esa-sceva/satcom-chunk-collection huggingface dataset:
###### Contents  
* 1 Introduction
* Light detection and ranging (LiDAR) sensors are advanced technologies that use light signals to generate three-dimensional maps of the surrounding environment. They significantly contribute to fields such as autonomous vehicles, robotics, and environmental monitoring.  
Particularly, LiDAR is essential in autonomous driving systems for accurate distance measurements and object recognition, both of which are critical for ensuring safety and reliability [1].  
However, contaminants such as dust, dirt, or water droplets on the LiDAR window can significantly reduce signal transmittance, which causes degraded sensor performance. This degradation increases the risk of false and missed detections, thereby posing serious threats to the overall safety of autonomous driving systems [2; 3].  
To prevent performance degradation due to dust accumulation, regular cleaning of LiDAR windows is essential for maintaining reliable operation [4].  
To systematically evaluate cleaning effectiveness, it is first necessary to understand how dust accumulates on the LiDAR surface. Various parameters, such as dust concentration, spray pressure, duration, and the number of sprays, directly impact the reliability of cleaning performance assessments [5].  
For example, previous research has shown that both dust concentration and particle size distribution are critical factors affecting LiDAR measurement accuracy [6]. Other studies have investigated how contaminant concentration and spray pressure influence detection performance, thereby offering foundational insights into optimal cleaning conditions [7]. Additionally, the combined effects of environmental factors and sensor contamination on system performance have been quantitatively assessed. Virtual testing environments have been employed to simulate contamination scenarios [8], whereas optimization studies have investigated effective cleaning strategies for LiDAR windows [9]. Addressing limitations in previous research, this study focuses on identifying optimal spray parameters for mitigating dust accumulation on the LiDAR window.  
Experiments were conducted using response surface methodology (RSM) with a face-centered central composite design (FCCD).  
The primary experiment evaluated four variables: spray pressure, speed, distance, and the number of sprays. Subsequently, a supplementary experiment was conducted, which replaced spray speed with spray angle. Each experiment comprised 30 optimization runs [10].  
Based on these experiments, the relationship between dust accumulation and cleaning performance was quantitatively analyzed. By deriving optimized conditions, this study provides practical guidelines for simulating LiDAR contamination, thereby contributing to improved sensor maintenance and supporting the overall safety and reliability of autonomous driving systems
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 3 Proposed Architecture for Real-Time Flood Monitoring
### Phase 2: Postprocessing Filters
#### 3.2.3 Temporal Filter  
The temporal filter averaged the frames and depth values depending on the number and history of previous frames. The alpha and delta factors also exist in this filter, serving  
Figure 3: Postprocessing flow.  
the same purpose as in the spatial filters. The number of averaged frames in the frameset was 15, the alpha value was set to 0.4 and the delta factor was set to a maximum value of 100. The threshold value in the spatial filter was set to the lowest value so the edges remained sharp. The threshold value in the temporal filter was set to its maximum value in order to achieve stable depth readings. This combination allowed for sharp edges as well as stability in the frames by reducing high-frequency components in the scene.
# Automated Trucks' Impact on pavement Fatigue Damage
## 4 Discussion
### Limitations and Future Research  
It is also essential to acknowledge a few limitations of the current study. The present study used numerical simulations using a specific tyre-pavement interaction and load model, which may not represent all possible tyre-pavement conditions. The accuracy of the results depends on the accuracy of the model assumptions, including the pavement layers' material properties and the load model. Therefore, the results of this study should be validated using experimental data from field tests or laboratory experiments. Furthermore, the present study used Miner's rule to compute fatigue damage, which assumes linear damage accumulation. This may not always be accurate in real-world situations. Additionally, [PERSON]'s rule does not account for variations in loading frequencies or amplitudes and the possibility of crack growth and propagation, which can affect the accumulated fatigue damage. Moreover, the present study focused only on the BU fatigue damage. Thus, investigating top-down fatigue damage could further corroborate the current understanding of pavement fatigue performance influenced by ATs.  
The findings highlight the complex and interdependent factors affecting pavement's fatigue performance under different AT deployment scenarios. This necessitates further research to investigate the other potential AT deployment scenarios affecting pavement fatigue damage.  
In addition to the limitations discussed above, it is imperative to recognise the need for a comprehensive evaluation of the integration of our findings with other pavement failure modes and distresses. This broader perspective will provide a more holistic understanding of pavement performance under varying AT scenarios. Furthermore, to enhance the practical applicability of our research, a thorough economic appraisal of interventions, such as modifying pavement thickness, width, or stiffness, is essential. These considerations are pivotal for offering cost-effective and sustainable solutions.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 4 Simulation Results and Discussion
### Dataset  
In this paper, the following \(\mathbf{M}=(BPKS,4 ASK,QPSK,16 QAM,64 QAM)\) modulation schemes were considered. The transmitted bit-stream was generated randomly to ensure each has equal probability. The received signal was pre-processed at the receiver end to obtain a complex based-band signal. The In-Phase-Quadrature (I-Q) samples of the received signal were sampled simultaneously to a frame of length N. They are combined later in a 2 X N matrix and input to CNN for feature extraction. In this study, the value of \(N\) is 2048 but some experiments are performed with \(N=1024\), 512. This is done by varying the number of symbols in the frame. A segment has the same results as that of a full observation. In this way, the proposed classifier would be independent of N. The range of AWGN noise with signal-to-noise ratio (SNR) = \(-8\) dB to \(+8\) dB in the dataset. For each modulation scheme and SNR values, 10,000 realizations of the received signal are generated according to the model described in Section 2. The rest of the parameters are summarized in Table 1 in detail.  
Figure 4: Neural-Induced Support Vector Machine (NSVM) Architecture.
## 3 Results
### Hyperparameter Optimization
#### 3.2.2 Loss Function  
The resulting model has been retrained using DICE Loss and several versions of FL. The average results from three trials are shown in Table 4. The best performance was achieved using FL, with the optimal parameters found \(\alpha=0.5\) and \(\gamma=1.1\). These results are in line with expectations, showing slight improvements over BCE due to FL's greater versatility. Although FL slightly increases the training time due to additional computations, it does not impact the model's inference speed, as it does not alter the model architecture.  
Despite the theoretical suitability of Dice Loss for this application, the results indicate poor performance. Dice's stringent nature may lead to noisy evaluations during training, complicating convergence to precise results. Figure 5 illustrates the significant amount of noise observed in the validation IoU curve. This noise likely arises from Dice's sensitivity to small perturbations and its difficulty in adjusting finely to pixel-level details. Pixel weight maps and pseudo-binarization of the inferred segmentations may contribute to the significant noise in the model's evolution during training. Pixels with high weights in the weight maps are inherently challenging, and will tend to be predicted with low confidence. Pseudo-binarization polarizes these predictions, increasing their confidence for better or worse, leading to high variance in the final value with small changes in inferred confidence. Nevertheless, using pseudo-binarization reduces noise from most pixels, and weighting pixels with weight maps is necessary for this application.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Loss Function** & **IOU** & **Precision** & **Recall** & **CCC** \\ \hline BCE (FL \(\alpha=0.5\); \(\gamma=0\)) & 0.799 & 0.919 & 0.860 & 0.831 \\ FL (\(\alpha=0.5\); \(\gamma=1.0\)) & 0.786 & 0.886 & 0.876 & 0.804 \\ FL (\(\alpha=0.6\); \(\gamma=1.0\)) & 0.796 & 0.871 & **0.903** & 0.794 \\ FL (\(\alpha=0.5\); \(\gamma=1.1\)) & **0.802** & 0.908 & 0.874 & **0.834** \\ FL (\(\alpha=0.5\); \(\gamma=1.2\)) & 0.793 & **0.920** & 0.853 & 0.816 \\ DICE Loss & 0.754 & 0.837 & 0.885 & 0.746 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained with various loss functions in the ResNet architecture with PReLUs.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Activation Function** & **IOU** & **Precision** & **Recall** & **CCC** \\ \hline ReLU & 0.795 & 0.913 & **0.861** & 0.822 \\ PReLU & **0.799** & **0.919** & 0.860 & **0.830** \\ \hline \hline \end{tabular} Bold numbers indicate the best results obtained.  
\end{table}
Table 3: Performance of the ResNet architecture using ReLU and PReLU activation functions.
## 4 Solutions to the Influence Line Equations of the Main Beam  
### Composition of the Vertical Deflection of the Main Beam  
By comparing the right terms of the equilibrium differential equations of the hybrid section, the pure suspension section, and the cable-stayed section in Equations (6), (9) and (15), respectively, it is found that the hybrid and pure suspension sections are equivalent to a concentrated load \(p\) and a uniformly distributed load \(H_{p}y^{\prime\prime}\) acting on the beam supported on the elastic foundation, while the cable-stayed section is equivalent to a concentrated load \(p\) acting on the beam supported on the elastic foundation.  
When a vertical load \(p\) acts at the position of \(\xi\) of the main beam, as shown in Figure 4(a), the hybrid bridge system can be simplified as a concentrated load acting on the elastic foundation and equivalent uniformly distributed load \(H_{p}y^{\prime\prime}\) acting within the pure suspension section and the hybrid section, as shown in Figure 4(b).  
For the beam on the elastic foundation, assuming that the vertical beam deflection is \(\
u_{1}(x,\xi)\) and \(\
u_{2}(x,\xi)\), under the concentrated load P and equivalent uniform load \(H_{p}y^{\prime\prime}\), respectively, then the vertical deflection of the beam of the hybrid system under the concentrated load is as follows:  
\[\
u(x,\xi)=\
u_{1}(x,\xi)+\
u_{2}(x,\xi) \tag{17}\]  
Figure 5: Simplified force diagram of the hybrid bridge system.  
Based on the general solution of the above differential equations for different sections, the vertical deflection of the beam on the elastic foundation under the concentrated load P can be computed based on the following:  
\[\
u_{1}(x,\xi)=A(\xi)f_{1}(x)+B(\xi)f_{2}(x)+C(\xi)f_{3}(x)+D(\xi)f_{4}(x) \tag{18}\]
## 7 Current Challenges--Future Trends
With this method, a dead drone battery can be replaced quickly with a fully charged replacement while multiple other batteries are being charged simultaneously. Additionally, this system can be set up to provide optional uptime expansion and a compact footprint, allowing for battery maintenance with minimal drone downtime. One more huge test confronting UAVs is the issue of network safety. Security remains a major issue that must be addressed, as a growing number of UAV-related cyberattacks are being reported. The danger is especially significant in military scenarios like combat missions, where falsification and Sybil attacks on UAVs present a considerable hazard. The matter of location privacy is also closely connected to localization and should be taken seriously. To avoid revealing their exact location to unintended parties, users can use various techniques, such as obfuscation. These issues are particularly pertinent when dealing with Cyber-Physical Systems (CPS) and the IoT. Another viable option is to utilize identity and site validation protocols that incorporate public-key-based authentication mechanisms and motion validation to confirm the authenticity of a group of UAVs.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 3 Admission Control
### Remarks  
Table 1 presents approaches using RL and DRL to perform admission control disjointly in RAN, TN, and CN of 5G. RL-based approaches (e.g., [43; 45]) used SARSA and Q-learning to admit slices in RAN. They mainly consider maximizing the operator revenue and minimizing the total network cost as the optimization objective. The worst-case complexity of these algorithms is \(O(|S|\times|A|)\)[39], where \(S\) and \(A\) are the sizes of the space of states and actions, respectively. RL-based approaches generally present scalability shortcomings when dealing with large state and action spaces. This shortcoming is relevant for slices of 5G/6G and beyond networks envisioned to support highly dynamic and complex services. DRL-based approaches [38; 42; 44; 45] employed algorithms such as N3 AC and DQN with one or two DNNs to admit slices in RAN while optimizing the operator revenue and network utilization as well as facing scalability issues. The worst-case complexity of these algorithms is \(O(|H|\times|N|)\)[38], where \(H\) and \(N\) are the number of hidden layers and neurons, respectively.  
Unfortunately, few works [39] have been developed to perform admission control involving end-to-end network slices. Indeed, most research has focused on proposing admission control approaches for 5G-RAN NSLs, neglecting the relevance of modeling aspects from the transport network and CN, for instance. In contrast, the TN, like the works [39; 40; 41], did by considering communication resources such as bandwidth and link optical backhaul and fronthaul. Since NSL is an end-to-end concept, novel admission control solutions in multitenant environments involving RAN, TN, CN, and edge networks are necessary to optimize revenue and utilization across the whole network. Since all cited works in Table 1 use centralized training, synthetic datasets, and nonperforming real deployments, it is essential to explore in-depth admission control based on decentralized, multiagent, and online RL/DRL to get a complete network view and cope with the dynamic of 5G/6G network slices. The use of datasets with real traces is essential to promote the deployment of approaches in real networks, which is, in turn, another imperative necessity. Furthermore, similar to [38; 42], the admission control solutions should consider the QoS/QoE performance requirements of 5G/6G vertical applications in their models and operate coordinately with resource allocation approaches.
# A Novel Piezoelectric Energy Harvester Using a Multi-Stepped Beam with Rectangular Cavities
## 1 Introduction
Two different structural tailoring approaches namely introduction of step section and cavities are employed to improve the magnitude of the harvested output with the change in operational frequency. Harvester design featuring two tapered beams with and without a cavity was proposed in Reference [24] to obtain high output in wideband simultaneously. The first two-mode frequencies were adjacent with improved output in both the modes. The harvester design proposed in Reference [25] used bimorph PZT films on Ni foil in a piezoelectric compliant mechanism to efficiently extract energy from low frequency vibrations. The strategy for enhancing the performance of the harvester using piezoelectric nano composite was presented in Reference [26]. The piezoelectric nanogenerator reported in Reference [27] showed that by properly choosing the shape of the driving force, the generator output could be enhanced. A harvester which utilizes mass tuning method for bandwidth enhancement was reported in Reference [28]. A harvester with an enhanced frequency bandwidth was presented in Reference [29], which used three bimorphs interconnected by two springs with three end masses. The research problem on energy harvesting for simultaneously achieving broad bandwidth and enhanced output is uncommon. In a harvester that has a normal cantilever beam with uniform dimension, the output is high only in the first mode of vibration. Higher mode frequencies are very far away compared to the first mode and harvesting power in these modes is very difficult. In this work, to improve the bandwidth and output simultaneously, a new broadband piezoelectric energy harvester designed using a multi-stepped cantilever beam shaped with rectangular cavities is proposed, which is the first research report of its kind. It has adjacent first two-mode frequencies with an enhanced output in both modes. In the regular cantilever beam harvester, the second resonant frequency is about 6.27 times higher than the first mode, whereas in the harvester proposed in this work it is 1.69 times higher than the first mode. This kind of harvester offers higher output both in the first two consecutive modes. This was proven through both an analytical model and an experimental test.
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
###### Abstract  
In order to detect kiwifruit quickly and accurately in orchard environments for the picking robot, this paper proposed a detection method based on a lightweight YOLOv4-GhostNet network. The implementations of the method are as follows: The original CSP-Darknet53 backbone network model was replaced by GhostNet, a feature layer facilitating small object detection was introduced in the feature fusion layer, and part of the ordinary convolution was replaced by a combination of \(1\times 1\) convolution and depth-separable convolution to reduce the computational pressure caused by the fused feature layer. The parameters of the new network are reduced, and the generalization ability of the model is improved by loading pre-training weights and freezing some layers. The trained model was tested, and the results showed that the detection performances were better than that of the original YOLOv4 network. The F1 value, map, and precision were improved on the test set, which were 92%, 93.07%, and 90.62%, respectively. The size of weight parameters was reduced to \(1/6\) of the original YOLOv4 network, and the detection speed reached 53 FPS. Therefore, the method proposed in this study shows the features of fast recognition, lightweight parameters, and high recognition accuracy, which can provide technical support for vision systems of kiwifruit picking robots.  
Keywords:kiwifruit detection; YOLOv4; GhostNet; light-weight; picking robot +
Footnote †: journal:
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 3 Proposed Methods  
### Materials  
As seen in Figure 3a, Puli is located in central Taiwan and is to the east of two potential pollution sources, the largest coal combustion power plant in Taichung city and a petrochemistry complex in Yunlin county. The landscape of Puli is a basin sitting at 100 m above sea level and surrounded by mountains. The mountains climb up from the west to the east of Puli with heights from 1000 to 2000 m, as shown in Figure 3b. As there is a river going across Puli, the main inlet for the external pollutions flowing into the basin is through the west river valley (indicated by red arrow). The highway 6, which is the major transportation route carrying goods from the west into Puli, was built along the river. The busy highway traffic also produces PM\({}_{2.5}\) concentrations. It is often observed that once the pollutants arise, they last longer in the Puli basin area than in the western metropolitan areas. This situation gets even worse in those days with temperature inversion. As the Taiwan Environmental Protection Administration (Taiwan EPA) deployed only one PM\({}_{2.5}\) monitoring supersite in Puli township (see the red dot in Figure 3b), we thus collected the PM\({}_{2.5}\) and the meteorological hourly data measured at the supersite ([[https://www.epa.gov.tw/](https://www.epa.gov.tw/)]([https://www.epa.gov.tw/](https://www.epa.gov.tw/)) accessed on 10 December 2021). The time span of the collected data is between 1 January 2016 and 31 December 2019. The Taiwan EPA supersite applies the beta attenuation monitoring (BAM) technique for PM\({}_{2.5}\) measurement ([[https://airtw.epa.gov.tw/cht/EnvMonitoring/Central/Tools.aspx](https://airtw.epa.gov.tw/cht/EnvMonitoring/Central/Tools.aspx)]([https://airtw.epa.gov.tw/cht/EnvMonitoring/Central/Tools.aspx](https://airtw.epa.gov.tw/cht/EnvMonitoring/Central/Tools.aspx)) accessed on 10 December 2021). The BAM employs the energy absorption of beta radiation by suspended particles extracted from the air flow. The attenuation caused by suspended particles is exponentially dependent on the particle mass in the sample. We obtained the Puli meteorological dataset from the Taiwan Central Weather Bureau ([[http://e-service.cwb.gov.tw/HistoryDataQuery/](http://e-service.cwb.gov.tw/HistoryDataQuery/)]([http://e-service.cwb.gov.tw/HistoryDataQuery/](http://e-service.cwb.gov.tw/HistoryDataQuery/)) accessed on 10 December 2021) which tallies atmospheric pressure, temperature, relative humidity, wind speed and direction, and precipitation.  
Figure 2: Relationship between temperature and PM\({}_{2.5}\) concentrations observed at Puli EPA supersite. (**a**) Entire year 2016 with a different color for each season; (**b**) summer; (**c**) autumn.