esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Results and Discussion
#### 3.2.3 Mapping the Water Bodies at a Given Instant of Time  
Solar installations are generally located in places away from large bodies of water such as rivers, lakes, or reservoirs. Sometimes, they are in areas close to water ponds that do not endanger the installation because they are far enough away (Figure 12).  
However, it is becoming more and more frequent to find episodes of heavy rains that can cause the accumulation of water, which could endanger the correct operation of this type of facility. An example of this occurred on plant 1 during a heavy rain episode during October 2020. The alert system detected a body of water with an area of 2134 m\({}^{2}\) that not only limited the accessibility to the plant but could have put the installation in risk (Figure 13).  
Although the NDWI is the most widely used spectral index for the detection of water masses, many authors are working on the development of new spectral indices. An example  
Figure 12: Water ponds detected with the NDWI index in the surroundings of (a) plant 1, and (b) plant 4.  
Figure 13: Body of water detected on plant 1 by the INSPECTORMAP alert system during an episode of heavy rain.  
of this is [36], who developed an \(SWI\) index for the detection of water masses using different spectral bands of Sentinel-2 (Equation (3)).  
\[SWI=\frac{Rededge\,1-SWIR}{Rededge\,1+SWIR}\frac{1}{1}, \tag{3}\]  
While in the case of NDWI all the pixels are part of the water body, in the case of \(SWI\) there are \"false positive\" pixels (Figure 14c). When quantifying the area affected by the water body using the NDWI on plant 4, it is obtained that with a 10-m resolution image the value is 31,166 m\({}^{2}\) and with a 20-m resolution the value drops to 27,171 m\({}^{2}\). The area affected by water when applying the \(SWI\) index almost doubles, obtaining a value of 43,154 m\({}^{2}\).
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 4 Results of Conventional Regression Methods with Traditional Features  
In our previous work [8], traditional engineered features were derived from the full OFDM waveform and were used with conventional regression methods to predict the communication performance. For the purposes of this paper, traditional features refers to signal statistics derived from the OFDM waveform, such as SNR and delay spread. For prediction performance comparison with CIR-based methods, Table 1 lists the prediction performance based on the features derived from the full OFDM waveform, including the time-domain SNR and the delay spread. While performance for most methods is quite good, the decision tree performs best for KWAUG14 and the linear regression works best for SPACE08.
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 3 Datasets used in State-of-the-Art Techniques
### Continuous Annotations  
Some datasets also annotate samples with continuous dimensions instead of only discrete categories. For example, the VAD model [76] is usually employed for emotion recognition. In this model, emotions are placed in a three-dimensional space: valence, arousal, and dominance. The valence (V) axis determines whether an emotion is pleasant or unpleasant to the perceiver, therefore distinguishing between positive and negative emotions. The arousal (A) axis differentiates between active and passive emotions. Finally, the dominance (D) axis represents the control and dominance over the nature of the emotion. Therefore, each emotion can be represented as a linear combination of those three components [77].  
The AfeW-VA dataset [52; 78] contains annotations of valence and arousal for 600 clips, which were also, in part, used in AfeW, to create an expanded dataset. The values for each axis of the model range within [10; 1; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 3  
weights between each annotation format to control their participation in calculating loss, for example.
# Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm
## 4 Proposed Algorithms
### PnP-FBS-CNC  
The basic idea behind PnP is to replace the proximal operator in the iterative algorithm with a denoiser. However, the FBS-CNC algorithm in Section 4.2, unlike the existingPnP algorithms, incorporates two proximal operators \(\text{prox}_{\frac{1}{p}\|\cdot\|_{1}}\) and \(\text{prox}_{\alpha\|\cdot\|_{1}}\), necessitating the utilization of two distinct denoisers \(D_{\sigma_{1}}\) and \(D_{\sigma_{2}}\) as replacements, where the parameters \(\sigma_{1}\) and \(\sigma_{2}\) are related to different noise levels. As a consequence, we obtain the following equation.  
\[x^{k+1}=D_{\sigma_{2}}\Big{[}v^{k+1}+\alpha\lambda b^{2}\Big{(}x^{k}-D_{\sigma _{1}}(x^{k})\Big{)}\Big{]}. \tag{22}\]  
After appropriately rearranging (20) and (22), we finally derive the PnP-FBS-CNC algorithm for image deblurring, as outlined in Algorithm 2.  
```
0:\(x^{0},y,F,\Lambda,\alpha>0,\lambda>0,b>0\).
0:\(x\). while \"stopping criterion is not met\" do \(v^{k+1}=x^{k}-\alpha\Big{(}F^{T}\Lambda Fx^{k}-F^{T}\Lambda^{T}Fy\Big{)}\); \(u^{k+1}=D_{\sigma_{1}}\Big{(}x^{k}\Big{)}\); \(w^{k+1}=v^{k+1}+\alpha\lambda b^{2}\Big{(}x^{k}-u^{k+1}\Big{)}\); \(x^{k+1}=D_{\sigma_{2}}\Big{(}w^{k+1}\Big{)}\). endwhile
```  
**Algorithm 2** PnP-FBS-CNC for image deblurring
## 3 Results and Analysis
### Effects of Different Additives on Microbial Community of Corn Stover  
#### 3.2.1 Effects of Different Additives on Microbial Community Structure of Corn Stalk Silage  
Effects of Different Additives on Microbial Community Diversity of Corn Stalk Silage  
Based on the analysis of the nutritional and fermentation quality of corn straw under different additive treatments, this study also conducted microbial research on the CK, LP, LPCE2, and LPXE2 groups. The \(\alpha\)-diversity of microbial communities after 30 and 60 days of silage is shown in Figure 1. In this study, the coverage of all silage treatment groups exceeded 99%, ensuring that all microorganisms in the samples were adequately captured. As shown in Figure 1A,B, the addition of cellulase and xylanase during corn straw silage did not significantly affect the microbial diversity indices (\(p>0.05\)), and the silage duration had no significant effect on the \(\alpha\)-diversity of the microbial communities (\(p>0.05\)). After 60 days of silage, there was no significant difference in community richness between the CE2 and XE2 groups (\(p>0.05\)); however, the community richness of the CE2 and XE2 groups was significantly higher than that of the CK group (\(p<0.05\)). Both the ACE and Chao1 indices showed similar trends. As shown in Figure 1C, after 60 days of silage, the Shannon index of the CK2 group was significantly lower than that of the LP2, CE2, and XE2 groups. Figure 1D shows that the Simpson index of the XE2 group had the lowest diversity, which was significantly different from the CK2 group (\(p<0.05\)).
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 4 Circuit Implementation
### Eight-Phase 12.5% Duty Cycle Clock Generator
#### 4.2.2 NAND and Duty Cycle Calibration  
The NAND circuit is shown in Figure 12. In the NAND combination of two sets of crossed inverters, the size of NMOS is 5 \(\upmu\)m/120 nm and the size of PMOS is 10 \(\upmu\)m/120 nm. Controlling the body voltage \(V_{bdn}\) of NMOS and \(V_{bdp}\) of PMOS in NAND adjusts the duty cycle of the output clock. The ideal clock duty cycle for an eight-path filter is 12.5%. A duty cycle that is too large can lead to filter aliasing; a duty cycle that is too small can lead to increased filter loss. The clock duty cycle is also adjusted by adjusting the bias voltage at the body end of the NMOS and PMOS in the duty cycle calibration circuit.
## 2 Principles
### Time Series Decomposition Algorithm (TD)  
In order to transform the multi-correlation analysis from the one-dimensional time domain to the TF domain, it is necessary to firstly transform the signal into a two-dimensional time domain. A time series decomposition algorithm (TD) can be derived from the SSST to achieve this. If the SSST is transformed by the inverse FT, a time series decomposition is obtained. For the TF spectrum attained by the SSST, if the time \(b\) in the TF domain is fixed but the frequency \(f\) is variable, then the TD is as follows:  
\[TD\big{(}t^{\prime},b\big{)}=\int_{-\infty}^{+\infty}SSST(f,b)exp\big{(}i2\pi ft ^{\prime}\big{)}\text{d}f \tag{13}\]  
where \(t^{\prime}\) denotes the time attained by the inverse FT on the frequency \(f\).  
Similar to the SSST, the TD is reversible:  
\[\int_{-\infty}^{+\infty}TD\big{(}t^{\prime},b\big{)}\mathrm{d}t^{\prime}=x(t). \tag{14}\]  
As the elements of the SSST are complex valued numbers, the elements of the TD are also complex valued. The TD can transform an arbitrary time series into a set of time-limited, localized two-dimensional time series composed of each component, which can highlight the local feature of the signal. The TD decomposes the signal into N-components, and the sum amplitude of these components is equal to that of the original signal. Therefore, in the process of decomposition, the amplitude of each decomposed component will be reduced compared with the original, but each component can highlight the local characteristics.
## 2 Materials and Methods
### Analysis of Handover Decision Factors
#### 2.2.2 Transmission Delay  
When designing the satellite handover algorithm for the power system, it is essential to fully consider the transmission delay of the star-ground link. This is because the transmission delay of the star-ground link directly impacts the real-time and accuracy of data transmission. In smart grid applications, a significant amount of power data requires timely collection and analysis to achieve real-time monitoring and management of the power grid operation status. An increase in transmission delay may result in delayed information transmission, thereby affecting the real-time monitoring and control capability of the system. Especially in the event of system state changes, faults, or emergencies, a short transmission delay is crucial to ensure the timely implementation of necessary measures. Therefore, the design of satellite handover algorithms in power scenarios should consider the transmission delay of the star-ground link to ensure that the power system can promptly and reliably meet the requirements of real-time communication and control. d is the propagation distance between the satellite and the power terminal, calculated as:  
\[d=\sqrt{h^{2}+(x-o_{x})^{2}+(y-o_{y})^{2}} \tag{4}\]  
where (\(o_{x}\),\(o_{y}\)) is the position directly below the satellite, (\(x\),\(y\)) is the coordinate position of the user node, and h represents the vertical height of the satellite from the ground. The transmission delay expression can be defined as:  
\[PD=\frac{d}{c_{light}} \tag{5}\]
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 1 Introduction  
Medical image security in the Internet of Medical Things (IoMT) presents several challenges that need to be addressed to ensure the confidentiality, integrity, and availability of medical images. The healthcare industry has always been at the forefront of incorporating new technology to improve patient care, increase efficiency, and enhance overall healthcare delivery. Information technology (IT) has revolutionized the healthcare industry, improving patient care, enhancing communication and collaboration, and optimizing healthcare processes [1; 2; 3; 4]. Its continued advancements hold significant potential for further transforming healthcare delivery and promoting positive health outcomes. In such a highly IT-oriented era, it is essential for healthcare organizations and technology providers to prioritize the implementation of robust security measures to protect the confidentiality and integrity of medical image transmission over the internet [5; 6; 7; 8]. By adopting security measures, the risks associated with eavesdropping and malicious activities can be significantly reduced, enhancing the overall security of medical image delivery. Three effective techniques--image encryption, image steganography, and image authentication--can reconcile the qualities of medical images with the need for security [8; 9; 10]. Deep learning's ability to automatically learn and extract complex features from medical images has significantly advanced the field of medical image processing [11; 12; 13; 14]. Its potential for improving diagnosis, treatmentplanning, and overall patient care is being explored across various medical imaging modalities, including radiology, pathology, and ophthalmology [15; 16; 17; 18; 19; 20]. As research in deep learning continues to evolve, it holds great promise for further revolutionizing medical image analysis and contributing to advancements in healthcare. Since these advancements have the potential to improve patient privacy, protect against malicious attacks, and ensure the integrity and authenticity of medical image analysis [7; 8; 10; 21; 22; 23; 24] by leveraging the capabilities of deep learning, researchers are actively exploring innovative approaches to enhance the security of medical image data.
## 2 Mathematical Model of Multi-Satellite Scheduling
### Constraints  
#### 2.3.1 Task Time Constraints  
A satellite can only observe one target at a time; that is, each satellite-borne remote sensor can only perform one observation task at any time. The mathematical expression is shown in Equation (1).  
\[t_{1},\,t_{2}\in\ T,\,\text{if}\ f_{t_{1}\ t_{2}}=1,\,\text{then,}\ \text{ST}_{t_{1}}+\text{D}\ t_{1}+\text{M}\ t_{1\ t_{2}}\ \leq\ \text{ST}_{t_{2}},\ \text{and}\ \theta_{t_{1}\ t_{2}}\leq\ 25^{\circ} \tag{1}\]  
In Equation (1), \(t_{1},\,t_{2}\in\ T\), if the targets \(t_{1},\,t_{2}\) occupy the satellite \(S_{m}\) and the observation task for the target \(t_{1}\) is followed by the task for the target \(t_{2}\), then \(f_{t_{1}\ t_{2}}=1\); otherwise, \(f_{t_{1}\ t_{2}}=0\). For the two adjacent observations of \(t_{1}\), \(t_{2}\), the next observation task can be performed only after the current task \(t_{1}\) ends and the necessary attitude maneuver is completed; the attitude maneuver angle must not exceed \(25^{\circ}\).  
The task start time and end time for each target \(t_{r}\) shall be within its corresponding visible window, as shown in Equation (2).  
\[ST_{rm}\geq WinST_{rm}\ \text{and}\ ET_{rm}\leq WinET_{rm} \tag{2}\]
## 2 The Selection Problem of the UAV-LEO Backhaul Links
### System Model  
With the increasing number of LEO satellites, the footprints of the adjacent satellites form more overlapping areas corresponding to the UAVs. Also, some UAVs fly in these overlapping areas. Thus, there are multiple backhaul links that can be selected by the UAV.  
In the association model of the UAV-LEO, multiple backhaul links can be established. At time \(t\), the association can be modeled as a graph \(G(t)=(U,S,E(t))\), where \(E(t)\) is the set of the backhaul links. One vertex set is the UAVs \(U=\{U_{1},U_{2},\ldots,U_{N}\}\), where N is the number of UAVs. The other vertex set is the satellites \(S=\{S_{1},S_{2},\ldots,S_{M}\}\), where M is the number of satellites. There are multiple selective backhaul links for the UAVs to transmit the data to the satellites. These selective links can be described as a candidate set \(\mathbf{H}(t)=\{\mathbf{H_{1}},\mathbf{H_{2}},\ldots,\mathbf{H_{N}}\}\). The element \(\mathbf{H_{i}}\) is an array that identifies the visible satellites for the UAV \(U_{i}\). Due to the higher processing capability of the satellites, multiple backhaul links can be connected to the same satellite simultaneously. In other words, one UAV has multiple visible satellites that can be selected for communication. These selected nodes are put in a set \(F(t)=\{f(1),f(2),\ldots,f(N)\}\). It means the \(i\)th UAV is associated with the \(f(i)\)th satellite. These nodes associated with UAVs are the edge \(E\) of graph \(G\).  
We can take an example as in Figure 2 to illustrate the backhaul link model at time \(t\). There are three satellites and five UAVs. The dashed lines represent the visibility between satellites and UAVs, and the red line is a kind of selected backhaul link. In the view of the UAVs, the visible satellites can be added to a candidate satellite set \(H=\{(1,2),(1,2,3),(1,2,3),(2,3),(3)\}\). One UAV has multiple visible satellites that can be selected for communication. The end nodes of the links are selected from the candidate set \(H\), which are put in a set \(F\), such as \(F=\{1,2,2,3,3\}\). Another selected node set is \(F=\{2,2,2,2,3\}\). There are four UAVs connected to the satellite \(S_{2}\), and the satellite \(S_{1}\) is idle. This situation will cause the system efficiency to decrease. Therefore, different methods of selecting satellites have an important influence on the performance of the system.  
Figure 1: The LEO relay, UAV BS, and IoRT sensor network.  
Figure 2: An association method between UAVs and LEO satellites.
# Self-Improved Learning for Salient Object Detection
## 2 Related Work
### Non-Fully Supervised Learning for SOD  
To alleviate the burden of human efforts on pixel-level annotations, semi-/weakly-supervised methods for SOD have been proposed. These methods require image-level [42; 43; 44; 45; 46], scribble-level [47; 48], or point-level annotations [49; 50]. For example, FIN [42] leverages the category labels and is jointly optimized with an FCN to capture potentially salient regions. WS3A [47] uses scribbles annotation to do local supervision while utilizing auxiliary edges to provide details. PSDD [49] uses point annotation to provide the locations of salient objects to obtain pseudo labels to gain the first round of supervision, and then again uses point annotation to suppress non-salient objects to obtain optimized pseudo labels for the second round.  
Recent years also witness the adoption of self-supervised learning for SOD, which requires no human annotations. A2S [51] and A2 Sv2 [52] employ large-scale unsupervised pre-trained networks. They utilize a two-step approach where the first stage involves constructing reliable pseudo labels and the second stage trains the final segmenter after intermediate label processing. UNSS [53] introduces a top-down context guidance strategy that extracts detailed signals for both global and local segmentation learning.  
In this paper, the training of our method consists of two parts: the Self-Improvement Training strategy (SIT) and Augmentation-based Consistent Learning. They are motivated by self-supervised learning, but both of them utilize pixel-level annotations. Specifically, SIT utilizes the SOD model, which uses pixel-level annotations, to generate smooth labels to alleviate the training difficulty of itself. ACL adopts the CutOut augmentation to remove part of the image and forces the model to make the results of the augmented image consistent with the results of the original image. The removed part is determined using the ground truth, and thus, ACL cannot be treated as unsupervised. In essence, the aim of ACL is to make the model robust instead of reducing human efforts on annotations.
## 2 Study Area and Datasets  
The Qingjiang River Basin (108\({}^{\circ}\)35\({}^{\prime}\)-111\({}^{\circ}\)35\({}^{\prime}\) E, 22\({}^{\circ}\)33\({}^{\prime}\)-30\({}^{\circ}\)50\({}^{\prime}\) N), a tributary of the Yangtze River, is located in southwestern Hubei Province in south-central China. It has an approximate total drainage area of 16,700 km\({}^{2}\) and a stream length of 423 km. This work focuses on the upper Qingjiang River basin (Figure 1), which is dominated by mountains and hills. The study area is dominated by a humid subtropical monsoon climate with four clear seasons. The annual average temperature is 14.1 \({}^{\circ}\)C, the coldest month is January, and the warmest month is generally July. The precipitation shows a significant time variation, with an annual average precipitation of approximately 1400 mm that is mostly concentrated in the flood season (April-September). The flow regime of the Qingjiang River is characterized by a seasonally fluctuating flow due to rainstorms in the rainy season [31]. Figure 1 shows the location of the Qingjiang River basin and the distribution of hydrological and meteorological stations.  
In this study, both observations and EPFs at the daily time step were used. The observations include daily air temperature, precipitation, and potential evaporation, as well as streamflow from 2014 to 2017. The meteorological data were obtained from 175 automatic weather stations, and hydrological data were obtained from the Shuibuya hydropower station (Figure 1). The ensemble precipitation forecasts used in this study were obtained from two well-behaved NWP centers in the TIGGE database, i.e., ECMWF and NCEP. Information of the models, including model sources, forecast length, number of ensembles, and initial date of TIGGE operational model, is shown in Table 1. The precipitation forecasts with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) at 1--7 lead days from 2014 to 2017 were evaluated in this study.
# Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision
## 4 Approach
### Text Features
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learner** & **Variant** & **Precision** & **Recall** & **F1-Measure** \\ \hline Naive Bayes & unigram & 0.3943 & **0.6376** & **0.4873** \\ \hline Decision Tree & unigram & 0.4219 & 0.4168 & 0.4193 \\ \hline SVM & unigram & 0.5878 & 0.1559 & 0.2465 \\ \hline Neural Network & unigram & 0.4471 & 0.3951 & 0.4195 \\ \hline XGBoost & unigram & **0.6630** & 0.0641 & 0.1170 \\ \hline Random Forest & unigram & 0.5226 & 0.4095 & 0.4592 \\ \hline Naive Bayes & bigram & **0.4486** & 0.1320 & 0.2040 \\ \hline Decision Tree & bigram & 0.4139 & **0.3178** & **0.3595** \\ \hline SVM & bigram & 0.5930 & 0.1029 & 0.1754 \\ \hline Neural Network & bigram & 0.3935 & 0.1414 & 0.2081 \\ \hline XGBoost & bigram & 0.4083 & 0.0596 & 0.1040 \\ \hline Random Forest & bigram & 0.4450 & 0.2945 & 0.3544 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performances of the learners with a bag-of-words model with 500 terms for unigrams and 250 terms for bigrams. The best performance for unigrams and biagrams is marked in bold.
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
###### Abstract  
The existing public key-based en-route filtering schemes are vulnerable to report disruption attacks or selective forwarding attacks, and they fail to consider any measure to detect and punish the malicious nodes. The authors propose a series of public key-based security mechanisms for wireless sensor networks (WSNs) in this paper, including a mechanism for verifying the partial signatures, a substitution mechanism, an effective report forwarding protocol, and a trust value-based mechanism to identify and punish the malicious nodes. Finally, the authors develop a public key-based authentication and en-route filtering scheme (PKAEF), which can resist false data injection attacks, report disruption attacks and selective forwarding attacks, and can mitigate the impact of malicious nodes. Detailed performance analysis and evaluation show that, in most cases, PKAEF outperforms previous works in terms of safety, filtering efficiency, and data availability.  
wireless sensor network; security; en-route filtering; elliptic curve cryptography 1