esa-sceva/satcom-chunk-collection huggingface dataset:
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Optimizations of QW
#### 3.1.3 Main Excited State of QW as a Function of Current and Temperature
The change in the main excited state reduces the single mode of the lasing light and the transmission ability of the signal. If TL is used for optical fiber communication, the base current and operating temperature should be properly regulated to avoid changes in excitation energy levels due to changes in these parameters, which will reduce the bandwidth and deteriorate single-mode properties.
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 4 Comparative Literary Deductions of Surveyed Privacy Preserving Techniques
### Survey Research Methodology for Literary Deductions  
The methodology adopted to conduct the survey is depicted in Figure 4. The goal of our research is to understand intrinsic concepts of blockchain with respect to privacy to understand the mechanisms of better privacy preserving techniques' formulation and appropriate evaluation. This will consequently result in wider adoption of the technology in privacy centric applications that are currently hesitant to deploy their systems to blockchains. Hence, the formulated research questions to achieve the study goal are:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
**RQ1:**_What are the latest trends in blockchain privacy preserving solutions?_  
**RQ2:**_How are blockchain privacy preserving techniques being evaluated?_  
**RQ3:**_How can the evaluation of blockchain privacy preserving techniques be improved?_  
For this survey, we used Google Scholar and IEEE Xplore digital repositories. The keywords used for our results were \"Blockchain Privacy\", \"Blockchain Privacy AND cryptography\" and \"Blockchain Privacy AND Smart Contracts\". We considered the data of past 5 years (i.e., 2017-2021) and picked up the first 300 results for our analysis. We excluded survey articles as they were not needed for the analysis. Moreover, we excluded manuscripts that either belonged to techniques of privacy breaching attacks or did not have any significant contribution to the body of the knowledge. We also excluded any articles that were not written in English language. Grey literature and duplicate articles were also removed for the analysis. The inclusion and exclusion criteria are comprehensively depicted in Table 3. We classified these articles based on the core mechanism of preserving privacy i.e., cryptography, smart contracts, hybrid of both or others. The last category included solutions that used deep learning, differential privacy, federated learning, clustering, and other computing approaches to retain privacy in blockchain based networks. The basic goal was to find out the blockchain based privacy preserving techniques that are currently being researched and experimented. The results of the analysis are summarized in subsequent sections.
## 2 Materials and Methods
### Data Preparation and Geospatial Layer
#### 2.2.4 Percentage of Agricultural Land Use  
The thematic map of land use inventory of Yunlin County is illustrated in Figure 2c. The study area is classified into nine land use types, including agricultural land, forestry land, transportation land, hydrological land, construction land, public facility land, recreational land, mine industry land, and other regulation district [28], as depicted in Figure 2c. As demonstrated in Figure 2c, the land use in Yunlin County is mainly categorized as agriculture.  
This study calculated the proportion of the agricultural land use within 250 m of each multi-layer compaction monitoring well (MLCW), as shown in Figure 4. The buffer analysis tool in ArcGIS creates buffer polygons around input features to a specified distance to adopt the spatial analysis and calculates the proportion of the agricultural land of the specified area. The percentage of agricultural land use is defined as follows.  
\[L_{f}=\frac{F_{a}}{T_{a}}, \tag{4}\]  
where \(L_{f}\) denotes percentage of agricultural land use, \(F_{a}\) denotes area of agricultural land use in the division unit, and \(T_{a}\) denotes total area of the unit.
# Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
###### Abstract  
The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.  
s +
Footnote †: journal: Applied Sciences  
1
## 4 Resultant Outcomes
A distinct feature is trusted as relevant if it has a strong or weak relevance score; otherwise, it is discarded from  
Figure 5: Density plot concerning significant happiness value. Figure 6: Heat map of correlation matrix representation between effective features selection. the dataset. An optimal Bayes classifier determines the strong relevance of a feature or variable in such a way that the resultant performance of the overall ML model could be deteriorated depending upon one variable/feature vi. Where the set of features/variables \(Fi=\{v1,\ldots,vi-1,vi+1,\ldots vn\}\), except _vi. vi_ is considered a strongly relevant feature if \(p(vi=xi,Fi=fi)>0\) for some existence of \(xi,y,\) and fi. A value-assignment fi denotes over each feature from Fi, such that:  
\(p(Y=y\ \ \ \ vi=xi;\ Fi=fi)\
eq p(Y=y\ \ \ \ Fi=fi)\). \(p\) stands for the performance variable of an optimal Bayes classifier. Figure 7 shows the relevancies of different features during model training. Similar to the previous case, we followed the random forest regressor [27] to split the dataset into train and test sub-datasets. And, in the case of hyper-parameters tuning, we set randomized search CV. Concerning the neutral emotion values, the density curve shows how much the range is relatively less than in the last case. The confidence region has reduced significantly as the arc is sharper in shape, and the alpha value is now 0.02, which is pretty much effective in the case of emotion detection. Figure 8 is the ultimate density curve for our improved ML model concerning the most stable emotional state, i.e., the neutral state. Figure 9 shows the scatter plot of the test dataset after the model\({}^{\prime}\)s training with best-parameter tuning and selection. Furthermore, MAE, MSE, RMSE, and R2 values [28] have evaluated the measures of the ultimate model performance [3], shown in Table 1. All the measurements support the success of our proposed model. Like the R2 value assured, 97% of the model\({}^{\prime}\)s inputs can explain the observed variation, which is favorable and satisfactory. Figure 7: Relevancies of different features during training. Semantic level interoperability through defined ontology has made it possible to collect data from distinct social media platforms; hence, we have integrated and illustrated training datasets. The model has been further improved compared to the previous case of feature selection. Consequently, we enhanced the proposed model's efficiency by selecting effective features from the collected dataset.
## 5 Results and Discussion
### Algorithm Performance Evaluation  
To comprehensively evaluate the performance of the proposed IGWOHHO algorithm, this subsection presents both theoretical analysis and empirical validation. The time complexity is examined to assess computational efficiency, while convergence performance is investigated through simulation experiments to demonstrate solution accuracy and stability.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 2 Autonomous DDboats
### Specification of Reference Trajectories  
The reference trajectories \(\mathbf{y}_{\mathrm{d}}^{(i)}(t)\) of both DDboats \(i\in\{\mathrm{A},\mathrm{B}\}\) are specified in terms of two Lissajous curves  
\[\mathbf{y}_{\mathrm{d}}^{(\mathrm{A})}(t)=\begin{bmatrix}A\sin(2\pi t)+x_{1,0 }^{(\mathrm{A})}\\ B\sin(\pi t)+x_{2,0}^{(\mathrm{A})}\end{bmatrix} \tag{1}\]  
and  
\[\mathbf{y}_{\mathrm{d}}^{(\mathrm{B})}(t)=\begin{bmatrix}-A\sin(2\pi t)+x_{1, 0}^{(\mathrm{B})}\\ -B\sin(\pi t)+x_{2,0}^{(\mathrm{B})}\end{bmatrix}. \tag{2}\]  
Both curves have the identical shape parameters \(A=40\,\mathrm{m}\) and \(B=20\,\mathrm{m}\). The midpoint positions of these two reference curves are specified by the coordinates  
\[(x_{1,0}^{(\mathrm{A})}\,\ y_{1,0}^{(\mathrm{A})})=(-35,\,0)\,\mathrm{m}\quad \text{and}\quad(x_{1,0}^{(\mathrm{B})}\,\ y_{1,0}^{(\mathrm{B})})=(-40,\,0)\,\mathrm{m}\enspace. \tag{3}\]  
For both reference trajectories \(\mathbf{y}_{\mathrm{d}}^{(i)}(t)\), with the corresponding desired velocities \(\dot{\mathbf{y}}_{\mathrm{d}}^{(i)}(t)\) of both DDboats \(i\in\{\mathrm{A},\mathrm{B}\}\), the period length for completing a full cycle is set to \(T=100\,\mathrm{s}\), leading to the velocity parameter \(\alpha=\frac{2\pi}{T}\). A graphical representation of these reference trajectories is shown in Figure 2 together with a video snapshot of the experiments in which both DDboats pass through the cycles according to (1) and (2) in opposite directions.  
Figure 1: An autonomous DDboat robot used for the experimental validation.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 5 Methods of Adjusting Duty Cycle  
Neighbor discovery is affected by nodes' duty cycles. Existing works have designed efficient discovery schedules for fixed duty cycles, but few of them study how to adjust the duty cycle during a node's lifetime. In this section, we present several methods to adjust the duty cycle according to the remaining energy and the latency constraints.
# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 3 Methodology
### Spatial and Channel Reconstruction Convolution  
CNNs are widely used in object detection but often require high computational resources. Researchers frequently explore model compression techniques or develop lightweight network models to reduce computational costs. However, these methods may lead to significant precision degradation or feature redundancy. SCConv is a novel method that exploits spatial and channel feature redundancy to compress CNNs while preserving key features of small objects, thereby improving precision while reducing model complexity [31]. SCConv consists of a Spatial Reconstruction Unit (SRU) and a Channel Reconstruction Unit (CRU), as illustrated in Figure 3.  
The Spatial Reconstruction Unit (SRU) performs separation and reconstruction operations. The separation operation isolates feature maps containing meaningful information from those with lower spatial relevance. The reconstruction operation then integrates information-rich and information-poor features to form a comprehensive feature set while optimizing spatial resource utilization.  
The Channel Reconstruction Unit (CRU) applies to split, transform, and fuse operations. The split operation divides spatially refined features into two channels and  
Figure 3: The schematic diagram of the SCConv module.  
compresses them using convolution. The transform operation employs efficient convolution operations to extract key representative information. Finally, the fuse operation merges the output features from the upper and lower transformations.  
In general, the parameters of a standard convolution can be calculated as follows:  
\[P_{Conv}=k^{2}C_{1}C_{2} \tag{5}\]  
where \(k\) is the kernel size of the convolution, and \(C_{1}\) and \(C_{2}\) are the number of input and output feature channels.  
The parameters of the SCConv consist of  
\[\begin{split} P_{SCConv}&=1\times 1\times\alpha C _{1}+k\times k\times\frac{\alpha C_{1}}{gr}\times\frac{C_{2}}{g}\times g\\ &+1\times 1\times\frac{\alpha C_{1}}{r}\times C_{2}+(1-\alpha)C_{1} \times\frac{(1-\alpha)C_{1}}{r}\\ &+1\times 1\times\frac{(1-\alpha)C_{1}}{r}\times\left(C_{2}-\frac{1- \alpha}{r}C_{1}\right)\end{split} \tag{6}\]  
where \(\alpha\) denotes the split ratio, \(r\) refers to the squeeze ratio, and \(g\) is the group size of the group-wise convolution operation. In the experiments, \(\alpha\), \(r\), and \(g\) are set to \(\frac{1}{2}\), \(2\), and \(2\), respectively [31]. Then, the parameters of the SCConv can be simplified as follows:  
\[P_{SCConv}=\frac{1}{8}[\,{C_{1}}^{2}+(k^{2}+4)C_{1}C_{2}\,] \tag{7}\]  
In a typical scenario, when \(k=3\), \(C_{1}=C_{2}=C\), the parameter counts are \(P_{Conv}=9C^{2}\) and \(P_{SCConv}=1.75C^{2}\)[31]. Consequently, SCConv significantly reduces the number of parameters to approximately \(\frac{1}{6}\) of that in standard convolution, highlighting its lightweight architecture.
## 3 Energy Efficiency Analysis
### Energy Efficiency of Two-Tier HCN  
Substituting Equations (10) and (12) into Equation (3), the energy efficiency of a two-tier HCN can be expressed as  
\[\eta_{EE}=\frac{A\lambda_{u}R_{1}+A\lambda_{u}R_{2}}{\lambda\lambda_{1}(N_{TR 1}\cdot P_{C1}+\theta_{1}P_{T1})+A\lambda_{2}(N_{TR2}\cdot P_{C2}+\theta_{2}P_ {T2})}=\frac{B\lambda_{u}R\left(\sum\limits_{k=1}^{2}\frac{\rho_{k}\lambda_{k} }{k+1\cdot 2\beta_{k}\lambda_{u}}\right)}{\lambda_{1}(N_{TR1}\cdot P_{C1}+ \theta_{1}P_{T1})+\lambda_{2}(N_{TR2}\cdot P_{C2}+\theta_{2}P_{T2})} \tag{13}\]  
where \(\eta_{EE}\) represents network EE. Actually, \(\gamma_{k}\), \(N_{TRk}\), \(\theta_{k}\), and \(P_{Ck}\) can be regarded as constants. From Equation (13), we can see that the network EE is only determined by the density of MU, the densities of BSs, and the transmit powers of BSs.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 2 Cubesat Specifications and Constraints  
The main motivation behind developing the CubeSat specification was to develop a standard interface with the launch vehicle to deploy nano-satellites without affecting the primary payload. The P-POD, the original CubeSat deployer, was designed to hold the CubeSats safely by interfacing over sliding rails at the corners [17]. However, the popularity of the CubeSats has grown so significantly that there are now programs to develop vehicles dedicated for CubeSat launches [18].  
Satellites comprise of various subsystems that are optimally interfaced to accomplish the space mission. The satellite is often treated as combination of payload and bus. The bus is designed to support the payload operation to achieve the mission objectives. The general subsystems of the bus include Mechanical System (MES), Electrical Power System (EPS), Communication System (COM), Attitude Determination and Control System (ADCS), and On-Board Computer (OBC). Other subsystems like propulsion and active thermal control may be required depending on the system requirements.
## 4 Results
### Optimal Credibility Threshold  
The ML model can autonomously adjust the weights of the evaluation factors. To reduce the risk of losing important information due to excessive removal of these factors, this study retains five evaluation factors (elevation, PD, SEI, RD, and SMC) identified through MIC nonlinear correlation analysis as inputs while using the slope state as the output. A training sample set was established using landslide and negative samples with different credibility thresholds, resulting in the development of FR-RF-coupled models (Models A-E). The predictive performance of these models was quantitatively assessed using the F1-score and AUC metrics (Table 3). We also calculated the ratio of landslides at each susceptibility level to the total number of landslides, denoted as A. Furthermore,  
Figure 8: Spatial credibility distribution of negative samples based on fuzzy membership and its validation with landslide samples. (**a**) Spatial distribution of negative sample credibility. (**b**) Distribution and cumulative proportion of landslides across credibility intervals. we determined the ratio of the area corresponding to each susceptibility level to the total area of the study region, referred to as B. Finally, we computed the landslide ratio (B/A), as detailed in Table 4.  
Table 3 indicates that as the credibility of the negative samples increases, the precision of the model increases and stabilizes. This suggests that in the early stages of enhancing the credibility of the negative samples, the model reduces the misclassification of the negative samples, thereby decreasing the FP. However, as the credibility of the negative samples continues to improve, the precision does not continue to improve indefinitely due to the limitations of the model. The recall rate initially increases and then decreases, indicating that in the early stages of enhancing the credibility of negative samples, the model reduces the misclassification of positive samples, which decreases the number of FN. However, as the credibility of the negative samples improves, to reduce the FP, the model increases the identification threshold of positive samples, thereby increasing the FN. Thus, establishing a suitable credibility threshold range is essential. This study employs the F1-score and AUC as evaluation metrics to identify an appropriate credibility threshold. Model C achieved the highest F1-score among the coupled models, indicating a lower omission rate (high recall) and a lower FP rate (high precision). Model C also exhibited the highest AUC value, demonstrating an effective balance between sensitivity and specificity, high global ranking performance, and strong discriminative ability. Further analysis indicates that when the credibility thresholds are set to 0.5-1 (Model A) and 0.6-1 (Model B), mixed negative and positive samples are obtained, resulting in reduced and unstable quality of negative samples. This makes it difficult for the model to distinguish between the samples, leading to poor global ranking and overall balancing ability. In contrast,
# Considering the Optimization Design of Urban Bus Network Scheduling
## 2 Synchronized Optimization Model for Network and Scheduling
### Parameter Definitions  
\(T_{ij}(X)\): Under the \(X\) bus network, the shortest travel time required for passengers to travel from Station \(i\) to Station \(j\) (s);  
Figure 1: Technical roadmap of Section contents.  
\(T_{w_{ij}}\): The waiting time for passengers traveling from site \(i\) to site \(j\) along their chosen shortest path (s);  
\(T_{S_{ij}}\): The bus's stopping time during its journey from site \(i\) to site \(j\) (s);  
\(T_{d_{ij}}\): Passenger's on-board time from site \(i\) to site \(j\) (s);  
\(T_{S_{ij}}\): The stopping time of a bus during its journey from station \(i\) to station \(j\) (s);  
\(T_{S}\): The average dwell time of a bus at a bus stop (s);  
\(T_{r}\): The operational time of route \(r\) (s);  
\(F\): Operating costs (s);  
\(M\): The cost of purchasing a bus (s);  
\(N\): The operating costs of each bus route (s);  
\(\overline{N}\): Cost per unit distance for a bus (s);  
\(\overline{M}\): The daily purchase cost per bus (s);  
\(X\): Bus network;  
\(Q_{ij}\): Traffic demand from station \(i\) to station \(j\);  
\(n\): The number of stops between stations \(i\) and j on the selected route;  
\(L_{ij}^{t}\): The length of the segment between stations \(i\) and \(j\) on route \(r\) (km);  
\(S\): The number of total stations represented;  
\(s_{ij}^{s}\): The number of stations between stations \(i\) and \(j\) on route \(r\);  
\(l_{min}\): The minimum length of a bus route (km);  
\(l_{max}\): The maximum length of a bus route (km);  
\(f_{min}\): The minimum departure interval for buses on a bus route;  
\(f_{r}\): Route \(r\)'s bus departure interval;  
\(f_{max}\): The maximum departure interval for buses on a bus route;  
\(L_{r}\): The length of route \(r\) (km);  
\(d_{r}\): The straight-line distance between the starting and ending points of route \(r\) (km);  
\(x\): The weighting coefficient between time cost and operating cost;  
\(\omega\): The conversion coefficient between time cost and operating cost;  
\(\delta_{w}\): The passenger waiting coefficient is due to passengers arriving evenly, and vehicle arrivals follow a Poisson distribution, \(\delta_{w}=0.5\);  
\(x_{ij}^{r}\): The value is \(1\) if the shortest path from site \(i\) to site \(j\) passes through route \(r\), and \(0\) if it does not;  
\(V\): The average speed of a bus (m/s);  
\(y_{ij}^{r}\): The segment \(l_{ij}\) between adjacent stations \(i\) and \(j\) on route \(r\) has a value of \(1\) when it is on the route \(r\), and a value of \(0\) when it is not;  
\(Q_{i}^{r}\): The station \(i\) in route \(r\).
## 3 Results
### Removal of TBC from Drinking Water by Filtration
#### 3.1.2 Removal of TBC from Drinking Water at a Flow Rate of 6 mL/min  
Six filter columns containing 32 g of granulated ODTMA-clay were operated simultaneously. Two types of micelle-clay granules were used: (1) ordinary complex, and (2) granules in which the amount of ODTMA added during the preparation of the granulated complex was increased by 15%. Before the start of this experiment, both types of granules were dried in a MW oven to remove possible extra humidity that might have accumulated during storage. Each sample was dried individually for 10 periods of 20 s, but the mass loss per sample of 40 g amounted to 0.18 g on average. The number of emerging TBC per mL after given volumes of continuous filtration by using fresh granules is given in Table 3. The average volume filtered per day was 8.64 L.  
Inspection of columns 2 and 3 of Table 3 indicates that the numbers of samples with 1000 or more TBC/mL were 3 and 1 in the case of ordinary (R) and enriched (E) columns, respectively. Hence, in accordance with Tables 1 and 2, the enriched complex yielded better results in filtration of bacteria.  
The average capacity of the column filters can be defined by dividing the number of liters of water passed with less than 1000 CFU per mL by the mass in grams of granulated material included in a column. For the enriched complex, the results in Table 3 gave a capacity of: (43.2 - 2/3 + 34.6) L/32 g = 2 L/g.
## 2 Results and Discussion
### Effect of Nanoparticle on Gene Expression
#### 2.5.1 FPPS1 Gene Expression Analysis Result  
The relative expression levels of the FPPS1 gene were analyzed in the leaves of _P. gravelens_ and _S. alba_ plants treated with 0.5 g/L of GW NPs and Mg NP/GW (Figure 10 and Table 5). The data revealed an overexpression of the FPPS1 gene in both _P. gravelens_ and _S. alba_ plants. The highest expression level (25.244 \(\pm\) 0.004) was observed in _S. alba_ plants treated with GW NPs (0.5 g/L), followed by 21.58 \(\pm\) 0.03 recorded in _P. gravelens_ plants treated with GW NPs (0.5 g/L). Additionally, a level of 13.4539 \(\pm\) 0.002 was observed in _S. alba_ plants treated with Mg NP/GW (0.5 g/L) compared to the control group.