esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a 90 GHz SOI Fin Electro-Optic Modulator for High-Speed Applications
## 3 Optimizing the Modulator Parameters
Along with \(L=7.5\) mm, we get \(IL=7.5+0.6=8.1\) dB, \(ER=3\) dB, and \(f_{\rm 3 dB}=10\) GHz. This design is called 'Modulator C'. Looking into the former range of results, 'Modulator B', with \(f_{\rm 3 dB}=90\) GHz, can be employed in the backhaul truck circuits of the 5G networks with a speed of 100 Gbps of non-return-to-zero (\(NRZ\)) coding [13], where also high voltage can be provided. 'Modulator C', which exhibits much lower bandwidth but also much lower voltage of operation, is then more suitable for the end user applications of the 5G systems where the speed of operation is 1-10 Gbps [13], which can be easily supported by a bandwidth of 10 GHz. A summary of the proposed variants of this modulator is given in Table 1.
## 4 Experiments and Results  
### Dataset  
For the university exam preparation dataset used in our study, we initially collected approximately 1 million images through a mobile application named Tosbik [37]. However, we conducted a sub-sampling process for this research, selecting 40,000 images related explicitly to mathematics courses. Additionally, we incorporated 100 query images uploaded by students into our system. These query images were intentionally chosen to be challenging. They encompassed a variety of conditions, including different smartphone brands, varying lighting conditions, diverse perspective views, and the possibility of containing scribbles or irrelevant portions of the page. All of these query images were known to have at least one or more relevant documents present within the document image dataset, forming a critical part of our research data.
# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 2 Related Work
### Bias in Presentation Attack Detection Systems  
Applying deep learning technology especially deep CNN has considerably improved accuracy in detecting presentation attacks ([2,22]). However, CNN-based PAD models need to be examined for any potential bias.  
To our knowledge there is only one research conducted to study the fairness of presentation attack detection technology [8]. This paper is related to iris-based presentation attack detection.  
In [8], three iris PAD algorithms were explored in order to observe their behavior toward different demographic groups. The NDCLD database proposed in [23] was used in this study but due to its limited diversity, only gender bias was measured. Using different dataset scenarios for training and testing, both the performance and results of iris PAD systems were monitored, analyzed, and compared for men and women. Researchers in [8] concluded that presentation attacks targeting women were noticeably harder to detect.
# Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment
###### Abstract  
A deep understanding of multipath characteristics is essential to design signal simulators and receivers in global navigation satellite system applications. As a new constellation is deployed and more applications occur in the urban environment, the statistical multipath models of navigation signal need further study. In this paper, we present statistical distribution models of multipath time delay, multipath power attenuation, and multipath fading frequency based on the experimental data in the urban canyon environment. The raw data of multipath characteristics are obtained by processing real navigation signal to study the statistical distribution. By fitting the statistical data, it shows that the probability distribution of time delay follows a gamma distribution which is related to the waiting time of Poisson distributed events. The fading frequency follows an exponential distribution, and the mean of multipath power attenuation decreases linearly with an increasing time delay. In addition, the detailed statistical characteristics for different elevations and orbits satellites is studied, and the parameters of each distribution are quite different. The research results give useful guidance for navigation simulator and receiver designers.  
Keywords:global navigation satellite system; multipath; statistical model; urban canyon +
Footnote †: journal: Sensors
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 4 Clutter Statistics and Detection
### K-Distribution  
Equations (11) and (12) are valid only when the clutter is Rayleigh distributed (=homogeneous clutter). However, in the case of relatively low grazing angles (\(10^{\circ}\)-\(50^{\circ}\)) and high resolution systems, the clutter is no longer Rayleigh distributed [36]. To address this problem, an additional independent random variable called texture is introduced. This leads to the following expression [37]  
\[Z_{\text{i}}=\
abla\cdot C_{\text{i}}+N_{\text{i}}+S_{\text{i}}, \tag{14}\]  
The introduction of the texture component \(\
abla\) leads to the product or compound clutter models. Compound models with a PDF \(P(I)\) in general are represented by the following expression  
\[P(I)=\int_{0}^{\infty}P_{\text{I}\text{X}}(I|x)P_{\text{X}}(x)dx, \tag{15}\]  
where \(x\) is the speckle and the underlying speckle component \(P_{\text{I}\text{X}}(I|x)\) is modulated by the texture \(P_{\text{X}}(x)\). One of the most popular and widely used compound models is the K-distribution. It is a comprehensive representation of a Gamma distributed texture over Rayleigh distributed homogeneous clutter. For \(L\) number of looks, it is represented as [36; 38; 39]  
\[f(I,v,b,L)=\frac{2I(v)^{-1}}{\Gamma(L)}(Lb)^{\frac{L+\gamma}{2}}I^{\frac{L+ \gamma}{2}}K_{\
u-\text{L}}\big{(}2\sqrt{LbI}\big{)}, \tag{16}\]  
where \(v,b,L,\Gamma(\cdot)\) and \(K(\cdot)\) are the shape, scale, number of looks, gamma function and the modified Bessel function of the second kind, respectively. The scale parameter \(b\) is related to the mean \(\mu\) of the data as \(b=v/\mu\).  
The shape parameter \(v\), also known as texture, mainly controls the shape of the K-distribution. The range of \(v\) is \([0.1,\infty]\), where for \(v=\infty\) the PDF reduces to a Rayleigh distribution. Lower values of \(v\) represents spiky clutter (cf. Figure 17). For a given threshold \(\eta\), above which a moving target is declared, the PFA for a given threshold \(\eta\) can be written as  
\[P_{\text{fa}}\ (\eta)=2\sum_{l=0}^{L-1}\frac{(vL)^{\frac{(v+l)}{2}}}{\Gamma(l+1) \Gamma(v)}\eta^{\frac{v+l}{2}}K_{\
u-\text{L}}\big{(}2\sqrt{vL\eta}\big{)}, \tag{17}\]There are in total three unknowns; shape (\(v\)), scale \(\left(\frac{v}{\mu}\right)\) and number of looks \((L)\) to be determined to fit the distribution. Different methods to determine the unknown parameters of the K-distribution are presented in the following.
## 4 Proposed Methodology
### The Deep Learning Detections
#### 4.2.2 RT-DETR: Real-Time Detection Transformer
After that, RT-DETR predicts bounding boxes in a manner similar to DETR, using a regression head to output the bounding box coordinates (x, y, w, h) and a classification head to predict the object class as in (9) [28]:  
\[\text{bbox}=(x,y,w,h) \tag{9}\]  
where \(x,y\) represent the center of the bounding box; \(w,h\) represent the width and height of the box. The model uses a combination of two main loss functions to train the detection model. The first is named Bounding Box Regression Loss, which measures the difference between predicted and ground truth bounding boxes, as in (10). ```
Define the main function:  Load the object detection dataset  Preprocess the dataset (cleaning, normalization, etc.) Split the dataset into training and testing sets  Define the object detection model (YOLO8 & RT-DETR)  Define the loss function (Cross-Entropy Loss)  Define the optimizer (Adam, SGD) for each epoch in total number of epochsdo for each batch in training datado  Forward pass the batch through the model  Compute the loss  Backward pass to compute gradients  Update the model parameters using the optimizer  end for  Evaluate the model on the testing set  Print the performance metrics  end for  End of the main function  Call the main function
```  
* Cross-Entropy Loss: is a measure used in machine learning and specifically in classification tasks to quantify the difference between two probability distributions. * Adam: Adaptive Moment Estimation is an advanced optimization algorithm used in training deep learning models. * SGD: Stochastic Gradient Descent is a core optimization algorithm used extensively in training machine learning models. \[\mathcal{L}_{\text{bbox}}=\sum_{i}(smooth_{L_{1}}(\hat{b}_{i}-b_{i})) \tag{10}\]  
where \(\hat{b}_{i}\) is the predicted bounding box, and \(b_{i}\) is the ground truth. The latter is named classification loss which measures the difference between predicted class scores and true class labels (often using a cross-entropy loss). The Total Loss is computed as follows in (11)  
\[\mathcal{L}_{\text{total}}=\lambda_{\text{class}}\mathcal{L}_{\text{class}}+ \lambda_{\text{bbox}}\mathcal{L}_{\text{bbox}} \tag{11}\]  
where \(\lambda_{\text{class}}\) and \(\lambda_{\text{bbox}}\) are weighting factors. Finally, RT-DETR used Hungarian matching to assign ground truth objects to predicted boxes. This bipartite matching process ensures that each ground truth object is matched to only one predicted object, which helps avoid duplicate predictions and enforces one-to-one matching.
To this end, a test rig is constructed, in which a robot is operated under various fault conditions with different severities. During the operation, the motor signal acquired to control its motion is exploited to diagnose the health of the system and its components. According to the robot company, the system performance is defined by a vibration magnitude of the end-effector during the operation, which is measured by an accelerometer. If the value exceeds a certain threshold, it is regarded as failure, that is, the robot fails to deliver the wafer to the target position, and the operation is aborted. In order to relate the motor control signal with the faults of the components, features are extracted by using the wavelet packet decomposition (WPD), which is known to be effective for nonstationary signals. A framework for system-level diagnosis is constructed,which enables the estimation of the current state of health of each component and the system performance. It consists of two models: the artificial neural network (ANN) for the diagnosis of component health and the Gaussian process regression (GPR) for the estimation of system health. To summarize, the contributions of the proposed work are as follows: First, multiple components with multiple degradation severities are examined to perform fault diagnosis of the robots. Second, the method leverages motor control signals, which are acquired automatically during operation, eliminating the need for supplementary sensors. Third, a framework for system-level diagnosis is proposed, assessing the component health in relation to the system performance, which is contrasted to the conventional component-level strategies. The rest of the paper is organized as follows: Section 2 presents the proposed methodology and theoretical background for the algorithms. Section 3 gives a brief explanation of the industrial wafer transfer robot and the designed experiment. Then, Section 4 presents the result of proposed methodology with the datasets of the designed experiment. Lastly, conclusions and potential future works are presented in Section 5.
## 3 Methods
### Wavelet Entropy Decompositions  
Wavelet transform can provide the time-frequency interpretation of radar data, and entropy is a parameter that quantitatively describes the order/disorder microstates. Wavelet entropy combines the advantages of the wavelet transform and entropy simultaneously [17; 18; 19]. As the regularity of the signal increases, the value of the wavelet entropy decreases. Thus, after wavelet entropy decomposition of the preprocessed radar data, the corresponding values at the range points of humans and mechanical vibrations are apparently lower because of their periodicity.  
To calculate the wavelet entropy, a discrete wavelet transform was performed first to generate a set of elementary functions \(\psi_{a,b}\) (\(t\)) by translations and dilations of the mother wavelet \(\psi(t)\)[20].  
\[\psi_{a,b}(t)=|a|^{-0.5}\psi\bigg{(}\frac{t-b}{a}\bigg{)},s.t.\;\;a,b\in R,\;a \
eq 0, \tag{5}\]  
where \(a\) denotes the scale for the description of the degree of compression, and \(b\) denotes the translation for determining the time location. As the value of \(a\) increases, the wavelet becomes narrower. By choosing \(a=2^{-k}\), \(b=2^{-k}n\) and \(k=-\{1,2,\ldots,log_{2}N\}\), the preprocessed radar data \(R_{AF}(t)\) can be reconstructed as follows:  
\[R_{AF}(x,t)=\sum_{k}\sum_{n}C_{k}(n)\psi_{k,n}(t), \tag{6}\]  
where \(n=64t\) denotes the slow time points, and \(C_{k}(n)\) denotes the local residual errors between the two adjacent scales of \(k-1\) and \(k\).  
The radar data were then divided into nonoverlapping temporal windows with lengths \(J=128\). Accordingly, the wavelet energy at each scale degree \(k\) of temporal window \(i=1,2,\ldots,I=N/J\) can be calculated using  
\[E_{k}(i)=\frac{1}{J}\sum_{n=(i-1)\times I+1}^{i\times J}|C_{k}(n)|^{2}. \tag{7}\]  
Subsequently, the relative wavelet entropy \(\tilde{E}_{k}\) at scale \(k\) of each temporal window \(i\) is given by  
\[\tilde{E}_{k}(i)=\frac{E_{k}(i)}{\sum_{k}E_{k}(i)}. \tag{8}\]  
Finally, the wavelet entropy at range point \(x\) is determined by  
\[WE(x)=\frac{1}{I}\sum_{i}\tilde{E}_{k}(i)\cdot\ln\big{[}\tilde{E}_{k}(i)\big{]}. \tag{9}\]  
The wavelet entropy of the target is much lower than that of the noise signal, whereas the subsequent CFAR algorithm can only detect targets whose values are much higher than noise. Thus, the wavelet entropy curve is flipped up and down by  
\[WE_{1}(x)=\frac{\underset{m}{\text{max}}WE(x)-\underset{m}{\text{min}}WE(x)}{ 2}-WE(x) \tag{10}\]  
\[\widetilde{WE}(x)=WE_{1}(x)-\underset{m}{\text{min}}WE_{1}(x), \tag{11}\]  
where \(WE_{1}(x)\) is an intermediate variable, and \(\widetilde{WE}(x)\) is the flipped value of \(WE(x)\).
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Point Cloud Preprocessing and Segmentation Analysis for Corn Populations
#### 2.6.1 Corn Population Point Cloud Preprocessing Results  
In this study, we performed preprocessing of the maize population point cloud, which included downsampling, statistical filtering, kd tree construction, and visualization. Figure 4 illustrates the various stages of preprocessing applied to the maize population point cloud derived from a single test set. The figure series shows the transformation of the point cloud data through different stages, as described below. Figure 4a presents a partial view of the large-scale field point cloud generated using CloudCompare 2.9.0. software. It represents the initial, raw point cloud data before any filtering or processing, providing an overview of the entire dataset. Figure 4b depicts the maize target point cloud extracted from the field. It represents the refined version of the point cloud, focusing specifically on the maize population by filtering out irrelevant background information. Figure 4c shows the point cloud after the ground points have been removed, leaving only the points associated with the maize population. The point cloud contains 6,122,494 points, representing the dataset after ground extraction and indicating the initial clean-up of the data. Figure 4d displays the point cloud after statistical filtering has been applied. Statistical filtering reduces noise and removes irrelevant points, resulting in a more accurate dataset. The processed point cloud contains 5,361,509 points. Figure 4e presents the final point cloud after downsampling, which reduces the point count to 29,939. The downsampling process makes the dataset more manageable for further analysis while retaining the key features of the maize population.  
Each figure represents a distinct stage in the preprocessing pipeline, clearly demonstrating the evolution of the point cloud as it undergoes various transformations. These steps significantly improve the quality and usability of the maize population point cloud for subsequent analysis.  
Figure 4: _Cont._
# Personalized Federated Multi-Task Learning over Wireless Fading Channels
## 3 Algorithm Description
### Definitions and Preliminaries  
In _FedGradNorm_, we aim to learn the dynamic loss weights \(\{p^{(i)}\}_{i=1}^{N}\) given in the lower-level optimization problem of (8). The main objective of the algorithm is to dynamically adjust the gradient norms so that the different tasks across clients can be trained at similar learning speeds. In the rest of the paper, _clients_ and _tasks_ will be used interchangeably as we assume that each client has its own different task. Before describing the algorithm in detail, we first introduce the notation:  
* \(\tilde{\omega}\): A subset of the global shared network parameters \(\tilde{\omega}\subset\omega\). _FedGradNorm_ is applied on \(\tilde{\omega}_{k}^{(i)}\subset\omega_{k}^{(i)}\), which is a subset of the global shared network parameters at client \(i\) at iteration \(k\). \(\tilde{\omega}_{k}^{(i)}\) is generally chosen as the last layer of the global shared network at client \(i\) at iteration \(k\).
* \(G_{\tilde{\omega}_{k}^{(i)}}^{(i)}(k)=\|\
abla_{\tilde{\omega}_{k}^{(i)}}p_{k} ^{(i)}F_{k}^{(i)}\|=p_{k}^{(i)}\|\
abla_{\tilde{\omega}_{k}^{(i)}}F_{k}^{(i)}\|\): The \(\ell_{2}\) norm of the gradient of the weighted task loss at client \(i\) at iteration \(k\) with respect to the chosen weights \(\tilde{\omega}_{k}^{(i)}\).
* \(\tilde{G}_{\tilde{\omega}}(k)=\mathbb{E}_{j\sim\text{task}}[G_{\tilde{\omega}_ {k}^{(i)}}^{(j)}(k)]\): The average gradient norm across all clients (tasks) at iteration \(k\).
* \(\bar{F}_{k}^{(i)}=\frac{F_{k}^{(i)}}{F_{0}^{(i)}}\): Inverse training rate of task \(i\) (at client \(i\)) at iteration \(k\), where \(F_{k}^{(i)}\) is the loss for client \(i\) at iteration \(k\), and \(F_{0}^{(i)}\) is the initial loss for client \(i\).
* \(r_{k}^{(i)}=\frac{F_{k}^{(i)}}{\mathbb{E}_{j\sim\text{task}}[F_{k}^{(i)}]}\): Relative inverse training rate of task \(i\) at iteration \(k\).  
Additional notation that is useful in algorithm description:  
* \(g_{k}^{(i)}=\frac{1}{\tau_{\omega}}\sum_{j=1}^{\tau_{\omega}}g_{k,j}^{(i)}\) is the average of gradient updates at client \(i\) at iteration \(k\), where \(g_{k,j}^{(i)}\) is the \(j\)th local update of the global shared representation at client \(i\) at iteration \(k\). Note that \(\|\
abla_{\tilde{\omega}_{k}^{(i)}}F_{k}^{(i)}\|\) is a subset of \(g_{k}^{(i)}\) since \(\tilde{\omega}\subset\omega\).
* \(h_{k,j}^{(i)}\) is the client-specific head parameters \(h^{(i)}\) after the \(j\)th local update on the client-specific network of client \(i\) at iteration \(k\), \(j=1,\dots,\tau_{\tilde{\eta}}\).
* \(\omega_{k,j}^{(i)}\) is the global shared network parameters of client \(i\) after the \(j\)th local update at iteration \(k\), \(j=1,\dots,\tau_{\omega}\). Additionally, \(\omega_{k}^{(i)}\) denotes \(\omega_{k,\tau_{\omega}}^{(i)}\) for brevity.
## 3 Problem Definition and Methods
### Gan  
Our approach uses the generative adversarial architecture [44] to facilitate multimodal trajectory prediction. The generator and discriminator are trained concurrently until the generator learns the distribution of pedestrian trajectories and can output diverse feasible future trajectories.  
Our generator employs an LSTM as a decoder. At each time step, a noise vector \(z\) is sampled from a multivariate normal distribution representing uncertain fluctuations and embedded with the social interaction vector and spatial interaction vector to generate the input \(C_{\text{G}}^{t}(i)\) for the decoder LSTM. Then, the decoder LSTM processes this input and generates the hidden state \(h_{\text{dec}}^{t}(i)\) at each time step. Subsequently, an MLP processes the hidden state to obtain the predicted trajectory coordinates as  
\[\begin{array}{l}C_{\text{G}}^{t}(i)=\varphi\Big{(}A_{\text{so}}^{t}(i),A_{ \text{sp}}^{t}(i),z\Big{)}\\ h_{\text{dec}}^{t}(i)=LSTM_{\text{dec}}\Big{(}C_{\text{G}}^{t}(i),h_{\text{ dec}}^{t-1}(i);W_{\text{dec}}\Big{)}\\ \hat{Y}_{i}^{t}=MLP_{\text{dec}}\big{(}h_{\text{dec}}^{t}(i)\big{)}\end{array} \tag{9}\]  
The discriminator consists of an LSTM and an MLP. The LSTM combines the observed trajectory \(X_{i}\) with a random sample of a future trajectory \(\hat{Y}_{i}\) from either a predicted path \(\hat{Y}_{i}\) or a ground-truth path \(Y_{i}\) as input. The corresponding input sequences are then encoded by the LSTM. At the final time step, a classification score is obtained by applying an MLP on the LSTM's final hidden state as  
\[\begin{array}{l}h_{\text{dis}}^{t}(i)=LSTM_{\text{dis}}\Big{(}\varphi\big{(} \big{[}X_{i},\hat{Y}_{i}\big{]};W\big{)},h_{\text{dis}}^{t-1}(i);W_{\text{dis }}\Big{)}\\ s_{i}=MLP_{\text{dis}}\Big{(}h_{\text{dis}}^{t_{\text{obs}}+t_{\text{pred}}}(i )\Big{)}\end{array} \tag{10}\]  
with a score of 1 indicating a true trajectory and a score of 0 indicating a false trajectory.  
The model's loss function comprises two components: an adversarial min-max loss \(L_{\text{GAN}}\), which trains the GAN network, and an \(L_{2}\) loss, which reflects the deviation from the real value. The total loss is determined as  
\[L=L_{\text{GAN}}+\lambda L_{2} \tag{11}\]  
where \(\lambda\) is a hyperparameter used to balance the two losses. The \(L_{\text{GAN}}\) follows the typical GAN loss formulation [44],  
\[L_{GAN}=\min_{G}\max_{D}\big{[}E\log D(X_{i},Y_{i})+E\log\big{(}1-D\big{(}X_{ i},\hat{Y}_{i}\big{)}\big{)}\big{]} \tag{12}\]  
To promote diverse predictions, the \(L_{2}\) loss adopts a minimum-over-N-like loss, which entails sampling the random noise \(z\) for \(k\) times to generate \(k\) predicted samples [21].  
Subsequently, the \(L_{2}\) loss is computed as the minimum deviation from the true trajectory among the \(k\) predicted samples, that is,  
\[L_{2}=\min_{k}\left\|Y_{i}-\hat{Y}_{i}^{(k)}\right\|_{2} \tag{13}\]  
Here, \(k\) is a hyperparameter that represents a trade-off between prediction accuracy and prediction diversity.