esa-sceva/satcom-chunk-collection huggingface dataset:
# Entity-Centric Fully Connected GCN for Relation Classification
## 3 Experiment
### Effect of Hyper-Parameters  
In this section, the influence of two hyperparameters in the method proposed in this paper is discussed through experiments, the number of attention heads \(h\) and the number of graph convolutional layers \(d_{l}\).  
Since multi-head attention allows the model to collectively pay attention to information from different representation subspaces from different locations. Choosing the right number of attention heads is crucial to the proposed model. As can be seen from the Figure 4(a), as the number of attention heads increases, the performance improves. Using 3 heads in the multi-head self-attention layer produces the best performance. Use more than 3 heads, each additional head, performance will gradually decrease.  
Then, we also study the influence of the number of graph convolution layers when the number of attention heads is fixed at 3. It can be seen from the Figure 4(b) that as the number of GCN layers increases, the performance improves. The best effect is achieved when using two layers in FCGCN. Using more than 2 layers, each additional layer, and the multi-layer GCN algorithm used for relation classification tasks will produce high space complexity.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 4 Solenoid Valve Failure-Mode Recognition  
SVM was developed from a linearly separable optimal classification surface and was initially used to solve the two-class classification problem, as shown in Figure 9. Its operating principle is as follows:  
In the data set, a hyperplane is constructed, where the largest \(w\) and \(b\) are chosen to maximize the edge; therefore, the distance between the hyperplane and any nearest training data point is the largest, and the points with \(y_{\text{i}}=1\) and \(y_{\text{i}}=-1\) separate.  
\[w\cdot x+b=0 \tag{14}\]  
Currently, several algorithms extend the classic two-class SVM to a multi-class problem, with the latter roughly divided into two categories. The first one constructs a series of two-class classifiers and combines them to achieve multi-classification. In contrast, the second type, entitled S-type, calculates the parameters for the multiple classification planes  
Figure 9: Two-class support vector machine principle.  
and merges them into an optimization problem utilizing a \"one-time\" optimization strategy to achieve multi-classification [25]. Given that the training sample cardinality is relatively small, the requirement for training speed is not high, and thus, the S-type approach is more accurate. Hence, in this work, the research group exploited the S-type method both for classification and recognition. It is worth noting that, for the S-type of problems, [PERSON] and [PERSON] [26] proposed a method to solve all hyperplanes at once.  
The fault identification process relying on SVM is divided into two stages:  
1. Training modeling: First, the information of the system under different fault types is obtained utilizing sensor measurement. Then, data processing is performed on the information to extract a feature vector and to form a training sample pair between the feature vector and its corresponding fault type, to ultimately establish the relationship between the sample and the fault type. This relationship is then exploited to train the SVM.
2. Model prediction: The feature information to be diagnosed is input into the trained SVM, and the latter's output presents the fault diagnosis classification.
## 4 Conclusions  
We have proposed and described a novel path finding algorithm and communication protocol to enhance autonomous ESSs. This path finding algorithm exploits the location of the communication nodes and hazards to calculate the direction from the hazardous area to the evacuee's current location to search for a safe path to a nearby exit. The communication protocol, is an extension of Epidemic Routing.  
A real-life example of a shopping centre was used as a case study to evaluate how these two algorithms can improve the performance of emergency evacuation systems. We evaluated the performance based on four different densities evacuees and with two different fire starting positions. Our experimental results indicate that an emergency evacuation system enhanced with our novel algorithms can evacuate a few percentage more evacuees and also improve the health level of the evacuees. These can lead to a significant number of people who are actually saved during an emergency such as a fire in a crowded shopping centre. In future work it will be quite interesting to evaluate the inclusion of more sophisticated guidance techniques that could include real-time visual matching with enhanced reality tools [25], in order to improve the decision making and provide evacuees with better and more reliable directions that take into account both the evacuees precise physical context and his/her location.
## 4 Defect-Centric Screen Methodology
### Channel Hole Bending  
The Hard bending channel hole defect originates when the bottoms of two adjacent channel holes are slightly stuck to each other. Similar to the Hard Bowing defect, the Hard bending defect can be detected using a program operation, but the test pattern should be modified due to its unique fail mechanism. Figure 9 shows the schematic of how to detect the Hard bending channel hole defect. As shown in Figure (a)a, channel holes \(\sharp\)1 and \(\sharp\)2 are assumed to be programmed and inhibited, respectively. When a channel hole is close to the neighboring one (i.e., Hard bending defect occurs), a leakage current will be observed between the two channel holes. However, the amount of leakage current is too low to measure using a normal program operation. To boost the amount of leakage current, we maximized the potential difference between channel holes by applying a 3D checkerboard (CKBD) pattern [27]. Figure (b)b shows the CKBD pattern in a diagonal and horizontal direction, respectively. (\"P\" indicates programmed ('0') states, and \"I\" indicates inhibited ('1') states). We used four different CKBD patterns to detect ChB defects: diagonal CKBD, reversible diagonal CKBD, horizontal CKBD, and reversible horizontal CKBD. (The reversible CKBD is a pattern in which the \"P\" and \"I\" of the original pattern are swapped.) Due to leakage current, the threshold voltage (\(V_{th}\)) of the program-inhibited flash cells (i.e., flash cells not to be programmed) in the bending channel hole increases compared to other normal channel holes. (The erased flash cells are not inhibited enough from being programmed, so they are partially programmed.) Therefore, after program operations with four 3D CKBD patterns, we can efficiently detect the Hard bending defect just by sensing the \(V_{th}\) of flash cells using the proper read level.
###### Abstract
To this end, we present an autonomous grasping method that firstly consists of detecting the object's position based on vision. Further, the DDPG is trained to become an inverse kinematics solver to calculate the joints angles to reach and grasp the targeted object. Certainly, our case solves the IK with DDPG for a 5-DOF robotic arm; however, this method can be applied to a numerous-degrees-of-freedom robotic arm (more than 6-DOF), which enhances the practicality of this approach. The main contribution of our paper can be represented as follows:  
* We suggest using YOLOv5 for more precise object position after its detection;
* We use backward projection for the extraction of the object's 3D position;
* We apply IK to compute the angles of the joints at the detected position;
* We employ the DDPG algorithm to teach the arm to autonomously reach the wanted object;
* Our method outperforms the state-of-the-art approaches by reaching the goal after only 400 episodes with 95.5% accuracy. This paper is organized as follows: in Section 2, an overview of reinforcement learning is given followed by a literature review and some related works in Section 3. In Section 4, detailed explanations of our method are presented. Experiments are clearly illustrated in Section 5. Finally, conclusions and future work are discussed in Section 6.
# Trusted Composition of Internet of Medical Things over Imperfect Networks
## 6 Behavior Specification Using BLESS Properties
### Respiration Monitor  
As depicted in Figure 4, Respiration_Monitor contains a Respiration_Sensor device to measure the respiration rate and a Respiration_Monitor_Process process (protected address space) which has two threads, Respiration_Monitor_Thread and RM_Communication_Thread.  
Figure 4: Respiration Monitor system structure.  
RM_Communication_Thread models COTS software such as a TCP/IP stack and device drivers. As such, RM_Communication_Thread is less assured. Respiration_Monitor_Thread performs a safety-critical function, so is formally verified with a deductive correctness proof. The type classifier of Listing 3 declares the interfaces, in terms of in and out ports, of the Respiration_Monitor_Thread.  
```
threadRespiration_Monitor_Thread features RP_Rate:indatportIoMT_Types::Respiration_Rate {BLESS::Value=>\"<<returnsquantititybrpn:=RESPIRATION_RATE>>\";} Lost_Confirmation:outeventport; RM_Alarm:outeventdataportBLESS_Types::Boolean {BLESS::Assertion=>\"<<RM_ALARM()>>\";} RM_Alarm_Echo:ineventdataportBLESS_Types::Boolean; properties Dispatch_Protocol=>Timed; Period=>20 sec; endRespiration_Monitor_Thread;
```  
BLESS::Assertion properties define predicates within << >>, which must match the values of boolean-typed ports. Therefore, port RM_Alarm outputs true when << RM_ALARM() >> holds and false otherwise. Because RM_Alarm is an out port, thread Respiration_Monitor_Thread guarantees its BLESS::Assertion property. Conversely, BLESS::Assertion of an in port is assumed. Connections from out ports to in ports have assume-guarantee verification conditions (further explained in Section 8). BLESS::Value properties define expressions which must match the values sent or received on data ports and event data ports. Port RP_Rate expects a value of type brpm with value RESPIRATION_RATE, which is a _ghost_ variable. Ghost variables represent the actual values sent or received by ports.
## 5 Comprehensive Analysis of Model Robustness, Generalizability, and Interpretability
### Analysis of Regional Transferability and Model Adaptability
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Serial**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{\(\mathbf{x_{1}}\)} & \multirow{2}{*}{\(\mathbf{x_{2}}\)} & \multirow{2}{*}{\(\mathbf{x_{3}}\)} & \multirow{2}{*}{\(\mathbf{x_{4}}\)} & \multirow{2}{*}{\(\mathbf{x_{5}}\)} & \multirow{2}{*}{\(\mathbf{x_{6}}\)} & \multirow{2}{*}{\(\mathbf{x_{7}}\)} & \multirow{2}{*}{\(\mathbf{x_{8}}\)} & \multirow{2}{*}{\(\mathbf{x_{9}}\)} & \multirow{2}{*}{\(\mathbf{x_{10}}\)} & \multicolumn{1}{c}{**Total**} \\
**Number** & & & & & & & & & & & & & & & \\
1 & 1993-07-12 & Hokkaido & 2 & 7.8 & 20 & 6 & 7 & \(-1\) & 61 & 5 & 4 & 3 & 2 & 230 \\
2 & 1994-12-28 & Sanriku & 2 & 7.6 & 30 & 6 & 7 & \(-1\) & 68 & 5 & 3 & 3 & 2 & 3 \\
3 & 1995-01-17 & Kobe & 1 & 6.9 & 17 & 7 & 7 & 0 & 2738 & 5 & 1 & 4 & 1 & 6434 \\
4 & 2003-09-26 & Hokkaido & 2 & 8.3 & 27 & 7 & 7 & 0 & 61 & 5 & 4 & 3 & 2 & 1 \\
5 & 2004-10-23 & Niigata & 2 & 6.8 & 13 & 7 & 7 & 0 & 172 & 5 & 2 & 3 & 2 & 68 \\
6 & 2005-03-20 & Fukuoka & 2 & 6.6 & 11 & 6 & 7 & \(-1\) & 1019 & 5 & 2 & 3 & 1 & 2 \\
7 & 2007-03-25 & Noto Peninsula & 2 & 6.9 & 10 & 7 & 7 & 0 & 167 & 5 & 4 & 3 & 2 & 1 \\
8 & 2008-06-14 & Iwate–Miyagi & 2 & 7.2 & 36 & 7 & 7 & 0 & 322 & 5 & 3 & 3 & 2 & 23 \\
9 & 2011-03-11 & Honshu & 1 & 9 & 30 & 7 & 7 & 0 & 338 & 5 & 3 & 3 & 1 & 18,500 \\
10 & 2011-04-07 & Miyagi & 2 & 7.1 & 45 & 6 & 7 & \(-1\) & 322 & 5 & 3 & 3 & 2 & 4 \\
11 & 2011-04-11 & Fukushima & 2 & 7 & 50 & 6 & 7 & \(-1\) & 147 & 5 & 4 & 3 & 2 & 1 \\
12 & 2016-04-16 & Kummoto & 1 & 7 & 10 & 7 & 7 & 0 & 300 & 5 & 2 & 4 & 1 & 273 \\
13 & 2018-06-17 & Osaka Prefecture & 2 & 6.1 & 13 & 6 & 7 & \(-1\) & 5200 & 5 & 1 & 4 & 1 & 5 \\
14 & 2018-09-06 & Hokkaido & 1 & 6.7 & 35 & 7 & 7 & 0 & 62 & 5 & 4 & 3 & 2 & 44 \\
15 & 2021-02-13 & Fukushima & 2 & 7.1 & 49 & 6 & 7 & \(-1\) & 147 & 5 & 3 & 3 & 2 & 1 \\
16 & 2022-03-16 & Fukushima & 2 & 7.3 & 63 & 6 & 7 & \(-1\) & 147 & 5 & 3 & 3 & 2 & 4 \\
17 & 2024-01-01 & Ishikawa & 1 & 7.6 & 10 & 7 & 7 & 0 & 167 & 5 & 4 & 3 & 2 & 618 \\
18 & 2000-10-06 & Tottori & 1 & 6.7 & 10 & 7 & 7 & 0 & 200 & 5 & 3 & 3 & 2 & 2 \\
19 & 1996-06-12 & Akita–Iwate & 2 & 6.1 & 13 & 6 & 7 & \(-1\) & 180 & 5 & 2 & 3 & 2 & 3 \\
20 & 1997-03-26 & Kagoshima & 2 & 6.3 & 11 & 6 & 7 & \(-1\) & 160 & 5 & 2 & 3 & 2 & 2 \\
21 & 2002-05-26 & Miyagi & 2 & 7.1 & 20 & 6 & 7 & \(-1\) & 310 & 5 & 3 & 3 & 2 & 6 \\
22 & 2007-07-16 & Niigata & 1 & 6.8 & 15 & 6 & 7 & \(-1\) & 178 & 5 & 2 & 3 & 2 & 11 \\
23 & 2008-07-24 & Iwate Coastal & 2 & 6.8 & 25 & 6 & 7 & \(-1\) & 320 & 5 & 3 & 3 & 2 & 1 \\
24 & 2013-04-13 & Awaji & 2 & 6.3 & 14 & 6 & 7 & \(-1\) & 240 & 5 & 2 & 3 & 2 & 1 \\
25 & 2015-05-30 & Ogasawara & 2 & 7.8 & 35 & 6 & 7 & \(-1\) & 130 & 5 & 3 & 3 & 2 & 2 \\
26 & 2023-02-02 & Chiba & 2 & 6.7 & 22 & 6 & 7 & \(-1\) & 143 & 5 & 3 & 3 & 2 & 1 \\
27 & 2025-04-12 & Kyushu & 2 & 6.8 & 20 & 6 & 7 & \(-1\) & 150 & 5 & 3 & 3 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Japanese earthquake sample data.
## 3 Results and Discussion
### The Lithosphere-Ionosphere Coupling  
The observations accomplished in time of the 4 March 2024 \(M6.1\) earthquake allowed us to draw a comprehensive analysis, which significantly extends the possibility of interpreting the data obtained as mutually confirming seismogenic anomalies accompanying the earthquake preparation. For the convenience of the analysis, a comparison is made in Figure 11 between the time series considered above on ENPEMF data, underground gamma rays, and DFS of ionospheric signal during the earthquake preparation period. In the figure, it is clearly seen sequential order of disturbance appearance: first in the intensity of ENPEMF (21 February), then in the subsurface gamma-ray flux (24 February), and then in the DFS (25 February).  
After the suppression of the characteristic diurnal ENPEMF cycle (upper graph), electromagnetic pulses reach their maximum intensity on the eve of the main shock. Apparently, this variation reflects the dynamics of deformation and destruction processes in rocks in the earthquake focus [2; 3; 4].  
In turn, the stress load and the change in the volume of porous space impact the emanation process of radon and its gamma-radioactive daughter products. This is revealed as a cove-like decrease in the intensity of gamma rays (middle graph). Consequently, the anomalies detected may carry information about mechanical processes that proceeded in the lithosphere in the short-term preparation period before the earthquake.  
Propagation of the seismogenic disturbance up to the ionosphere, as detected by the Doppler ionosonde, can be explained in the frame of the lithosphere-ionosphere coupling [27; 28]. The initial link in this process could be the varying ionization of the atmospheric boundary layer, which depends on the penetration speed of subsurface radioactive gases onto the Earth's surface. Actually, as seen in Figure 11, the negative anomaly in Doppler frequency follows, with a small delay, the anomalous cove-like depression in the gamma-ray background as measured at a depth of subsoil rocks.  
Figure 10: The ionospheric disturbance registered by the Doppler ionosonde before the \(M6.1\) earthquake on 4 March 2024. The daily Doppler frequency data are plotted after averaging over 4 points. Straight horizontal lines indicate the value of the mean counting rate (the solid one) and the level of \(-1\sigma\) standard deviation, both estimated over the period preceding the earthquake. Vertical lines label the date 8 days prior to the earthquake and its strict moment (\(EQ\)).
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 7 Conclusions  
In this paper, we have presented the GCMR scheme and analysed and compared its performance by simulation against three reference multicast routing algorithms, namely ST, PIM-SSM and BIER. In addition, we have described the implementation of a GCMR prototype using the library of the Quagga open source routing engine and the large experimental tests against PIM-SSM in the iLab.t Virtual Wall testbed. We have proved that GCMR provides a better trade-off between the decrease in the memory space required to locally store the routing information and the increase in the stretch produced. In the experimental platform, we demonstrated the successful operation of GCMR, the consistency between these results and the simulation ones, and the capability of GCMR to considerably reduce the recovery time in case of failure compared to PIM-SSM.  
On the contrary, the results obtained for the communication cost ratio between BIER (but also against PIM-SSM) and GCMR show that further improvements are still needed. Future work will focus on this aspect and aims at reducing the communication cost of GCMR at the expense of marginally increasing the memory space consumption. One possible solution is to configure a so-called source ball around the multicast source; nodes at a given path budget distance from the source will be considered inside this source ball and will store a path to the source node. In this way, when a new joining node begins to flood a search message and it reaches one of the nodes within the source ball, this can forward the message directly to the source, preventing the need for further exploration.  
Conceptualisation, D.C. and D.P.; methodology, D.C. and D.P.; software, F.A.; validation, F.A., D.C. and D.P.; formal analysis, D.P.; writing--original draft preparation, D.C.; writing--review and editing, D.C. and D.P. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under contract FEDER TEC2017-90034-C2 (ALLIANCE project) and supported but not funded by the Generalitat de Catalunya under contract 2017 SGR-1037.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ASAutonomous SystemBGPBorder Gateway ProtocolBIERBit Index Explicit ReplicationBIRTBit Index Routing TableGCMRGreedy Compact Multicat Routing
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 5 Discussion  
This systematic review comprehensively analyzes the transition from static to dynamic LEZs, underscoring the significant advancements and ongoing challenges in optimizing urban air quality and traffic management.  
One of the main findings obtained from the study is the lack of systematic studies that holistically address the problem associated with urban models that implement dynamic low-emission zones. In this sense, we can see how existing studies address various implementation strategies that could support the development of dynamic low-emission zones.  
The findings reveal that dynamic LEZs, which leverage real-time data and adaptive strategies, offer substantial improvements over their static and hybrid counterparts in reducing vehicular emissions without adversely affecting traffic flow. Notably, studies incorporating advanced technologies such as blockchain for secure and privacy-preserving access control, as well as sophisticated traffic simulation tools, demonstrate enhanced efficiency and scalability in LEZ implementations.  
The review encompasses 14 studies on transitioning from static to dynamic LEZs. The key findings can be summarized as follows:  
* Technological Integration: A significant number of studies emphasized the adoption of advanced technologies such as blockchain and smart contracts to enhance the security and privacy of access-control systems in dynamic LEZs [19; 26].
* Pricing Strategies: Dynamic pricing mechanisms, including electronic road pricing (ERP) systems with real-time fee adjustments based on emissions and traffic conditions, were identified as practical tools for reducing vehicular emissions without disrupting traffic flow [18; 19].
* Emission Reduction Efficacy: Studies demonstrated that dynamic LEZs can achieve substantial reductions in CO\({}_{2}\) emissions (up to 66%) and significant decreases in other pollutants such as NOx and PM2.5 [24; 25].
* Traffic Management Improvements: Optimized LEZ designs using simulation tools like SUMO and genetic algorithms resulted in improved travel times (up to 15%) and a better traffic distribution, indicating enhanced urban mobility [24; 29].
* Privacy Preservation: Several studies addressed privacy concerns by implementing anonymization protocols and decentralized access-control systems, ensuring that only non-compliant drivers are identified without tracking honest drivers [22; 23].
* Public Acceptance and Compliance: The review highlights the importance of transparent communication and stakeholder engagement in fostering public trust and compliance with dynamic LEZ regulations [20; 34].  
These findings collectively indicate that dynamic LEZs, when effectively implemented with supportive technologies and strategic planning, may significantly contribute to urban sustainability by reducing emissions and improving traffic management while maintaining public trust and compliance.
# Developing a Virtual World for an Open-House Event: A Metaverse Approach
## 3 Methods
### Investigation Procedure and User Task  
The investigation procedure is explained as follows. The open-house event was held on a Sunday, specifically for students to attend. It was an on-site event where students visited the university college to explore the five study programs and get a feel for its academic activities. While they were exploring the physical event, they were also invited to register and use our newly developed platform.  
During the platform usage session, visitors were encouraged to participate in the activities announced at the main stage of the metaverse. A notification message (see Figure 1 for the notification functionality) was also provided to inform users of upcoming activities. Additionally, a large screen at the main stage in the metaverse broadcast a livestream of the on-site event.  
Participants were free to explore the metaverse platform, which contained information about the curriculum inside each building. For our investigation, participants were informed and encouraged by our staff members to complete a questionnaire provided by designated NPCs located in the metaverse platform. Participants who completed the questionnaire were rewarded with a certain amount of points. Each participant's score was ranked and displayed on the leaderboard at the main stage, and the avatars of the top three participants were displayed on billboards around the main stage. At the end of the event session, physical giveaways were awarded to the top three participants on the leaderboard.  
User tasks are summarised in the following items:  
* Participants were able to explore the platform and discover their interest in each study program as if it were a physical open-house event.
* Participants could visit each of the curriculum-themed buildings to get a feel for the academic activities provided by each study program.
* Visitors were able to interact with information-conveying methods designed for each study program inside the buildings. Some of these methods provided additional points to users, such as answering short questions presented by NPCs about the curriculum and viewing informational digital poster boards for a certain amount of time.