esa-sceva/satcom-chunk-collection huggingface dataset:
# VBF Event Classification with Recurrent Neural Networks at ATLAS's LHC Experiment
## 1 Introduction  
Nowadays, machine learning (ML) algorithms are largely used in many scientific fields as well as in many industrial applications. High energy physics (HEP) collaborations have been using ML techniques progressively in recent years, publishing many results that show significant improvements with respect to standard data analysis techniques. Specifically, both ATLAS and CMS [1, 2] collaborations spent efforts to boost deep learning (DL) regime applications; indeed, the benefit of using such techniques is maximised since the huge amount of data available at the Large Hadron Collider (LHC) [3] comes from collisions between proton beams. Many different physics problems were addressed, such as the reconstructions of physics objects and their identification, the event classification of physics processes, anomaly detection analysis and many others.  
In this growing environment, a novel deep learning approach based on a recurrent neural network (RNN) was developed and is presented in this work. The technique was applied to the categorisation task of the production mechanism of high-mass particles in the context of new resonance searches within the ATLAS collaboration.  
The ATLAS detector is a multi-purpose experiment that has been gathering data since 2012 at the Large Hadron Collider at CERN. The second part of the data gathering, named Run-2, ended in 2018, and currently, many physics results have been released, and many others are expected soon, while a new round of data gathering, Run-3, is ramping up, and it is expected to gather data until 2024. Each sub-detector has the specific task of reconstructing a different kind of particles by measuring their kinematic quantities. In this work, final states with leptons and jets will be considered. A jet of hadrons isa typical signature produced from hadronization of quarks and gluons created during collisions. Specifically, the ATLAS jet reconstruction employs an anti-\(k_{t}\) algorithm based on calorimeters cells information [4].  
Useful information about the nature of the production mechanism of new heavy particles might be found in the reconstructed events, specifically, among the hadronic part of the events. Since the experimental environment of the data collected at the LHC are dominated by hadronic secondary processes, this task becomes more challenging, and machine learning approaches can help to improve the performances. The physics problem can be translated into a general binary classification task that aims to categorise the events according to the event topology of the initial state that produces the new particle; therefore, it represents a signal-to-signal classification problem, and it was one of the first approaches using RNN developed and published in the ATLAS collaboration.  
In this paper, an overview of the method is presented, and its application in the context of the diboson searches is performed by the ATLAS collaboration.
## 4 Methodology
### Prediction and Optimization  
With the fused user representation \(\mathcal{R}_{U}\in\mathbb{R}^{|U|\times d}\) and fused location representation \(\mathcal{R}_{L}\in\mathbb{R}^{|L|\times d}\), where \(|U|\) and \(|L|\) denote the total number of users and locations respectively, we compute the interaction score between user \(u\in U\) and location predictions \(l\in L\) based on their dot product in the latent space.  
Specifically, the predicted interaction probability \(\hat{y}_{u,l}\) is computed via a softmax function, as \(\hat{y}_{u,l}=\text{softmax}(\mathcal{R}_{U}\cdot\mathcal{R}_{L}^{\top})\), indicating the likelihood of the user visiting that location at the given timestamp. This probabilistic prediction is computed by the dot product of the fused user and location representations in the latent space, followed by a softmax function, ensuring that the output is a normalized probability distribution across the top-k locations.  
The recommendation objective is formulated as in Equation (27), a binary cross-entropy loss over all user-location prediction pairs.  
\[\mathcal{L}_{Rec}=-\sum_{u\in U}\sum_{l\in L}[y_{u,l}\log(\hat{y}_{u,l})+(1-y_ {u,l})\log(1-\hat{y}_{u,l})], \tag{27}\]  
where \(y_{u,l}\in\{0,1\}\) indicates whether user \(u\) visited location \(l\) (1 for visited, 0 otherwise).  
To enhance the learning signal, we adopt a multi-task objective by incorporating a self-supervised contrastive loss, \(\mathcal{L}_{CL}\), and a regularization term to mitigate overfitting. The final total loss function is defined as follows:  
\[\mathcal{L}_{\text{total}}=\mathcal{L}_{Rec}+\lambda_{CL}\mathcal{L}_{CL}+\| \Theta\|_{2}^{2}, \tag{28}\]  
where \(\lambda_{CL}\) is a hyperparameter controlling the relative importance of the self-supervised signal, and \(\Theta\) denotes the set of all trainable parameters in the proposed MPHCL model.
# Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain
## 3 Model Architecture
### Problem Definition and Preprocessing  
In this paper, all processing characteristics of the model are defined in the frequency domain. Problem definition and data flow are detailed below.  
Given a multivariate time series \(\mathcal{X}=\left\{x_{t}^{i}\right\}\in\mathbb{R}^{N\times T}\), \(\mathcal{X}\) is a real matrix, \(N\) is the number of variables, \(T\) is the length of time, and \(x_{t}^{i}\)\(x\) the value of the \(i\)th sequence at moment \(t\). The prediction length is \(L\):\(f(*)\) is the learning target for the model, and the output of the model is in the same format as \(\mathcal{W}_{k}\). The inverse of the \(reshape(*)\) operation is used to convert \(\mathcal{V}_{l}\) into a complex matrix \(\overline{\mathcal{V}}_{l}\), which is then subjected to inverse Fourier transformation to obtain the final prediction result \(\hat{\mathcal{V}}_{L}\) of length \(L\).  
In general, our goal is to obtain a frequency mapping model, which receives the frequency representation of the input time series and outputs the combination of the imaginary part and the real part of the prediction frequency. Thus, we can ignore the temporal features of input/output series to simplify our prediction problem.
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 3 The Channel Calibration Method for Bistatic ISAR System  
Channel calibration method based on a calibration tower at a fixed distance is effective for a monostatic ISAR system. SNR of the received signal is usually high because of the short distance and the gain of transponder. However, for the bistatic ISAR system, the baseline of the radar system is up to hundreds of kilometers. It is unrealistic to use a calibration tower to form the calibration loop. It is necessary to adopt this kind of target, which can be observed by bistatic radar system and can be regarded as a point target. The calibration satellite is also known as the standard spherical satellite, of which the RCS is generally within 1m\({}^{2}\), and the echo signal is stable. The calibration for transmitting and receiving channels can be completed using calibration satellite echoes. Hence, a novel channel calibration method is proposed based on the calibration satellite.
# Non-Invasive Hydration Monitoring with a Graphene Dual Sweat Sensor
## 3 Results and Discussion
### Sensor Characterization: Electrochemical Performance
(**a**) Repeatability tests via repeat measurements on the same sodium selective electrodes, with concentrations from 0.1 mM to 100 mM, increasing in tenfold steps; (**b**) reproducibility tests via identical measurements on the five different sodium selective electrodes, with concentrations from 0.1 mM to 100 mM, increasing in tenfold steps; (**c**) repeatability tests via repeat measurements on the same potassium sensors, with concentrations from 0.1 mM to 100 mM, increasing in tenfold steps; (**d**) reproducibility tests via identical measurements on the five different potassium sensors, with concentrations from 0.1 mM to 100 mM, increasing in tenfold steps. having a final concentration of 10 mM, was dispensed on the sensor. This test was repeated on five different sensors in order to observe the degree of difference of the results when all of these ions are combined in one solution. As seen in the last row of Table 2, the sensors were able to detect approximately 10 mM of sodium and potassium, as per what was added in the solution, with a 7% variance between sensors. These results indicate that the sodium and potassium electrodes are highly selective to the ions they were functionalized to detect, with minimal interference from other similar ions. Our technology advances the field beyond the relatively limited existing graphene-based sensor platforms. The dual graphene sensor we developed operates over a wide linear detection range, delivers a near-Nernstian response, and requires a lower sample volume. Additionally, we have validated our sensor against both commercial solutions and ICP-OES measurements to ensure accuracy and reliability. For comparison, we have included Table S1, comparing previous graphene-based sweat sensors with our own, highlighting key differences and improvements.
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
###### Abstract  
Nanotechnology is an exciting and innovative field when combined with tissue engineering, as it offers greater versatility in scaffold design for promoting cell adhesion, proliferation, and differentiation. The use of nanomaterials in craniofacial tissue regeneration is a newly developing field that holds great potential for treating craniofacial defects. This review presents an overview of the nanomaterials used for craniofacial tissue regeneration as well as their clinical applications for periodontal, vascular (endodontics), cartilage (temporomandibular joint), and bone tissue regeneration (dental implants and mandibular defects). To enhance periodontal tissue regeneration, nanohydroxyapatite was used in conjunction with other scaffold materials, such as polylactic acid, poly (lactic-co-glycolic acid), polyamide, chitosan, and polycaprolactone. To facilitate pulp regeneration along with the revascularization of the periapical tissue, polymeric nanofibers were used to simulate extracellular matrix formation. For temporomandibular joint (cartilage) engineering, nanofibrous-type and nanocomposite-based scaffolds improved tissue growth, cell differentiation, adhesion, and synthesis of cartilaginous extracellular matrix. To enhance bone regeneration for dental implants and mandibular bone defects, nanomaterials such as nanohydroxyapatite composite scaffolds, nanomodified mineral trioxide aggregate, and graphene were tested. Although the scientific knowledge in nanomaterials is rapidly advancing, there remain many unexplored data regarding their standardization, safety, and interactions with the nanoenvironment.  
d 28 December 2018; accepted: 15 January 2019; Published: 17 January 2019
###### Abstract
Nonlinear mechanisms of absorption of femtosecond laser radiation in an aqueous medium [22; 23] result in the localization of the laser effect within the point of the focused beam, significantly increasing the accuracy of microsurgical procedures. This, in turn, improves the safety of laser microsurgery of embryos and expands the scope of femtosecond lasers in the ART. Controlled laser-assisted hatching (CLAH) is a novel technology developed using femtosecond laser pulses [24]. It consists of the dissection of the zona pellucida and, in contrast to millisecond pulses, can be performed at a late stage of embryo development. At the blastocyst stage, embryo cells have already differentiated into the trophectoderm cells and the intracellular mass (ICM). So, CLAH enables the embryologist to choose the hatching site (\"ICM-forward\" or \"TE-forward\") for subsequent cell biopsy. Moreover, femtosecond CLAH procedures do not impair embryo development and increase the hatching rate. Femtosecond laser pulses have also been implemented in noncontact laser-tagging technology to tag preimplantation embryos to identify them. It could help to prevent reported medical accidents relating to embryo mix-ups [25]. This brings prolonged emotional distress to parents and the loss of reputation to the clinic. The technology consists of applying alphanumeric designations to embryo ZPs using laser engraving. The cuts, which are 1-1.5 \(\upmu\)m wide, form a set of arbitrary alphanumeric characters that can be recognized by the embryologist. The width of the cut is crucial since it affects the readability of the code. Despite the minimal risk of embryo thermal damage, parameters for femtosecond laser exposure should be chosen carefully. Microsurgery of the ZP is performed with a series of femtosecond laser pulses, and a cut of a given width can be obtained with a different combination of parameters, including the energy and the laser beam velocity. To the best of our knowledge, the issue of the influence of laser-radiation parameters on the formed cut of the zona pellucida has not been considered previously. The purpose of this research was to study the effect of laser parameters on the cut width, as well as to provide recommendations for choosing the optimal values for ZP microsurgery in the framework of assisted reproductive technologies.
## 2 Materials and Methods
### Devices  
EEG signals were measured with the GES 300 EEG system (Electrical Geodesics, Inc., Eugene, OR, USA) comprising a Net Amps 300 amplifier (input resistance 200 \(\mathrm{M}\Omega\); recording ranging 0.01-1000 Hz) and a 64-channel cap with active electrodes ActiCAP (BrainProducts, Munich, Germany). In the study, 64 electrodes were used an arranged according to the 10-10 international system. The impedance of the electrodes was kept below 10 k\(\Omega\) and the signal was referenced to an FCz channel throughout the recording. Data were sampled at 500 Hz and recorded with the Net Station 4.4 (EGI, Eugene, OR, USA). The experimental procedure was designed and displayed on a computer screen with the use of PsychoPy Software v. 3.0. Above the computer screen there was a stroboscope emitting white light. Responses were registered with a response pad built using non-ferrous materials to avoid magnetic induction. The processing of the EEG signal was performed with EEGLab version 2021.1 (USA) [44], and the statistical analysis as well as visualisation of the results was carried out with SPSS 20 (IBM, USA). The ICLabel, an automatic EEG independent component classifier plugin for EEGLAB [45], which is designed to recognise independent components (ICs) such as brain or non-brain sources, was used to remove the ICA components, but the final decision was made by an expert if the component had to be removed. The goal of this paper was to compare the methods which can be used automatically, so this is possible only if the IC components are classified by the algorithm. The clean_rawdata [18], which is also the EEGLab plugin, was used to calculate ASR. The method which was applied in calculating ASR also allows removing channels, line-noisy channels, poorly correlated channels or applying the high-pass filter. The ASR was calculated with parameter \"Standard deviation cutoff\" for the removal of bursts, which was set to the default value of 5. This is a conservative, not strongly restrictive value. It was set to 5 based on visual examination. The mentioned functions were calculated in separate steps.
# Disseminating the Past in 3D: O Corro dos Mouros and Its Ritual Landscape (Galicia, Spain)
## 4 Discussion  
The combination of UAV-LiDAR and SfM photogrammetry technologies implemented at O Corro dos Mouros involves certain technical and operational limitations that must be taken into account. The precision of data acquisition through UAS is conditioned by meteorological factors, particularly visibility and atmospheric stability. In this case, the climatic variability characteristic of autumn in the northwest of the Iberian Peninsula forced adjustments to the flight planning, optimising the operational windows based on the most favourable conditions to ensure adequate resolution and geometric accuracy. One of the key factors influencing the results was vegetation, especially in areas with dense forest cover. Although the campaign was conducted during a season with less vegetation than summer, the presence ofiferous forests with persistent foliage represented a significant challenge for obtaining accurate data. In regions with tall trees and/or dense foliage, dense vegetation obstructed LiDAR laser penetration, resulting in incomplete terrain point clouds.  
Figure 9: 3D models of the O Corro dos Mouros archaeological site, developed as tools for archaeological analysis and interpretation, and intended to promote scientific study and public dissemination through accessible visualisations, available on the Sketchfab platform.  
The processing of data obtained through photogrammetric techniques or LiDAR scanning requires the use of specialised software with advanced capabilities for 3D modelling, image alignment, and the generation of dense point clouds. Established commercial solutions such as Agisoft Metashape or DJI Terra provide high-precision results, but their acquisition represents a considerable economic investment (Table 1). It is important to note that the costs presented in Table 1 are approximate and may vary depending on factors such as geographic region, vendor, license type (e.g., perpetual, subscription-based, or floating), and other commercial conditions, which may exceed the budget capacity of many archaeological projects or research groups with limited resources. Although the choice of these commercial platforms was justified based on their advanced capabilities and the need to ensure high fidelity in the generated products, it is worth noting that open-source solutions such as Meshroom, despite their limitations in terms of metric accuracy, process automation, and technical support, can constitute viable alternatives in budget-constrained environments. Additionally, access to shared academic or institutional licences represents an effective strategy to increase the availability of these tools in research contexts with limited resources.
## 4 Empirical Insights from Sectorial and Regional Studies
### AI Synergies and Revenue Growth in European SMEs  
[PERSON] et al. [111] present robust econometric evidence on the impact of AI adoption on revenue growth among 11,429 European SMEs, using data from Flash Eurobarometer 486. Their findings reveal that AI adoption, especially when complemented by Big Data Analytics and the IoT, significantly increases the probability of revenue growth by at least 30%. Specifically, the combined adoption of AI with IoT or BDA led to a 21% increase in the likelihood of high-growth scenarios. This reinforces the view, consistent with our TOE-DOI framework, that technological readiness must be coupled with complementary infrastructure and organizational capabilities to produce strategic returns.  
The study also illustrates the value of modular and combinatory adoption strategies, where SMEs that take advantage of multiple digital assets achieve synergistic results. From a practical point of view, these results support the actionable pathway in our framework that encourages SMEs to adopt scalable, integrated technology stacks rather than isolated tools. Furthermore, [PERSON] et al.'s [111] findings validate the role of external ecosystem support and digital service platforms as critical enablers in overcoming capability gaps. By linking AI adoption with clear economic performance indicators, this study strengthens the external validity of our challenge-solution model and exemplifies how customized digital strategies can lead to measurable SME growth in national and sectoral contexts.
# Improving Sentence Retrieval Using Sequence Similarity
## 2 Previous Research  
Sentence retrieval is similar to document retrieval, and sentence retrieval methods are usually simple adaptations of document retrieval methods where sentences are treated as documents [6]. The sentence retrieval task consists of finding relevant sentences from a document base which has been given a query [6].  
Generally, when it comes to information retrieval, the TF-IDF method is still very much present today. For example, the authors of [12] presented a text document search system on distributed high-performance information systems, where initial document weighting was performed using the TF-IDF method. The weighting of text by the TF-IDF method, or the assignment of weight to each linguistic concept in comments from social networks, has also been described by the authors of [13].  
The authors of [14] presented two different techniques (BM25 and TF-IDF) to extract the keywords from data collection using Twitter. TF-IDF has also been also used for novelty detection in news events [15]. TF-IDF is widely used for text pre-processing and feature engineering [13]. There have also been attempts to outperform TF-IDF. For example, the authors of [16] presented a Phrase-Based document similarity, which effectively outperformed term-based TF-IDF. The authors of [17] proposed a refined TF-IDF method, called TA TF-IDF, for calculating the weights of hot terms. The vector-space model is one of the most commonly used models for documents and sentence retrieval [2]. The authors of [18] proposed a method of analyzing patent texts based on the vector space model, with the features of patent texts being excluded. Using the proposed algorithm, the authors of [19] calculated the distance between document and topics, and then each document was represented as a vector.  
When it comes to sentence retrieval, TF-ISF (the sentence retrieval variation of the TF-IDF method) is one of the first and most widely used methods for sentence retrieval [5]. TF-ISF has been shown to outperform other methods, like BM25-based methods and methods based on language modeling [5; 20]. The sentences are represented as a collection of unique words with the weights of words that appear in the selected sentence [21]. The second method popularly used with sentence retrieval is query likelihood [2; 6]. There have been multiple attempts to improve the standard TF-ISF method, which include analyzing the context in the form of a document and the previous, following, or current sentences [6]. The authors of [22] analyzed the effectiveness of contextual information for answer sentence selection.  
In our research, we adapted tree standard retrieval models, TF-ISF, BM25 and the language modeling-based method, to improve sentence retrieval using the partial matching of words.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 4 Experimental Setup
### Hardware Configuration  
Figure 4 depicts the communication between the server and clients. The server node, which functions as the SCADA, has been implemented on a high-performance machine equipped with an Intel Core-i9 32-core processor operating at 2.0 GHz, with 32 GB of random access memory (RAM) and a Gigabit Ethernet interface. This server has been assigned a static IP address of 192.168.1.1.  
In the experimental testbed, client nodes, which represent various distributed components of the smart grid, including smart meters and EV chargers, have been deployed on a diverse array of hardware configurations. These configurations range from embedded devices (e.g., ESP-8266 modules) to high-performance octa-core machines. Client machines have been allocated dynamic IP addresses within the range of 192.168.1.101 to 192.168.1.150.  
All nodes are interconnected through a Gigabit Ethernet local area network (LAN) that is managed by a network switch. Manual delays ranging from 0.1 ms to 5 ms have been introduced to emulate real-world conditions.