esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Hardware and Software for the Data Monitoring System
### Design of Hardware for the Data Monitoring System
#### 3.1.3 External Clock  
An external clock has been included in the printed circuit board. This clock can use a button battery which allows it continue counting even when the complete system is not connected to an energy source along more than a year. That way it is only necessary to set the clock on time once a year during the data-logger revision.  
Besides, the time accuracy of external clocks is usually more precise than microcontrollers in long periods. The microcontroller is continuously recording the time data and it is saved in the EEPROM memory. This aspect is really useful to recover the data time if the external clock stops.
## 5 Discussion
### Limitations of the Study
#### 5.2.2 Measurement Techniques for Residual Stress  
The experimental measurement of residual stress is another area where limitations arose. In this study, the \(\cos\alpha\) technique was employed, which is widely regarded as a reliable method for surface-level stress measurement. However, this technique is not as effective for measuring stress at deeper levels within the material. This limitation is noted in prior research as well. [PERSON], [PERSON] et al. (2019) discussed the challenges of using X-ray diffraction-based techniques, including the \(\cos\alpha\) method, in capturing accurate stress data beyond the surface layers. Their study indicated that while surface measurements were accurate, deeper residual stresses often required complementary methods, such as neutron diffraction or hole-drilling techniques, to provide a more comprehensive stress profile. In our study, the inability to measure deeper stresses with high precision may have contributed to discrepancies between the FEA results and experimental data in the material's lower regions. This limitation suggests that combining multiple measurement techniques would offer a more holistic understanding of stress distribution throughout the material, as highlighted by [PERSON] et al. (2019).
# Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities
###### Abstract  
The Internet of Things (IoT) is an emerging technology and provides connectivity among physical objects with the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematze daily routine tasks. In the literature, many solutions facilitate remote users to gather the observed data by accessing the stored information on the cloud network and lead to smart systems. However, most of the solutions raise significant research challenges regarding information sharing in mobile IoT networks and must be able to stabilize the performance of smart operations in terms of security and intelligence. Many solutions are based on 5G communication to support high user mobility and increase the connectivity among a huge number of IoT devices. However, such approaches lack user and data privacy against anonymous threats and incur resource costs. In this paper, we present a mobility support 5G architecture with real-time routing for sustainable smart cities that aims to decrease the loss of data against network disconnectivity and increase the reliability for 5G-based public healthcare networks. The proposed architecture firstly establishes a mutual relationship among the nodes and mobile sink with shared secret information and lightweight processing. Secondly, multi-secured levels are proposed to protect the interaction with smart transmission systems by increasing the trust threshold over the insecure channels. The conducted experiments are analyzed, and it is concluded that their performance significantly increases the information sustainability for mobile networks in terms of security and routing.  
Internet of Things; threats analysis; 5G; sustainable routing; mobile networks +
Footnote †: journal: _Sustainability_  
0021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 7 Conclusions  
This paper proposed a generalized multi-class NN-based decoding architecture. Simulation results indicated that the proposed method is superior to other DNN-based decoders reported in the literature [13; 14]. For the short linear block codes, the BER and CER performance of the developed method is close to the well-known OSD algorithm and slightly better than the Chase-II algorithm. It also required much less decoding time than the OSD scheme. Such an architecture is highly compatible and the training dataset is not affected by the SNR. However, the total number of nodes in the proposed NN will exponentially increase as the message length becomes longer. It becomes difficult for the current hardware equipment to train such a network. It is expected to solve the problem as the technology of quantum computing is well-developed.  
Conceptualization, S.-I.C., H.-C.C. and M.-Y.C.; Methodology, K.H.; Software, K.H. and Y.-W.L.; Formal analysis, K.H.; Data curation, K.H.; Writing--original draft, Y.-W.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data is available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **Chase-II** & **OSD(2)** & **[**13**]** & **[**14**]** & **Proposed (I)** \\ \hline BCH (15, 7) & 123 μs & 585 μs & 7105 μs & 3760 μs & 780 μs \\ BCH (15, 5) & 284 μs & 2360 μs & 6907 μs & 4100 μs & 761 μs \\ QR (23, 12) & 2896 μs & 14,340 μs & 8820 μs & 6300 μs & 2069 μs \\ BCH (31, 11) & 492,427 μs & 15,500 μs & 5470 μs & 4000 μs & 1398 μs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average Decoding Time.  
Figure 14: (a) BER for BCH (31, 11) Code, (b) CER for BCH (31, 11) Code.
## 2 DC-DC Converters--Background
Coupled inductors (CIs) are utilized instead of isolated inductors to improve the voltage output and converter-generated interleaved structures. The switching frequency required in CI-based conversions is readily satisfied by appropriately altering the switching frequency of the CIs. Microgrid applications use various DC-DC converters. A DC-DC converter with a multiport is suggested in [18] for microgrid applications. The author of this study conducted research to minimize many conversions and components, and a thorough analysis was conducted. Yet, this architecture produces a low output while using more switches and passive parts than usual. To sustain the boosted output voltage with high frequency in the Dual Active Bridge converter, a new control method is used [19]. This technique cannot be used in a non-linear system because classical techniques were used to construct the controller. In [20], a converter with an extremely high gain was suggested. The gain of the proposed structure is six-times greater, but the circuit does not employ inductance components. An explanation and demonstration of a DC microgrid with solar PV integration was presented in [21]. An efficiency of 92.3% for the hybrid combination's full load capacity was achieved. In this study, there are six operational modes and six switching devices, each of which suffers increased stress. For use with renewable grid applications, a reconfigurable structure was suggested in [22]. This configuration is utilized to link a variety of regenerative loads; however, additional diodes and capacitors are used for more operating modes.
## 5 Numerical Experiments
### Comparison of Flexible and Fixed Composition Mode
It should be noted that the blue curve in Figure 9 is the hourly arrival passenger flow curve, which represents the number of passengers who arrive at the station at each time period and choose the intercity railway to travel. If the passengers in a certain period cannot be fully served due to insufficient transport capacity, the unserved passengers will choose to wait for the next time period with probability \(\varepsilon\). Therefore, the number of passengers that are served in the next time period may be greater than the number of passengers that are arriving in the current period, which usually occurs after the peak period of passenger flow. Take the red rectangular box in Figure 9 as an example, when the flexible composition mode is adopted, 1594 people are not served during the period from 20:00 to 21:00 due to the capacity limit, of which 797 (\(\varepsilon=0.5\)) passengers choose to travel in the next time period. The number of arriving passengers from 21:00 to 22:00 is 1918 (the blue line). Therefore, the total number of passengers during the period from 21:00 to 22:00 should be 2715, while the actual number of passengers which can be served is 2472 (the red line) due to the limited transport capacity. Figure 9: \(Cont.\)  
Figure 10 shows the comparison of the line running frequency and service passenger flow at each time period under different composition modes when \(N=140\). Compared with the condition that the carriage resources are relatively tight, the service capacity that is provided by different composition modes at each time period is basically the same, but the frequency of departure in the flexible composition mode is still higher than that in the fixed composition mode. Based on the above analysis, the flexible composition mode is more conducive to adapting to the fluctuation of passenger flow, which can improve the matching degree of the train running structure and passenger flow at each time period, improve the utilization efficiency of carriage resources, and increase the social and economic benefits. Moreover, the advantages of the flexible composition mode are more obvious under the condition of tight carriage resources. Figure 10: _Cont._  
Figure 9: The operational frequency and service capacity analysis in the upstream and downstream direction when \(N=60\).
# A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition
## 5 Experiment and Discussion  
### Experiment Setting  
In this work, the standard dataset WSC-08 [43] was used to evaluate the performance of MSPEA2+. It has been used many times in web service composition [33; 34; 36]. Semantics were represented as ontologies written in OWL-S and services were represented in WSDL, which enabled the dataset to be easily parsed. All of the data in this dataset come from real public web services on the internet. Six representative QoS attributes were evaluated according to the normalized average method and user preference: time, cost, availability, reliability, SIM, and MT. The QoS attribute weights were \((W_{t}=1,W_{c}=1,W_{a}=1,W_{r}=1,W_{MT}=1,W_{SIM}=1)\). All experiments were conducted on a MacBook Pro equipped with Apple M1 Pro, 8 core (6 + 2) CPU, 32 GB RAM, macOS 13.4 operating system, and IDEA 2023. For the selection of test parameters, the population size was 50, and the number of iterations was 300. For the SPEA2+, NSGA2, and SPEA2 algorithms, the probabilities of crossover and mutation were set to 0.8 and 0.2. The mutation probability for MSPEA2+ was set to 0.2, the probability of preference crossover to 0.1, and crossover to 0.6. In the experiments described in Sections 5.3 and 5.4, the archive size of the SPEA2, SPEA2+ and MSPEA2+ was set to 40% of the population size. All experiments were conducted independently 30 times to avoid experimental errors.  
The main influencing parameter of the experiment was the inverted generational distance (IGD) [44]. This is a commonly used performance metric in multi-objective evolutionary algorithms. IGD is calculated through measuring the distance from the point on each Pareto front to the most recently generated solution and then averaging these distances. In this way, IGD represents the overall distribution difference between the algorithmically generated solution and the Pareto frontier.  
The first experiment studied the effect of archive size, based on tasks WSC-0803 and WSC-0804. In particular, six sets of archive-size settings, which were 5%, 10%, 20%, 40%, 60%, and 100% of the population size were studied. The second experiment studied the iteration performance of SPEA2, SPEA2+, NSGA2, and MSPEA2+ using the WSC-0801 and WSC-0802 datasets. The third experiment investigated mean IGD based on the WSC-08 experimental set.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 4 Recurrent Neural Network Models
### GRU Working Structure  
RNN architectures, such as the vanishing gradient problem and the incapacity to accurately capture long-term dependencies, are addressed by the GRU. Because GRUs are made to update and reset their internal state selectively, they can avoid consuming superfluous memory and retain pertinent information across longer sequences. The update gate and the reset gate are two of the main parts of the GRU design. Figure 6c illustrates GRU's internal organization. At every time step, these gates decide which data to keep and which to delete, controlling the flow of information through the network. At every time step, these gates decide which data to keep and which to delete, controlling the flow of information through the network. In addition, GRUs have a candidate state calculation phase that uses the reset gate and the current input to create a new candidate state. The final concealed state for the current time step is then created by combining the output of the update gate with this candidate state. GRUs have several advantages over conventional RNNs, one of which is their reduced parameter requirements and improved ability to capture long-term dependencies. Because of this, GRUs are especially well suited for sequential data tasks including time series prediction, machine translation, and language modeling. Furthermore, because of their more straightforward architecture, GRUs can be trained more quickly than LSTMs, which makes them a preferred option for many recurrent neural network applications. The GRU calculation procedure is explained using the formulas as follows [33]:  
\[U_{t}= \sigma(G_{U}X_{t}+O_{U}H_{t-1}+b_{U}) \tag{18}\] \[R_{t}= \sigma(G_{R}X_{t}+O_{R}H_{t-1}+b_{R})\] (19) \[\widehat{H}_{t}= \tanh(G_{H}X_{t}+O_{H}(R_{t}\otimes H_{t-1})+b_{H})\] (20) \[H_{t}= (1-U_{t})\otimes H_{t-1}+U_{t}\otimes\widehat{H}_{t}, \tag{21}\]  
where the biases are expressed as, respectively, \(b_{U}\), \(b_{R}\), and \(b_{H}\). Again, \(G_{U}\), \(G_{R}\), \(G_{H}\), \(O_{U}\), \(O_{R}\), and \(O_{H}\) are weight matrices. \(\sigma(c)=(1+e^{-c})^{-1}\) is used to calculate the gate activation function and expressed as a sigmoid function. The output of the hidden layer at the current instant is denoted as \(H_{t}\), while the input state \(X_{t}\) and the output of the hidden layer at the preceding instant are combined to yield \(\widehat{H_{t}}\). The state activation function is computed via the hyperbolic tangent function, \((\tanh)\). This uniformity makes it possible to assess the effectiveness of various RNN techniques for interference management fairly.
###### Abstract  
This study conducted in situ monitoring by means of distributed optical fiber sensors (DOFS) and multipoint borehole extensometers (MPBXs). Combined with the measurement data of water level depth, the development of separation was analyzed comprehensively for the first time. At first, the development height of the water-conducting fracture zone was predicted. As the results show, the predictive data is 173.95 m. According to the in situ monitoring data, the top boundary height of the water-conducting fracture zone is determined at a height of 186.1\(\sim\)207.9 m, which is in line with the predicted results. Based on the DOFS data, it can be inferred that the separation layer exists at the depths of 351.3\(\sim\)390.4 m. According to MPBXs data, the largest development of the separation layer is also inferred to be located at the depths of 324-388 m. The in situ monitoring methods in this study can directly obtain the development position of the separation layer in the internal overburden, which can provide good guidance for the on-site control of water disasters caused by water accumulation in separation.  
separation; water-conducting fracture zone; DOFS; MPBXs; water level depth Article
# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
## 5 Test Results  
The tests provided in this section are related with the two-step ML-based solution presented in previous section. Concretely, we performed multiple measurements in LTE network and, based on those measurements, we trained the two ML algorithms (ML\({}_{1}\) and ML\({}_{2}\)), each algorithm representing one link of the network (i.e., UAV-antenna and control station-antenna). ML\({}_{1}\) and ML\({}_{2}\) use the same models for resource usage prediction, however, since both links are measured using different parameters, then ML\({}_{1}\) and ML\({}_{2}\) will tune the layers in a different way. In addition, another algorithm, ML\({}_{3}\), is trained using measurements of both links, this is end-to-end measurements from UAV to control station. Moreover, we assume that the selection of the routing in link 1 and link 2 are subject to the following constrain: The connection (routing path) in link 1 and in link 2 must be served  
Figure 3: Machine Learning (ML)-based methodology for achieving final Utility functions used in two-steps routing selection.  
by the same operator in order to avoid inter-operator roaming (IPX network, see Figure 2). ML\({}_{3}\) has the end-to-end information from link 1 and 2, so the final routing selected by ML\({}_{3}\) is the optimal one. ML\({}_{3}\) corresponds to the best trained machine (named \(f\) in the previous section).  
The next step consists of setting a hypothesis space \(G\) with two potential functions and check which one of them fits better with function \(f\). The best function in \(G\) is considered the Utility function and will be used during the evaluation phase.  
The evaluation phase follows the footsteps of Machine Learning theory, so the measurements from the network are classified in different sub-groups and k-fold cross-validation is performed [25; 26; 27].
## 1 Introduction
Nevertheless, using the model requires preliminary validation to assess its outcomes under site-specific conditions. Several authors have assessed the suitability of Hydrus-2D to simulate the infiltration processes of water and solutes in the soil, evidencing fairly good agreements between simulated and observed SWCs [13; 21; 22; 23; 24]. To the knowledge of the authors, there are a few continuous long-term observed SWC datasets (e.g., [25; 26]), available at a very high temporal resolution, to be used for model calibration and validation. Model simulations have usually been run with daily time steps and have been compared with weekly to monthly simulation time domains. The objective of this paper is to assess the performance of the thermal inertia and Hydrus numerical models to estimate SWC in the upper soil layer. After the detailed calibration of the empirical equation relating thermal inertia and SWC under controlled laboratory conditions (thermal inertia based on the TLHS method), a remote-sensing-based approach (thermal inertia based on proximity sensing data) was applied in situ to assess the SWC under two extreme conditions of soil-water status (well-watered and air-dry). Despite the ability of remote sensing to describe the spatial patterns, in this application the technique was used to retrieve punctual values of SWC, which were compared with those obtained by other assessment methods. Continuous, independent, sensor-based measurements of SWC (TDR measurements) allowed the evaluation, qualitatively and quantitatively, of the performance of the thermal inertia and Hydrus models.
## Abbreviations  
The following abbreviations are used in this manuscript:  
List of Acronyms  
ADRC Active disturbance rejection control  
BAP Baromatic air pressure  
CBNC Cascaded backstepping nonlinear controller  
DOBC Disturbance observer-based control  
EKF Extended Kalman filtering  
ESO Extended state observer  
FCS Flight control system  
GPS Global positioning system  
IMU Inertial measurement unit  
LiPo Lithium polymer  
PID Proportional integral derivative controller  
PWR Power-to-weight ratio  
SKF Standard Kalman filtering  
TD Tracking differentiator  
UAV Unmanned aerial vehicle  
USB Universal serial bus  
Variables and Parameters  
\(\varphi,\theta,\psi\)  
Roll, pitch, yaw, respectively  
x-coordinate measured along the east-west axis, y-coordinate measured  
along the north-south axis, and z-coordinate measuring elevation  
\(m\) Quadrotor mass, \(kg\)  
\(I_{x}\), \(I_{y}\), \(I_{z}\)  
Rolling, pitching, and yawing moments of inertia, respectively, \(kg\cdot m^{2}\)  
\(l\) Distance between the propeller axis and the mass center of the UAV in m  
\(C_{d(x,y,z)}\)  
Translational drag coefficients in N/m/s  
\(C_{a(x,y,z)}\)  
Aerodynamic friction coefficients in N/m/s  
\(K_{b}\), \(K_{d}\)  
Thrust coefficient in N\(\cdot s^{2}\) and drag coefficient in N\(\cdot m\cdot s^{2}\), respectively  
\(g\)  
Gravitational constant in m/s
## 4 Establishment of the Model
### Computational Model  
#### 4.2.1 Electromagnetic Field Calculation Model  
The finite element analysis software COMSOL Mutiphysics 6.0 is used to simulate the Epstein square ring, and the transient field is used to simulate its electromagnetic characteristics. The following assumptions are made before performing the calculation: the core, winding, and other materials are isotropic; the influence of the lead current and displacement current on the magnetic field is not considered; and there is no free charge in the Epstein frame. The mathematical calculation of the Epstein square ring is used for the electromagnetic field; the equations involved are as follows:  
\[\bigtriangledown\times\mathbf{H}=\mathbf{J} \tag{1}\]  
\[\mathbf{B}=\bigtriangledown\times\mathbf{A} \tag{2}\]  
\[\mathbf{J}=\sigma\mathbf{E}+\mathbf{J_{e}} \tag{3}\]  
\[\mathbf{E}=-\frac{\partial\mathbf{A}}{\partial\mathbf{t}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Outer Perimeter** & **Inner Perimeter** & **Magnetic Circuit** & **N1** & **N2** \\ \hline
280 mm & 220 mm & 940 mm & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main design parameters of 0.10 mm UTGO silicon steel Epstein square rings.  
The constitutive relation of the equation is  
\[\mathbf{B}=\mu\mathbf{H} \tag{5}\]  
According to Equations (1)-(5), the vector magnetic potential equation of the magnetic field of the Epstein square ring is derived as follows:  
\[\frac{1}{\mu}\bigg{(}\frac{\partial^{2}\mathbf{A}}{\partial x^{2}}+\frac{\partial^{ 2}\mathbf{A}}{\partial y^{2}}+\frac{\partial^{2}\mathbf{A}}{\partial z^{2}}\bigg{)}=- \sigma\frac{\partial\mathbf{A}}{\partial t}+\mathbf{J_{e}} \tag{6}\]  
where \(\mathbf{B}\) represents the magnetic induction intensity vector; \(\mathbf{H}\) represents the magnetic field intensity vector; \(\mathbf{E}\) denotes the electric field intensity vector; \(\mathbf{A}\) represents the vector magnetic potential; \(\mathbf{J}\) represents the current density; \(\mathbf{J_{e}}\) represents the external current density; \(\sigma\) is the conductivity; and \(\mu\) is the permeability.