esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Primary Causes for Soil Loss, Land Use, and Land Cover Changes  
From the research conclusions, the estimated soil erosion reported in earlier studies based on the data from 2005 and 2017 was 15 t ha\({}^{-1}\) yr\({}^{-1}\) which has increased to 23.47 t ha\({}^{-1}\) yr\({}^{-1}\) in 2021 [80].  
The earthquake that occurred in the northern regions of Pakistan on 8 October 2005 resulted in a manifold increase in soil erosion in the area. This earthquake of magnitude 7.6 Mw (Moment Magnitude Scale) and its aftershocks were the sources of many landslides and soil erosion in the area [81]. Analysis shows that soil erosion has increased significantly near the epicenter of the earthquake. Moreover, soil erosion has also escalated due to the high average annual population growth rate of AJ&K (1.61%) [82] and the poor development of infrastructure [83]. The instability in the mountainous regions is further compounded by the fact that Pakistan has a forest cover of only 2.5%, and the annual deforestation rate is 2.1% [84]. Soil conservation methods face various challenges, including socioeconomic differences, violations of the law and court rulings, as well as a lack of awareness and education.  
Due to poor rock weathering, increased surface runoff, and natural disasters, soil erosion increased manifold. Additionally, landslides and forest degradation significantly contributed to soil erosion at higher altitudes throughout the sixteen-year study period (2005-2015) [76].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Study Area**} & **Soil Erosion Rate (t ha\({}^{-1}\) yr\({}^{-1}\))** \\ \cline{3-3}  & & **Calculated by the Respective Local Study** \\ \hline [PERSON] et al. [78] & Rawal watershed & 10.3 \\ \cline{2-3}  & Ghabbir watershed & 22 \\ \hline [PERSON] et al. [77] & Jhelum watershed & 14 \\ \hline SRC’s. [77] & Jhelum watershed & 17.51 \\ \hline [PERSON] et al. [75; 76] & Azad Jammu and Kashmir & 40.88 \\ \hline [PERSON] et al. [76] & Azad Jammu and Kashmir & 22.25 \\ \hline
**Current Study** & Jhelum watershed & 23.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing soil erosion estimates in neighboring areas with similar topography using both global and local studies.  
The results indicate that soil erosion values are higher in areas with steep slopes near drainage lines such as streams and rivers. This could be due to runoff carrying large amounts of damaged materials. The steeper sections and higher slopes are also impacted by rills, gullies, and significant amounts of silt, which were easily observable during field surveys. These features are common in the research area due to their delicate geomorphology.  
Moreover, the study found that areas with farming and dense mixed forests had the lowest erosion rates, whereas areas covered in snow and glacier patches had the highest rates of erosion [79].
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
###### Abstract  
The unique characteristics of underwater environment such as long propagation delay, limited bandwidth, energy-constraint and non-uniform topology are big challenges in designing a routing protocol for underwater wireless sensor networks (UWSNs). In this paper, a novel routing scheme is proposed through which two mobile sinks are used for efficient collection of sensed data packets. Moreover, a new metric \"Mobile Sink Utility Ratio (MUR)\" is also introduced that helps in measuring the usage of mobile sink in the collection of data packets. The proposed scheme is rigorously evaluated and compared with current state-of-the-art routing protocols. The simulation of the proposed scheme shows promising results in terms of throughput, network lifetime, packet drop ratio and MUR.  
underwater wireless sensor networks (UWSNs); energy efficiency; energy and performance aware routing; mobile sink; mobile sink utility ratio +
Footnote †: journal: Accepted: 23 May 2018; Published: 6 June 2018
## 3 Analysis of Kaunas WWTP Case
** Another potential energy source in WWTP is recoverable heat from wastewater by heat pump technology. Although this is a promising technology, theoretically studied in a number of articles [46; 106; 131; 132], its practical application in WWTPs is not yet widespread. Based on methodology and assumptions presentedin [131; 132] (wastewater temperature reduction of 5 K, coefficient of performance (COP) of 4.0, actual Kaunas WWTP wastewater outflow of 809 L/s), the theoretical potential of wastewater heat recovery Q\({}_{\text{rec}}\) is 16.98 MW, or 148,760 MWh/a. Considering COP equal to 4.0, the capacity of the wastewater heat pump Q\({}_{\text{cap}}\) is 22.64 MW. This means that the theoretical heat amount supplied from the heat pump to the district heating network could reach 198,341 MWh/a while consuming 49,585 MWh/a of electricity. This amount of heat significantly exceeds the demand of the nearest Kaunas city district heating subnetwork. The main Kaunas city district heating network is located on the other side of the river Nemunas. Although in 2024 Kaunas city implemented a project to connect another DH subnetwork, also located on the other side of the river Nemunas, there are no plans to connect the Kaunas WWTP DH subnetwork in the coming years. For this reason, heat recovery potential from wastewater is not included in the present Kaunas WWTP energy neutrality estimations. Increasing incoming waste streams.The annual wastewater volume of Kaunas WWTP is currently 25.5 million m\({}^{3}\). The treatment of this wastewater produces 3.815 million m\({}^{3}\) of biogas, or 27,161 MWh of energy value. Based on the results of the Alytus city (Lithuania) sorted food waste collection [133] and the data of the Vienna city (Austria) sorted food waste biogas power plant [134], the implementation of a sorted food waste collection system in Kaunas city would allow the collection of 11,772 t of food waste and the production of 4281 MWh of biogas per year. In this way, biogas production would increase by 15.8%. The use of additional agricultural residues in Kaunas WWTP is unlikely due to the resistance of the residents in the surrounding areas to the expansion of the WWTP. However, there are currently no agricultural biogas plants in the nearest places, so the use of agricultural waste may be technically and economically acceptable.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 7 Applications of AV Traffic Modeling
### Traffic Flow Optimization  
The optimization of traffic flow, particularly in the context of autonomous vehicles (AVs), has significantly advanced through various interconnected scientific studies. [PERSON] et al. presented a multi-scale approach that models the interaction between controlled AVs and surrounding traffic using scalar conservation laws coupled with ordinary differential equations, demonstrating how these interactions can reduce congestion and improve traffic flow [258]. This foundational work was complemented by [PERSON] and [PERSON]'s extensive review of machine learning techniques for traffic flow predictions in AVs, which emphasizes the importance of large-scale datasets and complex models in accurately capturing traffic dynamics [259]. The integration of these advanced modeling techniques is further illustrated by [PERSON] et al., who explore optimal control strategies for AVs, demonstrating, through simulations, that such strategies can significantly enhance traffic efficiency, reduce travel times, and increase road capacities [260].  
[PERSON] et al. expanded this framework by discussing the role of network softwarization in automating network security, indirectly supporting AV traffic modeling by ensuring secure communication networks--a critical component for reliable AV operations [261]. This interrelationship between network security and traffic modeling was further highlighted by [PERSON] et al., who proposed a novel two-phase approach using deep reinforcement learning (DRL) combined with Smart Rerouting (SR) techniques to optimize traffic flow during congestion periods, demonstrating significant performance improvements [262]. The effectiveness of these optimization strategies was further validated by [PERSON] et al., who explored discrete-event simulations for traffic flow, simplifying modeling efforts through the validation of exponential distributions for inter-arrival times [263].  
The importance of accurate simulation tools was underscored by [PERSON] et al., who analyzed traffic simulators like VISSIM and AIMSUN, emphasizing their crucial role in testing the impacts of AVs and Connected Autonomous Vehicles (CAVs) on various traffic scenarios [264]. Lastly, [PERSON] and [PERSON] contributed to this cohesive framework with their optimization algorithm for traffic flow predictions, employing wavelet neural networks and the beetle antennae search algorithm to enhance the prediction accuracy, further solidifying the theoretical underpinnings of traffic flow optimization in AV contexts [265].  
Collectively, these studies form a cohesive body of work that advances traffic flow optimization in AV systems. By integrating various modeling, control, and AI techniques, they establish a robust theoretical foundation for future research and practical applications in traffic management.
## 2 Model
### Matte-Grade Prediction Model Based on BP Neural Network
#### 2.2.1 Model Structure  
The input layer of the BP neural network can build a nonlinear model G = F(X) between the matte grade G and the influencing factor X, based on the main factor \(X\) that affects the matte grade, where the equation for the influencing factor \(X\) is as follows:  
\[X=[X_{1},\,X_{2},\,X_{3},\,X_{4},\,X_{5},\,X_{6},\,X_{7},\,X_{8}]^{\text{T}} \tag{2}\]  
In this formula, \(X_{1}\) is Cu content, \(X_{2}\) is S content, \(X_{3}\) is Fe content, \(X_{4}\) is SiO\({}_{2}\) content, \(X_{5}\) is CaO content in the copper concentrate, \(X_{6}\) is oxygen volume, \(X_{7}\) is blast volume, and \(X_{8}\) is flux amount.  
In the model, the influence degree of each factor on matte grade is reflected by the weight of each influence factor, and the final network output value is constantly approaching the actual value by using the self-training, learning, and adjustment abilities of the BP network. The model uses eight main influences \(X\) as the feature set with a complete dataset of 910 and one output term of matte grade; 900 sets of data were used as the training set, and 10 were used as the test set. The number of hidden layer units in the model is determined to be 13 by the empirical formula \(n_{1}=\sqrt{n+m}+\alpha\)[23] (\(n_{1}\) is the number of hidden layer units, \(n\) is the number of input layer units, \(m\) is the number of output layer units, and \(\alpha\) is a constant between 1 and 10). A matte-grade prediction model with the network structure of 8-13-1 is shown in Figure 1.
## 3 Decentralized Neural Block Control (DNBC-UKF)
### SI Optimization for UKF Learning  
Using the UKF to estimate the NN weights, and correcting for identification errors, the filter is updated at each step. Usually, \(P_{i}^{j}\), \(Q_{i}^{j}\), and \(R_{i}^{j}\) are initialized as diagonal matrices with entries \(P_{i}^{j}(0)\), \(Q_{i}^{j}(0)\), and \(R_{i}^{j}(0)\), respectively. Given that, typically, these entries are defined heuristically, we propose employing SI methods to improve the UKF training algorithm.  
According to the optimal control theory [24], it is common to use error-based performance measures such as those described in Table 1. On the other hand, in (3), the Bolza form [25] is described and used as an objective function to evaluate the overall performance, including information from the control input of the system.  
\[J=\overbrace{\underbrace{\mathbf{e}_{kf}^{\top}L\mathbf{e}_{kf}}_{\text{Mayer form}}+\underbrace{\sum_{k_{0}}^{k_{f}}\left[\mathbf{e}_{k}^{\top}Q\mathbf{e}_{k}+ \mathbf{u}_{k}^{\top}R\mathbf{u}_{k}\right]}_{\text{Lagrange form}}}^{\text{ Bolza form}}T \tag{3}\]where \(k_{0}\) is the initial iteration; \(k_{f}\) is the final iteration; \(T\) is the sampling time; \(\mathbf{e}_{k}\) is the error vector; \(\mathbf{u}_{k}\) is the control input vector; and \(L\), \(Q\), and \(R\) are gain matrices with appropriate dimensions.  
Although the Bolza form is a good performance criterion, it presents a serious disadvantage for this work. The final value of the error vector is not very useful for our methodology because it does not significantly represent the system identification and trajectory tracking; in other words, we need to know how it behaves throughout the simulation. For this reason, we propose an objective function, based on that found in [26], for the SI algorithms as follows:  
\[f_{obj}=\varrho_{1}\text{MSE}(e_{i,k})+\varrho_{2}\text{MSE}(z_{i,k})+\sum \limits_{k=0}^{\frac{t}{2}}[\varrho_{3}(u_{i,k-1}-u_{i,k})] \tag{4}\]  
where MSE represents the mean square error; \(t\) is the total time of the simulation; \(e_{i,k}\) represents the identification error; \(z_{i,k}\) is the tracking error; \(u_{i,k}\) represents the input control; and \(\varrho_{1}\), \(\varrho_{2}\), and \(\varrho_{3}\) are scaling factors to bring all the terms of the objective function to a similar order.
## 3 Results and Discussions
### The Target Signal Selection  
The impact components with different frequency were extracted from the original signal in Section 3.1, choosing a signal in one layer as the target signal needed for further analysis. The energy  
Figure 7: (**a**) Time domain of the expulsion signal in each layer; (**b**) energy distribution proportion of expulsion signal in each layer; (**c**) time domain of other signal 1 in each layer; (**d**) energy distribution proportion of other signal 1 in each layer; (**e**) time domain of other signal 2 in each layer; (**f**) energy distribution proportion of other signal 2 in each layer.  
kurtosis index were introduced. The peak-to-peak amplitude is the change between peak (highest amplitude value) and trough (lowest amplitude value, which can be negative), which measures the range of a signal value. The kurtosis index is the dimensionless parameters in statistics. It is sensitive to impact signal component, and the bigger the value is, the lower dispersion degree of the data is. Therefore, the kurtosis index is still an important basis for measuring the aggregation degree of impact components. The formulas of the indexes above are as follows:  
The peak-to-peak amplitude:  
\[U=Max(x)-Min(x). \tag{5}\]  
The kurtosis index:  
\[K=\frac{\frac{1}{N}\sum_{i=1}^{N}(|x_{i}|-\overline{x})^{4}}{X_{rms}^{2}}= \frac{\frac{1}{N}\sum_{i=1}^{N}(|x_{i}|-\overline{x})^{4}}{\frac{1}{N}\sum_{i=1 }^{N}x_{i}^{2}}, \tag{6}\]  
where \(x\) is the signal to be analyzed.  
The two indexes of the 20 set target signal were calculated. The result is shown in Table 1, data 1-3 and data 4-20 stand for the expulsion defects and other conditions, respectively.  
Due to the impact components, the peak-to-peak amplitude of the target signal should be higher than other conditions when expulsion occurs in theory. From the results in Table 1, the peak-to-peak amplitude of the expulsion signals were higher than some other data, while data 11, 12, 13, and 20 also stayed at a high level, even higher than the expulsion ones. The kurtosis index of the target signal (data 11, 12, 13, and 20) were higher than the expulsion ones.  
Figure 9 shows the time domain of these target signals (data 11, 12, 13, and 20), the impact components were not from the expulsion defect during the welding time, but from the early signal fluctuation. Therefore, the high indexes of the signals (data 11, 12, 13, and 20) could not be used as the indicator of the expulsion defect. Moreover, the early signal fluctuation in the expulsion signal will have influenced the impact component distribution, resulting in nonlinear trends of kurtosis index as shown in Table 1.  
Therefore, early signal fluctuation affected the extraction of the impact components during the welding time and index distribution, and the high peak-to-peak amplitude and kurtosis index resulted in misjudgment. Therefore, neither the peak-to-peak amplitude nor the kurtosis index could accurately judge the expulsion defect, individually.  
Figure 9: Time domain of the signal: (**a**) Data 11; (**b**) Data 12; (**c**) Data 13; (**d**) Data 20.
# Noninvasive Suspicious Liquid Detection Using Wireless Signals
## 5 Conclusions  
In this work, we explored the feasibility of using a wireless signal in a multi-band environment to detect suspicious liquids. Our work is novel because it demonstrates that it is possible to detect suspicious liquids accurately using radio signals without installing expensive liquid detection machines. Our system can not only detect whether the liquid is suspicious, but also further identify the types of suspicious liquids. In addition, we confirmed the feasibility of liquid concentration detection by using the WCI at a 5G frequency band.  
The results analysis shows that our method can accurately detect suspicious and nonsuspicious liquids (Experiment 1) with more than 98% accuracy, regardless of the type and size of containers, and can identify the type of suspicious liquids (Experiment 2) with more than 97% accuracy. For the detection of alcohol with different concentrations, the accuracy can reach up to 94%. This provides better performance and robustness than existing Wi-Fi technology.  
However, our method has limitations for liquids stored in metal containers. In that case, we recommend that security personnel intervene. Our method could be further improved by increasing the number of suspicious liquids prohibited in public and looking for ways to reduce the impact of metal containers on liquid detection.  
Conceptualization, J.D. and N.Z.; methodology, L.G.; software, J.D.; validation, L.G.; formal analysis, J.D.; writing--original draft preparation, J.D.; writing--review and editing, M.B.K.; supervision, W.S., A.R., J.Z., X.Y. and Q.H.A.; project administration, X.Y.; funding acquisition, X.Y.  
The work was supported in part by the Fundamental Research Funds for the Central Universities (No. JB180205).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Band Selection** & \multicolumn{3}{c}{**C-Band**} & \multicolumn{3}{c}{**S-Band**} \\ \hline
**Container** & **Paper** & **Plastic** & **Glass** & **Paper** & **Plastic** & **Glass** \\ \hline Experiment 1 & 0.98 & 0.99 & 0.98 & 0.99 & 0.99 & 0.99 \\ Experiment 2 & 0.99 & 0.97 & 0.99 & 0.99 & 0.99 & 0.99 \\ Experiment 3 & 0.91 & 0.91 & 0.94 & 0.81 & 0.80 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The KNN algorithm classification results of the system.  
Figure 11: Detection results of different concentrations of alcohol at the C-band and the S-band.
## 3 Materials and Methods
### Datasets
#### 3.5.2 Patient Dataset  
In low-income nations like Bangladesh, maternal mortality is increasingly a worrying aspect. In [9], an IoT-based maternal health care system was proposed to address this problem. Analyzing medical profiles, including age, weight, blood pressure, pre-existing conditions, heart rate, body temperature, and physical activity, allows for the prediction of risk levels for a certain patient based on these characteristics and their associated values. The research studied the symptoms as well as maternal health risk factors and collected data accordingly. The dataset comprises the most common readings that need to be monitored regularly from home via sensors. The readings of systolic blood pressure (SystolicBP), diastolic blood pressure (DiastolicBP), blood sugar level (BS), body temperature (BodyTemp), and heart rate (HeartRate) were reported along with the risk-level factor, categorized as low, mid, or high. This dataset is employed to mimic the patient-level monitoring presented in this research. The parameters of the dataset are shown in Table 3 along with a statistical analysis of each feature.
## 4 Algorithm Design
### Conflict Resolution Mechanisms for Limited Charging Resources
#### 4.1.1 Charging Sequence Feasibility Theorem
Its charging interval \([S_{b},F_{b}]\) must satisfy  
\[ES_{b}\leq S_{b}\leq LS_{b} \tag{30}\]  
\[F_{b}=S_{b}+(EF_{b}-ES_{b}) \tag{31}\]  
From theorem condition \(LS_{b}<EF_{a}\), combined with Equation (29) and \(S_{a}\geq ES_{a}\), we derive the following:  
\[F_{a}=S_{a}+(EF_{a}-ES_{a})\geq ES_{a}+(EF_{a}-ES_{a})=EF_{a}>LS_{b} \tag{32}\]  
Thus far, \(S_{b}\geq F_{a}>LS_{b}\), which contradicts \(S_{b}\leq LS_{b}\) in Equation (30). Therefore, no feasible charging scheduling scheme exists. (2) Scenario 2. Charging task \(b\) is the immediate predecessor. By advancing charging task \(b\), its charging interval \([S_{b},F_{b}]\) satisfies Equations (30) and (31). Vehicle \(a\) starts charging after \(b\) completes its task, implying \(S_{a}\geq F_{b}\). Its charging interval \([S_{a},F_{a}]\) must satisfy Equations (28) and (29). From Equation (28), \(S_{a}\geq ES_{a}\). To ensure feasibility, we verify the existence of \(S_{a}\) such that  
\[\max(ES_{a},F_{b})\leq S_{a}\leq LS_{a} \tag{33}\]  
From Equations (30) and (31), \(F_{b}\geq EF_{b}\), while \(EF_{b}=ES_{b}+T_{b}>ES_{b}\). Given \(ES_{a}\leq ES_{b}\), it follows that \(F_{b}\geq EF_{b}>ES_{a}\). Thus, \(max(ES_{a},F_{b})=F_{b}\), leading to  
\[F_{b}\leq S_{a}\leq LS_{a} \tag{34}\]  
By theorem condition \(LS_{a}>EF_{b}\), the interval \([EF_{b},LS_{a}]\) is non-empty. Since \(F_{b}\geq EF_{b}\), the interval \([F_{b},LS_{a}]\) is also non-empty. Therefore, a feasible solution exists. In summary, a feasible deferred charging schedule exists to simultaneously satisfy the charging demands  
Figure 5: Charging conflict resolution via task rescheduling at CI. of \(a\) and \(b\) if and only if the charging task of \(b\) is the immediate predecessor. This completes the proof of Theorem 1. \(\square\)  
Corollary 1: _At a given time, the internal capacity of the CI is 1. For any two electric logistics vehicles \(i\) and \(j\), if \(ES_{i}\leq ES_{j}\) and the following conditions hold:_  
\[ \begin{split} LS_{j}&<EF_{i}\\ LS_{i}&>EF_{j}\end{split}  \tag{35}\]  
_then the charging order of \(j\to i\) is feasible. Otherwise, an unresolvable charging conflict arises. The proof follows the same logic as Theorem 1 and is omitted here._