esa-sceva/satcom-chunk-collection huggingface dataset:
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Simulation Setup  
The developed MBMQA routing protocol was implemented, and comprehensive simulations were conducted to evaluate the performance of MBMQA along with the MEQSA-OLSRv2 and MP-OLSRv2 routing scheme in various scenarios. A simulation environment was established to investigate the performance of the proposed MBMQA scheme and obtain the multi-path routing conditions. The simulation was performed using network topology with 49 devices deployed over a network area of 1000 m \(\times\) 1000 m. Therefore, there were many multi-hop and neighboring devices with different resources in the proposed network topology. For the purpose of simulating the linear battery model, the monitoring interval of the battery energy level was set to one second and all the devices possessed an equal initial battery energy level of 10 mAh. The devices exhausted their battery during the conveying of data packets and eventually shut off due to a critically low battery level. A special set of eight source-destination joints were carefully selected, including mid-devices on each side of the four corner nodes, so that multiple paths could be achieved through an adequate number of intermediate devices. The constant bit rate (CBR) of 20 packets/s generates a 512 byte packet size in the network. The data transfer started after 15 s of the simulation, and enough time was spent exchanging routing messages. IEEE  
where \(N_{PD}\) is defined as the number of packets dropped throughout the simulation time, \(N_{PS}\) is the number of data packets transmitted by the source device, and \(N_{PR}\) denotes the number of data packets successfully delivered to the destination device;
5. Average Energy Consumption (\(Avg.E_{consume}\)). This refers to the average energy consumption of all devices during the simulation time in mAh. The device's energy consumption varies with respect to the device's state, such as transmission, reception, sleep and idle. The total average energy consumption (\(Avg.E_{consume}\)) of device \(j\) is calculated as follows: \[Avg.E_{consume}=\frac{1}{n}\sum_{j=1}^{n}E_{total}(j)\] (19) where \(n\) refers as the total number of devices in the network;
6. _Energy Cost per Packet_ (\(E_{\text{cos}\,t}\)). This is defined as the ratio between the average energy consumption by the network devices and the total number of data packets successfully received at the destination. The \(E_{\text{cos}\,t}\) can be calculated as below: \[E_{\text{cos}\,t}=\frac{Average\,\,Energy\,\,Consumption}{Total\,\,Packets\,\,Received}\] (20)
# Seismic Response Analysis of Buried Nuclear Power Plant Structures in Non-Bedrock Sites
## 1 Introduction
Research shows that site conditions greatly affect underground seismic responses [24; 25; 26; 27; 28; 29; 30], possibly being the most critical factor [31]. [PERSON] et al. [32] measured uncertainties in seismic risk assessment for RC buildings, providing a risk assessment tool for the retrofitting and design strategies of RC structures. This method offers a rapid and accurate assessment approach applicable to nuclear power structures. Research on underground structures and aboveground-underground structures has matured and extended to nonlinear site analyses [33; 34; 35; 36; 37]. However, these underground structures primarily include subway stations, tunnels, and similar constructions, with few studies on deeply buried large structures. The structure in this article is a large underground-aboveground single entity. Understanding the seismic performance of these unique structures under SSI requires specialized research and analysis. To systematically study the characteristics of embedded nuclear power plant structures under non-bedrock site conditions, this study developed a refined three-dimensional finite element model integrating site-structure interaction using GFE (2024) [38], an efficient finite element analysis software, through numerical modeling. The model adopts a wave input method and uses free-field loads and viscoelastic artificial boundaries to quantitatively study the effects of different structural burial depths and successfully reveals the law of change of seismic response patterns with depth. Under a variety of burial configurations, key parameters, such as the acceleration amplification factor and stress distribution characteristics, are analyzed in detail, providing strong data support for subsequent engineering design and safety assessment.
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 4 Discussion
### Stick-Slip  
Stick-slip motion is a manifestation of ice friction [25]. Many previous experiments have reported the stick-slip phenomenon in ice-ice friction using different types of loading system [23; 24; 35; 37] (see Table 1). In the present tests, stick-slip was obtained in ice-ice contact at a velocity between 0.02 m\(\cdot\)s\({}^{-1}\) and 0.10 m\(\cdot\)s\({}^{-1}\), which is higher than in previous reports. When this observation is considered with the data listed in Table 1, a trend becomes apparent: the softer the loading system, the higher is the velocity at which stick-slip of ice-ice friction occurs. Because the loading systems in the present study and [37] are less rigid, while the velocity ranges of stick-slip are higher. In present tests, stick-slip was also observed in steel-ice friction at a velocity of 0.02 m\(\cdot\)s\({}^{-1}\), which demonstrates that stick-slip occurs not only between ice and ice. Furthermore, results revealed that the frequency of load oscillations in stick-slip motion increased with increasing sliding velocity because the frequency of stick-slip in Top-Bottom friction rose from 0.28 Hz to 0.96 Hz with velocity increasing from 0.04 m\(\cdot\)s\({}^{-1}\) to 0.10 m\(\cdot\)s\({}^{-1}\). The frequency was found to be independent of the normal stress because the frequency of load oscillations of stick-slip of SS-Top remained at approximately 30.10 Hz in spite of the increase in normal stress from 3.10 kPa to 6.56 kPa.  
Because of the cyclic changes of motion state during stick-slip, the pulling force contains not only friction force but also inertial force. When calculating the \(\mu_{\rm k}\) of stick-slip in present study, the same way as calculating the \(\mu_{\rm k}\) of steady sliding was employed, which used the ratio of the average pulling force after initiation to the normal load [23; 37]. [PERSON] et al. [36] took into account inertial force when calculating \(\mu_{\rm k}\) of stick-slip in their field sea ice friction tests and developed a model based on the force history during stick-slip cycles. The \(\mu_{\rm k}\) of stick-slip in current study was determined using these two methods, and the value calculated using the former method was only 0.43% greater than the value calculated using the latter method.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Loading System** & **Test Temperature (\({}^{\circ}\)C)** & \(P\) **(kPa)** & \(V\) **(m\(\cdot\)s\({}^{-1}\))** \\ \hline
[23] & Uniaxial testing system & \(-3\) & 7–1000 & \(10^{-5}\)–\(5\times 10^{-2}\) \\  & \(-40\)—\(-10\) & 7–1000 & \(5\times 10^{-5}\)–\(5\times 10^{-2}\) \\ \hline
[24] & Rotation device and uniaxial testing system & \(-30\)–\(0\) & 1.2–370 & \(10^{-5}\)–\(10^{-3}\) \\ \hline
[35] & Pusher in a basin & \(-10\) & 10 & \(3\times 10^{-3}\) \\ \hline
[37] & Linear actuator in field & \(-20\)—\(-2\) & 4–36 & \(6\times 10^{-3}\)–\(3\times 10^{-2}\) \\ \hline \end{tabular}
\end{table}
Table 1: Information on stick-slip from previous experiments.
## 2 Results and Discussion  
### Synthesis, Electrochemical and EPR Data  
Radical **4** was previously prepared via a multistep route (Scheme 1) starting from the readily available 1-benzylidene-2-phenylhydrazine (**5**) [10]. Reaction of the benzylidene **5** with 1-fluoro-2-nitrobenzene and K\({}_{2}\)CO\({}_{3}\) in DMSO at _ca._ 100 \({}^{\circ}\)C for 1 day gave 2-benzylidene-1-(2-nitrophenyl)-1-phenylhydrazine (**6**) which upon treatment with excess NH\({}_{2}\)OH:HCl in pyridine and heating to _ca._ 80 \({}^{\circ}\)C for 1 day released 1-(2-nitrophenyl)-1-phenylhydrazine (7) in 80% yield. Acetylation and subsequent mild reduction of the nitro group followed by an acid-mediated cyclodehydration gave the fused triazine which upon alkali treatment afforded the desired adamantly-substituted radical **4**.  
The redox behavior of radical **4** is typical of 1,2,4-benzotriazinyls exhibiting two fully reversible waves corresponding to the \(-1/0\) and 0/+1 processes (Figure 2, left) [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Worthy of note is that the  
Figure 1: SOMO of Blatter radical **1** calculated at the UB3 LYP/6-311+G(d,p).  
oxidation potential of \(E_{1/2}\)\({}^{0/+1}\) = 0.08 V _vs_ Fc/Fc\({}^{+}\) couple makes radical **4** the best 1,2,4-benzotriazinyl donor reported to date. The solution EPR spectrum of radical **4** (Figure 2, right) exhibits the typical 1,2,4-benzotriazinyl seven-line multiplets consistent with coupling of the unpaired electron with the three similar but slightly nonequivalent \({}^{14}\)N nuclei. The hyperfine coupling constants (hfcc), determined by the simulation of the EPR spectrum, are \(a_{\text{N(1)}}\) = 7.51, \(a_{\text{N(2)}}\) = 4.94 and \(a_{\text{N(4)}}\) = 5.13 G with g = 2.0040.
## 3 Results and Discussion
### Washing Performance Test of Different Proteases
Elemental analysis on the cloth was performed using XPS before and after washing with alkaline protease, keratinase, and trypsin (Figure 3). The protein-fouling cloth and blood-dirty cloth before washing showed obvious peaks at 398-400 eV, indicating that they contain N elements. After washing with protease detergent, the peaks of N elements were significantly reduced. This result indicates that the protein on the surface of the dirty cloth was decomposed and removed by the proteases.
# Geophysical Monitoring of Leachate Injection in Pretreated Waste Landfill
## 4 Discussion
* [PERSON] et al. [33] investigated the leachate recirculation effects on a real landfill scale, testing wastes disposed of in about 600,000 m\({}^{3}\). The recirculation operation lasted about one month, and during this period, biogas and leachate composition were monitored to compare them to the corresponding ones achieved in the landfill zone managed without leachate injection. Methane concentration was higher in the -m\({}^{3}\) zone (53-54% by volume against 46-47% by volume). Regarding leachate composition, the study showed that COD decreased after the recirculation period due to the degradative activity of microorganisms. * [PERSON] et al. [1] measured the biogas composition and flow rate in a -ton landfill with leachate recirculation in two tests that lasted 20 and 30 days, respectively. Their findings showed the rather sudden growth of the biogas flow rate when the leachate started to be injected. At the same time, the methane concentration increased by some units (from 60 to 65% by volume).
# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 4 Conclusions  
To solve the situation that the frequency hopping sequence of the receiver is unknown due to the intelligent decision of the transmitter of the frequency hopping system, a CVDP network by combining the CNN network and improved VIT network has been designed. It is used for the hopping frequency estimation of the frequency hopping receiver system to realize the intelligent reception of the frequency hopping system. The simulation results show that the comprehensive performance of the CVDP network designed is better than other comparative networks and has better adaptability to the interference environment. And the performance of the CVDP-based frequency hopping intelligent reception system is almost the same as that of the conventional reception system, even when the hopping sequence is unknown.  
Conceptualization, Z.Y.; methodology, Z.Y.; software, Z.Y. and Y.Z.; validation, S.Z. and S.D.; formal analysis, S.Z.; investigation, S.D.; resources, Y.Z.; data curation, S.Z.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y. and Z.Z.; visualization, Z.Y.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on Intelligent Spread Spectrum Anti-interference Technology in Complex Electromagnetic Environment, U19B2016 National Natural Science Foundation of China.  
Figure 9: The BER curve in two fading channel environments (**a**) The modulation type is BPSK, (**b**) The modulation type is QPSK.  
**Institutional Review Board Statement:** Our studies not involving humans or animals.  
**Informed Consent Statement:** Our studies not involving humans.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Works
Key findings reveal high energy (62%) and exergy (55%) efficiencies with a cost of USD 0.123/kWh and an environmental impact of 524 kg/MWh. Optimization via GAs and artificial ANNs improves exergy efficiency to 61.1% and reduces costs to USD 0.03/kWh. Parametric studies show that lower fuel utilization factors and higher current densities enhance performance but increase power costs. Exergy destruction is most significant in the mixer, desalination unit, and fuel cells. Sustainability improves with lower equivalence ratios and optimized compressor pressure and fuel cell temperatures. This approach offers a path to cost-effective, sustainable energy and water production.
# Development of a Lightweight Prosthetic Hand for Patients with Amputated Fingers
## 4 Results  
### Prototype  
The prototype was composed of the prosthetic hand and operating part, as shown in Figure 1a. All parts except the shafts were manufactured from nylon using the MJF 3D printing, and the shafts were made of stainless steel. To satisfy the design conditions, the linear, torsional, and leaf springs adhered to the following specifications.  
Figure 8: Deflection (**left**) and stress distribution (**right**) when external forces were applied along (**a**) \(Y\)- and (**b**) \(X\)-coordinates, respectively.  
1. The available range of linear springs installed on the joint frames was 4 mm, the spring constant was 2.667 N/mm, and the length for preload applied by frames was 1 mm,
2. The available range of torsional springs was 20\({}^{\circ}\), the diameter and internal diameter of the coil and torsional spring were 1 mm and 5 mm, respectively, and the angle for preload was 5\({}^{\circ}\), and
3. The available range of leaf spring was 20\({}^{\circ}\), the thickness of leaf springs was 0.7 mm, and the lengths of the leaf springs were 10 mm and 20 mm, respectively.  
The diameter of the tendon was 0.6 mm, and the allowable load was 6 kg. Furthermore, the actuator has a gearbox with a 986.41:1 ratio and 31 rpm. The mounting part is installed on the back of the patient's hand, and the arrangement between the palm of the patient and the fingers of the prosthetic hand can be adjusted adequately. The weight and product cost of the prototype were 152.32 g and 1246 USD, respectively. The cost included 3D printing (460 USD), machining (403 USD), mechanical parts (333 USD), and electrical parts (50 USD). Because of the use of the actuator with 31 rpm, each phalanx moves 185.8\({}^{\circ}\)/s. When the applied external force is parallel to the \(X\)-coordinate (\(\eta=0\) in Equation (13)), and the variables in Equation (12) are \(h_{1}=h_{2}=6.5\) mm, \(\theta=45\), and \(\alpha=\beta=25\), one finger module is capable of lifting the object with 0.609 kg because the output torque from the gearbox is 210 Nmm. In other words, using four finger modules, the prosthetic hand can raise over 2.435 kg at the fingertip in these conditions. The specifications of the developed prosthetic hand are summarized in Table 2. The developed prosthetic hand is lighter than the previous models, and its size is similar to the average size of American female hands. The grasp speed (185.8\({}^{\circ}\)/s) exceeded the minimal acceptable speed (115\({}^{\circ}\)/s) suggested in [13].
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 2 Materials and Methods
### Evaluation of Classifier Performance  
The classification performance of the models can be comprehensively assessed using the confusion matrix and the receiver operating characteristic (ROC) curve. The confusion matrix is presented in Table 2.  
The classification performance of the model is typically assessed using the accuracy derived from the secondary indicators of the confusion matrix. The formula for classification accuracy is as follows:  
\[Accuracy=(TP+TN)/N \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{2}{c}{**Reference**} \\ \cline{2-3}  & **1** & **0** \\ \hline
1 & TP & FP \\
0 & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Beyond accuracy, secondary indicators within the confusion matrix also encompass recall (TPR) and sensitivity (FPR), with their respective formulas as follows:  
\[TPR=TP/(TP+FN) \tag{6}\]  
\[FPR=FP/(TN+FP) \tag{7}\]  
The Receiver Operating Characteristic (ROC) curve is a two-dimensional graph where the ordinate represents the True Positive Rate (TPR) and the abscissa represents the False Positive Rate (FPR). The area under the ROC curve, enclosed by the curve and the coordinate axes, is defined as the AUC (Area Under Curve), which ranges from 0 to 1. A larger AUC indicates superior model classification performance.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### Considerations for cross-layer approach  
The cross-layer mechanism fulfills parameters DL1 and DL4 for the dependability criteria. Next, for the adaptability condition, it fulfills parameters AL1 and AL4. Finally, the cross-layer mechanism fulfills parameter PL4 for the power-saving criteria.
## 4 Experimental Results and Analysis
### Experiment Case II  
For the second test case we considered a model with seven QTLs with increasing effect, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75 and 0.8 at the same locations as the first test case. In this test we intended to show the  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**DataSet Size** & \multicolumn{3}{c}{**Bayesian Interval Mapping**} & \multicolumn{3}{c}{**MOEA Method**} \\ \hline  & **Correct** & **Ex-L** & **Ex-UL** & **Correct** & **Ex-L** & **Ex-UL** \\ \cline{2-7}
100 & \(2.16\pm 1.06\) & \(0.82\pm 0.44\) & \(0.41\pm 0.37\) & \(4.54\pm 1.77\) & \(1.08\pm 0.52\) & \(0.08\pm 0.05\) \\
200 & \(5.21\pm 0  
power of the algorithm in detecting the QTLs ordered by their effect on the phenotype output. The Pareto front is depicted in Figure 5 and detailed results are shown in Figure 6. As results show, each marker's magnitude tends to be more consistently detected and be included in the simpler solutions, signalled by the deeper red color. The only exception is for makers on chromosome 2 that are linked antagonistically. Here, antagonistic markers are consistently detected only in solutions whose complexity is near or equal to the problem complexity. As in Case I, as solution complexity increases beyond the given problem size, there is an increase of nearby extraneous parameters, but little increase in accuracy. We performed the Kolmogorov-Smirnov test for the results of this experiment too. Like in the first experiment, the null hypothesis was rejected for all the tests under 1% and 5% significance levels.  
Figure 5: **Case II Pareto Front:** Pareto front showing the relation between \(-R^{2}\) and complexity for Case II. The values are average of 10 runs of 100 simulation replicates.  
Figure 6: **Case II Markers Identified:** in different complexity Pareto optimal solutions for Case II. The color is the proportion of random executions that the specific marker was found. Bright red signifies that in each run the marker was consistently found. Blue means that the marker did not appear in any solutions.  
As results in Table 3 show, the detection accuracy of case II is decreased compared to Case I; the reason for this is the difficulty of detection of markers with small effect of QTLs on the trait. Again, in this experiment the performance of the MOEA method is consistently better than WQTLC over all evaluation criteria. The best improvement is demonstrated with smaller data sets, a more likely case in real experiments. When the sample size is small, the MOEA method identified on average about one  
Figure 6: _Cont._  
near-by extraneous linked QTLs, which can still be of value to scientists when the correct linked QTLs are weakly detected.
## 1 Introduction  
Civil infrastructure is built to enhance the quality of life of the general public. Slopes are a part of these civil infrastructure systems, typically designed and constructed based on sound science and engineering knowledge. These slopes, however, occasionally fail, causing life losses, structural damage, and accessibility issues (e.g., road closure and detour). One of the methods that may fill the gap between practice and science and minimize the occurrence of slope failures may be field monitoring. By analyzing the behavior of the critical slopes based on accurate sensor data, engineers may be able to foresee the possibility of failure before a failure occurs. With well-developed modern sensors, these tasks are seemingly straightforward. However, it is noted that predicting the likelihood of failure is only reliable when the characteristics of different sensors and sensing systems are rationally evaluated.  
Many new types of sensors and sensing systems have emerged in recent years and decades. Some of them may have a high potential to be excellent sensor and sensor systems for health monitoring at a much lower cost than others. A large amount of detailed sensor information for traditional sensors is found in the literature, such as [PERSON] [1] and [PERSON] [2]. The information on newer sensors, such as fiber optic cable-based DistributedStrain Sensors (DSS) or Distributed Temperature Sensing (DTS), are addressed by [PERSON] et al. [3] and [PERSON] [4]. In addition, an Uncrewed Aerial System (UAS)-based deformation measurement system is also used to survey the deformation of a large area, as addressed by [PERSON] and [PERSON] [5], [PERSON] et al. [6], and [PERSON] [7]. The use of GPS itself as a stand-alone deformation sensor is also reported ([PERSON] et al. [8; 9]). However, the successful application of sensors in general may substantially depend on the proper understanding/evaluation of the characteristics of the sensors and sensor system as addressed by [PERSON] [10; 11; 12]. Particularly for these new kinds of sensors such as DSS, DTS, and UAS, all steps in field instrumentation--sensor selection, installation, data taking, and data analysis--should be carefully evaluated and cross compared. However, cross-comparisons of these new sensors hardly exist presently.  
This study evaluates the features and field applicability of these newer measurement systems based on experience in measuring the deformation for two roadside slopes. The two sites have varying conditions, such as soil type, geographical location, and weather condition. In addition, this study included the traditional Total Station (TS)-based measurement result in the comparison, even though its usage was limited to only the first site. Findings from the field instrumentations and performance data from these sites, therefore, will benefit the geotechnical instrumentation community as a whole.