esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 E-Consultation on the Spatial Development of the Tourist Sector in Greece
### E-Consultation Results
* _Level of intervention_, where comments are classified according to whether they proceed to certain policy recommendations, distinguishing between comments that proceed to general or concrete recommendations (91 percent) and comments that remain only to the criticism of the proposed policy reform (nine percent) (Figure 6d). * _Origin of participants_: most participants have not declared their place of origin (56 percent). From those who have (44 percent), the majority had as place of origin the regions of \"Central Greece\" and \"Attica\" (Figure 6e). * _Region-specific comments_: the majority of comments (71 percent) were not region-specific. From the rest, the region that was most often referred was the region of \"Central Greece\" (15 percent of comments) (Figure 6f). Moreover, the _impact_ of public engagement on the decision-making process is studied, where in an article by article approach, is presented the way from the draft to the final decision. More specifically, in Table 2 are presented the changes that were made from the draft to the final policy text, among which are:  
* Changes that are proposed merely by participants and are adopted by decision makers in the final policy decision (green color in Table 2);
* Changes that are proposed by both participants and policy makers (yellow color in Table 2); and
* Changes that are proposed merely by policy makers (orange color in Table 2). * By elaborating on the outcome of the participatory process presented in Table 2, it is obvious that there is a certain _impact_ of the views of participants on the final policy statement (green and yellow areas). By further inspecting the areas of impact, it is revealed that these are mostly focusing on the _objectives_ as well as the _spatial implications_ of the proposed policy reform. It should be noted that this conclusion is also supported by the elaboration of participants' comments in respect of the final legislation, where almost half of them (51%) have had certain impact on the way respective articles of the SF-T are articulated (fully adopted comments: 10% and loosely adopted comments: 41%). * Finally, the correlations among the specific attributes of e-consultation were explored by a bivariate correlation analysis, in which 272 relations between 17 variables were searched with the usage of Pearson correlation coefficient (_r_) and two-tailed test of significance. The result of this analysis is 15 different, statistically significant, correlations between variables (Table 3). The most important finding coming out of this elaboration is that comments of participants of a certain origin are mainly spatially delimited to this specific region of origin.
# Fast Reset Protocol for Superconducting Transmon Qubits
## 3 Results
### Summary of the Protocol  
The qubit reset scheme is based on the scalable superconducting quantum chip architecture [23; 24; 25; 26; 27; 28; 29; 30], where two superconducting qubits are connected to a frequency-tunable coupler, as shown in the Figure 2a. For simplicity, we consider only the circuit model of a single qubit. As sketched in Figure 2b, a frequency-tunable coupler (c) and a readout resonator (r) couple to a superconducting transmon qubit (q) with coupling strengths \(g_{cq}\) and \(g_{q\prime}\), respectively. In general, assuming \(\hbar=1\), the Hamiltonian of this circuit model is  
\[\begin{split} H_{0}&=\sum_{i\in\{c,r\}}\omega_{i}a_ {i}^{\dagger}a_{i}+\frac{1}{2}a_{c}^{\dagger}a_{c}^{\dagger}a_{c}a_{c}+\frac{ \eta_{q}}{2}a_{q}^{\dagger}a_{q}^{\dagger}a_{q}a_{q}\\ &\quad+g_{cq}\left(a_{c}^{\dagger}a_{q}+a_{c}a_{q}^{\dagger} \right)+g_{q\prime}\left(a_{q}^{\dagger}a_{r}+a_{q}a_{r}^{\dagger}\right),\end{split} \tag{6}\]  
Figure 1: (**a**) Simplified system diagram of the frequency-tunable coupler. (**b**) All capacitors and inductors in the circuit form an impedance network \(Z(\omega)\). (**c**) The impedance network \(Z(\omega)\) is decomposed into three series LC circuits by the Foster theorem and the two-port network theory.  
where \(a_{i}\) (\(a_{i}^{\dagger}\)) is the corresponding annihilation (creator) operator, \(\omega_{i}\) is the bare frequency of the corresponding mode and \(\eta_{c}\) and \(\eta_{q}\) are anharmonicity of a coupler and a qubit, respectively.  
Based on this circuit model, we introduce how to realize our superconducting qubit scheme. Here, we apply a mixing microwave pulse to the superconducting qubit. With the rotating wave approximation, the driving Hamiltonian is  
\[H_{d}=\epsilon(t)\Big{(}e^{i\omega_{d}t}a_{q}+e^{-i\omega_{d}t}a_{q}^{\dagger} \Big{)}, \tag{7}\]  
where \(\omega_{d}\) and \(\epsilon(t)\) are the frequency and the amplitude of the mixing microwave pulse, respectively. For simplicity, we only consider the case where the pulse amplitude is constant--that is, \(\epsilon(t)=\epsilon\). When the frequency \(\omega_{d}\) meet the condition of \(\omega_{d}=\omega_{c}+\omega_{r}-\omega_{q}\), the effective Hamiltonian will be in the needed qubit reset form as  
\[H_{t}=G\Big{(}a_{c}a_{q}^{\dagger}a_{r}+a_{c}^{\dagger}a_{q}a_{r}^{\dagger} \Big{)}, \tag{8}\]  
where \(G\) is the coupling strength. For the superconducting qubit in \(|e\rangle\) state, the state of this system could be transfer \(|0e0\rangle\) state to \(|1g1\rangle\) state under the action of Equation (8). Finally, the decay (the rate labeled as \(\kappa_{c}\) and \(\kappa_{r}\)) of the coupler and the readout resonator is used to dissipate the system to \(|0g0\rangle\) state, thereby realizing the reset of the superconducting qubit, as described in Figure 2\(c\).
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Fuel Cell Powered UAVs
#### 2.3.2 Fuel Storage  
Hydrogen has a low density at standard temperature and pressure, which poses a challenge for storing a sufficient amount of fuel for UAV missions. The low density means that hydrogen tanks need to be bulky to accommodate the required amount of fuel, which can impact the size and weight of the UAV. In addition to the size and weight considerations, safety is also a significant concern when it comes to storing hydrogen. Pure hydrogen cannot be stored under extremely high pressure and low temperatures due to safety reasons. Therefore, alternative techniques are employed for hydrogen storage in UAVs.  
The three main techniques currently used for hydrogen storage in UAVs are as follows:  
1. **Compressed hydrogen gas:** Hydrogen gas is stored in tanks under high pressure. This method allows for easier storage and refueling compared to other techniques. However, it requires high-pressure tanks, which can add weight and volume to the UAV.
2. **Liquid hydrogen:** Hydrogen is stored in a liquid state at very low temperatures. This method provides a higher energy density compared to compressed gas storage. However, it requires specialized cryogenic storage systems and insulation, which can add complexity and weight to the UAV.
3. **Chemical hydrogen generation:** Hydrogen is generated onboard the UAV through chemical reactions, such as the reaction between a metal hydride and water. This method offers the advantage of generating hydrogen as needed, eliminating the need for storing large quantities of hydrogen. However, it requires additional components and can have limitations in terms of hydrogen generation rate.  
Table 4 in the referenced study likely provides a detailed analysis of the advantages and drawbacks of each hydrogen storage technique in UAV applications, allowing for a comprehensive comparison and evaluation of the different options.  
\begin{table}
\begin{tabular}{c c} \hline
**Compressed H2** & **Liquid H2** \\ \hline \hline \multirow{7}{*}{Properties} & Hydrogen has a density of approximately 71 kg/m3 at 1 bar and a temperature of \(-\)252.87 \({}^{\circ}\)C, which is extremely low. To store hydrogen in its liquid form, a thermal insulation system is necessary to maintain such low temperatures. \\  & The most commonly used method for hydrogen storage in UAV applications is \\  & high-pressure storage. Hydrogen is stored at pressures ranging from 35 to 70 megapascals (MPa). At these high pressures, the density of hydrogen increases, reaching approximately 42 kg/m3 at 70 MPa \\ \end{tabular}
\end{table}
Table 4: Comparison between compressed and liquid hydrogen storage [80,89].
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 2 The NOMA Link-Level Simulator
### Receiver Design
#### 2.4.3 Code-Word-Level SIC  
The only difference between symbol-level SIC and code-word-level SIC is channel encoding and decoding at the transmitter and receiver sides, respectively. Code-word-level SIC involves channel coding after the modulation process before SC, whereas at the receiver side, channel decoding is performed before the demodulation process. Therefore, channel encoding/decoding results in a drastic reduction in the probability of error at the expense of an increase in system complexity. The perfect SIC provides the upper bound limit as a reference to compare the other extracted results from each new idea presented by the researchers. Therefore, the complexity of the perfect SIC is always greater than both the code-word-level and symbol-level SIC because the receiver knows the channel statistics to fully subtract from the received signal.
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 2 Methods and Data
### Methods
#### 2.6.2 K-Nearest Neighbor (KNN)  
KNN algorithms are supervised ML algorithms; however, they are also called lazy algorithms because they do not require learning [47]. KNN can be used to handle regression and classification issues. KNN computes the k nearest samples utilizing the distance between samples and uses their value to predict the value of the desired selection [75]. These k samples are most similar to the sample examined. Once the method has selected the k nearest samples, it may simply output a weighted sum of their values as the model's prediction for the target sample [75]. KNN's drawbacks include the necessity for extensive calculation and the requirement for a large memory [76]. The distance formula in KNN is as follows.  
\[L_{p}\big{(}x_{i},x_{j}\big{)}=(\sum_{l=1}^{n}[x_{j}^{l}-x_{j}^{l}]^{P})^{ \frac{1}{p}} \tag{5}\]  
where \(p=2\), the Euclidean distance used in this study.  
Figure 4: Flow chart for detecting flood points in Tarim city using Sentinel-I data.
# Dimensional Reduction-Based Moment Model for Probabilistic Slope Stability Analysis
## 5 Applications  
In this section, the proposed method was applied to two slopes. The first slope is an infinite soil slope with an analytical solution [42]. The limited equilibrium method was used to compute the factor of safety. Reliability index and failure probability were estimated using MDRM and FOSM. The results were compared with the results of MCS. In general, it is difficult to obtain an analytical solution for practical slopes. Therefore, the second slope is a soil slope with no analytical solution. The strength reduction method was adopted to compute the factor of safety [43]. The reliability index was computed by the proposed method.  
Figure 1: The reliability analysis of slope based on MDRM.
## 1 Introduction
Researchers can make detection models pay more attention to critical features more effectively by adding attention mechanisms to the backbone networks. However, commonly used channel or spatial attention mechanisms can only capture local information effectively without establishing functional dependency between space and channels, making it challenging to use the global information of forged images entirely. In this paper, the training data and model structure are combined to design an algorithm based on an adversarial dual-branch data augmentation framework and the improved ResNet50 [7], embedded with a modified attention mechanism module to improve the robustness of detection models. The main work and contributions are as follows:  
1. The performance of detection models deteriorates when presented with corrupted images. To mitigate this problem of feeble model robustness, we have introduced two data augmentation frameworks--Augmix [8] and Augmax [9]--to enhance and expand deepfake datasets in the preprocessing stage, respectively. The Augmax  
Figure 1: Face forgery classification: (**a**) Original real face image; (**b**) Face identity replacement; (**c**) Face expression reenactment. framework amalgamates Project Gradient Descent (PGD) [10; 11] with the Augmix framework, engendering more diverse and challenging exemplars. 2. In the training phase, we have designed and incorporated a novel attention mechanism module, designated MSE, by modifying and integrating Squeeze-and-Excitation Networks (SENet) [12] into ResNet50, thereby deriving an enhanced model (i_ResNet50) as the backbone. This module augments the model's capability to learn essential information in spatial and channel dimensions across multi-scale feature maps, enabling more efficacious mining of forgery traces in images. 3. We have deployed the cosine annealing algorithm to modulate the learning rate during training and expedite the model's convergence. 4. In the integrated decision-making phase, we have formulated a multi-branch loss function and introduced the Jensen-Shannon [13] divergence loss to improve the correlation between standard and augmented images. The structure of our treatise is organized as follows. Section 2 reviews the extant literature on deepfake detection algorithms, highlighting limitations that motivate our proposed approach. Section 3 delineates our method's underlying principles and implementation details, encompassing data augmentation during preprocessing and model enhancement during training.. Section 4 provides specifics on the experimental setup, datasets, and results that validate the improved robustness of our deepfake detector. Section 5 concludes with a summary of contributions and suggested directions for future inquiry to advance the field.
## 3 Results
### Microwave-Assisted Extraction Optimization Results
#### 3.1.3 Sample Mass  
For the next optimization experiments, the previous factors that proved to be appropriate for extraction were employed, and tests were performed with the amount of apple tissue reduced to 5 g. Figure 3 indicates that the difference in the obtained recoveries when using 5 and 10 g of sample is statistically insignificant, in most cases. In particular, the RE% of 38% of the compounds increased significantly when the sample mass was reduced to 5 g. The recoveries of abametcin, deltamethrin, flonicamid, tau-fluvalinate, thiabendazole, and thiamethoxam increased and were included in the acceptable values (>88%). The remaining 62% of the compounds showed slight differences which, according to the statistical processing, were considered negligible. Thus, subsequent experiments were conducted using 5 g sample mass.  
Figure 2: Percentage of pesticides and mycotoxins with acceptable recoveries in the range 70–120% when using acetonitrile and ethyl acetate as extraction solvents, at different temperatures (60, 80, 100 \({}^{\circ}\)C).
## 2 Material and Method
### CartoDEM V3 R1  
CartoDEM is a national DEM established by the Indian Space Research Organization (ISRO). CartoDEM version 3 release 1 is the latest version derived from the Cartosat-1 satellite sensor and is the first Indian remote sensing (IRS) satellite capable of acquiring high-resolution stereo imagery with 2.5 m pixel size. It was primarily designed for topographic mapping and the creation of digital terrain models [8; 9; 10]. CartoDEM V3 R1 comes with a spatial resolution of 1 arc-second (30 m) with 16 bits per pixel and WGS84 datum.
# Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks
###### Abstract  
Because of the increase of the data traffic demand, wireless access networks, through which users access telecommunication services, have expanded, in terms of size and of capability and, consequently, in terms of power consumption. Therefore, costs to buy the necessary power for the supply of base stations of those networks is becoming very high, impacting the communication cost. In this study, strategies to reduce the amount of money spent for the purchase of the energy consumed by the base stations are proposed for a network powered by solar panels, energy batteries and the power grid. First, the variability of the energy prices is exploited. It provides a cost reduction of up to 30%, when energy is bought in advance. If a part of the base stations is deactivated when the energy price is higher than a given threshold, a compromise between the energy cost and the user coverage drop is needed. In the simulated scenario, the necessary energy cost can be reduced by more than 40%, preserving the user coverage by greater than 94%. Second, the network is introduced to the energy market: it buys and sells energy from/to the traditional power grid. Finally, costs are reduced by the reduction of power consumption of the network, achieved by using microcell base stations. In the considered scenario, up to a 31% cost reduction is obtained, without the deterioration of the quality of service, but a huge Capex expenditure is required.  
wireless access network; energy price; demand and response; energy efficient; power consumption; LTE-Advanced; power grid; renewable energy; solar energy; energy storage 2018  
I1, 617; doi:10.3390/en11030617
## 2 Materials and Methods
### Improved Detection Setup  
As each combination of the input feature type and the measurement position (and thus the corresponding NIR sub-dataset) leads to a different configuration of the CNN model, we only present the process of constructing and evaluating the CNN model, which achieved the highest classification accuracy to make this article concise. Figure 3 describes the proposed CNN architecture in this case. The model includes one input layer, which contains 456 neurons as input data, representing the feature vector of size \(456\times 1\), which is of the type \"prep + der1\" (i.e., pre-processed spectrum concatenated with its first derivative). It consists of two convolutional layers, each of them followed by a pooling layer. The convolutional layers have kernels of size \(8\times 1\). They are alternated with two max-pooling layers with the pool size \(2\times 1\) and Rectified Linear Units (ReLUs) as the activation functions. The output of the final max pooling layer is streamed through a flattened layer in order to convert multi-dimensional data into one-dimensional data, which are then entered into the two fully connected (i.e., dense) layers. The first dense layer consists of eight neurons and a ReLU activation function. A dropout layer is placed before the two dense layers. Finally, the last dense layer contains two neurons where softmax classifier activation is used to predict the output (i.e., the safety label) of the model. The proposed CNN model consists of 7482 parameters.  
The training process of this model was implemented using the Keras framework with the Adam optimizer and the initial learning rate at 0.001. The learning rate was set to be reduced by a factor of 0.8 when the training result was not progressing. The validation set was used to stop the training process. Given the substantial parameter count associated with the initial CNN architecture and the limited availability of training samples, the issue of overfitting emerged as a significant concern. Consequently, the incorporation of a dropout layer was deemed necessary in order to mitigate this challenge effectively.  
Figure 4 shows how the cross-entropy-based loss function of the CNN model varied on the training and validation sets over training epochs. We stopped the training process after 50 epochs to prevent overfitting since the model had their losses converged on the validation set at this point.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Type** & **Vector Size** & **Description** \\ \hline orig & \(228\times 1\) & Original spectrum \\ prep & \(228\times 1\) & Pre-processed spectrum \\ der1 & \(228\times 1\) & 1 st derivative of pre-processed spectrum \\ der2 & \(228\times 1\) & 2 nd derivative of pre-processed spectrum \\ prep + der1 & \(456\times 1\) & Pre-processed spectrum + its 1 st derivative \\ prep + der2 & \(456\times 1\) & Pre-processed spectrum + its 2 nd derivative \\ \hline \hline \end{tabular}
\end{table}
Table 1: Six feature types of the NIR spectrum.
## 3 Results and Discussion
### Experimental Results
#### 3.2.3 Rear Interface Reflectivity  
Eventually, the OFI signal simulations and measurements are performed in the DF channels with and without the additional gold coating on the rear channel interface. The simulated OFI signal spectra in the two channels are plotted (red--without coating and blue--with coating) in Figure 7a while measured ones are plotted with the same color code in Figure 7b. As can be seen, simulated and measured spectra have clearly a different behavior in the low frequencies as the measurement exhibits a regular increase of the power at low frequencies. We believe this effect to be the result of unwanted vibrations induced fringes that are emphasized considering the higher reflectivity of the gold coating. However, a similar trend can be observed in the measured and simulated OFI signals: Both spectra have a visible lower frequency peak _fd_1 = 4 kHz that corresponds to the reflection induced feedback-power enhancement as depicted in Figure 4b,d. Moreover, the magnitude difference between the peak at _fd_1 obtained with the gold coating and the one observed without coating is of 15 dB in the simulated spectra and 13 dB higher in the measured ones, indicating a consistent agreement, proving the essential role of the rear interface reflectivity.