esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Deregulated structures offer a competitive market environment where participating customers can interact autonomously to enhance their social welfare [1]. A peer-to-peer (P2P) structure is a local competitive market framework where customers with surplus energy produced by renewable power, such as photovoltaic (PV) solar or previously stored in a battery energy storage system (BESS) can sell it to the other consumers in the same network. These consumers with excess energy are known as _prosumers_ in the P2P network. The basic intuition of the P2P platform is to efficiently utilize the PV energy of prosumers to improve the welfare of the P2P community. In traditional vertically aligned grids, the excess PV power can only be shared with the grid, which results in a limited flexibility for the prosumers. However, with the advancements in smart grids, the prosumers can interact with their neighbors to sell their PV energy at the desired price in the form of P2P trading. The main advantages of a P2P market are competitive buying/selling rates and increased flexibility for the market participants [2; 3]. In P2P markets, prosumers can sell their PV energy at a competitive price compared to a utility feed-in tariff (FIT), whereas customers can meet their demand at a price that is lower than the utility price [4]. This sets up a competitive trading environment, where the participants can get competitive rates compared to the traditional market prices [5; 6].  
P2P markets can be set up using a centralized method, i.e., the energy transactions are monitored by a central entity such as the market operator or an aggregator, or using a decentralized method, i.e., the customers trade without any central operator overlooking the transactions [7; 8]. The formulation of the P2P network depends on the bidding strategy used for the participants and the physical constraints of the network. The proper selection of the trading strategy can ensure a positive welfare for the P2P market by giving additional monetary advantages to the participants. Contrarily, if unaccounted for, the network constraints of the system can impair the welfare of the market. Different methods have been presented in the literature to model the behavior of such markets under the influence of the physical constraints of the network [9].  
The major challenge for P2P platforms is to decide the optimal type of trading strategy based on the network parameters of the system. An improper selection of the bidding framework can impair the performance of the P2P platform under varying network parameters [10]. In this paper, we provide a comparison of two different trading strategies for the P2P platform while incorporating the network parameters and their impacts on the social-economic welfare of the market. Additionally, the reluctance-based sensitivity of prosumers towards the network parameters was developed to adjust the trading strategy of the participants based on the system parameters.
# Detection of Frozen Soil Using Sentinel-1 SAR Data
## 6 Conclusions  
This paper investigates, from a semi-empirical dielectric constant model and times series of Sentinel-1 images, the potential of the new C-band SAR Sentinel-1 sensor for classifying frozen/unfrozen soils in an agricultural context.  
When the soil temperature decreases, the liquid water content also decreases, and, consequently, the backscattered signal decreases. The results indicate that frozen soils showed a lower radar backscattering signal than the unfrozen soils by approximately 3 dB. A classification of frozen soils using Sentinel-1 images was proposed to identify frozen and unfrozen soils for all crop fields. The method uses the difference between the radar backscattering coefficient of the image acquired at a date without any freezing condition (temperature higher than 0 \({}^{\circ}\)C) and the radar backscattering coefficient of acquired image under a frozen condition (temperature lower than 0 \({}^{\circ}\)C). Simulations performed from a semi-empirical dielectric constant model show that the difference between frozen soils and unfrozen soils increases when the soil moisture content increases.  
If the soil roughness of a given field changes between the acquisition dates of the two images used, the image under a freezing condition and the image under a non-freezing condition, the detection of frozen soils can be problematic. In addition, the presence of wet snow on the image acquired under a non-freezing condition can lead to poor identification of frozen soils.  
The results of this study provide an interesting way to map frozen soils using the high spatial and temporal resolutions of Sentinel-1 sensors. The frozen soil maps are of great interest to assess damage in agricultural areas.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the results; [PERSON] wrote the article; [PERSON], [PERSON], and [PERSON] revised the paper.  
This research was supported by IRSTEA (National Research Institute of Science and Technology for Environment and Agriculture) and the French Space Study Center (CNES, TOSCA 2018). Authors thank the European Space Agency that provided the Sentinel-1 data.  
The authors declare no conflict of interest.
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 3 Background on Convolutional Neural Networks
### Training Process of CNN  
The training process optimizes different layer parameters of a neural network to minimize differences between given labels on a training dataset and the output predictions. Commonly, the backpropagation algorithm is the most used method for training a neural networks. The training process with backpropagation is as follows:  
1. Select a training dataset of images, usually taken by batch with lesser dimensions.
2. Pass each batch over the network and obtain the output.
3. Compute the error between the given labels and the output predictions by using a loss function \(L\).
4. Propagate the error throughout the network by the backpropagation algorithm.
5. Update the weights \(W\) to minimize the error.
6. Repeat until converge or reach a limit of iterations.  
To carry out previous steps and train a CNN, we must consider the following aspects:  
* Define the CNN architecture: it consists of establishing the number of layers for each corresponding type, as well as the size and number of filters for each layer. The architecture design always depends on the objective of CNN.
* Loss function: it measures the difference between the given ground-truth labels and the outputs of the network. Typically, the Mean Squared Error function is applied and it is given by: \[L=\sum{(target-output)^{2}}\] (9) Hence, \(L\) must be minimized to find the contribution of each weight and optimized them. The gradient descent algorithm is widely adopted for the minimization procedure, which is mathematically expressed as partial derivative of the loss function. Then, the parameter update process is formulated as follows [19]: \[W_{k}=W_{k-1}-\alpha*\frac{\partial L}{\partial W},\] (10) where \(\alpha\) denotes the learning rate. Thus, the learning rate is a very important hyper-parameters and must be established before starting the training process. It should be noted that a lower learning rate can give a more accurate result, but the network may take longer to train.
* Training dataset: the available data is generally divided into three subsets: a training set to train the network, the validation set to evaluate the model during the training process, and the testing set to evaluate the final trained model. Most CNN frameworks require that all training data have the same shape (i.e., dimensions). Therefore, pre-processing the data is the first step before the training process to normalize the data.  
Another important point is that the dataset should be balanced, which means the same number of images for each class. In case the dataset does not have a sufficient number of images, it is recommended to apply a Data Augmentation technique. It consists of increasing the amount of training data by performing a series of transformations, such as rotations, translations, mirroring, among others.
## 1 Introduction
[18] proposed a safety-monitoring-system framework to integrate the said Bluetooth-beacon-based location-detection technology with building information model (BIM)-based hazard identification and a cloud-based communication platform. The integrated system successfully demonstrated the capacity to detect unsafe operating conditions, as well as collect and analyze trajectories of workers with respect to potential safety hazards in buildings. [PERSON] et al. [19] investigated inconsistencies and delays occurring in the operation of Bluetooth-beacon-based PWSs owing to variations caused by different settings and environmental conditions, such as equipment types and approaching speeds. Their study also discussed parameter adjustment and the use of adaptive signal-processing methods to reduce inconsistencies in alert distances and time delays resulting from the use of different types of equipment and high approaching speeds, respectively. Although Bluetooth-beacon-based PWSs have only been recently developed and investigated for use in civil and construction industries, little attention has been paid to the possibility of employing Bluetooth-beacon-based PWSs in underground tunnels. This possibility must be investigated to extend the utility of Bluetooth-beacon-based PWSs to the mining industry. The purpose of this study was to develop a Bluetooth-beacon-based underground proximity warning system (BBUNPROWS) for use in underground tunnels. A smartphone application that provided drivers with progressive collision-risk alarms based on the status of a received signal strength indicator (RSSI) was developed. The said alarms were issued after the receipt of signals--transmitted by Bluetooth beacons attached to workers and equipment inside a tunnel--by a smartphone mounted on a moving equipment. This manuscript describes the development of the proposed BBUNPROWS along with the presentation of results obtained during field tests performed inside an underground tunnel.
## 4 Modified Topology with Harmonic Control
Among these strategies, the combination of the \(L_{0}\) transformation with the addition of the 3 rd harmonic offers the most favorable trade-off, as evidenced by the key performance metrics presented in Figure 9.7 The efficiency-bandwidth response for this final topology, in comparison to the standard Topology 2.2.2, is illustrated in Figure 11, which shows a significant improvement over the bandwidth of 30%. Additionally, the time-domain current and voltage waveforms obtained from the ADS simulation are displayed in Figure 9.8
## 4 Computational and Mathematical Aspects
### Structural Modeling Techniques for Masonry Structures
#### 4.2.1 Modeling Masonry as a Three-Phase Material (Detailed Micro-Modeling Approach)  
In this approach, the masonry constituents (units, mortar and unit-mortar interfaces) are modeled as they are in masonry wall elements. In other words, units and mortar in the joints are represented by continuum elements, whereas the unit-mortar interface is represented by discontinuous elements accounting for potential slip planes as shown in Figure 3b. Even though the accuracy of this approach is high, it needs high computational costs and time. In addition, the elastic and inelastic material properties of both masonry units and mortar and the unit/mortar interface mechanical property as well, are required to be determined by some realistic masonry sample tests. Therefore, this approach is mostly preferred for the analysis of small-scale experimental specimens and structural details to determine accurately their components' stress distribution ([PERSON] and [PERSON], 2006 [95]; [PERSON], 2001 [96]; [PERSON], 1991 [97]; [PERSON] and [PERSON], 2003 [98]; [PERSON] and [PERSON], 2006 [99]).  
Figure 3: Modeling strategies for masonry: (**a**) typical masonry specimen; (**b**) detailed micro-modeling; (**c**) simplified micro-modeling; and (**d**) macro-modeling ([PERSON], 1996 [100]).  
Recently, [PERSON] and [PERSON] (2018) [101] proposed a detailed micro-modeling approach for the analysis of masonry couplelets and prisms. In this approach, masonry constituents are represented as an assemblage of densely packed discrete irregular deformable particles bonded together by zero thickness interface elements. The advantage of this approach is that failure can occur at the masonry units, mortar and/or brick-mortar interface in a realistic manner. In this approach, the crack initiation and propagation can be traced as well.
## 3 Results and Discussion
### Mechanical Property Analysis  
#### 3.2.1 Stress-Strain Analysis  
Figure 6 presents the axial stress-strain curves of the sandstone samples under different treatment conditions. Supercritical CO\({}_{2}\) soaking and acid treatments significantly alter their mechanical strength and deformation characteristics. As observed from the graph, the axial stress-strain curves of all samples can be divided into four stages: initial pore compaction, elastic deformation, pre-peak yielding, and post-failure. The y-1 sample produces a noticeable sound upon failure, followed by a sharp drop in axial stress, indicating a clear brittle failure of the original sandstone sample. In contrast, the four groups of samples treated with the acid-supercritical CO\({}_{2}\) composite action (sc-1, sc-2, sc-3, sc-4) do not exhibit this distinct characteristic. Compared with the original sample, the peak strength and deformation of the sandstone decrease monotonically after being subjected to supercritical CO\({}_{2}\) soaking, acid treatment, and the acid-supercritical CO\({}_{2}\) composite treatment. The peak strength of the four groups of samples treated with supercritical CO\({}_{2}\) at pressures of 8 MPa to 16 MPa decrease by 1.2% to 32.7%; the two groups of samples subjected to acid treatment decrease by 20.3% to 26%, confirming that within a certain range, higher acid concentration and soaking pressure have a more pronounced effect on the strength of sandstone. However, the strength reduction in the four groups of sandstone samples treated with the acid-supercritical CO\({}_{2}\) composite is even more significant, with decreases ranging from 37.4% to 49.7% compared to the original sandstone sample. Compared to other samples, the axial stress-strain curves of sh-2, sc-1, sc-2, sc-3, and sc-4 exhibit a distinct pre-peak yielding stage, a level of deformation that falls within the category  
Figure 5: Sandstone porosity under different treatment conditions (including original sandstone, supercritical CO\({}_{2}\)-treated sandstone, acid-treated sandstone, and acidification-supercritical CO\({}_{2}\) combined treated sandstone).  
of brittle-ductile failure. This indicates that the sandstone exhibits certain ductile failure characteristics after complete acid reaction.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## Appendix B Sampling Algorithm
### Conditional Distribution of \(\tau\)  
It can be noted from (10) that the introduction of the auxiliary variable \(\mathbf{u}\) induces some difficulty in the conditional distribution of \(\tau\) due to the presence of the term \(\mathbf{u}^{\top}\mathbf{\Gamma}^{-1}\mathbf{u}\) needing the inversion of \(\mathbf{\Gamma}\). We propose to partially marginalize the joint distribution (10) with respect to \(\mathbf{u}\) in the sampling step of \(\tau\). This is equivalent to sample \(\tau\) from its conditional distribution given \(\mathbf{y}\), \(\mathbf{x}\) and \(\mathbf{\Theta}\). The latter reduces to an Inverse Gamma distribution that is  
\[\tau|\mathbf{\Theta},\mathbf{x},\mathbf{y}\sim\mathcal{IG}\left(\frac{M}{2}, \frac{\|\mathbf{H}\mathbf{\Phi}^{-1}\mathbf{x}-\mathbf{y}\|^{2}}{2}\right) \tag{16}\]
## 3 Results and Discussion
### Pathogens
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Units**} & \multirow{2}{*}{**Results**} & \multicolumn{3}{c}{**Conama 498**} & \multicolumn{3}{c}{**Norm 503**} & \multicolumn{3}{c}{**Directive 86/27S EEC**} \\  & & & **(Brasil, 2020)** (\({}^{\text{th}}\)) & & & **(U.S EPA, 1994)** (\({}^{\text{th}}\)) & & & **(European Union, 1986)** (\({}^{\text{th}}\)) \\ \hline \multicolumn{11}{c}{} \\ \multirow{6}{*}{**Viable helminth**} & & & **Class** & **Class** & **B** & **Present** & **Class** & **B** & **Present** & **Class** & **Class** & **B** & **Present** \\  & & & **A** & **B** & **Work** & **A** & **B** & **work** & **A** & **B** & **Work** \\ \hline \multirow{2}{*}{**Viable helminth**} & egg g of & \multirow{2}{*}{\(<\)0.08 \(\pm\) 0.6 g\({}^{-1}\)} & \multirow{2}{*}{\(<\)0.25 g\({}^{-1}\) of TS} & \multirow{2}{*}{\(<\)10} & \multirow{2}{*}{A} & \(<\)11 m & \multirow{2}{*}{N} & \multirow{2}{*}{A} & \(<\)11 m & \multirow{2}{*}{N} & \multirow{2}{*}{A} & \multirow{2}{*}{N} & \multirow{2}{*}{B} \\  & TS\({}^{-1}\) & of TS & TS & TS & & & & & & & & \\ \multirow{2}{*}{_Salmonella_} & g of TS\({}^{-1}\) & ND & ND & NS & A & \(<\)3 MP & \multirow{2}{*}{N} & \multirow{2}{*}{A} & \multirow{2}{*}{ND} & \multirow{2}{*}{N} & \multirow{2}{*}{A} \\ \multirow{2}{*}{_E. Coli_} & & & & & & & & & & & \\ \multirow{2}{*}{**(MPN g\({}^{-1}\) TS\%)**} & & & & & & & & & & & \\ \multirow{2}{*}{**or fecal Kilform**} & & & & & & & & & & & & \\ \multirow{2}{*}{**(USA)**} & & & & & & & & & & & \\ \multirow{2}{*}{**Enteric viruses**} & & & & & & & & & & & & \\  & & & & & & & & & & & & \\ \multirow{2}{*}{**Enteric viruses**} & & & & & & & & & & & & & \\  & & & & & & & & & & & & \\  & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Concentration of pathogenic microorganisms in biosolids in the wastewater treatment plant of the present work in comparison with the legislation of Brazil, USA, and European Union.
# How Useful Are Strain Rates for Estimating the Long-Term Spatial Distribution of Earthquakes?
## 2 Method and Data
### Data  
The target earthquakes in this study are events from 1951 to 2020 with a hypocentral depth between 0 and 40 km and a magnitude greater than 4.95 (GeoNet preferred magnitude) within the CSEP testing region [17]. The events of interest were extracted from the GeoNet catalogue of NZ earthquakes (www.geonet.org.nz; last accessed on 1 July 2022). Magnitudes assigned by GeoNet are mostly local magnitudes, but moment magnitudes are preferred for larger events. The catalogue is consistent with that used by [PERSON] et al. [10,11] within the fitting and testing periods of those studies.
### Conceptual Framework and Related Work
#### 1.2.1 Research Stream 1: Personality and Technology Acceptance
The major conclusion they draw is that \"[f]rom the literature review, it can be observed that there are mixed results with respect to the influence of gender on technology adoption [...] while in few contexts, gender plays a significant role in determining the intention of accepting new technology, there are cases where gender differences cannot be discerned\" [28] (p. 56). In a pioneering study, [PERSON] and [PERSON] [18] examined sustained usage of technology in the workplace. Technology usage behavior was investigated over a five-month period among workers being introduced to a new software system. Results indicate that at all three points of measurement, compared to female users, the technology usage decisions of male users were more strongly influenced by their perceptions of the system's usefulness for task execution. In contrast, female users were more strongly influenced by ease of use perceptions and subjective norm perceptions (note that the effect of subjective norm diminished over time). Similar results are reported in a recent study by [PERSON], [PERSON], and [PERSON] [29], who studied acceptance of web-based learning systems. This study found that the influence of perceived ease of use and social norm on behavioral intention to use a system was stronger for women than for men. However, unlike [PERSON] and [PERSON] [18], this study did not find a significant moderating effect of gender on the relationship between perceived usefulness and behavioral intention to use the system. [PERSON] and [PERSON] [30] empirically investigated gender differences with respect to beliefs and use of e-mail. Findings indicate that men and women differed in their perceptions, but not use, of e-mail (use was measured by the number of sent and received messages). A major implication of this finding is that managers \"need to realize that the same mode of communication may be perceived differently by the sexes, suggesting that more favorable communications environments might be created, environments that take into account not only organizational contextual factors, but also the gender of users\" [30] (p. 389).
## 3 Methodology
### Error Analysis and Anomaly Detection
#### 3.4.2 Anomaly Detection Process  
The main detection process of the prediction-based anomaly data detection method proposed in this paper is as follows:  
1. First, the dataset is divided into a training set and a test set in a certain proportion;
2. The temporal features of the original traffic flow data are extracted using Bi-LSTM; the original data are constructed into graph data with spatial correlation using adjacency matrix, and the spatial correlation of the graph data is extracted by GAT;
3. The raw traffic flow data is subjected to FFT to transform the signal into the frequency domain space, and the Transformer model is used to learn the magnitude and phase features in the frequency domain;
4. Fully learn the time and frequency domain characteristics of the data to construct predictive models;
5. Based on the observed data at each node at historical time \(K\), predict the traffic flow at that node at time interval \(H\);
6. Analyze the error \(e_{t}\) between the observed value and the \(x^{t}\) predicted value \(\hat{x}^{t}\), determine the threshold of the outlier according to Chebyshev's inequality, and determine whether the error \(e_{t}\) falls within the threshold range to detect whether the data at the point is anomalous data or not.