esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Instrument  
Data collection was via a self-administered questionnaire with two major parts: firstly, respondents' demographic profile which includes, gender, profession, nationality, working experience, and marital status. The socio-demographic characteristics questionnaire was prepared on an ad hoc basis for this study. Secondly, the measurement instrument used in this research was the MBI-HSS, adapted from the Maslach Burnout Inventory-Health Services Survey Manual [1], consisting of 22 items improvised for human service. Using the MBI-HSS in this study means that each item is an affirmation of healthcare professionals' feelings and attitudes towards their work and their patients. The instrument used a 7-point Likert-scale starting from never (0), few times a year or less (1), once a month or less (2), a few times per month (3), once per week (4), a few times per week (5), to every day (6). From the manual, MBI-HSS comprised of three sub-dimensions: (a) Emotional Exhaustion (EE) with nine items (1, 2, 3, 5, 6, 8, 13, 14, and 16), (b) Depersonalization (DP) with five items (5, 10, 11, 15, and 22), and lastly, Personal Accomplishment (PA) dimension, encompassed of eight items (4, 7, 9, 12, 17, 18, 19, and 21).
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 3 Modifications of Information Criteria for the Case of Hankel Noise
### Red Noise  
In this section, we examine a more complicated case of red noise. Since there is no effective implementation of the MGN method for red noise with unknown autoregression parameters, we will not consider it in this section.  
In this study, we consider the red noise process in the form \(\xi_{n+1}=\phi\xi_{n}+\sigma\epsilon_{n}\), where \(\epsilon_{n}\sim N(0,1)\) are independent between themselves and with \(\xi_{n}\), \(0\leq\phi<1\)  
Figure 10: Example ‘log’: Dependence of relative RMSE on white noise level, TRMAT and MGN.  
Figure 9: Example ‘log’: Dependence of rank estimation on white noise level, MGN.  
\(\xi_{n}\sim N(0,\sigma^{2}/(1-\varphi^{2}))\). Accordingly, when calculating the baseline RMSE, it is necessary to normalize it by dividing by \(\sqrt{1-\varphi^{2}}\) in order to obtain 1 for the maximum relative RMSE.
## 2 Material and Methods
### Immunohistochemical (IHC) and Immunofluorescence (IFC) Staining Assays  
Immunohistochemical (IHC) analyses were performed on formalin-fixed paraffin-embedded (FFPE) sections from our PCa cohort consisting of patients with different tumorgrades (normal: Gleason score (GS) \(\leq\) 5; low: GS = 6-7; high: GS \(\geq\) 8) and therapy response. The study was approved by the Taipei Medical University Institutional Review Board (approval number: N202101071) and compliant with recommendations from the Declaration of Helsinki for biomedical research involving human subjects. Samples were probed with antibodies against HSD17B2, HSD17B3, SHBG, SRD5A1, KLF4, OCT4A, MRP1, MDR1, ALDH1, and GAPDH at 1:200 dilution following standard IHC protocol. Protein expression was scored by two independent pathologists using the quick-score (Q-score) formula Q = I \(\times\) P, where I is staining intensity (0 (no staining), 1+ (weak), 2+ (moderate), and 3+ (strong)), and P represents percentage of stained cells. Maximum Q-score = 300. For immunofluorescence (IFC) staining, WT, sgSRD5A1, or SHBG_OE PCa cells or tumorspheres derived from the PCa cells were plated onto poly-\(L\)-lysine-coated glass cover-slips, fixed with 4% paraformaldehyde, washed carefully with cold PBS thrice, permeabilized with 0.1% Triton X-100/PBS solution for 10 min, and then incubated with primary antibodies, followed by Cy5-labeled goat anti-mouse Alexa Fluor488 secondary antibodies (#R37120, Thermo Fisher Scientific Inc.) for 1 h. DAPI (4\({}^{\prime}\),6-diamidino-2-phenylindole; #D1306, Molecular Probes, Thermo Fisher Scientific Inc.) was used for nuclear staining. For cell visualization and imaging, the Nikon E800 fluorescent microscope (Nikon Instruments Inc., Melville, NY, USA) was used.
# Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification
## 3 Our Approach
### Loss Function  
We use Softmax cross-entropy loss and batch triplet loss as a loss function for our work. On the one hand, these two loss functions are used for a fair comparison with the baseline. On the other hand, because these two loss functions are proved to be our work is very suitable. We randomly select P identity samples and randomly select K sequences (each sequence contains N frames) from each identity sample to form a batch. Therefore, a batch contains P\(\times\) K sequences. The overall loss function can be described as:  
\[\text{L}=L_{softmax}+L_{triplet}, \tag{7}\]  
where \(L_{softmax}\) and \(L_{triplet}\) refer to the cross-entropy loss and batch triplet loss, respectively.  
The cross-entropy loss function encourages the network to classify the P\(\times\) K sequences to the correct identities. The cross-entropy loss function is defined as follows:  
\[\text{L}_{softmax}=-\frac{1}{B}\sum_{i=1}^{B}p_{i}\log q_{i}, \tag{8}\]  
where \(p_{i}\) and \(q_{i}\) are the groundtruth identity and the prediction of sample \(i\). \(B\) represents all sequences in a batch.  
Batch triplet loss is generally used to reduce the intra-class distance between each sequence and to increase the inter-class distance. The training instances contain an anchor, a positive instance, and a negative instance. The positive instance belongs to the same class as the anchor, and the negative instance belongs to a different class than the anchor. Let \(\left\{f_{I_{A}},f_{I_{P}},f_{I_{N}}\right\}\) be the video-level descriptors of three different sequences, where \(I_{A}\), \(I_{P}\), and \(I_{N}\) are the anchor, positive, and negative examples, respectively. The triplet loss function is defined as:  
\[L_{triplet}=\sum_{i=1}^{B}\left[m+D\big{(}f_{I_{A}},f_{I_{P}}\big{)}-D\big{(}f_ {I_{A}},f_{I_{N}}\big{)}\right]_{+}, \tag{9}\]where \(m\) is the margin between positive and negative samples, and \(\text{D}(i,j)\) indicates the distance function between two video-level descriptors \(i,j\). \(B\) represents all sequences in a batch, and \(I\) represents the \(I^{th}\) sequence in a batch.  
The Softmax cross-entropy loss function follows the fully connected (FC) layer for probabilities obtained for the identities. The batch triplet loss is applied to the video-level descriptors to backpropagate the gradients.
# Non-Newtonian Behaviors of Crumb Rubber-Modified Bituminous Binders
## 3 Results and Discussion
### Complex Viscosity as a Function of Temperature  
The viscosity of bituminous binders is highly temperature-dependent. Generally, there are two common equations to evaluate the viscosity-temperature characteristics of binders, namely the William-Landel-Ferry (WLF) function based on the free volume theory and the Arrhenius-type equation based on the absolute theory of reaction rate [36]. These two equations correspond to two temperature regions. At temperatures well above the glass transition temperature \(T_{g}\), i.e., \(T_{g}+100\)\({}^{\circ}\)C or \(\sim\)1.3\(T_{g}\), the viscosity follows the Arrhenius equation:  
\[\eta^{*}=Ae^{\frac{E_{a}}{kT}} \tag{4}\]  
where \(A\) is a pre-exponential parameter; R is the universal gas constant (8.314 J/(mol\(\cdot\)K)); and \(E_{a}\) is the flow activation energy (J/mol). \(T\) is temperature (K). At lower temperatures ranging from \(T_{g}\) to  
Figure 6: Yield stress calculation of different binders at 50 \({}^{\circ}\)C (**a**) entire curve; (**b**) partial curve.  
\(T_{g}+100\)\({}^{\circ}\)C, the WLF function should be used instead. A strictly equivalent form to the WLF function is described by the Vogel-Fulcher-Tammann (VFT) expression:  
\[\eta^{*}=A_{0}e^{\frac{E_{0}}{R(T-T_{0})}} \tag{5}\]  
where \(A_{0}\), \(E_{0}\), and \(T_{0}\) are material constants. Apart from these two common equations, a number of empirical expressions have been proposed to represent the viscosity-temperature dependence based on available experimental data. The proposed equations of viscosity-temperature characteristics have the following general form [8; 37]:  
\[\mathrm{ln}\eta^{*}=A+\frac{B}{T+C}+a\mathrm{ln}T+bT+cT^{2}+\frac{D}{T^{2}} \tag{6}\]  
where \(A\), \(B\), \(C\), \(D\), \(a\), \(b\), \(c\) are fitting constants. It is possible to add more power-law terms of \(T\) into the above empirical equation.  
Figure 7 presents the plot of logarithmic viscosity as a function of the reciprocal of temperature ranging from 283.15 K to 323.15 K at two extreme shear rates. At a low shear rate of 0.01 rad/s, the viscosity-temperature curve can be fitted by a linear line, which is an Arrhenius-type equation. The reason that Arrhenius equation requires elevated-temperature condition is to guarantee the material is in the Newtonian state. Based on the time-temperature superposition principle as well as the test results in this study, bituminous binders can be regarded as Newtonian-like fluids at a low shear rate of 0.01 rad/s, so that Arrhenius equation is applicable. However, this observation is invalid at a high shear rate of 100 rad/s, under which bituminous binders exhibit apparent non-Newtonian behaviors. The viscosity-temperature curve at the higher shear rate of 100 rad/s can be fitted by a second-order polynomial equation with enough high correlation degree instead of a linear one. Based on the above findings, it is not reasonable to fit the viscosity-temperature curve with only an Arrhenius equation over a wide range of temperatures or shear rates.  
Figure 7: _Cont._
## 3 Finite Element Simulation and Verification
### Finite Element Model Verification
#### 3.2.3 Sand Deformation  
The sand deformation of different cases obtained from the FEM is presented in Figure 13. Comparing Figures 10 and 13, it can be found that the differences (\(\delta_{\mathrm{v}}\)) between the FEM results (\(v_{\mathrm{FEM}}\)) and test results (\(v_{\mathrm{TEST}}\)) of different cases are basically within 10 mm. When the sand surface uplifts, the differences in \(w\) (\(\delta_{\mathrm{w}}\)) between the FEM results (\(w_{\mathrm{FEM}}\)) and test results (\(w_{\mathrm{TEST}}\)) of different cases are less than 0.1 m. When the sand surface subsides, \(\delta_{\mathrm{w}}\) is less than 0.2 m. \(\delta_{\mathrm{w}}\) in the case of sand subsidence is larger than that in the case of sand uplift. This is probably due to the fact that the sand in the test can subside more readily as discrete particles, while the sand in the FEM has better continuity.  
The sand strain contour plots of different cases obtained from the FEM are illustrated in Figure 14. It can be found that there is a continuous plastic zone in the sand after applying displacement and slip surfaces finally appear, indicating a general shear failure mode. The numerical failure mode is in accordance with the experimental sand deformation described in Section 2.5.3. Comparing Figures 10 and 14, it can be found that when the sand surface uplifts, the differences (\(\delta_{\mathrm{\theta}}\)) between the horizontal angle of the slip surface (\(\theta\)) obtained from the FEM (\(\theta_{\mathrm{FEM}}\)) and tests (\(\theta_{\mathrm{TEST}}\)) in different cases are less than 10\({}^{\circ}\). However, when the sand surface subsides, \(\delta_{\mathrm{\theta}}\) of different cases is between 20\({}^{\circ}\) and 24\({}^{\circ}\). This may be because  
Figure 13: Sand deformation diagram in FEM: (**a**) H21-Push; (**b**) H21-Pull; (**c**) H31.5-Push; (**d**) H31.5-Pull; (**e**) H63-Push; and (**f**) H63-Pull. (The yellow and purple grid represents sand and approach slab, respectively).  
the sand moisture content in the test and the FEM is different. In general, the FEM can be used to predict the sand deformation obtained from the test.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 8 Performance Evaluations
### Impact of Cache Size on Popular Messages  
Another very stressful scenario for OppNets is when the cache size is limited. Then, the data dissemination protocol needs to drop data items, but it might also encounter them again during their lifetime. We performed experiments where we decrease the available cache size by keeping the network traffic stable. Of course, it would have been more realistic to keep the cache size stable and increase the traffic, but this resulted in unacceptable simulation times where a single simulation ran for several months, due to our relatively large reference scenario. The results are compared to those for increasing number of nodes in Figure 8 (right).  
It can be seen that Epidemic quickly degrades its delivery ratio and delivery delay. Especially when the cache size becomes less than 500 KB (50 messages), we observe a tipping point, when Keetchi becomes better than Epidemic in all used metrics. In terms of overhead, they are more similar, as Epidemic simply  
Figure 8: Comparison between Keetchi and Epidemic for popular messages, with increasing number of nodes (**left**) and decreasing cache sizes (**right**). Mean and 99% confidence intervals of: (**a**) delivery ratio, popular messages with more network nodes; (**b**) delivery ratio, popular messages with decreasing cache size; (**c**) delivery delay, popular messages with more network nodes; (**d**) delivery delay for popular messages with decreasing cache size; (**e**) network overhead, popular messages with more network nodes; and (**f**) network overhead, popular messages with decreasing cache size.  
does not have space to store and/or exchange many messages. We believe this trend will be similar or even larger with increasing traffic in the network and the same cache size.  
_Take-home message._ With decreasing cache size, Keetchi performs better than Epidemic in all explored metrics.
## 3 Results
### Detection of Virulence Genes  
A multiplex polymerase chain reaction (PCR) was used to detect virulence genes in four different species: _E. coli_, _K. pneumoniae_, _P. aeruginosa_, and _S. enterica_. Twelve genes,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**AST**} & \multirow{2}{*}{\(\mathbf{E.coli}\) (\(\mathbf{\approx 10}\))} & \multicolumn{3}{c}{_K. pneumoniae_} & \multicolumn{3}{c}{_S. enterica_} & \multicolumn{3}{c}{_P. aeruginosa_} \\  & & & **(\(\mathbf{\approx 10}\))** & & & **(\(\mathbf{\approx 12}\))** &  
three for each species, were tested in this study (Table 1). The _FimH_ (E3) gene was positive at 465 bp for all of the _E. coli_ isolates that were tested. However, the _Sfa/focDE_ (E1) and _papC_ (E2) genes could not be found (Figure 3). In all _K. pneumoniae_ isolates, the _MrkD_ (K3) gene was detected at 340 bp. However, the other two genes, _rmpA_ (_K1_) and _ybtS_ (_K2_), were not found in any _K. pneumoniae_ isolates. For all tested _P. aeruginosa_ isolates, the _pcItH_ (P2) and _toxA_ (P3) genes were identified at 307 bp and 433 bp, respectively. The _exoS_ (P1) gene was not detected in any of the tested _P. aeruginosa_ isolates (Figure 4). The genes _agfA_ (S1), misL (S2), and _invA_ (S3), which are specific to _S. enterica_, were not found in any of the isolates examined in this investigation. This indicates that not all of these genes contribute to virulence or are necessary for the pathogenicity of _S. enterica_. The results of this study are consistent with those of other studies that have demonstrated _S. enterica_ can produce genes associated with virulence without the assistance of _agfA_, _misL_, or _invA_. It is possible that we could have obtained positive results if we had tested more _Salmonella_-specific virulence genes. However, due to financial limitations, we were only able to test three genes per species of bacteria under investigation.  
Figure 4: Agarose gel electrophoresis visualized using the Gel documentation system.3000 bp ladder (M), _exoS_ (P1) non-specific amplicon, _pcIt_ (P2)-specific amplicon, _toxA_ (P3)-specific amplicon, _rmpA_ (K1), _ybtS_ (K2) non-specific amplicon, _mrkD_ (K3)-specific amplicon, and negative control (Neg).  
Figure 3: Agarose gel electrophoresis visualized using a gel documentation system. The gel contained a 3000 bp ladder (M), _agfA_ (S1) non-specific amplicon, _misL_ (S2) non-specific amplicon, _invA_ (S3), _sfa/focDE_ (E1), _papC_ (E2) non-specific amplicon, and _fimH_ (E3)-specific amplicon.
## Appendix A
### Privacy and Security Concerns  
In this section, participants were asked to rate their beliefs about the increase in the security level from potential threats due to BSs. Since BSs are relatively new in Greek society, while their capabilities to provide privacy and security protection are not well-known, it is important to understand which techniques users believe utilize their information in a harmful way in cases of privacy loss or security incidents. These representations possibly affect their intention to use them as well.  
Many of them (10%) support that DNA and eye iris analysis increase security more than the others, while 9% of them consider that fingerprinting is also one of the more secure techniques. On the contrary, 8% and 7% of them, respectively, declare that typing analysis and signature recognition and pacing analysis are not very secure. In particular, 39% of the 18-22 age group considers that using fingerprinting gives them the confidence of security increasing against potential threats, unlike other biometric techniques. It is worth noting that among other demographics, only the age category shows such variations.  
Furthermore, the kind of service is also important to be identified regarding the level of protection offered, considering that BS adoption in Greece is promoted in several public services. Participants were also asked to report \"Which services/organizations they believe that use powerful security and identity protection methods\". In fact, the majority reported that public spaces do not (95%). Respondents believe (74%) that mobile operators, health services (75%) and airlines (66%) also do not provide secure solutions. Thus, 87% of them support that bank services use powerful methods. Figure 22 presents the respective results.  
Employment status seems to be important among those participants who support that the mobile industry does not offer powerful security methods. In particular, civil (82%) and private servants (76%) are more confident regarding this. However, regardless of the employment category, all of them support this point of view, as presented in Figure 23.
# Interlocking Settlement Induced by Widening Subgrade of Railway Line
## 4 Conclusions  
In this study, the long-term settlement behavior for a widened embankment using RSR was analyzed, and its performance was evaluated through test construction and field measurements. The conclusions of this study are as follows:  
1. When widening an embankment using RSR, interlocking settlement occurs within a range of 10 mm, which is the Korean track maintenance standard. Therefore, a widened embankment using RSR can secure train operation stability on existing tracks, even in ground conditions including shallow soft layers.
2. The interlocking settlement is hardly affected by the wall load compared with the ground and surface of the RSR. Owing to the wall installation location and structural characteristics of the RSR, in which the backfill shares the wall load, the farther from the center of gravity of the RSR, the less the influence of the wall load.
3. After wall construction, time-dependent settlement increased in the original ground and surface settlement, but there was little change in the interlocking settlement owing to the difference in the degree of consolidation in the lower bottom ground. Interlocking settlement mainly occurred in the early stage of the widening of the embankment (backfill construction and stabilization) and converged early. Therefore, interlocking settlements require intensive management during the early stages of construction.  
The operational stability of existing tracks and the importance of the early management of interlocking settlements during the widening of an embankment using RSR were confirmed in this study. However, this study is limited since it is a field study of a single case. The applicability of this study can be expanded in subsequent studies (e.g., conducting numerical analyses and field tests in other conditions).  
**Author Contributions:** Data curation and writing--original draft preparation, U.K.; supervision, D.S.K.; funding acquisition, Y.K.P.; writing--review and editing D.S.K. and Y.K.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by a grant from the R&D Program (Development and Establishment of BIM-based Integrated Operation System for Life Cycle Management of Railroad Infrastructures, BI22001) of the Korea Railroad Research Institute.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Architecture
### Use Case  
This section introduces our use case in order to illustrate how to leverage SDN technology in IIoT environments, focusing on the security and system reliability aspects. On one hand, the data generated by the IIoT have great commercial value, so secure management of the IIoT devices will be a great challenge to preventing data theft. On the other hand, security is important because humans and production machines work in strict collaboration, and an intrusion may cause serious problems. According to [PERSON] et al. [56], data management in Industry 4.0 is and will be the key enabler for all those applications that want to take advantage of IIoT devices. As stated before, SDN technology can assist with having dynamic networks in the IIoT environment that face a variety of devices, thus facilitating the extraction of data. Our use case is focused on cases that necessitate a combination of multiple complex accomplishments in terms of data requirements. For example, in a chemical plant, the chemical leakage must be informed in predefined times [57], which means that a decision must be made under specific time latencies in order to guarantee human safety.  
In general, data generated by IIoT devices about parameters and abnormal events are processed for decision making, thereby improving production and predicting maintenance and failures for the industrial equipment. Data usually come from sensor devices in small volumes, typically including sensor measurements of various types. Therefore, in IIoT environments, we cannot have high data latency or even data loss. Thus, potential attacks on the SDN network may cause data delivery time increments. Our use case's purpose is to offer an overview of what may happen if an SDN-enabled IIoT network is under attack, highlighting how the network performance degrades. In addition, this work would like to make clear the importance of having secure SDN networks in IIoT environments.  
In particular, we devise an Industry 4.0 application aligned with one of the 5G and factories of the future use cases [58], such as video-driven interaction between collaborative robots. Specifically, we focus on a scenario related to managing Autonomous Driving Vehicles (AGVs) that are connected to the 5G infrastructure to perform decentralized decision making, such as object recognition tasks. AGVs are commonly used for the internal movement of goods through an interconnected external logistics system to dynamically manage incoming and outgoing goods flows from the factories. Precisely for this reason, they are considered systems that need a high reliability rate (mostly 99.999%) and a cycle  
Figure 1: SDN-enabled architecture for the Industrial Internet of Things.  
time of less than 2 ms [59]. In particular, industrial AGVs are usually equipped with several sensors for transportation goods to detect obstacles and estimate their positions and orientations. All this will require stable wireless connectivity and resilient computing power at the cloud or edge with the use of wireless and 5G technologies.