esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
This section shows the experimental results and compares them with the results of [9; 15; 22; 35; 37; 38]. Three test images: Lena, Baboon and Jetplane, as shown in Figure 3. In addition, the measured metric PSNR (Peak Signal-to-Noise Ratio), as seen in Equation (10), is used to evaluate the visual quality. After the image is encrypted, the encrypted results of Figure 2(a)-c are shown in Figure 2(d)-f, respectively. The encryption result is meaningless to users. The PSNRs of Figure 2(d)-f are 8.816, 9.524 and 8.677 dB, respectively. Figure 2(g)-i shows that the attacker used the wrong key to decrypt the image, and the PSNRs are 8.797, 9.507 and 8.656 dB, respectively.  
\[PSNR=10\times log_{10}\frac{255^{2}}{MSE} \tag{10}\]\[MSE=\frac{\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}(p^{*}(i,j)-p(i,j))}{m\times n} \tag{11}\]  
Hidden capacity is expressed in bits per pixel (bpp). Table 2 shows the hiding capacity of the test images Lena, Baboon and Jetplane. Here, the field \"Label\" is mapped to the falling case according to the value of \(c^{*}(i,j)\) and the image type (smooth or complex image), and the \"distribution\" is the number of pixels classified into the same \"Label\". \"Payload\" refers to pure payload. The payloads of the images Lena, Baboon and Jetplane are 2.58, 1.19 and 3.43 bpp, respectively, which are higher than those in [35]. In addition, the experiments showed that the prediction results for smooth images (such as Lena and Jetplane) were better than those for complex images (such as Baboon), which resulted in smooth images having greater hiding capacity than complex images did. Moreover, thresholds \(t_{1}\) and \(t_{2}\) settings are the flexible factors for different images to make the prediction pixel close to the original ones. The close prediction causes a better hiding capacity. Table 3 compares the payload (bpp) of the proposed scheme with the methods in [9; 15; 22; 35; 37; 38]. The results show that the proposed method outperforms the six other algorithms.  
Figure 3: Test images and the corresponding encrypted and decrypted with wrong key images. (**a**) Lena. (**b**) Baboon. (**c**) Jetplane. (**d**) Encrypted result of (**a**). (**e**) Encrypted result of (**b**). (**f**) Encrypted result of (**c**). (**g**) Illegal decryption of (**d**). (**h**) Illegal decryption of (**e**). (**i**) Illegal decryption of (**f**).
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 5 Informal Security Analysis
### Man-in-the-Middle Attack  
An adversary, \(A\), could intercept messages transferred on a public channel. In our scheme, an adversary, \(A\), needs to make the \(GWN_{k}\) believe that it is from the user, \(U_{i}\). However, the adversary, \(A\), cannot pass the verification without the identity, \(id_{i}\), and password, \(pw_{i}\), to calculate \(M_{2}\). Meanwhile, only the \(GWN_{k}\) can calculate \(K_{U-G}\) to decrypt \(M_{5}\) and encrypted messages with the encryption key \(S_{j}\) to the sensor node \(Sn_{j}\), so the adversary cannot impersonate the user and the gateway node. In the same way, the adversary cannot impersonate the sensor node since the adversary does not know \(S_{j}\) to decrypt the encrypted message. Therefore, the scheme can withstand the man-in-the-middle attack successfully.
## 3 Results and Discussion
### Prediction of Antibiotics Removal Efficiency in Absence of Br\({}^{-}\)In Synthetic Water  
Searwater contains approximately 18,000 mg/L chloride ions and 60 mg/L bromide ions. As mentioned in the introduction, the primary oxidant in seawater ozonation is bromine (HOBr/OBr\({}^{-}\)), and not ozone, even with this high concentration of chloride ion in seawater. The reaction rate of ozone with bromide ion (160 M\({}^{-1}\)s\({}^{-1}\)) is 53,000 times higher than that with chloride ion (k\({}_{\text{Ozone,C1-}}\) = 0.003 M\({}^{-1}\)s\({}^{-1}\)). Even though chlorine is generated by the reaction of chloride ion with ozone, the chlorine reacts with bromide ion to generate bromine (2950 M\({}^{-1}\)s\({}^{-1}\)). In fact, bromine is the primary oxidant along with trace ozone, for seawater ozonation.  
The removal efficiency for the target antibiotics, FF and OA, by seawater ozonation was predicted using the reaction rate constants for the reaction of FF and OA with ozone and bromine determined from this study. To verify the value predicted using the calculated reaction rate, the removal efficiencies of FF and OA from synthetic seawater were evaluated; synthetic sea water was used owing to the difficulties of analysis under real seawater condition. Synthetic seawater was prepared using bromide ions of concentration 60 mg/L, at pH 8, without chloride ions. In this test, the effects of ozone and bromine were considered only with respect to their role in antibiotic removal. The ozone decomposition and bromine formation were measured and compared with the predicted values. Subsequently, the actual removal efficiencies for the target antibiotics, FF and OA, were compared with the predicted values.  
Figure 3: pH dependence of the apparent rate constants for the bromination of (**a**) florfenicol and (**b**) oxolinic acid. Symbols: experimental values; dotted line: calculated values of the apparent rate constant; solid line: speciation of bromine and florfenicol, ([Bromine]\({}_{0}\) = 3000 and 5000 \(\upmu\)M, [FF]\({}_{0}\) = 1 \(\upmu\)M, [OA]\({}_{0}\) = 1 \(\upmu\)M, pH 3–10, Temp. = 20 \({}^{\circ}\)C). ([Bromine]\({}_{0}\) = 20 \(\upmu\)M, [OA]\({}_{0}\) = 1 \(\upmu\)M, pH 5–10, Temp. = 20 \({}^{\circ}\)C).
## 2 Methodology and Networks
### Role of the Discriminator in Model Training  
Understanding the intricate dynamics within the realm of digital rock reconstruction, particularly the role of the discriminator in a Generative Adversarial Network (GAN) setup, is imperative. This section delves deep into elucidating this pivotal component.  
As illustrated in Figure 2, the GAN architecture hinges on two primary modules: the generator and the discriminator. While the generator's function is to produce synthetic rock images, the discriminator's responsibility lies in discerning these generated structures from real rock samples. This dual interplay forms the foundation of our methodology.  
Functioning of the Discriminator  
Input Assimilation: The discriminator concurrently receives both real rock images and the synthetic outputs generated by the model.  
Differentiation: Tasked with distinguishing between the real and the synthesized, the discriminator evaluates the authenticity of each image. This discernment process becomes the primary feedback mechanism for the model, guiding the generator's subsequent iterations.  
Figure 2: Discriminator’s quality control mechanism in GAN training.  
Feedback Mechanism: Based on the evaluation, the discriminator relays its findings to the generator. If the generated images deviate from real samples, the feedback instructs the generator on areas of improvement.  
Optimization Goal: The continuous feedback loop aims to refine the generator's output with each iteration. The ultimate objective is to reach a juncture where the discriminator, despite its adeptness, struggles to differentiate between genuine and generated rock structures. This indicates that the synthesized images have attained a realism level mirroring actual rock structures.  
In essence, the discriminator serves as the model's quality control mechanism. By constantly challenging the generator's outputs, it ensures that the end results are not only realistic but also of superior fidelity. This internal rivalry within the GAN setup accelerates the learning process and guarantees the production of high-caliber digital rock reconstructions.
# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 3 Proposed Model
### Regional Weighted Loss Function  
CycleGAN exhibits the phenomenon of changing the overall brightness tone. Therefore, a local weighted loss function for local tone training was proposed as the second training step. When the local tone area is trained from the L channel of a target image, the region of interest (ROI) comprises areas with a high local tone contrast. The RWL is the difference between the source and target images' local brightness. The luminance weight map of an image is devised using \(GF_{i}\) obtained using a Gaussian filter \(g(\bullet)\) and normalization. Equation (12) obtains \(W_{i}\) to train the dark and bright areas according to the gamma values of input images:  
\[GF(i)=N[g(i)], \tag{11}\]  
\[W(i,\gamma)=\left\{\begin{array}{cc}1-GF(i),\text{ }if\text{ }\gamma\geq 1\\ GF(i),\text{ }if\text{ }0<\gamma<1\end{array}\right., \tag{12}\]  
where \(GF\) represents a Gaussian filter of \(11\times 11\) size, \(i\) represents the luminance of images, and \(N(\cdot)\) represents the normalization function.  
A weight value \(W(\bullet)\) must be obtained according to the brightness level based on the gamma value of \(1\) because the training data are in the form of a gamma shift. \(\gamma\) represents the gamma value of the source image.  
These were performed to reduce the spatial consistency and brightness difference between the target and generated images.  
\[RWL_{G}(G,D_{Y},x,y)=\mathbb{E}_{y\sim p_{data}(y)}[logD_{Y}(y\times W(y,\gamma ))]+\mathbb{E}_{x\sim p_{data}(x)}[log(1-D_{Y}(G(x)\times W(y,\gamma))], \tag{13}\]  
where \(RWL_{G}\) represents the RWL of generator loss value and \(x\) and \(y\) represent the gamma shift source and target images, respectively.  
\[RWL_{F}(F,D_{X},y,x)=\mathbb{E}_{x\sim p_{data}(x)}[logD_{X}(x\times W(x,\gamma ))]+\mathbb{E}_{y\sim p_{data}(y)}[log(1-D_{X}(F(y)\times W(x,\gamma))], \tag{14}\]  
where \(RWL_{F}\) represents the RWL of reconstructor loss value and \(y\) and \(x\) represent the gamma shift source and target images, respectively.  
The role of the reconstructor is to restore a source image from a generated image. Thus, this function trains the weight map of \(x\). The training direction is the opposite of the generator's training direction. Figure 4a depicts the target image, whereas Figure 4b,c depict the images of \(W(y,\gamma)\) and \(1-W(y,\text{ }\gamma)\) obtained from the generator, respectively. By optimizing the loss values between the luminance maps, a source image is learned from the luminance ROI in a target image. The proposed model demonstrated an improved image enhancement performance compared with existing models. However, the proposed model did not guarantee full tone compression performance. It did not perfectly regulate the regions that increased and decreased brightness. Therefore, it is necessary to balance the bright region restoration speed with the dark region restoration speed.  
Figure 3: Difference in tone compression: (**a**) EV shift and (**b**) gamma shift.
# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 4 Simulation and Results
### Feature Analysis  
Several important indications were uncovered via the examination of feature importance in identifying DDoS attacks. These indicators were generated using the Boruta algorithm and correlation studies. Particularly important for identifying DDoS patterns are properties like orig_ip_bytes, resp_ip_bytes, tcp.flags.push, and tcp.srcport. Abnormal traffic bursts and transport layer resource depletion are two examples of DDoS behaviors that are strongly correlated with these characteristics. Since volumetric DDoS attacks sometimes take advantage of TCP handshake processes, the SYN and ACK TCP flags are very helpful in differentiating between malicious and benign traffic. Also, answer bytes (resp_ip_bytes) and packet sizes (frame.len) are related to DDoS attacks' impact on resource use. These results are in line with what we expect from DDoS attacks at the transport and network layers, where these characteristics are useful for detecting abnormalities.  
These important characteristics' relationships in the IoT-23 and APA-DDoS datasets are further shown in Figures 4 and 5. In order to improve the EffiGRU-GhostNet model's detection capabilities, these variables were highly prioritized for their predictive value throughout the Boruta algorithm's selection phase.
## 6 Experiment
### Experimental Setup  
In the experiment of DGS-HSA, we select the trajectory data within three kilometers of BUPT in the Geolife Trajectories 1.3 dataset as the historical location dataset \(G\). The processed historical location dataset has three first-level grids (\(|R^{sub}|=3\)) and approximately 1300 second-level grids (\(|U^{sub}|=1300\)). One hundred users are randomly distributed in different second-level grids. The basic service categories of Amap are used as the basic service set of the experiments, which has about 21 categories (\(M=21\)). Set (\(k=45\)), then (\(s=1,2,\cdots,k\)). Each user sends 100 query requests at a frequency of once per minute.  
In the experiment of L2P2, the second-level grids where the residential quarters and hospitals are located are set as sensitive areas. In order to ensure the privacy protection effect, we set the PID exchange probability \(\rho=0.5\) and \(l=\frac{k}{2}\).  
In the experiment of REGP, we divide all second-level grids into four _PLs_. The second-level grids where the confidential organizations are located are set as the first-level privacy zone \(w_{1}\), such as the military and scientific research departments. The second-level grids where the hospital and residential community are located are set as the second-level privacy zone \(w_{2}\). In \(w_{2}\), personal privacy is easily leaked. The second-level grids where the parks are located have relatively fewer people and are set as the third-level privacy zone \(w_{3}\). The second-level grids that the malls and the schools are located in are densely populated, and are set as the fourth-level privacy zone \(w_{4}\). We also set the parameter \(\lambda=4\) and \(w_{1}:w_{2}:w_{3}:w_{4}=8:6:4:1\).
## 3 Results
### GNSS Measurement
The result shows that the DInSAR result is still applicable at this location with lower accuracy. The descending data also shows low accuracy and is not appropriate at this location. The correlation coefficient is the relation between the GNSS and DInSAR measurements. If the coefficient approaches one, this indicates that these two data are the same. On the other hand, if the coefficient is less than zero, this means completely different results and even indicates minor mean errors. Thus, we can conclude that ascending SAR data is better than descending SAR data in this study area. The results show that ascending SAR data is suitable for the east aspect slope.
## 2 Materials and Methods
### Jamming Event Detector
They can be expressed by the following equations:  
\[\left(\overline{\text{C}/\text{N}_{0}}\right)_{f_{signal_{n}}}=\frac{1}{ \left(N_{sat}\right)_{n}}\sum_{k=1}^{\left(N_{sat}\right)_{n}}\left(\text{C}/ \text{N}_{0n}\right){}_{k} \tag{3}\]  
\[\left(\overline{\text{C}/\text{N}_{0}}\right)_{f_{signal_{j}}}=\frac{1}{ \left(N_{sat}\right)_{j}}\sum_{k=1}^{\left(N_{sat}\right)_{j}}\left(\text{C}/ \text{N}_{0j}\right){}_{k} \tag{4}\]  
In Equation (3), \(\left(\text{C}/\text{N}_{0n}\right)_{k}\) is the nominal C/N\({}_{0}\) in the absence of any deliberate jamming of the \(k^{th}\) satellite, \(\left(N_{sat}\right)_{n}\) is the total number of satellites observed by the receiver in the nominal situation. The \(\left(\overline{\text{C}/\text{N}_{0}}\right)_{f_{signal_{n}}}\) is derived only once for a specific \(f_{signal}\) in nominal situation. In Equation (4), \(\left(\text{C}/\text{N}_{0j}\right)_{k}\) is the observed C/N\({}_{0}\) at the \(j^{th}\) time instant of the \(k^{th}\) satellite, \(\left(N_{sat}\right)_{j}\) is the total number of satellites observed by the receiver at the \(j^{th}\) time instant. There is also a recommendation proposed in [20] to report the duration of each event (i.e., the time when interference starts and the time when it ends), which is implemented in this manuscript. For this purpose, the duration of jamming event detector is defined as follows:  
\[t_{j}=Jamming_{EndTime}-Jamming_{StartTime} \tag{5}\]  
In Equation (5), \(t_{j}\) is the duration of jamming in seconds. \(Jamming_{StartTime}\) is defined as the time when \(\Delta\left(\text{C}/\text{N}_{0}\right)_{f_{signal}}\) is either equal to or above a predefined threshold (i.e., 4 dB in this case). Similarly, \(Jamming_{EndTime}\) is defined as the time when \(\Delta\left(\text{C}/\text{N}_{0}\right)_{f_{signal}}\) is below the predefined threshold. However, there must always be a valid \(Jamming_{StartTime}\) in order to recognize \(Jamming_{EndTime}\) as a valid time. When jamming is observed on any GNSS signal, reporting a jamming incident to end-users becomes quite crucial. The end-users will be notified of a jamming incident immediately after the end of a jamming event.
## 1 Introduction
This information helps identify critical areas prone to crack initiation and propagation, allowing engineers to focus their efforts on preventive measures and inspections. By integrating machine learning models into structural health monitoring systems, early detection of crack propagation can be achieved, enabling timely maintenance and repair actions. This proactive approach enhances the dependability and longevity of structures by preventing failures and minimizing downtime. 2. Cost and Resource Optimization: Accurate crack propagation prediction enables optimized allocation of resources and cost-effective maintenance strategies. By identifying critical areas and accurately predicting crack propagation rates, engineers can prioritize inspection and maintenance efforts, allocating resources where they are most needed. This targeted approach optimizes resource utilization, reduces unnecessary maintenance activities, and lowers overall costs while ensuring the long-term dependability of structures. Our study aims to demonstrate the potential of machine learning models as effective tools for predicting crack propagation in materials. The remaining sections of this paper are structured as follows: Section 2 provides an overview of the materials used in this study, along with the machine learning models employed for crack propagation prediction under coupled load and temperature. In Section 3, we present the results obtained by applying the selected algorithms to three distinct material datasets. A comprehensive analysis of these results is provided, highlighting the performance and effectiveness of the various models. Finally, the concluding section summarizes the key findings of our research, shedding light on the comparative analysis of the machine learning models for crack propagation prediction under coupled load and temperature. It consolidates the main outcomes and implications derived from the study, potentially paving the way for further advancements in this field.
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 1 Introduction
We integrate the focusing mechanism Wise-IoUv3 [30] loss into the bounding-box regression loss calculation. This helps minimize the influence of dataset quality differences on gradient updates. 4. We propose a Multi-Location Cross-Attention Ship Detection Network (LCAS-DetNet), a multi-scale cascaded object detection algorithm. LCAS-DetNet was tested on two ship detection datasets (HRSID [31] and SSDD [32]), and the results demonstrated state-of-the-art performance.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 6 Wearable Devices for Heart Rate Monitoring  
Wearable healthcare devices that track physiological parameters are either non-invasive or minimally invasive--for example, blood glucose level monitoring [120]. Their design must conform to the needs of the user in terms of comfort and functionality and the output data must be informative and accurate to inform medical interventions when needed [34]. Recently, novel types of wearable devices have been developed; for example, a wearable device using triboelectric nanogenerator technology [121]. It is self-powered in a sense that it can harvest the energy released by the radial artery pulse in the wrist and its potential application is uninterrupted heart rate monitoring over prolonged periods. Nevertheless, technology readiness level for such exotic devices is relatively low and the following subsections will explore the existing solutions for wearable devices for heart rate monitoring based on PPG and bioimpedance signals whose nature is well-understood.
# An Overview of Fog Data Analytics for IoT Applications
### Need for Fog Computing in IoT  
We have gone through the paradigms of IoT and the introduction of fog computing into the IoT paradigm. Therefore, now, we will dive deep into the discussion as to why we should be using fog or edge computing in the IoT-driven world and why it is so disruptive [18]. We will first look at how our network would look with fog computing.  
Cloud has more power, while fog has more agility, and agility will beat power when real-time processing and decision making is involved. With great power comes great responsibility, so the cloud has to make computations for all of the devices. In contrast, the fog has to make computations related only to locally connected devices. The devices connected mainly consist of sensors or actuators, which generate huge amounts of data every second. These data are also very raw, contain noise, and need to be pre-processed before some meaning can be derived. For example, it has been shown that a typical driverless car generates several megabytes of data every minute. This is where fog computing might be useful. Fog computing extends the already existing concept known as edge Computing. The edge computing paradigm states that computing and storage are performed directly on the end devices or near them. At the same time, fog computing addresses near computing but is separated from edge devices. The fog computing model is a distributed computing or decentralized model as opposed to the centralized model of the cloud [19]. Every fog node forms a layer of the interconnected network with one another next to the layer of interconnected IoT devices. The role of these fog nodes is not only to process data generated by the devices but also to transmit the decisions to the cloud via other fog nodes so that all of the fog nodes can have a common view of the system as a whole [20].
# Nighttime Image Dehazing Based on Point Light Sources
## 4 Experiment
### Main Results  
In order to verify the universality of the algorithm, foggy images in various environments (city, road, riverside, park) are selected for experiments, and 150 real nighttime foggy scene images are used in the dataset for experiments and evaluation. The selected comparison algorithms include DCP [14], NDIM [25], and OSFD [35]. Through experimental comparison, the experimental results are analyzed and evaluated from both subjective and objective aspects.  
As shown in Table 2, We compare the differences between the proposed method and DCP in terms of the way to calculate atmospheric light and transmittance. For the atmospheric light value, DCP selects the first 0.1% of the pixel values in the dark channel of the image and then chooses the brightest of these values as the constant atmospheric light value. This method may cause the defogged image to be too light near the light source. Our method calculates the atmospheric light value by making the image through the median filter directly. For transmittance, the proposed method corrects the transmittance at the near light source with the light source influence matrix based on the dark channel prior, and, finally, blends the transmittance at the near and far light source areas with the edge-preserving filter.