esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images
## 4 Results
### Evaluation on UIEB Dataset  
The UIEB dataset [8] is used to test the proposed method first. In the UIEB dataset, the reference images are manually selected by 50 volunteers from the potential reference images generated by 12 image enhancement methods [8]. Following [8], the proposed method is also test on the five categories of images: greenish and bluish images, downward looking images, forward looking images, low backscatter scenes, and high backscatter scenes. The results of different methods and the corresponding references are shown in Figures 3-7. Zoom in the images to see more details.  
It can be seen from Figures 4 and 5 that the proposed method does not show obvious advantages on the images with little color cast, which is consistent with expectations. From Figure 6, we can see that the proposed method could correct the color cast well though the restored image still does not look very good due to the unimproved contrast. The restored images of the total 890 images with reference images in the UIEB dataset are compared between the test methods. We found the GW, WP and SB [21] methods often cause artifacts, especially in the dark regions of the images with severe color cast, as shown in Figure 8. [PERSON]'s method [37] often over-grays the images making the color compressed or lost, as shown in Figures 4 and 5, and Figure 9. The proposed method has consistently outperformed the other methods on the images with severe color cast. Many restored images obtained by the proposed method look even more natural than the reference images, as shown in Figure 10.  
Figure 10: Sample images on which the proposed method produces more natural results than reference images. From left to right are raw underwater images, the results of gray-world (GW) method, white-patch (WP) method, statistical-based (SB) method [21], [PERSON]’s (GWL\(\alpha\beta\)) method [37], proposed method and reference images.  
Figure 9: Comparisons on images with color prior. From left to right are raw underwater images, the results of gray-world (GW) method, white-patch (WP) method, statistical-based (SB) method [21], [PERSON]’s (GWL\(\alpha\beta\)) method [37], proposed method and reference images.  
Lower angular error indicates a more accurate color restoration. Many images in the Haze-line dataset contain multiple color charts at different distances from the camera. The charts are ordered by its distance to the camera and chart #1 is closest to the camera. The angular errors for each chart, image, and method are shown in Table 1. The lowest average angular error is got by our method, as evident from Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image No.** & **Raw** & **GW** & **WP** & **SB** & **GWL\(\alpha\beta\)** & **Ours** & **Haze-Lines** \\ \hline
3008\#1 & 31.41 & 26.87 & 27.51 & 9.85 & 7.42 & 4.67 & 2.55 \\
3008\#2 & 35.27 & 35.26 & 35.30 & 10.93 &
## 3 Experiment and Discussion
### Results and Discussion  
#### 3.2.1 Failure Localization  
When \(V_{0}\) is set at 3.5 V, the current of the sample \(I_{0}\) is 626 \(\mu\)A. According to the back-side CCD image (Figure 5a), the size of the sample is 2450 \(\mu\)m \(\times\) 2450 \(\mu\)m. Set the lower-left corner of the chip as the origin of the coordinates, the horizontal direction as the X-axis, and the vertical direction as the Y-axis (Figure 5a). After laser-scanning with a 40 mW laser beam, the abnormal spot is determined, and the coordinates are (\(X_{\text{fail}}=2064\)\(\mu\)m, \(Y_{\text{fail}}=2033\)\(\mu\)m) (Figure 5b). The abnormal current variation at the abnormal spot is about 2.5 \(\mu\)A, while the current variation at the normal spot is about 0.5 \(\mu\)A (Figure 5c). This result is in accordance with the result tested by Emission Microscopy (EMMI) (Figure 6), which is another common defect localization technology [18]. Figure 7 shows a possible physical reason for the failure, that is a cavity in the copper, found by the Focused Ion Beam (FIB) facility.  
Figure 5: Localization of the abnormal spot using the LSTS: (**a**) The back-side CCD image of the DUT. The scanning scheme is shown by the white line and arrow. (**b**) While the laser is scanning the abnormal spot, an abnormal current is tested by the current meter, and a red spot is marked on the failure spot. (**c**) The abnormal current variation is about 2.5 \(\mu\)A, while the normal current variation is about 0.5 \(\mu\)A.
## 4 Influences of Non-Gaussian Transverse Distributions
### Non-Axisymmetric, Non-Gaussian Transverse Distribution  
For non-axisymmetric distributions, a simple correction to the standard Gaussian is the skew-normal (SN) function [24]: \(f(x)=2\phi(x)\Phi(\alpha x)\), where \(\alpha\) is its form parameter, \(\phi(x)=(2\pi)^{-1/2}e^{-x^{2}/2}\) is the standard Gaussian with a cumulative distribution function of \(\Phi(x)=0.5\Big{[}1+\text{erf}\Big{(}x/\sqrt{2}\Big{)}\Big{]}\) and \(\text{erf}(x)=2\pi^{-1/2}\int_{0}^{x}e^{-t^{2}}\text{d}t\) is the error function. Here we only consider the SN distribution in the \(x\)-direction as shown in Figure 9a, while the \(y\)-direction beam profile is still in the standard Gaussian. It should be noted that the mean position in the \(x\)-direction is still zero, i.e., on the axis.  
Figure 8: Comparison of the influences of initial SG spatial profiles and the SG momentum profile. Results of the low-charge (120 pC) witness beams are shown. (**a**) The normalized transverse emittance \(\epsilon_{n}\). (**b**) The charge-in-core ratio \(Q_{core}/Q_{0}\).  
Figure 9: _Cont._  
To measure the degree of asymmetry of a distribution, the skewness \(\hat{\mu}_{3}=\left<x^{3}\right>/\left<x^{2}\right>^{3/2}\) can be used. As illustrated by Figure 9c, the initial skewness \(\hat{\mu}_{3}\) increases with the form parameter \(\alpha\) of the SN distributions. In the limit when \(\alpha\rightarrow\infty\), the skewness reaches the maximum of 1 and the SN distribution becomes triangular. The transverse asymmetry can arise due to the transverse wakefields induced by an off-axis beam in the RF cavities, higher-order dispersion such as the quadratic T166 term, as well as potential well distortion [11].  
Since this problem is non-axisymmetric, for these simulations we use the fully 3D code QV3D [25], built on the VLPL platform [26]. Similar to the case of super Gaussian distributions, Figure 9b shows that SN distributions have only a very small impact on the beam energy gain and the normalized RMS beam emittance \(\epsilon_{n}\). The beam emittance growth after saturation is only slightly smaller with the increase of the SN distribution form parameter \(\alpha\). It is also interesting to see that in Figure 9c the \(x\)-direction skewness \(\hat{\mu}_{3,x}\) soon damps and becomes almost negligible after a propagation distance of 1 m.  
The above results suggest that a degree of asymmetry of a beam transverse distribution is allowed for the witness beam acceleration in the studied case, and it can be automatically corrected by the plasma response. This is consistent with previous studies for the beam injection with minor transverse offsets [7]. It shows that the emittance of the witness electrons inside the bubble is also not strongly affected. This is due to the decoherence of the betatron oscillations along the bunch as described in Section 3, which helps to suppress the hosing instability [27; 28].
## 6 Results and Discussion
### Observing Application Performance from the TRA Layer  
A file transfer application is emulated using the proposed multipath and TCP New Reno (for single path 802.11 ad, HEKATON, BUSH). For data transfer, we set the packet size to 1500 bytes. Each user's SNR is set as physical layer traces, which are inputs into the NS-3 simulation to evaluate application performance at the TRA layer. The comparison of throughput (in Mbps) evolution over time is shown in Figure 8. We adopt Direct Code Execution, a framework for ns-3 that allows existing implementations of userspace and kernel space network protocols or programs to be handled flexibly without requiring source code modifications. As a result, the setup is almost similar to a real-world scenario. The data transmission takes around 100 seconds, as we randomly change the RTT of the  
Figure 7: Quantifying and comparing the overhead in milliseconds of MS-UCB over seven links EDMG-SC 802.11_ay_ networks with spatial streaming (up to eight) with MPTCP LIA over 802.11 ad.  
paths from 10 to 40 milliseconds over the duration. Despite all of the considered overheads, such as headers, timeouts, slow start and congestion avoidance, we observe in Figure 8 that multipath outperforms BUSH, HEKATON and 802.11 ad MU-MIMO.
## 4 Discussion
### Relationships Between SWC and Environmental Factors  
The correlation analysis underscores the stronger relationships between altitude, soil chemical properties, and SWC in coal mining areas. The positive correlation between altitude and SWC, particularly in coal mining regions, suggests that altitude may influence moisture retention differently due to the modified soil structure and chemistry resulting from mining [21]. Specifically, geological processes, such as the formation of mountainous terrain or hills, often result in different hydrological characteristics at higher altitudes. Mining in these areas could change the natural water flow patterns, influencing how water is retained in the soil. In higher-altitude coal mining regions, water may accumulate at the surface due to altered drainage patterns, creating pockets of water retention in disturbed soil layers. These changes in hydrology, combined with altered soil properties, contribute to the observed correlation between altitude and SWC. Conversely, the weaker correlations in non-coal mining areas imply a more stable environmental condition, where traditional relationships between topographic features and moisture content remain intact [22].  
The more substantial correlations between SWC and soil chemical properties in coal mining areas--especially for nitrogen--highlight how mining can enhance certain nutrient interactions with water retention [23]. Mining disturbs the natural soil horizons, exposing previously deep soil layers and minerals that were not directly in contact with the surface. This disturbance can alter the chemical composition of soil, especially increasing the availability of certain nutrients like nitrogen, which are often trapped in deeper layers. The disruption of these layers allows for better nutrient mobilization and increased interactions between water and soil minerals. This dynamic poses both opportunities and challenges; while it may improve short-term fertility, the long-term implications for soil health and sustainability warrant attention [24]. Several potential solutions could be beneficial. Firstly, to mitigate the long-term negative effects of mining on soil health and sustainability, a geological approach could involve soil restoration and stabilization techniques, such as the use of organic amendments (e.g., compost or biochar) to restore SOM. This would not only help in improving nitrogen retention but also stabilize the soil structure to reduce nutrient leaching. Secondly, revegetation with native or adaptive plant species could help restore soil balance, as plant roots can act as conduits for nitrogen and other nutrients, reducing leaching and promoting nutrient cycling.
## 2 Material and Methods
### Cross-Spectral Estimation from Kernel Mixtures
\[\kappa_{ij}(\tau) =\alpha_{ij}\exp\left(-\frac{1}{2}(\tau+\theta_{ij})^{2}\sigma_{ ij}\right)\cos\left((\tau+\theta_{ij})\mu_{ij}+\phi_{ij}\right), \tag{5}\] \[S_{ij}(\omega) =\frac{w_{ij}}{2}\exp\left(-\frac{1}{2}\frac{(\omega-\mu_{ij})^{2 }}{\sigma_{ij}}+\iota(\theta_{ij}\omega+\phi_{ij})\right)\] (6) \[+\frac{w_{ij}}{2}\exp\left(-\frac{1}{2}\frac{(\omega+\mu_{ij})^{ 2}}{\sigma_{ij}}+\iota(-\theta_{ij}\omega+\phi_{ij})\right),\]  
where the term \(\alpha_{ij}=w_{ij}\sqrt{2\pi}|\sigma_{ij}|^{1/2}\) absorbs the constant resulting from the inverse Fourier transform. Equation (5) allows computing the real-valued autocovariances \((i=j)\) and cross-covariances \((i\
eq j)\) with negatively and positively correlated channels through the magnitude parameter \(\alpha_{ij}\in\mathbb{R}\); delayed channels through the \(\theta_{ij}\
eq 0\) delay parameter, and channels out-of-phase through the \(\phi_{ij}\
eq 0\) phase parameter. Moreover, increasing the rank of decomposition \(Q\) corresponds to considering more components in the multiple-output spectral mixture (MOSM) kernel as shown in Equations (7) and (8). \[\kappa_{ij}(\tau)=\sum_{q=1}^{Q}\alpha_{ij}^{(q)}exp\left(-\frac{1}{2}\frac{( \tau+\theta_{ij}^{(q)})^{2}}{\sigma_{ij}^{(q)}}\right)cos\left((\tau+\theta_ {ij}^{(q)})\mu_{ij}^{(q)}+\phi_{ij}^{(q)}\right), \tag{7}\]  
\[S_{ij}(\omega) =\sum_{q=1}^{Q}\frac{w_{ij}^{(q)}}{2}\exp\left(-\frac{1}{2}\frac{( \omega-\mu_{ij}^{(q)})^{2}}{\sigma_{ij}^{(q)}}+\iota(\theta_{ij}^{(q)}\omega+ \phi_{ij}^{(q)})\right) \tag{8}\] \[+\sum_{q=1}^{Q}\frac{w_{ij}^{(q)}}{2}\exp\left(-\frac{1}{2}\frac{( \omega+\mu_{ij}^{(q)})^{2}}{\sigma_{ij}^{(q)}}+\iota(-\theta_{ij}^{(q)}\omega+ \phi_{ij}^{(q)})\right),\]  
denoting the superindex \((q)\) the \(q-\)th spectral component. Then, MOSM effectively computes autocovariance and cross-covariances through the spectral-mixture of positive-definite kernels from the Fourier transform of spectral functions \(S_{ij}(\omega)\). In practice, the adjustment of the cross-spectrum parameters should be performed on the evidence of the EEG data.
## 5 Simulations
### Reference-Tracking Performance in System without Uncertainties  
When there is no uncertainty in HDFRMS, the tracking performances of joints (see Figure 8a-c) and vibration suppression of the flexible link 3 (see Figure 8d-f) are tested. Both simulations and numerical results use the same control parameters under several different working conditions, including the end-effector without payload (\(M=0\) kg), with half payload (\(M=300\) kg) and with full payload (\(M=600\) kg).  
Figure 8a-c indicates that all joint angles can quickly track to the prospective trajectories in 3 s or less. In particular, the trajectory tracking speed of joint 1 is faster than other joints because of the rotary joint, which is insensitive to vibrations. Figure 8d-f shows that both first-order mode and second-order mode can be controlled effectively, even if the vibration suppression needs to spend 3 s handling full payload. However, only forced vibration caused by a large range of joint motion exists in system. In addition, Figure 9 illustrates that the weights of the critic neural network converge to \(\left[0.994\ \ 0.003\ \ 1.001\ \ 1.013\ \ 0.499\ \ -0.007\ \ 0.002\ \ 1.007\right]^{ \text{T}}\).
## 7 Challenges and Limitations
### Real-World Performance and Implementation Challenges  
While laboratory-scale advancements in RFEH have shown promising efficiencies and system improvements, real-world deployments reveal additional complexities that impact practical performance. Field studies indicate that ambient RF energy levels are often significantly lower and more variable than those reported in controlled environments, which affects the continuous power supply to energy-harvesting devices. For example, variations in user density, environmental obstacles, and temporal factors such as time of day or weather conditions can cause substantial fluctuations in harvested energy, challenging the reliability of autonomous devices [8]. Moreover, implementation trials exhibit highlighted integration issues when embedding RF harvesters within commercial IoT devices or wearable systems. These include challenges related to antenna placement, device form factor constraints, and interference from other onboard electronic devices, which often degrade the expected energy capture and conversion efficiency [14]. The thermal management and long-term durability of novel materials like graphene-based rectennas under real operating conditions also remain open concerns [16].  
In industrial and biomedical applications, maintaining stable operation amidst highly dynamic electromagnetic environments demands the use of hybrid energy-harvesting approaches and advanced power management algorithms to buffer against intermittent RF sources [19]. Several pilot deployments underscore the necessity of combining RFEH with auxiliary power sources or energy storage components to guarantee uninterrupted device functionality. Furthermore, the regulatory landscape complicates large-scale implementation, with field deployments sometimes restricted by emission limits and spectrum use policies that vary significantly across regions [22]. Collaborative efforts are increasingly required to harmonize these standards to enable the development of viable commercial solutions. These real-world insights emphasize the need for further research focusing on deployment-driven design, robust system integration, and adaptive energy manage ment strategies that can cope with the unpredictability and heterogeneity of ambient RF environments. Addressing these challenges will be critical to transitioning RFEH from experimental setups to reliable, mass-market applications.
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 4 Results and Discussion
### Assumption and Diagnostic Tests  
#### 4.1.1 Breusch-Pagan Test Results  
The Breusch-Pagan test is conducted to detect heteroscedasticity in residuals versus fitted values, as shown in Figure 6. The results indicate a significantly low \(p\)-value (\(3.138\times 10^{-115}\)), well below the 0.05 threshold, and a high-test statistic (0.431), confirming substantial heteroscedasticity. The residuals exhibit a distinct trend, dispersing as fitted values increase. Additionally, several outliers deviate significantly from the zero-line, suggesting influential data points that may affect model performance. The fan-shaped residual pattern indicates non-constant variance, necessitating modifications to the target variable or model selection adjustments.
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 4 System Design
### LoRa Mesh Protocol Design
#### 4.2.3 Routing Algorithm  
After receiving the broadcast packets forwarded by multiple nearby nodes, the node to be added to the network needs to select the most suitable parent node and join the network. There are two main routing algorithms in current mesh networking protocols. One algorithm is to choose the node with the lowest signal attenuation between themselves, namely the algorithm of maximizing RSSI. The other algorithm is to select the node with the smallest hop count to the gateway, that is, to minimize the hop count. However, these two routing algorithms cannot balance communication efficiency and communication quality well. The goal of the minimal hop count networking is to reduce the hop count of routes. However, when the RSSI between two nodes is lower than \(-80\) dBm, the link reliability deteriorates seriously, which is reflected as a high packet loss rate. The maximum RSSI algorithm selects the path that maximizes the overall RSSI value of the link, thus ensuring the quality of the connection between each hop. However, if the hop limit is not considered, the selected path may have unnecessary forwarding, which increases the transmission delay.  
A heuristic algorithm is designed to find the optimal route in order to take into account node load, packet arrival rate (PDR), and data transmission time (DTT). First, LoRa broadcast packets are modified. When each node forwards broadcast packets, the hop count of the node to the gateway and the current load of the node are updated. After receiving the broadcast packets forwarded by surrounding nodes, the node to be connected to the network can calculate the attractiveness of surrounding nodes. Attractiveness can be calculated based on self-measured RSSI and the information in received packets. We define the attraction of node \(i\) to node \(j\) as:  
\[K_{i,j}=\frac{r_{i,j}^{2}}{M_{j}^{7}}, \tag{1}\]  
\[r_{i,j}=\begin{cases}10\frac{-60}{-60},&r_{i,j}>-60\text{ dBm},\\ 10\frac{r_{i,j}}{-60},&r_{i,j}\leq-60\text{ dBm}.\end{cases} \tag{2}\]  
In the equations, \(K_{i,j}\) is the attraction of node \(j\) to node \(i\). Node \(i\) will choose a surrounding node \(j\) with the highest attraction and join it. \(r_{i,j}\) is originally the absolute power calculated from RSSI between node \(i\) and \(j\). Considering that if the RSSI is higher than \(-60\) dBm, the link is already reliable enough, the authors fix the RSSI value higher than \(-60\) dBm at \(-60\) dBm to avoid ignoring hop count and path load due to redundant RSSI. \(M_{j}\) is the path load on the node \(j\), which means how many routings are forwarded through the node \(j\). \(T_{j}\) is the number of hops from node \(j\) to the gateway. Considering the above three factors at the same time can make the routing algorithm more reasonable and reliable.
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Methodology Comparison  
For the scope of this paper, methodology comparison studies will cover experiments in which different methods were used to analyze the same sample to find statistical differences in enumeration or detection of microorganisms. Typically, comparison between methodologies is performed due to a lack of information about the method tested in certain matrices or limited information in the literature regarding discrepancies between methods to enumerate or detect a certain microorganism [45; 48; 49; 50; 51; 52; 53; 54; 55; 56]. Moreover, when new methodologies are developed by companies, it is common to compare methodology studies to validate their newly developed method [57; 58; 59; 60; 61].  
For instance, [PERSON] et al. 2011 compared an automated most probable number technique with traditional plating methods for estimating populations of total aerobes, coliforms, and _Escherichia coli_ associated with freshly processed broiler carcasses [52]. [PERSON] et al. 2010 evaluated the same automated most probable number technique (TEMPO(r)), but for the enumeration of Enterobacteriaceae in food and dairy products [55]. [PERSON] et al. 2021 compared TEMPO(r) with 3M(tm) Petrifilm(tm) for aerobic counts in order to support the study's validity, as the experimental design tested different lengths of time and used different microbial enumeration methods [56]. [PERSON] et al. 2016 validated Hygiena's MicroSnapTM method for enumeration of total viable count in a variety of foods to provide supporting data for AOAC certification consideration [54]. [PERSON] et al. 2023 developed a novel methodology for quantification of _Salmonella_ in pork and beef lymph nodes and also validated the methodology with a comparison study of commonly used methods [45]. These are just a few examples of method comparison studies with different objectives, following the same basic principle of validating a certain methodology while conducting an experiment.