esa-sceva/satcom-chunk-collection huggingface dataset:
### Entropy Images  
US B-mode images contain the anatomical information formed by reflections at large boundaries as well as microstructural properties inside a small local area, which may be related to the pathological status of soft tissue. These backscattered waves are generally shown as speckles, periodic patterns, or textures in a B-mode image. Therefore, many approaches have been used to analyze tissue characteristics from these randomly backscattered signals using various statistical models [16]. For example, the Nakagami [17], K-distribution [18], and homodyned K-distribution [19] are popular statistical distributions for assessing pathological information. For these approaches, called model-based methods, there are some limitations in estimating the distribution parameters for a specific model because a single model cannot be satisfied for the entire image area, and nonlinear signal processing such as log-compression may change the original statistical characteristics of raw data. Therefore, non-model-based approaches have been studied to compensate for the limitations of statistical model-based methods.  
The Shannon entropy, a measure of the average level of information, was proposed in the research area of information theory [20] and applied to many signal processing applications to estimate the signal uncertainty in a random variable. Since [PERSON] first utilized the Shannon entropy for the analysis of ultrasonic waveforms in a scattering medium [21], many studies using the same concept for ultrasound images have been proposed and exhibited performances comparable to those of model-based approaches [22; 23; 24]. The  
\begin{table}
\begin{tabular}{c c} \hline
**Class** & **Number of Images** \\ \hline Benign & 487 \\ Malignant & 210 \\ Normal & 133 \\ Total & 780 \\ \hline \end{tabular}
\end{table}
Table 1: Three classes of breast US images and the number of images in each class.  
Figure 1: Sample B-mode images of the breast ultrasounddataset [11]: (**a**) Normal, (**b**) benign, and (**c**) malignant.  
Shannon entropy of a discrete random variable \(X\) with possible values \(\{x_{1},\ x_{2},\ \cdots,\ x_{n}\}\) is defined in the following discrete form:  
\[H\ =\ -\ \sum_{i\ =\ 1}^{n}p(x_{i})\ \text{log}_{2}[p(x_{i})] \tag{1}\]  
where \(p(\cdot)\) represents the function of probability distribution.  
In this study, the entropy image was constructed from the B-mode image using a small moving rectangular window in both the axial and lateral directions. Although [PERSON] et al. showed that the appropriate window length in the axial direction for a stable statistical parameter was three times the pulse length [25], the window used in this study was a fixed size of 25 \(\times\) 25 pixels, which is the minimum size considering the average size of B-mode images, because detailed information on raw radiofrequency data was not available. The sample entropy images calculated from the B-mode image are shown in Figure 2b.
## 3 Distributed Jamming System Model for LEO Signal
### Identification of Primary and Relative Jammers  
When jamming the jamming target, the distributed jamming system will operate adaptively according to the current environmental situation and complete the early interception and forwarding jamming of the LEO signals. LEO satellites use narrow-beam signal transmission, and the coverage probability is inversely proportional to the satellite density, orbital angle, and orbital altitude [44]. In the literature [45], the downlink signal antenna reception is analysed using airborne jammers at different altitudes moving parallel  
Figure 2: Block diagram of the distributed interference optimisation algorithm flow.  
to the LEO satellite, illustrating that the relative angular difference is more important in the jamming-to-original-signal ratio than the distance between the ground station and the jammer or the satellite, confirming that the jamming-to-original-signal ratio increases as the relative angular difference decreases. Therefore, combined with the electromagnetic characteristics of LEO signals, the interference source is dynamically bisected by simplifying the calculation of the relative angular difference to complete the jammers. As shown in Figure 3, the primary jammer needs to receive the LEO signals for the first time, so the jammer with the smallest reception distance in the airspace should be selected as the primary jammer. Other jammers that are further away from the centreline of the beam are identified as relative jammers.  
The primary jammer is determined according to Equation (1).  
\[d_{i,k}=\frac{|(\mathbf{J}_{i}-\mathbf{S}_{k})\times\mathbf{L}_{k}|}{|\mathbf{L}_{k}|}\quad, \quad i_{p}=arg\;min\;(d_{i,k}) \tag{1}\]  
where \(d_{i,k}\) is the distance of each jammer from the beam, \(\mathbf{L}_{k}\) is the \(k\)th LEO beam, \(arg\;min\;(d_{i,k})\) is the corresponding jammer subscript \(i\) closest to the kth LEO satellite beam, and \(\mathbf{J}_{ip}\) is the primary jamming source.
# Localization of Radio Sources Using High Altitude Platform Station (HAPS)
## 4 Discussion  
### Localization of a Single Source  
In this study, we investigated the changes in estimation accuracy due to distance, the changes in estimation accuracy due to the transmission power of the source, the effectiveness of the proposed method against these factors, and the impact of considering edge diffraction around buildings.  
When the horizontal distance from the HAPS to the source was within 60 km, the estimation error was within 1 km for almost all sources. On the other hand, in cases where the distance exceeded 80 km, significant degradation in accuracy was observed. This is likely because the direct wave could not reach the receiver, and the radio waves that arrived after multiple reflections were buried in noise power. Furthermore, when the distance was 100 km, the estimation error was within 1 km in less than 10% of the cases. This is because the elevation angle becomes nearly horizontal, causing deviations in height estimation to significantly affect horizontal distance estimation. Additionally, compared to localization over shorter distances, angle estimation errors have a greater impact on horizontal distance errors over longer distances.  
When the transmission power of the source was reduced, a slight decrease in estimation accuracy was observed. On the other hand, adjusting the element panel's tilt to modify the directivity improved the estimation accuracy. This is likely because the SNR of the signal increased. In the original element arrangement, radio waves from sources located downward were not sufficiently received, indicating that modifying the element arrangement is important.  
When considering edge diffraction, no significant difference in estimation accuracy was observed at 5 km. However, at 20 km, a degradation in accuracy was confirmed. This is likely because considering edge diffraction increases the number of arriving radio waves. However, when the elevation angle is large, the variation in the angle of arrival is not significant, so the impact on accuracy is considered minimal. Additionally, even when direct or reflected waves do not reach the receiver, considering edge diffraction increases the likelihood of receiving an arriving wave, thereby improving the detection percentage.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 4 Cryptographic Preliminaries
### Attribute-Based Credentials from wBB Signature  
The Weak Boneh-Boyen (wBB) signature scheme [23] is a pairing-based short signature scheme. The scheme is provably secure, and it is proven to be existentially unforgeable against a weak (non-adaptive) chosen message attack. The scheme can be easily combined with zero-knowledge proofs as shown in the attribute-based credential scheme [21]. In this way, it is possible to prove the ownership of issued attributes in an unlinkable, anonymous, and efficient manner. The scheme was designed to be easily implementable and efficiently executed on computationally and memory-constrained devices such as smart cards. The scheme supports all the standard privacy-enhancing features of attribute-based credential schemes, such as anonymity, unlinkability, untraceability, and selective disclosure ofattributes. The main sign of the scheme is its symmetrical character, namely that the credential issuer also serves as the verifier, and therefore, they share secret keys. The scheme is briefly described below:  
* \((par)\)\(\leftarrow\)SetupI\((1^{\kappa})\): on the input of the security parameter \(\kappa\), the protocol generates the public system parameters \(par=(\mathbb{G},g,q)\) satisfying \(|q|=\kappa\).
* \((sk,ipar)\)\(\leftarrow\)CredKeyen\((par)\): on the input of the public system parameters \(par\), protocol chooses \(x_{i}\in_{R}\mathbf{Z}_{q}^{*}\) for \(i=(0,\ldots,n)\) and outputs secret key \(sk=(x_{0},\ldots,x_{n})\) and issuer parameters \
## 2 Frame Model Design and Structural Analysis
### Modal Analysis
#### 2.5.2 Calculation Process of Frame Mode  
1. Establish a finite element model of the structure;
2. Set solution methods and control parameters;
3. Extracting modal results;
4. As shown in Table 2, the low-order modal frequency and modal shape of the frame are obtained through calculation. Among them, the frequencies before the first-order mode are all lower than 1 Hz, which corresponds to the six-degree-of-freedom rigid mode in free modal analysis. Therefore, considering the extraction from modes greater than 1 Hz as the first order, the result is that the lowest first-order frequency is approximately 36 Hz. The frame possesses a natural frequency, which refers to the frame's elastic vibration frequency within a small range around its equilibrium  
Figure 8: Displacement variables of the frame in the \(z\)-axis direction.  
state. When the frame experiences external excitation close to its natural frequency, resonance occurs, increasing the frame's amplitude and consequently affecting the handling stability and driving safety of the vehicle. Generally, the frequency of road excitation is around 20 Hz, while the lowest first-order frequency of the frame is about 36 Hz. Thus, if the vehicle operates on a road with a frequency of around 20 Hz, it can avoid the frame's natural frequency to a certain extent and prevent the occurrence of resonance effects.
# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 2 Literature Review
### Text Summarization
#### 2.1.2 Abstractive Summarization  
In contrast to extractive summarization methods, the core of abstractive summarization methods is to fully understand the document content and then reorganize the language to generate a grammatically correct and coherent summary. Currently, the most widely studied approach is the sequence-to-sequence framework model. In 2015, [PERSON] et al. [19] first applied a model with an attention-based encoder and a neural network language model decoder to abstractive summarization. Subsequently, [PERSON] et al. [20] introduced a conditional recurrent neural network to construct the decoder based on [PERSON]'s work. [PERSON] [21] et al. were the first to propose using the seq2 seq framework combined with recurrent neural networks (RNN) to process long text summaries and introduced a generator pointer to solve the problem of out-of-vocabulary and low-frequency words. The Google team proposed the transformer model [22] in 2017, which solely uses the Attention mechanism and completely abandons traditional neural network units. Moreover, Claude3 [23], released by Anthropic; Gemini [24], released by Google; GPT-4 [25] and ChatGPT [26], released by OpenAI; \"Ernie Bot\" [27], by Baidu; and \"Tong Yi Qian Wen\" [28], by Alibaba, are all built based on the transformer architecture.  
Despite the significant progress made in abstractive summarization, several challenges remain: (1) Factual inconsistency: Abstractive models sometimes generate summaries that contain factual errors or hallucinations not present in the source text [29]. This is particularly problematic for news summarization, where accuracy is crucial. (2) Lack of domain knowledge: Current models often struggle with domain-specific texts, as they lack the necessary background knowledge to accurately interpret and summarize specialized content [30]. (3) Limited control over summary content: While these models can generate fluent summaries, they often lack fine-grained control over the specific information included or emphasized in the summary [31]. (4) Difficulty with long documents: Many abstractive models struggle with very long documents, often losing important information or focusing too heavily on the beginning or end of the text [32].
###### Abstract  
With the development of wireless sensor networks (WSNs), in most application scenarios traditional WSNs with static sink nodes will be gradually replaced by Mobile Sinks (MSs), and the corresponding application requires a secure communication environment. Current key management researches pay less attention to the security of sensor networks with MS. This paper proposes a hybrid key management schemes based on a Polynomial Pool-based key pre-distribution and Basic Random key pre-distribution (PPBR) to be used in WSNs with MS. The scheme takes full advantages of these two kinds of methods to improve the cracking difficulty of the key system. The storage effectiveness and the network resilience can be significantly enhanced as well. The tree-based path key establishment method is introduced to effectively solve the problem of communication link connectivity. Simulation clearly shows that the proposed scheme performs better in terms of network resilience, connectivity and storage effectiveness compared to other widely used schemes.  
wireless sensor networks; key management; tree-based; path key establishment; mobile sink +
Footnote †: journal: Sensors
## 3 Experiments and Analysis
### Performance Testing and Comparative Analysis
#### 3.3.3 Classifier Comparison
This enhanced discussion on the implications, now included in the revised manuscript, expounds on how the findings could contribute to the formulation of more proactive and responsive disaster management strategies, thereby representing a significant stride toward augmenting meteorological disaster capability assessments. A survey of contemporary literature underscores a predominant reliance on traditional neural network models and linear regression techniques. While these methodologies have their merits, they often fall short of capturing the intricate dynamics inherent in meteorological data. The Encoder-Adaptive-Focal model, on the other hand, is uniquely equipped to handle such complexities, a fact that becomes evident when it is juxtaposed with the outcomes of these conventional models. Furthermore, the recent surge in interest around convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in meteorological studies should be acknowledged. These models, though sophisticated, often require extensive computational resources and, at times, may not be adept at managing the nuances of time series data, especially in the context of meteorological disaster capabilities. In contrast, our proposed approach strikes an optimal balance, maximizing efficiency without compromising on depth and precision. It's also crucial to highlight the seminal works of researchers who have paved the way in this field with their groundbreaking methodologies. While their contributions have undeniably shaped the trajectory of disaster capability assessments, the Encoder-Adaptive-Focal model's adaptive nature and recalibration mechanisms offer a fresh perspective, potentially setting a new benchmark in the domain. In conclusion, by integrating insights from the extant literature and highlighting the novel facets of the Encoder-Adaptive-Focal deep neural network approach, we hope to enrich the academic discourse. This comparative analysis not only solidifies our model's position within the research landscape but also underscores its potential in reshaping the future of meteorological disaster capability assessments.
###### Abstract  
The control system for a micro-hydropower plant using an Archimedes screw turbine is the focus of this work. Three control systems were implemented based on a Barreda micro-hydropower plant (Spain) currently in operation: an optimal water level control (\(OWLC\)) system, a maximum power point monitoring (\(MPPT\)) system, and a water level control (\(WLC\)) system. The comparison was made using several assessment indicators: electricity production, micro-hydropower plant efficiency, and gearbox fatigue. The electricity production is similar in the \(OWLC\) and \(MPPT\) systems (energy gain \(+0.5\%\)) and significantly lower in the \(WLC\) system (energy gain \(-12\%\)). The efficiency of the micro-hydro plant is similar in the \(OWLC\) and \(MPPT\) systems (average efficiency gain \(+0.9\%\)) and significantly lower in the \(WLC\) system (average efficiency gain \(-15\%\)). The mechanical stress on the gearbox is similar in the \(OWLC\) and \(WLC\) systems and significantly higher in the \(MPPT\) system. It can be concluded that the \(OWLC\) system performs better as concerns the three assessment indicators used, followed by the \(MPPT\) system. The \(WLC\) system is not recommended for use, due to its low electricity production and low efficiency of the micro-hydropower plant.  
micro-hydropower plants; Archimedes screw turbine; control system; power production; mechanical stresses; micro-hydropower plant efficiency +
Footnote †: journal: Applied sciences  
0
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# Comparative Study on Physicochemical and Nutritional Qualities of Kiwifruit Varieties
## 2 Materials and Methods
### Methods
#### 2.2.5 Determinations of Total Sugar and Total Acid Content  
Determination of total sugar content as referred to in the literature [15] for the determination of reducing sugars in foods by anthracomic colorimetric method. Each sample was divided into 3 tubes. A total of 2 g of pulp was weighed with a straw and placed in a 50 mL centrifuge tube. Add water about 10 mL first, then slowly add 1 mL zinc acetate solution and 1 mL potassium ferrocyanide solution, shake gently, then add water to the scale, mix upside down, and let it rest for 30 min. After centrifugation at 4000 rpm \(\times\) 10 min at room temperature, 1 mL of the supernatant was transferred to a 15 mL centrifuge tube, and 9 mL of water was added to obtain a 10-fold diluted sample supernatant. A total of 0.5 mL of the above 10-fold diluted sample supernatant was aspirated and added to a 15 mL centrifuge tube with 1.5 mL of water to determine the absorbance value of the reaction solution at 620 nm. Total acid content as referred to in the literature [9], with some modifications. The 0.1 M sodium hydroxide solution was used to titrate the sample (50 mL) to a pH of 8.1, and the following formula was used to determine the total acid content:  
\[TAC(\mathrm{g}/100\ \mathrm{g})=C\times V_{2}\times K/V_{1}\times(V_{0}/W)\times 100 \tag{5}\]  
where _TAC_ was total acid content (g/100 g), \(C\) was the sodium hydroxide solution concentration (0.1 M), \(W\) was the sample mass (g), \(V_{2}\) was the volume of sodium hydroxide solution consumed by titration (mL), \(V_{1}\) was the titrated sample volume (mL), \(V_{0}\) was the total volume of crude extract (mL), \(K\) was the citric acid conversion coefficient, 0.070.
## 3 An Improved DPC Algorithm Based on Chebyshev Inequality and Differential Privacy
### Determination of Clustering Center Based on Chebyshev Inequality  
The core of DPC algorithm to select the clustering centers based on the decision graph is to select the points with relatively large local density \(\rho\) and center deviation distance \(\delta\) from dataset \(D\). While previous studies, such as those in the literature [34; 35; 36], have utilized the concept of Chebyshev inequality to achieve adaptive clustering center selection, their inequality parameters were determined as 3, 2, and 1, respectively, without universal adaptability.  
This paper establishes the constraint conditions for \(\rho\) and \(\delta\) through Chebyshev inequality, and determines the threshold values by adjusting parameters \(b_{1}\) and \(b_{2}\) to obtain the clustering center. Through this process, the clustering centers are obtained. The procedure is shown as follows:  
(1) The potential set of clustering centers \(ELC\) is obtained based on \(\delta\), and the constraint conditions of \(\delta\) can be expressed by Equation (11), where \(\mu(\delta)\) and \(\sigma(\delta)\), respectively, represent the mean value and the standard deviation of \(\delta\), \(b_{1}\) represents the coefficient parameter of \(\sigma(\delta)\), and \(I_{8}\) represents the set of all points.  
\[ELC=\{i|\delta_{i}>\mu(\delta)+b_{1}\sigma(\delta),i\in I_{8}\} \tag{11}\]  
(2) The true set of clustering centers, denoted as \(LC\), is screened from \(ELC\). If the average of the local density of all the points in \(ELC\) is less than that of all points in \(D\), it indicates that \(ELC\) contains a significant number of noise points; in this case, the true clustering center set can be obtained from Equation (12). Conversely, the true clustering center set can be obtained from Equation (13). Where \(\mu(\rho)\),\(\sigma(\rho)\) represents the mean value and the standard deviation of \(\rho\), respectively, _ave_ represents the mean of \(\rho\) of all points, and \(b_{2}\) represents the coefficient parameter of _ave_.  
\[LC=\{i|\rho_{i}>\mu(\rho)+\sigma(\rho),i\in ELC\} \tag{12}\]  
\[LC=\{i|\rho_{i}>b_{2}ave,i\in ELC\} \tag{13}\]
# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
## 5 Simulation Results
Then, the number of parameters (weights) of the network, including biases, is given by \(N_{\text{DNN}}=\sum_{i=0}^{l-2}n_{i}n_{i+1}+\sum_{i=1}^{l-1}n_{i}\) (=204,130 in our simulation). Because the DQN framework calculates Q-values using a DNN, the total number of parameters becomes \(D_{\text{state}}+D_{\text{action}}+N_{\text{DNN}}\). Moreover, because of the use of two Q-networks, the online network and the target network, the total number of parameters to be processed doubles, and the replay memory is required to store a collection of experience tuples, i.e., the parameters of the online network. In our simulation, we set the replay buffer size to 100 tuples. The computational complexity is similar to [11]. In our implementation, each selection takes less than \(10^{-4}\) s using GPU 2080 Ti. The computational speed is acceptable for vehicles thanks to the power of the GPU. The computational complexity of the DNN can be reduced by using lightweight DNNs [42; 43].