esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reliability, Availability and Fault Tree Analysis
### Availability  
Availability is a measure which is defined as the probability of a component/system is functioning at time \(t\). The availability at the instant \(t\) is referred as _instantaneous availability_\(A\left(t\right)\). The _steady-state availability_ expresses the percentage of time that a component is working properly. Formally it is defined as \(A=\lim_{t\rightarrow\infty}A(t)\) (note: this metric only makes sense in systems which have a stationary probabilistic condition [42]). Availability is closely related with repair actions. In fact, it is implied that the system is repaired after a failure, otherwise \(\lim_{t\rightarrow\infty}A(t)=0\). For a non-repairable system \(A(t)=R(t)\).  
Similarity to the failure rate \(\lambda(t)\), it is possible to define a _repair rate_\(\mu(t)\), as the rate at which a failed component is repaired. The MTTR (_Mean Time to Repair_) is defined as the expected (average) time that takes to repair a component. If failure and repair rates are assumed constant, respectively \(\lambda\) and \(\mu\), then it can be proved that \(A(t)\) is given by [42]  
\[A\left(t\right)\;=\;\frac{\mu}{\lambda+\mu}+\frac{\lambda}{\lambda+\mu}e^{- \left(\lambda+\mu\right)t} \tag{4}\]  
Finally, if failure and repair actions are independent and described by \(i.i.d\) (independent and identically distributed) random variables, than the following relationship applies [42]  
\[\lim_{t\rightarrow\infty}A\left(t\right)\;=\;A\;=\;\frac{MTTF}{MTTF+MTTR} \tag{5}\]  
This expression is independent of the CDF that characterize failure and repair processes.
# Advancing Digital Twin-Based Collision Avoidance:
## 2 Collision Prevention Mechanism
### Communication Strategy  
As depicted in Figure 3, three methods of collision alerting are outlined. The decentralized model leverages 5G to share positional data among vehicles with each vehicle responsible for assessing collision risk. In one centralized model, the edge server compiles and periodically broadcasts truck locations, easing the 5G network load. Another centralized method utilizes updated truck positions to predict collision events and informs  
Figure 3: Comparison of communication methodologies among decentralized and centralized systems from [13]. Arrows indicate the flow of packets, similar to a transmission diagram. The icons at the top represent vehicles. The left-side decentralized model uses 5G to disseminate position data among vehicles, which then individually assess collision risk. The middle centralized model involves a server consolidating location data from all trucks and relaying it periodically. The right-side centralized approach calculates potential collisions using incoming location data. The red icon on the right hand side indicates that a collision risk is detected and alerts are sent to all vehicles.  
Figure 2: An architecture blueprint from [13] suitable for industrial applications using private 5G networks, showcasing three key elements: the sensor-equipped forklift truck, the local IT services, and the cloud-connected services. Arrows indicate the flow of messages between modules.  
vehicles about potential risks. Centralizing computation reduces the volume of messages in the 5G network, which is a factor that will be further evaluated in the paper.
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 4 Multicast Mechanism  
The multicast source can send multicast packets without knowing the group members. After receiving the multicast packet identified by the MGEID for the first time, the SER needs to initialize the NFT entry of the MGEID and register the mapping between the MGEID and its NA with NRS. The establishment and destruction of multicast communication are completed through control signalings. The receiver can send the corresponding control signaling to join or leave the specified multicast group. In this section, we introduce the multicast mechanism of AHHM in detail from four aspects: joining a group, MJN selection, leaving a group, and data forwarding.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 9 Challenges and Future Directions
### Areas to Explore
#### 9.2.3 Machine-to-Machine Communications (M2M)  
BackCom is the key enabler for M2M in IoT [228]. M2M communications are widely used in emerging autonomous applications. M2M communications consider small chunks of sensed data from different sensors, and these chunks can be transmitted rapidly to establish low latency communications. The Amb-BackCom tags can transmit the small amount of data by backscattering cellular signals [229]. BackCom can be employed in M2M. This technique allows driverless cars and other autonomous vehicles to communicate with each other using BackCom. Eventually, M2M communications can be used to save power and to maintain reliable communications. Amb-BackCom is also used in M2M communication [230], to maximize energy harvesting and data transmission.
# Dark-Center Based Insulator Detection Method in Foggy Environment
## 5 Conclusions  
In this paper, a joint learning framework that combines image defogging with target detection, Dark-Center, is designed based on the DCP defogging algorithm and CenterNet target detection algorithm. First, a foggy simulation insulator dataset CPILDs is constructed using the dark channel of a prior algorithm. Then, the Dark algorithm is proposed to improve the robustness of the defogging algorithm by combining defogging and restoration modules. Meanwhile, the Center model is proposed to improve the feature extraction capability of the model for small targets on the basis of the CenterNet fused with M-SPP and E-ASPP modules. The target detection accuracy in foggy scenes is greatly improved via the joint learning of the defogging algorithm (Dark) and the target detection model (Center). The experimental results showed that the Dark defogging algorithm can not only remove most of the fog in the image but also retain the original image detail information and improve the image quality. The Center target detection model incorporating the M-SPP and E-ASPP modules can achieve the target detection of insulators in the foggy environment. The insulator detection accuracy reached 98.45%, and the defect detection accuracy reached 90.21% on the CPILD dataset. Moreover, the joint learning framework Dark-Center based on image defogging and target detection further improved the insulator detection accuracy to 98.85% and the defect detection accuracy to 94.67%. The experimental results confirmed the feasibility of Dark-Center-based insulator detection for foggy weather and the generalization of the model. It also has excellent detection accuracy and real-time response speed even in small target detection tasks. Considering the current model is only capable of dealing with foggy environments, the authors' future work will focus on intelligent algorithms that can learn insulator features in a wide range of complex environments for image recovery.  
Conceptualization, C.K., L.L., and H.L.; methodology, C.K.; software, C.K.; validation, C.K., L.L., and H.L.; formal analysis, C.K.; investigation, C.K.; resources, C.K.; data curation, C.K.; writing--original draft, C.K.; writing--review and editing, C.K. and H.L.; visualization, C.K.; supervision, C.K.; project administration, C.K.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University-Industry Cooperation Project \"Research and Application of Intelligent Traveling Technology for Steel Logistics Based on Industrial Internet\", grant number 2022H6005, Natural Science Foundation of Fujian Provincial Science and Technology Department, grant number 2022J01952, and Research Start-up Projects, grant number GY-Z12079.  
Not applicable.  
The data presented in this study are available upon request from the corresponding authors.  
The authors declare no conflict of interest.  
Figure 11: Performance comparison of different algorithms in a foggy environment.
## 3 Results
### The Optoelectronic Characteristics of the Carbon-Based Photodiode  
A single graphene/s-SWCNT film Schottky photodiode and the executive circuit is illustrated schematically in Figure 3a. The chemical residues created from the transfer process and the oxygen from the surrounding generally rendered a p-type graphene sheet [26]. The source-drain current of the Au-graphene-Au transistor as a function of V\({}_{\text{BG}}\) was tested, which is shown in Figure S3. An 8 V voltage of the Dirac point (V\({}_{\text{D}}\)) was observed, indicating the p-type characteristics of as-used graphene. The transfer curves of the graphene/SWCNT hybrid transistor formed by the A-B ends of the cross-shaped carbon-based structure (Figure 1 and the inset in Figure S2) were compared. We observed that the Dirac point of the hybrid transistor shifted from 8 to 15 V, indicating deeper p-type doping of the graphene/SWCNT junction. Figure 3b shows a set of output curves of the Schottky devices at different back-gate voltage (V\({}_{\text{BG}}\)) without illumination. The as-fabricated device presents a typical p-type doping characteristic ascribed to the doping effect by the surroundings and clearly exhibits a near perfect rectifying behavior. According to the electrical characteristic of SWCNTs  
Figure 2: Schematic (**a**) and optical image (**b**) of the graphene/s-SWCNTs cross-shaped photodiode. (**c**) The radial breathing mode (RBM) range of Raman spectrum for the s-SWCNT film before and after overlapping graphene. (**d**) The AFM image of the graphene/s-SWCNT film overlap area.  
analyzed in the supplementary materials (Figure S3a), it presents ohmic or near-ohmic contact between SWCNTs and Au electrodes [27]. In view of the fact that there is no obvious contact resistance between the graphene and Au metal, the Schottky characteristic observed in our device can be principally attributed to the graphene/SWCNT junction. By applying a reverse \(\mathrm{V_{BG}}\), the diode current (\(\mathrm{I_{Dcode}}\)) can be dramatically increased and an order of magnitude difference was observed at \(\mathrm{V_{BG}}=-30\) V. The photocurrent shown in Figure 3c exhibits a continuous and monotone increasing with increasing reverse \(\mathrm{V_{BG}}\) (source-drain voltage (\(\mathrm{V_{SD}}\)) \(=-5\) V). The back-gate tunability of the photocurrent is associated with the gate-tunable carrier density and polarity in the photoelectric conversion system. The \(\mathrm{I_{Dcode}}-\mathrm{V_{SD}}\) curves of the Schottky devices at zero back-gate voltage before and after illumination is shown in Figure 3d, in which a turn-on voltage (\(\mathrm{V_{T}}\)) of \(-6.5\) V was observed. Upon illumination of \(1000\) nm, \(\mathrm{V_{T}}\) was slightly decreased, which was ascribed to the enhanced image force barrier lowering (IFBL) induced by photoexcited carriers in s-SWCNTs [28; 29]. This resulted in an increased diode current. Hence, the dramatically increased photocurrent with the increasing of source-drain voltage is observed in the inset of Figure 3d. The corresponding curves of the carbon-based devices in log scale are shown in Figure S2.
## 5 Compensation for Frictional Disturbance Observer  
In servo systems, due to the presence of friction components, the system can't respond promptly when the speed crosses zero, causing the motor to remain stationary for a certain period. This results in a velocity dead-zone phenomenon on the speed response curve. This occurs because, at the moment of commutation, the servo system is unable to generate sufficient torque instantly from the applied command to overcome the static frictional torque and drive the motor. Therefore, compensation for the frictional force is necessary to ensure the normal operation of the motor. The disturbance suppression method based on the disturbance observer treats the frictional force as part of the external disturbance, without the need for a friction model for observation and estimation.
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 4 A Real Case Study
### Determining the District with Clustering
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**District** & **Population** & **SES Score** & \
\begin{tabular}{c} **Intentional** \\ **Murder Rate** \\ \end{tabular} &
\begin{tabular}{c} **Armed** \\ **Incidents** \\ \end{tabular} \\ \hline Armavutköy & 312,023 & 16.78 & 0.002954595 & 9.395613317 \\ Avclar & 457,981 & 43.75 & 0.004336695 & 13.79068973 \\ Bagcalar & 744,351 & 28.41 & 0.007048378 & 22.41384183 \\ Bahcelieveler & 605,300 & 45.45 & 0.005731682 & 18.22674848 \\ Bakurköy & 228,759 & 79.17 & 0.002166155 & 6.888373956 \\ Bagaksehir & 503,243 & 38.75 & 0.004765288 & 15.1536157 \\ Bayrampasa & 274,884 & 32.95 & 0.00260292 & 8.277286518 \\ Besiktas & 178,938 & 91.30 & 0.001694392 & 5.388167718 \\ Beylikdüzüü & 398,122 & 50.00 & 0.00376988 & 11.98821999 \\ Beyoglu & 233,322 & 45.28 & 0.002209363 & 7.025774672 \\ Buyukcekmee & 26,916 & 42.71 & 0.000254872 & 0.810492586 \\ Catalca & 76,131 & 13.78 & 0.000720897 & 2.292450997 \\ Esenler & 447,116 & 23.44 & 0.004233812 & 13.46352366 \\ Esenyurt & 977,489 & 31.10 & 0.009255999 & 29.4340759 \\ Eyüngultan & 41,736 & 35.27 & 0.000395205 & 1.256751321 \\ Fatih & 38,299 & 37.28 & 0.000362659 & 1.153256633 \\ Gaziosmanpasa & 493,096 & 33.59 & 0.004669204 & 14.8480722 \\ Güngören & 283,083 & 40.91 & 0.002680558 & 8.524174195 \\ Kägthane & 45,455 & 40.13 & 0.000430421 & 1.368737572 \\ Kücükcekmee & 80,593 & 35.71 & 0.000763148 & 2.426810408 \\ Sarryer & 349,968 & 57.57 & 0.003313903 & 10.53821033 \\ Silivri & 209,014 & 17.50 & 0.001979187 & 6.293813988 \\ Sultangazi & 54,338 & 23.33 & 0.000514535 & 1.636221806 \\ Sisli & 284,294 & 68.50 & 0.002692025 & 8.560639736 \\ Zeytinburu & 293,839 & 43.27 & 0.002782408 & 8.848058065 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average values of districts’ risk factors (IMM, 2021; [PERSON] et al., 2019; Adli Istatistikler, 2021). The lowest cluster total SES score of 29.75, the highest cluster total Intentional murder rate of 0.08%, and the cluster total Armed incident rate of 25.9% belongs to Cluster 3: Bagclar, Esenyurt. This district has been selected as a highly risky district. Because low social and economic status and high crime rates may lead to increased ATM theft and other crimes in the region, one of these districts must be selected for the ATM location selection problem. For this reason, by comparing the two districts, it is decided that Esenyurt is the riskiest area with a 31.1 SES Score, 0.09% Intentional murder rate, and 29.43% Armed incident rate.
# A Gamified AI-Driven System for Depression Monitoring and Management
## 2 Background and Related Works
### Adaptive Daily Surveys for Emotional Monitoring  
Daily surveys are a widely used tool for emotional check-ins in mobile mental health applications, ranging from structured clinical scales like the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7) to app-specific, simplified self-report instruments. These tools facilitate self-reflection, early symptom recognition, and tracking emotional trends over time [12].  
Nonetheless, survey fatigue is a common concern, especially when users are presented with the same set of questions each day. Repetitive and non-contextual prompts can lead to disengagement or incomplete data capture [13]. Researchers have explored adaptive surveys that personalize content based on prior responses, mood history, or contextual factors such as time of day or recent behavior [14,15]. Adaptive assessments have also shown promise in improving both adherence and the ecological validity of collected data, especially when aligned with users' daily routines [16]. When thoughtfully combined with gamification elements and feedback mechanisms, adaptive surveys can enhance user experience while preserving data richness and emotional accuracy.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 3 Results and Discussion
### Result Analysis
#### 3.1.4 HoL Blocking Degree  
On the one hand, wVegas paired with Reno at the path level exhibited the highest average and maximum HoL Blocking Degree, making it suboptimal for streaming applications. Conversely, BALIA with Reno and BALIA with BBR demonstrated the lowest average and maximum HoL, meaning superior performance in terms of packet reordering. Low HoL values are especially advantageous for streaming applications. With reduced jitter and diminished buffer requirements, they not only enhance the overall user experience, but also relieve the resource pressure for low-end and mobile terminals.  
A detailed examination of the HoL Blocking Degree can be found in Figure 4, specifically plots (a), (c), and (e). All three SPTCP CC algorithms displayed pronounced fluctuations, with peaks reaching up to 125 ms for the combination of BBR and wVegas, approximately 100 ms for Cubic and LIA, and 80 ms for Reno and LIA. When juxtaposed with the data from Table 3, these values are consistent, but for combinations like BBR with LIA and Reno with wVegas, values from other runs were observed to be four to six times higher than those depicted in the presented graphs. The magnitude of these fluctuations becomes evident when considering the averages computed from all the experiments. Except for BBR paired with LIA and Reno with wVegas, these averages oscillate around 20 ms. However, the graphs indicate that the fluctuation band spans from 0 to 40 ms and occasionally exceeds this range.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications
### TSN Support for Automated Driving  
This section highlights the correspondence between the various Automated Driving requirements and the TSN standards that can be used to meet them.
## 4 Diamond-Based 1D Metal Oxide Heterojunction Classes
### D N-ZnO/P-Diamond Heterojunction
#### 4.1.1 Epitaxial Growth of 1D ZnO NRs/Diamond Facet  
At room temperature and normal pressure, ZnO shows the hexagonal wurtzite structure (Figure 7). Each Zn\({}^{2+}\) ion is surrounded with four O\({}^{2-}\) ions corresponding to tetrahedral coordination (\(sp^{3}\) orbital hybridization) corresponding to the C\({}_{6V}\)\({}^{4}\) class and \(P6_{3}mc\) space group. The shape of Zn\({}^{2+}\)-O\({}^{2-}\) tetrahedrons, as well as the ionic-covalent nature of Zn-O bonding, define the intrinsic polarity in the ZnO crystal growing along the c-axis [0001] orientation. Therefore, various 1D nanostructures are readily formed in the ZnO growth process [79].  
Diamond is a typical atomic crystal with a face-centered cubic structure, belonging to the equiaxed crystal system. The carbon atoms in the diamond structure are bonded by \(sp^{3}\), with each carbon atom being bonded with four adjacent carbon atoms. The arrangement of diamond carbon atoms is shown in Figure 8, which contains a carbon atom centered in a regular tetrahedron and a carbon atom at each of four vertices, wherein carbon atoms at each vertex are covalently bonded with C-C bonds and shared by four tetrahedrons to form a three-dimensional network of diamond crystal [80].  
In terms of theoretical background, [PERSON]'s group proposed the epitaxial growth mechanism for the structure of ZnO-diamond. Since both ZnO crystal (0001) facet and diamond (111) facet have the same hexagonal atomic arrangement, ZnO (0001) planes and diamond (111) are geometrically matched. The [0001] oriented 1D ZnO NRs are normally aligned and epitaxial grown perpendicular to the diamond (111) plane. The epitaxial growth relation between the ZnO (0001) and diamond (111) is suggested in compliance  
Figure 7: (a) ZnO unit cell with the hexagonal wurtzite structure. (b) Multiple crystal facets of the ZnO hexagonal wurtzite [79].  
with (0001) [11\(\overline{2}\)0]ZnO//(111)[1\(\overline{1}\)0] diamond or (0001)[10\(\overline{1}\)0]ZnO//(111) [1\(\overline{1}\)0]diamond (Figure 9a), and the epitaxial growth relation between the (0001) ZnO and (100) diamond is primarily of (0001)[0001]ZnO//(101) [101] diamond (Figure 9b) [23]. An in-depth study of 1D ZnO/diamond system will not only contribute to understanding the physical mechanism for epitaxial growth but also to extending the area of ZnO/diamond applications in optoelectronics devices.
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 3 Results
### Model Performance Evaluation
#### 3.4.2 Computational Time Analysis  
Table 8 compares the average epoch time cost of different models across four datasets. MD-Autoformer shows a time cost increase of approximately 4.8% in Apparel Sales, 3.9% in ILI, 17.2% in Exchange, and 5.6% in Weather compared to Autoformer. This indicates that the MD-Autoformer achieves higher accuracy with only a slight increase in computational time, which is acceptable. DLinear, with its linear network structure, demonstrates high computational efficiency and good accuracy, such as taking only 2.882 s on the weather dataset. In conclusion, the trade-off between accuracy improvement and time cost increase in MD-Autoformer is reasonable, while DLinear maintains good accuracy with high efficiency, making them suitable for different scenarios.
## 2 Geometrical Details and Mathematical Models
### Grid Distribution and Grid Independence Study  
The grid used for the simulation is shown in Figure 2. The length of the grid in the z-direction is half of the experimental setup because of the use of the symmetry boundary condition. The computational grid is finer near the walls and the region of jet injection and development so that large gradients near the walls and the jet's separation and attachment can be accurately captured. In this paper, the first layer of the grid is placed in the viscous sublayer (\(y^{+}<1\)) to make sure that the low-Reynolds number \(k\)-\(\varepsilon\) models can directly solve the variation of physical quantities in the boundary layer.  
To verify that the grid is independent of the final calculated flow results, three grid sizes 251 \(\times\) 315 = 79,065, 301 \(\times\) 365 = 109,865, and 351 \(\times\) 415 = 145,665 of the test section are used for grid independence verification. As shown in Figure 3, with the increase in grid numbers, the dimensionless x-velocity profiles calculated by the AB model at x/L = 0.07 of the three grid sizes have a good consistency. Note \(U_{M}\) is the local maximum time-averaged x-velocity. This shows that the increase in grid density has little impact on the results. Considering the available computing resources and numerical simulation cycles, a grid size of 301 \(\times\) 365 = 109,865 is chosen for simulation. The minimum orthogonal quality of the grid is 0.99, the maximum skewness is 0.004, and the maximum aspect ratio is 19.14.