esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Permeability in the out-of-Plane Direction  
In the LCM process, the permeability along the out-of-plane direction (\(z\)-axis) is also important when the resin is first injected into the fiber surface because of the flow media or the gap between the mold and then impregnated in the vertical direction of the surface. Consequently, the permeability in the out-of-plane direction was measured using the equation in Section 2.3.2, and the results are shown in Figure 8 and Table 3 according to the three types of fiber volume fractions: 50%, 55%, and 60%. To investigate the effect of the binder treatment, neat fabric preform and two types of binder-treated fabric preform (without heat treatment and with heat treatment during the preforming process) were prepared. As with the tendency of change in the permeability in the in-plane direction, all three cases showed a tendency to decrease the permeability of the preform in the out-of-plane direction when the fiber volume fraction increased. In addition, the binder-treated preforms exhibited a lower permeability than that of the neat fabric preforms. When the fiber volume fraction was 50%, 55%, and 60%, the permeability of the binder-treated preform without heat treatment exhibited a 78-79% reduction, and the binder-treated preform with heat treatment exhibited a 97-98% reduction compared to the permeability of the neat fabric preform. These results indicate that the binder treatment hindered the impregnation of resin in the out-of-plane direction. In particular, the permeability of the binder-treated fabric preform with heat treatment during the preforming process decreased more than that of the binder-treated fabric preform without heat treatment because the binder was softened during the heat treatment and permeated between the fibers under the 1 atm pressure applied by a vacuum bag. This phenomenon can greatly affect the reduction in the permeability of the fiber preform. To support this argument, micro observations of the binder in the preform is discussed in Section 3.3 below.  
Figure 7: The value of K2/K1 according to the binder treatment.
# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 2 Materials and Methods
### YOLOv8 Architecture  
A thorough examination of the extant literature on related methods was conducted, leading to the determination that the YOLO algorithm would be employed due to its capacity to integrate a greater number of characteristics than those initially proposed and its exceptional adaptability to the images contained within the datasets. In addition, the authors of [33] demonstrate the efficacy of the method in identifying defects in welds. This task is analogous to the present one, but it is conducted using radiographic images. A comparative analysis of various algorithms is conducted, with YOLO emerging as the most effective option.  
In [34], the authors undertake a comparative analysis of all iterations of the selected algorithm. It is evident that, of all the iterations of YOLO, the YOLOv8 version has been the most effective in achieving optimal results, as evidenced by its superior performance in aligning with the aforementioned characteristics. While acknowledging the shortcomings of YOLO in consistently achieving optimal performance in detecting small objects, the YOLOv8 model was meticulously engineered to address these limitations. Its design aims to enhance localization accuracy and optimize the performance-to-computing ratio.  
This renders it particularly well-suited to the purposes of this study [35]. This version was released by a company, Ultralitycs, that develops tools to build, optimize, and implement deep learning models. In this version, all the characteristics of YOLO--performance, precision, robustness, efficiency, etc.--are far superior to its predecessors.  
Consequently, the YOLOv8 architecture has been selected for further investigation. The YOLOv8 model has been developed to incorporate new features and enhancements over its predecessor, YOLOv5 [22], with the objective of achieving an optimal speed-accuracy trade-off.The YOLOv8 architecture comprises five distinct models, designated as \"n\", \"s\", \"m\", \"1\", and \"x\", which vary in size and thus offer different levels of computational complexity and parameter requirements. The \"n\" model is the most lightweight, making it particularly well-suited for processes with limited computational resources. The YOLOv8 architecture is composed of three fundamental components: the backbone network, the neck, and the head. The backbone network is responsible for feature extraction, while the neck component integrates these features. Finally, the head component is responsible for object localization and regression.
# Road Traffic Gesture Autonomous Integrity Monitoring Using Fuzzy Logic
###### Abstract  
Occasionally, four cars arrive at the four legs of an unsignalized intersection at the same time or almost at the same time. If each lane has a stop sign, all four cars are required to stop. In such instances, gestures are used to communicate approval for one vehicle to leave. Nevertheless, the autonomous vehicle lacks the ability to participate in gestural exchanges. A sophisticated in-vehicle traffic light system has therefore been developed to monitor and facilitate communication among autonomous vehicles and classic car drivers. The fuzzy logic-based system was implemented and evaluated on a self-organizing network comprising eight ESP32 microcontrollers, all operating under the same program. A single GPS sensor connects to each microcontroller that also manages three light-emitting diodes. The ESPNow broadcast feature is used. The system requires no internet service and no large-scale or long-term storage, such as the driving cloud platform, making it backward-compatible with classical vehicles. Simulations were conducted based on the order and arrival direction of vehicles at three junctions. Results have shown that autonomous vehicles at four-legged intersections can now communicate with human drivers at a much lower cost with precise position classification and lane dispersion under 30 s.  
Advanced Driver Assistance System (ADAS); autonomous vehicles; extended blind-spot detection; fuzzy logic; gesture recognition; in-vehicle traffic light +
Footnote †: journal: Sensors
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 3 Results and Discussion
### Results from Quality Control of GNSS Observations  
#### 3.1.1 Carrier-to-Noise (C/N\({}_{0}\)) Analysis Results  
All three GNSS instruments were placed side by side with only 20 cm between the antennas to assure the same surveying conditions. In the case of the open-sky conditions, GD\(-1\) has a higher C/N\({}_{0}\) for the observations in F1 frequency than LC\(-1\) and LC\(-2\), which is in the range of 25 to 55 dBHz (Figure 4), even at elevation angles lower than 10\({}^{\circ}\). For LC\(-1\) and LC\(-2\), the C/N\({}_{0}\) is greater than 40 dBHz at high elevation angles, while it is less than 25 dBHz at low elevation angles. The C/N\({}_{0}\) for F2 is similar to F1; again GD\(-1\) has higher C/N\({}_{0}\) values than LC\(-2\) and LC\(-1\). The C/N\({}_{0}\) for LC\(-1\) and LC\(-2\) is lower than 25 dBHz at elevation angles lower than 15\({}^{\circ}\) in both F1 and F2, while it is higher in the case of GD\(-1\).  
The LC\(-1\) was additionally analyzed for its C/N\({}_{0}\) in the open sky for 24 h using the open-source application u-center [31]. From the results shown in Figure 5, the C/N\({}_{0}\) at elevation angles below 10\({}^{\circ}\) is less than 10 dBHz, while the C/N\({}_{0}\) at elevation angles above 10\({}^{\circ}\) is more than 30 dBHz. The overall average C/N\({}_{0}\) is 43.5 dB Hz with a deviation unit of 0.9 dBHz.  
In urban areas, worse results are achieved with lower C/N\({}_{0}\); this is especially true for low-cost GNSS devices (Figure 6). A C/N\({}_{0}\) of 25 dBHz is obtained at elevation angles of 30\({}^{\circ}\) for LC\(-1\) and LC\(-2\) in both F1 and F2, while the C/N\({}_{0}\) in open sky ranges between 30-35 dBHz at angles of 30\({}^{\circ}\). The use of a geodetic antenna does not significantly improve the C/N\({}_{0}\) for low-cost GNSS devices in urban areas. High-quality geodetic GNSS devices are found to perform better than low-cost ones; C/N\({}_{0}\) is higher than 25 dBHz at lower elevation angles (<20\({}^{\circ}\)) for GD\(-1\), while for LC\(-1\) and LC\(-2\), C/N\({}_{0}\) is always less than 25 dBHz for both F1 and F2.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 6 Implementations and Use Cases  
This overview of the challenges and opportunities in intelligent mobile edge computing for 5G networks is timely because of the recent implementations of 5G networks in multiple countries supported by international telecommunication companies. 5G promises faster connection experiences and enhanced security through eMBB, URLLC, mMTC and design decisions for resource sharing such as network slicing and prioritized traffic. Several key world powers are competing for technology dominance in the space that will define the future of communication with new hardware, software, and data processing paradigms.
# Dynamic-Breakdown of the Ball-Screw Inerter in ISD System
## 2 Modeling and Analysis of the DBoI in ISD System
### Response Analysis of the DBoI in ISD System  
On account of the dynamic relations, the details of the DBoI in the ISD system are further analyzed. Equation (3) can be solved and further expressed by Equation (5).  
\[b(v_{1}-v_{2})|_{t_{\rm c}}^{t_{\rm t}}+k\int_{t_{\rm c}}^{t_{\rm t}}(z_{1}-z_{2} )\mathrm{d}t+\left.c(z_{1}-z_{2})\right|_{t_{\rm c}}^{t_{\rm t}}=-P_{\rm t} \tag{5}\]  
During the DBoI in the ISD system, the inerter's two terminals remain in contact. Therefore, the relative displacement \(z_{1}\)-\(z_{2}\) can be seen as a constant. Then, the relative displacement variation during the dynamic-breakdown can be seen as zero. That means the influence of the damper is minor and can be seen as zero. That is why the influence of the damper in the ISD system is ignored at the beginning of Section 2.1. Equation (5) can be further expressed by Equation (6).  
\[b[(v_{1\rm t}-v_{2\rm t})-(v_{1\rm c}-v_{2\rm c})]+k(z_{1}-z_{2})(t_{\rm t}-t_{ \rm c})=-P_{\rm t} \tag{6}\]  
where \(v_{1\rm t}-v_{2\rm t}\) is the relative velocity of the two terminals, when the two terminals disconnect. \(v_{1\rm c}-v_{2\rm c}\) is the relative velocity of the two terminals, when the two terminals begin to contact.  
Actually, the duration of the impulse \(P_{\rm t}\) is short. That means the influence of the spring is minor. A dangerous situation is that the inerter causes almost the entire impulse. That means the impact force is much larger than the force \(k(z_{1}-z_{2})\). This dangerous situation ought to be focused on when designing the inerter and the ISD system. If the influence of the spring is ignored, Equation (6) can be simplified as Equation (7). Therefore, Equation (7) is an approximate equation.  
Under the dangerous situation, the huge impact forces caused by the DBoBSI in the ISD system may destroy the inerter and the system. The dangerous situation is mainly discussed below. The dynamic-breakdown issue is simplified based on the dangerous situation, and the approximate Equation (7) is applied.  
\[b[(v_{1\rm t}-v_{2\rm t})-(v_{1\rm c}-v_{2\rm c})]=-P_{\rm t} \tag{7}\]  
The left part of Equation (7) means the variation of the inerter's momentum in the ISD system during DBoI. The absolute value of the variation is denoted as \(M_{\rm c}\), as expressed by Equation (8).  
\[M_{\rm c}=b|(v_{1\rm t}-v_{2\rm t})-(v_{1\rm c}-v_{2\rm c})| \tag{8}\]  
The coefficient of restitution of the DboI in the ISD system is denoted as \(e_{\rm t}\), as expressed by Equation (9).  
\[\frac{v_{1\rm t}-v_{2\rm t}}{v_{1\rm c}-v_{2\rm c}}=-e_{\rm t},\;0\leq e_{\rm t }\leq 1 \tag{9}\]  
Synthesize Equations (7) and (9), an approximate equation is obtained and expressed by Equation (10).  
\[b(1+e_{\rm t})(v_{1\rm c}-v_{2\rm c})=P_{\rm t} \tag{10}\]  
Equation (10) can be verified by experiments of DBoBSI in the ISD system in Section 3. On account of the analysis above, the basic method of solving the dynamic-breakdown issue of DBoBSI in the ISD system is obtained.
# Variability of Diffuse Daylight Due to the Diversity of Cloud Arrays
## 3 Results
### Different Cloud Size and Altitude  
As mentioned in Section 2, we considered two different cloud radii \(R\) = 0.25 km and 0.5 km, and two cloud base altitudes \(h_{\rm C}\) = 0.5 km and 1 km. The diffuse illuminances calculated for each combination of these parameters at one specific cloud distribution and the fixed value of the cloud fraction \(C_{F}\) = 0.3 are shown in Figure 1. The illuminances on the horizontal plane and the three vertical planes are depicted as a function of the solar zenith angle.  
Figure 1: Diffuse illuminance on the horizontal plane (**a**) and vertical planes oriented to the south (**b**), east (**c**), and north (**d**) as a function of the solar zenith angle for a different cloud base altitude \(h_{\rm C}\) and radius \(R\). The same distribution of clouds at the cloud fraction of 0.3 was used and also the clear sky for comparison.  
One can see that lower clouds can cause noticeably greater diffuse horizontal illuminance (and, for example, north wall illuminance) than clouds of the same size but placed higher. Even more significant differences can be seen when clouds of different radii but with the same base altitude are compared--the smaller clouds with \(h_{C}=0.5\) km give greater illuminances than the bigger ones. However, these conclusions are not universal. A different distribution of clouds in the sky can result in completely different behavior. Figure 1 only demonstrates how different cloud parameters can have a noticeable effect on the magnitude of diffuse illuminance on various surfaces.  
The dependence of the illuminance on the solar zenith angle follows approximately the trend observed under clear sky conditions. This is except for the case of the south wall, in which the clouds simply shade the bright vicinity of the sun at the middle elevations.
# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
###### Abstract
This paper's novelty consists of using synthetic data created from dynamic EON simulations, focusing on connection-blocking prediction based on the spectrum status of network links. Even though it might take hours to train the CNN, it can ultimately analyze and give the output in milliseconds once trained. The main contributions of this study are:  
* Introducing CNNs as a method for blocking prediction in Elastic Optical Networks
* Proposing a CNN 1D and CNN 2D architecture for EON connections blocking prediction
* Generating synthetic data using dynamic EON simulations to train and test machine learning models
* Analyzing and implementing a 1D CNN and 2D CNN and comparing these with KNN and SVM methods  
We assess that the trained model is able to predict potential connection blocking. Deep learning neural network CNNs (1D CNN and 2D CNN) are implemented and are compared with other machine learning models used to predict connection blocking in EONs (support vector machine and K nearest neighbors). The remainder of this paper is organized as follows. In Section 2, the EON network simulator and CNN input data generation are presented. Section 3 is devoted to the structure and characteristics of CNNs. The main results obtained are presented in Section 4. Finally, Section 5 concludes the paper.
# A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding
## 2 Theoretical Design of CLSDAT
### Constructing a Data Set of Selected Circle Center Samples  
As shown in Figure 2, three randomly selected pixel coordinates from data set W are used to connect any two points to form a chord corresponding to the respective circular arc  
Figure 1: Grid method for dividing circles. (**a**) Horizontal straight line dividing circle. (**b**) Vertical straight line dividing circle.  
in the circular contour. Normal lines are drawn from the midpoints of the two chords, which must intersect at a single point. This intersection point is recorded as \(O_{i},i=1,2,\cdots,n\). Thus, the intersection point represents the center of the circle determined by these three coordinate points. In the ideal case, if all the points are from the same standard circle, all the obtained centers should converge to the same point. However, due to the imperfectly standardized circular contour obtained from image pre-processing, the calculated center coordinates may be in the vicinity of the true center coordinates. A significant portion of the calculated center coordinates will be close to or overlap with the true center position.  
By changing only one of the three coordinate points, it is possible to construct different circles and obtain different center coordinate data. By selecting any three-pixel coordinates from data set \(W\), according to the combination formula in the probability theory, the number (\(n\)) of circle center sample data points satisfies Equation (1), where \(N\) is the number of pixels of the circle outline. For example, 60 coordinates of circular contour pixels correspond to 34,220 center sample data points. If the number of center sample data points is too large, random sampling can be used to obtain the desired center sample data set.  
\[n=C_{N}^{3}=\frac{N!}{3!(N-3)!} \tag{1}\]  
After traversing the coordinate data set (\(W\)), the coordinates of the center points \(O_{1}\) to \(O_{n}\) of the selected circle are obtained, and they are saved in data set \(P\), which is called the sample data set of the selected circle center.
## 3 Methodology
### Delay Processing Model
#### 3.3.3 Re-Claiming  
The third part, re-claiming, is a core procedure to retrieve occluded objects after messy frames as in Figure 5. For all the objects in the shadow tracking mode, as lost verification of detection for recent frames, the re-claiming part also performs a second-time correlation between newly initialized tracks and shadow objects. Moreover, the model calculates the overlap rate between those new tracks and the shadow object. If the re-correlation score \(s_{recorrelate}\) and the overlap rate show that the new track is the doppelganger of the shadow object with a high probability, then the re-claiming phase chooses a more accurate bounding box from both tracks based on their tracking scores before, following a rule that each object can only appear a single time at a certain frame. One thing worth mentioning is that these kind of doppelgangers may be created by deformation or partial occlusion. This procedure can be viewed as a precaution in case of failures by the re-initiation part.  
Figure 4: An example of how attaching handles object retrieval after occlusion. The occlusion happens at time \(t\), the single object tracker belonging to the object in the yellow box at time \(t\) failed to track and save to memory. A new object in the white dotted box is initialized from detection at time \(t+1\) after occlusion. The attaching phase is able to attach the new object in the white dotted box to the lost one in the yellow box, eliminate the object in white dotted box, and keep tracking using the single object tracker from the yellow box object from before.
# Actimetry-Derived 24 h Rest-Activity Rhythm Indices Applied to Predict MCTQ and PSQI
## 2 Materials and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **Instrument** & **Mean** & **SD** & **S** & **K** \\ \hline Age, years & Self-report & 21.51 & 9.64 & 0.79 & 0.86 \\ BMI, \% & Self-report & 47.51 & 25.33 & 0.26 & \(-\)0.63 \\ PSQI, global score & PSQI & 5.65 & 2.82 & 0.37 & \(-\)0.83 \\ SIE, \% & MCTQ & 88.64 & 5.53 & \(-\)0.67 & \(-\)0.12 \\ SID, min & MCTQ & 413 & 72 & \(-\)0.94 & 0.19 \\ SJL, min & MCTQ & 114 & 80 & 0.71 & \(-\)0.77 \\ MSFsc, h:min & MCTQ & 04:13 & 01:22 & 0.07 & \(-\)0.10 \\ MESOR, a.u. & Actimetry & 10.36 & 1.97 & 0.09 & \(-\)0.27 \\ A, a.u. & Actimetry & 7.49 & 2.07 & 0.17 & \(-\)0.47 \\ \(\varphi\), hh:min & Actimetry & 14:52 & 01:07 & 0.17 & \(-\)0.20 \\ M10, a.u. & Actimetry & 17.28 & 3.29 & 0.21 & \(-\)0.45 \\ M10t, hh:min & Actimetry & 15:16 & 01:29 & 0.59 & 0.69 \\ L5, a.u. & Actimetry & 0.60 & 0.24 & 2.25 & 9.95 \\ \(2+\ln(\text{L5})\), a.u. & - & 1.42 & 0.36 & 0.29 & 0.76 \\ L5t, hh:min & Actimetry & 03:14 & 01:17 & \(-\)0.10 & 0.82 \\ IS, a.u. & Actimetry & 0.51 & 0.11 & \(-\)0.14 & \(-\)0.76 \\ IV, a.u. & Actimetry & 0.92 & 0.19 & 0.40 & \(-\)0.16 \\ AcoB, a.u. & Actimetry & 14.84 & 2.61 & \(-\)0.09 & \(-\)0.49 \\ AciB, a.u. & Actimetry & 1.65 & 1.01 & 1.97 & 4.47 \\ \(1+\ln(\text{AciB})\), a.u. & - & 0.36 & 0.52 & 0.53 & 0.02 \\ \hline \hline \end{tabular}  
* PSQI: sleep quality; SID: average weekly sleep duration; MSFsc: chromotype; SIL: social jeta; SIE: sleep efficiency; MESOR/\(A/A\); \(\varphi\): 24 h time series mean/amplitude/acrophose of 24 h rest-activity rhythm; M10(t): most active 10 h period (mid-phase of M10); L5(t): least active 5 h period (mid-phase of L5); IS: interdaily stability; IV: intradaily variability; AcoB/AciB: mean values of motor activity out of bed/in bed; a.u.: arbitrary units; _SD_: standard deviation; _S_: skewness; _K_: kurtosis.  
\end{table} 
Table 1: Descriptive statistics parameters studied. A series of multiple regression analyses were performed in which the continuous values 'MSFsc', 'SJL', 'SID', 'SIE', and 'PSQI global score' were used as dependent variables and 'age','sex', 'BMIc', and '24 h rest-activity rhythm characteristics' were included as independent variables. A procedure of stepwise inclusion of predictors in the model was used. Only predictors with significant regression coefficients were included in the final model. The variance inflation factor was used to evaluate multicollinearity in the model, as described in [42]. Predictors were excluded from the model if the variance inflation factor was equal to or greater than five.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Multi-Channel MAC Protocols
#### 4.3.7 MoMAC  
The mobility-aware MAC protocol was proposed to achieve collision-free transmission and to enhance the message delivery and reception rates of safety applications. It handles the hidden terminal problem. TDMA-based slot partitioning is used; vehicles use GPS to synchronize and use the same slot until collision is detected. Different time slots are selected by vehicles within the same OHS. To resolve the hidden terminal problem, vehicles in the same THS should choose different communication time-slots. During a THS, the hidden terminal problem can occur when two vehicles, located in the two OHSs, cannot hear each other and decide to send messages simultaneously. For instance, in Figure 8, vehicle A wishes to transmit a message to vehicle B, and vehicle C wishes to transmit a message to vehicle D at the same time. Vehicle C does not realize that vehicle A has already started transmitting because it is not within its communication range, which results in a collision with vehicle B. Since there is no RTS/CTS mechanism, each vehicle should collect and broadcast information about all other vehicles occupying time slots from one-hop neighbors to the other THS, so there is no hidden terminal problem. The protocol is implemented in a multi-lane road segment where the segment is divided into time slots. The collision detection scheme and distributed slot access are employed to eliminate hidden terminal problem [82].  
The protocol aims to minimize delays in access to and delivery of packets. The communication is decentralized, not RSU centric. Therefore, current traffic detection is not easy and cannot handle dynamic traffic scenarios.