esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### GA-Based Feature Selection  
In the paper, we set the number of chromosomes in each population as 50 and the maximum number of generations as 80, respectively, for GA-based feature selection. Stochastic universal sampling (SUS) is used as the selection operator, and uniform crossover is employed. The crossover rate is 0.63 and the mutation rate 0.018. Because the GA randomly selects different subsets of the SE-MF dataset to create a primary population of chromosomes, we iterate the GA 30 times and choose only those features that were selected more than 70% in 30 iterations. Table 3 lists the average number of selected features from the application of the GA to the SE-MF dataset for various IEEE standard systems.
# Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath
## 3 Accounting for a Protective Layer and Antenna Elements
For further consideration, we will select the last set of parameters (\(z_{res}=0.115\) cm; \(b=318\) nm) for the metal layer used as part of the bolometric detector with sufficiently strong signal absorption, see Figure 6 for example. Figure 3: Sketch of the dielectric resonator with protection and additional metal layer inside playing the role of the receiving antenna element. Figure 4: Mode distributions inside the structure (no metal layer). Parameters used: \(a\) = 0.5 cm, \(\epsilon_{d}\) = 700, \(c\) = 1 cm, \(\epsilon_{c}\) = 10, \(d\) = 10 cm. Figure 5: Dependence of maximum absorption \(\eta\) in the metal layer on the width of this metal layer \(b\). The parameters are \(a\) = 0.5 cm, \(\epsilon_{d}\) = 700, \(z_{\text{res}}\) = 0.115 cm, \(c\) = 1 cm, \(\epsilon_{c}\) = 10, \(d\) = 10 cm. The main characteristics of the considered radio technical device are the amplitude-frequency response (AFR) and phase-frequency response (PFR). In our case, the frequency dependence of absorbed energy in the metal layer plays the role of the AFR (\"1\" line in Figure 7), while the frequency dependence of phase shift of incident and reflected waves represents the PFR (\"2\" line in Figure 7). To obtain the PFR response, we provide a reminder that the reflection from the structure field is given by the expression  
\[E_{\omega}(z)=E_{0}\,exp(-ikz)+B_{\omega}\,exp(ikz)\]  
where the complex amplitude of the reflected wave is \(B_{\omega}=B_{0}\exp(i\varphi_{\omega})\) and \(k=\omega/c\). Taking into account that \(\varphi_{\omega}=\text{atan}(\text{Im}(B_{\omega})/\text{Re}(B_{\omega}))\), we have:  
\[\varphi_{\omega}(z)=\text{atan}\left\{\frac{Im\Big{[}E_{\omega}(z)\,exp(-ikz) /E_{\omega}^{(0)}-\,exp(-2 ikz)\Big{]}}{Re\Big{[}E_{\omega}(z)exp(-ikz)/E_{\omega }^{(0)}-\,exp(-2 ikz)\Big{]}}\right\} \tag{9}\]  
There are two peaks of absorption in the frequency range 20 GHz-27 GHz (27 GHz \(\approx(2\pi)^{-1}\omega_{p}\)). The first peak is rather small (\(\eta_{\omega}\approx 0.02\)) with central frequency 2.093 GHz. The second peak has \(\eta_{\omega}\approx 0.64\) with a central frequency 2.632 GHz and \(\Delta f_{2}=130\) MHz on the level of absorption of 0.025. The absorption scale was graduated from \(10^{-8}\) to 1, where the ground level corresponds to the case when there is almost no absorption of the external electric field in the metal layer, and \"level 1\" corresponds to the full absorption of the external electric field. Therefore, up to 64% of an external wave field can be absorbed at the carrier frequency \(\sim\)2.6 GHz.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 7 Performance Evaluation
### Computational Cost  
We utilize a device equipped with an Intel(R) Core (TM) i7-12700H processor running at 3.50 GHz and 16 GB of RAMs for the evaluation of the computational cost of the two phases of handover authentication. The cryptographic library employed to perform the required cryptographic operations of the proposed scheme was C/C++ OPENSSL. For the selected protocols, each protocol uses different encryption functions with unique key size requirements. To assess their performance comprehensively and fairly at the same security level, we follow the recommendation of NIST in [28] and use a 256-bit equivalent key strength throughout the entire simulation process. Therefore, for all operations based on ECC, secp256k1 is selected as the default elliptic curve. We run each encryption function 10,000 times to measure its average execution time. The measurement results obtained follow. The point multiplication \(T_{\text{PM}}\) is 0.201 ms. The point addition \(T_{\text{PA}}\) is 0.001 ms. The modular exponentiation \(T_{\text{E}}\) is 0.665 ms. The Rivest-Shamir-Adleman (RSA) signature and verification \(T_{\text{RV}}\) is 1.445 ms. The hash operation \(T_{\text{H}}\) is 0.021 ms. The symmetric encryption/decryption operation \(T_{\text{A}}\) is 0.02 ms. XOR, multiplication, and arithmetic operations have been neglected. The results are presented in Table 2, where \(T_{\text{MTCD}}\) and \(T_{\text{gNB}}\) represent the computation time for the MTCD and the gNB, respectively. We have only accounted for the operations during the first MTCD handover authentication and the group handover authentication of the BSPGH scheme.  
In Table 2, \"_n_\" represents the number of MTCD to substitute specific numerical values. For the 5G-AKA scheme, the computation costs are \(0.126n\) ms. For the PPHAP scheme, the computation costs are \(0.205n\)-\(0.063\) ms. For the NASS scheme, the computation costs are \(1.466n+2.775\) ms. For the BSPGH scheme, the computation costs are \(1.155n+1.068\) ms. The NASS scheme involves complete RSA verification for handover authentication, which increases the overhead. The PPHAP scheme primarily uses symmetric encryption and hash operations for authentication, resulting in lower computational overhead but posing a risk of DDoS attacks. The 5G-AKA scheme has the lowest computational overhead but has serious security vulnerabilities. In contrast, the proposed BSPGH scheme is the most secure in terms of security functionality and has lower overhead compared to the NASS scheme. Overall, the BSPGH scheme has been proven to be the most effective.
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 2 Materials and Methods
### Coherence Estimation  
Central to this study is the estimation of the amplitude of the complex correlation coefficient, called coherence. Given two complex SAR images \(s_{1}\) and \(s_{2}\) (e.g., Sentinel-1 SLC products), coherence is defined as:  
\[\gamma=\frac{|\langle s_{1}s_{2}^{*}\rangle|}{\sqrt{\langle s_{1}s_{1}^{*} \rangle\langle s_{2}s_{2}^{*}\rangle}},\quad 0\leq\gamma\leq 1 \tag{1}\]  
where \(|..|\) denote the absolute value, \(\langle..\rangle\) denote an averaging operation, and \({}^{*}\) denotes the complex conjugate product.  
Coherence reaches the maximum value of 1 when the position and physical properties of the scatterers within the averaging window \(\langle..\rangle\) are the same for both images \(s_{1}\) and \(s_{2}\). On the other hand, changes in the position or properties of the scatterers cause the coherence value to decrease. Additionally, a decrease of the coherence value or decorrelation can be caused by a mismatch in the properties of the two imaging systems involved caused by volume scattering, processing errors, and others--a full list is presented in [34]. These sources, however, are negligible in the context of this study (including volume decorrelation, given the relatively small baselines between interferometric acquisitions). Instead, we focus on temporal decorrelation. It is caused by changes in the scatterers over time; i.e., between the acquisitions. In the presence of vegetation, temporal decorrelation decreases the measured coherence. On the other hand, when the vegetation is removed, temporal decorrelation is expected to be lower due to a more coherent scattering from the soil, increasing the measured coherence. In order to evaluate the temporal decorrelation term that could indicate a moving event, additional decorrelation sources have to be taken into account. The estimated coherence \(\gamma_{total}\) can be defined as:\[\gamma_{total}=\gamma_{temporal}\gamma_{SNR}\gamma_{bias}\gamma_{other} \tag{2}\]  
where \(\gamma_{total}\) is the calculated coherence from Equation (1), \(\gamma_{temporal}\) is the temporal decorrelation, \(\gamma_{SNR}\) is decorrelation due to system noise (SNR: signal-to-noise ratio), \(\gamma_{bias}\) is influenced by the size of the averaging window, and \(\gamma_{other}\) are the other terms mentioned before as being negligible. Procedures for the evaluation of \(\gamma_{SNR}\) and \(\gamma_{bias}\) decorrelation terms are given in the following subsections.
## 4 Software/Hardware Overview
### ROS-Based Low-Level Architecture  
The proposed ROS-based software architecture is illustrated by using a node-graph in Figure 11. This shows a simplified view of the nodes and topics used to control \(n\) joints of _Serpens_ in the current implementation. The nodes are represented as ellipses while the topics as rectangles. The arrows represent publishers and subscribers, where arrows directed towards an ellipsis or box indicates a subscriber and an arrow directed outwards indicates a publisher.  
The controller node can run either on an external computer or the SBC embedded in the head module. This node provides all high-level control for _Serpens_ and acts as a hub for sensory data, such as the depth-sensor data collected by the _Intel RealSense D435_. As described above in this section, each joint module is provided with an embedded micro-controller (_OpenCM 9.04_) that is responsible for low-level control of the designated in the structure. Each of the boards acts as separate nodes in the ROS network architecture. In addition to being responsible for the low-level control, each joint module controller board also collects the feedback from the _XM 430W-210T_ actuator and from the _RoLin_ rotary incremental encoder. Each micro-controller implement a running ROS-node. This is shown in Figure 11 as _/serial_node_n_, where \(n\) denotes the micro-controller-index corresponding topics for each joint.  
Figure 10: The simulated Gazebo environment showing _Serpens_ in a pitch–yaw configuration, and the output of the simulated RGB (**top**) and RGBD (**bottom**) channels from the stereoscopic camera visualised through RVIZ.
## 3 Calibration of the Optoelectronic FDS-System
### Frequency Calibration
The FSR values obtained were \((42.580\pm 0.008)\) GHz for T-Sweeper, \((42.780\pm 0.022)\) GHz for TeraFlash, and \((42.540\pm 0.016)\) GHz for TeraScan. The values are in agreement within the measurement uncertainty. In order to reveal systematic errors or drifts in the frequency scale, Figure 4b plots the frequency residuals for each instrument, calculated as \(0.5f_{p}-p\cdot\text{FSR}\). In the case of T-Sweeper and TeraScan, the residuals are randomly distributed around zero, confirming the absence of systematic frequency errors. The standard deviation of the residuals of the T-Sweeper is smaller than those of TeraScan by a factor of 2.3. In the case of TeraFlash, the residuals follow a saw-tooth pattern arising from coarser frequency resolution; however, they are also evenly distributed around zero. Figure 3: (**a**) Absorption spectrum of atmospheric water vapour measured by T-Sweeper, TDS and FDS. The grey vertical lines correspond to the values from HITRAN. (**b**) Deviations of the measured lines from the line data listed by HITRAN [29].
## 4 Conclusions  
In this study, five nanocrystalline first transition-metal (Ti, Mn, Co, Ni and Zn) oxide nanofibers with regular microstructure and lattice structures were fabricated by electrospinning and controlled calcination. The obtained nanofibers composed of metal oxide nanocrystals have continuously twisted three-dimensional scaffold structures. Metal oxide nanocrystals with a grain size lower than 100 nm grow uniformly and orderly along the original nanofiber direction, showing a high individual independence and spatial orientation. The special structure can effectively prevent their own aggregation. Moreover, the calcined nanofibers have a high specific surface area and uniform porous structure, which follow the order of NiO nanofiber (393.645 m\({}^{2}\)/g) \(>\) TiO\({}_{2}\) nanofiber (121.445 m\({}^{2}\)/g) \(>\) ZnO nanofiber (57.219 m\({}^{2}\)/g) \(>\) Co\({}_{3}\)O\({}_{4}\) nanofiber (52.717 m\({}^{2}\)/g) \(>\) Mn\({}_{2}\)O\({}_{3}\) nanofiber (18.600 m\({}^{2}\)/g). Due to these special structures, the nanofibers expose many active sites, which can effectively improve the application performance. In addition, we used nanofibers to perform a photocatalytic reaction of methyl orange, which shows that ZnO, TiO\({}_{2}\) and NiO nanofibers have excellent catalytic activity and catalytic cycling. Therefore, we have reason to believe that nanocrystalline metal oxides nanofibers prepared by electrospinning and controlled calcination could have excellent properties in their respective fields.  
[PERSON], [PERSON], Y.C. conceived and designed the experiments; [PERSON], [PERSON], [PERSON], Y.S. performed the experiments and analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON], W.L. wrote the paper.  
This work was supported by State Natural Sciences Fund, China (Project No. 21506236, 51372276), Research and Application of Gelatin Green Manufacturing 2.0 Technology by Enzymatic method (KFJ-STS-ZDTP-016) and Hangzhou Research Institute of Technical Institute of Physics and Chemistry, CAS Fund (Project No. 2016050201, 2016050202).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 9: (**a**) Fitted curves with pseudo-first order kinetic model of nanofibers under the UV light irradiation. (**b**) Three successive cycles of photocatalytic experiments under UV light irradiation.
# Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study
## 4 Results
### Livestock Farming vs. Industrial Production
#### 4.1.1 Structural Characterization of Livestock Farming in the European Union and in Germany  
EU farms can be broadly characterized as either (i) semi-subsistence (ii) small and medium-sized farms or (iii) large agricultural enterprises [26]:  
Of the EU-27\({}^{\prime}\)s (Year 2020, without UK) 10.3 million farms, 4.0 million had an economic size in terms of standard output below EUR 2000 per year and were responsible for only 0.9% of the EU's total agricultural economic output. These very small farms are at the (semi-)subsistence end of the farming scale; about three quarters of such farms in the EU consumed more than one half of their own production.  
A further 3.0 million farms had an economic output within the range of 2000-8000 EUR per year. Together, these very small and small farms accounted for two thirds (68.3%) of all farms in the EU in 2016 but were responsible for only 4.6% the EU's total agricultural economic output.  
By contrast, the largest 278,000 farms (2.7% of the EU total) each produced a standard output of EUR 250,000 per year or more in 2016 and were responsible for a majority (54.4%) of the EU's total agricultural economic output; these farms can be characterized as being large agricultural enterprises.  
Consequently, the livestock sector in Europe is characterized by a high diversity in production systems, intensity, and farm size.  
In Germany, the structure of farming and rural life has changed substantially during the last 70 years. In 2020, the German government constituted the \"commission on the future of agriculture\" with 32 representatives of \"the full range of societal groups relevant to the agricultural policy with the involvement of the scientific community\" and the task of \"drawing up recommendations and proposals to ensure that agriculture in Germany is environmentally, economically, and socially sustainable into the future.\" [27]. In its final report, the commission summarizes the structural situation of German agriculture as follows: Agriculture in Germany has a broad range of different farm structures. This primarily reflects great variation in natural conditions from region to region but also differences in other conditions for agricultural production including socio-structural patterns (such as urban versus rural), economic factors (such as industrialization), and historical influences (such as inheritance law). There are thus major regional differences between the southwest of Germany, where farms are mostly small, and the north and east of Germany, where large farms predominate [27].
# The Use of Augmented Reality for the Management of Equipment Ageing with a Virtual Sensor
## 4 Results
### Augmented Reality Results
To obtain better results, a geostatistical technique (i.e., kriging) was used to interpolate or predict values at unsampled places in space [54]. This technique is based on a statistical model that evaluates the spatial autocorrelation of the data and estimates the value of a variable at a given location by combining nearby measured values of that variable and assigning them weights based on the spatial correlation between them. Weights are determined using a variogram or covariance function. The accuracy of the AR overlay depends on the technique used to implement AR in the virtual sensor. The tracking Image technique (contained within Unity) was used. For this purpose, a grid composed of a large set of vertices was created. The grid is a circumference with its centre at the origin of the axes (0, 0), which coincides with the centre of the bottom of the tank, and has a radius of 1. The dimensions of the real bottom are readapted to this grid. This allows the sampled points to be inserted inside the image in the correct position with respect to the centre. The accuracy of the RUL estimation is related to the corrosion model. It is derived from experimental tests when numerous inspection data are not available. These tests reproduce the conditions inside the tanks for the specific case study (i.e., fuel, impurity, trend of temperature, bottom material, etc.). The corrosion rate is correlated with the measure of the thickness of the samples of the bottom tank carried out in the laboratory. A microscope was used to measure the depth of the pits, but clearly, in a real case (i.e., tank miniature and large storage tank), it is not possible to use such an instrument and the accuracy is related to techniques applied (i.e., ultrasound or magnetic flux leakage). The visualisation of the corrosion map, combined with the estimation of the RUL and the other ageing-related parameters, allows the inspector to focus attention on the critical points when he/she is on-field. The whole system (virtual sensor) makes it possible to combine visual deductions with forecasts obtained from the models in order to understand the real expected evolution of the phenomenon, also on the basis of how it is managed.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
###### Abstract  
Voice over Internet Protocol (VoIP) is a technology that enables voice communication to be transmitted over the Internet, transforming communication in both personal and business contexts by offering several benefits such as cost savings and integration with other communication systems. However, VoIP attacks are a growing concern for organizations that rely on this technology for communication. Spam over Internet Telephony (SPIT) is a type of VoIP attack that involves unwanted calls or messages, which can be both annoying and pose security risks to users. Detecting SPIT can be challenging since it is often delivered from anonymous VoIP accounts or spoofed phone numbers. This paper suggests an anomaly detection model that utilizes a deep convolutional autoencoder to identify SPIT attacks. The model is trained on a dataset of normal traffic and then encodes new traffic into a lower-dimensional latent representation. If the network traffic varies significantly from the encoded normal traffic, the model flags it as anomalous. Additionally, the model was tested on two datasets and achieved F1 scores of 99.32% and 99.56%. Furthermore, the proposed model was compared to several traditional anomaly detection approaches and it outperformed them on both datasets.  
deep learning; autoencoders; network security; VoIP; SPIT +
Footnote †: journal: Applied  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
sEMG has been used to evaluate the Nordic hamstring curl [28], the leg curl [29], bilateral open chain [30] and unilateral closed chain exercises like forward lunge, Bulgarian squat and unilateral bridge [31]. Research has shown that these exercises targeting the quadriceps and hamstrings have proved beneficial not only for the rehabilitation of these specific muscle groups but also for the rehabilitation and prevention of anterior cruciate ligament injury, [32] patellofemoral pain syndrome [33] or groin injuries [34]. Studies using sEMG have required the normalization of sEMG signal [35]. Many normalization techniques are available with the maximal voluntary isometric contraction (MVIC) the most commonly used method [35]. However, some controversy exists about its reliability [36; 37], leading researchers to study the agonistic-antagonist coactivation of muscles as an alternative by normalizing the electrical activity of one muscle in relation to the other [20]. This can be useful because it gives information on how muscles work synergistically and permits a normalization that is not demanding for the athlete, facilitating its use at any time of the season. Such a synergistic muscle coactivation is observed in the hamstring and quadriceps in different movements in soccer such as sprinting, kicking, jumping and also in strengthening exercises used in soccer training. Submaximal strength exercises such as the Bulgarian squat, forward lunge and squat have been previously evaluated to study this hamstrings-quadriceps coactivation [7]; however, none have studied this coactivation pattern without the use of voluntary maximum isometric contraction. Such a study has not been carried out in soccer players either. Hence, this study aimed to compare: (i) the inter-limb differences in muscle activation; (ii) the intra-muscular group activation pattern and (iii) the activation pattern during different phases of the exercise. Based on previously published evidence, the following hypotheses were formulated: (1) Inter-limb differences would be noted for the different exercises. (2) Muscle activation would be different according to the phases of movement. (3) There would be a different pattern of intra-hamstrings and intra-quadriceps muscle group co-activation for each exercise.