esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN
### Performance Evaluation and Results
For related QoS metrics, the results show that the nRF2401A incremental technique can achieve a longer stability period and network lifetime, minimizes average network energy consumption and delivers the highest number of successfully transmitted packets. It is therefore a promising technique in the support of long-time healthcare services. For high data rate transmission or heavy traffic load requirement applications, the two-relay based approach would achieve better performance. Moreover, we compare the attributes of the other state of art existing routing techniques with our proposed protocol as summarized in Table 8. Further study will be required in order to optimise energy efficient routing protocols and to ensure data packet IP compatibility, both of which are considered essential for the ongoing health of IoT applications. Figure 16: The propagation delay versus the network lifetime. Figure 15: The successfully transmitted packets versus the network lifetime. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Protocol** & **Features** & **Weaknesses** & **Performance** \\ \hline \multirow{4}{*}{Incremental relaying (this paper)} & \multirow{4}{*}{Cooperative routing} & \multirow{4}{*}{*}{Not suitable for heavy load traffic} & \multirow{4}{*}{*} *} * & * Extend network lifetime \\  & & & & Higher throughput \\  & & & & Minimize energy consumption \\  & & & Reduce propagation delay \\ \hline Energy-Balanced Rate & \multirow{4}{*}{Routing strategy based on the residual} & \multirow{4}{*}{low bandwidth utilization} & \multirow{4}{*}{Minimum energy consumption} \\ Assignment and Routing protocol (EBRAR) & \multirow{4}{*}{Intelligent transmission routes} & \multirow{4}{*}{High drop packets} & *} * \\ Balance payload on sensors & & & High delay \\ \hline Enhanced cooperative critical data transmission in emergency in static WABAN (EnTo-CEStat) & \multirow{4}{*}{Incremental relaying} & \multirow{4}{*}{Decrease stability period} & *} * \\ \cline{2-2} Link-Aware and Energy Efficient protocol for WBAN (LAEEBA) & & & & \\ \cline{2-2} Relay based routing protocol for in-body sensor networks & \multirow{4}{*}{Energy efficient} & \multirow{4}{*}{Path loss model is not accurate} & * \\ \cline{2-2} \cline{4-
## 3 Results and Discussion
### Predictions
#### 3.4.3 The Best Growth Stage for Yield Prediction  
When using scenarios with each SAR variable individually in conjunction with EVI for stage 3, a maximum difference of 18% was obtained compared to the scenario that included all SAR variables for stage 3 (Figure 12, Table 10). Utilizing only one SAR variable reduces operational costs and makes the modeling process more efficient compared to using all SAR variables combined with EVI. Moreover, for harvest 3, we achieved better results by including only one SAR variable in the model along with EVI, specifically the models used DPSVI, RVI, \(VH\), and entropy separately with EVI. Among these variables, the advantage of using DPSVI is greater, as it is derived from backscatter data and is available in GRD data. Additionally, the predicted yield maps using this configuration showed a good visual correlation with the actual yield maps (Figure 13). The prediction error map, which compares predicted yield with actual yield, shows higher occurrences of errors at the field boundaries (Figure 13).  
Although SAR data can enhance soybean yield predictions, there are some limitations that may impact their implementation. Processing SAR data requires specialized knowledge, as they provide information with different processing levels compared to  
Figure 12: R\({}^{2}\) values obtained for Stage 3 using scenarios with separate SAR variables in conjunction with EVI, compared to using all SAR variables combined with EVI and using only optical data (EVI).  
Figure 13: Visual comparison between actual yield maps and predicted yield using DPSVI in conjunction with EVI for Stage 3, using the Boxcar filter. The actual yield data were interpolated using ordinary kriging. The error maps represent the difference between the actual and predicted maps, showing positive and negative variations.  
optical sensors. Furthermore, the inherent noise in SAR images can influence the accuracy of predictions, especially in areas with homogeneous vegetation cover, such as soybean fields [59; 60]. When compared with previous studies, such as Hosseni [23], which utilized only entropy and alpha data, we observed that by employing a different polarimetric decomposition method and a distinct deep learning algorithm, their prediction accuracy surpassed ours (R\({}^{2}\): [PERSON]: 0.81 and ours: 0.76). However, the complexity and high computational costs of these approaches make their implementation less viable for end users. The Random Forest regression algorithm stands out for its ability to handle large datasets, resulting in accurate predictions with lower computational costs than deep learning algorithms. This makes it a popular choice for yield prediction in agricultural scenarios. Additionally, simpler regression algorithms can be adapted for use with SAR data and have the potential to further enhance prediction accuracy in field-scale agricultural areas. Therefore, it is essential for future research to address these limitations to strengthen the validity and applicability of findings in precision agriculture.
# Interconnected Government Services: An Approach toward Smart Government
## 3 The Methodology
The role of IM, as a software entity, is multiple:  
* To create a UCN that would be associated with the user identity;
* To create subqueries for the required service function (one subquery for each database needed, not giving the details of the service function required);
* To create IM under UCN, with the task of collecting data and calculating a specific service function based on them;
* To provide the local databases with UCN, the identity parameters of the requesting person, and data specification (subquery) (necessary to calculate the respective function);
* To pass the decision obtained under the UCN from IM to the government portal (competent to give the information to the potential user) and the database of users of the specific service function. Based on the received packet consisting of a subquery, an identity parameter, and a code number, each local database's task is to search and find the requested data, which is delivered with the code to IM at its address (without identity information). The role of the IM is to obtain the results of all searches under the given UCN, calculate the service function derived from the data for that UCN, and submit the result back to IM. The IM has two features (Figure 2). First, it enables every user to formulate his/her requests and obtain an almost immediate decision upon them. Secondly, implementing the IM does not reveal information about the user's identity simultaneously with the sensitive data related to him/her. It also enables the use of tools for producing different on-demand aggregated (statistical) reports for higher-level functions of the smart government (such as the average age, salary, sex, and location of the users of a particular service). This additional function creates the analytic database needed for defining governmental policies, such as measures of social and population policies in society. With the implementation of the new model, governments can conduct complex analyses. They can foresee the necessary  
Figure 1: The design of the proposed model. funding for different benefits within the state budget; stop potential abuses; and make adequate, efficient, and targeted social and population policies.
# Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling
## 4 Proposed Thermal Model
### Thermal Boundary Conditions  
The boundary conditions expressed in Equations (7) and (8) need to be imposed on surface particles at each time step. The identification of particles located at the clamped surfaces is trivial and can be done once at the initial configuration. However, the detection of particles belonging to the open surfaces of the workpiece cannot be done without utilizing a particular measure. To do so, we incorporate a free-surface detection algorithm initially formulated by [PERSON] et al. [5] in fluid flow problems.  
In this algorithm, the parameter \(\lambda\) is calculated as the minimum eigenvalue of the matrix \(\underline{\underline{A}}\) in Equation (14), also known as the Randles-Liberky [36] renormalization matrix. Theoretically, the values of \(\lambda\) vary from 0 to 1 for exterior and interior particles, respectively. Preliminary studies by the authors of [5] demonstrated that taking a \(\lambda=0.75\) is generally a proper choice as it isolates at least one layer of surface particles. Figure 2 confirms this by displaying one example frame of an orthogonal cutting simulation with SPH, where the surface detection algorithm is active in the code. It demonstrates how the surface particles required for thermal boundary conditions can be effectively identified using this algorithm. Another advantage of detecting surface particles is linked with the stability of the Laplacian formulation. That is, [PERSON]'s Laplacian operator Equation (13) is computed for interior particles (\(\lambda\geqslant 0.75\)), and the approximation of the heat equation for all surface particles falls back to [PERSON]'s method in Equation (4). By doing so, the singularity of correction matrices in FMFS can be avoided. This matter has been elaborated by [PERSON] et al. [22] in several 3D heat conduction benchmark tests.
## 4 Experiments
### Orientation Estimation
The traditional model relies on a single\(-\)point approach, and our hybrid model measures the distance variation between two vehicles. The outcomes of this experiment are contrasted and compared in Figure 13. The vehicle distance variation, taken about frame 200, is plotted on the vertical axis. The horizontal axis represents the corresponding frame numbers. The single\(-\)point model necessitates the assumption of a constant camera pitch angle. Consequently, when a vehicle experiences jolts or abrupt movements, this model produces unreasonable maximum or minimum vehicle distance estimates. This discrepancy is evidenced in Areas C and D of Figure 13. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Error** & \(\mathbf{W_{1}}\) & \(\mathbf{W_{2}}\) & \(\mathbf{W_{3}}\) & \(\mathbf{H_{1}}\) & \(\mathbf{H_{2}}\) & \(\mathbf{H_{3}}\) & \(\mathbf{L}\) \\ \hline \(<\)0.12 m & 0.940 & 0.921 & 0.937 & 0.972 & 0.953 & 0.942 & 0.932 \\ \(<\)0.8 m & 0.862 & 0.893 & 0.892 & 0.872 & 0.883 & 0.867 & 0.881 \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation of parameter optimization. Figure 11: Normalized ranging error along the horizontal axis and cumulative proportion on the vertical axis. Unlike the single\(-\)point model, our algorithm can dynamically select the most appropriate ranging model and reference edge points, guided by the vehicle's yaw angle and gradient information. This adaptability renders our algorithm less susceptible to the disruptive influences of vehicle turbulence or shadow interference, ultimately contributing to its superior robustness in real\(-\)world applications.
## 2 Monitoring Method of Bridge Dynamic Deformation
### Digital Photography Deformation Monitoring Technology Based on an Iso-Metric Virtual Plane
\(\Delta p_{x}^{df}\) and \(\Delta p_{z}^{df}\) denote parallax values in the horizontal and vertical directions of the corresponding deformation points on the photo, respectively. Note that \(\Delta p_{x}^{df}\) and \(\Delta p_{z}^{df}\) contain systematic errors. Expressions for \(\Delta p_{x}^{df}\) and \(\Delta p_{z}^{df}\) are depicted in Formula (14):  
\[ \begin{array}{c}\Delta p_{x}^{df}=\left(x_{2}^{df}-x_{1}^{df}\right)-\left( dx_{2}^{df}-dx_{1}^{df}\right)\\ \Delta p_{z}^{df}=\left(z_{2}^{df}-z_{1}^{df}\right)-\left(dz_{2}^{df}-dz_{1}^ {df}\right)\\ \end{array}  \tag{14}\]  
where \(a(x_{1}^{df},z_{1}^{df})\) and \(b(x_{2}^{df},z_{2}^{df})\) denote points with the same name in the zero-point photograph and follow-up photograph, respectively. \((dx_{1}^{df},dz_{1}^{df})\) and \((dx_{2}^{df},dz_{2}^{df})\) denote systematic errors in the zero-point photograph and follow-up photograph with the same deformation point. \((\Delta p_{x}^{df},\Delta p_{z}^{df})\) denotes the parallax value of the deformation point on the image plane. The displacement parallax method based on the isometric virtual plane is shown in Figure 3.  
To eliminate the parallax value occasioned by digital camera vibration and tripod movement, the study adopts a fixed and stationary reference plane to match the zero-point photograph and follow-up photograph. Subsequently, the parallax correction value of the corresponding deformation point can be expressed as follows:  
\[ \begin{array}{c}Adv\Delta p_{x}^{df}=\Delta p_{x}^{df}-\Delta p_{x}^{df/0}\\ Adv\Delta p_{z}^{df}=\Delta p_{z}^{df}-\Delta p_{z}^{df/0}\\ \end{array}  \tag{15}\]  
Figure 3: Displacement parallax method based on the isometric virtual plane. where \((Adv\Delta p_{x}^{diff},Adv\Delta p_{z}^{diff})\) denotes the correction of parallax in the barycentric coordinate system, and \((x^{diff},z^{diff})\) and \((\Delta p_{x}^{d/0\
u},\Delta p_{z}^{d/0\
u})\) denote the coordinates and systematic errors of the deformation point in the barycentric coordinate system, respectively. Subsequently, based on the reference plane, the displacement change value of the deformation point is obtained as follows:  
\[\left. \begin{array}{l}Adv\Delta x_{IVP}^{df}=Q\cdot Adv\Delta p_{x}^{df}\\ Adv\Delta z_{IVP}^{df}=Q\cdot Adv\Delta p_{z}^{df}\end{array} \right\} \tag{16}\]  
where \((Adv\Delta x_{IVP}^{df},Adv\Delta z_{IVP}^{df})\) denotes the displacement change value of the deformation point on the reference plane. The displacement parallax method based on the isometric virtual plane requires the photographic axis to be perpendicular to the monitored object surface to ensure that the solution result represents the movement direction of the monitored body.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
###### Abstract
Hence, this paper also presents a modified [PERSON]'s scheme that cancels MUI even if an inverted code is used. Then, we investigate the average light intensity and normalized fluctuation of multiplexed optical signals. The results show that the systems with inverted MPSCs provide a higher average light intensity and lower normalized fluctuation in comparison with the conventional systems. Furthermore, we evaluate the bit error rates (BERs) for the systems with inverted and non-inverted MPSCs by computer simulation.
## 3 Results
### Assessment Methods of Cognitive Workload in the Use of Rehabilitative Exoskeletons  
We retrieved three assessment methods of cognitive workload in the use of rehabilitative exoskeletons. Of these, two deploy quantitative and subjective measurements, and one deploys quantitative and objective types of measurement. These methods are described in detail within the following subsections.  
2.1 Quantitative and Subjective Assessment Methods of Cognitive Workload in the Use of Rehabilitative Exoskeletons  
_Subjective Workload Assessment Technique (SWAT)_. Reported by one of the selected studies [45], this instrument enables assessing cognitive workload during the use of a rehabilitative exoskeleton. It consists of three multiple-choice items for rating time load, mental effort load, and psychological stress load. SWAT has been reported with a clear definition of its underlying conceptual model, as well as with good values of validity and reliability indices, such as content validity, construct validity, and Cronbach's \(\alpha\). On this basis, this method was assigned a score of 14 out of 17 in our critical appraisal checklist.  
_NASA-Task Load Index (NASA-TLX)_. Reported by one of the selected studies [45], this popular and well-accredited instrument was deployed to evaluate individuals' perceptions of task-related workload and can be used to gauge the cognitive workload experienced by users of rehabilitative exoskeletons while performing the task of using such robotic devices. It consists of six items for rating mental demand, physical demand, temporal demand, performance, effort, and frustration on a 10-point Likert-type scale. NASA-TLX has been reported with a clear definition of its underlying conceptual model, as well as with good values of validity and reliability indices, such as content validity, construct validity, and Cronbach's \(\alpha\). On this basis, this method was assigned a score of 14 out of 17 in our critical appraisal checklist.  
Quantitative and Objective Assessment Methods of Cognitive Workload in the Use of Rehabilitative Exoskeletons  
_Event-Related Potential (ERPs)_. Reported by one of the selected studies [46], amplitude analysis of ERPs corresponds to an electroencephalography-based measurement of peaks in the brain activity and, as such, can be deployed to assess the cognitive workload of exoskeleton users while performing rehabilitative exercises. This technique has been reported with good construct validity and content validity, but no evidence for reliability was provided. On this basis, this method was assigned a score of 9 out of 17 in our critical appraisal checklist.
# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
## 5 Conclusions and Future Directions
Although the USA and Europe have been significant contributors, engaging more stakeholders from other parts of the world could further propel progress in this domain. In addition to government initiatives, a few private companies are also contributing to space situational awareness, such as LeoLabs from the USA and Silentium Defense from Australia [103; 104]. While LeoLabs uses phase-array technology to precisely track targets in space, the Maverick radar from Silentium Defense utilizes passive FM signals to detect and determine the orbit of debris. Moreover, advances in computational power and the refinement of machine learning and deep learning techniques have driven increasing interest in applying these methods to improve orbit prediction accuracy and resident space object classification [105; 106; 107; 108]. Ongoing advancements in radar and radio telescope systems, along with improvements in data analysis and machine learning methods, are crucial for advancing future space debris tracking. By addressing existing technical issues and promoting international collaboration, researchers can devise more effective and scalable strategies to mitigate the increasing threats posed by space debris accumulation. **Author Contributions:** Bibliographic research and analysis: B.A. ; draft preparation: B.A. ; writing, review, and editing: B.A., L.G., A.K. and M.M; supervision: L.G. and M.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** Not applicable. **Acknowledgments:** This paper and related research have been conducted during and with the support of the Italian National inter-university PhD program in Space Science and Technology. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Performance Evaluation Metrics  
We employed five popular metrics to compare regression models' effectiveness: the mean absolute percentage error (MAPE), mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R\({}^{2}\)). N samples were used to establish a regression model. The actual value of each sample is denoted as \(y_{i}\), and its predicted value (per the regression model) is denoted as \(\hat{y_{i}}\). The chosen evaluation metrics can be defined as follows:  
* MAPE is common in statistics and machine learning when evaluating the performance of a predictive model or estimator. As Equation (3) shows, MAPE quantifies the average size of forecasting errors, representing the magnitude of the error in percentage form. A lower MAPE value indicates relatively smaller relative errors and better model performance. \[\text{MAPE}=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{\hat{y_{i}}-y_{i}}{y_{i}} \right|\times 100\%\] (3)
* MSE (see Equation (4)) reflects the mean of the squared differences between projected and observed values. More weight is given to large errors, resulting in increased sensitivity to outliers. \[\text{MSE}=\frac{1}{N}\sum(y_{i}-\hat{y_{i}})^{2}\] (4)
* MAE (see Equation (5)) is used in regression analysis to measure the average magnitude of errors between predicted and actual values. It provides a straightforward method to assess a regression model's prediction accuracy. \[\text{MAE}=\frac{1}{N}\sum\lvert\hat{y_{i}}-y_{i}\rvert\] (5)
* RMSE (see Equation (6)) refers to disparities between a model's anticipated and reported values. This metric indicates the square root of the average of squared differences between predicted and actual values. \[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}}\] (6)
* R\({}^{2}\) (see Equation (7)) evaluates a regression model's goodness of fit. This score typically ranges from 0 to 1, with a higher score demonstrating a better fit (i.e., the model explains a larger proportion of the variance in the dependent variable). In Equation (7), \(\overline{y}\) is the mean actual value. Importantly, a high \(\text{R}^{2}\) score does not necessarily indicate that the model fits the data well; such a score may instead suggest overfitting, in that the model captures noise in the data rather than underlying patterns. Therefore, one must consider other factors and use additional evaluation metrics when measuring models' performance.  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{N}(y_{i }-\overline{y})^{2}} \tag{7}\]