esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Architecture Overview  
The FSF forwarding decisions are based on both the social tie and selfishness between the nodes. To get information about these two characteristics, every network node uses two different systems: the social tie classifier system, and the selfishness assessment system.  
* **The social tie classifier system**. The main goal of this system is to update information related to the social ties between each pair of nodes in the network. The social ties can be classified into _friends_, _acquaintances_, and _unknown_. A node stores information about its social ties in two different lists: _my social ties_, which stores the own node's social ties_, and _global social ties_ which stores the social ties from the other network nodes. With the aim of updating _my social ties_ list, every node has a database containing information about their meetings with other nodes in the network. The database belonging to the node \(n_{i}\) is divided into \(n-1\) rows, where the row \(n_{k}\) stores data related to the history of meetings between nodes \(n_{i}\) and \(n_{k}\). Initially, this database is empty. Upon each contact opportunity, both the nodes in contact update the information related to the meetings between themselves in their own databases. Based on this information, they can classify the social tie with every other node participating in the network (as friends, acquaintances, or unknown). Such classification is performed periodically every \(T\) seconds. The _global social ties_ list is updated at every contact opportunity taking place after the first \(T\) seconds. In a contact opportunity between the nodes \(n_{i}\) and \(n_{k}\), they share information about their own social ties (_my social ties_ list) with each other, and then they include this information in the corresponding entry in their _global social ties_ list. More details about these two lists will be provided in Section 4.1.2.
* **Selfishness assessment system**--The main goal of this system is to update the node's reputation in terms of selfishness behaviour. In this work, a node can be classified as _individually selfish_, _socially selfish_, and _non-selfish_. Initially, the nodes have the same reputation and no information about the network. The node's reputation is updated according to their behaviour when they were selected as a relay node in the past. In such a situation, a node can behave in three ways: (i) it always accepts receiving the message, (ii) never accept the message, (iii) it accepts receiving the message only if it is not experiencing resource constraints. Upon each decision, nodes update their own reputation. The selfishness assessment system is divided into two main systems: a detection scheme, which is implemented as a collaborative watchdog system based on [37], and a reputation system, which is based on a logistic function. More details about these components will be provided in Section 4.2.
## 2 Evolution of Digital Certificate  
A p12 file contains a digital certificate that uses (Public Key Cryptography Standard #12) encryption. The p12 file builds on PKCS #8 by adding essential information and improving security through public keys' privacy and integrity modes [12]. It is a portable format for transferring personal private keys and other sensitive information. Various security and encryption programs use P12 files. P12 keys store a private key that encrypts information in such a way that decryption requires using the corresponding public key. In addition, data encrypted with the public key need the private key for decryption [13]. A P12 file may also contain a certificate revocation list, information about the chain of trust, and information about its holder, such as their name, surname, name of the company where they work, a position occupied, and other personal information data. Signing and encrypting this type of file blocks any modification, and thus, prevents unauthorized access. This helps validate the source of information transmitted. This method applies to migrating an end-entity certificate and its private key, with the signing certificate chain, from one platform to another. A certification authority to distribute a certificate chain and a private key can also be used when the certification authority generates the private key. For security reasons, the user authenticates directly with the PKCS#12 certificate, not the certificate stored in the operating system Keystore. For example, when using multiple desktop workstations and laptops, it is advisable not to leave the certificate on different devices. For increased security, access to the e-learning platform with the PKCS#12 certificate can be carried out using a USB stick [14]. The certificate standards are as follows: Rivest-Shamir-Adleman (RSA) encryption, RSA signature, password-based encryption, encrypted or cryptographic message syntax, private key information syntax, selected object category and attribute type, certificate request syntax or authentication, encryption interface or cryptographic token, personal information exchange syntax, encrypted information syntax or cryptographic token [15]. The issued digital certificate has a period of validity; it is protected against repetitive password attempts through a limited number of entries, with its blocking beyond this limit and the possibility of revocation when the student is no longer part of the system, see Table 2.
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 4 Mechanotransduction in Space (\(1\times 10^{-6}\) g)  
### Osteocytes  
Osteocytes are particularly susceptible to the adverse effects of zero gravity. In mice, one month of spaceflight decreased femoral trabecular bone volume by 64% and increased bone resorption by 140% when compared to ground controls. These changes were associated with osteocyte apoptosis, reductions in osteocyte lacunar volumes, and increases in lacunar vacancies (+344% vs. ground controls); fatty metamorphosis of mesenchymal stem cell precursors was also observed [18]. Similar findings were noted in iliac crest biopsies of monkeys flown for 14 days at zero gravity [19].
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 2 Related Work
### Traffic Demand  
Forecasting traffic demand is crucial for the success of taxi and ride-hailing services, as it allows providers to efficiently allocate their limited resources to areas with high demand. This optimization not only improves service availability for passengers but also promotes the use of alternative transportation options, such as public transit, when there is a limited availability of taxis or ride-hailing vehicles. Traffic demand refers to the expected need for travel, which is not always fully met. For example, ride requests on ride-hailing platforms represent demand, but some of these requests may go unfulfilled due to a shortage of available drivers, especially during peak hours. Accurate predictions of travel demand are essential for the effective operation of vehicle scheduling systems, such as those used in online ride-hailing services [15]. However, in some instances, collecting potential travel demand from passengers proves challenging, leading to the use of transaction records as an approximation of demand [16]. Yet, this method may underestimate actual demand.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
The above studies focus on the impact of individual factors of the number, area and mass of satellites in the constellation on the future environment, but lack of comprehensive analysis of the impact of the number, area and mass of satellites in the constellation on the long-term evolution results. and the impact of different deployment heights of constellations and the interaction between satellites in constellations on the environment is not deeply considered. In this paper, taking the Starlink constellation as an example, the interaction between the constellation and the space debris environment is discussed, and the orbital evolution of the constellation during the mission cycle is analyzed. In different mission stages, the flux analysis of individual satellite and overall satellite constellation is carried out, and calculate the collision risk assessment. According to the different operating heights and inclination of the constellation, the number of avoidance maneuvers and risk levels are evaluated, and the impact of the success rate of post mission disposal (PMD) on the space environment is analyzed. Finally, in the conclusion, the interaction between the space debris environment and the large satellite constellation is given, and the constellation satellite protection and mitigation suggestions are provided. The structure of the paper is as follows. Section 2 introduces the deployment and situation of the Starlink constellation. Section 3 evaluates the collision risk of satellites and constellations in different mission stages. Section 4 analyzes the avoidance maneuver of the constellation and the corresponding risk level. Section 5 gives the impact of the success rate of constellation PMD. Section 6 gives the discussion and conclusions.
## 2 Method  
### Physical-Based Reliability Model  
[PERSON] and [PERSON] developed a temperature cycling-based accelerated reliability model in 1969. It is more practical than using the semi-experimental Coffin-Manson equation which has difficulties in measuring the true strain associated with the thermal effect. The N-L equation is shown below:  
\[\text{AF}=\frac{N_{u}}{N_{a}}=\left(\frac{\Delta T_{a}}{\Delta T_{u}}\right)^{n }\left(\frac{f_{u}}{f_{a}}\right)^{m}\varphi(T_{max}) \tag{1}\]  
where AF is acceleration factor, \(N_{u}\) is the cycles to failure in normal use conditions (h), \(N_{a}\) is the cycles to failure in accelerated test conditions (h), \(\Delta T_{a}\) is temperature range of TC in accelerated test conditions (K), \(\Delta T_{u}\) is temperature range of TC in normal use conditions,\(f_{a}\) is cycle frequency of TC in normal use conditions,\(f_{a}\) is cycle frequency of TC in accelerated test conditions (24 h/cycle time), \(n\) and \(m\) are exponents, and \(\varphi(T_{max})\) is a function associated with the effect of maximum temperature in TC, and it is expressed particularly by the Arrhenius equation, shown below.  
\[\varphi(T_{max})=\exp\left[\frac{E_{a}}{k}(\frac{1}{T_{u}^{max}}-\frac{1}{T_{a }^{max}})\right] \tag{2}\]  
where \(E_{a}\) is activation energy (ev or kJ/mole), \(k\) is Boltzmann constant 8.625 \(\times\) 10\({}^{-5}\) eV/K, \(T_{u}^{max}\) is the maximum temperature in normal use conditions (K), \(T_{a}^{max}\) is the maximum temperature in accelerated test conditions (K). In this study, two temperature cycling test conditions named TC1 and TC2 were setup. They represent the difference from the maximum temperature, but the other parameters, temperature range and cycle frequency, remain the same. Equation (1) was simplified to be  
\[\text{AF}=\frac{N_{TC1}}{N_{TC2}}=\varphi(T_{max})=exp[\frac{E_{a}}{k}(\frac{1 }{T_{TC1}^{max}}-\frac{1}{T_{TC2}^{max}})] \tag{3}\]  
Thus, the activation energy is shown below:  
\[E_{a}=\frac{\ln(AF)\times k}{(\frac{1}{T_{TC1}^{\max}}-\frac{1}{T_{TC2}^{\max }})} \tag{4}\]  
It is noted that the activation energy in Equation (4) is from the two controllable dynamic stress variables: the power factor corresponding to charging-discharging currents and times and the environmental factor corresponding to temperature cycling at which they were implemented simultaneously.
## 4 Results and Discussion
### Driving Stability Analysis on Single-Factor
#### 4.1.4 Effects of Various Driving Conditions on RA  
The variations of RA over time under the influence of different factors are shown in Figure 11. The simulation results for the period of time without outside adverse disturbance indicate that the RA of the vehicle is generally less than 2\({}^{\circ}\) and varies slightly, even when driving in a curve segment with a lateral wind. However, when it crosses a rut, the RA changes rapidly, and the instantaneous changes in RA happen to the wheels crossing the rutting, one after another, respectively. In the case of WHR equal to 5, the maximum RA of the vehicle is about 14\({}^{\circ}\) when driving across the rutting, which exceeds the threshold  
Figure 10: Change of LTR over time under various driving conditions: (**a**) Vehicle speed; (**b**) WHR of the rutting; (**c**) Radius of circular curve; (**d**) Superelevation; (**e**) Crosswind angle; (**f**) Wind speed; (**g**) Friction coefficient between road and tire.  
value of RA, while the maximum RA under the rest of WHR conditions does not exceed 6\({}^{\circ}\), which is less than the limit value of RA. Thus, it is concluded that severe rutting damage will significantly reduce lateral stability, thus heightening the possibility of rolling over.  
Figure 11: Change of RA over time under various driving conditions: (**a**) Vehicle speed; (**b**) WHR of the rutting; (**c**) Radius of circular curve; (**d**) Superelevation; (**e**) Crosswind angle; (**f**) Wind speed; (**g**) Friction coefficient between road & tire.  
Based on the results and discussions in Sections 4.1.1-4.1.4, it is finally found that all LAs and SAs are within the threshold, but the maximum LA and maximum SA dramatically fluctuate up and down in certain cases. Regarding evaluating the roll-over stability of the vehicle, all LTRs are less than 1, showing satisfactory roll-over stability. However, the maximum RA far exceeded the threshold of 6\({}^{\circ}\), which is not compliant with LTRs. So, it seems that these two indicators do not show consistency in evaluating the roll-over stability of the vehicle. Obviously, it is not thorough and accurate enough to evaluate the lateral stability with only the aid of single and independent indicators.  
To overcome the limitations of a single indicator, a comprehensive indicator accounting for multiple indicators is essential for evaluating the lateral stability of the vehicle. So, in the subsequent section, [PERSON]'s coefficient was applied to verify the correlation between LA, SA, LTR, and RA. Then, the entropy weight method was utilized to weigh these four indicators. Finally, the comprehensive relation grade (CRG) was proposed to evaluate the effects of different factors on the lateral stability of the vehicle.
# Extending the Coverage of IEEE 802.15.4z HRP UWB Ranging
## 5 Conclusions  
In this study, we analyzed the link performance of the IEEE 802.5.4z HRP UWB standard, which has been commercialized in various fields recently, from the perspective of the link budget and proposed a method for configuring the optimal HRP UWB parameters for long-distance ranging up to hundreds of meters. Since UWB technology allows for various wireless link designs as long as they satisfy the technical criteria, we confirmed that ranging up to hundreds of meters is possible if designed optimally. The results analyzed in this study were tested with an actual commercial UWB module using a non-directional antenna. Therefore, if an actual directional antenna is used, then the possibility of long-distance ranging of hundreds of meters or more will increase even more.  
From a system design perspective, it is important to recognize that, despite the wide bandwidth (~500 MHz) characteristic of UWB systems, significant link performance asymmetry exists between the synchronization header and the data portion due to their different modulation schemes. While conventional assumptions often treat UWB links as uniformly robust across the packet, our analysis and experimental results demonstrate that the weaker link of the two--typically the DP--ultimately constrained the maximum achievable ranging distance. Therefore, future UWB system designs and standard developments should explicitly account for this inherent link margin disparity. By addressing and compensating  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **6.8 Mbps** & **850 Kbps** \\ \hline Short & 62.3 m & 68.8 m \\ Middle & 66 m & 71.77 m \\ Long & 65.5 m & Above 90 m \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum achievable ranging distance.  
Figure 14: Maximum ranging distances mapped onto the hallway layout of the engineering building.  
for the weaker link through adaptive modulation, coding, or dynamic parameter tuning strategies, further improvements in ranging performance, reliability, and coverage can be realized without violating regulatory constraints. In future UWB system designs, explicitly addressing the link margin imbalance between the SHR and DP sections will be key to maximizing performance and deployment scalability. We expect that UWB technology can be used in various applications that require long-distance ranging in the future.  
Conceptualization, S.H.; methodology, B.-J.J.; software, S.H.; data curation, S.H.; writing--original draft preparation, S.H.; writing--review and editing, B.-J.J.; visualization, S.H.; supervision, B.-J.J.; project administration, B.-J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN
### Related Works  
There are many studies on deep learning for the diagnosis of GC using endoscopic images, including studies on the classifications between GC and healthy subjects and the automated recognition of GC regions.  
[PERSON] et al. investigated the prediction of Helicobacter pylori infection using a convolutional neural network (CNN) and obtained a sensitivity of 88.9% and specificity of 87.4% [11]. [PERSON] et al. developed a method to discriminate between GC and normal tissue using magnified narrow-band imaging (NBI) [12]. They used Inception-v3 as the CNN model for classification and obtained a sensitivity of 91.18% and specificity of 90.64%. [PERSON] et al. developed a method to classify precancerous diseases (polyp, ulcer, and erosion) using CNN and obtained a classification accuracy of 88.9% [13].  
[PERSON] et al. developed a single-shot multi-box detector (SSD), an object detection model, for the automated detection of early-stage GC [14]. The sensitivity of detection was 92.2% and the positive predictive value was 30.6%. [PERSON] et al. also developed a method for object detection of GC by classifying GC regions and normal regions using micropatch endoscopic images [15]. The detection sensitivity and specificity of the method were 80.0% and 94.8%, respectively.  
We proposed a method for extracting the presence and invasive regions of early GC using Mask R-CNN, which can perform both object detection and segmentation [16]. We showed that the automated detection sensitivity for early GC was 96.0% and that the segmentation concordance was 71%. Although the method had sufficient detection sensitivity, the average number of false positives (FP) was 0.10 per image (3.0 per patient). The Mask R-CNN used in this study introduced an object detection model for common natural images. It captured the clear contour of the object in the image, so that lesions with a relatively clear shape that caused unevenness were detected correctly. On the other hand, many lesions of early GC, in which only the surface of the gastric mucosa was cancerous, were not detected correctly by the object detection model because contours were unclear.  
A CNN used for segmentation rather than object detection analyzes patterns in the local regions of the image and divides the entire image into regions by determining whether they match the patterns to be extracted. This behavior involved in determining individual regions while observing details is similar to that used by an expert physician when observing the gastric cavity, and segmentation techniques may be able to improve the accuracy of automated lesion detection. On the other hand, many small regions are often observed in the segmentation output by CNNs. Excluding these using the FP reduction technique may greatly reduce the number of FPs and improve detection performance. Therefore, segmentation techniques that exclude small excess regions are effective for the automated detection of GC and identification of the extent of invasion.
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
###### Abstract  
Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the \(K\)-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.  
Keywords:indexing; music information retrieval; nearest neighbor search; efficiency; runtime +
Footnote †: journal:
## 3 Illustrative Numerical Results
### Number of Optical Channels (\(\lambda\))  
We have evaluated the performance parameters for the following optical-channel configurations: 16 \(\times\) 10 Gb/s (100 GHz spacing), 32 \(\times\) 10 Gb/s (50 GHz spacing), and 64 \(\times\) 10 Gb/s (25 GHz spacing), _i.e._, \(N\) = 16, 32, 64, \(S\) = 4 and \(N\)/\(S\) = 4, 8, 16). We have designed each RR with a \(-\)3 dB bandwidth set to 20 GHz.  
Figure 7: Transfer function “-” and group “- ” delay at RXs after the RSC at plane 1.  
Insertion loss increases when the number of channels scales up, due to star coupler/splitter losses that increase with _N/S_ (see Table 1). The variability among TX-RX transfer-function peaks is due to the increasing number of cascaded RRs in the RSC (_i.e._, _N/S_ = 4, 8, 16). The \(B_{m}\) usually corresponds to a RX on the first output plane as signals traverse two drop functions in the PSC, whereas \(B_{M}\) corresponds to a RX on the third plane as signals traverse two through functions. The analysis shows that performance parameters are critically low at 25 GHz spacing, while they are fair at 50 GHz and even better at 100 GHz. In the following we show results for 32 channels, since a typical core router hosts 32 LCs at 10 Gb/s [12].