esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Metasurface Simulation and Experimental Measurement Results
### Metasurface Simulation and Results
The simulated results show that the pseudo Bessel beam using the proposed method can keep the energy concentrating on the propagation axis in the non-diffraction area, while the divergence of energy is obvious in the traditional method. So compared with previous methods, the proposed method shows its superiority. Based on the high-performance, off-axis pseudo Bessel beams we have generated, and the superposition of different off-axis pseudo Bessel beams according to Equation (8), multiple pseudo Bessel beams can also be generated simultaneously using the metasurface. We chose the dual pseudo Bessel beams as an example, and the propagation directions were set to be \(\theta_{1}=30^{\circ},\ \varphi_{1}=0^{\circ}\) and \(\theta_{2}=30^{\circ},\ \varphi_{2}=180^{\circ}\), respectively, while the angle \(\alpha=10^{\circ}\) for both of the two beams. The unit cell was still chosen to be the unit cell shown in Figure 2, and the metasurface has the whole dimension of 390 mm \(\times\) 390 mm \(\times\) 3 mm, while the coordinate of the feed horn in the coordinate system was still 0 mm, 0 mm, and 300 mm. Then, the required compensation phase distribution, the layout of the metasurface, and the simulated normalized E-field amplitude distribution of the generated dual pseudo Bessel beams on the xoz-plane are shown in Figure 5.  
Figure 4: The normalized E-field amplitude distributions of the off-axis pseudo Bessel beams and their corresponding compensation phase distributions when using (**a**) the proposed method; (**b**) the traditional method. Figure 3: The normalized E-field amplitude distributions of the off-axis pseudo Bessel beams with the propagation directions of (**a**) \(\theta_{k}=15^{\circ},\ \varphi_{k}=45^{\circ}\); (**b**) \(\theta_{k}=30^{\circ},\ \varphi_{k}=45^{\circ}\); (**c**) \(\theta_{k}=45^{\circ},\ \varphi_{k}=45^{\circ}\). It can be seen from Figure 5c that the dual pseudo Bessel beams have been generated by the metasurface simultaneously, and the propagation directions of them meet the predetermined settings. The energy is also concentrated along the propagation axes, and to further prove this, we can observe the normalized E-field amplitude distributions on two sets of observation planes that are, respectively, perpendicular to the two propagation directions. These observation planes with a size of 0.3 m \(\times\) 0.3 m are placed at different distances from the metasurface, and the distances are 400 mm, 600 mm, 800 mm, 1000 mm, and 1200 mm, respectively. As shown in Figure 6, it is obvious that the energy is concentrated on the center point of each observation plane, which is also the intersection of the propagation axis and the observation plane. In addition, the normalized E-field amplitudes of both the two beams decrease as the distances from the metasurface increase, while the normalized E-field amplitude distributions that on the same distance of the two beams are symmetrical. This phenomenon shows that the energy allocated to the two beams is basically the same, which is in line with our design.
## 3 Results
### Differential Analysis of Implant Deformation Data  
In case of PLS and HEF implant types the deformation values were within 1 mm from each other as it can be observed in Figure 14. Moreover, this difference decreased as the applied force value increased, reaching approximately 0.5 mm at maximum force value. If other factors of random nature are taken into consideration such as bone--implant bonding and measurement error, it can be argued that no clear-cut distinction exists between the performance of PLS and HEF. This effect is better observed in Figure 16, where a box plot of implant deformation data for the three implant types is presented.  
Figure 15: Relative error (in percentage) for each type of implant.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Implant Type** & **Mean** & **Mean (Absolute Values)** & **Standard Deviation** \\ \hline LP & 0.0228 & 0.0568 & 0.0423 \\ PLS & 0.154 & 0.0421 & 0.0307 \\ HEF & \(-\)0.158 & 0.0450 & 0.0345 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean and standard deviation of absolute error.  
From Figure 16 it appears that ID data interquartile range overlaps significantly for PLS and HEF types with a difference between mean values of less than 0.8 mm. It is important to establish if such a small difference between PLS and HEF types is statistically significant or it is caused by random factors, meaning that PLS and HEF perform similarly. This matter is of utmost importance for the selection of the appropriate implant device. If it can be confirmed that no significant difference exists between PLS and HEF at high values of the applied force, then the mechanical resistance criteria can be ignored in selecting one implant type over the other, leaving in the selection balance only relevant differences, such as the level of trauma the patient has to undergo for each implant device type.  
As the ANN model cannot provide an answer to this question, a machine learning classification algorithm was employed. The most suitable algorithm for this type of classification problem is Support Vector Machine.
## 4 Results and Discussion  
This section is dedicated to the preliminary analysis and subsequent discussion of the results obtained through environmental monitoring and the application of the LCA methodology to the selected case study. By interpreting the data collected during the spring and summer phases--considered in itself an original research output--the impact of local climatic conditions on the actual energy performance of the poultry tunnel under study is evaluated, identifying potential criticalities related to both thermal management and energy consumption. The comparative analysis between the scenario based on actual energy consumption data and the expedited scenario representing a potential retrofitting process (focused on photovoltaic system implementation) allows for an estimation of the reduction in ecological footprint and operational costs of the livestock facility. The findings provide replicable options for the effective energy efficiency improvement of similar livestock farming structures.
## 3 Results and Analysis
### Sensitivity to the Number of Col-Sample-Tree and Samples  
The optimized parameter \(col-sample-tree\) represents the portion of selecting features when building a tree in XGBoost. The choice of the \(col-sample-tree\) can be important for the variable importance estimation. As we showed above, the XGBoost algorithm was performed on the data set considering the input vectors (\(SNRrh\), \(Gr\)\(Pn\), and \(\theta\)) and the output vectors (\(permittivity\)). The figures illustrate the behavior of variable importance estimation for different \(col-sample-tree\) (0.5, 0.6) and samples \(n\). We tried to check if the variable importance was still stable when the parameters were changed in this case. The samples \(n\) were set to 2000 and 5000 (Figure 7) respectively.  
The optimization of parameters can help with differentiating the significant variable and increase the stability of the variable importance estimation. In this case, the value of \(SNRrh\) was the highest that was also observed in Figure 7a. When \(n=2000\), \(col-sample-tree\) increased from 0.5 to 0.6, \(SNRrh\) increased to a value of 0.6. The value of \(G_{r}\) decreased to 0.1 (10%). The value of \(\theta\) also decreased to nearly zero. When the samples \(n=5000\), the variables \(SNRrh\) showed the same behavior with the case of \(n=2000\), and the values of \(G_{r}\) and \(\theta\) (nearly zero) also decreased. If we increased the samples \(n\) from 2000 to 5000, \(Pn\) and \(\theta\) increased little, and the value of \(SNRrh\) decreased little, but the variable \(SNRrh\) still showed the highest value among the variables in those plots. Furthermore, the value of \(\theta\) is still the lowest value as mentioned before. In any case, the order of the variable importance was clear and quite stable in those four plots. This phenomenon also reported that the received SNR was predominate and the most sensitive parameter to the GNSS-R permittivity retrieval with the maximum contribution of 0.6 (60%). In addition, the value of the variable \(\theta\) (elevation angle) was much lower  
Figure 7: Variable importance sensitivity to \(col-sample-tree\) 0.5 (**a**) and 0.6 (**b**) for \(n=2000\) and 5000.  
than \(Pn\) (receiver noise) and \(G_{r}\) (receiver gain), which could be nearly zero indicating that the variable \(\theta\) was not very sensitive to the output (_permittivity_) in this case. It also confirmed that it was a less sensitive input parameter in permittivity retrieval of GNSS-R.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 4 PRC Modeling  
### PRC Data Collection  
The PRC data used in the present study were collected from the Javad Alpha GNSS receiver and Javad GrAnt antenna permanently installed at Inha University, South Korea. PRC modelling experiments were conducted on 23 April 2016. For the GPS, three-day PRC data prior to the experiment date were used. However for BDS, three-day data as well as data collected 7 and 14 days earlier than 23 April were utilized.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 3 Noise Model
### Internal Noise  
In practice, the data collected from the environment by sensors is not always clean. Numerous factors can influence measurement data. Specifically, this study examines the impact of thermal noise on the received data. Thermal noise is often described as Gaussian white noise with a zero mean and high variance \(\delta^{2}_{\text{noise}}\gg\delta^{2}_{\text{normal}}\). Adding Gaussian noise, \(S_{\text{noise}}\), causes the sensor output variance to increase significantly above the usual state. We express the receiver signal \(S_{\text{receive}}\) under the effect of noise as  
\[S_{\text{receive}}=S_{\text{normal}}+S_{\text{noise}}, \tag{21}\]  
where \(S_{\text{noise}}=\mathcal{N}(0,\delta^{2}_{\text{noise}})\). Based on [29], we set \(\delta^{2}_{\text{noise}}=0.1\) and \(\delta^{2}_{\text{normal}}=0.01\). In order to assess the impact of internal noise on sensor data, an experiment was conducted using real-world data and focusing on the presence of thermal noise. In this investigation, a publicly available dataset was used, including measurements of particulate matter (PM) 2.5 levels in Beijing City, China, spanning the time period from December 2013 to October 2022. Figure 2 shows normal data and the data affected by thermal noise when we run the experiment with the PM 2.5 dataset.  
Figure 2: Received signal at sensor nodes affected by noise.
# Inverse Solution Error Analysis and Correction of Beam Steering System Based on Risley Prisms
## 3 Causes and Correction of Inverse Solution Error
### Inverse Solution Error Correction Method  
According to the above analysis, both the prism rotation axis tilt and the incident light tilt will cause the deviation of the pointing field. Moreover, the TLS deformation caused by the prism rotation axis tilt is much smaller than its displacement. So, the inverse solution error mainly comes from the deviation of the pointing field. If we transform the light spot coordinates to the actual pointing field coordinate system for a solution, the main inverse solution error caused by the pointing-field deviation can be eliminated.  
However, as shown in Figure 8, the incident light tilt and the prism rotation axis tilt also cause the centers of the TLS to no longer coincide. This means that the points located on different TLS have different coordinate transformation values. It is necessary to first determine upon which TLS the target point is located. To solve this problem, a two-dimensional table needs to be established for table checking, but this would increase  
Figure 7: The inverse solution error caused by different systematic errors. (**a**) The incident light horizontally tilted by \(0.1^{\circ}\). (**b**) The incident light vertically tilted by \(0.1^{\circ}\). (**c**) The prism 1 rotation axis horizontally tilted by \(0.1^{\circ}\). (**d**) The prism 1 rotation axis vertically tilted by \(0.1^{\circ}\). (**e**) The prism 2 rotation axis horizontally tilted by \(0.1^{\circ}\). (**f**) The prism 2 rotation axis vertically tilted by \(0.1^{\circ}\).  
the amount of data and computation significantly, which is not in line with our original intention of using the two-step method.  
For this problem, by the aid of the error model, we obtain the coordinate transformation value that minimizes the overall inverse solution error of all points corresponding to one target beam deflection angle \(\Phi\) through the optimization method, and form a one-dimensional table. The procedures for the inverse solution are described below:  
**Step 1.** Find the coordinate transformation value of target position by table look-up. Then transform the target position to the actual pointing-field coordinate system by coordinate transformation. Finally, calculate the deflection angle and azimuth angle \(\left(\Phi^{\prime},\Theta^{\prime}\right)\) of the target position in the new coordinate system.  
**Step 2.** Let \(H_{I},\,V_{I},\ H_{R},\,V_{R}=0\) in the error model to eliminate the deviation of the pointing field. Then use the two-step method to calculate the prism rotation angle corresponding to \(\left(\Phi^{\prime},\Theta^{\prime}\right)\) under the current model.  
This method simultaneously calculates the coordinate transformation values of all points corresponding to a deflection angle, which can greatly reduce the amount of data and number of calculations.
## 3 Materials and Methods
### Photoreduction Setup  
The tests with the synthetized catalysts were performed in a special high-pressure photoreactor previously developed by our group and described elsewhere [45]. This reactor allows us to perform experiments in the liquid phase up to 20 bar and 100 \({}^{\circ}\)C. An AISI 316 stainless steel jacketed reactor (cylinder shaped) with an internal capacity of 1.7 L was used. A quartz sleeve able to withstand pressures up to more than 20 bar was placed in the middle of the reactor. Housed inside the quartz jacket was the UV lamp, consisting of two medium Hg-pressure UV bulbs, each one with a power of 125 W. The lamp irradiation was in the range of 254-365 nm, with the peak emission located at 365 nm. The irradiance was checked periodically using a photodadiometer (Delta OHM HD2102.2 equipped with a LP 471A-UVeff probe, producer Senseca Italy SrI (ex Delta OHM SrI), Caselle di Selvazzano (PD), Italy), active in the range of 250-400 nm and resulting in an average of 120 W/m\({}^{2}\). Sodium sulfite (Na\({}_{2}\)SO\({}_{3}\)) was used as a hole scavenger (HS). To test the catalysts, 1.2 L of water were loaded into the reactor, together with 1.66 g/L of Na\({}_{2}\)SO\({}_{3}\) and 31 mg/L of the catalyst dispersed in it [25; 46; 47; 48]. All of the photocatalytic tests were performed at a basic pH (pH = 14), 80 \({}^{\circ}\)C and 18 bar under intense and constant stirring to ensure the homogeneous dispersion of the catalyst. NaOH was used to obtain a basic environment. Each experiment lasted 6 h.  
The gaseous products were analyzed with a GC Agilent 7890A (producer Agilent Technologies, Santa Clara, CA, USA) equipped with two different columns, HP PlotQ and MS, respectively, used for the analyses of the gaseous product and permanent gases. The GC analysis was performed at 70 \({}^{\circ}\)C, He was used as a carrier gas and the quantification was performed by means of a TCD. The two columns were placed in a series, with the second (MS) being able to be bypassed and sent into isolation. At the beginning of the analysis, the two columns were connected in a series, which allowed the permanent gases (H\({}_{2}\), N\({}_{2}\), O\({}_{2}\), CH\({}_{4}\) and CO), which were the first to leave the HP PlotQ, to flow directly into the MS column. The MS column was then set into bypass mode, and the CO\({}_{2}\) coming out from the HP PlotQ was directly detected. After this, the bypass of the MS column was removed, and the permanent gases were quantified.  
The liquid products were evaluated through an HPLC analysis (Jasco, Tokyo, Japan), equipped with a PS-DVB sulphonated gel column (SepaChrom Benson Polymeric BP-OA BL0053 2000-0, Rho, Italy), a UV-Vis (Jasco 4070) set at 210 nm and a Refractive Index (RI) (Jasco 4030) detector. An aqueous H\({}_{2}\)SO\({}_{4}\) solution (0.05 mM) was used as an eluent, with a flowrate of 0.4 mL/min. The column was held in a bath at 45.5 \({}^{\circ}\)C. An iodometric titration was performed after the tests to evaluate the sodium sulfite conversion.
# Transient Friction Analysis of Pressure Waves Propagating in Power-Law Non-Newtonian Fluids
## 3 Results and Discussion
### Transient Friction at the Wall
#### 3.3.3 Influence of Frequency on the Wall Friction  
Figure 13 shows the variation of pulsating shear stress with frequency for different flow rates and power-law indexes. It can be seen from the figure that, regardless of how the flow rate and power-law index change, the pulsating shear stress decreases monotonically with an increase in frequency. The reason for this phenomenon is that as the pulsation frequency increases, the response amplitude of the fluid movement decreases, leading to a decrease in the shear rate. Therefore, the pulsation friction loss at the wall decreases. The ratio of the shear stress at the wall to the fluctuation of maximum velocity is referred to as the relative pulsating shear stress. Reorganizing Figure 13b yields the curves shown in Figure 14. The fluctuation of the maximum value of the flow velocity profile increases with the increase in the power-law index. Therefore, the variation pattern of friction loss with the power-law index in Figure 14 is opposite to that in Figure 13b. Moreover, the rate of decrease in velocity fluctuation with the increase in the frequency is greater than that of the shear stress, so the relative pulsating shear stress increases with the increase in frequency.  
Figure 12: Variation of pulsating shear stress at the wall for different power-law indexes. (**a**) \(Q=0.03\) m\({}^{3}\)/s; (**b**) \(P_{p}/P_{c}=0.2\).
## 5 Results
### Ablation and Sensitivity Analysis
#### 5.3.2 Sensitivity Analysis  
We conducted a sensitivity analysis on the key hyperparameters of CoTD-VAE. We established a strong baseline configuration (e.g., learning_rate = 5 \(\times\) 10\({}^{-4}\), \(\alpha\) = 0.5, all \(\beta\)s = 0.5) and then varied each parameter individually while keeping others fixed. The analysis revealed several key insights into the model's inner workings.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline
**CoTD-VAE** & **0.9981** & **1.0000** & 0.9878 & **0.9939** & **1.0000** \\ Random Forest & 0.9863 & 0.9234 & **0.9961** & 0.9584 & 0.9995 \\ LightGBM & 0.9819 & 0.9008 & 0.9949 & 0.9455 & 0.9993 \\ XGBoost & 0.9773 & 0.8804 & 0.9910 & 0.9324 & 0.9989 \\ LSTM & 0.9641 & 0.8214 & 0.9875 & 0.8968 & 0.9956 \\ Logistic Regression & 0.6002 & 0.2013 & 0.5162 & 0.2896 & 0.5983 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different models on the MIMIC-IV thrombus prediction task.  
Figure 4: Performance comparison of different models on the MIMIC-IV thrombus prediction task.  
As shown in Figure 5, the model's performance is notably sensitive to the KL divergence weights (\(\beta\)). A critical finding is that applying stronger regularization to the dynamic components (\(\beta_{\text{trend}}\) and \(\beta_{\text{event}}\)) while using weaker regularization for the static component consistently yields superior results. For example, increasing \(\beta_{\text{trend}}\) from 0.1 to 1.0 improved the F1-score from 0.9827 to 0.9947. Conversely, increasing \(\beta_{\text{static}}\) over the same range caused a significant drop in the F1-score, from 0.9921 to 0.9659. This outcome strongly supports our core hypothesis: an effective model should learn generalized, abstract representations for dynamic patterns (trend and event) while preserving the rich, specific information inherent in a patient's static baseline.  
Concurrently, the model exhibited considerable robustness to the temporal constraint weights. For instance, varying the trend smoothness weight \(\alpha\) from 0.1 to 1.0, and the event sparsity weights \(\gamma_{c}\) and \(\gamma_{p}\) across their tested ranges, resulted in only minor fluctuations in performance, with the F1-score remaining consistently high (above 0.99). This suggests that our proposed temporal constraints are beneficial and effective without requiring meticulous fine-tuning, which enhances the practical applicability and reliability of our model. Full results of the sensitivity analysis are provided in Appendix A.
# Game-Based Resource Allocation Mechanism in B5G HetNets with Incomplete Information
## 3 Distributed Resource Allocation Algorithm
### Leader Game
2:Input: Initial \(b_{j}(t)\), \(\forall j\in M=\{j|j=1,2,\ldots,m\}\)
3:Repeat:
4:do
5: Mobile users compute \(\dot{q}_{ij}(\tau)\), according to Equation (15), \(\forall i\in N\), \(\forall j\in M\)
6:\(\tau\leftarrow\tau+1\)
7: Mobile users obtain the adjusted strategy \(q_{ij}(\tau)=q_{ij}(\tau-1)+\lambda_{i}\dot{q}_{ij}(\tau-1)\),
8:while\(q_{ij}(\tau)\in\underset{q_{ij}}{\arg\max}\overline{u_{i}^{E}}(\dot{q}_{ij},\textbf{q}_{-i^{\prime}}\textbf{s}_{-i^{\prime}}\textbf{h}_{i},\textbf{h}_{ -i})\), \(\forall i\in N\), \(\forall j\in M\)
9: Networks obtain the updated strategy profile of users \(\textbf{q}=(\textbf{q}_{1},\textbf{q}_{2},\ldots,\textbf{q}_{i},\ldots, \textbf{q}_{n})\)
10:do
11: Networks compute \(\dot{b_{j}}(t)\) from Equations (19) and (20), \(\forall j\in M\)
12:\(t\gets t+1\)
13: Networks obtain the new pricing strategy \(b_{j}(t)=b_{j}(t-1)+\mu_{j}^{\cdot}\dot{b}_{j}(t-1)\),
14:while\(b_{j}\in\underset{b_{j}}{\arg\max}u_{j}^{L}\big{(}b_{j},\textbf{b}_{-j^{ \prime}}\textbf{q}\big{)}\), \(\forall j\in M\)
15:Until iterating to the convergence
16:Output:\(\textbf{q}_{i}^{\cdot}\) and \(\textbf{b}^{*}\)
```  
**Algorithm 1** Stackelger Game-Based Distributed Resource Allocation Algorithm
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 2 Proposed EWS-16T SRAM Cell
### Design and Operation of the EWS-16T Cell
#### 2.1.3 Write Operation  
To write a logic '1' into the cell, BL is pre-discharged to VDD, and BLB is precharged to GND. WL and WWL are set to VDD and GND, respectively, turning on all access transistors connected to the internal and storage nodes. As a result, nodes Q and S1 are charged through transistors N7 and P8, respectively, while nodes QB and S0 are discharged through transistors N8 and P7. As the voltages of Q and S1 increase, the pull-up paths for QB and S0 become weakened, facilitating the rapid discharge of QB. When the voltage of QB drops below the threshold voltage of the NMOS transistor, the pull-down transistor N6 turns off, further accelerating the charging of node Q. The storage node QB is weakly held at logic '1' by the surrounding NMOS transistors, while the internal node S1 is weakly held at logic '0' by the surrounding PMOS transistors. These weakly driven nodes can be easily overridden by the strongly driven access paths, thereby improving the write efficiency of the proposed EWS-16T cell.  
Figure 3: Transient waveforms of the EWS-16T cell during basic operations.