esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study of the Availability of Electric Buses in the Public Transport System
## 4 Discussion of Results
Electric buses, due to their simpler mechanical design, have fewer moving parts, reducing the number of potential tribological failure points compared to diesel buses, which require regular maintenance of many components. In addition, electric buses have lower operating and maintenance costs [26]. The greater reliability of electrical and design structures, combined with higher build quality, means that these buses require less frequent service interventions, which reduces operating costs and makes them more efficient for long-term use. Considering the entire period under study, electric vehicles showed a technical availability that was 11.2% higher (based on the arithmetic mean); in individual years, it was, respectively, 0.9%, 19.3%, and 13.5%. The low difference in the first year was strongly influenced by the low technical availability index in the first month of electric vehicle operation, K\({}_{\text{G}}\) = 0.28. It is worth noting that the first implementation of a new technology, as was the case described in this article, required a period of adaptation, such as solving the problem of adapting the charging system to local conditions. With regard to diesel vehicles, it should be noted that there is a significant drop in the technical availability index after the first year of operation. This is due to the impact of mechanical wear and tear on vehicle components. The described system of the Lublin electric bus is unique. It allows for a diesel bus to be replaced with an electric bus on the same routes. For all-day operation, a necessary condition is the location of the bus charging point at one of the end-route stops. In the case of the implementation of the peak period, there are no differences. Therefore, it is possible to compare both of these vehicles performing transport tasks, in the same conditions, without taking into account differences related to the limitations of operability for one group of vehicles. Future research should consider a wider range of specific factors affecting vehicle technical availability, such as intensity of use, qualification of technical staff, and quality of servicing. An important future topic is the extension of the analysis to include issues of repairaability of electric buses, both in terms of quantity and type of failure. Mainly, an extensive analysis should be conducted while considering a powertrain failure comparison between diesel and electric vehicles.
## 4 Optimization Model
### Non-Uniform Deployment Optimization Model  
In the uniformly distributed homogenous WSN, sensors that are close to the sink consume energy faster than those far away from the base station due to the unevenly distributed forwarding workloads among sensors. [PERSON] and [PERSON] [1] have proved that the uneven energy depletion phenomenon is intrinsic to the system and no routing strategy can avoid the creation of an energy hole around the sink. However, the uneven energy depletion can be prevented by judicious system design, which can result in balanced energy consumption in the network. In a non-uniform deployment, \(n_{i,j}\) is a variable based on different grids. The problem of finding the optimal \(d\) and \(n_{i,j}\) that maximize the WSN's lifetime subject to coverage, connectivity, success transmission rate and number of sensors can be expressed as  
\[\begin{array}{ll}\max&\min\limits_{i=1}^{L}\left[\min\limits_{j=1}^{6i} \left(\frac{n_{i,j}E_{0}}{E_{i,j}}\right)\right]t\\ \mathrm{subject\ to}&S>S^{*},\\ &R_{s}<d\leq\sqrt{3}R_{s},\\ &\frac{\sqrt{3}}{3}R_{t}<d\leq R_{t},\\ &\sum\limits_{i=1}^{L}(\sum\limits_{j=1}^{6i}n_{i,j})\leq N^{*},\end{array} \tag{10}\]  
where \(N^{*}\) is the maximum number of sensors. The new constraint \(\sum\limits_{i=1}^{L}(\sum\limits_{j=1}^{6i}n_{i,j})\leq N^{*}\) is a requirement that the number of sensors cannot exceed the maximum allowable number. For uniform deployment optimization problem, the lifetime of the WSN is proportional to the number of sensors in the sensor location. Hence, once the optimal sensor deployment scheme is determined for a WSN with only one sensor at each sensor location, it is the optimal deployment scheme for cases with more sensors at one location. Unlike the uniform deployment optimization problem discussed in Section 3.2, the number of sensors in a sensor location is not limited to 1. If no maximum allowable number of sensors is specified, the optimization objective, _i.e._, the WSN lifetime, steadily increases with rising number of sensors in the network.  
A genetic algorithm can be applied to solve this nonlinear integer programming problem. If the lifetime requirement of the WSN is specified as \(\tau^{*}\), the minimal number of sensors can be obtained by \[N=\sum_{i=1}^{L}\left(\sum_{j=1}^{6i}\left\lceil\frac{\tau^{*}E_{i,j}^{\prime}}{tE _{0}}\right\rceil\right), \tag{11}\]  
where \(E_{i,j}^{\prime}\) is the optimal energy consumed by the sensor at location \((i,j)\) in a data gathering cycle.
## 2 Field Monitoring Project
### Monitoring Sections and Measuring Points Layouts  
Since the Gangkou double-arched tunnel is structurally symmetrical and the geological conditions in the tunnel area are basically similar, instead of monitoring the deformations and stresses of the surrounding rocks and lining structures in the entire cross section of the double-arched tunnel, only one bore needs to be monitored. Typical monitoring sections were chosen in each rock mass classification in the tunnel area. These three monitoring sections are the K89+949 section (class III rock masses), K90+105 section (class IV rock masses) and K89+729 section (class V rock masses), as shown in Figure 1. The burial depths of the cross sections are, respectively, 56.0 m, 39.2 m and 40.5 m. According to the Code for Design of Road Tunnel in China [26], these three cross sections are deep-buried. Therefore, the same monitoring parameters in different cross sections can be compared. The layouts of the measuring points in different classes of surrounding rocks are shown in Figures 8 and 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Parameters** &
\begin{tabular}{c} **Sensors and Sensing** \\ **Devices** \\ \end{tabular} & **Range** & **Accuracy** & **Frequency** \\ \hline \multirow{3}{*}{1} & internal displacements of & multi-point & & & 2–4 times/day (displacement velocity \(>\) 10 mm/day); \\  & surrounding rocks & extensometer, dial & 100 mm & 0.1 mm & once/day (5–10 mm/day); \\  & surrounding rocks & indicator & & & once/2 days (1–5 mm/day); \\  & & & & & once/week (\(<\)1 mm/day). \\ \hline \multirow{3}{*}{2} & contact pressures between & vibrating-wire & & & \\  & surrounding rocks and & pressure cell, & & & \\  & primary linings & cytometer & & & \\ \hline \multirow{3}{*}{3} & axial forces in rock bolts & vibrating-wire bolt & & & 2–4 times/day (construction time 1–15 days); \\  & & force gauge, & & & once/day (16–30 days); \\  & & cytometer & & & once/week (30–90 days); \\ \hline \multirow{3}{*}{4} & internal forces in & & & & \\  & steel arches & & & & once/month (\(>\)90 days). \\ \hline \multirow{3}{*}{5} & internal forces in & & & & \\  & secondary linings & & & & \\ \cline{1-1} \cline{3-5}  & internal forces in & & & & \\ \cline{1-1}  & secondary linings & & & & \\ \cline{1-1} \cline{3-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Monitoring parameters and methods.  
Figure 7: Layouts of the concrete strain gauges.
###### Abstract  
Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting in significant communication overhead. This overhead not only increases energy consumption but also diminishes device longevity within IoT networks. By focusing on model updates rather than raw data transmission, FL reduces the volume of data communicated to the base-station; however, FL still faces challenges due to the multiple communication rounds required for convergence. This research introduces an innovative approach that leverages the in-network processing capabilities of IoT devices by integrating a hierarchical clustering routing protocol with FL. This approach enhances energy efficiency through single-round pattern recognition, minimizing the need for multiple communication rounds to achieve convergence. It is envisaged that the proposed approach will prolong the lifespan of IoT devices and maintain high accuracy in event detection, all while ensuring robust data privacy.  
federated learning; internet of things; sensor networks; artificial intelligence; machine learning; distributed learning; nearest neighbor; pattern recognition; in-network processing; communication efficiency
## 6 Conclusions
### Validation of the Evaluation Method  
The validations performed have demonstrated the validity of the evaluation method with the results of a series of indicators in scenarios where a determined set of ECMs have been applied.  
As input for the evaluation methodology, the prioritisation criteria are needed and the pre-defined weighting schemes developed for the evaluation methodology have been tested. As a conclusion of this analysis the following has been extracted:  
* The cost and benefit functions obtained after evaluating the candidate scenarios with the pre-defined weighting schemes are reasonable.
* While some pre-defined schemes presented graphs where the point distribution was more spread it is to be highlighted that in schemes such as 3 or 3A (those schemes related to energy generation through renewables) points corresponding to sets of scenarios where the same type of ECM was implemented were more concentrated, indicating that these schemes left little space to appreciate the differences among the same type of ECM.
* Also noticeable was the great difference between the schemes where renewables were favoured (as schemes 3 or 3A) and the rest. In these first schemes it can be observed that the scenarios where renewables have been applied have greater benefits. This was the main intention of the configuration of the scheme. However, it might be advisable to make the user aware of these facts when applying the pre-defined schemes.
* For the economically driven prioritisation schemes (1A, or 3A) a higher number of scenarios appear in the Pareto front. It can be deducted that the use of these schemes makes possible to better discriminate scenarios with similar measures by intensifying their main differences.  
The analysis of the numerical values of the Pareto front did not provide highly relevant insights to the analysis, since the point distributions and related observations had already been carried out. However, it is to be highlighted that regardless of the scheme, the extreme benefit values do not vary significantly, whereas major differences can be seen in the cost values.  
These validations have served for the tool to address more complex cases, evaluating and optimising districts with several buildings covering the complete assessment of the effects among buildings and how the optimisation at district scale can derive into wider benefits than when tackling buildings individually. Under these cases the scale of the optimisation problem rises dramatically and requires enough power in order to compute the simulation models in a reasonable time. This is addressed through techniques as parallel computing in order to cope with the number of models required.
# Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks
## 5 Weighted Grid Clustering Algorithm  
### Weighted Grid Clustering  
For energy conservation, we propose a technique which consists of four models namely grid clustering, building the Hop tree, selection of a dynamic cluster head, and multi-hop routing and Hop tree update [38].  
The process starts with the division of even-sized grids in the WSN. Once the grid structure is built, a node called sink broadcasts the Hop configuration message. This message helps in selecting a dynamic cluster head (CH) among all the other member nodes by their remaining battery capacity. Subsequently, the corresponding cluster head in the grid collects the data from all its member nodes and forwards it to the base station. The overall routing process is shown in Figure 2.  
The grid-based clustering technique directly depends on the formation of a CH, which plays the role of a gateway between nodes and the base station. The sensor nodes send their data to the CH and then the CH manages all the functions of delivering data. Moreover, the CH acts as a bridge between the nodes for information exchange. The CH can send the data to the concerned node and base station instantaneously, or it uses any other CH as a medium if needed. In conclusion, two main observations for saving more energy are noticed, as follows:  
1. A CH saves energy and space by creating only one routing table for all nodes rather than making an individual routing table for each node as in single-hop routing.
2. A CH schedules the activities for all nodes in its specified region. These activities are maintained by broadcasting a message to all the member nodes. This message contains different schedules in which nodes can perform their operations (sending/receiving) with the CH and other nodes as well. After that, nodes can turn on their sleep mode.  
After completing the clustering structure, all the nodes are free to transmit data locally with the help of the CH. Then, the CH forwards data to the base station via neighbor grids. The proposed scenario describes that grid-based routing provides the multi-hop routing between CHs and it also prevents the long range data transmission.
## 4 Experimental Results and Analysis  
The experimental platform used in this study consisted of 32 GB of memory, an AMD Ryzen 7 5800H @ 3.2 GHz CPU, an NVIDIA GeForce RTX 3070 (8G) Laptop GPU, and Windows 10. The proposed model was implemented using the programming framework TensorFlow, accelerated by a GPU, and the DL library Keras. After hyperparameter optimization, the method proposed in this paper employs the hyperparameters, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline
**Actual Positive** & True Positive (TP) & False Negative (FN) \\
**Actual Negative** & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix.  
We conducted an evaluation of the proposed CBF-IDS using three imbalanced datasets: NSL-KDD, UNSW-NB15, and CIC-IDS2017. In order to assess the effectiveness of CBF-IDS, we compared it with several algorithms. Additionally, we measured the performance of various CNN and BiLSTM structures and their hybrid models in multi-classification, and we conducted an evaluation of the performance of the focal loss function on minority class samples and compared it with other loss function methods, including CBC-IDS (using the CE loss function), CBA-IDS (using the \(\alpha\)-balanced CE loss function), and the proposed CBF-IDS (using the focal loss function). As highlighted in Section 3.5, these datasets are imbalanced, and when evaluating model performance, it is necessary to consider metrics such as recall, precision, and F1 score in addition to accuracy. The F1 score, in particular, is a crucial metric for evaluating the effectiveness of IDS.
To address these concerns, we have summarized the key requirements for V2X data sharing, particularly for AI-driven applications, as follows:  
* Establishing trust quickly between vehicles and roadside units;
* Maintaining anonymity of vehicle identity while providing a means for validating messages and identifying/tracking misbehaving vehicles;
* Ensuring secure messages are only accessible by intended users;
* Implementing a threshold cryptography system where no single intermediary can reveal secret information, but a group of authorized entities can access it. The primary contributions of this research work include proposing a novel threshold cryptography-based key exchange protocol for V2X networks to address the outlined data sharing requirements. This proposed protocol employs a threshold secret-sharing scheme that integrates a certifiateless scheme, which is based on the approach developed by [PERSON] et al. This approach has been discussed in a series of publications [12; 13; 14; 15; 16] for a certifiateless key management scheme using verifiable secret sharing. Additionally, the feasibility of the proposed protocol was evaluated in a tailored hybrid V2X network testbed, leveraging the NS-3 network simulator, a commercial 5G network, and public cloud infrastructure. In this testbed, the cloud platform and 5G network were implemented in actual environments, while the RSUs and vehicles were simulated. Finally, this research work provides recommendations for future research and development in V2X security systems.
## 5 Forest Fire Risk Prediction in China
### Forest Fuel Moisture Content Estimation Model  
The estimation of forest fuel moisture content (FMC) is important for predicting forest fire danger. With use of remote sensing sensors, it is possible to retrieve vegetation water content. Several studies demonstrated the linkage between leaf-level reflectance in the 400-2500 nm spectral region and the FMC [99-102]. A weak absorption in the 950-970 nm wavelength range was found to be useful for detecting the water status of vegetation and FMC [99]. The spectral features in reflectance spectra of green vegetation in 1300-2500 nm regions are mainly dominated by the liquid water absorption [100]. [PERSON] _et al._ calculated the normalized difference water index (NDWI) as (R860-R1240)/(R860+R1240) in a theoretical study and the results demonstrated that the index has potential applicability for FMC detection [101]. The AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) imagery also was used to study the possibility of retrieving FMC [103,104]. The NDWI (860, 1250) derived from EOS/MODIS data has been used to estimate FMC (_i.e._, EWT, equivalent water thickness) in the northern and eastern China in July 2006, which showed a potential of detecting FMC on a regional scale [105].
# Status, Features, and Future Development of the LIFUS5/Mod4 Experimental Facility Design
## 3 Materials and Methods
### Instrumentation
#### 3.4.2 Foreseen Upgrades  
The violent nature of the phenomena being investigated in the first phase of the experimentation required that the facility would be simplified to make it able to withstand the repeated high loads on its components. For this reason, several components such as the recirculation pump and the cold-trap mock-up have been excluded from the system to avoid damage. However, since the goal of the second phase of the facility operation is testing the performance of the TBS components, several upgrade points have been foreseen to allow the installation of additional equipment.  
In particular, the facility includes:  
* Two attachment flanges on the lines P-LLL-08 and 09, between the TEU equivalent volume and the recirculation tank. These flanges can be used to attach to the system a mock-up or prototype of the cold trap for performance or design testing.  
* A predisposition in the main recirculation tank to allow the insertion of a pump. This would convert the LIFUS5/Mod4 from a stagnant to a circulating PbLi loop and allow effective testing of the components under representative conditions. Other than this, the mock-up attachment points are predisposed to accommodate a full-scale TBM mock-up, even if for the first tests smaller ones will be used, representing only one or few breeding units. The CAD drawing of the facility with the upgrade points highlighted in red is shown in Figure 4.
# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 3 Proposed Method
### Traffic Accident Risk Estimation  
The estimation model demonstrated in Section 3.3 can predict the probability of black ice formation from freezing rain at the locations where AWS and ASOS are installed. However, as listed in Table 1, there are about 1500 locations nationwide where these devices are installed, thereby making it impossible to apply the model to individual sections of the roads. Therefore, this model can be used to provide hazard information over vast regions, i.e., similar to weather forecasts. The information can be used for response planning by local road management organizations rather than by individual drivers. In this study, shaded areas were extracted from road segments, at the time of black ice warning issuance, based on the results from hillshade simulation analysis. Further, the segments with a slope angle of \(\geq\)5\({}^{\circ}\) were extracted to determine hazardous locations.
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 4 Conclusions  
Velocity is one of the main parameters in visual navigation and the improvement of its accuracy is beneficial for promoting the development of visual navigation. An improved optical flow algorithm based on Mask-R-CNN and K-Means is studied in this paper to enhance the performance of velocity calculation in visual navigation. By integrating the technique of Mask-R-CNN and K-Means, the proposed algorithm can effectively rule out the outliers of optical flow values by reducing the effect of the dynamic objects in UAV captured images with similar color mask. More importantly, it can largely improve the accuracy of velocity with relatively correct optical flow values. The performance of the proposed algorithm is validated by an unmanned aerial vehicle flight experiment in the campus of North University of China. The experimental results demonstrate that dynamic objects on captured images can effectively be recognized and covered, and its side effect on optical flow and velocity calculation can be largely reduced, resulting in more optimal accuracy and reliability compared with traditional optical flow-based methods.  
[PERSON] and [PERSON] contributed equally to this paper. Funding acquisition, C.S., H.C., H.H. and X.G.; investigation, Y.P.; methodology, Y.P., X.L., C.S., H.H. and X.G.; software, Y.P.; writing--original draft, Y.P.; writing--review and editing, C.S., D.Z., H.C. and X.G.  
This work was supported in part by the National Natural Science Foundation of China (61603353, 51705477, 61703098), and the Pre-research Field Foundation (6140518010201) and the Scientific and Technology Innovation Programs of Higher Education Institutions in Shanxi (201802084), the Shanxi Province Science Foundation for Youths (201601D021067), the Program for the Top Young Academic Leaders of Higher Learning Institutions of Shanxi, the Young Academic Leaders Foundation in North University of China, Science Foundation of North University of China (XJ)201822), the 15 th Graduate Science and Technology Project of North University of China (20181537), and the Fund for Shanxi \"1331 Project\" Key Subjects Construction, Natural Science Foundation of Jiangsu Province (BK20160699).  
The authors declare no conflict of interest.
## 2 Problem Background
### Need for Routing Fairness in OppNets  
The resource-constrained nature of portable handheld user devices has prompted various studies in OppNet routing. Mechanisms have been proposed to detect and relieve congested devices [25], cooperation of selfish users is motivated by incentive schemes (e.g., [26; 27]), and energy awareness (e.g., [28; 29]) considers the battery level of devices during routing. Despite these studies, failure to address routing unfairness leaves a gap to be filled in OppNets for sensed data collection. This section justifies the need for routing fairness and the role it plays by presenting how fair routing complements congestion control, how it can enhance incentive schemes and how it augments energy aware algorithms.