esa-sceva/satcom-chunk-collection huggingface dataset:
# A Preliminary Study on the Potential of the New Red-Fleshed 'JB' Clone Apple for Cider Production
###### Abstract  
Apples are one of the most widely consumed fruits in the world, resulting in the continuous creation of new cultivars with different properties, both in terms of breeding as well as extended shelf lives and increased bioactive substance contents. This study aimed to determine the potential of a new red-fleshed apple 'JB' clone developed at the Warsaw University of Life Sciences to produce high-sensory-quality cider. Fermentation was carried out using the _Saccharomyces cerevisiae_ UCLM S325 yeast strain, commonly used for quality white wines (Fermentis, Lesaffre Polska SA, Warsaw, Poland), at 15 \({}^{\circ}\)C for 21 days. The must and the cider were subjected to physicochemical analysis to determine their color, sugar content, total acidity, and polyphenol levels. Additionally, volatile acidity and alcohol content were measured in the cider, followed by an organoleptic evaluation. A red color characterized both the must and the cider, which had total acidities of 8.78 \(\pm\) 0.05 and 6.61 \(\pm\) 0.03 g malic acid/L, respectively, as well as polyphenol contents of 307.28 \(\pm\) 1.29 and 240 \(\pm\) 7.00 mg chlorogenic acid/L, respectively. Trained panelists evaluated the cider obtained from fermentation. The study's results indicate that the new 'JB' apple clone has strong potential for producing juice and cider with a unique red color and high sensory attributes.  
apple; color parameters; monosaccharides; total sugars; polyphenols; total acidity
## 2 Methodology
### Articles Selection  
Based on developed protocol for this study, articles were searched by utilizing the keywords, BIS adoption/acceptance, theories, determinants/factors and models/frameworks in individual, technological, organizational and environmental contexts.  
The inclusion and Exclusion Criteria for SLR  
Inclusion /exclusion search criteria was set to make sure that studies must be relevant to the research objectives [33]. The inclusion criterion included: (1) behavioral studies that comprised the conceptual models, theories and preposition, (2) studies that observed the BIS adoption and acceptance. The exclusion criterion consisted of: (2) all technical studies related to BIS adoption/acceptance, and (3) studies about the BIS research in other languages except English language. As the review study is a part of the information technology (IT)/IS field, which is changing rapidly. The process of inclusion and exclusion is also presented in Table 2. Thus, the time span of this study is from 2011 to 2020. The studies are also excluded those published before 2011. An additional check was applied by all authors to maintain high reliability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Years** & **2011–2020** \\ \hline \multirow{3}{*}{Keywords} & “Business intelligence systems”; “Business intelligence adoption/acceptance”, “Individual determinants/factors”; “Organizational determinants/factors”; “Technological determinants/factors”; “Environmental determinants/factors”; “Model/framework”; “Theories”, (for business intelligence systems adoption and acceptance) \\ \hline \multirow{3}{*}{Databases} & ScienceDirect; Emerald Full text; ABI/INFORM; Google Scholar; IEEE/IEE Electronic Library; Elsevier; Springer link; Taylor \& Frances online; MDPI; Sage Journals; Web of Science \\ \hline \hline \end{tabular}
\end{table}
Table 1: Keywords and databases.  
Figure 1: Papers selection and analysis process.  
The retrieval process of studies has been done by two coders independently with IS background. If any article was confronted with a disagreement, the paper was discussed by all co-authors until reaching an agreement. By looking at the title and keywords, a total of 1032 articles were selected. 749 articles were downloaded after reading the abstract. 665 studies were discarded by applying the inclusion and exclusion criteria. Only 84 studies were selected for the current study. Table 3 presents the list of selected journals.
# A Survey on Immersive Cyber Situational Awareness Systems
## 6 Discussion
### Limitations  
This survey provides a structured synthesis of ICSA systems; however, several methodological and practical limitations should be acknowledged.  
First, the literature search was limited to the Scopus database. While Scopus is a widely recognized and comprehensive indexing source, this constraint may have excluded relevant studies indexed in other databases such as IEEE Xplore, ACM Digital Library, or Web of Science. Future reviews could benefit from a multidatabase search strategy to ensure broader coverage and minimize selection bias. Second, this study excluded workshop papers, white papers, and other non-peer-reviewed sources. Although this choice supports academic rigor, it may have omitted valuable early-stage insights and innovations, particularly from industry and interdisciplinary research, where immersive technologies often emerge. Third, the 43 selected studies were included based on topical relevance without a formal quality assessment. Treating all studies equally may limit the ability to weigh the reliability or strength of individual findings.  
Beyond these methodological constraints, several broader issues emerged from the reviewed literature. Many user studies were based on small participant samples, often fewer than 30 individuals, limiting the generalizability and statistical robustness of reported outcomes, particularly those concerning usability, cognition, and engagement. This limitation warrants stronger emphasis in future analyses, especially when interpreting system effectiveness. In terms of scope, while this survey focuses on XR modalities such as AR and VR, it does not explicitly account for immersive environments like digital twins or CAVE systems, which are increasingly relevant in simulation-based cybersecurity applications. Their exclusion reflects limited coverage in the current body of ICSA literature but represents a promising direction for future research. Lastly, practical usability challenges, such as motion sickness, interface fatigue, and cognitive overload, are rarely discussed in the reviewed studies, despite their critical impact on the long-term viability and user experience of immersive systems. These human factors must be more thoroughly addressed to ensure that ICSA systems are not only effective but also accessible, comfortable, and operationally sustainable.  
By acknowledging these limitations, we aim to improve transparency and support future research efforts that seek to expand methodological rigor, practical relevance, and inclusivity in the design and evaluation of immersive cyber situational awareness systems.
# Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
## 7 Evaluation of IDBA: The Experiments
### Experiment 3: Comparing IDBA with Another DC-Based Trading Strategy  
The objective of this experiment was to compare the performance of IDBA with another DC-based trading strategy named TSFDC [13]. As explained in Section 3.5, TSFDC consists, firstly, of forecasting market's trend under the DC framework. TSFDC, then, decides whether to trigger a buy or sell order based on the made prediction. The authors introduced two versions of TSFDC, namely TSFDC-down and TSFDC-up. However, they reported that there is no significant difference between the performances of these two versions. Therefore, in this paper, we arbitrarily chose to compare IDBA with TSFDC-down (henceforth TSFDC). We applied TSFDC to the six currency pairs previously considered in Section 6.2. We set the trading parameters of TSFDC with the same values employed in [13]: \(STheta=0.1\%\) and \(BTheta=0.13\%\).  
We wanted to compare IDBA to TSFDC in terms of profitability and risk. For this purpose, we employed the Wilcoxon rank sum test [32] twice. Firstly, we applied the Wilcoxon test with the null hypothesis being that \"the median difference between the rate of return produced \(RR\) by IDBA and TSFDC is zero\" and with the alternative hypothesis being that \"the \(RR\) provided by IDBA are greater than those provided by TSFDC\". Secondly, we applied the Wilcoxon test with the null hypothesis being that \"the median difference between the maximum drawdown \(MDD\) produced by IDBA and TSFDC is zero\" and with the alternative hypothesis being that \"the \(MDD\) provided by TSFDC are greater than those provided by IDBA\". For each of the six considered currency pairs, we applied the initial version of TSFDC and observed the results of RR and MDD. We then compared these results to those obtained by IDBA (from Section 7.1) using the Wilcoxon test.
# A Reference Model for Monitoring IoT WSN-Based Applications
## 4 Model Description
### Information Layer
#### 4.2.1 Data Format  
To achieve greater universality, the acquisition mechanism of each probe delivers its data in raw format, in continuous streams of bytes. To convert these raw bytes into significant information, each probe must define its own data coding/decoding element. These elements must apply a syntax description, common to all the probes in the monitoring platform, to make its data compatible with the rest of the system. The final aim of this layer is the standardization of the description language in order to universally unify the WSN-MP specification, which would lead to the previously mentioned advantages.  
Each significant data unit obtained must be expressed in a standard way. Each piece of information obtained should include what information reflects, where has been obtained, when has been captured (time stamp) and why is relevant (trigger conditions).  
For instance, description languages such as XML may be highly adequate for this purpose. Each piece of data may be contained in a meaningful XML record. The Information Layer Protocol must allow the Standard data interchange. As an example, the events captured in PIMOTO [32] may be described in a XML record as follows:  
<trace date=\"date of the trace\" monitoring_experiment=\"monitoring experiment identification\"... >  <event monitor_address=\"MAC address of monitor node\" source_address=\"BMAC source address\"  destination_address=\"BMAC destination address\"  data_length=\"Lenght of captured data\" type=\"application type\"  time=\"global time in which event occurs\"....... >  data_collected </event>  <event... >data_collected </event> ..  
The used synchronization algorithm must be part of the Information Layer Protocol, which must offer the user the possibility to select the most appropriated synchronization protocol for the characteristics of the monitoring application.
# Cross-Correlation and Fractal Analysis in the Images Diatoms Symmetry
## 3 Results and Discussion
### Power Spectrum in the Self-Similarity and Symmetry  
The preprocessing steps presented in Figure 2 only allow evaluating a symmetry rotation at 180\({}^{\circ}\) in the image (R180), where other applied rotations generate a different CC size than CCauto. For that reason, it is impossible to calculate the projections because the dimensions of the vectors are different. Furthermore, the centering of the image is critical in the \(\theta\) calculation. Because of this, new preprocessing steps were proposed (see Figure 5). Where the image is resized to a square image (254 \(\times\) 254) in order to match the dimension of the vector found with and without the symmetry operation (CC) to CCauto, and the power spectrum (PW) is determined by 2-D Fast Fourier Transform with a shift zero-frequency component to the center of the spectrum. Most of the information is obtained from the contrast image, whereby a grayscale preprocessing is applied before finding the PW [42].  
Figure 4: Polar plot for all the diatom images where the radius is Nf and the angle is the projection for each symmetry operation (**a**) FSv, (**b**) R180, (**c**) PSo and (**d**) score plot PC 1 vs. PC2 for all diatoms images using Nf, and projection parameters.  
Figure 5: Scheme preprocessing steps using power spectrum to calculate \(\theta\).  
This PW preprocessing allows us to explore other symmetry operations, such as rotations in small angles of 90\({}^{\circ}\), 60\({}^{\circ}\), and 45\({}^{\circ}\). Figure 6a shows the projection values for each symmetry operation. In this case, the symmetry operations used above (Psv, PSo, and R180) generated \(\theta\) ranges lower than those for range for the preprocessing steps used in Figure 4. It is because the CC calculated for the images on the frequency scale is better aligned than the CC calculated in the spatial scale. The range for the smallest angle rotation (R90, R60, and R45) is higher than the other symmetry operation. The diatoms with the highest symmetry (see the first five diatoms images in Figure 1) have \(\theta\) values close to zero, regardless of the operation applied, which is expected for structures with high symmetry similar to a disk. On the other hand, a PCA using Nf and projections parameters for all symmetry operations (see Figure 6b) shows discrimination for diatom's species, classified by their similarities in structural geometry. Additionally, an analysis of the loading was conducted (see inset plot in Figure 6b), where it is evidenced that the symmetry operations that have the biggest contribution to the correct differentiation between diatoms species. The PSo operation shows a poor contribution in comparison with the other operations as R90, R60, R45, R180 and PSv, and the Nf parameter.
## 1 Introduction
On the top-left: the OCTA cross-sectional B-scan showing retinal layers. a comparison between training using the transfer learning approach and training from scratch. The obtained accuracies were 78.1% for transfer learning and 83% without transfer learning. More recently, [PERSON] et al. [21] developed a DL architecture so called \"AMDnet\" on OCT images to predict the conversion from early/intermediate AMD to advanced wet AMD. The study included 71 patients with confirmed early/intermediate AMD that were imaged with OCT three times over 2 years. Results showed a ROC-AUC of 0.89 at the B-scan levels and 0.91 for volumes. [PERSON] et al. [22] used 35,900 labeled OCT images from AMD patients to train three types of Convolutional Neural Networks (CNNs), VGG19, InceptionV3 and ResNet50, to perform AMD diagnosis. The authors developed an AI and cloud-based telemedicine interaction tool dedicated to diagnosis and therapeutic of AMD. The image discrimination rates obtained by expert clinicians (92.73% and 91.90%) and provided by the AI-based platform (above 90%) were almost the same. Further works on OCT imaging, such as the recent study of [PERSON] et al. [23], proposed a treatment predictive model using a densely connected neural network (DenseNet) and a recurrent neural network (RNN) on longitudinal OCT scans for neovascular AMD patients (281 patients for training and 69 for tests). The CNN model achieved 0.85 AUC in detecting patients with low treatment requirements and 0.81 AUC for patients with high treatment requirements. In what concerns the use of OCTA in DL, [PERSON] et al. [17] tested the feasibility of using DL for DR detection from OCTA including 77 patients and 20 control subjects. The authors applied transfer learning on a VGG16 network for robust OCTA classification. The obtained results showed an accuracy of 87.27% in differentiating healthy, no DR and DR eyes. In the same period, [PERSON] et al. [16] developed an algorithm based on two CNNs to classify input OCTA images (using structural volumes and enface retinal angiograms) as CNV or Non-CNV and then segment the CNV membrane when present. The proposed neural network included a cutoff threshold for CNV area to overcome the residual artifacts limitation that could be confounded with CNV. CNV binary classification ROC-AUC was 0.997. In this work, we aim to fill the gap of CNV screening on OCTA images using DL by promoting a novel application of CNNs on OCTA images using the choriocapillaris slab. The main contribution of this paper is the deep learning-based solution to classify AMD on two major forms: advanced wet AMD (CNV) and intermediate AMD (drusen/pigmentary changes) including a healthy control group (no AMD) using choriocapillaris OCTA images. A second contribution is the adaptation of a pre-trained VGG19 model on non-medical ImageNet dataset to medical domain using an adapted densely connected classifier on our limited OCTA data. Additionally, class activation mapping is used to interpret the CNN prediction on choriocapillaris OCTA images, which is a promising DL application for CAD systems in retinal clinical routine.
# Satellite Image Compression Guided by Regions of Interest
## 2 Materials and Methods
### Baseline Algorithm for Cloud Detection  
The task of detecting clouds in satellite images is still a challenge. Different algorithms have been proposed over the years aiming at even lower FAR for the achievable values of PD. A survey of cloud detection methodologies is given in [15], in which 59 approaches are discussed among the classical and machine learning approaches.  
Approaches vary in accuracy and output values. The results of classification can vary from a simple cloud/no-cloud to a wider range of labels, such as: thin cloud, thick cloud, cloud shadow, snow, ice, and others. In fact, different properties of the clouds are explored by the approaches to perform the detection and classification, which can be physical parameters (e.g., shape attributes) or optical properties (e.g., spectral content, brightness temperature, and polarization characteristics).  
This paper makes use of the threshold-based algorithm described in [12] to perform the detection of the clouds. More precisely, the algorithm makes use of the RGB bands as input signals to explore the brightness content, for which the threshold value may vary by season, sun elevation, and other factors, to avoid false alarms. Figure 2 shows the processing steps of the cloud detection algorithm, which can be described as follows.  
First, one threshold operation (1) of value \(\tau_{1}\) is applied to each one of the RGB-bands (\(I_{red}\), \(I_{blue}\), and \(I_{green}\)) of the tiled image. Next, the resulting binary matrices from each band are multiplied element-wise (2). Then, the values are summed and divided by \(\delta^{2}\) (the area of the tile) (3), resulting in one value for each tile as a ratio of brightness per area. Finally, another threshold operation (4) is applied to the value calculated for each tile, with a threshold value of \(\tau_{2}\), resulting in the binary flag \(F_{bin}\). In fact, the last threshold operation labels each tile as \"No-cloud\" or \"Cloudy\". The labels \"mapSY\" and \"zipWith3 SY\" represent the process constructors of the ForSyDe framework [13].
# RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System
## 5 Results and Discussions
### Effect of DC Bias  
In this study, the average electrical power is kept at a constant of \(P_{\text{Eave}}\) = 8 dBm, and \(I_{\text{DC}}\) is swept from 5 to 25 mA [14]. The EVM performance for different QAM modulation orders are depicted in Figure 9. For 4-QAM to 32-QAM modulation, it can be found that the favorable range is from 10 to 20 mA, and the system achieves the lowest EVM value nearby 13 mA. In addition, the EVM is increased when \(I_{\text{DC}}\) is beyond 20 mA, and \(n_{\text{dis}}\) is expected to dominate noise when operating at high working points. Whereas for 64-QAM and 128-QAM signals, the EVM performances are little improved, which indicates that high order modulations are more sensitive to the nonlinear distortion. Therefore, the selection of modulation orders must be considered for the system design to avoid the performance degradation.
# Urban Road Collapse Risk Assessment Study Based on InSAR Spatiotemporal Data +
## 2 Study Area and Data Sources
### Data Sources  
The historical road collapse accident data utilized in this study originated from two sources. One part was derived from the Guangzhou historical road collapse accident reports compiled by the Guangzhou Natural Resources Bureau and the Emergency Management Bureau. These reports include relevant information such as the time, geographical coordinates (longitude and latitude), and causes of the collapse accidents. The other part came from the national geological disaster bulletins released by the Ministry of Natural Resources. Based on the acquired raw data, this study employed ARCGIS geographic information system software to process and transform the raw data, mapping the spatial distribution of road collapse accidents in Guangzhou from 2014 to 2021, with a total of 210 accident points, as shown in Figure 2.  
The ground subsidence data employed in this study spanned from 2014 to 2020 and were sourced from the Sentinel-1 satellite launched by the European Space Agency (ESA) during the same period. This spacecraft is one of the Earth observation satellite constellations under the European Union's Copernicus Program and consists of two satellite platforms. They are equipped with C-band Synthetic Aperture Radar (SAR) remote sensing payloads, possessing all-weather and all-time observation capabilities, enabling continuous acquisition of imagery data under any light and meteorological conditions, with a global coverage spatial resolution of 50 m. This study processed Single Look Complex (SLC), Digital Elevation Model (DEM), and Synthetic Aperture Radar Precise Orbit Ephemerides (SARPOE) data from Sentinel-1 based on the Small Baseline Subset InSAR (SBAS-InSAR) technique. The processed data are shown in Figure 3.  
Figure 2: Distribution of collapse accidents in 5 urban areas of Guangzhou.