esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### HPGe Gamma Spectrometry Measurements  
For the gamma spectrometry analysis, samples were enclosed in 1 L capacity plastic Marinelli containers and counted for 70,000 s. The 661.66 keV \({}^{137}\)Cs and 1460.8 keV \({}^{40}\)K gamma ray lines were used to determine their specific activity.  
The experimental setup consists of an Ortec HPGe detector and integrated digital electronics. In detail, it is a negative biased detector (GMX), cooled by the Ortec recycl condensing liquid nitrogen cooling Mobius system [35], characterized by a Full Width at Half Maximum FWHM of 1.94 keV, a peak to Compton ratio of 65:1 and a relative efficiency of 37.5% at 1.33 MeV (\({}^{60}\)Co). The Eckert and Zigler Nuclitec GmpH traceable multinum-clide radioactive standard, number AK-5901, with an energy range of 59.54 to 1836 keV, reproducing the exact samples geometries in a water-equivalent epoxy resin matrix, was employed to perform efficiency and energy calibrations [36].  
The Quality Controls (QC) of radiation measurements were conducted according to [37]. The Gamma Vision software (Ortec) was used to acquire and analyze the data [38].  
The specific activity (Bq kg\({}^{-1}\)) of the investigated radioisotopes was calculated using the following formula [39]:  
\[C=\frac{N_{E}}{\varepsilon_{E}t\gamma_{d}M} \tag{1}\]  
where \(N_{E}\) indicates the net area of the peak at the energy \(E\); \(\varepsilon_{E}\) and \(\gamma_{d}\) are the efficiency and yield of the photopeak at the energy \(E\), respectively; \(M\) is the mass of the sample after treatment (kg) and \(t\) is the live time (s).  
In measuring the error, at the 95% confidence level, the components taken into account were: uncertainties of count assessment, calibrating source, calibration efficiency, background subtraction, and \(\gamma\)-branching ratio [40].  
The quality of the gamma spectrometry experimental results was certified by the Italian Accreditation Body (ACCREDIA) [41]. This implies continuous testing (annually) of whether the performance characteristics of the gamma spectrometry method are maintained [42].  
Figure 1: The map of the sampling area (**a**), with the site IDs (1–3) indicated (**b**).
## 4 Discussion
### Limitations  
The limitations of the current study should be taken into consideration while evaluating its results. Limitations include the following:  
Search Strategy: The choice of specific keywords for the literature search may have omitted relevant studies that used different terminology. This could potentially lead to the exclusion of valuable research on wearable devices.  
Publication Bias: The study focused on open access articles published in English, which might introduce publication bias by excluding non-English articles.  
Time Span: We narrowed the time filter to see the effect of pandemic on our subject but the results showed large number of unspecified studies which resulted in unconclusive results. A longer timeframe might provide a more comprehensive understanding of trends, especially in relation to the ongoing development and adoption of wearable technology.
# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 1 Introduction
The proposed method eliminates accumulated errors before pre-integration and obviates the need for additional IMU hardware. The system performs an external fusion ZUPT followed by tight coupling optimization within the sliding window to update the pose graph, reduce localization and mapping drift, and enhance motion attitude estimation and the construction of the scene's three-dimensional structure. The experimental results show that the proposed system effectively solves the common accumulated error problem in VIO while accurately estimating motion attitude and constructing the scene's three-dimensional structure.
# An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
## 4 Experiment and Analysis of Surface Defects Detection Based on MB-LBP
### Comparison of Experiment Result between MB-LBP and Other Algorithms
However, it uses the original image as input, and the dimension of input vector was as high as \(224\times 224\). Additionally, its network structure is too complicated. Therefore, its time efficiency was only 434 ms/image, much higher than the minimum requirement (less than 100 ms/image) of online real-time detection in this paper. Table 3 shows the recognition accuracy of each class of defects with the MB-LBP. The recognition accuracy of all defects met the requirement of online detection (at least 90%). The recognition accuracy of most classes was above 94%, except for scales. This is because scales have complex morphological features and are difficult to classify. Recognition of scales is also one of the future improvement directions. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Feature Dimension** & **Recognition Accuracy** & **Time Efficiency (ms/image)** \\ \hline SIFT & 128 & 91.43\% & 583 \\ SURF & 128 & 86.89\% & 142 \\ GLCM & 20 & 84.27\% & 248 \\ LBP & 256 & 89.05\% & 41 \\ MB-LBP & 59 & 94.40\% & 63 \\ CAE-SGAN & \(224\times 224\) (original image) & 96.21\% & 434 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the recognition accuracy and time efficiency between the MB-LBP and other algorithms. Figure 16 shows some examples of false matching. Cracks vary in size and shape, in particular, the detection of small cracks is difficult. Figure 16(a1,a2) show false-matching cracks, as they were too small and not obvious enough. Scratches were relatively easy to detect, but were greatly affected by light. Figure 16(b1,b2) show false-matching scratches. Figure 16(b1) was misclassified as a crack as it shows a thin dark stripe with weak light; and Figure 16(b2) was misclassified as a scale, as it shows an irregular area shape with strong light. Indentations vary in size and direction. Figure 16(c1,c2) show false-matching indentations. Two indentations with different sizes and directions were classified as different types of defects. Pits have irregularly shaped edges and complex backgrounds, like scales. Figure 16(d1,d2) show false-matching pits that were misclassified as scales. Scales have complex shapes and great grayscale changes, as such, they are the main misclassification defects. Scales have the lowest recognition accuracy, many other types of defects are misclassified as scales, or vice versa. Figure 16(e1,e2) show false-matching scales. Figure 16(e1,e2) were separately misclassified as a pit and crack due to their similar shapes. Figure 16: _Cont._ \begin{table}
\begin{tabular}{c c} \hline \hline
**Defects** & **Recognition Accuracy with MB-LBP** \\ \hline Crack & 95.61\% \\ Scratch & 94.29\% \\ Indentation & 94.25\% \\ Pit & 95.68\% \\ Scale & 93.87\% \\ Total & 94.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition accuracy of each class of defects with the MB-LBP.
## 5 Results
### Dropped Messages over Time
#### 5.4.2 Low Threshold: All Three Available Channels Will Be Used  
When the dynamic channel-switching mechanism is used, the number of dropped messages on the CCH is significantly lower (Figure 19). Vehicles that have been on the map since the beginning of the simulation had about 40 such lost packets.  
On the SCH1 (Figure 20), packet drops can only be observed after 15 s. This is because, at the beginning of the simulation, messages are transmitted on CCH, and the congestion rate of the SCH1 is only considerable towards the end of the simulation. The number of dropped messages is even lower than CCH.  
Figure 19: Dropped messages of every vehicle on CCH using dynamic channel selection.  
Figure 20: Dropped messages of every vehicle on SCH1, using dynamic channel selection.  
On SCH2, no packet loss was measured at all. As can be seen, using more than one channel considerably reduces the number of dropped messages. Before the DCC mechanism kicks in, it transfers the messages to another channel.
## 3 SWIFTNET: Protocol for Fast-Reactive Monitoring
### Learning Technique: Normalized Least-Mean Square Filter
#### 3.2.1 Adaptive Sampling Strategy  
We design an adaptive sampling mechanism that takes inputs from the monitored environment with units in seconds. We express the sampling interval \(\gamma\) in the form:  
\[\gamma=\beta\cdot\theta_{min(t)}/X_{sense} \tag{15}\]  
where \(\theta_{min|max(t)}>0\) denotes the minimum or maximum non-negative value that could be attained within a specified sampling period. For example \(\theta\) is \(min\) when our intention is to capture high values, and conversely, \(\theta\) is \(max\) if the interest is to capture low values in the monitored environment. From Equation (15) \(\beta\) denotes the initial sampling interval specified for the WSN deployment. \(X_{sense}\) is the sensed parameter e.g., temperature, humidity, wind speed at time \(t\). Both \(\theta_{min(t)}\) and \(\beta\) values can be set by the user based on the application-specific knowledge.  
Suppose we consider a wildfire monitoring application as a use-case, where our interest is to monitor high temperature values, \(\theta\) is chosen as \(min\) e.g., \(\theta_{min(t)}\) could be set to 2 \({}^{\circ}\)C (assuming it is the lowest non-negative and non-zero temperature value recorded for the sampling period). Obviously the lower this value is, the smaller the sampling interval \(\gamma\), hence the higher the sampling rate. Due to the inverse relation between \(\gamma\) and \(X_{sense}\) from Equation (15), an increasing air temperature automatically increases the sampling interval and vice versa. For each data transmitted during the prediction phase, the time stamp is added to the data packet with the newly computed sampling interval. With this design, the prediction algorithm is adaptive and highly responsive to changes in the air temperature signal. The same metric is applicable to other parameters such as humidity, wind speed, _etc_. For the remainder of this article, we shall refer to the integration of the adaptive sampling strategy and prediction model (Section 3.2) as an \"Adaptive prediction\" method.
## 4 Experimental Verification Results
At \(t_{2}\), both the voltage and current reach 0 V and 0 A, respectively, indicating the completion of the discharge cycle. Through experimental comparison, it was observed that at time \(t_{1}\), the circuit suddenly closes, causing the voltage to drop sharply to 2 V, while the current surges from 0 A to 10 kA. This phenomenon occurred consistently across all three test groups. The abrupt voltage drop and current spike indicate a sudden and significant change in the battery's internal impedance, revealing a common characteristic of internal impedance variation under high-current conditions [50; 51; 52]. This rapid response highlights the dynamic behavior of the battery during high-amperage discharge and confirms the swift activation of the electrochemical reaction when the circuit is closed. During the continuous discharge stage (form \(t_{1}\) to \(t_{2}\)), notable differences emerged among the three test groups. Specifically, the time interval \(t_{2}\) \(-\)\(t_{1}\) was 0.75 s in the C0 group (as shown in Figure 8), 0.8 s in T1 group (as shown in Figure 9), and extended to 1.73 s in T2 group (as shown in Figure 10). The optimized design (T2 group) demonstrated a substantial increase in current-carrying duration, achieving 230% of the control group's duration (C0). In contrast, the conventional design (T1 group) showed only a negligible improvement, with a duration of 107% of that of the control group. While the simulation has inherent limitations, leading to slight discrepancies between experimental and simulated results, the measured fusing time of the conductor structures closely aligns with the electrothermal coupling simulation outcomes. This consistency not  
Figure 10: Variation curves of voltage and current with discharge time for T2 group. only validates the accuracy of the simulation analysis and experimental verification but also provides deeper insight into the dynamic characteristics of battery performance under high-ampergage discharge conditions.
## 5 Validation of Proposed Method
### Evaluation Results
#### 5.3.3 Travel Time  
Travel times for both methods were longer when the pedestrian density was higher, as shown in Figure 18. In the condition with 20 pedestrians (pedestrian density 0.08/m\({}^{2}\)), the average travel time was also significantly longer for the proposed method compared to the existing method (\(p<0.01\)). On the other hand, in higher pedestrian density conditions, the travel time of the proposed method was shorter compared to the existing method. Particularly, in the condition of 50 pedestrians (pedestrian density 0.21/m\({}^{2}\)), the average travel time for the existing method was 33.1 s. By contrast, it was 32.8 s for the proposed method, and this difference between the methods was significant (\(p<0.01\)). In the scenario with no pedestrians, the PMV takes 29.2 s to reach the destination. Thus, this time can be taken as the baseline, which any pedestrian avoidance strategy cannot further reduce. Among the highest pedestrian density conditions, the existing method resulted in a 3.9-s increase in travel time from the baseline, and the proposed method resulted in a 3.6-s increase. From the comparison of the increased time from the baseline due to pedestrian avoidance among the two methods, the proposed method reduced the increased time from the baseline by approximately 8%, which can be regarded as practically significant.  
Figure 17: Comparison of the time-averaged PS invasion ratio of intolerable pedestrians.  
Figure 18: Comparison of the travel time among different pedestrian densities.  
The numerical simulation results showed that the proposed method satisfies the required function of suppressing the avoidance behavior of the PMV for tolerable pedestrians, as set in Section 4.2. It can shorten the travel time without increasing passenger discomfort of autonomous navigation, particularly in crowded environments with high pedestrian density.
## 3 Results and Discussion
### Biochar Characterization
#### 3.1.4 Morphological Analysis  
The scanning electron microscopy (SEM) images of the prepared biochar samples are shown in Figure 3. The SEM images shows that BC samples have random shape structures with sharp-edged particles. It can be observed that the BC average particle size distribution is in the range of 2 to 20 \(\upmu\)m (Figure 3a); however, some bigger particles of larger size are also observed. On the other hand, high magnification images (Figure 3b) displays clearly the porous structures in the biochar samples. The appearance of deep channels and pores became more prominent with pyrolysis temperature caused by organic materials volatilization. However, destruction of the porous structure is also observed at higher temperature and can be seen in the BC-700 sample. This can be due to the accelerated and intensified degree of carbonization. The porous structure in biochar is vital aspect, because of the fact that these pores on the BC might enable the molten polymer chains to flow into this pores, which will lead to better mechanical interlocking and formation of stronger BC-PP interface [28; 29].  
Figure 2: BET specific surface area and pore volumes of biochar samples.  
Figure 3: _Cont._
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Packet-Based Attack Detection  
Packets, which are the basic units of network communication, represent the details of each communication. Packets consist of binary data, meaning that they are incomprehensible unless they are first parsed. A packet consists of a header and application data. The headers are structured fields that specify IP addresses, ports and other fields specific to various protocols. The application data portion contains the payload from the application layer protocols. There are three advantages to using packets as IDS data sources: (1) Packets contain communication contents; thus, they can effectively be used to detect U2L and R2L attacks. (2) Packets contain IPs and timestamps; thus, they can locate the attack sources precisely. (3) Packets can be processed instantly without caching; thus, detection can occur in real time. However, individual packets do not reflect the full communication state nor the contextual information of each packet, so it is difficult to detect some attacks, such as DDOS. The detection methods based on packets mainly include packet parsing methods and payload analysis methods.
# The Efficacy of MOOC to Support Students in Pedagogical Research
## 3 Materials and Methods
### Dataset  
We have realized our experiment on a sample of 114 bachelor's and master's degree students in both, full-time and extra-mural study. This sample also included 14 PhD students. The MOOC was realized at the end of the winter semester in the academic year 2019/2020 at the Constantine the Philosopher University in Nitra. Five students did not attend the study or interrupted their studies, we excluded them from the data file. The sample was divided into two groups, control (CON) with 68 participants and experimental (EXP) with 41 participants:  
* bachelor studies, full-time study--CON (57 participants)  
Figure 2: Example from the course—content, video, quiz.  
* bachelor studies, extramural study--EXP (17 participants),
* master studies, full-time study--EXP (10 participants),
* master studies, extramural study--CON (11 participants),
* doctoral studies (PhD.)--EXP (14 participants).  
In the case of bachelor and master students, the sampling was selected based on availability. The project was assigned to students during the ongoing subjects focused on data analysis. In the case of PhD students, the sampling was selected based on voluntary.  
The control group was educated conventionally, the experimental group completed the above mentioned MOOC called Experimental Data Processing focused on statistical processing of the experiment. Each participant completed the pretest before the start of the experiment and after its completion had to work out a project according to the assignment (posttest). The pretest represented the final mark from the solved tasks in statistics (A-FX), where A (1) means excellent knowledge and FX (3.5) insufficient knowledge. The posttest was performed after the end of the experiment and takes values in the interval 0 to 1, where 0 is 0% success and 1 is 100% success of the submitted project solution (Table 2).  
The posttest was the evaluation of the submitted project (Figure 3) focused on the processing of experimental data. The participants had to correctly realize and interpret the following phases from the processing of the experiment:  
* the analysis of reliability/items + descriptive statistics, correlation,
* ANOVA + descriptive statistics, assumptions,
* ANCOVA + descriptive statistics, assumptions.
# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
## 3 Comparison with Volterra Method
### Modelling Approach  
The ILA is utilized for the DPD model coefficient computation, which is characterized in Figure 5. In the training phase, the coefficients for the predistortion are computed. As explained in [6], the input of predistorter \(z(n)\) comes through the RoF output \(y(n)\) where \(z(n)=y(n)/G\). \(G\) represents the gain of the RoF link. The estimation of coefficients is  
Figure 4: Schematic showing block diagram of NN structure used in this article, feedforward fully connected (FC) NN. FC-NN contained \(N\) hidden layers and \(K\) neurons per hidden layer.  
performed using the least squares algorithm. As soon as the error \(e(n)\) converges, the coefficients are fed to the DPD block. The MP and GMP model utilized is discussed below.
## 2 Materials
### Data Preparation
#### 2.2.2 Conditioning Factors  
A total of 11 condition factors were selected based on the study region's characteristics, the data's availability, reliability, and practicality, including elevation, slope, maximum elevation difference (MED), plan curvature, profile curvature, topographic wetness index, distance to faults, distance to roads, distance to streams, annual rainfall, and NDVI [27].  
Elevation, which was divided into five sub-classes by 1000 m intervals, has an influence on both rainfall and vegetation [28; 29] (Figure 4a). Slope, which controls shear strength at potential slide surfaces as well as subsurface flow [30], was reclassified into six classes by 10\({}^{\circ}\) intervals (Figure 4b). It was calculated in ArcGIS 10.2 that the MED reflects potential kinetic energy of slope units. By 100 m intervals, the thematic map was reclassified into six classes (Figure 4c). Erosion and deposition can be determined from slope curvature since it is essential to the geometry of slopes [31]. Six classes were established for profile and plan curvatures (Figure 4d,e). The TWI represents basic terrain, which was divided into six categories (Figure 4f). With the spatial resolution of 30 m, six topographic factors were extracted from the digital elevation model (DEM). The DEM was originally sourced from the Geospatial Data Cloud, which was accessed on 6 January 2023.  
Bulk-rock strength could be reduced as a result of faults acting as potential weak planes in slopes, and distance to fault was constructed with six classes by 2000 m intervals (Figure 4g). In a similar manner, distances to the road and river were divided into six categories with an interval of 2000 m (Figure 4h,i).  
Figure 3: Remote sensing image interpretation: (**a**) debris flow in Kangbu township; (**b**) debris flow in Duina township.  
Figure 4: _Cont_.  
An existing 1:50,000 geological map was used to extract fault information, and Landsat 8 LOI images were used to determine road and river network information. Using the Euclidean Distance ArcGIS Tool, the distances from each raster unit of the area to the nearest fault, road, stream, and highway were calculated.  
The unique trigger factor considered in the study was rainfall, and it has been applied numerous times. Based on the data from 14 precipitation stations near the study area, an ordinary kriging interpolation was used to generate the thematic map in ArcGIS. The thematic map was reclassified into 4 classes (Figure 4j).  
The consolidation of vegetation roots can stabilize soil, which alleviates the effect of rainfall on the stability of the slope [32]. The vegetation normalization index (_NDVI_) was applied to evaluate the vegetation, and it is calculated by:  
\[NDVI=\frac{NIR-RED}{NIR+RED} \tag{1}\]  
where _RED_ is the reflection value of the red wavelength and _NIR_ is the near-infrared wavelength.  
_NDVI_ ranges from \(-1\)-\(1\) with a positive value indicating coverage, and the greater the value, the greater the coverage. The thematic map was reclassified into 6 classes (Figure 4k). The _NDVI_ was derived from Landsat 4-5 TM satellite images.