esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Study  
This section describes how a Monte Carlo simulation study is conducted to compare the Bayesian estimates and the CI of residual life with the maximum likelihood estimate (MLE). The performance of the Bayesian method and MLE are compared under different parameter settings. Parameter values of \(\lambda\), \(\beta\), current time \(\tau\) and sample size \(n\) are necessary for this simulation. The values of the Weibull parameters (\(\lambda\), \(\beta\)) are set to (\(2\times 10^{-8}\), 3), (\(4\times 10^{-8}\), 3), and (\(2\times 10^{-10}\), 4), respectively. \(\tau\) is set to 100, and the sample size \(n\) is set to 3, 5, and 10. For convenience, the sample size of the historical lifetime data and the similar data is set to 5. The expert information is the true value of reliability at time \(\tau\).  
The simulation steps are listed as follows:  
* Draw historical lifetime data, similar data, expert information and lifetime data predicted by degradation data from \(W(\lambda,\beta)\). Generate field data with size \(n\).
* The Bayesian estimate and 90% CI of the residual life, denoted by \(\hat{\mu}_{\tau}^{r}\), and \((\hat{\mu}_{L}^{r},\hat{\mu}_{H}^{r})\), can be calculated by Algorithm 1.
* MLEs of the Weibull parameters \(\lambda\) and \(\beta\) can be obtained by field data. Then the point estimate and the 90% CI of the residual life, denoted by \(\hat{\mu}_{m}^{r}\) and \((\hat{\mu}_{Lm}^{r},\hat{\mu}_{Hm}^{r})\), can be calculated by Equations (59), (61) and (62).
* Repeat Steps 1-3 100 times and compare the collected results using bias, mean absolute error (MAE), and mean square error (MSE) of the point estimate, coverage probability (CP) and the average interval width (AIW) of the CI [48].  
More detailed comparing results are provided in Tables 1-3.  
From Tables 1-3, the following conclusions can be found.  
1. The sample size of lifetime data has a remarkable effect on both the Bayesian method and the MLE method. Generally, the bias, MAE, and MSE decrease as the sample size increases. Simultaneously, the CP increases, which means that a more accurate interval can be obtained.
2. The difference between these two point estimates is gradually eliminated as the sample size of the field data increases. When the sample size is small, the advantage of using Bayesian estimation is obvious. The bias, MAE, and MSE found using Bayesian estimation are apparently smaller than those found using the MLE method, which indicates that the former estimation is more accurate, stable and robust. Also, when the number of failures of the field data is equal to one or zero, MLE is restricted and cannot be used.
3. Generally, the CP of the Bayesian method is larger than that of the MLE method, indicating that the Bayesian CI is more useful as it fuses various kinds of prior information. Therefore, the simulation results indicate that the multi-source information fusion method could significantly outperform other conventional approaches that are based on any single input [49].
## 2 System Model
### Channel Model  
A narrowband block-fading channel model is adopted as the propagation environment between transmitters and receivers [12; 13; 24; 25], which is known as geometric Saleh-Valenzuela channel model, and it is considered a typical channel model for uplink cell-free mm-Wave massive MIMO systems. The channel between the \(m_{th}\) AP and \(k_{th}\) UE is expressed as  
\[h_{k,m}=\sqrt{\frac{G_{a}N_{r}}{\beta_{k,m}P_{k,m}}}\sum_{p=1}^{P_{k,m}}\alpha _{r}^{(p)}(\phi_{k,m}^{(p)}), \tag{1}\]  
where \(G_{a}\) denotes antenna gain, and \(\beta_{k,m}\) represents the pathloss between the \(m_{th}\) AP and \(k_{th}\) UE, which can be expressed from [18; 26; 27] as  
\[\beta_{k,m}[dB]=10\log_{10}(\frac{4\pi d_{o}}{\lambda})^{2}+10\;\varepsilon \log_{10}(\frac{d_{km}}{d_{o}})+\chi_{k,m}, \tag{2}\]  
where \(d_{o}\) is the reference distance, which is equal to one, \(\lambda\) is the wavelength, \(d_{k,m}\) represents the distance between the \(m_{th}\) AP and \(k_{th}\) UE, the average pathloss exponent over the distance is represented by \(\varepsilon\), and \(\chi_{k,m}\sim\mathcal{N}(0,\varsigma^{2})\) gives the shadow fading component with zero mean Gaussian random variable and \(\varsigma\) standard deviation. Moreover, \(P_{k,m}\) represents the number of propagation paths, the complex small scale fading gain is denoted by \(\alpha_{k,m}^{(p)}\sim\mathcal{CN}(0,1)\) for all the APs and UEs in the service area, and \(\phi_{k,m}^{(p)}\in[0,2\pi]\) is known as the azimuth angle of arrival (AoA) for each channel path. Each AP is assumed to be equipped with a uniform linear array (ULA), and this structure of the antenna array is utilized to obtain the receive array response vector at the \(m_{th}\) AP, where \(\alpha_{r}\) is given by  
\[\mathrm{a}_{r}(\phi)=\frac{1}{\sqrt{N_{r}}}[1,e^{i\frac{2\pi}{\lambda}d_{o} \sin\phi},\ldots,e^{i(N_{r}-1)\frac{2\pi}{\lambda}d_{o}\sin\phi}]^{T}, \tag{3}\]  
Figure 1: Hybrid beamforming structure for each AP in uplink cell-free massive MIMO systems with CPSs connected to RF chains via switch network.  
where \(d_{s}\) denotes antenna spacing of half of a wavelength [24]. Finally, let us consider \(\mathcal{A}_{k,m}=[\mathrm{a}_{r}(\varphi_{k,m}^{(1)}),\ldots,\mathrm{a}_{r}( \varphi_{k,m}^{(\Gamma_{k,m})})]\in\mathbb{C}^{N_{r}\times P_{k,m}}\) and \(\mathrm{Y}_{k,m}=[\alpha_{k,m}^{(1)},\ldots,\alpha_{k,m}^{(\Gamma_{k,m})}]\in \mathbb{C}^{\Gamma_{k,m}\times 1}\). Then, \(h_{k,m}\) can be expressed as [18]  
\[h_{k,m}=\sqrt{\frac{G_{a}N_{r}}{\beta_{k,m}P_{k,m}}}\mathcal{A}_{k,m}\mathrm{Y }_{k,m}. \tag{4}\]  
Thus, \(h_{k,m}\sim\mathcal{CN}(0,\sqrt{\frac{G_{a}N_{r}}{\beta_{k,m}P_{k,m}}}\mathbb{ E}\{\mathcal{A}_{k,m}A_{k,m}^{H}\})\). In addition, the channel matrix between \(K\) UEs and the \(m_{th}\) AP is \(H_{m}=[h_{1,m},\ldots,h_{K,m}]\in\mathbb{C}^{N_{r}\times K}\), and the total channel between \(K\) UEs and all APs in the coverage area is defined as \(H=[H_{1},\ldots,H_{M}]^{T}\in\mathbb{C}^{MN_{r}\times K}\).
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 6 Discussion
### Innovative Aspects and Contributions  
Our research extends beyond traditional applications of neural networks in sports science, offering several novel contributions to the field:  
1. Multimodal Data Integration: Our model uniquely combines QE metrics with biomechanical data, providing a more comprehensive analysis of badminton performance. This integration captures complex interactions between visual attention and physical execution that were previously unexplored in racket sports.
2. Real-time Predictive Modeling: To our knowledge, this is the first study to develop a neural network model capable of real-time shot accuracy prediction in badminton using QE metrics. This capability goes beyond verifying QE's importance to provide actionable, in-moment insights for players and coaches.
3. Advanced Interpretability Techniques: By employing SHAP (SHapley Additive Explanations) values, we offer unprecedented insights into the relative importance of different QE and biomechanical features in badminton performance. This approach enhances the interpretability of our complex neural network model, making it more accessible and applicable for sports scientists and coaches.
4. Adaptive Learning Potential: The architecture of our model allows for continuous learning and adaptation to individual players' patterns. This opens new avenues for developing personalized training regimens in badminton, potentially revolutionizing how players are coached and how they improve their skills.
5. Cross-skill Level Analysis: By applying our neural network model across different skill levels, we establish how the relationship between QE, biomechanics, and performance changes with expertise. This provides us with a nuanced understanding of how badminton players develop their visual and motor skills. Unlike earlier studies, our findings shed new light on the intricate process of players coordinating what they see as they move around the court.
# High-Tech Defense Industries: Developing Autonomous Intelligent Systems
## 2 Conceptual Background
### Military Applications of Autonomous Intelligent Systems
Intelligent unmanned aircraft systems are beginning to be developed mainly in the civil context, such as package delivery [50], agriculture, and agroindustry [51], to name a few. The intelligent unmanned maritime and underwater systems have pushed the technology beyond imaginable limits in order to deal with complex ocean and sea missions [23]. These systems present new opportunities for naval use, in particular for dangerous missions, such as highly efficient mine sweeping. Recognizing the potential of autonomous underwater vehicles for both science and the military, in 1997 the Massachusetts Institute of Technology and the North Atlantic Treaty Organization joined efforts to develop robotic technologies applicable to mine countermeasures [52]. These systems have evolved with the use of disruptive technologies until today [53]. For example, [PERSON] [54] developed very relevant methodologies in this regard. He studied deterministic artificial intelligence for unmanned underwater vehicles, proposing an automated control and learning methodology that requires simple user inputs. Noteworthy is also the attention that has been given to unmanned ground vehicles. Of these, an interesting example is the BigDog, developed by Boston Dynamics, which is a four-legged robot for transporting loads in difficult terrain [46] and has been adapted with autonomous navigation [55]. Moreover, mathematical structures and expectation-maximization and Gaussian mixture models algorithms have been developed [53]. Besides its applications in robotics and unmanned ground vehicles, these algorithms can also be used in various domains such as cybersecurity, object detection, and military logistics. The practical application of these algorithms has resulted in modern technology that can be applied on the battlefield, as presented by [PERSON] and [PERSON] [53], who give us examples of solutions such as Spot and Atlas (also developed by Boston Dynamics). In addition to the potential for civil society, much is speculated about the use of these autonomous robots in the revolution of military ground operations [6].
## 2 Related Work  
### Text-to-SQL Models  
The current state-of-the-art performance has been achieved by PLM-based models using GPT [7]. [PERSON] et al. [8] introduced C3, which employs schema linking to rank relevant  
Figure 1: Examples of a false positive yielded by EXE (**a**) and a false negative yielded by ESM (**b**). (**a**) Semantically distinct queries having the same execution result, as there are no dogs with age \(\geq\) 10. (**b**) Syntactically distinct but semantically equivalent queries to find the weight of the heaviest dog.  
tables and columns and prompts GPT to generate SQL queries. [PERSON] and [PERSON] (2019) proposed DIN-SQL, which predicts schema links, classifies query difficulty, and prompts GPT using template-based queries with debugging prompts. [PERSON] et al. (2020) presented DAIL-SQL, which searches for similar questions in the training set and uses them to create a few-shot prompt with GPT to generate an initial query. This is then used to find more similar queries in the training set, and the most similar ones are used in a second few-shot prompt to generate the final query. Despite achieving high ranks on the Spider leaderboard, evaluated on EXE, none of these PLM-based models appear on the CoSQL leaderboard, evaluated on ESM (2020).  
Several FLM-based models, such as fine-tuned T5 ([PERSON] et al., 2019), have also been introduced, showing comparable results to PLM-based models on Spider. RASAT (2019), coupled with PICARD (2019), incorporates relation-aware self-attention, enabling better schema understanding while inheriting pre-trained weights from T5. [PERSON] et al. (2020) introduced Graphix-T5, which augments T5 with graph-aware layers to integrate semantic information from transformer blocks with structural information from graph neural networks. [PERSON] et al. (2020) presented RESDSQL that utilizes an encoder to identify relevant schema items and a decoder to first generate the SQL skeleton with keywords. [PERSON] et al. (2020) introduced CodeS, an open-source series of language models specifically designed for Text-to-SQL. CodeS undergoes incremental pre-training with a curated SQL-centric corpus and uses schema filtering and prompt construction techniques. SuperSQL (2020), a hybrid FLM/PLM framework, uses a genetic learning algorithm to swap model components to improve its output.
## 2 Vision and Key Features for Future 6G Networks
### Extremely Large Bandwidth (THz Waves)
Figure 7: Effect of free space loss and water vapor absorption at a distance of 10 m [35]. The consensus among wireless communication researchers is that the high-frequency spectrum must be further exploited with the adoption of multi-cellular topology for achieving improved wireless communication systems [1]. The former approach is in line with the global trend of migrating from the cellular RF spectrum to the mm wave, THz radio spectrum, and visible-light spectrum. To fully exploit the high-frequency spectrum domains, the electronic components such as the drivers must be readily available. Recent advances in the microwave communications leading to the design of THz electronic, photonic, and hybrid electronic-photonic technologies are encouraging. Most likely, 6G will witness the evolution of a THz/free-space-optical hybrid deployed in hybrid electronic-photonic transceivers. In this configuration, the optical laser can either produce a THz signal or transmit an optical signal [36]. Some of the advantages of using a hybrid link include exploiting the THz-wide bandwidth for signal transmission and immunity against unfavorable weather climates [37]. Furthermore, THz transmission has a crucial function to perform in the uplink, as a line-of-sight link is not a prerequisite. From the channel model perspective, this condition will tilt the 6G technology into the Rayleigh channel model and drastically reduce the need for the Rician channel model. With the adoption of the THz, a reliable uplink communication link in the visible-light communication (VLC) networks is assured in contrast to the infrared solution that requires a tracking and positioning system. A system consisting of hybrid VLC/terahertz system ushers in a rugged communication technology that can withstand ambient light, thereby ultimately leading to a reduction in the signal-to-noise ratio of the VLC system. Moreover, super-efficient, short-range connectivity solutions will be the driving force for 6G, a domain in which the higher-frequency bands will play a significant role in the future. Molecular absorption has a substantial impact on the path loss, especially at longer distances (\(\sim\)1... 10 dB/km at frequencies up to 400 GHz) [38]. A photonic solution is a viable approach for providing high data rates at low propagation losses, but the component size is two orders of magnitude larger than the counterpart required for the THz band case. By contrast, plasmon-based THz link components present a promising solution for THz communications due to their extremely small size and ability to operate at ultra-high data rates. Moreover, they could be perfectly combined with photonic technology, particularly with dielectric wave guiding, as plasmonic waveguiding is quite lossy concerning relatively long interconnect distances. _[PERSON]_[39] thoroughly investigatedthe in-depth reference material of the current fundamental aspects of plasmonic technology and hybrid combinations, and highlighted the future roles of plasmon in THz band wireless communication and wireless THz nano-applications.
## 3 Results and Discussions  
### XRD Analysis  
X-ray diffraction patterns (XRD) for samples of _H. thebaica_ Co\({}_{3}\)O\({}_{4}\) NPs were carried out using a Bruker Advanced D8 diffractometer with monochromatic Cu K\(\alpha\) radiation of wavelength 1.5406 A operating at a current of 40 mA and a voltage of 40 kV in the Bragg-Brentano geometry. The observed intensity peaks at 31.50\({}^{\circ}\), 37.05\({}^{\circ}\), 38.78\({}^{\circ}\), 44.98\({}^{\circ}\), and 59.53\({}^{\circ}\) represent the corresponding planes at (220), (311), (222), (400), (511), respectively. These sharp peaks confirm that the synthesized Co\({}_{3}\)O\({}_{4}\) NPs are crystalline in structure as shown in Figure 2. The fabricated Co\({}_{3}\)O\({}_{4}\) NPs diffraction 20 values correspond to the face-centered cubic crystalline phase of cobalt oxide (JCPDS card 073-1701) [68]. The average crystallite size of Co\({}_{3}\)O\({}_{4}\) NPs was calculated by using [PERSON]'s formula.  
Figure 1: Schematic diagram of green synthesis of Co\({}_{3}\)O\({}_{4}\) NPs.  
\[D=\frac{0.9\lambda}{\beta\cos\theta} \tag{1}\]  
where \(\lambda\) is the wavelength of Cu-k\(\alpha\) radiation, \(\beta\) is the full-width half-maximum (FWHM) in radians, and \(\theta\) is the angle of diffraction (in radians). The average particle size and the lattice parameter (a = b = c) were found to be 11.87 nm and 8.082 A, respectively. No peaks of contaminants were observed, indicating the pure crystal structure of Co\({}_{3}\)O\({}_{4}\). Thus, XRD confirms the formation of Co\({}_{3}\)O\({}_{4}\) NPs with desired crystalline structure.
## 3 Results and Discussion
### Adsorbent Characterization
#### 3.2.2 FT-IR Analysis of ADAV and TTAV300  
The FT-IR spectra of ADAV and TTAV300 are shown in Figure 5. The revealed spectrum of ADAV exhibited a broad band positioned at 3300 cm\({}^{-1}\), which was attributed to the stretching vibration of the -OH groups that characterized the carbohydrate monomers together with mannose and uronic acid. Moreover, the spectrum showed two absorption bands at 2916 and 2850 cm\({}^{-1}\), which could be assigned to the symmetrical and the asymmetrical C-H stretching. The absorption band at 1731 cm\({}^{-1}\) was characteristic for C=O stretching and confirmed the presence of a carbonyl moiety. The absorption peak at 1586 cm\({}^{-1}\) may have been associated with the asymmetrical and the symmetrical -COO\({}^{-}\) stretching of carboxylate compounds. Furthermore, the two absorption peaks at 1250 and 1153 cm\({}^{-1}\) corresponded to the presence of C-C stretching and C-O-C stretching of aliphatic ether, respectively. The absorption peak at 1019 cm\({}^{-1}\) may have been due to C-O stretching associated with rhamnocalaturonan, a side-chain component of pectins [43; 44].  
Figure 4: TGA/dTGA analysis of ADAV sample.  
On the other hand, the spectrum of TTAV300 showed the presence of some peaks similar to those of ADAV, however, with a lower absorption intensity due to the thermal treatment. Moreover, two strong absorption peaks appeared at 1559 and 1409 cm\({}^{-1}\), which could be attributed to the presence of -COO' stretching of carboxylate compounds in the thermally treated sample.
# Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
###### Abstract  
With the increasing number of heavy-duty trucks and their high fuel consumption characteristics, reducing fuel costs has become a primary challenge for the freight industry. Consequently, accurately predicting fuel consumption for heavy-duty trucks is crucial. However, existing fuel consumption prediction models still face challenges in terms of prediction accuracy. To address this issue, a model named Cross-LSTM Multi-Feature Distillation (CLMFD) is proposed. The CLMFD model employs the Crossformer model and the LSTM model as teacher and student models, respectively, utilizing multi-layer intermediate features for distillation. Fuel consumption data from a vehicular networking system was used in this study. Initially, the raw data were preprocessed by segmenting it into two-kilometer intervals, calculating sample features, and handling outliers using box plots. Feature selection was then performed using XGBoost. Subsequently, the CLMFD model was applied to predict fuel consumption. Experimental results demonstrate that the CLMFD model significantly outperforms baseline models in prediction performance. Ablation studies further indicate that the CLMFD model effectively integrates the strengths of both the Crossformer and LSTM, exhibiting superior predictive performance. Finally, predictions on data with varying masking rates show that the CLMFD model demonstrates robust performance. These findings validate the reliability and practicality of the CLMFD model, providing strong support for future research in fuel consumption prediction.  
Keywords:fuel consumption forecast; vehicle networking data; feature selection; cross-LSTM multi-feature distillation +
Footnote †: journal: Applied sciences
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 5 Quantitative Performance Analysis of Leading AIS Methods in IoT Malware Detection
### Memory and Time Complexity  
IoT devices are lightweight with limited computing power; therefore, reducing memory usage and computing time when applying security methods is essential. We calculate the space complexity for the NPS, MNSA, and NSNN using Equations (5) and (6), where \(m\) is the alphabet size (\(m=2\) in binary representation), \(L\) is the string size, \(N_{S}\) is the amount of self-data, and \(N_{R}\) is the number of detectors. Table 3 shows the values of the three methods for each parameter. Using 16-bit strings with an equal number of detectors in both negative and positive sets in the NPS results in a 65% decrease in memory usage compared to generating 12-bit strings with larger detector sets in the MNSA. To calculate the space complexity for the NSNN, we assume that the string length is \(\geq\)7 since the R-Continuous Bit Matching (RCBM) is 7. RCBM is the number of matching bits between two strings: self and non-self. In this case, the NPS uses 90% less memory space than the NSNN.  
When calculating the time complexity using Equation (5), the results show that the NSNN needs less computing time compared to the other two methods--MNSA and NPS. The following Figure 5 shows the result of the space and time complexity analysis.  
\[Time=(m^{L}\times N_{S}\times N_{R}) \tag{5}\]  
\[Space=(L\times N_{S}\times N_{R}) \tag{6}\]  
Figure 5: Memory and time complexity of NPS, MNSA, and NSNN.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 3 Dynamic RID Model with Sliding Time Window
### Heterogeneous Vehicles Routing (HVR)  
In the dynamic relief items distribution model, a variant of the VRP considering a heterogeneous fleet with a limited number of vehicles at supply points is applied. The objective of HVR is defined to select the vehicle sets and routes from the supply points by optimising the selection criteria. In this model, the best-fit method is applied as the selection criteria for the vehicle selection at each supply point in each time slot. The best-fit selection gives the most suitable vehicle settings based on the relief item demand volume and the vehicle's loading capacity for distribution. For the HVR, the following assumptions are applied to the constraints set:  
1. Only the disaster regions with non-negative relief demand are considered for vehicle routing at each time slot.
2. Thirty minutes duration is set as the delay (service) time for unloading relief items from a vehicle in a disaster region.
3. Additional thirty minutes duration is set as offset time for each vehicle after a round trip. This offset time is defined for vehicle refueling, cleaning and other small maintenance work. Any vehicle is available for the next trip after the roundup time with round trip journey time + service time + offset time.  
For the HVP, four categories of vehicles have been considered with their cost, capacity and speed. A synthesised value on vehicle cost, capacity and speed are considered for each vehicle type as presented in Table 3.