esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Result
Therefore, as shown in Figure 5, the background intensity of the single-point hologram decreases according to an increase in the hologram reconstruction distance. In order to perform a practical experiment, a custom-made pinhole array mask with every pinhole having a diameter of 3 \(\upmu\)m was fabricated by photolithography, as shown in Figure 6. The mask was then carefully attached to the SLM by a custom-made alignment stage. The experimental conditions were identical to those in the numerical simulation, apart from the \(C\) value. The reconstructed hologram was captured with a digital camera. Figure 7 shows the optical system used in the experiment. In addition, the \(C\) value in the practical context was determined as the  
Figure 5: The intensity distribution of a single-point hologram according to the reconstruction distance (log scale). Figure 6: Magnified image of the custom-made non-periodic pinhole array filter. effective pixel aperture divided by the pixel pitch. The effective pixel aperture was determined by the fill factor of the spatial light modulator. Our spatial light modulator had a 58% fill factor with a 36 \(\upmu\)m pixel pitch; hence, the effective pixel aperture was only 20.88 \(\upmu\)m. Therefore, unfortunately, the \(C\) value could not exceed 0.58 in this experiment. Although the random pinhole array filter was created with a \(C\) value of 0.6, which was suitable for our SLM, Figure 8a,b demonstrate feasible performance in that the high-order images were eliminated. Figure 8: (**a**) Reconstructed holographic image without using a filter and (**b**) with the proposed non-periodic pinhole array filter. Figure 7: Optical configuration used in the experiment. (BS: Beam-Splitter, LP: Linear Polarizer). Due to the small pixel size of the pinhole, the diffraction efficiency of the system decreased. The light intensity was measured using a laser power meter (Sanwa instrument, Tokyo, Japan). When there was no pinhole array, the intensity of the light before the SLM was 1.453 mw and after the SLM was 0.723 mw, and the light passing through with the random pinhole array filter was measured as 5.38 \(\upmu\)m. The diffraction efficiency ratio with and without the pinhole array was 0.744%.
# The Impact of Technical Error of Measurement on Somatotype Categorization
###### Abstract  
Inquiry into somatotype often seeks to assign participants into somatotype groups. The aim of this study was to demonstrate how the intra-tester reliability of anthropometric measures can influence how somatotype is categorized. Sixty-eight physically active males (mean [SD] 24.8 [7.9] ); 79.8 [14.4] kg; 1.81 [0.07] m) had their anthropometric profiles measured and somatotype components calculated. Technical error of measurement (TEM) was used to calculate 95% confidence intervals (CI) for overall somatotype calculation (RTEM) for the data collected by the lead researcher. CIs were further calculated based on the International Society for the Advancement of Kinanthropometry accreditation Level 1 and 2/3 thresholds. Somatotype groups were categorized as either simple (four groups) or detailed (13 groups). RTEM had the smallest TEM values (0.05 somatotype units). Detailed somatotype categorization demonstrated larger potential for misclassification (39.7-72.1%) versus simple categorization (29.4-38.2%). Researchers investigating somatotype should keep technical skill high and group according to the four simple somatotype categories in order to maintain acceptable categorization reliability.  
reliability; physique; endomorphy; mesomorphy; ectomorphy 2022
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 3 Results and Discussion
### Microbial Community Composition
#### 3.1.2 Archaea  
Large differences were observed in the ratios of the archaeal part of the microbial community microbiota. The archaea ratio was 10.4% for I-WWP7 and 10.2% for I-WTT14, which was almost 2.5 times higher than the ratio of I-LRD (4.1%) as well as higher than that of I-LRS (6.3%) and I-BGP (8.3%). [PERSON] et al. [17] recommend a high abundance of methanogens for a good performance of the inocula.  
The majority of archaea (93%) were members of the phylum _Euryarchaeota_. The I-BGP and I-LRD were dominated by members of the genus _Methanosaea_ (I-BGP 88% and I-LRD 73%). This reveals that within both inocula the acetoclastic methanogenesis pathway is favored and that both inocula seem to have a low VFA and ammonium (NH4+-N) concentration. It was shown, that the genus _Methanosaea_ decreases with high VFA and NH4+-N concentration [19; 42]. In contrast, the archaeal community of I-LRS consisted of the genera _Methanoculleus_ (43%) and _Methanosarcina_ (8%). Both species could also be found in smaller ratios (1%-11%) in all of the other inocula. The high abundance in I-LRS is probably due to the high NH4+-N concentration within this inoculum (Table 6). Members of the genera _Methanoculleus_ and _Methanosarcina_ can replace those of to the genus _Methanosaeta_ in habitats with higher VFA and NH4+-N concentrations. [PERSON] et al. [46] described the occurrence of _Methanoculleus sp._ in many biogas plants with different feedstocks and that this genus uses the hydrogenotrophic methanogensis pathway. Member of the genus _Methanosarcina_ are described as stress resistant, hydrogenotrophic archaea [38].  
In I-WWP7 and I-WWP14, the archaeal community composition was similar. Both communities consisted of 80% _Methanomicobia_. The genus of this archaeal class could not be detected.
# Real-Time Monitoring of Passenger's Psychological Stress
## 2 Results
### Learning Phase
#### 2.1.3 Validation Output  
Model performances during the cross-validation are displayed in Table 1. For each partition of the initial database (numbered in the left sidebar), columns 2, 3 and 4 display the performances reached at the end of the training phase (e.g., linear regression on the training set). Columns 5, 6 and 7 show the same results for the validation stage (the previously learnt model is used on the remaining data). Columns 2 and 5 indicate the number of examples in each set for the non-stressful (Ns) and stressful (S) condition. Columns 3 and 6 display the root mean square error (RMSE) computed between participant's reported stress values and the output of the linear model. The margins of error are given by the difference between RMSE and the upper bound \(U\) expressed in Equation (1). Columns 4 and 7 give the part of correctly classified examples for both datasets: the margins of error refer to the number \(m\) defined in Equation (2). Finally, the end line displays the mean value and margin of error over each column.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trial** & \multicolumn{2}{c}{**On the Training Set**} & \multicolumn{2}{c}{**On the Validation Set**} \\ \hline
**N\({}^{\circ}\)** & **No. Ex. Ns/S** & **RMSE** & **Cr \%** & **No. Ex. Ns/S** & **RMSE** & **Cr \%** \\ \hline
1 & 56/74 & 0.066 \(\pm\) 0.009 & 100 \(\pm\) 0 & 28/63 & 0.090 \(\pm\) 0.015 & 100 \(\pm\) 0 \\
2 & 56/72 & 0.048 \(\pm\) 0.007 & 100 \(\pm\) 0 & 28/65 & 0.143 \(\pm\) 0.024 & 100 \(\pm\) 0 \\
3 & 56/128 & 0.070 \(\pm\) 0.008 & 100 \(\pm\) 0 & 28/9 & 0.086 \(\pm\) 0.025 & 100 \(\pm\) 0 \\ _Mean_ & - & _0.061 \(\pm\) 0.008_ & _100 \(\pm\) 0_ & - & _0.106 \(\pm\) 0.021_ & _100 \(\pm\) 0_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performances for each trial during cross-validation.  
For two out of the three learning and validation trials, a good generalization of our model from the learning to the validation dataset was obtained, with similar RMSE values despite the class imbalance in the number of examples. In each case, the linear model had a good discriminative power on this dataset, as can be seen from the maximum classification rates. Validation results suggest that the model output may lack accuracy (RMSEs being around 0.1) but still remain discriminative for stress with respect to the no-stress condition.
# Development and Biomechanical Evaluation of a Back Exoskeleton to Support Lifting Tasks
## 2 Exoskeleton Functionality and Design
### Exoskeleton Design
#### 2.2.4 Control  
An Arduino Uno serves as the central processing unit for handling the sensory input signals and controlling the actuation output. It processes real-time data from inertial measurement units (IMUs) and translates them into control commands for the exoskeleton motors. To ensure a reliable and intuitive assistance system, a straightforward control algorithm is implemented, which adjusts the actuation force based on the bending angle of the hip joint.  
To effectively categorize movement patterns, the algorithm classifies the wearer's posture and actions into three distinct phases: (1) bending forward, (2) holding the position, and (3) bending upward (lifting). No active support is provided during the forward bending. However, a low-level pre-tension of 10% of the maximum motor torque is applied to prevent slack in the steel cables, ensuring a smooth and rapid engagement during subsequent movements. When the user reaches a holding position, the support force is dynamically calculated based on the bending angle. From an upright posture (0\({}^{\circ}\) hip flexion) to a 45\({}^{\circ}\) bending position, the supportive force follows a sinusoidal curve, gradually increasing from 0% to 70% of the maximum motor torque. The final 30% of the motor torque is allocated to support the lifting phase, providing assistance as the wearer straightens up.  
To enhance robustness and prevent unintentional activation, an additional filtering mechanism is implemented. The system monitors angular velocity, applying a minimum threshold of 25 rad/s to reliably detect lifting movements while preventing false activation caused by minor swaying of the upper body in the sagittal plane.
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### The 5G Project Use Cases
#### 3.2.3 Smart Grids  
Different sensors (acoustic, accelerometers, temperature, ozone and other gases, deterrents for copper theft) will be installed on a selected electrical grid, in some secondary substations, and on the connected low voltage lines, for monitoring the network and for predictive maintenance and infrastructure security. A management architecture inspired by the blockchain protocol will be built to enable new services and management methods of the load and generation assets, in order to facilitate the integration of distributed generation in medium- and low-voltage networks. The 5G will then provide radio access to the peripheral plants (primary and secondary cabins) for the construction of an integrated network that has a core component and that can also include a mix of different connectivity solutions (fiber, xDSL, etc.).
_Applied_  
_sciences_  
_Article_  
**Selection of the Informative Frequency Band**  
**in a Bearing Fault Diagnosis in the Presence**  
**of Non-Gaussian Noise--Comparison of Recently**  
**Developed Methods**  
**[PERSON] 1,*(), [PERSON] 1 and [PERSON] 2 ()**  
1 Faculty of Geoengineering, Mining and Geology, Department of Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland  
2 Faculty of Pure and Applied Mathematics, Department of Pure Mathematics, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland  
* Correspondence: [EMAIL_ADDRESS]  
Received: 28 January 2020; Accepted: 6 April 2020; Published: 12 April 2020  
**Abstract:** The vibration signals acquired on machines usually have complex spectral structure. As the signal of interest (SOI) is weak (especially at an early stage of damage) and covers some frequency range (around structural resonance), it requires its extraction from a raw observation. Until now, most of the techniques assumed the presence of Gaussian noise. Unfortunately, there are cases when the non-informative part of the signal (considered as the noise) is non-Gaussian due to the random disturbances or nature of the process executed by the machine. Thus, the problem can be formulated as the extraction of the SOI from the non-Gaussian noise. Recently this problem has been recognized by several authors and some new ideas have been developed. In this paper, we would like to compare these techniques for benchmark signals (Gaussian noise, cyclic impulsive signals, non-cyclic impulsive signals with random amplitudes and locations of impulses and a mixture of all of them). Our analysis will cover spectral kurtosis, kurtogram, stability index (Alpha selector), conditional variance-based selector, spectral Gini index, spectral smoothness index and infogram. Finally, a discussion on the efficiency of each method is provided.  
**Keywords:** local damage; vibration; time-frequency analysis; band selectors; statistical analysis
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 5 Evaluation of the Numerical Results
### Comparison to Analytical Buckling Curves  
Using the final advanced computational model, an additional numerical parametric study was executed to obtain the most accurate local buckling resistances, which could be compared to the local buckling curves depending on the relative slenderness of the analyzed cross-section. The numerical parametric study was made on 100 virtual models, where the input data of the yield strength, cross-section size and plate thickness values were generated using Matlab's built-in uniformly distributed random number generator. The parameter limits were set to the lowest and highest value used in the experimental tests, ranging from 280 to 1350 MPa, 70 to 680 mm, and 1 to 14 mm, respectively. Figure 15 illustrates the numerical results depending on the relative slenderness ratio (as presented on the horizontal axis). The reduction factor for local buckling (presented on the vertical axis) was back-calculated from the numerically calculated buckling resistances. The obtained numerical results were compared to different analytical buckling curves and the best-fit curve is also indicated on the graph. Three different analytical buckling curves were compared to the numerical results, which were (i) the local buckling curve of EN 1993-1-5 [4] using [PERSON]'s formula; (ii) the local buckling curve of EN 1993-1-5 [27] using formula of Annex B; and (iii) the buckling curve proposal of [PERSON] [6]. The fitting process was performed using the software tool \"ODRPACK\" version 2.01, employing a weighted orthogonal distance regression method (ODR). Based on the obtained result, it can be seen that the best approach with results closest to the data points calculated by the most accurate numerical model was the buckling curve proposed by [PERSON] [6]. The figure also proves that the buckling curve given by Annex B of EN 1993-1-5 [4] leads to safe side resistances, especially for very slender plates. The results also prove that the Winter-type buckling curve would overestimate the buckling resistance for the welded box section columns, as proven by previous research results as well.
## 3 Identity Authentication and Secure Transmission Based on Blockchain  
The management model proposed needs to transmit data between blockchain and UAV, and between UAV and UAV. Although blockchain can guarantee the tamper-proof of on-chain data, it cannot guarantee the security of data transmission. Therefore, it is necessary to combine cryptography to realize the secure transmission of data.
# Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4
## 5 Conclusions  
In order to realize the automatic monitoring of irregular behavior of substation personnel, this paper proposes an improved YOLOv4 lightweight detection model based on MobileNetV3 and the ECA attention mechanism. Case studies were carried out and the detection results were compared and analyzed with different improvement methods, object detection models, and gamma correction preprocessing. The following conclusions can be drawn.  
1. Using MobileNetv3 as the feature extraction network to replace CSPDarkNet53, increasing the scales of feature extraction, and using depthwise separable convolution and an ECA attention mechanism to optimize the SPP and PANet networks effectively enhanced the accuracy of the model, which can provide a reference for other researchers to improve such models.
2. The improved YOLOv4 lightweight model proposed in this paper had good performance in detecting irregular behavior of substation personnel, with high detection accuracy and fast detection speed. The mAP and FPS reached 83.51% and  
Figure 12: Detection results with gamma correction: (**a,c**) original images; (**b,d**) gamma corrected images.  
38.06 pictures/s, respectively, and the overall performance was better than that of Faster RCNN, SSD, YOLOv3, and YOLOv4.
3. Gamma correction preprocessing can effectively enhance image samples, improving the detection accuracy of the improved YOLOv4 lightweight detection model, and increasing the mAP to 83.51%. The proposed method was conducive to accurate, automated, and fast detection of irregular behaviors of substation personnel and was suitable for the requirements of real-time detection in practical engineering contexts.  
Certain problems should be further studied and solved in the future. The indoor and outdoor environments of substations result in images with various backgrounds. Effective and automatic image preprocessing methods need to be studied, as the accuracy and robustness of detection models strongly depends on the training image samples. More image samples should therefore be added to the dataset. In addition, the object model would be deployed on computing devices to develop a visual image analysis and real-time detection system; thus, related hardware issues and practical application effects will need to be studied and verified in the future.  
Conceptualization, X.L. and J.F.; methodology, J.F.; software, J.F.; validation, X.L. and J.F.; formal analysis, J.F.; investigation, J.F.; resources, J.F.; data curation, X.L.; writing--original draft preparation, J.F.; writing--review and editing, X.L.; supervision, X.L.; project administration, X.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data used in this research can be provided upon request.  
The authors declare no conflict of interest.
## 1 Introduction
### Research Gaps and Challenges  
This study addresses this gap by examining three distinct delay mechanisms (constant, uniform, and exponential) across diverse network structures. We initially analyze real Reddit thread data to establish baseline patterns, but encounter limitations in sample size. To overcome this constraint, we develop a comprehensive synthetic network approach, generating 100 networks with controlled properties representing five distinct topological structures. Our methodology combines empirical analysis, simulation modeling, and machine learning to identify the key factors influencing information diffusion.  
This research specifically investigates the following: (1) how different delay mechanisms affect information coverage; (2) which network structural properties most strongly influence diffusion outcomes; (3) the relative importance of spread probability versus network topology; and (4) the predictive power of temporal dynamics in determining ultimate information reach. By integrating both real and synthetic data analysis, this study provides a robust foundation for understanding how information propagates through social networks with time-delayed sharing.  
Our findings reveal that temporal dynamics, particularly the timing of peak diffusion speed, play a crucial role in determining information coverage. Moreover, we identify specific combinations of network structure, spread probability, and delay mechanisms that maximize information reach. These insights contribute to both the theoretical understanding of diffusion processes and the provision of practical guidance for optimizing information dissemination strategies in social networks.
## 3 Results
### Phytochemical and Antioxidant Activities of Extracts Derived from Plasma-Exposed Muslrooms  
Total phenolic content and total flavonoid content of the mushroom fruiting body extract were calculated from calibration curves. Phenolic compounds have redox properties that allow them to act as antioxidants [22]. Flavonoids, including flavones, flavonols, and condensed tannins, are plant secondary metabolites, the antioxidant activity of which depends on the presence of free OH groups, especially  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene Name** & **Sequence (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Size (bp)** \\ \hline ACTIN-forward & GGC ATC CTC ACC CTC ATG AAG TA & 82 \\ ACTIN-reverse & AGG TGT GGT GCC AGA TTT TC & 82 \\ BAX-forward & AAG AAG CTG AGC GAG TGT CTC & 186 \\ BAX-reverse & GCT GGC AAA GTA GAA AAG GGC & 186 \\ BCL2-forward & TTC TTT GAG TTC GGT GGG GTC & 208 \\ BCL2-reverse & TCT TCA GAG ACA GCC AGG AGA & 208 \\ BCL-XL-forward & TCA GTG GAG GAG GTG TT & 170 \\ BCL-XL-reverse & GGC CTC AGT CCT GTT CTC TT & 170 \\ TP53-forward & GCC CCT CCT CCT CAC CAT CT ATC & 246 \\ TP53-reverse & AAA GCT GTT CCG TCC CAG TAG & 246 \\ H2 AX-forward & CAA CAA CAA CAA GAC GCG AAT CA & 223 \\ H2 AX-reverse & CGG GCC CTC TTA GTA CTC CT & 223 \\ Casp3-forward & ATG TCG ATG CAG CAA ACC TC & 173 \\ Casp3-reverse & TCC TTC TTC ACC ATG GCT CA & 173 \\ Casp7-forward & AGA AGC TGT TAC CAC ACC CA & 185 \\ Casp7-reverse & ACT CCA TCT CAG TCA GTG GGC & 185 \\ Casp8-forward & CCC AAA TCA ACA ACA GGC TGC & 219 \\ Casp8-reverse & TCA GAC AGT ATC CCC GAG GTT & 219 \\ CytC-forward & CAA CAC CTC TCA CAT CTT AAA GGA & 185 \\ CytC-reverse & GGC AAA AGC ACC ATT CCC AA & 185 \\ PUMA-forward & CCC ACC ACC ATC TCA GGA AA & 185 \\ PUMA-reverse & GTG GTC ACG TTT GGC TCA TT & 185 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of primer sequences used in this study.  
3-OH. These flavonoids exhibit antioxidant activity in vitro and act as antioxidants in vivo [23]. To determine whether plasma could affect these biological components, we measured the levels of phenols and flavonoids in untreated/plasma-treated mushroom-derived extracts using Folin-Ciocalteu and aluminum chloride colorimetric methods, respectively. Here, the 30 s plasma-treated mushroom groups produced the highest levels of phenolic and flavonoid compounds as compared with the 60 s and 0 s treatment groups (Figure 2B,C). These findings suggest that plasma exposure for 30 s increases phenol and flavonoid content in the mushroom extracts, which might possess antioxidant properties. Therefore, we assayed DPPH radical scavenging that relies on color changes due to free radicals. Decreasing absorption values are directly proportional to the amount of produced DPPH [24]. The DPPH inhibition activity was notably higher in the samples from the 30 s groups than in those from the 60 s and 0 s groups. These results were consistent with the increased phenol and flavonoid contents under similar exposure conditions (Figure 2D). These results suggest that plasma exposure can improve phytoconstituent levels and antioxidant properties in mushroom extracts.