esa-sceva/satcom-chunk-collection huggingface dataset:
# MALDI-TOF Mass Spectrometry Applications for Food Fraud Detection
###### Abstract
MALDI mass spectrometers are capable to rapidly generate spectra, profiles, and/or fingerprints from food matrices, often with simple preparation or no sample preparation at all. The potential of the MALDI approach applied to the fight against food fraud, sometimes coupled to multivariate statistical methods to extract information from complex analytical data such as mass spectra, has been clearly understood, as demonstrated by a considerable number of related publications. The purpose of the present review is to provide a comprehensive overview of the relevant literature. Based on the research carried out, most of the existing MALDI applications dedicated to the field of food fraud have concerned the analysis of milk and dairy products, oils, fish and seafood, vegetables, fruit, meat, and, to a minor extent, a few other categories. Most of the works have been focused on the evaluation of food quality and detection of adulterations, while only few applications have been devoted to the investigation of the geographical origin.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 4 QKD Applications
### Long-Range Fiber Communication  
Including QKD encryption in existing optical metro and core networks is widely recognized as an important application scenario. This means reusing infrastructure designed for classical networks to enable QKD encryption along the telecom services. This is the reason why CV-QKD gives an advantage in respect to DV-QKD, due to better compatibility with commercial devices. However, the current limits in terms of security and reachable distance mean that CV-QKD is still not comparable to DV-QKD, meaning that both approaches are still relevant. For this use case, guided phase or spin-modulated DV-QKD with possible MDI, decoy states, and trusted nodes (where acceptable) could be the right choice, especially considering that some integration work has been already taken out [121; 122]. However, the compatibility issues with the existing classical networks remain dramatic, and too few works exist in this area [52; 123; 124]. Most of the QKD systems are assumed to work over point-to-point nodes, without the chance of using optical amplification.  
The situation is different for critical environment services or military applications, where due to the amount of resources invested it is possible to build dedicated QKD infrastructures, while also adopting high-efficiency cooled systems to increase the rate and distance. In some scenarios, even the one-time pad approach might be acceptable.  
Leakage of data from critical infrastructure that may be vulnerable to cyber attacks is considered of utmost importance at the European level, and has been defined in a previous directive [125], which identifies energy, digital services, air transport, bank systems, water supply, and healthcare as critical services.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 5 Optimization on the ABE Scheme
### Energy Consumption Evaluation  
We have also evaluated the energy consumption of the hybrid ABE scheme on a Raspberry Pi 3 and a OnePlus A6000 smartphone. Figure 14 shows the power consumption (mAh).
## 2 Study Area and Data Analysis
### Pleiades-1B Triplet  
The Pleiades satellite system is composed of two identical satellites, Pleiades 1A/1B, which were launched by the French space agency (CNES) in December 2011 and December 2012, respectively. Both satellites are flying on the same sun-synchronous low-Earth orbit at an altitude of 694 km with a phase of 18\({}^{\circ}\) and an orbital period of 98.8 min. The sensors are able to collect both panchromatic and multispectral images [3].  
The Pleiades-1B tri-stereo images used in the current investigations were acquired in the late morning of 13 June 2017 within 23 s. Within this time, the satellite travelled a total distance (Base) of 167.87 km, leading to an intersection angle of rays on the ground of 12\({}^{\circ}\). The B/H ratios are of 0.10, 0.11, and 0.21 for forward-nadir, nadir-backward and forward-backward image combinations. The pansharpened images composing the triplet are available in 16 bit, each of them with four spectral bands, i.e., red, green, blue, and near-infrared. Depending on the viewing angle, the mean values for the GSD vary between 0.70 and 0.71 m.
# An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM
## 2 Sound Frequency Analysis Technology  
A motor generates sound when it is running, and the sound data can be analyzed in depth through signal-processing technology and machine learning algorithms so as to implement the accurate monitoring of the motor status and fault warning. This study intended to identify the modes of normal operation of a motor and three fault types: out-of-phase fault, unbalanced voltage fault, and misalignment fault, as shown in Figure 1.  
FFT is a key step in converting the motor operating sound from the time domain to the frequency domain. The original sound signal is decomposed via FFT into a series of components of different frequencies, and a spectrogram is generated to display the amplitude of each frequency component in order to understand the characteristic frequency and energy distribution of operational sound. The steps of the FFT algorithm are as follows [29]:  
1. Prepare input signals: The signal data requiring frequency-domain analysis are obtained. They are usually discrete signals in the time domain.
2. Choose the FFT length: The length of FFT is determined; a power of 2 is usually chosen to accelerate the calculation using the FFT algorithm. If the length of the input signals is not an integer multiple of the FFT length, the required length can be achieved by filling zeros at the end of the signals.
3. Calculate FFT: The FFT algorithm is used to perform a Fourier transform on the zero-filled signals. The FFT algorithm converts the time-domain signals into frequency-domain signals, and the spectral information is generated.  
Figure 1: Diagram of fault identification.  
Calculate the spectral amplitude: The spectral amplitude information is extracted from the FFT results; the energy distribution of the spectrum is usually represented using the amplitude spectrum.
5. Draw a spectrogram: The spectral amplitude or spectral phase information is visualized so as to analyze and understand the frequency-domain characteristics of signals.  
The time-domain signals of the sound are converted into frequency-domain signals, and the feature information of the signals in the frequency domain is obtained, such as the spectral amplitude and phase. The audio sampling time in this paper is a sampling frequency of 21,000 samples per second, and the same audio information is subjected to different FFT lengths. The operating sound included a normal operating mode and three failure modes. Each mode is divided into 16 operating sound samples with different loads from light load to full load. The total of sound samples is 64. Figure 2 shows the no-load, half-load, and full-load frequency-domain signals of FFT with 128 Hz, 256 Hz, 512 Hz, and 1024 Hz.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 5 AI and Different Applications in Ophthalmology and Other Fields
### Precedents
#### 5.1.4 AI in Pediatric Ophthalmology  
Pediatric Cataract  
Cataracts in children are a common and avoidable cause of visual abnormalities and possible everlasting vision loss at the international level [126]. Left untreated, mainly during the critical interval from birth to age 5, it can lead to irreversible amblyopia. In this way, early identification and surgical procedure are essential. Cataracts are nowadays diagnosed by means of the slit lamp, an examination that can often be hard to perform on children.  
CC-Cruiser is an AI-based cloud platform that visualizes data and suggests treatment for patients with congenital cataracts. It comprises three CNNs that (1) visualize and identify potential cataract patients, (2) assess disease severity (lens opacity, density, and location) as well as risk stratification, and (3) provide advice--surgery or follow-up--based on the risk analysis.  
A multicenter randomized study by means of the CC-Cruiser showed the accuracy of the platform in detecting cataracts and recommending specific treatment. The study obtained 87.4% and 70.8%, respectively, compared to experts, who achieved significantly better accuracies of 99.1% and 96.7%, respectively [127]. Yet, the CC-Cruiser provided a diagnosis in an average of 3 min, compared to almost 9 min for the experts.  
However, cataract imaging is lacking. Therefore, similar to eye tumors, ML/DL algorithms cannot be trained reliably on small image sets. In addition, pediatric cataracts show certain characteristics and risk patterns, and thus, available adult cataract representations and datasets cannot be used.  
Strabismus  
Strabismus is a condition of ocular misalignment and is diagnosed through simple clinical examinations such as the Hirschberg test and coverage test.  
In a hospital environment with specialized equipment, CNNs can identify strabismus from eye-tracking fixation divergence (95% accuracy) and from retinal birefringence scanning (100% accuracy) with high sensitivity and specificity [115].  
Ref. [128] describes the application of AI techniques to analyze the red pupil reflex (Bruckner test) in video and photorefraction images for amblyogenic factors. The decision tree made the same referral/non-referral decision 77% of the time as the clinicians, who were considered the \"gold standard\".  
For situations with limited access to ophthalmic clinics, a new model was proposed for the development of a CNN for telemedicine. Supporting both tele-ophthalmology and clinical strabismus diagnosis, an AI-based mobile platform localizes eyes and classifies them into nine gaze positions [129].
# Analysis of the Development Status of eLoran Time Service System in China
###### Abstract  
This article introduces the eLoran timing system principle, the characteristics of the eLoran and GNSS systems, and the current development status of eLoran in China. This article elaborates on the significance and scale of this high-precision ground time service system currently being constructed in China and describes the technical methods used in the high-precision ground time service system. Finally, it analyzes and elaborates on the signal and data channels of the eLoran time service system.  
eLoran; Loran-C; time service +
Footnote †: journal: Applied sciences
## 6 Conclusions  
In this study, a millimeter-wave FMCW radar sensor system was developed for small displacement sensing, and its phase accuracy and precision were experimentally evaluated using two test scenarios. The sensor demonstrated a phase accuracy shift of +0.137 degrees with a standard deviation of \(\pm\)0.222 degrees. The maximum observed shift was +0.359 degrees, corresponding to a displacement offset of +1.907 \(\upmu\)m. This offset falls within a 3-sigma condition, confirming the sensor's accuracy. Similarly, the 3-sigma deviation of \(\pm\)0.222 degrees corresponds to a displacement precision of \(\pm\)1.180 \(\upmu\)m.  
The industrial shaker test verified the sensor's capability to measure displacements ranging from 15.198 \(\upmu\)m to as small as 0.019 \(\upmu\)m with good linearity, as confirmed by regression analysis. Applying a high-pass filter and calibration using regression coefficients  
Figure 17: Calibrated radar data from radar and shaker.  
effectively aligned the measured displacement with the reference values. After calibration, the sensor precision (\(\pm\)1.180 \(\upmu\)m) equaled its accuracy.  
Future work will address identifying the root causes of the observed errors to further enhance reliability. Additional measurements under varied vibration conditions, including different frequencies, accelerations, and shaker types, will be conducted to better understand the influence of radar MMIC characteristics and the mechanical properties of the shaker machine on sensor performance.  
Conceptualization, H.T. and F.S.; methodology, H.T.; software, K.S.; validation, K.S., H.T. and N.H.; formal analysis, H.T., N.H. and K.S.; investigation, H.T.; resources, H.T. and F.S.; data curation, H.T.; writing--original draft preparation, H.T.; writing--review and editing, H.T. and F.S.; visualization, H.T.; supervision, F.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request due to restrictions.  
Authors [PERSON] and [PERSON] are employed by Analog Devices KK. Other authors [PERSON] and F.S. receive compensation from Sakura Tech Cooperation.
# From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies
## 4 Neural Network Models for Thresholding Prediction Scores
### ThresNet\({}_{CSS}\)--A Residual Variant  
ThresNet can learn complex patterns for seemingly easy tasks of thresholding score vectors. Although this ability is crucial to be able to correctly predict mis-scored labels, making a prediction about a label should fundamentally be based on its label score. However, ThresNet architecture does not allow for this easily, because there is no direct connection between the \(i\)-th input \(\mathbf{ss}[i]\) and the \(i\)-th output \(\mathbf{\hat{y}}[i]\). The signal between these units has to go through three bottleneck layers and nonlinearities of activation functions.  
ThresNet\({}_{CSS}\), depicted in Figure 3, is a variant designed specifically to remedy the aforementioned problem. The idea here is to combine the powerfulness of ThresNet with traditional CS-cut thresholding, implemented as CS-net (shown in Figure 1a and Equation (12)). The model adds the input score vector just before the final activation function of ThresNet, transforming the architecture into a form of a _residual_ network [45]. The residual connection joins the corresponding elements of large sparse input and output vectors, which are otherwise connected through bottleneck layers, similar to what is done to images in UNet-like image segmentation models [46].  
Figure 2: ThresNet—a thresholding model with label embeddings.  
Additionally, it is possible to _transfer knowledge_ from a CS(S)-cut traditional strategy into the model via a non-trainable bias vector \(\mathbf{t}_{\mathit{CSS}}\). The overall function of the model is as follows:  
\[\mathrm{ThresNet}_{\mathit{CSS}}(\mathbf{ss})=\sigma\Bigg{(}\underbrace{\mathrm{ dense}(\mathrm{dense}_{\mathrm{ReLU}}(\mathrm{dense}_{\mathrm{ReLU}}(\mathrm{ Emb}\cdot\mathbf{ss})))}_{\mathrm{ThresNet\ part}}+\underbrace{\mathbf{ss}-\mathbf{t}_{\mathit{CSS}}}_{ \mathrm{CS}\mathrm{-Net\ part}}\Bigg{)} \tag{15}\]  
During training, this residual model learns to exhibit behaviors that differ from simply applying _a priori_-given CSS-cut thresholds. The intuition is that complex pattern-based thresholding should be applied only when it outweighs the default CSS-cut strategy. The space complexity of \(\mathrm{ThresNet}_{\mathit{CSS}}\) is the same as the primary ThresNet.
## 5 Computer Simulation
### Traffic Model  
Downlink traffic directed to user terminals that stay under relay nodes is all generated at the core node and forwarded to each relay node. Uplink traffic caused by user terminals is gathered at the relay nodes where users belong to and forwarded to the core node. The Poisson origination is employed as a traffic model. The number of data packets per session is randomly determined according to the log-normal distribution, the mean of which is 20 for downlink and 3 for uplink [50]. Assuming user datagram protocol (UDP), the ratio of the total offered load between downlink and uplink is set to 10:1 [51].
# Efficient Data Structures for Range Shortest Unique Substring Queries +
## 1 Introduction
### Main Problem and Main Results  
An alphabet \(\Sigma\) is a finite nonempty set of elements called letters. We fix a string \(\mathsf{T}[1,n]=\mathsf{T}[1]\cdots\mathsf{T}[n]\) over \(\Sigma\). The length of \(\mathsf{T}\) is denoted by \(|\mathsf{T}|=n\). By \(\mathsf{T}[i,j]=\mathsf{T}[i]\cdots\mathsf{T}[j]\), we denote the substring of \(\mathsf{T}\) starting at position \(i\) and ending at position \(j\) of \(\mathsf{T}\). We say that another string \(\mathsf{P}\) has an occurrence in \(\mathsf{T}\) or, more simply, that \(\mathsf{P}\) occurs in \(\mathsf{T}\) if \(\mathsf{P}=\mathsf{T}[i,i+|\mathsf{P}|-1]\), for some \(i\). Thus, we characterize an occurrence of \(\mathsf{P}\) by its starting position \(i\) in \(\mathsf{T}\). A prefix of \(\mathsf{T}\) is a substring of \(\mathsf{T}\) of the form \(\mathsf{T}[1,i]\) and a suffix of \(\mathsf{T}\) is a substring of \(\mathsf{T}\) of the form \(\mathsf{T}[i,n]\).  
We next formally define the main problem considered in this paper.  
**Problem \(r\)S**  
_Preprocess:_ String \(\mathsf{T}[1,n]\).  
_Query:_ Range \([\alpha,\beta]\), where \(1\leq\alpha\leq\beta\leq n\).  
_Output:_\((p,\ell)\) such that \(\mathsf{T}[p,p+\ell-1]\) is a shortest string with exactly one occurrence in \([\alpha,\beta]\).  
If \(\alpha=\beta\) the answer \((\alpha,1)\) is trivial. So, in the rest we assume that \(\alpha<\beta\).  
**Example 1**.: _Given \(\mathsf{T}=\boldsymbol{caabcadaacaddaaanabac}\) and a query \([\alpha,\beta]=[5,16]\), we need to find a shortest substring of \(\mathsf{T}\) with exactly one occurrence in \([5,16]\). The output here is \((p,\ell)=(10,2)\), because \(\mathsf{T}[10,11]=\boldsymbol{ac}\) is the shortest substring of \(\mathsf{T}\) with exactly one occurrence in \([5,16]\)._  
Our main results are summarized below. We consider the standard word-RAM model of computations with \(w\)-bit machine words, where \(w=\Omega(\log n)\), for stating our results.  
**Theorem 1**.: _We can construct an \(\mathcal{O}(n\log n)\)-word data structure that can answer any rSUS query on \(T[1,n]\) in \(\mathcal{O}(\log_{w}n)\) time._  
**Theorem 2**.: _We can construct an \(\mathcal{O}(n)\)-word data structure that can answer any rSUS query on \(T[1,n]\) in \(\mathcal{O}(\sqrt{n}\log^{\epsilon}n)\) time, where \(\epsilon>0\) is an arbitrarily small constant._
## 3 Materials and Methods
### Traffic Density
#### 3.2.1 Grid Cell Map  
IIO digitalizes and standardizes hydrographic and navigational information by defining the structure, content, and format of data to enable the consistent and efficient transmission, storage, and utilization of marine and hydrographic data. Although the IHO and the IMO used the S-57 data processing standard, they began developing the S-100 standard in 2012 to support various data formats, including 3D data, temporal changes, videos, and images [32; 33].  
Research using grids in maritime transportation has diverse applications, including maritime traffic analysis and network management [34; 35] for object detection and abnormal behavior monitoring [36; 37]. To easily and intuitively provide maritime traffic information to users, a grid system supporting multi-scale data analysis was utilized in a target sea area. In a grid system, selecting the appropriate grid cell size is crucial. If the grid cell size is too small, a large amount of data will be crowded on one screen, causing visual clutter. Conversely, if the grid cell size is too large, users might miss detailed data.  
The Korea Hydrographic and Oceanographic Agency (KHOA) has established a novel one-stop platform to integrate and provide dispersed marine information, enabling both public and private sectors to easily access and use marine data, contributing to the revitalization of marine-related industries [38]. Since 2014, KHOA has been providing services using a grid system for observing and forecasting marine weather. To deliver marine information services, KHOA defines the grid cell sizes in six levels, as presented in Table 1. In this study, a comparative analysis was conducted to determine the grid size suitable for the simulation area by adding a seventh grid cell size proposed by the author to the six levels defined by KHOA.
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 2 State of Art about Collective Perception for Connected and Autonomous Vehicle
### Communication Aspect
[PERSON] and [PERSON] [28] investigated three schemes for filtering the number of objects in messages, and thus adjusting network load to optimize the transmission of perceived objects. The simulation-based performance evaluation shows that filtering is an effective method for improving network-related performance metrics, with only a minor reduction in perceived quality. The authors also investigate whether filtering perceived objects carried in CPM is an appropriate mechanism for optimizing vehicle perception capabilities. The authors see filtering as a supplement to the message generation rules based on vehicle dynamics.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 1 Introduction
### The Motivation for the Research  
The study presents an innovative method, the ABBSOA and MSSOA techniques, for solving the energy shortage issues in WBANs. Using the ABBSOA and MSSOA, the approach aims to revolutionize cluster-based routing and provide optimal energy consumption. It can increase network reliability and improve WBANs' potential to provide essential technical services to the general population.