esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Intelligent Reflecting Surfaces (IRSs) enable us to have a reconfigurable reflecting surface that can efficiently deflect the transmitted signal toward the receiver. The initial step in the IRS usually involves estimating the channel between a fixed transmitter and a stationary receiver. After estimating the channel, the problem of finding the most optimal IRS configuration is non-convex, and involves a huge search in the solution space. In this work, we propose a novel and customized technique which efficiently estimates the channel and configures the IRS with fixed transmit power, restricting the IRS coefficients to \(\{1,-1\}\). The results from our approach are numerically compared with existing optimization techniques.The key features of the linear system model under consideration include a Reconfigurable Intelligent Surface (RIS) setup consisting of 4096 RIS elements arranged in a 64 \(\times\) 64 element array; the distance from RIS to the access point measures 107 m. NLOS users are located around 40 m away from the RIS element and 100 m from the access point. The estimated variance of noise \(\mathcal{N}_{\text{C}}\) is 3.1614 \(\times 10^{-20}\). The proposed algorithm provides an overall data rate of 126.89 (MBits/s) for Line of Sight and 66.093 (MBits/s) for Non Line of Sight (NLOS) wireless communication.  
5G/6G; wireless networks; smart systems; non line of sight (NLOS); programmable meta-surface; line of sight (LOS); intelligent reflecting surface (IRS); beamforming; orthogonal frequency division multiplexing (OFDM)
## 2 Materials and Methods
### Equipment
#### 2.4.2 Surface EMG  
The Noraxon Surface EMG System (TELEMYO 2400, Noraxon, Scottsdale, AZ, USA) was used to record the AD muscle activity changes of groups A and B as well as the AD, LD, and PD muscle activity changes of groups C and D. The EMG signal was at a sampling rate of 1000 Hz, and the EMG electrodes were placed over the muscle measured for each participant with 20 mm between the electrodes. Before using the EMG electrodes, the skin was cleaned with an alcohol cleansing pad and shaved to reduce any noise. The EMG electrodes were fixed using elastic bands [23]. The grounding electrode was placed on the 7 th cervical vertebra (C7), and the EMG information was examined and analyzed using MyoResearchXP Basic Edition 1.07.01 software. A bandpass filter at 80-250 Hz was added to the data using MyoResearch-XP 1.07 (Noraxon, Scottsdale, AZ, USA) software, and the EMG signals were rectified. The EMG signals used to measure muscle activities were processed using the root mean square (RMS) value to quantify the EMG signals. The EMG amplitude was calculated by the RMS values of the raw EMG. The EMG amplitude of each muscle was normalized by %MVC. The %MVC was compared with the RMS and MVC, as characterized by Equation (1). Equation (2) illustrates the PULE effect. The mean values of the amplitude were used to measure the degree of muscle contraction [25; 26], whereas the area under the amplitude curve was used to approximate muscle work [27].  
\[\%MVC=\left(\frac{RMS_{force\ application-RMS_{force\ relaxation}}}{RMS_{ MVC}-RMS_{force\ relaxation}}\right)*100\% \tag{1}\]  
\[\text{the PULE\ effect}=\left(\frac{\%\lambda VVC_{W-ES}-\%\lambda VVC_{ES}}{ \%\lambda VVC_{W-ES}}\right)*100\% \tag{2}\]
# The Study of Thermal Properties of Blackberry, Chokeberry and Raspberry Seeds and Oils
## 3 Results and Discussion
### Study of Oil Obtained from Seeds
#### 3.2.3 PDSC of Extracted Oils  
PDSC analysis is a tool to determine the value of induction time of an oxidation reaction. This parameter is essential to describe oil stability in conditions of oxidative degradation. In this study, PDSC was conducted at isothermal conditions at a temperature of 120 \({}^{\circ}\)C. The PDSC curves for blackberry, chokeberry, and raspberry seed oils are presented in Figure 4.  
Figure 3: Chromatogram of blackberry seed oil (16:0—palmitic acid, 16:1—palmitoleic acid, 18:0—stearic acid, 18:1—oleic acid, 18:2—linoleic acid, 18:3—\(\alpha\)-linolenic acid, 20:0—arachidic acid, 20:1—paulmitic acid, 22:0—behenic acid).  
It can be observed that values of induction time of oxidation reaction for oils are different. At 120 \({}^{\circ}\)C, chokeberry seed oil has the shortest induction time lasting 41.20 min, followed by raspberry seed oil with a result of around 56.02 min. The highest value is seen in blackberry seed oil which amounts to over 90.29 min. The results for chokeberry and raspberry oils from the following study were lower than for rapeseed oils at 120 \({}^{\circ}\)C, valued from 66.61-74.78 min in a study by [PERSON] et al. [41] or than soybean oil with a 63.1 min induction time [15]. [PERSON] et al. reported induction times for hazelnut oil and olive oil, and the results were higher than for berry seed oils in the following study. Times of oxidative reactions ranged from 119.95-191.06 min and 134.15-180.07 min for hazelnut oil and olive oil, respectively [42]. Considering that, the studied berries oils are not as oxidatively stable as hazelnut or olive oil. However, the examined berry oils have higher induction times and may be considered as more stable in 120 \({}^{\circ}\)C than the linseed oil studied by [PERSON] et al. with an induction time of 21.20-24.72 min [43], amelina oil with the result of 26.81-35.85 min [44], and sunflower oil with a 33.4 min time of oxidative reaction [15].
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 2 Security Challenges in 5G Network
### Authentication
The authors also modified the existing 5G-AKA protocol for prevention strategies. The authors in [26] developed an advanced authentication and key agreement protocol for the 5G mobile network. The proposed scheme applies random numbers, which resulting robust security and decreased communication cost. The authors utilized public-key cryptography to encrypt the SUPI and generating the SUCI. Furthermore, since current USIMs can execute random asymmetric encryption procedures, a random number can be used in the 5G-AKA protocol. Moreover, the proposed solutions guarantee forward security and post-compromise security. An efficient and seamless vertical handover authentication protocol for a 5G wireless mobile network is presented in [27]. The proposed mechanism offers fast, strong, and mutual authentication. Furthermore, the authors suggested using a certificate-based approach called Extensible Authentication Protocol-Transport Layer Security (EAP-TLS). [PERSON] in [28] investigated handover authentication schemes using cross-layer collaborative techniques for a 5G heterogeneous network. The proposed method offers reliable and secure services by utilizing the cross-layer such as physical layer to implement the EAP-AKA authentication. Moreover, the proposed scheme executed the physical layer authentication using the non-parametric Kolmogorov-Smirnov (K-S) test. In [29], the authors considered the 5G security model in their proposed protocol. They developed a 4G+ relative authentication protocol (4G + RAM) that depends on 4G+ frequency re-authentication protocol (4G + FRP) and privacy-protected authentication and key agreement protocol (PEPS-AKA). [PERSON] et al. presented two protocols to provide anonymity and secure device-to-device (D2D) group communications [30]. The first protocol is the privacy-preserving authentication protocol (PPAKA-HMAC), where the session key is created and combined with the hash-based message. The second protocol is the key agreement protocol (PPAKA-IBS) where it protects against internal attacks by utilizing IBS instead of HMAC. [PERSON] in [31] introduced a two-factor authentication scheme in heterogeneous WSN for IoT to provide anonymity, mutual authentication, and protection against various attacks.
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 5 Conclusions  
To solve the problem that the single mode of an image or point cloud cannot accurately calculate the ore rock fragmentation, this paper proposes a calculation method for ore rock fragmentation based on the point cloud and image multi-mode fusion. The main contributions are summarized as follows:  
1. Aiming at the problem that a single mode cannot accurately calculate the ore rock fragmentation, a multi-mode fusion algorithm was proposed, which used the spatial position information of the point cloud to calibrate the size of image pixels, and accurately achieved ore rock fragmentation calculation without relying on external markers to calibrate the pixel size;
2. To solve the problem of image under-segmentation caused by weak edges, the construction of the standard deviation map and multi-scale adaptive edge detection had effectively enhanced weak edges. This achieved an improvement in the marked watershed image segmentation algorithm and improved the accuracy of image segmentation;
3. Experiments demonstrated that the proposed method could accurately calculate the fragmentation of ore rock blocks with different morphologies in the local excavation area of the bucket. The average error of the equivalent diameter of ore rock blocks was 0.66 cm, the average error of elliptical long diameter was 1.42 cm, and the average error of elliptical short diameter was 1.06 cm, which could effectively meet practical engineering needs.  
The ore rock fragmentation calculation method proposed in this paper gives full play to the advantages of point cloud and image multi-mode fusion, effectively overcomes the shortcomings of the ore rock fragmentation calculation method based on a single mode, and improves the accuracy of ore rock fragmentation calculation and the practicability of the algorithm, which is of great significance for improving the reliability of intelligent mining shovel autonomous mining operations and reducing the failure rate of equipment.  
Methodology, J.P.; Writing--original draft, Y.C. and Z.Z.; Programming and editing, Y.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grants 62173055, in part by the Major Science and Technology Project of Henan Province under Grant 23111222900, in part by the Joint Fund of Science and Technology Research and Development Plan of Henan Province under Grants 232103810038 and 222103810030, in part by the Key Research Projects of Higher Education Institutions of Henan Province under Grant 24A460009 and 20A460012, in part by the Natural Science Foundation of Liaoning Province under Grant 2023-MS-093, and in part by the Science and Technology Major Project of Shanxi Province under Grant 20191101014.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the confidentiality requirements of the project partners.  
The authors declare no conflict of interest.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 3 Analysis of the Research Object Maintenance Process
### Airworthiness of Research Object  
The starting point of the analysis is the airworthiness of the M-346 aircraft. Airworthiness refers to the condition in which the aircraft is capable of performing its intended tasks safely and effectively. The airworthiness of considered A/C is registered in dedicated IT System called Samanta [3,18]. Generally, airworthiness can be characterized by the following formula:  
\[F=\frac{D}{C-X}\cdot 100\% \tag{2}\]  
where  
* _C_--A/C on the records [pcs.];* \(D\)--airworthy A/C [pcs.];
* \(X\)--A/C not counted for airworthiness [pcs.];
* \(F\)--airworthiness [%].  
The exact airworthiness percentage of the analyzed aircraft are sensitive data belonging to the organization in which it is used. This information should not be publicly disclosed. As a consequence, the following relationship was used to present the airworthiness tests conducted:  
\[F^{\prime}=F.\frac{100}{\max(F)} \tag{3}\]  
where  
* \(\max(F)\)--the highest recorded percentage airworthiness of the M-346 aircraft in 2018-2020.  
On the basis of data from IT System Samanta, an airworthiness analysis of the aircraft was carried out M-346 in the years of 2018-2020, the results of which are presented in the table below (Table 1).  
From the above data, it was concluded that higher airworthiness in the first year of operation is mainly due to the fact that new aircraft did not require as many periodic maintenance processes. In 2019, most of the higher-level periodic maintenance (conducted every two years) was performed, which significantly reduced the total airworthiness.
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 4 Results and Discussion
### Processing Time  
To consider the processing as real time, the algorithms execution time should be less than the acquisition time between consecutive images. In our case, the images are captured by the UAV every 2 s. The time to process one image on the Raspberry Pi 3 is shown for each algorithm separately in Table 5. The mean and standard deviation have been calculated from processing 100 images. Images are stored in high resolution (3 Mb), but are scaled to about one-fourth (from 21.5-24.5% depending on the distance to the ground of each image) during the execution of the segmentation.  
Observe that the time of the three processing phases is detailed separately, including the two proposed methods for segmentation: color-based and stereo vision-based. Except for stereo vision, which spans for more than 3 min, the rest of the processing times are below one second. Assuming a sequential execution of the algorithms after the capture of every new image, we will execute color-based segmentation, then the global mapping algorithm, and finally, the geolocation of the new trees. The total execution time would be 1.0355 s. Moreover, using a pipeline configuration, we could exploit the benefits of the four cores of the RaspberryPi 3 and execute the algorithms in parallel. Then, the added execution time could be reduced to 0.5840 s per each new image. In both cases, the execution time is less than the elapsed time to receive a new image.  
The variability of the execution time is very relevant in real time. Observing the standard deviations of the three algorithms, we see small values and, thus, a beneficial low variability.  
Figure 11: Tree detection in a sequence of 10 images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Mean (s)** & **StdDev (s)** \\ \hline Color-based segmentation & 0.5840 & 0.0148 \\ Stereo vision-based segmentation & 205.2 & 27.1 \\ Registration and merging & 0.4505 & 0.0120 \\ Geolocation & 0.0010 & 0.0002 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Execution time of the proposed algorithms on the RaspberryPi 3. Mean and standard deviation per image. Time is in seconds.  
Assuming the Gaussian confidence interval of 99% of the values (\(3\sigma\)), the maximum sequential execution time of the three algorithms (1.91) is still below 2 s. In summary, less than 1% of the time, the processing of an image can take more than 2 s, and the following image might have to wait for its processing, or the current image processing might be aborted. Considering the overlap of the images, even the abort of one every 100 images will not be noticed in the final count.  
Stereo vision-based segmentation is not a good solution, as proposed in this paper. A UAV with a stereo camera, with a fixed geometry, could be a better solution to solve the segmentation. In the same manner, a depth sensor, such as a LiDAR, could also obtain better results, but at the expense of higher payload costs.
## 5 Enhancing QoS and QoE in ICN
### Media-Streaming Strategies
[120] proposed solutions for CCN audio and video transmissions and investigated the impacts of different caching strategies on QoE and application-layer QoS. In [121], authors proposed a strategy to improve QoS and QoE for repeatedly accessed multimedia content (\"multi-view\") through coordinated scheduling of content retrieval initiation times across users. Comparative Analysis of Media-Streaming Strategies  
To compare the strategies listed in Table 6, we can divide them into the following subgroups: (i) adaptive and multi-source streaming and (ii) media-focused cache optimization. The first category of strategies improves QoE and QoS through adaptive mechanisms and by leveraging multi-source streaming or temporal availability of content. These approaches employ adaptive streaming algorithms that switch quality based on network \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**\#** & **Proposal and Publication Year** & **Architecture** & **Experimental Environment** & **Optimized QoS/QoE Metric** \\ \hline
1. & [PERSON] et al. [115], 2016 & CCN & CCNx 0.8.2, LAN & QoE, throughput \\ \hline
2. & [PERSON] et al. [116], 2019 & CCN & ccnSim, CCNx [63], LAN and 802.11n WLAN & QoE (MOS from human subject test), delay, bit rate/request \\ \hline
3. & [PERSON] [118], 2022 & CCN & CeforeNet, LAN & MOS, video media unit (MU) loss ratio, video error conceal ratio, audio media unit loss ratio \\ \hline
4. & [PERSON] and [PERSON] [121], 2023 & CCN & Cefore [122], LAN & Audio media unit loss ratio, video media unit loss ratio, viewpoint change delay, MOS \\ \hline
5. & [PERSON] and [PERSON] [119], 2024 & CCN & Cefore, LAN & MOS, cache utilization ratio, video media unit loss ratio \\ \hline
6. & [PERSON] and [PERSON] [120], 2024 & CCN & NS-3 ccnss3 sim [123] & Cache hit ratio, audio media unit loss ratio, video error conceal ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Media-streaming strategies: architectures, experimental platforms, and QoS/QoE metrics. conditions and potential source switches, aiming to provide a higher-quality viewing experience despite network variations or the distributed nature of content in ICN/CCN. Some techniques also delay access to already-cached content to improve the chances of retrieving content from closer, faster sources [118]. A potential drawback is the added complexity in implementing these dynamic adaptations and the overhead of continuous monitoring of cache availability. The second category focuses on optimizing cache management and policies to improve efficient media delivery. It includes approaches such as linking different viewpoints in multi-view video to improve cache utilization [121] and deleting duplicate cached content to obtain better hit rates when handling multiple streams at once [120]. This inherently improves network performance by reducing traffic on core network links and lowering content retrieval latency. The improved cache hit ratios and reduced loss ratios contribute to better QoS, which in turn positively impacts QoE.
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 6 Discussion
### Consideration on a Possible Color Effect  
The mineral effect observed at granites and migmaties may suggest a sort of color-dependance of emissivity. In fact, it has been experienced herein that higher \(\varepsilon\) values correspond to darker minerals (micas). This would also justify the higher mean emissivity of red granites (darker variety) if compared to the lighter ones (Figure 4a).  
Nevertheless, single mineral phases are not the target of IRT for engineering geological applications, which is instead focused on framing the whole rock surface, on which both pore and mineral effects take place. In this perspective, a color-emissivity dependance was not found herein, as dark colored rocks are not always characterized by the highest emissivity values. An example is the case of light beige limestone vs. dark grey calcisiltite, which returned the same emissivity although showing an evident color difference to the naked eye; similarly, black slate holds a lower \(\varepsilon\) than white marble (Figures 2-4). This is not surprising; in fact, colors surely affect the visible light wavelength, which has the highest energy in the electromagnetic spectrum according to [PERSON]'s law, but at the infrared eye, only slight differences occur. Rather, such discrepancies can be explained by a mix of simultaneous mineral and pore effects, although differently weighted on the framed rock surface. In fact, the mineral effect plays a preponderant role on the emissivity of  
Figure 6: (a) IRT image of a granite highlighting the different thermal behavior offered by minerals, m—micas, \(\ell\)—feldspars, q—quartz; (b) digital image of the corresponding granite.  
macroscopically holocrystalline rocks, where crystals dominate the framed area. Similarly, in the case of light beige limestone vs. dark grey calcisiltite, the carbonate nature is common to both rocks, as well as the absence of macropores. On the contrary, the emissivity of a rock affected by the presence of voids is strongly influenced by the pore effect, which overcomes the eventual mineralogical differences.  
These considerations further confirm the reasonability of choosing an average emissivity value for IRT applications on rocks when the framing of an \"averaged\" rock surface is required.
## 3 Materials and Methods
Union of current and corner nodes we called _edge nodes_. Let us consider for simplicity rectangular plate (Figure 2). Its contour has no double nodes. In this case, we can say that each edge node is generated by single initial point. From this reason each function \(f_{1 ps}(x_{s})\) corresponds to single initial point. At each edge node two boundary conditions are written. Corner nodes, say \(\overline{K}_{32}\) and \(\overline{L}_{33}\), have the same coordinates \((a_{1},a_{2})\) but they lie at the different edges of the normals \(\mathbf{n}_{2}\) and \(\mathbf{n}_{3}\). Thus, they are different nodes. Each edge node is generated by single initial point. At the first approximation \((K=1)\) we have eight unknown coefficients \(R_{1 psv}\). Thus, eight boundary conditions written at four edge nodes can be satisfied. To that four initial points must be chosen at the semi coordinates axes \((x_{1m}=1,2;\ x_{2n}=1,2)\). Following for approximation \(K\) there must be \(2K\) points \(x_{1m}=1,\ldots,2K\); \(x_{2n}=1,\ldots,2K\). For illustration of effectiveness of the proposed method as an example we consider rectangular plate (Figure 2) with thickness \(h\) and plane sizes \(2a_{s}\ (s=1,2)\). This example allows easily compares author's results with results obtained by other available methods. To obtain the particular solution \(w_{*}\) of Equation (1) we present outer load \(q(x_{1},x_{2})\) in the form of double trigonometric series [70; 73]  
\[q(x_{1},x_{2})=q_{mnpq}\cdot T_{mp1}(x_{1})\cdot T_{nq2}(x_{2}),\ \ \ m,n=1,2,3,\ldots \tag{25}\]  
For rectangular plates parameters \(q_{mnpq}\) can be determined as coefficients of expansion of the outer load in Fourier series on the surface of the plate. New symbols have been introduced here. \[T_{m1s}(x_{s})=\cos\left(\gamma_{ms}x_{s}\right);\ \ \ T_{m2s}(x_{s})=\cos \left(\delta_{ms}x_{s}\right); \tag{26}\]  
\[\gamma_{ms}=\frac{m\pi}{a_{s}};\ \ \delta_{ms}=\frac{(2m-1)\pi}{2a_{s}}. \tag{27}\]  
For rectangular plates force functions \(W_{mnpq}\) are taken in the form  
\[W_{mnpq}(x_{1},x_{2})=T_{mp1}(x_{1})\cdot T_{nq2}(x_{2}).\ \ \ p,q=1,2 \tag{28}\]  
Figure 2: Distribution of current nodes at the contour of rectangular plate. Substituting expressions (25), (28) into Equation (1) with taking into account (19,20) and equating coefficients at the same expressions in the both sides of Equation (1) we find coefficients \(C_{mnpq}\) expressed by outer load.