esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parameter Analysis  
The feasibility of the proposed method was verified by comparing it with finite element simulation, which analyzes the influence of surface surcharge on the existing operating tunnel. A parameterized analysis was adopted in this experiment to further understand the influence of various factors in the surcharge load-soil-tunnel model on the existing tunnel. The main influencing factors include the relative positions of the tunnel and the load, the geometric size of the load, and the relative stiffness of the tunnel and soil. The relationship between the tunnel axis and the position of the load in the load-tunnel model is shown in Figure 12. The shape of the load is rectangular, and the length \(L\) and width \(B\) are 48 m and 24 m, respectively. The existing tunnel is assumed to have an outer diameter \(R_{o}\) of 6.2 m, an inner diameter \(R_{i}\) of 5.5 m, an annulus width of 1.2 m, and an axis buried depth of \(z_{0}=15\) m. The spacing between the surface surcharge center and the tunnel axis d is 0. [PERSON]'s ratio \(\
u\) of the soil in the stratum in which the tunnel is located is 0.35, and the elastic modulus is 15 MPa. The calculated equivalent bending stiffness of the tunnel is 7.8 \(\times\) 10\({}^{4}\) MN m\({}^{2}\), and the structural parameters of the tunnel are shown in Table 2.  
Figure 11: Comparison between the measured and computed results for the Zhongjiu section of Shanghai Metro Line 9.
## 3 Results  
### Mothers and Litter Endpoints  
The effects of daily exposure at 900 MHz on the mothers and their litters are shown in Figure 3. The weights of the mothers with the corresponding wbSAR are presented in Figure 3a. The mixed-effects analysis indicated no effect of 900 MHz exposure on the mothers' weight (Table 1), but an effect of interaction between age and exposure was observed (F72,599 = 2.514, \(p\)-value \(<\) 0.0001, Table 1). Multiple comparisons confirmed no effect of exposures for all the ages but showed an effect of age due to the increase in weight during gestation. The mixed-effects analysis indicated a significant difference in estimated SAR over time (F36,397 = 20.35, \(p\)-value \(<\) 0.0001). The variation in the mean SAR was calculated between 0.067 W/kg and 0.081 W/kg (max standard deviation: 0.005) for the PuM group and between 0.336 W/kg and 0.407 W/kg (max standard deviation: 0.028) for the OcM group.  
The mother's food intake per cage is presented in Figure 3b. The multiple t test indicated that maternal food intake was impacted punctually by 900 MHz exposure. For the OcM group, the mothers ate less at 29 days postfertilization (t-ratio = 2.559, \(p\)-value = 0.037). For the PuM group, the mothers ate less at 9 days (t-ratio = 2.408, \(p\)-value = 0.047), more at 17 days (t-ratio = 3.596, \(p\)-value = 0.0088), and less at 38 days postfertilization (t-ratio = 2.623, \(p\)-value = 0.039).  
No effect of exposures was shown at PND 0 on the sex balance of the litters presented in Figure 4a, the percent of the pups born alive presented in Figure 4b, or the size of the litters presented in Figure 4c, (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Source of** \\ **Interaction** \\ \end{tabular} } & \multicolumn{1}{c}{**dF**} & \multicolumn{1}{c}{**MS**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \hline \multirow{4}{*}{Mixed effects} & \multirow{2}{*}{Mother’s body weight} & Age * & 36 & / & 68.18 & / \\ \cline{3-6}  & & RF exposure & 2 & / & 0.01807 & 0.9821 \\ \cline{3-6}  & & **RF exposure \(\times\) Age * & **72** & / & **2.514** & **<0.0001** \\ \hline \multirow{3}{*}{One-way ANOVA} & Size of litters & RF exposure & 2 & 5.593 & 1.632 & 0.2183 \\ \cline{2-6}  & Pups born alive & RF exposure & 2 & 0.1256 & 1.156 & 0.3331 \\ \hline \multirow{3}{*}{Two-way ANOVA} & \multirow{3}{*}{Sex balance of litters} & Sex & 1 & 2.2 & 0.6137 & 0.4374 \\ \cline{3-6}  & & RF exposure & 2 & 2.463 & 0.6869 & 0.5082 \\ \cline{1-1} \cline{3-6}  & & **RF exposure \(\times\) Sex** & **2** & **13.26** & **3.699** & **0.0324** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the analysis of variance for physical development and SAR exposure.
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 3 Materials and Methods
### Sound Data Preprocessing Methods  
The audio recordings, extracted from the constructed IoT devices, are processed at a sampling rate of fs = 22,400 Hz, and filtered using a Finite Impulse Response (FIR) 50-tap Low Pass Filter (LPF) with a cutoff frequency of 10 kHz and 1 kHz transition width. Then the filtered data were split into minute time-length blocks.  
The raw data preprocessing methodology included a Fast Fourier Transformation (FFT) per block. Therefore, only the frequencies mentioned in the literature range of 150-600 Hz have been evaluated with other frequency responses being discarded. For the frequency range of 150-600 Hz, 20 frequency 25 Hz bins have been created, wherein in each bin, the aggregative sum of frequencies amplitude response per minute (in mV RMS) is accumulated.  
For the SVM and k-NN classifiers, FFT transformation is executed for every minute block to convert audio recordings into frequency vectors, and then frequencies not in the range of 150-650 Hz are excluded. Next, a label _Swarming_ is assigned to every vector. Its recording date is at most ten days before the swarming event. Similarly, a label Non-Swarming is assigned to every vector that had been recorded at least 20 days before the swarming event.  
The recorded audio used for training and testing the classifiers contain data from different beehives, where the queen is of the same age (2 years old) for swarming events on 8 May 2022 and 17 May 2022. For each event, the dataset includes 5 h of data every day, starting ten days before the swarming event, reaching a total of 1435 vectors with a Swarming label for each beehive after removing identical vectors. Additionally, the dataset includes 2788 vectors labeled with Non-Swarming, that arise from 20 days of 5 h for each beehive (150 vectors) every day, on dates at least twenty days before the swarming event, after removing the identical vectors from the dataset. Eventually, two swarming and Non-swarming tagged data datasets are formed for the k-NN and SVM algorithms.  
Only one-third of data are labeled Swarming because of the probability of a swarming event, which does not exceed 20%. Finally, the recording data from the experiment shows that one minute is long enough to illustrate the requested information by dividing the x-axis into 128-time points of 0.468 ms duration, as shown in Figure 1.  
For each single-minute sound described above, the corresponding Mel Spectrogram 128 \(\times\) 128 px image is created, illustrating time points on the x-axis and frequency on the y-axis (center frequency of 128 equally-spaced Mel bands in the frequency spectrum of 0-10 kHz). The brightness of the pixels represents the per band aggregate sum RMS values expressed in dBmV values (on the logarithmic Voltage scale transformation, see Appendix A).  
Therefore, the data we used for the algorithm training consists of 23,760 Mel Spectrogram images that had either the Swarming or the Non-Swarming label. This set will be applied to the U-Net CNN algorithm for training and testing.
## 5 Discussion
### Condition Analysis of Current Detection  
The GOCI-derived current field is affected by conditions such as cloudiness and tracers. For the analysis of the conditions for effective detection of SSC, we attempted to change the types of tracers and compare the differences in the GOCI current field derived from different tracers. Table 6 and Figure 15 show the statistical and comparative results of the SSC field derived by the GOCI inversion using TSM, Chl-a, and remote sensing reflectance (Rrs) at 555 nm as tracers at noon in the central Yellow Sea with a better inversion effect. The currents derived from different tracers are different from each other. The table shows that TSM gives the best effect in selected cases and obtains the largest number of current field vectors.  
Figure 14: (a–g): SSC vectors obtained under each window size parameter. Red dots in (**h**) are the position of the target vectors selected.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Target** & \multicolumn{2}{c}{**W1**} & \multicolumn{2}{c}{**W2**} & \multicolumn{2}{c}{**W3**} & \multicolumn{2}{c}{**W4**} \\ \cline{2-9}
**Vector** & **AME** & **AAE(\({}^{\circ}\))** & **AME** & **AAE(\({}^{\circ}\))** & **AME** & **AAE(\({}^{\circ}\))** & **AME** & **AAE(\({}^{\circ}\))** \\ \hline I & 0.07 & 10.43 & 0.10 & 9.70 & 0.09 & 2.08 & 0.09 & 13.63 \\ II & 0.24 & 54.72 & 0.36 & 42.48 & — & — & 0.76 & 39.24 \\ III & 0.07 & 16.70 & 0.26 & 29.73 & — & — & 0.45 & 29.94 \\ Average & 0.13 & 27.28 & 0.24 & 27.31 & — & — & 0.43 & 27.60 \\ \hline \hline \multirow{2}{*}{**Target Vector**} & \multicolumn{2}{c}{**W5**} & \multicolumn{2}{c}{**W6**} & \multicolumn{2}{c}{**W7**} \\ \cline{2-9}
**Vector** & **AME** & **AAE(\({}^{\circ}\))** & **AME** & & **AAE(\({}^{\circ}\))** & **AME** & **AAE(\({}^{\circ}\))** \\ \hline I & 0.20 & 5.21 & 0.10 & 13.18 & 0.13 & 11.76 \\ II & 6.61 & 59.76 & 0.69 & 56.20 & 0.38 & 20.41 \\ III & 0.46 & 23.16 & 0.49 & 32.10 & — & — \\ Average & 2.42 & 29.37 & 0.43 & 33.83 & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 5: AME and AAE values of the three target vectors and their adjacent vectors under each window size.
# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 3 Proposed SDR-Based LTE-LAA Architecture and Its Resource Management Scheme
### Network Model
To further clarify the functionality of the control server, as shown in Figure 2, users A1, A2, A3 (from network 1), B1, B2 (from network 2), C1, C2, and C3 (from network 3) are edge users at their vendor networks. Upon receiving and processing the updates, the control  
Figure 2: System model. server can recognize the benefits which edge users may achieve if they are offloaded to the nearby SDR-based BSs, particularly if these nearby BSs can supply them with far more radio resource blocks or carriers using LTE-LAA technology. Hence, it generates a new optimum user-BS association list and notifies the SDR-based BSs to adapt accordingly. In peak hours, such a scenario requires substantial procedures and communications between BSs, whereas the control server can easily determine the allocation which balances the load and issue an instruction to the SDR-based BSs to act accordingly. It is worth noting that offloading the above-mentioned users to nearby BSs allows the macro BSs to use cell zooming techniques [30] to reduce their power consumption and save a considerable amount of power, as the BS is responsible for more than 60% of the overall network power consumption [48].
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 4 Current State of Technology
### Adoption of Reactive Programming
PayPal has also chosen to contribute and make use of the concept of \"squbs\", a stack that simplifies the creation and management of loosely coupled components. Through this approach, manageability is improved, ensuring symmetry and loose coupling between services [46]. Cloud Foundry is a Platform as a Service (PaaS) that experimented with reactive programming to provide an efficient and scalable solution for building and deploying cloud-native applications. Reactive programming is a paradigm that focuses on data flow, as well as efficient manipulation of that data, using programming models that are non-blocking and event driven. The implementation of the reactive components in Java attempted by Cloud Foundry is based on Project Reactor, the standard implementation assumed by the Spring development team. Their platform leans towards reactive programming for robust management of the request, and data flows between different application components, such as the frontend and backend, therefore going a long way towards ensuring that the system remains responsive and scalable even when faced with high volumes of data and traffic. At the same time, the adoption of reactive programming in Cloud Foundry may also help in reducing latency and downtime by enabling real-time processing of data and events [47]. Moreover, other important companies have recognized the value of reactive programming and are accelerating its development. Facebook, Alibaba, and other companies decided to collaborate and organize a community under the Linux Foundation, focusing on the advancement of reactive programming and technologies such as RSocket, designed to ensure efficient, resilient communication between microservices, ensuring robust and scalable application performance across diverse environments and devices, showcasing the growing commitment and highlighting its potential to improve modern software development industry wide. A summary of the main aspects analyzed in this section is provided in Table 4.
# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 2 Related Work
### Feature Selection Techniques in Intrusion Detection Systems  
[PERSON] et al. proposed a method that utilizes Pareto dominance sets with mutual information and linear correlation to filter features for intrusion detection [12]. Their approach focused on balancing information quantity and correlation among features, which significantly reduced the feature set and achieved over 95% accuracy with only 14% of the original features, making it particularly suited for IoT applications.  
[PERSON] and [PERSON] minimized the number of features by optimizing feature selection for the IDS in IoT networks, based on recursive feature elimination via cross-validation and decision trees [13]. The efficiency of the anomaly-based IDS was increased by reducing the feature count from 42 to 15 while maintaining an accuracy of 95.3% in the classification task.  
[PERSON] et al. studied the trade-offs between the tasks of feature selection and feature extraction [14]. They demonstrated that while feature extraction may contribute to accuracy in the task of detection, feature selection contributes to reducing computational complexity. They evaluated these tradeoffs and their influence, using specific IoT scenarios.  
[PERSON] et al. proposed a two-stage approach, using a model based on dynamic search firework optimization for selecting features [15]. This helped the system focus on the most relevant features, thus eliminating irrelevant ones and enhancing the task of intrusion detection. The accuracy reached 96.11%, using benchmark datasets.
# Class-Ceramics Processed by Spark Plasma Sintering (SPS) for Optical Applications
## 1 Introduction
In the last years, novel sintering techniques, and particularly SPS, have been used to obtain well-densified transparent glass-ceramic materials [15; 16; 17]. Single crystals, ceramics, glasses, and glass-ceramics (GCs) are important materials in optical applications with the condition of high transparency, which allows light transmission without scattering. Transparent ceramics synthesized by SPS technique, such as ZrO\({}_{2}\)[18], Al\({}_{2}\)O\({}_{3}\)[19; 20], MgO [21], non-oxide AlN [22], yttrium aluminum garnet (YAG) [23; 24], and MgAl\({}_{2}\)O\({}_{4}\)[25; 26] have been widely reported, even though these materials appear as transparent in nature, and the same phenomenon does not apply for the transparent glass-ceramics. The physical properties of transparent GCs are influenced by crystallization kinetics, crystallinity, microstructure, the nature of the glassy and crystalline phases, and their chemical composition. The optical properties, transparency, in particular, can be controlled by the precipitation of nanocrystals, similar refractive index values for the glass matrix and the crystalline phase, and low birefringence of the crystalline phase [27; 28]. Transparent GCs are very important materials for optical applications because of the possibility of mass production, shaping as required, and high incorporation of dopant concentration. In particular, oxyfluoride transparent glass-ceramics in which fluoride nanocrystals can precipitate offer a high solubility for the rare-earth ions in the crystal phase, providing more efficient optical emissions [29; 30]. Transparent GCs can fulfill the gap between glasses and ceramics, in particular, as optical materials, and SPS turns to be an alternative method to produce dense materials. It is noteworthy that, to the best of the author's knowledge, no review article about state-of-the-art is available that summarizes the benefits by SPS towards the development of transparent GCs. Hence, the major motivation for this review is to shade light in this area. After short information about the SPS method (brief history and sintering mechanism), this review paper has focused on the SPS processing of transparent ceramics and transparent glass-ceramic materials for optical applications and the comparison with other processing methods, evaluation of the mechanisms of sintering and crystallization, and final properties. Some recent studies concerning GCs prepared by SPS have been included and discussed. However, there is no relevant literature about transparent glass-ceramics, the effect of glass powder size and their distribution, sintering behavior, and the effect of processing parameters on the transparency. Finally, some future prospects about SPS processing are given.
## 3 Methodology
### Model Modulation
A priori information plays a primary role, which is used to introduce essential knowledge to the model to identify possible geological alterations in the area, which can potentially affect the final results. This was enhanced to improve the accuracy of the data significantly, by introducing area information and providing subsurface details, before fitting the model with the observed data, which, in turn, mitigates ambiguity in the interpretation of the data. This helped establish a framework for comparing various stages of the inversion process. It also helped create various initial models, which were used to find the best adjustment of values in the hyperparameters. To prepare these models, various machine learning (ML) algorithms were used. Once the properly adjusted inversion model was obtained, it was applied to real data (seismic data and well logs) from the Norme field in the North Sea. To select the best regression model, the four developed models were analyzed, with precision and standard deviation mainly evaluated to evaluate the fit and understand the predictive capacity. With the information on the precision of the regression model, we ensure that we obtain the best value fitted to the training data; this allows us to generalize the process to data not used by the model (blind data). The results of the standard deviation will be applied to be able to evaluate how far (dispersed) the data resulting from the model are concerning the initial observed data. Figure 7 shows us the analysis results of the different models, using precision and standard deviation as indicators. The SVR model is shown to have the highest precision and one of the lowest standard deviation values. Once the model with the highest precision and lowest dispersion was determined, the model was trained with the available data using the logs from 10 wells (P and S wave speed logs, density logs, and clay quantity logs) using the classic 70-30 split. This method divides the dataset into two parts: 70% of the data was used to train the model, allowing it to learn and adjust its parameters. At the same time, the remaining 30% was reserved for testing and validating its performance. Figure 8 shows the result of this process. Figure 7: The results of the analysis of the precision and standard deviation carried out on the regression models are shown, this represents the level of precision in the estimation and the degree of dispersion or variability that the data has. Among the tested models, support vector regression (SVR) demonstrated the best performance, exhibiting the highest precision and one of the lowest standard deviation values.
## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
#### 5.2.1 Resource Allocation in 5G Network Slicing
As can be seen in Figure 8, during the establishment of NSs, it is possible that some network elements may not be used at all, given that both slices satisfy the specified QoS parameters with the allocated network resources. Thus, these non-used resources of the 5G network are not essential for the services offered by NSs. As these elements can enter sleep mode while working, energy consumption in the entire 5G system will be reduced in comparison with the unsliced legacy 5G network presented in Figure 7. Thus, through NSs' orchestration (NSs' management), the adaptation of the 5G network resources to the offered service in the NS is achieved by adding or reducing necessary resources to preserve the required QoS while ensuring an improvement in network energy efficiency. Figure 7: Non-sliced 5G mobile network segments. If there is an increase in the traffic of some NS, for example, in Slice 1 (Figure 8), additional resources are engaged through MANO at that moment to handle all traffic demands, as presented in Figure 9. Since a much higher volume of traffic is processed in the NS, the performance of the slice is raised to a significantly higher level. However, this additional engagement of network resources will lead to an increase in consumed energy due to the additionally activated network elements. If we consider the NS energy efficiency KPI, it is clear that the network performance, compared with the scenario presented in Figure 8, has increased in the scenario presented in Figure 9 to a level of performance equal to the one presented in Figure 7, and consequently the energy consumption also increases to the same levels. This leads to the conclusion that the adaptation of network resources in network slicing can contribute to an increase in energy efficiency, since in the periods of 5G network activity presented in Figure 8, the network consumes less energy when compared with the legacy 5G network presented in Figure 7 or the sliced network presented in Figure 9.  
Additionally, in a situation where there is a reduction in traffic or service demand in some NS(s) compared to the configuration with fully activated NS(s) resources presented in Figure 9, the adaptation mechanisms of the MANO entity can act to reduce the use of 5G network resources. In this situation, the resources freed up by adaptation mechanisms can be in two states, either being used by another NS due to increased traffic demands in that NS, as presented in Figure 10, or put into a sleep state, as presented in Figure 10b. In both cases, the energy efficiency of the 5G network will be improved in comparison with the efficiency of the legacy unsliced 5G network presented in Figure 7, since at least some  
Figure 8: Allocation of network elements (NEs) for two NSs in the 5G mobile network. Figure 9: New network elements (NEs) allocation in the 5G network for NS1. elements of the 5G network resources will be in the sleep operation state (Figure 10) and thus in an energy-saving state.