esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 6 Discussion and Conclusions
All case studies show that a set of features selected by considering only some health conditions can also reveal other conditions. Therefore, it is not necessary to permanently store all raw signals. On the contrary, only features can be stored, which can be used to construct more complex models. In this way, the data quantity is notably reduced, and machinery can be monitored continuously. In addition, real-time clustering allows for labeling the data during the collection. Because the algorithm is incremental, the data corresponding to a novel condition are assigned to the same cluster and will have the same label. From the industrial point of view, obtaining the label in real time allows for detection of faults or anomalous behaviors as they occur, and, therefore, it is possible to intervene before more severe faults occur and the system breaks down. In conclusion, using the proposed approach, it is possible to implement an edge-cloud infrastructure to deal with data availability, completeness, and quantity issues. In particular, feature extraction can be extracted in real time to reduce the quantity of data to analyze. In addition, fault and novelty detection approaches should be applied to streaming data to obtain information on the setting, which corresponds to the operating condition of the system, and the event, which corresponds to the occurrence of a particular event, such as a fault, a novel behavior, or a change in the system setting. This information is provided in real time to have feedback on the health condition of the machinery; at the same time, it is included in the dataset so that the extracted file can be directly used as input for diagnostics models without any preprocessing phase. Conceptualization and methodology, F.C. and A.R. ; software development, F.C. ; experimental dataset collection, A.R. ; new approach application, F.C. and F.G.G. ; results analysis and discussion. F.C. and M.B. ; writing--original draft preparation, F.C. ; writing--review and editing, A.R. and M.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from Authors (lab dataset) and third parties (industrial datasets) and are available from the authors with the permission of third parties (industrial datasets). **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Method
### Improved VGG16 Model
#### 3.5.2 Multi-Loss Function Fusion  
The cross-entropy loss function is usually used for common image-classification problems to find the loss. Cross entropy represents the difference between the actual probability distribution and the predicted probability distribution [22]. The smaller the value of cross-entropy, the better the model prediction. The cross-entropy loss function is defined in Equation (16). However, in the problem of classifying pipeline-corrosion patterns, the situation of corrosion-free and lightly corroded pipelines is very similar, resulting in little feature differentiation.  
\[C=-\sum_{x=1}^{N}\Big{[}y_{(x)}\log\widehat{y}_{(x)}+\Big{(}1-y_{(x)}\Big{)} \log\Big{(}1-\widehat{y}_{(x)}\Big{)}\Big{]} \tag{16}\]  
where \(N\) is the number of samples in the batch, \(x\) is the index symbol for the sum, \(y\) is the label value, and \(\widehat{y}\) denotes the actual output.  
The advantage of the center loss function is that it can learn features with smaller intra-class distances, thus reducing intra-class variation and, to some extent, increasing inter-class variability, thereby improving classification accuracy [23]. The definition of the central loss function is given in Equation (17):  
\[L_{c}=\frac{1}{2}\sum_{i=1}^{n}||x_{i}-C_{yi}||_{2}^{2} \tag{17}\]  
where \(n\) is the number of batch samples, \(x_{i}\) denotes the \(i\) feature of category \(y_{i}\), and \(C_{yi}\) denotes the centroid of category \(i\) features.  
Therefore, the cross-entropy and center loss function's joint loss function is used. Including center loss ensures that the feature distance within a class is minimized, allowing samples from the same type to be close to the feature center of the corresponding class, making training more accessible and facilitating optimization. The joint loss function is calculated as in Equation (18).  
\[L=C+\lambda Lc=-\sum_{x=1}^{n}\left[y\log\widehat{y}_{x}^{l}+(1-y)\log\Big{(} 1-\widehat{y}_{x}^{y}\Big{)}\right]+\frac{\lambda}{2}\sum_{i=1}^{n}||x_{i}-C_ {yi}||_{2}^{2} \tag{18}\]  
Figure 10: Before and after comparison of VGG16 model improvements.  
The fusion coefficient \(\lambda\) ranges from 0 to 1, and the fusion coefficient that gives the highest classification accuracy was found to be 0.4 through several experiments.
# Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree
## 4 Monitoring Results and Discussion
### Tree Impedance Depending on Probe Insertion Depth  
Figure 11 shows a graph of the normalized impedance of the zelkova tree according to the probe insertion depth. The insertion depth of the probe varied from 5 mm to 30 mm at 1 mm intervals. Since the tree impedance was measured using a DC current using a reference instrument (B2912B, Keysight, Santa Rosa, CA, USA), the measured values refer to the resistance. To show the relative ratio rather than the absolute value, it was normalized and expressed based on the resistance value of 13.7 k\(\Omega\) at a depth of 9 mm. The impedance tended to decrease as the probe was inserted deeper. Since the results were measured using the four-point method, the impedance may not have decreased because the contact area of the probe increased in proportion to the insertion depth. The probe insertion depth of up to 7 mm corresponds to the tree bark, so the impedance value is relatively high. The region with an insertion depth of 7 mm to 13 mm is where the phloem, cambium, and xylem are located, and since these structures are involved in the physiological activities of the tree, the impedance measurement in this region is appropriate. Water is delivered from the roots to the leaves through the xylem, and nutrients are delivered through the phloem. The region exceeding the probe insertion depth of 13 mm corresponds to the sapwood of the tree, the impedance of which gradually decreases with increasing depth. Since the physiological activities of trees occur in the cambium, electrical impedance measurements should also be performed in the cambium [20].  
Figure 11: Tree resistance as a function of insertion depth (Keysight B2912B).  
Figure 10: Impedance change over 14 days after insertion of measurement probe.
# Early Ransomware Detection with Deep Learning Models
## 3 PARSEC Dataset
### Ransomware Processes  
We started from a dataset comprising 38,152 ransomware samples, obtained from VirusShare.com [46]. To verify this site's virus classification, we employed an automated pipeline to verify the authenticity of these samples as ransomware. The objective was to identify at least 62 ransomware programs within this dataset to match the number of benign processes described in Appendix A.1. The identification pipeline is a multi-stage process designed to differentiate actual ransomware from potential threats. It includes two VirtualBox virtual machines (VMs) and a host machine, each playing a critical role in screening, analyzing behavior, and confirming ransomware candidates. The full pipeline of ransomware API calls collection flow is shown in Figure 2.  
The first virtual machine (denoted as VM1) starts the process by querying the VirusTotal API for each entry in the \"VirusShare_CryptoRansom_20160715\" collection, which consists of 38,152 potential samples. Its objective is to filter and prioritize samples based on the frequency of detections via various antivirus engines. Prioritized samples are forwarded to the second virtual machine (denoted as VM2) for a detailed behavioral analysis.  
VM2 receives prioritized samples (one by one) from VM1 and executes each in a secure, controlled setting. It focuses on detecting encryption attempts targeting a \"honey spot\", which refers to a deliberately crafted and strategically placed element within a system or network designed to attract ransomware or malicious activities [47]. All API calls made during execution are recorded. If a sample is confirmed as ransomware (i.e., it encrypts the \"honey spot\"), VM2 compresses the API call data into an Excel file, packages it with WinRAR, and sends it back to VM1.  
The host machine maintains a consistent testing environment by resetting VM2 after each analysis. It gathers the compressed Excel files containing API call data from confirmed ransomware samples and compiles them into a single list of these verified programs. This process resulted in a dataset of 62 validated ransomware programs from the initial 38,152 candidates after it ran for two weeks.
# TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning
## 3 Results and Discussion
### Tick Identification with TickPhone App  
To evaluate and validate the performance of our TickPhone app, we tested 31 adult tick specimens as independent samples. Each tick was photographed three times using a smartphone. For comparison, all of these ticks were also identified by trained professionals at the CVMDL, a fully accredited diagnostic laboratory at the University of Connecticut. Table S1 summarizes the test results of both our TickPhone app and trained professionals. The test results show that our TickPhone app achieved recognition accuracies of 95.69% and 96.77% using the Samsung Galaxy S6 and Huawei P20 Pro, respectively. In a previous study [16], primary care providers were only able to achieve identification accuracies of 10.5%, 46.1%, and 57.9% for adult female blacklegged tick (enggorged), dog tick, and lone star tick, respectively. Thus, our TickPhone app shows a much better identification accuracy compared to the previous method. As shown in Figure S5, misidentification of ticks was mainly due to the poor quality of the tick photos, caused by glare or lack of focus. Therefore,  
Figure 5: Ternary phase diagram of performance scores with various network layers, training–test distributions, and picture normal sizes: (**A**) color pictures and (**B**) grayscale pictures.  
it is crucial to take high-quality tick photos with the smartphone to achieve highly accurate tick identification.  
Further, we used receiver-operating characteristics (ROC) curve analysis to evaluate the detection sensitivity and specificity of our approach [37; 38]. As shown in Figure 7, we achieved an average area under the curve (AUC) of ~93% for all three types of tick species. In particular, our TickPhone app can identify adult female deer ticks and adult male deer ticks with an AUC of 98% and 96%, respectively. In our testing, there were relatively low AUCs (86% for males and 89% for females) for lone star tick identification, which can be attributed to the limited number of lone star tick photos available to train our deep learning model. The identification accuracy can be further improved in the future with the addition of more lone star tick photos. Therefore, all of these results showed that our TickPhone app is a simple, reliable, and accurate approach for rapidly identifying tick species without the need for well-trained personnel and expensive equipment.  
Figure 6: Accuracy curves for training and validation of our optimized deep learning model. Error bars represent the standard deviations of three replicate experiments using the same parameters, 64: 3: Color: 80–20.
# Joint Power Allocation and Link Selection for Multi-Carrier Buffer Aided Relay Network
## 1 Introduction
### Organization  
The remaining of this paper is organized as follows. The list of acronyms is in Table 1. The dual hop BAR-based transmission model and the joint optimization problem are presented in Section 2. Section 3 provides the proposed algorithms. Finally, the simulation results and the conclusion are presented in Sections 4 and 5, respectively.
## 2 Background
Under this hypothesis, D2D-enabled UEs must be equipped with a Single-Carrier Frequency Division Multiple Access (SC-FDMA) receiver [18]. The phrase network-controlled hints at the fact that the eNB is still in control of resource allocation on the SL, i.e., it decides which UE can use which resources. Two schemes have been envisaged to do this: a Scheduled Resource Allocation (SRA), and an Autonomous Resource Selection (ARS). SRA is an on-demand scheme, similar to resource allocation in the UL for standard communications: the UE must send a RAC request to the eNB, which grants enough space for it to send its BSR. Then, the eNB schedules SL resources accordingly and issues the grant to the UE for D2D  
Figure 1: LTE-A (Long-Term Evolution-Advanced) protocol stack. IP: Internet Protocol; PDCP: Packet Data Convergence Protocol; RLC: Radio Link Control; MAC: Media Access Control; PHY: physical. communications, as shown in Figure 2a. On the other hand, in ARS the eNB configures a static resource pool, e.g., \(M\) RBs every \(T\) TTIs, and UEs can draw from it without any signaling. With reference to Figure 2b, the UE has new data to transmit at \(t=1\), but it needs to wait for the next eligible TTI, i.e., at \(t=4\). If more than one UE selects the same resources, then collisions will ensue. Note that P2 MP D2D transmissions are not acknowledged, hence the sender cannot know which neighboring UEs received a message, and H-ARQ (Hybrid Automatic Repeat reQuest) is disabled. This is because (N)ACKs should be sent on a dedicated control channel to the sender, but dimensioning the latter would be impossible: in fact, with P2 MP D2D transmission, there is no way to know in advance how many and which UEs will actually receive the message, or were meant to in the first place. P2 MP D2D transmissions use UM RLC.
## 1 Introduction
In contrast to multifocal designs, CLs based on this concept are not only aimed at inducing a peripheral myopic defocus to compensate for the potential peripheral hyperopic defocus promoting the axial elongation of the eye. EDOF CLs contain non-monotonic, non-aspheric, and/or aperiodic power profiles, utilizing alterations of spherical aberration as well as of other multiple higher order spherical aberration terms to obtain a relatively stable retinal image quality with minimal susceptibility to variations in the pupil, inherent ocular aberrations and decentration [23]. Therefore, these CLs promote the generation of an acceptable retinal image quality minimally affected by central and peripheral defocus [23]. Currently, the only CL model for myopia control based on this principle is marketed under the name of Mylo (Mark'ennovy Personalize Care SL, Majadahonda, Madrid, Spain), which is based on the use of a non-progressive distribution of power from the center to the periphery, leading to a controlled induction of some high-order aberrations, allowing an increase in the eye depth of focus [24]. This specific myopia control CL has shown a rate slowdown in myopia progression of 32% and 25% in terms of spherical refractive equivalent (SE) and AXL, respectively [25]. Despite the abundant scientific evidence on CLs for myopia control, it is mainly focused on the demonstration of their efficacy in terms of the control of SE and AXL changes. However, few studies have exhaustively analyzed the impact of these optical aids on visual acuity, accommodative function and binocularity. Considering the peculiar power profile of myopia control CLs, with significant power modifications across the pupillary area, accommodative and binocular changes are expected. The knowledge of these changes may elucidate the mechanism of action and real visual impact of myopia control CLs. The aim of the current study was to characterize the optical and visual changes occurring in the short term with the EDOF CL Mylo, which has been already demonstrated to be efficacious for myopia control in children in the long term.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 3 Enabling Technologies for a High-Speed VLC System
### LED Technology
Thus, in contrast to the limited modulation bandwidth of sub-GHz for LEDs, the modulation bandwidth of LDs is higher than 5 GHz [20]. In addition, LDs has higher current densities and output powers per unit wafer area than LEDs, and recent research has shown that lighting based on multicolor LDs has no health concern on the human eye [31]. Furthermore, the output light has the feature of coherent and collimated, which is fit for point-to-point data transmission. Thus, by leveraging the advantages mentioned above, a LD-based white VLC system is promising to outperform the ones based on LEDs. According to the recent studies, multi-Gigabit data rates have been achieved in VLC  
Figure 4: The schematic diagram of a typical RC-LED. Reproduced with permission from [23], John Wiley and Sons, 2004. (Reprinted from Phys. Status Solidi, 201, [PERSON] etc., InGaN/GaN resonant-cavity LED including an AlInN/GaN bragg mirror. 2675-2678, Copyright (2004), with permission from John Wiley and Sons.) Figure 5: Micro-LEDs proposed in Ref. [28]. Reproduced with permission from [28], Chinese Laser Press, 2017. (Reprinted from Photonics Research, 5, [PERSON] etc., Towards 10 Gb/s OFDM-based visible light communication using a GaN violet micro-LED. A35-A43, Copyright (2017), with permission from Chinese Laser Press.) systems based on III-nitride LDs [15; 16; 17; 18; 19]. However, the safety concerns involving the use of LDs is the main limitation for the rapid development of a LD-based VLC system [32].
## 3 Efficient Hierarchical Co-Occurrence Graph Convolution Networks
### Efficient Hierarchical Co-Occurrence Convolution  
Similar to CNNs, GCN can be seen as the aggregation of local features in the spatial domain of skeleton data and the global feature aggregation across channels. Since it is locally aggregated in space, it may be difficult to capture remote joint interactions involved in actions. As the output of the convolution is the global response of all input channels, if the joint and channel dimensions are transposed, the convolution layer can easily learn the co-occurrence of all joints. Therefore, we adopt a hierarchical co-occurrence framework to adjust the degree of feature aggregation. First, we learn the local features of joint through SGC operations. After transposing the joint and channel dimension, we can use CNN operations to learn the global features of all joints while using \(\Gamma^{*}1\) convolution kernels to extract temporal features, as shown in Figure 4(left), where \(C_{in}\) and \(C_{out}\) represent the number of input and output channels, respectively, and N represents the total number of body joints.  
To further improve the efficiency of the model and reduce the computational cost, an advanced separable convolution architecture [33] is applied, as shown in Figure 3b,c. This architecture replaces the standard TC by stacking depth-wise convolution and point-wise convolution layer in order, and introduces residual connections to reduce the difficulty of model training. Additionally, we have introduced a visualization chart (details can be seen in Appendix B) to illustrate the step-by-step transformation of skeleton data through different network layers.  
Separable convolution operation consists of two parts: depth-wise convolution (D_Conv) and point-wise convolution (P_Conv). D_Conv is to perform separate group convolution on each input channel. P_Conv employs \(1\times 1\) convolution kernels to combine the outputs of all channels. A visual comparison of standard and separable convolution is illustrated in Figure 5. Assuming that the dimensions of the input features are \(D_{f}\times D_{f}\), the kernel dimensions are \(D_{k}\times D_{k}\), and the counts of input and output channels are \(C_{in}\) and \(C_{out}\), respectively, the computational expense of standard convolution is calculated as \(D_{k}\times D_{k}\times C_{in}\times C_{out}\times D_{f}\times D_{f}\), as depicted in Figure 5a. In contrast, for  
Figure 4: Hierarchical co-occurrence module (**left**) and efficient co-occurrence temporal convolution layers (**right**).  
the separable convolution represented in Figure 5b,c, the computational requirement is represented as \(D_{k}\times D_{k}\times C_{in}\times D_{f}\times D_{f}+D_{k}\times D_{k}\times C _{out}\times D_{f}\times D_{f}\). It can be seen that replacing standard convolution with separable convolution layers can effectively reduce the computational cost of the model.
## 4 Experiments and Results
### Model Description and Parameter Settings  
For our baseline models, we employed pre-trained transformer-based language models, namely BERT and RoBERTa. Specifically, we used the \"hfl/chinese-bert-wwm\" and \"hfl/chinese-RoBERTa-wwm-ext\" [38] variants, which are widely used in natural language processing tasks due to their strong performance on various benchmarks. We extensively experimented with the parameter settings to determine the optimal configuration. The experimental parameters are shown in Table 5:  
Furthermore, we explored various combinations of loss functions. Ultimately, we found that augmenting the cross-entropy loss with three inconsistency loss functions, each scaled by a factor of 0.1, yielded the most favorable experimental outcomes.
## 4 Materials and Methods
### Cell Culture
#### 4.1.3 RNA-Seq  
The RNA-seq protocol was performed as described previously [15; 26]. Briefly, total RNA was extracted from cultured cells using the E.Z.N.A. Total RNA Kit I. RNA clean-up was performed (if required) using ethanol precipitation. Subsequent Qubit results determined a RNA integrity number (RIN) score > 9.5 for all samples. Complementary DNA (cDNA) library preparation was performed with polyA selection using Illumina HiSeq, 2 \(\times\) 150 base pair (bp) configuration, single index, per lane. \(\sim\)350M raw paired-end reads per lane. Library preparation and sequencing were performed by GeneWiz (Leipzig, Germany). Raw data quality was evaluated with FastQC. Sequence reads were trimmed using Trimmomatic v.0.36 and subsequently mapped to the Homo sapiens GRCh38 reference genome using the STAR aligner v.2.5.2b. Unique gene hit counts were calculated by using featureCounts from the Subread package v.1.5.2. Downstream differential expression analysis was performed using DESeq2 [51]. \(p\)-values and log2-fold changes were generated using the Wald test. Significant differentially expressed genes (DEGs) were called genes with an adjusted \(p\)-value (\(p\)-adj) < 0.05 (Supplementary Tables S1-S4). Initial stringent gene ontology (GO) analysis was performed using GeneSCF v1.1-p2 to generate GO graphs, where a Fisher Exact test was used to determine \(p\)-values (Supplementary Figure S5). Principal component analysis and read count distribution analysis were performed by GeneWiz (Supplementary Figure S2). Volcano plots of differentially expressed genes were generated using GraphPad Prism, with log2 FC on the x-axis and an adjusted \(p\)-value on the y-axis (Figures 2 and 3). Significant differentially expressed genes (DEGs) were determined as any genes from the DESeq2 workflow that had an adjusted \(p\)-value (\(p\)-adj) < 0.05. Nested comparisons for multiple groups were completed by cross-referencing lists of DEGs from pairwise comparisons to generate secondary lists of either common or specific DEGs. Raw data from the RNA-seq experiment is displayed as mean TPM (+/ - SEM) to best visualise expression changes between several groups. Statistical comparisons between individual groups used DESeq2 analysis of normalised counts. The bulk RNA-seq data presented in this study are deposited in the Gene Expression Omnibus (GEO) repository, accession number GSE251925.