esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability-Based View Synthesis for Free Viewpoint Video
## 5 Conclusions  
In this paper, we present a reliability-based view synthesis framework using depth refinement and an adaptive background modeling method. Multiple viewpoints are employed to render desirable virtual images. In the proposed algorithm, the disocclusion regions are filled by a combination of two sources. The first one comes from two reference viewpoints; the disocclusion regions generated from one reference view are more likely to be found from another reference view due to different position and viewing angle. If the disocclusion regions are lost in both reference views, the updated background image is utilized to fill the static regions. Experimental results indicate that depth refinement obviously improves the accuracy of the depth map, thus improving the performance of the proposed adaptive background modeling and forward (and backward) warping. In addition, an adaptive median filter and DMPM are proposed to replace the classical median filter due to their ability to eliminate unwanted effects and noise while ensuring high-quality texture images. The experimental results show that the combination of proposed techniques yields satisfactory subjective and objective improvement. There are three aspects to focus on in our future research. First, we will focus on improving synthesis quality while reducing computing complexity. Second, we will explore how to construct a stable temporal correlation for complex scenes with moving cameras. Finally, as deep learning is becoming more popular in various types of research, deep view synthesis seems to have a bright future.  
[PERSON] and [PERSON] designed the experiments. [PERSON] performed the experiments. [PERSON] wrote the paper and analyzed the data. [PERSON] contributed simulation tools. [PERSON] supervise the whole work.  
Figure 6: Frame-by-frame objective comparisons of PSNR and SSIM.  
**Funding:** This research was funded by Shenzhen fundamental research project under Grant JCYJ20170412151226061 and Shenzhen fundamental research project under Grant JCYJ20170808110410773.  
**Acknowledgments:** The authors would like to thank the Interactive Visual Media group at Microsoft Research for providing the video sequences _Ballet_ and _Breakdancers_. This research is funded by Shenzhen fundamental research project (Grant JCYJ20170412151226061 and Grant JCYJ20170808110410773).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Methodology
### Hyper-parameters Selection (HS)  
Machine Learning (ML) models need their hyper-parameters to be tuned in order to perform accurately in the addressed problem. Two well-known strategies have been traditionally used for this purpose: _Grid Search_[23; 36] and _Random Search_[37]. The former explores several combinations of certain values provided for each of the hyper-parameters to be tuned, while the latter simply try a given number of random settings obtained from a specified range of values for each hyper-parameters. On the one hand, a _Grid Search_ has the disadvantage of only exploring a subset of the entire search space limited by the values provided for the hyper-parameters. Additionally, this strategy is very costly, in terms of computational time, for ML models with more than 2-3 hyper-parameters, since the search space increases exponentially. On the other hand, a _Random Search_ strategy may be faster and independently of the number of hyper-parameters to be tuned, but its main drawback is that some irrelevant settings may be tested using this strategy (e.g., two hyper-parameters may be linked in such a way that either both have high or low values, but not high/low or low/high at the same time).  
Even though a _Grid Search_ strategy was applied in [38] providing acceptable results, a more recent and powerful hyper-parameters selection strategy, which has provided excellent results overcoming the limitations mentioned above, has been used in this study: Bayesian optimization [39]. In this strategy, hyper-parameters search is modelled by an underlying Gaussian process in such a way that, on each step of the iterative search, the next hyper-parameters setting to be tested is the one with more uncertainty (higher variance) defined by this Gaussian process. Therefore, this strategy ensures that a pseudo-optimal hyper-parameters setting will be achieved in a few number iterations. Bayesian optimization is performed within the train set to obtain the optimal hyper-parameters setting, fit a ML model to the train set using this optimal hyper-parameters setting, and finally evaluate the performance of the fitted ML model on unseen test data (see Figure 1).
# Seismic Wave Amplification Characteristics in Slope Sections of Various Inclined Model Grounds
## 2 Experimental Method for Model Ground Using Shaking Table  
### Experimental Equipment  
Figure 1 shows the 1 g shaking table and the Laminar Shear Box (LSB) used in this experiment. The LSB was placed on the 1 g shaking table, and the model ground was constructed inside it. The LSB allows for layer shear, enabling each layer to move during shaking with the input seismic waves, thereby minimizing boundary effects during the experiment. The dimensions of the LSB are 2000 mm (width) \(\times\) 600 mm (length) \(\times\) 600 mm (height), with each layer being 50 mm thick, comprising a total of 12 layers. Ball bearings were used between the layers to minimize friction, and rubber packing was employed to absorb impact. Each layer can move independently with a maximum displacement of 5 mm.
## 3 Results Analysis
### Metamaterial Property  
Figure 7a,b present the metamaterial properties of the proposed MMA for TE and TM modes, respectively. In Figure 7a, the permittivity is negative from 427 to 541 nm and from 605 to 750 nm. The permeability is negative from 380 to 394 nm and from 506 to 750 nm. Both permeability and permittivity are negative from 505 to 540 nm and from 602 to 750 nm. A negative value of refractive index appears from 472 to 750 nm. As a result of this negative value of metamaterial property, the proposed MMA exhibits high-level broadband absorption properties in the TM mode. The TE mode alternatively achieves the negative value of metamaterial property, where a negative value of permeability is reached from 514 to 750 nm. A unique absorption curve is completed due to the structural symmetricity shown in Figure 4.
## 3 Conclusions and Perspective  
Recent studies have demonstrated that MOFs hold a prominent position in electrochemistry due to their large surface area, excellent mass transfer, porosity and effective active sites. Inspired by this, we have systematically summarized various literature focussing on the use of MOFs in Fuel-cell systems in tabular form. At the present time, considerable efforts have been made to integrate MOF-based materials for commercial use. However, technical difficulties still exist for tuning the MOF chemistry towards Fuel-cell technologies for industrial production. By using environmentally friendly rechargeable batteries, we can reduce energy consumption and protect the environment. It is crucial to mention that fuel cells are proficient in directly converting chemical to electrical energy. This technology has become known for its ultra-high power density, excellent reaction efficiency, quick start-up, easy manufacturing, low operating temperatures, and convenience of industrial production. However, the electrochemical performance and the cost-effectiveness of these batteries need to be enhanced to ensure their long-term reliability and durability. To enable better enhance Fuel-cell-related performance technology at the present time, electrolytes and electrocatalysts need to be optimized for their interfacial effects. Further, proton-transport capacity has to be increased and the resistance must be reduced. Electrocatalytic reactions involving oxygen reduction reactions are substantially affected by the coordination surrounding the active sites inside MOFs. Real-time monitoring of degradation processes and electrocatalytic activity can improve Fuel-cell life and stability. There are still a variety of practical issues associated with Fuel-cell technology despite the major scientific and technological advancements in these fields. These issues may include local and material degradation and side reactions. In hindsight, one of the main factors restricting the commercial success of PEMFCs is their durability. Hence, the present scenario is to predict the process lifetime while making informed judgements about the degradation and performance of PEMFCs under Fuel-cell operation conditions.
## 2 Cu-Based Catalyst-Activated PMS Processes
### Cu-Based Designed Catalyst Materials
#### 2.4.3 Cu-Based DESs
This interest is driven by the ease of synthesizing these materials from inexpensive and renewable starting materials, their low toxicity, and their biodegradable nature. In summary, Cu-based designed catalyst materials, including Cu-MOFs, Cu-LDHs, and DES-assisted catalysts, offer several distinct advantages. These catalysts feature highly tunable properties such as increased surface area, improved stability, and enhanced catalytic efficiency. Their ability to be specifically designed to target contaminants makes them highly effective for customized applications, positioning them as a versatile and powerful tool in environmental remediation. However, these advanced materials also come with certain disadvantages. The complexity involved in their synthesis often leads to higher costs and potential scalability issues, which may limit their widespread adoption. Additionally, despite their advanced design, some of these materials may still face challenges related to metal leaching or decreased stability over multiple cycles of use, necessitating further refinement and innovation. In terms of applications, Cu-based designed catalysts are at the forefront of innovative water treatment technologies. They are particularly valuable in high-value applications such as the degradation of emerging contaminants, the disinfection of pathogens, and in advanced treatment processes that require tailored catalytic properties. Their versatility and effectiveness make them a key component in the development of next-generation environmental technologies.
## 3 Experimental Results and Discussion
### Experimental Results of the Proposed Crack Detection Model  
During the training process, the loss value stabilized at 0.05 after 50,000 iterations, and the best test result was obtained at 56,000 iterations. Figure 8 presents examples of the experimental results generated by the proposed crack detection model.  
In order to verify the crack detection model performance, a total of four comparative experiments were conducted. Table 3 shows the detection precision of Faster-RCNN, YOLOv3, YOLOv5, and the proposed network. Comprehensively judging from the performance of the four classes, our proposed model performs the best, followed by YOLOv5 and YOLOv3, and the typical two-stage detection network Faster-RCNN performs the worst. Obviously, there is a great improvement for the AP of the transverse crack, longitudinal crack, and alligator after the network improved, but the AP of the block is slightly reduced. It can also be seen from Figure 8f that the bounding box generated by the proposed model does not describe the shape of the block crack very well. There is a high possibility that block cracks are mostly large and medium targets with significant semantic information; the proposed network added more shallow features which are helpful to identify the characteristics of transverse cracks and longitudinal cracks but may not be conducive to the identification of block characteristics.  
To further observe the improvement of the proposed crack detection model in small-target detection, follow-up experiments were conducted. When the number of iterations is between 45,000 and 57,000, a comparative experiment is performed every 2000 times to observe the detection precision of transverse cracks and longitudinal cracks. Figure 9 compares the AP changes in longitudinal cracks and transverse cracks during 45,000 to 57,000 iterations before and after network improvement. The evidence supports that when the number of iterations is greater than 45,000, as it increases, the AP values of transverse cracks and longitudinal cracks are both more than 5% higher than those of the original YOLOv5 network. As can be seen from Figure 9, there is a maximum difference of 8.57% between the APs of transverse cracks before and after YOLOv5 improvement at 53,000 iterations, while the APs of longitudinal cracks differ the most by 6.83% at 47,000 iterations.  
Figure 8: Examples of experimental results: (**a**) Single target; (**b**) Multiple targets within a single class; (**c**) Multiple targets within two classes; (**d**) Multiple targets within two classes; (**e**) Multiple targets within multiple classes; (**f**) Block crack with an inaccurate bounding box.  
Figure 9: Comparison of changes in the AP of transverse and longitudinal cracks before and after network improvement.
# Chemical and Enzymatic Treatment of Hemp Biomass for Bioethanol Production
## 2 Materials and Methods
### Enzymatic Hydrolysis Process  
The selection of the enzyme complex for the enzymatic hydrolysis process was made by conducting enzymatic tests using selected enzymes, their mixtures (compositions -- 30/70, 50/50 and 70/30%) and by supplementing them with glucosidase, xylanase and their mixture (50/50%). The enzyme test was carried out under the following conditions: substrate concentration 5%, enzyme dose 10 FPU/g, temperature 55 \({}^{\circ}\)C (SHF process)/38 \({}^{\circ}\)C (SSF process), pH 4.8, time 24 h. The selection parameter was the content of released reducing sugars determined by the Miller method with 3,5-dinitrosalicylic acid (DNS).  
The optimization of the enzymatic hydrolysis of hemp biomass as the SHF process was carried out acc. to the Response Surface Methodology (RSM) using the parameters: biomass content 5-10% _w/v_, temp. 50-70 \({}^{\circ}\)C, time 24-72 h, pH 4.2-5.4, dose of Flashzyme (AB Enzymes) 10-30 FPU-g\({}^{-1}\). Also, the Flashzyme enzyme supplementation by using glucosidase 20 CBU-g\({}^{-1}\) and xylanase 500 XU-g\({}^{-1}\) (Sigma-Aldrich, St. Louis, MI, USA) was tested. Next, the fermentation process was carried out in 100 mL Erlenmeyer flasks containing 40 mL of medium with added _S. cerevisiae_ (1 g dry matter/L) in the following process conditions: 37 \({}^{\circ}\)C, pH 4.8, 120 h.  
To optimize the SSF process according to the RSM, the ranges of process parameters were selected: substrate content 5-7% _w/v_, dose of Flashzyme/Celluclast 1.5 L enzymes 10-30 FPU g\({}^{-1}\) using _S. cerevisiae_ yeast (1 g dry matter/L) at 37 \({}^{\circ}\)C, pH 4.8 and 120 h.
## 7 VLC-IoT: VLC Benefits for IoT  
The potential benefits of VLC for IoT are immense. In this section, we investigate the benefits of VLC for IoT.  
1. Massive IoT device connectivity  
With IoT moving from a limited usage machine-to-machine (M2M) type communication system to a global IoT network service needing massive connectivity, a high-speed network connectivity becomes essential. Conversely, due to the usage of LEDs in almost every aspect of our daily life, VLC is providing massive connectivity for various types of massive IoT communications ranging from M2M, vehicle-to-infrastructure, infrastructure-to-vehicle, chip-to-chip, and device-to-device [28].  
2. Ultra-high-capacity hybrid radio-optical wireless networks  
The high data rate, intrinsic security, and safety of VLC joined with the flexibility and scalability of radio communications create a high-performance and robust hybrid communications system to meet the ultra-high network capacity requirements of IoT networks. Having hybrid devices and a reconfigurable network that can adapt to their radio environment by dynamically selecting the best operating modes will result in a high-performance wireless communications network. Research contributions in VLC have proposed and demonstrated that nodes or devices equipped with both radio and optical interfaces can exploit the best features of both communication systems to support high data rate services [26].  
3. Low-energy-consuming networks with energy autonomous nodes  
The vision of IoT is to connect virtually all the devices in the world to the Internet. As a result, there is an exponential rise in the number of devices participating in IoT with the number expected to hit an unprecedented landmark rapidly [28]. Therefore, one of the big challenges IoT is expected to face is the energy to operate these massive number of nodes since most of them will run on batteries in remote locations. Fortunately, LED sensors will help IoT to save a lot of energy since LED sensors consume less energy compared to RF sensors [62]. Similarly, the concept of LIoT where network nodes can harvest the energy they need to transmit and receive information from light has resulted in low energy-consuming networks with energy-autonomous nodes [26].
4. Highly reliable dense small cell networks for large scale VLC-IoT communications  
The vision of IoT to connect billions of devices can be achieved by deploying highly dense small cell networks. Moreover, the VLC concept supports the deployment of highly reliable dense small networks through the high flexibility and controllability of LED lights [26]. For example, hundreds of non-interfering small cell networks with a high signal-to-noise ratio can be created in a large room using several LED light sources. By extension, multitier networks comprising of satellite, macro cells, and small cells can be created using hybrid communications systems. In this case, the small cell can contain both VLC networks and RF femtocell to meet the demand of massive connectivity of IoT networks as well as for large-scale VLC-IoT communications.
# The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning
## 2 Theory of BDS-3 TGD/DCB Correction Models  
### Current Status of BDS-3 TGD and DCB  
In order to maintain signal consistency, B1I Open Service Interface Control Document (ICD) [10] defined a \"equipment group delay\" \(TGD_{1}\) for users using single-frequency B1I, B1C open Service ICD [12] defined \(TGD_{B1 Cp}\) for users using the pilot component of B1C single-frequency, B2a open Service ICD [11] defined \(TGD_{B2 ap}\) for users using the B2a pilot component single-frequency.  
BDS-3 as a developing constellation, CAS has started to provide BDS-3 official DCB products for users at present. Note that the pseudorange observation of the B1I and B3I signals are represented by C2I and C6I, respectively; C1X and C1P are represented in the pseudorange observation of BIC signal, and C5X and C5P are represented in the pseudorange observation of the B2a signal. Estimates of DCB have been averaged over a
## 3. Sacrificial Anode CP  
Sacrificial anode CP is based on the principle of electrochemical galvanic cells. The metal to be protected is connected to a metal or alloy with a more negative potential to form a galvanic cell. The protected metal acts as the cathode, and the metal or alloy with strong reducibility is consumed as the anode for the oxidation reaction to provide the required CP current. The negative shift in the potential of the protected metal reaches the best protection potential of CP, which protects the metal from corrosion.  
The protective effect of sacrificial anode CP is directly related to the performance of the anode material itself. The sacrificial anode materials commonly used are zinc-based alloys, aluminum-based alloys, magnesium-based alloys, and iron-based alloys. Zinc-based alloys have a high aspect ratio, low electricity generation, and low driving potential for steel. Aluminum-based alloys have a high aspect ratio, high current efficiency, large electricity generation, and moderate driving potential for steel. Magnesium-based alloys have low current efficiency and high driving potential for steel (easy over-protection) and are often used in high-resistivity environments. Magnesium-based alloys are, therefore, commonly used in soil and freshwater [10; 11; 12], and zinc-based and aluminum-based alloys are often used in environments with low resistivity, such as marine environments. Iron-based alloys are mainly developed for CP of copper alloy pipelines in the marine environment and have been applied in marine seawater management protection [13].
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 5 Experiments
### Experiment I  
The statistics for the NSOC2017 dataset are given in Table 2, revealing a wide variation in the number of alerts depending on different detection mechanisms, with some IDSs generating only a few dozen alerts and others producing several hundred thousand. Out of the 133.77 million alerts in the dataset, only 593 were escalated as part of incident handling. To expedite the review process, we downsampled the negative class, resulting in an evaluation dataset with approximately 0.488% negative samples while retaining all positive samples. This process led to a substantial increase in the number of positive samples (from 593 to 2239). The NSOC2017 dataset includes 2239 positive samples and 672,000 negative samples, with a positive sample ratio of approximately 0.333%, highlighting the challenge of class imbalance in the classification task. Note that we adopted a subsampling approach used in previous studies [70] to maintain comparability.
## 1 Introduction
Crucially, we examined the influence of the adopted activation functions in the attention mechanism, such as Rectified Linear Unit (ReLU), Hyperbolic Tangent (tanh), and Logistic Sigmoid, on the proposed approach's forecasting quality. Moreover, the influence of the attention type, including multiplicative and additive, on the forecasting accuracy has been investigated. * Finally, this study investigated both single- and multi-step-ahead forecasting. Data sets from six pedestrians and bicycle traffic flows are utilized to evaluate the forecasting quality of the considered methods. Results reveal that the proposed GAHD-VAE method offers satisfying performance to forecast different types of traffic flows and consistently performed better than the other methods. The rest of the paper consists of three sections. Section 2 highlights literature reviews on the related works. Section 3 describes preliminary material and the proposed GAHD-VAE methodology. Section 4 presents the forecasting results and discussion based on six pedestrians and bicycle traffic flow datasets. Lastly, Section 5 summarizes the paper and provides future directions for possible improvements.