esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation
## 2 Related Works
### Sparse Filtering  
Sparse filtering is an unsupervised feature extraction algorithm. Unlike auto-encoder and sparse coding, it does not seek to model the data explicitly, but attempts to obtain ideal features from the perspective of so-called good features. First of all, it gives three requirements on the question of What is good features.  
* Population Sparsity. Each example should be represented by only a few active features. Ideally, a sample should be a vector which contains lots of zeros and few non-zero values.
* Lifetime Sparsity. Good features should be distinguishable, therefore, a feature is only allowed to be activated in few samples. On the contrary, if a feature is activated by all the samples, we cannot classify samples according to this feature, so it is not a good feature.
* High Dispersal. It requires each feature to have similar statistical properties across all samples, which seems to be useless. The authors indicate that this can prevent feature degradation, such as similar features across samples.  
[PERSON] et al. pointed out that we can obtain the features that meet all the requirements by optimizing population sparsity and high dispersal [13]. Suppose now we have \(n\) samples, each with \(m\)-dimensional features that can be written as \(x=x_{1},x_{2}\ldots,x_{n}\in R^{m}\). The learning paradigm of sparse filtering is as follows:  
(1) Linear feature extraction and non-linear activation. Let \(f_{j}^{(i)}\) represent the \(j\)th feature of the \(i\)th sample, \(f_{j}^{(i)}=x^{(i)}w_{j}\). Then we can use some activation functions to make it more expressive, such as soft-absolute function.  
\[f_{j}^{(i)}=\sqrt{\epsilon+(x^{(i)}w_{j})^{2}}\approx\left|x^{(i)}w_{j}\right| \tag{1}\]  
(2) Solving high dispersal. Each feature is divided by the \(l2-\)norm of the feature on all samples.  
\[\widetilde{f_{j}}=\frac{f_{j}}{\left\|f_{j}\right\|_{2}} \tag{2}\]Remember that the requirement of high dispersal is that the statistical properties of each feature are similar. This step forces the sum of the squares of all features to be 1 roughly.  
(3) Solving population sparsity. Each sample is divided by its own \(l2-\)norm. Then we can get the objective function.  
\[minimize\ \sum_{i=1}^{n}\left\|\frac{\widehat{f}^{(i)}_{i}}{\|\widehat{f}^{(i)} \|_{2}}\right\|_{1} \tag{3}\]  
This objective function can be easily understood through an example. Imagine that we have different expressions of a sample, \((0.6,0.8)^{T}\) and \((1,0)^{T}\). We say \((1,0)^{T}\) is a sparser representation since it has more zero, so it has a smaller loss, \(\frac{1}{\sqrt{1^{2}+0^{2}}}+\frac{0}{\sqrt{1^{2}+0^{2}}}=1\leq\frac{0.6}{ \sqrt{0.6^{2}+0.8^{2}}}+\frac{0.8}{\sqrt{0.6^{2}+0.8^{2}}}=1.4\).
## 5 Discussion
### Performance of the Deep Learning Models  
All three deep learning models performed well, with DSC higher than 0.80. Statistical results indicated that their similar dice scores achieved no significant difference in their detecting the arteries of interest. However, the Standard U-Net model showed lower accuracy in segmenting the vessels in Moyamoya patients than in the HC cohort, likely due to vessel occlusion and/or stenosis in Moyamoya cases. Interestingly, the Nested U-Net and Attention U-Net did not show this discrepancy while their performances were not significantly different from the Standard U-Net. These findings were further supported by the ROC curves of the three models, showing the Standard U-Net having generally lower AUC than the Attention and Nested U-Nets. It can also be observed that although the Attention U-Net can achieve better true positive performance than the Nested U-Net, the Nested U-Net marginally outperformed the Attention U-Net if minimizing false positives is desired. Based on these findings, Attention and Nested U-Nets should be the preferred choices due to their superior and non-discriminative performances over the Standard U-Net.  
We avoided splitting the data into three sets due to the small size of our dataset. Instead, we employed a variety of augmentations to artificially reduce the dataset size to prevent overfitting, which left us with 20 samples for validation and 130 samples for  
Figure 5: Bland–Altman plot comparing the flow volume measurements between the deep learning models and ground truth. Overall, good agreements were achieved for all 3 deep learning models. The blue line indicates the mean value and the red dash lines show the \(\pm\)1.96 SD.  
training. Dividing this data further into a test set would result in high variance estimates of test accuracy due to low sample count. The augmentations increased the number of images without increasing the underlying information content. In other words, all the augmentations of a given image would seem redundant to a human observer, since they would obviously depict the same underlying structure. However, these augmentations are non-trivial to our model and force invariance along certain axes that are obvious to humans such as rotations, translations, shears, etc. In total, a given image can appear in approximately at least 45 million different ways.
## 7 Experimental Results
### Analysis of the Discrepancies for the Urban Environment Case
As stated by the analysis of the live and replayed signals at IF, the playback system was able to faithfully re-generate the RF signal except for the time interval when the front-end went into saturation. Therefore, by feeding the two signals to the GBPT under test and comparing the HPEs, one would expect a similar behavior. In other words, the two curves should match for all the signal durations except for the time interval when the front-end went into saturation, which in this specific case was from 352 to 355 s.  
The experiment was conducted by using different levels of attenuation for the re-generated RF signal before feeding it to the GBPT. The resulting HPEs are plotted in Figure 13(a). Regardless of the different attenuation levels, the HPE of the replayed signal (dashed curve) was larger than the HPE of the live signal (solid curve). As a summary, it is possible to state that in constrained environments, despite the recorded and replayed signals having the same time and frequency behavior, they did not provide the same HPE when fed to the GBPT under test. In other words, in such harsh scenarios, the record and replay system was not capable of fully reproducing the detailed recorded environment, despite the good matching of the time and frequency representations. This is also visible from the \(C/N_{0}\) estimation in Figure 13(b), which shows the difference of the \(C/N_{0}\) values between the live and replayed signals, for all the different levels of attenuation. The receiver reacted in a different way to the recorded data with respect to the live operation. It has to be remarked that in harsh environments the receiver is forced to react to the variability of the environment, performing a number of operations for the management of the channels, the re-acquisition of the signals, and the logic for the allocation of the resources. Such operations are based on the monitoring of some parameters and according to a rationale that is unknown to the user. The values of the monitored unknown parameters may be slightly different in the replayed signal with respect to the real case, causing the receiver to behave in a different way when the signal is far from the nominal conditions (e.g., presence of interference, distortion of the Gaussian statistics, etc. ), as shown in Figure 13.  
Figure 13: Test case for the urban environment. Histograms (**top**) and PSDs (**bottom**) of the recorded and re-recorded GNSS raw samples at seconds 155 (**left**), 351 (**middle**), and 353 (**right**). The results show that the record and replay approach should be used carefully when the recorded signal is far from the GNSS nominal condition, and it is hard to check the fidelity of the replayed signal in terms of metrics that take into account features that are important drivers for the receiver logic, but are unknown to the testing user who treats the receiver as a \"black box\".
## 3 Pose Estimation Based on Visual-IMU Fusion
### Reprojection Error Based on Vision  
The monocular camera collects forest image information and provides the initial image sequence for the forest point cloud reconstruction system. At the same time, in order to avoid the loss of the feature track, this paper establishes the following constraints to define the key frames of the obtained image sequence:  
\[\left\{\begin{array}{c}\frac{\sum\limits_{k=1}\left(V_{k+1}-V_{k}\right)}{k}> \alpha\\ F_{k}<\beta\end{array}\right. \tag{1}\]  
where \(V_{k+1}-V_{k}\) is the parallax between the \((k+1)\)th frame and the \(k\)th frame; \(F_{k}\) is the number of feature points of the \(k\)th frame; and \(\alpha\) and \(\beta\) are the predefined thresholds. In Formula (1), there are two strategies for key frame selection. The first one is the average parallax of the key frames. If we add one new frame, the average parallax of the tracked features in all key frames exceeds the specified threshold, and we treat the frame as a new key frame. Another strategy is the number of feature points. If the number of tracked feature points is below the specified threshold, the frame is regarded as a new key frame.  
This paper uses the KLT sparse optical flow algorithm [31] to extract and track the feature points of image key frames. Therefore, we can form matching feature point pairs between adjacent key frames. At the same time, for each new image, we use the RANSAC algorithm [32] with the basic matrix model for outlier elimination.  
In the feature point method, camera motion is often estimated by minimizing the reprojection error based on point pairs from adjacent frames. This paper adopts the difference between the spatial coordinates and the pixel coordinates of the feature points to obtain the visual reprojection error between two adjacent key frames:  
\[r_{\mathrm{C}_{\mathrm{L}p}}=u_{k+1}-\frac{1}{z_{k}^{p}}KT_{k}^{w}P_{k} \tag{2}\]  
In Equation (2), \(P_{k}=\left(x_{k}^{p},y_{k}^{p},z_{k}^{p}\right)^{T}\) is the 3D spatial coordinate of the \(p\)th feature point in the \(k\)th frame; \(u_{k+1}=\left(u_{k+1}^{p},v_{k+1}^{p},1\right)^{T}\) is the pixel coordinate of the \(p\)th feature point in the \(k+1\)th frame, expressed in homogeneous coordinates; \(T_{k}^{w}\) represents the transformation matrix from the \(k\)th frame to the \(k+1\)th frame, which is composed of the camera's position and attitude, \(p_{k}^{w}\) and \(q_{k}^{w}\), respectively; and \(K\) is the camera internal parameter.  
The visual reprojection error is obtained from the forest images, and it provides visual constraints for the pose estimation of visual-IMU fusion.
## 2 Materials and Methods
### Geometrical Modelling and Governing Equations
\begin{split} Re\beta\
abla^{1}u+\frac{\partial p}{\partial x}= \
abla u+\lambda_{1}\beta\bigg{(}\frac{\partial}{\partial x}(u\
abla u)+ \beta^{2}\bigg{(}\frac{\partial}{\partial x}(v\
abla v)-v\
abla\frac{ \partial v}{\partial x}\bigg{)}+v\
abla\bigg{(}\frac{\partial u}{\partial y }\bigg{)}\bigg{)}\\ -\bigg{(}M^{2}+\frac{1}{K}\bigg{)}(u+1)+\frac{1}{4}(3\lambda_{1 }+2\lambda_{2})\frac{\partial A}{\partial x}\\ Re\beta^{3}\
abla^{1}v+\frac{\partial p}{\partial y}=\beta^{2} \
abla v+\lambda_{1}\beta\bigg{(}\frac{\partial}{\partial y}(u\
abla u)+ \beta^{2}\bigg{(}\frac{\partial}{\partial y}(v\
abla v)-u\
abla\frac{ \partial u}{\partial y}+\beta^{2}u\
abla\frac{\partial v}{\partial x}\bigg{)} \bigg{)}\\ +\frac{1}{4}(3\lambda_{1}+2\lambda_{2})\frac{\partial A}{ \partial y}-\frac{1}{K}\beta^{2}v\\ \text{where }A=2\bigg{(}\beta^{2}\frac{\partial v}{\partial x}+\frac{ \partial u}{\partial y}\bigg{)}^{2}+4\beta^{2}\bigg{(}\bigg{(}\frac{\partial u }{\partial x}\bigg{)}^{2}+4\bigg{(}\frac{\partial v}{\partial y}\bigg{)}^{2} \bigg{)}\end{split}  \tag{7}\]  
\[\
abla=\beta^{2}\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{ \partial y^{2}},\ \ \ \ \
abla^{1}=\bigg{(}u\frac{\partial}{\partial x}+v\frac{ \partial}{\partial y}\bigg{)}\]  
The relevant boundary conditions are:  
\[u=u_{0}=-1-2\pi\varepsilon a\beta\cos(2\pi x)\]  
\[v=2\pi\varepsilon\sin(2\pi x)+\beta(2\pi\varepsilon)^{2}\alpha\sin(2\pi x) \cos(2\pi x)\]  
\[\text{at}\ y=h=1+\varepsilon\sin(2\pi x) \tag{8}\]  
\[\frac{\partial u}{\partial y}=0,\ \ \ \ \ \text{at}\ y=0 \tag{9}\]  
The stream function \(\psi\) is defined as:  
\[u=\frac{\partial\psi}{\partial y},\ \ \ v=-\frac{\partial\psi}{\partial x} \tag{10}\]  
After eliminating the pressure gradient and using the assumption \(\beta<1\), i.e., the mean width of the mucus is less than the wavelength [31], the governing equations in terms of \(\psi\) take the following form:\[\frac{\partial^{4}\psi}{\partial y^{4}}-\left(M^{2}+\frac{1}{K}\right)\frac{ \partial^{2}\psi}{\partial y^{2}}=Re\beta\left(\frac{\partial\psi}{\partial y} \frac{\partial^{3}\psi}{\partial x\partial y^{2}}-\frac{\partial\psi}{\partial x }\frac{\partial^{3}\psi}{\partial y^{3}}\right)-\lambda_{1}\beta\left(\frac{ \partial\psi}{\partial y}\frac{\partial^{5}\psi}{\partial x\partial y^{4}}- \frac{\partial\psi}{\partial x}\frac{\partial^{5}\psi}{\partial y^{5}}\right) \tag{11}\]  
In the above expression, higher powers of \(\beta\) are ignored, as \(\beta<1\). In addition, if \(Re\) and \(\beta\) tend to zero, then the above expression can be reduced into the model flow of inertial free flow of a second-grade fluid due to ciliary flow, as shown by [PERSON] et al. [31]. When \(\lambda_{1}=0\), the present study turns to the study of an MHD Newtonian fluid flow through ciliated channel embedded in a porous medium at a moderate Reynolds number, as proposed by [PERSON] et al. [25].
# Self-Organizing Networks for 5G and Beyond: A View from the Top
### Motivation and Main Drivers for SON Deployment in Next-Generation Networks
Finally, step 3 includes the monitoring of the changes and reconfiguration deployed during step 2 and it provides feedback about how beneficial these are. The process repeats until the optimum result is achieved in a manual manner, leading to a long and time-consuming process. Network automation is the key to eliminating delays and the error-prone nature of manual activities by replacing the prementioned steps with automated procedures performed by ML algorithms [9; 28; 29]. Key drivers for SON deployment in existing and next generation networks:  
* B5G networks further increase the complexity of network planning and optimization processes since the former must coexist and counteract with the existing networks (2G/3G/4G/5G) in parallel. The network parameter design, tuning, and management lead to a high complexity in terms of smooth coordination among all existing networks, and it is obvious that as more complex architectures and B5G evolve, network management shall not be feasible without the use of automated SON functions operating in real time and taking direct feedback from the network [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Figure 1: Traditional vs. automated optimization. * The standardization of network traffic characteristics and parameters in order to support the basic 5G use cases (i.e., e-MBB, URLLC and MTC) should be embedded in SON systems and applications so that already set targets for 5G, such as low latency and high reliability, can be achieved. * The benefits of AI and ML for the operation of next-generation networks should be applied as the basic building blocks for B5G intelligent operation. SON platform advancements and network automation are clearly dependent on advanced ML algorithms and their implementations [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Simplistic automation solutions do not enable a distributed approach and they are not effective and efficient in complex environments. * Academia and the telecommunications industry to combine 5G use cases and intelligent techniques, such as C-RAN, mmWave, cognitive radio (CR), network slicing, spectrum sharing, NFV/SDN, mm-Wave (Millimeter-Wave), automated backhauling and massive MIMO (massive multiple-input and multiple-output). with next-generation SON platforms for maximum performance.
## 3 Results
### Overall Statistical Evaluation  
Based on the previous OOP evaluation, an overall statistical evaluation for all pulse durations (at a \(\kappa\)-value of 13) is presented in Table 1 For this data set, the algorithm-based RPE cell damage evaluation achieved an overall sensitivity of 85% and an overall specificity of 93%. The optimum sensitivity was achieved for laser pulses of 14 and 20 us duration (sensitivity for both: 90%). The optimum specificity was achieved for a laser pulse of 2 us duration (specificity: 97%).  
\begin{table}
\begin{tabular}{c|c c c c c|c} \hline
**Pulse Duration** & **2 μs** & **6 μs** & **10 μs** & **14 μs** & **20 μs** & **Overall** \\ \hline TP & 54 & 116 & 106 & 130 & 133 & 539 \\ TN & 112 & 65 & 69 & 51 & 47 & 344 \\ FP & 3 & 4 & 6 & 5 & 6 & 24 \\ FN & 31 & 15 & 19 & 14 & 14 & 93 \\ \hline Sensitivity & 0.64 & 0.89 & 0.85 & 0.90 & 0.90 & 0.85 \\ Specificity & 0.97 & 0.94 & 0.92 & 0.91 & 0.89 & 0.93 \\ Accuracy & 0.83 & 0.91 & 0.88 & 0.91 & 0.90 & 0.88 \\ PPV & 0.95 & 0.97 & 0.95 & 0.96 & 0.96 & 0.96 \\ NPV & 0.78 & 0.81 & 0.78 & 0.78 & 0.77 & 0.79 \\ \hline \end{tabular}
\end{table}
Table 1: Overall statistical evaluation for all pulse durations (OCT algorithm: \(\kappa\)-value of 13).  
Figure 7: **(a)** ROC curve of the dosimetry algorithm showing the performance at different pulse durations with respect to different \(\kappa\)-values. The best performance was obtained for a pulse duration of 6 μs (AUC = 0.97) and 14 μs (AUC 0.97); **(b)** in order to define the optimal operating point (OOP), the data points were fitted using OriginLab’s Logistic function (Logistic-FitFunc, indicated as solid lines). The data point that is closest to the 45\({}^{\circ}\) OOP reference line was determined as \(\kappa\)-value of 13 at a laser pulse duration of 6 μs (sensitivity: 0.89, specificity: 0.94).
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
###### Abstract  
This study aims to analyze electrocardiogram (ECG) data for the classification of five cardiac rhythms: sinus bradycardia (SB), sinus rhythm (SR), atrial fibrillation (AFIB), supraventricular tachycardia (SVT), and sinus tachycardia (ST). While SR is considered normal, the other four represent types of cardiac arrhythmias. A range of methods is utilized, including the supervised learning technique K-Nearest Neighbors (KNNs), combined with dimensionality reduction approaches such as Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP), a modern method based in Riemannian topology. Additionally, logistic regression was applied using both maximum likelihood and Bayesian methods, with two distinct prior distributions: an informative normal prior and a non-informative Jeffreys prior. Performance was assessed using evaluation metrics such as positive predictive value (PPV), negative predictive value (NPV), specificity, sensitivity, accuracy, and F1-score. Ultimately, the UMAP-KNN method demonstrated the best overall performance.  
Bayesian approach; UMAP dimensionality reduction; ECG; maximum likelihood; supervised machine learning Article
# A Geospatial Framework of Food Demand Mapping
## 4 Discussion
Additionally, a larger sample will allow for more detailed sub-group analyses, improving the understanding of different factors influencing food demand. Looking ahead, the research intends to develop an agent-based model of the food supply chain, utilizing the insights gained from the mapping exercise. To circumvent the challenges associated with conducting extensive surveys, we plan to integrate satellite imagery as a novel approach for predicting food demand at a granular level. This methodology promises to streamline the data collection process, offering more efficient and scalable means of understanding food demand dynamics without the logistical complexities of survey distribution and collection. However, it is important to note that, while satellite imagery can provide valuable data, it may also introduce its own set of biases, such as the potential misinterpretation of imagery data and the limitations of detecting certain food demand indicators from space. Future studies should consider these potential biases and develop strategies to address them, ensuring the accuracy and reliability of the findings. Another promising direction for future research is to integrate socio-economic indicators with food preferences obtained from surveys. By combining this information with census data, we could estimate actual food demand in precise quantities, rather than simply identifying trends. This approach would allow for a more comprehensive and accurate assessment of food demand, enabling policymakers and stakeholders to make more informed decisions regarding food security and resource allocation.
# Estimating Tail Probabilities of Random Sums of Phase-Type Scale Mixture Random Variables +
## 3 Simulation Methods
### Asmussen-Kroese-Type Algorithm  
The Asmussen-Kroese estimator [7] is efficient for estimating tail probabilities of light-tailed random sums of heavy-tailed summands [13].  
The key idea is based on the law of total probability and \(\mathbb{P}(S_{n}>u,\max\{Z_{i},i=1,\ldots,n\}=Z_{i})\) being the same for \(i=1,\ldots,n\) (as \(\{Z_{i},i=1,\ldots,n\}\) are i.i.d. random variables). We then have the following identity:  
\[\mathbb{P}(S_{N}>u|N=n)=n\mathbb{P}(S_{n}>u,\max\{Z_{i},i=1,\ldots,n\}=Z_{n})= n\mathbb{E}\overline{B}(Z_{n-1}^{*}\vee(u-S_{n-1}))\,,\]  
where \(\overline{B}(\cdot)\) is the complementary CDF of \(Z_{i}\), \(Z_{n-1}^{*}=\max\{Z_{i},i=1,\ldots,n-1\}\), \(S_{n-1}=\sum_{i=1}^{n-1}Z_{i}\), and \(x\lor y=\max(x,y)\).  
In our setting, the summands \(Z_{i}=W_{i}\,X_{i}\) are heavy-tailed whenever \(W_{i}\) has unbounded support [11] and so it is natural to consider this estimator here. Unfortunately, the Asmussen-Kroese approach is usually not directly implementable in our setting because the CDF of \(Z_{i}\) is typically unavailable.  
Instead, we consider a simple modification, by conditioning on a single scaling random variable \(W_{N}\) or on the PH random variable \(X_{N}\). We further consider applying a change of measure to this single random variable. Conditioning on \(N\), the Asmussen-Kroese estimator (without applying the control variate method for \(N\)) for \(\ell(u)\) takes the form  
\[\widehat{\ell}_{\text{AK}}(u)=N\,\overline{B}\big{(}Z_{N-1}^{*}\vee(u-S_{N-1} )\big{)}\,. \tag{2}\]  
In our context, when conditioning on the \(W_{N}\) associated with \(Z_{N}\), we arrive at  
\[\widehat{\ell}_{\text{ConAK1}}(u)=N\,\overline{F}\bigg{(}\frac{Z_{N-1}^{*} \vee(u-S_{N-1})}{W_{N}}\bigg{)}\,, \tag{3}\]  
where \(\overline{F}(\cdot)\) is the complementary CDF of a PH random variable.  
When conditioning on the \(X_{N}\) associated with \(Z_{N}\), we arrive at  
\[\widehat{\ell}_{\text{ConAK2}}(u)=N\,\overline{H}\bigg{(}\frac{Z_{N-1}^{*} \vee(u-S_{N-1})}{X_{N}}\bigg{)}\,, \tag{4}\]  
where \(\overline{H}(\cdot)\) is the complementary CDF of a scaling random variable.  
We then consider improving the efficiency of our algorithm by implementing IS over the distribution \(H\) or \(F\), ensuring that the random sum after conditioning is equal in expectation to \(u\). Inspired by popular methodologies drawn from light-tailed and heavy-tailed problems, we suggest two alternative approaches.