esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Medical Information System Architecture
### Image Classification Console  
The console is dedicated to the team of medical engineers specialized in Artificial Intelligence. The console contains the necessary objects for the acquisition and processing of medical images:  
* Patient Procedures: Procedures that contain AI processing.
* Acquired Images: Each record contains: \(\odot\) Classes: Classes of acquired images (pathology/pathologies, normal). Each class contains either the acquired images or an archive containing the acquired images. Both variants can be uploaded simultaneously.
* Skin Pathology Detection Module: Similar to the example in [38], but using multiple image classes: Melanoma, Actinic Keratosis, Seborrheic Keratosis, Basal Cell Carcinoma, and a class for images without pathologies. Video solution--a custom classification application for real-time classification of skin images. The module operates based on the camera of a computer or mobile device where the open platform of  
Figure 10: Scanning the QR code on the Medical Chart.  
the information system is running. It encompasses the area with a lesion and displays, in real time, one of the four classes for which the classifier was trained (Figure 11).  
* Ocular Pathology Detection Model: This model was created for classes such as Glaucoma, Cataract, and Diabetic Retinopathy. Image solution is a custom classification application for classifying fundus images acquired and stored in the database. The module operates by uploading an image and receiving a response based on the three pathologies for which the classification model is trained (Figure 12).  
All these objects contribute to the management of workflows in the medical institution, such as medication ordering, sending notifications to teams with different roles in patient care, creating records when a team member needs to perform a medical procedure or take specific analyses. These activities are scheduled for easy management, and there is a notification flow upon completion of certain activities or upon receiving medical results, etc.  
Figure 11: Automatic classification module for skin pathologies.
## 3 Results
### ANOVA Analysis  
The mean RPS volumes obtained using manual and automatic segmentations were 2697.57 cubic centimeters (cc) (SD = 4075.73 cc) for the manual approach, 2539.88 (SD = \(\pm\)3464.10 cc) for ENet, and 1701.48 cc (SD = \(\pm\)1996.71 cc) for ERFNet. Table 1 shows the minimum, 25 th percentile, median, 75 th percentile, and maximum RPS volume obtained using manual and automatic segmentation.  
Table 2 reports no significant difference between the RPS volumes obtained using the manual approach and ENet (_p_ = 0.935), the manual approach and ERFNet (_p_ = 0.544), or ENet and ERFNet (_p_ = 0.119).  
The correlation graph showed a high positive correlation between manual and ENet segmentation [r\({}^{2}\) = 0.99] and a moderate correlation between manual and ERFNet segmentation [r\({}^{2}\) = 0.79], as shown in Figure 1a,c. In the same way, the Bland-Altman plots showed a high consistency between manual and ENet segmentation and a reproducibility coefficient (RPC) of \(\leq\) 1400 cc or \(\leq\)16% of values; values of 0% or 100% indicate a high or low consistency, respectively, and a low consistency between manual and ERFNet segmentation (RPC of \(\leq\)5200 cc, or \(\leq\)100% of values), as shown in Figure 1b,d.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Minimum** & **25 th Percentile** & **Median** & **75 th Percentile** & **Maximum** \\ \hline Manual & 227.20 cc & 515.30 cc & 1108.00 cc & 2836.00 cc & 13,820.00 cc \\ \hline ENet & 205.90 cc & 550.00 cc & 1173.00 cc & 3024.00 cc & 11,780.00 cc \\ \hline ERFNet & 163.10 cc & 519.50 cc & 605.70 cc & 1577.00 cc & 6196.00 cc \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference values of manually and automatically segmented RPS volumes from CT examinations in cubic centimeters (cc).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **F-Value** & **F Critic Value** & _p-Value_ \\ \hline ENet vs. Manual & 0.0069 & 4.494 & 0.935 \\ \hline ERFNet vs. Manual & 0.3854 & 4.494 & 0.544 \\ \hline ENet vs. ERFNet & 2.3263 & 4.494 & 0.119 \\ \hline ENet, Efficient Neural Network; ERFNet, efficient residual factorized ConvNet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **F-Value** & **F Critic Value** & _p-Value_ \\ \hline ENet vs. Manual & 0.0069 & 4.494 & 0.935 \\ \hline ERFNet vs. Manual & 0.3854 & 4.494 & 0.544 \\ \hline ENet vs. ERFNet & 2.3263 & 4.494 & 0.119 \\ \hline ENet, Efficient Neural Network; ERFNet, efficient residual factorized ConvNet. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of variance (ANOVA) on retroperitoneal sarcoma volumes showed no significant difference between manual and automatic segmentations.
## 6 Conclusions  
The studies here aimed to evaluate the geotechnical properties (specific gravity, water absorption, and soundness), strength tests (UCS and BTS), petrographic analysis, and geochemical properties of the Gahirat Marble rock samples in each Chittral area vicinity. The tests were performed based on the ASTM specification. The highest strength values of the mechanical properties were uniaxial compressive strength in GMKB1 (100 MPa) and Brazilian tensile strength in GMKB1 (13.47 MPa) from the Gahirat Gol area. Specific gravity (2.71 gcm\({}^{-3}\)) was calculated as the greatest value of the physical attributes in several places. The minimum values of water absorption (0.03%) were from various vicinities GMKB1 and GMD1, and the minimum value of soundness GMKB1 was for Khairabad (0.14%). The petrographic study reveals that the Gahirat Marble is composed of more than 90% calcite and exhibits an interlocking texture with the accessory minerals dolomite, mica, quartz, garnet, and opaque minerals. Two varieties of texture, homeoblastic and heteroblastic, are observed under the microscope. The petrographic analysis of all the Gahirat Marble samples showed the presence of a smaller number of microcracks and a well-developed mineral grain boundary, which approved the high strength. The data generated by XRF analysis confirm the high concentrations of CaO in all studied samples. The UCS results indicate that the Gahirat Marble has high resistance to crushing and bending effects. The specific gravity values show its ability to bear the impact of the degree of polishing and grinding. The low rate of water absorption makes it appropriate for floor tiles and outdoor cladding due to its resistance to weathering. The geotechnical properties, e.g., UCS, BTS, and specific gravity, of Gahirat Marble are higher than the Nikani Ghar Marble and Nauseri Marble, which indicates that the Gahirat Marble is more durable and suitable to be used as a dimension stone. The obtained analytical results may provide a solid foundation for further exploitation and utilization of Gahirat Marble, both domestically and internationally.  
Conceptualization, S.A.A.B. and M.B.; methodology, S.A.A.B., M.B., M.S.M. and A.G.; software, S.A.A.B., M.B., M.S.M. and H.T.J.; validation, M.B., M.S.M., H.T.J. and G.K.; formal analysis, S.A.A.B., M.B. and M.S.M.; investigation, S.A.A.B., M.B. and M.S.M.; resources, M.B. and M.S.M.; data curation, S.A.A.B., M.B. and M.S.M.; writing--original draft preparation, S.A.A.B. and M.B., writing--review and editing, M.S.M., H.T.J., G.K. and C.V.; visualization, M.S.M., H.T.J. and G.K.; supervision, M.B.; project administration, M.B.; funding acquisition, G.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research is part of the Ph.D. thesis work of the first author. The first author is very grateful to the Institute of Geology, University of Azad Jammu and Kashmir for providing him with the opportunity and all the facilities to complete his research work.  
The authors declare no conflict of interest.
filters with tuneable infinite-rejection responses is presented. Wave theory is developed to explain the principle of multipath loss compensation, and high attenuation is confirmed by experiment. Designs are extended to allow multiple stopbands with improved rejection. Three-port directional filters are also shown to be feasible and can be made using a section of MI waveguide with the addition of as few as three resonators. In each case, wave theory is used to reduce the complexity of the circuit equations. Simple multiplexers can be implemented by cascading directional filters. Together, these features can introduce additional functionality and complexity in magnetoinductive systems, since the filter components can be integrated directly with MI waveguides. Additionally, such devices can be successfully used in conventional systems.  
Essential background is first reviewed in Sections 2.1 and 2.2. An analytic model for MI directional filters is presented in Section 2.3, the circuit equations are solved using discrete travelling wave analysis, and methods for tuning are proposed. Conditions for infinite rejection are identified in Section 2.4, and the theory is extended to multiple passbands. Methods for constructing tuneable devices with high rejection and multiple passbands are described. Theoretical predictions are shown to be in good agreement with experimental results in the HF band in Section 3. Performance parameters including power handling and frequency scaling are also considered, with conclusions in Section 4.
# Biologically Derived Gels for the Cleaning of Historical and Artistic Metal Heritage
## 4 Organogels for Organic Coatings Removal
### Bio-Based Solvents
#### 4.2.2 n-Butyl Alcohol and Derivatives  
n-Butyl alcohol (or n-butanol) is a colorless solvent, which, apart from a petrochemical origin, can be bio-sourced therefore acquiring the title of \"bio-butanol\". It can be produced by bacterial fermentation of renewable feedstocks sugar- or lignocellulosic-storing and, remarkably nowadays, this green process is economically equal to the non-sustainable counterpart [90].  
The inherent low acute toxicity makes n-butyl alcohol accepted in the food and personal care industry, as well as in the field of art conservation [90; 96]. Indeed, its efficacy as a lacquer solvent is well-known for example on hard copal and shellac. However, its fumes can have a toxic effect by lengthened inhalation; thus, despite the bio-origin, this solvent is harmful under the irrevocable criterion of human health. Nevertheless, its employment is not only limited as a solvating agent but also in the manner of raw material to produce other widely used substances, including n-butyl acrylate and n-butyl acetate.  
n-Butyl acrylate is widely used as a precursor to several synthetic binders and adhesives, used in contemporary art and conservation praxis [97; 98]. On the other hand, n-butyl acetate is well-recognized as a strong solvent for cellulose nitrate-based and other synthetic resins, and most interestingly its vapors are considered not toxic [96]. As confirmation, it is an active compound in personal care goods and household cleaners, as well as an artificial flavoring in the food industry [90].  
The employment of n-butyl acetate for the formulation of cleaning organogels is noteworthy. A series of methyl methacrylate-based (MMA) gels has been loaded with various organic solvents and performances compared for precise removal of unwanted adhesives and varnishes from easel paintings [82].
## 3 Results
(**b**) Polar spectrogram at 224 \(\times\) 224 resolution. (**c**) Rectangular spectrogram at 128 \(\times\) 128 resolution. (**d**) Polar spectrogram at 128 \(\times\) 128 resolution. (**e**) Rectangular spectrogram at 96 \(\times\) 96 resolution. (**f**) Polar spectrogram at 96 \(\times\) 96 resolution. Figure 5 presents t-Distributed Stochastic Neighbor Embedding (t-SNE) visualizations of the penultimate feature distributions. Averaged penultimate features from the five cross-validated models were used as input to TSNE's fit_transfron() function in Scikit-Learn. The sample distributions indicate that the polar spectrograms are on par with rectangular spectrograms across all three models. Notably, polar spectrogram samples exhibited tighter clustering compared to rectangular spectrogram samples, as observed with DenseNet121 (Figure 5e,f). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Baseline Network** & **Input Image Dimensions** & **The Number of Weighted/Bias Parameters in the Baseline network** & **The Number of Weighted/Bias Layers** & **Feature Map Dimensions (After the First Layer of the Baseline Network)** & **Feature Map Dimensions (After the Last Layer of the Baseline Network)** \\ \hline \multirow{3}{*}{ResNet50} & 96 \(\times\) 96 & 23,587,712 & 23,719,364 & (64, 48, 48) & (2048, 3, 3) \\  & 128 \(\times\) 128 & 23,587,712 & 23,719,364 & (64, 64, 64) & (2048, 4, 4) \\  & 224 \(\times\) 224 & 23,587,712 & 23,719,364 & (64, 112, 112) & (204 Figure 6 compares confusion matrices for test data predictions using polar and rectangular spectrograms with the DenseNet121 model at various input dimensions. For the normal sinus rhythm and other rhythm classes, the prediction performances of polar and rectangular spectrogram models are comparable. However, for the atrial fibrillation class at 96 \(\times\) 96 resolution, the polar spectrogram model demonstrates slightly superior prediction performance compared to the rectangular spectrogram model (compare Figure 6e,f). Figure 5: t-SNE scatter plots obtained using the (**a,b**) ResNet50, (**c,d**) MobileNet, and (**e,f**) DenseNet121 models for the prediction of four classes using test datasets with the image dimensions of 96 \(\times\) 96. (**a,c,e**) Rectangular spectrograms as input. (**b,d,f**) Polar spectrograms as input.
## 1 Introduction
### Related Work  
Common GNSS interference source localization methods include Direction of Arrival (DOA)-based [14], Time Difference of Arrival (TDOA)-based [15; 16] and Received Signal Strength (RSS)-based [17; 18] approaches. Among these, DOA-based localization methods are limited by the number of antenna array elements, and the commonly used cross-localization technique suffers from positioning ambiguity when multiple interference sources coexist [19]. TDOA-based methods, however, face challenges in accurately extracting time differences between various interference sources and receivers due to the difficulty in separating multiple interfering signals. Additionally, commercial off-the-shelf (COTS) receivers typically cannot provide the raw data samples required for TDOA estimation [20].  
Due to their low implementation complexity, RSS-based methods have been widely adopted for GNSS interference source localization [21]. The concept of Jamming 911 (J911) was first proposed by [PERSON] at the 2011 Institute of Navigation (ION) GNSS conference. It introduced a crowdsourcing-based approach for interference detection and localization, leveraging carrier-to-noise ratio (C/N\({}_{0}\)) measurements reported by smartphones to identify and locate GNSS jammers [22]. Smartphones enjoy near-ubiquitous global penetration. For instance, China's Ministry of Industry and Information Technology (MIIT) reported in its 2023 Communications Industry Statistical Bulletin (published in January 2024) that the country hosts 11.62 million mobile communication base stations and 1.727 billion mobile phone subscribers [23]. However, the developer of the J911 system did not specify how to efficiently process such massive-scale GNSS receiver data, nor did they provide an effective solution for multi-interference source localization [24]. Reference [25] proposed a centroid-based localization method for multiple GNSS interference sources using clustering and centroid convergence algorithms on C/N\({}_{0}\) data. While this method achieves multi-source localization, its performance degrades as the number and variety of interference sources increase. Moreover, it fails to address scenarios involving directional GNSS jammers or low-power interference sources.
# Development of an Automatic Low-Cost Air Quality Control System: A Radon Application
## 2 Materials and Methods
### The Prediction Algorithm
This behavior reduces the amount of data sent between the server has to send to the devices and the MQTT, Telegram, and email clients, so the processing capacity needed for the server is reduced, and we can deploy it on machines with low resources. \(\circ\) Currently, the algorithm runs on the server, and its behavior is the same for all monitoring devices. However, we established this requisite so the algorithm could run on an edge device (the monitoring device). This would allow the system to work when the devices cannot connect to the server due to communication failures. This also enables the possibility of deploying a reduced system consisting of a monitoring device and a control device placed in a residence and connected to the same local network, where they communicate directly with each other without the need for a server. Requisite 4: the ability to change the prediction algorithm's behavior, and the results it provides, is achieved by changing the interpolation time window. The algorithm uses this value as the point in time where it predicts the future radon concentration. Increasing this parameter would make the algorithm turn on the airflow system sooner. 9. The algorithm uses some fixed values to operate. The most critical fixed value is the radon concentration used as a threshold in the algorithm. The WHO handbook [12] states \"...establishing a national annual average concentration reference level of 100 Bq/m\({}^{3}\), but if this level cannot be reached under the prevailing country-specific conditions, the reference level should not exceed 300 Bq/m\({}^{3}\)\". This work was carried out in Galicia, in the northwest of Spain, where the indoor radon concentration could naturally be above 400 Bq/m\({}^{3}\). Thus, the algorithm uses the 300 Bq/m\({}^{3}\) limit of the WHO handbook as the radon gas level threshold. The RD200M sensor can read radon concentration from 7.4 to 3.700 Bq/m\({}^{3}\), so its characteristics make it suitable for the air quality control system. Another fixed value is alpha, the exponential moving average factor. The algorithm sets it to 0.85, a value that mitigates the brief radon concentration spikes' effect while giving more weight to the current radon concentration [47]. Another fixed value is the time between readings that makes the algorithm discard the old readings. The algorithm implements this behavior, so if there were a communication failure, the system would not work based on old data that could not resemble the actual radon concentration. The algorithm sets this to one hour, i.e., if the last reading was 1 h before the current one, the algorithm resets the moving average, so it only takes the current value into account. Algorithm 1 shows the algorithm's implementation pseudocode, and it depicts all the explained behavior that implements the requisites.
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
###### 1 Introduction
suggested dynamic power control for NOMA transmissions to minimize the transmission delay with the consideration of each user's transmission deadline and the total power constraint [20]. [PERSON] et al. proposed machine-learning- and deep-learning-based power allocation schemes that find near-optimal solutions with regard to sum capacity and provide low computational costs in NOMA systems [21]. As mentioned in the above studies, DNNs can be used for resource allocation for wireless communication systems due to the low computational complexity [22]. Moreover, VLC is considered for industrial applications [3]. There have been reliability issues for industrial applications [23; 24]. Therefore, in this study, we proposed DBPA and PBUP as solutions to improve reliability and real-time transmission. Even though the VLC-NOMA system can provide efficient transmissions to reduce the usage of resources, the decoding error is larger than that of the VLC orthogonal multiple access (OMA) system. The VLC-OMA allocates the whole transmit power for one data transmission in a resource. Instead, the VLC-NOMA system transmits superposed signals splitting transmit power; then, the decoding error is larger than that of the VLC orthogonal multiple access (OMA) systems. It can cause significant problems for factory automation. Therefore, the reliability of the VLC-NOMA system should be improved. DBPA can be considered a solution to cover the decoding error caused by splitting transmit power. In this study, DBPA is proposed to improve the reliability of the VLC-NOMA system for factory automation. Moreover, the proposed VLC-NOMA system should handle two signals at a timeslot. As the VLC-NOMA system should handle a maximum of two signals in a timeslot, each signal for each node should be paired. Instead, the VLC-OMA system has to only schedule transmitting orders in timeslots. However, the VLC-NOMA system should pair two nodes before scheduling. Therefore, in this study, PBUP is considered a way to pair two nodes based on transmission priority. The contributions of this study are as follows:  
* Evaluating the reliability of the VLC-NOMA system in terms of the perfect and imperfect SICs. It provides the simulation and numerical results in terms of BER. * Proposing VLC-NOMA system for a factory automation scenario to solve missed deadlines caused by a lack of resources. * Proposing DBPA to solve the degradation of reliability caused by adopting the VLC-NOMA system. * Proposing PBUP to pair two nodes based on priority and channel state for the VLC-NOMA system. The remainder of this paper is structured as follows: The system model is described in Section 2; Section 3 proposes DBPA and PBUP; Section 4 explains the evaluation and analysis of the downlink VLC-NOMA system in terms of reliability and of DBPA compared with other power allocation algorithms, and evaluates PBUP in a factory automation scenario; Section 5 concludes the paper and describes future work.
## 2 Materials and Methods
### Estimation of the Ionospheric TEC
In the same way, if DoY 120 was the day of the eclipse in 2022, we used as reference DoYs 115 and 125. To represent the ionospheric TEC maps, we take all the VTEC values at IPPs at 350 km altitude within a range of 2.5\({}^{\circ}\) latitude and 2.5\({}^{\circ}\) longitude (starting from the Geographic Equator) and average them to generate a single VTEC value within a spatial resolution of 2.5\({}^{\circ}\times\) 2.5\({}^{\circ}\) and temporal resolution of 2 min. Furthermore, to correctly carry out the algebra of maps using Equations (1) and (2), we only consider the quadrants (2.5\({}^{\circ}\times\) 2.5\({}^{\circ}\)) that show values during all the days analyzed (reference days and eclipse days) at the same time of the day for each eclipse.