esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Design
### Dual Band-Notched UWB Antenna
The length of the slot is computed using Equations (4) and (5) [10], and their optimal values are listed in Figure 1:0  
Figure 1:1  
where \(\lambda_{g}\) is the guided wavelength and is equal to \(\lambda_{g}=\frac{\lambda_{0}}{\sqrt{\epsilon_{eff}}}\)  
Figure 1:2 \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Ls** & **Ws** & **b1** & **b2** & **b3** & **b4** & **b5** & **b6** & **b7** & **b8** \\ \hline
21.5 & 13 & 5.6 & 3.8 & 7 & 7.1 & 2 & 4 & 3 & 2 \\ \hline \end{tabular}
\end{table} 
Table 1: Optimal values of the UWB antenna dimensions (mm). where \(\epsilon_{eff}\) is the effective dielectric constant, \(f_{r}\) is the resonant frequency in GHz, and \(c\) is the speed of light. \[L_{inverted\ U-shape\ slot}=2(n1)+n2=2(6)+6=18\ \mathrm{mm}\]  
\[f_{r\ U-shape\ slot}=\frac{c}{2L_{inverted\ U-shape\ slot}\sqrt{\epsilon_{eff}}}=5 \ \mathrm{GHz}\]  
\[L_{U-shape\ slot}=2(n3)+n4=2(5)+1.4=11.4\ \mathrm{mm}\]  
\[f_{r\ U-shape\ slot}=\frac{c}{2L_{U-shape\ slot}\sqrt{\epsilon_{eff}}}=8\ \mathrm{GHz}\]  
Figure 3: The projected dual band-notched UWB antenna: (**a**) structure (**b**) S-parameter curve. The center frequency and bandwidth of the notching frequency are controlled by altering the physical parameters of the slots. At the notch frequencies, the U-shape slots on the radiator attract the maximum current and neutralize the current by creating the same magnitude and out-of-phase currents within the slots, as illustrated in Figure 4. Therefore, nothing radiates from the radiator to the free space. Further, the effectiveness of the slots introduced for notching is witnessed by the impedance mismatching at the notched bands in the impedance curve (Z11), as in Figure 5, and the negative gain of \(-5.62\) dB and \(-5.72\) dB at the center frequency of the notched bands \(5\) GHz and \(8\) GHz, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline
**n1** & **n2** & **n3** & **n4** & **n5** & **n6** \\ \hline
6 & 6 & 5 & 1.4 & 0.25 & 0.8 \\ \hline \end{tabular}
\end{table} 
Table 2: Optimal values of the notch elements used in band-notched UWB antenna dimensions (mm). Figure 4: The current distribution of band-notch UWB antenna at (**a**) \(5\) GHz, (**b**) \(8\) GHz. Figure 5: Impedance curve of the UWB and UWB notch antenna.
# SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells
## 2 Methodology
### Deterioration Information Integrate for SOH Prediction
#### 2.3.1 Input Data Preparation  
The critical point of overall SOH prediction is to obtain the degradation trend of cells under interaction. This input information can be learned from the earlier degradation process of the battery cells. If \(S_{ori}\) represents the training dataset collected in the experiment, the normalized dataset can be expressed as \(S\). In order to provide a clear description, this paper describes the mathematical expressions under the condition that the health status indicators only include the SOH. The health status data of the cells is expressed as \(S_{c}=\left(X_{1},X_{2},\cdots,X_{n},\cdots,X_{N}\right)^{T}\), \(n\in N\), where \(N\) is the number of cells located in the battery pack. The health status data of the battery pack is expressed as \(S_{P}=\left(Y_{1},Y_{2},\cdots,Y_{m},\cdots,Y_{M}\right)^{T}\), \(m\in M\), where \(M\) represents the indicator types of the battery pack. The simplified indicator expression can be written as \(S_{P}=\left[Y\right]\). The health status data \(X_{n}\) of each battery cell can be expressed as a high-dimensional time series \(X_{n}=\left(\begin{array}{c}X_{1,n}\\ X_{2,n}\\ \vdots\\ X_{p,n}\end{array}\right)\)\(p\in P\) represents the types (dimensions) of the health status indicators. \(X_{p,n}=\left(x_{p,1}^{n},x_{p,2}^{n},\cdots,x_{p,t}^{n},\ldots,x_{p,T}^{n}\right)\) is the captured data for the \(p\)th indicator. \(t\in\left\{1,2,\ldots,T\right\}\) is the time moments and \(T\) is the collection time. Thus, the input data and the output data structure in this model were chaired. However, considering the generally poor prediction performance of a time series, a large error may have been introduced in the input link. The time label and the ambient temperature were used as auxiliary input data to help estimate the SOH.
* Time label: For the circumstance of no external impact, the degradation process of battery cells is consistent. The main deterioration of battery cells can be determined by taking the number of cycles as an estimation benchmark. Here, the time label input is expressed as \(L=\left(l_{1},l_{2},\ldots,l_{T}\right)\). Similarly, \(T\) is the collection time of the time series.  
Figure 5: Flow chart of the proposed method.  
* Ambient temperature: A slight fluctuation of the ambient temperature has an influence on the charge and discharge performance of battery cells. Consideration of the ambient temperature improves the prediction accuracy to a certain extent. Here, the environmental temperature input is expressed as \(D_{temp}=(d_{1},d_{2},\dots,d_{t},\dots,d_{T})\). Based on the above description, it can be concluded that the input of the cells prediction model was \(X^{c}_{input}=\left(\begin{array}{c}S_{c}\\ L\\ D_{temp}\end{array}\right)\), and the input of the pack prediction model was \(X^{p}_{input}=\left(\begin{array}{c}S_{p}\\ L\\ D_{temp}\end{array}\right)\). The input mode of the training data for the pack model is shown in Figure 6.
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 5 Evaluations
### Efficiency Analysis
#### 5.2.2 Theoretical Analysis on Communication Overhead  
The major communication overhead in system usage includes a blockchain service query, directory service query, new block creation, and data encryption/decryption. The estimated costs of different operations are listed in Table 2. Specifically, the Add Data operation (see Def. (1) and (3)) includes the time of inventory query (\(T_{Qd}\)), the time of inventory encryption (\(T_{E}\)), the time of digest computation (\(T_{D}\)), and the time of block generation (\(T_{B}\)). If a new chunk of data stream is added, the time of the last chunk query from the blockchain service is also included (\(T_{Qb}\)). In the Add Session operation (see Def. (2) and (4)), the overall latency is related to the number of sections (\(k\)) in a session. For each section, the cost includes the healthcare data entry retrieval (\(T_{Qd}\)) from the directory service as well as the local cost from the section encryption (\(T_{E}\)). For data retrieval (Figure 6), the overhead includes the time of session query (\(T_{Qd}\)), decryption for \(k\) sections (\(k\cdot T_{E^{\prime}}\)), and digest verification for all the retrieved data (\(k\times n\) (\(T_{Qb}+T_{D}\)), \(n\): the number of entries in a section). \(T_{Qb}\) and \(T_{B}\) depend on the network size and the selected protocol. For the directory service, \(T_{Qb}\) increases in \(O(log_{2}(m))\)[40] where \(m\) is the number of super-peers. For the blockchain service, \(T_{Qd}\) minimally involves five communication steps if BFT-SMaRt [39] is applied.  
Table 2 also lists the communication overhead in the existing blockchain-based data sharing schemes [32; 33; 34], which only uses asymmetric encryption for data sharing. Due to the lack of session management, they cannot effectively revoke data access. They also do not involve a directory service to manage mutable data, which results in reduced communication overhead. However, they will incur more overhead in description modification, which will be discussed in Section 5.3.
Fifteen triangular node elements were examined for deformation and stability. The comprehensive analysis integrated flow-deformation dynamics and safety considerations through a structured approach. Initially, the flow analysis-calibrated model was employed, ensuring accuracy and reliability in depicting the system's behavior. Subsequently, numeri  
Figure 11: Selected HWE for numerical SMS variation analysis for both high total rainfall (233.9 mm) and low total rainfall (101.6 mm) (1 kPa = 20.88 psf). Figure 12: Selected HWEs for numerical stability analysis for total rainfall of 119.38 mm with different rainfall durations (1 mm = 0.0032 ft and 1 m = 3.28 ft). cal comparisons were drawn between slope deformation and empirical data obtained from diverse field sites. This examination facilitated an understanding of the slope's behavior under field conditions. FEM analyses were conducted to scrutinize the slope's stability and safety across various rainfall scenarios. This allowed insights into the intricate interplay between flow dynamics and slope deformation and provided a robust framework for evaluating safety thresholds, thereby informing proactive failure management strategies. Figure 12 depicts the deformation analysis for the selected HWE slope ratio with varying total rainfall intensities at fully softened shear strength. During the coupled flow-deformation analysis performed for the individual HWEs (Figure 12), selected geometries and soil properties were determined, and various rainfall volumes were applied to monitor the slope FS for multiple scenarios. HWE FSs were analyzed for different geometries with different rainfall volumes and soil layer strength conditions (for instance, the top soil layer was considered to have peak, full, and residual soil strength parameters). A comprehensive 2D numerical analysis was conducted as part of the numerical investigation for the development of the EWP, with a particular emphasis on the variation in the FS with varying rainfall volumes. The analysis was performed on three different slope ratios on six field slopes. Various rainfall volume scenarios were modeled using projected data obtained from NOAA for the same region where the field slopes were located. A coupled flow-deformation analysis was implemented to evaluate the variation in slope FS, as presented in Figure 12. Based on the stability analysis, the changes in the slope FS were examined, and the results show a clear decrease in the FS as rainfall volume increases.
## 6 Conclusions  
In this study, in order to examine the proposed 3 DFPHPCM, the three-dimensional vector PE was used to provide the initial field strength for three-dimensional RT, allowing the aforementioned 3 DFPHPCM to be developed--taking into account that the initial ray produces different polarization effects on the wall and the ground--in order to analyze the effects of different scattering angles and the different number of scattered rays on prediction accuracy in the millimeter wave. In addition, measurements at 39 GHz were carried out in urban microcellular environments. The results showed that, when the number of scattered rays increases, the runtime increases accordingly. Meanwhile, the change in the scattering angle has no effect on the runtime. When the scattering angle was \(5^{\circ}\) and the number of scattered rays was nine, the RMSE and MAE between the theoretical calculation and experimental data were 4.1778 and 3.4927, respectively, and the runtime was 94.56 s, all of which reached the minimum values, as confirmed through multiple tests. Compared with traditional RT and the three-dimensional PE, the proposed 3 DFPHPCM had the smallest RMSE and MAE, higher computational accuracy, and the shortest runtime. The results presented herein provide a theoretical basis for the optimization of base station locations. Our proposed model is only suitable for urban microcellular environments, and we will conduct experiments for other scenarios in the future to optimize the proposed model.  
**Author Contributions:** Each author discussed the details of the manuscript. [PERSON], [PERSON] and [PERSON] designed and wrote the manuscript. [PERSON] implemented the proposed technique. [PERSON] and [PERSON] provided the experimental data. [PERSON] and [PERSON] collated the results of the experiment, [PERSON], [PERSON] and [PERSON] reviewed and revised the article. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China, grant number U2006207.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Different Method** & **RMSE (SP)** & **MAE (SP)** & **Running Time (s)** \\ \hline Three-dimensional PE & 12.0166 & 10.7764 & \(5.4892\times 10^{3}\) \\ Three-dimensional & & & \\ RT & 8.5142 & 7.3333 & 302.344 \\ SP\_N9\_A5 & 4.1778 & 3.4927 & 94.56 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RMSE and MAE when using the different methods.  
Figure 12: The path loss varies with distance when using different methods.  
**Acknowledgments:** The authors would like to thank all the editors and reviewers for their valuable comments that greatly improved the presentation of this paper. Moreover, the authors thank the people who have assisted with the experiment.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Performance Comparison with Other Existing Approaches
[PERSON], & [PERSON] [52] proposed a model for CCP using both classification and clustering algorithms. The fuzzy particle swarm optimization (FPSO) technique was applied for feature selection and the divergence kernel-based support vector machine (DKSVM) technique was used to categorize churn customers. After the classification step, the hybrid clustering-based kernel distance possibilistic fuzzy local information C-means (HKD-PFLICM) model was employed for cluster-based retention. Results of this study reveal that the proposed approach achieved a 76.51% accuracy rate on the Cell2 Cell dataset and outperform other existing algorithms.
# Lean Management Framework in Healthcare: Insights and Achievements on Hazardous Medical Waste
## 2 Literature Review on HMW Management  
### Technological Advances in HMW Treatment  
The management of HMW continues to present significant environmental and public health challenges, prompting a sustained research effort to develop advanced technologies and policy frameworks that align with CE principles. As global healthcare systems generate increasing quantities of medical waste [11; 12], especially in the aftermath of global health crises, the pressure to transition from linear disposal models to more sustainable and integrated systems has intensified. Within this context, the recent literature has increasingly focused on how innovation in treatment technologies, regulatory compliance, environmental assessment, and resource valorization can be harmonized under the CE paradigm.  
The incorporation of sustainability assessments such as life cycle assessment (LCA) has further strengthened the scientific basis for selecting and validating treatment technologies. As a decision-making tool, LCA facilitates the comparison of environmental burdens across the entire life cycle of disposal options--from collection to treatment and final disposal--helping identify the most efficient and least harmful alternatives [13].  
Concrete examples from Romania illustrate the practical application of these principles. For instance, Stericycle Romania has implemented high-temperature sterilization units that utilize hot air to disinfect medical waste while simultaneously reducing its volume [14]. These installations demonstrate the feasibility of minimizing water and energy use through internal heat recirculation mechanisms. Similarly, the Oradea sterilization station uses pressurized steam to decontaminate various medical waste categories while optimizing resource efficiency [15]. These cases underscore the potential of combining technological innovation with operational best practices to achieve CE goals at the local level.  
In response to these policy shifts, significant progress has been made in the development and application of treatment technologies capable of aligning with CE objectives. Among these, high-temperature processes such as pyrolysis and gasification have received particular attention for their dual capacity to neutralize hazardous components and generate energy or valuable secondary products. Pyrolysis, for example, has been demonstrated to break down medical plastics and organic matter into useful by-products such as syngas, pyrolytic oil, and char, which may be repurposed as fuels or additives [16]. Similarly, gasification enables the conversion of carbon-rich medical waste into a combustible gas mixture, offering a cleaner energy pathway with lower emissions compared to conventional incineration.  
Alongside these thermal methods, anaerobic digestion has emerged as a biological alternative. The integration of anaerobic digestion with thermal or sterilization methods can provide a complementary route to manage complex waste streams, further enhancing system resilience [17].
# Influence of Ozone on the Biochemical Composition of Birch Sap
## 3 Results  
### Ozone Concentration in Birch (Betula pendula) Sap  
The dispersion of ozone gas in the sap was investigated first. For comparison, changes in ozone concentration in water (10.4 \(\pm\) 0.7 \({}^{\circ}\)C), which had a pH of 7.67 \(\pm\) 0.07 before ozonation (\(n\) = 6), were also monitored. According to the study data (Figure 2), it can be seen that the distribution of ozone in water is more even and easier to distribute over the entire volume of water. After 5 min, the ozone concentration in the water was 0.52 \(\pm\) 0.028 mg L\({}^{-1}\), and no significant difference was observed between further ozonation intervals (Tukey HSD test, \(p\) < 0.05, \(n\) = 6). From 5 min onwards, the ozone concentration in water averaged out to 0.53 \(\pm\) 0.01 mg L\({}^{-1}\). The pH of ozonated water was found to rise to 8.16 \(\pm\) 0.07 (\(n\) = 6).  
The distribution of ozone concentration in the sap was not stable throughout the ozonation period. After 5 min, the ozone concentration in the sap was 0.087 \(\pm\) 0.009 mg L\({}^{-1}\), which is 83.3% less than in water. Fresh sap is not sterile, and ozone first reacts with microorganisms and thus decomposes faster and does not retain. The initial number of microorganisms is the highest and after most of the microorganisms are eliminated, the ozone saturates the liquid more easily. Therefore, there is a subsequent increase in ozone concentration in the sap. Later, i.e., after 10 min, it can also be seen that the ozone concentration in the sap is not as stable as in water. As is known, microorganisms are destroyed throughout the entire period of ozonation. Thus, it is assumed that the sap mixed, and the ozone reacted with the microorganisms unevenly during ozonation. It is also believedthat the unequal ozone concentration was influenced by the trace elements and other components in the sap. The highest ozone concentrations in the sap were recorded after 10 and 20 min: 1.173 \(\pm\) 0.09 mg L\({}^{-1}\) and 1.25 \(\pm\) 0.08 mg L\({}^{-1}\), respectively. There was no significant difference in ozone concentrations after 15, 25, and 30 min. The average ozone concentration in the sap treated with ozone for 10 min was 0.99 \(\pm\) 0.09 mg L\({}^{-1}\).
## 5 Comparisons and Experiment
### Proof of Concept Experiment and Discussion  
In the performed indoor experiment, three aspects related to the feasibility of trajectory generation are presented, which are (1) curvature constraint, (2) arrangement of obstacles, and (3) sensor detection error. First, while the maximum curvature constraint decreases, it is more difficult for robots to achieve primitive paths. Second, a robot needs to keep enough clearance from the obstacle to ensure the pursuit of all the three primitive paths possible. Third, we rely on low-cost sonar and infrared sensors to estimate the obstacle location. Furthermore, in order to guarantee obstacle avoidance, the obstacles are arranged so that only one obstacle is detected within a pre-specified look-ahead distance of the mobile robot's current location at a time. Table 1 lists the parameters of the robot platform related to the experiment.  
Figure 16: The two obstacle environment. Comparison of the pure pursuit path and weighting streamline path.  
Figure 17: A cluttered environment: upper plot = the paths, lower plots = the curvature. Black dashed path denotes the path generated by the method of superposition that collides with the obstacle.
## 5 Experimental Simulation Analysis
### Experimental Parameters  
In this paper, the UAV path planning is simulated in the Matlab environment and is run on a server with a 2.1 GHz CPU, 16.00 GB of RAM, and 64-bit operating system, and the effectiveness of the proposed technique is verified. Table 2 shows the parameters of PSO and its variant applications. The experimental environment model is an actual digital elevation model map acquired from the lidar sensor. A 3D terrain environment with x, y, and z coordinates is generated by augmenting a terrain structure area on Christmas Island, Australia. When setting up threats of radius r in these scenarios, the number and location of threats (indicated by way of red cylinders) are chosen according to the environment of different complexity, as shown in Table 3 and Figure 3.  
There are three algorithms in the experiment, specifically the PSO algorithm, the VAINDIWPSO algorithm, and the IC-VAINDIWPSO algorithm. The damping rate of the PSO algorithm is a constant value \(\omega_{damp}\), the maximum and minimum values of the inertia weight of the VAINDIWPSO algorithm and the IC-VAINDIWPSO algorithm are \(\omega_{\max}\) and \(\omega_{\min}\), respectively, and the value of \(\omega\) of the corresponding algorithm is acquired from Table 4. When the three algorithms draw the path curve in the environment, \(n=10\) path points are received between the coordinates of the starting point and the target point according to the flight regulation of the UAV to structure the flight path of the UAV. The coordinates of the starting point are (200,100,150), and the coordinates of the target point are (800,800,150).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **PSO Algorithm** & **VAINDIWPSO Algorithm** & **IC-VAINDIWPSO Algorithm** \\ \hline Velocity and position & Equations (10) and (11) & Equations (10) and (11) & Equations (21) and (11) \\ inertia weight & \(\omega(\mathbf{k}+1)=\omega(\mathbf{k})*\omega_{damp}\) & Equation (19) & Equation (19) \\ update velocity & no & Equation (20) & Equation (20) \\ Chaos initialization & no & no & yes \\ Logistic chaos map & no & no & Equation (22) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The difference between the three algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Parameter Notation** & **Parameter Value** \\ \hline learning factors & \(c_{1}=c_{2}\) & 1.5 \\ disturbed coefficients & \(\alpha\) & 0.2 \\ disturbed coefficients & \(\beta\) & 0.3 \\ control parameter of the chaotic state & \(\mu\) & \(\mu\in(0,4]\) \\ neighborhood radius & \(\gamma\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Threat Parameter** & **Threat Number** & **x** & **y** & **z** & **r** \\ \hline
1 & 900 & 100 & 250 & 80 \\
2 & 300 & 750 & 150 & 80 \\
3 & 700 & 550 & 150 & 70 \\
4 & 350 & 200 & 150 & 70 \\
5 & 500 & 350 & 150 & 80 \\
6 & 600 & 200 & 150 & 70 \\
7 & 400 & 500 & 100 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Threats parameter setting.