esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions
[22] found that the MDA and NO levels in the spinal cord tissues of the prenatal + postnatal group were higher than the control group in the prenatal + postnatal group of puppies with a 50 Hz electric field for 24 h during the prenatal and postnatal period. In the postnatal group, there was no significant difference in MDA and NO levels compared to the control group. In this study, similar findings were obtained in terms of MDA level. It was revealed that in rats exposed to a 50 Hz 01 mT magnetic field continuously for 10 days, young rats developed neurotrophic signaling and anti-oxidative enzymatic defense against a possible increase in oxygen radical species application of ELF-MF. On the contrary, aged rats could not develop anti-oxidative enzymatic defense systems against ELF-MF application. GPX1, GR and GST enzyme activities were also significantly increased compared to young people. It was determined that there was no significant change in SOD1, CAT, GPX1 or GST in young rats. In aged rats, there was a strong decrease in glutathione-induced detoxification enzymatic activity, except for SOD, while a significant decrease was observed in CAT activity. The result of the study showed that exposure to ELF-MF is a risk factor for age-related oxidative stress-based nervous system pathology [23]. In this study, while the change in MDA and CAT activities was not significant, the differences between the groups due to the increase in SOD activity in the electric field group were found to be statistically significant. SOD, CAT and GSH activities in the brain tissue of rats exposed acutely to a low-frequency electromagnetic field (60 Hz) for 2 h were found to be significantly lower than the control group. However, when the NO level was compared with the control group, no significant difference was found [24]. In our study, the lowest CAT activity was determined in the EF group, while the lowest SOD activity was detected in the Juglone + EF group. The cell cycle molecular mechanism of judging and protein expression levels by apoptosis were measured by Cyclin D1 blot analysis, bcl-2, Bax, cytochrome C, caspase-9 and caspase-3. SKOV3 cells treated with juglone to investigate their motility abilities were demonstrated to inhibit SKOV3 cell proliferation. It has been determined that juglone has an antitumor effect on SKOV3 cells, causing cell cycle arrest and preventing SKOV3 cell proliferation [25]. In our study, the number of apoptotic cells was determined by caspase-3 and tnf-\(\alpha\), and it was concluded that juglone reduced apoptosis because the number of apoptotic cells in the EF group was higher than in the EF + Juglone group.
## 3 Proposed CNN + DWFs Methodology and Dataset
### Tool Wear State Labeling and Raw Signal Segmentation  
In the International standard ISO 8668-2, regarding the tool-life criteria, the first recommendation is the width of the flank wear land (VB). Nevertheless, due to the lack of necessary measurement equipment, the author failed to obtain this data during the experiment. On the other hand, as the cutting time increases, the tool will inevitably wear out gradually. A total of 25 milling tasks were performed in the experiment. The authors therefore chose six out of the 25 milling tasks at the same interval (the first, fifth, tenth, fifteenth, twentieth and twenty-fifth) and defined that the tool was in different wear states during the six milling tasks. Then, the raw monitoring data of the six milling tasks were libeled as \(\#1,\;\#2,\cdots\;\#6\) respectively.  
The spindle-vibration-acceleration signal for each cutting task is completely collected, with the data of the cutting period being approximately 12 min. The vibration during the idle period is significantly weaker than the cutting period, and there are significant differences in the spectrum. In this paper, the turning point of the effective value of the signal is adopted in order to determine the intersection time of the idle and the milling, and then partition the data of the idle period and the milling passes. According to experience, cutting conditions such as spindle speed and feed rate can decisively affect the cutting process and even obscure the loss of sharpness. Due to different feed rates, the cutting time for each milling pass is not the same. The 9 th to 12 th steps are the shortest, 120 s; the 5 th to 8 th steps are the longest, 240 s. For each milling pass, 50 data segments of 1-s duration are extracted from a random initial time in the valuable data of each milling pass. Thus, there are 800 raw signal samples for each cutting state, and the total capacity of the raw signal data set is 4800.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Combination** \\ **of Cutting** \\ **Conditions** \\ \end{tabular} & \begin{tabular}{c} **Milling** \\ **Pass** \\ \end{tabular} & \begin{tabular}{c} **Radial Cutting** \\ **Depth (mm)** \\ \end{tabular} & \begin{tabular}{c} **Axial Cutting** \\ **Depth (mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Feed Per Tooth** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Spindle Speed** \\ **(r/min)** \\ \end{tabular} \\ \hline \# 1 & 1–4 & 1 & 5 & 0.08 & 800 \\ \# 2 & 5–8 & 2.5 & 5 & 0.06 & 800 \\ \# 3 & 9–12 & 1 & 5 & 0.08 & 1200 \\ \# 4 & 13–16 & 2.5 & 5 & 0.06 & 1200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cutting conditions.  
It should be noted that in this paper, the signal segments in the same milling task are set to the same label. That is, the cutting conditions of the signal samples in the data set are unknown.
# Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals +
## 3 Data Annotation and Ground Truth Generation
### Flight Simulations  
The experiments were designed considering the importance of collecting experimental data that could be useful to quantifying the impact of a task load increment to pilots through operational interruptions by an air traffic controller (ATC), cabin crew (TCP), and electronic centralized aircraft monitor (ECAM) warnings, in order to assess to what extent the system presented to discriminate between low and high workload can be transferred to a more complex environment.  
Four scenarios with different levels of complexity were designed, all of them assuming pilot monitoring (PM) incapacitation in order to check how interruptions can overload pilot flying (PF).  
* Flight 1. It is based in a nominal standard flight. This experiment is used to take reference parameters. Thus, nominal flight without considering any interrupting event from abnormal procedures due to system failure nor ATC vectoring instructions. In this scenario, ATC provides a minimum number of instructions which the pilots are used to. This scenario the lowest complexity and is considered as the BL class.
* Flight 2. It also relies on the approach phase and it is modified from the nominal scenario, by three different interruptions which increase the PF workload. This scenario has an overall high WL demand.
* Flight 3. This scenario is based on the previous experiment with similar interruptions, but they are slightly advanced or delayed to times at which the PF workload is low and the pilot can attend the interruption without a negative performance impact. Given that interruptions were issued at the most appropriate times, this scenario has a lower level of WL demand than Flight 2.
* Flight 4. This last scenario is based on the previous experiment with the same interruptions, but they are fired at times in which PF is attending to concurrent actions, considerably increasing the workload and impacting the PF performance. This scenario has a similar or greater WL than Flight 2.  
The functional resonance analysis method (FRAM) [39] is an agent based modeling framework to identify those factors that affect the performance of the pilot in cockpit functionalities considering different socio-technical operational conditions. According to this agent, the impact of an interruption on the PF workflow depends largely on the time at which the interruption occurs. Consequently, FRAM provides a reliable measure of the workload that will be faced by the pilot, and thus, it was used to design simulation scenarios with interruptions triggered at times when the pilot had a low and high WL peaks, and thus to provide realistic flying situations of controlled difficulty. In addition, FRAM output (both, number of tasks and its complexity) was used to assess the ability of ML models to detect WL peaks associated with highly demanding tasks. In this case a single pilot flew the 4 scenarios.  
Figure 1 illustrates a volunteer during a session for the dual n-back test task (a) and a pilot during a simulated flight session (b).
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 4 Standards Evaluation Results
### Synthesis  
As a result of the evaluation, it can be said that STIX is de-facto standard in the threat intelligence context. First, STIX is the most popular and compatible one, being supported by many platforms and tools and used by most organizations. Second, due to its holistic architecture and its capability of addressing a lot of scenarios in the threat intelligence scope, can be considered the most complete standard.  
Even though the other standards are still supported by some platforms and have satisfactory applicability, the features offered by STIX have stood out. So, considering the characteristics of STIX and the capacity of possible results it can generate, it can be said that the standard has the most satisfying performance.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
###### Abstract  
Mobile communication security techniques are employed to guard the communication between the network entities. Mobile communication cellular systems have become one of the most important communication systems in recent times and are used by millions of people around the world. Since the 1990s, considerable efforts have been taken to improve both the communication and security features of the mobile communications systems. However, these improvements divide the mobile communications field into different generations according to the communication and security techniques such as A3, A5 and A8 algorithms for 2G-GSM cellular system, 3G-authentication and key agreement (AKA), evolved packet system-authentication and key agreement (EPS-AKA), and long term evolution-authentication and key agreement (LTE-AKA) algorithms for 3 rd generation partnership project (3 GPP) systems. Furthermore, these generations have many vulnerabilities, and huge security work is involved to solve such problems. Some of them are in the field of the public key cryptography (PKC) which requires a high computational cost and more network flexibility to be achieved. As such, the public key infrastructure (PKI) is more compatible with the modern generations due to the superior communications features. This paper surveys the latest proposed works on the security of GSM, CDMA, and LTE cellular systems using PKI. Firstly, we present the security issues for each generation of mobile communication systems, then we study and analyze the latest proposed schemes and give some comparisons. Finally, we introduce some new directions for the future scope. This paper classifies the mobile communication security schemes according to the techniques used for each cellular system and covers some of the PKI-based security techniques such as authentication, key agreement, and privacy preserving.  
PKI security; mobile communication security; GSM-authentication and key agreement (AKA) security; CDMA-AKA security; long term evolution-authentication and key agreement (LTE-AKA) security +
Footnote †: journal: _Symmetry_
## 5 Data Analysis and Main Findings
### Confirmatory Factor Analysis (CFA)  
A critical measurement test of a theory, CFA examines the links between structures and searches for important correlations between them [59; 63]. As testing the validity of the constructs is a primary aim of data analysis, a CFA was conducted after internal consistency and reliability testing to fully assess the theoretical framework of this present  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Initial Eigenvalues**} & \multicolumn{3}{c}{**Extracted Sums of Squared Loadings**} \\ \cline{2-7}  & **Total** & **\% of Variance** & **Cumulative \%** & **Total** & **\% of Variance** & **Cumulative \%** \\ \hline
**1** & 11.676 & 48.652 & 48.652 & 11.676 & 48.652 & 48.652 \\ \hline
**2** & 03.339 & 13.913 & 62.565 & 03.339 & 13.913 & 62.565 \\ \hline
**3** & 01.688 & 07.035 & 69.600 & 01.688 & 07.035 & 69.600 \\ \hline
**4** & 01.292 & 05.384 & 74.983 & 01.292 & 05.384 & 74.983 \\ \hline
**5** & 01.104 & 04.599 & 79.582 & 01.104 & 04.599 & 79.582 \\ \hline
**6** & 00.784 & 03.265 & 82.847 & 00.784 & 03.265 & 82.847 \\ \hline
**7** & 00.739 & 03.079 & 85.926 & 00.739 & 03.079 & 85.926 \\ \hline
**8** & **00.586** & **02.443** & **88.369** & **00.586** & **02.443** & **88.369** \\ \hline
**9** & 00.558 & 02.323 & 90.693 & & & \\ \hline
**10** & 00.527 & 02.197 & 92.890 & & & \\ \hline \hline \end{tabular} Note: Extraction method: PCA—eight components extracted.  
\end{table}
Table 3: The Total Variance Explained including Initial Eigenvalues and Sums of Squared Loadings.  
study. One of the primary advantages of CFA is structural equation modelling (SEM), which is a subset that generates a more rigorous interpretation of a theoretical framework than EFA [59; 64]. The results of the CFA refined and supported the conceptual framework of this present study.
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 4 Simulation Results
### Results on Detection Probability  
Figure 5 shows the normalized acquisition results of the proposed algorithm with different \(FCR\) when \(SNR\) is \(-12\) dB. We can find that with the decrease of \(FCR\), the ratio of the highest correlation peak to the second correlation peak increases, which means the detection probability increases.  
Figure 6 shows the detection probabilities of the proposed algorithm with different \(SNR\). We can compute that when the \(SNR=-12\) dB, the detection probabilities of \(FCR\) = 2:1, 4:1, and 8:1 are 96.3%, 67.8%, and 22.4%, respectively. With the increase of \(FCR\), the number of overlaps of the mapped signals with the same code phase increases, which makes it difficult to distinguish the correlation peaks and reduces the detection probability.
## 2 Materials and Methods
### Image Analysis Application  
To describe the specific construction system, a sufficiently representative sample of the wall from each tower was used. In each case, a square 1.0 \(\times\) 1.0 m section of the elevation at Level 1 in each tower is selected to create an image that would allow us to compare the different models. They were compared using ImageJ Fiji (v. 1.52p) (National Institutes of Health, Bethesda, MD, USA) image processing software [31]. Based on that, a measurement and analysis protocol were drawn up to obtain the most relevant data from each image to classify the construction systems:  
* Area--selected area in square pixels. Area is in calibrated units, in this case, square centimetres.
* Perimeter--the length of the outside boundary of the selected area.
* Shape descriptors (previously circularity). Calculate and display the following shape descriptors:
* Circ. (circularity): 4\(\pi\)\(\times\) area/perimeter2. A value of 1.0 indicates a perfect circle. The nearer the value is to 0.0, the more elongated its shape is.
* AR (aspect ratio): major_axis/minor_axis
* Round (roundness): 4\(\times\) area/(\(\pi\)\(\times\) major_axis2), or the inverse of the aspect ratio.
* Solidity: area/convex area. The Edit \(>\) Selection \(>\) Convex Hull command makes a selected area convex.
* Feret diameter--the longest distance between any two points along the selected boundary, also known as maximum caliper. Feret angle (0\({}^{\circ}\)-180\({}^{\circ}\)) is the angle between the Feret diameter and a line parallel to the \(x\)-axis of the image. MinFeret is the minimum caliper diameter. The starting co-ordinates of the Feret diameter (FeretX and FeretY) are also displayed. This data can characterise the homogeneity and settlement of the successive courses and therefore define the quality of the construction.  
The results of the image analysis enabled conclusions to be drawn both at the general level of the elevation and by studying each of the materials and elements individually. Some of these calculations, such as the circularity of the rough stone or Feret diameters, are highly variable, i.e., their values are highly dispersed as they are directly linked to the original conditions of the area in which the construction is located and from which the materials for each tower were collected. Therefore, these parameters are not valid as a way of defining a complete typology and although parameters that can be used for more detailed analysis of the masonry were calculated, the only functional parameter for differentiating one type from another is the percentage (%) of rough stones. Indeed, given that the study was carried out on several towers of similar type and age, the proportion of masonry and mortar present in each sample enables thresholds to be established that sort each specimen into one or other construction technique. The rest of the parameters enable the visual data of the elevation to be depicted.
## 4 Discussions
### Generalization and Potential Application to Open-World Scenarios  
Tables 4 and 5 illustrate that the highest accuracy is achieved by models that are trained using supervised learning, especially if re-ranking is applied (see Table 5). This, encourages models to provide higher scores to true matches, thus increasing mAP accuracy. This observation makes it tempting to render the parameters in Tables 1 and 2 to be trainable in order to possibly improve the accuracy in our experiments. However, this would render the generalization worse due to the bias the model would develop towards training datasets. Indeed, such effects are well-known in machine learning.  
On the contrary, the absence of trainable parameters renders our approach dataset-agnostic. Our results regarding the Market1501 dataset in Table 4, are obtained using exactly the same model as in case of the CUHK03 results in Table 5. Therefore, this can be considered analogous to transfer learning experiments in context of neural networks.  
Figure 6: Search results for a human-generated query of a person wearing a red shirt, black pants, and black shoes in the CUHK03 dataset.  
Figure 7: Search results for a request to find ten people wearing white shirts.  
As such, our 62.9% Market1501-CUHK03 rank-1 \"transfer\" accuracy is significant when compared to the 50.1% DukeMTMC-Market1501 transfer in [19], as shown in Table 6. Furthermore, Table 6 shows that 20-25% mAP accuracy is common for generalization experiments. In addition, in certain cases, the proposed method results in a transfer-learning mAP that is higher than that of APNet-C. Having said that, it should be noted that the DukeMTMC dataset has since been retracted, which prevents us from conducting exactly the same experiments as in [19]. However, the DukeMTMC dataset can be considered less challenging than the CUHK03 [61, 62] dataset.  
A number of recent works have suggested domain adaptive clustering techniques as another approach for the purposes of generalization improvement [66, 67]. However, such methods still require DL-model training on re-ID datasets and a fine-tuning of clusters on target datasets, which indicates dataset-dependence. On the contrary, the proposed method in this paper is that it is re-ID dataset-agnostic. Furthermore, it is very promising from the perspective of the development of highly generalizable models. This also suggests that when accuracy improvements are achieved on one dataset it is likely to improve the accuracy on other datasets too. Such behavior, although desirable, is not guaranteed for DL models. In contrast with the current trends towards end-to-end DL models, this paper indicates a significant potential in combining machine learning (human parsing) with human intelligence (analytical features) in order to obtain more flexible systems that are more robust in open-world scenarios, while also being easier for human interpretation and understanding.
## 2 PV/PDC-CTR-BSF Approach  
### PV/PDC-CTR-BSF Design  
The PV/PDC-CTR-BSF system is made up of a parabolic dish collector, conical receiver cavity, PV cell panel and beam splitter filter. The geometry modeling equations for each component are first developed in order to build the complete system. The design of the system under investigation is shown in Figure 1.  
One can observe that the BSF is placed between the two receivers (conical receiver cavity and PV cell panel) and above the parabolic dish collector (PDC). The main goal of the BSF placement is to manage the path of the reflected solar rays due to its wide spectral selective coating [17]. This last one allows one to split the concentrated sun rays intercepted by the BSF according to their wavelengths.