esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The relative incidence of two sources of uncertainties on the calculation of stresses induced by meteoroid impact on a lunar regolith shield has been investigated. In particular, the effects of assumptions concerning the soil stiffness and the duration of collision have been pointed out. From the designer's point of view, changing the assumed collision time in the calculation of meteoroid impacts should necessarily imply a change in the maximum impact force. The reason for this lies in the fact that the \"design impact\" should be defined on a probabilistic base, by selecting both mass and velocity of the impactor, that is, by selecting a given momentum. The uncertainty related to the stiffness of a regolith layer has been considered to arise from the different strain intervals the designer can adopt for the measurement of the elastic moduli.  
Based on the FEM results, the following conclusions can be drawn:  
* For the assumed impact, the discrepancy in terms of maximum stress at the layer bottom related to the implemented regolith stiffness is 43-52% of the average value, depending on the damping assumption.
* This discrepancy is comparable with the percentage differences in terms of maximum stress (at the bottom) between the loading vertical and a vertical 2m far: for the case of stiffness 33 MPa and force 360 kN, for example, they are 38-43%, depending on the damping assumption.
* Changing the collision duration of one order of magnitude, for a given stiffness, leads to changes in maximum stress at the bottom of one order of magnitude.  
Concluding, the results suggest that the uncertainty of the impulsive stress prediction related to the implemented soil stiffness, although not negligible, could be managed by suitable safety factors to avoid damage to the buried structure, whereas the collision duration to be implemented in the calculation should be defined with very great accuracy. In this respect, the combined use of numerical simulation and physical modelling of impact processes is probably the strategy to be pursued.  
Conceptualization, F.C. and E.D.; methodology, F.C.; software, A.V.; investigation, A.V., O.A., O.B. and F.C.; writing-original draft preparation, F.C.; writing-review and editing, F.C., O.A. and O.B.; visualization, A.V.; supervision, O.B., F.C. and E.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussions
### Results
#### 4.1.1 Feature Visualization  
The Kernel Density Estimate (KDE) is a useful technique to visualize the distribution of observations in the given dataset. It is equivalent to a histogram with the difference of using a continuous probability density curve with one or more dimensions. The proposed scheme employed the KDE plot to visualize the separation of the features related to different character classes. For this purpose, the features were transformed from multi-dimensions to two-dimensions with the help of the PCA method. Figure 10a was plotted with the features received directly from images of the handwritten character dataset, while Figure 10b was plotted with fusion-based hybrid features of Dataset 15. A clear distinction can be made between these two. In Figure 10a, the separation of various character-classes in the feature space was so poor that some of the classes were completely overlapped by the others, and were either poorly or not visible; the examples were the classes 4(%), 5(%), 13(%), 14(%), 15(%), and 23(%). On the contrary, Figure 10b shows a promising separation between all the character classes, except 28(%) and 32(%). Figure 10 justifies the effectiveness of features collected from the fusion-based approach.  
Figure 9: Confusion matrix generated for Dataset 15 by: (**a**) MLP classifier; (**b**) SVM classifier.
# Study on Microseismic Monitoring of Landslide Induced by Blasting Caving
## 2 Engineering Background
### Introduction to the Microsesmic Monitoring System  
To monitor the stress distribution characteristics during pillar recovery operations, a 30-channel microseismic monitoring system was established in the mining area in 2008. The microseismic monitoring system of the Canadian ESG Company was adopted, with 24-bit analog-to-digital conversion and sampling frequency ranging from 3 Hz to 10 kHz. This system consists of sensors, paladin signal acquisition and processing system, time  
Figure 1: Mining plan view: (**a**) before pillar recovery, (**b**) after pressure-relief shaft formation.  
Figure 2: Mining Sectional View: (**a**) before pillar recovery, (**b**) after pressure-relief shaft formation.  
synchronization system, optical fiber data communication system and ground data comprehensive processing and analysis system. The system comprises five data acquisition units, each equipped with six single-axis acceleration microseismic sensors (sensitivity: 30 V/g; maximum output signal of the sensor is \(\pm\)5 V; frequency response range: 50-5000 Hz). The 30 sensors were distributed across three mining levels: 12 sensors at the 514 m level, 6 sensors at the 558 m level, and 6 sensors at the 630 m level, as shown in Figure 3. The system consists of two major components: the surface monitoring system and the underground monitoring system. The surface monitoring system includes a local monitoring station and a remote data analysis station, while the underground monitoring system primarily comprises the underground microseismic sensors and data acquisition units. A schematic of the system's configuration and signal transmission method is shown in Figure 4. In 2010, the mine further expanded the microseismic monitoring system to 36 channels.  
Figure 3: Sensor distribution map: (**a**) 514 m, (**b**) 558 m, (**c**) 630 m.
# SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks
## 5 Results and Discussion
### Assessment of SEHIDS with ReNN Architecture (Anomaly-Based IDS)  
The results presented in the previous subsections show that SEHID is able to evolve a simple version of the MLP architecture in such a way that allows it to accurately detect different types of attacks without consuming a significant number of resources. However, since the MLP architecture is by far a supervised learning approach, the resulting model acts as a signature-based IDS. This section is dedicated to assessing the ability of SEHID to develop an anomaly-based IDS system. The results presented in the previous subsections show that SEHIDS is able to evolve a simple version of the MLP architecture in such a way that allows it to accurately detect different types of attacks without consuming a significant number of resources, which attests to its suitability for IoT networks. However, since the MLP architecture is by far a supervised learning approach, the resulting model acts as a signature-based IDS. This section is devoted to assessing the ability of SEHIDS to develop an anomaly-based IDS system. In order to conduct a thorough investigation of the performance of SEHIDS in the domain of anomaly-based IDS, three new SEHIDS models are created, each of which is used with one of the three datasets described in Section 3. The number of neurons in the input and output layers of the three initial ReNN architectures fed into the three SEHIDS models is set to 128, which is the number of MTU's octets. These models are trained on benign traffic in an unsupervised manner (i.e., without labelling data) using a 10-fold cross-validation strategy. In this strategy, the considered dataset is randomly split into 10 subsets where each subset is used in the training phases while the remaining is used in the testing phase. During the training phase, the SEHIDS is used to evolve the initial architectures until the evolving criteria defined in the pseudocode are met, i.e., until the error calculated by the loss function in a time instance becomes smaller than the errors produced by the model up to that point. Thereafter, the resultant architecture is maintained, SEHIDS is halted, and a mixture of benign and malicious traffic is used to test the performance of the model. During the testing phase, the minimum error obtained during the training phase is used as the confines for normal instances. The performance of the three SEHIDS-based models is measured using standard statistical metrics as the average metrics that are obtained during the testing phase and then compared with the results of related work. Table 2 summarises these results, where the abbreviations are defined as follows. Figure 7: RAM utilisation of (**a**) BoT-IoT, (**b**) IoTID20 and (**c**) TON-IoT.
###### Abstract
However, he also notices the hyperparameter tuning and comprehensive understanding of data issues are challenging tasks with SARIMA. Furthermore, the model's performance has been validated in various contexts, including large-scale wind farms, where it has been used to model uncertainty in wind speed forecasts [27]. The application of SARIMA is not limited to short-term predictions; it has also been effectively utilized for long-term forecasting of climatic parameters, including wind speed [22; 23]. This versatility is particularly beneficial for energy production planning in wind farms, where accurate wind speed forecasts are critical for optimizing energy output and managing grid stability [28]. The integration of SARIMA with other forecasting techniques, such as neural networks, has also been explored, leading to hybrid models that leverage the strengths of both statistical and machine-learning approaches [29]. These hybrid models have shown promises in improving prediction accuracy, particularly in complex environments where multiple variables influence wind patterns [30]. This study evaluates and compares three advanced methodologies for wind prediction, integrating machine learning, signal processing, and stochastic techniques. Specifically, the stochastic approach employed in this study is the SARIMA model, which is assessed alongside machine learning techniques for their effectiveness and adaptability to diverse regional contexts in Bode, Norway. The machine learning methods under investigation include LSTM: A type of recurrent neural network that excels at capturing long-term dependencies in sequential data, making it particularly effective for forecasting seasonally influenced variables like drought indices; SVM: A supervised learning algorithm adept at handling complex relationships between climate variables. Its robustness makes it well-suited for classification tasks and severity prediction, even with limited data availability; ANN: A model composed of interconnected processing units (\"neurons\") that work collaboratively to model nonlinear patterns in data. To enhance the predictive performance of these methods, WT is employed as a preprocessing step. This versatile signal processing technique effectively captures both high-frequency and low-frequency patterns in climate data, thereby improving the input data quality for models such as LSTM and SVM. The primary objective of this study is to determine the most effective machine learning algorithm and model architecture for describing wind speed at a monthly timescale. The performance of each method will also be evaluated in predicting maximum monthly wind speed. Moreover the study investigates the impact of data feature selection and the application of signal decomposition techniques in improving the accuracy and computational efficiency of wind speed forecasting. Through a comparative analysis of these approaches, this study aims to improve wind forecasting capabilities and offer practical insights in selecting the most effective methodologies across varying regional and climatic conditions.
## 4 Results and Discussion
### Descriptive Analysis (EDA)
#### 4.1.2 Differences by Transport Mode and Commune  
To explore waiting time differences in greater depth, an analysis was conducted based on the origin commune and the mode of transport (Figure 2). The key findings were as follows:  
* Urban bus: Exhibits highly variable waiting times, particularly in Valparaiso (median 6.0, std. 7.2 min) and Vina del Mar (median 5.0, std.6.3 min). Other communes show medians around 5.0 min with significant variability as well (Quilpue std.6.2, Concon std.6.0, Villa Alemana std.4.4). Peaks of up to 30-40 min, reflected in the high standard deviations, highlight frequency issues during peak hours, particularly in densely populated areas.
* Metro Valparaiso: Shows lower and more consistent waiting times. The median is generally 5.0 min (Valparaiso std.3.2, Vina del Mar std.3.6, Quilpue std.4.0, Villa Alemana std.2.9), with low standard deviations suggesting higher reliability and punctuality. Concon registers a median of 0.0 (std.0.0). No instances above 20 min were observed in the communes analyzed.
* Taxi Colectivo: Medians are relatively low, observed at 5.0 min in Valparaiso (std.6.1) and Vina del Mar (std.5.8), and 4.0 min in Quilpue (std.4.6), Concon (std.5.6), and Villa Alemana (std.3.7). However, the standard deviations (ranging from 3.7 to 6.1 min) and observed peaks up to 40-50 min indicate variability and suggest instances of longer waits, likely due to congestion or an insufficient fleet during periods of high demand.
* Funicular and Trolleybus: In Valparaiso, both display low, stable waiting times. Funicular shows a median of 2.0 min (std.1.8), while the Trolleybus has a median of 5.0 min (std.2.7). The low standard deviations support their perceived stability, possibly related to their operation on fixed, short routes. (Note: Funicular data also exist for Villa Alemana: median 2.0, std.0.0; Vina del Mar: median 5.0, std.3.6).
* Informal services: Show very short waiting times with little variability. Medians range from 0.0 (Villa Alemana std.0.0, Vina del Mar std.0.0) to 1.0 min (Quilpue std.1.9) and  
Figure 1: Analysis of waiting times in public transportation.  
4.0 min (Valparaiso std.2.0). The low standard deviations (0.0 to 2.0 min) confirm low variability, although use appears limited to specific areas.
* Interurban or Rural Bus: Waiting time characteristics differ significantly between communes. Medians range from 3.0 min in Villa Alemana (std.5.6) and 5.0 min in Valparaiso (std.2.7) and Vina del Mar (std.8.0), up to 10.0 min in Quilpue (std.5.2). The wide range in both medians and standard deviations (from 2.7 to 8.0 min) indicates inconsistencies in scheduling and frequency.  
In summary, notable disparities emerge depending on the commune and the mode of transport. Some services (e.g., Metro, Funicular) prove highly efficient, whereas others (e.g., Urban Bus, Taxi Colectivo) are prone to delays and may require targeted frequency and service-level adjustments.
## 4 Materials and Methods
### Satellite Spectral Analysis  
As mapped 2D structures are often so difficult to visualize, the newly identified archaeological features were expanded into a geo-referenced 3D model. This reconstruction is intended to help archaeologists conceptualize what these structures may have looked like in antiquity and, more generally, to give a tangible picture of the understudied eastern section of Hermopolis. The final model was constructed in two main sections: First, the architectural footprint of the structures was created, and then the materials/textures were mapped using the 3D-modeling computer program, SketchUp Pro. Second, the materials, textures, and landscape of the model were realistically rendered using the powerful rendering and simulation software, Chaos V-Ray. It should be noted that while SketchUp has been often utilized in archaeology for the creation of 3D models [49; 50], the use of Chaos V-Ray for rendering archaeological structures/features is still very new.  
The fabrication of the 3D reconstruction started with determining the general parameters of the model. The size parameters, the length (modelLength) and width (modelWidth), of the entire area were first measured from the 2D model and then rendered in SketchUp Pro. It should be emphasized that the exact height (modelHeight) is unknown, as remote sensing analysis merely detects the foundations of a structure. However, similar surviving structures in Egypt were used as the prototype for the model's height (more in the Results and Discussion section). Next, the roomLength and roomWidth of each individual room within the structures was measured and mapped into the model. As the type of ceiling of the uncovered structures is also unknown, extant examples from ancient Egypt were utilized for this model. After the addition of the ceiling, realistic materials used in ancient Egyptian structures--mudbrick with both vegetal and non-vegetal inclusions--were mapped on the exterior and interior surfaces. Grain plants and grass features were then added around the structures to enhance the landscape of the model. Finally, the completed  
Figure 3: Deteriorating remnants of mudbrick structures at Hermopolis (photo taken by author).  
model was transferred to Chaos V-Ray and the geometry settings/presets for the model were adjusted for the materials/texture and landscape features (e.g., grass) in order to render them in the most realistic way possible.
## 4 Discussion
### Modifiable Risk Factors  
#### 4.1.1 Biomechanical--Hip Range of Motion  
The current search identified the hip range of motion as the only modifiable risk factor to have prospectively investigated ACLI risk in semi-professional American football. Hip internal rotation reduction on either side demonstrated significant association to left ACLI(left hip internal rotation reduction OR: 0.95, 95%CI: 0.93-0.98, \(p\) = 0.0001 and right hip internal rotation reduction OR: 0.95, 95%CI: 0.93-0.97, \(p\) < 0.0001) [35]. While the authors reported significantly increased odds of left ACI with greater reductions in hip internal rotation motion, an odds ratio of <1 contradicts this notion [44]. Further to this distinction is the 5% difference in odds represents a small effect size, and a reported confidence interval close to 0 limits a clear interpretation. It is intriguing that no significant associations were found between hip internal rotation reduction and right ACII, but significant associations existed for left ACII. Whether this outcome is an artefact of limb dominance bias or of statistical relevance requires further investigation.  
A retrospective study not meeting inclusion criteria for this review reported that non-professional athletes (including American footballers) with hip internal rotation reduction and >60\({}^{\circ}\) alpha angle (measurement defining hip cam deformity) were at greater risk of ACII (OR: 2.7, 95%CI: 1.4-5.2, \(p\) = 0.001) [45]. It is important to consider that hip internal rotation reduction can also be classified as a non-modifiable risk factor given the potential for hip morphology, such as a cam deformity, to cause reduction in hip internal rotation motion [35; 45].  
Careful interpretation of these results for hip internal rotation reduction and ACII risk are required as the primary cause of hip internal rotation reduction is uncertain and may stem from other risk factors. The potential for modifying hip internal rotation and its associated benefits is questionable and highly dependent on the individual athlete's primary cause of reduced hip internal rotation. For instance, repetitive end-range hip internal rotation mobility work in attempt to increase hip internal rotation motion in an athlete with underlying anterior femoroacetabular impingement syndrome may not be appropriate as this may consequently, over time, lead to posterior capsular laxity through a fulcrum effect [46]. A more thorough examination of the role hip range plays in ACII risk is warranted given the findings from this review.  
Future prospective randomized control trials would be required to determine a causal relationship between hip internal rotation reduction and ACII. An intervention group performing exercises aiming to improve hip internal rotation motion would be plausible whilst monitoring for various confounding variables such as fatigue, game time, footwear, etc. The hip range of motion testing is a cost and time-effective screening test that can be used in the elite field-based team sports. However, the ability of coaching and training staff to modify the outcomes may be limited.
## 3 Results and Discussion
### Objective Functions
\[\text{TFC}_{\text{loss}}=\frac{71}{1+\exp^{-(-2.2407326)t_{1}-2.488371847 _{2}+0.33508437_{3}-0.0248867_{4}+6.309837_{5}-1.4838487_{6}-9.336848467_{5}+3.5 381487)}} \tag{12}\] \[\text{A}_{\text{loss}}=\frac{80.1}{1+\exp^{-(2.208830071_{1}-0.7975 487_{2}+2.388373_{3}-1.19125037_{4}-2.981842507_{5}+1.4821837_{6}-1.6851907_{7 }-0.3315937_{5}+0.3829776)}}\] (13) \[\text{TPC}_{\text{loss}}=\frac{62.91}{1+\exp^{-(6.032897_{1}+7.2 318087_{2}+2.248687_{3}-1.3440237_{4}+3.386837_{5}+1.48608487_{6}+0.0057027_{7 }+1.1086137_{5}-3.3009243)}}\] (14) \[\text{CD}=\frac{42.69}{1+\exp^{-(3.217007481_{1}+10.3999502_{2}+1.4703687_{3}-3.2780417_{4}+2.272789_{5}+0.376889_{6}+1.12460519_{7}+0.8402208 _{5}-1.2884037)}} \tag{15}\]  
where \(F_{(i=1\cdot 8)}\) from  
Figure 3.0  
\(W_{1}\)-\(W_{5}\) from  
Figure 3.1  
and \(D_{ji}\) are shown in Figure 3.2  
Equations (10)-(15) (respected normalization) were used for algorithm (Equation (9)). The validation of the model (ANN) using the validation set and, additionally, all data, to demonstrate the reliability of predicted values was conducted. The R for validation and all data was 0.9974 and 0.9922 (Figure 3), whereas mean square errors (MSE) were 0.00026 and 0.00029, respectively. This confirms the accuracy and consistency of the proposed model.
# A Back-to-Back Gap Waveguide-Based Packaging Structure for E-Band Radio Frequency Front-End
## 2 Unit Design of the Packaging Structure  
As shown in Figures 2 and 3, the proposed packaging structure consists of a hybrid coupler with back-to-back GW structure, a 71-76 GHz and 81-86 GHz filter based on the GW structure, and a microstrip (MS)-GW transition. Since there is no interaction among the channels of the hybrid-coupled duplexer, we can design the required components separately and connect them directly, which greatly simplifies the design process.
## 4 Results
### Statistical Analysis
#### 4.7.3 [PERSON]'s Post Hoc Test  
[PERSON]'s post hoc test with Bonferroni correction was conducted to identify which model pairs differed significantly. The pairwise \(p\)-values are summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Modulation** & **CNN** & **ResNet** & **SCRNN** & **CLDNN** & **CV-CNN-TCN** & **CV-CNN-TCN-DCC** \\ \hline
8 PSK & 64.79\% & 57.33\% & 40.77\% & 44.10\% & 84.18\% & 85.04\% \\ AM-DSB & 96.52\% & 73.52\% & 99.95\% & 99.31\% & 99.97\% & 43.52\% \\ BPSK & 97.31\% & 95.81\% & 95.68\% & 97.27\% & 96.18\% & 98.04\% \\ CPFSK & 99.93\% & 95.39\% & 98.64\% & 99.31\% & 99.29\% & 99.14\% \\ GFSK & 99.77\% & 98.97\% & 99.35\% & 99.31\% & 99.14\% & 98.81\% \\ PAM4 & 98.08\% & 95.87\% & 95.27\% & 95.60\% & 98.47\% & 98.29\% \\
16-QAM & 20.54\% & 18.83\% & 16.37\% & 31.49\% & 26.60\% & 35.94\% \\
64-QAM & 77.79\% & 71.97\% & 71.31\% & 61.20\% & 79.14\% & 69.68\% \\ QPSK & 56.66\% & 80.45\% & 60.06\% & 63.58\% & 73.25\% & 74.58\% \\ WBFM & 42.04\% & 55.25\% & 38.74\% & 41.20\% & 35.81\% & 79.72\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recognition accuracy at 4 dB SNR.  
Statistically significant differences (\(p<0.05\)) were found between the following model pairs:  
* CLDNN and CV-CNN-TCN, CLDNN and CV-CNN-TCN-DCC;
* SCRNN and ResNet, SCRNN and CNN, SCRNN and CV-CNN-TCN, SCRNN and CV-CNN-TCN-DCC.  
These findings suggest that CV-CNN-TCN and CV-CNN-TCN-DCC offer statistically significant performance improvements over certain baseline models.