esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, this study has addressed the performance evaluation of the physical downlink control channel (PDCCH) in two different scenarios: one without losses and another with losses generated by the path. Through the implementation of a transmission-reception system in MATLAB R2021b and the analysis of BER vs. SNR and BER vs. transmit power curves, valuable insights have been gained into how key parameters impact channel performance. In the first scenario, where a lossless channel was considered, the significance of the number of resource blocks (NRB) in enhancing PDCCH performance was highlighted. As the number of NRB increases, a reduction in the probability of bit errors was observed, indicating a more reliable transmission. The second scenario, incorporating channel losses, revealed the positive influence of user equipment (UE) height and elevation angle on attenuation loss reduction. By increasing the UE's height or elevation angle, the link distance decreased, leading to improved BER performance and ultimately enhancing the system's efficiency. Furthermore, the analysis of carrier frequency variation demonstrated that higher carrier frequencies result in higher bit error rates, underscoring the importance of carefully considering frequency selection in system design.  
Figure 6: BER vs. PTx for hUE = 1.5 m, angUE = \(\{\pi/18,\pi/12,\pi/19,\pi/6\}\) rad.  
Figure 7: BER vs. PTx for hUE = 1.5 m, hUE = 60\({}^{\circ}\), and Fc = [3.5, 6, 24.5] GHz.  
**Author Contributions:** Conceptualization, M.C.P.-P.; methodology, P.B.-C., F.G.-G. and M.C.P.-P.; software, P.B.-C.; validation, P.B.-C., F.G.-G. and M.C.P.-P.; formal analysis, P.B.-C., F.G.-G. and M.C.P.-P.; investigation, P.B.-C.; resources, F.G.-G.; data curation, P.B.-C.; writing--original draft preparation, P.B.-C.; writing--review and editing, F.G.-G. and M.C.P.-P.; visualization, P.B.-C. and F.G.-G.; supervision, M.C.P.-P.; project administration, M.C.P.-P.; funding acquisition, F.G.-G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Escuela Politecnica Nacional.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Self-Sensing Geosynthetics
### Carbon-Black-Based Self-Sensing Geosynthetics
Their results showed that the conductive network in coated components composed of higher-structured CB exhibited more resilient piezoresistivity and suffered less damage during cyclic loadings. In addition, the strain sensitivity of the coated specimens was also increased using pre-strain. The ratio of the initial peak strain to the subsequent operating peak strain was also used to determine the degree of pre-straining applied to the samples. However, the composite piezoresistivity was more consistent and steadier when the ratio was higher. These studies on self-sensing geosynthetics primarily investigated the performance of unitised and coated yarn-based self-sensing geosynthetic prototypes in isolation. However, in field applications, the soil-confining pressure is expected to affect the piezoresistivity of self-sensing geosynthetics. [PERSON] et al. [120] employed a modified direct shear test set up to evaluate the effect of confining pressure on the piezoresistivity of a PVC-coated PET-yarm-based self-sensing geosynthetic. They conducted in-soil tensile experiments at 10, 30, and 50 kPa confining pressures to mimic the upper levels of reinforced soil structures subjected to greater strain and deformations [121] and reported that the piezoresistivity response of self-sensing geosynthetics was reduced by increasing the confining pressures and strain rates. However, the amplitude and repeatability of the piezoresistivity responses recorded in the in-soil experiments were deemed adequate for civil engineering applications.
## 6 Uniformity Index  
How can we measure the difference between real cases-fade durations fragmented in many different intervals with changing threshold and site-and the limiting case of identity/uniformity of the two processes when all fades last the same time? We propose the following index \(U(S)\), referred to as the uniformity index and a function of threshold \(S\), defined by:  
\[U(S)=\frac{\int_{0}^{1}P_{S,B}(D)d(P_{S,A}(D))}{0.5} \tag{6}\]  
In other words, Equation (6) is the ratio between the area under each function \(P_{S,B}(D)=f(P_{S,A}(D))\) shown in Figure 9 and in Appendix C, and the area under the 45\({}^{\circ}\) line, which is 0.5. Therefore \(0<U\leq 1\).  
Figure 10 shows uniformity index versus threshold, for each site. We can observe the following features:  
1. \(U(S)>\sim\)0.4 for all sites.
2. For \(S=0\) dB, \(U(0)\) ranges from \(\sim\)0.42 to \(\sim\)0.58. Since this value refers to the single rain events (i.e., the time series \(R(t)\) of each rain event), then the duration of rain events does change in a large range. In fact, if all rain events were of equal duration \(D\), they would give \(P_{S,B}(D)=P_{S,A}(D)=\delta(1)\), \(U(0)=1\). This case would be represented by the point (1,1) in Figure 9, with probability \(\delta(1)\), a Dirac impulse of unit area.
3. Some sites show marked dips at low thresholds. In other words, at these thresholds the two processes are the furthest away from the uniformity model (Spino d'Adda, Gera Lario, Madrid, Vancouver).
4. As the threshold increases, \(U(S)\) increases; therefore, the two processes tend to be closer to the uniformity model at larger rain attenuation. This is a sound physical result because fades tend to be more similar in duration at large rain attenuation (see, for example, Figure 1). \(U(S)\) approaches 1 at very large thresholds (Madrid, Vancouver).
5. The red line, Equation (3), gives \(U=0.2605/0.5=0.521\) (a line \(y=0.521\) in Figure 10) practically the mid-range value at \(S=0\) dB: \((0.42+0.58)/2=0.5\).
## 2 Modeling of a Mobile Satcom Antenna
### Gimbal Coordinates Definition  
An orthogonal coordinate system is defined: Base coordinate frame \((\alpha x_{b}y_{b}z_{b})\), azimuth gimbal frame \((\alpha x_{a}y_{a}z_{a})\) and elevation gimbal frame \((\alpha x_{e}y_{e}z_{e})\). The rotating transformations are shown in Figure 1. As we can see from the Figure 1, \(\theta_{a}\) and \(\dot{\theta}_{a}\) are the relative angular and relative angular rate between the azimuth gimbal and the base carrier, respectively. \(\theta_{e}\) and \(\dot{\theta}_{e}\) are the relative angular and relative angular rate between the elevation gimbal and the azimuth gimbal, respectively:  
\[\text{Rotation matrices}\quad\text{can}\quad\text{be written}\quad\text{as} \quad C_{b}^{a}=\left[\begin{array}{ccc}\cos\theta_{a}&\sin\theta_{a}&0\\ -\sin\theta_{a}&\cos\theta_{a}&0\\ 0&1&1\end{array}\right]\quad\text{and}\]  
\(C_{a}^{e}=\left[\begin{array}{ccc}1&0&0\\ 0&\cos\theta_{e}&\sin\theta_{e}\\ 0&-\sin\theta_{e}&\cos\theta_{e}\end{array}\right]\), which represent the transformation from the base coordinate to the azimuth coordinate and transformation from azimuth coordinate to the elevation coordinate, respectively.  
According to the rotation matrix \(C_{b}^{a}\) and \(C_{a}^{e}\), the angular rate relations of the two gimbals can be obtained as follows:  
\[\omega_{ia}^{a}=C_{b}^{a}\omega_{ib}^{b}+\omega_{ba}^{a}=\left[\begin{array}[] {c}p\cos\theta_{a}+q\sin\theta_{a}\\ -p\sin\theta_{a}+q\cos\theta_{a}\\ r+\dot{\theta}_{a}\end{array}\right] \tag{1}\]  
\[\omega_{ie}^{e}=C_{a}^{e}\omega_{ia}^{a}+\omega_{ae}^{e}=\left[\begin{array}[] {c}p\cos\theta_{a}+q\sin\theta_{a}+\dot{\theta}_{e}\\ -p\cos\theta_{e}\sin\theta_{a}+q\cos\theta_{a}\cos\theta_{e}+r\sin\theta_{e}+ \dot{\theta}_{a}\sin\theta_{e}\\ p\sin\theta_{a}\sin\theta_{e}-q\cos\theta_{a}\sin\theta_{e}+r\cos\theta_{e}+ \dot{\theta}_{a}\cos\theta_{e}\end{array}\right] \tag{2}\]  
in which, \(\omega_{ib}^{b}\) is the angular rate of the base carrier; \(\omega_{ia}^{a}\) is the angular rate of the azimuth gimbal; \(\omega_{ie}^{e}\) is the angular rate of the elevation gimbal; \(\omega_{ba}^{a}=\left[\begin{array}{ccc}0&0&\dot{\theta}_{a}\end{array} \right]^{T}\) represents the angular rate of the azimuth gimbal with respect to the base; \(\omega_{ae}^{e}=\left[\begin{array}{ccc}\dot{\theta}_{e}&0&0\end{array} \right]^{T}\) represents the angular rate of the elevation gimbal with respect to the azimuth gimbal; \(\omega_{ib}^{b}=\left[\begin{array}{cc}p&q&r\end{array}\right]^{T}\) is the angular rate of the mobile carrier.
## 3 Methodology
### Audio Segmentation and Feature Extraction  
In our experiments, we were not looking for the best feature/classifier, which led to the best accuracy, but instead were interested in what is the achievable accuracy and the execution time of the classic ML techniques reported in the literature.  
Figure 1 details the audio segmentation and the feature extraction. A HANN window [12] with a length of 44,100 samples and 50% overlap is used to divide every 5-second segment of the audio files into frames. Since the sampling frequency is 44,100 Hz, the duration of each audio frame is one second. The choice of the particular window is based on the fact that the sounds included in the data sets, although non-stationary, do not contain abrupt changes in time. The choice of the sampling frequency is based on [1].  
The audio features are extracted per audio frame. The selection of the audio features can be done using software environment tools such as Weka [13]. However, an analysis about the impact in accuracy based on the selection of the features is discussed in Section 5.4.2. The first 12 Mel-Frequency Cepstral Coefficients (MFCC) are extracted per audio frame. The per-frame values of each coefficient are summarized across time using statistics. As a result, each audio frame contains a vector containing:  
Figure 1: A 5-second input audio signal is split in frames of one second with a 50% overlap. Each frame is sampled at 44.1 kHz and a set of audio features are extracted.  
mean, median, minimum, maximum, variance, skewness and kurtosis. Other features, which are extracted per frame are the spectral contrast, the spectral centroid, the Root-Mean Square (RMS), the spectral bandwidth, the spectral roll off and the zero crossing. This results in a feature vector of 90 elements per frame. All of these feature vectors per frame conform the feature set, which is used by the ML techniques to perform the sound classification.
###### Abstract  
The key performance parameters of proppant are mainly the crushing rate and fracture conductivity, which are usually evaluated using physical experimental methods. However, the testing method for fracture conductivity has limitations, such as its long time-consumption, high testing costs, instability, and even the presence of large errors in testing results under the same conditions. The purpose of this paper is to propose a calculation method that can replace physical experiments. Firstly, we analyze the random and deterministic phenomena in the contact relationship between proppant particles from a microscopic perspective. Subsequently, we develop a physical model of the microscopic arrangement of these particles, enabling us to conduct further computer simulations of their microscopic configuration. Secondly, we conduct a microscopic mechanical analysis of the contact between proppant particles and between particles and boundaries and establish a corresponding mathematical model. Then, utilizing the simulation and mechanical analysis results of the proppant, we calculate the crushing rate. Considering the crushing rate of proppant, we improve the Kozeny-Carmen equation to determine the fracture permeability, and subsequently calculate the fracture conductivity. Finally, the calculated results are compared with the experimental results. The results show that the calculated values for the proppant crushing rate and fracture conductivity matched well with experimental data, and that the model's calculation values were more accurate. As the number of simulations increased, the accuracy of the calculation results became higher. Research shows that the fracture conductivity is influenced by factors such as the particle size, microstructure, and crushing rate. Numerical calculation methods can replace physical experiments and provide theoretical support for engineering applications of hydraulic fracturing proppant materials.  
numerical calculation; proppant; crushing rate; fracture conductivity Article
## 4 Empirical Insights from Sectorial and Regional Studies
### AI Synergies and Revenue Growth in European SMEs
#### 4.2.3 AI and Robotics as Innovation Drivers in European SMEs [113]  
[PERSON] et al. [113] provide a large-scale empirical study using data from the Flash Eurobarometer 486 survey, covering over 16,000 SMEs in Europe. Their analysis explores the adoption of artificial intelligence (AI) and robotics and their association with different types of innovation outcomes, including product, process, organizational, and marketing innovations. The study applies a two-stage residual inclusion model and confirms that start-ups and scale-ups are significantly more likely to adopt AI and robots, aligning with the emphasis of our TOE-DOI framework on organizational readiness and dynamism as key enablers. Quantitative indicators reveal that 6.9% of SMEs adopted AI and 8.3% adopted robots, with adoption strongly correlated with internationalization, firm size, digital infrastructure and country-level information and communication technology skills.  
Importantly, AI adoption is associated more significantly with product, process and organizational innovations, while robot adoption is closely linked to product and process improvements. The findings highlight clear sectoral differences: manufacturing firms use these technologies for specialized innovation tasks, while service sector SMEs integrate them more broadly. These insights strengthen the external validity of our challenge-solution framework, particularly with respect to the technological and environmental dimensions of the TOE model, and demonstrate the role of digital complementarity in shaping innovation trajectories.
# Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit
## 6 Results
#### 4.2.2 Augmented Reality Experiences on iOS Devices  
**Augmented reality application**. We successfully implemented the trained YOLOv3 and YOLOv3 tiny models using our proposed synthetic dataset on an iOS device (Figure 18). We tested the application's performance on the iPhone X model, which has 3 GB of RAM and the Apple-designed A11 Bionic chip. The results showed that the iOS application could detect the markers under different lighting conditions at an average rate of 60 frames per second (FPS). The added animations work efficiently at an average rate of above 50 FPS. However, due to the limitations of the hardware, the frame rate drops to 30 FPS after 30 min of running continuously. This is a known issue with current iOS devices where the neural engine (ANE) inside the CPU is responsible for machine learning tasks. The CPU will have thermal throttling after an extended time period using the ANE and forces the system to slow down CPU performance to protect the device's components. Therefore, it could lead to low augmented reality experiences and detection accuracy rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **Precision** & **Recall** & **mAP** & **mAP50** & **Time (GPU/CPU) (s)** \\ \hline YOLOv3 416 & Ours & 98.73\% & 99.31\% & 99.5\% & 60.5\% & 0.01/2.738 \\ YOLOv3 tiny & Ours & 97.64\% & 97.98\% & 98.9\% & 57.7\% & 0.003/1.006 \\ YOLOv3 416 & MS-COCO & - & - & 55.3\% & 31.0\% & 0.01/4.587 \\ YOLOv3 608 & MS-COCO & - & - & 57.9\% & 33.0\% & 0.012/5.821 \\ YOLOv2 & MS-COCO & - & - & 44.0\% & 21.6\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the accuracy results and detection time for different datasets using YOLO models.  
Figure 18: The prototype iOS application shows that the proposed method can predict the augmented reality markers under different lighting conditions and successfully render the corresponding virtual models.
## 4 Result and Discussion
### Comparison of LSGB and BRFFNN Based on UAV Movement Pattern
#### 4.8.1 Circular UAV Movement  
In the scenario where the UAV follows a circular path, the QoS metrics obtained are shown in Figure 11.  
The results show that both the LSGB and BRFFNN models achieved a 100% PDR, as illustrated in Figure 11a. However, the throughput recorded using the BRFFNN was 3.843 million packets per second (pps), compared to 3.232 million pps using LSGB, as  
Figure 10: Throughput using BRFFNN and LSGB for bandwidth allocation with random UAV movement.  
shown in Figure 10(b). Additionally, Figure 10(c) presents the data transmission delay, where the BRFFNN achieved a lower delay of 0.00647 ms, while LSGB recorded 0.00773 ms. These results indicate that while both models perform well in terms of the PDR, the BRFFNN demonstrates superior performance with higher throughput and reduced latency. The analysis highlights key differences in QoS for data transmission between the UAV and GS when using LSGB and the BRFFNN for bandwidth allocation. These findings emphasize the importance of applying robust machine learning techniques to enhance network performance in real-world UAV communication systems.
# The Influence of Metro Tunnel Construction Parameters on the Settlement of Surrounding Buildings
## 2 Materials and Methods
The division of soil mass characteristics into two conditional parts, \"at the tunnel face\" and \"above the tunnel\" was carried out in order to detail the forecasting model and localize the area of influence of the parameters on the dependent variable. Table 1 shows the parameters on which the model was compiled and the ranges of values that advise them. The analyzed sample does not include above-normative displacements of monitoring objects and technical parameters obtained during emergency situations. Consequently, the statistical model for predicting absolute settlement considers continuous operation of the tunnel boring machine, excluding cases of stoppage for routine maintenance and scheduled works (cleaning of the primary grouting system, splicing and extension of the conveyor belt, high-voltage cable, planned gyroscopic orientation in the tunnel, etc.). The regression model was created using the \"Data Analysis\" option in Excel. The final form of the model was compiled through an iterative process, and the algorithm for its formation is provided below. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Parameter** & **Range of** & **Unit of** \\  & **Values** & **Measurement** \\ \hline
1 & Distance from the center of the tunnel to the deformation control benchmark (r) & 0–59.1 & m \\ \hline
2 & Depth of the tunnel from the surface to the tunnel crown (H) & 14.27–35.63 & m \\ \hline
3 & Average face-support pressure at a distance of 10 m (P\({}_{\text{cp},10}\)) & 1.33–3.15 & bar \\ \hline
4 & Average ground output at a distance of 10 m (V\({}_{10}\)) & 65.40–84.54 & t \\ \hline
5 & Number of stories of the building (L) & 0–12 & fl. \\ \hline
6 & Cohesion of the soil at the tunnel face (c\({}_{1}\)) & 4–33.99 & kPa \\ \hline
7 & Internal friction angle of the soil at the tunnel face (\(\varphi_{1}\)) & 24.69–37.00 & deg. \\ \hline
8 & Deformation modulus of the soil at the tunnel face (E\({}_{1}\)) & 23,000–31,283 & kPa \\ \hline
9 & Cohesion of the soil above the tunnel (c\({}_{2}\)) & 22.92–37.53 & kPa \\ \hline
10 & Internal friction angle of the soil above the tunnel (\(\varphi_{2}\)) & 19.47–27.92 & deg. \\ \hline
11 & Deformation modulus of the soil above the tunnel (E\({}_{2}\)) & 29,025–34,098 & kPa \\ \hline
12 & Average volume of grout injection a distance of 10 m (V\({}_{10}\)) * & 3.68–4.76 & m\({}^{3}\) \\ \hline \hline \end{tabular}  
* Injection volume data are provided only for the RAL; Note: The range of values of soil characteristics is represented by weighted averages.  
\end{table} 
Table 1: The range of values of the selection parameters.
# An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced
## 6 Conclusions  
This paper has inspected the downlink radio resource management problem with CA for the LTE-A network. The problem involves with CC selection and RBs allocation of the selected CCs under specific constraints as specified in LTE-A standard. The resource management problem has been formulated and an improved method based on several parameters, such as the head of line delay and delay threshold, has been developed. This proposed method can increase the system throughput and maximize the QoS of the user while ensuring better spectral efficiency and low computational complexity. The queue length of each CC is taken into account to balance the load among all CCs. A set of system-level simulations have been performed to support the proposed method. The obtained results demonstrated that the proposed method significantly improves the user throughput up to 39.40% compared to the well-known method of previous studies. Thus, it can be concluded that the proposed method is a better implementation alternative as it is capable of improving the overall performance of LTE-A system with CA.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 4 Intrusion Detection and Prevention Systems
### Intrusion Detection System Functionalities  
Intrusion detection and prevention systems are widely used to monitor and investigate events related to cyberattacks. While their specific techniques may be different, intrusion detection and prevention systems (IDPS) there are some common functionalities:  
1. Ensuring records of information are correlated to actual events. Information can be stored and made available also to disconnected systems;
2. It is essential that security administrators be notified of important events. The system can send notifications (alerts) by using different channels: messages on the system console, emails, text messages (SMS), trigger user-defined scripts, etc. The content of the message has mainly the purpose of notification. Full details are stored on the IDS;
3. Report generation. Such outputs summarize detected events or provide relevant details about them;
4. An IDS should be able to trigger defensive measures when a new threat is detected. For example, banning specific IP addresses or IP ranges associated with the origin of the anomalous activities. It may also protect the access to specific resources that need to be protected or are in danger by reconfiguring network devices such as routers and firewalls to prevent access to those resources;
5. An IDS may alter the traffic to block malicious content (for example, mail attachments containing malware) and forward the part deemed safe;IDS technologies are not 100% accurate. Therefore, harmless activities may be classified as malicious (a false positive) or vice versa (a false negative). While detection rates can improve, errors cannot be totally eradicated.