esa-sceva/satcom-chunk-collection huggingface dataset:
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 6 Conclusions  
In this paper, we propose a pseudo-phoneme labels (PPL) loss for text-independent speaker recognition and compare several dominant losses on the VoxCeleb database. By comparing different losses, we conclude that variants of margin loss (AM, AAM) on the sphere and log-likelihood ratio cost (CLLR) loss performs better than the classic Cross-Entropy loss on the text-independent speaker verification task. Adding the PPL loss can boost the performance of most existing loss functions for introducing content information with unsupervised deep clustering at a low computation cost. Additionally, PPL loss is applicable to softmax, AM-softmax and AAM-softmax loss. PPL loss can guide network training to refine frame-level features and obtain speaker features containing speech information. This method does not require additional training of the ASR model and only requires a simple clustering pseudo-label to calculate frame-level information loss, so phoneme information can be introduced into speaker representation, greatly simplifying network computation and model complexity. Experimental results on the VoxCeleb database demonstrate the effectiveness of our proposed method.  
Writing--original draft, M.N.; Writing-review & editing, L.H., Z.F., B.Z. and K.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 5: The total number of frames is 10,000; the above figure (**a**) is the feature distribution before training with PPL loss, and the following figure (**b**) is the feature distribution after training.
# Recovering Texture with a Denoising-Process-Aware LMMSE Filter
## 2 Preliminaries and Notation
This process corresponds to a closed-form solution of the iterative reweighting method applied to the singular value matrix of \(\mathbf{Y}_{i}\)[16]. The singular value thresholding described above is applied to each focused patch and similar patches, and estimates of the original patches are obtained by adding the columnwise average to \(\hat{\mathbf{Y}}_{i}\). Then, the estimated original patches are combined to obtain a reconstructed image (overlapping patches are subjected to pixelwise averaging). In WNNM, this process is iterated to estimate the original image. In each iteration, the denoising target \(\mathbf{y}^{(k)}\) is updated by calculating a weighted sum of the most recently estimated image \(\hat{\mathbf{x}}^{(k-1)}\) and the original noisy observation \(\mathbf{y}\) (where \(k\) is the index of the iteration) with a weight parameter \(\delta\), as follows: \(\mathbf{y}^{(k)}=\hat{\mathbf{x}}^{(k-1)}+\delta(\mathbf{y}-\hat{\mathbf{x}}^ {(k-1)})\). This process is called iterative regularization, and the parameter \(\delta\) is fixed to 0.1 in [16]. The WNNM algorithm is given in Algorithm 1. For additional details on WNNM, please consult [16]. ```
0: Noisy observation \(\mathbf{y}\)  Initialize \(\hat{\mathbf{x}}^{(0)}=\mathbf{y}\) and \(\mathbf{y}^{(0)}=\mathbf{y}\) for\(k=1:K\)do  Iterative regularization: \(\mathbf{y}^{(k)}=\hat{\mathbf{x}}^{(k-1)}+\delta(\mathbf{y}-\hat{\mathbf{x}}^ {(k-1)})\) for\(i=1:I\)do  Find the similar patch matrix \(\mathbf{Y}_{i}^{(k)}\)  Estimate the corresponding original patch matrix \(\mathbf{X}_{i}\) by applying Equation (2); the result is \(\hat{\mathbf{X}}_{i}^{(k)}\) endfor  Use the \(\hat{\mathbf{X}}_{i}^{(k)}\) to reconstruct the estimated image \(\hat{\mathbf{x}}^{(k)}\) endfor Output: Obtain the denoised image as \(\hat{\mathbf{x}}=\hat{\mathbf{x}}^{(K)}\)
```  
**Algorithm 1** Image denoising via WNNM.
## 4 Discussion  
Metagenomics has extensive potential in unveiling the mechanisms and correlations between the human intestinal microbiome and diseases. Recent advances in functional profiling of metagenomic samples, encompassing genes and biochemical pathways, have revolutionized our ability to elucidate the links between the functional capabilities of microbial communities and human diseases [63]. The functional characterization of metagenomic samples enables the estimation of relative abundance values for genes encoding enzymes, which is critical for uncovering the molecular underpinnings of diseases such as CRC, a leading cause of cancer-related mortality worldwide. The gut microbiota plays a pivotal role in this context, with dysbiosis potentially triggering colonic carcinogenesis through chronic inflammation, involving specific bacterial strains in this multifaceted process. Various factors, such as antibiotic usage and specific dietary habits, have been recognized as key drivers in the development of dysbiosis [64]. Despite the precise mechanisms underlying how dysbiosis leads to colonic carcinogenesis not being fully elucidated, chronic inflammation is widely acknowledged as a primary instigator [65; 66]. Different risk factors associated with life styles, such as obesity, smoking, and alcohol consumption, have been found to contribute to CRC formation and accelerate the worsening of the disease [2].  
Figure 14: Network of top 16 scoring enzymes that were identified either by the XGB or SKB feature selection methods as part of their top 10 scoring lists and their associated organisms. A total of 85 species that synthesize the top scoring enzymes are presented. Node size is associated with betweenness centrality.  
Although dysbiosis of the gut microbiome has been strongly associated with CRC, leveraging biological insights in metagenomic data analysis is essential for understanding the disease mechanisms.  
To address the limitations of the conventional feature selection techniques that fail to utilize the biological insights embedded in multi-omics data, in this study, a novel method called \"EC-nomenclature-based Grouping-Scoring-Modeling (G-S-M)\" was developed. This approach integrates biological domain knowledge into feature grouping and selection. Specifically, in this study, community-level EC abundances were used as features and were grouped based on their third-level EC categories to provide biologically informed groupings. The EC-nomenclature-based G-S-M method was evaluated through randomized 10-fold cross-validation and compared against traditional feature selection methods such as IG, SKB, XGBoost, CMIM, MRMR, and FCBF. These were paired with classifiers like Adaboost, decision tree, LogitBoost, RF, XGBoost, an optimized Support Vector Machine, and ensemble models like Stack_LogitBoost_KMeans and Stack_SVM_KMeans. Our experiments demonstrated that the EC-nomenclature-based G-S-M approach outperforms the conventional methods, offering more reliable insights into the molecular and biological associations between the microbiome and CRC.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 7 Security Analysis
### Malicious-Attack Analysis
#### 7.2.3 Collusion between DLT Participants  
We adopted a practical Byzantine fault tolerance (PBFT) consensus protocol which supports e-voting resilience and trust [39]. A PBFT consensus ensures that the system stays available and accessible by voters even if a node goes offline due to a malicious attack. This is an advantage in comparison to e-voting systems which adopted a centralised infrastructure, which is vulnerable to a single point of failure. An additional strength of PVPBC is that we designed the PBFT so that the collusion between PVPBC blockchain members is very challenging. In other words, PBFT prevents the system from reaching an agreement if there are faulty or malicious nodes. To reach an agreement in PBFT, nodes vote on the validity of the information (transactions/blocks). This process provides safety because breaching the data integrity would require \((n-1)/3\) faulty nodes out of a total of \(n\), the honest nodes, to collaborate. The PBFT protocol ensures resilience by including the election authority and other trusted parties as known participants in the permissioned group. They cannot compromise the integrity of the ledger unless the number of participating collaborators surpasses a threshold.
# A Study on the Channel Expansion VAE for Content-Based Image Retrieval
## 4 Experiment
### Experiment
#### 4.2.5 Comparison to Unsupervised IR Models  
For a fair comparison with the results reported in a UAIR paper [18], we used the MNIST dataset. The number of the database and test dataset is the same as the reference paper. Since the MNIST dataset contains small-sized images, we amended the network size to 4 layers. Table 4 contains the mAP for the full rank of the supervised and unsupervised models.  
The UAIR reports the performance for supervised models (NDH, OANIR) and unsupervised models (ITQ, Spherical, DH). In addition, Table 4 had the top-2 mAP of VAE [19] on the MNIST dataset. Unsupervised methods achieved less than 50%, but CEVAE achieved 52.71%. Even though the performance of the unsupervised models was worse than the supervised model and GAN, it is promising without the annotation process. Moreover, CEVAE was competitive in easy learning and the compression rate. In Table 4, 52.7% is a 128-dimensional vector, and mAP was kept high with 51.7% in an 8-dimensional vector.  
Figure 6: Regional masking on the Flowers dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Descriptor**} \\ \cline{2-5}
**Dimension** & **GMP** & **GMP + AMP** & \multirow{2}{*}{**Encoder Output**} & **Inference Time** \\  & **From Block\_6** & **From Block\_6** & & \\ \hline
**2304** & - & 45.2\% & - & 0.277 \\
1152 & 45.3\% & 45.2\% & - & 0.106 \\
512 & 45.1\% & 45.3\% & 43.0\% & 0.067 \\
256 & 44.9\% & 44.8\% & 43.0\% & 0.060 \\
128 & 44.3\% & - & 43.0\% & 0.057 \\
62 & 43.5\% & - & 42.2\% & 0.055 \\
32 & 41.8\% & - & 40.0\% & 0.053 \\
16 & 39.5\% & - & 33.9\% & 0.051 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of channel expansion on the Flowers dataset (mAP for top 5). GMP = global max pooling.
## 5 Experimental Results  
### Performance of MILP and CP Models  
In this section, the performance of the proposed models is evaluated. We use ILOG Cplex 12.10 software and CP Optimizer (CPO) for solving the MILP model and the CP model, respectively, using a DELL personal computer equipped with an Intel(r) Core(tm) i5-8250U @ 1.6 1.8 GHz CPU, 8 GB RAM, and Window 10 operating system.  
This section begins with a description of the numerical instances that were tested. Then, the different results tables are presented and, at the end, comparisons between the different algorithms are made.
# Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks
## 3 Design of the Controller Scheme  
### Modeling of Periodic DoS Attacks  
Due to the inherent unreliability of the information transmission channels between multiple agents, the system is prone to being affected by DoS attacks during the information transmission process. Generally speaking, DoS attacks are divided into two types: attacks on individual agents within the multi-agent system and attacks on the communication channels between the agents. This paper primarily focuses on the latter and assumes that the DoS attack is energy-constrained and periodic. The attacker performs attacks on the communication channels between the multi-agent system during the attack period, while reserving energy during the dormant period for the next attack. For each iteration, the attacker's attack period is within \(k\in\{0,1,2,\ldots,T\}\), as shown in Figure 2.  
In Figure 2, \(k_{h}^{an}\) is the start time of the attack in the attack period \(h^{th}\), and \(k_{h}^{off}\) is the end time of the attack in the attack period \(h^{th}\). The time interval \(k\in[k_{h-1}^{off},k_{h}^{an})\) is defined as the attack period when the DoS attacker is actively attacking, and the time interval \(k\in[k_{n}^{on},k_{h}^{off})\) is defined as the dormant period when the DoS attacker is resting. For the data transmission process in the BFMASs, the dormant period of the attacker is defined as \(\alpha_{p}(k,i)=0\), and the attack period is defined as \(\alpha_{p}(k,i)=1\), where:  
\[\alpha_{p}(k,i)=\begin{cases}0,&\text{if }k\in[k_{h-1}^{off},k_{n}^{on}),\\ 1,&\text{if }k\in[k_{h}^{on},k_{h}^{off})\end{cases} \tag{7}\]  
In MASs under periodic DoS attacks, the probability of successful data transmission between agents follows a Bernoulli distribution. Let \(\beta_{\alpha_{p}}(k,i)\) represent the success or failure of data transmission between agents. A successful data transmission is indicated by \(\beta_{\alpha_{p}}(k,i)=1\), whereas a failure data transfer is shown by \(\beta_{\alpha_{p}}(k,i)=0\). The probability of successful transmission is denoted as \(P(\beta_{\alpha_{p}}(k,i)=1)=\overline{\beta}_{\alpha_{p}}\), and the probability of transmission failure is \(P(\beta_{\alpha_{p}}(k,i)=1)=1-\overline{\beta}_{\alpha_{p}}\).  
To reduce the influence of DoS attack, a DoS attack compensation is designed as below:  
\[\xi_{ap}(k,i)=\beta_{\alpha_{p}}(k,i)\xi_{p}(k,i)+(1-\beta_{\alpha_{p}}(k,i)) \xi_{p}(k,i-1) \tag{8}\]  
**Remark 2**: _The two primary components of the attack compensation system are \(\xi_{p}(k,i)\) and \(\xi_{p}(k,i-1)\). If information transmission fails, the distributed formation error of agents is denoted as \(\xi_{ap}(k,i)=\xi_{p}(k,i-1)\). In this case, the controller will use the most recent data received stored in the buffer \(\xi_{p}(k,i-1)\) to compensate for the effects of the DoS attack._
## 2 Taxonomy of Techniques for Co-Located LTE and Wi-Fi Networks  
This section presents a taxonomy of techniques that can be applied when an LTE network is co-located with a Wi-Fi network and both networks operate in the same frequency band. This taxonomy is presented in Figure 2. As can be seen, co-located LTE and Wi-Fi networks can be classified into three big categories depending on the techniques that are applied between them.  
In the first category, the networks operate next to each other in the way they were initially designed, without any technique that improves the symbiosis between them. This is the worst type of co-location scenario as LTE transmissions result in a severe impact on Wi-Fi [7].  
The other two categories classify two co-located networks based on whether the applied technique aims to provide coexistence or cooperation between the networks. Coexistence and cooperation are two terms that differ significantly. With the term coexistence, we refer to methodologies that enable peaceful operation of a wireless technology next to another. The technologies must respect each other, as well as the regional regulations and seek equal opportunities to access the wireless medium, under the condition that there is no exchange of any information between different technologies. On the other hand, the cooperation term refers to methodologies that seek collaboration among the technologies towards harmonious coexistence and optimal spectrum usage by exchanging information. The cooperation between different technologies is in line with the 5G vision, where all of the available wireless technologies will act towards enhancing the user experience.  
As can be seen from Figure 2, coexistence techniques include the 3 GPP LTE LAA mechanism and other techniques that are described later in Section 3. Although coexistence techniques between LTE operating in the unlicensed spectrum and Wi-Fi have been studied widely, the literature, to the best of our knowledge, consists of only a limited number of studies focusing on cooperation techniques among the two technologies. This paper targets covering this gap by classifying the possible cooperation schemes and proposing potential techniques that can be applied in each category. The different cooperation techniques can be classified into the following two big categories, based on the way that the participating networks communicate with each other:  
* Direct cooperation via in-band energy level patterns
* Indirect cooperation via a third-party entity  
The first category includes techniques that make use of one or multiple in-band energy patterns in order to perform technology identification, inform about their actions or achieve synchronization between multiple networks that participate in the cooperation scheme.  
The second category refers to the ability for different wireless technologies to exchange messages via a third-party entity, in order to maintain synchronization and explicitly describe their characteristics and requirements.
## 4 Discussion
### Calculation of Overturning and Stabilizing Moments
Now, the coefficient \(C_{T}\) of the total moment M\({}_{T}\) induced by the wind can be computed again according to Equation (3). The presence of the people on the platform also increases the total weight of the structure:  
Figure 13:6  
Finally, the values of the critical wind speed causing the tip-over of the investigated scissor lift with the various number of workers on the platform can be determined with the use of Equation (6). Table 9 presents the appropriate values of the critical wind speed for studied variants of scissor lift configurations as a function of the number of workers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Position of the** & & & \\
**Human Body against** & **Wind Speed [m/s]** & **Drag Force \(F_{w}\) [N]** & **Coefficient \(C\)** \\  & & & \\  & & & \\ \hline \multirow{2}{*}{Frontal position} & 20 & 125.956 & 0.900 \\  & 30 & 272.009 & 0.857 \\  & 40 & 479.672 & 0.850 \\ \hline \multirow{2}{*}{Lateral position} & 20 & 69.4320 & 0.840 \\  & 30 & 155.612 & 0.837 \\ \cline{1-1}  & 40 & 277.650 & 0.840 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The values of the drag force acting on the human body according to [59]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Workers** & & & \\ \(n_{w}\) & **Configuration 1 st** & **Configuration 2 nd** & **Configuration 3 rd** \\ \hline
1 & 22.096 & 22.510 & 33.961 \\
2 & 21.889 & 22.284 & 33.412 \\
3 & 21.696 & 22.073 & 32.917 \\
4 & 21.513 & 21.874 & 32.459 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The critical value of the wind speed \(V_{CRT}\) [m/s]. In the case of configurations 1 st and 2 nd, the presence of a single worker does not reduce the critical wind speed dramatically. However, the maximal number of persons on the platform causes the value of the critical wind speed to be lower in comparison with the value for the empty platform, about 7 m/s (2 km/h). In the case of configuration 3 rd, the reduction of the value of the critical wind speed is the greatest, about 18 m/s (6 km/h).
# Ultrasonic Non-Destructive Testing of Accelerated Carbonation Cured-Eco-Bricks
## 4 Discussion of Results
### UNDT (Ultrasonic Pulse Velocity Test) and DT (Compression Test)
#### 4.1.2 DT (Compression Test)
The exponential model with an acceptable trendline gave the most preferred relationship between the wave velocities and the CS for the 0, 5, and 10% TRC WC samples (Figure 9). A correlation coefficient (R\({}^{2}\)) of 0.98 was obtained and was considered the most reliable coefficient. Figure 8: DT results considering 1.9 MPa compression pressure. Figure 7: UNDT results considering 1.9 MPa compression pressure. The exponential model obtained in Equation (6) below can predict the laboratory compressive strength values for the 0, 5, and 10% TRC WC carbonated samples when cast samples are unavailable. \[f_{c}=4084e^{0.0011V} \tag{6}\]  
where  
\(f_{c}=\) Compressive strength;  
\(V=\) Wave velocity. The developed exponential model is a useful tool for predicting the CS of the carbonated samples produced in this study in an event where cast samples are unavailable. However, further investigation is recommended to verify the model considering different \begin{table}
\begin{tabular}{c c c c} \hline \hline
**TRC (\%)** & **P-Wave (m/s)** & **CS (MPa)** &
\begin{tabular}{c} **CO2 Curing Duration**— \\ **Hydration Condition** \\ \end{tabular} \\ \hline \multirow{6}{*}{0} & 1800 & 3.4 & 72 h HC \\  & 2138 & 4.5 & 30 h NWC \\  & 2300 & 5.2 & 30 h WC \\  & 2330 & 5.4 & 48 h NWC \\  & 2414 & 5.7 & 48 h WC \\  & 2486 & 6.3 & 72 h NWC \\  & 2550 & 7.2 & 72 h WC \\ \hline \multirow{6}{*}{5} & 1467 & 2 & 72 h HC \\  & 1532 & 2.8 & 30 h NWC \\  & 1646 & 2.9 & 30 h WC \\  & 1694 & 2.9 & 48 h NWC \\  & 1801 & 3.5 & 48 h WC \\  & 1951 & 3.7 & 72 h NWC \\  & 2050 & 4 & 72 h WC \\ \hline \multirow{6}{*}{10} & 1081 & 1.2 & 72 h HC \\  & 1187 & 1.3 & 30 h NWC \\  & 1234 & 1.5 & 30 h WC \\  & 1239 & 1.7 & 48 h NWC \\  & 1456 & 2 & 48 h WC \\  & 1467 & 2 & 72 h NWC \\  & 1571 & 2.3 & 72 h WC \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of UNDT and DT results considering varying TRC. Figure 9: An exponential relationship between the CS (\(t_{\rm{C}}\)) and the P-wave velocities (V) for 0, 5, and 10% TRC WC samples. cementitious materials, water cement/binder ratios, specimen sizes, curing durations, methods employed in testing the samples, and types of aggregate from those used in this study.
# A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection
## 1 Introduction
This phenomenon makes the sensor data streams seem to be uncorrelated. As the example in Figure 1, because the coal mill needs a few minutes to grind the imported coal, the power of the coal mill does not increase as soon as the coal flow increases. In Figure 1, the two curves of \"power\" and \"coal flow\" have a time lag by about 3 min. When we compare the data segments of such two curves (as the square in Figure 1), there seems to be no correlation if we do not consider time lags. Figure 1: The correlated power and coal flow of the coal mill. The second problem is that industrial equipment sensor data analysis may involve tens of thousands of sensors, including huge amount of sensor data correlations to be taken care of, which leads to enormous online computation cost. To solve the time-lagged correlation problem, we conduct curve registration to study the sensor data correlation. Considering the time lags are dynamic, we use dynamic time-wrapping methods to study the appropriate data segment for data correlation study. To solve the high-dimension problem, we conduct sensor clustering based on curve registration method to investigate the correlated sensors. By monitoring the correlation changes among correlated sensors, we manage to reduce the load of correlation monitoring. In the rest of this paper, we first introduce few preliminaries of this paper in Section 2. In Section 3, we lay out the formal statement of our problem. We present the proposed methodology we propose in Section 4. We evaluate our methods with experiments in Section 5. We also compare our work with related works in Section 6 and conclude our work with comments on future research in Section 7.