esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 1 Introduction  
The problem of reliability of UAVs, like problems of maintenance and safety, have become extremely important in recent years: engines became more robust, avionics was improved, etc. Despite this, the approach regarding the reliability of drones is still too fatalistic.  
Obviously, by means of reliability analyses which are available nowadays, we consider that the absence of a driver or person on board does not allow us to design and realize the UAV with less stringent standards with respect to those used for airplanes. The commercial aviation failure rate is about \(1/10^{5}\) flight hours, while for drones, it has been verified at about \(1/10^{3}\) flight hours, so a higher magnitude of two orders. From a different point of view, sophisticated UAV systems have an overall failure rate of 25%. The aim of the paper, which is an extended version of [1], is to provide new ideas to increase the reliability of a drone optimizing maintenance activities. For this, we start from the philosophy of apportioning the percentages of reliability assigned (on average) to each system (and subsystem), trying to optimize them according to safety requirements. On the other hand, it is necessary to optimize the time intervals (and consequently the costs) of maintenance, taking into account that all critical systems must absolutely support preventive maintenance criteria: in these cases, we are helped by the concepts of _soft_ and _hard_ failure [2, 3].
# _microorganisms_
## 3 Results
### Sequential Model II  
Under aerobic or anaerobic conditions, the amount of viable _E. faecalis_ in Sequential model II was similar to that of the _E. faecalis_ as monospecies in the positive control group (\(p>0.05\)) (Figure 2a). The log10/mL values for _E. faecalis_ monospecies culture under aerobic and anaerobic conditions were 8.10 and 8.13, respectively, whereas the values for Sequential model II were 8.01 and 8.08, respectively (Figure 2a). The dual-species culture model and oxygen tension did not significantly alter the viable cell counts of _E. faecalis_ (\(p>0.05\)).  
Under aerobic conditions, the amount of viable _P. gingivalis_ in the Sequential model II was similar to that of the _P. gingivalis_ as mono-culture in the positive control group (\(p>0.05\)) (Figure 2a). Under anaerobic conditions, there was a 1-log reduction observed when _P. gingivalis_ was grown in the Sequential model II without significant difference (\(p>0.05\)) (Figure 2b). The log10/mL values for _P. gingivalis_ as mono-culture under the aerobic and anaerobic conditions were 6.85 and 8.10, respectively, whereas the values for Sequential model II were 7.13 and 6.95, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Organisms and Strains** & **(a) Primer Sequences (5\({}^{\prime}\to\) 3\({}^{\prime}\))** & **(b) DNA Probe Sequences (5\({}^{\prime}\to\) 3\({}^{\prime}\))** \\ \hline _Enterococcus faecalis_ & Forward: GTTATGCCCGCATGCCATAGCAG & 6 AM-CGGCTCACCAAGGCCA-TAMRA \\ (ATCC 4/077) & Reverse: CAGGTCGGCTATGCGA & \\ \hline _Porphynomonas gingivalis_ & Forward: ACCTTACCCGATGAATG & \\ (ATCC 33277) & Reverse: CAACATGCAGCACTCATAGAA & \\ \hline \end{tabular}
\end{table}
Table 1: (a) Species-specific primer sequences used in this study; and (b) Species-specific DNA probe sequences used in this study.  
Figure 1: The viability of **(a)**_E. faecalis_ and **(b)**_P. gingivalis_, cultured under aerobic and anaerobic conditions based on the Sequential model I and mono-culture models. Data represent the mean and standard deviation.
## 7 Limitation and Future Recommendations  
While the adoption of the distributed dynamic on/off scheduling (D-DOSS) approach has shown promise in enhancing EE within HetNets, it is important to recognize inherent limitations. D-DOSS, by forming small clusters and evaluating key quality-of-service (QoS) parameters, offers ease of implementation and manageability. However, transitioning to a centralized system from distributed sleep approaches like D-DOSS may introduce higher computational demands and complexity. Moreover, simulations conducted in an interference-limited environment underscore D-DOSS's compatibility with distributed setups, but transitioning to a centralized system could amplify computational complexity and overhead.  
In addressing these limitations, future research should prioritize real-world deployment and field trials to validate D-DOSS's efficacy in practical HetNet environments. Additionally, efforts should focus on enhancing D-DOSS's dynamic adaptation capabilities to enable it to respond more adeptly to evolving network conditions and traffic patterns. Development and refinement of optimization algorithms, considering factors like user mobility and varying environmental conditions, can further bolster D-DOSS's energy efficiency and performance. Integrating AI techniques, such as machine learning and reinforcement learning, holds promise for enhancing D-DOSS's intelligence and adaptability in optimizing HetNet energy efficiency. Lastly, standardizing and integrating D-DOSS into existing  
Figure 13: Average achievable per user throughput.  
network architectures will facilitate widespread adoption and deployment across diverse network environments, advancing the frontier of HetNet technologies and fostering the development of more sustainable wireless communication networks  
Conceptualization, A.S. and S.R.; methodology, A.S. and S.R.; software, A.S.; validation, A.S., S.A. and A.A.A.; formal analysis, A.S.; investigation, A.S.; resources S.A. and A.A.A.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, M.F.S.; visualization, A.S.; supervision, S.R.; project administration, A.S.; funding acquisition, S.A. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by EIAS Data Science & Blockchain Lab, Prince Sultan University. The authors would like to thank Prince Sultan University for paying the APC of this article.  
All data supporting the findings of this study are included within the manuscript.  
The authors would like to thank Prince Sultan University for its support.  
The authors declare no conflicts of interest.
## 4 Experimental Evaluation and Analysis
### Qualitative Analysis  
The effectiveness of the VTP-GCN proposed in this paper is qualitatively analyzed by visualizing several representative driving scenes using the datasets I-80 and US-101. As shown in Figure 9, the coordinates and velocity data of all vehicles in the scene in the past 3 s are entered into VTP-GCN, and then our model generates the trajectories of the vehicles in the following 5 s.  
Figure 9a shows a mild driving scene in which there are no neighboring vehicles that can interact with the predicted vehicle; the predicted vehicle, therefore, chooses to go straight at a high speed. The prediction trajectory of our model fits the solid blue line well, which proves that the proposed model is fully capable of performing vehicle trajectory prediction tasks in simple scenes. In Figure 9b, the traffic density is moderate and there are complex interaction behaviors between vehicles. It is noteworthy that a vehicle in the middle lane will change lanes to the left in search of a better driving experience. Our VTP-GCN accurately predicted the lane change intention, and the error between the predicted trajectory and the ground truth is small. Meanwhile, the predicted trajectories of the rest of the vehicles in this scene are also accurate. In Figure 9c, we show a prediction result in a heavy traffic scene with 13 vehicles. The observation shows that the vehicles in the upper lane move faster, while vehicles in the other lanes drive at relatively slow speeds. Faced with the complex driving scene, our proposed VTP-GCN still predicted the future trajectories of all vehicles accurately. Through the above analysis, we can conclude that the VTP-GCN model that we propose in this paper is applicable to most traffic scenes and that it is able to operate with accuracy and robustness.  
Figure 8: Prediction errors of VTP-GCN and its variants.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 3 System Model  
This section introduces the proposed system model, including an integrated satellite-terrestrial network, users, contents, and a cooperative caching and resource allocation model.
# Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables
## 1 Introduction
### Physicochemical Properties  
#### 2.1.1 Gel Content Test  
The gel content is the insoluble fraction of a polymer sample and is an indicator of the degree of cross-linking in cross-linked polymers. We performed this test in accordance with the standard ASTM D2765-11 to investigate the changing law of cross-linking in cross-linked polymers throughout the aging process. The test methods for the gel content test are as follows.  
In preparation, a stainless steel mesh bag for placing samples was cleaned with anhydrous ethanol carefully, and then left to dry (until the quality of it (denoted as \(W_{1}\)/g) no longer decreased) in a dry air oven, where the temperature was constant in 40 \({}^{\circ}\)C. Then, samples with a quality of 0.4 \(\pm\) 0.02 g cut from XLPE cables were placed into the bag and the quality was denoted as \(W_{2}\)/g.  
When testing, all samples were dipped in xylenes at 150 \({}^{\circ}\)C for 12 h and then cleaned with anhydrous ethanol. After drying in a dry air oven until the quality did not increase, the quality of samples, denoted as \(W_{3}\)/g, was obtained. The gel content can be obtained as follows:  
\[C_{ge}=\frac{W_{3}-W_{1}}{W_{2}-W_{1}}\times 100\% \tag{1}\]  
where \(C_{ge}\) is the average value of gel content, g; \(W_{3}\) is the quality of the sample after being dipped in xylenes, g; \(W_{2}\) is the quality of the stainless steel mesh bag with samples placed in it, g; \(W_{1}\) is the quality of the stainless steel mesh bag, g.  
The test results for \(C_{ge}\) are shown in Figure 1.  
According to the test results obtained in Figure 1, with an increasing operating time, the gel content increases before decreasing. During the aging process, chemical bonds break under the joint action of high temperatures and strong electrical fields, which, in turn, lead to a decrease in gel content. Moreover, the free radicals in chemical bonds polymerize and reformate in chemical bonds under the action of high temperature, which increases the gel content. At an operating time between 0 and 2.5 years, a mass of free radicals exists in cross-linked polyethylene material and the increase in the bonding rate is the dominant factor, which, in turn, leads to an increase in the gel content of samples. At an operating time between 2.5 and 8 years, thermal aging causes molecular chains in cross-linked network structures to break, which, in turn, leads to a decrease in the gel content of samples.
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 2 Methods  
In this paper, we proposed a robust localization model using CNN-based spectral analysis. Firstly, a two-dimensional Mel-spectrogram is generated by dividing a one-dimensional audio signal into frames, windowing, discrete Fourier transform, and Mel filter. The Mel-spectrogram [20; 21] visually reflects the spectral characteristics of audio signals, with the horizontal axis representing time and the vertical axis representing frequency. The color of the corresponding coordinate points represents the energy value of the speech data (the higher the energy value, the brighter the color). The frequency of the Mel-spectrogram follows a logarithmic distribution, which is sensitive to changes in the low-frequency range and insensitive to changes in the high-frequency range, which is more in line with the human ear's perception characteristics of frequency. For images, the copy-move forgery area can appear in any region. However, audio is different. As shown in Figure 2, the forgery only reflects similar rectangular areas on the Mel-spectrogram, and the duration of the forgery directly affects the width of the similar rectangular areas.  
Figure 2: Mel-spectrogram for copy-move forged audio.  
Figure 2a is a Mel-spectrogram generated from a forged audio with a duration of 10 s. The segment of 1-2 s in the audio is copy-moved to the 5-6 s. Figure 2b,c compares the magnified copy-move forgery area, and it can be observed that the graphic similarity between the two areas is extremely high. The localization model analyzes similar regions in the Mel-spectrogram to locate audio copy-move forgery segments. For the convenience of analysis, the Mel-spectrogram size of the audio samples was preprocessed to 3 \(\times\) 128 \(\times\) 256.  
The method of locating audio copy-move forged segments is shown in Figure 3. Firstly, the one-dimensional audio signal is calculated as a two-dimensional Mel-spectrogram, which is preprocessed and used as input for the model. Next, the localization model locates forged segments through three sub-modules: Feature Extraction Module, Correlation Detection Module, and Mask Decoding Module. Among them, the Feature Extraction Module extracts deep features from the Mel-spectrogram. The Correlation Detection Module calculates the similarity between features and generates statistical features. The Mask Decoding Module decodes statistical features to generate a similarity region mask, which visualizes the position of copy-move forged segments.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 2 Principles  
This section dedicates to the physical principles of integrated OPAs, which can be fully explained by the Fraunhofer diffraction. OPAs can be described using a diffraction model or multi-slit diffraction model, which is the simplified version of the former.  
There are two kinds of OPAs, namely the waveguide grating antenna (WGA) OPAs with nearly vertical radiation and the end-fire (EF) OPAs with power radiated from the facets of waveguides. Typically, the EF OPAs enable 1D beam forming and steering. Two-dimensional (2D) beam steering can be achieved by the WGA-based OPAs.
## 2 Materials and Methods
### Materials, Manufacturing, and Characterization Methodologies
#### 2.2.4 Dynamic Fracture Mechanics Tests  
At 156 days (\(\approx\) 5 months), mode I dynamic fracture tests were conducted using the servo-hydraulic machine Instron 8805, previously employed for quasi-static mechanical characterization (Figure 2). Four prismatic specimens measuring \(420\times 100\times 100\) mm\({}^{3}\), each featuring a centrally sawn notch with a depth equal to 1/6 of the specimen's height, were tested for each type of hydraulic lime concrete (with and without PVA fiber reinforcement) and at two different loading rates. The selected velocities, aiming to encompass the spectrum from quasi-static to low dynamic regimes, were denoted as \(v_{1}=4\times 10^{-4}\) mm/s and \(v_{2}=4\) mm/s, respectively.
## 2 Fundamentals of Terahertz Communication
### The Terahertz Spectrum
#### 2.1.1 High Bandwidth
FSPL (L\({}_{\text{FSPL}}\)) further contributes to signal degradation, as higher frequencies experience greater power losses over long distances. To mitigate these effects, techniques such as multi-band adaptation, AI-driven power optimization, and low-loss material integration are essential for enhancing the stability and efficiency of THz links, particularly for 6G, inter-satellite links, and high-speed backhaul applications. Figure 3: Bandwidth comparison of communication technologies. Figure 2: Data rates and stability comparison. These equations highlight how the use of THz MIMO systems can further enhance the communication capacity of existing systems, making them ideal for high-demand applications like 6G and ultra-HD streaming. The THz spectrum is also shown to outdo traditional technologies in terms of bandwidth in Table 2, which depicts the data rates required for next-generation networks.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 3 System Architecture
### Two Types of Radio Systems  
Primary radio networks (PRNs) and CR-enabled LPWANs are considered for the two types of radio systems as shown in Figure 2. The PRNs are mobile cellular networks where the fixed and licensed spectrum bands are often observed to be underutilized. CR-LPWANs work with PRNs and use their spectrum at a fixed frequency. Specifically, the spectrum channel contention between PRN and CR-LPWAN only takes place within the PRN radio spectrum range.  
The licensed primary users (PUs) can exclusively use the spectrum bands allocated in PRNs. When PRN radio spectrum is not fully utilized, however, many spectrum holes in the PRN radio system can be temporarily used by the cognitive users (CUs) in CR-LPWANs. That is, CUs can identify and exploit these spectrum holes occurring in the PRN radio system. Specifically, CU should be able to exploit them to transmit in an opportunistic manner only when it does not interfere with any PUs.  
In this architecture, IoT sensor devices send their reading values to the base station of LPWAN depending on spectrum availability by cognitive radio capability. The readings are eventually delivered to the data center, which is not indicated in Figure 2 for simplicity. That is, cognitive radio-equipped IoT  
Figure 1: Low power wide area network (LPWAN) end-to-end network architecture for Internet of Things.  
devices communicate with each other according to cellular-like topology over both licensed PRN bands and unlicensed CR-LPWAN spectrum bands. This topology creates less control data in communication and makes more accurate results in spectrum sensing. If IoT devices in a CR-LPWAN have mobility, it produces a more dynamic topology and enlarges the existing challenges.
## 4 Discussion
### Yield Simulation  
After the application of calibrated crop parameters for yield simulation, there was a strong relationship between the observed and simulated yields in Group B, which used manual transplanting. The Nash-Sutcliffe model efficiency coefficient (EF) was 0.97, indicating a near-perfect match between the observed and simulated yields. [PERSON]'s index of agreement or compatibility (d) is close to 1, which signifies an almost-perfect agreement between the observed and simulated yields. The RMSE of rice group B was 0.36, indicating a few errors. The NRMSE is 7.24%, indicating an excellent relationship between the measured and predicted yields. The simulated yield of Group A was evaluated as anacceptable result with statistical indicators of 0.40%, 0.60%, 1.35% and 18.99% for EF, d, RMSE and NRMSE, respectively. Nevertheless, the validation of the simulated yields presented good results (MAE = 0.159, RMSE = 0.182 t ha\({}^{-1}\) and R\({}^{2}\) = 0.88) as shown in Table 8 and Figure 8.
## 3 Results
### A20 Was Involved in MSF-Mediated TNF-\(\alpha\) Tolerance  
The mRNA levels of negative regulators such as SOCS-1 and A20 were examined. The SOCS-1 mRNA level increased 4.8-fold, and the A20 mRNA level increased 35-fold in LPS-treated cells compared with untreated controls (Figure 5A,B). In cells treated with only MSF, the SOCS-1 mRNA level did not increase, but the A20 mRNA level increased approximately 20-fold. A20 mRNA showed the greatest increase (of 60-fold) when THP-1  
Figure 4: Signaling variation by MSF pretreatment. (**A**) THP-1 cells were pretreated with 200 \(\upmu\)g/mL MSF for 18 h and then re-stimulated with 500 ng/mL LPS for the indicated time. The levels of p-p38, p-ERK, p-JNK, p-p65, and \(\beta\)-actin were then quantified by western blot. (**B**) THP-1 cells were pretreated with dimethyl sulfoxide (DMSO) as a control and 10 \(\upmu\)M of inhibitors for 30 min and re-stimulated with 500 ng/mL LPS for 4 h. (**C**) THP-1 cells were pretreated with 200 \(\upmu\)g/mL MSF for 18 h and either DMSO or 10 \(\upmu\)M of inhibitors for 30 min. The cells were then stimulated with 500 ng/mL LPS for 4 h. The levels of cytokines in the culture supernatants were measured by ELISA. Statistical analysis was conducted with one-way ANOVA followed by [PERSON]’s HSD post hoc test. ***\(p\) < 0.001.  
cells were co-treated with LPS and MSF (Figure 5B). We further examined the protein levels of the negative regulators by western blot. Similar to mRNA expression, SOCS-1 and A20 proteins were most induced in THP-1 cells treated with a combination of MSF and LPS (Figure 5C). THP-1 cells were transiently transfected with siRNAs to confirm whether MSF increases the expression of SOCS-1 and A20, which would result in LPS-induced TNF-\(\alpha\) tolerance. When cells were transfected with siA20 RNA, MSF-mediated TNF-\(\alpha\) inhibition significantly recovered, unlike in siSOCS-1-transfected cells (Figure 5D), suggesting that MSF increased mRNA and protein levels of A20 to inhibit LPS-induced TNF-\(\alpha\) production. At a concentration of 200 ug/mL, MSF induced 80% greater A20 protein expression compared to K8 lysates when treated with LPS (Figure 5E), suggesting that the increased TNF-\(\alpha\) inhibition by MSF was due to A20 expression.
## 2 System Model of MC\(-\)ODBR\(-\)DCSK
### Chaotic Signal Generator
#### 2.1.4 Two-Dimensional Logistic (2D-Logistic) Map  
The 2D-Logistic chaotic map is obtained by increasing the dimension of the Logistic map, which is defined as:  
\[\begin{cases}x_{i+1}=r(3y_{i}+1)x_{i}(1-x_{i})\\ y_{i+1}=r(3x_{i}+1)y_{i}(1-y_{i})\end{cases} \tag{4}\]  
where, the control parameter \(r\) satisfies \(r\in[0,\ 2]\). When the initial value of iteration is \((x_{0},y_{0})=(0.40,\ 0.40)\) and the parameter \(r=1.19\), the time domain diagram of the map is shown in Figure 1b.  
As can be seen from Figure 1, the time-domain waveform of a chaotic signal exhibits noise-like characteristics, and as a carrier signal, it has good confidentiality.
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 3 Heterogeneous Network Architecture
### Data Formats for Hybrid LoRa-Satellite Networks
The summary of data presentation formats with their key properties is presented in Table 1.  
Considering the predetermined nature of data structures transmitted from IoT sensors, Protobuf seems to be the best choice, as it minimizes the amount of service information while still preserving the key-value approach. The key technical task, however, is to seamlessly integrate the protocol into an existing network infrastructure.