esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effect of Boundary Conditions on Dynamic Characteristics of Cable
### Accuracy Verification of Boundary Condition Range  
The validation example in the literature [21] is selected to verify the conclusions of this paper. In this literature, a suspender with fixed boundaries at both ends is selected,  
Figure 4: The #4 cable frequency variation of each order: (**a**) to (**f**) are the variations of the first to sixth order frequencies of #4 cable under different boundary condition coefficients for each material influence factor, respectively.  
and its free length \(l=30.323\) m, elastic modulus \(E=1.95\times 10^{11}\) N/m\({}^{2}\), section moment of inertia \(I=7.26\times 10^{-7}\) m\({}^{4}\), linear mass per unit length \(m=24.556\) kg/m, and suspender axial force \(H=8.25\times 10^{5}\) N are known. The method in this paper is used to calculate the frequency value when the boundary condition coefficient is 0.4. Here, due to symmetry, the stiffness ratios \(\lambda_{AB}\) are 0.3 and 1.0, respectively. In order to verify the accuracy of defining the fixed support with the determined boundary condition coefficients, the relative errors between the suspender frequency values calculated in this paper and the measured values under the fixed support are compared, and the results of the calculation are shown in Table 3.  
It can be clearly seen from Table 3 that this paper uses the value of the boundary condition coefficient to define the cable boundary with sufficient accuracy. The determination of the boundary conditions is independent of the stiffness ratio \(\lambda_{AB}\), as the upper and lower boundary stiffnesses of the cable are reciprocal, the cable ends will be solidly supported when any of the boundary condition coefficients reaches 0.4. Similarly, in a single cable, the value of the corner stiffness \(k^{\theta}\) can be determined by the boundary condition coefficient \(v\), from Equation (20), \(k^{\theta}\in\left[0,\frac{2\sqrt{H\cdot EI}}{3}\right]\) can be obtained by \(v\in\left[0,0.4\right]\). When \(k^{\theta}=0\), both ends of the cable are simply supported; when \(k^{\theta}=\frac{2\sqrt{H\cdot EI}}{3}\), both ends of the cable are solidly supported, replacing the theoretical condition of the cable being solidly supported.
# Power Management Strategy for an Autonomous DC Microgrid
## 2 Autonomous DC Microgrid Configuration and Component Description  
The configuration of a typical autonomous DC microgrid, which consists of a PV source, electrochemical batteries, a DG, a SC, and a DC load, is shown in Figure 1.  
In this microgrid, excepting the load, every component is connected to a common DC bus via the dedicated power converters. The DC load can be connected directly to the DC bus if its rated voltage is equal to that of the DC bus. However, in most cases, the voltages are different and so a DC-DC buck or boost converter may be involved. In addition, the AC load requires a DC-AC power inverter to change the current's form. Considering that this paper focuses on a real-time power control and power management strategy, both the AC load and DC load are regarded as the DC power demanded on the DC bus. Furthermore, in this study, the DC load is seen as the electric appliances of a building equipped with PV panels on the roof [29] and the microgrid controller can cut some appliances off, so that some load power \(p_{L\_S}\) can be shed. Therefore, there is also a local load controller which can partly reduce power demand upon receiving the load shedding order from the central power management block or fault detection program. Similarly, the PV source is controlled by a maximum power point tracking (MPPT) algorithm to exploit PV energy as much as possible, but the microgrid controller can shed some PV power as \(p_{PV\_S}\). To ensure the power balance, a voltage loop compares the instant DC bus voltage \(v_{DC}\) with the reference \(v_{DCref}\) and obtains the power \(p_{dyn}\) needed to restore the rated voltage of the bus. The power flow management and control strategy will then transform this power into different current references (\(i_{Bref}\), \(i_{SCref}\), and \(i_{DGref}\)), and then distribute them to the controller of each source. DC bus voltage fluctuations are considered to verify system stability. The control of each source is carried out by a control loop including the classic proportional and integral regulator and the PWM controller. In particular, a Phase-Locked Loop (PLL) is needed to control DG power, calculating the electrical rotation angle \(\theta\) from the DG AC voltage \(v_{DG}\). The bandwidth of each control loop is carefully chosen to avoid stability issues. In fact, all these controls are implemented in a single central digital signal processor controller in the experimental platform.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion
### Jiangsu Power Grid  
As an important component of the eastern China power system, the Jiangsu power grid has put into operation two HVDCs in 2017, named Jinsu and Longzheng. It is a typical AC/DC hybrid receiving terminal power grid. Jinsu's HVDC is fed into the Sunan district, which is the major load center in the Jiangsu power grid with weak reactive power support ability. Jiangsu power grid's data of generators, HVDC, and load message are shown in the supplementary. To illuminate the effectiveness of the proposed method, two SCUC cases are built for the Jiangsu 500 kV power grid with 24 h:  
* SCUC solution with minimum frequency limits constraints and security constraints after secondary frequency regulation.
* SCUC solution with minimum frequency limits constraints, security constraints after secondary frequency regulation and voltage stability constraints.  
It is worth mentioning that the Jinsu HVDC is located in the Sunan district, which is the major load center in the Jiangsu power grid and has a shortage of reactive power support ability. Jinsu's HVDC feed-in would cause generating units to shut down in this area so that the weaker voltage support capacity may further lead to a voltage stability problem. In this section, two UC cases are built for the Jiangsu 500 kV power grid within 24 h:  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **Case 1** & & **Case 2** \\ \hline  & **HVDC 1’s Power** & **Lines’ Over Limits** & **HVDC 1’s Power** & **Lines’ Over Limits** \\ \hline Hour 10 & 1000 & Power between bus-9 and bus-39 is 393.9 MW & 956.0 & – \\ Hour 11 & 1000 & Power between bus-9 and bus-39 is 390.7 MW & 1000 & – \\ Hour 12 & 1000 & Power between bus-9 and bus-39 is 404.2 MW & 873.7 & – \\ Hour 13 & 1000 & Power between bus-9 and bus-39 is 398.7 MW & 981.4 & – \\ \hline \end{tabular}
\end{table}
Table 2: HVDC 1 transmission power and AC transmission power HVDC1 block fault.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Case 1** & & **Case 2** \\ \hline  & **HVDC 2’s Power** & **Lines’ Over** & **HVDC 2’s Power** & **Lines’ Over** \\ \hline Hour 7 & 1000 & Power between bus-16 and bus-15 is 505.9 MW & 1000 & – \\ Hour 8 & 989.5 & Power between bus-16 and bus-15 is 535.0 MW & 1000 & – \\ Hour 15 & 985.6 & Power between bus-16 and bus-15 is 540.7 MW & 1000 & – \\ Hour 19 & 980.4 & Power between bus-16 and bus-15 is 520.6 MW & 989.5 & – \\ Hour 21 & 1000 & Power between bus-16 and bus-15 is 505.3 MW & 989.5 & – \\ \hline \end{tabular}
\end{table}
Table 3: HVDC 2 transmission power and AC transmission power HVDC1 block fault.  
Forty-six generators in the Jiangsu 500 kV power grid provide a total capacity of 40,980 MW. The rated capacity of Jinsu and Longzheng are respectively 7200 MW and 3000 MW, accounting for 19.9% of the total installed generator capacity. Load data and the HVDC parameter are shown in the Appendix A.
# Evaluation and Characterization of Alginate Extracted from Brown Seaweed Collected in the Red Sea
## 2 Materials and Methods
### Characterization of Sodium Alginate--\({}^{1}\)H-NMR Spectroscopy Analysis  
The extracted sodium alginate was dissolved in D\({}_{2}\)O (deuterium dioxide) with a concentration of 6 g/L at 80 \({}^{\circ}\)C, and the proton number was identified and confirmed by \({}^{1}\)H NMR experiments using a Bruker AVANCE 400 MHZ NMR spectrometer [41]. The NMR spectroscopy analysis was done based in studies of [PERSON] et al. [24], [PERSON] et al. [42], [PERSON] et al. [43], and [PERSON] et al. [44].  
The M/G ratio, the molar fractions of the monads (FG and FM), and the dyad (FGG, FMM, FMG, and FGM) sequences were calculated from the area of \({}^{1}\)H NMR signals, employing the formula given by [PERSON] et al. [42]:  
FG = AI/(AII + AIII);  
FM = 1-FG;  
FGG = AIII/(AII + AIII);  
FGM = FMG = FG-FGG;  
FMM = FM-FMG;  
M/G = (1 - FG)/FG;  
\(\eta\) = FMG/(FM \(\times\) FG);  
AI is in the region of anomeric hydrogen of guluronic acid at 5.1-5.2 ppm, AII is at the anomeric hydrogens of mannuronic acid and the H-5 of alternating blocks overlapping at 4.7-4.9 ppm, and AIII is the H-5 of guluronic acid residues in the homopolymeric G blocks at 4.5-4.6 ppm.
## 2 Materials and Methods
### Architecture of the Proposed Framework  
The overall architecture of the proposed framework is illustrated in Figure 1. The processing pipeline begins by gathering the software components that will be deployed on the IoT platform. The source of each such software component is stored in the corresponding code repository. This step is instrumented by the additional testsuite framework [18] and detailed in Section 2.2.  
Subsequently, feature selection is applied to each software component, producing the respective tailored software. Recall from the introduction that feature selection is a step that assists in limiting the attack surface and the associated risk, while it can also lead to configurations with smaller memory footprint and resource requirements. The outcome of the feature-selection step is a set of software components that are tailored to the needs of the specific IoT platform deployment. In order to perform feature-based tailoring of software components, the proposed framework utilizes the relevant capabilities of the additional testsuite framework [18]; the additional testsuite framework is summarized in Section 2.2, while Section 2.3 provides details on the feature selection capabilities of the additional testsuite framework.  
Afterwards, each software component is analyzed to identify security issues, forming a list of software-related vulnerabilities related to the individual software component. In this context, the additional testsuite framework submits the relevant projects to the SonarQube static code-analysis platform, and for each project, it gathers the results through the SonarQube API [[https://sonarquube.inria.fr/sonarqube/web_api/api/](https://sonarquube.inria.fr/sonarqube/web_api/api/)]([https://sonarquube.inria.fr/sonarqube/web_api/api/](https://sonarquube.inria.fr/sonarqube/web_api/api/)) (accessed on 14 May 2023). Project-specific lists are then merged to formulate a comprehensive vulnerability list for the whole of the platform. Security analysis is presented in Section 1.2.  
Each vulnerability in the comprehensive platform-wide list is assessed to estimate its impact on the platform. To this end, a statistical approach is used. Vulnerability impact estimation is discussed in Section 2.5.  
Finally, the vulnerabilities are prioritized, taking into account the impact of each vulnerability and the cost to fix it, as well as the overall security budget, producing the final list of vulnerabilities to be remedied. This is accomplished through an integer programming-based optimization scheme, which is detailed in Section 2.6.  
This list can guide developers to the process of maintaining the software so as to minimize the overall _residual risk_, i.e., the risk owing to the vulnerabilities that will not be fixed, due to security budget constrains.
# Challenges of Hydrodynamic Cavitation of Organic Wastes
###### Abstract  
The aim of this study was to evaluate the effectiveness of hydrodynamic cavitation (HC) as a pre-treatment method for selected organic wastes. In these HC experiments, municipal wastewater (MW) and mature landfill leachate (MLL) as well as mixtures of lignocellulosic waste (LB) suspended in these waste streams were investigated. For all HC tests, the same operational parameters were assumed: an inlet pressure of 7 bar, and 30 recirculations through the cavitation zone. A steel orifice plate with a conical concentric hole of 3/10 mm was used as the HC inductor. In almost all the materials analysed, solubilisation and decomposition of complex organic matter were observed, which were confirmed by an improved biodegradability index (BI) and soluble chemical oxygen demand (SCOD) content in the cavitated mixtures. The exception was the series with sole MW; in this case, the BI was reduced. In turn, regarding the multicomponent mixtures, more beneficial results were found for LB and MW, which were confirmed by improved BI, alkalinity and SCOD content. The results obtained indicate that HC might be applied as a pre-treatment method for selected organic wastes for further biomethane production. However, a key factor in its successful application is the selection of suitable operational conditions chosen individually for each waste type.  
hydrodynamic cavitation; municipal wastewater; mature landfill leachate; lignocellulosic waste; biodegradability index; anaerobic digestion Article
## 2 Theoretical Model
### Channel Model
\begin{array}{l}\frac{L}{k!_{x}}=\frac{1}{\kappa_{x}}\approx\left\{\begin{array} []{l}\sqrt{\frac{L}{k}},\quad\sigma_{R}^{2}(\kappa),\widetilde{\sigma}_{R}^{ 2}(\alpha)<<1\\ \frac{L}{k!_{0}(\alpha)},\quad\sigma_{R}^{2}(\kappa),\widetilde{\sigma}_{R}^{ 2}(\alpha)>>1\end{array}\right.\\ \\ l_{y}=\frac{1}{\kappa_{y}}\approx\left\{\begin{array}{l}\sqrt{\frac{L}{k}}, \quad\sigma_{R}^{2}(\kappa),\widetilde{\sigma}_{R}^{2}(\alpha)<<1\\ \rho_{0}(\alpha),\quad\sigma_{R}^{2}(\kappa),\widetilde{\sigma}_{R}^{2}(\alpha)>> 1\end{array}\right.\\ \\ \end{array}  \tag{6}\]where \(L\) is the distance of the propagation path, \(k\) represents the wave number, with \(\lambda\) being the wavelength, \(\rho_{0}(\alpha)\) is the coherent length, \(l_{x}\) is the lower limit of the large-scale eddies, \(l_{y}\) is the upper limit of the small-scale eddies, the functions \(\sigma_{R}^{2}(\alpha)\) and \(\widetilde{\sigma}_{R}^{2}(\alpha)\) are the Rytov variance of the Kolmogorov turbulence and non-Kolmogorov turbulence, the spectral power-law can suppose the values \(11/3\) and \(5\) for the Kolmogorov turbulence and non-Kolmogorov turbulence, respectively. The coherent length has the form of  
\[\rho_{0}(\alpha)=\left[\frac{-2^{3-\alpha}\pi^{2}\Gamma\big{(}1-\frac{\alpha} {2}\big{)}k^{2}A(\alpha)\widetilde{c}_{n}^{2}}{\Gamma\big{(}\frac{\alpha}{2} \big{)}}\right]^{\frac{1}{2-\alpha}}, \tag{7}\]  
where \(A(\alpha)\) denotes the generalized modified amplitude factor; the expression is defined as  
\[A(\alpha)=\frac{1}{4\pi^{2}}\Gamma(\alpha-1)\cos\Big{(}\frac{\alpha\pi}{2} \Big{)}. \tag{8}\]
###### Abstract  
The high-precision positioning and navigation of agricultural machinery represent a backbone for precision agriculture, while its worldwide implementation is in rapid growth. Previous studies improved low-cost global navigation satellite system (GNSS) hardware solutions and fused GNSS data with complementary sources, but there is still no affordable and flexible framework for positioning accuracy assessment of agricultural machinery. Such a low-cost method was proposed in this study, simulating the actual movement of the agricultural machinery during agotechnical operations. Four of the most commonly used GNSS corrections in Croatia were evaluated in two repetitions: Croatian Positioning System (CROPOS), individual base station, Satellite-based Augmentation Systems (SBASs), and an absolute positioning method using a smartphone. CROPOS and base station produced the highest mean GNSS positioning accuracy of 2.4 and 2.9 cm, respectively, but both of these corrections produced lower accuracy than declared. All evaluated corrections produced significantly different median values in two repetitions, representing inconsistency of the positioning accuracy regarding field conditions. While the proposed method allowed flexible and effective application in the field, future studies will be directed towards the reduction of the operator's subjective impact, mainly by implementing autostereing solutions in agricultural machinery.  
real-time kinematic (RTK); precision agriculture; ISO standard; global positioning system (GPS); GLONASS; agricultural tractor +
Footnote †: journal: Applied Sciences  
Article  
A Low-Cost Global Navigation Satellite System Positioning Accuracy Assessment Method for Agricultural Machinery
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 2 Methods
### Bottleneck  
In the bottleneck, the resolution and dimension of the feature remain unchanged. Two Swin Transformer blocks are used to replace the CNN-based bottleneck block for better deep feature representation [51].  
An MSA layer can be used as a convolution layer in the bottleneck. Consider an MSA layer consisting of \(N_{h}=K^{2}\) heads of dimension \(D_{h}\) and output dimension \(D_{out}\). Let \(\mathbf{f}:[N_{h}]\rightarrow\Delta_{K}\) be a mapping of heads onto shifts and \(\Delta_{K}\) contain all possible shifts in the convolution. Suppose that the following holds for every head \(h\):  
\[\text{softmax}\Big{(}\mathbf{A}_{\mathbf{q}}^{(h)}\Big{)}_{\mathbf{k}}=\left\{\begin{array} []{ll}1&\text{if }\mathbf{f}(h)=\mathbf{q}-\mathbf{k}\\ 0&\text{otherwise}\end{array}\right. \tag{8}\]  
where \(\mathbf{q},\mathbf{k}\) stand for query and key pixels. \(\mathbf{A}\) stands for the attention scores and the softmax outputs attention probabilities. Then, for any convlution layer (Conv) with \(D_{out}\) output dimension, there are \(\left\{\mathbf{W}_{\text{val}}^{(h)}\right\}_{h\in[N_{h}]}\) such that \(\text{MSA}(\mathbf{X})=\text{Conv}(\mathbf{X})\) for every \(\mathbf{X}\in\mathbb{R}^{W\times H\times D_{\text{in}}}\). For one output pixel of MSA:  
Figure 4: Swin Transformer blocks with the spatial fusion (SF) module.  
\[\text{MSA}(\mathbf{X})_{\mathbf{q}:}=\sum_{h\in[N_{h}]}\left(\sum_{\mathbf{k}}\text{softmax} \Big{(}\mathbf{A}_{\mathbf{q}:}^{(h)}\Big{)}_{\mathbf{k}}\mathbf{X}_{\mathbf{k}}\right)\mathbf{W}^{(h)}+ \mathbf{b}_{\text{out}} \tag{9}\]  
where \(\mathbf{W}\) is the weight tensor and \(\mathbf{b}\) is the bias vector. For the \(h\)-th head, the probability of attention is \(1\) when \(\mathbf{k}=\mathbf{q}-\mathbf{f}(h)\) and \(0\) otherwise. The output is then equal to:  
\[\text{MSA}(\mathbf{X})_{\mathbf{q}}=\sum_{h\in[N_{h}]}\mathbf{X}_{\mathbf{q}-\mathbf{f}(h):}\mathbf{W} ^{(h)}+\mathbf{b}_{\text{out}} \tag{10}\]  
When \(K=\sqrt{N_{h}}\), the above is equivalent to a convolution layer. Therefore, the bottleneck using Swin Transformer blocks performs the same function as a convolutional bottleneck.
## 5 Simulative Assessment
### Simulation Results
#### 5.2.2 Impact of the Maximum Number of Backoffs  
In this experiment a LoRaWAN network with 200 EDs is assessed. The duration of a backoff unit (\(U_{backoff}\)) is heuristically set to 5 ms. To evaluate the impact of the maximum number of backoffs, the value of \(MAC_{nBackoff}^{max}\) varies from 5 to 15, in steps of 2.  
Table 8 shows the average values of MLossR, MDropR and MDelivR.  
The results show that also in this case the MLossR value remains around 2%, without significant variations, thus confirming that in the evaluated scenarios the value mainly depends on the wireless channel properties. Conversely, the MDropR decreases when the value of the \(MAC_{nBackoff}^{max}\) parameter grows. In fact, an increase of \(MAC_{nBackoff}^{max}\) corresponds to a higher probability that a message will be transmitted before reaching the maximum number of backoffs, as a higher number of transmission attempts can be run for each message. For the same reason, MDelivR grows with the \(MAC_{nBackoff}^{max}\) parameter, which also entails a higher energy consumption due to the larger number of CCAs performed. Also in these scenarios, all the messages generated are transmitted.  
Table 9 shows the average and the maximum e2 eDs.  
The results in Table 9 show that when the maximum number of allowed backoffs grows, both the average and maximum e2 eDs increase. This results is because some of the messages experience a high number of backoffs before being transmitted. For this reason, the maximum number of backoffs shall be set depending on the application requirements in terms of both average MDelivR and e2 eDs.
## 5 Results and Discussion
### Performance Evaluation Metric  
The proportion of samples that are successfully identified to all items is known as classification accuracy (CAc). When the classes are uneven, accuracy is less helpful. When the classes are balanced, it offers helpful information [36]. The following formula is used to calculate Classification Accuracy:  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)}\times 100\;\% \tag{6}\]  
Figure 10: A sample of the visualized version of malware binary file segments and their associated parts.  
Figure 9: Dispersion of malware across classes inside the Malimg dataset.  
Both existing and expected malware kinds might be viewed as positive in order to calculate the _TP_ (true positive). Both existing and expected malware kinds might be viewed as negative in order to calculate the _TN_ (true negative). In order to compute the _FP_ (false positive), the present malware kinds might be seen as negative, and the expected malware kinds might be seen as positive. In order to determine the _FN_ (false negative), the existing malware kinds may be seen as positive and the expected malware kinds as negative. Figure 11 illustrates an estimation of the _TP_, _TN_, _FP_, and _FN_ parameters. In contrast to the conventional confusion matrix of binary classification tasks, these parameters represent the confusion matrix of multi-classification tasks.