\(\upmu\)m, and was confirmed to converge at approximately four million meshes. In this study, the value of the phase delay was derived using approximately 6250 thousand meshes.  
The relative position phase delay results of the numerical analysis were verified through cross-validation with the same quantity as derived from the theoretical analysis conducted by [PERSON] et al. [27]. The surface temperature of the pure material combined with the single copper layer was compared with the value calculated when inputting the material properties of copper (as shown in Table 3) for the light absorption film and target material. Comparison of the analytical and numerical results, as shown in Figure 6, demonstrated that the temperature distribution tendencies relative to the position were 99.84% consistent with each other using the least-squares method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Mesh** & **Phase Delay (\({}^{\circ}\))** \\ \hline
125,375 & 145.6057 \\
784,036 & 147.5570 \\
1,434,175 & 148.1252 \\
2,873,162 & 148.0972 \\
4,000,894 & 147.5738 \\
6,254,729 & 147.5345 \\
7,345,315 & 147.5596 \\
8,732,682 & 147.5505 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mesh dependency evaluation.  
Figure 5: Double-layer structure model and boundary conditions.
## 5 General Recommendations for Various Stakeholders
### Industry  
Many industries assess the risks associated with their products, e.g., medical devices, food, pharmaceutical, cosmetics and chemical industries. It was reasonable to assume that those industries that develop nanotechnology-enabled products are conducting additional safety evaluations, despite the fact that current regulations do not oblige them to do so for materials of equivalent chemical composition to ones that are already approved in bulk-scale. However, these data, if in existence, generally do not get published in the scientific literature, and thus are not available to the wider scientific community. It would be very helpful if information about NMs that industry has found to be safe or unsafe by stated criteria were made more widely available to the scientific community.  
Industry and their research teams should provide data about production and use of NMs, together with detailed description of used materials, consistent with the legal framework on commercial confidentiality, health and safety and the environment. To this end industry needs to be incentivised to cooperate more closely with the nanosafety agenda. It should evaluate and define suitable positive and negative controls for toxicological research and for evaluating different measurement concepts and devices; report as much information as possible about the experiments and the characterisation of materials when publishing the data in scientific publications; and contribute to the harmonisation of data descriptors for pooling safety and health information in joint databases. It should play a more active role in defining research needs, and contribute to networking activities and collaborative projects; and support the definition of clear terms of reference for industry participation.  
Industry data are often proprietary information and investments will need to be protected. Firms quite legitimately think hard about which partners they might share data with. However, many academics and public sector researchers believe it would be a great leap forward if industry scientists could be convinced to share their core exposure or dose-response data, for example, in public communications or peer-reviewed journals, thus enabling comparative assessments.  
We recommend that industry revise waste management practices to be more relevant to the nanoscale nature of particles, such as incinerator temperatures (e.g., for fullerenes), explosivity, _etc._ All mixed nanoparticle-biological waste must be first treated with a sodium hypochlorite solution (bleach) or similar to remove biological component. The remaining low volumes of nanoparticles should be disposed of as aqueous chemical waste, which goes for incineration.  
Meanwhile, waste management research should obtain data on the leach rate or decay rate of nanoproducts (products containing NMs) under realistic landfill conditions (See Recommendation 3 above).
###### Abstract  
Sensor localization remains a crucial function within the context of wireless sensor networks (WSNs) and is a delicate concern that has attracted many researchers' attention. Undoubtedly, a good distance estimation between different wireless sensors allows us to estimate their accurate locations in the network well. In this article, we present a simple but very effective anchor-free localization scheme for wireless sensor networks called the contextual received signal strength approach (CRSSA) localization scheme. We use the received signal strength (RSS) values and the contextual network connectivity within an anchor-free WSN. We present and thoroughly analyze a novel joint estimation methodology for determining the range, path loss exponent (PLE), and inter-node distances in a composite fading model that addresses small-scale multipath fading and large-scale path loss shadowing effects. We formulate analytical expressions for key parameters, the node's communication range and the PLE value, as functions of the sensor's number, the network's connectivity, and the network density. Once these parameters are estimated, we estimate the inter-node distances and the positions of nodes, with relatively high accuracy, based on the assumed propagation model in a two-dimensional anchor-free WSN. The effectiveness of the CRSSA is evaluated through extensive simulations assuring its estimation accuracy in anchor-free localization.  
wireless sensor network; network connectivity context; RSS; inter-node estimation; anchor-free; small/large-scale fading channel; localization +
Footnote †: journal: Sensors
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 4 Simulation Results
### Tracking Error Assessment  
This subsection assesses the tracking errors obtained by different methods for an intuitive view.  
Figure 3 shows the code phase and carrier phase tracking errors. These results are defined as the estimated value minus the actual value recorded by the signal simulator. The constant carrier phase bias introduced by the adaptive array was subtracted in advance. From Figure 3a, it is shown that that there is substantial code phase error in the PI algorithm, because it suffers satellite signal attenuation while suppressing interference and not all signal power of different channels is used. In contrast, the other methods can significantly reduce the code phase error compared to the PI algorithm. From Figure 3b, it is shown that the carrier phase error curves of the MVDR-like and MMSE algorithms are smoother and show lagging characteristics (see the zoomed-in subfigure) compared to those of the other two methods. This is because the additional carrier tracking error is introduced by array weighting, which affects the carrier tracking results, as mentioned in Section 3.4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Antenna array type & 10-element ULA \\ Array spacing & 0.5 wavelength \\ Interference type & Gaussian broadband \\ Interference-to-noise ratio & 40 dB \\ Interference’s DOA & 60\({}^{\circ}\) \\ GNSS signal type & GPS L1 C/A \\ GNSS’s C/\(N_{0}\) & 35 dB-Hz \\ GNSS’s DOA & 20\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the simulation scenarios.  
Figure 3: Code phase and carrier phase tracking errors. (**a**) Code phase. (**b**) Carrier phase.  
Figure 4 shows the cumulative distribution functions (CDFs) of the code phase and carrier phase absolute errors. The following results can be obtained from the figure. (1) The MVDR-like and MMSE algorithms can significantly reduce the code phase error but also sacrifice carrier phase precision compared to the PI algorithm. This is in agreement with the analysis in Section 3.4. (2) The MVDR-like algorithm outperforms the MMSE algorithm at the cost of increased complexity. Moreover, the proposed method performs the best with an approximate MVDR-like complexity, as analyzed in Section 3.4. (3) It should be noted that the MVDR-like and MMSE algorithms reduce the maximum carrier phase error compared to the other two methods, which is mainly due to the smoother results obtained by these algorithms. (4) The proposed method slightly reduces the carrier phase error compared to the PI algorithm because it has better code tracking, which also benefits carrier tracking, since they are coupled.