esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Less Regular Connectivity Models
### Shadowing  
In real networks, obstacles block the path of the signal, and cause random anisotropic signal strengths. This effect, called _shadowing_, means that some links may exist to quite distant nodes, while there may be no links to quite close nodes.  
The most widely accepted model of shadowing is log-normal shadowing [59]. This adds a random, normally distributed component to the path loss expressed in decibels, or equivalently multiplies the path loss by a log-normal random variable. In [60], it was shown that the optimal transmission power in a spread spectrum system should be selected such that the average number of nodes closer to the transmitter than the receiver is \(\phi(G/b)^{2/\zeta}\), where \(\phi\) is a constant depending on the physical systems, \(G\) represents the spread spectrum processing gain, \(b\) is the threshold for the outage signal to noise ratio and \(\zeta\) is the power loss factor.  
[PERSON] and [PERSON] [61] studied the probability that a single node is isolated in a log-normal shadowing environment with Poisson distributed nodes. A tight bound for the Poisson density to achieve a highly connected subnetwork is derived. It was shown that the fading would influence the connectivity significantly.  
Other models of shadowing are also possible. An interesting result arises if we consider the coverage area of each node to be a deterministic but irregular region, rather than a disk. [PERSON] _et al._[62] show that this irregularity enables the network to percolate in a lower density if the footprint of each node is convex, which indicates that sufficient conditions for connectivity obtained by disk-based models hold in general for unreliable and non-rotational symmetric wireless channels. However, for networks that each node produces arbitrary footprint, the connectivity is still an open problem.
# Ni-Al Alloys as Alternative EUV Mask Absorber
## 3 Film Characterization
### Composition and Density Profile

Table 4: Comparison of bulk density and composition as determined by XRR to crystalline density and nominal composition for Ni\({}_{3}\)Al, NiAl, and Ni\({}_{2}\)Al\({}_{3}\). Crystalline density is calculated from lattice parameters found in literature on Material Springer database [46]. Figure 8: X-ray photoelectron spectroscopy (XPS) depth profile of Ni\({}_{2}\)Al\({}_{3}\), showing Al2p, Ni2p, and Ni2p satellite (sat.) peaks. The density calculated with XRR, can differ from crystalline density from literature, due to non-optimized packing order or due to dissimilar bulk composition from nominal, as some Al diffuses away from bulk to form the native oxide. Two peaks are observable in the Ni\({}_{2}\)Al\({}_{3}\) density profile: just below the surface oxide and on top of SiO\({}_{2}\) substrate, corresponding to the Ni-rich layers where Al has diffused away. Figure 10: (a) XRR spectra of Ni\({}_{2}\)Al\({}_{3}\) showing measured data (blue dots) and fitting result (red line); (b) Density profile of Ni\({}_{2}\)Al\({}_{3}\) calculated by XRR spectra fit, with the dashed black line representing layer thicknesses based on TEM result and crystalline density based on literature values. Figure 9: (a) TEM-EDS images of Ni\({}_{2}\)Al\({}_{3}\) showing O, Al, and Ni traces; (b) TEM-EDS line scan along the arrow direction. The bulk composition can be calculated from bulk density using the rule of mixtures for the mass density of alloy composites, with \(w_{i}\) and \(\rho_{i}\) the weight percentage in the alloy and density of the \(i\)'th element respectively:  
\[\rho_{alloy}=\frac{1}{\sum_{i}\frac{w_{i}}{\rho_{i}}} \tag{3}\]  
Only Ni and Al were considered for composition determination without taking into account the O content. The calculated bulk composition of Ni\({}_{3}\)Al and NiAl are very similar, around 3:1 Ni:Al ratio. Although the NiAl sample contains relatively more Al than Ni\({}_{3}\)Al, more Al has diffused away towards the surface, resulting in a thicker surface oxide layer in the NiAl sample (cf. Figure 4) and a higher Ni content in the bulk compared to nominal composition. Similarly, the bulk composition of Ni\({}_{2}\)Al\({}_{3}\) ends up closer to 1:1 Ni:Al ratio. With the bulk alloy density \(\rho_{alloy}\) and the bulk composition \(w_{j}\) from XRR spectra, we can proceed to calculate the optical constants \(n_{,K}\) with the Equation (1).
## 2 Numerical Analysis
### CFD Models and Governing Equations
The composition PDF transport governing equation is derived from the Navier-Stokes equations as [39]:  
\[\frac{\partial}{\partial t}(\rho P)+\frac{\partial}{\partial x_{i}}(\rho u_{ i}P)+\frac{\partial}{\partial\psi_{k}}(\rho S_{k}P)=-\frac{\partial}{\partial x_{i}} \big{[}\rho\big{\langle}u_{i}^{\text{-}}\big{|}\psi\big{\rangle}P\big{]}+ \frac{\partial}{\partial\psi_{k}}\bigg{[}\rho\bigg{\langle}\frac{1}{\rho} \frac{\partial J_{i,k}}{\partial x_{i}}\bigg{|}\psi\bigg{\rangle}P\bigg{]} \tag{2}\]  
where,  
\(\rho=\) mean fluid density;  
\(P=\) Favre joint PDF of composition;  
\(u_{i}=\) Favre mean fluid velocity vector;  
\(\psi=\) composition space vector;  
\(S_{k}=\) reaction rate for specific \(k\);  
\(j_{i,k}=\) molecular diffusion flux vector;  
\(u_{i}^{\text{-}}=\) fluid velocity fluctuation vector. In Equation (2), the notation \(\langle\ldots\rangle\) denotes expectations, while \(\langle A|B\rangle\) is the conditional probability of event A, given that event B occurs. Meanwhile, the two terms on the right-hand side of the equation represent the PDF change due to scalar convection caused by turbulence (turbulent scalar flux) and molecular mixing/diffusion, respectively. The turbulent scalar flux term is modeled by the gradient-diffusion assumption:  
\[-\frac{\partial}{\partial x_{i}}\big{[}\rho\big{\langle}u_{i}^{\text{-}} \big{|}\psi\big{\rangle}P\big{]}=\frac{\partial}{\partial x_{i}}\bigg{(} \frac{\rho\mu_{t}}{Sc_{t}}\frac{\partial P}{\partial x_{i}}\bigg{)} \tag{3}\]  
where \(\mu_{t}\) and \(Sc_{t}\) are the turbulent viscosity and Schmidt number, respectively. The Extended Zeldovich mechanism was utilized to model the NO formation occurring inside the engine cylinder [33, 40]. This reaction mechanism consists of 7 species and 3 chemical reactions and can highly accurately predict NO formation over a wide equivalence ratio range. This model is presented in Appendix A of this article and can also be referenced in detail in our previous publications [11, 12, 21]. The soot formation during the combustion process of the engine was modeled by the Moss-Brookes model [33, 41, 42]. The collisions and interactions between the injected fuel droplets and combustion chamber walls were modeled using the Stochastic Collision sub-model. This model is derived from the O'Rourke algorithm [33, 41, 42]. The CFD models used in this study have been successfully applied and can be found in our previous publication [21] or references [33, 40, 41]. Table 5 shows a summary of the CFD numerical models used in this study.
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## 5 Results
### Ablation Studies
For GPT-4o (new), Table 2 shows Type 5 errors (query logic) rising significantly, contributing \(\sim\)82% of the total gain over BIRD. Errors increased substantially in domains like formula_1 and california_schools. This underscores how resolving logical misalignments [12] is pivotal for improving baseline reasoning accuracy, thus enhancing the model's reliability and generalization potential on logically complex tasks. 2. Impact of Schema Enhancement: Excluding schema enhancement reduced accuracy (\(-2.75\%\) to \(-3.56\%\)). For GPT-4o (new), Type 2 (forgetting requirements) and Type 4 (data handling) errors rose, reflecting reduced guesswork. Domain errors also increased. This supports the role of schema enhancement in reducing guesswork and improving cross-domain generalization by providing clearer data context, leading to more reliable outputs. 3. Impact of Prompt Guidelines: Dropping prompt guidelines lowered accuracy moderately (\(-1.23\%\) to \(-1.78\%\)). For GPT-4o (new), Type 3 (syntax) and Type 6 (edge cases) errors increased slightly. Domain impacts were smaller. This indicates that while data quality is primary, prompt guidelines play a significant secondary role in fine-tuning reasoning and improving precision, contributing to overall model reliability across different models. Overall Analysis: The total accuracy gain over BIRD (7.53-9.59%) matches the main experimental trends. (1) For GPT-4o (new), logical alignment drove \(-82\%\), schema enhancement \(\sim\)41%, and prompts \(\sim\)19% of the uplift. (2) Errors increased upon removing refinements confirm BIRD's ambiguities degrade performance. (3) O1-Preview's higher baseline and resilience highlight its superior reasoning, while GPT-4o-Mini's drops underscore sensitivity to data quality. (4) The dominant contribution of logical alignment confirms that clean, unambiguous data are the most critical factor for robust zero-shot performance, directly impacting model generalization and reliability.
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 3 Optimization Model
### Fleet Placement Problem
#### 3.2.1. FPP Parameters  
In the fleet placement problem (FPP), there are two types of parameters, inputs and instance parameters. Input parameters are the number of fleet stations, maximum walking distance, and station radius as shown in Table 1. Instance parameters are related to the problem graph instance, which are the set of street nodes, buildings, population, and distance between street nodes and buildings as summarized in Table 2. Stations are put on the street node in \(S=\{s_{1},s_{2},\ldots,s_{n}\}\) and have a unique size \(r\) (see Figure 2). Users are assumed to start the trip from their residence (referred to as building) in B = {b_1, b_2,..., b_m} where each residence or building has a different number of users defined as \(P=\{p_{1},p_{2},\ldots,p_{m}\}\). Each street node covers a set of buildings, which depends on the defined walking distance \(w\) and a set of distance \(D=\{d_{11},d_{12},\ldots,d_{ij}\}\) where \(d_{ij}\) denotes walking distance between node \(s_{i}\) and building \(b_{j}\) where a distance function \(d(s_{i},b_{j})=d_{ij}\). A station \(s_{i}\) covers a building \(b_{j}\)_iff_ that station locates at most \(w-r\) meters from the building.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Parameters** & **Description** & **Type** \\ \hline \(f\) & Maximum number of desired fleet stations. & \(\mathbb{Z}^{+}\) \\ \(w\) & Maximum walking distance allowed. & \(\mathbb{R}^{+}_{*}\) \\ \(r\) & Station area radius. & \(\mathbb{R}^{+}_{*}\), \(r\leq\frac{w}{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: FPP input parameters.  
Figure 2: An illustration of a virtual area of a station on streets.
## 4 Simulation Results  
To validate the effectiveness of the proposed carrier phase difference strategy, dual-axis VSIs driving two PMSMs are tested in the PLECS simulation program. A field-oriented control algorithm using an SVPWM technique is used to control the PMSMs. The carrier waves are triangular, and their frequency, known as the switching frequency, is 10 kHz. The input voltage of the system is provided by a 48 V battery, with a dc-link capacitor connected in parallel with the battery. The parameters of the PMSMs are given in Table 3. In the dual VSI system, two PMSMs are powered by two VSIs, both utilizing the same dc-link voltage and dc-link capacitor. The PWM signals, used to control the power switches of the inverters, are generated separately for each motor. This means that both motors can either rotate at the same speed with the same load or different speeds with different loads. Therefore, three cases are examined in this part of this paper.
# A Benchmark for the Evaluation of Corner Detectors
## 3 Benchmark Evaluation Analysis
### Subjective Evaluation  
Subjective evaluation plays a considerable role in the assessment of corner detection algorithms. In fact, a corner detector with better subjective performance according to human vision can solve visual tasks better. Figure 6 shows the detected results of the 18 corner detectors when using two test images. Figure 6a indicates that most of the intensity-based corner detectors (e.g., SUSAN [2], Harris-Laplace [3], SOGGDD [7], and Zhang & Sun [8]) performed worse than the contour-based corner detectors (e.g., CPDA [22], Fast-CPDA [37], New-Curvature [27], and ANDD [25]), because the intensity-based corner detectors were sensitive to small changes in the images' gray values. In Figure 6b, it can be seen that many corner detectors produced false positives due to the interference cloud, while such a nonrigid object is not supposed to be taken into account in corner detection.  
Figure 6: Subjective evaluation results for test images. (**a**) Test image 1. (**b**) Test image 2.  
According to the objective and subjective experimental results, there are still some challenging problems in corner detection. Intensity-based methods usually use first-order or second-order derivatives to detect corners and can thus identify non-corner edge points as corners incorrectly. Contour-based corner detectors are based on edge extraction and can thus yield large localization errors. Another fundamental problem is that both kinds of methods exploit a small amount of image smoothing to suppress noise at the beginning of corner detection, which is generally performed with the linear Gaussian kernel. However, non-linear kernels, such as those in [44; 45], have the merit of edge preservation and can thus be used instead.
## 1 Introduction
In addition, we consider the degree of completion of the computational request offloading task, which makes the system network fairer, and introduce a scheduling dominance degree to determine the fairness metrics. Such improvements can simultaneously improve user-friendliness and fairness. The main contributions of this paper are as follows:  
* We propose an adaptive dingo optimization algorithm (DOA) based on greedy strategies to search for the optimal solution to the PA problem, called AGDOA. The DOA incorporates a greedy algorithm, to optimize the initial value of the DOA, which improves the convergence speed. It also makes its parameters adaptively adjusted according to the convergence speed of the algorithm, to prevent it from falling into a local optimum;
* We advocate utilizing a binary salp swarm algorithm (SSA) method, known as BSSA, for the JRORS problem. We can use our approach for federated learning tasks in edge cloud computing environments;
* Simulations showed that the individual improvements of AGDOA significantly improved on the original algorithm, in terms of optimization results and convergence speed, while the search results outperformed the traditional algorithm. BSSA had a superior performance compared to the conventional algorithm for different numbers of mobile users, different workloads, and different configurations. The rest of this study is organized as follows: Section 2 reviews related work on task allocation in edge cloud computing. Section 3 introduces the network architecture and problem analysis. Section 4 describes the original structure and construction process of the BSSA algorithm and the AGDOA algorithm. Section 5 details the configuration of the experiment, and Section 6 presents the results and a discussion of the experiment. Finally, Section 7 presents our conclusions and makes recommendations for future research.
## 4 Computation of \(\epsilon\) Proper Equilibria
### Mixed 0-1 Linear Formulation
**Example** Consider the three players polymatrix game \((4\times 3\times 2)\), where the following matrices \(A_{1}\), \(A_{2}\), and \(A_{3}\), detail the payoffs of players I, II, and III. \[A_{1}.=\left( \begin{array}{cccc|c}1&1&3&1&1\\ 1&2&0&4&1\\ 3&4&1&3&2\\ 2&3&2&1&2\end{array} \right)\ \ \ A_{2}=\left( \begin{array}{cccc|c}5&2&2&3&2&5\\ 2&5&3&4&1&2\\ 1&4&2&1&4&1\end{array} \right)\]  
\[A_{3}.=\left( \begin{array}{cccc|c}1&1&2&3&5&4&3\\ 2&1&2&1&2&1&3\end{array} \right)\]  
Using exact arithmetic, the \(\Sigma\chi\)-MIP algorithm [4,25] enumerated three different extreme Nash equilibria for this game, as detailed in Table 1.  
The optimization results in Table 2 (as shown in [20], a triplet of linear programs was used to conclude on the perfectness of the extreme Nash equilibria) and Table 3 points towards the existence of \(\epsilon\)-proper equilibria sequences close to extreme Nash equilibria 1 and 3. The optimization results in Table 3 show how the value of \(\sigma(\epsilon)\) decreases while \(\epsilon\) converges to 0 and the quest of \(\epsilon\)-proper equilibria close to extreme Nash equilibrium 3 is successful. The following figure (Figure 1) illustrates how the value of \(\sigma(\epsilon)\) converges to 0, when \(\epsilon\) converges to 0, for the extreme Nash equilibrium 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(Eq\) & \(\alpha_{1}\) & \(\alpha_{2}\) & \(\alpha_{3}\) & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) \\ \hline
1 & 4 & 6 & 5 & \((0,0,1,0)\) & \((0,0,1)\) & \((1,0)\) \\
2 & 4 & 8 & 4 & \((0,1,0,0)\) & \((0,0,1)\) & \((1,0)\) \\
3 & \(\frac{35}{9}\) & \(\frac{31}{6}\) & \(\frac{89}{18}\) & \((\frac{1}{6},0,\frac{5}{6},0)\) & \((\frac{1}{18},0,\frac{17}{18})\) & \((\frac{7}{9},\frac{5}{9})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Extreme Nash equilibria. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Eq.** & **Perfect** & \(\epsilon\)**-Proper** & \(\epsilon\) & \(\sigma(\epsilon)\) \\ \hline
1 & yes & yes & \(\frac{1}{1000}\) & \(\frac{1}{100,100,100}\) \\
2 & yes & no & \(\frac{1}{1000}\) & 0 \\
3 & yes & yes & \(\frac{1}{1000}\) & 5,998,996 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimization results. Figure 1: Eq3; Plot of \(\sigma(\epsilon)\).
# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 1 Introduction
However, customized CNN layers can heavily affect the object-detection accuracy and would not yield any better results when two object classes have very similar results. This can be overcome by combining CNN with other models such as SVM, which can solve simple classification problems such as classification based on size. This also provides better accuracy over fixed thresholds, while being lightweight, which enables real-time implementation. Recently, it was seen that some works included different combinations of both the CNN and the SVM to resolve some of the complex issues and improve the object detection and classification accuracy. One study carried out face detection using CNN [20]. SVMs were integrated with this model using kernel combination. Another study applied this technique to scene recognition by using features from different layers of CNN in the SVM [21]. The CNN-SVM classifier has also been used for recognizing handwritten digits [22]. In [23], [PERSON] et al. developed a hybrid object recognition and detection scheme for an autonomous driving vehicle by combining CNN and SVM technique where multiple CNNs are employed for determining local robust features and SVM recognizes and detects all objects. Even though there exists much literature demonstrating the benefits of deep-learning technique for various robotic application, none of this work is targeted towards debris classification for indoor floor-cleaning robots using deep-learning techniques. In this work, we present a debris-detection and classification task for floor-cleaning robot applications using cascaded SSD MobileNet and SVM frameworks. By adjusting the network parameters and structure, the deep-learning network can recognize and classify the debris as solids or liquids. Furthermore, the SVM classifier [24; 25; 26] is used to classify the CNN detected liquid spillage as smaller or larger regions. The experimental results demonstrate that the proposed cascaded scheme achieves better debris detection and classification capabilities than Faster RCNNInception architecture and achieves 97.5% classification accuracy. The CNN-SVM cascaded approach takes approximately 71 milliseconds for debris detection and classification on the captured floor image. It implies that the proposed technique can be implemented in real time for floor-cleaning robots to recognize debris and avoid mess which is hard to clean. The remainder of this article is organized as follows: related work is reported in Section 2 and a brief introduction of CNN and SVM is given in Section 3. Section 4 describes the debris-detection and classification methodology in detail. The experimental results are given in Section 5. Finally, conclusions and future work are provided in Section 6.
# Flexible Antennas: A Review
## 3 Fabrication Techniques for Flexible Antennas
### Special Fabrication Techniques for Flexible Wearable Antennas
#### 3.5.2 Stitching and Embroidery  
Weaving or knitting the conductive textile on the substrate is another method for fabricating flexible wearable antennas. Knitting copper on a fleece substrate, the first compact fabric antenna design for commercial smart clothing was presented in an earlier article [124]. Embroidery with conductive yarn is a simple fabrication method with great potential in flexible wearable antenna fabrication due to its compatibility with nonelectronic textile processing capabilities. An embroidered antenna-IC in-erconnections in passive UHF RFID electrotextile tags and the possibility of creating a planar dipole tag by only embroidering the borderlines of the full antenna shape was studied earlier [125]. A 160-mm-diameter Archimedean spiral antenna was weaved using seven-filament silver-plated copper Elektrisola E-threads on a Kevlar fabric substrate [126]. The application area of this antenna covered several wideband, conformal, and load-bearing applications, such as airborne and wearables. Recently, a mix of these two methods (stitching and embroidery) was presented in a study [127] (Figure 10c).  
This work demonstrated the possibility of implementing an all-textile antenna, reducing the backward radiation via the use of a SIW topology. A novel embroidered metamaterial antenna based on as split ring resonator (SRR) electromagnetic bandgap (EBG) shielding structure was presented in [128] (Figure 10a). Split ring resonator (SRR) has been introduced in the antenna to reduce the SAR value, which would improve the antenna performance. For efficient antenna design, the quality, strength, and flexibility of the conductive yarns, the accuracy of the embroidery machine, stitching density, and direction on the fabric are the main factors to consider [129]. Figure 10b shows the embroidery metamaterial manufacturing process.  
Figure 9: Antennas fabricated using the substrate integrated waveguide (SIW) method. (**a**) The prototype of the SIW antenna using conductive fabrics [122] and (**b**) prototype of the circularly polarized SIW antenna [121].
# Trustworthiness of Situational Awareness: Significance and Quantification
## 4 Quantifying Trustworthiness of SA  
As discussed in Section 3, inaccuracy, incompleteness, and untrustworthiness of COP can adversely impact a commander's SA and C2 operation. Hence, to restore a commander's trust in SA and COP, it is worth quantifying the trustworthiness of SA and COP. Discussion of different approaches for incorporating security and trust in devices, links, and computing systems providing SA is beyond the scope of this paper (interested readers can refer to [3] for a summary of these approaches). Different SA devices and systems integrate different levels of security primitives because of cost and feasibility constraints. Thus, overall trustworthiness estimation of COP and perceived SA need to consider the complex organization of heterogeneous devices with different integrities that are connected with various communication links with different integrity levels. This section presents a model for quantifying the trustworthiness of COP and SA.