esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 4 Results
### Ablation Experiment
#### 4.2.2 Channel Attention Mechanism  
Channel attention mechanisms enable our model to focus more on important features while suppressing less important ones, thereby better accomplishing the low-light image enhancement task. In our design, to prevent degradation of model generalization capacity caused by using attention at every layer, we strategically implemented attention only after two network layers: specifically following the second and fourth convolutional layers. We now need to conduct comparative validation between implementations with and without our global attention mechanism to demonstrate the effectiveness of our model. We again selected a combined dataset comprising LOLv1, LOLv2-Real, LOLv2-Synthetic, and LSRW-huawei for validation. As shown in Table 2, compared to the configuration without the GCA attention mechanism, the model incorporating the GCA module clearly achieved higher scores across the PSNR, SSIM, and LPIPS metrics. This demonstrates that our designed GCA attention module contributes significantly to the image enhancement task. All the subsequent experiments were conducted on the foundation of models incorporating the GCA module.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & \(a=0.10\) & \(a=0.11\) & \(a=0.12\) & \(a=0.13\) & \(a=0.14\) & \(a=0.15\) & \(a=0.16\) \\ \hline PSNR \(\uparrow\) & 18.672 & 18.926 & 19.063 & 19.079 & 18.955 & 18.790 & 18.602 \\ SSIM \(\uparrow\) & 0.610 & 0.628 & 0.629 & 0.635 & 0.646 & 0.644 & 0.635 \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison of our method with hyperparameter a ranging from 0.1 to 0.16.
# SAHA: A String Adaptive Hash Table for Analytical Databases
## 1 Introduction
### Background
#### 1.1.3 Hash Table  
The core data structure for the group and join operators is the **Hash Table**. A hash table is a data structure that associates keys with values. It supports querying given keys in constant time by choosing an appropriate hash function. The basic idea is to pick a hash function that maps arbitrary keys to numeric hashes, and these hashes serve as indices to slots used to store values in the hash table.  
Figure 1: An typical implementation of the group operator of vectorized databases.  
Figure 2: A typical implementation of the join operator of vectorized databases.  
However, two different keys might end up having the same hash, and thus the same index. If any key collides in the hash table with an already existing key of the same index, a probing function is used to resolve the collision.  
There are two main ways of resolving collisions, namely, open addressing and separate chaining, as shown in Figure 3. In separate chaining, elements are stored outside of the hash table with linked lists. Items that collide are chained together in separate linked lists. To find a given record, one needs to compute the key's hash value, get back the corresponding linked list and search through it. It is usually inefficient compared to open addressing because of additional cache misses and branch conditions. However, it is easy to implement, and has other properties, such as reference stability (references and pointers to the keys and values in the hash table must remain valid until the corresponding key is removed). Thus, it is still widely used and is the default implementation of C++'s unordered hash tables.  
In open addressing, all elements are stored in the hash table. In order to find a given record, table slots are examined systematically during search until the desired element is found. This process is called probing through the conflicting chain, and there are many different probing schemes, such as linear probing, quadratic probing and double hashing. These schemes are intuitive and are named according to the probing process. There are also more sophisticated strategies, such as RobinHood [7] and Hopscotch [8], which contains complex element adjustments. When hash tables become full, many conflicting chains are quite long and probing can become expensive. To remain efficient, rehashing is used by scattering the elements into a larger array. The indicator of doing rehashing is called the load factor, which is the number of records stored in the hash table divided by the capacity. Open addressing hash tables have better cache locality, less pointer indirections and are well suited for vectorized processing. Thus, for analytical workloads, open addressing schemes should be used to achieve superior performance. To save words, unless otherwise specified, all mentioned hash tables use the open addressing scheme in this paper.
_Applied_  
_sciences_  
_Article_  
**Latex-Based Membrane for Oily Wastewater Filtration: Study on the Sulfur Concentration Effect**  
**[PERSON] 1\({}^{\text{(c)}}\), [PERSON] 2, [PERSON] 2, [PERSON] 1\({}^{\text{,*}}\) and [PERSON] 1**  
1 Department of Mechanical Engineering, College of Engineering, University of Bisha, P.O. Box 001, Bisha 67714, Saudi Arabia; [EMAIL_ADDRESS] (K.A.); [EMAIL_ADDRESS] (M.A.)  
2 Department of Petrochemical Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Petaling Java 31900, Malaysia; [EMAIL_ADDRESS] (Y.T.J.); [EMAIL_ADDRESS] (Y.M.)  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** Nitrile butadiene rubber (NBR) latex/graphene oxide (GO) membranes were fabricated through a latex compounding and curing method which is a relatively new method to produce membranes for wastewater treatment. Hence, the steps in the production of the membrane through this new approach need to be evaluated to optimize the performance of the membrane. In this paper, the effect of sulfur loading in the range of 0.5 to 1.5 parts per hundred rubber (phr) on the morphology, crosslink density, tensile properties, permeation flux and oil rejection rate performance of NBR/GO membranes was studied. The sulfur loading was found to influence the surface morphology and integrity of the membrane which in turn affects the performance of the membrane in terms of strength, water flux and rejection rate of oil. Inaccurate sulfur loading produced a membrane with micro cracks, low surface area for filtration and could not withstand the filtration pressure. In this research work, the membrane with 1.0 phr sulfur provides the highest water flux value and oil rejection rate of 834.1 L/m\({}^{2}\)-hr and 92.23%, respectively. Surface morphology of 1.0 phr sulfur-loaded membrane revealed the formation of continuous membrane with high structural integrity and with wrinkles and folded structure. Furthermore, micro cracks and a less effective surface area for filtration were observed for membranes with 0.5 and 1.5 phr sulfur loading.  
**Keywords:** graphene oxide; nitrile butadiene rubber; oily wastewater; membrane filtration; sulfur
## 4 Experimental Results
### AR Registration Based on QR Code  
This experiment mainly takes the assembly disassembly animation and industrial virtual instrument as examples to demonstrate the function of AR registration based on QR code.  
(1) Assembly disassembly animation demonstration  
In this study, the assembly disassembly animation steps are configured in the AR configuration software. The assembly disassembly animation AR demonstration is realized by recognizing the QR code in the scene with the script parsing and AR display software. Figure 12 illustrates the assembly disassembly animation demonstration in the real scene. To better realize AR interaction and show the assembly disassembly steps more intuitively, the system designs three graphical interaction buttons to control the demonstration of the disassembly animation, including the \"next step\", the \"previous step\", and the \"stop\" buttons. Developers can achieve the animation demonstration of the entire disassembly process by clicking these buttons.  
Figure 12: Cont.
# Comparisons of Dynamic Landslide Models on GIS Platforms
## 4 Results and Discussion
### Reason for Differences
#### 4.1.2 Differences Caused by Algorithms  
Differences in parameters and numerical schemes can affect the runout zones significantly (Figure 4). In this case, we used Rockfall Analyst to analyze the influence of algorithms. The results obtained with ArcGIS 9.x and ArcGIS 10.x differ because of different point extraction algorithms (Figure 4). The small difference in the point extraction of the GIS module will affect the runout zone. The rock fell into the river in ArcGIS 9.x. However, the rock stops on the road in ArcGIS 10.x, although the DEM is the same. The results show that a small difference in the algorithm can produce large differences in trajectory.  
In the Eulerian-based model, some numerical schemes can handle one property of ADEs but process badly with another one [17]. Therefore, the balance between numerical diffusion and numerical oscillation is key to obtaining a suitable physical solution. For the model in the Eulerian description, we simulated the uniform linear motion of a block with 1 m/s. In this simulation, the space interval is 0.1 m, and the time interval is 0.01 s. The Lax-Friedrichs scheme, a first-order in time and second-order in space method, shows numerical diffusion during motion (Figure 5A) but the Lax-Wendroff scheme, a second-order in both space and time method, shows numerical dispersion (Figure 5B). The errors are generally caused by neglecting high-order terms. However, higher-order linear schemes such as 3 rd order, although more accurate for smooth solutions, are not TVD and tend to introduce spurious oscillations (wiggles) where discontinuities or shocks arise. Various high-resolution schemes use flux/slope limiters to maintain the TVD, thereby reducing the impact of numerical dissipation and numerical diffusion [66; 67]. In these methods, the  
Figure 3: Bilateral dam break simulation with different models: (**A**) the initial state of the simulation, (**B**) obtained by OpenFOAM at 0.5 s, (**C**) obtained by PySPH at 0.5 s, and (**D**) obtained by the depth-averaged model at 0.5 s.  
accuracy is high in the smooth area, and the flux/slope limiter method is used in the shock area to avoid producing nonphysical solutions.
## 3 Clustering Methods
### Support Vector Clustering (SVC)  
Support Vector Clustering (SVC) is a kernel-based approach that adapts the principles of support vector machines (SVMs) to unsupervised learning. In SVC, the normalized data points are mapped to a high-dimensional feature space using a kernel function, typically the radial basis function (RBF) [19], which is defined as follows:  
\[K\big{(}x_{i},x_{j}\big{)}=e^{-\gamma\|x_{i}-x_{j}\|^{2}} \tag{5}\]  
where \(\gamma\) is a hyperparameter controlling the width of kernel. In this feature space, SVC determines the smallest hypersphere that encloses the majority of the data points.  
Upon convergence, the projection of this hypersphere back into the original input space produces a decision boundary. A data point \(x\) can be tested by evaluating the following:  
\[f(x)=\|\Phi(x)-a\|\ ^{2}-R^{2} \tag{6}\]  
where \(f(x)<0\) indicates that \(x\) is inside the sphere, and \(f(x)>0\) signals it is an outlier or lies in another region. When different enclosed regions emerge in the input space, multiple clusters can be formed. In the context of second-life battery regrouping, equal number SVC applies an additional post-processing step to ensure each cluster contains a fixed number of cells. Although SVC excels at discovering arbitrarily shaped clusters, it demands careful parameter tuning. The results of clustered cells with SVC are shown in Figure 4.
## 4 Research Methodology
### Application-Based Approaches
#### 4.8.1 Monitoring  
[PERSON] and [PERSON] presented a healthcare monitoring technique based on IoT and fog for the treatment of hypertension. This healthcare monitoring technique was contrasted with cloud computing technology to demonstrate its high precision in reaction time, low latency, and good bandwidth efficiency [75]. Furthermore, an artificial neural network was used for predicting the severity of hypertension. As reported by [PERSON] et al., their smart tracking student interactive healthcare system measures symptoms of aquatic illness for the purpose of identifying certain diseases using machine learning techniques based on k-cross validation [76]. Based on the simulation, it was shown that the suggested methodology performed better in terms of quick responses and accuracy.
## 2 Research Framework  
The research framework employed in this study is shown in Figure 1. This paper is based on multi-source satellite data (Sentinel-2, EnMAP, and GF5-02-AHSI), proposing a tiered monitoring approach by combining high-frequency observations and high-resolution spectral data to detect and quantify methane super-emitters. This study first employs a matched filtering algorithm to identify methane plumes from high-resolution data and estimates emission flux rates using the Integrated Methane Enhancement (IME) model. Subsequently, it leverages the high revisit frequency of Sentinel-2 to conduct the long-term monitoring of selected hotspot regions, analyzing the spatiotemporal characteristics of methane emissions. Ultimately, the research demonstrates the potential of satellite remote sensing technology in methane emission monitoring and provides a scientific basis for global methane mitigation efforts.
## 1 Introduction
In summary, the contributions of this study are as follows:  
* Proposing a dynamic key generation method that provides short-lived authentication keys per vehicle in each zone. * Proposing a Transmitter Centric Authentication (TCA) method where signature generation at transmitters and signature verification at receivers are based on the transmitter zone parameters. * Proposing a Signature Concatenation-based Authentication (SCA) method, in which the transmitter generates a concatenated signature that can be individually verified by all receivers, using their corresponding zone parameters. * Proposing a Receiver Centric Authentication (RCA) method, where transmitters and receivers aggregate the security parameters of the overlapped zones to generate and verify signatures. * Comparing the three proposed authentication methods in terms of signature generation time, signature verification time, and communication cost. * Comparing the communication cost in terms of message size for the three proposed authentication methods and six previous related methods for single-zone and multi-zone scenarios. * Comparing the computation cost in terms of signature generation time and signature verification. The rest of this paper is organized as follows. Section 2 describes the previous V2X security methods based on the bilinear pairing and free of certificates solutions. Section 3 presents the proposed protocol architecture and the three proposed authentication methods. Section 4 describes security analysis, and the proposed communication overhead analysis is presented in Section 5. In Section 6, the computation overhead is analyzed. Conclusions are provided in Section 7.
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 5 MIMO Antenna Diversity Parameters  
### Envelope Correction Coefficient  
Evaluating ECC in our MIMO antenna system is integral for measuring its diversity performance, with ideal ECC values being below 0.5, and measuring how effectively this prototype provides robust diversity based on this threshold. ECC measures isolation and correlation across communication channels, as expressed by Equation (3).  
\[\rho=\frac{\left|S_{11}^{*}S_{12}+S_{21}^{*}S_{22}\right|^{2}}{\left(1-\left|S_{ 11}\right|^{2}-\left|S_{12}\right|^{2}\right)\left(1-\left|S_{21}\right|^{2}- \left|S_{22}\right|^{2}\right)} \tag{3}\]  
The determination of the error correction code (ECC) for the envisioned MIMO system involves the utilization of the measured S-parameters. The ECC curve in Figure 11a distinctly reveals that the ECC value consistently maintains a level below 0.025 throughout the entirety of N79, Wi-Fi-5/6, and V2X/DSRC in the sub-6 GHz band. This finding confirms that the prototype excels in multiplexing performance, ultimately boosting data throughput. This achievement holds significant importance in contemporary communication systems.  
Figure 11: The proposed antenna diversity parameters. (**a**) ECC. (**b**) CCL.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 5 Performance Simulation and Analysis
### Limited Bandwidth  
In a band-limited system, the energy of the BOC signal's high-frequency portion is limited. The discrimination curve of the band-limited signal is relatively smooth compared to the discrimination curve of the non-bandlimited signal. However, if the local reference signal is non-bandlimited, the received band-limited signal has little effect on the resolution of the digital discriminator.  
Figure 8 shows the simulation results of the code phase discriminator and subcarrier phase discriminator of the BOC signal under different RF front-end bandwidth settings. A spreading code sequence with a PRN of 1 for the GPS L1C/A signal is selected. The sampling frequency, the spreading code rate, and the subcarrier symbol rate are set to 11.251, 1.023, and 2.046 MHz, respectively. The code phase correlation interval, the subcarrier phase correlation interval, and the coherent integration time are set to is 1 chip, 0.25 chips, and 1 ms, respectively. The simulation results show that the limited bandwidth affects the slope, phase resolution and zero-bias of the digital phase discriminator, the effects are not serious.  
Figure 9 shows the simulation results of the zero-bias of the BOCs (1, 1) signal under different bandwidth conditions. The simulation results show that the limited bandwidth has little effect on the variation of the zero-bias of the BOCs (1, 1) signal reception. In fact, although the linear bandpass filter limits the high-frequency portion of the BOC signal, it does not destroy the symmetry of the correlation curve or the characteristics of the phase response curve.
# The Use of Machine Learning for Comparative Analysis of
###### Abstract
The method of amperometric determination of antioxidant properties is the closest to the method of electrochemical oxidation, which allowed [PERSON] to formulate the \"Free Radical Theory of Aging\". The amperometric method for evaluating AOA consists of measuring the strength of the electric current that occurs during the oxidation of the test substance (or mixture of substances) on the surface of the working electrode at a certain potential and then comparing the received signal with the signal from the standard (quercetin) under the same measurement conditions. The chemiluminescence method is the most standardized method for determining antioxidant properties in Europe and imitates the ability of living cells to participate in photosensitizing processes. Chemiluminescence methods for determining antioxidant activity are based on the phenomenon of chemiluminescence--the ability of radicals (superoxide of anion radicals, hydroxyl, and lipid radicals) to glow during the recombination reaction as a result of the optical excitation of particles of a photosensitizing substance added to a certain amount of the analyzed sample. To measure the antioxidant activity (AOA) in world practice, various chemical and physicochemical methods are currently used, most of which are based on determining the effect of antioxidants on the degree of oxidation, while the determination of the AOA indicator itself is carried out in relation to free radicals [9]. Currently, the most widely used methods for determining AOA include: photocolorimetric and fluorimetric methods; methods based on electron paramagnetic resonance spectroscopy; chemiluminescent and amperometric methods [10; 11; 12; 13; 14; 15; 16]. The phenolic substances that form the phenolic profile of wines exhibit a high antioxidant capacity due to the high reactivity of the hydroxyl groups that are a part of them [17; 11]. We also note that in wines, the AOA value can be affected by phenolic compounds as well as organic and amino acids. The aim of the study is, in particular, to identify the most informative and accessible methods for determining the antioxidant activity of table wine materials and compare the results of regression analysis of factors affecting AOA in the standard Excel program and in the Neural Network Regression Model (NNRM).
## 3 Kinematic Analysis
### Inverse Velocity Solving and Jacobian  
The speed of the thrust system determines the propulsive efficiency of the shield machine, so it is important to perform velocity analysis of the thrust system. The previous research on speed control of the thrust system mainly focused on the elongation or shortening speed control of a certain group of cylinders based on electro-hydraulic control. However, the thrust system controls the changes in position and attitude of the shield machine. The change speed in the shield pose includes both the forward speed and the rotating speed component. Therefore, the elongation or shortening speed of cylinders is insufficient to describe the pose change speed of the shield machine. The velocity analysis model of the thrust system should be established from the equivalent mechanism.
## 6 Results and Analysis
### Energy Consumption  
The more energy the network's nodes have, the higher the probability of detecting an attack and the longer the network will live to perform its services. Figure 12 illustrates a comparison between the three designs in terms of energy consumption.  
Figure 11: Number of nodes alive measurement.  
Figure 10: Packet delivery ratio measurement.  
As can be seen in Figure 12, the Watchdog design has consumed the least energy among other designs. This is due to the Watchdog monitoring mechanism, as it has reduced the number of message transmissions required and hence it has reduced the energy cost. The Watchdog technique has eliminated the need to have communication between CHs with their associated nodes and between SCHs and the associated nodes of the monitored CHs. The most expensive design is the Broadcasting design. This is due to the message transmission process performed at the \"Schedule Creation Phase\" and \"Data Transmission Phase\". This design is exactly the opposite of the Watchdog design. As for the Scheduling design, it is more similar to the Broadcast design than the Watchdog design. The only difference is that the CH uses the same scheduling message used in the \"Schedule Creation Phase\", however with small extra data added to it. Thus, this design does not initiate a new message transmission to inform the associated nodes with the identity of the SCH. It only increases the message size. Equations (7)-(9) provide analytical analysis for the cost of the three designs only in the \"Schedule Creation Phase\".  
Equation (7) calculates the cost in the case of Scheduling design after adding the identity of the second cluster head ((SCH-BS)\({}_{\text{MSG}}\)) to the schedule message (TDMA\({}_{\text{MSG}}\)). This addition increases the message size and consequently increases the transmission cost. _Scheduling Design Cost_ =  
\[\left(SCH-BS\right)_{MSG}+\sum_{i=1}^{NC}CNi[TDMA_{MSG}+SCH_{MSG}] \tag{7}\]  
Equation (8) shows the new message added by the Broadcasting design (SCH\({}_{\text{MSG}}\)). This new message with the new added headers will be sent to all cluster nodes which will cost even more energy. _Broadcasting Design Cost_ =  
\[\left(SCH-BS\right)_{MSG}+\sum_{i=1}^{NC}CNi[TDMA_{MSG}]+\sum_{i=1}^{NC}CNi[SCH _{MSG}] \tag{8}\]  
Figure 12: Energy consumption measurements.  
Equation (9) shows the original messages initiated by LEACH. Only the message sent to the base station is generated by this design, which is a common message in all three proposed designs: _Watchtog Design Cost_\(=\)  
\[\left(SCH-BS\right)_{MSG}+\sum_{i=1}^{NC}CNi[TDMA_{MSG}] \tag{9}\]