esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Filtering Based on a Variational Gaussian Mixture Model
## 3 Method
### Variational Autoencoder  
A variational autoencoder is a generative model, whose purposed is to discover the principle of data generation in the form of probability distribution. For each user \(u\), the model samples an \(M\)-dimensional latent factor \(z\) from a standard Gaussian prior distribution. It is assumed that the input \(x\) is generated by the following process:  
\[z \sim\mathcal{N}(0,I_{M}) \tag{1}\] \[x|z \sim p_{\theta}(x|z),\]  
where \(z\) is a latent factor that cannot be observed, and the dimension is lower than the dimension of the input \(x\). The goal of VAE is to maximize the log likelihood (maximum likelihood estimation (MLE)) of the data to estimate the parameter \(\theta\). The MLE is calculated through:  
\[p_{\theta}(x)=\int p_{\theta}(x|z)p_{\theta}(z)dz. \tag{2}\]  
Equation (2) needs to calculate all latent representations, which is a problem that cannot be solved directly. To make this problem tractable, we usually use a posterior distribution \(q_{\phi}(z|x)\) to approximate the true posterior \(p(z|x)\), where \(\phi\) is a parameter. In traditional variational autoencoders, we usually use a standard Gaussian distribution, and the similarity between \(q_{\phi}(z|x)\) and the posterior distribution \(p(x|z)\) is measured by minimizing the Kullback-Liebler(KL) divergence. Since the marginal likelihood function is difficult to obtain, it is usually obtained approximately via the evidence lower bound (ELBO):  
\[logp_{\theta}(x) \geq\mathbb{E}_{q_{\phi}(z|x)}[logp_{\theta}(x|z)-KL(q_{\phi}(z|x )||p_{\theta}(z))] \tag{3}\] \[=\mathcal{L}(x;\theta,\phi),\]  
where \(\mathbb{E}[\cdot]\) denotes the expectation. The first term of the reconstruction error is to reduce the distance between the input \(x\) and the reconstruction \(x^{\prime}\), while the second term is the KL divergence between the variational posterior \(q_{\phi}(z|x)\) and the standard Gaussian distribution \(p_{\theta}(z)\). It should be noted that \(p_{\theta}(z)=\mathcal{N}(0,I_{M})\), \(q_{\phi}(z|x)\) embeds the input \(x\) into a low-dimensional manifold.  
In fact, \(p_{\theta}\) and \(q_{\phi}\) determine the parameters through two deep neural networks, corresponding to the encoder \((f_{\theta})\) and decoder \((g_{\phi})\), respectively. These parameters are optimized using stochastic gradient algorithms with the help of a reparameterization trick [26; 27]. Finally, the encoder network determines the parameters, such as mean \(\mu\) and variance \(\sigma\), by defining the distribution of each data point \(z\). Since the sampling operation is non-differentiable, it is transformed as follows:  
\[z=\mu+\sigma\odot\epsilon, \tag{4}\]  
where \(\epsilon\) is sampled from the standard normal distribution. Since only linear transformation is involved, the stochastic gradient algorithm can be used to update the parameters.
## 2 USBL Positioning Principle  
The USBL achieves the underwater target positioning by transmitting and receiving acoustic signals [12]. The USBL consists of at least three hydrophones and a transmitter placed on a ship bottom. After a transmitter emits an acoustic signal, the transponder placed on the underwater target receives the transmitted signal. According to the time that passes from transmitting to receiving of the underwater acoustic signal, the distance between the target and an ultra-short baseline array can be obtained. The azimuth of the underwater target to the transceiver head is calculated based on the time difference or phase difference of arrival of the acoustic wave at each hydrophone, and the three-dimensional position coordinates of the underwater target are obtained through the geometric formula [13].  
As shown in Figure 1, the ultra-short baseline adopts a right-angled triangle array with a spacing of \(d\). The right-angle triangle model is simple, streamlined, easy to calculate, and quick to understand.  
Let set No. 2 hydrophone as an array center (0, 0, 0), then the phase difference [14] between the baseline and the other two hydrophones is \(\psi_{12}\) and \(\psi_{22}\) respectively. The obtained sound speed is converted to the wavelength using the relation  
\[c=f\cdot\lambda \tag{1}\]  
where \(f\) is the center frequency of the received signal, \(c\) is the speed of the sound, and \(\lambda\) is the wavelength corresponding to the frequency \(f\); all quantities are measured by the underwater acoustic monitoring equipment. Using the USBL principle and geometry physics, the three-dimensional coordinates of the target are  
\[l^{2}=\mathrm{X}^{2}+\mathrm{Y}^{2}+\mathrm{H}^{2} \tag{2}\]  
\[\psi_{12}=\frac{2\pi}{\lambda}\bigg{[}\sqrt{l^{2}}-\sqrt{\left(\mathrm{d}- \mathrm{X}\right)^{2}+\mathrm{d}^{2}+\mathrm{H}^{2}}\bigg{]} \tag{3}\]  
\[\psi_{22}=\frac{2\pi}{\lambda}\bigg{[}\sqrt{l^{2}}-\sqrt{\mathrm{X}^{2}+\left( \mathrm{d}-\mathrm{y}\right)^{2}+\mathrm{H}^{2}}\bigg{]} \tag{4}\]  
where \(d\) is the spacing between the array elements, \(\psi_{12}\) and \(\psi_{22}\) are the phase difference between the second and the other two hydrophones, and H is the height of the target array element, X is the distance of the target along the x-axis direction, Y is the distance of the target along the y-axis direction, \(l\) is the slant distance of the target from the USBL, and triple (X, Y, H) denotes the three-dimensional coordinates of the target relative to the array.  
Sound propagation speed has a very little effect on the phase difference of the signals received by the hydrophones in the transceiver head. One of the main factor affecting the positioning accuracy of the USBL is the measuring error caused by sound propagation in the slant range. Therefore, it is particularly necessary to take into account the form of the sound propagation path to improve the slant range accuracy of the target and the array.
## 2 Data and Segmentation Methods
### Dataset
#### 2.1.3 Spine Dataset  
Spine dataset consists of 10 CT scans of different patients in the age 16-35 years old. The pictures had resolution 512 \(\times\) 512 pixels and were provided in NRRD format. Number of slices in each scan was in range from 520 to 600 slices in the third dimension. Scans cover lumbar and thoracic spine region and were acquired without intravenous contrast. Slice thickness is 1 mm per slice and the in-plane resolution is between 0.31 and 0.45 mm. The data have been acquired at the Department of Radiological Sciences, University of California, Irvine, School of Medicine and scanners used include Philips or Siemens multidetector. Data were published as a part of 2014 CSI workshop challenge of the web [[http://spineweb.digitalimaginggroup.ca](http://spineweb.digitalimaginggroup.ca)]([http://spineweb.digitalimaginggroup.ca](http://spineweb.digitalimaginggroup.ca)). Dataset can be used for development, training and evaluation of spine segmentation algorithms. Image data are provided in NRRD format. An example image slice and according ground truth mask can be seen in Figure 3.  
Ground truth segmentation masks have been semi-automatically segmented and verified for complete thoracic and lumbar vertebrae for each scan. Segmentation masks are also stored in NRRD filed and originally each verteb had assigned different label. The first verteb was labelled as 100, the second as 200 and so on. For the semantic binary volumetric segmentation task the masks have been thresholded to greyscale 8 bit PNG files and had format where vertebrae tissue was assigned with a value of 255 and non-vertebrae tissue with the value 0 [19].  
Figure 2: Example of brain dataset sagittal image slice (**left**) and according ground truth mask (**right**).
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 2 Related Works  
### Decision Map-Based Deep Learning Methods  
Decision map-based deep learning methods have significantly advanced MFF by providing interpretable and precise fusion guidance. This approach typically involves a neural network learning to identify focused or defocused regions, which then informs the final fusion. A notable advantage of this approach is that the models are generally smaller than end-to-end fusion networks. This is because they only need to learn to classify or regress a decision map for each pixel or patch, which is a simpler task than learning to directly synthesize a complete fused image. Consequently, these networks are more efficient and easier to train.  
A pioneering work by [PERSON] et al. in 2017 [PERSON] et al. (2017) introduced a CNN-based method to generate decision maps through binary patch classification, laying the groundwork for deep learning in MFF. Subsequent efforts enhanced this foundation: ECNN [PERSON] et al. (2017) utilizes an ensemble of CNNs for more robust focus detection, while DRPL [PERSON] et al. (2017) reframes focus detection as a regression task to learn continuous focus measurements, significantly improving accuracy in boundary regions.  
Recent years have seen further innovations in this paradigm. Unsupervised learning frameworks like SESF [PERSON] et al. (2017) emerged, eliminating the need for labeled data. Generative adversarial networks (GANs) were leveraged in MFIF-GAN [PERSON] et al. (2017) to improve decision map quality. MSFIN [PERSON] et al. (2017) introduced multi-scale feature interaction for more precise fusion. ZMFF [PERSON] et al. (2017) proposed a zero-shot learning framework using a deep image prior, enhancing generalization to unseen scenes.  
Most recently, MFIF-STCU-Net [PERSON] et al. (2017) proposed synthesizing training datasets using a depth estimation model and achieved state-of-the-art performance with a multi-level feature fusion (MFF) model that uses a hybrid U-Net and Transformer architecture. DMANet [PERSON] et al. (2017) introduced a novel framework that integrates explicit defocus blur modeling into the MFF process, improving both interpretability and performance. LSKN-MFIF [PERSON] et al. (2017) is a new MFF network that uses a dynamically adjusting \"large selective kernel\" module to capture both global and local features, leading to more accurate fusion results that outperform existing methods.
## 6 Discussion  
(1) It is important to point out that our scheme is only applicable to cases which do not need to cancel the central optical carrier. If our designed mothed is adopted in a system that needs to cancel the central carrier, the data signal will be modulated onto the carrier component in addition to the +nth-order sidebands, and the center carrier that contains data cannot be completely cancelled out.  
We proposed a method to solve this problem. A band-rejected filter is added before the PD to filter out the carrier with data. This method can ensure the data signal is modulated only on the +nth-order sidebands. Adopting a filter will increase the cost of the system and limit the tenability of the system.  
(2) The method proposed in this paper can be suitable as a generic method to generate 2n frequency multiplication MMW, with the data carried only on the +nth-order sidebands.  
According to the theoretical analyses, the key parameter for the realization of the data carried only on +nth-order sidebands is the selection of the P of PM and G of EG.  
Figure 19: Relation of the Q factor with the P and the G.  
Figure 18: Relation between the G of EG and system Q factor.  
We suppose the RF drive signals loaded onto the MZMs can be expressed as follows:  
\[V_{i}(t)=V_{RF}\cos[\omega_{RF}t+Ps(t)+\varphi_{i}]+Gs(t) \tag{13}\]  
The final output expression after multiple MZMs can be expressed as follows:  
\[E_{out}=E_{0}e^{i\omega_{0}t}\sum_{n=-\infty}^{\infty}J_{2n}(m)e^{j2n[\omega_{ RF}t+Ps(t)]+j\frac{\varphi_{i}G}{\varphi_{n}^{\mathrm{c}}}(t)} \tag{14}\]  
To make it possible to carry data only on the +nth-order sidebands, it is necessary to satisfy the following equation:  
\[\left\{\begin{array}{l}2 nP+\pi G/V_{\pi}=(2k_{1}+1)\pi\\ -2 nP+\pi G/V_{\pi}=2k_{2}\pi\end{array}\right. \tag{15}\]  
where the \(n\) is the order of the sidebands and \(k_{1}\) and \(k_{2}\) are integers.  
From Equation (15), we can obtain the following:  
\[G=(k+1/2)V_{\pi},P=\left(k^{\prime}+1/2\right)\pi/2n \tag{16}\]  
where \(k=k_{1}+k_{2}\) and \(k^{\prime}=k_{1}-k_{2}\) are integers.  
Due to the limitation of the manuscript length, this paper did not discuss the issues presented above; they will be the subjects of our future research.  
(3) The experiments in our manuscript are simulation experiments with \"Optisystem 15.0\" software. According to the literature available to us, due to the limitations of the experimental conditions in the schemes for generating high-frequency MMWs using MZMs, some schemes in which the frequency multiplication factor (FMF) is less than 12 were verified through experiments, and the innovation of most schemes in which the FMF is greater than 12 were generally verified by simulation. Due to the limitations to conditioning in our laboratory, it is still difficult to experimentally verify the method proposed in this paper. In the future, if the experimental conditions are available, we will continue with the related physical experiments.
## 2 Methodology
### Feature Aggregation Module  
The feature aggregation module primarily serves to integrate both global and local features, resulting in a new feature that encompasses local and global information. The global features are derived from the global feature extraction module, while the local features are obtained from the feature coding module. The module comprises 6 transformer units within the network architecture, with the detailed computation described as follows:  
\[x_{gl}=Transformers\Big{(}Concat\Big{(}x_{l}^{4},\ x_{g}\Big{)}\Big{)}, \tag{3}\]  
where \(x_{g}\) represents the global feature output of the global feature extraction module, \(x_{l}^{4}\) represents the last layer of local features output by the feature coding module, \(Concat\) stands for feature concatenation operation, which connects two sets of features together along the channel dimension to form a new, larger set of features, \(Transformers\) represents the network structure adopted by the feature aggregation module in this paper, which consists of 6 multi-head transformer units, and \(x_{gl}\) represents the new features after feature aggregation. The transformer unit used here is the same as the transformer unit of TransUNet [30] in Section 2.2.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### Right to Rectification and LLMs  
Article 16[7] of the GDPR provides individuals with the right to have inaccurate personal data corrected or completed. This ensures that data controllers maintain accurate and up-to-date information about data subjects.  
Rectification in LLMs is challenging because, once trained, the models lack mechanisms to update or correct specific data points without comprehensive retraining easily. Personal data within these models is often intertwined with other information, making isolated corrections difficult. While methods such as fine-tuning with corrected data, continuous learning frameworks, or modular updates have been proposed, each approach has its limitations. Fine-tuning may not fully eliminate residual inaccuracies if the model retains influences from the original data, and retraining large-scale models is resource-intensive. Consequently, significant advancements in model architecture and training methodologies are needed to effectively implement the Right to Rectification in LLMs [23; 37; 39; 41; 42; 55; 56].
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## Using Mobile Radio Frequency (RF) Power Charger to Charge the Batteries of Sensors in a WSN  
### System Model and Design Objectives  
In [9], a WSN was considered in a smart grid with immobile nodes that utilize radio frequency (RF) energy of a mobile charger that visits specific landmarks to replenish their batteries. This was an extension to previous work (the scheme SuReSensE) that minimized the number of landmarks by considering priorities of the monitored equipment in the grid. The proposed scheme in [9], _Differentiated RF Power Transmission_ (DRIFT), aims to deliver more power to high priority nodes using a mobile power transmitter by solving an integer linear program (ILP) to determine its optimal landmark positions. The possible landmarks of the mobile charger were assumed to be known in advance.
## 3 Experimental Results and Analysis
### Hysteresis Loop  
By observing the hysteresis curves of the five specimens in Figure 8, it can be seen that all five specimens were pinched together in an inverse S-shape. In the elastic stage, the hysteresis curves of the specimens plus and minus the hysteresis curves basically overlapped. The hysteresis ring enclosed a small increase in area from the specimen cracking to the gradual yielding stage. The residual deformation was small. With the increasing loading displacement and the damage accumulation, the area of the hysteresis loop gradually increased. The energy dissipation of the specimen increased accordingly.  
In comparison, W-5 was a solid wall, and the integrity was better compared with the remaining four sandwich wall panels. By comparing the hysteresis curves of specimens W-1 and W-5, it could be found that the hysteresis phenomenon of the W-1 recovering deformation was not obvious. This phenomenon indicated that the uneven force in the sandwich wall panel ensured that the steel reinforcement did not play a full role.  
The load-bearing materials selected for W-1 and W-4 were different. According to a comparison of the hysteresis curves of the two specimens, the difference was the instance of the load of W-4 decreasing faster after the peak load. This indicated that the ductility of W-1 was better based on the design scheme of the sandwich wall panel studied in this project.  
Figure 8: _Cont._Finally, the hysteresis curves of W-1 were compared with those of W-2 and W-3. W-2 showed a significant recovery deformation hysteresis. The result indicated that the addition of fibers could alleviate the problem of poor integrity caused by the sandwich core. Additionally, it was conducive to the establishment of force transfer paths and the energy dissipation capacity of the wall panel. In contrast, although the hysteresis curve of W-3 showed a more obvious inflection point, its hysteresis loop area was reduced, and the overall trend was more biased toward the displacement axis with a larger slip. This  
Figure 8: Hysteresis loops of specimens.  
phenomenon indicated that fiber agglomeration was easily caused when the fiber doping was higher. This, in turn, caused a decrease in the load-bearing capacity.
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms
### TOF Estimation  
The algorithm of the whole system focuses on finding the start point of the reflected ultrasound signal, namely TOF estimation. In this section, the NRLM algorithm based on the GN model and the DE model is proposed and applied in the system.
# Designing a Vertical Handover Algorithm for Security-Constrained Applications
## 5 Conclusions  
In performing seamless vertical handover, VHO packet loss, VHO delay, and VHO throughput remain major obstacles. Moreover, the security aspect of triggering VHO has not been adequately studied, particularly when an MU plans to utilize a payment application. We therefore present a comprehensive performance evaluation for a new secure VHO algorithm, taking into account these obstacles, for co-located Wi-Fi and UMTS networks. The performance of the proposed algorithm is tested in a number of scenarios with various numbers of MUs. We have seen that the proposed algorithm performed significantly compared to the MIH standard, which enables the seamless transfer of connection among heterogeneous networks in terms of various factors, such as VHO delay, VHO handover time, VHO throughput, and VHO packet loss. During the experiments, we saw that the MIH standard suffered from high VHO delay and VHO packet loss due to frequent handovers. However, the proposed algorithm always performs well, and the VHO packet loss is always less than the 3% packet loss threshold defined by the MIH standard. Finally, in the future, we will implement the proposed system on real a testbed for advanced networking technology, such as 5G and beyond.  
Conceptualization, O.K. and M.K. Methodology, O.K., M.K. and B.A. Writing--original draft, O.K., M.K. and B.A. Writing--review & editing, O.K. and M.K. Visualization O.K., M.K. and B.A, Supervision, O.K. and M.K. All authors have read and agreed to the published version of the manuscript. This work is not supported by any funding agency.  
Not applicable.AcknowledgmentsWe deeply acknowledge Kuwait College of Science and Technology for supporting and providing a research environment to conduct this study.  
The authors declare no conflict of interest.  
Figure 7: Analysis of the number of frequent handovers during the mobility of MUs in the simulation scenario.
# Design of a Digital Array Signal Processing System with Full Array Element
## 2 System Architectural Design
### System Software Architecture Design
#### 2.2.2 Broadband Calibration Design
Figure 15b shows that the amplitude error between the channels is small, and the maximum difference is only 0.5 dB. The results verify that the proposed method can achieve broadband calibration. Figure 14: Time-domain waveform after broadband calibration. Figure 13: Process of broadband calibration implementation.
## 3 Results
### Fabrication and Characterization of Organic Complementary Ring Oscillator Array  
Organic five-stage ring oscillators with a 10 \(\times\) 10 device layout were fabricated, as shown in Figure 4a. By integrating an odd number of inverters, we were able to fabricate a ring oscillator circuit. The circuit diagram of the device is shown in Figure 4b. An optical microscope image of our ring oscillator is shown in Figure 4c. The average W/L values for p-type and n-type semiconductors were 960/60 and 960/55 \(\upmu\)m, respectively. Oscillation frequency measurements were performed by measuring the voltage of the buffer inverter, whose input was driven by the oscillator output, using a digital oscilloscope with a high input impedance probe (66.7 M\(\Omega\), 3 pF). Figure 4d shows the signal propagation delays per stage and oscillation frequency measured with these ring oscillators plotted as a function of the operating voltage containing standard deviation error bars with 10 devices. The average value of the coefficient of variation, being the ratio of the magnitude of the data variation to the average, is 6.0%, showing that the variation is small. The measured stage delay is 28 ms at an operating voltage of 1 V, 8.2 ms at 2.5 V, 3.1 ms at 5 V, 1.7 ms at 7.5 V, 1.1 ms at 10 V, 0.54 ms at 15 V, and 0.34 ms at 20 V. The delay time of each inverter is defined as 1/2 Nf\({}_{\text{osc}}\), where N and f\({}_{\text{osc}}\) are the number of stages and the oscillation frequency, respectively. This value is a little slower than that in our previous research [18]. Comparing this delay time with the representative report results, ring oscillators fabricated by combining printed electrodes and semiconductors are in the high-speed class. Further high-speed operation can be expected by improving the mobility and shortening the channel by increasing the resolution of the source and drain electrodes. Figure 4e displays the output signal of the ring oscillator as a function of the operating voltages of 2 V, 5 V, 10 V, and 20 V.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operating Voltage (V)** & **Gain** & **Switching Point Forward/Reverse (V)** & **Noise Margin (V)** \\ \hline
2.5 & 2.3 & 1.5/1.4 & 0.19 (15\%) \\
5.0 & 4.3 & 2.6/2.4 & 0.73 (29\%) \\
7.5 & 8.2 & 3.5/3.4 & 1.34 (36\%) \\
10.0 & 8.9 & 4.8/4.4 & 1.96 (39\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of static characteristics of complementary inverter circuit.  
Figure 3: (**a**) Circuit diagram of complementary inverter circuit, (**b**) symbolic, and (**c**) input-output characteristics of the fabricated device.