esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 2 Related Research  
### Ad Hoc Network and Its Application  
With the development of wireless communication technology, some new network types have emerged, such as Vehicular Ad Hoc Networks (VANETs). By realizing the direct and indirect connection between vehicles and connection between vehicles and road facilities, it can realize the maintenance of vehicle driving safety, real-time map and route updates, location-based network, information services, collaborative driving, etc. At the same time, VANETs will be affected by road terrain and the speed of vehicles in the network will change rapidly, so research on network access and route maintenance is essential. [PERSON] et al. [8] proposed an enhanced routing mechanism based on AODV, so that roadside units (RSUs) can provide continuous services to vehicles that may be intermittently located outside the RSUs' coverage area. [PERSON], [PERSON] et al. [9] studied the optimal access network control problem of vehicles, so that each vehicle can make an access decision to maximize a single utility. [PERSON], [PERSON] et al. [10] proposed an enhanced hybrid ant colony optimization routing protocol (EHACORP), which uses the shortest path to improve the packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate. [PERSON], [PERSON] et al. [11] made two improvements to AODV routing and route discovery to improve its performance in packet transmission rate and communication link stability in VANET, and to obtain a new vehicle V-AODV that meets the specific requirements of autonomous vehicle communication.  
In addition, with the emergence, development, and application of unmanned aerial vehicles (UAVs), a flying ad hoc network (FANET) has been gradually developed [12]. UAV groups are generally task-based, and they need to achieve high-level goals through collaboration. In such a network model, any two nodes within the network communication range need to connect and communicate directly or indirectly, and also need to meet the requirements of node mobility, connectivity, message routing, quality of service, etc. [13].
# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 4 Experimental Verifications
### Experimental Data Collection  
The Case Western Reserve University Bearing Data Center provides experimental data. As shown in Figure 5, the test stands mainly composed of a torque transducer/encoder (center), a 2 hp motor (left), and a dynamometer (right). Motor bearings were used to inoculate faults by electro-discharge machining (EDM). The fault 0.007 inches, 0.014 inches, and 0.021 inches in diameter were introduced separately at the inner raceway, rolling element (i.e., ball), and outer raceway. Therefore, totally we have one normal condition (no-fault) and nine different faults. Bearings are tested under different conditions consisting of normal condition, ball fault (BF), outer race fault (OR), and inner race fault (IR).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Residual Block**} & \multicolumn{2}{c}{**Parameters of the Convolution Layer**} \\ \cline{2-3}  & **Conv1** & **Conv2** \\ \hline Res-block1 & 1\(\times\)3, 8, 1 & 1\(\times\)3, 8, 1 \\ Res-block2 & 1\(\times\)3, 8, 1 & 1\(\times\)3, 8, 1 \\ Res-block3 & 1\(\times\)3, 16, 1 & 1\(\times\)3, 16, 1 \\ Res-block4 & 1\(\times\)3, 16, 1 & 1\(\times\)3, 16, 1 \\ Res-block5 & 1\(\times\)3, 32, 1 & 1\(\times\)3, 32, 1 \\ Res-block6 & 1\(\times\)3, 32, 1 & 1\(\times\)3, 32, 1 \\ Res-block7 & 1\(\times\)3, 64, 1 & 1\(\times\)3, 64, 1 \\ Res-block8 & 1\(\times\)3, 64, 1 & 1\(\times\)3, 64, 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the convolution layer in the residual block.  
Figure 4: The complete structure of the designed ResNet.  
Vibration data was collected for motor loads of 3 horsepower (motor speeds of 1730 RPM) at 12,000 samples per second.  
For training our proposed ResNet model, we employ enough training samples.
## 3 Results and Discussion  
### Forming Quality  
Figure 2 shows the quality of the weldments obtained via optical observation and ultrasonic inspection. The results reveal that dissimilar stainless steel 316L multi-layer bellows and one-layer B610 CF high-strength steel plates were fully welded, and that the FZ was free of defects, such as a lack of fusion, cracks or porosities. By comparing the weldment surface welded by GTAW and laser welding, the width of the FZ and HAZ in GTAW was much wider than that in laser welding due to the big heat accumulation of GTAW. Furthermore, cross-sectional specimens of the weldments were observed. As seen in Figure 3, the profiles of the FZs of both welding methods were different. The widths of the FZs in the upper region were 11.7 mm under GTAW and 8.2 mm under laser welding, respectively. The widths of the FZs in the middle region were similar. However, there existed a defect of insufficient fusion in the bottom region of the FZ in the GTAW experiment, which resulted from the limited penetration of the arc. The results of ultrasonic inspection and optical observation indicated that, although the dissimilar plates were welded by GTAW without obvious and continuous defects, a lack of fusion was detected in the bottom zone of GTAW FZ. The joint created by laser welding exhibited a full weld without visible defects, demonstrating excellent metallurgical compatibility between the 316L stainless steel and the B610 CF high-strength steel.  
Figure 2: The quality of the welded joints fabricated by GTAW and laser welding: (**a**) the optical observation of GTAW, (**b**) the optical observation of laser welding, (**c**) the ultrasonic inspection of the GTA-welded sample, (**d**) the ultrasonic inspection of the laser-welded sample.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 2 Background to Interference Monitoring
### Mitigation  
A temporary solution to interference signals is to mitigate them through advanced signal conditioning [75; 76; 77; 78]. Interference mitigation methods perform well against some interference signals, but poorly against others. Hence, a single perfect algorithm is unlikely. However, a combination of approaches in a multi-layer approach together with classification yields improved the mitigation capability. By monitoring and classifying the interference signals, the interference threat is quantified. It, in turn, allows a mitigation algorithm designer to focus on the most common interference waveforms, thereby optimizing the mitigation efficiency. Therefore, mitigation algorithm development benefits from interference monitoring. This benefit from surveying is also called electronic support (ES) [53].
## 4 Results and Discussion
### Divergence of Attributes
#### 4.3.2 Safety/Hazards Concerns  
Seven papers reported safety concerns when implementing AR on construction sites [30; 36; 45; 46; 49; 50]. Although some of these papers recommended training to overcome this challenge [48], other papers showed variance and considered that no safety/hazardous issues were faced during AR application for synchronized communication on site [10; 51]. Reviewing the context and the type of project during which each paper recorded these findings, only one paper among the seven papers that reported that safety was a challenge, was conducted in a real environment [45]. This study implemented AR in a real environment to facilitate the communication of site issues related to thermostats, showing no movement for the user during the application. Moreover, regarding the two studies that considered that no safety issues were faced during AR application, one study was conducted in an uncontrolled environment [10], while the second study was conducted in a controlled environment [51]. The only difference between these papers was the type of project studied; one paper studied AR in a vertical project (i.e., a 10-story building) [51] whereas the second paper studied AR in horizontal road infrastructure work [10]. This indicates that safety issues affecting the use of AR devices could be related to the type of projects and the movement of the practitioner while using AR.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 3 Concurrent Transmission Based on Distributed Scheduling
### Protocol Description
#### 3.2.4 ACK Stage  
The ACK/Negative ACKnowledgment (NACK) packets are sent using the same method as the data packets in the ACK stage. When all ACK/NACK packets are sent, the cluster turns idle and is ready for another new transmission cycle.  
The fundamental difference between the proposed scheme and the basic RTS/CTS mechanism is that the former supports multiple successful handshakes and concurrent transmissions per transmission cycle, whereas the latter only allows one successful handshake and one pair of nodes to communicate. In the RTS and CTS stages, multiple RTS and CTS packets can be sent in the proposed scheme, which means that multiple successful handshakes are allowed. In the data and ACK stage, the data and ACK packets are transmitted concurrently at an optimized time. This batch transmission mode effectively makes use of the long propagation delay in underwater acoustic channels so that the total transmission time can be shortened when there is more than one node that has data to transmit. So, the proposed scheme is superior to the basic RTS/CTS mechanism in most cases.
## 5 Simulative Assessment
### Simulation Results
#### 5.2.1 Impact of the Number of EDs  
In this experiment the duration of a backoff unit (\(U_{backoff}\)) and the maximum number of backoffs (\(MAC_{nBackoff}^{max}\)) were heuristically chosen based on both the network workload and the typical ToA duration for LoRaWAN transmissions. In particular, the duration of a backoff unit was set to 5 ms and the maximum number of backoffs was set to 5. Four different scenarios were evaluated with an increasing number of EDs from 50 to 200, in steps of 50 EDs.  
Table 6 shows the average values of MLossR, MDropR and MDelivR obtained by varying the number of EDs.  
The measured MLossR in all the evaluated scenarios maintains values around 2%, with negligible variations (less than 0.05%). A possible explanation is that in this case the 2% value mainly depends on other factors, e.g., the wireless channel properties, therefore the effect of increasing the number of EDs from 50 to 200 is not significant. Instead, when the number of EDs increases, the MDropR also increases, as there are more EDs competing for the same channels. In fact, with a high number of EDs, the channels are sensed busy multiple times and several messages, after exceeding the maximum number of backoffs (5 in these simulations), are discarded. For the same reason, the MDelivR decreases as the number of EDs grow. Note that, in the evaluated scenarios, all the messages generated are transmitted. This means that the number of messages transmitted by the MAC layer is equal to the number of messages that the MAC layer received from the Application layer, therefore the measured MDelivR value corresponds to that calculated as the difference \(100\%-(MLossR+MDropR)\).  
Table 7 shows the average and the maximum e2 eDs obtained in the evaluated scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of EDs** & **50** & **100** & **150** & **200** \\ \hline
**MLossR** & 2\% & 2\% & 2\% & 2\% \\
**MDropR** & 2\% & 6\% & 15\% & 26\% \\
**MDelivR** & 96\% & 92\% & 83\% & 72\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average values of MLossR, MDropR and MDelivR varying the number of EDs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline ADR & Disabled \\ BW & 125 kHz \\ CR & 4/5 \\ \(LBT_{backoffs}^{max}\) & 5 \\ \(MAC_{BE}^{min}\) & 3 \\ \(MAC_{BE}^{max}\) & 8 \\ Message generation pattern & Exponential distribution with a mean of 60 s. \\ Mobility pattern & Chiang-Mobility [96] \\ Physical payload & 32 bytes \\ Propagation Model & LoRa Path Loss Oulu [95] \\ Simulation time & 12 h \\ TP & 14 dBm \\ \(\mathcal{T}_{CCA}\) & 160 \(\mu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.  
As it can be seen, the highest e2 eD values are experienced in the scenario with the highest number of EDs, as on average the EDs run more backoffs when the workload increases.
## 2 Materials and Methods
### Statistical Analysis and Machine Learning Model Development
#### 2.4.1 Prediction of Early BCR Using Classification Methods  
In order to predict patients with early BCR after RP, supervised learning algorithms, which mean classification methods, are used in a previous study of [PERSON] et al. [16]. Supervised learning algorithms analyze the characteristics of patients in two groups statistically, and based on these statistical differences, it can provide a prediction model that classifies patients with prostate cancer that are likely to experience BCR after RP. In this paper, we use KNN and DT to build a prediction model. In KNN, we apply 3-NN with a Euclidean distance measure, and 4 layers with 20 hidden neurons MLP, and DT with gain index (GINI) are applied.  
To check the robustness of prediction performance, we perform cross-validation. The data that are composed of 80 non-BCR patients and 24 BCR patients are divided into 10 groups. Eight randomly selected groups are used for training and the other two groups are used for verification. A total of 10 such trainings and verifications are conducted to verify performance.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
###### Abstract  
Society 5.0 represents an opportunity to transform the economy and create a digital society with the goal of long-term sustainable development and economic growth. There is a growing importance of boosting ICT as an effective and efficient means of achieving this transformation, and Target 9c of the UN Sustainable Development Goals is to 'Significantly increase access to information and communications technology and strive to provide universal and affordable access to the Internet in least developed countries'. Mobile telecommunication systems have become the most effective and convenient means of communicating in the world, and as such, they are revolutionizing business operations. Nigeria is the fastest growing telecommunication market in Africa, with approximately 298 million subscribers accommodated by over 53,000 base transceiver stations (BTSs) which are largely concentrated in urban areas. As a result of increasing subscribers, all mobile network service providers in Nigeria are building new BTSs, often without considering existing infrastructure. This has led to a proliferation of masts, defacing the environment and causing unnecessary environmental pollution as BTSs are largely powered by diesel generators. It is therefore becoming paramount for the telecommunication regulatory body in Nigeria to enforce principles of infrastructure sharing and the collocation of sites for all mobile network service provider BTSs to improve network availability, reliability, scalability, customer satisfaction and sustainability. This paper argues, through the development of ICT tools and their application to a case study, that infrastructure sharing and colocation of sites is not only feasible if supported correctly but also offers the potential to reduce operational and capital expenditure, reduce the number of BTSs required for the rapidly growing mobile telecoms industry in Nigeria and in doing so reduce environmental pollution.  
Society 5.0; Industry 4.0; ICT infrastructure sharing; sustainability; ICT tools +
Footnote †: journal: Data
# Robust Fuzzy Adaptive Sliding Mode Stabilization for Fractional-Order Chaos
## 2 Preliminaries
### Basic Definitions
#### 2.1.1 Riemann-Liouville (R-L) Definition  
It is one of the most popular definitions of the fractional order integrals and derivative [26].  
The R-L integral of fractional order \(\lambda\), (\(0<\lambda<1\)) is given as,  
\[I^{\lambda}_{RL}g(t)=D^{-\lambda}g(t)=\frac{1}{\Gamma(\lambda)}\int_{0}^{t}(t- \zeta)^{\lambda-1}g(\xi)d\zeta \tag{1}\]  
and the R-L derivative of fractional order \(\mu\) is:  
\[D^{\mu}_{RL}g(t)=\frac{1}{\Gamma(n-\mu)}\frac{d^{n}}{dt^{n}}\int_{0}^{t}(t- \zeta)^{n-\mu-1}g(\xi)d\zeta \tag{2}\]  
where the integer \(n\) verifies: \((n-1)<\mu<n\). Definition (2) can be represented as:  
\[D^{\mu}_{RL}g(t)=\frac{d^{n}}{dt^{n}}\left\{I^{(n-\mu)}g(t)\right\} \tag{3}\]
## 2 Related Concepts
### Setting of Parameters in ACDE Algorithm  
For the ACDE algorithm, the parameters should be reasonably set to obtain better parameter optimization results. The main parameters of the ACDE algorithm are scaling factor F, population size \(D\), maximum iteration number \(M\) and crossover probability \(cr\). Among them, the selection of scaling factor is introduced in Part 2.2, which is related to the number of iterations \(k\). The bases for setting the remaining three parameters are as follows:  
(1) Population size D. According to experience, D should be 5 to 10 times the dimension of the problem but not less than 4; otherwise, the mutation operation will not be possible. Generally speaking, as \(D\) increases, the population diversity will gradually increase along with the probability of reaching an optimal solution. However, this will also greatly increase the calculation time. In general, \(D\) ranges from 20 to 50.  
(2) Maximum number of iterations \(M\). This parameter is the termination condition of the evolutionary operation. A larger \(M\) increases running time of the program and yields a more accurate optimal solution. However, when \(M\) is too large, the accuracy of the optimal solution will not increase correspondingly, because the population has reached an extremely single state at this time and the algorithm cannot jump out of the local optimal solution [39].  
(3) Crossover probability \(cr\). The value range of this parameter is [0, 1] and the value range of this parameter is (0.0, 0.2) when the objective function is separable [40]. If \(cr\) is large, the population convergence is gradually accelerated and precocity is prone to occur.
## 3 Results
### rsmX-2 Expression Is Exclusively GaCA-Dependent  
The Northern blot analysis (Figure 3A) and 5\({}^{\prime}\) end determination of the transcript by 5\({}^{\prime}\)RACE method confirmed that the novel 110-nt long Rsm sRNA is transcribed only in WT and not in the \(\Delta\)_gacA_ mutant. This was confirmed by the analysis of _rsmX-2_ expression in wild-type and \(\Delta\)_gacA_ mutant cells during growth in 10-fold diluted TSB medium, by measuring \(\beta\)-galactosidase activity of the transcriptional _rsmX-2_ + 16-lacZ_ fusion (Figure 3B).  
Moreover, the overexpression of _rsmX-2_ from a vector under the control of _Ptac_ promoter restored the wild-type phenotype in _gacA_ mutant cells (Figure 4), indicating that the overall function of RsmX-2 is likely similar to that of RsmX-1, RsmY and RsmZ [18].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strains** & **Gene** & **Begin** & **End** & **Size (nt)** & **Sequence** & **Number of** _rsmX-Like_ \\  & & & & & **Identity (\%)** & **Genes** \\ \hline \multirow{2}{*}{_Pseudomonas brassicacarum_ NFM421} & _rsmX-1_ & 4839892 & 4840003 & 111 & \multirow{2}{*}{75} & \multirow{2}{*}{2} \\  & _rsmX-2_ & 4839681 & 4839791 & 110 & & \\ \hline \multirow{2}{*}{_Pseudomonas fluorescens_ F113} & _rsmX-1_* & 2113839 & 2113950 & 111 & \multirow{2}{*}{75} & \multirow{2}{*}{2} \\  & _rsmX-2_* & 2114052 & 2114160 & & & \\ \hline \multirow{2}{*}{_Pseudomonas stutzeri_ A150} & _rsmX-1_* & 356904 & 357013 & 109 & \multirow{2}{*}{81.5} & \multirow{2}{*}{2} \\  & _rsmX-2_* & 357143 & 357250 & 107 & & \\ \hline \multirow{2}{*}{_Pseudomonas syringae_ pv. _phaseolicola_} & _rsmX-4_** & 160448 & 160336 & 112 & \multirow{2}{*}{85} & \multirow{2}{*}{5} \\  & 1448A & _rsmX-3_** & 160740 & 160627 & 113 & \\ \hline \multirow{2}{*}{_Pseudomonas syringae_ pv. _syringae_} & _rsmX-3_** & 5867169 & 5867282 & 113 & \multirow{2}{*}{84} & \multirow{2}{*}{5} \\  & _rsmX-4_** & 5867461 & 5867572 & 111 & & \\ \hline \multirow{2}{*}{_Pseudomonas syringae_ pv. _tomato_ str.} & _rsmX-3_ & 6144830 & 6144943 & 113 & \multirow{2}{*}{87} & \multirow{2}{*}{5} \\  & DC3000 & _rsmX-4_ & 6145122 & 6145235 & 113 & \\ \hline \hline \end{tabular}  
* in comparison to _P. brassicacarum_ NFM421. ** in comparison to _P. syringae pv._ tomato str. DC3000.  
\end{table}
Table 1: Tandem copy of rsmX gene in _Pseudomonas_.  
Figure 3: _rsmX-2_ expression is GaCA dependent. (**A**) Northern blot analysis of _rsmX-2_ expression in WT and _gacA_ backgrounds, 55 rRNA is used as loading control; (**B**) \(\beta\)-galactosidase measurements of a transcriptional _rsmX-2_ + 16-lacZ_ fusion in the WT and _AgacA_ strains.  
This is illustrated by the restoration of protease activity (Figure 4A), colony morphology (Figure 4B) and biofilm formation in a _gacA_ mutant (Figure 4C). These findings indicate that RsmX-2 is part of the Gac-Rsm regulatory system.
# The Influence of Pillar Displacements on Geodetic Measurements
###### Abstract  
This paper deals with the effects that displacements of the measuring pillar have on precise geodetic measurements. The changes in the position of the control points on the object or its surroundings can only be determined with well stabilized and stable reference points. These points are usually stabilized with measuring pillars which are not always constructed in an optimal manner. If they are placed in a dark tube with a high absorption factor, solar heating on one side of the pillar can cause the pillar to deflect considerably due to the temperature difference on the two sides of the pillar. This paper presents the influence of such a displacement, if the pillar is a survey point, orientation point, or control point. We show that even small displacement of the survey point can have important influence on all measurements and that the error in some cases significantly increases, e.g., if the standard deviations of the coordinates of the survey point are 1 mm and their covariance is assumed to be zero, the standard deviation of the distance between measured and exact position of the control point exceeds the value of 2.2 mm.  
Keywords:influence of temperature; deflection of the measuring pillar; displacement of the measuring pillar; control measurements +
Footnote †: journal: Applied Sciences