esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Ego Vehicle Travel Speed  
We investigated the ego vehicle travel speed in near-miss incidents immediately before the driver started braking to avoid a collision. The composition based on the vehicle travel speed range for the Left-Pedestrian and Right-Pedestrian is listed in Table 3. The incidence rate for the Left-Pedestrian was the highest in the \(\leq\)20 km/h range (51.4%), followed by the \(\leq\)10 km/h range (36.1%). For Right-Pedestrian, the rate was highest in the range of \(\leq\)20 km/h (56.1%), followed by \(\leq\)30 km/h (31.7%). The rate in the range of \(\leq\)10 km/h for Left-Pedestrian (36.1%) was significantly higher than that for the Right-Pedestrian (12.2%), whereas that in the range of \(\leq\)30 km/h for the Left-Pedestrian (12.5%) was significantly lower than that for the Right-Pedestrian (31.7%).  
Table 4 lists the average ego vehicle travel speeds for the Left-Pedestrian and Right-Pedestrian scenarios. They were 13.4 km/h (SD 6.3 km/h) and 17.1 km/h (SD 5.7 km/h) for the Left-Pedestrian and Right-Pedestrian, respectively. The speeds were significantly slower for Left-Pedestrians than for Right-Pedestrians.  
Figure 5: Incidence rate according to road width to/through the intersection in the presence of Left-Pedestrian (\(n=72\)) and Right-Pedestrian (\(n=40\)): (**a**) to the intersection; (**b**) through the intersection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vehicle Travel Speed** & \multicolumn{2}{c}{**Left-Pedestrian**} & \multicolumn{2}{c}{**Right-Pedestrian**} & \multicolumn{2}{c}{**A [\%] vs. B [\%]**} \\ \cline{2-5}
**[km/h]** & **N** & **A [\%]** & **n** & **B [\%]** & \(p\)-Value (\({}^{\text{(1)}}\)) \\ \hline \(\leq\)10 km/h & 26 & 36.1 & 5 & 12.2 & 0.007 ** \\ \(\leq\)20 km/h & 37 & 51.4 & 23 & 56.1 & 0.534 \\ \(\leq\)30 km/h & 9 & 12.5 & 12 & 31.7 & 0.023 * \\ Total & 72 & 100.0 & 40 & 100.0 & \\ \hline \hline \end{tabular}  
* A significant difference was determined using a two-tailed statistical test of sample rates.
* \(p\)-value \(<\) 0.05, **\(p\)-value \(<\) 0.01.  
\end{table}
Table 3: Near-miss incidents according to range of vehicle travel speed in the presence of Left-Pedestrian and Right-Pedestrian.
## 5 Conclusions  
The implemented 433 MHz down-converting repeaters with variable attenuators and 433 MHz up-converting receiver with variable attenuator have been used in 2D positioning successfully for the first time. It is also shown that, with the help of cascaded LNA and variable attenuator, the proposed system for 2D positioning has gained immunity to the non-line-of-sight conditions. The power level of the signals that are transmitted to the receiver can be adjusted by varying the attenuation so that a receiver can pick up different satellite signals coming from different indoor paths almost equally. The GPS signals, which are down-converted and retransmitted by repeaters, are sent to the indoor environment and picked-up by an up-converting receiver. After up-conversion, the raw data are collected by an off-the-shelf receiver and then transmitted over Wi-Fi to a remote computer for processing. The estimated positions are found to be only 54 and 98 cm away from the actual receiver position, for line-of-sight and non-line-of-sight cases, respectively. Therefore, experiment results show that sub-meter accuracy can be achieved with the transmission of GPS signals in the 433 MHz ISM band in an indoor environment.  
Conceptualization, I.T. and H.Y.; funding acquisition, I.T. and H.Y.; software, F.A.G., A.U., and A.M.A.N.; investigation, A.U. and F.A.G.; testing, A.U., F.A.G., A.M.A.N., H.Y., and I.T.; writing--original draft preparation, A.U. and F.A.G.; writing--review and editing, A.U., F.A.G., H.Y., and I.T.; project administration, I.T. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK), Grant No: 116E752.  
Not Applicable.  
Figure 20: Deviation from the real position for each of 30 points.
# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 3 Results  
### Analysis of Ground-Measured Chlorophyll Fluorescence  
The variations in maximum quantum yield (Fv/Fm) characterizing chlorophyll fluorescence in different vegetation growth periods at the wetlands in 2022 are shown in Figure 4. The most ChF records were made in late June and early July (115), while due to unfavorable weather conditions in the period September-October, only 48 field measurements were carried out. The Fv/Fm reached its maximum value (0.82) at the late flowering stage in August-September. It was noted at its lowest at the late beginning and at the end of the growing season (0.76), in the periods April-May and September-October, respectively. The lowest standard deviations appeared in the mid-season (July-August-September), at 0.010-0.011, whilst the highest standard deviations appeared in the early (April-May) and late stages (September-October) of wetland vegetation development, at 0.026 and 0.029, respectively.
# Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment
## 2 Materials and Methods
### Model Validation  
The models were validated with a dataset that was not used during model training. In the case of ARX/ARMAX, a training window was utilised to systematically pick a part of the dataset, train a model and compare the results over the rest of the dataset. In the case of regression models, k-fold cross-validation was used to divide the datasets into training and test data. K-fold cross-validation divides the dataset into k number of folds (or partitions) that are nearly equal in size. After the data were divided, the k \(-\) 1 number of folds was used for model training, and the remaining data were used for model validation. This procedure was iterated k times, which means that each fold was successively utilised in validation, and the remaining data were used as training data [38]. The value for k was chosen to be 5 because there a large dataset was available and a separate independent validation dataset to test the effluent model on. With this k-value, it was believed that the results would be the most realistic as opposed to biased or optimistic. Monte Carlo repetitions were utilised to repeat this process 2000 times each time model training was performed. Both models were also subsequently tested with independent validation data afterward.  
There are many ways to evaluate the performance of an identified model. Commonly utilised measurements include the root mean squared error (RMSE), mean absolute percentage error (MAPE) and the correlation coefficient (r). The following equations were used to calculate these performance metrics for the identified models to evaluate their performance [39]:  
\[MSE=\frac{1}{N}{\sum_{t=1}^{N}}(\hat{y}_{t}-y_{t}), \tag{7}\]  
\[RMSE=\sqrt{MSE}, \tag{8}\]  
\[APE_{t}=\frac{|\hat{y}_{t}-y_{t}|}{y_{t}}.100\%, \tag{9}\]  
\[MAPE=\frac{1}{N}{\sum_{t=1}^{N}}APE_{t}, \tag{10}\]  
\[r_{xy}=\frac{\sum_{t=1}^{n}}{(x_{i}-\overline{x})(y_{1}-\overline{y})}{(n-1) S_{x}S_{y}}, \tag{11}\]  
where \(\hat{y}_{t}\) represents the predicted values, \(y_{t}\) is the measured values, \(N\) is the number of data points, \(\hat{y}\) is the response variables mean and \(t\) represents time.
## 7 Conclusions and Future Work  
In this paper, we have developed a congestion mitigation approach by employing the distributed power control framework for SSNs in the rectangular grid based SN-CRN. Particularly, we defined the relative divergence between the detection probability and false alarm probability for a SSN under any uplink channel by adopting a Kullback-Leibler divergence framework. After deriving the detection probability and false alarm probability distributions for SSN according to mathematical statistics, we characterized the stability metric of local spectrum sensing based on entropy modeling framework. Aiming to gain the tradeoff between channel capacity and energy consumption, the distributed power control framework for IT and MT SSNs was proposed, and the power control problem was formulated as differential game model by taking into account the utility function maximization with linear differential equation constraint in regard to energy consumption. Further, we derived the theoretic optimal solutions to this game model under the scenario of cooperation or noncooperation via dynamic programming. Based on the obtained optimal transmit power of SSNs, we devised the congestion mitigation approach for bottleneck SU by alleviating buffer load over its internal buffer, and validated its performance with simulations.  
Figure 12: Reduction of data sequence of spectrum sensing resultsfor of selected spectrum sensor nodes vs. the slot \(\tau_{rp}\) (\(r=0.3\), \(l=250\) iterations). NoCoPC: noncooperative power control; CoPC: cooperative power control; PB: power balancing.  
What we have discussed in this paper is the portion of the foundation for SN-CRN. Possible directions for future work within this research involve examining the effect of imperfect CSI and outage constraint on distributed power control for SSNs by formulating the uncertain relation between the wireless channel conditions and the corresponding estimates. As another future work, we will try to investigate congestion mitigation approaches in future 5G mobile systems with the novel network architecture and networking technologies [36], e.g., fog computing-based radio access networks and network slicing-based mobile networks in currently practical applications.  
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China under Grant No. 61402147 and No. 61501026, the Research Program for Top-notch Young Talents in Higher Education Institutions of Hebei Province, China under Grant No. BJ2017037, the Research and Development Program for Science and Technology of Handan, China under Grant No. 1621203037, and the Natural Science Foundation of Hebei Province of China under Grant No. F2017402068.  
[PERSON] conceived the idea of this paper and determined the structure of research work; [PERSON] provided valuable insights for the scheme and revised the manuscript; [PERSON] and [PERSON] performed the simulations and wrote the paper; [PERSON] contributed to data analysis.  
The authors declare no conflict of interest.
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
###### Abstract  
As an important part of the Internet of Things, the Internet of Vehicles (IoV) has achieved efficient interconnection and collaboration between vehicles and road infrastructure, and between vehicles through advanced information and communication technologies. However, the high-speed movement of vehicles has generated a large number of cross-domain behaviors, which has greatly increased the number of authentications. Existing authentication protocols face challenges such as high cost, high computational overhead, and easy eavesdropping, interception, or tampering. To this end, this paper proposes an ECC-based IoV secure and efficient handover authentication protocol. The protocol adopts a \"non-full key escrow\" mechanism. The private key of the vehicle is jointly generated by the Trusted Authority (TA) and the vehicle. The TA only holds part of the private key. Even if the TA is malicious, the security of the vehicle's private key can be ensured. At the same time, the proposed protocol uses the time tree technology in trusted computing to share part of the vehicle's private data, which not only ensures the security of authentication, but also improves the efficiency of authentication, and solves the high-latency problem caused by the use of blockchain in previous protocols. When the vehicle moves across domains, there is no need to re-register and authenticate, which reduces the authentication overhead. Compared with existing protocols, this protocol is lightweight in both computational and communication overheads, effectively solving the problem of excessive cost.  
Internet of Vehicles; handover authentication; elliptic curve cryptography; time tree +
Footnote †: journal: Applied Sciences  
17 April 2025  
Revised: 20 May 2025  
Accepted: 22 May 2025  
Published: 23 May 2025
## 4 Results
### Intensity of SNSAs Use  
Participants answered two survey questions related to the daily amount of time spent using SNSAs and the level of involvement with friends via SNSAs, with results outlined in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **\%** & **M** & **SD** \\ \hline Q1: Time spent using SNSAs on an average day & & 3.29 & 1.37 \\
30 min or less & 13.5 & & \\ Between 30 min and 1 h & 15.2 & & \\ Between 1 and 2 h & 26.1 & & \\ Between 2 and 3 h & 18.3 & & \\ More than 3 h & 26.6 & & \\ Q2: Level of involvement with friends SNSAs accounts & & 3.15 & 1.29 \\ Rarely visit & 13.4 & & \\ Only read comments & 17.8 & & \\ Read posts and comments & 26.2 & & \\ Read posts and comments and then respond & 24.4 & & \\ Read posts/comments, respond, and introduce new topics & 17.7 & & \\ \hline \hline \end{tabular} Note: Individual items were recorded from 1 = low intensity to 5 = high intensity in order to calculate an overall mean score for each question.  
\end{table}
Table 1: Intensity of SNSAs use.  
By using the following mean ranges (mean below 2.5 represents Low intensity, between 2.6 and 3.9 represents Moderate intensity, above 4 represents High intensity), the mean scores for the two questions indicated a moderate level of intensity for SNSAs use by participants. The measurement index for intensity of engagement associated with mean score ranges was determined as appropriate and representative of the range of scales included and the various interpretations used for similar scales.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 3 Results
### The Optimal Constellations
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Nsat**} & **Orbit Altitude** & \multirow{2}{*}{**\(\hat{\imath}^{(\circ)}\)**} & \multirow{2}{*}{**e**} & \multirow{2}{*}{**No**} & \multirow{2}{*}{**Nw**} & \multirow{2}{*}{**Nso**} & \multirow{2}{*}{**Nc1**} & \multirow{2}{*}{**Nc2**} & \multirow{2}{*}{**Nc3**} & \multirow{2}{*}{**Fitness Value**} \\  & **(km)** & & & & & & & & & \\ \hline
6 & 520.038 & 89.617 & 0.01 & 3 & 1 & 2 & 3 & 1 & 2 & 0.68191 \\
7 & 505.997 & 88.544 & 0.01 & 7 & 1 & 1 & 2 & 1 & 2 & 0.73264 \\
8 & 529.264 & 99.078 & 0.01 & 8 & 1 & 1 & 3 & 1 & 2 & 0.59394 \\
9 & 633.961 & 89.317 & 0.02 & 3 & 1 & 3 & 1 & 1 & 2 & 0.66609 \\
10 & 572.877 & 92.260 & 0.01 & 5 & 2 & 1 & 1 & 2 & 2 & 0.72608 \\
11 & 532.137 & 89.130 & 0.02 & 11 & 1 & 1 & 1 & 1 & 1 & 0.60108 \\
12 & 537.006 & 89.130 & 0.01 & 3 & 1 & 4 & 1 & 1 & 1 & 0.66744 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The parameters of the optimal 3D-LFC configurations.
# Digital Twin Framework for Road Infrastructure Management
## 4 Digital Twin Framework for Road Infrastructure Management
### Analysis of Existing DT Frameworks for Road Infrastructure Management
#### 4.1.2 Road Bridges
The core components of the bridge DT system include the physical entity, data, DT virtual model, users, and connections. The bridge's physical entity serves as the primary data source for the DT and can be subdivided into components and units, which reflect its structural composition and functional roles in data acquisition. Data are the most critical component of the bridge DT, flowing across the physical asset, user, and DT model. Specifically, data circulating from the physical structure to its virtual counterpart include bridge monitoring metrics (e.g., stress and strain), environmental data (e.g., temperature, wind speed and direction, and extreme weather events), structural condition data (such as cracks and corrosion), and traffic information. Conversely, data transmitted from the bridge DT to users include bridge condition assessments, predictive insights, maintenance planning recommendations, emergency response guidance, and decision-support outputs. Users can also send information to the physical bridge, such as maintenance actions, traffic control measures, and other relevant instructions. The virtual model forms the core of the bridge DT, using data from the physical entity to generate insights and feedback for both users and the physical asset. To support the full lifecycle of the bridge, the DT should integrate a 3D surface model, a finite element analysis or simulation model, a data-driven surrogate model, and an information management model (Figure 5). Users--typically operators and managers--interact with the DT, while the system's connections determine the direction of data flow, level of automation, and degree of real-time responsiveness. Figure 5: Core models used in bridge DT applications. These foundational components are further detailed into specific processes that characterize an ideal bridge DT across the asset's entire lifecycle. Table 2 summarizes the findings from DT layered approaches in road bridge applications. In Section 4.1.2, we conducted a thorough review of six papers focused on road bridge maintenance and engineering. These papers primarily addressed topics such as bridge management, semantic bridge maintenance systems (BMS), smart bridge safety management, DT-based improvements in BMS, advancements in bridge DTs, and BIM-based bridge DTs. The bridge DT models typically included common components: a 3D surface model, a finite element analysis (simulation) model, a surrogate model, and an information management model. The layers of these bridge DTs consisted of at least three layers and could extend to as many as eight layers. While the DT layers used in these studies had different names, they generally served the same purposes and functionalities, similar
# Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage  
[PERSON]  
1 Faculty of Mechanical Engineering, [PERSON] Radom University, Stasieckiego 54, 26-600 Radem, Poland; [EMAIL_ADDRESS] (L.C.)1 Faculty of Mechanical Engineering, [PERSON] Radom University, Stasieckiego 54, 26-600 Radem, Poland; leszek.  
expenses on conveyor monitoring equipment were, in 2018, as high as USD 200 million, and they are expected to exceed USD 0.25 trillion in 2024.  
However, apart from collecting the signal from a monitoring system conveying the belt's actual condition, a proper analysis of the registered signals should be performed, and the relevant decision should follow [15]. Among others, machine learning (ML) algorithms are widely applied for this purpose. For instance, [PERSON] and co-authors attempted to identify the correlations between significant damage to the rubber-textile material of the conveyor belt and some parameters, such as the type of material and the impact height [16]. Using multispectral imaging, [PERSON] et al. assessed a conveyor belt's wear condition, classifying it into three wear states using a deep learning approach [17]. In order to prevent the belt from flipping over, [PERSON], together with the team, proposed an algorithm based on machine learning and analytical algorithms to determine the forces acting on the individual components of the conveyor and subsequently make a decision on classifying the category of the obtained results [18]. [PERSON] and co-authors reported the application of a new detection method able to simultaneously detect multiple faults using a special dataset for various types of damage to the conveyor belt [19]. [PERSON] and colleagues proposed a novel method for conveyor belt damage detection based on fusion knowledge distillation and a deep neural network using the data collected via an image acquisition module [20]. Signals obtained from both machine vision [21] and acoustic systems [22] were reported to be analyzed with a machine learning algorithm. [PERSON] and [PERSON] proposed a digital twin to perform a predictive maintenance approach through the analysis of data collected from various sensors placed along an operating conveyor belt, exploiting a machine learning algorithm [23]. Also, [PERSON] and co-authors reported the application of a combination of wavelet packet decomposition and gradient boosting decision tree for the diagnosis of failure modes, including the surface wear of laboratory belt conveyor idlers [24].  
In this paper, a novel system for conveyor belt monitoring is presented based on strain gauges. Strain gauges provide a continuous signal corresponding to the belt's tension in different work conditions. Considering some of the features of the registered signals, an attempt was undertaken to identify them using several machine learning algorithms.
## 2 Experimental System and Measurement Methods
### Parameters  
To evaluate the dynamic behavior of droplets and qualitatively analyze the morphological changes of droplets, several nondimensionalized factors are adopted to characterize the droplet behavior. Spreading factor, calculated by \(\beta=D/D_{0}\), is a key parameter to evaluate the dynamic behavior of droplets, representing the ability of droplet spreading, where \(D\) is the spreading diameter of droplets after impact, and \(D_{0}\) is the initial diameter of the droplet before impact. A dimensionless spreading time \(\tau=\frac{IV_{0}}{D_{0}}\) is also employed, where \(t\) is the time duration of the droplet impacting process. The impacting velocity is calculated by using the equation [17]:  
\[V_{0}=\sqrt{\frac{g}{A}(1-\exp(2A(D_{0}-H)))} \tag{2}\]  
\[A=\frac{3}{4}\frac{\rho_{\text{air}}\text{C}_{\text{f}}}{\rho D_{0}} \tag{3}\]  
where \(H\) is the falling height and Friction coefficient \(C_{\text{f}}\) in this formula is 0.796 according to [PERSON] and Feuillebois [17]. The equivalent initial diameter \(D_{0}\) is calculated by \(D_{0}=\left(D_{\text{h}}^{2}D_{\text{v}}\right)^{\frac{1}{3}}\)[15; 35], as shown in Figure 3, because the droplet is not perfectly spherical, where \(D_{\text{h}}\) and \(D_{\text{v}}\) are diameters in horizontal and vertical directions. Three dimensionless numbers on droplet impinging dynamics are mainly studied: _We_ represents the ratio of inertial force and liquid surface tension, _Re_ represents the ratio of inertial force and viscous  
Figure 2: Image of (**a**) conical nozzle and (**b**) comparison of droplet diameter distributions resulting from the flat and conical nozzles.  
force, and _Oh_ represents the relationship between viscous force, inertial force, and surface tension. Their mathematical expressions are as follows:  
\[We= \frac{\rho D_{0}V_{0}^{2}}{\sigma} \tag{4}\] \[Re= \frac{\rho D_{0}V_{0}}{\mu}\] (5) \[Oh= \frac{\mu}{(\rho\sigma D_{0})^{0.5}} \tag{6}\]  
where \(\rho\), \(\sigma\), \(\mu\), \(D_{0}\) and \(V_{0}\) represent the density, surface tension, viscosity, initial diameter, and impact velocity of droplets, respectively. Dynamic parameters of droplets are obtained from images shot by a high-speed camera.