esa-sceva/satcom-chunk-collection huggingface dataset:
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 1 Introduction
The rise in the popularity of drones has increased the frequency of cyber assaults against UAV systems during the last decade. An adversary will target the radio connections of UAV systems in order to hinder the systems' ability to communicate with user equipment. This includes data required by the user's cellular equipment that control signals and GPS signals. This is an example of an interception of information giving enemies the ability to steal data that drones transmit and request, as well as foregoing direct control of the drones through control signals. To guarantee the security of the wireless communication channels,  
Figure 1: IoD environment. guaranteeing that both control and data signals are sent has become a critical element of the overall UAV system [7]. This review focuses on the recent advancements in IoD development. The key contributions to this paper, which are presented from Sections 3-8, are to:  
* Investigate the main requirements of IoD (Section 3). * Provide the main parameters-based thematic taxonomy (Section 4). * Discuss the most commonly used commercial case studies of IoD (Section 5). * Investigate the progress recently described in the literature (Section 6). * Discuss the IoD open challenges and prospective study directions (Section 7).
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 5 Health Implications and Allergic Reactions to Small Berries
### Allergic Reactions  
Several small fruits, usually from the _Rosaceae_ family, like strawberries, raspberries, or blackberries, were implicated in allergenic reactions [147]. Although no official information resulting from national studies regarding the occurrence of allergic reactions caused by berry consumption is available, the data available in the scientific literature suggest rather low incidence. For instance, according to the studies reviewed by [PERSON] et al. [148], the prevalence of strawberry allergy ranges between 0.008% and 1.19% in different countries in Asia and South and North America, the most susceptible to allergic reactions being infants and children. The scarce information regarding the prevalence of berry allergies might be explained by the lack of specific molecular markers for allergy diagnosis [149]. The hypersensitivity is believed to be caused by putative IgE-reactive proteins from four families: TLPs and PR-5 thuumatin-like proteins (31 kDa); PR-10 proteins belonging to pathogenesis-related proteins (17.5 kDa); nsLTPs and PR-14 non-specific lipid transfer proteins (9 kDa); and profilins (12-15 kDa) [147].  
Despite being considered a superfood, goji berries (_Solanaceae_ family) showed allergenic potential in high-risk individuals, most probably due to cross-reactivity with lipid transfer proteins from other foods [150]. Although such cases were rarely reported, clinical symptoms may vary from mild responses such as oral allergic syndrome to more severe reactions including edema, dyspnea, urticaria, and rhinitis [151].  
The allergenic potential of berries depends on the cultivar and their composition. For example, [PERSON] et al. [152] found that different cultivars of honeysuckle berries (_Lonicera caerulen_ L.) presented different allergenic potencies. On the other hand, quercetin, a flavonoid found in many berries, fruits, and vegetables, was reported to restrain antigen-specific IgE antibody formation [153]. High quantities of quercetin were found in Saskatoon berries (_Amelanchier alinfolia_) (up to 307 mg/kg) [154], correlian cherries (_Cornus mas_) (120-360 mg/kg) [155], and black chokeberries (_Aronia melanocarpu_) [156]. Moreover, it should be noted that during processing, the allergenicity can either increase or decrease, depending on many factors such as the processing method, temperature, molecular characteristics of proteins, and reactivity (such as Maillard reaction formation) [157].
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 3 Methodology
### Accuracy Assessment  
A confusion matrix was constructed to assess the accuracy of the classified maps using the overall accuracy (OA), producers' accuracy (PA) and users' accuracy (UA). The most recently proposed allocation quantity (QD) and allocation (AD) disagreements [52] were also calculated from the classification confusion matrix to evaluate the reliability of the classification map and to measure the agreement between the predicted classification features and the reference field data (OOB samples). Class-wise accuracy assessment was performed for each class using F1-score [18]. This measure represents the harmonic mean between PA and UA for each class i as follows:  
\[\left(\mathrm{F1}\right)_{\mathrm{i}}=\frac{2\times\mathrm{PA_{i}}\times \mathrm{UA_{i}}}{\mathrm{PA_{i}}+\mathrm{UA_{i}}} \tag{1}\]  
The advantage of using the F1-score for class accuracy evaluation is to give equal importance to both precision and recall, by combining PA and UA into a fused measure.
## 3 The Objective Electromagnetic Environmental Effects Assessment
### Grey Theory and the Analytical Hierarchy Process
[34] employed time occupancy, space coverage, spectrum occupancy, average power spectral density, a large signal ratio, a dynamic signal variance ratio, and background signal intensity as the set of basic evaluation indexes of the electromagnetic environment's complexity. Grey information fusion technology and D-S evidence theory are used in their research. For the improvement of the evaluation resolution and accuracy, a new method for intelligent classification of the electromagnetic environmental complexity is presented. In the literature review, we can see that grey theory and the AHP can effectively solve the uncertainty problem of the electromagnetic environment. The evaluation system is always modeled at two or three levels, where target and indicator levels are necessary. As shown in Table 2, we can choose the indicators according to the signal, the interference source, the equipment efficiency, the object in the electromagnetic environment, and so on, which suggests that, except for the four domain characteristics (time, frequency, airspace, and energy domains), the electromagnetic behavior characteristics (electromagnetic behavior subject, object, and pattern) are also involved in their environmental complexity evaluation. The model's performance is always demonstrated by a consistency check (CC) of the index weightings, which should be less than 0.1. The lower the CC, the higher the model's reliability. A three-tiered evaluation system is better than a two-tiered one due to the combination of objective and subjective evaluations. Additionally, the complexity classification is the same. However, the quantitative threshold value (or range) is expressed differently, which will lead to an inconsistent description of the electromagnetic environment's complexity. Yet, it still needs to rely on the definition of the electromagnetic environment threshold. Furthermore, the target hierarchy structure, selection of the evaluation matrix, and gray level often follow expert opinion, which is more subjective. Hence, the assessment results are not convincing enough, and the objectivity and accuracy of the assessment model need to be improved.
# Point Deflection in Topological Interlocking Plates
## 6 Discussion  
The point deflection observed under point loading is a consequence of the relative block rotations followed by the delamination of contacts of the neighboring blocks and elbowing. In the TI assemblies, the neighboring blocks do not resist local tension. Subsequently, there is no local bending stiffness. This makes the macroscopic bending of the TI beams resemble that of a string.  
The second important point is that the constraint required for the structural integrity of TI assemblies is achieved either by an external frame or post-tensioning cables; both are characterized by a linear increase in the constraining force with the deflection-produced axial displacement, and hence behave similarly. Opposite to it in a model in which the constraining force is kept constant the point deflection will turn into the deflection of the central area, which enlarges with an increase in the deflection-generating force [53]. However, the deflection still has a tendency for localization in the central area and post-peak softening is also observed in the force-deflection diagram.  
When a TI plate is loaded by a central force, the point deflection turns into a line-localized deflection with lines oriented according to the symmetries of the interlocking blocks. For instance, in the case of cubic blocks, the central plane of the TI plate has hexagonal symmetry [4]; subsequently, three localization lines inclined at 120\({}^{\circ}\) to each other are observed [47]. In the case of a TI plate assembled from tetrahedra, the localization lines are observed to be perpendicular to each other [51], in line with the square symmetry of the central plane of the TI assembly of tetrahedra [4].  
When a TI beam is subjected to several point loads, the deflection will have inflection points equal to the number of the applied forces. Between these points the deflection is linear. If, however, the load is continuous, there shall be no inflection points. For instance, in the case of uniform load \(q(x)=q_{0}\) (in the coordinate frame shown in Figure 8b) under the constraining force \(F_{0}\), the solution of loaded string, e.g., as in [54], gives a parabolic shape, \(u_{y}(x)=\frac{q_{0}}{2F_{0}}\left(l^{2}-x^{2}\right)\).  
Discontinuous damping is an intrinsic property of TI structures. The damping is produced by the transfer of oscillating energy to the kinetic energy of the air squeezed out due to the gap closure between the neighboring blocks. Thus, damping is achieved without additional dampers and therefore can be used for designing hybrid materials and materials with internal architecture, where sound absorption/attenuation is a design parameter. The discontinuous nature of the damping may impose additional numerical difficulty for modeling oscillations of interlocking structures, so special algorithms may need to be developed to deal with the discontinuity of this kind.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 4 Autoencoder Deployment on ESP32-CAM
### Model Architecture and Training Strategy
Samples with a reconstruction error that exceeded a defined threshold were classified as anomalies. This anomaly threshold was set using the 95 th percentile of reconstruction errors computed on the training data, effectively treating 95% of the training samples as normal and allowing up to 5% to be considered potential outliers. As previously mentioned, no separate validation set was used during training. Instead, the training set was used for tasks typically handled by a validation set, such as loss evaluation, anomaly threshold calculation, and early stopping check. This choice was driven by the strict memory constraints of edge devices, where maximizing the size of the training set can improve generalization. While using a dedicated validation set is certainly possible and may be preferable in less constrained environments, reusing the training data allows all available memory to be allocated to the training set or the model, avoiding the overhead of reserving memory for validation. The models were initially implemented and trained on a PC using Python 3.10.0 and the TensorFlow 2.17.0 library [51]. This allowed us to experiment with different configurations and evaluate model behavior under various conditions. Each architecture was trained using multiple batch sizes (1, 10, and 50) to analyze their impact on performance, particularly in preparation for deployment on memory-constrained edge devices. Model evaluation was conducted using several performance metrics, including final training loss (measured by MSE), training and inference time, AD rate (defined as the percentage of test samples flagged as anomalous), and the F1 score based on AD results. After evaluation, a suitable training configuration was selected for each model based on batch size and performance trade-offs.
###### Abstract  
In recent years, wireless multimedia sensor networks (WMSNs) have emerged as a prominent technique for delivering multimedia information such as still images and videos. Being under the great spotlight of research communities, however, multimedia delivery over resource- constraint WMSNs poses great challenges, especially in terms of energy efficiency and quality-of-service (QoS) guarantees. In this paper, recent developments in techniques for designing highly energy-efficient and QoS-capable WMSNs are surveyed. We first study the unique characteristics and the relevantly imposed requirements of WMSNs. For each requirement we also summarize their existing solutions. Then we review recent research efforts on energy-efficient and QoS-aware communication protocols, including MAC protocols, with a focus on their prioritization and service differentiation mechanisms and disjoint multipath routing protocols.  
wireless multimedia sensor networks; energy-efficiency; wireless communication protocols; multipath routing; medium access control; quality of service +
Footnote †: journal: Sensors
# Real-Time Path Planning of Driverless Mining Trains with Time-Dependent Physical Constraints
## 3 Problem Description  
Figure 1 shows a sample layout of mining railway network, where trains start from station A, travel via the tracks, load mine from load-out G, and return back to station A.  
The mining railroad optimisation problem is to determine the best feasible timetable for a set of trains in order to load as many mines as possible. The related constraints are to satisfy restrictive operational constraints (e.g., track capacity, travel speed, safe distance, etc.) and to avoid possible conflicts when using each single track.
## 7 Conclusions  
To sum up, this study assessed the effect of vibrations from wind turbines on the lifespan of the FP50R12 KT4 IGBT module. Modal analysis showed that the minimum natural frequency of the module is around 19 kHz, which is much higher than the maximum load frequency typically encountered in wind turbine operations. This large difference suggests that resonance is unlikely. Furthermore, the random vibration analysis revealed that the maximum deformation at the knee points of the bond wires was only 2.92 \(\times 10^{-6}\) mm, a very small value that has little to no effect on the module's performance. With a minimum calculated lifetime of 2.14 \(\times 10^{12}\) cycles (67.4 years) and a maximum damage rate of just 4.67 \(\times 10^{-13}\), the impact of vibration on the IGBT's lifespan is negligible. Overall, the findings confirm that vibrations in wind turbine nacelles do not pose a significant risk to the IGBT module's longevity, supporting the continued reliability of IGBT-based power electronics in these environments. While this study is focused on the impact of vibration on IGBT lifetime, it is acknowledged that other environmental stressors, such as thermal cycling and humidity, also play a crucial role in IGBT reliability. Solder fatigue and bond wire degradation can be caused by thermal cycling, whereas insulation breakdown and corrosion may be induced by humidity exposure. In future work, the effects of all these stressors will be considered to provide a more comprehensive understanding of their combined impact on IGBT lifetime in wind turbine applications.  
Conceptualization, S.A.H. and S.P.; methodology, S.A.H.; software, S.A.H.; validation, R.W. and S.P.; formal analysis, S.A.H.; investigation, S.A.H. and S.P.; resources, S.A.H.; data curation, S.A.H.; writing--original draft preparation, S.A.H.; writing--review and editing, R.W. and S.P.; visualization, S.A.H. and R.W.; supervision, S.P.; project administration, S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Vestas. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 11: Knee points of the bond wires exhibiting the maximum damage (4.67 \(\times 10^{-13}\simeq 0\)).
## 4 Results
### Allocentric Distance Perception  
One participant failed to complete this task. For each environmental condition, the outlier data points were dropped from the analyses, which led to the omission of about 16% of the data. The analyses were conducted on the data from the remaining 26 participants.  
Overall, participants' responses showed an overestimation of the distance between the two targets under the environment with moderate cues (Mrelative error = \(-\)16.4, SD = 50.78), while showing an underestimation of the distance in environments with maximum (Mrelative error = 0.44, SD = 0.36) and minimum (Mrelative error = 0.36, SD = 0.66) visual and gravitational cues.  
Again, for further analyses, the absolute relative error was used. A one-way repeated-measures ANOVA on perception of allocentric distance perception revealed an effect of the environmental condition that approached significance; F (1, 25) = 2.82, \(p\) = 0.11 (Greenhouse-Geisser correction). In estimation of the distance between the two targets, participants made larger errors under the environment with moderate visual and gravitational cues (Mrelative error = 17.24, SD = 50.49) than the environment with minimum cues (Mrelative error = 0.66, SD = 0.32) than the environment with maximum visual and gravitational cues (Mrelative error = 0.51, SD = 0.27; see Figure 4).  
To determine if the absolute relative error of allocentric distance perception under each environmental condition was significantly greater than zero, we conducted single sample t-tests (one-tailed) against zero for each environmental condition. The results revealed significant deviation from zero, i.e., an accurate estimation under all environments with maximum (t (25) = 9.25, \(p\) < 0.001, d = 1.82), moderate (t (25) = 1.74, \(p\) = 0.047, d = 0.34), and minimum (t (25) = 10.43, \(p\) < 0.001, d = 2.05) visual and gravitational cues.  
Together, the results of both egocentric and allocentric distance estimation tasks showed an underestimation of distance in the environment with both visual and gravitational cues (maximum: cityscape) and the environment with no cues at all (minimum: outer space). However, there was a tendency for an overestimation of distance in the environment that had gravitational cues but no familiar visual cues (moderate: space space).
# Cross-Entropy Method in Application to the SIRC Model
## 1 Introduction
### Application to Optimization Problems  
This section is intended to show how extensive the CE method is. The examples will show the transformation of the optimization problems to the Monte Carlo task of estimation for some expected values. The equivalent MC task uses the CE method. Other very good examples can be found in [16].  
The first variational problem presented in this context is the specific multiple stopping model. Consider the so-called secretary problem (v. [PERSON] [17] or the second author's paper [18]) for multiple choice, i.e., the issue of selecting the best proposals from a finite set with at most \(k\) attempts. There is a set with \(N\) objects numbered from \(1\) to \(N\). By convention, the object with the number \(1\) is classified as the best and the object with the number \(N\) as the worst. Objects arrive one at a time in random order. We can accept this object or reject it. However, if we reject an object, we cannot return. The task is to find such an optimal stopping rule that the sum of the ranks of all selected objects is the lowest (their value is then the highest). So we strive for a designation  
\[\inf_{\tau}\mathbf{E}X_{\tau}=\mathbf{E}X_{\tau^{\#}}.\]  
Let \(a_{i}\) be the rank of the selected object. The goal is to find a routine for which the value of \(\mathbf{E}(a_{\tau_{1}}+\cdots+a_{\tau_{k}})\) for \(k\geq 2\), is the smallest. More details are moved to Appendix A.1.  
Next, the problem was transformed into the minimization of the mean of the sum of ranks. The details are presented in Appendix A.2. This problem and its solution by CE was described by [PERSON] [19]. Its correctness was checked and programmed in a different environment by [PERSON] [20].  
The second example is presented in detail in Appendix A.3. It is a formulation of the vehicle routing problem (v. [21; 22]). This is an example of stochastic optimization where the cross-entropy is used.
## 4 Research Methodology
### Adversary Model  
Let there be \(N\) total number of sensors in a complete sensor network. The attacker can only compromise \(k\) out of \(N\) sensors. Step 1, randomly choose \(k\) sensors to be compromised: generate a random number for each sensor. Sort the sensors based on the corresponding random numbers in ascending order. Select the first \(k\) sensors for compromising. Step 2, record each compromised sensor's untampered readings over the next 500-time steps. Build and train a pair of convolutional neural networks (CNN) for each compromised sensor. The first CNN predicts the conditional mean of the individual time series at time step \(t\)--the inputs of this network are the past values of the time series, and the desired outputs are one-step-ahead values of the time series. The second network predicts the conditional variance of the conditional mean at time step \(t\)--the inputs of this network are the past squared residuals of the first CNN, and the desired outputs are one-step-ahead squared residuals from the first CNN. Step 3, to estimate the modified readings for the compromised sensors, construct a 95% confidence interval for the reading of each compromised sensor \((I)\) at time step \(t\) based on the corresponding conditional mean \(\left(\overset{\wedge}{m_{I}}(t)\right)\) and conditional variance \(\left(\overset{\wedge}{v_{I}}(t)\right)\) forecast. Step 4, Generate \(k\) random numbers in the range [**0,1**], one for each compromised sensor. If the random number is less than 0.5, the corresponding compromised sensor \((I)\) uses the following formula to calculate its tampered measurement \((m\upsilon_{I})\) at time step \(t\):  
\[m\upsilon_{I}=\overset{\wedge}{m_{I}}(t)+\overset{\wedge}{\theta_{I}}(t)\ \times r \tag{16}\]  
where \(r\) is the normal random number with mean 0 and standard deviation 1.  
Otherwise, it reports the upper or lower bound of the 99.7% confidence interval as the reading of the compromised sensor at time step \(t\) to shift the trend upward or downwards, respectively. Step 5, if the defender fails to identify the \(I\)th compromised sensor at time step \(t\), retrain the corresponding CNN pair with the reported measurement at time step \(t\). Step 6, repeat steps 3-5 to estimate the readings for the compromised sensors at each time step.