esa-sceva/satcom-chunk-collection huggingface dataset:
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 6 The New Privacy Amplification Strategy Based on So-Called Huffman-Renyi Difference  
As is known, refs. [14; 15; 42], in order to efficiently utilize a particular CR source, it is necessary to adaptively determine the degree of compression of the PA block. In this way, the speed of generated secret keys is adjusted to the side information available to Eve. Complex machine-learning systems developed for these purposes can be replaced by simpler yet still very effective procedures for certain classes of CR sources. Figure 14 shows a histogram of the difference  
\[D_{HR}=|w_{i}|-R(w_{i}|e_{i}) \tag{32}\]  
between the length of the sequence \(|w_{i}|\) at the output of the Huffman encoder and the conditional Renyi entropy \(R(w_{i}|e_{i})\) of that same sequence observed by [PERSON] for synthesized signals with SNR = 39.9 dB. We will call the quantity \(D_{HR}\) the Huffman-Renyi difference. We notice that the mean value is very close to 0, more precisely 1.84 bits, and that there is a negligible number of samples outside the range of 3\(\sigma\) = 9.02 bits. Based on (32), we can derive a simple estimator  
\[\hat{R}(w_{i}|e_{i})=|w_{i}|-\hat{D}_{HR}. \tag{33}\]  
Figure 13: [PERSON], [PERSON], and [PERSON] have the same Huffman encoder. Blue line: Probability density of conditional Renyi entropy \(R^{*}(w_{i}|e_{i},K_{i-1})\) when the random LSP parameters of [PERSON], [PERSON], and [PERSON] synthesizers are the same, i.e., \(LSP_{A}=LSP_{B}=LSP_{E}\). Orange line: Probability density of conditional Renyi entropy \(R(w_{i}|e_{i})\) when \(LSP_{A}=LSP_{B}\
eq LSP_{E}\).  
From (33), we see that with a quality estimate for \(\hat{D}_{HR}\) and knowing \(|w_{i}|\), we can also obtain a quality estimate for \(R(w_{i}|e_{i})\). If we set \(\hat{D}_{HR}\) equal to the mean value of \(\overline{D}_{HR}\), then with high probability  
\[\hat{R}(w_{i}|e_{i})\geq|w_{i}|-\overline{D}_{HR}-3\cdot\hat{\sigma}_{D_{HR}}. \tag{34}\]  
Bearing in mind that the degree of compression of the PA block is equal to \(\hat{R}(w_{i}|e_{i})-|K_{i}|\), we arrive at three possible PA strategies:  
\[|K_{i}|=|w_{i}|-\overline{D}_{HR},\,\text{``mean''} \tag{35}\]  
\[|K_{i}|=|w_{i}|-\overline{D}_{HR}-3\cdot\hat{\sigma}_{D_{HR}}\,\text{``$3\sigma ''} \tag{36}\]  
\[|K_{i}|=|w_{i}|-\overline{D}_{HR}-3\cdot\hat{\sigma}_{D_{HR}}-s\,\text{``$3 \sigma+s''} \tag{37}\]  
The strategies are ordered according to the increasing degree of compression. Strategy (37) allows the security margin \(s\) to be chosen by the predefined value of the leakage rate.
## 8 Open Research Challenges and Research Directions
### Monitoring, Mobility, and Connectivity  
Monitoring, mobility, and connectivity are essential elements of healthcare, as they enable services to be accessible to more people and facilitate tracking and monitoring of patient health. This allows healthcare providers to monitor their patients from anywhere and provide better care. Connectivity also enables patients to remotely access their medical records and data, making it more convenient to seek medical advice and treatments. Additionally, mobility facilitates healthcare providers to reach more people, as they are not limited to being in the same location as their patients. In a connected smart health system, both patients and medical staff should have the ability to move freely betweenmultiple locations. However, the presence of several wireless networks operating in close proximity leads to collisions, particularly when mobility is involved. Healthcare delivery faces disruptions and reduced performance, which may lead to disastrous situations. Therefore, it is essential to ensure the accurate operation of medical devices by connecting them through various wireless technologies. The challenges associated with monitoring, connectivity, and mobility in IoT/mobile applications are numerous. Despite significant progress in sensors and IoT technology, achieving the same level of accuracy as hospital-grade devices while maintaining energy efficiency and wearability remains a challenge. To address this, we need new technologies that are cost-aware, powerful, fully connected, and efficient. The evolution and innovation of smart and connected healthcare are expected to provide efficient solutions and services to patients, paving the way for next-generation healthcare. Research and development efforts are necessary to ensure data security and user privacy. Additionally, there is a need for research to improve methods for monitoring healthcare services and enhancing their mobility and connectivity.
# Fractional-Order Sliding Mode with Active Disturbance Rejection Control for UAVs
## 3 Flight Controller Design  
This section focuses on the FOSM-ADRC method applied to quadrotor flight controller design. In addition, the propeller is very light compared to a brushless motor, and the moment of inertia induced by the propeller is neglected here.  
The structure of the inner and outer loop structure control system designed in this paper is shown in Figure 2. The outer loop of the control system is the position controller, which adopts a PID controller; the inner loop is the attitude control, which adopts the FOSM-ADRC. The input to the position controller is the body position; the output is coupled to generate an attitude angle signal to the attitude controller, which generates an output to control the quadrotor body to track the reference input.  
In the attitude controller design, the controller is divided into two inner and outer loops for control. In view of the good robustness and strong adaptability of the SMC as well as its fast response characteristics [20], a fractional-order reaching law sliding mode controller (FO-SMC) is used in the outer loop, and since we use a separate SMC alone, the control output oscillates at a higher frequency near the desired signal, which puts a higher demand on the quadrotor motor. In order to suppress this, the inner loop uses an ADRC, which can soften the output of the SMC and, at the same time, obtain a good anti-interference capability, which is a two-for-one approach. Figure 3 shows the control framework of this paper more visually.  
Compared with the traditional single-loop PID controller, which is widely used in UAVs, the control structure proposed in this paper can achieve finer control accuracy and combine the strong robustness and anti-interference ability of both the SMC and ADRC to realize the stable operation of the quadrotor under strong interference conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \(m\) & 0.886 & kg \\ \(l\) & 0.225 & m \\ \(K_{i}(i=1,\cdots,6)\) & 0.001 & Ns/m \\ \(l_{x},I_{y}\) & 0.016 & Ns\({}^{2}\)/rad \\ \(I_{z}\) & 0.0274 & Ns\({}^{2}\)/rad \\ \(J_{r}\) & 0.02 & Ns\({}^{2}\)/rad \\ \(g\) & 9.8067 & m/s\({}^{2}\) \\ \(b\) & 5 & Ns\({}^{2}\) \\ \(d\) & \(1.239\times 10^{-5}\) & N/(rad/s)\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: UAV modeling parameters (parameter reference AMOVLAB [19]).  
Figure 2: Control system structure diagram.
# Datamator: An Authoring Tool for Creating Datamations via Data Query Decomposition
## 4 Data Query Analyzer
### Calibration Network (\(\mathcal{C}\))
#### 4.2.1 Loss Function  
The above calibration network is trained using one feedback \((q_{x},s_{c})\) a time. The training samples consist of various data queries \(q_{x}\), their original decomposition results \(s_{d}\) from the decomposition network, and the desired results \(s_{c}\) are given by users. In each round of training, the following loss function is minimized:  
\[L=\alpha L_{r}+L_{p} \tag{8}\]  
Figure 4: The calibration network (**a**) takes the gradient (**c**) from the decomposition network as input to predict parameter calibration \(\Delta\theta\). It is a collection of auxiliary MLPs (**b**) with two consecutive blocks (**b1,b2**).  
where \(L_{r}\) is the cross-entropy loss designed to make sure that the calibration is efficient. It estimates the similarity between the decomposition result after calibration and the desired results \(s_{c}\):  
\[L_{r}=-\sum_{i=1}^{n}\log p_{(\theta^{\prime})}(t_{i}\mid t_{1},\dots,t_{i-1},q _{x}) \tag{9}\]  
where \(p_{(\theta^{\prime})}(\cdot)\) estimates the probability of the model output token \(t_{i}\) aligned with the desired result \(s_{c}\), after calibrating the decomposition network based on \(\theta^{\prime}\).  
In Equation (8), \(L_{p}\) is the Kullback-Leibler divergence, which is used to ensure that there is minimal impact on the data queries \(q_{\text{other}}\), which do not need to be adjusted. It estimates the similarity of the output distributions of decomposition network \(\mathcal{D}\) over \(q_{\text{other}}\) before and after calibration:  
\[L_{p}=KL(\mathcal{D}(q_{\text{other}}|\theta),\mathcal{D}(q_{\text{other}}| \theta^{\prime})) \tag{10}\]  
Intuitively, minimizing \(L_{r}\) ensures that the feedback will be accepted by the decomposition network and minimizing \(L_{p}\) will prevent our calibration from affecting the decomposition results beyond the feedback.
# Indole Degradation in a Model System and in Poultry Manure by _Acinetobacter_ spp.
## 4 Discussion
For microorganisms, indole can induce bacterial membrane and oxidant toxicity, prevent cell division by modulating cell membrane potential, inhibit adenosine triphosphate production and protein folding, and cause reparable DNA damage [15; 55; 61; 62]. Due to the toxic nature of indole compounds to microorganisms, their concentrations were kept low when supplied to culture medium [24; 25; 48; 63; 64]. Aromatic compounds are generally lipophilic [28], but it has been observed that _Pseudomonas_ sp. ST-200 can grow at a high concentration of indole when an organic solvent-water system consisting of two phases is used to avoid the toxicity effects. In another study, [PERSON] et al. [20] reported that higher concentrations of indole is toxic and inhibits bacterial growth. Similarly, at a concentration \(>\)300 mg/L, growth of NTA1-2A and TAT1-6A strains were inhibited and no colony formed on the agar plate. Indole also decreases cell growth of a fungus _Aspergillus niger_[2]. Bacteria play an important role in aerobic and anaerobic fermentation of manure to release nutrients and transform harmful organic compounds. Aerobic fermentation is one of the most effective ways of treating manure harmlessly and resourcefully [65; 66]. Animal manure has become a major source of ecological and environmental pollution [67] which is not only harmful for the environment and human health, but also influences the sustainable development of poultry and livestock industries. NTA1-2A and TAT1-6A strains are non-pathogenic, ubiquitous microbes of promising candidates capable of removing 78.67% and 83.28% indole, respectively and 83.70% in a mix from poultry manure in eight days. Many bacterial strains do have the potential of removing pollutants like indole compounds and others from the environment under different conditions via the production of intracellular or extracellular enzymes. For example, many non-indole producing bacteria encode enzymes that can degrade indole and produce indole derivatives [68; 69]. [PERSON] et al. [35] reported n-alkane degradation by _Acinetobacter_ sp. attacked by a dioxygenase enzyme. [PERSON] et al. [70] revealed methyl oxidation of 3-methylindole by cytochrome P450 enzymes. [PERSON] et al. [4] reported supernatant fluids of culture extracted from 3-methylindole degrading isolates showed 3-methylindole degradation activities. In this study, the supernatant fluid extract obtained from the two strains' culture medium completely removed 120 mg/L of indole within 48 h. This is a clear indication that the strains have a promising potential of degrading indole compounds which are challenging the rapidly expanding intensive poultry and livestock industries.
## 1 Introduction
These empirically derived methods and equations often relate to the intrinsic properties of the rock mass itself [23]. The inverse velocity method (INV) [24; 25], a classic approach to landslide prediction, originated in the early 20 th century, and has been gradually developed and refined in subsequent research. This method, based on the three-stage model of slope creep theory, particularly focuses on the accelerated deformation stage before landslides, and uses the variation in deformation velocity over time to estimate the timing of landslides. [PERSON] [26] et al. conducted experimental research and derived various relationship patterns between the reciprocal of velocity and time, initially establishing the foundation for the application of the INV in landslide prediction. They believed that the reciprocal of the velocity-time relationship curve can be approximately fitted into a straight line, and the intersection of this line with the time axis represents the time of landslide instability. [PERSON] [27] conducted research on plastic-accelerated creep in soils, rocks, and other materials, concluding that under constant effective stress conditions, the reciprocal of velocity-time fitting curve is actually linear. However, the traditional INV has limitations, such as failing to consider that the velocity during actual landslides is not infinite, resulting in predicted landslide times that may lag behind actual landslide times. Due to the complex heterogeneous nature of rocks, the INV has limitations in certain aspects. It has good adaptability for landslides in accelerated creep states but poor adaptability for progressive and instantaneous landslides. The reason for this is that in practical applications, factors such as rainfall and external force disturbances can cause the reciprocal of the velocity-time fitting curve to exhibit concave or convex properties. [PERSON] [16], and [PERSON] [24], raised the issue of reselecting the Time of Start Point (\(T_{SP}\)) after the onset of acceleration (OOA) during the accelerated deformation stage. Combining the characteristics of arc GB-SAR monitoring data, this paper proposes an improved INV method for landslide time prediction to enhance the accuracy of landslide time predictions. In response to the lag issues present in traditional inverse velocity methods, this paper proposes a time logarithm model and utilizes the normal distribution characteristics of deformation velocities to set confidence intervals, accurately identifying the onset of accelerated deformation. This approach enhances the accuracy of landslide disaster predictions.
# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 3 Proposed Methods
### Local Detail Enhancement
#### 3.4.1. _Brighter_ and Darker Area Determination Criteria  
Since the darker areas of the low-exposure images and the brighter ones of the over-exposed images do not have any detailed information, there is no need to enhance these details, as shown in Figure 8d,e. Figure 8d,e show the horizontal pixel intensity of the red area (the brightest area in Figure 8a) and the yellow area (the darkest area in Figure 8c) in Figure 8a-c, respectively. In Figure 8d, the brightest area of the over-exposed image has a straight line, indicating that this part of the area does not contain any details. However, the same area in Figure 8b,c contains more information; in other words, the under-exposed image contains the texture information of the brighter area of the over-exposed image, as shown in Figure 8e. It is necessary to exclude the DoE of the low-exposure image and the BoE of the over-exposed image, then use the DoE as the enhanced area of the over-exposed image and the BoE as the enhanced area of the low-exposure image. Although the DoE region does not have any detailed information in the low-exposure image, it contains this part of the detailed information in the over-exposure image.  
The brightest and darkest regions are determined by the mean value \(\hat{l}(x,y)\) at \((x,\ y)\) of \(N\) input images, as shown in Equation (4). When \(\hat{l}(x,y)>0.9\), it means that the current pixel belongs to the BoE, as shown in Figure 8f; when \(0.1<\hat{l}(x,y)\leq 0.9\), it means that the current pixel belongs to the normal area; when \(\hat{l}(x,y)<0.1\), it indicates that the current pixel belongs to the DoE, as shown in Figure 8g.
## 2 Results  
### De Novo Transcriptome Assembly and Analysis  
RNAseq data from the leaf of _P. amarylifolius_ was used to perform de novo assembly of the transcriptome. Raw data comprising 37,583,042 reads were obtained from the library. The SRA sequences were submitted to NCBI (BioProject ID: PRJNA692823). After removing the low-quality reads and all possible contamination, a total of 29,464,902 clean reads with Q \(\geq\) 30 and 46% of GC content were obtained. Trinity-based de novo assembly generated contigs with N25 of 1663 bp, N50 of 1010 bp, and N75 of 555 bp. The N50 value of assembly is the sequence length of the shortest contig at 50% of the total transcriptome assembly length. It is a length-weighted median. N50 statistic defines assembly quality in terms of contiguity. Likewise, N25 and N75 are the minimum contig length to cover 25% and 75% of transcriptome assembly, respectively. A total of 34,672 transcript sequences were produced that had an average transcript size of 802 bp. A total of 27,734 transcripts had homologous genes in the NCBI nt database, while 6938 transcripts had no significant homology to any known genes. The assembly statistics are given in Supplementary Table S1.
## 3 Materials and Methods
### Scale Adaptation Loss  
The purpose of convolution is to extract features, but in the process of extracting features, information is lost. The ideal situation is to extract critical information and ignore noncritical information. This ideal is difficult to achieve, and the loss of information is unavoidable. To minimize the loss of key information, it is necessary to effectively discriminate between critical information and noncritical information. For the detection task in this paper, there are obvious differences in the corresponding object scales, but in the calculation process, the same weight optimization was carried out on the features of different scales, increasing the computational burden of the system and leading to a lack of precision. Different numbers of convolutions will result in differences in the fineness of features. The more convolution processing is performed, the more obvious the feature retention is, and the effect is optimized for small targets. Similarly, large targets need to be close to the tail-end features.  
In practical applications, the acquisition system continuously acquires new image data, and the system needs to iterate quickly to adapt to the new data.  
Figure 3: Enhancement of ship target data: (**a**,**c**,**e**,**g**,**i**,**k**) images before data enhancement; (**b**,**d**,**f**,**h**,**j**,**l**) corresponding images after data enhancement.  
A new LOSS was designed for the ship target characteristics of SAR images. The specific process is described as follows:  
The first step is to calculate statistics based on 30% of the data and count the size and number of ship targets in the data source.  
In the second step, the collected targets are divided into three categories according to their size. The smallest 30% are regarded as small targets, the largest 30% are regarded as large targets, and the remainder are regarded as medium targets.  
The third step is to set the attenuation factor according to the overall distribution of the target in each image. For example, if there are many small targets in a certain picture, the underlying structure in the convolutional layer should play a greater role, thereby reducing the information loss of small targets. The attenuation factor is adjusted according to the size of the target. The specific mediation method is as follows: for the feature output of the three scales in the YOLO structure, when there is no target, in the process of calculating loss, it corresponds to targets of different sizes multiplied by a and \(\beta\), and the 1-\(\alpha\)-\(\beta\) decay factor constrains the features.  
The main goal of loss is balance. Due to the addition of loss design, the target of the corresponding area will produce a large loss, so that it accounts for a relatively high proportion. We focus on optimizing it to make it more balanced. Through adaptive design, each part of the model takes its due responsibility in the face of different scales.
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies
## 4 Discussion  
### Correspondence between Geomagnetic Anomalies and Earthquakes  
According to the previous literature, geomagnetic anomalies can only be observed before large earthquakes. These two sets of data proves that the magnitude of the earthquake ranges from 3.0 to 6.8, and the distance from the epicenter to the geomagnetic network center ranges from 158 km to 460 km, all of which have observed geomagnetic anomalies before the earthquakes. Based on comprehensive historical data and observations up to now, it has been shown that the majority of earthquakes undergo geomagnetic anomalies before they occur regardless of the earthquake intensity, and there is a one-to-one correspondence between geomagnetic anomalies and earthquakes. The pre-earthquake geomagnetic anomalies can serve as an important parameter for earthquake prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **M** & **Location** & **Epicenter Distance** \\ \hline
4 April 2022 & 3 & Qianwei County, Leshan, Sichuan Province & 353 \\
12 April 2022 & 3 & Li County, Aba Prefecture, Sichuan Province & 450 \\
16 April 2022 & 4.6 & Ninglang County, Lijiang City, Yunnan Province & 253 \\
22 April 2022 & 3.2 & Kangding City, Ganzi Prefecture, Sichuan Province & 290 \\
30 April 2022 & 4.2 & Ninglang County, Lijiang City, Yunnan Province & 251 \\
20 May 2022 & 4.8 & Hanyuan County, Ya’an City, Sichuan Province & 252 \\
1 June 2022 & 6.1 & Lushan County, Ya’an City, Sichuan Province & 374 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of earthquakes from 1 April 2022 to 1 June2022.  
Figure 6: Schematic diagram of early warning points from1 April 2022 to 1 June 2022 (the red circle is the early warning time point).  
However, the mechanism of earthquake magnetism is still unclear, and we have not yet conducted research on this issue, which requires further exploration.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 3 The Proposed Methods
### Optimization of Atmospheric Light Values
The fusion result is shown in Figure 4d, and the mathematical expression for this result is as follows:  
\[D(x)=I_{E1}(x)\cap(-G(x)) \tag{25}\]  
where \(D(x)\) denotes the fusion result; since \(D(x)\) contains holes and cracks as well as a large amount of noise, in order to improve the localization accuracy of the atmospheric light region, an expansion operation is performed on \(D(x)\) to obtain the approximate region of the atmospheric light, which is set to be \(D_{1}(x)\), as shown in Figure 4e. The values of the pixels at the positions corresponding to the brightest pixels of the top 0.1% in \(D_{1}(x)\) in the enhanced foggy image \(I_{E}(x)\) are selected, the average of which is calculated to be the atmospheric light estimation value.