esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions
**Nomenclature**  
Abbreviations  
C VSI  
DSTATCOMs  
Distribution static synchronous compensators  
FLC  
Fuzzy logic controller  
FC  
Fuel cell  
FOPI-FOPI  
Cascading Fractional-Order Proportional-Integral  
ITSE  
The integral of time-weighted square error  
MPC  
Model predictive control  
MPPT  
Maximum power point tracking  
NN  
Neural network  
PI-PI  
Cascading Proportional-Integral Controller  
PEMFC  
Proton exchange membrane fuel cell  
PQ  
Power quality  
PV  
Photovoltaic  
WSO  
White Shark Optimization  
WOA  
While Optimization Algorithm  
WSO-WOA  
The hybrid algorithm of WSO and WOA  
Symbols  
C1, C2, C3, and C4  
The loop control units of the DSTATCOM  
\(C_{\rm C}^{dc}\)  
DC link capacitor of the C VSI  
\(I_{PV}^{mod}\), \(V_{PV}^{mod}\)  
The output current and voltage of the PV module  
\(I_{FC}\), \(V_{FC}^{c}\), \(P_{FC}\)  
The output current, voltage, and power of the FC system, respectively  
\(V_{imp}^{dt}\), \(V_{imp}^{dt}\)  
The CSVI's d- and q-axes output voltage components  
\(V_{imp}^{ds}\), \(V_{imp}^{ds}\)  
The reference signals of CSVI's d- and q-axis output voltage components  
\(V_{B1}^{d}\), \(V_{B1}^{d}\)  
The d- and q-axes voltage components at B1  
\(V_{B2}^{d}\), \(V_{B2}^{d}\)  
The d- and q-axes voltage components at B2  
\(r_{f}^{e}\), \(I_{f}^{e}\), \(I_{f}^{e}\)  
Resistance, inductance, and capacitance of the CVSI's output filter, respectively. \(r_{tr}\), \(L_{tr}\)  
Resistance, and inductance of the transformer connected between bus B1  
and B2  
\(I_{imp}^{d}\), \(I_{B2}^{q}\)  
The CSVI's d- and q-axes output current components  
The reference d- and q-axes current components at bus B2  
\(\omega\)  
The microgrid frequency in rad/sec  
\(I_{S}^{d}\), \(I_{S}^{q}\)  
The DSTATCOM's d- and q-axes output current components  
\(I_{S}^{ds}\), \(I_{S}^{qs}\)  
The reference d- and q-axes of the DSTATCOM's current components at  
bus B2  
\(V_{S}^{d}\), \(V_{S}^{q}\)  
The DSTATCOM's d- and q-axes output voltage components  
\(V_{S}^{d*}\), \(V_{S}^{q*}\)  
The reference signals of DSTATCOM's d- and q-axes output voltage components  
\(I_{f}^{s}\), \(I_{f}^{s}\)  
Resistance, and inductance of the DSTATCOM's output filter, respectively  
\(P_{S}\), \(Q_{S}\)  
The DSTATCOM's active and reactive powers  
\(V_{S}^{dC*}\), \(V_{DC}^{*}\)  
The reference DC link voltage of the DSTATCOM and the  
CVSI, respectively. \(k_{p,an}^{at}\), \(k_{l,an}^{l}\),\(\lambda_{m}^{n}\)  
The control parameters of the cascading control units C1, C2, C3, and C4  
\(LB\), \(UB\)  
Lower and upper bounds of the PI-PI and FOPI-FOPI controllers. \(N\), \(T_{max}\)  
The population size and the maximum number of iterations used in the  
optimization run  
\(X_{gbest}^{t}\)  
The global best solution in iteration t  
\(\rm{OF}^{best}\)  
The best fitness value
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 6 Conclusions
All authors have read and agreed to the published version of the manuscript. **Funding**: This research was jointly funded by the National Key Research and Development Program of China (Grant No. 2023 YFB2604001), the National Natural Science Foundation of China (Grant Nos. 42371460, U22A20565, and 42171355), the Natural Science Foundation Project of Chongqing (Grant No. CSTB2022 NSCQ-MSX1671), and the Natural Science Foundation Project of Shandong Province (Grant No. ZR2021 MD082). **Data Availability Statement**: The GPS data can be downloaded from [[https://gage-data.earthscope.org/archive/gnss/rinex](https://gage-data.earthscope.org/archive/gnss/rinex)]([https://gage-data.earthscope.org/archive/gnss/rinex](https://gage-data.earthscope.org/archive/gnss/rinex)) (accessed on 11 April 2024), and the soil moisture reference data can be downloaded from [[https://www.unavcco.org/data/gps-gnss/derived-products/pbo-h2o/pbo-h2o.html](https://www.unavcco.org/data/gps-gnss/derived-products/pbo-h2o/pbo-h2o.html)]([https://www.unavcco.org/data/gps-gnss/derived-products/pbo-h2o/pbo-h2o.html](https://www.unavcco.org/data/gps-gnss/derived-products/pbo-h2o/pbo-h2o.html)) (accessed on 11 April 2024). **Acknowledgments**: The authors thank the UNAVCO ([[https://www.unavco.org/](https://www.unavco.org/)]([https://www.unavco.org/](https://www.unavco.org/)), accessed on 11 April 2024) for providing the GNSS site (P037 and P041) data and soil moisture reference data. The authors would also like to thank [PERSON] professor from Laboratoire Geosciences Environnement Toulouse, Universite Paul Sabatier, France for providing GNSS and soil moisture data collected in Lamasquere, France. The authors also thank the editors and all anonymous reviewers for their constructive and excellent reviews of our work. **Conflicts of Interest**: The authors declare no conflicts of interest.
## 3 Results and Discussion
### Thermo-Regulated Performance of the DP/Gypsum Composite  
Figure 14 illustrates the indoor temperature variations of different room models. When the room models were heated for 2.83 h, the maximum indoor temperature of the control model (G-C) was 27.4 \({}^{\circ}\)C. However, the highest indoor temperatures of the G-10% DP and G-20% DP models were 25.9 \({}^{\circ}\)C and 24.5 \({}^{\circ}\)C, reducing by 1.5 \({}^{\circ}\)C and 2.9 \({}^{\circ}\)C, respectively. Obviously, the indoor temperature decreased as the DP content increased. Nevertheless, when 1 wt. % carbon fiber was added, the peak temperature of G-10% DP-1% CF was 25.7 \({}^{\circ}\)C, reducing by 1.7 \({}^{\circ}\)C compared to the control, while the maximum indoor temperature of G-20% DP-1% CF was 24.3 \({}^{\circ}\)C, which is 3.1 \({}^{\circ}\)C lower than that of the control room.  
Figure 13: The specific heat capacity of diatomic/paraffin/gypsum composites.  
As shown in the schematic diagram of Figure 15, the reasons for the reductions in indoor temperatures are summarized as follows: (1) Once the outdoor temperature exceeded the phase change temperature of paraffin, the PCM began to melt and absorb heat, so the heat passing through the gypsum panel could be reduced. This in turn caused the indoor temperature to rise slowly, and resulted in a drop in the maximum indoor temperature; (2) CF greatly improved the thermal conductivity of DP/gypsum and the heat transfer of the PCM.  
Although the DP used in this study has a small impregnation ratio (15%), it could help improve the thermo-regulation performance by reducing the indoor temperature by 3.1 \({}^{\circ}\)C.  
Figure 14: The curves of the indoor temperature variation of five small house models.  
Figure 15: The schematic diagram of the thermo-regulating mechanism of different diatomic/paraffin panels.  
The application of DP within walls and roofs could not only reduce energy consumption, but also shift peaks in electricity consumption, contributing to the economic benefits and CO2 emission reduction.
## 4 Discussion
### Hybrid Methodology: Deep Learning and Traditional ML Classifiers  
This study deliberately employed a hybrid methodology combining deep learning feature extraction (VGG16 pretrained networks) with traditional ML classifiers rather than implementing end-to-end deep neural architectures. This hybrid approach aligns with successful previous work in DME classification where traditional ML classifiers demonstrated strong performance when combined with carefully engineered features [17]. However, this approach involves certain trade-offs. While potentially limiting the model's ability to learn hierarchical OCT-specific features that might emerge from end-to-end training, it offers significant advantages for our specific binary classification task. The pretrained VGG16 feature extraction combined with traditional ML classifiers provided superior interpretability, required less training data, and enabled precise calibration of sensitivity-specificity trade-offs through transparent threshold adjustments--critical factors for clinical adoption. Logistic Regression demonstrated particularly strong performance (accuracy: 0.9396, AUC: 0.9832) compared to more complex alternatives, offering an optimal balance between computational efficiency and classification power while requiring significantly fewer resources than full deep learning implementations. Our comparative analysis confirmed that this hybrid approach achieved equivalent discrimination power with enhanced interpretability compared to more complex alternatives. However, we acknowledge that for future multiclass pathology classification tasks, where increased feature complexity and inter-class relationships are more nuanced, full deep learning implementations may become necessary to capture hierarchical relationships across diverse OCT presentations beyond the binary classification presented in this initial study.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 4 Parameter Tuning and Evaluation through Simulations  
In this section, we use realistic simulations for four different goals. First, we perform sweeps on the values for the configuration parameters of the proposed broadcast suppression algorithms. We will use two different city maps so that we can choose the most convenient values for almost any scenario. Second, we will be able to compare the performance of each alternative in its best configuration, in order to choose the final form of our dissemination scheme for urban environments. Third, we want to compare the three different alternatives for store-carry-forwarding and see how much they improve the performance of the scheme. Lastly, we simulate UV-CAST in the same conditions in order to find out how our solution compares to a well-known protocol that has been selected for many other comparisons in the state of the art.  
Now, we are going to present the metrics that we will be measuring to compare the results of each algorithm. Next, we detail the configuration details of our simulations, so that they could be replicated. Finally, we present and comment on the results.
# Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
## 2 Materials and Methods
### Discriminant Analysis  
A discriminant analysis was conducted to investigate the banana peel alteration over 21 days of ripening and assess the ripening days according to the banana-peel texture. First, all bananas were photographed on the predetermined measurement days (ripening days 2, 4, 7, 9, 11, 14, 17, 21). Using a specially designed software, six grayscale rectangular regions-of-interest (ROIs) from the photographed peel of each banana, three of each banana-side, were selected. Second, from each ROI, 15 textural features were generated, the mean feature values from the 6 banana ROIs were computed, and 15 feature values that had been consistent across a 15-long feature-vector were selected to represent each banana. Second, eight classes were formed, corresponding to eight measurement days, and each class contained the feature-vectors of eight bananas photographed on each measurement day. These classes were used in both the discriminant and statistical analyses.  
For the discriminant analysis, machine-learning (ML) classifiers were employed. A classifier is an algorithm that, when suitably designed, could assign the banana ROIs to a predetermined class (or ripening day). Ten different ML-classifiers were tested on our data. The classifiers were included in the Python programming language library scikit-learn [[[https://scikit-learn.org/](https://scikit-learn.org/)]([https://scikit-learn.org/](https://scikit-learn.org/)) (accessed on 7 March 2023)] and were the following: classification and regression decision tree, \(k\)-nearest neighbor, linear discriminant analysis, logistic regression, multi-layer perceptron, naive Bayesian, nearest centroid, perceptron, random forest, and support vector machines. The accuracy of a classifier in assigning the banana ROIs to the correct ripening day was assessed by evaluating labelled ROIs in the banana peel images. For this, an ML-system was constructed by choosing one classifier, selecting a sub-set of the 15 features, and using a precision evaluation method (leave-one-out, LOO). The ML-system's design precision was tested by employing a different classifier each time, normalizing the features to a zero mean and a unit standard deviation, and using all possible feature combinations. As a result, the ML-system design that could provide the highest precision was determined. For dimensionality reduction, each feature combination was compacted into two principal component analysis (PCA) components, PCA1 and PCA2, which were then input into the ML system. PCA-based two-dimensional scatter plots with class-separating surfaces were produced for displaying the results of the discriminant analysis [21].
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Membrane Distillation (MD)  
Membrane Distillation (MD) is a desalting process based on hydrophobic membranes. These kinds of membranes can be crossed by water as vapor molecules [61].  
MD is theoretically able to reject all non-volatile solutes (like salts). The main drawback of the MD process is the large amount of energy that is consumed during the liquid-vapor phase change process and the incomplete recovery of the latent heat. For these reasons, the MD process is energy-inefficient if used as a standalone system [62].  
However, this technology works at lower temperatures than other thermal-driven phase transition technologies (MSF, MED). Similarly, the required pressure is lower than other technology based on membranes (RO).  
MD can be run by using low-grade waste heat as an energy source while operating at a low pressure and shows a negligible sensitivity to varying feed salinity. As an alternative, solar radiation can be used to supply MD units [28].  
MD units can be assembled in four configurations, as shown in Figure 28[62; 63]. The simplest one is the Direct Contact Membrane Distillation (DCMD), where two solutions are in direct contact with the hydrophobic membrane. Due to the difference of pressure between the two solutions, the vapor produced on the hot solution surface can cross the membrane, going inside the cold solution. This technology is commonly used in desalination and concentration processes of aqueous solutions in the food industry [64; 65; 66].  
In Air Gap Membrane Distillation (AGMD) a layer of stagnant air is added between the membrane and the cold solution. The reason is the reduction of the thermal energy expenditure, as the air gap increases the thermal resistance between the two fluids. However, the adding of the air gap obstructs the mass flow through the membrane. This technology can be used in desalination application or to remove volatile compounds from an aqueous solution [63; 67].  
Figure 28: The possible configurations of a Membrane Distillation (MD) unit.  
Another technology is the Sweeping Gas Membrane Distillation (SGMD), where the hydrophobic membrane separates the hot solution from a sweep gas. Like the AGMD, the thermal efficiency is higher than the DCMD. The mass transfer is promoted by the sweep gas since it is not stationary. As a disadvantage, a large condenser is required. This technique can be used to remove volatile compounds from aqueous solutions [68].  
The last technique is the Vacuum Membrane Distillation (VMD), where the sweep gas (or the air gap) is replaced by the vacuum produced by a pump [69]. The condensation is externally realized by the distillation unit. This solution has a high thermal efficiency and is used to separate volatile aqueous solutions [70].  
A common problem of all membrane distillation technologies is due to fouling and wetting phenomena, which are in part controlled by using different strategies like pretreatments [71], surface modification [72], increasing flow rate to promote turbulence [73], and periodical hydraulic and chemical cleanings [74; 75; 76].
## 2 Basics of CAD systems  
### CAD systems and pattern recognition  
In general, radiologists carefully look into medical images, and diagnose a patient's disease by obtaining useful information and interpreting medical images with their experience, knowledge, and wisdom. For the purpose of attaining a correct diagnosis, radiologists need to learn and archive a huge number of cases with various types of diseases in their own mind according to their specialty. A good radiologist does not need to have a particularly good eye, but rather a sensible ability to identify precisely what he or she is looking at when looking at medical images. Thus, it can be said that good radiologists have high and flexible abilities of learning and pattern recognition. Since CAD systems provide radiologists with a \"second opinion\" for assisting them in the diagnosis of patients' diseases, it entails the CAD system to have similar abilities as the radiologists in terms of learning and pattern recognition, even though good CAD systems cannot reach radiologists' high diagnostic abilities. Consequently, machine learning and pattern recognition techniques are substantially important in developing CAD systems.  
Pattern recognition is the act of extracting features from some objects in raw data and making a decision based on the classifier output such as classifying each object into one of the possible categories of various patterns [32]. In general, there are two types of CAD systems. One CAD system is for classifying all candidates into abnormal and normal candidates such as intracranial aneurysms or white matter hyperintensities in MR images, _i.e._, two-class categorization system. The other is a CAD system for classification of unknown cases into several types of abnormalities, which are more than two, _i.e._, multi-class categorization system. For instance, one CAD system would be for the  
Figure 1: An illustration of the output by a CAD system for detection of intracranial aneurysms in MRA images [25,26].  
classification of brain tumors into high grade glioma, metastatic brain tumor, low grade glioma, malignant lymphoma, _etc._  
Figure 2 shows the difference between a general pattern recognition system and a CAD system. Basically, both systems consist of preprocessing, feature extraction, and classification. However, there is one difference between the two systems. The CAD systems would suggest a \"second opinion\" to radiologists, not an output \"decision,\" which should be made by the radiologists using the CAD systems. Nevertheless, algorithms of major CAD systems have been developed by using techniques and theories developed in the pattern recognition field, and thus we believe that a computer-aided diagnosis is academically included as one of the pattern recognition fields. The techniques of the CAD systems on preprocessing, feature extraction, and machine learning classification are described in the subsequent sections.
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 1 Introduction
### Related Work
#### 1.1.1 NSS-Based Methods  
The main assumption behind NSS-based methods is that natural images of high quality have certain statistical features which lose strength in the presence of image noise and degradation. A typical NSS-based algorithm has three distinct stages, i.e., feature extraction, NSS modeling and regression [8]. A representative example of NSS-based methods was proposed by [PERSON] and [PERSON] [9]. Namely, the statistical properties of wavelet coefficients were utilized as quality-aware features using a Gaussian scale mixture model in the authors' work. Another well-known example is the blind image quality index (BIQI) [10] which works with wavelet transform to decompose an input image into three scales and orientations. Next, generalized Gaussian distributions (GGD) were fitted onto the wavelet coefficients and the mean, the variance and the shape parameters of the GGDs were considered as quality-aware features. These were mapped onto perceptual quality scores with a trained support vector regressor (SVR). NSS-based methods have been proposed in other transform domains, i.e., curvelet [11], contourlet [12], quaternion wavelet transform [13] and discrete cosine transform [14] and in the spatial domain [15], as well.
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
###### Abstract  
The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In this work, we evaluate the performance of Generic Header Compression (6 LoWPAN-GHC), defined in RFC 7400, for IEEE 802.15.4-based networks running the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Through simulation and real-device experiments, we study the impact of 6 LoWPAN-GHC on energy consumption and delays and investigate for which scenarios 6 LoWPAN-GHC is beneficial. We show that all RPL control packets are compressible by 6 LoWPAN-GHC, which reduces their transmission delay and as such their vulnerability to interference. However, for the devices under study transmitting at 250 kbit/s, the energy gain obtained from sending a compressed packet is outweighed by the energy needed to compress it. The use of 6 LoWPAN-GHC causes an energy increase of between 2% and 26%, depending on the RPL packet type. When the range is more important than the bandwidth and a sub-GHz band is used at 10 kbit/s, an energy gain of 11% to 29% can be obtained, depending on the type of RPL control packet.  
6 LoWPAN; Generic Header Compression; Internet of Things; Low-power Wireless Personal Area Networks; Modified and Improved Header Compression 20242473390/s24010073
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 3 Proposed Solution
### Hardware Architecture
#### 3.1.4 Signal Acquisition Module (ADC)  
This application requires high-accuracy measurements of the grid voltage and current values. To achieve this, we used a Texas Instruments ADS131M04 analog-to-digital converter (ADC). It features 4 input differential channels, a 24 bit resolution, programmable data rate up to 64 kSPS, and a serial peripheral communication interface (SPI). We selected this 4-channel model due to the stock shortage of the 2-channel version (ADS131M02), while also considering potential future improvements of the device. Through the SPI interface, the microcontroller sends the ADC configuration commands and reads the values of the signal samples. To signal when new conversion data are ready in the internal registers, the ADC uses an active low digital output pin. We connected this pin to a hardware interrupt-capable input pin of the microcontroller so we could read the new ADC data as soon as they are available. The converter does not incorporate an internal clock generator; therefore, an external clock generator with a frequency of 8.192 MHz was added.