esa-sceva/satcom-chunk-collection huggingface dataset:
# Boundary-Aware Concurrent Queue: A Fast and Scalable Concurrent FIFO Queue on GPU Environments
## 3 BACQ Proposal
### Utilize Virtual Caching Layer to Broadcast Base Index  
GPU architectures structure memory in three primary levels: global memory, shared memory (thread-block level), and registers (thread level), as shown in Figure 6. However, CUDA does not provide a dedicated memory layer for warps, which can introduce inefficiencies in algorithms (e.g., BACQ) operating at the warp level due to the lack of an explicit caching mechanism.  
To overcome this limitation, BACQ introduces a virtual caching layer for threads within a warp by leveraging registers instead of relying solely on thread-block shared memory. Starting with the Kepler microarchitecture in 2012, CUDA introduced the shuffle instruction, enabling communication between threads within a warp using the thread's registers. The shuffle primitive, \(shfl(r,t)\), allows a calling thread to read the register value r from another thread t within the same warp. Figure 7 illustrates how the shuffle function works: Thread 1 and Thread 2 can access values from Thread 0's register, while Thread 0 reads the value from Thread 2's register.  
BACQ utilizes this technique to compute the current tail and head positions locally without needing to access the global tail constantly and head counters. The leader thread uses the shuffle primitive to broadcast its old tail and head counters to all active threads in the warp. Each thread then adds its thread rank to the old tail or head value. Since thread ranks increase sequentially starting from 0, we add an extra \(1\) when calculating the tail position as shown in (Figure 8):  
\[localtail=shuffle(oldtail,0)+thread\_rank+1\]  
We apply this technique at several critical points in the BACQ algorithm, such as in Listing 2 Line 21, Line 22, Line 26, Line 31, Line 38 and Listing 3 Line 21, Line 23, Line 27, Line 32.
# Power Control Techniques for Interference Management-
### Significance of the Study  
A systematic literature review of the existing literature was conducted to present a comprehensive overview of the current literature on power control techniques on 5G wireless networks. This review can open new approaches to power control techniques to address interference. Implementing this successfully could bring efficient technique(s) to address interference. This review delivers a clear and comprehensive overview of available evidence on the topic under review.
## 5 Digital Modulation and Demodulation Circuits
### Receiver
#### 5.3.2 BPSK Demodulator Core
A subsequent decision stage maps the counters output \(s_{\text{o}}[n]\in\{0,\dots,8\}\) to a single binary value corresponding to the decoded bit; a hysteresis avoids frequent toggling of this asynchronous data output, so that \(s_{\text{o}}=4\) does not change the logic level _data_out_sync_. In summary, the stage synchronizes to the incoming carrier and performs down-conversion and symbol mapping to yield an asynchronous data stream, which is processed by the circuitry described in Section 5.3.1.
## 2 Design of the 3.7 kW WCS
### Design of Resonance Coupling Coils  
#### 2.2.1 Design of Coupling Coils  
The primary and secondary coils with the same geometric size and shape are selected, and the Archimedes spiral coil arrangement with better anti-offset characteristics is selected. The ferrite is selected at a long and short staggered spoke layout. This layout can maintain the magnetic field  
\begin{table}
\begin{tabular}{c c c} \hline Parts & Structure & Parameters \\ \hline PFC & Boost PFC & \(L_{PFC}\), \(C_{PFC}\) \\ Filter & LC filter & \(L_{o}\), \(C_{o}\) \\ \hline \end{tabular}
\end{table}
Table 1: Main components of the input and output modules.  
strength at offset and save ferrite [22]. The coupling coils are made of multi-strand Litz wire, which can reduce the skin effect when the coils work at a high frequency. The 3D model of coupling coils with ferrite is shown in Figure 3.  
Parameters of the coupling coils and ferrite are shown in Table 2. The three-dimensional model of coupling coils is established in ANSYS Maxwell. After the materials of coils and ferrite are correctly assigned, the electrical parameters of the entire coupling coils model can be efficiently extracted. For power coupling of the two coils, three main parameters are concerned, namely self-inductance, mutual inductance and coupling coefficient.  
According to the design parameters, the coupling coils with a ratio of 1:1 is wound. The test \(LC\) instrument is used to measure the self-inductance and mutual inductance when the coils are flat, as shown in Figure 3b. It can be seen that the difference between the simulated and measured parameters is within 10% in Table 3, which verifies the accuracy of the three-dimensional model of coupling coils.
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 2 Materials and Methods
### Determination of Total Phenolic Content  
An amount of 500 \(\upmu\)L of honey solution was added to 2.5 mL of Folin Ciocalteu (0.2 N). After 5 min, 2 mL of sodium carbonate solution (75 g/L) was added and the solution was incubated for 2 h in the dark. The absorbance was measured at 760 nm in a spectrophotometer [37; 38]. The standard curve was defined by known concentrations of gallic acid, ranging between 0 and 200 mg L1. The results were expressed in milligrams for gallic acid equivalents (CE)/kg of honey.
# Remote Sensing and Machine Learning for Safer Railways: A Review
## 3 Descriptive Analysis
### Literature Network  
This literature review aimed to acquire insights into the existing scholarly works. The authors discovered a cluster of interconnected research that formed a cohesive body of related works within the realm of RS-ML railway inspection. Figure 1 illustrates this relationship by using the 2024 version of an online tool called Litmaps [11].  
The connecting lines trace the citations in the selected body of literature. The horizontal axis represents the publication date. Hence, lines leading from the left of an article connect to its references. The vertical axis encodes the number of citations, with larger circles representing a higher number of citations. The clustering of most publications suggests a close interrelation, indicating shared concepts and common methodologies within the field. However, certain publications, such as [12; 13; 14], stand out as outliers because of their more distant or divergent research focus compared with the closely connected majority.  
The network diagram helps to visualize the interconnections and thematic concentrations within the existing research. This type of mapping facilitates the identification of key research clusters and outlier studies, illustrating how different methodologies and findings relate to one another. It also helps to pinpoint influential studies and emerging trends to provide a clearer picture of the research landscape. Furthermore, this visualization  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Search Phrases** & **Publications** \\ \hline
1 & “[PERSON]” as an author in Google Scholar & 4 \\
2 & “human eye” railway inspection ML & 1 \\
3 & allintitle: comparative assessment Convolutional Neural Network & 2 \\
4 & allintitle: deep learning rail detection & 4 \\
5 & allintitle: deep learning railroad detection & 1 \\
6 & allintitle: Deep Networks rail & 1 \\
7 & allintitle: Generative Adversarial Networks rail & 2 \\
8 & allintitle: Generative Adversarial Networks railway & 1 \\
9 & allintitle: learning rail detection aerial & 1 \\
10 & allintitle: learning rail detection UAV & 1 \\
11 & allintitle: ML RS & 2 \\
12 & allintitle: ML transportation infrastructure & 1 \\
13 & allintitle: multispectral transportation & 2 \\
14 & allintitle: rail RS & 1 \\
15 & allintitle: rail sensors & 2 \\
16 & allintitle: railroad crack detection & 1 \\
17 & allintitle: railroad imaging & 1 \\
18 & allintitle: railroad RS & 3 \\
19 & allintitle: railway LiDAR & 1 \\
20 & allintitle: RS transportation & 4 \\
21 & allintitle: RS transportation infrastructure (since 2015) & 3 \\
22 & allintitle: RS transportation infrastructure (since 2018) & 2 \\
23 & RS transportation & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial search phrases used to search Google Scholar.  
helps to enhance our understanding of how the field evolved by illustrating how newer methodologies built upon or diverged from established practices. These insights can help to inform future research directions and potential areas for innovation.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 3 Theoretical Analysis of Prestress Loss of BFRP Tendons
### Results and Discussions  
On the basis of the appearance time, the prestress loss can be classified into two stages, i.e., prestressing stage and in-service stage, given that the losses in prestressing stage and in-service stage are instantaneous losses and time-dependent losses, respectively [27]. In general, the prestress loss caused by anchorage deformation and prestress retraction (\(\sigma_{11}\)), friction between prestressed tendons and their surrounding casing pipes (\(\sigma_{12}\)), and elastic compression of concrete (\(\sigma_{13}\)) is produced in the prestressing stage. The prestress loss caused by the relaxation of prestressed tendons (\(\sigma_{14}\)), and concrete shrinkage and concrete creep (\(\sigma_{15}\)) is produced in the in-service stage, usually lasting for a long period.
## 5 Ray Tracing Methods  
### Constant Sound Speed Ray Tracing Method  
Assume that the sound beam propagates in an underwater area composed of \(n\) layers with different sound speeds. Also, assume that a sound wave propagates along a straight line at a constant velocity in one layer, while the sound speed in each layer is different. Then, the sound wave's propagation trajectory can be subdivided into several straight lines [27], as shown in Figure 3. The path propagates in the \(i\)th layer beam is \(S_{i}\), the angle between the incident beam and the horizontal plane is \(\theta_{i}\), \(c_{i}\) is the propagation speed of a sound wave in each layer, \(t_{i}\) is the wave propagation time through the \(i\)th layer, and \(z_{i}\) is the water layer thickness of the \(i\)th layer.  
According to the physical and geometrical functions, the trigonometric function of the acoustic wave at the incident angle \(\theta_{i}\) of each medium is  
\[\cos\theta_{i}=\frac{z_{i}}{S_{i}} \tag{14}\]  
The horizontal displacement \(x_{i}\) and time \(t_{i}\) of the acoustic wave passing through the \(i\)th layer are defined by  
\[x_{i}=z_{i}\cdot\tan\theta_{i}=\frac{p\cdot c_{i}\cdot z_{i}}{\sqrt{1-\left(pc _{i}\right)^{2}}} \tag{15}\]  
\[t_{i}=\frac{S_{i}}{c_{i}}=\frac{z_{i}}{\cos\theta_{i}\cdot c_{i}}=\frac{z_{i} }{c_{i}\cdot\sqrt{1-\left(pc_{i}\right)^{2}}} \tag{16}\]  
The total horizontal displacement \(X\) of the signal received from the underwater target is defined by  
\[X=\sum_{i=1}^{n}x_{i}=\sum_{i=1}^{n}\frac{p\cdot c_{i}\cdot z_{i}}{\sqrt{1- \left(pc_{i}\right)^{2}}} \tag{17}\]  
Figure 3: Constant sound speed ray tracing method.  
This method is simple and iterative. Besides, there is a small amount of calculation iterations and fast ray tracing can be achieved. However, the error made by constant sound speed ray tracing method is larger than the equal gradient ray tracing method; especially in the ocean areas where the sound speed changes greatly, so an accurate tracing cannot be achieved.
## 2 Material and Methods
### Airborne Laser Scanning Processing Chain
#### 2.2.3 Assessment of Aquatic Reed Extents and Classification Accuracy  
Extents of aquatic need beds were evaluated based on two criteria. First, the extents derived from LiDAR data for every AOI were compared with the ones obtained from field measurements, by means of correlation analysis. Since measured and derived surfaces can have the same extents but different shapes, the correlation analysis of the extents was complemented by an assessment of geometrical similarity. As a second criterion, the separation distance of two geometrical objects (polygons) in a metric space was measured (Hausdorff Distance Pairwise). The smaller the resulting value, the more similar the geometrical shapes are [38]. In addition and to support this last metric, the Root Mean Squared Error (RMSE) of the lengths of cross-sections measured on the field in relation to the lengths obtained from LiDAR data was assessed.  
The assessment of classification accuracy was achieved by a confusion or error matrix. The classification result was compared with independent validation data. Two approaches were implemented for the accuracy assessment of aquatic need classification. First, the classification was compared with on-field collected data. A total of 249 square sample plots of 1 m\({}^{2}\) each were collected on-field and used for validation, out of which 127 and 152 were measured in dense and sparse aquatic need beds, respectively. Second, RGB imagery with a resolution of 0.1 m/pxl, collected at the same time as the ALS survey took place, was used for visual interpretative validation. Samples were collected randomly and stratified (i.e., classes). Since the map has less than 12 classes and the study area is less than 1 million acres (4046.9 km\({}^{2}\)), a minimum of 50 points should be collected for every single class [39]. A total of 150 points were generated and proportionally distributed (Table 1) across the total extent of each class.  
Figure 4: Collection of Reference Points (RPs) as an example of AOI-3. Background is an orthorectified aerial photograph in true colour of the Landesamt für Digitalisierung, Breitband und Vermessung (LDBV), 2015 (**a**). Differential Global Positioning System (DGPS) Trimble GeoXT and an antenna Hurricane L1 used for georeferencing RPs (**b**). Aerial view of square transect measurements along a cross-section (**c**). One-hundred metre line parallel to the shore with 10 m sections (**d**).  
The error matrix shows a percentage of pixels from each class labelled correctly as well as the proportions of pixels erroneously labelled into a different class [40]. These percentages are expressed as the overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA). Additionally, the chance of agreement was also calculated with the Kappa coefficient [41].
# New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
## 2 Materials and Methods
### Feature Extraction
#### 2.3.6 Grouped Tripeptide Composition (GTPC)  
GTPC extends the tripeptide composition by grouping tripeptides with similar physicochemical properties. This allows for capturing higher-level structural and functional patterns in the protein sequence.  
The GTPC is a 125-dimensional feature vector. Each feature vector represents the frequency or composition of tripeptides grouped into predefined categories or classes based on their physicochemical properties or structural similarities.
## 3 Results
### Amorphous Phase Analysis  
C8F120 with 7 curing days, 14 curing days, and 28 curing days and C8F280 with 28 curing days were selected for TG-DTG. On the one hand, the same selection of objects as in XRD can be analyzed. On the other hand, it is possible to study the phase changes throughout the reaction process. Figure 10a presents the results of the TG-DTG analysis on C8F120. A weight loss peak was observed between 60 and 200 \({}^{\circ}\)C due to the dehydration of several hydrates (including C-S-H, AFm, AFt, and gypsum) [52]. The dihydroxylation of Ca(OH)\({}_{2}\) typically occurs between 400 and 500 \({}^{\circ}\)C. The mass reduction observed between 600 and 700 \({}^{\circ}\)C can be attributed to the decomposition of calcite formed during the carbonation process [52]. The diagram suggests that the weight loss associated with Ca(OH)\({}_{2}\) dihydroxylation at 400-500 \({}^{\circ}\)C decreased as the curing time increased. In addition, the mass loss of hydration products at 600-800 \({}^{\circ}\)C was significantly greater at 28 days compared to the samples after 7 and 14 days. This increased mass loss may be due to the carbonation of the test block surface due to exposure to air, leading to the formation of CaCO\({}_{3}\). The much lower solubility of CaCO\({}_{3}\) further consumes Ca\({}^{2+}\), thereby affecting the production of CaF\({}_{2}\).  
Figure 10b compares the TG results for C8F120 and C8F280 against the results of the XRD analysis to investigate the effect of fluorine on the strength of PCPB. The mass difference between the 120 mg and 280 mg IFC samples between 60-200 \({}^{\circ}\)C indicates that more free and bound H\({}_{2}\)O was lost, likely due to a delayed hydration reaction. The hydration reaction of PCPB with higher IFC was delayed, resulting in more free and bound water in the system, as confirmed by the weight loss peak gap in Figure 10b. The mass loss curves gradually converge at 400-500 \({}^{\circ}\)C, indicating that the sample with an IFC of 280 mg contained less Ca(OH)\({}_{2}\). It is hypothesized that a higher fluorine content in the hydration process further delays the hydration reaction and affects the formation of Ca(OH)\({}_{2}\). The presence of portlandite is another factor that affects the strength of cementitious materials [58]. The mass gap of hydrdcalcite is corroborated by the results in XRD. The XRD analysis of C8F120 has much less C\({}_{2}\)S and C\({}_{3}\)S, which are the main sources of hydrdcalcite during the hydration reaction [60]. The results further support the negative effect of fluorine on the strength of PCPB in both the early and late stages of the cement hydration process.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
#### 3.2.5 Cosine k-NN  
Cosine k-NN [52] is founded on the assessment of angular similarity between two vectors in a multidimensional space, as opposed to their absolute magnitudes. This method utilizes cosine similarity as the distance metric, distinguishing it from the traditional k-NNalgorithm, which relies on Euclidean distance. By focusing on the directional alignment of vectors, Cosine k-NN is particularly effective in capturing relationships in high-dimensional and sparse data.
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Hyperspectral Imaging (HSI)
#### 4.3.5 Visualisation
In a similar study, [PERSON] et al. [240] investigated instrumental pork tenderness using a different reference method and statistical analysis, resulting in a high R\({}^{2}_{cv}\). Moreover, [PERSON] et al. [247] and [PERSON] et al. [248], through the detection of the total volatile basic nitrogen (TVB-N) and the use of HSI, employed similar performance values for the freshness of pork meat. In comparison, [PERSON] et al. [245,246] used the K value and biogenic amine index (BAI) to predict the freshness of pork meat, reaching slightly higher performance values relative to the previous authors. Finally, in a different approach, [PERSON] et al. [243], using a novel algorithm, showed the superiority of the determination of the TVB-N content in pork meat using HSI technology. [PERSON] et al., in two different studies [250,251], used HSI in the NIR region to evaluate lamb quality attributes. The results indicated that NIR HSI has great potential to be a non-destructive tool for predicting lamb colour and tenderness (instrumental and sensory) Also, in different study, [PERSON] et al. [252] tested an HSI system in the Vis-NIR range to develop an online monitoring system for red meat colour, obtaining high coefficients of determination (R\({}^{2}_{p}\)). The results from the previous studies discussed in this review confirmed that HSI is well suited for predicting the sensory properties of meat, including the tenderness, freshness and colour in different meats. Through the integration of advanced chemometric multivariate data processing techniques (PLSR, MLR, SNV, ANN, etc. ), HSI methods present significant opportunities for both qualitative and quantitative analysis within the meat industry [10]. The non-invasive, remote, and multivariate nature of HSI methods offers an intriguing opportunity for advancing the monitoring and control of meat quality. This technology has the potential to shift the measurement of meat quality from traditional laboratory settings to processing lines. HSI achieves high performance values in predicting the sensory traits of meat through its ability to capture detailed data at multiple wavelengths, providing comprehensive information about the composition and quality of meat samples. HSI overcomes the problems of the insufficient repeatability and accuracy of single-point detection and the variety of methods employed in these studies highlights the versatility of HSI in predicting sensory traits across different types of meat.
## 1 Introduction
In the EU, the production of local varieties specifically adapted to the local environments can be protected by Protected Designation of Origin (PDO) or Protected Geographical Indication (PGI) labels. The granting of their status is based on the fulfilment of several conditions, and the protection of such plant material is based on morphological characteristics, which can be time-consuming and not always precise enough. [PERSON] et al. [16] used seven SSR markers and four AFLP markers to characterise 36 traditional apricot varieties from southern Italy, all of which are approved for the PGI label 'Albicocca Vesuviana' (Vesuviana apricot). Their data showed that DNA fingerprinting should always be used to complement the use of morphological characteristics in the description of plant material during the establishment of a PDO or PGI label and to evaluate the presence of genetic inconsistencies in orchards. Recently, [PERSON] [17] successfully discriminated the apricot 'Salak' from closely related cultivars using SSR primers by the high-resolution melting (HRM) method based on monitoringthe DNA denaturation and quantification by a real-time PCR. [PERSON] et al. [18], using eight SSR markers, confirmed high genetic diversity among thirty-eight commercial apricot genotypes collected from 14 nurseries in provinces of Azerbaijan and Iran. However, they also detected synonyms--some cultivars with different names had the same genetic background, so the names of these samples should be unified. Of the 38 genotypes, 26 were genetically distinct. When checking the genetic identity of samples with the same name from different nurseries, it was found that they had different genetic backgrounds. As the cultivars are propagated asexually by nurseries, this showed that not enough attention was paid to the selection of propagating material. Plant certification and registration institutes should carry out tests in mother orchards and nurseries to ensure that propagated trees are genetically uniform and original [18]. The aim of our study was to examine the genetic variation of selected apricot cultivars of different countries of origin obtained from Slovak and Austrian gene banks, using highly polymorphic SSR markers. We focused especially on old regional cultivars but also on modern cultivars grown in Slovak and Austrian orchards and on the evaluation of their genetic relationships with cultivars of different geographical origins. Another aim was to create a control database of apricot genotypes/cultivars and to use this database for identification/verification of apricot samples collected from orchards and stores in the Slovak and Austrian regions.