esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Architecture
### Task Priority Framework and Definitions
Once an action is defined, for each priority level \(k\), the following quantities are computed [42]:  
* \(\hat{\mathbf{x}}_{k}\triangleq[\hat{x}_{1,k}\dots\hat{x}_{m_{k},k}]^{\top}\in \mathbb{R}^{m_{k}}\) is the collection of reference rates of the \(m_{k}\) scalar task for priority level \(k\);
* \(\mathbf{J}_{k}\) is the Jacobian relation that connects the time derivative \([\hat{x}_{1,k}\dots\hat{x}_{m_{k},k}]^{\top}\) of the \(k\)th task vector with the system velocity vector \(\hat{\mathbf{v}}\); and
* \(A_{k}\triangleq diag(a_{1,k},\dots,a_{m_{k},k})\) is the diagonal matrix constituting the activation functions. Now, by exploiting the task priority inverse kinematics defined in [37,39], the desired system velocity vector \(\hat{\mathbf{v}}\) that satisfies the task priority requirement can be found. In detail, the following sequence of nested minimization problems needs to be solved, corresponding to the so called Task Priority Inverse Kinematics (TPIK):  
\[S_{k}\triangleq\left\{\arg\!R-\min_{\hat{\mathbf{v}}\in S_{k-1}}\|\mathbf{A}_{k}(\hat{ \mathbf{x}}_{k}-\mathbf{J}_{k}\hat{\mathbf{v}})\|^{2}\right\},k=1,2,\dots,N, \tag{11}\]  
where \(S_{k-1}\) is the manifold of solutions of all the previous tasks in the hierarchy, \(S_{0}\triangleq\in\mathbb{R}^{n}\), \(N\) is the total number of priority levels, and finally where the notation \(R-\min\) is introduced for underlining that each minimization is performed via the specialized form of [39]. Within such a specialized framework (named iCAT: inequality Constraints And Task transitions), the above TPIK problem (11) results in the following algorithm, to be initialized with \(\mathbf{\rho}_{0}=0\), \(\mathbf{Q}_{0}=I\) and then, for \(k=1,\dots,N\)  
\[\mathbf{W}_{k}=\mathbf{J}_{k}\mathbf{Q}_{k-1}(\mathbf{I}_{k}\mathbf{Q}_{k-1})^{\#,\mathbf{A}_{k},\mathbf{ Q}_{k-1}}, \tag{12}\]  
\[\mathbf{Q}_{k}=\mathbf{Q}_{k-1}\Big{(}\mathbf{I}-(\mathbf{J}_{k}\mathbf{Q}_{k-1})^{\#,\mathbf{A}_{k}, \mathbf{I}}\mathbf{J}_{k}\mathbf{Q}_{k-1}\Big{)}, \tag{13}\]  
\[\mathbf{\rho}_{k}=\mathbf{\rho}_{k-1}+\text{Sat}\Big{(}\mathbf{Q}_{k-1}(\mathbf{J}_{k}\mathbf{Q}_{ k-1})^{\#,\mathbf{A}_{k},\mathbf{I}}\mathbf{W}_{k}(\hat{\mathbf{x}}_{k}-\mathbf{J}_{k}\mathbf{\rho}_{k-1}) \Big{)}, \tag{14}\]  
where the special pseudo-inverse operator \((. )^{\#,\mathbf{A},\mathbf{Q}}\) has been introduced in [39] to cope with certain invariance problems arising in (11) and where the function \(\text{Sat}(. )\) implements the management of control variable saturations suggested in [52]. At the end of the above iterative process, the desired final system velocity vector is \(\hat{\mathbf{v}}=\mathbf{\rho}_{N}\). This can also be further extended to vehicles with manipulators [40, 41, 42, 43, 44, 45].
# Investigating Attention Mechanism for Page Object Detection in Document Images
## 2 Related Work
### Page Object Detection
#### 2.1.1 Traditional Approach  
To achieve table detection, the earlier approaches have established a specific underlying structure for tables in a document. [PERSON] et al. [25] utilized Optical Character Recognition (OCR) to extract tabular information. The algorithm attempts to determine probable table regions by evaluating the keywords and white spaces. The primary drawback of this strategy is its total reliance on the presumptions about the tables' structure and the gathering of the employed keywords. In the area of table analysis, [PERSON] et al. [26] introduced a new approach. To detect table lines, it uses the distance between consecutive words. Following that, table entity candidates are proposed by grouping adjacent vertical lines with successive horizontal words. However, the fundamental premise is that a table can have no more than two columns. As a result, three different layouts (single, double, and mixed columns) are created using this method. This approach has the disadvantage of application to a small number of designed tables.  
Figure 1: Visual representation of the detections obtained by the proposed model on publicly available benchmark datasets, i.e., DocBank [19], PublayNet [20], and IIIT-AR-13K [21]. Colors are used as visual embeddings to distinguish various detected objects. Blue, orange, green, and black represent paragraphs, title, figures, and tables, respectively. The first, second, and third rows represent samples from DocBank, IIIT-AR-13K, and PublayNet, respectively.
## 3 Design and Construction  
Magnetic fields are solved in two ways. A quick axial/longitudinal (z-axis) solution can be determined by Biot-Savart current coil model summation. Alternatively, finite element analysis (FEA) using [PERSON]'s equations [8] provides a more complete solution profiling longitudinal or z-axis and transverse or x-axis B-fields.  
\[\begin{array}{l}\overset{\rightarrow}{H}\equiv-\overset{\rightarrow}{ \
abla}\Phi\\ \overset{\rightarrow}{\
abla}\cdot\overset{\rightarrow}{B}=0\\ \overset{\rightarrow}{B}=\mu_{0}\left(\overset{\rightarrow}{H}+\overset{ \rightarrow}{M}\right)\end{array} \tag{1}\]  
\(H\) is magnetic field strength, \(\Phi\) is scalar magnetic potential, \(B\) is magnetic field intensity, and \(M\) is magnetization. Outside the magnetic material, \(M=0\):  
\[\overset{\rightarrow}{\
abla}\cdot\overset{\rightarrow}{B}=\mu_{0}\left( \overset{\rightarrow}{\
abla}\cdot\overset{\rightarrow}{H}+\overset{ \rightarrow}{\
abla}\cdot\overset{\rightarrow}{M}\right)\quad\Rightarrow 0 =\mu_{0}\left(\overset{\rightarrow}{\
abla}\cdot\left(-\overset{ \rightarrow}{\
abla}\Phi\right)+0\right)\quad\Rightarrow 0=\
abla^{2}\Phi \tag{2}\]  
Inside magnetic material bulk of constant magnetization, \(\overset{\rightarrow}{M}\)= constant. Magnetization may change on the magnet edges due to mutual coercion with other magnets. This effect is a small contribution as indicated by close agreement of measured and COMSOL-simulated [8] values of the B-field (detailed in Section 4). The COMSOL model did not include mutual coercion.  
\[\overset{\rightarrow}{\
abla}\cdot\overset{\rightarrow}{B}=\mu_{0}\left( \overset{\rightarrow}{\
abla}\cdot\overset{\rightarrow}{H}+\overset{ \rightarrow}{\
abla}\cdot\overset{\rightarrow}{M}\right)\quad\Rightarrow 0 =\mu_{0}\left(\overset{\rightarrow}{\
abla}\cdot\left(-\overset{ \rightarrow}{\
abla}\Phi\right)+0\right)\quad\Rightarrow 0=\
abla^{2}\Phi \tag{3}\]  
For FEA modeling, \(\overset{\rightarrow}{B}\) parallel to the surface or _Dirichlet_ boundary condition (BC) is applied to the far bounding surfaces while \(\overset{\rightarrow}{B}\) perpendicular to the surface or _Neuman_ BC is employed at symmetry faces involving or very close to the magnet(s), e.g., the floor in Figure 4. The model space is reduced by exploiting symmetry planes to minimize simulation overhead. The whole solution is generated by duplicating the minimized volume.  
Figure 4: Reduced FEA simulation volume.  
Exploration FEA simulations shown in Figure 5 examine different configurations of similar annular magnets. Clearly, a parallel magnetized pair stabilizes the central field. The addition of a repelling pair (one on each end) reduces the far-field decay distance.
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA
#### 4.3.9 One-End Access Schemes  
The basic system of the BOCDA basically has access configuration involving both ends to propagate the pump and the probe lightwave in opposite directions in the sensing fiber in relation to each other. On the contrary, one-end access BOCDA configurations have also been proposed and demonstrated. In some proposals, a reflection mirror or a fiber loop is set at the far end of the fiber to make the probe lightwave [63; 64]. These configurations have the possibility of simplifying the sensing-fiber installation procedure. Recently, new works have also been presented [65].
# Evaluation of English-Slovak Neural and Statistical Machine Translation
## 4 Results  
### Comparison of MT Quality  
Based on the results of the adjusted univariate tests for repeated measures (Greenhouse-Geisser adjustment) among GT_SMT, GT_NMT, mt@ec_SMT, and eTranslation_NMT, there are significant differences in the MT quality in terms of the scores for BLEU_n (\(n=1\), \(2\), \(3\) and \(4\): \(G\)-\(G\)_Epsilon_ \(<0.898\), \(p<0.001\)).  
From multiple comparisons (Bonferroni adjustment) in the case of BLEU_1 (Table 2a), there is a significant difference between NMT (GT_NMT or eTranslation_NMT) and GT_SMT, as well as mt@ec_SMT (\(p<0.001\)), in favor of NMT (Figure 1a). Similar results (Table 3a, Figure 2a) were achieved in the case of the BLEU_3 measure (\(p<0.01\)).  
The BLEU_2 measure (Table 2b) showed not only significant differences between statistical machine translation and neural machine translation (\(p<0.001\)) in favor of NMT (Figure 1b), but also a significant difference between neural machine translations themselves, i.e., between GT_NMT and eTranslation_NMT (\(p<0.05\)) in favor of GT_NMT.  
Concerning the BLEU_4 measure (Table 3b), there is a significant difference between GT_NMT and the statistical machine translations (GT_SMT or mt@ec_SMT) (\(p<0.001\)) in favor GT_NMT (Figure 2b), but there is not a difference between neural machine translation (eTranslation_NMT) and statistical machine translation (GT_SMT) (\(p>0.05\)).  
To sum up, the assumption concerning the better quality of NMT regardless of the system used (GT_NMT or eTranslation_NMT) has been confirmed. There were statistically significant differences between SMT and NMT in favor of NMT based on all BLEU_n metrics.  
Subsequently, the results were also verified using error rate metrics (CDER, PER, TER, and WER). Based on adjusted univariate tests for repeated measures and multiple comparisons, statistically significant differences between the NMT and SMT translation technologies were proved in favor of NMT (CDER, PER, TER, and WER: \(G\)-\(G\)_Epsilon_\(<0.944\), \(p<0.001\)). The NMTs achieved statistically significantly lower error rates than the SMTs, regardless of the MT system. The results matched, and we can consider them robust.
# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 4 Computational Simulations
### Example 1
As shown in Figure 5, the traditional ant colony algorithm considers the influence of the ocean current and is oriented by the optimal path length, and it selects the area with large current resistance relative to the sailing direction. On the basis of continuous experiments on the optimized ant colony algorithm, the path with less energy consumption is selected. The reason why the energy consumption becomes lower while the path length increases is that the energy consumption of bypassing sea areas with high current resistance is far less than the sum of the additional energy consumption resulted by passing through the sea area with large current resistance plus the normal energy consumption. Meanwhile, the longer sailing time is due to the longer path length. In conclusion, experiments proved that the optimized ant colony algorithm has a longer navigation time and an energy saving rate at about 3.326%.
the network. We propose three different power consumption modes for BSs and a novel MDP-based algorithm to control these modes of the BSs under QoS constraint.  
**Novelty of our work:** As already presented in some pioneering works [2; 3; 4], switching off a base station within a HetNet is a very good approach to save energy consumption during low traffic condition. However each sleeping BS takes significant amount of time to wake up and become fully operable. As a consequence, some of the users might experience a delay in accessing the network, even some calls might be dropped as well. In order to overcome the problem of call dropping due to the wake up delay, we propose an energy efficient '3-state MDP model' for the BSs within the network. We propose three different modes namely 'Active mode', 'Stand-by mode' and 'Sleep mode' for the BSs. In this paper, we utilize Markov model to find the optimal policy in terms of energy efficiency using Markov decision process (MDP). We have also shown the energy-delay trade-off in order to design an energy efficient as well as delay aware network. We utilize the stand-by mode and have differentiated it from the sleep mode by defining that a BS in stand-by mode consumes some small amount of energy but takes negligible time to go back to the active mode; whereas a BS in sleep mode does not consume any energy but takes more time to wake up. Hence sleep mode gives us the advantage of power saving, whereas stand-by mode allows us to avoid the wake-up delay. That is why we are implementing both the stand-by mode and sleep mode in our algorithm so that we can reduce energy consumption as well as wake up delay of BSs. The hardware and software setups for these low power consumption and zero power consumption modes have already been proposed in the literature [5; 6]. We have explained the operation and switching procedure among these modes in the following sections. Another novelty of our work is in defining a novel reward function for the Markov decision process (MDP) which helps us to get an optimal policy in selecting a particular mode for each BS. Because of applying the proposed 3-state MDP model, we find that a significant amount of energy can be saved in low traffic condition along with fulfilling the QoS requirement in terms of data rate.  
The rest of the paper is organized as follows: Section 2 outlines related work in the concerned area. Network model, three state Markov model and proposed MDP-based algorithm model are presented in Sections 3-5, respectively. Section 6 presents the power consumption model. The simulation results and performance analysis are presented in Section 7. Finally Section 8 concludes the paper.
## 4 Experiments
### Penetration Testing Scheme  
The vehicle penetration testing scheme, as presented in Table 2, is based on the results of cybersecurity threat analysis and authorized testing scope to effectively address potential cybersecurity threats. The mobile device primarily serves for remote vehicle control and virtual Bluetooth key functionalities. However, since the Bluetooth digital key function of the target cannot be utilized normally, mobile device testing is considered as a form of remote vehicle control evaluation. Due to hardware limitations preventing access to firmware through disassembling the ECU, testers initially treat the vehicle as a black-box during penetration testing. Consequently, they rely on fuzzing technology to identify potential cybersecurity vulnerabilities by manipulating input parameters related to various vehicle functions and observing corresponding responses. Fuzz testing technology is predominantly employed in assessing communication channels carrying baseband data such as Bluetooth, Wi-Fi, network communication protocols etc. while vision-based sensors like in-vehicle cameras and ultrasonic sensors are excluded from the fuzz testing scope.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threat** & **In-Vehicle System** & **Attack Entry** & **Threat Fields** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Infotainment} & Bluetooth & \multirow{2}{*}{Radio Threat/ System Threat} \\ \cline{3-4}  & & Wi-Fi & \\ \cline{3-4}  & & 4G & \\ \cline{3-4}  & & USB & \\ \hline \multirow{2}{*}{2} & CAN Bus & OBD-II & In-vehicle Bus Threat \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Mobile Device} & APP & APP Threat \\ \hline \multirow{4}{*}{4} & \multirow{2}{*}{Sensors} & PKES & \multirow{2}{*}{Sensor Threat} \\ \cline{3-4}  & & GPS & \\ \cline{3-4}  & & Camera & \\ \cline{3-4}  & & Ultrasonic Radar & \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{Network} & Infotainment & \multirow{2}{*}{Network Threat} \\ \cline{3-4}  & & APP & \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Cloud Platform} & APP & \\ \cline{3-4}  & & Infotainment & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cybersecurity threats.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**In-Vehicle System** & **Test Case** & **Test Case Set** \\ \hline \multirow{4}{*}{Infotainment} & Bluetooth Test & \multirow{2}{*}{Radio Test Case Set} \\ \cline{3-3}  & Wi-Fi Test & \\ \cline{3-4}  & 4G Test & \\ \cline{3-4}  & \multicolumn{2}{c}{\"Vehicle-Cloud\" Network Test} & Network Test Case Set \\ \cline{3-4}  & In-vehicle Operating System Test & \multicolumn{2}{c}{System Test Case Set} \\ \cline{3-4}  & USB Test & Hardware Test Case Set \\ \hline CAN Bus & CAN Bus Test & In-vehicle Bus Test Case Set \\ \hline \multirow{2}{*}{Remote Vehicle Control} & APP Test & APP Test Case Set \\ \cline{3-4}  & Network Test & Network Test Case Set \\ \hline \multirow{4}{*}{Sensors} & PKES Test & \multirow{2}{*}{Sensor Test Case Set} \\ \cline{3-4}  & GPS Test & \\ \cline{3-4}  & Camera Test & \\ \cline{3-4}  & Ultrasonic Radar Test & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Penetration testing scheme.
## 3 Results and Discussion
### Mechanical Properties
In comparison, our SCLC mixes, such as SCLC-EC and SCLC-SMR37, achieved compressive strengths up to 36.72 MPa and densities between 1.94 and 2.03 g/cm\({}^{3}\), demonstrating strong performance with unconventional aggregates like SBA and SMR. Additionally, studies by [PERSON] and Rossignolo [24] and [PERSON] et al. [32] reported similar results for expanded clay and pumice-based lightweight concretes, with compressive strengths around 30 MPa and densities of approximately 1.85 g/cm\({}^{3}\). Our SCLC mixes not only matched but exceeded these values, reinforcing the potential of SBA and SMR as viable alternatives for producing high-performance self-compacting lightweight concrete. Finally, the efficiency factor (EF) was evaluated, which is the ratio between compressive strength and concrete bulk density. In this study, the efficiency factor was calculated for the concretes at 28 days, using the average dry bulk density value. Figure 16 presents the efficiency factor for each SCLC mix. Figure 15: Dry bulk density at 7 and 28 days of curing. Based on the results, it can be observed that the efficiency factor for the self-compacting lightweight concretes ranged from 13 to 18 MPa\(\cdot\)dm\({}^{3}\)/kg, with the highest values found in SCLC-SMR37 and SCLC-EC, which were the mixes that exhibited the highest compressive strength and lowest bulk density. Previous studies have found efficiency factors ranging from 22 to 28 MPa\(\cdot\)dm\({}^{3}\)/kg [31]. However, in both studies, different aggregate sizes were used, in addition to partial replacement of lightweight aggregate with normal coarse aggregate, which increased the compressive strength of the concretes and, consequently, the efficiency factor. According to [PERSON] and [PERSON] [24], the efficiency factor is an important parameter in structural design, and concretes with efficiency factors above 25 MPa\(\cdot\)dm\({}^{3}\)/kg can be considered high-performance concretes. In this study, all the mixes were classified as structural concretes, meeting the compressive strength and bulk density parameters defined by [35].
# Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
## 3 The Methodology of Multi-Objective Optimization
### The Minimum Distance Selection Method (TMDSM)  
Through the above optimization method, the Pareto optimal front is obtained so that the designer can consult and make a determination according to the amount of candidate solutions. For all that, sifting the most satisfactory individual solution is essential to evaluate the final optimization result [60]. The most commonly used methods for determining the optimal solution include the weight decision-making method and the minimum distance selection method (TMDSM). The former assigns weights to the optimization objectives based on their relative importance and determines the optimal solution. However, considering different actual operating conditions and preferences, it is difficult to determine appropriate weights [61]. In this paper, TMDSM, which searches the \"keen point\" by calculating the distance between the ideal solution and each candidate solution, is applied to conduct the selection of the optimal solution. The formulation below exhibits the principle of TMDSM:  
\[\text{min}D=\sqrt{\sum_{\tau=1}^{K}(f_{\tau\tau}-\min(f_{\tau}(x)))^{2}} \tag{18}\]  
where \(D\) is the distance between the ideal solution and the \"keen point\". And \(K\) denotes the quantity of the objective elements;\(f_{\tau\tau}\) represents \(\tau\)-th objective value corresponding to the \(c\)-th Pareto solution.
## 3 DGT Assessment Influenced by Environmental Factors
In addition, iron minerals that are ubiquitous in soil will also complex with heavy metal ions to form relatively stable complexes, thereby reducing the biological availability of heavy metals. For example, a higher degree of stable binding between Sb(V) and organic matter and Fe/Al minerals is expected to reduce the ecotoxicity, bioavailability, and health risks of Sb(V) [75]. However, a study had shown that As and Sb exhibited contrasting results in soils and white icicle radish (_R. sativus_) [66]. Sb was most likely predominantly associated with sulfide minerals, silicates, and organic matter, while As was mainly associated with Fe oxides. Fe and Al oxides play an important role in As retention in contaminated soil due to the formation of inner spherical, monodentate and mononuclear, and bidentate binuclear complexes with soil Fe oxides. Numerous studies have shown that the addition of soil amendments can effectively reduce the bioavailability of heavy metals, which is closely related to the characteristics of soil DOM and Fe plaque [65; 76; 77]. Fe plaque usually exists on the surface of plant roots, mainly iron oxides or hydroxides formed by \(\mathrm{O_{2}}\) secreted by roots, which chelate heavy metals through co-precipitation and adsorption, restricting the migration of heavy metals from soil to plants, resulting in the reduced bioavailability of heavy metals [65; 76]. [PERSON] et al. [48] assessed the human health risks caused by lead in urban park soils by using in vitro methods, such as simulated human gastrointestinal tract extraction and DGT, and found that Pb bioaccessibility in the gastric phase decreased with the increasing Fe, Mg, and clay content in park soils. In fact, the DGT and DIFS model can be used to assess the risk of heavy metals release from soils. While [PERSON] et al. [47] analyzed the effect of soil chemistry on the correlation between Pb availability and DGT-Pb bioaccessibility, they found that total Fe, total Mn, and total organic carbon (TOC) had no effects on the correlation coefficients. Meanwhile, DGT measurement was less affected by different soil properties than UBM (Unified bioaccessibility research group of Europe (BARGE) Method). Compared to traditional chemical extraction methods, the DGT technique can not only simulate the root environment of plants and measure the available concentration of heavy metals in soil [78], but also shows a high correlation with the concentration of heavy metals in earthworms [68; 79], which is an effective method for predicting and evaluating the bioavailability of heavy metals in different types of soil.