esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 8 Trade-Offs Made by the Protocols  
The trade-offs made by the selected routing protocols are given in Table 4 3D-SM has increased throughput, however, at the cost of increased packet drops. The transmission pattern here is direct; after sensing the data, nodes wait for the MS or CN to send them data. In this way, end-to-end delay is minimized by introducing an efficient tour of the MS, as well as CNs. The presence of the CN is contributing to the NLT maximization; however, at the same time, this is the reason for the TL in 3D-SM. We applied a random uniform model for calculating the packet drops, where the packet drops are directly proportional to the throughput; the higher the throughput, the higher the packet drop ratio.  
DDRP minimizes the TL and path loss at the cost of reduced throughput, increased end-to-end delay and reduced NLT. DDRP has long end-to-end delay due to the random trajectories of the MS. Furthermore, there is no restriction that the MS will gather data from all nodes in a tour.  
AUV-PN is a cluster-based routing protocol in which the PN receives data from the MN and sends them to the AUV. Its NLT is less as compared to the rest of the schemes, because the nodes consume surplus energy in the clustering process. AUV-PN minimizes delay and prolongs NLT at the cost of TL and path loss.  
Figure 24: Path loss of fixed and dynamic routing.  
Figure 25: End-to-end delay of fixed and dynamic routing.
## 3 Results and Discussion
### Anticrorosion Effect of the Employed Ionic Liquids  
In order to investigate the anti-corrosion impact of the employed ionic liquids, potentiodynamic polarization experiments were carried out. Figure 6 shows the potentiodynamic polarization curves of mild steel electrodes in 3.5% NaCl solutions with and without different concentrations of [OH-EMIm]Cl. It is clearly seen that both anodic and cathodic branches of the polarization curves wereinfluenced in the presence of the employed ionic liquid. The current densities shifted to lower values and the corrosion current significantly decreased, and also the corrosion potential shifted to the anodic direction. At a concentration of 10 ppm, the shift in the corrosion potential increased and the corrosion current exhibited a further decrease. This signifies the corrosion inhibition influence of the ionic liquid [OH-EMIm]Cl. Increasing the ionic liquid concentration above 10 ppm didnot lead to a significant change in the polarization curves (the curves are not shown). The inhibiting effect couldbe attributable to the adsorption of the ionic liquid species, cations, onthe surface of mild steel forming a protective layer against the corrosion attack. Once the protective layer of the adsorbed ionic liquid is formed, a further increase in the ionic liquid content would not lead to a subsequent increase in the inhibiting influence.  
The potentiodynamic polarization curves of mild steel in 3.5% NaCl solutions without and with two different concentrations (5 and 10 ppm) of the ionic liquid [EMIm]Cl are shown in Figure 7. As in the case of [OH-EMIm]Cl, the addition of [EMIm]Cl to the employed electrolyte led to a significant inhibiting effect of the corrosion of mild steel as the corrosion current decreased. No further improvement in the inhibiting influence of [EMIm]Cl upon increasing the concentration above 10 ppm occurred. It is obvious that the corrosion inhibition influence of the ionic liquid [OH-EMIm]Clwas higher than that of the ionic liquid [EMIm]Cl. This indicates the synergistic inhibition effect due to the incorporation of the hydroxyl group into the side chain of the imidazolium cation.The presence of the hydroxyl group in the imidazolium cation enhanced the adsorption capability of the cation of the employed ionic liquid due to the existence of two heteroatoms, O and N.  
Figure 5: The inhibition efficiency of the bacterial growth in presence of different concentrations of [EMIm]Cl.
# Research on an Evaluation Method of Snowdrift Hazard for Railway Subgrades
## 2 Methods  
### Factors  
Whether snowdrift-related disasters occur in railway subgrade is determined by many factors, which have certain characteristics, and many factors are not related, which makes the evaluation of the risk of wind and snow-blowing disaster complex and uncertain. Therefore, in the process of railway subgrade wind and snow-blowing disaster evaluation, it is a particularly important work to accurately identify the risk factors and establish the corresponding risk evaluation indicators.  
1. Environment  
Site environmental conditions [4] are the basis for evaluating the severity of snowdrift disaster. Therefore, considering the characteristics of the site environment of the railway subgrade snowdrift disaster, it is subdivided into two categories: the topographic slope and vegetation cover conditions. The topographic and landform features are the comprehensive embodiment of site geological conditions, topographic fluctuation, and other characteristics, and are important factors affecting snowdrift disaster; vegetation coverage is the distribution of vegetation in the study area. If the distribution is rich, the harm of snowdrift can be effectively weakened. Generally speaking, the flatter the area and the lower the amount of vegetation coverage, the more prone the area is to snowdrift.  
2. Snowfield  
Snowfield conditions [8] are the main factor affecting snowdrift disasters on railway lines. Because the snowfield conditions provide the snow source for snowdrift on the line, it is very necessary to fully grasp the regional snowfield conditions to evaluate the risk of snowdrift disaster in railway subgrade. Meanwhile, the average snow thickness, the average annual snow amount, and the duration of heavy snow in the study area reflect the environmental characteristics related to snow in the research area, which is of great significance for the research of snowdrift disaster in railway subgrade.  
3. Windfield  
Windfield conditions [9] are the dynamic conditions of snowdrift. They are among the important factors in snowdrift disaster. At present, research on the windfield in railway line areas is mainly focused on the average wind speed, maximum wind speed, wind direction,and wind duration in the area. It is generally believed that when the wind speed is high enough, the phenomenon of snowdrift will occur. Then, combined with the structural form of the roadbed, it is necessary to judge whether there will be a snowdrift disaster that will affect the operation of the line. On the other hand, the direction of the wind and the roadbed also have an obvious impact on snowdrift disasters on the line.  
4. Subgrade design parameters  
The design parameters of subgrade are an important factor affecting snowdrift disasters relating to railway subgrade. Cuttings are more likely to suffer from snowdrift disaster than embankments, and low embankments and deep cuttings are more likely to suffer from snowdrift disasters.
## 2 Layout and Characteristics of the Studied Community Microgrid System  
According to the real configuration in Figure 1, the layout of the studied C-\(u\)grid is illustrated in Figure 2. It consists of following major elements: (1) an utility grid that is in charge of grid-connected support and provides general alternating current (AC) electric power to each home user; (2) when daily solar insolation is sufficient, photovoltaic (PV) systems act as major DERs on site that supply electric power to each home user as well as ESS charging; (3) battery energy storage (BES) units are used to store remaining electricity from PV systems, and release these electricity to home users at nighttime; and (4) electric load units that are formed by an independent or an aggregated home users. In this study, the C-\(u\)grid is connected to a single-phase 220 V utility grid and normally operates on a grid-connected mode. In this study, the C-\(u\)grid is connected to a single-phase 220 V utility grid and normally operates in grid-connected mode. According to the distinction between the daytime and nighttime, C-\(u\)grid includes following operation states:  
* Daytime with sufficient solar insolation--almost all the home users' electricity is supplied by PV systems in the C-\(u\)grid, and the utility grid may only contribute a little or does not provide any electricity; it depends on the level of the load demand. Meanwhile, PV systems may also supply power to BES units for charging.
* Daytime with insufficient solar insolation--all home users' electricity is mainly supplied by the utility grid and a little from PV systems in the C-\(u\)grid. Meanwhile, BES units may charge with limited PV power. Once the PV power is all consumed by home users, the BES unit may stop charging.
* Nighttime with sufficient BES electricity--all home users' electricity is first supplied by BESs until their power exhausted. Then, the utility grid may take the place of BESs to continue to supply power to home users.
* Nighttime without BES electricity--all home users' electricity is only supplied by the utility grid.  
Later, a proposed control strategy is developed under these operational principles.
## 4 Experiment Verification
### Relation Completion Task  
In this paper, the evaluation indicators of relation completion tasks are also used, including MRR, HITS@1, HITS@3 and HITS@10.  
It can be seen from the results in Table 3 that compared with the original representation learning model, the improved model considering the entity type information has a significant improvement in each evaluation index, and the conclusion is similar to the conclusion of the entity completion task. It should be noted that because the built data set does not indicate complex relation patterns such as symmetric/antisymmetric relation and synthetic relation, the RotatE model has excellent performance in relation completion, but its advantages are not fully manifested.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model Name** & **Batch Size** & \begin{tabular}{l} **Negative** \\ **Sample Size** \\ \end{tabular} & \begin{tabular}{l} **Hidden** \\ **Layers** \\ \end{tabular} & \
\begin{tabular}{l} **Learning** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{l} **Maximum** \\ **Iterations** \\ \end{tabular} \\ \hline RotatE & 1024 & 256 & 1000 & 0.0001 & 20,000 \\ DistMult & 1024 & 256 & 1000 & 0.001 & 20,000 \\ ComplEx & 1024 & 256 & 2000 & 0.001 & 20,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters setting of each embedded learning model.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model Name** & **MRR** & **HITS@1** & **HITS@3** & **HITS@10** \\ \hline RotatE (Before improvement) & 0.625 & 0.481 & 0.689 & 0.945 \\ RotatE (after improvement) & **0.826** & **0.796** & **0.850** & **0.951** \\ DistMult (Before improvement) & 0.618 & 0.488 & 0.676 & 0.835 \\ DistMult (after improvement) & **0.859** & **0.854** & **0.859** & **0.872** \\ ComplEx (Before improvement) & 0.708 & 0.612 & 0.788 & 0.865 \\ ComplEx (after improvement) & **0.795** & **0.773** & **0.808** & **0.873** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Entity completion task evaluation indicators.  
In the table above, the bolded value indicates that its model performs better in this index.
## 3 Experiment Results and Discussion
### Seismic Performance Analysis
#### Bearing Capacity, Displacement, and Ductility  
The bearing capacity, displacement, and ductility coefficients of all specimens at different loading stages, obtained from the skeleton curves in Figure 12, are listed in Table 4. To evaluate the ductility of the specimens, we defined the ratio of the ultimate displacement \(\Delta_{u}\) to the yield displacement \(\Delta_{y}\) as the ductility coefficient \(\mu\), calculated using Equation (1), as follows:  
\[\mu=\frac{\Delta_{u}}{\Delta_{y}} \tag{1}\]Stiffness degradation  
The secant stiffness \(K_{i}\) was used to characterize the stiffness degradation of the specimens during the loading process, which was calculated using Equation (2):  
\[K_{i}=\frac{|+F_{i}|+|-F_{i}|}{|+\Delta_{i}|+|-\Delta_{i}|} \tag{2}\]  
where \(F_{i}\) represents the peak load of the first displacement cycle in the \(i\)-th loading step, and \(\Delta_{i}\) represents the corresponding displacement of the \(F_{i}\); \(+\) and \(-\) represent the values obtained in the positive and negative directions, respectively.  
The stiffness degradation curves of all testing specimens are shown in Figure 14; each specimen's stiffness degradation curve was relatively symmetrical, with minor differences caused by construction deviation and material anisotropy [68]. The stiffness of all specimens decreased rapidly in the early stage with increased lateral displacement and then gradually flattened out.
# Interdependent Transcription of a Natural Sense/Antisense Transcripts Pair (_SLC34A1_/Pfn3)
## 4 Materials and Methods
PCR was performed using GoTaq(r) Long PCR 2 \(\times\) Master Mix with the following conditions: denaturation at 94 \({}^{\circ}\)C for 30 s; annealing 59 \({}^{\circ}\)C for 30 s; extension at 65 \({}^{\circ}\)C for 4 min (primer sequences, Table S6). PCR products were separated on a 1% agarose gel and clones which showed mono- or biallelic HDR cassette insertion were confirmed by sequencing (GATC Biotech). \({}^{32}\)P uptake measurements: HEK293 cells were treated with dexamethasone (100 nM) for 5 days prior to uptake measurements, with replacement of the drug containing growth medium every 48 h. On the day of the 32P uptake experiment, the number of cells was counted from one well for each treatment condition using a Cellometer Auto T4 Bright Field Cell Counter (Nexcelom, Lawrence, MA, USA). For uptake, the cells were washed gently three times with 500 \(\mu\)L of warm phosphate-free Kreb's buffer (140 mM NaCl, 5.4 mM KCl, 1.2 mM, MgSO\({}_{4}\), 4 mM glucose, 2 mM CaCl\({}_{2}\), 10 mM HEPES/Tris pH 7.4) and adapted to Kreb's buffer on a thermostat-controlled platform at 37 \({}^{\circ}\)C for 30 min. Cells were then exposed to 1 mM phosphate containing 1 \(\mu\)C/mL \({}^{32}\)P tracer for 5 min, followed by three washes with ice cold Kreb's buffer. Cells were lysed with 250 \(\mu\)L of 0.1% SDS (_v/w_) and \({}^{32}\)P was quantified by scintillation counting (Beckman LS5000). Xenopus oocyte expression: Oocytes were purchased from the European Xenopus Resource Centre (Portsmouth, UK) and defolliculated with collagenase using standard procedures [59]. Oocytes were injected with 50 ng of total RNA isolated from HEK293 cells as described above. Cells were incubated for 3-5 days in modified Barth's solution. Uptake was performed as described using \({}^{32}\)P as a tracer [60]. Statistical analysis: Unpaired student _t_-testing was used to compare pyrosequencing data. For RT-qPCR data, one-way ANOVA followed by Games-Howell Post-Hoc test or Tukey's test for multiple comparisons was performed using GraphPad Prism version 6.00 (GraphPad Software, San Diego, CA, USA). Values of \(p\) < 0.05 were considered as significant, unless otherwise mentioned. Data are presented as mean \(\pm\) standard error of the mean (SEM).
# Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision
###### Abstract
In this method, multiple sets of binocular cameras are used to perform 3D reconstruction and point-cloud registration on laser markers projected on the cross section of a pipe under test so as to achieve rapid on-site measurement of pipe diameter. The following research is completed in this paper: a feature-point-matching algorithm is designed with the aid of affine distance transformation to realize exact matching of feature points; singular value decomposition (SVD) is used to solve the rigid-body transformation; normal-vector constraints are then added; and the Gauss-Newton method is adopted to improve the robustness and accuracy of the measurement algorithm. An experimental system is built to verify the proposed method under laboratory conditions.
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 5 Results and Discussion
### Convergence Time
\[\text{T}=\text{T}_{\text{LF}}-\text{T}_{\text{PD}} \tag{3}\]  
The convergence time of the protocol after fast recovery without any additional flow is shown in Table 1. In this scenario, Fast TCP, MFast TCP, EMFast TCP, TCP New Reno, and SCTP attain a maximum congestion window of size 52, 52, 52, 57, and 56, respectively. EMFast TCP attains its congestion window size to consume available bandwidth in 29% less time compared to the Fast TCP and MFast TCP, 54% less time compared to TCP New Reno, and 52% less time compared to the SCTP. The 29% improvement is due to the CCaFR algorithm whereas 52% and 54% are the cumulated effect of delay-based congestion control algorithm and CCaFR algorithm. Convergence time of the protocol after fast recovery with four additional flows is also shown in Table 1. In this scenario, Fast TCP, MFast TCP, EMFast TCP, TCP New Reno, and SCTP attain a maximum congestion window of size 27, 27, 27, 34, and 32, respectively. EMFast TCP consumes available bandwidth in 55% less time compared to the Fast TCP and MFast TCP, 74% less time compared to TCP New Reno, and 73% less time compared to the SCTP. This is because it quickly re-acquires a small congestion window compared to the large congestion window size of SCTP and TCP New Reno. Therefore, EMFast TCP with CCaFR algorithm improves the performance in terms of congestion window recovery after the fast-recovery phase in long-distance networks. Thus, it is concluded that CCaFR improves the performance of the protocol in a network where there is a high loss rate.
# Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication
## 4 Experimental Results and Discussion
### Discussion  
We still need to put more effects in several aspects compared to the current well-known biometric techniques such as fingerprint identification [42]. For instance, although more and more ECG datasets are available now benefitting from great advancement of data acquisition systems, the amount of data is still not comparable to the fingerprint data. Generally, hundreds or thousands of records of fingerprint can be obtained, which is much higher than the dataset sizes used in this study. Moreover, there are also more diversity induced by different heart health conditions, which brings more challenges to the generalization ability of the identification algorithms [43; 44]. Our proposed method includes random segmentation for data enriching purpose as non-fiducial approach, which effectively avoids complicated and data-dependent signal preprocessing efforts such as QRS peaks detection and segmentation effort. It also removes phase difference among random-chosen signal segments by the auto-correlation approach. However, further consideration for more data representation methods and deep neural network techniques should be considered, to explore the interesting connection between the data representation network topology and the feature learning ability. Our experimental results have also suggested that the cardiac organisms react physiologically to stimulations such as danger and threat, and corresponding ECG signal also changes due to the cardiac defense mechanism (CDR). Therefore, identifying these external stimulation-related ECG patterns in future work is also expected to further improve the user identification performance.
## 4 Case Study: Application of the Modified FMEA-DEA for the RFID System  
### An Overview of the RFID System  
RFID is a wireless technology using the radio frequency electromagnetic (EM) field. The RFID system consists of the tag, reader, interface software, and host. The proper functionality of the RFID system is contingent upon the logical connection and compatibility of all of these components with each other. The components and the functioning of the RFID system can be found in [36; 37; 38] and are illustrated in Figure 4.  
The collection and the identification of failure modes, their causes, and the effects, as well as current control processes, have been performed based on the team members' brainstorming, interviews, and communications with many experts, along with the information retrieved from different websites and studies from the existing literature. The effects of RF radiation on human health were disclosed in [39]; meanwhile, the environmental impacts of the RFID and ICT technologies are discussed in [40; 41; 42]. A failure modes, mechanisms, and effects analysis (FMMEA) was used for testing the environment of qualification of the RFID system [43]; in the same context, the reliability of RFID tags was tested in humid [44] and harsh environmental applications [45] and under thermal storage [46; 47]. Conversely, as stated in [48], faults may arise due to temporary antenna tearing, electromagnetic interferences, and optical inductions; the results showed that most vulnerability was related to implementation. Under the same scope, different errors between readers and RFID tags were mentioned in [49; 50; 51]. FMEA was applied to assess the analog part of the RFID system, presuming faultlessness in the digital segment, as indicated in [52]. Additionally, the FMEA methodology was utilized to scrutinize failure modes within the RFID middleware [53] and to investigate user misconfigurations [54]. Evaluations of RFID device performance within the blood supply chain were conducted, utilizing FMEA to identify departments necessitating reengineering [55]. A review of security concerns in RFID systems was presented in [56]. Furthermore, [57; 58; 59] outlined the issues and challenges encountered with RFID technology.  
Figure 3: FMEA-DEA model.
## 3 DSSS in Electric Field Communication  
Spread spectrum technology involves the use of a specific spreading function. This function expands the spectrum of a signal carrying information to be transmitted. The result is the transformation of the signal into a wideband signal. The signal is then modulated onto a carrier and transmitted through the channel. At the receiving end, a correlation-based process is used to compress the spread spectrum, thus recovering the original baseband signal's bandwidth and enabling information transmission [19]. In the spread spectrum system, the signal transmitted through the channel has a wider bandwidth than the original signal. Spreading is achieved by using pseudorandom codes that are independent of the information, expanding the signal's spectrum significantly beyond that of the original signal. Due to the spread spectrum communication technology expanding the signal spectrum, it possesses strong resistance to narrowband interference and noise. DSSS is a type of spread spectrum technology, and compared to other spread spectrum techniques, it exhibits stronger resistance to interference.
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.2.4 Platform Specific Trends and Corporate Usage  
Table 6 presents a summary of malware occurrences grouped by CPLS. An occurrence is either an in-the-wild reported malware incident or a proof-of-concept (PoC) malware developed by security researchers. The data showcase the diverse range of CPLS exploited as C&C communication channels. CPLS like Dropbox, Google Docs, Google Drive, Outlook, and OneDrive, widely utilized in corporate environments, are particularly appealing to threat actors due to their integral roles in data storage, collaboration, and communication.  
Figure 6: Correlation between number of users and abuse occurrences.  
Our analysis reveals that these platforms have been exploited using a variety of techniques over the years, including botmaster credentials, encoding, cryptography, and compromised accounts. Their popularity in both the public sphere and corporate settings, coupled with their common usage for file storage and sharing, marks them as attractive targets for data theft and ransomware attacks. This emphasizes the need for robust security measures tailored to these commonly targeted platforms.