esa-sceva/satcom-chunk-collection huggingface dataset:
# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
## 6 Conclusions  
In this paper, we have presented a novel technique for improving the resolution of tomographic volumes using a custom scanning procedure combined with on-the-fly machine learning. The technique relies on combining high-resolution projection data of a small region of interest with low-resolution projection data of the entire object. Reconstructions of both projection sets are used to train a neural network, which is then able to improve the resolution of the low-resolution reconstruction of the entire object. The effectiveness of our approach was tested on simulated foam phantom data and real-world experimental cone-beam tomography data. Using the learned high-resolution structure, the proposed approach is able to recover a large fraction of high-resolution features without introducing additional artifacts. Moreover, it requires a limited increase in scanning time and radiation dose. We have proposed two variants of our approach (A and B). The first variant (A) performs better on synthetic data but is more computationally costly to compute. The second variant (B) is considerably cheaper to compute, results in smaller output images, and produces results qualitatively similar to method A on real-world experimental data. Our proposed approach has the added advantage of removing noise from reconstructions of experimental data. The results of this paper show that the proposed machine learning method is able to significantly improve resolution of tomographic reconstructions without requiring a training set of similar objects.  
Conceptualization, A.A.H., D.M.P., and K.J.B.; Methodology, A.A.H., D.M.P., W.J.P., and K.J.B.; Software, A.A.H., D.M.P., and W.J.P.; Validation, D.M.P. and K.J.B.; Investigation, A.A.H. and S.B.C.; Resources, K.J.B. and S.B.C.; Writing-original draft preparation, A.A.H. and K.J.B.; Writing-review and editing, D.M.P., W.J.P., S.B.C., and K.J.B.; Funding acquisition, K.J.B. The authors acknowledge financial support from the Netherlands Organisation for Scientific Research (NWO), project numbers 639.073.506 and 016.Veni.192.235.  
We acknowledge XRE NV for their role in the FleX-ray collaboration.  
The authors declare no conflict of interest.
## 6 Simulation and Evaluations  
### Simulation Settings  
Simulations were conducted to evaluate the effectiveness of the proposed velocity optimization schemes for MASS. The simulation was conducted in MATLAB 2018. We simulated the scenario when an MASS was sailing in an \(L*W\) region from (0 km, 0 km) to (1000 km, 1000 km) when there were \(K\) conventional ships moving. We assume the MASS can detect the shape, the location, and the velocity of the conventional ships. This simulates the case when the locations and velocities of other ships are provided by AIS. We simulated three algorithms:  
1. greedy: the greedy one-step velocity optimization;
2. greedy + risk: the greedy one-step velocity optimization with consideration of collision risk;
3. DP: the dynamic programming-based multiple interval motion planning;
## 5 Classification Analysis  
After a thorough analysis of the entropy patterns, we decided to use all seven features--packet size, inter-packet delay, packet process time, Modbus frame length, function code, protocol overhead, and data--for classification. We extended our anomaly detection method by conducting a classifier-based analysis of the datasets. Eight different classifiers, including tree-based classifiers, were compared: BayesNet, Naive Bayes, J48, Simple Logistic, SVM, Multilayer Perceptron, random forest, and Decision Table. Binary classification, performed using Weka's default values for each classifier, used the features packet size, inter-packet, packet process time, two additional Modbus application protocol header features, Modbus frame length, and function code value. The last two additional features were used since they had high information gain rankings based on the work of [10].  
Due to the imbalanced nature of the data, the classifiers were run using three different data resampling configurations: no resampling; a 50-50 split (50% non-attack data and 50% attack) and an 80-20 split (80% non-attack data and 20% attack). Resampling was conducted using random undersampling and random oversampling. Two different split combinations of the training vs. testing data were used: 60-40% training vs. testing and 75-25% training vs. testing data. For each run combination, the Accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Precision, F-measure, and ROC values were recorded. The objective was to have a lower FPR but higher accuracy, TPR, Precision, F-measure, and ROC.  
* Accuracy is the number of correct classifications divided by the total number of classifications given by (TP + TN)/(TP + FP + TN + FN).
* TPR measures the proportion of actual attacks that were identified as attacks, given by TP/(TP + FN).
* FPR, where a non-attack is identified as an attack, is given by FP/(FP + TN).
* Precision measures the proportion of positive identification of attacks that were actually attacks, given by TP/(TP + FP). F-measure is given by 2 \(\times\) ((Precision \(\times\) Recall)/(Precision + Recall)).
* ROC plots the relation of the TPR vs. FPR.
# Usage of V2X Applications in Road Tunnels
## 3 Methodology
### Methodology of the Data Acquisition and Evaluation Procedure  
During the test, both OBUs logged incoming and outgoing communication. It was logged in the PCAP (Packet Capture) file format, which is commonly used in telecommunications. Therefore, it was possible to check in real time whether these vehicles are connected or not.  
Furthermore, when the received C-ITS message was valid, the information was expressed in a mobile app that was used for the purposes of the test as a Human-Machine Interface (HMI). It should be noted that the validity of the C-ITS message is based on many parameters, and it does not matter whether the receiving OBU has the correct time and position. The message may be invalid even in this case, for example, because the traces leading to the C-ITS event are incorrectly defined.  
Although the mobile app was only used in real time and no data are available after the test, the data captured from the OBUs have been prepared for further post-process analysis. These data have been essential in the test evaluation procedure. Each of the C-ITS messages  
Figure 4: Schema of the pre-test.  
Figure 5: Schema of the major test scenario.  
contains several parameters, but for the analysis, the timestamp and the position (latitude, longitude) were the main ones.  
From these C-ITS data, a filtration was applied to obtain the position of the vehicle at the time the C-ITS message was received from the stationary vehicle. It allowed for the calculation of the difference between the positions of the stationary vehicle and the arriving vehicle, and thus the communication range was known.  
Another approach within the test evaluation was a map projection of the positions of the arriving vehicle during the entire test. It provides an opportunity to check whether the positions were correct in terms of matching with the layout of the map, but also provides a general overview, where it is possible to receive and obtain C-ITS information concerning the position of the source that generates C-ITS messages.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Interference Management
#### 7.2.1 Cell Densification and Offloading  
Network densification can be defined as a simple but important instrument that is used in increasing the capacity of network capacity in 5G cellular networks [84]. This approach has been used over several cellular generations to enhance the network capacities [85]. Furthermore, ultra-dense networks make resource allocation more challenging because of the massive number of devices and the random device locations. Due to the frequent recurring movements that lead to change of device positions, it is essential to develop an efficient resource allocation scheme that can ensure the QoS of conventional network users [86]. Generally, the idea for performance enhancement in network densification proposes small coverage cells, such as picocels and femtocells, to be deployed into close range with the intended devices, which leads to multiple favorable channel conditions between receivers and transmitters. As a result, the transmission power can be reduced, which results in reduced interference with other network elements that co-exist. However, the main issue in the integration of small cells and D2D technology that supports in-band D2D communication is the interference management. Resource allocation and interference management issues in multi-tier heterogeneous networks are considered more challenging compared to the proposed solution in the literature that caters to traditional single-tier systems due to the different transmission power of each BS. Additionally, different tiers produce different levels of interference due to the different access restrictions (private, public, hybrid, etc.). The interference among small-cell cellular links, macro-cell cellular links, and D2D links all need to be considered and managed efficiently to enhance the spectrum efficiency. Besides, the algorithms that are used in the mode selection need to be modified for this heterogeneous environment so that it can make a dynamic decision depending on the condition of the networks. Therefore, it is important to consider how effective interference management can be obtained.
# Hybrid Dual-Polarization Synthetic Aperture Radar
## 3 Hybrid Dual-Polarization  
The hybrid dual-polarization version of compact polarimetry [23] has emerged as the preferred method for operational systems [24; 25]. An HCP SAR transmits a circularly polarized field (either L or R) and receives on two orthogonal linear polarizations (nominally H and V).
# Interpolation-Filtering Method for Image Improvement in Digital Holography
## 7 Conclusions  
The method is proposed to increase the quality of images reconstructed from a single digital hologram. The method contains an additional increase in size by interpolation before filtering, and it decreases after. Filtering with interpolation is tested on computer-generated and optically registered holograms.  
Compared to digital filtering on the computer-generated holograms, the quality was improved by up to 17% in terms of the SSIM and up to 29% in terms of the NSTD. On optically registered holograms, speckle contrast was reduced by more than three times compared to the original reconstructed image. In comparison with digital filtering, the speckle contrast was reduced by 1.4 times. Two-fold upscaling can be considered as the optimum.  
Additionally, the proposed method offers only digital operations to improve the efficiency of image processing. Therefore, it can be added to systems of data analysis, nondestructive testing, medicine, biology, etc. Due to the versatility of the approach to increase the statistical basis, the development of the proposed method can be combined with optical and hybrid methods to increase the quality of reconstructed images, as well as with image processing chain components.  
Conceptualization, A.V.K., P.A.C. and N.N.E.; methodology, A.V.K. and P.A.C.; software, A.V.K. and V.G.R.; validation, A.V.K.; formal analysis, A.S.S. and R.S.S.; investigation, A.V.K. and P.A.C.; resources, N.N.E., P.A.C. and R.S.S.; data curation, A.S.S. and V.G.R.; writing--original draft preparation, A.V.K., P.A.C. and A.S.S.; writing--review and editing, A.V.K. and P.A.C.; visualization, A.S.S. and A.V.K.; supervision, N.N.E. and V.G.R.; project administration, R.S.S. and V.G.R.; funding acquisition, N.N.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grant 24-19-00899 from the Russian Science Foundation (RSF).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Methodology
### Case Study
#### 2.1.2 Electricity Sub-Metering  
An extensive network of electrical energy consumption sub-metering is installed in the building, where there are dedicated meters for different zones and also for different sub-systems of building service systems (cooling system, ventilation, etc.). The data used in this study were collected with meters measuring the power consumed by the lighting system and the equipment connected via sockets in the office. For these two categories of electricity consumption, occupants have a direct impact. Most of the lights on the floor are workstation-based and are occupant operated, so the assumption was that lighting electricity has a good correlation with occupancy. The same goes with the equipment electricity consumption, where most of the devices connected were personal computers; desktop and laptops. Both desktops and laptops were connected to either one or two computer screens.  
The floor consisted of three wings which had their own sub-meters for lighting and equipment. The consumptions of each zone were combined into floor-level consumptions in order to be comparable with available ground truth occupancy.
# Optical Cable Lifespan Prediction Method Based on Autoformer
###### Abstract
For instance, [PERSON] and his team have developed an innovative digital twin method for cable temperature prediction based on RF-GPR (random forest-Gaussian process regression), which combines finite element analysis with artificial intelligence techniques [6]. Furthermore, they have proposed a data-driven cable insulation defect model based on a convolutional neural network approach to address the problem that the electric field is not easily measured under the operating condition of transmission cables with insulation defects [7]. Inspired by these approaches, this paper introduces deep learning into the realm of optical cable lifespan prediction. Autodformer, a novel sequence model, demonstrates proven success in fields including natural language processing and image recognition. Adopting an approach that diverges from traditional sequence analysis, it combines time series decomposition with an auto-correlation mechanism, allowing for a more accurate grasp of data trends and dependencies [8; 9]. Considering its high compatibility with the characteristics of optical cable data, this study applies the Autodformer model to the task of predicting the remaining service life of optical cables. Optical cable data typically exhibit strong time-dependencies, reflecting the gradual degradation process influenced by factors such as temperature, wind, and load. The Autodformer model, combining time series decomposition with an auto-correlation mechanism, is particularly well suited for capturing these long-range dependencies. The model's ability to effectively analyze temporal patterns and extract long-term correlations makes it a promising approach for accurately predicting the remaining service life of optical cables. This paper examines how fiber length, stress, and specific loads under varied weather conditions (e.g., temperature, lightning strikes, icing, wind vibrations) affect optical cable lifespans. Using the Autodformer model, it forecasts trends in excess fiber length alterations, confirming the model's superiority over others through simulation validation. This method not only offers a more accurate and reliable means of predicting cable lifespans but also presents a new technical direction for the optical cable industry by leveraging deep learning and time series analysis for lifespan prediction, moving away from traditional methods that rely on limited factors and idealized conditions. This shift towards data-driven approaches can provide vital support for ensuring the stability and reliability of communication networks. While this study focuses on the impact of temperature, wind, and residual length on optical cable lifespan, other factors such as humidity, UV exposure, and installation quality can also play a significant role. We acknowledge the importance of these additional factors and plan to investigate their influence on cable lifespan in our future research.
## 1 Introduction  
The human gut harbors around \(10^{14}\) microbes, comprising a genome size almost 150 times larger than that of the host itself [1, 2]. These microbes interact with each other through various metabolites, such as short-chain fatty acids (SCFAs) and amines, to create a microenvironment inside the gut, which also affects the host. Studies have shown that gut microbes act differently in response to different diets, such as vegan, vegetarian, high-fat diet, Mediterranean, etc., and such dietary interventions have been reported to induce distinct changes in the microbial ecology and metabolic profile of the host [3, 4].  
In the last decade, next-generation sequencing (NGS)-based culture-independent fecal microbiome analysis has been successfully used to analyze the effects of dietary interventions on the intestinal microbiome. The roles of individual dietary components, such as carbohydrates, proteins, and fats, have all been evaluated in an effort to understand their contribution to microbial diversity and population. Previous studies have shown that indigestible dietary fiber and polysaccharides are used by the gut microbiota to produce SCFAs. Three major SCFAs, acetic acid, propionic acid, and butyric acid, are known to play important roles in maintaining gut health. Similarly, amino acids produced by the enzymatic digestion of proteins can be used by gut microbes as an energy source and facilitate SCFA production [5]. Although amino acids are neither efficient energy sources nor preferentially consumed by gut microbes when compared to carbohydrates, studies have shown that amino acid fermentation results in highly diverse by-products, which can interact with the gut microbiome in many ways [5,6]. Previous studies have also shown that a protein diet can be metabolized by the gut microbiota to produce beneficial substances such as SCFAs and toxic substances, such as sulfur metabolites, depending on the type of microbes found in the gut [5, 6, 7].  
Most evaluations of dietary interventions have relied on animal model data. However, there is a significant difference between the animal and the human gut microbiome, and each human microbiome is unique to the individual; thus, the effects of dietary interventions may vary depending on the composition of the gut microbiota in each individual. It would be ideal to use personal gut microbiota to investigate the effects of specific dietary interventions in each person, but the practicality of this approach remains lacking. The INFOGEST network was created by the European Cooperation in Science and Technology to standardize static in vitro digestion practices [8,9], which are harmonized according to the human body and adopted to mimic gastrointestinal digestion. This study used six unique fecal microbiota samples from six subjects to analyze the response of the fecal microbiome to the digestion of beef proteins using the INFOGEST protocol. Here, we investigated the possibility of using simple in vitro fecal fermentation to evaluate the fundamental differences in the metabolism of individual gut microbial communities in response to specific dietary interventions.
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 2 Parallel Auto-Encoder Framework
### The Proposed Approach
#### 2.2.2 Sparse Auto-Encoder  
An auto-encoder is trained to reconstruct its input to its output [35]. However, if an auto-encoder succeeds in simply copying its input to its output, it may not extract any useful features. A sparse auto-encoder is an extension of the auto-encoder whose training criterion involves a sparsity penalty term on the hidden neurons inspired by sparse coding [9], along with the reconstruction error. By regularizing the auto-encoder to be sparse, it must respond to unique statistical features of the training dataset, rather than purely acting as an identity function [35]. Hence, training a sparse auto-encoder to perform reconstruction task can produce useful features.  
The detailed formulation of sparsity penalty term will be explained further here. Let \(a_{j}\big{(}x^{(i)}\big{)}\) denotes the activation of hidden neuron \(j\) when the network is given the \(i\)th input \(x^{(i)}\). Then, the average activation of hidden neuron \(j\) that averaged over the whole training datasets can be expressed as:  
\[\hat{\rho}_{j}=\ \frac{1}{m}\sum_{i=1}^{m}\big{[}a_{j}(x^{(i)})\big{]} \tag{3}\]  
where \(m\) is the number of samples. The constraint is enforced as:  
\[\hat{\rho}_{j}\approx\rho \tag{4}\]  
Figure 2: Proposed parallel auto-encoder model.  
where \(\rho\) is a sparsity parameter that needs to be pre-determined as a small value (typically close to zero). In this process, the average activation of each hidden neuron \(j\) would be close to zero. In the other words, the hidden neurons are mostly 'inactive.' To achieve this, an extra penalty term is introduced to the optimization objective function that penalizes \(\hat{\rho}_{j}\) if it deviates significantly from \(\rho\) as below:  
\[KL\big{(}\rho\parallel\hat{\rho}_{j}\big{)}=\rho\log\frac{\rho}{\hat{\rho}_{j }}+(1-\rho)\log\frac{(1-\rho)}{(1-\hat{\rho}_{j})} \tag{5}\]  
where \(KL(\cdot)\) is the Kullback-Leibler (KL) divergence [PERSON] and [PERSON] (1941): a measure of the similarity between two distributions \(\rho\) and \(\hat{\rho}_{j}\). In the other words, as \(\hat{\rho}_{j}\) diverges from \(\rho\), the KL divergence increases monotonically. Hence the sparsity penalty term can be formulated as below:  
\[J_{sparse}\big{(}W,\overline{b}\big{)}=\sum_{j=1}^{r}KL\big{(}\rho\parallel \hat{\rho}_{j}\big{)} \tag{6}\]  
where \(r\) is the number of neurons in the hidden layer and the index \(j\) is summing over the hidden neurons in the network.
## 2 Related Works
### Link-Aware Video Encoding and Streaming  
CoSA, a cognitive video streaming system, streams video with compression rates set according to the communication state [24; 25]. The important area in a video is identified using an IR depth camera. Then, CoSA streams the important area with higher quality and the unimportant area with lower quality. Image encoding and decoding are handled by the CoSA encoder and decoder. The CoSA encoder adopts high-frequency deletion techniques to encode a video, and the decoder adopts high-frequency restoration techniques to decode the video. The CoSA decoder not only decodes the video for viewing but also periodically transmits the link state to the encoder to adjust the compression rate. The experiments show that the system can deliver video data with two varying compression rates.  
Even though CoSA introduced a new image streaming system that is robust to the dynamic link state, it is not compatible with H.264 standards. Videos encoded using the CoSA codec cannot be decoded with the original H.264 standard decoder, so the system's practicality is limited. CoSA's codec is efficient and well-suited for conversational video, but the incompatibility with the standardized codec limits its applicability. Moreover, CoSA utilizes an IR depth camera to identify important areas. The requirement of an IR depth camera further limits the applicability of CoSA. The proposed system in the paper produces a more lightweight video and uses image processing for ROI selection, which does not require additional hardware.