esa-sceva/satcom-chunk-collection huggingface dataset:
# Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data
## 7 Discussion  
The results complete a probabilities matrix by one eigenvector. Fifty-six probabilities are obtained by the optimizing program. The 28 results in block 2 of the DTBM represent the conditional probabilities of each medical regimen under each symptom, while the other 28 results in block 3 of DTBM represent conditional probabilities of each symptom under each medical regimen. All the completed elements follow [PERSON] rule [14] that the eigenvector of the completed matrix is exactly what we assumed. The completed conditional probabilities can reveal the relationship between diabetes symptoms and medication regimens.  
Due to the crude nature of the previous data, it is difficult to find the symptoms of each patient in the early stages of diabetes and the treatment plan given by the doctor for each characteristic. However, statistics are more readily available on the overall proportion of treatment options allocated to the case and the probability distribution of the occurrence of each symptom. The accuracy of the treatment recommendation can be significantly improved if the treatment plan for each case for each symptom is completed with known outcome information from successful treatment cases. Current research in the literature on missing value completion focuses on low-rank matrix completion methods such as alternating least squares. Alternating Least Square is doing a pretty good job at solving the scalability and sparseness of the rating data, and it's simple and scales well to very large datasets. But Alternating Least Square or other matrix completion methods are based on matrix factorization algorithms like SVD that need to have parts of the matrix. That can not be useful in the problem of this paper that the information of matrix was all lost. Comparing the existing methods, such as alternative least squares [15], this model produced more information from less.  
The produced information we found respected [PERSON]'s theorem [16], but at the same time, the relations between symptoms and regimens are dug out of overall links due to the eigenvectors! These relations can be used to support doctors, especially the completed conditional probabilities of regimens under each symptom. Doctors can make a medical plan according to these probabilities.  
The restricted condition of our model is the probability of different symptoms in diabetic patients, and the probability of each medication regimen being used are the stable probability distributions of the symptoms and medication regimens in people with diabetes, and it mathematically conforms to the stable state theory of the Markov matrix. Based on the above information, the sum of the completed values in each column must be one, which limits this model to apply specific problems.
## 3 Results
### ERP Analyses
#### 3.1.3 ERP Analyses: Signal Variance  
There were significant effects of condition on standard deviation of the grand averaged epochs for both targets and non-targets, at both Pz and in the pooled signal (all \(\chi^{2}(2)\) values \(\geq\) 8.083; all \(p\) values \(\leq\) 0.018). In the pooled signal, small jitter target averages (\(Mdn\) = 2.30) demonstrated smaller standard deviations than no jitter targets (\(Mdn\) = 2.64), \(Z\) = 3.031, adjusted \(p\) = 0.007. Similarly for pooled signal non-targets, small jitter variance (\(Mdn\) = 0.88) was smaller than no jitter (\(Mdn\) = 1.15), \(Z\) = 3.175, adjusted \(p\) = 0.004, and the same was true for the large jitter condition (\(Mdn\) = 0.79), \(Z\) = 5.052, adjusted \(p\) < 0.001. At Pz, standard deviations of small jitter target averages (\(Mdn\) = 3.05) were smaller than either no jitter targets (\(Mdn\) = 3.78), \(Z\) = 2.454, adjusted \(p\) = 0.042, or large jitter targets (\(Mdn\) = 3.41), \(Z\) = \(-\)2.742, adjusted \(p\) = 0.018. For non-targets measured at Pz, standard deviation estimates were significantly lower in the large jitter condition (\(Mdn\) = 0.88) than in the no jitter condition (\(Mdn\) = 1.03), \(Z\) = 2.742, adjusted \(p\) = 0.018.  
Unlike the grand averages, there were no effects of condition on the point-by-point averages of standard deviations within the window of 200-600 ms post-stimulus onset. The only item of note was a trend in the pooled signal toward smaller average ERP non-target standard deviations for the small (\(Mdn\) = 8.97) and large jitter (\(Mdn\) = 8.99) conditions, compared to no jitter (\(Mdn\) = 9.37), \(\chi^{2}(2)\) = 5.250, \(p\) = 0.072.
# Translating Speech to Indian Sign Language Using Natural Language Processing
### HamNoSys V/S ISL Gestures  
Unlike other sign language scripts, HamNoSys is not intended to be a practical writing tool for everyday communication. Rather, its motivation is similar to that of the International Phonetic Alphabet, which is intended to consistently transcribe the sounds of any spoken language [1]. Because the increased number of possible parameter versions precluded the use of a well-known alphabet, newly produced glyphs must be developed in such a way that memorizing or deducing the meaning of the symbols is as simple as possible [2].  
HamNoSys consists of handshapes and gestures as shown in Figure 1.  
Indian Sign Language consists of gestures as shown in Table 1.  
HamNoSys contains pointers as shown in Figure 2.  
Figure 1: Various Handshapes in HamNoSys.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**One Hand** & **Open Hand** & **Zero Hand** & **Five Hand** \\ \hline U-hand & L-hand & Y-hand & C-hand \\ \hline V-hand & C-hand & Full U-hand & Full C-hand \\ \hline Fist hand & Claw hand & Closed two hands & Closed four hands \\ \hline \hline \end{tabular}
\end{table}
Table 1: ISL Handshapes.  
When it comes to body gestures, as the name suggests, it includes body movements other than hand gestures, such as head movements, facial expressions, mouth shapes, etc.andtaken as modulation, when speaking we modulate our voice to show the variation in our speech.  
Talking about one-handed, it is explained as static and dynamic movements which are further divided into manual and nonmanual.  
Two-handed signs are divided into the above sections too, but movements can be further divided into Type 0 and Type 1. Explaining the terms, Type 0 includes the signs which are performed by both hands, whereas Type 1 includes the signs which too involve both hands, but one hand is dominant over the other, taking the lead.
## 6 Simulation Results  
This section presents simulation results based on laboratory tests carried out using the testbed described in Section 5.1, where satellite IoT terminals (either from the MASSIVE TGR or terminal) transmitted bursts according to the ME-SSA protocol using one of the MASSIVE waveforms defined and implemented in the project (see Table 1). The aggregated traffic was then received and demodulated via the ME-SSA receiver described in Section 4 at the MASSIVE Gateway.  
First, Section 6.1 validates the implementation of the new algorithms and waveforms in the test-bed, discussing also the causes of the implementation loss in the proposedreal-time ME-SSA receiver compared to the theoretical results in the literature, based on a non-real time receiver with perfect and full knowledge of the interfering scenario.  
Section 6.2 provides results in different a scenario of interests, considering the case of power spreading across bursts, the application to non-GEO satellite scenarios and the adoption of a QPSK modulation with an SUMF only receiver.
## 2 Development of a Numerical Model  
### Computational Domain and Scanning Pattern  
In this study, a numerical simulation using the time-dependent heat transfer module of COMSOL Multiphysics (version 5.5) was conducted to analyze the temperature distribution during laser dopant activation annealing. The computational domain used in the simulation is presented in Figure 1. The size of the computational domain is 7.5 mm \(\times\) 2.7 mm \(\times\) 0.75 mm (length \(\times\) width \(\times\) height). Through preliminary numerical tests, it was found that the temperature change at the boundary of x and y direction computational domain (7.5 mm \(\times\) 2.7 mm) was negligible. The real thickness of a commercial wafer was adopted for the height of the computational domain. In order to increase the accuracy of the calculation, the area irradiated by the laser beam was finely meshed to a size of 40 \(\upmu\)m. For the surrounding area, a growth-distributed mesh capable of increasing the mesh size was adopted to reduce the computation time. In this simulation, two scanning patterns (zig-zag and bidirectional pattern) were tested, as shown in Figure 2. The dashed line with the arrows indicates the scanning path of the laser beam. \(\Delta x\) (4 mm) represents the distance at which the laser beam scans along the \(x\)-axis direction. \(\Delta y\) is the distance between two adjacent scan tracks, and it varies from 200 \(\upmu\)m to 400 \(\upmu\)m according to the beam overlap ratio (0-50%). For the zig-zag pattern, the laser beam continuously irradiates the surface along the scan paths. On the other hand, for the bidirectional pattern, the laser beam is switched off when it moves in the \(y\)-axis direction. The laser beam is again switched back on as it proceeds to the next track.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 2 Related Work
### Normalizing Flow  
Normalizing Flow [18] is a branch of generative model approaches that has received less attention. It aims to parametrize a complex distribution \(p_{\mathbf{y}}(\mathbf{y})\) into a simple distribution \(p_{\mathbf{z}}(\mathbf{z})\) (e.g., standard Gaussian) using an invertible neural networks \(f_{\theta}\), and it maps the samples from \(\mathbf{z}\sim p_{\mathbf{z}}(\mathbf{z})\) using the reverse forward step \(\mathbf{y}=f_{\theta}^{-1}(\mathbf{z})\) during inference. Thus, the network can be trained directly by minimizing the negative log-likelihood \(-\log p_{\mathbf{y}}(\mathbf{y})\) with any back-propagation algorithms. In recent years, various works [19; 20; 21] have been proposed to improve the performance of normalized flow on image generation tasks, but there are few works on image degradation learning. In this paper, we will learn the image degradation via conditional normalizing flow in a supervised scheme from existing real-world SR datasets.
## 4 Findings
### Generic Approaches
For the consensus algorithm, the blockchain is divided into the main-chain and side-chain to ensure better performance. A novel DWD mechanism is used for dynamic weight distribution with many metrics, such as Cardinal Value (CV), Data Validity (DV), and Contact Degree (CD). This architecture is implemented and evaluated; it results in a better performance than the NKC scheme. A study proposed a blockchain-based privacy-preserving and trust-centric approach and Proof-of-Trust (PoT) consensus algorithm to tackle the challenges related to trustworthiness and create an affordable and lightweight consensus mechanism [97]. This study included a trust evaluation mechanism, PoT consensus algorithm, and privacy protection mechanism. The commitment scheme and ring signature combine to create a robust privacy protection mechanism. On the other hand, PoT is designed by connecting the trust value of network miners with mining difficulty. The design of the proposed DSA system included four phases: individual sensing, sensing fusion, spectrum allocation, and spectrum access. This system offers decentralization, transparency, automation, and flexibility. The proposed consensus algorithm increased the scalability and reduced computation cost. With blockchain technology, a lightweight multi-chaincode model is proposed to address central authority management issues that lead to a lack of privacy, low scalability, and single point of failure [98]. The proposed system includes various layers, such as Consortium Blockchain Manager (CCBCM) for access control, an Aggregated Edge Blockchain Manager (AEBCM) layer for communication purposes, and Edge Blockchain Managers (EBCMs) that contain network devices. To achieve the required scalability, low latency, and high throughput, a hierarchical permissioned blockchain is used. EBCM is used within the cluster to manage the data securely. This model eliminates Trusting Third Parties (TTP) by incorporating self-executed smart contracts. The authors provided a security analysis discussion on how the proposed model offers availability, integrity, and confidentiality.
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 2 Related Work
A database of RPs is constructed using fingerprinting, and nearest neighbor RPs are obtained through an iterative search process. Afterward, the position of the UE is determined using a weighted average. In [23], the location of the UE could be determined by sequentially applying simple techniques. As the number of reference points increases, the time for repeated searches also increases significantly. The authors of [24] performed transmitter identification and UE position determination using RGB LEDs. The transmitter transmits an LED ID using frequency division multiplexing (FDM), and the UE receives it to identify the location of the transmitter. Afterward, the location of the UE is determined using a triangulation technique. In [24], the authors achieved high positioning accuracy using RGB LEDs. However, there is a limitation that it can only be applied to indoor environments using the CIE 1931 standard. This study improved the performance of the double deep Q-Network model by applying fingerprinting and Cell-ID techniques based on an analysis of the existing literature. In the case of reinforcement learning, there is an issue with the initial state being set randomly, which leads to prolonged optimization processes. Therefore, the time for optimization can be reduced by limiting the initial state of the agent using the Cell-ID method. In addition, quick optimization was performed using the learned agent without searching all reference points in the fingerprint database. Detailed information regarding this is covered in Section 4.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 1 Introduction
Having stated that, this work will concentrate on Sentinel-1 C-band cases while also comparing them to other bands. This paper provides a thorough explanation for readers who are not familiar with InSAR techniques and will also provide the fundamental knowledge of InSAR error sources for those who wish to advance their skills in InSAR phase interpretation.
# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
###### Abstract
They make some experimental changes on the MLP layer or the output layer: for instance, changes in the number of layers, activation, or loss function. Two or more approaches are also merged to predict the ratings. However, to the best of our knowledge, there is no study that performs preprocessing on data before providing it into the DNN. It is important because input quality determines output quality. Therefore, the research focuses on preprocessing the original data to improve the input data quality. This is completed as follows: (1) identifying the dataset's noisy (inconsistent) ratings and correcting them using the matrix factorization approach; and (2) assigning weightage to the recent ratings using the time function, i.e., Power. The resultant data matrix is provided as the input into the DNN model for training purposes. The embedding layers are used in the DNN because they reduce the input size and computational complexity, leading to faster training times. As such, we summarize the contributions as follows:  
1. In this paper, a Time Decay-based Deep Neural Network (TD-DNN) is proposed, in which, firstly, the noisy ratings are identified from the dataset and corrected using the Matrix Factorization approach. After this, to give the weightage to the most recent ratings, a power decay function is used. The resultant data matrix is inserted as the input into the DNN model for the training purposes. 2. The model incorporates the embedding layer to a Multi-Layer Perceptron to learn the N-dimensional and non-linear interactions between users and movies. The Huber loss function, which has shown outstanding efficacy across all the loss functions, is used. 3. The experiment has been conducted on benchmark datasets to examine the new model's efficiency and compare it with the existing approaches. The results on the MovieLens-100k, ML-1M and Epinions datasets show that the proposed TD-DNN architecture enhances recommendation efficiency and solves the data sparsity problem. The paper is organized as follows: The literature review is examined in Section 2, which is followed by the problem statement discussed in Section 3. The proposed model is evaluated in Section 4, and the results are presented in Section 5. The paper comes to a conclusion in Section 6.
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
## 3 Impact on _i_GNSS-R Altimetric Performance  
With the existence of the IM component in _i_GNSS-R waveform verified, we analyzed the impact of the IM component and revised the _i_GNSS-R altimetric performance of [13] by using the PSD and WAF models derived in the previous section. In this analysis, a spaceborne scenario with a height of 600 km (as is intended for the PARIS-IoD mission [13]) is considered. Table 2 shows the main parameters of our analysis, which focuses on the comparison of two cases:  
* Case 1: considering only the ranging components, i.e., the composite of the C/A, P and M codes as in previous studies,
* Case 2: considering the fully composite signal including both the ranging, as well as the extra IM component.
## 2 Formulation of the Proposed BMUA
### Probabilistic Damage Detection  
To measure the probability of the variation in mass and stiffness parameters, the MPVs of the updated parameters and associated uncertainty are used together to quantify the damage probability given a specific damage fractional level \(d\) compared with its healthy condition. Based on asymptotic Gaussian approximation, the probability of damage in the \(l\)th structural parameter can be given by [53]  
\[\begin{split}& P_{l}^{dam}(d)=P\Big{(}\mathbf{\Omega}_{l}^{pd}<(1-d) \mathbf{\Omega}_{l}^{ud}\Big{|}C\Big{)}\\ &=\int_{-\infty}^{\infty}P\Big{(}\mathbf{\Omega}_{l}^{pd}<(1-d)\theta _{l}^{ud}\Big{|}\mathbf{\Omega}_{l}^{ud},\,C\Big{)}p(\mathbf{\Omega}_{l}^{ud}\Big{|}C \Big{)}d\mathbf{\Omega}_{l}^{ud}\\ &\approx\Phi\Bigg{[}\frac{(1-d)\mathbf{\Omega}_{l}^{ud}-\mathbf{\Omega}_ {l}^{ud}}{\sqrt{\left(1-d\right)^{2}\left(\sigma_{l}^{ud}\right)^{2}+\left( \sigma_{l}^{ud}\right)^{2}}}\Bigg{]}\end{split} \tag{54}\]  
where \(\Phi(\cdot)\) represents the standard Gaussian cumulative distribution function for random variables; \(\mathbf{\Omega}_{l}\) and \(\sigma_{l}\) represent the optimal value of the \(l^{th}\) structural parameter and its standard derivation, respectively. The superscripts \(ud\) and \(pd\) represent the undamaged and possible damaged structural state, respectively.
# Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss
## 2 Materials and Methods  
### YOLOv5  
The network structure of YOLOv5 mainly consists of four parts: Input, Backbone [41], Neck [42], and Head, as shown in Figure 1.  
Incorporating Mosaic data augmentation at the input stage, YOLOv5 combines four randomly scaled, cropped, aligned, concatenated, and padded sample images to form a new composite sample image. This technique merges multiple images into a singular entity, thereby enhancing the image dataset and accelerating the training speed of the network. Moreover, this augmentation method not only diminishes the requirement for numerous GPUs but also implements adaptive anchor box computation to derive high-quality anchors. Additionally, it utilizes adaptive scaling to standardize the image sizes to an optimal dimension, further optimizing the training process.  
The Backbone part adopts the New CSP-Darknet53 [40] model and utilizes the Focus module (Figure 2) to perform slicing operations, transforming the 640 \(\times\) 640 \(\times\) 3 image into a 320 \(\times\) 320 \(\times\) 12 tensor. The structure of CSP1_X is shown in Figure 3, where X represents the number of residual modules (Res units) in the structure. The CBL module consists of a convolutional layer, a batch normalization layer (BN), and an activation function (Hard-Swish activation function [43]). Each Res unit consists of a series of convolutional layers and a residual connection. The two convolutional layers (Conv) are used to adjust the number of channels in order to add the two calculations together (Concat).  
Figure 1: YOLOv5 model framework.  
In the Neck component, YOLOv5 integrates Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) modules to bolster multi-scale feature representation and accurate localization details. The addition of CSP2_X intensifies the feature fusion from the preceding stage, as depicted in Figure 4. Within the Prediction segment, the GloU_Loss function is utilized to determine the localization loss. This function aids in optimizing the model's ability to precisely pinpoint objects within an image, enhancing the overall accuracy of the detection process.
## 3 Shadow Encoding Scheme for Power Spectrum Density
### Transparency  
The modulated form of the digital bits into the signal at the receiver end is called transparency. Transparency makes it possible that a waveform should not affect the accuracy of the message at the destination. The long stream of 0 s and 1 s causes an error due to the stable state of the signal in a unipolar and AMI digitizing scheme. The transmission of the signal may modulate such a pattern; it should deliver at the destination faithfully. Each digital bit should represent a specific waveform for signal modulation. The proposed SES provides good transparency. The 1 bit represents the positive amplitude voltage level, and the 0 bits are modulated on a negative level [22].
## 4 Results and Discussion
### Phase 1--Analysis of the Reference Context
#### 4.1.2 Identification of the Average Property Internal Area  
In order to identify the average area of the residential units located on the Italian territory, the analysis has been developed by consulting the data published by ISTAT collected for the 15 th General Census of the population and housing in 2011 [1,68]. From the data consulted, the average surface area of occupied Italian dwellings is equal to 99.3 m\({}^{2}\).