esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Digital Modulation and Demodulation Circuits
### Receiver  
#### 5.3.1 Overview  
The receiver's digital circuit architecture is summarized in Figure 11 and composed as follows: An RF detection block checks the incoming signal from the LVDS comparator _rf_in_ for RF transients and enables the subsequent circuit blocks upon detection. These include the fully custom BPSK demodulator block as a synchronous receiver, which extracts a raw data stream formally corresponding to the recovered stream of _data_tx_ by the digital circuits detailed in the next subsection. A symbol clock is extracted from the raw data signal, dynamically restarting a symbol clock period at every signal transition of _data_async_out_. This clock is then used to sample _data_async_out_ at the middle of a symbol clock period into the preamble FIFO, which is analyzed for the presence of the expected preamble pattern. As soon as it is detected, both data FIFO and packet byte counter are activated, so that the subsequent 64 bytes of payload data are acquired by the RX packet handler. After a fixed packet size of 64 bytes, reception is terminated and the receiver stage is reset to its initial state.  
Figure 11: Block diagram as well as data and control signals of the fully digital BPSK demodulator.  
Figure 10: Signals and circuits of the custom NFC transmitter.
## 2 Methodology
### Determining Design Parameters Weights  
#### 2.2.1 DEMATEL Process  
DEMATEL is recognized as an effective analysis tool for identifying the cause-and-effect chain components within complex systems. This technique evaluates the interdependent relationships among factors, discerning critical ones through a visual structural model [27,28]. In the context of highway geometric design, especially in challenging conditions like mountainous regions, DEMATEL helps to understand the complex relationships between various design parameters. Based on previous research, [PERSON] et al. [6] utilized DEMATEL to assess the geometric design quality of highways. In this study, relationships between risk factors were established, the relative importance and net effect of risk factors were calculated, then normalized, and the weight of each risk factor was determined. This paper will adopt a similar methodology to comprehend the inter-relationships among different risk factors.  
* **Step 1: Formulation of Matrices** The process begins by creating three matrices: the direct-relation matrix (\(Z\)), the normalized direct-relation matrix (\(X\)), and the total-relation matrix (\(T\)) are formulated as shown in Equations (1)-(3). 1. **Direct-Relation Matrix (\(Z\)):** This matrix \(Z=\left(z_{ij}\right)_{n\times n}\) represents the direct influence of factor \(i\) on factor \(j\). Each element \(z_{ij}\) is set to 1 if there is an influence, and 0 if there is not. \[Z=\left(z_{ij}\right)_{n\times n}\] (1) 2. **Normalized Direct-Relation Matrix (\(X\)):** The direct-relation matrix is normalized to form \(X=\left(x_{ij}\right)_{n\times n}\), ensuring that all values are between 0 and 1. \[X=\left(x_{ij}\right)_{n\times n}=\frac{Z}{max\Sigma_{j=1}^{n}z_{ij}}\] (2) 3. **Total-Relation Matrix (\(T\)):** This matrix represents the overall influence and relationship among factors and captures both direct and indirect influences among factors. It is calculated using the formula: \[T=\left(t_{ij}\right)_{n\times n}=X(I-X)^{-1}\] (3) where (\(I\)) is the identity matrix or self-influencing factor, where diagonal elements equal to 1 and the remaining matrix is 0.
* Relative Importance (\(q_{j}\)) \[q_{i}=D_{i}+R_{j}\] (6)
- Net effect (\(n_{i}\)) \[n_{i}=D_{i}-R_{j}\] (7) The relative importance (\(q_{i}\)) indicates how important each factor is in relation to others, with a larger value indicating a closer relationship and higher importance. The net effect (\(n_{i}\)) classifies factors into causal (positive values) and resultant (negative values), where causal factors have more influence.
* **Step 3: Normalizing Relative Importance** To facilitate comparison, the relative importance (\(q_{i}\)) is normalized to derive the weight of each factor (\(w_{i}\)). The weight indicates the factor's impact on traffic safety, with higher weights indicating more significant effects. \[w_{i}=\frac{1}{1+e^{-q_{i}}}\] (8) The normalized weight (0 to 1) of each factor is then calculated as: \[w_{i}=\left(\frac{w_{i}}{\sum_{i=1}^{i}w_{i}}\right)\] (9)
## 5 Performance Evaluation
### Authentication between Entities
#### 5.2.3 Communication Cost  
The total communication cost of the proposed protocol and other related protocols during the Authentication and Key Agreement phase is analyzed and summarized in Table 10 and Figure 14 as follows:  
* 3-Tier AKA: In lines 2, 5, 6, 8, 10, and 12, there are a total of 10 AES encrypted messages, as shown in Figure 6.
* 5G-AKA: In lines 1 and 11, there are a total of six IDs involved during the communication process. During line 3, it includes a sequence number, a random number, two hash functions, and a key. Then, in line 5, there is a random number and two hash functions transferred. In line 7 and line 9 of Figure 10, there are a total of four hash functions transferred.
* 4G EPS-AKA: In the 4G EPS-AKA, it is similar to 5G-AKA, except there is no ID encryption process in the beginning, as shown in Figure 11.  
Figure 13: Signaling cost comparison.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol** & **Signaling Cost** \\ \hline
3-Tier AKA & 6 \\ \hline
5G-AKA [31] & 9 \\ \hline
4G EPS-AKA [31] & 8 \\ \hline TLS 1.3 [58] & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Signaling cost.  
* TLS 1.3: In line 1 of Figure 12, the client sends a ClientHello message to the server. Once the server receives the message, it sends a ServerHello message, certification, certification timestamp, and certification verify back to the client. After the client authenticates the server, in line 3 of Figure 12, it transfers its certification, certification timestamp, and certification verify back to the server.
* 3-Tier AKA only performs 10 AES encryption/decryption operations. Hence, the overall communication cost of 3-Tier AKA amounts to 1280 bits, significantly lower than that of other protocols.
## 3 Results
### Characterization of Spatial and Temporal Changes in Drivers  
#### 3.3.1 Characterization of Interannual Variation in Drivers  
The drivers selected for this study were precipitation, air temperature, radiation, vapor pressure deficit (VPD), leaf area index (LAI), evapotranspiration (ET).  
Figure 7(a) demonstrates a statistically significant increasing trend in annual precipitation, with a rate of 1.75 mm/year. The peak precipitation (892.76 mm) was recorded in 2020, while the minimum (741.49 mm) occurred in 2015, yielding a multi-year average of 832.23 mm.  
Figure 7(b) reveals a fluctuating but significantly volatile upward trend in annual mean temperature. The highest recorded value (\(-\)1.86 \({}^{\circ}\)C) occurred in 2021, while the lowest (\(-\)3.82 \({}^{\circ}\)C) was observed in 2012, with an overall warming trend of 0.04 \({}^{\circ}\)C/year and a long-term average of \(-\)3.02 \({}^{\circ}\)C.  
Figure 6: Interannual mean difference between GPP and MODIS GPP simulated by the Qinghai-Tibet Plateau model from 2001 to 2022.  
Figure 7: Rates of change and significance of interannual mean GPP on the Qinghai-Tibet Plateau, 2001–2022: (**a**) Spatial distribution of rates of change; (**b**) spatial distribution of significance.  
Figure 8c indicates a declining trend in annual radiation levels at a rate of \(-\)4.51 MJ/m\({}^{2}\)/year, albeit with considerable interannual variability. The peak radiation level (7531.72 MJ/m\({}^{2}\)) was recorded in 2015, contrasting with the minimum value (7243.17 MJ/m\({}^{2}\)) observed in 2021, resulting in a mean annual radiation of 7390.16 MJ/m\({}^{2}\).  
Figure 8d displays a modest increasing trend in annual radiation levels, though with substantial interannual variability. The maximum saturated vapor pressure difference (245.50 Pa) was observed in 2022, whereas the minimum (197.12 Pa) occurred in 2005, exhibiting an overall increasing rate of 0.861 Pa/year and a long-term mean of 217.96 Pa.  
Figure 8e illustrates that annual evapotranspiration ranges from 529.79 to 741.49 mm, exhibiting a fluctuating yet increasing trend with notable interannual variations. The time series reaches its minimum value (529.79 mm) in 2017 and peaks at 741.49 mm in 2015, demonstrating an overall increasing rate of 2.1 mm/year and a multi-year average of 487.96 mm.  
Figure 8f reveals that the annual leaf area index (LAI) ranges from 0.42 to 0.46, exhibiting a fluctuating but significantly increasing trend (\(p<0.05\)). The maximum LAI value (0.46) was recorded in 2020, while the minimum (0.42) occurred in 2008, showing an overall growth rate of 0.0013 year\({}^{-1}\) and a long-term mean of 0.44.  
Figure 8: Characterization of interannual temporal variation of drivers on the Qinghai-Tibet Plateau from 2001 to 2022: (**a**) precipitation; (**b**) temperature; (**c**) radiation; (**d**) vapor pressure deficit; (**e**) evapotranspiration; (**f**) leaf area index.
## 1 Introduction
The right and left Porion points are challenging to locate, have poor reproducibility in CBCT scans, are positioned in curved areas, and are, frequently, asymmetric; for these reasons, they might not be shown due to a limited field of view or when the patient's head is tilted [12; 27; 28; 29]. Given the technical difficulties in identifying Porion for defining the FHP, a new reference plane is presented, the Acta plane, which stands for Academisch Centrum Tandhelekunde Amsterdam. Instead of Porions, this plane is defined by reference points more central to the skull. Therefore, the aim of this study was to determine whether the newly defined Acta plane affects the examiner's reliability when locating anatomical landmarks and performing measurements. Cone Beam Computed Tomography (CBCT) has transformed imaging with superior 3D skull images, offering enhanced resolution. It competently analyzes dentofacial changes, comparable to or surpassing 2D cephalometrics. However, the precision of landmarks depends on factors like anatomy, examiner expertise, and scan orientation. CBCT addresses challenges in 2D cephalometrics using virtual orientation, yet it struggles to reproduce identical skull positioning. The Acta plane, introduced due to difficulties with the traditional Frankfort Horizontal Plane (FHP) relying on hard-to-locate Porion points, aims to offer an alternative. Given the flexibility of the new CBCT scans with adjustable software to quantify the desired field of view, opting for the Acta plane over the Frankfort Horizontal Plane or other reference planes relying on more external landmarks is expected to significantly decrease radiation exposure. These adjustments can be seamlessly integrated into routine clinical procedures.
## 2 System Model and Formulation  
Inspired by the study by [30], this study sketches a green-cooperative IoT wireless network, as shown in Figure 1 consisting of an IoT hub \(S\), a coupled IoT relay, and the number of \(N\) IoT devices. Assuming all IoT nodes have been solar-powered enough for their operation and equipped with a single antenna. The study employed coupled IoT relays \(R_{1}\) and \(R_{2}\) to assist IoT devices. The coupled IoT relays adopted the DF protocol for forwarding superimposed signals to IoT devices. To prolong IoT network online time, the study employed the SWIPT technique with the PS protocol to transmit simultaneously information for information processing and energy for EH at coupled IoT relays. Furthermore, the study proposed an RS strategy to select the relay for the forwarding signal. The selected relay used RF-EH for forwarding the signal instead of its own energy. However, the IoT network as shown in Figure 1 had an eavesdropper \(E\) that eavesdropped the IoT relay \(R_{1}\) or \(R_{2}\) by wiretapping channel \(h_{R_{1},E}\) for odd transmission block or \(h_{R_{2},E}\) for even transmission block since the broadcast function of RF. Therefore, the study proposed the rest IoT relay to transmit the jamming signals to the eavesdropper \(E\) using RF-EH instead of its own energy. Table 1 shows a comparison of the works in this study, in contrast to previous studies. Please note that the IoT devices \(D_{n}\) for \(n\in N\) had distances from IoT hub \(S\) as ordered \(d_{S,D_{1}}<\ldots<d_{S,D_{N}}\).
## 3 Results and Discussion
### Characterization of the 3D-Printed Bioceramics
Indeed, the porosity assessed by BET/PSD experiments (3-200 nm) can be used to gain information on \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Ra** & **SSA** & **Total Pore** & **Compressive** \\  & **(nm)** & **(m\({}^{2}\)/g)** & **Volume (mL/g)** & **Strength (MPa)** \\ \hline HPMC\_5 & 464 \(\pm\) 152 & 5.6 & 0.035 & 0.55 \(\pm\) 0.16 \\ HPMC\_10 & 212 \(\pm\) 67 & 3.7 & 0.017 & 0.51 \(\pm\) 0.11 \\ CMC\_5 & 360 \(\pm\) 72 & 3.5 & 0.016 & 0.40 \(\pm\) 0.06 \\ CMC\_10 & 180 \(\pm\) 94 & 2.1 & 0.013 & 0.38 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Roughness Ra, SSA, total pore volume, and results of the compressive strength tests performed on the 3D-printed bioceramics. The reported arithmetic mean roughness values (Ra) are the average of five maps acquired on different regions of the samples \(\pm\) standard deviation. The compressive strength values are the average of three tests \(\pm\) standard deviation. Figure 6: FE-SEM images of the cross-sections of the printed bioceramics HPMC_5 **(A)**, HPMC_10 (B), CMC_5 **(C)**, and CMC_10 **(D)**. the structure's phases, as it is closely related to the complex network of pores characteristic of dried cementitious materials [24]. As anticipated, cellulose derivatives are considered responsible for the regulation of water in the cementitious pastes, which plays a major role in the formation of the hydrated reaction products. According to results gained from confocal Raman experiments, when using 10% of HPMC or CMC, only Mg\({}_{2}\)SiO\({}_{4}\) forms. Considering that all samples were prepared with an Mg/Si molar ratio of 2/1, we can assume that in the presence of abundant cellulose derivatives, we achieved very efficient reaction conditions, consuming almost all the reactants and thus resulting in smaller SSA and total pore volumes. At the same time, the samples prepared with 5 wt% of HPMC/CMC show compressive strengths very similar to their counterparts containing 10 wt% of cellulose. This could be explained considering that the mechanical properties are related to the total porosity (from nanoscale pores up to millimetric pores), and the strength is highly affected by large pores [66]. Nonetheless, all these values could be compatible with biological applications as cancellous bone scaffolds [67].
## 4 Fabrication, Measurement, and Results  
### Fabrication  
The overall structure of the proposed pattern reconfigurable antenna and RF MEMS switches was fabricated on a high resistivity silicon substrate with a thickness of 400 \(\upmu\)m and a dielectric constant of 11.9. The SiO\({}_{2}\) layer, which acts as an insulating layer, with a thickness of 0.3 \(\upmu\)m is formed by thermal oxidation. Then, a 0.2-\(\upmu\)m-thick layer of Al is deposited and patterned to define DC bias pads afterward and to form coplanar waveguide (CPW) transmission lines. Next, thin SiAl (approximately 0.05 \(\upmu\)m) is patterned by lifting off to form the bias lines after deposition. A Si\({}_{3}\)N\({}_{4}\) layer with a thickness of 1500 A is patterned on the top of the electrode and bias lines by a plasma-enhanced chemical vapor deposition (PECVD) process. A 1.5-\(\upmu\)m-thick layer of Al, which acts as an anchor, is evaporated. Polyimide as the sacrificial layer was cut down by a chemical mechanical polishing (CMP) process. The beam uses 0.6 \(\upmu\)m of SiAl. Finally, the wafer is released in a plasma dryer to avoid the collapse of the membrane. The photographs of the proposed antenna and its close-up are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Switch State** & **(0,0)** & **(0,1)** & **(1,0)** & **(1,1)** \\ \hline \((Z_{2},Z_{3})\)\(\Omega\) & (\(\prime\)98, \(\prime\)98) & (\(\prime\)97.5, \(\prime\)30.7) & (\(\prime\)30.7, \(\prime\)97.5) & (\(\prime\)30.7, \(\prime\)30.7) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The four kinds of combination for driven patch load conditions.  
Figure 5: The simulated gain results of the proposed RF MEMS pattern reconfigurable antenna.  
Figure 6: The photograph of the proposed pattern reconfigurable antenna taken by a microscope. (**a**) The antenna with testing holder. (**b**) Close-up of the antenna.
## 3 Results and Discussion
### Seasonal Variation of the Aerosol Layer over Saudi Arabia
Moreover, the seasonal variations in regions II and III were significantly higher than those of region I (Figure 13), seemingly because dust intensity in the aforementioned regions remained unchanged at nighttime during spring and summer, while the human activities significantly decreased. This resulted in the observation of the accumulation of more large particles of sand and dust in the bottom aerosol, which might also be related to the passage time of the satellite [42]. Figure 16: Seasonal variation in the optical and physical properties of aerosol layer over Saudi Arabia (regions I, II, and III) from 2007 to 2019. (**a**,**d**) represent PAOD\({}_{1}\), (**b**,**e**) represent VDR\({}_{1}\), and (**c**,**f**) represent CR\({}_{1}\). Figure 15: Seasonal variation in the optical and physical properties of aerosol layer over Saudi (regions I, II, and III) from 2007 to 2019. (**a**,**d**) represent N, (**b**,**e**) represent AOD\({}_{1}\), and (**c**,**f**) represent HTT\({}_{1}\).
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 5 Experiments
### Tiny ImageNet Classification
The results show that ShuffleNetV2\(\_10\times+\) FSRF performs well at \(\sim\)35.6 top-1 error level with comparable complexity than ShuffleNetV2\(\_10\times\), ShuffleNetV2\(\_10\times+\) SE and ShuffleNetV2\(\_10\times+\) SK. Notably, we note that MobileNetV2\(\_0.75\times+\) FSRF outperforms MobileNetV2\(\_1.0\times\) by above 0.66% accuracy, although MobileNetV2\(\_1.0\times\) is 25.7% larger in parameter. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MobileNetV2** & **\#P** & **GFLOPs** & **top-1 err. (\%)** \\ \hline \(0.75\times(\text{our impl.})\) & 1.61M & 0.22 & 41.31 \\ \(0.75\times+\)SE(our impl.) & 7.35M & 1.61 & 40.68 \\ \(0.75\times+\)SK(our impl.) & 8.01M & 2.13 & 39.83 \\ \(0.75\times+\)FSRF(ours) & 8.89M & 2.85 & 39.07 \\ \(1.0\times(\text{our impl.})\) & 2.48M & 0.31 & 39.73 \\ \(1.0\times+\)SE(our impl.) & 12.63M & 2.67 & 39.22 \\ \(1.0\times+\)SK(our impl.) & 13.52M & 3.54 & 38.43 \\ \(1.0\times+\)FSRF(ours) & 14.76M & 4.36 & 37.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Single 224 \(\times\) 224 cropped top-1 error rates (%) on the Tiny ImageNet validation set and complexity comparisons, with all models based on the corresponding MobileNetV2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ShuffleNetV2** & **\#P** & **MFLOPs** & **top-1 err. (\%)** \\ \hline \(0.5\times(\text{our impl.})\) & 1.47M & 37.62 & 47.82 \\ \(0.5\times+\)SE(our impl.) & 1.59M & 36.25 & 46.51 \\ \(0.5\times+\)SK(our impl.) & 1.54M & 38.78 & 45.01 \\ \(0.5\times+\)FSRF(ours) & 1.87M & 40.41 & 43.33 \\ \(1.0\times(\text{our impl.})\) & 2.35M & 142.35 & 40.17 \\ \(1.0\times+\)SE(our impl.) & 2.72M & 140.93 & 38.64 \\ \(1.0\times+\)SK(our impl.) & 2.67M & 143.66 & 37.41 \\ \(1.0\times+\)FSRF(ours) & 3.23M & 144.57 & 35.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Single 224 \(\times\) 224 cropped top-1 error rates (%) on the Tiny ImageNet validation set and complexity comparisons, with all models based on the corresponding ShuffleNetV2. Figure 6: Relationship between performance and the number of parameters: FSRFNet compared with the corresponding lightweight model ShuffleNetV2.
## 4 Results and Discussion
### Results of Estimation and Verification of the Power Model
The F statistic, given the truth of the null hypothesis, has an F Snedecor distribution with 1 degree of freedom of the numerator and 27 degrees of freedom of the denominator. The empirical value of the statistic is F = 254.865, and the corresponding critical level of significance F = 2.821 \(\times\) 10\({}^{-15}\) is less than the accepted significance level \(\alpha\) = 0.05. We reject hypothesis H\({}_{0}\) in favor of H\({}_{1}\). There is no basis for rejecting the hypothesis that brake pad wear depends on at least one of the variables X\({}_{2}\). For the regression model coefficient \(\alpha_{2}\), we test the hypothesis of its significance. We verify it with a statistic of \(t\)-Student's distribution with 27 degrees of freedom. The empirical values of the \(t\)-Student's statistic and the corresponding values of the critical level of significance (\(p\)-value) are shown in Table 9. The coefficients of the model are significantly different from zero (the values of the critical level of significance are less than the accepted level of significance \(\alpha\) = 0.05). There are no grounds for rejecting the hypothesis that the coefficients of the tested model are significantly different from zero. The current structure \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{R^{2}}\) & \(\widetilde{\mathbf{R^{2}}}\) & **Std Error** & **Observation** & \\ \hline
**0.904** & **0.901** & **0.043** & **29** & \\ \hline  & **Coefficients** & **Std Error** & **t Stat** & **\(p\) Value** & \\ \hline Intercept & 9.075 & 0.132 & 68.877 & 7.01 \(\times\) 10\({}^{-32}\) & \\ X\({}_{2}\) & 0.362 & 0.023 & 15.964 & 2.82 \(\times\) 10\({}^{-15}\) & \\ \hline ANOVA & df & SS & MS & F & Significance F \\ \hline Regression & 1 & 0.466 & 0.466 & 254.865 & 2.821 \(\times\) 10\({}^{-15}\) \\ Residual & 27 & 0.049 & 0.002 & & \\ Total & 28 & 0.516 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Multiple regression results for the new power model. of the quasi-linear power model is correct. After simple transformations, we obtain a power model in the form (24) and geometric interpretation in Figure 4:  
\[\hat{y}_{3}=8725.971\cdot\chi_{2}^{0.362} \tag{24}\]