esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 2 System Models for RIS-Assisted Systems
### MIMO Systems
#### 2.2.1 Cell-Free Communication System
\tag{9}\]  
* Channel Model of Cell-Free Communication System  
In [80], the BSs and RISs are supplied with a uniform linear array (ULA), and the physical channels defining the geometric arrangement are  
\[F_{mj}=\sqrt{\frac{{\rm N}N_{m}}{P_{f,m,j}}}\sum\limits_{p=1}^{P_{f,m,j}}\beta _{m,j}e_{L}\Big{(}\frac{2\pi d}{\lambda}\sin\theta_{m,j,p}\Big{)}\times a_{D}^ {H}\Big{(}\frac{2\pi d}{\lambda}\text{sin}\ \varphi_{m,j,p}\Big{)}, \tag{10}\]  
\[h_{jk}=\sqrt{\frac{{\rm N}}{P_{h,jk}}}\sum\limits_{k=1}^{P_{h,k,k}}\gamma_{j, k,b}a_{L}\Big{(}\frac{2\pi d}{\lambda}\text{sin}\ \varphi_{j,k,b}\Big{)}, \tag{11}\]  
where \(P_{f,m,n}\) and \(P_{h,j,k}\) are the number of paths of \(F_{mj}\) and \(h_{jk}\), accordingly, \(\beta_{m,j,p}\) and \(\gamma_{j,k,b}\) are the complex gains of the \(p\)-th and \(b\)-th paths of the two channels, accordingly. Additionally, \(\varphi_{m,j,p}\) and \(\varphi_{j,k,b}\) are AoDs from the \(m\)-th BS to the \(j\)-th RIS and from the \(j\)-th RIS to the \(k\)-th UE, accordingly. \(\theta_{m,j,p}\) is the AoA from the \(m\)-th BS to the \(j\)-th RIS. \(d_{BS}=d_{RIS}=d\) is the antenna inter-element distancing. \(a_{D}(\cdot)\) and \(a_{L}(\cdot)\) are ULA steering vectors of BSs and RISs, accordingly. Not losing the generality, we have  
\[a(\theta)=\sqrt{\frac{1}{\rm L}}\Big{[}1,\ldots,e^{i\theta},\ldots,e^{i(N-1) \theta}\Big{]}^{T}, \tag{12}\]  
where L is the number of antenna elements.
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 5 Conclusions
Although our model could successfully evaluate the visibility of images accurately, it still had some limitations. In terms of calculation time, it was quite time-consumingto carry out the preprocessing and gray level averaging of the whole dataset to obtain the coordinates of the effective subregions. However, the preprocessing stage is only necessary during the initialization and pre-tuning stage. While the new images are taken by CCTV and added into dataset, the SVR models will be updated. In the case of increased application noise, this would affect the choice of the effective area. In addition, the fusion of the sub-region visibility assessment model proposed in this paper can reduce the effect of increased application noise to some extent. Another limitation of the proposed method in this paper is that it is applicable to daytime images only. Further modifications of the algorithm are needed when it is applied to night-time images. These limitations will be further investigated in the future. In addition, in our future research, we will also focus on optimizing the selection of effective subregions. So as to minimize the number of subregions in a particular image dataset while the visibility estimation accuracy can be maintained at reasonable level. J.L. : conceived and designed the algorithms, formal analysis, performed the experiment, managed projects, write articles and draft original manuscripts. W.L.L. : conceived and designed the algorithms, analyzed the data, raised funding, managed projects, write articles and draft original manuscripts. H.F.: reviewed the manuscript. H.S.H.C. : analyzed the data and revised the manuscript. All authors have read and agreed to the published version of the manuscript. This research received external funding from Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference No. : UGC/FDS13/E02/18). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data are not publicly available as the restricted data are obtained from Hong Kong Observatory. The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference No. : UGC/FDS13/E02/18). The authors would also like to thank Hong Kong Observatory for the providing the weather photos and visibility reports as database. The authors declare no conflict of interest.
## 2 Prerequisites
### The Information Bottleneck Method  
The information bottleneck method is a generic unsupervised clustering framework from the field of machine learning [21; 22]. The so-called information bottleneck setup is visualized in Figure 2. Having defined a random variable \(X\) termed the _relevant random variable_, the principal aim of the information bottleneck method is to extract all _relevant_ information contained in an observation \(Y\), by squeezing \(Y\) through a compact bottleneck represented by the _compression variable_\(T\). More precisely, the information bottleneck method attempts to design a compression mapping \(p(t|y)\) which maps an observation \(Y\) onto a compact compression variable \(T\).  
Typically, \(|\mathcal{Y}|\gg|\mathcal{T}|\). The key idea is to design \(p(t|y)\) such that the maximum possible amount of mutual information \(\mathrm{I}(T;X)\leq\mathrm{I}(X;Y)\) is preserved under a constraint for the cardinality \(|\mathcal{T}|\). Several information bottleneck algorithms exist, intended to find a locally-optimum compression mapping \(p(t|y)\)[22]. All information bottleneck algorithms input a joint distribution \(p(x,y)\) and yield the compression mapping \(p(t|y)\) and a joint distribution \(p(x,t)=p(x|t)p(t)\). Typically, in the context of signal processing, one assumes a deterministic input-output mapping (e.g., at a quantizer). Hence, it is often sufficient to consider only deterministic mappings \(p(t|y)\) (i.e., \(p(t|y)\in\{0,1\}\,\forall(t,y)\)). Such mappings can be interpreted as look-up tables mapping \(y\) onto \(t\). More details related to the information bottleneck method can be found in [21; 22]. Throughout the paper, we use the terms mapping and clustering synonymously. A more detailed overview and comparison of information bottleneck algorithms can be found in [22; 23]. Please note that when performing a mapping \(p(t|y)\), any physical meaning contained in \(Y\) is lost (i.e., the compression variable \(T\) on its own has no distinct meaning and the event space \(\mathcal{T}\) can be chosen arbitrarily). Hence, a coupling between the relevant variable \(X\) and \(T\) is needed, given by \(p(x|t)\). We sometimes refer to this distribution as the _meaning_ of a cluster index \(t\) with respect to \(x\).
## 2 Materials and Methods
### Experimental Site and Sampling  
The experiment was conducted at China Agricultural University's Shangzhuang experimental station (40\({}^{\circ}\)14\({}^{\prime}\) N, 116\({}^{\circ}\)20\({}^{\prime}\) E, 49 m altitude) for 3 consecutive vintages(2018-2020). In this field experiment, 3 rootstocks (1103P, SO\({}_{4}\), 5A) were used to graft Cabernet Sauvignon (CS) clone 685. These vines were spaced at 2.5 \(\times\) 1.2 m, in north-south rows, and were planted in 2012. Furthermore, a modified vertical shoot positioning training system was employed in the vineyard [15], which retained 12 to 15 nodes per linear meter. Each graft combination contained 3 replicates and each replicate had 15 vines. Nutrition and pest control methods were conducted in accordance with local industry norms. Grape growing season average monthly temperatures, sunlight hours, and precipitation were gathered from the China Meteorological Data Interchange Platform ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/)), accessed on 15 June 2025) (Table S1). Soil sampling referred to the previous study by [PERSON] et al. [20] and briefly described as follows: 9 sample plots were randomly taken in the vineyard and mixed into 3 replicates, and each sample plot was taken at 5 different depths (0 to 20, 20 to 40 cm, 40 to 60 cm, 60 to 80 cm, and 80 to 100 cm). Soil basic physicochemical indices were tested with specific reference to [PERSON] et al. [21]. The Kjeldahl method was used to determine N in soil, which consisted of 3 steps: sample digestion, distillation, and ammonia determination [22]. The vine growth indexes are shown in Table S2, and the physicochemical parameters of graft combinations in berries and wines are shown in Table S3, and the physical-chemical indices of vineyard soil are shown in Table S4 [23; 24].
## 5 Discussion
### Limitations  
One limitation of this study is that it focussed solely on the use of FaceNet as a facial recognition model. While FaceNet has been shown to have high accuracy rates, there may be other models that are better suited for recognizing people while wearing masks. Additionally, the study was limited to the use of the CASIA and VGG-Face datasets, which may not fully represent the diversity of faces and masks in the real world. Another limitation is that the study focussed on the effect of data preprocessing techniques on the accuracy of the facial recognition model. However, other factors such as lighting and camera angle may also play a role in the accuracy of facial recognition when masks are worn, and these factors were not considered in the study. The study also did not consider the potential ethical and privacy implications of using facial recognition technology to identify people while wearing masks. This may raise concerns about surveillance and the collection of personal data. The study considered only the faces of healthy people; however, it is known that some diseases such as facial palsy can significantly distort the characteristics of the face [95] while distorting its symmetry features [96], which can negatively affect biometric recognition using face. Additionally, the study did not consider adversarial attacks or attempts at face forgery with the aim of concealing identity or performing impersonation, which can decrease the performance of face recognition [97]. Finally, the study did not address the fact that the use of masks to conceal faces may be done for legitimate reasons and not only by criminals. Therefore, the study did not consider the impact of increasing facial recognition accuracy in individuals who wear masks for safety or medical reasons.
## 2 Materials and Methods
### vePOD YOLO: Privacy Preserving Object Detection on the Edge
```
1:Input: feature map \(F_{i}\) from the previous convolution layer on edge server \(E_{i}\)
2:for each channel \(j\) in feature map \(F_{i}\)do
3:\(F_{ij}\) is the feature map \(F_{i}\) in the \(j_{th}\) channel
4:\(w,h\) are the max-pooling strides
5:for each stride region \(R\) in \(F_{ij}\), \(w\) in a range \([0,1]\), \(h\) in a range \([0,1]\)do
6: pooling index \(\alpha=0,\beta=0\)
7:\(I_{i}=R[\alpha][\beta]-R[w][h]\)
8:\(E_{i},F_{q}\) exchange \(I_{i}\), \(I_{q}\) and compute \(I=I_{i}+I_{q}\)
9:if\(I<0\)then
10:\(\alpha=w,\beta=h\)
11:endif
12: return \(R[\alpha][\beta]\)
13:endfor
14:endfor
```  
**Algorithm 2** Secure Max Pooling Function  
The secure max pooling in vePOD YOLO bears similarity to the secure ROI pooling in vePOD Faster R-CNN, but they are different. The former adopts region-based index tracking to find the most significant element, while the latter uses relative difference tables to select patch-based maximum-valued elements. Both methods exploit the additive secret sharing theory to enhance non-linear operations to handle secret shares.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 3 Method
### Decision-Making Mechanism  
Continuous authentication yields a decision of \"accept\" or \"reject\" after every re-authentication. In general, longer re-authentication time improves authentication performance due to better-accumulated information; however, few works have discussed how to make decisions with variable re-authentication time.  
Primary decision-making mechanisms are categorized into the sensor, decision, and score-level fusion [36]. Some prior works have followed the sensor-level fusion, enlarg the window size (input sequence length) according to re-authentication time (\(T_{w}=T_{r}\)) [2,3,5,10].  
However, due to the long-term dependency, networks such as RNN may not fully benefit from longer input sequences. Moreover, a larger window size increases the model size of CNNs, increasing the output feature map size [5]. Additionally, the TCN requires more layers or a larger kernel size to have a larger receptive field to cover input sequences. Therefore, sensor-level fusion is problematic regarding performance stability and model complexity.  
We consider the decision and score-level fusion as alternatives to the sensor-level fusion, which maintains the window size and is free from the above problems.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 4 Proportional-Integral-Derivative Parameter Design
### Normalization  
The plant model and the control law are normalized to obtain a universal design method. The normalization parameters for the plant model, \(\tau_{0}\) and \(\tau_{a}\), are given as follows:  
\[\tau_{0} =-d\log a_{1}+\log\left(\frac{b_{0}a_{1}+b_{1}}{a_{1}(b_{0}+b_{1})}\right) \tag{18}\] \[\tau_{a} =-\log a_{1} \tag{19}\]  
Moreover, the normalization parameters for the control law, \(\kappa_{p}\), \(\tau_{i}\), and \(\tau_{d}\), are given as follows:  
\[\kappa_{p} =\frac{b_{0}+b_{1}}{1-a_{1}}Kp \tag{20}\] \[\tau_{i} =-\frac{T_{i}\log a_{1}}{T_{s}}\] (21) \[\tau_{d} =-\frac{T_{d}\log a_{1}}{T_{s}} \tag{22}\]  
The derivation of the normalization parameters is shown in Appendix A.  
The expression of the plant model using the normalization parameters has merits for optimization analysis and controller design. Since the optimization problem is stated in terms of just two parameters \(\tau_{0}\) and \(\tau_{a}\), the analysis and elaboration of coefficient parameters are simplified. Furthermore, the controller parameters are tuned independent of the process gain and time constant.
## 3 Method
### Behavioral Classification
#### 3.4.2 Abnormal Behavior Recognition and Recording  
The spatio-temporal graph convolutional network (ST-GCN) [34] is introduced to perform feature extraction of human keypoints in graph structures. As shown in Figure 9, the unit of ST-GCN consists of an adjacency matrix, a weight matrix with learnable edge weights, a spatial graph convolution, a temporal graph convolution, and a residual structure (used to introduce the original skeleton information). The adjacency matrix is a matrix of size \(V\times V\) (\(V\) is the number of keypoint categories) obtained from the maximum paths of predefined neighboring points. It describes the connection relationship between each skeleton point. At each layer of the convolutional unit, a learnable weight matrix is introduced for the adjacency matrix. This weight matrix is of the same size as the  
Figure 8: The structure of the space–time skeleton diagram.  
Figure 7: Heat map regression method for human keypoints.  
adjacency matrix. It is initialized as an all-ones matrix and is updated along with the model training implementation.  
The ST-GCN network accomplishes the behavior recognition task through graph structure feature extraction and behavior classification. Specifically, the batch normalization (BN) layer performs normalization operations on the input data. The nine spatio-temporal map convolutional units are divided into three groups. The output channel dimensions of the units in the three groups are 64, 128, and 256. Finally, the feature vectors are sequentially fed into the pooling layer, the fully connected layer, and Softmax to output the behavioral category labels. The graph convolution operation at position \(x\) in the input feature map can be expressed as follows:  
\[f_{\text{out}}(x)=\sum_{h=1}^{K}\sum_{w=1}^{K}f_{\text{in}}(p(x,h,w))\cdot w(h,w) \tag{7}\]  
where \(p(x,h,w)\) is a sampling function that traverses the region around position \(x\) according to the \((h,w)\) index. \(w(h,w)\) is the weighting function.  
In order to accurately record the time points at which abnormal behaviors occur, it is necessary to capture the beginning and ending moments of the abnormal behavior in the video. The task of recording abnormal behavior can be divided into two stages. In the first stage, a sliding window algorithm is designed to implement the classification of timing behaviors. In the second stage, the system traverses the category records for each frame and judges them to determine if the behavior is abnormal.  
The flow of abnormal behavior detection based on a sliding window is shown in Figure 10. The length of the sliding window is predefined as \(n\). The entire process pseudo-code is shown in Algorithm 1. During the process of performing the timing of abnormal behavior detection task, frame-level behavioral information is recorded for each target subject that occurs.  
Figure 9: ST-GCN network structure.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 6 Conclusions  
A-MAC protocol for secure transmission of data in V2V environment in VANETs is proposed in this article. Along with that, an overview and detailed discussion on five-level architecture to enhance vehicular communication in IoV is presented. The IoV architecture shows great potential in enabling future autonomous driving scenarios. An authentication scheme, A-MAC is proposed. The system necessitates hash operations and upholds the necessary security level. Additionally, the privacy and integrity of the message are protected. We made our system inconsequential by taking less memory and decreasing the number of variables to be stored. The results show that A-MAC protocol outperforms other similar protocols based on hash mechanism. It can withstand common security attacks during data transmission in vehicular scenario.  
In future, we look to work on spatial correlation for further analysis of the proposed model. DCF shall be further customized to reduce latency and loss ratio. A hybrid technique that works well with safety as well as security of the data shall be devised. We shall come up with new communication protocols for the IoV framework to resist cyberattacks by verifying security strengths.  
**Author Contributions:** Conceptualization, N.G., R.M. and B.S.; methodology, N.G., R.M. and B.S.; validation, N.G., R.M. and B.S.; writing-original draft preparation, N.G., R.M., B.S. and A.T.; writing--review and editing, N.G., F.S. and A.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Criteria** & [18] & [19] & [20] & [21] & [43] & [44] & [45] & **A-MAC** \\ \hline Security against replay attack & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Security against impersonation attack & ✓ & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ & ✓ \\ Security against tampering attack & ✗ & ✓ & ✓ & ✗ & ✓ & ✓ & ✗ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of performance of various schemes under comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & [18] & [19] & [20] & [21] & **A-MAC** \\ \hline Communication cost & High & High & Medium & Low & Low \\ Storage cost & High & Medium & Medium & Low & Low \\ Execution time & High & Medium & Medium & Medium & Low \\ RSU’s overhead & High & High & Medium & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of performance of various schemes under comparison.  
**Acknowledgments:** The authors [PERSON] and [PERSON] would like to thank FAPEMA (State of Maranhao Research Funding Agency) for supporting their research projects.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiments and Evaluation
### Abnormal Behavior Data Generation  
Abnormal behavior occurs mostly due to an unexpected load on the motor running the conveyor system. The behavior of this unexpected load could be periodic (e.g., a gear in the motor is causing extra friction when it is on a certain angular position), constant (e.g., something rubbing the belt) or random. On the real setup, it is hard to create such unexpected torque requests on the motor; however, we use a friction pad placed under the belt to emulate an abnormal behavior. Whereas on the digital twin, such torque behaviors are simulated through five different functions including step, ramp, linear, logarithm, exponential and sinusoidal. In our experiments, we mainly use a sinusoidal disturbance (in Equation (6)) as it emulates a periodic behavior and it allows to test the system's adaptation capability, flexibility and smoothness of its responses when a disturbance is introduced and removed.  
\[\tau_{dist}=\frac{a}{2}\cdot sin(\frac{2\pi}{b}\cdot t+\frac{3\pi}{2})+\frac{a}{2}, \tag{6}\]  
where \(a\) and \(b\) are two parameters defining the magnitude (i.e., the wavelength) and \(\tau_{dist}\) is the sinusoidal torque disturbance that is added to the system load torque at time \(t\).
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
## 3 Diagnosis: Tissue Characterization in Early Disease  
Although diagnosis of cardiac phenotype in FD has hitherto been based primarily on measurement of MWT and development of LVH, it is understood that sphingolipid deposition precedes the development of hypertrophy and is a continuous process that results in insidious progression of organ involvement over time. In most men with a classical phenotype, LVH develops in their 30 s, and in most women approximately 10 years later [18], then continues to rise inexorably regardless of treatment with ERT [19]. Data consistently suggest that ERT, and perhaps OCT, have the most significant effect on stabilizing cardiac disease if given early enough [20]. A critical development in advanced cardiovascular imaging techniques is the detection of cardiac involvement before the onset of LVH, an aim that is particularly suited to innovative imaging tools both in echocardiography and CMR.  
Figure 3: CMR analysis of wall thickness using manual contouring versus AI. **(A)**: Manual contouring and corresponding quantification of MWT using a 3D short-axis stack image of the mid-wall of the LV. **(B)**: Corresponding 4-chamber view demonstrating the mid-wall slice through the LV where the manual/AI contouring is completed. **(C)**: Semi-automated AI-generated quantification of wall thickness derived from 3D short axis imaging demonstrating end-diastolic MWT. **(D)**: Corresponding map of AI-generated MWT measurements (millimetres) from base-apex of the LV. Images contoured and semi-automated AI conducted using cvi42 version 5.11.2 (Circle Cardiovascular Imaging, Calgary, AB, Canada).
# Robust Design of 3D-Printed 6-18 GHz Double-Ridged TEM Horn Antenna
## 2 TEM Horn Antenna Design
### Horn Aperture Design  
Figure 5 shows cross-sectional views of the horn aperture which is realized with an exponentially tapered structure along both the \(x\)- and \(y\)-axes. The tapered structure is advantageous for broadband matching because its characteristic impedance varies smoothly. The width \(w(z)\) of the horn aperture and the spacing \(d(z)\) between the upper and lower plates are correspondingly given by  
\[w(z)=2(exp^{az}-1)+s,(0\leq z\leq l_{h}) \tag{1}\]  
\[d(z)=2(exp^{\beta z}-1)+d,(0\leq z\leq l_{h}) \tag{2}\]  
where \(\alpha\) and \(\beta\) are constants, and \(l_{h}\) is the length of the horn aperture. The characteristic impedance of the aperture is obtained by means of parallel plate waveguide theory [19]. The characteristic impedance \(Z(z)\) of the horn aperture can be expressed as follows:  
\[Z(z)=\frac{d(z)}{w(z)}120\pi,(0\leq z\leq l_{h}) \tag{3}\]  
In Equation (3), the fringing fields at the edges of the parallel plate waveguide are ignored for a simple and rapid design of the horn aperture. Based on Equations (1)-(3), the horn aperture is designed. The values of \(\alpha\) and \(\beta\) are respectively set to 0.092 and 0.1 so that the characteristic impedance at the open end matches the intrinsic impedance (377-\(\Omega\)). \(l_{h}\) is fixed at 25 mm, which is half of the wavelength at 6 GHz.