esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Care-Gnn  
The key idea of CARE-GNN is to convert heterogeneous graph into homogeneous graph (i.e., aggregating features separately under each relation) [2]. At each layer, the input node embeddings (i.e., a batch of dataset) are fed through a MLP. Then, the label-aware similarity measure will be performed to calculate the \(l_{1}\)-distance between each central node in the minibatch and its neighbors:  
\[D^{(k)}(v,u)=\left\|\sigma(mlp^{(k)}(h_{v}^{(k-1)}))-\sigma(mlp^{(k)}(h_{u}^{( k-1)}))\right\|_{1} \tag{4}\]  
where \(D^{(k)}(v,u)\) denotes the \(l_{1}\)-distance between central node \(v\) and one of its neighbors \(u\) at \(k\)-th layer. The similarity between two nodes is defined as  
\[S^{(k)}(v,u)=1-D^{(k)}(v,u) \tag{5}\]  
where each layer has its own similarity measure module. A reinforcement learning (RL) module is designed to dynamically learn threshold, \(p_{r}^{(k)}\in[0,1]\), which is used by similarity-aware neighbor selector to perform top-p sampling of neighbors for feature aggregate (see in [2] for more details on the threshold and top-p sampling). The ability of similarity measure module, discriminating whether the categories of neighbors are the same as that of central node, is in a dynamic state during the training. If the algorithm choose same nodes in every epoch, it will be difficult to effectively adapt to the state of network. If the average distance between a central node and its neighbors is decreasing, it means that similarity between the central node and its neighbors is increasing and there may be more homogeneous nodes. In this case, more neighbors can be selected to extract richer information to help model convince better of the identity of the central node (i.e., we increase the corresponding \(p_{r}^{(k)}\)). Conversely, it means that there are too many different nodes that have been selected to perform aggregate. Moreover, the aggregate operation will cover up the true characteristics of the central node. Therefore, we need to decrease the corresponding \(p_{r}^{(k)}\). This process is done automatically by the RL module.  
As CARE-GNN applies multi-relation aggregate, the node embedding at each layer is composed of two steps: First, the intra-relation aggregate is performed under each relation:\[h_{v,r}^{(k)}=\sigma(AGG_{r}^{(k)}(\{h_{u}^{(k-1)},u\in N(v),(u_{i},v)\in E_{r}\})) \tag{6}\]  
where \(h_{v,r}^{(k)}\) denotes the embedding of node \(v\) after intra-relation aggregate under relation \(r\) and nonlinear transformation at \(k\)-th layer. We take mean aggregator as the intra-relation aggregator. When the first step is completed, we obtain the embeddings under all the relation \(\{h_{v,1}^{(k)},h_{v,2}^{(k)},\cdots,h_{v,3}^{(k)}\}\). Then, we use the threshold \(p_{r}^{(k)}\) as the weight of embedding under relation \(r\) to perform inter-relation aggregate:  
\[h_{v}^{(k)}=\sigma(h_{v}^{(k-1)}+AGG^{(k)}(\{p_{r}^{(k)}\cdot h_{v,r}^{(k)},r= 1,2,\cdots,R\})) \tag{7}\]  
where \(h_{v}^{(k-1)}\) denotes the embedding of node \(v\) at previous layer.
## 4 Distributed Strain Monitoring Using the BOCDA System
### Distributed Strain Monitoring of the Composite Bogie Using an Embedded Optical Fiber  
As shown in Figure 9, the side beams on both sides of the composite bogie frame were fixed using four supporting jigs; two vertical load actuators were placed above the composite side beams on both sides. The location and amount of the load applied to the composite side beams were the same as in the vertical train load tests. The optical fiber embedded in the composite bogie frame was connected to the BOCDA system to measure the strain distribution in the composite bogie frame during the vertical train load test.  
Figure 10 shows the strain distribution results obtained using the embedded optical fiber. The strain at the left and right composite side beams had positive values when a vertical train load was applied to the composite bogie frame; however, the crossbeam was not deformed because the crossbeam is a torsion-resisting structure in the composite bogie frame, and is unaffected by the vertical train load. The maximum strain occurred at the intersection of the side beam and crossbeam, and the distribution of strain changed sharply according to the shape of the composite bogie frame. The left and right composite side beams had the same strain distributions. This symmetry shows that they were well fabricated. The point at which the left composite side beam is attached to the crossbeam showed more drastic changes in strain than the same part on the right-hand side. The maximum strain in the left joint was 1.3 times higher than that in the right joint.  
Figure 9: Equipment for the vertical train load test of a composite bogie frame.  
The Brillouin gain spectrum in Figure 11 shows a side peak at this joint. This shows that the strain distribution within the short section (i.e., a spatial resolution of 3 cm) was not uniform, and that an abrupt strain change occurred on the optical fiber at this joint [15; 16]. This means that a fabrication mismatch occurred during the assembly of the composite bogie frame, and hence, structurally weak areas were present. Therefore, concentrated monitoring would be needed for this joint during actual operation. The maximum strain in the composite bogie frame during the 182-kN vertical train load test was an allowable value, and no residual deformation occurred after the test, confirming that the bogie frame was structurally stable.  
Figure 10: Strain distribution measured using an embedded optical fiber during a vertical train load test of a composite bogie frame with a: (**a**) 140 kN vertical load; and (**b**) 182 kN vertical load.  
The embedded optical fiber could also be used to measure changes in maximum strain and strain distribution in real time in subsequent tests; thus, allowing for the detection of internal cracks.
## 4 Diversity Performance
### Comparison Table with Existing Models  
Comparison Table 2 shows the advantages of the proposed UWB-MIMO antenna with other previous works. The proposed antennas have better performance in different parameters with the reported design [8, 11, 12, 13, 20, 23, 24, 25, 26, 27]. The proposed design has one advantage over the previous one and takes advantage of the mutual coupling of notching elements to create a third notch band. The proposed antenna achieved a three band notching characteristic with compact size due to only two notching elements. The other parameters such as ECC, DG, TARC, and CCL have better diversity performance compared to the reported work.
# Chemical Characterization and Volatile Profile of Trebbiano di Lugana Wine: A Case Study
## 3 Results
### Chemical Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **LS \#** & \multicolumn{4}{c}{**Users of Storage**} \\ \cline{3-10}  & & **(2005)** & **(2009)** & **(2011)** & **(2013)** & **(2015)** & **(2016)** & **(2017)** \\ \hline \multicolumn{1}{c}{L*} & ns & 94.14 \(\pm\) 2.82 \({}^{a}\) & 97.24 \(\pm\) 2.92 \({}^{b}\) & 97.54 \(\pm\) 2.93 \({}^{a}\) & 98.51 \(\pm\) 2.96 \({}^{a}\) & 99.26 \(\pm\) 2.97 \({}^{a}\) & 99.16 \(\pm\) 0.97 \({}^{a}\) & 99.18 \(\pm\) 0.98 \({}^{a}\) \\ \multicolumn{1}{c}{a*} & \({}^{**}\) & \(-\)03.8 \(\pm\) 0.01 \({}^{a}\) & \(-\)13.7 \(\pm\) 0.04 \({}^{b}\) & \(-\)1.08 \(\pm\) 0.03 \({}^{c}\) & \(-\)1.22 \(\pm\) 0.01 \({}^{a}\) & \(-\)0.84 \(\pm\) 0.03 \({}^{c}\) & \(-\)0.71 \(\pm\) 0.02 \({}^{c}\) & \(-\)1.02 \(\pm\) 0.03 \({}^{c}\) \\ \multicolumn{1}{c}{b*} & \({}^{**}\) & \(19.16\) \({}^{a}\) & 11.87 \(\pm\) 0.32 \({}^{b}\) & 10.73 \(\pm\) 0.24 \({}^{c}\) & 7.84 \(\pm\) 0.14 \({}^{d}\) & 4.56 \(\pm\) 0.14 \({}^{e}\) & 4.48 \(\pm\) 0.16 \({}^{e}\) & 5.18 \(\pm\) 0.16 \({}^{f}\) \\ \multicolumn{1}{c}{Chroma} & \({}^{**}\) & \(19.16\) \({}^{a}\) & 11.95 \({}^{b}\) & 10.78 \({}^{c}\) & 7.94 \({}^{d}\) & 4.64 \({}^{e}\) & 4.54 \({}^{e}\) & 5.28 \({}^{f}\) \\ \multicolumn{1}{c}{Hue} & \({}^{*}\) & 91.16 \({}^{a}\) & 96.61 \({}^{b}\) & 95.76 \({}^{b}\) & 98.83 \({}^{c}\) & 100.4 \({}^{d}\) & 99.04 & 101.12 \({}^{d}\) \\ \multicolumn{1}{c}{AE} & 14.87 & 6.97 & 5.78 & 2.75 & 0.65 & 0.76 & \(-\) \\ \multicolumn{1}{c}{} & OD 420 nm (AU) & \({}^{**}\) & \(0.303\) \(\pm\) 0.01 \({}^{a}\) & \(0.178\) \(<\) 0.01 \({}^{b}\) & 0.155 \(\pm\) 0.00 \({}^{c}\) & 0.109 \(\pm\) 0.00 \({}^{d}\) & 0.063 \(\pm\) 0.00 \({}^{c}\) & 0.064 \(\pm\) 0.00 \({}^{c}\) & 0.071 \(\pm\) 0.00 \({}^{c}\) \\ \multicolumn{1}{c}{} & OD 420 (\%) & \({}^{**}\) & \(72.7\) \({}^{a}\) & 78.1 \({}^{b}\) & 77.5 \({}^{c}\) & 80.7 \({}^{d}\) & 82.9 \({}^{e}\) & 82.1 \({}^{e}\) & 84.5 \({}^{e}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Color parameters determined for the investigated Trebbiano di Lugana wines.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 5 Results
### Multi-Pass Approach
#### 5.1.3 Urban scene  
We switch from the alpine area to a typical urban scenario. Just as retaining walls, we expect tall buildings to negatively affect GNSS measurements and thus the georeferencing accuracy of MLS point clouds. Since MLS is also used for change detection in cities (cf. [48]), the best possible co-registration of multiple MLS point clouds is thus required.  
The urban scene provides a suitable testbed with a narrow road next to multi-story office buildings. The area was traversed four times within half an hour with an MLS at low speed (max. 20 km/h). We denote the resulting georeferenced point clouds of the right scanner as I, II, III, and IV. An example of the point-to-point deviations between runs I and II is given in Figure 15.  
The point clouds of the two passes deviate up to 4-5 cm (Figure 15 left). The median value of the differences (cf. Table 4) for all combinations is about 2 cm, which is in the order of differences of RW2. Remarkably, although we can minimize most of the deviations (Figure 15 right and Table 4), differences of up to 2 cm remain  
is large, and the angle of incidence is small. Since our method retains the inner profile geometry, we can attribute these systematics to the laser measurements.
# Free-Hand Input and Interaction in Virtual Reality Using a Custom Force-Based Digital Thimble
## 2 Related Work
### Teleportation in Virtual Reality  
Locomotion is a crucial aspect of VR that enables users to explore virtual worlds. Researchers have investigated various locomotion techniques, such as simulated walking, which allows users to walk in place while navigating the virtual environment. This can be achieved through the use of treadmills [40], low-friction surfaces [41], novel devices like spheres [42], and other motion-based systems. One issue with this approach is that when users physically move, their eyes and inner ear are not always in sync, which can lead to symptoms such as nausea and dizziness [43]. Additionally, these methods require heavy and expensive setups, which are not always practical or scalable. As a result, teleportation has become the dominant method for navigating the virtual environment. It is a virtual locomotion technique that allows users to move around a virtual environment without physically moving their body [43]. This is achieved by instantly transporting the user's avatar or point of view (POV) to a new location. Since teleportation does not involve any physical movement, it is less likely to cause motion sickness compared to other locomotion techniques [43]. Point-and-click is the method most commonly used for teleportation, allowing users to point to their desired destination and then click a button on their controller to teleport [43; 44]. However, researchers have explored alternative approaches, including teleportation by eye gaze [45], mid-air gestures [44], jump gestures [46], and head movements [45].
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 3 Handshake Performance Measurements
### Handshake Reliability  
IEEE802.15.4 provides a reliable link and physical layer. Channel hopping and dedicated cells in the schedule mitigate many transmission errors due to multipath fading and collisions. The link layer uses acknowledgments, and OpenWSN performs up to 15 retransmissions combined with a back-off mechanism to minimize loss. However, constrained devices can drop IEEE802.15.4 frames due to limited packet buffer space on the constrained devices. To test how both TLS and DTLS behave when the lower layers do not provide 100% reliability, we set up an experiment where 6 LoWPAN fragments have a 5% probability of being dropped when traversing the 6 TiSCH network.  
Figure 6 shows the results for the TLS handshake. We compare the lower whiskers (which correspond to a handshake with no losses) with the median. We notice, with Nagle active, an increase between \(\pm\)10% and \(\pm\)2% in the number of transmitted bytes for an MTU of 356 B and 864 B, respectively. Similarly, we see an increase between \(\pm\)10% and \(\pm\)15% when Nagle is not active. Comparing the handshake duration, we measure an increase between \(\pm\)15% and \(\pm\)10% with Nagle active and \(\pm\)11% with Nagle inactive. We also notice that when TCP uses a larger MSS, the worst-case scenario deteriorates quickly.  
Figure 5: The duration for the (D)TLS handshake increases fast when the TSCH schedule is sparsely allocated.  
We conclude that a lower MSS/MTU results in more bytes exchanged to establish the secure connection due to the header overhead. However, when there are losses, the small TCP segments allow to more precisely indicate, through the TCP SACK option, which segments require retransmission. When the receiver can accurately indicate which losses occurred, the transmitter can minimize the data it has to retransmit.  
In a second experiment, we look at the behavior of the DTLS handshake over lossy links and compare it to the values obtained for TLS. Figure 7 depicts the comparison between the lossy TLS and DTLS handshake. For an MTU of 529 B and 1041 B we measure an increase of \(\pm 31\%\) and \(\pm 45\%\), respectively. Handshake duration roughly doubles.  
Figure 6: Limiting the MTU, by negotiating a small Maximum Segment Size (MSS) in the TCP handshake, reduces the number of bytes retransmitted and improves the handshake duration.  
Figure 7: Loss of a 6 LoWPAN fragment causes significant retransmission activity during the DTLS handshake. A properly configured TCP stack recovers the lost fragment with minimal impact on the 6 TISCH network.  
The lack of control over the retransmission mechanism of DTLS results in a drastic increase in the number of bytes exchanged and total handshake duration. Since UDP combines many DTLS records of a flight in large datagrams, losing a single 6 LoWPAN fragment results in the loss of all DTLS records. DTLS will then have to retransmit the entire flight.
# Mass Processing of Sentinel-1 Images for
## 2 Study Definition and Datasets
### Sentinel-1 Data
These images are included in the image density map in Figure 2, but are excluded from the overall detection and correlation statistical results (Section 4.3) and from the detection density maps (Section 4.5). The area off Israel in the eastern Mediterranean is severely affected by RFI. Almost every image there is affected by strong RFI. Since identifying and labelling the ones affected by RFI would have  
Figure 3: Example of Sentinel-1 images affected by RFI (radiofrequency interference): (**a**) from ground source (image name: SIA_IW_GRDH_ISDV_20160712T164623_20160712T164648_012118_012C3E_5B16); and (**b**) from Radarsat-2(SIA_IW_GRDH_ISDV_20150909T165710_20150909T165735_007641_00A97F_B8 EB). The VH polarimetric channel is shown in both cases. Azimuth is from top to bottom; range from left to right. The RFI (indicated with arrows) is visible as horizontal rows or short bright vertical stripe artefacts. Sentinel-1 images © Copernicus 2015, 2016. Figure 2: Number of observations for each geographic location used in the study (11,647 images, only Sentinel-1A). Calculated with a grid size of 0.1\({}^{\circ}\). imposed a heavy burden on the amount of manual assistance provided, it was decided for that area not to attempt to label the images but instead delineate the area most affected by the RFI-related detections and exclude that area from the overall ship detection and correlation statistics (presented in Section 4.3). The area is small and the impact of this exclusion on the overall statistics is minor: only 3% of the detections, 0.5% of the interpolated positions, and 1% of the correlations are inside the area excluded. The area is indicated in Section 4.5. While Sentinel 1B data were not considered for ship detection, a comparison between the revisit times obtained with Sentinel-1A only and with the full Sentinel-1A + Sentinel-1B constellation in the period from 29 November 2016 to 9 February 2017 during which they were both in full operational capability was carried out. For this, only the metadata of the Sentinel-1 images were used.
# FRP Bridges in the Flanders Region: Experiences from the C-Bridge Project
## 5 In Situ Testing
### Static Load Tests  
Static load tests have been carried out on two composite footbridges in Bruges and one composite footbridge in Puurs. They provide deflection values and, if internal sensors (e.g., BRAGG optical sensors) are present in the bridge deck, internal stresses or strains. The following experiences are relevant:  
* When comparing the theoretically calculated deflection values, care must be taken to exclude the conversion factors that account for the environmental influences and the aging effects. Such values might not be directly available from the calculation notes.
* When performing static load tests, the effects of temperature and solar radiation are relatively large compared to the load effects. A relevant example relates to the static measurements on the Canada Bridges in Bruges (bridge No. 4 in Table 1) where, during the reference measurement (without load), the temperature in the upper flange's temperature had already risen to 46 \({}^{\circ}\)C due to direct solar radiation. The variable load was generated by water containers placed on the bridge surface. The containers, and the resulting shade they provided, decreased the temperature in the upper flange to 34 \({}^{\circ}\)C. During the execution of the static load test, i.e., the gradual filling of the containers, the temperature in the upper flange decreased further and finally reached the temperature of the bottom flange (25 \({}^{\circ}\)C). As such, during the test, a differential temperature between the upper and lower flanges of about 20 \({}^{\circ}\)C was measured without more exact information due to the changing temperature and solar conditions, including local shade, at every measurement. An estimate of the influence of a temperature gradient of 40 \({}^{\circ}\)C, viewed as an upper bound for in service conditions, between the upper and lower decks was simulated with engineering software and resulted in a maximum vertical upward deflection at midspan of 7.5 mm. It should therefore be noted that the temperature and solar radiation on a composite bridge deck have a non-negligible effect on the accuracy of the deflection measurements during an in situ test.
* When preforming an on-site creep test, two problems arise. Firstly, the duration of such a test is limited in time (e.g., one week) and is essentially too short to draw accurate extrapolations for creep effects. Secondly, the recorded strains or deflections caused by temperature and solar radiation are much larger than those resulting from creep-controlled climate circumstances (indoor) prior to installation on-site.
###### Abstract  
This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder-decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting.  
Keywords:yeast counting; image segmentation; pixel interval down-sampling; tiny objects +
Footnote †: journal: _Sciences_
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Data Preparation and Generation Module
#### 4.2.1 Data Encoding  
Participants negotiate preprocessing parameters via smart contracts to standardize data formats and provide initial privacy protection, ensuring consistency for subsequent processing. Quasi-identifier attributes are then encoded into Bloom filters to efficiently match similarity while preserving ambiguity for enhanced privacy protection. Consider a dataset \(D=\{r_{1},r_{2},\ldots,r_{N}\}\), where each record \(r_{i}\) has a set of quasi-identifier attributes  
Figure 1: The overall process of multi-party PPRL.  
Figure 2: The data processing flow of data preparation and generation module.  
\(A=\{a_{1},a_{2},\ldots,a_{p}\}\). All participants negotiate Bloom filter parameters--bit length \(m\) and number of hash functions \(k\)--through a blockchain smart contract, with results recorded on-chain to ensure consistency and transparency. For example, [PERSON] maps the quasi-identifiers of record \(r_{A_{i}}\) to a Bloom filter \(BF_{A_{i}}=[a_{i1},a_{2},\ldots,a_{im}]\) (\(a_{ij}\in\{0,1\}\)) using \(k\) hash functions. [PERSON] performs the same operation for record \(r_{B_{i}}\), generating \(BF_{B_{i}}=[b_{i1},b_{i2},\ldots,b_{im}]\). Parameter selection must balance security and efficiency: a larger \(m\) and appropriate \(k\) reduce false positives and enhance privacy, but excessively large values decrease the efficiency of Boolean secret sharing decomposition and matching. Thus, parameters are chosen to optimize both aspects.
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
## 3 Datasets and Processing Strategy
### Processing Strategy  
Table 2 shows the algorithm processing strategy of network RTK server and terminal RTK. It can be seen that compared with terminal RTK processing, the main difference of network RTK processing is that there is no need to estimate coordinate parameters, but the observation equation needs to add tropospheric wet delay and ionospheric delay as the parameters to be estimated. In addition, the non-combined model used in the algorithm processing in this paper can assist the rapid convergence of long-distance baseline ambiguity parameters by adding reasonable variance constraints of ionospheric and tropospheric parameters to the equation (virtual observation equation).