esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup and Scheme
### Experimental Scheme  
The particles tested were as follows: 304 stainless steel particles (2 mm diameter) (Defined as Fe in the legend), lead particles (2 mm diameter), stainless steel powder (about 0.048 mm diameter), tungsten carbide powder (about 0.5 mm diameter), as shown in Figure 5. The density of the chosen material: stainless steel (\(7.93\times 10^{3}\) kg/m\({}^{3}\)), lead (\(11.3\times 10^{3}\) kg/m\({}^{3}\)) and tungsten carbide (\(15.6\times 10^{3}\) kg/m\({}^{3}\)). In practical engineering applications, the mass ratio between the damper and the primary structure should be small, usually no more than 10%. All of the experiments were conducted with additional mass (sum of the adapter, the particle cavity, and particles mass) no more than 1 kg, which corresponds to a mass ratio of 8.8%.  
The loading conditions of the standard sine sweep were described as follows: the acceleration was maintained 1 g the scope of sweeping frequency was from 20 Hz to 1000 Hz, and the sweep rate was set as 2 oct/min. The random excitation loading conditions were shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Range** & **Power Spectral Density (g\({}^{2}\)/Hz)** \\ \hline
10–100 & 3 dB/oct \\
100–200 & 0.08 \\
600–2000 & \(-9\) dB/oct \\ RMS acceleration & 8.1 g \\ Loading time & 1 min \\ Loading direction & Z \\ \hline \hline \end{tabular}
\end{table}
Table 2: Random excitation loading conditions.  
Figure 5: Particle type (**a**) 304 Stainless steel particles (2 mm diameter); (**b**) Lead particles (2 mm diameter); (**c**) Stainless steel powder (about 0.048 mm diameter); (**d**) Tungsten carbide powder (about 0.5 mm diameter).  
The primary structure with the adapter was as a reference scheme (Figure 6), numbered Sup_1. Figure 7 showed the response of the main structure of Sup_1. Consistent with the modal analysis results in Section 2.1, the maximum response direction of the primary structure for the fundamental mode shape is Y direction, and the value is about \(68\,g\).
## 6 Conclusions  
As shown in Table 2, the results of the speed test of a smart wheelchair with a weight of 50 kg running at a distance of 10 m show that it can reach full speed in just 86.20 s, and if a weight of 90 kilograms travels a distance of 10 m, this takes 45.90 s longer. Table 3 shows the results of the control of the tilt angle of the smart wheelchair with a weight of 50 kg running at a distance of 10 m, it can move as fast as 127.60 s, and if a weight of 90 kg travels a distance of 10 m, this can take an extra 62.90 s. Figure 9 shows the performance of the smart wheelchair in mobility tests with a load of up to support no more than 100 kg with a distance of up to 3250 m with one battery charge. In addition, the results of the three GPS tracking displacement tests with varying longitude and latitude values, the mobility of the electric wheelchair in position 1 had an accuracy of 97.66 percent of the assigned route, position 2 had an accuracy of 98.58 percent of the designated route, and position 3 had an accuracy of 96.45 percent of the given route (see Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GPS Tracking** & **Item** & **Longitude** & **Latitude** \\ \hline \multirow{7}{*}{1} & & 100.61239 & 14.13312 \\  & & assign & 100.61165 & 14.13331 \\  & & 100.611135 & 14.13311 \\ \cline{2-4}  & & 100.613284 & 14.13312 \\  & & round1 & 100.611705 & 14.13311 \\  & & 100.611128 & 14.13311 \\ \cline{2-4}  & & round2 & 100.612386 & 14.13312 \\  & & round2 & 100.611709 & 14.13311 \\  & & 100.61170 & 14.13311 \\ \hline \multirow{7}{*}{2} & & 100.612343 & 14.13318 \\  & & assign & 100.611196 & 14.13322 \\  & & 100.611165 & 14.13469 \\ \cline{2-4}  & & 100.612316 & 14.13322 \\  & & round1 & 100.611120 & 14.13307 \\  & & 100.611158 & 14.13384 \\ \cline{2-4}  & & 100.612309 & 14.133126 \\  & & round2 & 100.611180 & 14.133159 \\  & & 100.611150 & 14.133155 \\ \hline \multirow{7}{*}{3} & & 100.611378 & 14.13434 \\  & & 100.612405 & 14.13432 \\  & & 100.612384 & 14.13312 \\ \cline{2-4}  & & 100.613728 & 14.13432 \\ \cline{2-4}  & & round1 & 100.612397 & 14.13367 \\  & & 100.61323 & 14.13314 \\ \cline{2-4}  & & 100.613808 & 14.133426 \\ \cline{2-4}  & round2 & 100.612414 & 14.133164 \\  & & 100.612380 & 14.132457 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Longitude and latitude (GPS tracking 1 to 3).  
**Author Contributions:** Implementation, B.M. and K.W.: data collection, B.M.: data analysis, B.M. and K.W.: funding acquisition, B.M.: project development, B.M. and K.W.: system design, B.M. and K.W.: supervision, B.M.: writing--original draft preparation, B.M. and K.W.: and writing--review and editing, B.M. and K.W. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by Mahasarakham University, Thailand.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors also thank [PERSON] for his English language consulting time and for proofreading the whole paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Magnetic Analysis  
Dry lichen samples were placed in standard 8 cm\({}^{3}\) palaeomagnetic plastic cubes for the analysis of magnetic susceptibility and in pharmaceutical gel caps #4 for the hysteresis measurements. Mass magnetic susceptibility (k) was calculated dividing the values measured with a KLY5 Agico meter for the net weight of the samples. The coercive force (Bc), the saturation remanent magnetization by mass (Mrs) and the saturation magnetization by mass (Ms) were measured using a vibrating sample magnetometer (Micromag 3900, PMC) equipped with a carbon fibre probe, under cycling in a maximum field of 1.0 T. Concentration dependent hysteresis parameters were calculated subtracting the high field paramagnetic linear trend before dividing the magnetic moments for the net weight of the samples. The coercivity of remanence (Bcr) values were extrapolated from backfield remagnetization curves up to \(-1\) T, following forward isothermal remanent magnetization up to a \(+1\) T field.  
The percentage decay of Mrs after 100 s was calculated as:  
\[\text{Mrs (SP)}\%=100\times(\text{Mrs}_{0}-\text{MRs}_{100})/\text{Mrs}_{0}, \tag{1}\]  
where SP refers to the superparamagnetic fraction, \(\text{Mrs}_{0}\) is the remanent magnetization measured as soon as the magnetic field is reduced to noise levels after the application of a 1 T field, and \(\text{Mrs}_{100}\) is the remanence measured 100 s later. The values of Mrs (SP)% are indicatory of the fraction of remanent magnetization due to viscous magnetic components, which are usually carried by ultrafine magnetic particles, dimensionally in the superparamagnetic/stable single domain boundary, which is around 20-35 nm for magnetite [16; 17].  
The domain state and magnetic grain-size of the samples were compared to theoretical magnetite according to the hysteresis ratios Mrs/Ms vs. Bcr/Bc in the \"Day plot\" [18; 19; 20].  
First order reversal curves (FORCs) [21; 22] were measured using the Micromag operating software; FORC diagrams were processed, smoothed and drawn with the FORCINEL Igor Pro routine [23]. FORCs were measured in steps of 2.5 mT, with 300 ms averaging time and maximum applied field being 1.0 T. The optimum smoothing factor was evaluated by FORCINEL software.
## 3 Application Community
### Application Community Detection Method
#### 3.2.2 Node Clustering Based on Traffic Behavior
If there is an \(N\) pair of IP pairs (i.e., instances of clustering) in a partition, the scaling formula is  
\[f_{ik}=\frac{F_{ik}-\min(F_{k})}{\max(F_{k})-\min(F_{k})} \tag{4}\]  
where \(i\) represents the instance number to be clustered and \(F_{ik}\) the \(i\)-th eigenvalue of the \(k\) instance. \(k=1,2\ldots,15\), and \(F_{k}\) represents a sequence of all the eigenvalues of the \(k\)-th dimension of all instances to be clustered. Values of \(\max()\) and \(\min()\) represent the maximum and small values of \(F_{k}\), respectively. Variable \(f_{ik}\) is the normalized eigenvalue. Finally, in this paper we use the dbscan-clustering algorithm [27] to process all session traffic in each subgraph. This algorithm is a density-based clustering algorithm that does not require the number of clusters to be specified. Without any prior knowledge, the number of types of traffic flows in a partition and the number of flows in each category are uncertain. Therefore, it is suitable to use the dbscan algorithm. When the dbscan algorithm runs clustering, there are three important parameters to specify:  
1. eps  
This parameter is the maximum distance that is allowed to constitute a cluster between instances. In this paper, we set the parameter value to 1.0. If the distance between two instances is less than the value, they are considered to be within the same cluster. If an instance is more than the distance of epsfrom all other instances, it is considered an outlier and to be labeled as \(-1\). To simplify the end result, we consider that all outliers belong to the same community. The parameter min_samples denotes the minimum number of instances in a cluster. In this paper, the parameter value is set to 2. This means, there are at least two instances in a cluster. This parameter, together with eps, determines how densely the instances are in the same cluster. For different data sets, the values of these two parameters should also be adjusted accordingly. 3. metric  
This parameter is the way the distance between instances is calculated. In this paper, we use the Manhattan distance to indicate the similarity between two instances. For instances \(i\) and \(j\), the calculation formula of the Manhattan distance between them is  
\[\text{Manhattan distance}(i,j)=\sum_{k=1}^{15}\left|f_{ik}-f_{jk}\right| \tag{5}\]  
The dbscan algorithm takes the normalized eigenvector matrix as input. Each row of the matrix represents a traffic flow between a pair of IPs and each column represents a feature dimension. The algorithm outputs an array of labels, each element of the array corresponding to each row of the matrix. The tag is used to indicate the class of the traffic flow represented by the corresponding vector. All IPs involved in the same traffic flow are the results of the final network community identification.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 3 Methodology
### Loss Function  
Given that the proposed FreANet operates within the embedding spaces of both the spatial and frequency domains, we adopt the main spatial \(\mathcal{L}_{\text{Spatial}}\) loss and an auxiliary spectral \(\mathcal{L}_{\text{Frequency}}\) loss:  
\[\mathcal{L}_{\text{Spatial}}=\|\hat{I}-Y\|_{1},\quad\mathcal{L}_{\text{ Frequency}}=\|\mathcal{F}(\hat{I})-\mathcal{F}(Y)\|_{1}, \tag{12}\]  
where \(Y\) is the corresponding clear image. Additionally, we employ contrastive regularization \(\mathcal{L}_{\text{CR}}\)[20] to further improve performance:  
\[\mathcal{L}_{\text{CR}}=\sum_{i=0}^{n}w_{i}\frac{\|\Phi_{i}(\hat{I})-\Phi_{i}( Y)\|_{1}}{\|\Phi_{i}(\hat{I})-\Phi_{i}(\,I\,)\|_{1}}, \tag{13}\]  
where \(\Phi_{i}\) refers to the hidden features extracted from the \(i\)-th layer of the pre-trained network. \(w_{i}\) is the weight coefficient. Then, the overall loss is expressed as a combination of the above-mentioned losses:  
\[\mathcal{L}=\mathcal{L}_{\text{Spatial}}+\lambda_{1}\mathcal{L}_{\text{ Frequency}}+\lambda_{2}\mathcal{L}_{\text{CR}}. \tag{14}\]  
In this work, \(\lambda_{1}\) and \(\lambda_{2}\) are set to 0.1 and 0.05, respectively.
# Application of the Clark-Wright Method to Improve the Sustainability of the Logistic Chain
## 5 Results
### Combined Routes  
The last category of proposed routes combines both part of the city of Zilina and part of the surrounding area. These routes result from calculations in cases where it was more advantageous to supplement the unused collection capacity from the surrounding  
\begin{table}
\begin{tabular}{c c c c} \hline
**Route** & **Length (km)** & **Required Capacity** & **Number of Served Points** \\ \hline ZA1 & 12.6 & 120 & 18 \\ ZA2 & 19.0 & 120 & 27 \\ ZA3 & 14.6 & 120 & 30 \\ ZA4 & 11.1 & 120 & 20 \\ ZA5 & 13.2 & 120 & 32 \\ ZA6 & 17.7 & 120 & 23 \\ ZA7 & 19.4 & 120 & 21 \\ ZA8 & 11.1 & 119 & 16 \\ ZA9 & 12.3 & 117 & 26 \\ ZA10 & 3.0 & 107 & 16 \\ ZA11 & 9.3 & 77 & 16 \\ Total & 143.7 & 1260 & 245 \\ \hline \end{tabular}
\end{table}
Table 10: Overview of new routes in the city of Zilina.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Route** & **Length (km)** & **Required Capacity** & **Number of Served Points** \\ \hline O1 & 140.0 & 118 & 7 \\ O2 & 103.3 & 168 & 12 \\ O3 & 108.6 & 231 & 12 \\ O4 & 108.9 & 215 & 15 \\ O5 & 98.9 & 231 & 15 \\ O6 & 99.7 & 226 & 13 \\ O7 & 97.6 & 238 & 13 \\ O8 & 97.7 & 235 & 18 \\ O9 & 82.9 & 224 & 15 \\ O10 & 87.7 & 239 & 14 \\ O11 & 84.0 & 233 & 15 \\ O12 & 84.7 & 228 & 14 \\ O13 & 84.7 & 237 & 14 \\ O14 & 82.8 & 230 & 15 \\ O15 & 68.8 & 240 & 12 \\ O16, O17 & 72.7 & 240 & 12 \\ O18, O19, O20 & 62.8 & 240 & 12 \\ Total & 1764.0 & 4493 & 264 \\ \hline \end{tabular}
\end{table}
Table 11: Overview of new routes in the vicinity of the city of Zilina.  
municipalities with smaller volumes of waste from Zilina. A total of fourteen routes were designed in this way, which serve the remaining 58 collection points in the territory of the city of Zilina, and 82 points located in the surrounding villages. An overview of the basic quantitative indicators can again be seen in the following Table 12.  
Based on the compiled routes calculated using the Clark-Wright method, as the last step of the proposal, the circulations of individual vehicles and their crews were compiled within five working days of each week in such a way that all nine considered vehicles are used evenly. At first, the circulations were designed so that some vehicles were still assigned routes in Zilina and some in the surrounding area, but in this case, there were big differences in the total weekly traffic of vehicles, as shown in the following Table 13.  
With such a planned circulation of vehicles on individual routes, the maximum weekly run was 303.5 km (vehicle 8) and the minimum was 226.9 km (vehicle 1); the difference was therefore 76.6 km, which represents a significant improvement in this disparity compared to the first proposed circulation, where it starts, up to 496.1 km.
## 3 Techno-Economic Analysis at 2060
### Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Baseline 2030** & CC 2060 & CC + TEC(C) 2060 & CC + TEC(C) 2060 & TEC(C) 2060 & CC + TEC(C) 2060 \\ \hline Water depth (m) & \(-\)463 & \(-\)463 & \(-\)463 & \(-\)463 & \(-\)463 & \(-\)463 \\ Distance from shore (Km) & 40 & 40 & 40 & 40 & 40 & 40 \\ Distance from grid (Km) & 44.9 & 44.9 & 44.9 & 44.9 & 44.9 & 44.9 \\
**Mean wind speed at h height (m/s)** & **8.05** & 7.58 & 7.58 & 7.84 & **8.36** & **7.84** \\ Wind farm capacity (MW) & 1000 & 1000 & 1000 & 2000 & 2000 & 2000 \\ Number of turbines & 100 & 100 & 100 & 100 & 100 & 100 \\ Turbine rate (MW) & 10 & 10 & 10 & 20 & 20 & 20 \\ Hub height (m) & 119 & 119 & 119 & 168 & 168 & 168 \\ Terrestrial cable cost (M/Km) & 0.25 & 0.25 & 0.25 & 0.25 & 0.25 & 0.25 \\ Offshore cable cost (M/MW) & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Total grid-connection costs (M) & 41.23 & 41.23 & 41.23 & 41.23 & 41.23 & 41.23 \\ CAPEX (excluding cables costs) (M/MW) & 2.30 & 2.30 & 2.07 & 2.07 & 2.07 & 1.50 \\
**Total CAPEX (MM/MW)** & **2.34** & **2.34** & **2.11** & **2.09** & **2.09** & **1.52** \\
**OPEX (MM/MW)** & **0.125** & **0.125** & **0.119** & **0.119** & **0.119** & **0.063** \\
**WACC (s)** & **7.00** & **7.00** & **5.00** & **3.75** & **3.75** & **2.50** \\ Farm lifecycle (year) & 30 & 30 & 30 & 30 & 30 & 30 \\ Total production efficiency (\%) & 83.3 & 83.3 & 83.3 & 83.3 & 83.3 & 83.3 \\ Gross annual production (M/Wh) & 3,602,000 & 3,300,000 & 3,300,000 & 6,956,000 & 7,560,000 & 6,956,000 \\ Net annual production (M/Wh) & 2,999,423 & 2,747,945 & 2,47/945 & 5,792,334 & 6,295,291 & 5,792,334 \\ Specific productivity (M/Wh/MW) & 2999 & 2748 & 2748 & 2896 & 3148 & 2896 \\ Capacity Factor (\%) & 34.2 & 31.4 & 31.4 & 33.1 & 35.9 & 33.1 \\
**LCE (M/MWh)** & **104.6** & **114.1** & **93.2** & **81.5** & **75.0** & **46.6** \\
**LCE variation referring to baseline (s)** & / & **+9.2\%** & **-10.9\%** & **-22.1\%** & **-28.3\%** & **-55.5\%** \\ \hline \end{tabular}
\end{table} 
Table 7: Case study 2 (Sicily); net wind speed variation: \(-\)0.50 m/s. (Variable parameters in bold).
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 4 Case Study
### Experimental Verification  
#### 4.3.1 Modal Test  
To investigate the applicability of the proposed method in practical engineering, a simplified aluminum solar panel frame is designed at the dimensions shown in Figure 13. This only considers the frame components and ignores the influence of supporting structure and hinge. The connection between plate and plate is directly integrated.  
Figure 11: Comparison of FRFs data measured at Node 13 under 5% random white noise.  
Figure 12: Comparison of FRFs data measured at Node 26 under 5% random white noise.  
The piezoelectric accelerometers were adopted in this modal test, which with a mass of 20 g, charge sensitivity of 6.05 pC/ms\({}^{2}\), and frequency range from 0.5 Hz to 5 kHz. The CL-YD-303 hammer was also adopted, the reference sensitivity is 3.99 pC/N. CRAS V7.0, a vibration and dynamic signal acquisition and analysis system developed by Nanjing AnZheng Software Engineering Company, was used as the analytical instrument, and MaCras was used as the modal analysis software.  
The layout of test site is shown in Figure 13(a). The end of the solar panel frame is fixed with a support, and the two accelerometers are installed on Position 1 and Position 2, where the mass of Accelerometer 1 is \(\mathbf{m}_{1}=20\) g and the mass of Accelerometer 2 is \(\mathbf{m}_{2}=20\) g. Then make a single point excitation at Position 3 with the hammer to measure the FRFs within 0-30 Hz of the solar panel frame. The FRFs obtained after 5 excitations are fitted by MaCras.  
The FE model of the solar panel frame was modeled by the FE analysis software 'Nastran' as shown in Figure 13(b), which consists of 86 shell elements and 148 nodes. The mass points were established at Node 1 and Node 2 to conduct the model updating and both the mass identification of the two accelerometers through mass perturbation of the mass points.
# Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows
## 3 Materials and Methods
### Restricted Auto-Encoding Structure for Shadow Estimation  
Let \(x\in[0,1]^{H\times W}\) be an input grayscale US image with a size of \(H\times W\). Its brightness is assumed to be normalized to \([0,1]\). We introduce an encoder DNN \(E\colon[0,1]^{H\times W}\to\mathbb{R}^{m}\) and a decoder DNN \(D\colon\mathbb{R}^{m}\to[0,1]^{H\times W\times 2}\), where \(m\) is the number of dimensions of a latent vector. Note that the decoder \(D\) outputs an image with two channels. An auto-encoding procedure that reconstructs the input \(x\) into \(\hat{x}\) is defined as  
\[\hat{x}=\delta\odot\hat{c}, \tag{1}\] \[\tilde{x}:=D(E(x)),\hat{s}:=\tilde{x}_{1},\hat{c}:=\tilde{x}_{2}, \tag{2}\]  
where \(\odot,\tilde{x}_{i},\hat{s}\) and \(\hat{c}\) are element-wise product, the \(i\)-th channel of \(\hat{x}\), the estimated shadow image, and the estimated clean image without shadows, respectively. For each element in the estimated shadow \(\hat{s}\), 1.0 means that no shadows expected in the pixel, and the lower the value, the intensity of the estimated shadow is higher. Figure 0(a) shows the proposed auto-encoding structure.  
The reconstruction \(\hat{x}\) is given as Equation (1) because we assume that the input image with shadows is generated as an element-wise product of an ideal shadow-free input image and an image of semi-transparent shadows. This is different from the actual generation process of US images but we model US images with shadows in this way for simplicity.
## 4 Methods
### Mapping Artificial Areas from OBI4
#### 4.1.3 Map Validation  
As metric accuracy of boundary locations was not required to satisfy the main objective of the study (_i.e._, building artificial patches and indicators from these patches) we did not check the planimetric accuracy of the boundaries, but focused on the semantic quality of the classification (_i.e._, the nature of the objects). Obviously semantic quality also depends on planimetric accuracy. Semantic quality is assessed using a confusion matrix [29]. Given that the features used for classification are calculated at the scale of the object, objects or polygons were chosen as sampling units for the selection of control data [30, 31, 32, 33]. As the resulting map contains objects of different sizes, from tiny to very large, the confusion matrix was computed using the area of the selected control objects (expressed as a number of 5 m pixels). To select control data (required for a sample that is spatially and thematically well-distributed over the study area [34]), we used an algorithm available in \"Hawths Analysis Tools for ArcGis\" ([[http://www.spatialecology.com](http://www.spatialecology.com)]([http://www.spatialecology.com](http://www.spatialecology.com))). Next, stratified random sampling was performed using grid cells as geographic strata (equal area for all strata). To obtain at least one object from each class in each grid cell, the number of cells (N) was made equal to the desired sample size. As suggested by [35] and used in other studies (e.g., [7]), 50 samples were taken from each class to build the confusion matrix. Considering the size of the study area (and mapping regions), collecting field data for the control sample would normally be extremely labor-intensive and time-consuming. As suggested by [22] and [36], selected control objects were photo-interpreted using the image with the highest spatial resolution as control data. To ensure photo interpretation was objective, the classified map was not viewed during the process and the manual photo interpretation was made by an experienced photo-interpreter who was not involved in developing the method. Three statistics were extracted from the confusion matrix to assess the accuracy of the land cover pressure map [35]: (i) overall accuracy. _i.e._, the proportion of map pixels that are correctly classified (_i.e._, equivalent to the proportion of real pixels that are correctly classified); (ii) user accuracy, _i.e._, for each class, what percentage of this map class corresponds to this class in reality; (iii) producer accuracy, _i.e._, for each class what percentage of the reality is correctly classified on the map.