esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Publication Volume Trends (2000-2025)  
Based on statistics from the WoS database, this study systematically presents the academic publication dynamics of wearable technology in the field of VRU traffic safety. In response to RQ1, our research reveals that academic publications on wearable technology in VRU traffic safety have experienced three distinct developmental phases (Figure 3): the initial exploratory phase (2000-2012), characterized by sporadic and intermittent research output with an average of less than one publication per year; the emergent phase (2013-2017), showing small-scale but steady growth with an annual output of one-three papers; and the rapid growth phase (2018-2025), with significantly increased research density and annual output stabilizing at five-seven papers.  
The significant increase in publications after 2018 marks the field's entry into an accelerated development stage, attributable to two key factors: first, the increasing maturity and market penetration of smart wearable device technologies; second, a series of policy initiatives and practical guidelines for VRU safety issued by the WHO [27], which effectively raised global awareness and research investment in VRU traffic safety issues. Although research output experienced minor fluctuations after reaching a peak in 2021 (7 publications), it has generally maintained a high level, indicating that this research direction has formed a stable and active academic ecosystem. Notably, the 2025 data had already reached three publications by May, suggesting that the full-year publication volume is likely to continue or even exceed the output scale of previous years. This sustained growth trend in research activities indicates that as wearable technologies continue to deeply integrate with cutting-edge technologies, such as artificial intelligence and the Internet of Things, the field of VRU traffic safety--an area critical to life protection and social welfare--will continue to attract academic attention and innovative investment.  
Figure 3: Trends in scholarly publications on wearable technology for VRU traffic safety (2000–2025).
## 5 Conclusions and Future Works  
In this study, the object detection performances of the YOLO models are optimized, especially if the hyperparameters of YOLO models used for object detection in autonomous driving in AWCs are optimized. When the datasets are compared, especially in the DAWN dataset, which contains only traffic images and only images with AWCs, an increase of more than 6% in performance is observed by optimizing the YOLO models with optimization algorithms. For the RTTS dataset, optimizing YOLO models does not show a noticeable performance increase. The fact that there is generally only one adverse weather condition (hazy/foggy) in the RTTS dataset and that it does not contain only traffic data distinguishes the RTTS data set from the DAWN dataset. In the DAWN dataset, even if the YOLOv5 model is optimized using GWO, ARO, or CLEO, it does not perform better than the more recent YOLOv7 and YOLOv9. However, the mAP values of all the training results with the YOLOv7 model and the YOLOv9 model were close to each other. According to these results, the YOLO models including AWCs perform well in object detection in autonomous vehicles. YOLO with optimized hyperparameters by using optimization algorithms in AWCs performs much better. Since the objects labeled in the datasets are not balanced, there are difficulties in detecting some objects. To solve this problem, it is necessary to find images containing objects such as bicycles, motorcycles, and buses in AWCs and to label these objects. In future studies, we aim to combine different optimization algorithms and make changes to the activation functions of the YOLO model to increase the object detection performance of the YOLO models in AWCs to better levels.  
Conceptualization, Y.A. and I.O.; methodology, Y.A. and I.O.; software, I.O., Y.A. and C.P.; validation, I.O. and Y.A.; formal analysis, I.O., Y.A. and C.P.; investigation, I.O. and Y.A. resources, C.P., I.O. and Y.A.; data curation, I.O.; writing--review and editing, I.O., Y.A. and C.P.; visualization, I.O.; supervision, Y.A.; project administration, Y.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Duzce University Scientific Research Projects Coordination Office with the Scientific Research Project grant number BAP--2020.06.01.1060.  
Steps to run the codes are included. [[https://colab.research.google.com/drive/1](https://colab.research.google.com/drive/1) XzvxOnKKQsX1 NGWN9 RkIDKLEBXr8g3 QF?usp=sharing]([https://colab.research.google.com/drive/1](https://colab.research.google.com/drive/1) XzvxOnKKQsX1 NGWN9 RkIDKLEBXr8g3 QF?usp=sharing) (accessed on 27 June 2024).  
The authors declare no conflicts of interest.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 5 Performance Evaluation
### Attack-Based Network
#### 5.1.6 Experimental Conclusions on Attack-Based Network  
FuGeF's performance in minimizing the possibility of choosing an attacker in scenarios explained in Sections 5.1.2 and 5.1.5, was mainly due to the dynamism employed and its spontaneous selection scheme. The dynamism (pseudo-random) adds a slight shift to the normal routing semantics. This eventually causes unpredictability in timing and selection of the forwarding node, thus abating attacker's effect. In DWSIGF, a random time(ms) is employed for the collection window. Rushing a CTS signal by the attacker is often presumed to be successful as the timing lacks fixed interval for collection to begin. In the case of FuGeF, the pseudo-random time utilized to collect CTS reply has a given time interval. This interval causes a slight delay in the start of a collection which prevents the CTS rushing attacker from participating in the selection process due to its early arrival.  
Additionally, spontaneity in FuGeF's selection scheme caused by FLS consensus (based on node's remaining energy, connectivity cost, and progressive distance) has also influenced the protocol's resistivity to attacks. This is because if a drop is noticed in any of the parameters (associated to a node), FuGeF disregards such a node until all its neighboring nodes are of equal or lesser value than the selected node. Thus, the drop noticed in malicious node's parameters that have been repeatedly selected would influence the selection of other non-malicious nodes during the routing process.  
Finally, FuGeF's packet delivery averagely outperforms DWSIGF-P and DWSIGF-R by 48.80% and 25.55%, and, 16.44% and 21.00% for strategic (Section 5.1.2) and random (Section 5.1.5) based attack scenario, respectively.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 3 Proposed Method
### FDNN Model
#### 3.2.4 Model Training  
The FDNN receiver transforms symbol detection into a classification problem. We chose Kullback-Leibler (KL) divergence to measure the distribution differences of vectors. The mathematical expression for solving the target can be expressed as  
\[\min_{\mathrm{F}(\cdot),\mathrm{F}(\cdot)}D_{KL}(\mathbf{x}_{i}||\hat{\mathbf{x}}_{i} )=\sum_{k=1}^{M}\mathbf{x}_{i}^{(k)}\log\frac{\mathbf{x}_{i}^{(k)}}{\hat{x}_{i}^{(k)}}\,, \tag{24}\]  
in which \(\mathbf{x}_{i}\) and \(\hat{\mathbf{x}}_{i}\  
\[L(\hat{\mathbf{x}},\mathbf{x})=-\sum_{i=1}^{N}\sum_{k=1}^{M}x_{i}^{(k)}\log\hat{x}_{i}^{(k )}+\lambda\sum_{k}w_{k}^{2}\,. \tag{25}\]  
The neural network employs stochastic gradient descent for training, representing the parameter update method for the detection module as  
\[w_{mn}^{k}(t+1)=w_{mn}^{k}(t)-\alpha\
abla w_{mn}^{k}\,, \tag{26}\]  
\[b_{n}^{k}(t+1)=b_{n}^{k}(t)-\alpha\
abla b_{n}^{k}\,, \tag{27}\]  
in which \(w_{mn}^{k}(t)\) represents the values of the weight matrix \(\mathbf{W}_{k}\) in the \(m\)-th row and \(n\)-th column of the \(k\)-th layer during the \(t\)-th iterations. \(b_{n}^{k}(t)\) represents the values of the bias vector \(\mathbf{b}_{k}\) in the \(n\)-th column of the \(k\)-th layer during the \(t\)-th iteration. \(\alpha\
abla w_{mn}^{k}\) and \(\alpha\
abla b_{n}^{k}\) are the gradient values and \(\alpha\) is the learning rate of the neural network.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
## 2 Materials and Methods
### Electrical Analysis of an SC Working in a Fast-Charging System
#### 2.1.2 Charging Time and Maximum Current  
The value of the inductance must be chosen as a function of the charging time and the peak current during the transient state. As can be deduced from (8), the transient current consists of two decreasing exponential functions, it will, therefore, die out when the slower component has done so. The time constant of the circuit, \(\tau(s)\), can be assumed to be as follows:  
\[\tau=\frac{1}{\alpha-\beta}. \tag{12}\]  
Considering that a period 7 times longer than \(\tau\) is sufficient for the transient current to extinguish, it is possible to define the maximum time for charging, \(t_{charge}\), as follows:  
\[t_{charge}=7\cdot\tau=\frac{7}{\alpha-\beta}=\frac{14\cdot L}{R_{T}-\sqrt{R_{T }^{2}-\frac{4L}{C_{eq}}}}. \tag{13}\]  
One of the requirements to be imposed for calculating the inductance value is that \(t_{charge}\) shall not exceed a certain value, which shall be set at the design stage. To calculate the instant at which the maximum current occurs, the current shall be derived with respect to time and equaled to zero as follows:  
\[\frac{di(t)}{dt}=\frac{\Delta U}{2\cdot L\cdot\beta}\cdot\left((-\alpha+\beta )\cdot e^{(-\alpha+\beta)\cdot t}+(\alpha+\beta)\cdot e^{(-\alpha-\beta)\cdot t }\right)=0. \tag{14}\]  
The time instant, \(t_{max}\), at which the transient current reaches its maximum value is therefore  
\[t_{max}=\frac{1}{2\cdot\beta}\cdot Ln\left(\frac{\alpha+\beta}{\alpha-\beta} \right). \tag{15}\]  
If this value is entered in (8), the peak current, \(I_{max}\), will be obtained as follows:  
\[I_{max}=\Delta U\cdot\sqrt{\frac{C_{eq}}{L}}\cdot\left(\frac{\alpha-\beta}{ \alpha+\beta}\right)^{\frac{\alpha}{\alpha+\beta}}. \tag{16}\]  
For the sake of simplicity, a new dimensionless constant, \(\gamma\), will be defined as  
\[\gamma=\frac{\alpha}{\beta}. \tag{17}\]  
In an overdamped system, it will be satisfied when \(\gamma>1\), since \(\alpha>\beta\).
## 5 Results and Discussion
### BLER Analysis  
In our study of BLER, we observed trends in LoRa link performance. Figure 11 clearly shows that LoRa's BLER hovers around 5 dB when interference is introduced into the environment. This observation highlights the sensitivity of the system to external factors and prompts a deeper investigation into its resilience.  
In addition, Figure 12 provides critical insight by showing that the overall BLER, as estimated by LoRa, remains at a remarkably low value of 4%. This finding is indicative of the robustness of the system in maintaining data integrity under typical operating conditions. However, it is important to highlight a significant aspect of our analysis: in certain locations where the calculated SINR dropped below 15 dB, the BLER calculations showed a significant increase, approaching an alarming 45%. This divergence in BLER values underlines the key role that SINR plays in determining the error rate of the system and  
Figure 11: LoRa BLER results, measured while the vehicle is moving across the defined path.  
Figure 10: LoRa SINR histogram measured while the vehicle is moving across the defined path.  
highlights the need for strategies to mitigate BLER peaks in areas of weaker signal strength.
## 6 Discussion and Conclusions  
The main contribution of conducted research is the application of ML techniques to problems of network design and capacity planning, and, in particular, spectral efficiency evolution modeling in growing network load conditions. Out of four tested ML models, XGBoost provided the best results and was selected for future work. The number of network performance indicators was introduced to the analysis, with the idea to improve modeling accuracy through the segmentation of network fragments.  
A strong correlation is observed between CQI and PRB utilization, where a slightly stronger correlation was observed with PRB utilization when aggregated over surrounding cells; however, authors decided to introduce an average utilization and number of surrounding cells within the radius as separate features.  
Higher order modulation usage shows the highest level of correlation with CQI, as expected due to the direct link between the two. It is introduced to capture information about user distribution with the reflection of general radio signal quality in a cell, and has a very high **F** score with a value of 176. The heavy data factor, intended to capture the type of the traffic mix in a cell in terms of burstiness, with a slightly lower level of correlation, shows the highest **F** score with a value of 190. The remaining three introduced features, frequency band, number of neighbors and average surrounding PRB utilization with an **F** score value of around 100, are also examined as very relevant features for model accuracy.  
For the newly introduced feature number of surrounding cells within a radius, an optimal radius value of 5 km was estimated.  
The continuation of the work and planned future activities are to test the model on a significant number of mobile networks, and investigate the impact of various network characteristics (topology, operating frequencies, maturity, design, traffic mix) on model behavior and accuracy.
## 4 Quantum Integrated Photonic Devices
### Integrated Quantum Circuits for Quantum Cryptography  
In this subsection we present two examples of integrated quantum circuits. The first one is a generator of quantum states intended for quantum cryptography by using the so-called BB84 protocol [214]. It is made on a hybrid glass as the ones described in Sections 2 and 3. Moroever, we show that the passive part of this integrated optical circuit can be also used as a quantum projector. The circuit is made with four paths but can be generalized to an arbitrary number of channel guides. The second integrated circuit is a Bell states measurement device of interest for the so-called MDI (measurement device independent) protocol [217] or in general for measuring the Bell states \(\Psi^{\pm}\).
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### Grid Anomaly Detection Based on Satellite Positioning
#### 4.2.1 Intelligent Unmanned Inspection Systems  
As shown in Figure 5, the use of drones has become an essential means of transmission line inspections. At the same time, there are also explorations of related applications in disaster surveys, emergency repairs, and drones to remove foreign objects in ground wires. At present, the application of drones is mainly based on the manual operation of drones for inspections. There are problems of high requirements for operators and high labor costs. Autonomous inspections by drones are the future trend. Due to safety considerations, there are high requirements for high-precision positioning and navigation performance. Therefore, there are many studies based on GNSS data to explore high-precision positioning methods for inspection drones and inspection robots.  
As we all know, there is substantial electromagnetic interference in the power grid environment, which will cause severe interference to inspection drones that use GNSS for positioning. [PERSON] et al. [10] has conducted a comprehensive evaluation UAV positioning's vulnerability to GPS spoofing attacks in the smart grid scenario. Although, with the development of satellite positioning technology, the emergence of pseudo-range differential positioning, RTK, and PPP alleviate, to some extent, the positioning problem of using GNSS in the power grid, the robustness of UAV positioning is still driving progress in this field. Therefore, in response to the GNSS challenge in the power grid scenario, ref. [11] proposed an experimental flight test evaluation of a cooperative navigation strategy in which an unmanned aerial vehicle (UAV) that is subjected to very poor GNSS satellite geometry is provided ranging updates from an unmanned ground vehicle (UGV). However, more research is required to use multi-sensor fusion to solve the problem of GNSS interference in the power grid. The most classic use of GNSS and IMU for fusion, the GNSS/INS integrated navigation system, can operate robustly in most scenarios [98]. In recent years, there have also been studies on the integration of GNSS with vision and lidar SLAM [99], which has also achieved good results and can operate stably in challenging GNSS environments such as power grids. The high-precision positioning results of GNSS are applied to the intelligent unmanned inspection system to provide protection for the abnormal detection of the power grid environment.
# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
## Materials and Methods
### Experimental Paradigm  
#### 2.4.1 Donning and Doffing Protocol  
Eight participants were used to analyze the effects of donning and doffing the armband on the system performance. The right arm was set in the same position for all sessions, with the elbow flexed 90 degrees so not to add more variables. All participants executed the experimental protocol shown in Figure 3 (testing and resting days) in the same schedule to obtain comparable results.  
The first day the participants completed a training phase. The model learned during the training was used in all the tests sessions in order to study its degradation through time and donning/doffing. The first test after the training was performed without any electrode shifting and without removing the sensor. This was considered the baseline test. Once the first test was completed, the Myo Armband was removed, and the participant had a 5-min break. Afterwards, a second test phase was performed relocating the Myo Armband.  
The positioning was done in realistic and manual conditions without any technical procedures. The researcher photographed the initial position to place the sensor as similar as possible in further sessions. No other re-positioning considerations were taken into account trying to reproduce a realistic environment where a daily-based prosthesis user places it without absolute precision.  
The following days the participants performed one test phase per day, with the same trained model, completing three more tests. At the end of the entire process, each participant had carried out one training session and five test sessions.
# Conductivity Enhancement of Graphene and Graphene Derivatives by Silver Nanoparticles
## 3 Results and Discussion
Considering that a conductive graphene/AgNPs hybrid is much more cost-effective and contains a lower Ag percentage, it is concluded that hybrid \(f\)-rGO/AgNPs has an advantage. Based on this, the \(f\)-rGO/AgNP\({}_{2}\) sample (containing approximately 18% Ag \(w/w\)) was further selected as the functional pigment for preparing a conductive ink with a ratio of solid pigment/resin of 50/50. The as-prepared ink was applied with the gravure printing method using two different kinds of coated papers under the same printing and environmental conditions (temperature and relative humidity). Generally, the interaction between ink and substrate affects various parameters such as wetting, penetration, and solvent rate evaporation. In this case, the best printing quality was obtained with the glossy photo paper (lower contact angle than IGT standard coated paper), where more adequate wetting and more uniform spreading of the water-based ink on its top layer were achieved (see Figure 8). The formation of such a smooth ink film on the glossy substrate denotes that \(f\)-rGO/AgNPs remained dispersible and stable in the ink as water evaporated. Thus, the photo paper was selected for further printing investigation and study in this work (see Figure 9). Figure 8: A flowchart with the procedure from the raw graphite to the final gravure-printed items on a high-gloss photo paper (**a**) and on a standard IGT coated paper (**b**) using the \(f\)-rGO/AgNP\({}_{2}\) ink. According to example Figure 8, the correct choice of paper for electronic applications is critical for the emergence of ink characteristics. Substrates with good wetting properties provide reproducible printed patterns, and their high quality is required for direct contact between conductive particles and, by extension, the production of working electronic devices [32; 33]. As a general rule, if the surface energy of a substrate is higher than the surface tension of an ink, then the contact angle is small and the ink is highly spread. Additionally, the porosity of papers is another important parameter relevant to the uniform deposition of inks on the printing substrates. Recently, [PERSON] et al. [33] reported that improved electrical properties were obtained when papers with porous coatings were used as substrates for graphene ink deposition. Usually, printed graphene or Ag/graphene-based conductive inks require a thermal stage to create conductive paths. In this case, the high dispersion of the composite hybrid \(f\)-rGO/AgNP\({}_{2}\) in the resin matrix allows the formation of conductive pathways without the addition of dispersants, rheology modifiers, or organic alcohols and without any thermal posttreatment. The R\({}_{8}\) values of the printed zones in Figure 9a showed a gradually increased conductivity that followed the thickness of the printed zones. Similarly, multiple printing passes via ink-jet printing [34] using the \(f\)-rGO ink showed that the resulted printed item can be used for practical applications, e.g., sensors or microheaters [34; 35; 36].