esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Feature Guided Low-Light Image Enhancement
## 1 Introduction  
When the object is in a low-light environment, such as backlight, at night, or the image is underexposed during the capture process, the resulting low-light image will affect visual perception and lose information. The loss of image information will cause many adverse effects. For example, in the target detection task at night, it will cause the accuracy of the detection results to be reduced, and even missed and false detections.  
Image enhancement based on low-light images is a current research hotspot and difficulty in the field of image processing. This technology is widely used, and most commonly used to enhance photos taken by the camera at night or in other low-light scenes. [PERSON] et al. [1] propose a new image processing pipeline to avoid the noise amplification and error accumulation problems caused by traditional camera processing pipelines. [PERSON] et al. [2] combine this method with target detection technology to improve the accuracy of detecting targets at night. However, due to its own characteristics, low-light images are often mixed with a lot of noise, which increases the difficulty of image enhancement. In order to achieve the desired enhancement effect, researchers have proposed many ideas and methods. In the early days, high-quality images were obtained by enhancing image contrast; the representative method is histogram equalization. However, this type of method is incapable of restoring image details and colors. With the development of deep learning in recent years, data-driven neural network methods have also been widely adopted and improved. The neural network-based method can learn more complex color and contrast transformations and has strong expressive power, but there are still many problems. The current mainstream traditional image enhancement methods and the existing problems of neural network enhancement methods are shown in Figure 1:It can be seen from Figure 1 that traditional models such as histogram equalization (HE) [3] only consider contrast enhancement when enhancing images, while Retinex-Net [4] and NPE [5] methods will cause serious color distortion. Methods such as MF [6], LIME [7], SRIE [8], and MSR [9] are lacking both in image quality and visual expressiveness, which affect the sensory aesthetics and later target detection work.  
In this paper, we propose a new deep neural network architecture to enhance low-light images. For noise removal and illumination enhancement, feature maps are extracted through two sub-networks to obtain noise and illumination features. Then, it is guided by the extracted features, and the denoising and enhancement work are performed at the same time, which avoids the limitations caused by the first enhancement and then the denoising, or the first denoising, and then the enhancement. In addition, we also use the method of bilateral grid upsampling to accelerate the image processing process and achieve the purpose of fast image enhancement without reducing the image quality.
## 3 Sliding Mode Controller Design
### Fuzzy Sliding Mode Controller Design
#### 3.2.2 Fuzzy Rule Design  
The real field operating environment is complicated by uneven terrain and variations in the depth of the mud foot as well as several unknowable factors. This work presents a control frame that combines sliding mode control and fuzzy control in order to better enhance the control effect. Fuzzy control is used to alter the parameters of the non-smooth terms and the function terms in the controller (14), and a tradeoff between disturbance rejection and quick reaching is discovered. The controller parameters may be tuned using the changes in sliding mode variables and their derivatives since the sliding mode variables defined in this work comprise information such as the vehicle position error, heading error, articulated angle, etc. To make parameter adjustments simpler, Equation (14) is further revised as  
\[\delta=\arctan\left(\frac{L_{1}}{v(\beta_{1}L_{2}-\beta_{2}L_{3}+\beta_{2}L_{ 2})}\begin{pmatrix}\beta_{2}v\text{sin}\varphi-L_{2}v\text{asin}\theta-\\ L_{2}K_{1}s-L_{2}K_{20}K_{21}|s|^{\beta}\text{sign}(s)\end{pmatrix}\right) \tag{17}\]  
where \(K_{20}\) is the preset initial parameter and \(K_{21}\) represents the output of the fuzzy controller.  
A single-output and double-input fuzzy controller is established in this study, i.e., the sliding variable (\(s\)) and its derivate (\(\dot{s}\)) are the inputs, and \(K_{21}\) is the output. The universes of discourse for \(s\), \(\dot{s}\) and \(K_{21}\) are arranged as [0; 1] and [1; 40]. The fuzzy rules define NB,NM, NS, ZO, PS, PM, and PB to represent negative large, negative medium, negative small, zero, positive small, median, and superficial, respectively. Z, LE, MS, ML, and LA denote zero, small, medium, medium, and large, respectively. The fuzzy rules are shown in Table 2 and Figure 3. The fuzzy logic controller was designed using the fuzzy logic toolbox in MATLAB 2018b.
## 4 Experiments
### Experiment Results
This stability can be attributed to its static nature, which, while less adaptive to dynamic changes, provides consistent results even under imbalanced conditions. These findings collectively emphasize the advantages of SCTD in handling real-world datasets with inherent imbalances and sparsity, further validating its practical applicability and robustness. In addition to validating the robustness of the model by constructing the Imbalanced-Yelp dataset, we supplemented our experiments with the Jingdong dataset, which has 25,152 user ratings and only 8310 reviews, and we conducted comparative experiments on such a sparse user ratings-reviews matrix, obtaining the experimental results shown in Table 4. Through the experiments, we could find that SCTD is optimal in all metrics on the Jingdong dataset. It is worth noting that CISD performs causal inference on user reviews to mitigate sentiment bias, and thus, the performance degradation is severe in real scenarios where user reviews are severely missing. While the model TMF-DANCER has less performance degradation, unlike the experiments on the Yelp dataset, the increase in data sparsity causes TMF-DANCER to be worse than our model SCTD in all the metrics. This proves the enhanced robustness of our model in real datasets where data sparsity exists. Figure 4: MSE on Imbalanced-Yelp. To rigorously validate the significance of performance improvements, we conducted paired t-tests between SCTD and the best baseline (TMF-DANCER) on the Yelp dataset. We ran each model 10 times with different random seeds and recorded MSE, MAE, and ACC. The results (Table 5) show that SCTD achieves statistically significant improvements in MSE (\(p\) = 0.003) and ACC (\(p\) = 0.021). This confirms that SCTD's superiority is not due to random variation. The extensive experiments conducted validate that our proposed approach not only adapts well to real-world imbalanced datasets but also consistently outperforms the baseline models, offering a robust solution for practical recommendation scenarios.
## 3 Results
### Research Questions' Answers
#### 3.2.2 Continents Analyzed  
The geographical areas modeled are analyzed at a continent level and discussed in more detail in this section. There are 17 articles modeling North America, 15 modeling Asia, 14 modeling Europe, and 1 modeling Africa. Figure 5 presents a comparison of the frequency of articles analyzed in the current SLR based on their scale and the analyzed continents. It is evident that Asia has been the primary focus of researchers, with all three types of scales being applied in various analyses. On the other hand, Africa has received the least attention, with only urban city scale being analyzed in this continent. In addition,  
Figure 4: Frequency of articles with different spatial scales in current SLR.  
it should be noted that the urban city scale has been the most commonly used scale in previous research, while the community scale has been the least applied scale.  
The vast majority of articles modeling Asia are concentrated on China [17]. This could be influenced by the fact that China has a history of summer flooding, but recent deforestation, reclaiming of wetlands, and storage of water for power generation and irrigation have made it more vulnerable [18].  
Of the articles modeling Europe, [PERSON] and [PERSON] [19], [PERSON] et al. [3], [PERSON] et al. [20], and [PERSON] et al. [21] focused their models on the UK; [PERSON] et al. [22], [PERSON] et al. [23], and [PERSON] et al. [24] on Spain; [PERSON] et al. [25] and [PERSON] et al. [26] on Poland; [PERSON] et al. [27, 28] on Italy; [PERSON] et al. [29] on Greece; and [PERSON] et al. [30] on Portugal. The distribution of articles across Europe highlights the high and frequent levels of disruption in traffic caused by flooding. This may be due to the inadequate drainage systems that are unable to cope with flood runoff and also as mentioned by Britain and [PERSON], predicted increases in rainfall resulting from climate change could exacerbate this situation [31]. This prediction justifies the growing research in this field worldwide.  
Most North America-focused articles modeled the US, except [PERSON] et al. [32], who modeled Canada. [PERSON] et al. [33] and [PERSON] [34] modeled the US more broadly, whereas others had narrowly defined geographic focuses. For example, [PERSON] et al. [35] focused on the Memorial super neighborhood in Houston to simulate and assess the effects of floods on traffic. The single article modeling Africa [36] focused on Kinshasa.  
Overall, researchers have primarily investigated the indirect effects of flooding on transportation in major metropolitan areas of developed countries with well-established infrastructures. Some examples of these areas include York [19, 21], New York [37], Shanghai [38], Shenzhen [18], and Seoul [39]. On the other hand, there has been limited research publications in the literature for municipalities with inadequate or low-quality infrastructure, particularly in developing countries.
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
### Existing Limitations  
Manufacturing businesses operate in remote areas, which makes status visibility and data backup difficult. When exploited, these vulnerabilities may result in DoV (Technique T0815), DoC (Technique T0813), and, worse, DoS [8; 9; 10; 11; 12]. Threats include possible natural disasters and other unfavorable events in addition to cyberattacks, which could prevent the provisioning of manufacturing services. Available options at this time, such as integration with third-party networks, are reliant on the provider's accessibility in the region and may require the private entity's willingness to give up control over OT traffic management to a third party or vendor. Data integrity may be jeopardized by possible third-party issues [33] (pp. 47-54), [34] (pp. 4543-4572), [35] (pp. 680-693).  
The research community has been actively exploring both software-defined network (SDN)-based network security and the security of the SDN architecture, as highlighted in [36]. Additionally, cybersecurity mechanisms within software-defined wide area net works (SD-WAN) architectures have been examined, comparing private and open-source solutions [37, 38]. However, to the best of our knowledge, and as will be discussed in the Related Works Section, existing research primarily focuses on the CIA triad and IT infrastructure, without adequately assessing the effectiveness of SD-WAN in mitigating prevailing cybersecurity risks within OT infrastructure. Moreover, current studies rely on simulation tools to evaluate the security capabilities of SDN and SD-WAN, focusing on their functionalities rather than mapping them to OT-specific risks. Most SDN studies concentrate on LAN traffic analysis, whereas this paper emphasizes SAI and the risks associated with extending OT infrastructure using WAN technology. In other words, this paper highlights the importance of analyzing a reliable and secure OT WAN infrastructure for data management and visibility across remote manufacturing sites.
# Optimal Cluster Expansion-Based Intrusion Tolerant System to Prevent Denial of Service Attacks
## 3 Proposed ITS System
### Unprocessed Packet Distribution  
Whenever a cluster needs expansion because of a massive QoS attack, it takes time for the cluster to be extended. During the cluster expansion time, the packets from DoS attacks accumulate in the online state VM queues. Thus, QoS will be necessarily degraded. To mitigate this problem, an additional packet distribution process is performed while the cluster expands. The total number of unprocessed packets, denoted as \(S\), can be computed by summing all unprocessed VM packets. \(S\) can be expressed as  
\[S=\sum_{i=1}^{n}Z_{i}, \tag{3}\]  
where \(n\) is the number of online VMs before cluster size expansion, and \(Z_{i}\) is the number of unprocessed packets of the \(i\)th VM. After cluster size expansion, the unprocessed packets are distributed to all online VMs, including newly created VMs. Let \(D\) be the average number of packets allocated to each online VM, which can be calculated as follows:  
\[D=\frac{S}{N}, \tag{4}\]  
where \(N\) is the total number of online VMs after cluster size expansion. Therefore, in our proposed scheme, an unprocessed packet distribution process is performed whenever the cluster size is extended, in order to preserve the required QoS under a massive QoS attack.
_Article_  
**Strain Rate Dependent Behavior of Vinyl Nitrile**  
**Helmet Foam in Compression and Combined**  
**Compression and Shear**  
**[PERSON] 1,2,*, [PERSON] 1,2(r), [PERSON] 1,2, [PERSON] 1,2, [PERSON] 3 and [PERSON] 1,2**  
1 Department of Mechanical Engineering, Ecole de Technologie Superieure, 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; [PERSON]RESS] (Y.P.); [EMAIL_ADDRESS] (J.-M.D.); [EMAIL_ADDRESS] (O.L.); [EMAIL_ADDRESS] (E.W.)  
2 Research Center, Department of Traumatology and Acute Care, Hopital du Sacre-Cccur de Montreal, 5400 Gouin Blvd, Montreal, QC H4J 1C5, Canada  
3 CCM, 3400 Raymond-Lasnier St., Montreal, QC H4R 3L3, Canada; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel: +1-(514)-567-8296  
Received: 5 October 2020; Accepted: 18 November 2020; Published: 23 November 2020  
**Featured Application: Results of this work can be used to compare VN foams to other helmet liner materials and could help in the design of new protective devices.**  
**Abstract:** Vinyl nitrile foams are polymeric closed-cell foam commonly used for energy absorption in helmets. However, their impact behavior has never been described in isolation. This study aims to characterize the strain rate dependent behavior of three VN foams in compression and combined compression and shear. Vinyl nitrile samples of density 97.5, 125, and 183 kg/m\({}^{3}\) were submitted to quasi-static compression (0.01 s\({}^{-1}\)) and impacts in compression and combined compression and shear (loading direction of 45\({}^{\circ}\)). For impacts, a drop test rig was used, and a method was developed to account for strain rate variation during impactor deceleration. Young's modulus and stress at plateau were correlated with foam density in both compression and combined loading. Vinyl nitrile foams were strain rate dependent: The absorbed energy at the onset of densification was two to four times higher at 100 s\({}^{-1}\) than at 0.01 s\({}^{-1}\). In combined loading, the compressive stress at yield was reduced by 43% at a high strain rate. Compared to expanded polypropylene, vinyl nitrile foams transmitted less stress at the onset of densification for equivalent absorbed energy and presented a larger ratio between the compression and shear stresses in combined loading (0.37 at yield). This larger ratio between the compression and shear stresses might explain why vinyl nitrile helmet liners are thought to be better at reducing head rotational acceleration than expanded polypropylene helmet liners.  
**Keywords:** polymeric foam; impact; compression and shear; helmet
# Metamaterial Based Ku-Band Antenna for Low Earth Orbit Nanosatellite Payload System
A linear polarized dual-band patch dipole antenna was developed in [8] for Ku and Ka-band uses, with resonant frequencies at 16.5 and 32.5 GHz. In [9], a larger size patch antenna was presented for S, C and Ku band frequencies. The antenna shows the resonant frequency at 2.4, 5.0, and 15.5 GHz frequencies with a low gain value. Different designs of Ku band antenna are presented in [10; 11; 12; 13; 14], where all the design has linear polarization and lower gain. Several researchers also focused on designing compact circular polarized (CP) high-gain antenna for small satellite communication systems [7; 15; 16]. In [12], a circular patch antenna was designed, which has CP, but the antenna size is 40 \(\times\) 48 mm\({}^{2}\). In [17], a patch antenna has been developed for Ku-band satellite application with a size of 20 \(\times\) 20 mm\({}^{2}\). The antenna shows circular polarization from 12.30 to 12.46 GHz. However, the gain of this antenna at this band is 1.6 dB. Similarly, [PERSON] et al. proposed a patch antenna with the size of 22.13 \(\times\) 21.9 mm\({}^{2}\), which operates at 12.2-14.5 GHz and has a linear polarized gain of 4.8 dB at 12.2 GHz [18]. In [19], a frequency selective surface (FSS) integrated patch antenna has been presented for a wide X and Ku band communication system, where the antenna can operate from 5 GHz to 24.5 GHz with the highest gain of 5.9 dB. The major limitation of this antenna is the overall size of 52.8 mm \(\times\) 52.8 mm \(\times\) 21.2 mm, which is not compatible with the 1 U (One unit) nanosatellite system. In [20], a printed monofilar antenna is offered for the Ku-band CubeSat application, which provides a gain of 8.5 dBi at 12.2 GHz with an overall size of 18 mm \(\times\) 18 mm \(\times\) 14 mm. The square cavity structure of the antenna makes complexity to mount with a 1 U nanosatellite structure, which is a major concern for nanosatellite antenna researchers. Therefore, from the study, it is shown that there is a great demand for designing a Ku-band antenna for a 1 U nanosatellite communication system to overcome the limitations of lower gain, larger size, complex structure, and circular polarization, etc. The metamaterial structure is attractive in antenna design, improving the gain and reducing interference with the satellite structure [21; 22; 23]. This paper proposes a metamaterial-inspired Ku band antenna for a 1 U nanosatellite communication system. The proposed antenna is a non-deployable planar structure with sufficient mechanical robustness to realize the maximum design flexibility of the limited volume 1 U nanosatellite structure.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
### Security Assumptions and Models
#### 2.3.1 Implementation of CONIKS in PYTHON  
The successful implementation of CONIKS in Python requires a well-defined development environment and a set of tools to manage cryptographic operations effectively. Python, with its rich ecosystem of libraries and frameworks, provides a robust platform for implementing key transparency mechanisms. The development process begins by setting up a suitable environment that supports both the cryptographic and algorithmic requirements of CONIKS. This includes selecting the appropriate libraries for cryptographic functions, such as hashing and digital signatures, and utilizing development tools that facilitate efficient coding and debugging. By establishing a solid foundation with these tools, developers can ensure that the implementation is secure, reliable, and aligned with the theoretical principles of CONIKS.
## 2 Materials and Methods
### Spatio-Temporal Characteristics Analysis  
To analyze the spatio-temporal change characteristics of DE and GD quantitatively, spatial kernel density estimation and standard deviation ellipse (SDE) models were chosen. Compared with the traditional kernel density estimation, spatial kernel density estimation incorporates temporal and spatial factors and estimates the probability density function of state transitions in random processes. This approach allows for the accurate identification of change patterns using three-dimensional graphs and density contour maps of the dynamic variable distributions [53]. It can also be used to explore trends in various regions over time [54]. SDE is widely applied for measuring the spatial statistical distribution features of geographical elements as it visualizes the correlation and variability among variables, revealing the directional distribution, trends, and ranges of abstract point data [55]. The SDE model calculates the mean, variance, and covariance matrix of multivariate data to define an ellipse representing the dataset variability [56]. Key parameters including the centroid, major axis, minor axis, and orientation angle describe the spatial distribution characteristics of the study subject [57].
# The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin:
## 1 Introduction
[14] also used the WRF model to perform 11 years (October 2000-September 2011) of simulations over the Tibetan Plateau at 30 km and 10 km grid cell resolution in the outer and inner domain, respectively. [PERSON] et al. [15] emphasized the importance of finer grid cell resolution over this region to simulate climatic variables, especially precipitation. However, investigating the long-term high-resolution meteorological variability (at 4 km) over the Karakoram Mountain Range is lacking, which is very important for the glaciological and hydrological communities. In view of the above, we used the WRF model Version 3.8.1 [26] to dynamically downscale 16 years (2000-2015) over the Indus Basin. Our purpose in this study is to investigate spatiotemporal variability in the climate over the UIB and to assess the ability of WRF to capture observed variations using an analysis of correlation, bias, and trend. Potential issues with robustness stemming from record length are captured in our results via rigorous significance testing on all trends. The WRF model has been configured with three nested domains (d01-d03) with the boundary conditions derived from the National Oceanic and Atmospheric Administration (NOAA) National Center of Environmental Prediction (NCEP) 6 hourly Climate Forecast System Reanalysis (CFSR) data [27], which has a 38 km horizontal resolution. This study provides an analysis of the WRF model's applicability over the complex terrain of the UIB at an annual and seasonal scale.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 2 IoT Tide Gauge Network Design
### Atmospheric Pressure Sensor  
Atmospheric pressure needs to be known when converting the measurements made with the Blue Robotics pressure sensor into the depth of water. The instrument used is a Bosch BME280 environmental sensor, an I\({}^{2}\)C sensor with temperature, barometric pressure and humidity. It is a low current sensor, 3.6 uA at 1 Hz for humidity, pressure and temperature, and 0.1 uA while in sleep mode.
## 3 Results and Discussion
### Evaluation Metrics  
There were three evaluation metrics in this paper's experiment including the mean absolute error, root mean square error, and mean absolute percentage error. _MAE_ (mean absolute error) represents the average absolute value of the error between the predicted value and the true value, _RMSE_ (root mean squared error) is the square root of the average error, and _MAPE_ (mean absolute percentage error) is the average percentage of the error between the predicted value and the true value.