esa-sceva/satcom-chunk-collection huggingface dataset:
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Impact of the Proposed Technique on the Healthcare Sector  
The integration of the DICA into the IoMT can significantly enhance the efficiency and usefulness of the healthcare sector. By improving data deduplication and reducing redundant transmissions, the proposed technique lessens the bandwidth and storage requirements. This improvement can lead to cost savings for healthcare facilities, permitting the distribution of resources toward more critical areas, such as patient care and medical research. Furthermore, the reduced need for extensive data storage and transmission lessens the load on the cloud and fog servers, increasing their performance and reliability. This efficiency can lead to more responsive healthcare systems capable of holding larger volumes of patient data with minimal delays, thus supporting real-time decision-making. The possibility to process and transmit patient data more efficiently has direct implications for patient outcomes. By guaranteeing that critical health data are transmitted with minimal delay and without redundancy, healthcare providers can monitor patients' conditions more accurately and respond to emergencies more swiftly. This is particularly valuable for patients with chronic conditions or those requiring frequent monitoring, as it decreases the need for frequent hospital visits and allows for timely medical intervention.  
Moreover, the reliable and efficient aggregation of health data supports the development of predictive analytics and personalized treatment plans. By comprehensively and accurately evaluating the conditions of patients, healthcare providers can identify patterns and trends to inform their clinical decisions, eventually improving patients' care and outcomes.
###### Abstract  
Industrial augmented reality (AR) applications demand high on the visual consistency of virtual-real registration. To present, the marker-based registration method is most popular because it is fast, robust, and convenient to obtain the registration matrix. In practice, the registration matrix should multiply an offset matrix that describes the transformation between the attaching position and the initial position of the marker relative to the object. However, the offset matrix is usually measured, calculated, and set manually, which is not accurate and convenient. This paper proposes an accurate and automatic marker-object offset matrix calibration method. First, the normal direction of the target object is obtained by searching and matching the top surface of the CAD model. Then, the spatial translation is estimated by aligning the projected and the imaged top surface. Finally, all six parameters of the offset matrix are iteratively optimized using a 3D image alignment framework. Experiments were performed on the publicity monocular rigid 3D tracking dataset and an automobile gearbox. The average translation and rotation errors of the optimized offset matrix are 2.10 mm and 1.56 degree respectively. The results validate that the proposed method is accurate and automatic, which contributes to a universal offset matrix calibration tool for marker-based industrial AR applications.  
augmented reality; AR registration; offset calibration; industrial AR; pose estimation; image descriptor; image alignment; CAD model +
Footnote †: journal: Applied Sciences
# _WebTrackingScore:_ A Combined Web Tracking Risk Score System for Websites
## 1 Introduction  
Nowadays, we can see how user tracking has increased with the aim of offering personalization and advertising. For advertising, it is essential to unequivocally identify users (real identity or user profile) [1, 2]. User tracking is predominantly carried out using cookies [2, 3], but more sophisticated techniques, such as browser fingerprinting, have also emerged [4, 5]. As a response to growing concerns about privacy, various technical solutions (ad-blockers, Virtual Private Networks (VPNs), anonymous communications networks [6, 7]) and regulatory frameworks (General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Brasil [8, 9, 10]) have been developed to protect users' privacy. These regulations have improved transparency, ensuring that users are informed about how their data are being tracked. However, there are still websites that are not properly adopting them or searching for mechanisms to bypass them [11, 10, 12].  
In addition to regulatory advances, several tools have been introduced that allow users to assess or score the privacy risks of websites so that they can understand the possible risks of accessing those websites [13, 14, 15]. These tools typically employ different methods to assess website privacy and obtain scores or metrics that show in a user-friendly way the level of privacy protection, helping users understand the risks they will possibly be exposed to. However, the metrics defined so far either are not fully transparent and we cannot know how they are calculated or do not evaluate websites by jointly considering the usage of cookies (as a stateful tracking mechanism) or fingerprinting (stateless tracking). Instead, these metrics focus solely on assessing either stateful or stateless mechanisms individually.  
In this paper, we introduce _WebTrackingScore_, a Web tracking scoring system that provides an easy-to-understand metric that encompasses both stateful and fingerprinting (stateless) tracking methods. Using _WebTrackingScore_, we analysed and classified the top-ranked websites based on the Alexa top sites list, evaluating their privacy risks and showing the level of privacy to which users are exposed if they visit them. Thus, this analysis aims to give users a clearer understanding of the potential tracking risks they may face when surfing the Web, leveraging the _WebTrackingScore_ scoring system to highlight these risks effectively.  
The rest of the paper is organized as follows. Section 2 presents different Web tracking mechanisms and privacy scanners. In Section 3, we analyse related works, such as those concerned with privacy risk metrics and systems. Then, in Section 4, we present _WebTrackingScore_, along with an evaluation of the top websites according to this metric. Next, in Section 5, we discuss the limitations of our work. Finally, in Section 6, we present our main conclusions and future work.
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
## 4 Equipment Operation Testing and Visual Scanning Simulation
### Equipment Operation Testing  
In this section, the equipment was operated to perform working condition scanning tests, yielding the average operating speed of the equipment, the rated power errors of the robotic arm joints, TCP, and U-axis rail, and the positioning errors under full power. The specific experimental power range for the rail motor and robotic arm is (1500-6000) W. The robotic arm joint axes and TCP operate using world coordinates, with the robotic arm joint axes corresponding to their respective encoder values. When the external U-axis runs along the rail, the movement distance is recorded based on servo motor feedback. Through error analysis and operating speed analysis, the equipment test results meet the processing requirements for small assemblies, as shown in Figure 34. Figure 34a shows the equipment positioning error under the full power range; Figure 34b shows the equipment positioning error under the rated power range; Figure 34c shows the rail operating speed under rated power; and Figure 34d shows the robotic arm TCP operating speed under rated power.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 2 Physical Layer Issues
### Channel Modeling
It was shown that the ML-based models achieved better air-to-air PL prediction by relying on training data related to propagation distance, UAV altitude, elevation angle, and LoS conditions. Moreover, among the ML-based approaches, RandF offered the best prediction results while the most important data was that of path visibility with propagation distance and elevation angle, also having significant influence. Another study focused on PL prediction for UAV networks communicating with ground nodes in Reference [42]. However, departing from radio-frequency (RF) PL prediction, the authors examined instead, PL and delay spread prediction in mmWave channels, employing the RandF and KNN ML algorithms. Moreover, the problem of requiring massive data sets for training of the ML algorithms is mitigated through transfer learning based on limited data sets and training results that were acquired from previous training phases. From the results, it is concluded that the proposed methods offer reduced mean square errors when compared to Okumura-Hata and COST 231-Hata models.
## 4 Discussion
### Behavior of Residual Space Charges
#### 4.1.2 Gas Type  
The insulation recovery performance and repetitive breakdown stability of N\({}_{2}\) and air were poorer compared to the strong electronegative gas SF\({}_{6}\). This is because N\({}_{2}\) and air have weak electronegativity, resulting in a low ability to absorb electrons and form negative ions during the preceding breakdown [36; 37]. The enhancement in the residual space charges in the strong electronegative gas SF\({}_{6}\) during corona discharge was almost non-existent in N\({}_{2}\) and air. Therefore, the relative difficulty of subsequent breakdown compared to the first breakdown did not significantly increase, and the insulation recovery characteristics of the switch were not significantly improved. In addition, due to the weak corona-stabilization effects of N\({}_{2}\) and O\({}_{2}\), the repetitive breakdown stability of corresponding gas switches was also inferior to that of SF\({}_{6}\).  
A \"plateau\" was also observed in the insulation recovery curves of N\({}_{2}\) and air (see Figure 15). Since the electronegativity of these gas media was weaker than SF\({}_{6}\), the mechanism behind this \"plateau\" was also different from that of SF\({}_{6}\). Taking air as an example, residual space charges (positive and negative ions) and metastable particles will be generated in the electrode gap during the preceding breakdown. The lifetime of residual positive and negative ions is relatively short, and the recombination coefficient between ions is about \(10^{6}\) cm\({}^{3}\)/s under atmospheric pressure and room temperature [22; 38; 39]. Therefore, ions will quickly disappear through diffusion and recombination. However, the lifetime of metastable particles of N\({}_{2}\) and O\({}_{2}\) is very long (in seconds), and they are retained in the gaps even after almost all of the residual space charges have decayed [40]. These low-energy particles can release photons by colliding with other particles, causing the  
Figure 15: Insulation recovery curve of the corona-stabilized switches under different parameter conditions.  
photoelectric effect on the cathode, or directly colliding with the cathode to release many initial electrons [38]. This improves the probability of discharge initiation for subsequent breakdown and reduces the level of breakdown voltage. Therefore, the relative difficulty of subsequent breakdown of the switch mainly depends on the concentration of metastable particles in the gap. During the existence of these metastable particles, the insulation recovery level (relative breakdown difficulty) will be maintained at a stable level, leading to the occurrence of the \"plateau\" phenomenon.
# Enzymatic Degumming of Soybean Oil for Raw Material Preparation in BioFuel Production
###### Abstract
This work is aimed at assessing the effectiveness of the application of the enzyme complex Purifine(r) 3G (manufactured by DSM Food Specialties (Royal DSM), Netherlands, Heerlen; distributor in Ukraine Tempo LLC, Horodok, Lviv region, Ukraine) to crude soybean oil obtained from soybean seeds grown in Ukraine. The content and qualitative composition of phospholipids significantly depend on agroclimatic conditions, soil-climatic factors, soybean variety, and technological parameters of its primary processing, which, in turn, directly affect the efficiency of the degumming process [10]. Therefore, even when using the same enzyme complex, the optimal degumming conditions can vary significantly and require local adaptation--this is exactly what became the subject of our study. In addition, this work focuses on the study of enzymatic degumming without prior treatment with phosphoric acid, which is typical for hybrid enzyme technologies. This approach minimizes the formation of phosphate-containing effluents and simplifies the integration of the degumming process into first-generation biofuel (FAME) production lines. Thus, the applied goal of our research is to verify experimentally the possibility of environmentally safe and technologically efficient enzymatic preparation of Ukrainian plant raw materials for further use in the production of biofuels. The scientific novelty of the work lies in the comprehensive study of the adaptation of the process of enzymatic degumming of crude soybean oil of Ukrainian origin using Purifine(r) 3G without prior acid activation. The establishment of optimal process parameters that ensure a reduction in the residual phosphorus content to a level of <10 ppm--which is a critical indicator for the use of oil in the production of FAME without additional purification.
## 2 Related Works
They performed binary classification in the arousal and the valence dimensions using their minimalistic acoustic feature set and achieved 95.3% accuracy in the arousal dimension. Nevertheless, the highest accuracy was 78.1% in the valence dimension. This standard minimalistic acoustic feature set is much more powerful than other large-scale brute-force acoustic feature sets. Moreover, the most effective acoustic feature set in the field of SER so far was reviewed in [25]. [PERSON] et al. reported that the most acoustic features were shared between anger and happiness, and between neutral and sad emotions [26]. Even though these pairs of emotions have a similar correlation in the arousal dimension, they are different in the valence dimension. Moreover, [PERSON] and [PERSON] investigated many acoustic features of speech and music to find the communication of emotions in vocal expressions and music performances, and they reported that most of the acoustic features of anger, happiness, and fear were the same (Table 1) [9], but we can differentiate between them in the valence dimension. Therefore, it is crucial to find the acoustic features that can differentiate emotions with similar energy, pitch, loudness, duration, and so on. In recent years, along with exploring the speech features for SER, deep learning has been applied to various speech-related tasks. A trend in the deep learning community has emerged towards deriving a representation of the input signal directly from raw, unprocessed data. The motivation behind this idea is that the deep learning models learn an intermediate representation of the raw input signal automatically. For instance, in [27], a raw input signal and a log-mel spectrogram were used as an input to a merged deep convolutional neural network (CNN) to recognize emotion in speech. Furthermore, deep neural networks [28] and end-to-end multi-task learning for emotion recognition from raw speech signal [29] are recent examples of this approach. However, these frameworks might suffer from overfitting or from the limited size of the training data. In this work, we aimed to analyze the acoustic features of audio signals, which has not been explored for SER tasks. The timbre model is used to distinguish two sounds with the same pitch and loudness in research into musical sounds. In general, timbre distinguishes between two sounds that have the same pitch and loudness [16]. [PERSON] proposed audio descriptors along with the acoustic feature extraction tool named Timbre Toolbox, which could potentially characterize the timbre of musical signals [30]. These audio descriptors comprise the temporal energy envelope (attack, decay, release), the spectral (spectral centroid, spread, slope), and the harmonic (harmonic energy, inharmonicity). Based on the theoretical, as well as the practical significance of these acoustic features of an emotionally expressed speech signal, we aimed to analyze them in the valence dimension as well as to classify discrete emotions in this paper.
# Seismic Risk Analysis of Existing Link Slab Bridges Using Novel Fragility Functions
## 4 Failure Rate and Result Discussion
### Failure Rates from Classic Element-Based Fragility Functions  
First, classic fragility curves are presented in Figure 8 to provide a global overview of the different vulnerabilities shown by the bridge at the damage and collapse limit states. The considered failure mechanisms are those presented in Section 3.4 and involve the structural elements here briefly recalled: Link bars, Piers, Bearings, and Abutments. The latter can show three different failure modes, related to longitudinal active or passive response and transversal response.  
The annual rates of exceedance associated with each of the aforementioned failure modes (obtained through the convolution between fragility and hazard functions) are collected in the chart of Figure 9. Markers with different colors identify damage (grey) and collapse (red) failure rates. Using the same colors, two horizontal dotted lines are superimposed on the chart to identify the code-based MAFs corresponding to the considered limit states (\(\
u\) = 1 \(\times\) 10\({}^{-2}\) 1/year for damage LS, \(\
u\) = 5 \(\times\) 10\({}^{-4}\) 1/year for collapse LS). Quantitative assessment of the reliability of the system can be carried out by comparing the obtained failure rate values with the reference MAF levels defined by the codes; in particular, annual rates above the line corresponding to the code-based MAF denote \"unsafe\" conditions, while annual rates below the line identify \"safe\" situations; the vertical distance between the marker and the line is a measure of the safety margin against that specific mode of failure.  
In light of this, the outcomes presented in Figure 9 can be interpreted as follows:  
* at the collapse limit state, piers and abutments (and all related mechanisms) show satisfactory safety margins, while bearings and links manifest failure rates higher than desired (slightly higher for bearings, notably higher for links);
* at the damage limit state, all the elements/mechanisms present rates of exceedance which are, on average, around the target MAF value, except for the markers of links and abutment active response which are above the reference line.  
It shall be noted that these results might be not sufficient to provide a clear understanding of the level of vulnerability of the bridge, because information on the extension of the damage is missing. Indeed, failure rates stemming from classic fragility functions  
Figure 8: Fragility curves for different elements and failure modes evaluated for two limit states: (**a**) damage, (**b**) collapse.  
refer to the occurrence of at least one unsatisfactory event and thus any detailed evidence concerning the number of elements experiencing damage is totally hidden. To complete this information, one should refer to Section 4.3, where the novel fragility functions are used to estimate the risk related to different damage extent scenarios.
## 3 Materials and Methods
### Dimensionality Reduction Stage
#### 3.3.2 t-Stochastic Neighbor Embedding (t-SNE)  
Features described by high-dimensional vectors or pairwise dissimilarities are embedded in a low-dimensional space while maintaining their neighbor identities using a probabilistic method called t-stochastic neighbourhood embedding (t-SNE) [15]. Overall, the t-SNE method involves centring a Gaussian on each object in the high-dimensional space and then using a small number of dissimilarities under this Gaussian to define a probability distribution over all of the possible neighbors of the features [16]. The conditional probabilities \(p_{j|i}\) and \(q_{j|i}\) will be equivalent for the map points \(y_{i}\) and \(y_{j}\), respectively, that accurately model the similarity between the high-dimensional data points \(x_{i}\) and \(x_{j}\), respectively. This insight serves as the inspiration for t-SNE, which seeks to identify a low-dimensional data representation that reduces the discrepancy between \(p_{j|i}\) and \(q_{j|i}\). The Kullback-Leibler divergence, which in this instance equals the cross-entropy up to an additive constant, is a natural indicator of how accurately \(q_{j|i}\) models \(p_{j|i}\). t-SNE employs a gradient descent technique to reduce the total of Kullback-Leibler divergences across all datapoints [15]. Equation (2) gives the cost function \(C\).  
\[C=\sum_{i}KL(P_{i}||Q_{i}))=\sum_{i}\sum_{j}\frac{P_{i|j}}{q_{i|j}} \tag{2}\]  
Applying the t-SNE approach to the factor scores that were produced as a result of factor analysis will accomplish this. To visualize high-dimensional data in a low-dimensionalsetting, a non-linear dimensionality reduction method known as t-SNE may be applied. This method has the potential to be exploited. This is feasible due to the fact that t-SNE employs sparse networks in order to represent the data. The procedure of mapping the factor scores to a two- or three-dimensional space is what allows t-SNE to be of assistance in the process of locating patterns and clusters within the BC data. This can be done in any dimension.
## 5 Functions Regulated by Quorum Sensing in Rhizobia  
Several studies have shown that QS regulation plays an important role regulating functions which are crucial for rhizbial survival as a free-living bacterium, as well as for different stages of the symbiotic interaction with the legume host. Processes such as growth, transfer of plasmids and symbiosis islands, regulation of EPS production, motility, biofilm formation, nodulation or nitrogen fixation can be influenced by QS with different effects depending on the rhizbial species (Table 1). The plethora of genes whose expression is modulated in an AHL-dependent manner in rhizobia has been unveiled in genome-wide transcriptomic analyses [29; 30; 88; 90]. Here, we will focus on the role of QS on relevant traits such as EPS production and motility and we will discuss the relevance of this regulatory system in the establishment of symbiosis. For information about QS-controlled transfer of plasmids and symbiosis islands, readers are referred to more detailed studies [69; 77; 83; 87; 92; 93; 94].  
Figure 2: Quorum sensing regulatory circuit in _R. leguminosarum_ bv. _viciae_. The Cin, Rhi and Rai systems are shown. The Cin system is at the top of the hierarchical regulatory cascade and is required for the activation of the Rhi and Rai systems through the action of the antirepressor CinS. The transcriptional repressor PraR is displaced by CinS at high population densities, allowing activation of the Rhi and Rai systems. ExpR also contributes to the activation of Rhi and Rai by repressing _praR_ transcription. Genes are represented with wide arrows and proteins with rectangles. Green arrows represent activation; red flat-ended lines indicate repression. AHL: \(N\)-acyl homoserine lactone.