esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
## 5 Scheme Feasibility Analysis
### Complexity Analysis  
The consensus algorithm is the core of the blockchain and the main overhead of the system resources. The main purpose of this scheme is to dynamically reconfigure the network according to the nodes' own state changes, including changes in trustworthiness, energy consumption and location, in order to ensure the availability and feasibility of the drone network. However, the drone nodes in the mission are limited in terms of bandwidth, computation, storage and energy resources, and the consensus algorithm must coordinate the complexity of computation, communication and storage to achieve a reasonable allocation of resources, especially energy consumption. The private blockchain created by the scheme in this paper, the main decentralized data asynchronous provable consensus algorithm used. The consensus algorithm uses a smart contract for authentication and transaction verification in the Genesis block to prove the authenticity of the source of the transaction and the integrity of the data with a computational complexity of \(O(1)\). The POS and PBFT consensus algorithms do not need to compete for bookkeeping rights, and their computational complexity for verifying transactions is of constant order \(O(C)\). The computational complexity of the POW consensus is \(O(1/2N^{2}+2/3N)\), which is as high as \(O(N\log N)\) even after immediate optimization, while the computational complexity of the POW consensus is mainly in the solution of the Hash problem. In terms of communication complexity, the nodes communicate with each other in a similar way as PBFT, and the consensus is completed when \(2/3\) of the nodes have confirmed the received transaction. The length of the data that requires consensus is \(|m|\), and the required communication complexity is \(O(|m|N^{3})\). The decentralized asynchronous provable consensus method of this party divides the transaction data based on the number of proxy agents and the size order of their IDs, and uses decentralized stable broadcast control communication (DRBC), if the number of proxy agents is M and the number of nodes in the whole network is N, \(M<<N\), the communication complexity is \(O(M|m|M^{2}/N)\). In the POW or POS consensus algorithm, the communication complexity is \(O(1)\), but its consensus result is probabilistic and requires multiple consensus rounds to determine, with Bitcoin's POW requiring six consensus rounds to determine a transaction and POS requiring longer consensus rounds. Table 4 shows the performance comparison of different consensus algorithms in terms of their associated complexity.
## 2 Background and Knowledge Gap  
Gram-negative bacteria have a multilayered cell envelope that consists of an inner (cytoplasmic) membrane, a periplasmic peptidoglycan layer, and an outer membrane [13; 14; 15; 16; 17; 18]. The outer membrane functions as both a selective barrier against harmful agents and a gateway for small hydrophilic solutes, including ammonium [7; 16; 17; 18; 19; 20]. Trimeric \(\beta\)-barrel proteins, known as porins/membrane proteins, form aqueous channels that allow ions and small molecules to pass passively along electrochemical gradients [16; 17; 18; 21; 22; 23]. General diffusion porins, such as OmpF in _Escherichia coli_ and Omp-Pst2 in _Providencia stuartii_, provide pathways whose throughput depends on pore architecture and ion concentrations on either side of the membrane [16; 22; 23; 24; 25; 26; 27; 28].  
To date, understanding of ammonium movement through porins has primarily been based on indirect observations. Measurements such as total nitrogen consumption or per-cell ammonium uptake may reflect the combined influence of multiple transport mechanisms. While high-resolution structural studies--for example, the 1.35 A structure of AmtB--have provided insights into active ammonium transport, comparable structural or electrophysiological data specifically related to porin-mediated ammonium flux appear to be limited [5; 6; 7; 19; 29].
## 5 Optimization Techniques for Routing in Wireless Sensor Networks
### Multipath Communication  
With this technique, nodes use multiple paths from an origin to a destination in the network. As multipath communications are intended to increase the reliability and the performance of the network, these paths should not share any link. Multipath communications can be accomplished in two ways.  
Firstly, one path is established as the active communication routing while the other paths are stored for future need, i.e. when the current active path is broken. On the other hand, it is also possible to distribute the traffic among the multiple paths.
# Trustworthiness of Situational Awareness: Significance and Quantification
## 1 Introduction
These attacks encompass both passive and active attacks that target the sensors, communication links, electronics, and artificial  
Figure 1: Overview of situational awareness [1]. intelligence (AI) of SA devices, equipment, and systems [3] as further discussed in Section 3. To protect SA devices and systems against these security attacks and to impart trust in these SA systems, mitigation techniques need to be incorporated to alleviate these security attacks on SA. This paper contemplates security and trust issues in SA and proposes a model for quantifying the trustworthiness of an SA system. To the best of the authors' knowledge, the proposed model is the first of its kind to quantify the trustworthiness of SA systems. The proposed model is a simple yet practical model, that enables efficient estimation of trustworthiness for different SA sources. Our main contributions in this paper are as follows:  
* We discuss the significance of security and trust in SA systems from a military and an air force perspective;
* We elaborate the significance of quantifying the trustworthiness of SA systems for improving the trust of SA systems;
* We propose a model for quantifying the trustworthiness of an SA system;
* We present numerical examples that demonstrate the quantification of the trustworthiness of an SA system using our proposed model. To the best of the authors' knowledge, this is the first paper that targets the quantification of the trustworthiness of SA, and can serve as a guideline for future research in this area. The remainder of this paper is organized as follows. Section 2 provides a summary of relevant works in the literature related to the assessment and security of SA. Section 3 discusses the security and trust of SA and identifies the different components of an SA system that can be subjected to adversarial attacks. Section 4 illustrates the significance of quantifying the integrity of SA on the trust of SA systems and also presents our proposed model for quantifying the trustworthiness of an SA system. Section 5 shows numerical results and examples of the trustworthiness quantification of the SA system. Finally, Section 6 concludes this paper.
# Development and Testing of a Two-UAV Communication Relay System
## 4 Software Development
### Mission Control Software  
The mission control software, which runs on the UAV's on-board computer, consists of the following seven modules, and the software's block diagram is shown in Figure 11.  
1. Flight Controller Interface: manages direct communication with Pixhawk flight controller over USB port.
2. UAV Status Monitor: monitors flight and mission parameters of the UAV, and reports them regularly to the GCS.  
Figure 11: Block diagram of mission control software.  
Figure 10: Format of frame.  
* Command Interface: translates user commands into low-level instructions to the flight controller; records and reports command execution status.
* Communication System Interface: manages low-level message traffic between mission control subsystem and ration telemetries; executes message framing, frame reception, and error checking.
* Communication Relay Control: controls the relay of radio communication.
* Startup Control: bootstraps the whole mission control software system during UAV power-up process.
###### Abstract  
Background: The aim of this study was to calculate and compare the peak acceleration and peak negative velocity of uke's shoulder during the course of two judo throwing techniques, Sode-Tsurikomi-Goshi (STG) and Tsurikomi-Goshi (TG). Methods: This study recruited male adult elite judo players with a mean age 26.5 of (SD = \(\pm\)8.916), a minimum black belt ranking, and no injuries in the past six months. Participants were selected from the Aris Judo Club, Thessaloniki, Greece, ensuring high-level technique for safe and reliable outcomes. Informed consent was obtained. A Huawei 5T with a 48-megapixel camera was used to record the throws. Kinovea (version 0.8.15) software was used for two-dimensional motion analysis of shoulder displacement during throws and IBM SPSS Statistics (version 25.0, Armonk, NY, USA) with Microsoft Excel were used for the statistical analysis of the data. Results: The peak acceleration of uke's shoulder during STG was lower than in the case of TG, but the statistical test did not confirm this difference (\(p\) = 0.1). The mean peak negative velocity of uke's shoulder in TG was higher compared to STG, and this difference was statistically significant (\(p\) = 0.04). The test-retest reliability of STG throws was good (ICC = 0.74), and for TG throws, it was moderate (ICC = 0.60). The effect size was large for negative velocity in TG (d Cohen = 1.12) and moderate for acceleration in STG (d Cohen = 0.43). The individual test did not show a statistically significant difference between STG and TG (\(p\) = 0.2). Conclusions: A statistically significant difference in peak negative velocity in TG compared to STG would reveal that uke is thrown quicker by tori. Further research on impact forces to determine the effective mass of uke's shoulder is advised.  
Keywords:injury; judo; velocity; acceleration; impact force +
Footnote †: journal: Applied sciences
## 3 Results  
According to Table 2, DarkNet-19 achieved the highest accuracy in age prediction alone, with an accuracy of 80.59% and a mean absolute error (MAE) of 0.4265. On the other hand, DarkNet-53 achieved the highest accuracy in sex prediction alone, with an accuracy of 87.94%. The detailed prediction matrix for DarkNet-53 is shown in Figure 1.  
Table S1 (Supplementary Materials) provides a detailed statistical comparison of the MAE of all networks in the study. Accordingly, the differences between DarkNet-19 and DarkNet-53, the networks that provide the closest results to chronological age, and AlexNet and Xception, which give the most inaccurate results, are not statistically significant. In contrast, the difference between DarkNet-19 and Xception is significant.  
Additionally, when age and sex predictions were combined, the accuracy was lower than when they were studied separately. DarkNet-53 achieved the highest accuracy in combined prediction, 70.88%.  
When examining the prediction success distribution graphs in Figures 1 and 2, it can be observed that CNNs mostly made predictions close to the actual sex and age but sometimes made significantly incorrect predictions. For example, DarkNet-53 incorrectly predicted that one in ten radiographs of a 19-year-old female was that of a 5-year-old male (Figure 1). There were also cases where certain age groups were collectively mispredicted, such as GoogLeNet failing to make any correct predictions for the 17-year-old female group (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **Input Res.** & **Age** & **Sex** & **Age and Sex** & **n** & **Mean** & **95\% Confidence Interval** & **Min.** & **Max.** \\  & **olution** & **Prediction** & **Prediction** & **Prediction** & **Absolute** & **Sed. Dev.** & **Sed. Err.** & **Lower** & **Upper** \\  & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Age Error** & & & & & \\ \hline DarkNet-19 & 256 \(\times\) 256 & 80.59\% & 86.18\% & 70.95\% & 340 & 0.4265 & 1.18641 & 0.06  
Figure 1: Detailed prediction distribution of test radiographs by age and sex groups for the DarkNet-53 network. Correct predictions are shown in shades of blue, while incorrect predictions are displayed in shades of red. For example, all 10 radiographs of 5-year-old girls (05F) were correctly predicted by DarkNet-53. However, among the ten radiographs of 6-year-old females (06F), only one was correctly predicted, while one was predicted as a 5-year-old male (05M), five as 6-year-old males (06M), one as a 7-year-old female (07F), and two as 8-year-old females (08F).  
Figure 2: Distribution of predictions for the GoogLeNet network. Correct predictions are shown in shades of blue, while incorrect predictions are displayed in shades of red.
# Effect of Crack Orientation on Fatigue Life of Reinforced Concrete Bridge Decks
## 2 Methodology and Studied Cases
### Randomized Artificial Crack Patterns (RACP)  
To complement missing crack patterns over real decks, randomized artificial crack patterns are utilized. Figure 8 shows the basic RACP procedure concept. Here, the random variable is the number of elements (1-336). To avoid the overlap of case studies and reduce the effect of crack locations as much as possible, the randomization range of elements was limited to the first quarter of the deck.  
Figure 7: Load cycles versus central total deflection for the referential RC deck.  
The other quarters were provided with the same cracks obtained from the first quarter by using the laws of symmetry in both the longitudinal and transverse directions, as shown in Figure 9.  
The crack angles were arranged in sequential order (each crack pattern has a single crack orientation) to cover all possible ranges from 0 degrees to 180 degrees with the step-interval of 15 degrees. The cracks with angles close to 0 degrees, 90 degrees, 180 degrees most extend the remaining fatigue life. Therefore, the smaller step interval of two degrees was further applied for the cases near 0 degrees, 90 degrees, and 180 degrees. The RACP produces continuous cracks, and three shapes of continuity are produced: x-direction, y-direction, or diagonal, on the basis of the sequential angle.  
Both the crack width (0.3 mm) and crack density (average strain = 0.05%) were kept fixed in the RACP scheme to allow independent study of the effect of crack orientation, with the average strain parameter calculated by Equation (3). Although the RACP is based on randomization of elements, it can avoid repetition of cracking patterns. If a crack pattern is repeated, the RACP will continue randomization until a unique one is obtained.  
It should be noted that the induced pre-crack is set to reach the upper mesh layer whose size is almost the same as that of the neutral axis of the studied RC deck [26].  
\[\varepsilon_{\text{avg.}}(\text{A.S})=\frac{\sum_{\text{k=1}}^{\text{k=n}}\! \left(\varepsilon_{\text{xx}}+\varepsilon_{yy}\right)}{\text{n}}, \tag{3}\]  
where \(\varepsilon_{\text{avg.}}\) is the average strain on the bottom surface of the RC deck, \(\varepsilon_{\text{xx}}\) is the concrete's normal strain in x-direction for the kth element, \(\varepsilon_{\text{yy}}\) is the one in y-direction, and n is the total number of elements (336 in this study).  
Figure 8: Randomized artificial crack pattern (RACP) scheme.
# A Review Paper on \"Graphene Field Emission for Electron Microscopy\"
## 3 Graphene-Based Cold Field Emitters
### Graphene Point-Cathode Field Emitters
(**A**) Field emission from individual freestanding graphene edge. Reproduced with permission from [70], Copyright Wiley, 2013; (**B**) Field emission from atomically thin edges of reduced graphene oxide. Reproduced with permission from [71], Copyright American Chemical Society (ACS) publications, 2011. These features enable it to provide stable and repeatable emission properties, something which is not usually associated with single point cathode cold field emission sources. The Graphene-Ni point cathode was fabricated by a two-step process, which consists firstly of an electrochemical etching process to obtain a sharp Ni tip, and secondly, a graphene coating step of chemical vapor deposition (CVD). The Graphene-Ni point cathode for cathode tip-radius of 400 nm was shown to have a high emission current of about 5 \(\upmu\)A under a relatively low applied electric field strength (0.5-1.5 V/nm) on the tip apex. In their study, direct ray tracing simulations of electron trajectory paths leaving the emitter surface were carried out to estimate \(m_{\alpha}\) and \(F\), from which the virtual source size \(d_{\mathrm{v}}\) and gun brightness \(B_{\mathrm{r}}\) were then calculated by using Equations (2) and (3). The source brightness values range from \(7\times 10^{7}\) Am\({}^{-2}\)sr\({}^{-1}\)V\({}^{-1}\) to 2.51 \(\times\) 10\({}^{9}\) Am\({}^{-2}\)sr\({}^{-1}\)V\({}^{-1}\) for cathode tip radii in the range of 130 nm to 800 nm. It is well known that there is a trade-off between the source brightness value and the tip size. On the one hand, a larger tip size has a greater emission area, thereby having a better current stability and can operate in much less stringent vacuum conditions [78]. One the other hand, a larger tip size has a larger virtual source size and a lower source brightness value. This trade-off is applicable to all CFE sources, and most of the previous studies on CNT, LaB\({}_{6}\), and CCnT tips made great efforts to maximize gun brightness at the price of having poor gun current stability and stringent vacuum requirements. Another stand out feature of the Graphene-Ni point cathodes is their low energy spread (Figure 5B). The measured energy spread ranges from 0.246 eV to 0.420 eV for tip radii in the range of 260 nm to 500 nm. The Graphene-Ni point cathode as presented here appears to have overcome many of the practical engineering difficulties. A comparison of typical operating parameters and electron optical properties for the Graphene-Ni point cathode and the state-of-the-art electron sources is listed in Table 1.  
Figure 5: Graphene-coated point cathode field emitter. (**A**) SEM images of graphene-coated point cathode emitter. Reproduced with permission from [77], Copyright Springer Nature, 2018; (**B**) Source brightness and energy spread. Reproduced with permission from [77], Copyright Springer Nature, 2018. CVD: chemical vapor deposition.
## 6 Data Preprocessing and Standardization
### Elimination of Stop Words  
Stop words are the units of text that carry the least semantic information and are non-informative components of the data. They are usually removed since they do not add any value to the analysis. These are the high-frequency words in a sentence that, if added as a feature in text classification, would have produced noise [43]. The larger word surface in textual data also poses the problem of the curse of dimensionality and data sparsity. Therefore, stop words were eliminated from corpora using a domain-specific stop word list. The list was developed from Roman Urdu hate speech corpora using statistical techniques and human evaluation by linguistic experts. The statistical techniques involved direct term frequency (TF), inverse-document frequency (IDF), and term-frequency-inverse-document-frequency (TFIDF) weighting models. The list was then evaluated by bilingual experts and the final list comprises 173 words. The detailed method of domain-specific stop word list compilation is given in our previous work [40]. The sample stop word list is given in Table 4.  
The output of stop word removal on Roman Urdu text is presented in Figure 5.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 4 User Association Scheme for Collaborative RSUs  
In this section, we propose a hybrid RSU method, which is the mutual cooperation scheme of BS/UE-type RSUs for V2X communication when deploying UE-type RSUs in the coverage area of urban Macro BS-type RSUs in a 5G-NR-V2X network. The proposed hybrid RSU method efficiently allocates V2X messages using BS/UE-type RSUs, to secure efficient user services. The hybrid RSU method minimizes shaded areas generated in 5G-NR-V2X environments, to use the data more efficiently and reliably for real-time changing road and traffic environments. For simulation evaluation, we assume a directional antenna-based cellular network, and a single antenna VUE can be adjusted vertically and horizontally through feedback for the backhaul link and signal delay control in the simulation. Additionally, we assume a cellular network that is always activated in the downlink direction for all sessions when using the hybrid RSU method, and analyze the impact of the proposed hybrid RSU method. In this case, vehicle terminals access the wireless channel to communicate with each RSU, and vehicles accessing the wireless channel undergo resource allocation through a proportional fair scheduler [28]. The scheduler performs resource allocation based on CQI estimated from the received signals. The received signal of each vehicle terminal and the estimated CQI for each vehicle are used to calculate the SINR for all PRBs. The calculated SINR value is used to select the modulation and coding rate MCS system for each vehicle terminal, and affects the average processing throughput value of the vehicle terminal.
## 5 Discussion
In addition, RSSI is also affected by fading, which is the deviation from the attenuation experienced by a signal, as well as by shading, which is when the signal is lost by surrounding objects or people. Finally, the _Energy_ is a symptom that is easier to quantify in monetary costs for a company, and for this reason, it is important to measure it and know which are the independent variables that affect it in a greater way. The jamming metrics used can be complemented by other metrics such as SNR or BER, so that the proposed algorithm can be extended for networks with lower densities. The _detection algorithm_ has laid the foundations for the mitigation of jamming since the damage caused to the network by each type of jamming has been detected, and it has been shown that in higher density networks, its detection is better.