esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current Status of GNSS Integrity Service
### Current Status of BDS-3 Integrity Services
#### 2.1.2 Integrity Services of BDSBAS  
Figure 2 demonstrates the basic principles of the BDSBAS. As shown in this figure, the BDSBAS is implemented based on the ground regional monitoring network, including 13 type-A monitoring stations and 34 type-B monitoring stations. The monitoring stations send raw observation data to the SBAS data processing center, where the SBAS messages are generated. Then, the SBAS messages are uploaded to the GEO satellites with the ground uplink stations. The parameters in the SBAS message include correction parameters, accuracy parameters, and degradation parameters, as shown in Table 3. Based on the principles above, the BDSBAS provides integrity monitoring capabilities for four GNSS constellations, including the BDS-3, GPS, Galileo, and GLONASS. Currently, China is actively working on passing the single-frequency APV-I certification of the BDSBAS from the ICAO. Previous studies have showed that the BDSBAS can provide APV-I services to China and surrounding areas with an availability of higher than 99% [35; 36]. Therefore, the BDSBAS can meet the performance requirements of APV-I.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline
**SISMA** & The signal-in-space (SIS) monitoring accuracy, which describes the errors in satellite orbits and satellite clock offsets. \\ \hline
**SISA** & The SIS accuracy, which describes the predictive accuracy of the orbital parameters and clock correction parameters in the broadcast message. \\ \hline
**SIF** & The signal integrity flag, which represents whether the signal is healthy. \\ \hline
**AIF** & The accuracy integrity flag, which represents whether the SISMA value is valid. \\ \hline
**DIF** & The data integrity flag, which represents whether the error of the broadcasted message parameters exceeds the predicted accuracy. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fundamental integrity parameters of BDS-3.  
Figure 2: Architecture of satellite-based augmentation system (SBAS).
# Priority-Based Content Delivery in the Internet of Vehicles through Named Data Networking +
## 4 The Priority-Based Forwarding Framework
### Face Selection and Forwarding  
The face selection module in pNDN decides the outgoing network face(s) for every Interest. In the considered IoV scenario, where two network faces are available for a vehicle, i.e., 3G/LTE and IEEE 802.11 OCB, pNDN implements the following selection strategy:  
* Interests for Low Priority (LP) contents (i.e., with prefix name /low) are forwarded only over the 802.11 OCB interface.
* High Priority (HP) contents (i.e., with prefix name set to /high) are forwarded by consumers according to a parallel forwarding strategy, i.e., the Interest is simultaneously forwarded on both faces. This is to ensure the low latency and reliable delivery of such sensitive data. Vice versa, vehicles acting as forwarders may decide, according to their own user-defined preferences (e.g., monetary costs), whether to use only the IEEE 802.11 OCB face or also apply the parallel strategy. In case the LTE face is not available for a forwarding vehicle, it can only forward the Interest over the IEEE 802.11 OCB face.  
Whatever the priority of content, the processing of an Interest packet at an intermediate node \(N\) can be summarized as follows. \(N\) first looks for a CS matching. In the presence of a CS hit, the receiving node checks if the freshness parameter specified in the Interest is satisfied by the stored Data. If this is the case, the Data is sent back. Otherwise, the Data packet is deemed outdated for the consumer, and the Interest is forwarded until the actual provider or a node caching a fresher copy is located. Multi-hop packet delivery over the 802.11 OCB interface is performed according to legacy protocols, like BF or LFBL, extended with additional priority-based forwarding rules, as specified in the following.  
Figures 3 and 4, respectively, depict the processing of an Interest packet at the consumer and at an intermediate vehicle node.
## Operational Ship Monitoring System Based on Synthetic  
**Aperture Radar Processing**  
**[PERSON] *, [PERSON] and [PERSON]**  
User Segment Division (URS), Science and Earth Observation Processing Systems (SEOPS), GMV Aerospace C Balmes, 268-270, 5\({}^{\mathrm{th}}\) floor, E-08006, Barcelona, Spain;  
E-Mails: [EMAIL_ADDRESS] (J.A.B.M.), [EMAIL_ADDRESS] (A.T.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34 93 292 24 84; Fax: +34 91 8072199  
_Received: 8 June 2009; in revised form: 30 July 2009 / Accepted: 4 August 2009 / Published: 14 August 2009_  
**Abstract:** This paper presents a Ship Monitoring System (SIMONS) working with Synthetic Aperture Radar (SAR) images. It is able to infer ship detection and classification information, and merge the results with other input channels, such as polls from the Automatic Identification System (AIS). Two main stages can be identified, namely: SAR processing and data dissemination. The former has three independent modules, which are related to Coastline Detection (CD), Ship Detection (SD) and Ship Classification (SC). The later is solved via an advanced web interface, which is compliant with the OpenSource standards fixed by the Open Geospatial Consortium (OGC). SIMONS has been designed to be a modular, unsupervised and reliable system that meets Near-Real Time (NRT) delivery requirements. From data ingestion to product delivery, the processing chain is fully automatic accepting ERS and ENVISAT formats.  
SIMONS has been developed by GMV Aerospace, S.A. with three main goals, namely:  
1) To limit the dependence on the ancillary information provided by systems such as AIS.  
2) To achieve the maximum level of automatism and restrict human manipulation.  
3) To limit the error sources and their propagation.  
Spanish authorities have validated SIMONS. The results have been satisfactory and have confirmed that the system is useful for improving decision making. For single-polarimetric images with a resolution of 30 m, SIMONS permits the location of ships larger than 40 m with a classification ratio around 50% of positive matches. These values are expected to be improved with SAR data from new sensors. In the paper, the performance of SD and SC modules is assessed by cross-check of SAR data with AIS reports.  
keywords:ship detection; ship classification; SAR; ship monitoring
## 4 Experiments and Results
### Qualitative Results on the Real-World Images  
To further verify the effectiveness of the proposed algorithm in this paper, we used it and several other algorithms to conduct experiments on real haze image datasets. As shown in Figure 10, the DCP algorithm had more serious color distortion, such as the color distortion in the sky area in the second image in Figure 10b. The AOD-Net algorithm not only had incomplete haze, but also changed the brightness of the image; for example, the overall brightness of the second image in Figure 10c is very dark. The GCANet algorithm could remove haze, but it tended to have uneven haze removal and uneven color blocks; for example, the haze in the distant woods in the third image in Figure 10d was not removed. The haze-free image obtained by the EPDN algorithm had the problem of unclear details; for example, some areas were blurred in the fourth image in Figure 10e. The YOLY algorithm caused color distortion in the haze-free image, such as the color distortion in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **PSNR** & **SSIM** \\ \hline DCP & 17.01 & 0.803 \\ AOD-Net & 19.68 & 0.835 \\ GCANet & 21.37 & 0.874 \\ EPDN & 20.37 & 0.877 \\ YOLY & 21.02 & 0.905 \\ Ours & **24.06** & **0.912** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative results on the HSTS.  
Figure 9: Visual results on the HSTS dataset: (**a**) hazy images; (**b**) corresponding haze-free images; (**c**) DCP; (**d**) AOD-Net; (**e**) GCANet; (**f**) EPDN; (**g**) YOLY; (**h**) proposed method.  
the sixth image in Figure 10f. The dehazing effect obtained by the proposed algorithm in this paper was better than that obtained by the other mentioned algorithms, which could both remove haze more thoroughly and better recover the detailed features of the original image. For the eighth image, no methods could recover the detailed features of the original image well, while the algorithm in this paper could recover the detailed features of the original image, which is a certain improvement compared with other algorithms.
## 4 Discussion
As a crude plant extract, MET70 contains a complex mixture of bioactive compounds capable of simultaneously targeting multiple cellular pathways. Contrary to our expectations, the data reveal a selective modulation of the ER stress response. Co-treatment with MET70 did not further alter BiP, CHOP, ATF4, or ATF6 expression but significantly upregulated PDIA2 compared to doxorubicin alone. These findings suggest that MET70 does not amplify doxorubicin-induced ER stress but may instead enhance protein folding capacity via PDIA2 upregulation [36,37]. This modulatory effect could support ER homeostasis under chemotherapeutic stress, indicating that MET70 enhances cytotoxicity primarily by promoting redox imbalance rather than intensifying UPR-mediated cell death. We also observed that MET70 downregulated Beclin-1, a key initiator of autophagy that interacts with the PI3 KC3 complex to promote autophagosome formation and activate the autophagic pathway. Beclin-1 functions as a tumor suppressor during early cancer development. However, in established tumors, autophagy is often activated as a protective mechanism in response to chemotherapy-induced ER stress and ROS generation. Therefore, MET70-mediated suppression of Beclin-1 may impair this survival pathway, thereby enhancing doxorubicin cytotoxicity [38]. Finally, we evaluated the expression of multidrug resistance (MDR)-related ABC transporters. Doxorubicin alone downregulated ABCB1 and ABCG2, while MET70 treatment selectively reduced ABCC1 expression in combination. These transporters are major contributors to drug efflux and resistance in cancer therapy [39, 40, 41]. Notably, while ABCG2 was upregulated in the co-treatment condition, the downregulation of ABCC1 and ABCB1 may still result in a net increase in doxorubicin accumulation within the cell.
# Optimization Strategies in Quantum Machine Learning: A Performance Analysis
## 3 Model Architecture
### Model QNN  
A. Data preprocessing:  
A two-step feature selection process was proposed to identify and select the most influential features from the dataset. The first step is based on information gain (IG), which involves calculating the gain ratio for each feature, as shown in the following equation:  
\[\text{IG (C,X)}=\text{H(C)}-\text{H (C\,|\,X)}\]  
Here, H(C) represents the entropy of the class label C, quantifying the level of uncertainty or randomness in the class distribution. It serves as a measure of the unpredictability or variability in the target variable. In contrast, H (C | X) denotes the conditional entropy of class C given a specific feature X, representing the remaining uncertainty about C when X is known. By comparing these entropy measures, we can evaluate the information gain provided by each feature and determine its relevance to the class labels [32].  
After computing the information gain for all features, they are ranked in descending order based on their scores. Features with low information gain are considered to have a minimal impact on the classification task and may be excluded from further analysis (Figure 2).  
For the second step, we employ Principal Component Analysis (PCA) to reduce dimensionality. PCA is a linear projection technique that transforms data from a high-dimensional space to a lower-dimensional space, minimizing information loss. It is widely used for feature extraction in healthcare classification [30].  
To apply PCA, we first standardize and normalize the top eight features (selected based on information gain, IG) using standard scaling. This prevents features with larger scales from disproportionately influencing the PCA results. Then, PCA is used to reduce the dimensionality of the original eight features to four principal components. These components, linear combinations of the original features, capture the essential data while eliminating redundancy, simplifying the input for quantum machine learning (QML) algorithms. The choice of four components was determined empirically. Figure 3 illustrates the contribution of the original features to each principal component:
## 4 Research Methods and Results
### Results  
\({}^{40}\)Ar-\({}^{39}\)Ar geochronology of muscovite from albite-mica-quartz schist (QY0807-1 and QY0807-4). Muscovite grains separated from albite-mica-quartz schist samples (QY0807-1 and QY0807-4, corresponding to the quartz fabric analysis samples described earlier) were subjected to \({}^{40}\)Ar-\({}^{39}\)Ar step-heating geochronology. The analytical results are summarized in Table 1. Both samples yielded consistent age spectra, as illustrated by their plateau age diagrams (Figure 6b,d) and inverse isochron plots (Figure 6a,c). For sample QY0807-1, eight heating steps between 840 and 1180 \({}^{\circ}\)C defined a well-constrained plateau age of 79.21 \(\pm\) 1.06 Ma, representing 72.23% of the total \({}^{39}\)Ar released. The corresponding \({}^{36}\)Ar/\({}^{40}\)Ar vs. \({}^{39}\)Ar/\({}^{40}\)Ar isochron age is 82.14 \(\pm\) 2.72 Ma. Similarly, sample QY0807-4 produced a plateau age of 79.65 \(\pm\) 1.04 Ma (72.63% \({}^{39}\)Ar release) over the same temperature range, with an isochron age of 79.73 \(\pm\) 2.45 Ma. The initial \({}^{40}\)Ar/\({}^{36}\)Ar ratios derived from the isochron regressions are 175.0 \(\pm\) 101.5 (MSWD = 0.11) and 294.0 \(\pm\) 57.0 (MSWD = 2.25) for QY0807-1 and QY0807-4, respectively. The weighted mean age of \(\sim\)79 Ma is interpreted to represent the timing of muscovite crystallization, corresponding to a Late Cretaceous metamorphic event linked to regional tectonism. Microstructural observations reveal that micas (muscovite and sericite) are preferentially aligned to define the S\({}_{1}\) foliation. S-C fabric analysis indicates that micas dominate the S-surfaces, while felsic minerals (e.g., quartz and feldspar) define the C-surfaces. Given the low-temperature deformation conditions inferred for this fabric development, the \({}^{40}\)Ar-\({}^{39}\)Ar ages of mica are interpreted as directly recording the timing of syn-tectonic metamorphism and associated ductile deformation.  
Figure 6: \({}^{40}\)Ar-\({}^{39}\)Ar plateau (**a,c**) and inverse isochronal (**b,d**) ages for mica from the quartz samples used in EBSD analysis. All age uncertainties are reported at the 2\(\sigma\) level. tp = plateau age; ti = inverse isochron age; MSWD = mean square of weighted deviates.
## 4 Results and Discussions
### Determination of Non-Clay Minerals  
It should be noted that the quantification of non-clay mineralsfound in the clays by XRD alone was difficult, since some components are hardly detectable by X-ray. Thesenon-clay minerals in the raw material were marked by different peaks. An X-ray diffraction pattern of raw clay mineral (S\({}_{0}\)) is shown in Figure 2.  
Figure 2: X-ray diffraction (XRD) patterns of raw material (S\({}_{0}\)).  
Quartz appeared as major non-clay minerals by the reflection peaks observed at d = 3.34; 2.28; 2.13; 1.819 A and d = 4.26 A. Other non-clay minerals are also detected, such as the calcite at 3.858; 3.04; 1.915, 1.878 A and gypsum at 2.09 A. It is remarkable that the non-clay minerals were still present even after purification. This is probably due to their fine particle size which makes their complete separation impossible during clay pre-treatment.
## 2 Materials and Methods  
### DSC Studies  
Hydrated hydroxypropyl cellulose (HPC) type MF and its mixtures (1:1 \(w/w\)) with lowly soluble salicylic acid (SA) or highly soluble sodium salicate (NaSA) were investigated by differential scanning calorimetry (DSC). The basic physicochemical properties of HPC type MF as well as of salicylic acid and sodium salicate were previously presented [20]. The same work also presented sample preparation, DSC instrumentation, software, thermal protocol, and other experimental details. The water concentrations of the hydrated samples under study, expressed as W\({}_{\text{c}}\), ranged from 0.2 to 5.0 g/g. The DSC curves obtained in that study, in the section related to melting processes, were the subject of deconvolution analysis. Based on the previously reported results [20], the main principles of the current research were set as follows:  
* the water concentrations of the hydrated samples under study were expressed as W\({}_{\text{c}}\), although W\({}_{\text{c}}\) is defined as the water fraction m\({}_{\text{H2O}}\) related the to dry mass m\({}_{\text{dry}}\) of raw HPC or an HPC mixture (W\({}_{\text{c}}=\) m\({}_{\text{H2O}}/\)m\({}_{\text{dry}}\))
* the decomposition of the curves was used for the DSC plots, from the lowest to the highest hydration level W\({}_{\text{c}}\)
* only melting peaks were used for decomposition analysis
* the amount of NFW water calculated for raw HPC was equal to 0.54 g/g, which is comparable to the amount calculated for HPC/NaSA 0.48 g/g
* NFW calculated for HPC/SA was equal to 0.18 g/g, which is almost 1/3 of the NFW amount measured for raw HPC and the HPC/NaSA mixture
* for water contents below NFW, all water found in the matrix is considered non-freezing NFW
* below the NFW value, the melting peaks for raw HPC and its mixtures are not visible in the DSC measurements; for that reason, the decomposition procedure was performed only for samples with a water concentration W\({}_{\text{c}}\) > NFW, usually, above 0.7-0.8 g/g
* the weight of freezing-bound FBW and non-freezing NFW water per g HPC or HPC/mixture is constant
* the developed model should consider the contribution of specific nanostructures, the so-called nanocavities, in water holding
* the developed model should consider the strong dissociative effect of the Na+ cation
## 6 Conclusions  
The current experimental investigation scrutinised the mechanical properties, including compressive strength, tensile strength, and modulus of elasticity, of SCGC specimens exposed to ambient and marine weather conditions. This study thus records an exciting observation of the mechanical strength developments of the short-term exposed ambient cured self-compacting geopolymer concrete, and provides a novel contribution to the body of knowledge on self-compacting geopolymer concrete. The following conclusions can be made from the experimental observations of this study:  
1. When exposed to marine conditions under accelerated wet-dry cycles, the specimens did not report any loss of mass or leaching of salts in the exposure period.
2. The cylinder specimens that were exposed to hybrid exposures (28-day ambient curing followed by 3 months of immersion in artificial seawater) reported improved mechanical properties, specifically a 30% increase in compressive strength compared to the 28-day strength of 40 MPa.
3. This study confirms that the selected SCGC mix can be successfully used for marine applications, and can acquire a design strength of 40 MPa with added strength development of up to 50 MPa, under harsh marine conditions without any strength deterioration from this exposure.
4. The improved mechanical performance of SCGC confirms that the heated and humid environments favour the strength development of the mix by aiding geopolymerisation of any unreacted species in the core of the specimens.
5. This study also warrants more research to promote geopolymer concrete as a sustainable solution for marine water structures.  
The authors acknowledge the need for further investigation, and ongoing research into long-term mechanical properties, and studies on sorptivity, porosity, and microstructural analysis, are underway.  
S.K.R.: Methodology, Investigation, Writing--Original Draft; R.A.-A.: Supervision, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Available on request from authors.  
The authors gratefully acknowledge the material assistance received from Independent Cements, Melbourne, and Fly Ash Australia for supplying the slag and micro fly ash, respectively, for our research work. The authors also extend their gratitude for the wholehearted support from Technical Staff [PERSON] and [PERSON] in the completion of the experimental investigation at the Structures Laboratory, Deakin University.  
The authors declare no conflict of interest.
## 4 Results
### Testing the Sensor Network's Survivability  
In the next computer simulation, the experimental sensor network's survivability was estimated in two cases: without the sensors' bandwidth and with the sensors' bandwidth. Parameters of the estimated network are presented in Table 3.  
In Table 1, the values of the sensors' importance were provided by experts. A value of 1 means that this sensor is very important and if the sensor is not important, the value of importance is equal to zero. The results of the sensor network's survivability estimation are presented in Figure 12:  
According to the proposed method, a vitality value of 1 means that this network is viable in case of loss of the sensor, connection between sensors and maximum bandwidth elements. The obtained results indicate that the network is very vulnerable with total vitality of 6%. The developed user interface allows the user to clearly identify vulnerable network elements according to the relevant criteria and the results are displayed in the relevant tables and textboxes. Note, \"Max flow\" in the corresponding result columns means that the given sensor or connection is weak in vitality testing considering bandwidth. The user can also upload data about the network settings in the form of spreadsheets. The developed software can work in two modes: with and without bandwidth consideration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Coordinates** & **Coverage Radius, m** & **Importance** & **Bandwidth, Mb/s** \\ \hline (15;15) & 10 & 0.75 & 100 \\ (20;45) & 10 & 0.85 & 95 \\ (35;40) & 10 & 0.69 & 87 \\ (17;30) & 8 & 0.8 & 120 \\ (20;45) & 10 & 1 & 110 \\ (50;55) & 13 & 0.9 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the estimated network.  
Figure 12: Sensor network’s survivability estimation results considering bandwidth.
# Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
## 6 Discussion
### Use of Edge Computing  
An ML model was deployed in the cloud as an API; data can be uploaded and analyzed using a cloud-based computing analytics service. Many IIoT applications employ a cloud data center as a central server for processing data. For textile manufacturing, cloud computing is not suitable for all practical manufacturing problems; edge computing may be more suitable in certain situations. Edge computing is predicated on moving certain computational loads toward the edge of the network to harness computational capabilities currently untapped in edge nodes such as base stations, routers, and switches [44]. Edge computing can assist equipment in making autonomous setting decisions based on anomaly detection and assessments of product quality. In certain manufacturing processes, where anomaly detection currently relies on the inspection of the product by an operator, machine vision can be implemented by edge computing to provide rapid responses for production line problems. In 5G communication technology, vision is the primary application for edge computing. Certain researchers have reported that edge computing can be used for continuous manufacturing processes such as weaving in fiber production and textile manufacturing [44; 45].