esa-sceva/satcom-chunk-collection huggingface dataset:
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 3 MADDPG-Based Resource Allocation
### Computational Complexity Analysis  
The computational complexity of Algorithm 1 mainly arises from two iterative phases: the convergence phase for updating the estimation covariance \(\mathbf{P}_{n}^{ijk}\) and the scheduling phase involving nested loops over time slots (\(K\)), agents (\(N\)), and groups (\(G\)). Specifically, the convergence phase's complexity is \(\mathcal{O}(k_{\max}\times N\times G)\), and the scheduling phase's complexity is \(\mathcal{O}(K\times N\times G)\). Thus, the total complexity of Algorithm 1 is \(\mathcal{O}(N\times G(k_{\max}+K))\), reflecting linear scalability in terms of the agents and groups.  
For the MADDPG algorithm used to solve Problem 2, the complexity consists of two parts: centralized critic network training and decentralized actor network execution.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Parameter and Value** \\ \hline \multirow{5}{*}{Transmission model} & N = 9, G = I = 3, M = 16, \(p_{\max}=20\) dBm \\  & \(\delta_{R}=\delta_{R}=4\)\(P_{\text{RHS}}=32\) dBm \\  & \(P_{\text{tot}}=30\) dBm, \(P_{\text{DC}}=-5\) dBm, \(P_{c}=-10\) dBm \\  & \(\alpha_{R}=2.7\), \(\alpha_{B}=1.5\), \(\alpha_{S}=1.6\), \(\alpha_{m}\in[1.1,2]\) \\  & \(\zeta=0.8\), \(\alpha=10^{17}\), \(\sigma_{1}^{2}=-70\) dBm, \(\sigma_{2}^{2}=-80\) dBm \\ \hline KF model & \(\mathbf{A}_{n},\mathbf{C}_{n},\mathbf{Q}_{n},\mathbf{R}_{n}\in[0.5,1.5]\) \\ \hline MADDPG & E = 10, K = 4000, \(\mathcal{B}_{n}=128\), \(\mathcal{C}=2000\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
The centralized training's complexity is \(\mathcal{O}(E^{\max}\times N)\), where \(E^{\max}\) is the maximum number of training episodes. The decentralized execution complexity per agent is constant per episode, resulting in an overall execution complexity of \(\mathcal{O}(E^{\max})\). Hence, the total complexity is \(\mathcal{O}(E^{\max}\times N)\). Compared to centralized approaches like DDPG (with complexity \(\mathcal{O}(E^{\max}\times N^{2})\)), MADDPG significantly reduces the computational overhead, thus enhancing the scalability and efficiency for multi-agent scenarios.
## 4 Results
### Joint Performance Test  
The Vickers hardness test was carried out on the cross section of the welded joint. The test results are shown in Figure 11. It can be seen that the joint hardness exhibits a \"W\"-shaped distribution. The curve marks the position of the outer contour of the shoulder with a vertical dashed line. The Vickers hardness of the weld TMAZ, HAZ, and WNZ areas is lower than that of the base metal. The minimum Vickers hardness value of the joint appears in the vicinity of the TMAZ and HAZ interfaces on both sides of the weld.  
Figure 11: Vickers hardness distribution of joint.  
Figure 10: Microstructure of different regions: (**a**) Upper layer of the weld nugget zone (WNZ); (**b**) middle layer of the WNZ; (**c**) lower layer of the WNZ; (**d**) thermo-mechanically affected zone (TMAZ); (**e**) heat-affected zone (HAZ); (**f**) base metal (BM).  
Table 4 gives the joint tensile test results. It can be seen that the performance test results of the three tensile specimens are similar. Under the selected process parameters, the tensile strength of the joint is 379.32 MPa, which is 82.5% of the strength of the base metal, but the plastic deformation ability of the joint during stretching is weak, and the elongation after fracture is only 58.5% of the base metal.  
Figure 12 shows the fracture position of tensile specimens, where the left side of each tensile specimen is the weld advancing side and the right side is the weld retreating side. It can be seen that all three tensile specimens are fractured on the retreating side of the weld, and the inclined fracture surface is at an angle of nearly 45\({}^{\circ}\) with the tensile direction. The strength of the material flow intersection area on the backward side of the joint is relatively weak, where microcracks tend to initiate and propagate during stretching, and eventually lead to fracture of the joint.
## 2 Background
### Multi-Channel Operation
#### 2.2.2 The USA MCO Standard  
Similar to the EU, the FCC (US Federal Communication Commission) [15] has set up a 75 MHz wide band around 5.9 GHz for C-ITS systems in the US. The band is divided into seven non-overlapping channels of 10 MHz each, plus a 5 MHz unused part at the end of the band. In addition, the usage of 20 MHz channels, combined versions of two 10 MHz bands, is also allowed [24]. As in Europe, different limitations have been introduced for transmission power, mainly to avoid interference. In general, this means values between 10 and 20 dBm per channel.  
The CCH is channel number 178, reserved for sending WSM (WAVE Short Message) and WSA (WAVE Service Advertisement) messages containing safety and channel information, respectively. The other six channels are called Service Channels (SCHs), where not necessarily safety-related packets are carried [24].  
In the US, a new protocol was introduced that supports the MCO solution in the WAVE architecture. This is the MAC 1609.4 version of the IEEE 1609 family of standards [25], which is an addition to IEEE 802.11p to allow multi-channel communication. The standard is intended to provide a mechanism where C-ITS entities in a multi-channel environment can find each other and communicate successfully using the same channel. We distinguish four different time-sharing-based channel utilization solutions in total [26] in this MCO technique:  
**Continuous method:** The system always uses and listens exclusively to the CCH channel. This approach is not actually multi-channel yet, but such a configuration is possible [2].  
**Alternating method:** In this case, time is divided into intervals (CCH and SCH intervals), and the devices involved in the communication must synchronize to a common time service to know which interval is active [27]. In this case, the CCH and SCH intervals are 50 ms long in duration, which includes a 4 ms guard to ensure that there is no overlap when switching so that there is no case where receivers cannot receive because they are still in the other time slot [2].  
**Immediate method:** The CCH and SCH intervals may change, for example, according to the actual traffic load. In this case, the time intervals are constantly recalculated before the period. The length of the CCH or SCH time interval may change, but the total period time does not; it remains 100 ms. In the immediate mode, the CCH interval could be completed way before its standard ending phase completes [2].  
**Extended method:** The SCH interval can be extended by several times its size. It is typically used when a lot of non-safety-related data are to be sent over the SCH timeframe. In such cases, the advertising service must indicate in the WSA that it can operate the advertising service in CCH time intervals in addition to SCH intervals. If the advertiser can do so, the receiving side can decide whether to switch to extended mode and maintain a connection to the service over multiple intervals [2].
# Study on the Mechanism of Stress Sensitivity Changes in Ultra-Deep Carbonate Reservoirs
## 2 Stress-Sensitive Modelling of Ultra-Deep Carbonate Reservoirs  
Based on the characteristics of carbonate reservoirs and the influence of the mechanical parameters of rocks (such as Young's modulus and Poisson's ratio) under different stresses, quantitative evaluation formulas for the stress sensitivity of matrix-type and fracture-type carbonate reservoirs were derived using the classical mechanics solution by [PERSON], [PERSON]'s law [21; 22].  
The matrix-type carbonate reservoir is a significant type of rock that contains both oil and gas resources. To evaluate this reservoir quantitatively, we must consider its petrophysical properties as well as its mechanical parameters, such as permeability, porosity, and Young's modulus. These factors primarily affect the stress sensitivity of the reservoir. A formula has been developed to reflect the stress sensitivity of the matrix-type carbonate reservoir, which considers changes in permeability, increases in porosity, and changes in Young's modulus under different stresses.  
The fractures carbonates reservoir is a complicated structure made up of matrix-type carbonate reservoirs and a series of fractures. A formula for evaluating stress sensitivity has been developed, which takes into account the permeability, number, and size of fractures under different stress conditions. This formula can guide the development and stability of intensive exploited fracture systems under various stress levels, and effective exploitation and development can be achieved in carbonate fracture-type reservoirs.  
These quantified evaluation formulas provide theoretical support and practical references for the evaluation and management of carbonate reservoirs and their oil and gas resources under different stress conditions.
## 4 Converters Design
### DC-DC Full Bridge Converter Design  
For all versions of this topology, the inductor current ripple criteria is applied to avoid high frequency components in the system. In the DCMC, the different DC-DC full bridge converter versions perform particular tasks. 400 W, 190 V input and 48 V output version supplies up to six tandems driver-LED lumininary from the secondary bus. 1.2 kW, 380 V input and 190 V output version takes power from primary bus to supply electronic loads, like personal computer and others in teachers offices. To carry out the transfer between the primary bus and the secondary bus, there are two more versions of the DC-DC full bridge topology, both with a capacity of 3.3 kW. The first one performs a step-down function by transferring power from the primary bus at 380 V to the secondary bus at 190 V. The second of these versions has step-up capacity and performs the opposite operation, transfering energy from the secondary bus with a voltage of 190 V to the bus primary bus at 380 V.  
On the other hand, the DCMR architecture is a multibus array, so each bus has at least one GD, one ESS and some loads. In the selected system, there are two main buses, so there is one ESSs in each bus. The ESS of the primary bus is made up of a serial arrangement of twenty batteries. To carry out the power management in the ESS batteries in the primary  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Version** & \(I_{L}\) & \(R_{o}\) & \(L\) & \(C_{1}\) & \(C_{2}\) & \(R_{D1}\) & \(R_{D2}\) & \(\Delta i_{L}\) & \(\Delta v_{C2}\) \\ (\(V_{out}\)) & (A) & (f) & (mH) & (\(\mu\)F) & (\(\mu\)F) & (mG) & (mG) & (\(\%\)) & (\(\%\)) \\ \bus, two versions of the full bridge DC-DC converter are used, the first of these versions with a capacity of 2.5 kW is used to charge the SAE from the 380 V bus at 250 V output. The second version for this task, also in 2.5 kW to take energy from the ESS at 250 V and set the primary bus to 380 V. The ESS in the secondary bus is composed by a series arrangement of ten batteries. To carry out the power management in the ESS batteries, another couple of versions of the DC-DC full bridge converter are used; the first of these versions has a capacity of 1.25 kW and is used for the ESS load from the 190 V bus and output at 125 V. In addition, a 1.25 kW version is included in the system to take power from the ESS at 125 V and sets the secondary bus at 190 V. The design procedure is based in procedure given in [38]. All of the versions perform in 50 kHz switching frequency. The values calculated for all the versions mentioned appear in Table 5.  
For each version, passive elements minimum obtained values for this topology are \(L_{min}\) and \(C_{min}\); then, three times \(L_{min}\) value to meet current inductor ripple criteria and to keep the converter performing in CCM operation.
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
## 2 Background to Quantum Key Distribution (QKD) Networks
### QKD Network Type
#### 2.3.3 Quantum Repeater Network  
The network topology of a quantum repeater network is similar to that of a trusted node network; however, the main difference is that the quantum node is equipped with a quantum repeater [63], which uses quantum teleportation [64; 65] or entanglement swapping [66], which assists the two remote quantum nodes in transmitting single photons or sharing entanglement states. Although the quantum repeater network can provide a more complete environment than the other networks in terms of applications, the quantum repeater technology is not mature enough, and the cost of the quantum repeater is still high. Thus, a quantum repeater cannot be generally adopted in existing QKD networks.
###### Abstract  
The main objective of this paper is to present a methodology for the reliable estimation of the energy consumption of electric vehicles, focusing on the main electrical subsystems of passenger cars. This paper presents a comparative analysis of the available regression models and the results of our simulation experiments. While numerous regression models have been documented in the literature, their accuracy is not always satisfactory. Consequently, there is a need to develop a sufficiently accurate and comprehensive generalized simulation framework, which is presented in the paper. Currently, most of the major vehicle manufacturers have developed pure electric vehicle platforms and are using them in the production of many models available on the market. The estimation of consumption data for these vehicles is still based on traditional techniques, namely, prediction from historical operation data. To overcome this problem, in this article, we have constructed a multi-element, model-based simulation for the purpose of implementing an energy consumption monitoring system. In order to create a simulation that reflects real-life vehicle behavior, the input data are based on empirical measurements, while the simulation model is based on actual electric vehicle parameters. In the main simulation model, it is possible to simulate the energy consumption of the vehicle's drive system and to extract the requisite input data for the simulation of the other vehicle subsystems. In regard to the simulation, the subsystems that have been incorporated are the electric vehicle steering system, the vehicle lighting system and the HVAC system. After running the simulation, the total system consumption for a given trip segment is obtained by running each vehicle subsystem simulation. The findings were validated with real data and compared with two relevant regression models. Our preliminary expectation is that, given the level of detail of our simulation, the developed model can be considered validated if the error of the estimate remains below 4% and if the simulation model in question yields superior results in comparison to other regression models.  
BEV; model-based simulation; power consumption estimation; electric passenger car +
Footnote †: journal: Applied Sciences
## 3 Analysis Model
```
#init model model <-keras_model_sequential() initializer <- tfkeras$initializers$GlorotNormal() unite<-512 # Build Model model <- model %\(\otimes\)% layer_dense (units = unit, activation ='relu', input_shape = shape(data_dim), kernel_initializer = initializer, name = \"input_layer\") %\(\otimes\)% layer_dropout (rate = 0.5) %\(\otimes\)% layer_dense (units = unit_activation ='relu', kernel_initializer = initializer,name=\"hidden_layer1\") %\(\otimes\)% layer_dropout(rate = 0.5) %\(\otimes\)% layer_dense(units = unit_activation ='relu', kernel_initializer = initializer,name=\"hidden_layer2\") %\(\otimes\)% layer_dropout(rate = 0.5) %\(\otimes\)% layer_dense(units = unit_activation ='relu', kernel_initializer = initializer,name=\"hidden_layer3\") %\(\otimes\)% layer_dense(units = unit_activation ='relu', kernel_initializer = initializer,name=\"hidden_layer4\") %\(\otimes\)% layer_dropout(rate = 0.5) %\(\otimes\)% layer_dense(units = unit_activation ='relu', kernel_initializer = initializer,name=\"hidden_layer5\") %\(\otimes\)% layer_dropout(rate = 0. ```
#init model model <- keras_model_sequential() n_filter<512 # Build Model model <- model %\(\infty\)% layer_conv_2d (filters = n_filter, kernel_size = c (3,3), activation ='relu', input_shape = input_shape, padding ='same', strides = c(2,2)) %\(\infty\)% layer_dropout(rate = 0.50) %\(\infty\)% layer_conv_2d (filters = (n_filter^2), kernel_size = c (3,3), activation ='relu', padding ='same', strides = c(2,2)) %\(\infty\)% layer_dropout(rate = 0.5) %\(\infty\)% layer_conv_2d (filters = (n_filter^4), kernel_size = c (3,3), activation ='relu', padding ='same', strides = c(2,2)) %\(\infty\)% layer_max_pooling_2d (pool size = c (2,2), padding ='same') %\(\infty\)% layer_dropout(rate = 0.5) %\(\infty\)% layer_flatten() %\(\infty\)% # # # # #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>  
We divided the students in BMI 4 into two parts, one with BMI\(\%\) < 97 th percentile, and the other with BMI\(\%\)\(\geq\) 97 th percentile. No student had an exact BMI \(\%\) of the 97 th percentile. A total of 17 students among 25 students in BMI 4 were over a BMI \(\%\) of the 97 th percentile. Among the 17 students with a BMI\(\%\)\(>\) 97 th percentile, 10 students were used to train the feedforward and convolutional neural networks, and 7 students were used for testing the neural networks. The students with a BMI \(\%\) < 97 th percentile, as well as those in BMI 1, 2, and 3, were merged and labeled \"0\". The students with a BMI \(\%\)\(\geq\) 97 th percentile was labeled \"1\". The feedforward and convolutional neural networks were trained with the new dataset coded in \"0\" and \"1\". The feedforward and convolutional neural networks with the new dataset show the highest rate of accuracy among all the tested models. The accuracy of feedforward neural networks using the new dataset is 92.6\(\%\), as displayed in Figure 4(a). As is shown in Figure 4(b), the accuracy of convolutional neural networks using the new dataset is 87.1\(\%\). Figure 4: Losses and Accuracies of the Four Models.
## 5 Discussion and Future Directions
### Limitations of SCI Device-Based Therapies
#### 5.2.5 Cost and Accessibility  
SCI devices require extensive equipment and top-of-the-range biomaterials, which results in high development and operational costs. This tends to limit widespread use. Advanced devices like BCIa and robotic exoskeletons are expensive to produce and maintain, with limited insurance coverage or reimbursement pathways. Similarly, implantable systems require surgical infrastructure and follow-up care, making them inaccessible in low-resource settings. Few studies include cost-effectiveness analysis or address economic strains in device availability. For example, the [PERSON] et al. study for the ultrasonically powered FES device implies cost reduction due to the elimination of external batteries,as well as reducing the long-term cost due to the reduction of surgical replacements and external hardware [16]. The traditional FES device, in a separate randomised control trial, had shown to cost GBP 14,300 per quality-adjusted life year, over two years, which is lower than the GBP 20,000 NHS quota, making it cost-effective for the NHS [51]. [PERSON] et al. covered robotic gait training with exoskeletons; however, they did not mention cost. The cost of this was analysed in a study performed in Singapore, which showed chronic stage treatment cost SGD 440,388 (equivalent to GBP 256,233), deeming it very unfavourable [52]. Wearable FES garments, by [PERSON] et al., have emphasised low-cost mass production potential; however, they do not provide the unit price of the device. Yet the reusability of the product proves a future cost-effectiveness [19]. It is important to note that most studies do not report direct device prices, especially for prototypes or implants, hence why the other studies mentioned in this review do not have a cost analysis.
## 4 Analysis and Discussion
### Future Work
More detailed land-cover classes can be involved to obtain some other indicators related to human activities at the fine scale such as human-impact intensity [46]. Additionally, bi-temporal Google-Earth images may come from different satellite sensors, and the large variations between two images would lead to a poor change-detection result. Moreover, historical Google-Earth images may not be available at a desired time. Recently, satellite constellations constructed by a number of artificial satellites have gained attention [47], providing four- or eight-band multispectral images with a 3-5 m spatial resolution and covering the entire land surface of the Earth every day [48]. These images have the ability to monitor CHS with a high frequency and low cost based on the proposed workflow, which can be an interesting point for future research.
## 7 Conclusions  
With the rise of the IoT and the number of smart applications in various domains, there is an increased demand to provide assurances that the applications will satisfy the  
Figure 5: Pareto front derived from the experimental evaluation with existing storage nodes located in Europe. The plot on the left illustrates all available Fog storage nodes, whereas the plot on the right illustrates only the non-dominated nodes that form the Pareto front.  
Figure 6: Trade-off between the nodes forming the Pareto front.  
requested QoS. Hence, providing means to establish the connection between software engineers and service providers by stipulating SLAs is of high importance.  
By introducing a novel SLA specification and Pareto-based decision-making method, this paper provides means to deploy databases and file systems with guarantees for high QoS in the Things-to-Cloud continuum. The newly proposed SLA specification, extended the existing CSLA specification with attributes allowing to define a node in the Things-to-Cloud continuum. Moreover, this paper investigated specific QoS parameters that are particularly important for data management operations. Significant effort was made to precisely define SLA parameters, and chose explicit formulae for their calculation based on lower-level QoS parameter measurements. In the context of this work, three distinctive SLA parameters (i.e., throughput, availability and cost), are used, however, the choice can be extended towards more parameters. In addition, the applicability of the Pareto-based decision-making method for the provisioning stage was evaluated, which showed that the proposed solution was able to select optimal storage nodes and thus remove up to 85% of the initially allocated nodes that were not optimal storage nodes in respect to the defined QoS requirements.  
The usability of the proposed solution was validated by using the DECENTER Fog Computing and Brokerage platform and can now be used with the platform. As a result to the successful results, our specification can be used for interoperability purposes with other Cloud storage platforms. In further research, the developed SLA approach will be used in various smart applications involving the IoT and will be further improved with a goal to become a standard.  
Conceptualization, P.K., U.P., V.S. and M.C.; Methodology, P.K., U.P., V.S. and M.C.; Software, P.K. and U.P; Validation, P.K., U.P., V.S. and M.C.; Resources, V.S. and M.C.; Writing--review and editing, P.K., U.P., V.S. and M.C.; Supervision, V.S. and M.C.; Project administration, V.S.; Funding acquisition, V.S. All authors have read and agreed to the published version of the manuscript.  
The research and development reported in this paper have received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 815141 (DECENTER: Decentralised technologies for orchestrated Cloud-to-Edge intelligence) and grant agreement no. 957338 (ONTOCHAIN: Trusted, traceable and transparent ontological knowledge on blockchain).  
Not applicable.  
The authors declare no conflict of interest