esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 2 System Model
### Coded-Slotted ALOHA
Therefore, in FA-CSA there also exists a time period of \(n\) slots for sending the copies of a packet, but the nodes are no longer enforced to be synchronized in global frames. Nodes are active (i.e., they have some replica of a packet awaiting for transmission) during the \(n\) slots following the new arrival, and again the total number \(M\) of active nodes in a slot is a random value. Unlike FS-CSA, where \(M\) only changes from frame to frame, here in FA-CSA \(M\) varies on a lost basis. In the literature, two minor variants of FA-CSA have been considered. One is the system described here, when the transmission of the first copy of a packet is immediate. The second consists in treating the first copy in the same way as the remaining ones, i.e., the \(\ell\leq n\) replicas are transmitted randomly and uniformly over the next \(n\) time slots. The decoding process in FA-CSA is performed following the same steps as with FS-CSA, but with the important difference that the receiver needs to keep memory of all the slots that precede the current one (FS-CSA works only on the set of slots of the current frame, \(n\)). However, it is clear that in practice the receivers in FA-CSA would need a finite memory during the decoding decisions in order to keep the memory finite. We will denote by \(n_{\text{RX}}\) this finite size of the receiver memory counted in number of slots. To all matters, \(n_{\text{RX}}\) operates as a sliding window for the recovery of the packets, and we will assume that any packets received in a slot out of this window and not yet recovered are dismissed.
# A Novel Encryption Scheme in Ship Remote Control against Differential Fault Attack
## 3 Prevention of DFA  
The encryption key can be recovered by \((8\times 2)\times 4=64\) failure inducement at most, but in fact, it only needs an average of 47 failure inducements. These calculations take no more than 1 ms to complete on a computer with ordinary performance. Therefore, on the one hand, the encryption devices should be physically protected to prevent the attacker from inducing its failure. On the other hand, it can also optimize the algorithm itself, confuse the results of fault induction, and increase the amount of calculation consumed by the attack as much as possible. It can provide the sensing system with time to discover the attack behavior, and take effective protective measures in time.  
The infection prevention scheme proposed by [PERSON] et al. implicitly verifies the ciphertext difference \(\triangle C\) by adding redundant encryption, so that the wrong ciphertext and ciphertext difference cannot be sent [16]. Based on adding redundant encryption, [PERSON] et al. [17] processed the ciphertext difference of twice encrypted output by a random vector, which masks the output of the original ciphertext and outputs the processed ciphertext, so that the attacker cannot get the correct ciphertext difference, and prevents double fault attack by adding a message verification code to the redundant encryptedplaintext. The above methods resist DFA by adding external protection and verification mechanisms, which to a certain extent ensures that the sensitive information induced by the fault cannot be sent out. They do not fundamentally solve the problem of \"exposure\" to sensitive information.
## 1 Introduction
However, difficulties can arise in on-site measurements of walls U-value in the existing building stock, leading to inaccuracies [32]. These difficulties can be classified into three groups, according to the IEA EBC Annex 58-project [33]: errors related to the measurement accuracy, errors related to the analysis of data and errors related to the boundary conditions of the in situ measurement. Difficulties related to the measurement accuracy include factors like reading and calibration of heat flow meters and temperature sensors and had been deeply analysed by authors such as [PERSON] et al. [17], [PERSON] [34] and [PERSON] et al. [35]. Difficulties related to the analysis of data were recently analysed in depth [32; 36; 37; 38; 39]. Difficulties related to the boundary conditions include factors as an imbalance of the heat flow, edge heat loss and accuracy on the position of sensors, which were highly analysed by [PERSON] and [PERSON] [32], [PERSON] et al. [35], [PERSON] et al. [40], [PERSON] et al. [41] and [PERSON] et al. [42]. Delving into boundary monitoring conditions, the factor of contact between the wall surface and the heat flow meter was analysed by simulations but has not been analysed in experimental tests [40], so as for the present study focuses on this aspect. The IEA EBC Annex 58-project [33] and authors such as [PERSON] et al. [40], [PERSON] et al. [43] and [PERSON] and [PERSON] [44] highlight the importance of ensuring good thermal contact between the heat flow meter plate and the wall to be measured to obtain accurate results. However, conducting HFM in situ measurements during the operational stage of dwellings is challenging due to the need for avoiding damage to wall surfaces. In this sense, the usual practice is placing a PVC film to preserve the wall surface [27; 45]. Considering all the aspects aforementioned, the study aimed to analyse the influence of the non-perfect thermal contact of heat flow meter plates on the accuracy of in situ measurement of the facades' U-value when a film was applied to avoid damage to the wall surface, as a usual equipment installation procedure during the building's operational stage. This research provides valuable additional evidence on the accuracy of in situ monitoring of the actual U-value of existing buildings' facades and will therefore assist practitioners in pre-retrofit diagnosis. The method used to analyse the influence of the non-perfect thermal contact of heat flow meter plates on the accuracy of in situ measurement of the facades' U-value is explained in the second section following this introduction. The third section presents the results. Finally, the discussion and conclusions are given in the fourth section.
# Spectral Shaping of a Superluminescent Diode for Terahertz Cross-Correlation Spectroscopy
## 3 Results and Discussion
### Analysis and Comparison of Structured Spectra to Continuous Spectra
While the measured FWHM for 20 GHz is close to the theoretical value at 45 ps, there are large deviations for 10 GHz and especially 5 GHz. There are two possible explanations for this discrepancy. First, the FWHM is only an estimate for the coherence time. Furthermore, the FWHM is not measured directly, as for example in a Fourier-transform infrared interferometer, but as a cross-correlation of the terahertz signal with the optical signal. Thus, effects at the detector are not taken into account. Second, due to the limited resolution, the feature width applied to the POF is not necessarily the feature width that is generated. The error becomes larger with smaller feature width. Nevertheless, this proves the correlation between the coherence length and the FWHM in the time-domain. A small feature width leads to a long coherence length and hence a larger FWHM. The extreme case is a mode-locked laser with an optical mode width on the order of a few MHz [18], which leads to a terahertz pulse train with constant amplitudes [19]. Further, it helps to understand why a smaller feature width increases the DR even if the \(\Delta\)pp is reduced: within the broader envelope more pulses are included. Since all pulses contain the same information, an averaging effect occurs in the frequency-domain which increase the DR. All values are summarized in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline
**Power** & **Feature Width** & **Mean FWHM** & **Mean Slope** \\ \hline
15 mW & 2 THz & 0.9 ps & \(-\)29.9 dB/THz \\
15 mW & 5 THz & 0.9 ps & \(-\)33.8 dB/THz \\
15 mW & 5 GHz & 74 ps & \(-\)36.8 dB/THz \\
15 mW & 10 GHz & 66.3 ps & \(-\)35.4 dB/THz \\
15 mW & 20 GHz & 45.8 ps & \(-\)34.4 dB/THz \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of the results in Figure 7. Mean slope and mean FWHM with respect to the POF parameters. The first two entries represent the 2 THz continuous bandpass and the case if no filter is applied (5 THz). Figure 7: **(a)** Slope m at the receiver antenna with an incident optical power of 15 mW. The arithmetic averages are rising for larger feature widths. The 5 THz continuous bandpass filter which is given by the maximum bandpass of the POF has the lowest value for m. For the 5 GHz feature at \(\Delta\)f = 0.03 THz, the optical power is too low to get a signal with a bandwidth higher than several hundreds of GHz. **(b)** FWHM at the receiver antenna with an incident optical power of 15 mW. The smaller the feature width of the optical spectrum, the larger the FWHM of the envelope of the time-domain trace.
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 4 Discussion
### Costs of the Solution  
As the presented solution is deployed in production commercially, several aspects related to the costs of the solution should be discussed. HPU without an anomaly detection system also contains PLC and also contains sensors that are useful in the context of an anomaly detection system. HPU enhanced with an anomaly detection system requires an edge computer attached. As mentioned in Section 2.1.1, the edge computer used in the solution is MOXA-UC8100. The cost of this edge computer is about 3000 PLN [84] gross. The approximate cost of Microsoft Azure Cloud resources costs is about EUR 10, as presented in Section 2.3.4. A component of the overall cost of implementing the solution is also the time that developers and analysts spent on the software development and analytical part of the solution. The quantifiable added value, in terms of saved funds, energy, and time, of the implemented anomaly detection system will be able to be estimated over a longer time horizon of production deployment. Due to confidentiality, it is not possible to present detailed calculations (including, for example, spare parts cost or cost of the time spent on software development) that prove that the solution is worth implementation. The presented use case involves not only anomaly detection but also machine monitoring in the context of machine parameters visualization in the cloud. Not quantifiable, but an important business benefit of the project, is the process of building a competitive advantage on the market having described the solution in the portfolio. According to consultations with domain experts from PONAR Wadowice, analyzing both costs and benefits of the solution from a business perspective, it is worth implementing.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 5 Simulation Analysis  
In order to test the performance of the proposed method, this section describes, taking the IEEE 33-node power distribution system and IEEE 69-node power distribution system as examples, fault location simulation tests performed on the MATLAB simulation environment on an Intel Core i7-1260P CPU 4.70 GHz platform, and the results are compared with the current typical fault location algorithms in terms of accuracy, fault tolerance, and computational efficiency. The compared algorithms include the binary particle swarm optimization algorithm (Algorithm 2) [20], bald eagle search algorithm (Algorithm 3) [31], and linear integer programming algorithm (Algorithm 4) [26]. The specific fault location methods for each algorithm are shown in Table 5.
# Community Detection Framework Using Deep Learning in Social Media Analysis
## 1 Introduction
The framework consists of three stages:  
* Social Network Topology Construction: A user topology graph is built based on connections between individuals. To efficiently assist investigators, key members that are identified through their central roles in the topology are prioritized. * Communication Content Analysis: Important information hidden within large volumes of social media data are extracted. By analyzing communication content such as email, relationships between individuals and sensitive information can be uncovered [6]. The Node2 Vec method [7] is used to vectorize users based on the textual content of their communications. It is an algorithm used to learn low-dimensional vector representations of nodes in a graph by capturing the structural and relational information of a network. * Community Detection Module: Combining the topology graph and communication content, communities are detected using the ClusterNet model [8]. This end-to-end model optimizes graph learning and clustering, outperforming traditional methods that rely solely on network topology. The Enron dataset is extensively used for evaluation, demonstrating that the proposed framework excels in detecting communities by using both topology and communication content, improving modularity index performance over baseline models. The main contributions are as follows:  
(1) This paper proposes a practical and extensible community detection framework, which achieves superior performance over the baseline models and can reflect network structure and node characteristics in social networks. (2) The method of network representation learning is adopted to map social networks into a vector space. Technically, we combine Node2 Vec and Word2 Vec to vectorize each user feature, a synthesis of topology and communication content features. (3) Modularity computation is adopted to evaluate the optimal community detection results. The modularity index is a metric used to evaluate the quality of community detection in networks. It measures how well a network is divided into distinct communities by comparing the density of edges within communities to the density of edges between different communities. Experimental results reveal that our unsupervised community detection framework achieves a modularity score 1.1% higher than the latest baseline model, indicating a significant advancement in the identification of community structures. This enhancement highlights our model's superior ability to capture the underlying community features compared to existing state-of-the-art models. The remaining sections are organized as follows. Section 2 introduces the related work in this domain. Section 3 describes the workflow, steps, and methodology applied in the proposed framework. Section 4 contains the experimental details and analysis. Section 5 discusses the significance and challenge of social media analysis. Finally, conclusions are presented in Section 6.
## 3 PCAWF- and ARPCAWF-Based OMA Methods and Their Limitations
### Limitations of PCAWF and ARPCAWF-Based OMA for LTV Structures  
1. When the PCA models are updated using the PCAWF or ARPCAWF algorithm, the PCs and eigenvectors must be calculated through matrix decomposition, which is computationally expensive. Furthermore, the algorithm process requires several large matrices to be stored, and these occupy a large amount of memory. Therefore, PCAWF- and ARPCAWF-based OMA methods have high time and space complexity.
2. In the process of dealing with the observed data, the results are always sensitive to observation noise. In the field of mathematics, this is known as an ill-posed problem [43], and matrix decomposition is particularly susceptible to such issues. In practical engineering applications, the crux of solving ill-posed problems is to replace the matrix decomposition method [44]. When a new data vector is added to the PCAWF model, there is a serious risk that the updated matrix will be ill-posed. This may result in biased matrix decomposition, inaccurate PCs and eigenvectors, and poor adaptive PCAWF-based OMA.
3. As ARPCAWF recursively updates the autocorrelation matrices rather than all PCA models, it has lower time and space complexity than PCAWF. For the autocorrelation covariance matrix, the  
Figure 3: Procedure of PCAWF-based OMA for LTV structures.  
recursive solution of the modal parameters requires repeated recursive computations to determine the covariance matrix at the next moment, which consume a lot of time and occupies large amounts of memory. For the PCA solution, if the variance of a variable changes, the characteristic structure of the covariance matrix decomposition will change completely, affecting the load vector assignment. If the mean value of the variable changes, the PC orientation will deviate. However, the PCs and eigenvectors are also calculated by matrix decomposition, thus the problems of matrix decomposition persist.
4. PCAWF- and ARPCAWF-based OMA suffer from the problem of modal exchange. For LTV structures, the engineering parameters (mass, stiffness, and damping) change over time, thus the order of each modal contribution rate may vary. Because of this modal exchange, the contribution order is not equal to the frequency order. The modal exchange phenomenon will cause the eigenvalues in the PCA decomposition results to be similar or even the same, and the PCA method based on singular value decomposition or eigenvalue decomposition appears singular or ill-conditioned, leading to a reduction in the accuracy of recognition results or even wrong results [34]. However, the order of the mode identified by PCA depends on its contribution rather than its frequency. In PCAWF- and ARPCAWF-based OMA, the eigenvectors and PCs are calculated by eigenvalue decomposition or singular value decomposition of the autocorrelation matrix and re-ordered according to the associated eigenvalues.
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 3 Cross-Layer Performance Analysis of Two-Hop Relay System Based on NOMA  
Previous research on the MAC layer of wireless or power line communication is often based on the ideal physical layer transmission, without considering the interruption caused by channel fading or noise, transmission failure is only caused by the conflict of the MAC layer. In this section, a dynamic relationship between the transmit power and the number of competing nodes is established for a wireless power line communication system, and the impact of physical layer outage on the overall performance of the MAC layer is studied. Considering the NOMA network with two users' cooperation, the normalized throughput, packet loss rate, and delay of the cross-layer system between physical layer and MAC layer are analyzed and calculated. The algorithm process is shown in Algorithm 1.  
```
1:Step 1: The SNR \(\gamma_{\text{R}_{\text{R}_{\text{R}_{\text{R}_{\text{R}}}}}}\), \(\tau(\text{D}_{n},x_{n})\) of different links is calculated by Formulas (1) to (8)
2:Step 2: Calculate \(P_{\text{out}}^{\text{D}_{\text{D}}}\), \(P_{\text{out}}^{\text{D}}\) and \(P_{\text{out}}^{\text{sys}}\) by Formula (15) to Formula (21) by comparing the relationship between SNR and threshold
3:Step 3: Calculate n by Formulas (24) and (25), substitute the value of n into Formula (22)
4:to solve the nonlinear equation and get and \(p\)
5:Step 4: Substitute the value of \(n\), \(\tau\) and \(p\) into Formula (22) to calculate \(P_{tr}\), \(P_{s}\), and \(P_{c}\)
6:Step 5: Formulas (23) to (36) are used to calculate the relevant probabilities of system idle, successful transmission, collision and interruption and the time used in different cases. Step 6: Substitute Formulas (37) to (39), and comprehensively consider the channel
7:conditions of physical layer and the backoff process of MAC layer to calculate the system performance index \(SNOMA\), \(p_{sys}\), \(\overline{TNOMA}\).
```  
**Algorithm 1** Cross-Layer Performance Analysis Method Combining Physical Layer and MAC Layer
## 2 Materials and Methods
### Transcriptome Data Analyses  
Raw data (raw reads) of FASTQ format were firstly processed through in-house perl scripts. Clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low-quality reads from raw data. Q20, Q30, and GC content of the clean data were calculated and listed in Table S1. The datasets were functionally annotated by pitaya genome using Hisat2 (version 2.0.5) [35]. The quantification of each gene expression level (fragments per kilobase millions (FPKM)) was counted using featureCounts (version 1.5.0-p3). The differential expression analyses of two libraries were performed using the DESeq2 package (version 3.10) of R software (version 4.1.2). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using KofamKOALA ([[https://www.genome.jp/tools/kofamkoala/](https://www.genome.jp/tools/kofamkoala/)]([https://www.genome.jp/tools/kofamkoala/](https://www.genome.jp/tools/kofamkoala/)), accessed on 5 February 2022). The transcript abundance of candidate genes was drawn by Tbtools software [36]. The accuracy of the RNA-Seq data was verified by RT-qPCR with specific primers (Table S2) according to the method of [PERSON] et al. [32].
## 4 Dynamic Modeling of the Vibration Isolator  
This section describes the deduction of a dynamic equation for the vibration isolator based on the Lagrangian equation. Here, damping of the rotation joint is analyzed as the difference in vibration isolation during translation and rotation. Subsequently, dynamic characteristics, including the amplitude-frequency characteristic and transmissibility, can be obtained using an average approach.
## 1 Introduction
Although the feedback mechanism is used in some SR methods, it is rarely used in lightweight SR methods. The feedback mechanism enables the previous layers to perceive high-frequency information from latter layers, but no new parameters are introduced. Therefore, it is very applicable for lightweight networks. Moreover, most medical devices are resource-constrained, so lightweight feedback SR networks with good performance are desired. In order to meet the demand, a lightweight dual mutual-feedback network (DMFN) is proposed for artificial intelligence in medical image super-resolution. The DMFN feeds the HR features generated by the up-projection unit back into the down-projection unit, and feeds LR features generated by the down-projection unit back into the up-projection unit, which forms a dual mutual-feedback architecture, as shown in Figure 1. Our method that was trained using natural images is named DMFN, and our method that was trained using medical images is named DMFN+. They were tested on MRI13 from [22] and compared with other state-of-the-art SR methods, as shown in Figure 2. Our method performs very well with little computational cost. Our contributions are summarized as follows:  
* To better perceive the high-level information from each other, we designed a dual mutual-feedback structure. The HR features generated by the up-projection unit are fed back into the down-projection unit, and the LR features generated by the down-projection unit are fed back into the up-projection unit. * To boost the expressive ability of the network, we propose a contrast-enhanced residual block (CRB) for use as each cell block in the projection units. CRB uses the contrast-enhanced channel and spatial attention within residual learning. The contrast-enhanced channel attention module learns the pixel contrast of each feature map to restore the textures, structures, and edges of images. The contrast-enhanced spatial attention module learns the pixel contrast in the same spatial location along the channel dimension to infer finer spatial-wise information. * To narrow the search domain of the SR function, we designed a unity feedback. We down-sampled the SR result to LR image as the inverse process of SR. We then compared it with the input LR to calculate the unity feedback loss. The proposed unity feedback is helpful in terms of learning a better SR function with very few introduced parameters, which can be applied as a module to other SR networks.