esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 3 Proposed Approach
### Boundary-Refinement Block  
The second module of the proposed network is the boundary-refinement block (BRB). It is a residual framework implanted after first five convolutional layers to maintain the boundary information of objects. This block is added for the refinement of feature maps and to enhance the accuracy of an object's spatial location. The BRB was included to improve localisation near boundaries, which can significantly improve object detection in low-contrast images.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology
### LoRa Measurement Descriptions
#### 3.2.2 Receiver Pycom Module  
Like the previous module, this node was also programmed to perform two functions:  
* Receive the characters sent by the other module.
* Carry out an analysis of the data obtained in order to be able to estimate both the signal quality rate (BER) and the signal power (RSSI) at each point.  
In this module, a program was implemented that was capable of receiving data at the maximum possible speed. Furthermore, since the characters sent were known, the module was capable of detecting those characters that were wrong or that had not been received directly during a given sampling time. In this way, it was possible to estimate the quality rate of the signal generated by the emitter module. In addition, the value of the signal power (RSSI) with which data was received at each of the study points was captured.
# Assessment of Nanobag as a New Safety System in the Frontal Sled Test
## 1 Introduction  
The vehicle safety is as old as mobility started. The improvement of vehicle occupants' safety was approached in different ways, where all of them led to enforcement through vehicle manufacturers by embedding safety standards into legislation and policies. Whilst the history of safety belts dates back to the early 20 th century, the airbag came more than 50 years later [1]. Although the active systems strongly support present vehicles' safety, the passive systems still play a main role. Nowadays, the airbag is an inherent safety system in almost all road vehicles, protecting passengers not only in frontal and side impacts, but special airbag systems mitigate injury to other parts of the human body (knee, chest, etc.) that are also considered vulnerable and likely to be injured [2].  
Current trends in the automotive industry bring new challenges for active and passive safety technology. Non-traditional seating configurations in autonomous vehicles and complex crash scenarios including multi-directional impacts are to be considered [3; 4; 5] in future automatic vehicles. The expected future scenarios will cover complex and highly unpredictable loading from various directions [6]. This study is testing a new restrain system that can find its benefit in these non-standard seating configurations, where standard airbags can lose their performance. However, nanobag technology needs to be tested and certified firstly in the standard seating position. Moreover, the main aim here is to assess the performance of the nanobag compared to the standard airbag, and it is certified for a standard seating configuration only. Thus, only the standard seating configuration and frontal impact are considered in this study.  
The aim of this study is to assess a brand new concept of the interior safety system for front and back seats made of elastic ultralight materials [7; 8]. The assessment concerns a virtual numerical simulation using validated models of both the occupant and the restrain system. The new supplemental restraint system called the nanobag concerns a foil folded in front of the passenger to serve as an airbag. The system is built in the way that it can be easily adapted for side impact. The main advantages of this new technology are in minimizing the volume of the folded airbag, decreasing the car weight, simplifying technology production and maintenance, the low cost of materials and the assembly and possible applicability to multidirectional accident scenarios with non-standard seating configurations, which is going to be a critical issue within new technologies of future mobility ecosystems.  
For assessing the safety of future mobility, the traditional anthropometric testing devices (dummies) are not suitable due to their unidirectional bio-fidelity, which cannot address non-traditional seating [9]. The new state-of-the art ATDs (THOR) are still in the process of exploration for the automated vehicles crash scenario application [10]. Therefore, the paper utilised an existing validated human body model, Virthuman [11]).
## 5 Conclusions  
We conclude by outlining the main results of the study:  
* A mathematical algorithm for adjustment of measured analytical line intensities in individual pixels at a CCD upper limit and data processing of the recording system was developed. It can be implemented in the software of modern emission spectrometers.
* The algorithm was tested in a numerical experiment and when determining the concentration of Al in Fe-based alloys.
* It was shown that if relative frame dispersion is significant (20% or more), the adjustment coefficient can reach 2.
* It was clarified that the actual dispersion of analytical line intensities' frame-by-frame distribution can exceed the measured dispersion by one-and-a-half times.
* It was shown that the half-width of the recorded spectral line contour exceeds its real contour.
* All detected patterns were interpreted within the framework of the mathematical algorithm and model.  
These results enable us to improve the software for emission spectrometers that use CCDs as recording elements, in order to adjust the calibration curves in the area of high alloying element concentrations. The algorithm can be used not only in emission spectral analysis, but also in any kind of equipment where spectral line intensity is measured with a CCD.  
Conceptualization, V.S.S. and A.S.M.; methodology, A.N.P., V.S.S. and A.S.M.; software, A.N.P. and V.S.S.; validation, A.N.P. and V.S.S.; formal analysis, A.N.P. and V.S.S.; investigation, A.N.P.; resources, A.N.P. and V.S.S.; data curation, A.N.P. and V.S.S.; writing--original draft preparation, A.N.P.; writing--review and editing, A.N.P. and V.S.S.; visualization, A.N.P.; supervision, V.S.S. and A.S.M.; project administration, A.N.P.; funding acquisition, A.N.P. All authors have read and agreed to the published version of the manuscript.  
This research was carried out within the state assignment of the Russian Science Foundation (RSF), the grant No 21-19-00139.  
Not applicable.  
All the data used is available in the article.  
The authors declare no conflict of interest.
# Cost-Aware IoT Extension of DISSECT-CF
## 3 Cost Modeling in DISSECT-CF
### IoT Pricing  
In our simulation model, we aim to investigate certain IoT cloud applications; therefore, we need to define and monitor the following parameters: the number of sensors or devices used, the total number of messages (and their data) sent in a certain period of time, and the uptime and capacity of virtual machines used to provide ingest services. Based on these parameters, we can estimate how our application would be charged after operating its system for a certain amount of time at a concrete IoT cloud provider.  
The calculation of the prices depends on different methods. Some providers bill only according to the number of messages sent, while others also charge for the number of devices used. The situation is very similar if we consider the virtual machine rental or application service prices. One can be charged after GB-hour (GigaByte-hour or uptime) or according to a fix monthly service price. This price also depends on the configuration of the virtual machine or the selected application service, especially the mount of RAM used or the number of CPU cores or their clock signal.  
We considered the following, most popular providers as the base of our extension: (i) Microsoft and its IoT platform called Azure IoT Hub [16], (ii) IBM's Bluemix IoT platform [17], the services of (iii) Amazon (AWS IoT) [18], and (iv) Oracle's IoT platform [19]. We took into account the prices publicly available on the websites of the providers and if it necessary we asked for clarifications via email.
# eSCorpius-m: A Massive Multilingual Crawling Corpus with a Focus on Spanish
## 1 Introduction  
Deep Learning (DL) models in Natural Language Processing (NLP) have achieved unseen performance and thus have largely replaced conventional machine learning approaches across multiple applications, such as machine translation, natural language understanding, or natural language generation. One of the main tasks in these areas is language modeling, for which the current state-of-the-art is to use models pre-trained on a data-rich tasks or languages that are subsequently fine-tuned to the target language or task at hand, so that stakeholders do not have to perform expensive pre-training themselves.  
To tackle these challenges, in this paper we present the eSCorpius-m multilingual corpus with the following properties:  
* It is cleaner than state-of-the-art corpora and deduplicated.
* It maintains both document and paragraph boundaries allowing language models to deal with textual data in the same way as humans do, thus unlocking the capabilities of Natural Language Generation to understand paragraph representation.
* The data downloaded maintains the traceability of the origin of each document. This level of traceability makes it possible to apply the right of withdrawal of individual website owners or individual persons whose data are cited on websites and are protected by GDPR. It also allows for systematically excluding blacklisted websites.  
* It is a high-quality multilingual corpus that excels in content cleaning and deduplication. Depending on the language, e.g., in Spanish, it is the largest web corpus of this quality available for the development of large language models.  
This paper is an extension of our prior work with the Spanish massive corpus esCorpius[1]. In this paper, we present the esCorpius-m multilingual corpus that comprises 34 languages different from English. This study aims to provide the scientific community with an extensive, multilingual corpus for training large language models. Our corpus stands out for its superior cleanliness and reduced duplication when compared to similar-sized corpora. Furthermore, we present the innovative pipeline employed to ensure its high quality.  
The rest of the paper is organized as follows. Section 2 presents the state-of-the-art providing the context for esCorpius-m, which is described in detail in Section 3. Section 4 describes the process followed to download, clean, and deduplicate the data and Section 5 details the technical environment employed to accomplish these tasks. Finally, Section 6 provides the conclusions drawn from our research and outlines avenues for future work.
## 3 Deep Learning Structure Design
### Hybrid LSTM-CNN Model  
To fully capitalize the strengths of both LSTM and CNN architectures, a new hybrid model is proposed. Figure 4 shows the overall design of the HLSTM-CNN model. It consists of two bi-directional LSTM (bi-LSTM) and six convolutional layers, together with rectified linear unit (ReLU), batch normalization, and dropout layers specifically placed between weighted layers to avoid overfitting. Traditional activation functions utilize either sigmoid function or the hyperbolic tangent function, but to conserve computation time, ReLU functions are used as given by Equation (7).  
\[f(x)=max(0,x) \tag{7}\]  
To lessen the effects of generalization errors whilst maintaining accuracy, dropout and batch normalization layers are utilized. Dropout is a strategy wherein an ensemble of possible sub-networks, from the original network, are randomly selected and applied on the hidden layer, with a zero-multiplication effect for each training iteration. On the other hand, batch normalization improves stability and speeds up network training [28]. Essentially, the features are shifted to have zero mean with standard deviation dominating the results.  
Following the results from [PERSON] et al. [29] on the Resnet model skip connections are introduced to avoid vanishing and exploding gradients of very deep layers. This idea has shown improved performance on images, but has not been further exploited on 1D inputs.  
Figure 4: Proposed HLSTM-CNN model.
# Investigations of the Energy Transfer
## 2 Materials and Methods
### Data Analysis  
The pre-processing of the TA data included removal of the pump spectral range and correcting the WL dispersion. The data were calibrated using a holmium oxide filter. The analysis of the samples kinetics was performed in CarpetView software (Light Conversion) by applying global analysis. Briefly, the global analysis assumes a scheme of discreet compartments that are connected by linear rate kinetics. The excitation pulse will populate one or more compartments, and the population willflow between the compartments, according to the selected connectivity scheme. The time-dependent populations of the compartments, \(c_{i}\) (f), are described as follows:  
\[\frac{dc_{1}(t)}{dt}\ =\ I(t)-\frac{1}{\tau_{1}}c_{1}(t) \tag{1}\]  
\[\frac{dc_{i}(t)}{dt}\ =\ \frac{1}{\tau_{i-1}}c_{i-1}(t)-\frac{1}{\tau_{i}}c_{i}(t ),\ i\
eq 1, \tag{2}\]  
where \(I(t)\) is the excitation pulse and \(\tau_{i}\) is the characteristic decay time of the \(i\)th compartment. When a sequential model is applied, each time-dependent population will be assigned a spectrum known as evolution-associated fluorescence spectrum (EAFS) in the case of time-resolved fluorescence data and evolution-associated difference spectrum (EADS) in the case of transient absorption data, which will represent the different evolutionary steps in the spectral evolution. In general, EADS represent mixtures of coexisting molecular states, but provide information about the spectral dynamics and time evolution of the system. In the ideal case, the EADS represent the true species associated spectra of the real photochemical species.  
Additionally, the TCSPC data were investigated using the EasyTau 2 software (PicoQuant) using a multi-exponential reconvolution method. This type of analysis performs a convolution of the kinetic decays obtained at each of the probed emission wavelengths with the measured instrumental response function. The IRF was measured separately for each pump wavelength.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 3 System Model
### UAV Threats and Vulnerability
#### 3.1.3 Eavesdropping and Spoofing  
Another prominent attack issue is eavesdropping and spoofing when attackers obtain critical information by listening to the communication between source and destination points via spoofing the Address Resolution Protocol (ARP) packets. In UAVs, once a hacker obtains the secret keys of the UAV, the whole device is compromised. The hacker can eavesdrop and steal their data through the open communication channel. In case of spoofing, the intruder will impersonate other UAVs and then take control of the UAV system by providing false information. GPS spoofing is a typical example of a spoofing or forgery attack in UAVs.
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Routing Protocols** & \multirow{2}{*}{**Key Features**} & \multirow{2}{*}{**Reliability**} & **Energy Consumption** & **Scalability (Long-Hop)** \\ \hline \multirow{3}{*}{Multipath Routing} & \multirow{3}{*}{MMSEED} & High reliability and & & & \\  & & supporting real-time & High & High & High \\ \cline{1-1}  & & transmission & & & \\ \cline{1-1}  & & Reliability based on & & & \\ \cline{1-1}  & & node-disjoint multipath & & & \\ \cline{1-1}  & & High reliability based on & & & \\ \cline{1-1}  & & radio-disjoint multipath & & & \\ \cline{1-1}  & & Maximizing network life & & & \\ \cline{1-1}  & & time based on load & & & \\ \cline{1-1}  & & balancing & & & \\ \cline{1-1}  & & ERMDT & Avoiding congestion area & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reliable routing protocols based on multipath routing, opportunistic routing, and a learning-based algorithm.
# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 2 Materials and Methods
### Results
The scores of different methods in Table 4 prove that our method has the best performance in various indicators. In addition, Figure 5 shows the visualization results of the centerlines. Figure 5 shows the fundus image, ground truth, and centerline results of various methods for each image instance. The displayed results include those of Skeleton, U-Net, and our proposed MSC-Net. Figure 5 shows that our method obtains centerline results with better topological continuity and fracture situation than other methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **SE** & **SP** & **ACC** & **AUC** \\ \hline \multirow{3}{*}{DRIVE} & Skeleton [28] & 0.7224 & 0.8993 & 0.9352 & - \\  & U-Net [17] & 0.7403 & 0.9313 & 0.9310 & 0.9045 \\  & MSC-Net & **0.7905** & **0.9552** & **0.9510** & **0.9583** \\ \hline \multirow{3}{*}{STARE} & Skeleton [28] & 0.7565 & 0.9156 & 0.9148 & - \\  & U-Net [17] & 0.7836 & 0.9463 & 0.9589 & 0.9467 \\  & MSC-Net & **0.8546** & **0.9625** & **0.9423** & **0.9683** \\ \hline \multirow{3}{*}{CHASE} & Skeleton [28] & 0.6785 & 0.7636 & 0.9048 & - \\  & U-Net [17] & 0.7560 & 0.8581 & 0.8902 & 0.9186 \\ \cline{1-1}  & MSC-Net & **0.8144** & **0.8865** & **0.9267** & **0.9541** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SE, SP, ACC, and AUC scores of MSC-Net and other methods for centerline extraction on the test set. Figure 4: Visualization results of different blood vessel segmentation methods. The segmentation contained in the yellow dashed circle is our concern. Our method has better results in small parts.
## 3 Results and Discussion
### Microbiological Analysis of the Tested Beef Hams  
The total microbial count (TVC--total viable count) after production in all ham variants was at a low level (3.53-4.39 log CFU/g). After production, the TVC in the A samples was higher (4.39 log CFU/g) than in the control sample (3.89 log CFU/g) (Table 2). In studies by other authors on chicken meat and cooked marinated beef meat, a reduction in TVC of approximately 1.0 log CFU/g after the marinating process was found, [76]. This can be explained by the higher pH of the studied samples (Table 1), as well as a different type of meat, marination formulation, temperature, and time of marination [29]. The lactic acid bacteria (LAB) counts were also low and equalized (4.92-4.99 log CFU/g) (Table 2). The number of lactic acid bacteria reached the highest values after 3 months of storage in all beef ham samples and remained high until the end of the storage period. After production and during the storage of beef hams, there were no significant differences of LAB count in C, A, and AW variants. This indicates the possibility of beef autochthonous LAB strain predominance. This should be confirmed in further bacterial strain typing studies. According to [PERSON] et al. (2020), LAB levels in fermentation processes are dynamic; they decrease throughout fermentation [77]. LAB are pivotal microorganisms of fermented meat products. Unique bacterial enzymes (esterase, protease, and peroxidase), along with endogenous meat enzymes, decompose proteins, fats, and carbohydrates, producing flavor compoundsand shaping the flavor of the final fermented meat product [78]. Moreover, LAB have antioxidant and antimicrobial activity, contributing to the extended shelf life of fermented meat [3]. LAB degrade nitrates, reducing potentially carcinogenic N--nitrosamines in meat products [79]. The strategy of replacement chemical preservatives, including nitrate and nitrite, should consider the microbiological safety of fermented meat products. In nitrate-free products, the potential microbiological hazard should be confronted by potential chemical risk. _Enterobacteriaceae_, including _Escherichia coli_, were below the detection level of the method used in all variants of hams after the fermentation process (<1.00 log CFU/g). _Staphylococcus aureus_ was also not observed in all tested hams. This proves that the ham technology process was properly carried out in terms of microbiological safety. The tested hams were free from pathogens _Salmonella_ and _Listeria_ spp. (including _L. monocytogenes_) after production and after storage. The amount of tested natural preservatives were appropriate for the microbiological safety of the studied beef hams. However, further microbiological assessments are needed for the industrial implementation of this technology. In the design of studies on the variety of available natural preservatives, its changeable content should be considered, as well as the possible multiple interactions with the food matrix. In this regard, the use of chemical preservatives is more predictable [40].
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 3 Categorization of Semi-Bridgeless Boost Converter Topologies
### Semi-Bridgeless Topology with Internal Snubber Circuit  
Figure 14 depicts the SBBC with an internal snubber circuit [108]. This converter has an additional switch (\(Q_{3}\)) in comparison with the topology with clamped diodes in order to switch the converter with zero voltage transition. The main function of \(Q_{3}\) is to control the snubber circuit made of diodes \(D_{5}\), \(D_{6}\), \(D_{7}\), \(D_{8}\), inductor \(L_{3}\), and capacitor \(C_{1}\). The snubber circuit provides a soft switching for \(Q_{1}\), \(Q_{2}\), and \(Q_{3}\) in order to reduce switching losses and reverse recovery losses from \(D_{1}\) and \(D_{2}\). Other topologies with a snubber circuit for SBBC soft switching can be consulted in [109; 110].  
Figure 12: Semi-bridgeless topology with clamped diodes and switches.  
Figure 13: Semi-bridgeless boost topology with clamped capacitors: (a) conventional; (b) coupling inductors.  
In conclusion, SBBC topologies present better behavior than BBC topologies in terms of efficiency, \(THD_{i}\), and losses. Therefore, SBBC topologies are suggested in applications of low voltage and medium power instead of BBC topologies [32; 36; 37; 112].