esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithm Based on Morphological Operators for Shortness Path Planning
## 3 Experimental Analysis and Results
### Results
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Approach** & **Workspace** & **Complexity** & **Weighted** & **Remarks** \\ \hline Local search with & & & & An approach based on a local search aided by the Levenshtein distance to quantify the dissimilarity among graphs. The dissimilarity is used to prioritize in terms of cost the possible path equivalences. \\ \hline Contextual heuristic based on local constraints [43] & Graph & \(O(n^{3}\times\log n)\) & No & Introduces a framework to locate the best path trajectories in a net via the introduction of local heuristics and constraints based on the process functionality. \\ \hline Dijkstra enhancement [44] & Graph\&Grid & \(O(n^{2})\) & No & Improves the Dijkstra algorithm via the enhancement of the data structure for the storage and searching of the sorted nodes. The matrix node combination is expressed as a single list for better indexing. \\ \hline Morphological search (proposed) & Graph & \(O(c_{1}|x^{*}|^{2}\times\lambda)\) & Yes & It defines a general framework based on mathematical morphology operators and introduces two new operators, which help to build a natural relation for the most reliable path. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._The final expression becomes simplified to the expression \(O(n^{2})\); the complexity is comparable to that of the Dijkstra algorithm without a priority queue. Finally, the \(\lambda\) parameter might increase the complexity; however, this parameter represents the number of elements at the border to be analyzed by each iteration (structural element dimension). The considerable increment in the complexity is compensated because, with each iteration, the number of covering steps is reduced in the best case by a factor of \(\log_{\lambda}(|\mathbf{\cdot}^{\star}|)\). The complexity will likely be increased for higher \(\lambda\) values, but the number of iterations needed to cover the nodes is reduced in a logarithmic ratio. Figure 7 shows the complexity in terms of the changing \(\lambda\) value. It is concluded that the total complexity is similar to that of Dijkstra for small \(\lambda\) values. This analysis confirms that the morphological search framework is equivalent to the Dijkstra algorithm in this particular case. Figure 6: Example of the searching algorithm in a graph space with (**a**) an avenue scenario, including the origin and destination nodes, and (**b**) the frequency with which dilation is applied and the computation of the best path trajectory.
## 3 Materials and Methods
### Experimental Setup  
All experiments were implemented using Python 3 and the TensorFlow2.18/Keras3.8 framework on Google Colab. The input images were resized to 224 \(\times\) 224 pixels. For training, the Adam optimizer was used with a fixed learning rate of 0.0001. The unimodal model was trained with a batch size of 16, while the multimodal model used a batch size of 32. MobileNet was adopted as the base architecture, and global average pooling was applied before the dense layer. MobileNet's lightweight architecture enables fast and  
Figure 3: System framework and technical roadmap.  
efficient feature extraction without sacrificing accuracy, making it especially appropriate for edge-device and mobile deployments [47; 48]. The unimodal model included a dense layer with 1024 units, whereas the multimodal model used 256 units. The number of output classes was set to 140 in both models. Fusion strategies--namely feature-level, score-level, and hybrid--were applied only in the multimodal system.
###### Abstract  
Articular cartilage injuries are a common source of joint pain and dysfunction. As articular cartilage is avascular, it exhibits a poor intrinsic healing capacity for self-repair. Clinically, osteochondral grafts are used to surgically restore the articular surface following injury. A significant challenge remains with the repair properties at the graft-host tissue interface as proper integration is critical toward restoring normal load distribution across the joint. A key to addressing poor tissue integration may involve optimizing mobilization of fibroblast-like synovocytes (FLS) that exhibit chondrogenic potential and are derived from the adjacent synovium, the specialized connective tissue membrane that envelops the diarthrodial joint. Synovium-derived cells have been directly implicated in the native repair response of articular cartilage. Electrotherapeutics hold potential as low-cost, low-risk, non-invasive adjunctive therapies for promoting cartilage healing via cell-mediated repair. Pulsed electromagnetic fields (PEMFs) and applied direct current (DC) electric fields (EFs) via galvanotaxis are two potential therapeutic strategies to promote cartilage repair by stimulating the migration of FLS within a wound or defect site. PEMF chambers were calibrated to recapitulate clinical standards (1.5 \(\pm\) 0.2 mT, 75 Hz, 1.3 ms duration). PEMF stimulation promoted bovine FLS migration using a 2D in vitro scratch assay to assess the rate of wound closure following cruciform injury. Galvanotaxis DC EF stimulation assisted FLS migration within a collagen hydrogel matrix in order to promote cartilage repair. A novel tissue-scale bioreactor capable of applying DC EFs in sterile culture conditions to 3D constructs was designed in order to track the increased recruitment of synovial repair cells via galvanotaxis from intact bovine synovium explants to the site of a cartilage wound injury. PEMF stimulation further modulated FLS migration into the bovine cartilage defect region. Biochemical composition, histological analysis, and gene expression revealed elevated GAG and collagen levels following PEMF treatment, indicative of its pro-anabolic effect. Together, PEMF and galvanotaxis DC EF modulation are electrotherapeutic strategies with complementary repair properties. Both procedures may enable direct migration or selective homing of target cells to defect sites, thus augmenting natural repair processes for improving cartilage repair and healing.  
pulsed electromagnetic fields; galvanotaxis; DC electric fields; synovium; migration; in vitro models +
Footnote †: journal: Applied Sciences
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 4 Theoretical Analysis and Experimental Results
### Experimental Settings  
In our network scenario, each node has a maximum transmission power of 10 W. The area is fixed at \(1.5\times 1.5\times 1.5\) km\({}^{3}\). During network initialization, the sink node is positioned at a specific location on the water surface, while the source nodes are fixed at the bottom of the water. The relay nodes are uniformly and randomly distributed throughout the area, and they should satisfy the condition of having approximately equal numbers of nodes within every 100 m of depth (it should be noted that due to the randomness in node distribution, each data point in this paper represents the average of 30 experiments.) Subsequently, the routing initialization is performed by the sink node (refer to Algorithm 1 in the paper). After the initialization is completed, and the network enters the data forwarding phase, where source nodes are responsible for sensing data and sending data  
Figure 8: Example of Multi-hop Transmission with \(k+1\) Relay Nodes.  
packets every 40 s. These are then relayed by the relay nodes to the sink node. To cope with network drift, we employ a localized-globalized integrated strategy (see Section 3.3.3). In this process, if a certain relay node fails to forward data, it triggers the Localized Recovery mechanism to reconstruct its own routing table. Furthermore, if the sink node receives a number of packets below the set threshold within a time interval T, it triggers the Global Recovery mechanism, where the sink node sends beacon packets to reconstruct the network routes.  
Regarding the models used in the simulation, the underlying models are all default models in AquaSim-ng. Specifically, the underwater acoustic channel follows the model described in reference [22], and the energy model aligns with Equation (5). The detailed parameter settings are shown in Table 1.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 4 Reconfigurable and Smart Antennas
### Reconfigurable and Smart Antennas: The Field of Use
The corresponding combination of the slot position, length, and width can create a hybrid mode between the TE\({}_{101}\) and TE\({}_{102}\) modes, which depends on their phase. In [252], the authors proposed and developed two microwave systems with horizontal and vertical polarization to select the optimal configuration for pipeline imaging applications. First, a pipeline containing crude oil was modeled, and its thermal and dielectric properties were proposed. Then, the characteristics of the antenna array were optimized. Different numbers of antenna elements were successfully investigated in both vertical and horizontal polarization to find their optimal number for pipeline applications. These systems can be used as accurate sensors to manage fluid flow in pipelines during the production process in the oil and gas industry because they can display changes in phase ratio in real time. By contrast, other, less accurate methods including mechanical, optical, X-ray or gamma-ray, ultrasound, nuclear magnetic resonance (NMR), and, in rare cases, microwave methods cannot do this. In addition, a new type of frequency-reconfigurable antenna, the ferrite slab-loaded substrate integrated waveguide antenna, was proposed in [253]. Due to the loaded ferrite, the operating frequency of the antenna can be tuned in a wide frequency range by bias magnetic field and the position of ferrite slab in the antenna. The experimental results proved the frequency tunability of the antennas and showed that the tunable range could be much larger than that of electronic reconfigurable antennas. This type of antenna's radiation performance may be promising in wireless communication systems. A reconfigurable reflect-array antenna, which exploits the magnetic anisotropy of a biased ferrite substrate, was studied in [254]. The unit cell of such an antenna is composed of two centered metal square loops placed on the grounded ferrite substrate. The analysis of such a unit cell for extracting its scattering information was carried out by [PERSON]'s MoM. The results obtained in [255] showed that the use of a reconfigurable antenna and intelligent antenna selection strategy onboard a UAV provides a higher average signal-to-noise ratio compared to an omni-directional antenna in both line-of-sight (LOS) and non-LOS scenarios, and is more resilient to co-channel interference. It is necessary to note that now, studies on reduced time of SLAE solution are mostly based on the effective use of computing resources of workstations (multicore, computer cache memory, graphics processor, etc. ), whereas the hidden resources of algorithms that format and solve SLAEs themselves remain practically unexplored. When geometrical dimensions of a structure change, there are significant changes in the matrix elements, which makes the use of direct methods ineffective. Iterative methods also become inefficient without additional adjustments or recalculation of the preconditioning matrix.
# Uniform Pooling for Graph Networks
## 4 Experiments
### Graph Classification on Brain Connectivity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **If Pooling** & **GATConv** & **GCNConv** & **SAGEConv** & **SGCConv** \\ \hline \multirow{2}{*}{D\&D} & No Pooling & 76.15 \(\pm\) 0.82 & 76.50 \(\pm\) 0.85 & 74.70 \(\pm\) 0.85 & 73.93 \(\pm\) 1.00 \\  & UGPool & **76.92 \(\pm\) 0.84** & **77.69 \(\pm\) 0.90** & **74.87 \(\pm\) 0.93** & **75.38 \(\pm\) 0.79** \\ \hline \multirow{2}{*}{ENZYMES} & No Pooling & 34.83 \(\pm\) 1.85 & 33.00 \(\pm\) 2.24 & **36.00 \(\pm\) 1.87** & 28.00 \(\pm\) 3.34 \\  & UGPool & **37.17 \(\pm\) 2.06** & **35.17 \(\pm\) 2.04** & 33.50 \(\pm\) 2.42 & **36.17 \(\pm\) 1.81** \\ \hline \multirow{2}{*}{PROTEINS} & No Pooling & **75.41 \(\pm\) 0.62** & 74.05 \(\pm\) 0.62 & 74.05 \(\pm\) 0.72 & 74.50 \(\pm\) 0.79 \\  & UGPool & 74.68 \(\pm\) 0.67 & **75.14 \(\pm\) 0.83** & **74.59 \(\pm\) 0.61** & **74.86 \(\pm\) 0.85** \\ \hline \multirow{2}{*}{NCI1} & No Pooling & **76.25 \(\pm\) 1.33** & 76.16 \(\pm\) 1.45 & 75.33 \(\pm\) 1.96 & **76.55 \(\pm\) 1.77** \\  & UGPool & 76.06 \(\pm\) 1.88 & **77.01 \(\pm\) 1.73** & **75.55 \(\pm\) 1.67** & 76.01 \(\pm\) 1.89 \\ \hline \multirow{2}{*}{NCI109} & No Pooling & 75.22 \(\pm\) 0.51 & 75.83 \(\pm\) 0.76 & 74.00 \(\pm\) 1.53 & **75.32 \(\pm\) 1.19** \\  & UGPool & **75.44 \(\pm\) 0.91** & **75.87 \(\pm\) 0.96** & **74.61 \(\pm\) 1.11** & 73.88 \(\pm\) 1.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy on benchmark datasets with and without global UGPool architecture. Table 5 lists the gender classification accuracy and autism classification accuracy via the GCN models with and without global UGPool on the HCP and ABIDE datasets, respectively. Based on different GCN models, our UGPool method can significantly improve the classification accuracy of the connectivity graphs. The experimental results show that the GCN model is effective in representing the brain connectivity graph. Moreover, our UGPool method can be integrated into general GCN models to improve graph embedding and classification performance.
# A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks
## 5 Experiments
### Results and Analysis
#### 5.3.3 Learning Weights of Meta Graphs In MCHIN  
From our experiments and comparisons, we can see that different meta graphs express different semantics. To utilize rich semantics of meta graphs, it is possible to weight meta graphs differently and assign higher weights to meta graphs with higher impacts on accuracy.  
Figure 6 shows the performance of MCHIN under meta paths and meta graphs on DBLP. The meta graphs' relative effectiveness is determined by MCHIN via its weight assignment mechanism. Table 6 denotes different weights of meta graphs computed by MCHIN. MCHIN has assigned the highest weight to the meta graphs \(AP\{A;C;T\}PA\), as \(AP\{A;C;T\}PA\) performs the best in the classification. This matches with the intuition. On the other hand, the weight of APA is around 0.01\(\sim\)0.15 due to its poor ability to capture the main features for the classification task. The results of meta graphs' weights on the Yelp dataset are also shown in Table 6. Because the labels of the links between restaurants are related to categories, our model assigns more weights 0.30\(\sim\)0.40 to \(RCR\) than \(RUR\). The meta graph \(RU(E;R)UR\) achieves the highest weight among other meta graphs. In the IMDB dataset, \(MA\{D;M\}AM\) has higher weight in average than \(M\{A;T\}M\), as the \(MA\{D;M\}AM\) expresses more semantic than \(M\{A;T\}M\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Meta Graphs** & **Weights** \\ \hline DBLP & APA & 0.01\(\sim\)0.15 \\  & AP(A;C;T)PA & 0.85\(\sim\)0.90 \\ \hline \multirow{3}{*}{Yelp} & RCR & 0.30\(\sim\)0.40 \\  & RUR & 0.01\(\sim\)0.10 \\  & RU(E;R)UR & 0.80\(\sim\)0.90 \\ \hline \multirow{3}{*}{IMDB} & M(A;T)M & 0.45\(\sim\)0.55 \\  & MA(D;M)AM & 0.70\(\sim\)0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Weights of meta graphs on DBLP, Yelp and IMDb dataset.  
Figure 6: Accuracy comparison of MCHIN corresponding to different meta paths and meta graphs.
# Position-Based Formation Control Scheme for Crowds Using Short Range Distance (SRD)
## 3 Proposed Algorithms
### Proposed Algorithms
#### 3.3.3 Steering Behavior for Control Inertia  
When the crowd suddenly has to turn or stop, the inertial force of the crowd makes it difficult for them to do it smoothly. To overcome this problem, we add the seek behavior [14] to the velocity of the final agents so that the crowd can move smoothly along the path or stop at the particular position. Basically, steering behaviors apply the steering forces to adjust the velocity of the moving agent. Among the various types of steering behavior proposed in [14], the seek behavior first detects the target and then applies a steering force so that the agent does not suddenly change its velocity when the distance to the target becomes short. Equation (10) explains the seek behavior applied to our algorithm. By adding the steering behavior, \(v^{s}\), to the agent's velocity, we can prevent rapid changes in velocity.  
\[v^{d}=\left(\frac{p^{c}-s^{c}}{|p^{c}-s^{c}|}\right)\kappa \tag{10}\]  
\[v^{s}=v^{d}-v^{c}\]  
where \(p^{c}\) is the current position of the agent, \(s^{c}\) is the SRD which is the position of the target, \(v^{c}\) is the current velocity of the agent, \(v^{d}\) is the desired velocity to the target, and \(\kappa\) is a constant, which is the maximum speed for \(v^{d}\). Figure 13 shows the seek behavior.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
### Contributions  
Common routing mechanisms, such as route requests and route replies, have been well investigated in the literature [27; 28; 29; 30]. This paper focuses on route selection and makes three contributions. _Firstly_, a hybrid route selection scheme called CenTri is proposed to select routes that minimize SU interference to PUs in available licensed channels in 5G networks characterized by the dynamicity of channel availability and ultra-densification. The purposes are to: a) improve load balancing through traffic offload from the macrocell layer to the small-cell layer; and b) the overall spectrum utilization. _Secondly_, RL models and algorithms are proposed for CenTri. _Thirdly_, the issues associated with implementing CenTri on a real-world platform consisting of heterogeneous nodes embedded with universal software radio peripheral (USRP) units are presented.
## 3 Results
### Copy Phrase Performance  
No statistically significant differences were observed among overall accuracy rates in the no jitter (\(Mdn=0.90\)), small (\(Mdn=0.90\)), or large jitter (\(Mdn=0.80\)) copy phrase tasks, \(\chi^{2}(2)=1.853\), \(p=0.396\). P300 target effects measured from both signal sources during calibration significantly predicted copy phrase accuracy rates in all experimental conditions (all \(r_{\rm s}(22)\) values \(\geq 0.453\); all \(p\) values \(\leq 0.026\)), with the single exception that the effect did not quite reach significance in the pooled signal during the small jitter condition (\(r_{\rm s}(22)=0.386\), \(p=0.062\)). N200 target effects measured during calibrations did not predict copy phrase performance, with the single exception that N200 target minus non-target differences in the pooled signal predicted accuracy rates during the no jitter condition (\(r_{\rm s}(22)=-0.532\), \(p=0.007\)).
## 3 Methodology
### Experimental Setup  
Both the pilot study and the user study took place at the Schleissheim Airfield. The area was closed to the public for the tests. The road surface is an even tarmac and the road boundaries were artificially represented using pylon lanes.  
As the first step of the methodology is to define driving maneuvers, the maneuvers \"Double 90\({}^{\circ}\)--turn with different radii\", \"Double 90\({}^{\circ}\)--turn with different radii from a standstill\", \"Turn from a standstill\", \"Single lane change\", \"Salam with rising pylon distances\", \"Salam with rising and falling pylon distances\", as well as \"Decelerate to a slower vehicle and change the lane\", were chosen.  
For one test drive in the pilot study, there were two occupants in each vehicle (test driver, test supervisor), and for the user study, four occupants (test driver, test supervisor, two test persons). The use of a human test driver results in increased demands on driving skills. The driver must be able to reliably simulate different driving styles. An employee of the Institute of Automotive Technology with a high level of driving experience and driving training was selected as the test driver. The test supervisor was seated in the passenger seat. He instructed the test persons and operated the measuring electronics. The test subjects were located on the rear seats of the vehicle and were assigned to one side of the vehicle based on their test subject number.
# Decentralized Identity Authentication with Auditability and Privacy
## 5 Privacy-Preserving Protocol
### Intermediate Protocol  
While investigating the drawbacks of the baseline scheme, it becomes clear that the privacy issues are caused by the linkage between the sent counters and the identity that issued them. One idea for breaking such a linkage is to allow a client to prove that the counters are consecutive and authentic (i.e., created by the client), but without leaving public evidence of that fact. We present a protocol that requires services to verify the linkage (thus, they become trusted in that scope). In our construction, we use a VRF that outputs a unique and deterministic hash for a given counter with a proof guaranteeing its authenticity. Hash-proof pairs can ensure services that the hashes are authentic, while hashes recorded in the blockchain ensure privacy (since without their proofs, they cannot be attributed to identities) and give clients the ability to audit which of their counter values have been used. Based on those insights, in the following sections, we give more details of this protocol. (Designs alternative to those presented in this section could include a system in which authentication counters are encrypted under the identity's public key and the corresponding ciphertexts are submitted to the blockchain. Unfortunately, such a solution (a) would require clients to constantly keep validating all new ciphertexts (in order to audit them) and (b) would allow anyone to use the public key to generate and submit a valid ciphertext, which would be interpreted by the client as a credential misuse.)