esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications
## 3 Proposed Load-Balanced and Energy-Efficient Mobile Routing Protocol
### Overview  
In this section, the detail of the proposed algorithm is presented. It comprises two phases. The first phase deals with traffic distribution over the mobile nodes with an efficient scheme. In addition, the second is for establishing reliable and trusting communication links with the support of fog nodes. In the beginning, nodes advertise their initial parameters in their communication range. All the nodes receive such information and record it in their local tables. The local table is updated with the analysis of QoS factors. Whenever any node shifts to another range, its record is removed from the table and the new node associates its information. Moreover, the dynamic routing metrics explicitly reduces the time of route formulation and offers more effective connections from node to node (N2N). Each route has a unique identity, and such information copes with a replay attack between the devices. The security keys provide improved authentication methods for mobile devices against data anomalies. Such actions decrease the chances of network compromising and intelligently manage the overall communication under the supervision of the sink node.  
Figure 1 depicts the three stages of the proposed algorithm. It includes communications equipment, network services, and secured services. In the deployed environment, communication entities include IoT devices, fog nodes, and cloud systems. IoT devices act as sensors that sense the data and send it to the fog nodes, which then send it to the sink node. Later, the sink node collaborates with the cloud system for data processing and storage. The first phase of the proposed algorithm provides clustering and services for link performance. Based on the adjustable transmission range, the nodes are separated into distinct regions. Furthermore, cluster heads are identified and their locations are made known to all members. The final stage concerns secured sessions for both low-level and high-level communication. N2N communications take place at the low level, whereas fog-to-sink node communication is carried out at the high level.
###### Abstract
* The closed-form expressions of outage probabilities of secondary users are obtained by deriving the corresponding distribution functions, and the impact of transmit power, number of relays, interference threshold and target data rate on the system outage probability is revealed. * Numerical results and simulations validate the advantages of the established cooperation, and the proposed adaptive relay selection and power allocation strategy shows better outage performance compared to the existing schemes. This paper is organized as follows. In Section 2, we describe the system model and transmission process. Then, a two-stage adaptive relay selection and power allocation strategy is introduced in Section 3. Section 4 investigates the outage probabilities of two secondary users and the numerical results are presented in Section 5. Finally, Section 6 concludes this paper.
## 3 Correlation Analysis Between Macroscopic Mechanics and Microscopic Pore Characteristics
### Porosity Analysis of Three-Dimensional Images Based on SEM
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Disturbance Degree** & \begin{tabular}{c} **Working Conditions (Hz-min)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Pixels** \\ \end{tabular} & \begin{tabular}{c} **Particle** \\ **Volume** \\ \end{tabular} & \begin{tabular}{c} **Pore** \\ **Volume** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **Volume** \\ \end{tabular} &
\begin{tabular}{c} **Porosity/\%** \\ \end{tabular} \\ \hline
0 & 0–0 & 1,495,269 & 129,007,866 & 252,285,729 & 381,293,595 & 66.16\% \\
0.22 & 20–30 & 1,957,713 & 165,039,616 & 334,177,199 & 499,216,815 & 66.94\% \\
0.28 & 20–60 & 1,559,995 & 128,533,902 & 269,264,823 & 397,798,725 & 67.69\% \\
0.28 & 20–90 & 1,645,788 & 129,942,243 & 289,733,697 & 419,675,940 & 69.04\% \\
0.29 & 20–120 & 1,669,952 & 128,927,677 & 296,910,083 & 425,837,760 & 69.72\% \\
0.31 & 35–30 & 1,304,772 & 91,322,570 & 241,394,290 & 327,168,860 & 72.55\% \\
0.39 & 35–60 & 665,118 & 46,308,014 & 123,297,076 & 169,605,090 & 72.70\% \\
0.43 & 35–90 & 1,910,660 & 129,993,659 & 357,224,641 & 487,218,300 & 73.32\% \\
0.47 & 35–120 & 830,767 & 56,173,278 & 155,672,307 & 211,845,585 & 73.48\% \\
0.42 & 50–30 & 1,200,595 & 81,144,957 & 225,006,768 & 306,151,725 & 73.50\% \\
0.52 & 50–60 & 622,836 & 38,140,816 & 120,682,364 & 158,823,180 & 75.99\% \\
0.58 & 50–90 & 1,732,772 & 100,327,395 & 341,529,465 & 441,856,860 & 77.29\% \\
0.61 & 50–120 & 1,114,456 & 58,219,540 & 225,966,740 & 284,186,280 & 79.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Porosity values calculated based on the three-dimensional model. Figure 4: Relationship between porosity and disturbance degree.
## 4 Simulations and Results
### Influence of Packet Size  
Because the handshaking and data transmission are implemented as a pipeline in the proposed protocol, the best performance will be achieved when the period of handshaking is equal to the duration of data transmission theoretically. Therefore, the data packet length is an important factor to the transmission efficiency and needs to be chosen properly.  
Figure 8a shows the durations for the handshaking period and data transmission under different packet sizes, where the node number is set to 10 and the offered load is set to 5.0 pkt/s. It can be seen that the period of TDMA based handshaking is quite stable even when the network nodes are randomly distributed in the test zoom. The duration of data transmission increases linearly as the packet size becomes large. The duration for handshaking and data transmission is nearly equal when the packet size is around 400 bytes.  
Figure 8b shows the throughputs of DSCT and the proposed protocols under different packet sizes. It can be seen that when the packet size is smaller than 200 bytes, the throughput of the proposed protocol is lower than DSCT. This is because when the packet size is small, the drawback of long handshaking period becomes significant and will reduce the throughput of the proposed protocol. On the other hand, when the packet size is larger than 200 bytes, the data transmission efficiency is large enough to compensate the negative effect of the long handshaking period and will become much higher than DSCT. It should be noted that the maximum throughput of the proposed protocol is achieved when the packet size is larger than 600, which does not coincide with the packet size at which the time for handshaking period and data transmission is nearly equal. This is because that although the total data transmission time increases with the packet size linearly, the number of packets  
Figure 8: Influence of packet size. (**a**) durations for handshaking and data transmission under different packet sizes; (**b**) throughputs of DSCT and the proposed protocols under different packet sizes.  
transmitted is not a linear function of the data transmission time due to concurrent transmission. A proper packet size might fill the vacant time better during the concurrent transmission so that the throughput can still increase when the packet size is larger than 400 bytes.
## 1 Introduction
Figure 2: Geological map of the central Betic Cordillera including the Granada Basin, modified from [17; 35; 36; 37; 38; 39]. ESGB: Extensional system of the Granada Basin; AD: Alboran Domain; SID: South Iberian Domain; NQB: Neogene–Quaternary Basin. The main active faults and their hanging wall block motion (top-right legend) are taken from the Quaternary Active Fault Database of Iberia (QAFI) [40] and are indicated by circled numbers corresponding to Table 1. NF: Normal fault. SS: Strike-slip fault. The minimum horizontal stress axes of the latest paleostresses obtained from striated microfaults are shown (bottom left legend) [41; 42; 43; 44; 45; 46]. Green axes indicate normal faulting (NF), which is defined by a \(\sigma_{1}\) dipping more than \(45^{\circ}\). Blue axes indicate strike-slip faulting (SS), which is defined by a \(\sigma_{2}\) dipping more than \(45^{\circ}\). Red axes indicate reverse faulting (RF), which is defined by a \(\sigma_{3}\) dipping more than \(45^{\circ}\). The greater the intensity of the colour of the axis, the closer to \(90^{\circ}\) of \(\sigma_{1}\) in NF, \(\sigma_{2}\) in SS, and \(\sigma_{3}\) in RF. The minimum horizontal stress axes correspond to a \(\sigma_{3}\) in normal (NF) and strike-slip faulting (SS) and to a \(\sigma_{2}\) in reverse faulting (RF). In the case of normal faulting and prolate stress ellipsoid, both \(\sigma_{2}\) and \(\sigma_{3}\) are considered as \(\sigma_{\rm{hmin}}\). In the cases of reverse faulting and oblate ellipsoid, no axis is considered as \(\sigma_{\rm{hmin}}\). This contribution aimed to analyse in detail the heterogeneous extensional tectonics of the Granada Basin and its propagation towards the surrounding areas of the central Betic Cordillera. To this aim, a comprehensive compilation of existing and new geological, seismological, and geodetic data was conducted. A new tectonic model is proposed to enhance understanding of the ongoing tectonic evolution in this complex region of the western Mediterranean. This model can serve as a valuable reference for future investigations in similar tectonic settings worldwide.
###### Abstract  
This short communication offers a preliminary view on ongoing research conducted on the as-built EOS managing steel 300. The material's cyclic elastoplastic characteristics under strain-controlled loading have been investigated experimentally. Specimens fabricated under two primary orientations, horizontally and vertically to the build plate, have been tested. The obtained stress-strain hysteresis loops exhibited symmetry, with the vertical specimen showing a higher plastic strain energy dissipation capability than the horizontal specimen. Modelling of the material's elastoplastic behaviour was performed with a commonly used kinematic hardening rule, combined with both isotropic and anisotropic yield functions and elasticity moduli. The obtained simulations of the hysteresis loops, from the implementation of these two plasticity models, indicate the advantage of the anisotropic modelling approach over the isotropic approach. The anisotropic plasticity model describes in a more representative way the inherent elastic and plastic anisotropy of the as-built material. Further research is underway to explore the low cycle fatigue performance of this additively manufactured metal.  
m 2  
the EOS EOSINT M280 system. In particular, the experimental data from strain-controlled tests on vertically and horizontally fabricated test coupons are presented, analysed and discussed. This work offers an early look at experimental data examining the cyclic elastoplastic characteristics of this additively manufactured metal, complementing and extending the existing dataset of [PERSON] et al. [10]. It also allows a comparison with a subset of the same previously published data and the evaluation of anisotropic plasticity modelling for simulation purposes. An isotropic plasticity modelling approach is commonly used by researchers and industry practitioners; however, it is a practice that may lead to higher computation error, which could, consequently, contribute to a substantial over- or under-estimation of the LCF life of this metal and parts produced with it.
# Review on Rail Damage Detection Technologies for High-Speed Trains
## 4 Railway Non-Destructive Testing Platforms and Equipment
### Comparison and Discussion of Platform Characteristics for Four Detection Methods
However, rail surface quality detection systems based on visual technology have poor robustness and huge computational complexity and makes it difficult to achieve high-speed rail detection, making it impossible to directly determine the rail operating status and conduct life health monitoring. Although ultrasonic testing methods can theoretically detect the surface quality of railway tracks well, there is still room for research and verification in practical applications, especially in large-scale trial and formal testing applications. Although terahertz technology has high detection accuracy in rail surface quality inspection and has certain advantages in non-contact detection and high-speed detection, there are certain limitations on the detection thickness for rail surface defects. In recent years, a research hotspot has been rail inspection robot systems, which are undoubtedly pieces of rail inspection platform equipment with high reliability and authenticity in the field of rail inspection. They can be equipped with multiple sensors to achieve the real-time online detection of rails in all directions and angles and realize active detection with long detection distance. Rail inspection robots are currently widely used in the field of rail inspection platforms and equipment. However, there is still room for improvement in remote control, remote communication, remote interaction, and other aspects of rail inspection robot systems, as Bluetooth WIFI, local area networks, and other technologies cannot meet the requirements of remote control. At the same time, with the rapid development of modern transportation technology, digital twin technology will be a very important research method and technology in the field of rail inspection, realizing the full life-cycle detection of rail surface quality and providing accurate predictions of alignment life.
## 5 Cost Analysis for the Proposed Preventive Control Methods
### Cost Analysis for the DLC and EDRP Methods  
On the other hand, the cost for DLC and EDRP methods take into account the rate discount for load participants. Generally, the rate discount depends on the electricity bill and demand-response contracts. In Taiwan, Taiwan Power Company provides the discounts of demand charge and energy charge to EDRP participants and the corresponding scheme concerning the Taiwan Power Company EDRP is presented in Table 4. EDRP participants pre-register their contracts on the suppression capacity. Participants can select to provide 2 h or 4 h load reduction and the average of load reduction (\(\mathbf{P}_{\text{EDRP},\text{avg}}\) (MW)) should be no less than the contracted suppression capacity. Therefore, the corresponding cost for the EDRP method can be calculated by (10)-(13),  
\[\mathbf{C}_{\text{EDRP}}=\mathbf{C}_{\text{EDRP},\text{DC}}+\mathbf{C}_{\text{EDRP},\text{ EC}} \tag{10}\]  
\[\mathbf{C}_{\text{EDRP},\text{DC}}=\mathbf{P}_{\text{CSC}}\mathbf{D}_{\text{DC}} \tag{11}\]  
\[\mathbf{C}_{\text{EDRP},\text{EC}}=\mathbf{P}_{\text{EDRP}}\mathbf{D}_{\text{EC}} \tag{12}\]  
\[\mathbf{P}_{\text{EDRP},\text{avg}}\geq\mathbf{P}_{\text{CSC}}, \tag{13}\]  
where \(\mathbf{C}_{\text{EDRP}}\) (NTD) is the cost for the EDRP method; \(\mathbf{C}_{\text{EDRP},\text{DC}}\) (NTD) is the cost for demand-charge discount in one month; \(\mathbf{C}_{\text{EDRP},\text{EC}}\) (NTD) is the cost for energy-charge discount; \(\mathbf{P}_{\text{CSC}}\) (MW) is the pre-registered contracted suppression capacity; \(\mathbf{D}_{\text{DC}}\) (NTD/MW) is the demand-charge discount per MW; \(\mathbf{D}_{\text{EC}}\) (NTD/MWh) is the energy-charge discount per MWh.  
In Taiwan, the government is currently comparing and analyzing the cost and benefits of different DR programs. However, there is no DLC program being implemented in Taiwan nowadays. References [4; 39] review the international experiences about DLC and EDRP programs. In California, three different DLC programs are provided by Pacific Gas & Electric Company, Southern California Edison Company (SCE) and San Diego Gas & Electric Company [40]. These California DLC programs provide load participants with different incentive payments, which are useful references to evaluate the cost for the DLC method in Taiwan.
## 5 The INS-Aided Acquisition Experiments with Different Grade INS  
In order to analyze the performance of different grade INS in the INS-aided acquisition experiments, a software simulation platform was firstly designed. As shown in Figure 5, the block diagram of the simulation platform includes: trajectory generator module, dual-mode GNSS signal simulation module, INS generating module and GNSS software receiver module [21]. Then, a trajectory with high dynamics, which is similar with missiles and aircrafts trajectory, is designed and simulated by the trajectory generator module. Under these conditions, the constellation of BDS and GPS are built. As shown in Figure 6, the constellation of BDS and GPS each has six satellites respectively in the simulation. Then, a dual-mode GNSS signal simulator is used to transmit the satellite radio frequency (RF) signal, which is nearly the same as the real signal. After that, the RF signal is received by an RF frontend which is designed based on MAX2769, as shown in Figure 7. Later, the received RF signal is converted to the intermediate frequency (IF) digital in the process of software reception. The gyro and acceleration data of different grade INS are simulated using the designed trajectory. Finally, the IF data and IMU data are input into the GNSS software receiver module for INS-aided acquisition experiments.  
Figure 5: The block diagram of simulation platform.  
Figure 6: The constellation of BDS and GPS in the simulation.
## 2 Materials and Methods
### Edge Sensor Node  
Figure 1 illustrates the overall architecture of the multi-spectral sensor-based edge sensor node designed for milk detection. This device consists of three main components: hardware, software, and power supply. The hardware includes an Arduino Nano 33 BLE microcontroller (Arduino, Turin, Italy), an AS7265x multi-spectral sensor (SparkFun Electronics, Niwot, CO, USA), and a specially designed tubular measurement point that blocks external light. The device's power needs are met by a 5 V power supply module. The software was developed using the Python programming language (Version 3.7) and the Arduino IDE (v.1.8.19). The data collection process begins by operating the device in measurement mode; the collected data are then used by a computer for model training, and the trained model is loaded onto the microcontroller.  
The integration of these hardware and software components has facilitated the creation of an edge sensor node used for measuring milk composition. This device is specifically designed for use in the dairy industry and is distinguished by its portability as well as its built-in data processing and real-time analysis capabilities. The microcontroller processes and analyzes the collected data, transmitting it to other devices or edge data collectors via Bluetooth.  
The software supports data transmission via the MQTT protocol used for the Internet of Things (IoT), thus enabling the collected data to be transmitted to software applications, databases, or other devices. This integration allows for continuous monitoring of milk quality, immediate interventions, data logging, and system integration.  
In conclusion, this edge sensor node is specifically designed for the collection, processing, analysis, and integration of critical data in the dairy industry. The device's advanced features support decision-making processes in dairy processing facilities and farms, enhancing production efficiency and saving time and costs.
# Bending Behavior of Corroded H-Shaped Steel Beam in Underground Environment
## 3 Analysis of Results
### Bending Test of Corroded Beam
#### 3.2.2 Analysis of Bearing Capacity  
Figure 11a,b shows that both the yield load and ultimate load of the corroded H-shaped beams decreased as the corrosion rate increased. As presented in Table 5, when the corrosion rate was in the range of 3.65-13.07%, the yield load decreased from \(-\)12.34 to \(-\)27.70%, while the ultimate load decreased from \(-\)5.31 to \(-\)22.96%. This indicates that the effect of the corrosion rate on the yield load was greater than the effect on the ultimate load.  
As presented in Table 5, both the corrosion rate and ultimate load of GB-60-6 are lower than those of GB-120-0. The same law can be observed by comparing GB-120-6 and GB-180-0. This indicates that, in addition to the corrosion rate, the sustained load is also one of the factors influencing the residual bearing capacity of corroded H-shaped steel beams. The reason for this may be that H-shaped steel beams deform to a certain extent owing to the sustained loading and corrosion-produced pits and other defects on the surface of H-shaped steel beams. When the sustained load is eliminated, the pits and other defects hinder the elastic recovery of corroded H-shaped steel beams, which affects the  
Figure 11: Relationship between yield load, ultimate load, and corrosion rate: (**a**) yield load—corrosion rate; (**b**) ultimate load—corrosion rate.  
Figure 10: Failure diagram of corroded beam GC-180-6.  
residual bearing capacity of corroded H-shaped steel beams. However, this issue requires further investigation because only a small number of samples were used in this test.
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 3 Design of Partially Information-Coupled Polar Code based on Parity Check
### Systematic PCCA-Polar Code
2: Generate submatrix \(\boldsymbol{G}_{\mathcal{A}^{\prime}\mathcal{A}^{\prime}}\) and calculate the row's weight \(W_{t}=\left\{W_{t}^{1},\ldots,W_{t}^{|\mathcal{A}^{\prime}|}\right\}\) of \(\boldsymbol{G}_{\mathcal{A}^{\prime}\mathcal{A}}\). 3:\(w_{t}=1\). 4:while\(|\mathcal{P}_{c}|<n_{pc}\)do
5:\(\mathcal{S}=O\). 6:for\(i=1\rightarrow|\mathcal{A}^{\prime}|\)do
7:if\(W_{t}^{i}=w_{t}\)then
8: Get \(1^{i}\) of \(g_{i}\). 9:\(\mathcal{S}^{\prime}=\left|1^{i}\cap\mathcal{S}\right|\). 10:if\(\left|1^{i}\right|-|\mathcal{S}^{\prime}|=1\)then
11: update the set \(\mathcal{S}=\mathcal{S}\cup\{\mathcal{A}^{\prime}_{i}\}\). 12:endif
13:endif
14:endfor
15:if\(n_{pc}-|\mathcal{P}_{c}\cup\mathcal{S}|<0\)then
16: Select \(n_{pc}-\mathcal{P}_{c}|\) elements with the lowest reliability of \(\mathcal{S}\) as set \(\mathcal{S}^{\prime}\). 17: update the set \(\mathcal{P}_{c}=\mathcal{P}_{c}\cup\mathcal{S}^{\prime}\). 18:else
19: update the set \(\mathcal{P}_{c}=\mathcal{P}_{c}\cup\mathcal{S}\). 20:endif
21:\(w_{t}=w_{t}+1\). 22:endwhile
```  
**Algorithm 1** Get parity check indices \(\mathcal{P}_{c}\)  
Finally, we can give the encoding method of systematic PCCA-polar code in Algorithm 2. The rate-matching pattern \(\mathcal{E}\) is generated according to 3 GPP TS 38.212. ```
1:\(\mathbf{b}\), \(N_{m}\), \(\mathcal{A}^{\prime}\), \(n_{PC}\) and rate-matching pattern \(\mathcal{E}\), where \(|\mathcal{E}|=N_{e}\). 2:\(\mathbf{c}^{\prime}\). 3:Initialize: Set a \(p\)-length cyclic shift register \(y[0],\ldots,y[p-1]\) to \(0\), and \(\mathbf{a}=0\), where \(|\mathbf{a}|=N_{m}\). 4:Generate the generator matrix \(\mathbf{G}\). 5:Get the information and CRC indices \(\mathcal{A}=\{\mathcal{A}^{\prime}\backslash\mathcal{P}_{c}\}\), and generate the submatrix \(\mathbf{G}_{\mathcal{A}\mathcal{A}}\). 6:Calculate \(\mathbf{b}^{\prime}=\mathbf{b}\mathbf{G}_{\mathcal{A}\mathcal{A}}\). 7:for\(i=1\to N_{m}\mathbf{a}o/\text{PC bits generation}\)do
8:Cyclic left shift the register. 9:If \(i\in\mathcal{A}\): set \(y[0]=a_{i}\oplus y[0]\). 10:If \(i\in\mathcal{P}_{c}\): set \(a_{i}=y[0]\). 11:endfor
12:Perform the basic polar coding to obtain mother code by \(\mathbf{x}=\mathbf{a}\mathbf{G}\). 13:Get systematic PCCA-polar codes \(\mathbf{c}^{\prime}\) by rate-matching pattern \(\mathcal{E}\). ```  
**Algorithm 2** Systematic PCCA-polar encoding