esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Receiving Process of the Composite Signal
### The Application Scenario  
In addition to saving spectrum resources, the composite signal can also be suitable for highly dynamic communication under spacecraft formation or UAVs. The paper uses Figure 6 as an application scenario to explain this. In the scenario, the spacecraft or UAVs are replaced by different devices.  
Device C is the master device that can continuously transmit composite signals to slave device A and device B. In addition, devices A and B send two different BOC signals to device C, which are used to distinguish the two devices. In addition, the BPSK signal of the uplinks are controlled of device C. When the communication target of device C is switched from A to B, the chip offset and Doppler frequency of the BOC signal are resynchronized to the BPSK demodulation module. In this way, slave devices switch in a clock cycle without the Doppler frequency search process, even under highly dynamic conditions.  
In the system, we use three different PN codes in BOC signals to differentiate between devices A, B, and C. However, in the code division multiple access (CDMA) system, the near-far problem will cause the cross-correlation interference of different spacecraft. To prevent the interference of near spacecraft, we adopt the regenerative BOC signal to  
Figure 5: Bit synchronization and demodulation of the BPSK signal.  
Figure 6: The application scenario.  
suppress the influence of near-far effect [33]. In the algorithm, the receiver will regenerate the BOC signal of near spacecraft with same frequency and phase through tracking loop. Then, the receiver subtracts the regenerative signal from the received signal, and obtains the transmitting signal of far spacecraft without interference.
## 5 Results and Analysis
### Hosting Capacity Evaluation by PSO Algorithm  
In this case, the PSO algorithm is used to find the PV hosting capacity of the test system. It is assumed that the connection node of the DC bus, as well the BESS SOC profile is determined in such a way that the presented OF in (7) is minimized. Note that in this case, we is set to 0.7, and BESS specifications are based on Table 2. The obtained results are summarized in Table 3. Maximum and minimum hourly voltages as well as energy loss and SOC profiles in a sample day in January are, respectively, depicted in Figures 11-14.  
Note that in the presented charge/discharge power, voltage and energy loss profiles, it is assumed that the load and PV power values remain constant during the one-hour time steps, and the voltage values and hourly energy losses are calculated accordingly. For example, the energy loss at 1 denotes the energy loss during the first time-step, i.e., 0:00-1:00, and the energy loss at 2 denotes the energy loss during the second time-step, i.e., 1:00-2:00, etc.  
Figure 10: Charge/discharge power profile.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**PV Size (MW)** & **Location** & **Total Daily Loss (MWh)** \\ \hline
2.2917 & Node 28 & 0.7542 \\ \hline \end{tabular}
\end{table}
Table 3: PSO HC assessment results.  
Figure 9: State of charge of battery.  
Figure 11: Minimum voltage hourly profile using PSO.  
Figure 12: Maximum voltage hourly profile using PSO.  
Figure 13: Energy loss hourly profile using PSO.  
Moreover, in contrast to the presented EMS, which runs in real-time, SOC scheduling in HC assessment module is based on average monthly load and PV output power profiles, and is implemented on a daily basis. Therefore, SOC levels at start and end of each day should be the same, as can be observed in Figure 14.  
As can be observed, minimum and maximum hourly voltages are within the permissible range. Moreover, during 12:00-15:00, when the PV power reaches the highest level, the voltage level also increases. Referring to Figure 13, energy losses also increase during this interval. As can be observed in Figure 14, BESS is charged during this interval to absorb some power of the PV unit and repress the overvoltage in MG. That is how the overvoltages beyond the allowable range are avoided. Note that for higher PV capacity values, voltage constraints could not be met, and thus the PV hosting capacity of the MG under the introduced conditions is equal to 2.2917 MW. Referring to Table 3, the optimal connection node of PV and BESS is node 28. Moreover, total daily energy loss is equal to 0.7542, equivalent to 5.13% of total energy consumption, which is an acceptable value.
# Modification of Nonlinear Controller for Asymmetric Marine Vehicles Moving in Horizontal Plane
## 4 Numerical Simulations
### Vehicles and Test Conditions
#### 4.1.2 Comparison with Classic Controller  
Analogous tests, that is, for the same vehicles, trajectories and initial conditions, as well as the same operating conditions, were carried out using the control scheme of [38] (referred to here as the classic controller).  
The parameters of the classical regulator, which guaranteed acceptable error convergence, were chosen as follows:  
\[k_{v_{x}}=k_{v_{y}}=25,\;\;k_{p_{x}}=k_{p_{y}}=5,\;\;k_{I_{x}}=k_{I_{y}}=0.5, \tag{46}\] \[\text{XX}\;\text{AUV}\;\;k_{v_{x}}=k_{v_{y}}=30,\;\;k_{p_{x}}=k_{ p_{y}}=5,\;\;k_{I_{x}}=k_{I_{y}}=0.5. \tag{47}\]  
Attempting to change these values did not yield better results than those presented in this section of the article.  
**Linear trajectory**. The results obtained for Kambara are given in Figure 6. They are similar to those presented in Figure 2. As can be seen from Figure 6a-d, the controller is working properly. However, the velocity error states, as shown in Figure 6c, have larger values at the beginning and are more disturbed.  
Acceptable results were also obtained for the XX AUV, as shown in Figure 7a-d. The velocity error states (Figure 7c) do not change as quickly as for Kambara.  
**Sine-cosine trajectory**. For this trajectory, the simulation results are presented for the Kambara vehicle only in Figure 6a-d. The controller works properly, but, again, the errors \(\Delta\zeta_{3}^{2}\), \(\Delta\zeta_{4}\) (Figure 6g) significantly change their values when the vehicle starts moving, which is not noticeable when an algorithm containing QV is used (Figure 4c).  
**Cycloid-like trajectory**. Figure 7e-h present the results for the XX AUV only. As can be easily seen, the algorithm works correctly and the desired trajectory is tracked. The effects are similar to those shown in Figure 5g-j. However, the changes in the error states here are not as fast as for the QV controller (cf. Figures 5h,i and 7f,g).  
**Analysis of results based on indexes**. The performance of the classic controller is given in Table 5. Slightly better results were obtained for the XX AUV than for the Kambara for the actual errors, although this was not always consistent with the error values of the position and velocity states. In addition, there is a greater reduction in the tracking accuracy for the linear trajectories than for the curvilinear trajectories. The mean thrust values \(\tau_{u}\) are much larger than the mean torque values \(\tau_{r}\), leading to the conclusion that the linear motion of the vehicle dominates.
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 7 Discussion  
The experiments carried out on the application have proven quite illustrative of the characteristics and operations of the three implemented ZKP algorithms. Focusing on the role of the global parameters, which in a real case would be defined by a trusted third party outside the verifier and prover, it has been possible to study the effectiveness of brute-force attacks. Thus, some aspects of the implementation of the protocols that would be naive for a real application case have been identified. However, they do not interfere with the academic objective of this work and may even prove instructive:  
* Allowing an unlimited number of connection attempts: The security of the authentication system is exposed in brute-force attacks because it allows the malicious user to make as many connection attempts as necessary to bypass the protocol. A real Verifier can protect itself, or at least make the attacker's job significantly more difficult, by refusing connections from an address for a set amount of time, similar to IPS protection against SYN Flood attacks on TCP servers. One may also block authentication for the specific user whose public key is being used after a certain number of attempts.
* Using small numbers as global parameters: The parameters with which the protocols work to compute the calculations must be large enough so that random matches do not occur in the calculations within modular arithmetic, since this increases the probability of a successful attack.
* Insecure value of the key: In the case of the Feige-Fiat-Shamir protocol, preventing repetition among the elements of the vector that constitute the public key is essential. In any of the protocols, a public key with value one would be equally insecure. The key generator should be implemented to assign key values inside a high and wide enough range.  
Figure 8: MITM Attack.  
Other types of attacks such as MITM have been theoretically analyzed. ZKP tests are not designed to protect against MITM attacks, only to protect against information leakage.  
The number of input sources is small enough so that the implementation of the application is not overly complicated, even when adding security checks. The interaction with the user is fluid and allows the user to be able to carry out their own analysis of the execution of the protocols since all of the necessary information is shown.  
The main objective of the application is to allow the interested student to carry out first-hand experiments in order to achieve a greater understanding of the ZKP algorithms in a pleasant and practical way. In this sense, the expectations for this work have been met.
## 5 Discussion
### Policy and Market Barriers to Digestate Utilization
#### 5.5.1 Legal Complexity in the Classification of Digestate  
The focus lies on the legal framework related to the use of digestate as a fertilizing product, and it is important to acknowledge that any legal assessment must also consider the nature and origin of the input materials fed into the anaerobic digestion process. The legal acceptability of digestate for specific uses may be constrained by the classification of these inputs. However, a detailed evaluation of feedstock admissibility is beyond the scope of this work. Instead, the following sections focus on the regulatory pathways governing the commercialization and agricultural application of digestate, assuming compliance with relevant quality and safety standards.  
A central legal challenge is the classification of digestate. Depending on its origin, treatment process, and intended use, digestate can be legally defined as a product, by-product, waste, or as a material that has achieved \"end-of-waste\" status. This classification significantly affects how digestate can be stored, transported, applied to land, and marketed. In practice, the legal status of digestate is often determined on a case-by-case basis, considering both feedstock characteristics and processing conditions.  
At the EU level, digestate is generally considered waste unless it satisfies the conditions set forth in Article 6 of the Waste Framework Directive [122] to be classified as having reached end-of-waste status. According to this directive, a material ceases to be waste after undergoing a recovery operation and meeting specific criteria, such as being commonly used for specific purposes, having a market demand, and posing no adverse environmental or human health impacts. Additionally, the material must comply with all relevant legislation. However, the absence of harmonized EU-wide end-of-waste criteria for digestate results in divergent interpretations and implementations across Member States.  
In Spain, digestate is legally defined by Article 3 (e) of Royal Decree 1051/2022 [123] as \"organic material obtained through anaerobic digestion in accordance with the requirements of Component Material Categories 4 and 5 (CMC4 and CMC5) of Anne II of Regulation (EU) 2019/1009\" [124]. The decree further clarifies that digestate may be regarded as a product recovered from waste treatment--i.e., a recovered product--provided it complies with end-of-waste criteria established in the Spanish Waste Law 7/2022 [125] and associated regulations. Consequently, under Spanish law, digestate can exit the waste regulatory framework and be treated as a product, subject to quality, traceability, and usage conditions. This status enables its commercialization and use as a fertilizing material, albeit often requiring case-specific evaluations and administrative approvals.
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 2 Materials and Methods  
For the purpose of this review, six main bibliographic databases (Web of Science, Scopus, ACM Digital Library, CiteSeer, DBLP, and Pubmed) were analyzed using specified keywords in English (M2M, 6G, IoT, IIoT, Industry 4.0, Industry 5.0, and related). The inclusion criteria were defined so as to return scientific articles and chapters in monographs (including post-conference materials) published in the last 10 years. Letters to the editor were excluded. The identified publications underwent content analysis, and duplicate studies were removed. The results of the analysis are presented below.  
The following presentation of the results stems from the way in which the introduction and diffusion of 6G networks will affect M2M--initially mainly in the area of communication and only later in the area of production, creating conditions favorable to completely new solutions, perhaps not yet known or defined. This approach also stems from the gradual planned development of 6G, which will be fully defined only in the years to come.
## 3 System Model and Problem Formulation
### Security Assurance Model  
We adopt the wiretap coding scheme to protect the vehicle MEC. According to the Shannon formula, the channel capacity \(C_{i,j}\) between the vehicle \(i\) and the UAV \(j\) is  
\[C_{i,j}=B_{j}\text{log}_{2}\bigg{(}1+\frac{P_{i}^{\Xi_{i}}G_{i,j}}{P_{n}}\bigg{)}. \tag{13}\]  
For our proposed UAV-assisted VANETs, we assume that there is an eavesdropper, which can eavesdrop the transmitted information \(\Xi_{i}\). To prevent eavesdroppers from eavesdropping transmitted information \(\Xi_{i}\), we have  
\[R_{e}=C_{i,j}-R_{s}\leq R_{th}, \tag{14}\]  
where \(R_{s}\) represents the target secure transmission rate, \(R_{e}\) represents the wiretap rate, and \(R_{th}\) represents the maximized permitted wiretap rate.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 3 Preliminaries
### Performance Metrics
#### 3.4.5 Path Loss  
Path loss refers to the attenuation or reduction in signal strength as it propagates over a distance through the wireless medium between the transmitter (i.e., the source node) and the receiver (i.e., the sink node). It is an important factor to consider in WBANs, as it directly impacts overall performance and network lifetime. Path loss increases bit error rate and packet loss, and it degrades the communication system throughput. The selection of an appropriate path loss model is crucial to accurately depicting the signal attenuation behavior in WBANs. A commonly used model is the log-distance path loss model [64], explained previously in Section 3.3.  
The impact of path loss on the overall performance of WBANs can be significant, as explained above. Higher path loss results in weaker received signals, requiring higher transmission power to maintain reliable communication. This leads to an increase in the energy consumption of the sensor nodes, which can significantly reduce the lifetime of the network. Several techniques can be employed to mitigate the effects of path loss and improve network performance. These include optimizing the placement of sensor nodes to reduce the distance between the nodes and the sink, using higher transmission power or directional antennas to compensate for the signal attenuation and deploying parent nodes to improve signal coverage.
## 4 Numerical Results and Discussions
### Critical Failure Surface  
This subsection discusses the effects of nonlinear parameters and seismic coefficients on the shape of critical slip surfaces, as presented in Figures 10 and 11. It is observed from Figure 10a that the surface surcharge has a small impact on the area of the critical slip surface. The increase in the initial cohesion leads to an extensive failure area and higher bearing capacity of the foundation; on the contrary, it is deserved to be noticed that the greater value of the nonlinear coefficient and uniaxial tensile strength results in a decrease in both failure regions and the bearing capacity, as shown in Figure 10c,d. As for the influence of seismic coefficients, Figure 11 displays the variation in the failure region of critical slip surfaces with horizontal and vertical seismic coefficients. It can be found that  
Figure 8: Seismic bearing capacity versus equivalent surcharge and horizontal seismic coefficient.  
Figure 9: Influence of nonlinear parameters on the seismic bearing capacity: (**a**) initial cohesion; (**b**) uniaxial tension strength; (**c**) nonlinear coefficient.  
the critical failure region becomes shallower as the horizontal seismic coefficient increases. However, the vertical acceleration intensity seems to not influence the failure region.
# Multi-Layer QoE Learning System Implemented by Fiberhost
###### Abstract  
Modern telecommunications networks, despite their ever increasing capacity, mostly attributed to optical fiber technologies, still fail to provide ideal channels for transmitting information. Disruptions in ensuring data throughput or the continuous flow of data required by applications remain as major unresolved problems. Most network mechanisms, protocols and applications feature adaptations that allow them to change the parameters of the transmission channel and try to minimize the negative impact of the network on the perceived quality, for example by temporarily changing the modulation scheme, or coding scheme, or by re-transmitting lost packets, or buffering to compensate for the interruptions in transmission. To respond appropriately, network operators are interested in knowing how well these adaptations are performing in order to assess the ultimate quality of their networks from the user perspective, i.e., Quality of Experience (QoE). Due to the huge amount of data associated with the collection of various parameters of the telecommunications network, machine learning methods are often needed to discover the relationships between various parameters and to identify the root cause of the observed network quality. In this paper, we present a Multi-layer QoE learning system implemented by Fiberhost for QoE analysis with a multi-layer approach based on machine learning tools.  
multi-layer QoE; learning system; fiberhost Article
## 4 Experiments and Discussion
### Balance Control Experiment Results  
For the first experiment with no external disturbances, Figure 15 shows the TWVS tracking responses using the proposed PISMC and the conventional SMC. In experimental results, the original tilt angle was approximately \(\pm\)2 degrees, and the tilt rate was approximately \(\pm\)25 degrees/s. Comparing the top left plot (PISMC) to the top right plot (SMC), one notes that the tilt angle result of the proposed PISMC controller had no overshoot, shorter rise time, and shorter settling time than that of the conventional SMC controller. Importantly, the experimental result shows that the PISMC held balance well for the developed TWVS. The results of the second TWVS experiment with external disturbances are shown in Figure 16, which shows that the output of tilt angle by the PISMC can be tracked well to follow the desired angle. The robustness of the TWVS with the PISMC is better than that of the conventional SMC. It also demonstrates that the PISMC can give reasonable angle output of the tilt motion in experiments. Comparing the PISMC results with the SMC results, the PISMC has smaller oscillation, smaller overshoot, shorter rise time, and shorter settling time than the conventional SMC. The results of the TWVS experiments validate the balance control capability of the PISMC.  
Figure 14: Attitude sensors: (**left**) accelerometer; (**right**) gyroscope.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
###### Abstract  
Electronic equipment in space is constantly exposed to high-energy particles, which can induce Single Event Upsets (SEUs) in memory components, threatening system reliability. To address this critical challenge, we propose RHLP-18T, a radiation-hardened 18-transistor (18T) Static Random-Access Memory (SRAM) cell designed to enhance robustness against radiation-induced faults. The proposed cell integrates circuit-level Radiation-Hardened-by-Design (RHBD) techniques to mitigate both SEUs and multi-node upsets. Comprehensive simulations were conducted using 90 nm CMOS technology, benchmarking RHLP-18T against nine existing RHBD cells (RHBD14T, HPHS12T, NRHC14T, QCCS12T, RHMC12T, RHWC12T, SEA14T, SIMR-18T, and SERC16T). Simulation results demonstrate that the proposed RHLP-18T cell exhibits superior SEU tolerance, achieving a Read Static Noise Margin (RSNM) over three times higher than the next best design. Moreover, the proposed cell achieves the lowest hold power consumption among all evaluated cells. These improvements result in the highest Figure of Merit (FOM), indicating that RHLP-18T provides an optimal trade-off between robustness and overall performance for operation in radiation-exposed environments.  
critical charge; single event upset (SEU); static random-access memory (SRAM); read stability; low power Article
## 5 TAFLRLR Protocol
### Best Forwarding Node Selection and Data Forwarding
Obviously, the FP of node b is greater than that of node c. Therefore, the sending node will select node b as the best forwarding node. After the best forwarding node is determined, the ID of the best forwarding node is filled in the field of the sender's ID of the packet; then, the DATA packet is sent to the best forwarding node. The flow chart is shown in Figure 11, where the N_ID and Sink_ID represent the ID of the neighbor node and the ID of the sink node, respectively. From the flow chart, we can see the procedure includes the following five steps. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rule** & **ND** & **CTV** & **FP** \\ \hline
1 & Low & Low & Rather Low \\
2 & Low & Medium & Low \\
3 & Low & High & Medium \\
4 & Medium & Low & Low \\
5 & Medium & Medium & Medium \\
6 & Medium & High & High \\
7 & High & Low & Medium \\
8 & High & Medium & High \\
9 & High & High & Rather high \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fuzzy rule table in TAFLRR protocol. Figure 10: The rule triggering and defuzzification process. **(a)** The values of input variables are 0.85 and 0.13. **(b)** The values of input variables are 0.75 and 0.27. Figure 9: Membership degree functions and FP of the candidate node c.  
Step 1: When a sending node N has a DATA packet to send, it first searches its neighbor information table for the sink node. If it cannot find the sink node, Node N finds the nodes whose layers are 1 lower than its layer from its neighbor information table as candidate forwarding nodes. Step 2: The sending node N calculates the CTV and ND for each candidate forwarding node. Step 4: Determine the candidate forwarding node with the largest FP as the best forwarding node and return the ID of the best forwarding node. Step 5: Node N updates the field of Rece ID in the packet with the ID of the best forwarding node and sends the DATA packet to the best forwarding node.