esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Modal Entity Alignment Method Based on Feature Enhancement
## 3 Experiment and Discussion
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Set Ratio**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**FB15K-DB15K**} & \multicolumn{3}{c}{**FB15K-YAGO15K**} \\ \cline{3-8}  & & **_H@1_** & **_H@10_** & **_MRR_** & **_H@1_** & **_H@10_** & **_MRR_** \\ \hline \multirow{8}{*}{20\%} & PoE [5] & 0.126 & 0.251 & 0.170 & 0.113 & 0.229 & 0.154 \\  & HMEA [9] & 0.127 & 0.369 & - & 0.105 & 0.313 & - \\  & MMEA [7] & 0.265 & 0.541 & 0.357 & 0.234 & 0.480 & 0.317 \\  & EVA [8] & 0.134 & 0.338 & 0.201 & 0.098 & 0.276 & 0.158 \\  & MCLEA [11] & 0.445 & 0.705 & 0.534 & 0.388 & 0.641 & 0.474 \\  & MEAFE (Ours) & **0.617** & **0.817** & **0.686** & **0.567** & **0.745** & **0.628** \\ \cline{2-8}  & Improv. best & 0.172 & 0.112 & 0.152 & 0.179 & 0.104 & 0.154 \\ \hline \multirow{8}{*}{50\%} & PoE & 0.464 & 0.658 & 0.533 & 0.347 & 0.536 & 0.414 \\  & HMEA & 0.262 & 0.581 & - & 0.265 & 0.581 & - \\  & MMEA & 0.417 & 0.703 & 0.512 & 0.403 & 0.645 & 0.486 \\  & EVA & 0.223 & 0.471 & 0.307 & 0.240 & 0.477 & 0.321 \\  & MCLEA & 0.573 & 0.800 & 0.652 & 0.543 & 0.759 & 0.616 \\  & MEAFE (Ours) & **0.712** & **0.880** & **0.771** & **0.678** & **0.837** & **0.734** \\ \cline{2-8}  & Improv. best & 0.139 & 0.080 & 0.119 & 0.135 & 0.078 & 0.118 \\ \hline \multirow{8}{*}{80\%} & PoE & 0.666 & 0.820 & 0.721 & 0.573 & 0.746 & 0.635 \\  & HMEA & 0.417 & 0.786 & - & 0.433 & 0.801 & - \\  & MMEA & 0.590 & 0.869 & 0.685 & 0.598 & 0.839 & 0.682 \\ \cline{1-1}  & EVA & 0.370 & 0.585 & 0.444 & 0.394 & 0.613 & 0.471 \\ \cline{1-1}  & MCLEA & 0.730 & 0.883 & 0.784 & 0.653 & 0.835 & 0.715 \\ \cline{1-1}  & MEAFE (Ours) & **0.804** & **0.934** & **0.851** & **0.740** & **0.884** & **0.791** \\ \cline{1-1} \cline{2-8}  & Improv. best & 0.074 & 0.051 & 0.067 & 0.087 & 0.049 & 0.076 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative results of MEAFE against other baseline methods on two cross kg datasets. The best results are marked in bold and the second best results are underlined. effect increased. The experimental results demonstrate the effectiveness of extracting textual information that may be included in the visual modal information and adding additional embedding of entity modal distribution for multi-modal entity alignment. We analyzed the fusion weights of various model information during multi-modal fusion, and the results are shown in Figure 4. On two kg datasets, MEAFE tends to give higher weights to image embedding and structural embedding, which is consistent with our preliminary assumption. Our newly added OCR embedding has similar attention weights to attribute, relation, and name embedding, proving its effectiveness. The attention weight of the modal distribution embedding is lower, possibly due to its lower original embedding dimension.
## 5 Tightness of Instance-Based Lower Bounding Technique
### Center-Conflict  
**Definition 2** (center-conflict).: _For two conflicting triples \((x,c,v)\) and \((x^{\prime},c^{\prime},v^{\prime})\) we say that they are_  
* _forward center-conflicting if_ \(x=x^{\prime}\) _and_ \(\pi(c,v)\cap\pi(c^{\prime},v^{\prime})=\{c\}=\{c^{\prime}\}\)__
* _backward center-conflicting if_ \(v=v^{\prime}\) _and_ \(\pi(x,c)\cap\pi(x^{\prime},c^{\prime})=\{c\}=\{c^{\prime}\}\)__  
Intuitively, we say that triples are _center-conflicting_ if the only reason for their conflict is the common center node.  
**Definition 3** (only-center-conflict).: _We call a set \(|W|\) only-center-conflicting if each triple has up to one forward and up to one backward center-conflict and is otherwise conflict-free._  
**Lemma 4**.: _A only-center-conflicting set \(|W|\) still lower bounds the sum of search spaces of all nodes in the network._  
Proof.: The argumentation builds up on Lemma 3. The difference is that if a node \(c\) is the highest node in both of two center-conflicting triples \((x,c,v)\) and \((x,c,v^{\prime})\), \(c\) seems to be doubly accounted in the forward search space of \(x\). However, we can let account w.l.o.g. the triple \((x,c,v)\) for the node \(c\) in the forward search space of \(x\) and at the same time we do not have to let \((x,c,v^{\prime})\) go away empty handed: We let \((x,c,v^{\prime})\) account for the node \(c\) in the backward search space of \(v^{\prime}\). Now, it could happen that in the backward search space of \(v^{\prime}\) there is a center-conflict of \((x,c,v^{\prime})\) with another triple \((x^{\prime},c,v^{\prime})\). Then, we let \((x^{\prime},c,v^{\prime})\) account for the node \(c\) in the forward search space of \(x\) and so on. Either this process terminates because there are no center-conflicts anymore or we come back to \((x,c,v)\) from where we started, but then we are done because \((x,c,v)\) already contributes to our search space. Therefore, each center-conflict can be resolved such that no double counting occurs.  
As a side note, we experimented with other triple definitions where, e.g., \(c\in E\) which can deliver as big sets \(W\) but we found the presented only-center-conflicting definition the easiest for the following analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Side Length** & **1** & **5** & **10** & **15** & **20** & **25** \\ \hline \(LB\) & 2.00 & 8.80 & 14.68 & 19.0978 & 23.82 & 26.83 \\ \(|DSS|(avg.)\) & 2.00 & 13.00 & 27.13 & 41.6178 & 56.85 & 69.59 \\ \(\frac{LB}{|DSS|}\) & 1.000 & 0.676 & 0.541 & 0.458 & 0.419 & 0.385 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results on grid with almost uniform weights.
## 3 Multiple Object Detection with YOLOv5
### Multiple Object Detection Pipeline  
To implement multi-object detection, YOLOv5 is utilized. YOLOv5 can perform real-time multi-object detection and inference simultaneously and is available as open-source software with various versions, depending on the network size. Figure 3 shows the structure of the YOLOv5 model for multi-object detection.  
The input image passes through the backbone, neck, and head, ultimately returning the final prediction. The features of the input image are extracted in the backbone field. The multiple convolution and pooling layers alter the image resolution, allowing features to be extracted at various resolutions. The extracted features then pass through the neck field, where they are fused.  
At this stage, a path aggregation network [35] is used to fuse the features of low and high resolutions to improve performance. The fused features are then moved to the head field, where convolution layers transform them into the final output. The final output includes the bounding box parameters (x, y, w, and h), confidence score indicating the probability of the object's presence, and class probability, thereby completing the final recognition.  
Figure 2: Structure of the proposed system.  
In this study, YOLOv5 was fine tuned using training data that allowed visually impaired individuals to recognize the appearance and status of obstacles they needed to avoid or be aware of when crossing roads in urban areas.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 5 Analysis
### Flying Height and Overlay  
#### 5.1.1 Flying Height  
During the bundle adjustment process, the external orientation of the UAV images is calculated using the ground control points, and the AGL altitude data are calculated by combining the elements of the exterior orientation of each image with the DSM. The altitude of each image presented in the grid-type is shown in Figure 11. The average altitude is 217 m, the highest altitude is 290 m, and the lowest altitude is 155 m, as shown in Figure 11a.  
Figure 11: _Cont._  
Figure 10: The distribution of control points and check points.  
Using the same method of calculating the flying height as above, the results of the contour-type regularization are shown in Figure 11b, from which the average flying height is 244 m, the highest peak is 286 m, and the lowest height is 200 m.  
We analyzed the height parameter; in this study, we set a height of 250 m. The results in Figure 11 show an average value of 244 m with a standard deviation of 22 m. In comparison, the grid-type method yielded an average value of 217 m with a standard deviation of 36 m. These findings suggest that the contour-type approach aligns more closely with the mission planning value, exhibiting a minor standard deviation compared to the grid-type method.
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
###### Abstract  
The digitization of industries enables a rapid transformation from mass production to individualized manufacturing. Communication plays an essential role in this digital transformation; in particular, wireless communication enables a high degree of flexibility, dynamic interactions, and mobility support in production systems. This paper presents an implementation of a 5G system with Time-Sensitive Networking (TSN) and analyzes a typical industrial use case involving cloud-controlled mobile robots. A prototype setup integrating 5G in a TSN network has been completed to evaluate the 5G-TSN performance for industrial applications. The integrated 5G and TSN prototype has been evaluated with over the air tests in an industrial shopfloor using TSN features of traffic shaping and scheduling.  
5G; URLLC; Time-Sensitive Networking; mobile robotics; time synchronization; factory cloud; IEEE 802.1 Qbv scheduled traffic; edge-control Article  
Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics.
## 3 Results and Discussion
### Scanning Electron Microscope (SEM) Analysis  
SEM imaging allows for determining the structure information of the obtained biopolymer. The results of the analysis of the fibre parameters from SEM imaging are presented in Figure 5. As can be seen, every tested BC sample reveals its characteristic net-like 3D structure with high density and porosity between the fibrils simultaneously (Figure 5a-k).  
The microfibres' arrangement was essentially random. The diameters of the fibrils were similar, mainly in the range of about 39.13 nm (\(\pm\)9.28 nm) (Figure 5e,f) from a medium with sucrose and \(\beta\)-D-Fru, 41.30 nm (\(\pm\)10.28 nm) (Figure 5h,i) in a medium with lactose and \(\beta\)-Gal, and 40.19 nm (\(\pm\)10.82 nm) (Figure 5k,l) obtained from starch and GLA medium and about 40.46 nm (\(\pm\)9.80 nm) (Figure 5b,c) from standard HS (the mean values do not differ significantly between the different sample trials (\(p<0.005\)). These values are in line with the literature [40]. As a result of the analysis, it can be concluded that, regardless of the culture medium used, the structure of BC was not affected. Moreover, the diameters or porosity of the membranes were similar to control samples, which was also confirmed by the visual assessment of the samples.  
Figure 3: BC synthesis yield and efficiency for samples growing on medium containing sucrose (**a**,**b**), lactose (**c**,**d**), and starch (**e**,**f**), supplemented accordingly with \(\beta\)-D-Fru, \(\beta\)-Gal, and GLA in proper units’ concentration. Bars marked as HS refers to parameters of _K. xylinus_ cultures maintained on standard Hestrin-Schram medium with glucose as the sole carbon source. The means with common superscript are not significantly different from each other (\(p<0.05\)). Error bars represent standard deviation.  
Figure 4: The representative ATR-FTIR spectra (**a**) and XRD diffractograms (**b**) of bacterial cellulose obtained from cultures with enzymatic conversion of sucrose, lactose, and starch to accessible carbon sources. The spectra from analysis BC samples obtained from modified media with the highest enzyme units amounts.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 3 Numerical Simulation Analysis and Verification
### Analysis Results and Case Validation
Figures 5-7 show the verification comparison between the monitoring data from inclinometer SID1 on the north side of the construction site and the analyses using three different models. In Appendix A, Figures A1-A3 shows a similar comparison for inclinometer SID3 on the west side of the construction site. Upon verification, it was observed that the MC model resulted in the smallest wall deformation, the HSS model showed moderate deformation, and the HS model indicated the largest deformation. All three models were feasible for analysis, as their trends were consistent. The author suggests that due to the occasional lack of complete triaxial test data in pre-construction geotechnical reports, using simplified parameters and the MC model for soil parameters input and simulation is a practical approach for confirming the safety of the project. However, to ensure construction safety, it is recommended to conduct soil mechanics tests after drilling to establish a comprehensive database of parameters required for the HS and HSS models. This would support more accurate simulation analyses, ensuring the safety of the construction. After verifying the soil parameters used in this study, the author proceeded with subsequent research using all three constitutive soil models. Figure 8 shows the settlement at monitoring point SM14 on the east side of the construction site. Among the three models, the numerical analysis using the MC model was closest to the field observation data. In summary, this study adopted a structural loading model and utilized the MC, HS, and HSS models. The numerical analysis results generally matched the field observation data. Therefore, in the next chapter, this research method will be used to establish a model of the neighboring buildings. The study will investigate the impact of the depth of basements, the height and load of the floors, and the proximity to the diaphragm wall on the deformation of the diaphragm wall and ground settlement. Figure 8: Comparison of monitoring surface settlement results with the MC, HS, and HSS models.
# Safety Issues Referred to Induced Sheath Voltages in High-Voltage Power Cables--Case Study
## 2 Description of the Analyzed 110 kV Cable Line
Impedance \(Z\) and admittance \(Y\) of cables are defined in two matrix equations presented in [33] and implemented in the software as well as described in its guide [34]:  
\[\frac{\partial}{\partial x}\big{|}\underline{\bigvee}\big{|}=-\big{|} \underline{Z}\big{|}\cdot\big{|}\underline{\bigvee}\big{|} \tag{4}\]  
\[\frac{\partial}{\partial x}\big{|}\underline{\bigvee}=-\big{|}\underline{ \bigvee}\big{|}\cdot\big{|}\underline{\bigvee}\big{|} \tag{5}\]  
where: \(|\underline{\bigvee}|\) and \(|\underline{\bigvee}|\) are the voltage and current vectors respectively, at distance \(x\) along the cable line; \(|\underline{\bigvee}|\) and \(|\underline{\bigvee}|\) are square matrices of the impedance and admittance respectively. The complexity of \(|\underline{Z}|\) and \(|\underline{\bigvee}|\) matrices depends on the number of cables and the number of layers per single cable. The impedance and admittance matrices can be described in the following way:  
\[[\underline{Z}]=\big{[}\underline{Z_{i}}\big{]}+\big{[}\underline{Z_{p}} \big{]}+\big{[}\underline{Z_{c}}\big{]}+\big{[}\underline{Z_{0}}\big{]} \tag{6}\]  
\[[\underline{Y}]=s\cdot[C]^{-1} \tag{7}\]  
\[[C]=[C_{i}]+\big{[}C_{p}\big{]}+[C_{c}]+[C_{0}] \tag{8}\]  
where: the matrices with subscript \(i\) include cable parameters (core, sheath and armour); the matrices with subscript \(0\) include parameters of the cable outer media (air, earth); the matrices with subscripts \(p\) and \(c\) include parameters of a pipe (an enclosure--if applied); \(C\) is a potential coefficient matrix; \(s=j\omega\). Detailed information on the calculation of individual components of the impedance and admittance matrices can be found in [33; 34]. Figure 3: A simplified view of the power cable used in the analyzed case. Figure 4: Real cable route of the analyzed cable system reflected in the PowerFactory software (**a**); an example part of the computer model of the cable system (minor section between point p.0 and point p.1) (**b**). In the considered case, the cable line model consists of two electromagnetically coupled circuits: cores electric circuit (c) and sheaths electric circuit (sh) (Figure 4b). The results of the multi-variant computer analysis included in the next sections show the levels of voltages induced in the cables' sheaths and risks referred to them, as well as the power losses influencing the current-carrying capacity of the cables.
# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
## 4 Results  
### Validation of AOT and WVP Maps  
Figure 3a,b present, respectively, validation of AOT and WVP estimates using coincident to reference estimates from AERONET stations. The overall root mean square error (bias) for AOT was 0.06 (0.05) for S2C-AOT and 0.09 (0.07) for ESA-AOT in comparison to AERONET. Site-specific RMSE and bias were higher for Barrax versus La Crau and Harth. S2C-AOT has consistently lower RMSE and bias than ESA-AOT for all sites although, except at Barrax, the difference between products was typically within the uncertainty of AERONET AOT.  
For WVP, Figure 3b indicates that, over all sites, the RMSE (bias) was 0.20 cm (\(-\)0.02 cm) for ESA-WVP and 0.22 cm (\(-\)0.10 cm) for S2C-WVP in comparison to AERONET. RMSE was lowest over  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Sentinel-2 Bands**} & **Central Wavelength (\(\mu\)m)** & **Resolution (m)** \\ \hline Band 2—Blue & 0.490 & 10 \\ Band 3—Green & 0.560 & 10 \\ Band 4—Red & 0.665 & 10 \\ Band 5—Vegetation Red Edge (VRE-1) & 0.705 & 20 \\ Band 6—Vegetation Red Edge (VRE-2) & 0.740 & 20 \\ Band 7—Vegetation Red Edge (VRE-3) & 0.783 & 20 \\ Band 8A—NIR & 0.865 & 20 \\ Band 11—SWIR (SWIR-1) & 1.610 & 20 \\ Band 12—SWIR (SWIR-2) & 2.190 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Sentinel-2 spectral bands evaluated in this study.  
Figure 3: AOT (**a**) and WVP (**b**) from ESA-L2A and S2C-L2A products derived from the same input ESA-L1C data compared to measurement from coincident AERONET stations.  
Earth (0.18 cm for ESA-WVP and 0.12 cm S2C-WVP) and slightly higher at La Crau (0.21 cm for ESA-WVP and 0.27 cm for S2C-WVP) and Barrax (0.20 cm for ESA-WVP and 0.27 cm for S2C-WVP).
## 6 Performance Analysis
### Comparing Algorithms  
Similar to the metrics, the comparative algorithms are as follows:  
* **Feature-based grouping:** Groups devices based on specific features such as device type, data requirements, or application.
* **Location-based grouping:** Groups devices based on their geographical proximity to leverage the spatial correlation of channel conditions. It provides the foundation for group-based services [22; 59].
* **Best fit decreasing (BFD):** A heuristic algorithm that groups devices based on their task load and the application's time constraint to minimize the number of failed RACH attempts. This algorithm also aims at minimizing the number of groups created [24].
* **MILP grouping:** Formulates the device grouping problem as a mixed-integer linear programming problem, including a relaxed version of BFD for reduced computational complexity [55].
## 2 Lempel-Ziv Data Compression
### LZ2 Compression  
The LZ2 factorization of a string \(S\) is \(S=f_{1}f_{2}\cdots f_{i}\cdots f_{k}\) where \(f_{i}\) is the shortest substring which is different from one of the previous factors. As for LZ1 the encoding of each factor leaves one character uncompressed. To avoid this a different factorization was introduced (LZW factorization) where each factor \(f_{i}\) is the longest match with the concatenation of a previous factor and the next character [18]. \(f_{i}\) is encoded by a pointer \(q_{i}\) to such concatenation (LZW compression). LZ2 and LZW compression can be implemented in real time by storing the dictionary with a trie data structure. Differently from LZ1 and LZSS, the dictionary is only prefix.
## 3 Results and Discussion
### Vertical Line
#### 3.2.2 Effect of Frequency  
Based on the results obtained from the previous experiment, the effect of the laser frequency on the deposition was examined. The laser pattern used in the experiment was applied in the same shape as in Figure 4a, and A was fixed at 50 \(\upmu\)m and B at 12.5 \(\upmu\)m. The average laser power was fixed at 20 W, and the scan speed was 1 mm/s, and only the frequency was changed to 25-500 kHz. In addition, the CW mode was also tested.  
As shown in Figure 5, the stability of the deposited structure significantly increased as the laser frequency increased. If the laser's average power is fixed, the laser energy per pulse is changed in inverse proportion to the frequency. Therefore, in the laser ablation process, the energy per pulse is increased by lowering the frequency for vaporizing the  
Figure 4: Vertical scan result according to patterning space B: (**a**) schematic diagram; (**b**) 100 \(\upmu\)m; (**c**) 50 \(\upmu\)m; (**d**) 25 \(\upmu\)m; (**e**) 12.5 \(\upmu\)m; (**f**) 6 \(\upmu\)m.  
material with instantaneous high energy. On the other hand, heating the substrate, such as welding or chemical reaction, the high frequency or CW mode is generally used to maintain a stable temperature rise by continuously applying constant energy. In the LCLD process, it is very important to maintain a uniform temperature rise because chemical reduction is the main reaction. Therefore, in this study, stable deposition was performed when the frequency was over 250 kHz. The following experiments were carried out while fixing the laser mode as the CW, which is widely used in chemical reactions.