esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Music Identification Experiment
#### 4.2.1 Dataset  
For the music identification experiment, 9118 songs of various countries and genres were collected; 1823 songs were used for the training and 7295 songs were used for the test. Landmark information was extracted from all sections of 7295. The length of the query signal was 12 s.  
The speech data was extracted only in a section where only pure speech exists in 90 h of broadcast content of various genres. The duration per genre of broadcast content was 30 h each for drama and entertainment, and 15 h each for documentaries and kids. The extracted speech signals were divided into 12-s intervals to generate a total of 3646 speech data corresponding to about 12 h; 1823 speech samples were used for the training and the remaining 1823 speech samples were used for the test. The speech data was recorded in a stereo format at 44,100 Hz sampling rate.  
The music signal of the training data was cut to 12 s in an arbitrary section. The truncated music signal was mixed with the speech signal to have an arbitrary signal-to-noise ratio (SNR) between -30 and 0 dB to apply the characteristics of the broadcaster where the louder speech signal was mixed. The music signal of the test data was cut off for 12 s in two arbitrary sections of each song. The truncated 12-s music signal was mixed with the test speech data and tested. In the test, the SNR of 0 dB and -10  
Figure 9: Overall structure of music identification experiment.  
dB were mixed to measure the separation performance according to each SNR. There are 14,590 test query data for each SNR. The music signal was recorded in a stereo format at a 44,100 Hz sampling rate.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 2 Materials and Methods
### Ideal Retroreflection from a Ceiling
#### 2.1.2 Finite-Difference Time-Domain (FDTD) Calculations  
The FDTD calculations were carried out in two stages. Firstly, a pulse was emitted from an omnidirectional source in free space, bounded by a perfectly matched layer. The envisaged reflector square was transparent, and entirely populated by receivers. The wave from the source arriving at the receivers was recorded. The wave 1 m from the source was also recorded for reference. Simulations were run for 25 source positions per scenario, with sources mostly within a 2.4 \(\times\) 2.4 m square area in line with the reflector (one exception was made so that a source could be at the focus of a 3.6 m patch for the large reflector).  
In the second stage, the received waves were emitted by hard sources corresponding to the receivers from the first stage, thereby imitating a reflector. Apart from the hard sources, the space was free, and bounded by a perfectly matched layer. Receivers in the second stage were on a plane the same distance from the reflector as the original source, and included the original source. However, only 500 receivers were used in the second stage, and were mostly distributed over a 2.4 \(\times\) 2.4 m square area in line with the reflector (with the one exception as noted above). All source positions in the first stage were included as receivers in the second stage.  
Between the first and second stages, the transformation of square patches (if applicable) within the reflector was carried out as outlined in Section 2.1.1: i.e., space-reversal and/or delay-based focusing.  
To manage computational resources, two simulation resolutions were used. For the small reflector, the emitted wave upper cutoff frequency was 11,233 Hz (the cutoff of the 8 kHz octave band), with a sampling rate of 112,330 Hz (10 \(\times\) cutoff), and a rectilinear inter-element spacing of 5.3 mm. For the large reflector, the emitted wave cutoff frequency was halved, sampling rate was 56,165 Hz, and inter-element spacing was 10.6 mm, to cover frequencies to the upper limit of the 4 kHz octave band. Hence, 206,116 elements were used to represent the small reflector, and 463,761 to represent the large reflector. The speed of sound in simulations was \(c\) = 344 ms\({}^{-1}\). The time periods for the simulations were sufficient for the receivers to completely capture the incident waves.  
FDTD simulation was implemented in C++, interfaced using MATLAB, following the framework proposed by [PERSON] et al. [62]. The 10-element perfectly matched layer design was based on Chern [63]. The simulation was significantly accelerated using GPU parallel computing based on the CUDA toolkit [64].
# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 3 Materials and Methods
### The General Process of Our Method  
Our general methodology is divided into three main parts:  
First, we prepare the data necessary for our study. This step includes collecting, transforming, and normalizing the data to fit our model. We describe how we divide 31-band images into 8-band images using Gaussian projections. These transformed data are then organized into mosaics and demosicked using a bilinear method, which makes it possible to simulate the images obtained by the camera developed as part of the CAVIAR project.  
The second part of our methodology consists of implementing different models based on deep-learning algorithms. We use advanced architectures of algorithms that we have modified to adapt to our study, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution network (LapSRN), and deeply recursive convolutional network (DRCN). Each model is adjusted to optimize image reconstruction, exploiting the specific characteristics of their respective architectures to improve image resolution and quality. These models are trained with the prepared data, thereby learning to predict the spectral responses of the 31-band images from the 8-band images. Finally, we evaluate our models using test data. This step measures the performance and efficiency of the models. We use various metrics such as loss function, PSNR (peak signal-to-noise ratio), and SSIM (structural similarity index) to evaluate the quality of spectral responses. The evaluation allows us to identify the strengths and limitations of each model and make the necessary adjustments to improve their performance. Figure 11 illustrates the general outline of our methodology.  
Figure 11: Block diagram of the methodology.  
Figure 10: Modified LapSRN architecture.
## 1 Introduction
For the model with a known number of processes, a Las Vegas algorithm is developed, called the Multichannel Beep Naming (MCBN) algorithm, which assigns unique names to all processes with an optimal time complexity of \(\mathcal{O}(n\log n)\) rounds. The Unknown Multichannel Beep Naming (UMCBN) algorithm is developed for the case in which the number of processes is not known. It is a Monte Carlo algorithm that assigns unique names to all processes with a probability of success that is at least \(1-\frac{1}{2}^{\Omega(\log n)}\). The algorithm has an optimal time complexity of \(\mathcal{O}(n\log n)\) rounds. These are the first naming algorithms that work in the multiple beeping channel environment, and there is no existing work on such a model. Our developed naming algorithms allow anonymous processes to have unique names even when they are assigned to different channels and communication capability is very limited. For the single beeping channel case, our naming algorithms have the same time complexity as [20; 21], whereas the authors in [21] developed deterministic and randomized algorithms that require processes to have internal unique IDs. However, their randomized algorithm does not guarantee consecutive unique names in the range \((1,n)\). The MCBN and UMCBN algorithms will generally speedup the initialization step in real-world applications and solve the performance bottleneck of a single beeping channel. These algorithms will be useful for many applications in several fields such as wireless sensor networks and distributed cloud computing. This paper is organized as follows. Section 2 presents an overview of related work from the literature. Section 3 describes some technical preliminaries of the studied model. After that, the MCBN algorithm is described in Section 4 including the proof of correctness and analysis. Then, in Section 5, the UMCBN algorithm is described along with the proof of correctness and analysis. Finally, in Section 6 we present the conclusions of our work.
## 4 System and Threat Model
### System Model  
In this paper, we focus on how to aggregate a global models in federated learning while protecting the user's private data, as well as enabling the user to validate the server's aggregation results. Moreover, the global model can still be correctly aggregated when some users drop out. The system model in this paper involves three main parties:  
* **Users**. Data ownership and local execution of the training algorithm fall under the purview of the users. Upon finishing a training round, users encrypt the local gradient parameters and transmit them to the fog node. Moreover, users receive the encrypted global gradient parameters that have been aggregated by the aggregation server. They then update their local models and proceed to conduct a next round of model training with their respective datasets. This iterative process continues until the model's training is deemed successful. Users possess the capability to authenticate the correctness of the aggregation outcomes produced by the server.
* **Server**. The fog node dispatches ciphered gradients to the server, which then conducts aggregation calculations without breaching user confidentiality. The server's access is limited to the aggregate of locally encrypted model gradients, precluding any direct exposure to users' unencrypted gradients. As an added measure, the server creates a proof of correctness for its aggregated results, subsequently transmitting this proof alongside the aggregated results to the respective users.  
Figure 1: System model.  
* **Fog Node**. The Fog Node (FN), equipped with robust computing, storage, and networking functionalities, is strategically positioned in proximity to the user. Its primary duty involves gathering data from all online users involved in the current round. Following this collection phase, the aggregated information is seamlessly transmitted to the central aggregation server for further processing.
# Unusual Animal Behavior as a Possible Candidate of Earthquake Prediction
## 3 Electromagnetic Effects as the Major Component of Short-Term EQ Prediction  
For the purpose of short-term EQ prediction, we are strongly required to find EQ precursors [1; 2; 3; 4]. Then during the last few decades, crucial efforts have been made in this field, and enormous progress has been achieved in finding various kinds of EQ precursors (e.g., [3; 5; 6; 7]). Specifically, we have found that EQ precursors are not seismological effects, but rather electromagnetic effects, which take place mainly before an EQ (sometimes together with some after-effects). Even though the main agents are the nucleation and fracturing processes in the lithosphere due to the accumulated pressure in fault regions, various phenomena are found to take place not only in the atmosphere but also in the upper atmosphere (ionosphere), which was revealed with both ground- and satellite-based observations (e.g., [6; 7; 8]). Finally, we have found that three layers of the Earth, that is, lithosphere, atmosphere, and ionosphere, are strongly coupled with other--a phenomenon which is called \"lithosphere-atmosphere-ionosphere coupling (LAIC)\" [30; 31; 32]. Further, it is surprising that the upper-most ionosphere seems to be the most sensitive to the pre-EQ lithospheric activity, and so the recent greatest concern of our scientific society is to elucidate the mechanism of the LAIC process. Even though a few hypotheses have been proposed so far [3; 6; 8; 9; 33; 34; 35; 36; 37; 38; 39], it is not well established which channel is most plausible at the moment, or it may be possible that different mechanisms are working for different EQs. In order to gain more insights into the LAIC process, [PERSON] et al. (Eds) (2018) [7] have emphasized the importance of multi-parameter observations; however, such multi-parameter and multi-layer observations are difficult to carry out due to the difficulty of the multidisciplinary nature of observations, so only a limited number of papers have been published only recently [40; 41; 42; 43; 44; 45; 46; 47]. [PERSON] et al. (2022) [46] is a typical example of such multiparameter observations for the 7 October 2021 Tokyo EQ, for which cows' unusual anomalies are presented in this paper. It is no doubt that these electromagnetic phenomena will remain as the highest priority for short-term EQ prediction.
## 1 Introduction
The design point and sensitivity factors (also the reliability index) obtained from simulated data are compared with those obtained from FORM; they are derived from a multi-linear regression of the simulated data. It is worth to mention that these methods, and others developed in the 1990s, used to state that a large number of simulations were not feasible; nevertheless, the computer power has increased substantially in the last decades, and the limitations of those days may not be as restrictive as before and thus the applicability could be currently extended. Furthermore, the commercial software available nowadays for engineering applications normally includes amenable built-in functions for linear and multi-linear regression analysis which simplifies the programming. The main objective of this study is to define the error statistics in the reliability index by using normality polynomials and to reassess the feasibility and adequacy of this method and the multi-linear regression approach for estimating the reliability of structural and coastal engineering systems including the determination of sensitivity factors. This study is of significance to the coastal and structural engineering fields because the number of simulations required to compute reliabilities can be reduced by accepting defined error levels when using normality polynomials, which was not established in previous studies. This is possible because the error in the estimation of the reliability index is mathematically defined as a function of the number of simulations for the cases investigated. Additionally, other contribution is the use of multi-linear regression applied to simulated results as a mean to determine sensitivity factors, design points and the probability of failure; not only a slightly modified (improved) version of the multi-linear regression approach but also the number of simulations and tolerances required to achieve adequate results are provided for guidance.
# Random Violation Risk Degree Based Service Channel Routing Mechanism in Smart Grid
## 4 Routing Mechanism Based on SCVRD
### SCVRD Based Routing (SCVRD-R) Algorithm  
Since the service channel is composed of \(v_{i}\) and \(e_{ij}\), \(v_{i}\) and \(e_{ij}\) is independent of each other, the service channel violation risk minimization problem \(Min(P_{R}(DT>T_{Thr}))\) can be converted into the shortest path problem with \(SCVRD(v_{i})\) and \(SCVRD(e_{ij})\) as the weight. To solve this problem, \(SCVRD\)-\(R\) algorithm based on Dijkstra is proposed in this paper. Here the nodes and edges that service channel passes are all called as channel section (CS), then in order to simplify the description of the algorithm, \(SCVRD(v_{i})\) and \(SCVRD(e_{ij})\) will be denoted as \(SCVRD(CS_{i})\). The steps of \(SCVRD\)-\(R\) algorithm are described as follows. Figure 6 is the flowchart of \(SCVRD\)-\(R\) Algorithm 1.  
```
(1) Initialize the graph \(G\)(\(N\),\(E\)) and the a\(TTR\)bitute vector of service (\(s\), \(d\), \(B\), \(A\), \(r\)). \(s\), \(d\), \(B\), \(A\) and \(r\)(\(s\),\(d\)) denote the source node, destination node, bandwidth requirements, availability threshold and the violation risk degree between \(s\) and \(d\) respectively. \(r_{0}(s,d)=0\). When there is no channel between s and d, \(r\)(\(s\),\(d\)) +\(\infty\). Set \(S=\{s\}\), \(D=G\)-\(s\). (2) If the bandwidth of any path in the graph G(\(N\),\(E\)) does not meet the bandwidth requirements \(B\), then the path is deleted from \(G\)(\(N\),\(E\)). Thus, a new sub-graph G'(\(N\),\(E\)) is generated. (3) For each \(CS_{i}\in G\)(\(N\),\(E\)), Calculate the probability of violation risk \(P_{CS_{i}}(TR>T_{Thr})\) according to Equation (37). End for each. (4) Choose the \(CS_{i}\) with minimum violation risk probability \(P_{CS_{i}}\) from \(D\), then \(D=D-CS_{i}\), \(S=S+CS_{i}\). (5) Take \(CS_{i}\) as the middle path, if the violation risk from \(s\) to \(CS_{k}\) shrinks, then modify \(r(s,CS_{k})\). (6) Repeat step 4 and step 5 until \(CS_{i}=d\). (7) If the new route is different from the original one, then a new service channel is created. Repeat Step 2 to Step 7.
```  
**Algorithm 1**_SCVRD-R_ Algorithm.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 5 ALOHA Software, Strengths, Limitations, and Need for Improvements
### Comparison with the Present Study  
This research aligns with many of these findings, particularly in its use of ALOHA for emergency response planning. As demonstrated in the study by [PERSON] and [PERSON] (2013), ALOHA is highly effective for initial hazard prediction and evacuation planning. However, this work also highlights the need for technological improvements, such as real-time data integration, which could significantly enhance ALOHA's adaptability in dynamic industrial environments. This is in line with the conclusions from [PERSON] et al. (2024), where the authors argued that improving ALOHA's real-time data-processing capabilities would lead to more accurate risk assessments during rapidly changing conditions [20; 23].  
Additionally, the focus in this study on evacuation modelling complements the findings of [PERSON] and [PERSON] (2013), which underscore the importance of ALOHA in planning effective evacuation strategies during chemical accidents. The integration of drones and AI into ALOHA, as proposed in this study, could enhance the system's ability to provide accurate, real-time predictions, improving evacuation efficiency and overall crisis management [15; 23].
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 2 Background
### Cache Side-Channel Attacks
#### 2.3.2 Conflict-Based Cache Attacks  
Unlike sharing-based attacks, conflict-based attacks do not require address sharing. This type of attack has a cache set granularity. The idea is that when the victim's addresses and the attacker are mapped to the same cache set, they may evict each other from the cache (this phenomenon is called \"cache contention\"). The attacker can use such evictions to monitor the victim's access patterns.  
In a conflict-based attack, such as Prime+Probe[28; 31], the attacker first constructs a set of addresses that are mapped to a particular cache set (called an _eviction set_). Then, the attacker uses the eviction set to create the target cache set in a known state by overloading the cache set with its data. When the victim accesses the same cache set, one of the attacker's addresses is replaced. The attacker determines whether the victim accessed its set or not by measuring the access time for each element of the eviction set.  
Figure 3 illustrates an example of using the Prime+Probe attack as a conflict-based cache attack on a two way set-associative cache involving three steps: _Prime, Wait_ and _Probe_. The attacker targets set one by accessing an eviction set of two addresses in the Prime step. Then, the attacker waits to see whether the victim accesses set one or not. When the victim accesses the same cache set, it forces one of the attacker's addresses to be evicted. Later, in the Probe step, the attacker re-accesses the eviction set and measures the access latency of  
Figure 2: The cache-based side-channel attack procedure.  
each address. When the access time for all of the addresses is low (i.e., cache hit), it means that the victim did not access the target set. However, as shown in Figure 3, when one address has a longer access time (i.e., cache miss), the attacker learns that the victim process accessed set one during the _Wait_ phase.
# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 5 Synthetic Data Manipulation  
The synthetic data used in this study were recorded using the simulation model of an IM with a broken rotor bar shown in Figure 2. The outputs of the model present several signals that can be analyzed to extract fault-related information and to better interpret the degradation behavior over time.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 3 Results
### Robustness Analysis of Signal Classification
#### 3.3.2 K-Fold Cross-Validation  
To further validate the reliability of the classification system, K-fold cross-validation techniques were employed. This approach not only provided a robust evaluation of the model performance but also eliminated bias by ensuring that the model was tested across multiple subsets of the data. The K-fold validation results underscored the model's consistent accuracy and its capacity to generalize well across unseen data, which is vital for deploying the system in diverse underwater conditions (see Table 3).  
The findings from both the noise analysis and K-fold validation experiments affirmed the robustness of the signal classification approach. Notably, the model's ability to retain high classification accuracy across different noise levels and validation subsets demonstrated its potential for reliable application in underwater acoustic signal processing tasks.
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 3 Results and Discussion
### Prediction and Analysis
#### 3.2.4 Experiment IV: The Performance of the Proposed Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** \\ \hline CNN-BiLSTM & 4.932 & 45.864 & 6.772 & 0.980 & 5.346 & 43.327 & 6.582 & 0.981 \\ CNN-SBiLSTM & 4.565 & 34.383 & 5.864 & 0.985 & 4.928 & 40.505 & 6.364 & 0.983 \\ MCNN-SBiLSTM & 3.702 & 26.934 & 5.190 & 0.988 & 3.916 & 29.964 & 5.474 & 0.987 \\ AM-MCNN-SBiLSTM & **3.076** & **15.069** & **3.882** & **0.994** & **3.115** & **18.375** & **4.287** & **0.992** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Error metrics for 2-step, 3-step forecasts: 24 h ahead of schedule. As the forecast horizon increases in Figure 14, our proposed model still shows more stable forecasting performance compared to other models. These all indicate that AM-MCNN-SBiLSTM possesses not only excellent forecasting capabilities but also exhibits good robustness when facing longer forecasting time spans. Figure 14: Comparison of multi-step ahead 24 h forecasting performance. Figure 13: \(R^{2}\) prediction fitting effect of 1-step, 2-step, and 3-step performance.