esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Challenges and Opportunities
### Incorporating Ad-Hoc Security in Legacy Protocols  
An upgrade of existing hardware on existing vessels may be prohibitively expensive. As such, manufacturers need to focus on legacy protocols that are currently in use and provide software updates that would enable encryption, as well as authorization/authentication by default. Our analysis has shown that many marine protocols have no such security protections relying instead on the application layer to provide such features. However, additions to protocols have in the past been demonstrated to work. An example of this is the SSL/TLS implementation that did not need to overwrite the existing network stack and its addition has been effective at reducing confidentiality risks in relation to network communication channels. Similarly, AIS can be replaced by newer protocols that run over VHF, such as the VHF Data Exchange System (VDES) [41].  
Similarly with software, one protocol that can be incorporated in existing marine network protocols is the Timed Efficient Stream Loss-Tolerant Authentication (TESLA) protocol [42], which is a variation on traditional asymmetric cryptography. With TESLA, senders distribute a random key (without revealing it) to their receivers. Next, the sender sends a message with an encrypted Message Authentication Code (MAC). After a certain threshold (time interval or number of messages), the key to decrypt the sender's MAC is revealed. Receivers who have received the sender's previous messages can then verify using the key and MAC. If the verifications are correct and out-of-band packet protection is guaranteed by the transfer protocol, then a receiver knows that the packet is authentic. TESLA's advantages include high scalability and minimal overhead. TESLA is advantageous to use in environment where continuous authenticity over radio or satellite is necessary, while packet loss is expected to be high [43]. Marine communications often can fit this profile.
# A Key Node Mining Method Based on K-Shell and Neighborhood Information
## 4 Experiments and Results
### Performance of KCH  
#### 4.3.1 Connectivity Analysis  
In this paper, method accuracy is measured by the changes in robustness and the values of the robustness indicator R during the process of node removal in experiments. The accuracy performance of KCH in the Dolphins, USpowerGrid, WikiVote, and HepPh networks is shown in Figure 2, while the accuracy performance of all networks is displayed in Appendix A.  
Figure 2: Subfigures (**a–d**) represent the accuracy performance of KCH on Dolphins, USpowerGrid, WikiVote, and HepPh networks. The horizontal axis in the graph represents the proportion of nodes removed, and the vertical axis represents the ratio of the number of nodes in the largest remaining connected subgraph after node removal to the number of nodes in the original network.  
To gauge the accuracy of the algorithm, node rankings are generated using KCH and baseline methods, followed by sequential network destruction. Figure 2 and Appendix A visually display the destruction process of 10 real-world networks. In each experiment, the node's importance is initially assessed using KCH and benchmark methods. Nodes are ranked based on these assessments, and network destruction begins with the most important node. The robustness indicator R-value is then calculated based on the destruction outcomes, with the method's accuracy measured accordingly. From the results shown in Figure 2, the change curve of KCH generally resides at the lower end in nine networks, with the area formed by the curve, the \(x\)-axis, and the \(y\)-axis being the smallest. This suggests that in these nine real networks, deleting nodes according to the node importance ranking generated by KCH leads to the fastest destruction of the network. In essence, the KCH method can destroy the network more quickly than other methods, which indicates that the KCH method can more accurately mine important nodes. Table 3 comprehensively shows the comparison of R-values of various methods in 10 real network datasets, with KCH having the smallest R-value in 9 datasets and the second smallest in 1 dataset. Overall, in terms of important node mining, the KCH method demonstrates superior performance.
## 3 Results and Discussion  
### The Analysis of Speech Features between MFCC and PPG  
Figure 2 shows the performance of these two speech features through the \(t\)-SNE approach, and the results indicated that these 19 commands of MFCC showed divergence and overlap in most frames; in contrast, the PPG feature showed more convergence and less overlapping frames in the same test condition. Specifically, from the \(t\)-SNE results, the PPG achieved more robust performance than MFCC to extract dysarthric speech features. Therefore, these results imply that the PPG could help the deep learning model achieve better performance in the dysarthric patient SCR task.
## 6 Practical Implications  
Based on our findings, the following recommendations may help integrate vitamin D supplementation into MRONJ management:  
1. Assess Serum Vitamin D Levels: We advise routine measurements of serum 25(OH)D in patients undergoing antiresorptive therapy (e.g., bisphosphonates, denosumab), particularly before invasive dental procedures such as tooth extractions.  
Target Vitamin D Concentration: Maintaining serum levels \(\geq\) 30 ng/mL appears optimal. In cases of marked deficiency (<20 ng/mL), short-term high-dose supplementation (e.g., 10,000 IU/ day for 4 weeks) may be necessary to promptly correct levels.
* Maintenance Supplementation: After correcting deficiencies, a maintenance dose (e.g., 2000-5000 IU/ day) is recommended, with periodic monitoring of 25(OH)D (every 3-6 months), especially in patients receiving prolonged antiresorptive therapy.
* Combine Supplementation with Standard MRONJ Care: Vitamin D supplementation should be used alongside other standard measures--such as antibiotic prophylaxis (when indicated), meticulous oral hygiene, and risk factor control--to optimize outcomes.
* Prophylaxis vs. Treatment: Our data suggest that preventive vitamin D supplementation (prior to MRONJ onset) may help reduce the risk of advanced-stage necrosis. However, once MRONJ is already established, vitamin D alone does not significantly alter the rate of postoperative complications, although it may still support overall bone health.  
By offering these specific guidelines, we provide clinicians with a practical framework for incorporating vitamin D testing and supplementation into the wider MRONJ management protocol, potentially improving patient outcomes. Nonetheless, further randomized controlled trials are warranted to refine optimal dosing regimens and confirm the preventive versus therapeutic efficacy of vitamin D at different stages of MRONJ.  
Conceptualization, M.D., F.M. and K.G.-L.; methodology, F.M. and K.G.-L.; software, D.K.; investigation, F.M.; resources, F.M. and D.K.; data curation, F.M., J.K. and K.G.-L.; writing--original draft preparation, F.M. and K.G.-L.; writing--review and editing, M.D., J.K., P.P., J.M. and K.G.-L.; visualization, P.P. and J.K.; supervision, M.D. and K.G.-L.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
The APC was financed by a subsidy from Wroclaw Medical University.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board: No. KB-27/2021.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# RoseCliff Algorithm: Making Passwords Dynamic
## 3 RoseCliff Algorithm  
In this section, we introduce our innovative algorithm crafted to enhance the resilience of authentication systems. This algorithm not only makes password dynamic but can be used to build an advanced trigger mechanism. Termed the RoseCliff Algorithm, its primary objective is to implement dual authentication without adding any extra complexity or burden on users, all while ensuring the continuous evolution of stored passwords in hashed forms called ciphertext in cryptography. The algorithm itself can be broken down into five distinct phases: Input Detection, Password Splitting, One Time Number, Dynamic Password/Ciphertext Password Generation, and Authentication.  
A summary of the steps in the RoseCliff Algorithms is as follows:  
1. Input Detection * Ensure input device used to enter at least part of a the password.
2. Password Splitting * Split password into two segments. * Identify and record the positions of characters prior to their separation.
3. One Time Number * Authentication system generates one-time number.
4. Dynamic Password/Ciphertext Password Generation * Utilize public-private key encryption to generate a randomizing value using the confidential one-time number and a segment of the password. * Merge the randomizing value with the portion of the password not used in randomization, thus generating a temporary password. * Encrypt and send the temporary password.
5. Authentication * During authentication, decrypt the encrypted password, as well as the previously stored password to be used for verification. * Verify that the nonrandomized portion of the decrypted password matches that of the nonrandomized portion of the previously stored password. 1. If there is a match, the system proceeds to check if the randomization value at the authentication system matches that obtained from the decrypted password. 1. If there is a match, the user is authenticated. 2. If there is not a match, the authentication system signals a potential breach. 2. If there is no match, the authentication system records a failed attempt. If the number of failed attempts reaches a preset threshold, the authentication system activates its security measures.
* If the user is authenticated, update the previously stored password to a new one.
## 2 System Model
### JP-CoMP Sum Capacity  
The cooperating base stations in JP-CoMP will transmit data to all users in the cluster. This case requires a channel aggregation scheme in the JP-CoMP clustering. Suppose that a \(Y\) cluster consists of \(K(\leq\)C) base stations. The \(Y\) cluster will serve transmission data for users in the cluster. Cooperating base stations overall in the \(Y\) cluster have \(X\) transmitter antennas and each user has \(V\) receiver antennas, where \(X=KN_{t}\) and \(V\)= \(N_{r}\). With the assumptions of ideal transmitter beamforming and receiver beamforming, the channel aggregation can be defined, as follows,  
\[\mathbf{H}_{Y}=\begin{bmatrix}\mathbf{h}_{11}&\cdots&\mathbf{h}_{1X}\\ \vdots&\ddots&\vdots\\ \mathbf{h}_{V1}&\cdots&\mathbf{h}_{VX}\end{bmatrix}, \tag{2}\]  
where \(\mathbf{H}_{Y}\in\mathbb{C}^{N_{r}\times KN_{t}}\) represents the aggregated channel between the transmitter antennas of the cooperating base stations in the Y cluster and the selected user in the cluster. Utilizing Shannon's capacity equation, the sum capacity in the cooperating base stations can be defined, as follows,  
\[R=\sum_{Y=1}^{N}\log_{2}\left|\mathbf{I}+\mathbf{H}_{Y}^{H}\mathbf{H}_{Y}\right|, \tag{3}\]  
where \(\mathbf{I}\) represents the identity matrix and \(N\) denotes the total number of clusters.
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
## 4 Global TTL Coupling  
Due to assembly error, thermoelasticity of the optical bench, and possibly other factors, there is an offset between the reference point and the CoM of the satellite. Apart from the planned drag-free mission by ESA in the joint NASA/ESA mission MAGIC [16], future NGGM missions will rely on the magnetic torquer for attitude control, supplemented by cold gas thrusters when a magnetic torquer is not working properly. The origins of attitude jitter come mainly from solar pressure, atmospheric drag, and albedo in the pitch direction. The attitude jitter originated from solar pressure gradient, and atmospheric drag will generate random motion of the laser link between two spacecraft. Coupled with the non-zero separation between the reference point and the CoM of the satellite, a global source of TTL is generated between two satellites which, dependent on the offset distance, does not seem to be avoidable. At the same time, the local TTL noise of the optical bench in one spacecraft will propagate with the TX beam to the distant spacecraft and couple with the random attitude jitter of the distant spacecraft. We shall study both sources of global TTL noise in this section. Due to certain computational limitations of the IfoCAD software employed in this work [17], the relative motion between satellites is simplified as a single optical bench rotating around the RP point. A remote photodiode is placed 100 km away from the TX-RP point, acting as a simplified model of the remote satellite, as depicted in Figure 10[7; 9]. The optical bench layout shown in Figure 3 is only a possible setup to reduce the offsets between the CoM and the reference point. Our main purpose is to analyze the performance of the optical bench given in Figure 1. The setup of Figure 10 is based on the layout described in Figure 1. The TX beam transmitting from the local optical bench propagates to the remote PD and interferes with a Gaussian beam placed on the surface of the remote photodiode; the beatnote is then captured by the remote photodiode. The Gaussian beam on the remote PD represents the LO beam of the remote optical bench, and the waist radius should be large enough to reduce the effect caused by the wavefront of the beams. As in the previous section, a mode expansion method is employed to study the propagation of a flat-top beam. The global TTL coupling is then obtained by combining the LPS variations derived from the local and the remote photodiodes. In addition, linear regression is employed here to estimate the margin on TTL coupling variation.
## 3 System Architecture  
The architecture of satellite networks differs significantly from that of ground networks, as shown in Figure 1. The entire network system can be divided into three segments: the space segment, the ground segment, and the user segment. The space segment comprises various types of satellites, including geostationary Earth orbit (GEO) satellites, medium Earth orbit (MEO) satellites, and low Earth orbit (LEO) satellites. The ground segment mainly consists of ground stations. The congestion mitigation method proposed in this paper primarily targets LEO satellite scenarios, employing a hybrid strategy. Global information is necessary to construct a continuously visible neighbor set, followed by local route modifications near congested nodes. In this approach, specific ground stations serve as the network control center, overseeing route control.
## 2 Model and Equations
### Adimensionalization of the System  
Transforming the system into a dimensionless coordinate system ensures the uniformity of units and prevents issues arising from disparate scales when performing numerical calculations. To achieve this, we perform the following transformations: \(t=\tilde{t}t_{0}\), \(\vec{x}=\tilde{\vec{x}}x_{0}\), \(g=\tilde{g}g_{0}\), \(\vec{\Psi}=\tilde{\Psi}\Psi_{0}\), \(V=\tilde{V}V_{0}\), and \(\rho=\tilde{\rho}\rho_{0}\), where tilde variables are dimensionless and are said to be code units, while nontilded ones are physical. Appropriate scale factors for the GPP system are:  
\[t_{0} = \frac{m}{x_{0}^{2}\hbar},\] \[g_{0} = 4\pi Gm^{2}x_{0}^{2},\] \[\Psi_{0} = \frac{\hbar}{\sqrt{4\pi Gm^{3}}x_{0}^{2}}, \tag{11}\] \[V_{0} = \left(\frac{\hbar}{mx_{0}}\right)^{2},\] \[\rho_{0} = \frac{\hbar^{2}}{4\pi Gm^{3}x_{0}^{4}}.\]  
Thus, the system effectively possesses a single degree of freedom, which we express in terms of a scaling factor \(x_{0}\), equivalent to the transformation (3). With these new variables, the GPP system can be rewritten in dimensionless units as:  
\[\mathrm{i}\frac{\partial\vec{\Psi}}{\partial t}=\left[-\frac{1}{2}\
abla^{2}+V +g|\Psi|^{2}\right]\Psi, \tag{12}\]  
\[\
abla^{2}V=|\Psi|^{2}, \tag{13}\]where we omit the tilde in all variables. Our objective now is to determine the ground state of the stationary version of the GPP system (12) and (13) since excited states are unstable [6].
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 4 Experimental Results
### Comparison Results Between Only PS and PS + PL  
Table 3 compares the execution times of a PS-only configuration with those of a combined PS + PL implementation. As the edge data grows, the number of nodes the KSP algorithm must explore increases, because KSP expands all possible paths the computational load rises sharply, lengthening the runtime. Execution time also varies with the source node, since the complexity of the connectivity graph changes; accordingly, in the TLE snapshot used here, the graph rooted at source 2 (with destination fixed at node 98) is denser than the graph rooted at source 38. Across all four test cases, the PS + PL design consistently outperformed the PS-only baseline, achieving up to a 3.81\(\times\) speed-up. These results indicate that the pipelined PL implementation was effective and demonstrate that an FPGA with pipelining is highly advantageous for executing the KSP and EAR algorithms on large graph workloads.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Specification** \\ \hline SOC & Zunq UltraScale+TM MPSoC ZU7 EV \\ \hline Programmable-logic resources & 504 K system logic cells, 461 K CLB flip-flops, 6.2 Mb distributed \\ \hline On-board memory & 2 GB DDR4 component memory \\ \hline Power consumption & 15 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: AMD Zynq UltraScale+TM MPSoC ZCU104 board major specification.
## 3 Results and Discussion
### MMP-1 and MMP-3 Facilitate MSC IL-6-Induced Migration in Monolayer  
MSCs were incubated in IL-6 (1 ng/mL); culture media was then collected after 3 h and 24 h and analysed for the presence of matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs). Changes in MMP/TIMP levels were quantified and expressed graphically (Figure 4). MMP transcription is regulated by cytokines [35]; hence, as the cell is exposed to IL-6, secretion of collagenases MMP-1 and MMP-3 is upregulated. Secretion of the corresponding inhibitor [34], TIMP-1, also rises as a balance is sought to prevent pathological conditions [36]. In this way, IL-6 will facilitate cell migration until it is exhausted. An increase in the MSC rate of migration has previously been described in response to cytokines MCP-1 and IL-8 [37].  
Figure 4: Mean pixel density of target proteins secreted into culture media by IL-6-induced MSCs at 3 h (black) and 24 h (grey) hour intervals. Culture medium was analysed using Abcam human MMP antibody array membranes, viewed on Agilent 2100 Bioanalyser, followed by quantification using ImageJ. Values analysed via \(t\)-test, \({}^{\ast}p<0.005\).  
Figure 3: Analysis of images generated during the 24 h MSC time-lapse imaging using the manual tracking plugin for ImageJ and the chemotaxis tool provided by Ibidi. IL-6 containing media was situated toward the positive axis. (**A**) Vector plot based on the tracking of 8 randomly selected cells showing the vector each cell has moved from origin with (**B**) showing the directionality of the cells as a rose plot.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 2 Literature Review
### Unsupervised Anomaly Detection  
The unsupervised learning-based anomaly detection method refers to the input of an unlabeled dataset to study and analyze the network traffic data via the machine learning (MRL) method to find out the data structure and features in it and then perform anomaly detection. Unsupervised techniques are typically employed in a situation where no prior knowledge of the dataset is known [26].  
Methods, such as variable selection and anomaly detection for automatic k-means clustering [19], an improved hierarchy-based clustering and anomaly detection algorithm and its application to data mining platforms [27] and research on parallel network traffic anomaly detection method based on Spark [28], solve the dependence of supervised learning on markers, but they only analyze from a statistical point of view and ignore the potential features among data, leading to bias due to ignoring the actual meaning of outliers; for example, they cannot solve the situation due to the aggregation of outliers. Therefore, how to address unsupervised learning to learn and link the actual meaning of network data is a key issue to improve online anomaly detection [29].
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 3 Arabic Dialect Identification Model
### Discussion  
While our models used AOC data for training and testing, we can compare our results to state-of-the-art models that used the same data. As mentioned in the Related Work section, the authors of [13] proposed the AOC data-based state-of-the-art models. They reported accuracy of 87.41% and 82.45% for 3-way and 4-way classification tasks respectively. According to these results, we have over 3% absolute gain in the 3-way classification task, and over 3.4% absolute gain when dealing with the 4-way classification task. When applied to the Twitter COVID-19 dataset, the proposed model performance was notable to the level that makes it an important knowledge source for applying geo-based analysis of pandemic disasters text data.  
Regarding the limitations of the proposed work, it is worth mentioning that the Twitter dataset we used for verification is domain-specific. So, it is expected to get a higher accuracy above the result achieved in the AOC-based test. In addition, the current versions of the suggested models do not support some Arabic dialects like Maghrebi. However, the dialects we have addressed in the experiments cover about half of the Arab population along with the modern standard Arabic that is common among all Arabs.