esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Extreme Learning Machine (ELM)-Based Moving Cast Shadow Detection
### Moving Cast-Shadow Detection Algorithm
It can be seen that the proposed method has the best performance on PeopleInShade, BusStation, Cubicle, Seam, Senoon and Sepm compared with the existing methods, while it has lower accuracy than the second-highest method proposed by [PERSON] et al. [24] of about 4.52% on Bungalows. Moreover, we can see that the proposed method is superior to the comparative methods according to the averages of seven scenes. In particular, the average of our method is higher than the second-highest method [24] of about 6.35%. In addition, for the three scenes, such as Seam, Senoon and Sepm, from the same camera with different time, our method exhibits the best performance. As summarized above, it demonstrates that our method is effective and robust in various indoor and outdoor scenes. To validate the effectiveness and superiority of the proposed method visually, the qualitative comparison results obtained by our method and the recent well-known method presented in [22] on seven different scenes are displayed in Figure 4. Apparently, it can be seen that the proposed method can separate moving shadows accurately from foreground frames in different outdoor scenes with strong shadows as well as the indoor scene with weak shadows. Similarly, it is also robust to noises and guarantees the integrity of moving shadows and moving objects. Figure 3: Visual shadow detection results of six scenes generated by the proposed method and comparative methods on the popular dataset. (**a**) input frames; (**b**) groundtruths; (**c**) results obtained by [21]; (**d**) results obtained by [20]; (**e**) results obtained by [31]; (**f**) results obtained by [26]; (**g**) results obtained by [22]; (**h**) results obtained by our method. ### Comparisons with Some Representative Deep-Learning Methods

Furthermore, the proposed method is also compared with two state-of-the-art methods based on the convolutional neural network and the quantitative comparative results listed in Table 5 which are obtained from the literature [34]. Considering the metrics of shadow detection rate \(\eta\), shadow
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## 3 Challenges  
While data from older datasets like Spider are currently too easy for LLMs [14], the more complex BIRD benchmark [12] is used here as a data source to ensure sufficient difficulty. Yet BIRD has various issues that harm its utility for assessing LLMs' capabilities in SQL reasoning [22]. Key problems identified include  
Logical and structural errors in SQL: Mismatches, such as omitting requested fields or providing overly complex SQL [12] (Figure 11), and ambiguities leading to misaligned logic [11] (Figure 12) are prevalent.  
Inconsistent data representation: Discrepancies between database descriptions and actual data (e.g., 'Y/N' vs. '0/1' in Figure 13) [12] or misleading naming conventions [7] can lead to incorrect reasoning.  
Ambiguities and poorly defined questions: Vague questions leading to multiple interpretations [13] (Figure 14) or failure to define terms clearly [25] (Figure 15) cause issues.  
Redundancy and unreasonable difficulty tags: The excessive repetition of specific test points [6] (Figure 16) or inaccurate difficulty labeling [12] (Figure 17) can skew evaluations.  
Incorrect domain-specific knowledge assumptions: Problems requiring domain knowledge not provided or based on wrong assumptions [37] (Figure 18) render data unsuitable for reasoning assessment.  
Evaluation challenges: Questions with multiple plausible answers or undefined criteria make validation difficult [34] (Figure 19).  
These numerous issues highlight the necessity for significant refinement of the BIRD dataset to ensure valid benchmarking of LLM reasoning capabilities [35]. Rigorous alignment between questions and SQL, clearer question phrasing, and consistent data representation are crucial. Further examples illustrating these error types and their frequency are provided in Appendix A.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Attack Attempts
#### 4.7.2 Internal Adversary  
Against internal adversaries, \(T_{2}\) offers no protection, as all members know the group key \(K\). However, no single group member (except the sender) knows all _family keys_ corresponding to the sets \(F_{1},\ldots,F_{\ell}\). Indeed, consider a malicious (compromised) node \(a\) attempting to forge the \(T_{1}\) part of the tag. The key point of the construction is that for each victim node \(b\) there is at least one \(j\leq\ell\) such that the \(j\)-th set from the separating family \(\mathcal{SF}\) separates \(b\) from \(a\). Consequently, recalling Equation (12), \(a\) does not know the key used by node \(b\) to verify the tag \(t_{j}\). In this case, any forgery attempt will be detected with probability at least \(2^{-u_{j}}\) under standard assumptions about the underlying cryptographic primitives, e.g., the random number generator from (12).  
Depending on the design of a separating family, it may be the case that more than one set from the family can be used to separate two nodes. For example, in the case of the family from (5), node 1 can be separated from node 6 by two sets from the family. Consequently, a forgery will be undetected with a much lower probability: \(2^{-2u}\) instead of \(2^{-u}\) if every \(u_{i}\) equals \(u\).
# The Polar Code Construction Method in Free Space Optical Communication
## 4 Construction Method for the Polar Code
\begin{table}
\begin{tabular}{c c} \hline \hline \(N\) & **M-SNR/dB** \\ \hline
32 & \(\sim\)5.25 \\
64 & \(\sim\)5.12 \\
128 & \(\sim\)5.08 \\
256 & \(\sim\)5.05 \\
512 & \(\sim\)5.03 \\
1024 & \(\sim\)5.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: M-SNR of the relationship change point in different code lengths. Through the UPO, the reliability relationship of some sub-channels can be determined, then the MC simulation is performed on the sub-channel pairs whose reliability relationship cannot be determined, and finally, the reliability of the sub-channels is evaluated by the PW. It is different from the traditional MC method which needs to calculate the reliability of each sub-channel online. It obtains the reliability relationship of each sub-channel in a turbulent intensity interval by offline simulation. It is versatile and can construct polar code directly according to the PW which is obtained by offline simulation. Polar code is constructed by PW linear complexity \(O(N)\), so the complexity of the STOP method for constructing the polar code is \(O(N)\). It has a lower coding complexity than the traditional MC method.
## 2 Framework for Coordinated Operation
### Traditional Decentralized Operation  
The structure of traditional decentralized operation (DO) is shown in Figure 2 composed of two steps:  
Step 1: ENO carries out the optimal local scheduling of electricity network which described as ENO sub-problem, and the corresponding consumption of natural gas information will deliver to the GNO.  
Figure 1: ADMM information flow with coordination operator.  
Step 2: GNO carries out the optimal scheduling of natural gas network and is described as GNO sub-problem. If GNO cannot supply enough expected gas volume in Step 1 as the premise of resident gas demand has a higher priority, ENO needs to re-dispatch in the electricity network.  
The main defect with this traditional DO is that ENO as an advance decision-maker, cannot guarantee the local scheduling result at coupling units will always be a feasible solution in the GNO scheduling feasible domain shown in Figure 3. The decision sequence is ENO-GNO-ENO.
## 7 Discussion
### Major Security Threats and Attacks
The defense mechanisms against edge device malware injection/modification are not very successful. There are no mature countermeasures defending against zero-day injections, firmware modification attacks, and remote WebView infections. In addition, current solutions such as code-level static analysis provide actions that cannot be used in real time; therefore, they cannot prevent damage; moreover, they need full access to the firmware or source codes, which may not be possible all the time. Some other defenses that are based on weak signature verification are also ineffective. Therefore, defending against malware injection attacks, especially on the edge device side, is still quite challenging. _Authentication and authorization attacks and defense mechanisms_: attacks such as brute-force attacks can be easily launched in edge computing systems with little or a reasonable amount of effort. The main reason for brute-force attacks is credential weaknesses in authentication protocols that can be defended by adding a stronger layer of authentication or hardening the password verification processes. Other attacks, such as overprivileged attacks, occur because of protocol/implementation-level flaws and can be defended by code-level analyses or patching/strengthening the current protocols. Strengthening authentication security in edge computing systems is critical, as it is the security entry point of a system. Most attacks begin with breaking into the authentication of systems in the first place. Defending against dictionary and brute-force attacks might be challenging. Attackers may have their own resources to build their dictionaries/passwords that can have great data to break the authentication system. It has been proven that mechanisms such as two-factor authentication are not effective and can be broken practically. Attacks exploiting the weaknesses of authentication/authorization protocols are not easy and need more effort since the attacker should identify the vulnerabilities of the protocol and compromise the edge servers, but by cracking an authentication/authorization protocol, attackers can break into the system and then begin other attacks. Overprivileged attacks are quite common in IoT and mobile devices, and there are many real-world overprivileged apps [178; 179]. However, the corresponding countermeasures are generally not very effective.
## 5 Conclusions and Future Work
In conclusion, the proposed technology in this paper marks a significant stride in the quest to enhance the security and efficiency of vehicular ad hoc networks (VANETs). However, we acknowledge certain limitations inherent in our research. First, the scalability of blockchain technology in a highly dynamic environment such as VANETs remains a challenge due to the extensive computational resources required for consensus mechanisms. Furthermore, the latency induced by blockchain could impact the real-time necessity for decision making in VANETs. The attenuation factor and numerical threshold, while effective, may not account for the complex and evolving patterns of vehicular behavior over longer periods. Our experimental setup, although comprehensive, was limited to simulated environments that may not fully capture the unpredictable nature of real-world vehicular networks. To address these limitations, future work will focus on optimizing the blockchain's scalability and reducing latency to meet the stringent real-time requirements of VANETs. Research will also be directed toward developing adaptive algorithms for the attenuation factor and numerical threshold to better reflect the evolving nature of vehicular behaviors. Moreover, we plan to conduct extensive field trials to validate our architecture in real-world scenarios. This will help in fine-tuning the system's parameters and improving its applicability and robustness. Additionally, we aim to explore the integration of emerging technologies like artificial intelligence and machine learning to further enhance the predictive capabilities of our system. By continually pushing the boundaries of current technology, we aim to develop a VANET framework that is not only secure and efficient, but also adaptive and scalable, thereby being capable of withstanding the test of an ever-evolving cyber landscape. Conceptualization, R.J.; methodology, [PERSON] and B.B. ; software, [PERSON]; validation, R.J.; formal analysis, B.B. ; investigation, R.J.; resources, B.B. ; data curation, [PERSON]; writing--original draft preparation, R.J.; writing--review and editing, B.B. ; visualization, [PERSON]; supervision, B.B. ; project administration, B.B. ; funding acquisition, B.B. All authors have read and agreed to the published version of the manuscript. This work is supported by the Comunidad de Madrid within the framework of the Multiannual Agreement with the Universidad Politecnica de Madrid to encourage research by young doctors (PRINCE project). Data are contained within the article. The authors declare no conflict of interest.
## 3 Results and Discussion
### Entropy Results  
The calculations made with formulas in the research of [PERSON] et al., are presented in Table 6 with a normalized matrix, and Table 7 with the entropy and weight of the criteria.  
Figure 10: Yield strength vs. flexural modulus on composites.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Index**} & \multirow{2}{*}{\begin{tabular}{c} **Price** \\ **(USD-kg-1)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Density** \\ **(kg-m-3)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Young’s** \\ **Modulus** \\ **(GPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Yield** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Flexural** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Embediod** \\ **Modulus** \\ **(GPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **CO2** \\ **(MJ-kg-1)** \\ \end{tabular} } \\ \hline Bamboo & M1 & 2.01 & 800 & 15 & 35.9 & 17 & 35.9 & 1.16 \\ Cement Bonded & M2 & 0.27 & 1300 & 1.8 & 3.6 & 3 & 8.87 & 0.66 \\ Particle Board & M3 & 0.21 & 1790 & 25.2 & 24.1 & 25.2 & 5.54 & 0.88 \\ Coir (RC) & M4 & 1.25 & 1860 & 34.1 & 28.4 & 34.1 & 6.97 & 0.49 \\ Palm 0.5 (RC) & M5 & 0.427 & 1460 & 24.2 & 8.95 & 24.2 & 6.02 & 0.569 \\ Polypropylene & M6 & 0.77 & 2110 & 21 & 9.06 & 21 & 18.3 & 0.857 \\ Sisal (RC) & M7 & 0.579 & 1830 & 25.1 & 28.4 & 25.1 & 6.37 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Candidate materials.  
The results show that for this weight method, the most important criteria are the embodied energy, followed by the price and the yield strength, in this case it would be useful when prioritizing the environment, but also considering the economical need of implementing a cheap resisting beam.
## 2 Beam Steering Antenna Technologies
The classification of various beam steering techniques is illustrated in Figure 2.  
Conventional beam-steering antenna technologies, such as electronically scanned phased arrays and mechanically scanned parabolic dishes, are well-established and widely deployed in various commercial applications. In contrast, passive metasurface-based beam-steering antennas are relatively new and are still being investigated to explore their full potential and capabilities. A high-level comparison of various beam steering techniques is given in Table 1, while some example designs for each category are illustrated in Figure 3.  
Figure 2: Classification of beam steering techniques.
## 4 Simulation Study on Strata Movement Characteristics in UCG
### Numerical Simulation  
The numerical models were developed based on the geological mining conditions of the gasification experiment station in Ulanqab, China, with a length of 600 m, a width of 600 m, and a height of 332 m (Figure 3). The model was built by ignoring the special combustion cavity morphology; only the expanding process of temperature and burnt fields is considered. The model consists of a square grid, and the Mohr-Coulomb model is chosen for the present model. Tables 1 and 2 show the initial thermodynamic and material mechanics parameters chosen for the model thermal coupling calculation process. FLAC3D 5.0 numerical simulation software was used for numerical calculation. In the numerical calculation process, materials' thermal parameters were considered constant, and their mechanical parameters changed with temperature. The law of mechanical parameters of coal and rock that change with temperature in Section 2 was introduced into the simulation calculation process using the fish language.  
The model uses fixed boundary conditions where the horizontal boundary of the model is fixed (displacement is 0 and velocity is 0), the lower boundary of the model is fixed (displacement is 0 and velocity is 0), and the upper boundary of the model is a free boundary. The model was tuned for the best simulation results using the measured surface subsidence data (maximum sink volume of 36 mm).  
To analyze the main controlling factors affecting rock movement and surface subsidence, the following four simulation scenarios were adopted: (1) traditional mining model, (2) under thermal stress only, the mechanical properties of the coal rock do not change with temperature, (3) under high-temperature burnt only, the mechanical properties of the coal rock change with temperature, and the coefficient of thermal expansion is 0, (4) synergistic effect of thermal stress and coal-rock high-temperature burnt.
## 5 Discussion  
Our observations constrain the gas plume composition at Gaua as hydrous (\(\sim\)95.9 mol% H\({}_{2}\)O), with SO\({}_{2}\) dominating over H\({}_{2}\)S (H\({}_{2}\)S/SO\({}_{2}\) of 0.13 \(\pm\) 0.01), and with a H\({}_{2}\)/H\({}_{2}\)O ratio of \(\sim\)0.001.  
Therefore, by using gas-phase H\({}_{2}\)/H\({}_{2}\)O (\(\sim\)0.001) and H\({}_{2}\)S/SO\({}_{2}\) (\(\sim\)8.4) redox couples and using published thermodynamic constants [52; 53], we can estimate the oxygen fugacity (fO\({}_{2}\)) of Mount Garet's volcanic gases (e.g., [PERSON] et al., 2019). Equations (1) and (2) also provide the means to estimate the gas-melt equilibrium temperature:  
\[log\frac{H_{2}}{H_{2}O}=\frac{-12707}{T}+2.548-\frac{1}{2}logfO_{2} \tag{1}\]  
\[log\frac{SO_{2}}{H_{2}S}=\frac{27377}{T}+\frac{3}{2}logfO_{2}-logfH_{2}O \tag{2}\]  
The value of \(f\)H\({}_{2}\)O used in (2) is 0.959 given that, at \(\sim\)1 bar the (Ptot \(\times\) nH\({}_{2}\)O)/ntot = [(1 bar) (0.959 ntot)]/(ntot) = 0.959 bar (where fugacity of a gas is equal to its partial pressure and that P(H\({}_{2}\)O) = P is the pressure in bar and ni the amount of species i in mol%) [54].  
Solution yields an equilibrium temperature of \(\sim\)641 \({}^{\circ}\)C and a \(log\)/O\({}_{2}\) equivalent to \(\Delta\)QFM = +2.0 where QFM refers to the quartz-fayalalite-magnetite buffer [55], and where \(\Delta\)QFM = \(log\)/O\({}_{2}\)-\(log\)/O\({}_{2}\) of QFM at the corresponding temperature range estimated above. This solution plots well within the global trend highlighted in [22].  
As such, an equilibrium temperature of \(\sim\)641 \({}^{\circ}\)C is higher than the temperature at which sulfur partitioning into the liquid hydrothermal system or gas scrubbing reactions in a shallow subsurface environment may occur [56; 57]. Therefore, the absence of contamination of Gaua's magmatic gas allows for comparison with other high-temperature arc gases worldwide (Figure 5) [17; 22; 28]. Being the first for Gaua, our results contribute to our understating of volcanic gas signature and fluxes along the Vanuatu arc, as discussed below.  
Figure 5: H\({}_{2}\)O–CO\({}_{2}\)–S\({}_{T}\) (S\({}_{T}\) = SO\({}_{2}\) + H\({}_{2}\)S) ternary plot with gas composition for Mount Garet volcano (in black). Ambrym [33; 34] and Yasur [31] are also plotted for comparison, as well as Manam (Papua New Guinea) [37]. For within-plot field delimitations (e.g., hydrothermal gases and arc volcanism) see [28].
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.17 AvgRLEACH and VarRLEACH  
Regarding CH selection, the threshold equation is modeled using first and second-order statistics to give prominence to network information on a local and global scale [92]. To meet this requirement, overall energy information must be collected and used to aid in selecting the CH. Not only can this help to extend the network's life by maintaining a balanced network power distribution, but it can also help increase overall throughput. To account for the average and variance of energy, the threshold was modified as in AvgRLEACH given in Equation (18) and VarRLEACH given in Equation (19), where \(N_{a}(r)\) is the number of alive nodes in each round. \(A_{n}(r)\) denotes the normalized average value of the node energy concerning initial energy \(E_{0}\) for each round \(r\) and \(V_{n}(r)\) is the normalized variance of the energy of nodes in round \(r\).  
\[T(n) =\frac{p}{1-p\Big{(}r\;mod\Big{(}\frac{1}{p}\Big{)}\Big{)}}\Big{(} \frac{E_{i}(r)}{E_{0}}\Big{)}A_{n}(r)N_{a}(r) \tag{18}\] \[T(n) =\frac{p}{1-p\Big{(}r\;mod\Big{(}\frac{1}{p}\Big{)}\Big{)}}\Big{(} \frac{E_{i}(r)}{E_{0}}\Big{)}V_{n}(r)\;N_{a}(r) \tag{19}\]
# A Comparative Study of Differential Quadrature Methods for METE Nanobeam Vibrations
## 5 Discussion  
In this section, we introduce the potential advantages and limitations of Ritz methods compared to the DQMs employed in this study: Ritz methods, such as those employing Ritz power series interpolation, often involve simpler discretization procedures compared to DQMs. They typically require fewer grid points, which can lead to reduced computational cost, especially for problems with simple geometries. The use of analytical basis functions, such as polynomials, can provide insights into the analytical behavior of the system and facilitate the understanding of the underlying physics. However, Ritz methods can be challenging to apply to problems with complex geometries or boundary conditions. The convergence of the Ritz method can be sensitive to the choice of basis functions and the number of terms included in the series. Implementing complex material properties, such as those encountered in METE materials, within the Ritz framework can be more intricate than in DQMs. DQMs are highly versatile and can be applied to problems with complex geometries, boundary conditions, and material properties. DQMs can achieve high accuracy with relatively few grid points, making them computationally efficient for many problems. DQMs can be easily adapted to different types of differential equations and boundary conditions. However, the accuracy and convergence of DQM solutions can be sensitive to the choice of grid point distribution. In some cases, DQMs can exhibit numerical instability, particularly for higher-order derivatives. While Ritz methods offer a simpler discretization process, DQMs demonstrate greater versatility and adaptability for handling complex problems encountered in the analysis of METE nanostructures. The choice of the most suitable method depends on the specific characteristics of the problem under consideration, including geometry, boundary conditions, material properties, and computational resources. This brief comparison provides a broader perspective for future readers and encourages the further exploration of alternative numerical techniques for analyzing the dynamics of METE nanostructures.