esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Security Proof and Analysis
### Security Proof
Otherwise, \(\mathcal{C}\) generatesa random \(h_{2,i}=H2(PID_{i}\|L_{i}\|\ T_{t}\|R_{i}\|PK_{i}\|M_{i})\), inserts \(L_{H2}:(PID_{i},L_{i},T_{t},R_{i}.PK_{i},M_{i},h_{2,i})\) into the list and yields \(h_{2,i}\) to \(\mathcal{A}\). _Sign Oracle_: When \(\mathcal{A}\)'s query with message \(M_{i}\) and pseudo identity \(PID_{i}\) is received, \(\mathcal{C}\) checks if \((sk_{i},L_{i},T_{t},h_{0,i})\) and \((T_{t},h_{1,i})\) already exist in \(L_{H0}\) and \(L_{H1}\), respectively. \(\mathcal{C}\) gains \(h_{0,i}\) from \((sk_{i},L_{i},T_{t},h_{0,i})\) and \(h_{1,i}\) from \((T_{t},h_{1,i})\). Otherwise, \(\mathcal{C}\) selects three random numbers \(\sigma,w_{i},PID_{i}\in Z_{q}\), where \(PID_{i}=f1(h_{0,i},h_{1,i})\), \(\sigma=f2(w_{i},PID_{i})\). Then \(\mathcal{C}\) sends \(\{M_{i},T_{t},PK_{i},R_{i},PID_{i}\}\) to \(\mathcal{A}\). It is feasible to verify that \(\sigma\cdot P=PK_{i}+w_{i}\cdot R_{i}\) holds. According to the Forking Lemma, assuming that \(\mathcal{A}\) has produced two valid signatures, we have \(\sigma\cdot P=PK_{i}+w_{i}\cdot R_{i}\) and \(\widetilde{\sigma}\cdot P=PK_{i}+\widetilde{w}_{i}\cdot R_{i}\). To violate \(\sigma\), \(\mathcal{A}\) will perform the following steps. \[ \begin{split}(\sigma-\widetilde{\sigma})\cdot P&= \sigma\cdot P-\widetilde{\sigma}\cdot P\\ &=(PK_{i}+w_{i}\cdot R_{i})\cdot P-(PK_{i}+\widetilde{w}_{i} \cdot R_{i})\cdot P\\ &=w_{i}\cdot Ri\cdot P-\widetilde{w}_{i}\cdot Ri\cdot P\\ &=(w_{i}-\widetilde{w}_{i})\cdot r_{i}\cdot P^{2}\end{split}  \tag{5}\]  
By calculating \((\sigma-\widetilde{\sigma})((w_{i}-\widetilde{w}_{i})\cdot P^{2})^{-1}\), \(\mathcal{C}\) demonstrates that \(\mathcal{A}\) has effectively resolved the ECDLP problem within a polynomial time, thereby contradicting the principles defined in Definition 1. As a consequence, we reach the conclusion that the communication within our scheme remains impervious to adaptive chosen message attacks when operating within the random oracle model.
# Tampered and Computer-Generated Face Images Identification Based on Deep Learning
###### Abstract  
Image forgery is an active topic in digital image tampering that is performed by moving a region from one image into another image, combining two images to form one image, or retouching an image. Moreover, recent developments of generative adversarial networks (GANs) that are used to generate human facial images have made it more challenging for even humans to detect the tampered one. The spread of those images on the internet can cause severe ethical, moral, and legal issues if the manipulated images are misused. As a result, much research has been conducted to detect facial image manipulation based on applying machine learning algorithms on tampered face datasets in the last few years. This paper introduces a deep learning-based framework that can identify manipulated facial images and GAN-generated images. It is comprised of multiple convolutional layers, which can efficiently extract features using multi-level abstraction from tampered regions. In addition, a data-based approach, cost-sensitive learning-based approach (class weight), and ensemble-based approach (eXtreme Gradient Boosting) is applied to the proposed model to deal with the imbalanced data problem (IDP). The superiority of the proposed model that deals with an IDP is verified using a tampered face dataset and a GAN-generated face dataset under various scenarios. Experimental results proved that the proposed framework outperformed existing expert systems, which has been used for identifying manipulated facial images and GAN-generated images in terms of computational complexity, area under the curve (AUC), and robustness. As a result, the proposed framework inspires the development of research on image forgery identification and enables the potential to integrate these models into practical applications, which require tampered facial image detection.  
Keywords:image manipulation; deep learning; imbalanced dataset; image forgery detection; GAN; tampered image +
Footnote †: journal: Applied Sciences
## 2 Related Work
In closed-loop, the BS directly controls the phase alignment among the modes by measuring a function of the received phases of the mode transmissions and then giving feedback to them in order to compensate for its overall phase offset. Nevertheless, in open loop, the modes interact among themselves with only minimal signaling from the BS. Rather than providing feedback to be used for adapting the motes' phases, the destination may simply broadcast an unmodulated sinusoidal beacon to the WSN. The motes use this beacon, as well as the signals from neighboring motes, to achieve appropriate phase compensation for beamforming to the BS. The emphasis of open-loop systems is on using local interactions between the sources to minimize interaction with the distant BS. It should be noted that similar phase synchronization algorithms are shown in [3; 7]. However, always mindful of what really matters, we must consider that the BS might be far from the WSN and bidirectional communications might not feasible when using CB. In other words, CB could be achieved to transmit to the BS but could not receive from the BS. CB is simplex. Then, the above approaches (open and closed phase synchronization methods) are not feasible in practice, and they require a further analysis. As seen from previous works, they are based on theoretical issues and our goal is to analyze its feasibility from a practical point of view. Thus, to do collaborative beamforming in real scenarios, we will consider in the following sections the requirements based on commercial motes and their constraints.
## 3 Materials and Methods
### Hand-Crafted Feature Extraction
#### 3.6.4 Integration with Deep Features  
To improve classification performance, hand-crafted features (Gabor, GLCM, HSV) were fused with deep CNN-based features. This integration combines the discriminative power of deep models with the interpretability of handcrafted descriptors. The fusion process is explained in Section 3.7.  
This multi-feature approach enhances the system's robustness by leveraging spatial, textural, and color-based information--particularly useful for distinguishing different levels of pest-induced damage in maize leaves.
# Enhancing Traceability of Italian Almonds through IR Spectroscopy and Chemometric Classifiers
## 1 Introduction  
Almonds are the edible seed of the _Prunus Amygdala_ tree, a plant native to Asia Minor which, over the years, has spread throughout the Mediterranean basin, the United States of America, and Asian countries. In Italy, almond cultivation covers an area of 37,000 hectares, with plantations mostly located in Apulia (South of Italy), Calabria (South of Italy), and Sicily (South of Italy), even though almonds can be successfully grown in any Italian region [1]. This fruit is highly nutritious for humans and has found applications in traditional medicine, demonstrating the interest in this nut for the treatment of numerous diseases. Furthermore, _Prunus amygdalus_ is widely used in the food industry. The high global consumption of almonds is attributed to their versatility and nutritional value. Almonds play a significant role in the confectionery and baking industry, being utilized in a wide range of products as sources of essential nutrients such as lipids, proteins, vitamins, fiber, and minerals like calcium, iron, magnesium, phosphorus, and potassium. In addition, they have anti-inflammatory properties and promote cardiovascular health, helping to reduce cholesterol [2]. Thanks to these health benefits, almonds are becoming increasingly popular.  
As for many other agro-food products, the chemical and organoleptic properties are strictly linked to the harvesting area, and, therefore, it is important to ensure the authenticity and geographical origin of these nuts [3; 4; 5]. Given the current scenario, the foundation of this research rests upon the imperative to effectively tackle the mounting economic pressures linked to the export of Italian almonds, as demonstrated by the increase in prices per ton of unshelled almonds reported by the Chamber of Commerce of Bariin November 2023, amounting to EUR 3600.00 per ton [6]. Simultaneously, it seeks to safeguard the essence and integrity of this renowned product from any potential threats of food adulteration [7]. Consequently, the primary objective of this study is to formulate a comprehensive analytical methodology dedicated to the determination of the geographical origin of Italian almonds.  
In light of this, attention was firmly fixed on three cultivation regions situated in South Italy, namely, Apulia, Calabria, and Sicily. These regions boast a rich heritage and longstanding tradition in the cultivation of almonds. In order to accomplish this aim, the potential of infrared spectroscopy in addition to advanced chemometric classifiers was explored [8]. This approach was chosen owing to its demonstrated efficacy in analogous investigations [9; 10; 11; 12; 13; 14; 15], rendering it a fitting choice for our research endeavors.
###### Abstract
[30] proposed a model order reduction method based on POD technology to improve the efficiency of dynamic response analysis of stiffened shells, and a global order reduction method and a model iterative update method were used to optimize the design of complex stiffened thin-walled structures for vibration reduction. Most studies on model order reduction methods based on POD technology are limited to a single configuration. When the design configuration changes, the reduction basis needs to be reconstructed. However, if the reduced basis matrix is repeatedly constructed for any configuration during the optimization design, it would undoubtedly cost substantial computation. This paper aims to construct a global POD ROM that meets the global accuracy of the design space in the offline stage to greatly improve the efficiency of structural frequency response analysis and optimization. This paper is arranged as follows: the frequency response analysis method of structures is introduced in Section 2. The process of constructing the global POD ROM is described in Section 3. Then, Section 4 presents the framework of vibration reduction optimization approach based on the global POD ROM. Next, two examples of vibration reduction optimizations for an S-shaped curved stiffened shell structure and a PAF are investigated in Section 5. Finally, the conclusions and prospects of this paper are given in Section 6.
## 3 Materials and Methods
### Questionnaire  
It is necessary to reasonably estimate the emission reduction effect of its usage phase. Therefore, the goal of this study was to investigate what pattern of FFBS is applied in a trip chain, and what kind of transport mode is usually abandoned by users to ride FFBS. The questionnaire begins with the two key questions: \"What is the last usage pattern of free-floating bike sharing, directly for short-distance trip (Pattern 1) or combined with bus/metro (Pattern 2)?\" and \"If FFBS was not available, which transport modes would you have used primarily for this trip?\" The first question requires respondents to choose whether to use FFBS on Pattern 1 or Pattern 2. According to the respondent's choice, it jumps to the corresponding questions to collect the respondent's choice of the alternative transportation modes for the trip chain under the specific usage pattern. The emission reduction effect of FFBS is calculated more accurately through considering the trip chain of users.  
There are some standard questions about respondents' personal socio-economic attributes (such as age, gender, occupation, income, and education) and trip characteristics (trip purpose, trip origin, and destination type). Additionally, we weighted the results according to the FFBS usage frequency of each respondent to measure the differences between individuals. The answers to the following questions will be used in this paper.  
Q1: How often do you use the FFBS?  
Q2: What was the last usage pattern of FFBS, directly for short trips or for connecting public transport?  
Q3: What was the distance for the last time you use FFBS (one-way, in km)?  
Q4: What was the duration for the last time you use FFBS (one-way, in minutes)?  
Q5: If FFBS were not available, which mode have you used primarily for this short-distance trip instead of using FFBS directly (Pattern 1)? If you use this other transport mode, how long would it have taken you to reach the destination on the same route?  
Q6: If the last FFBS trip was used to connect the bus/metro, what kind of public transport do you connect to? And how much time was spent on the bus/metro (one-way, in minutes)?  
Q7: If you could not use FFBS to connect the bus/metro (Pattern 2), what traffic mode would you have chosen for this trip? And if you used this other traffic mode, how long would it have taken you to reach the destination on the same route (one-way, in minutes)?  
Considering the universality and convenience of the survey, this survey was achieved online in nearly an eight-week period in April and May 2021 in Nanjing through sample service provided by the survey platform. The temperature varied from 15 to 30 degrees Celsius during the survey period, and most of the weather was cloudy, which was suitable for riding FFBS.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 3 Results
### Spatial Autocorrelation in Residuals  
We used Global Moran's I to detect spatial autocorrelation in the prediction errors of all models to test if the models capture spatial heterogeneity [91,92]. Table 3 depicts the results of the Moran's I analysis. In terms of spatial autocorrelation, the residuals of PLSR and SVR model are more clustered when compared with other non-spatial machine learning methods. However, GWRFR outperforms other models with the lowest Moran's I value [11,93]. This means the errors are less clustered. Figure 10 illustrates the spatial distribution of prediction errors for all six models. The prediction errors of GWRFR are more randomly distributed as compared to non-spatial models. Smaller predictions errors are observed in the models trained with full-length features compared with those trained with other sets of features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Moran’s I** & **Z Score** & \(p\)**-Value** \\ \hline MLR & 0.277 & 21.28 & 0.00 \\ PLSR & 0.295 & 21.34 & 0.00 \\ SVR & 0.295 & 22.60 & 0.00 \\ DTR & 0.277 & 21.38 & 0.00 \\ RFR & 0.269 & 20.61 & 0.00 \\ GWRFR & 0.139 & 10.68 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Moran’s I value of residuals for MLR, PLSR, SVR, DTR, RFR, and GWRFR.  
Figure 9: The scatterplots of actual yield and predicted yield derived from (**a**) MLR, (**b**) PLSR, (**c**) SVR, (**d**) DTR, (**e**) RFR, and (**f**) GWRFR using only soil data.
# Research on Tracking Technique Based on BPSK-CSK Signals
## 3 Results
### BER Comparison and Analysis
Table 3 gives the specific ratio values of the BERs of Method 2 and Method 3 with respect to Method 1, from which it can be seen that the BER of the CSK-modulated signal is much higher than that of the conventional BPSK-modulated signal when a is 10, while the BER of the BPSK-CSK-modulated signal is improved. \begin{table}
\begin{tabular}{c c c} \hline \hline
**CNR/(dB-Hz)** & \
\begin{tabular}{c} **Method 3 Higher than** \\ **Method 1/\%** \\ \end{tabular} &
\begin{tabular}{c} **Method 2 Higher than** \\ **Method 1/\%** \\ \end{tabular} \\ \hline
44 & 25.43 & 3.85 \\
45 & 21.12 & 3.11 \\
46 & 17.56 & 2.31 \\
47 & 13.31 & 1.52 \\
48 & 10.54 & 1.15 \\
49 & 7.27 & 0.78 \\
50 & 4.15 & 0.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 7: BER varies with CNR when Doppler is constant.
## 3 Results and Discussion
### Test Results with Hybrid Paper-Based Sensors
The recorded courses, presented in Figures 11 and 12, are characterized by limited repeatability and stability. It can be attributed to the realization of experiments involving a human being whose presence introduces additional, unexpected disturbances. As a result, conditions close to those found in everyday life are obtained. The proper interpretation of the observed effects and differences requires using signal analysis with the application of advanced mathematical tools, such as artificial neural networks, and it will be the subject of future authors' works. At the present research stage, it is still rather early to distinguish the most suitable solution in terms of the content of the electrically conductive material. It is primarily dictated by the intended application of the sensor and the associated type and magnitude of interacting forces. The absence of the electroconductive material or its use in a pure, homogeneous form leads to a lack of response in the measurement system. It can therefore be assumed that there is a certain optimum amount of AgNWs for a given mechanical interaction and application. The use of CNTs did not show an additional improvement in sensor properties. Nevertheless, it can be expected that the AgNWs-CNTs composition can provide a long life cycle when using such a material, as suggested by other authors [23]. Figure 11: Effect of selected vital activities: (**a**) cyclic bending of the finger (sample #2), (**b**) blowing on the flat sensor (sample #5) surface by the test participant.
## 3 Materials and Methods  
### Experimental Design and Data Collection  
Two identical aquaponics testbeds were designed and installed for the experiment: a standard aquaponics testbed and a vermiponics testbed, both with 12 g of red tape earthworms added onto the growbeds. Both testbeds have the same specification with 100 L fishtanks and were equipped with artificial LED lights. A total of 6 Swiss chard plants and 6 cold-water carp fish, scientifically known as _Cyprins carpio_, were chosen to grow on both testbeds. Fish water was recirculated on both systems every 30 min. Initially, the experiment was run without replenishment for 91 days. Then, water replenishment of 19 L in 100 L tanks of both testbeds were performed weekly, for the next 91 days. Manual data collection for fish and plant attributes was performed weekly in both terms of experimentation. IoT-based data collection for water quality data and environmental data was performed daily, in both terms. Plant length, leaf breath length, stem size and leaf length were chosen as plant attributes, whereas fish length and fish weight were used as fish attributes, contributing to evaluating the growth rate of the yields. Water quality parameters such as pH, dissolved oxygen, Total Ammonia and Nitrogen (TAN), temperature, turbidity and TDS values were also collected along with environmental parameters such as room temperature, humidity and CO2, whose values were collected using sensors and IoT. Arduino UNO was used for data collection and processing. Data were collected and stored in a local computer and in the cloud. Cloud-based and IoT-based predicative analysis has been proven to be effective in sustainable aquaponics systems [18]. Figure 1 shows the IoT communication of this experimental setup.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 4 Discussion
[PERSON] [33] developed a ship motion control model to solve the monocular camera motion pose, ultimately addressing the automatic berthing problem. While cameras can provide high-resolution image data, they are limited by lighting conditions and viewing angles. In 2023, [PERSON] et al. [34] developed an autonomous berthing system based on symbol recognition using cameras and LiDAR. In autonomous ship berthing recognition technology (Table 11), cameras and LiDAR can be integrated to effectively detect and track both static and dynamic obstacles during the berthing process, providing spatial and speed information. An inertial measurement unit (IMU) can provide accurate motion data to support the positioning and control of a vessel during berthing. High-precision location and speed information can also be obtained using accelerometers, gyroscopes, GPS, and other sensors. [PERSON] [35] designed a method to estimate the ship's operating state based on point cloud matching using LiDAR. The accuracy and stability of the system were verified through comparative testing with an unmanned boat experimental platform based on the Robot Operation System (ROS) and integrated GNSS/IMU navigation. Technologies such as LiDAR and cameras are used to detect hulls or obstacles, whereas high-precision location and speed information is provided by accelerometers, gyroscopes, GPS, and other sensors. The automatic berthing support program proposed in this study recognizes and displays docks based on GPS location information and calculates and provides the distance between the ship and the dock. In addition, it calculates and presents, in real time, the output of the tug or thruster needed for safe berthing and unberthing, depending on the ship's berthing speed and the magnitude of external forces in various external force environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **Distance** & **Difference** & **Actual** & **Standard** \\
**(h/min)** & **from Pier** & **(m)** & **Berthing** & **Berthing** \\  & **(m)** & **Speed (m/s)** & **Speed (m/s)** \\ \hline
13:13 & 285 & & & \\ \hline
13:14 & 224 & 61 & 1.02 & \\ \hline
13:15 & 156 & 68 & 1.13 & \\ \hline
13:16 & 113 & 43 & 0.72 & 0.15 \\ \hline
13:17 & 78 & 35 & 0.58 & \\ \hline
13:18 & 50 & 28 & 0.47 & \\ \hline
13:19 & 30 & 20 & 0.30 & 0.10 \\ \hline
13:20 & 15 & 15 & 0.25 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Difference between actual and standard berthing speeds.