esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Background
Both XRF and LIBS can obtain the surface characteristics of materials, so they are also easily affected by the surface pollution of materials to be sorted [6]. In addition, the essence of EMS is to employ the principle of electromagnetic induction. According to this principle, excitation coils generate a variable excitation magnetic field which interacts with the materials to be measured, resulting in an eddy current effect. Meanwhile, the eddy current generates an anti-magnetic field which interacts with the excitation magnetic field to change the impedance of the excitation coils. EMS is suitable for sorting NM-SS (non-magnetic stainless steel) with very small electrical conductivity [5]. However, impedance change is not only closely related to the conductivity and permeability of the measured material, but also affected by many uncontrollable factors, such as excitation frequency, excitation coil structure, material shape, internal mechanical defects and material-to-sensor distance, etc. Therefore, there are many difficulties in the industrial application of EMS sorting. Compared with methods above-mentioned, the XRT method theoretically has the following advantages [5; 19; 20; 21; 22; 23; 24; 25; 26]: (i) no specific requests for materials, (ii) less sensitive to surface pollution, (iii) not easily affected by the material shape (including hollow pipes) and detection environment, (iv) without residue or secondary pollution. Meanwhile, as one of the dry-type sorting methods, XRT can realize rapid detection and large throughput. Furthermore, DE-XRT can identify materials on the basis of the high correlation between transmission value, once detecting the effective atomic number of materials, and obtaining certain shape features too. It has become one of the most promising sorting methods in the field of solid waste sorting. The main content of this study is the application of DE-XRT to the in-line automatic sorting of non-ferrous waste metals.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 6 Open Research Challenges
### Channel State Information Knowledge and Feedback  
In many cases, to carry out IA in CR networks, local CSI knowledge is required. It is a crucial to investigate suitable blind and semi blind IA techniques so that the burden for acquiring the channel knowledge at the CRs is minimized. CSI is key to evaluating IA precoders. Pilot transmission require adequate resources to be assigned, and in certain circumstances, to CSI feedback increasing the readiness of precise CSI. Subsequently, IA precoders need be recalculated during an appreciably change in channel conditions, in high-mobility fast-fading networks, the overhead of CSI acquisition can reduce the gains of IA [133]. Hence, minimal overhead signalling dimension techniques need be developed for an appropriately trade-off between CSI quality and CSI acquisition overhead. Temporal correlation can be used to both improve the precision of CSI and decrease the feedback overhead. The effects of feedback overhead and feedback delay in IA CR networks are a significant future research issue [88; 90].
###### Abstract  
The study assessed the quality parameters of grain and flour, the rheological properties of dough and the quality of bread prepared from flour of hybrid cultivars of wheat in comparison with population cultivars of wheat. As the interest in wheat hybrids cultivars from the agricultural and milling industry is growing, their technological value of grain and flour was evaluated at two levels of nitrogen fertilisation (N1--110 kg/ha, N2--150 kg/ha). Increasing the fertilisation (N2) produced a significant influence on the crude protein and gluten content in the flour, as well as the moisture of the crumb and the yield of the dough without impacting other rheological traits and parameters of bread baking process. The performed principal component analysis (PCA) allowed for identification of the best cultivars among the studied wheat cultivars (Hybery and Hyvento). The hybrid cultivar Hyvento was characterised by favourable qualitative traits of the grain (vitreousness, crude protein content) and rheological parameters of the dough (bread volume), however, it had lower baking quality parameters. Among the hybrid cultivars, the best applicability for baking purposes was Hybery due to the favourable values of the baking process parameters and bread quality (bread yield, bread volume, Dallmann porosity index of crumb). Hybrid cultivars of wheat can therefore be used for the production of bread and be an alternative in agricultural production for population cultivars, which will contribute to filling the knowledge gap for the hybrid wheat cultivars.  
hybrid wheat; rheological properties; bread-making quality; N fertilisation Article
## 2 Video Camera Imaging Based Method
### Basic Framework
#### 2.1.4 Noise Artefact Removal  
The raw signal may contain unwanted noise due to factors such as subject movement, illumination changes, camera movement and skin tone. To remove unwanted noise from the raw signal various signal processing techniques may be applied such as low pass filtering, bandpass filtering, adaptive bandpass filtering, signal decomposition, blind source separation (BSS) and model-based methods.
# Study on Single-Event Transient Hardness of Semi-Enclosed Gate NMOS
## 3 Analysis of Simulation Results
### Analysis of Electric Potential Gradient Distribution at Different Times  
To further analyze the mechanism of the semi-enclosed gate NMOS device transitioning from a non-steady state to a steady state after heavy ion incidence and to interpret the changes in the simulation curve, the simulation captured three time periods: the moment of incidence, the moment when the drain current approached its peak, and the moment when the device returned to a steady state. The visual tool was used to analyze the potential distribution of the device at these three time nodes.  
From Figure 8a, it can be observed that, at the moment of incidence, the drain exhibits a very high potential due to the application of a 1 V driving voltage, while the source has a low potential. The source and drain are separated by an even lower potential in the conductive channel region, effectively blocking current flow. Figure 8b shows that, except for a weak potential distribution at the very top, there is almost no potential distribution in the lower regions, resulting in almost no current generation at this stage.  
Figure 7: Heavy ion charge density gradient distribution.  
Subsequently, Figure 9a, which depicts the cross-sectional view of the device during the mid-incidence period, reveals the most significant impact of the heavy ion incidence. The drain remains at a high potential, and the source remains at a low potential. However, the potential in the conductive channel region connecting the two terminals is at the same gradient as the source, effectively linking the drain and source. This causes the potential to decrease from the drain to the source, creating a gradient. Compared to Figure 8, the potential gradient in Figure 9, ranging from 9.272 \(\times\) 10\({}^{-1}\) V to 3.197 \(\times\) 10\({}^{-1}\) V, spreads across 80% of the plane. This condition leads to significant current generation, reaching the peak current value.  
Finally, Figure 10 shows that the potential gradient returns to a state similar to that in Figure 8, interrupting the current and restoring the device to its pre-incidence steady state. In summary, the simulated drain current output curve exhibits a sudden rise from a very low current state to a peak, followed by a rapid decline back to a very low drain current state, forming the single-event transient current. This behavior is consistent with the observed transient response of the device under heavy ion incidence.  
Figure 8: Electric potential gradient distribution at 0 s time. (**a**) Sectional view parallel to the conductive channel. (**b**) Sectional view perpendicular to the conductive channel.  
Figure 9: Electric potential gradient distribution at 10\({}^{-10}\) s time. (**a**) Sectional view parallel to the conductive channel. (**b**) Sectional view perpendicular to the conductive channel.
## 3 Analysis Process
### Data Analysis
#### 3.1.1 Search 1: Carbon Footprint and Manufacturing Processes  
A search conducted in the SCOPUS database with the keywords \"Carbon Footprint and Manufacturing Processes\" yielded the following findings:  
* **Number of Published Articles:** A significant increase in studies on carbon footprint and manufacturing processes was observed over the last 10 years. A total of 13,004 articles were identified from 1992 to 2022, with notable growth in interest in recent years.
* **Key Findings:*
* Geographical Contributions: The United States and China emerged as the leading contributors to carbon footprint research.
* Research Focus: LCA and carbon management were identified as the most frequently studied topics.
* **Evolution of Research:** There has been a progression from basic carbon footprint assessment methods to more advanced analyses, including life cycle assessments and predictive modeling.
* **Major Challenges and Opportunities:*
* Challenges: The complexity of managing supply chains and difficulties in measuring emissions remain significant hurdles.
* Opportunities: The development of clean energy strategies and interdisciplinary collaborations present promising avenues for reducing carbon emissions.  
The review highlights that the U.S. and China make significant contributions to the literature, and there has been a marked increase in carbon footprint studies. Methodologies such as LCA play a critical role in reducing carbon emissions, while bibliometric analysis and process mining have also been widely used. Challenges include the accurate measurement of emissions and managing complex supply chains. However, clean energy strategies and advanced modeling techniques present important opportunities for reducing carbon footprint. In conclusion, these studies contribute to the development of sustainable manufacturing processes and the alignment of environmental goals with economic balances.  
The section titled \"Challenges and Opportunities\" in the graph addresses the key issues encountered in carbon footprint management and the innovative technologies and policy approaches developed to address these challenges. Subsections like \"Innovative Technology and Policy Approaches\" are crucial factors that enable strategic decision-making aimed at reducing carbon emissions and achieving environmental sustainability goals.  
The \"Methodologies\" section in the graph highlights the scientific tools used in carbon footprint studies. Methods such as Environmental Science and Bibliometric Analysis are critical for analyzing the evolution of academic literature in this field and identifying which topics have gained more prominence. Particularly, studies focused on sustainable development and emission control reveal strategies aimed at reducing carbon emissions in manufacturing processes.  
Thus, the Table 1 illustrates a broad spectrum of research on carbon footprint management and sustainable manufacturing processes and is presented as an important resource for understanding future trends in this area.
## 2 Theoretical Framework and Methods  
### Subway Station Flood Resilience System  
#### 2.1.1 Causes of Flood Disasters in Subway Stations  
The causes of subway station flood disasters are the sudden changes in disaster-causing factors and the destabilization of the disaster-prone environment under the impact of sudden events such as heavy rain, which lead to losses or damage to the disaster-affected bodies. Disasters result in varying degrees of consequences for the subway, including operational disruptions, damage to facilities and equipment, and casualties. For instance, during the incident involving Zhengzhou Line 5, significant water accumulation at the tunnel entrance of the Wulongkou parking lot breached the water barrier, causing water to flow into the subway station at Yueji Park. Conversely, at the Jin'anqiao subway station in Beijing, the area's relatively low elevation causes rainwater from surrounding roads to converge at this location during heavy rainfall, resulting in the backflow of rainwater.
## 6 Experimental Results
Another key challenge for real-world clinical deployment is robustness to artifacts such as muscle movements, eye blinks, and electrical interference, which are common in clinical EEG recordings. Since the UCI dataset is pre-cleaned, our current model has not been tested under such noisy conditions. Furthermore, the model has not yet been evaluated in a patient-specific or longitudinal context, where intra-subject variability over time can impact performance. Future studies will focus on testing the model with raw, multi-channel clinical EEG and assessing performance consistency across individual patients to support personalized seizure monitoring.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 2 Depleted/Depleting Hydrocarbon Reservoirs (DHRs) for H\({}_{2}\) Generation
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Process Proposed** & **Selected References** \\ \hline Microbial H\({}_{2}\) generation (DORs) & Dark fermentation (anaerobic) of sugar. This is a derivative of microbial EOR (MEOR) to improve oil production. & [11; 12] \\ \hline \multirow{4}{*}{Catalytic methane conversion (DGRs)} & In situ steam methane reforming (SMR), water–gas shift (WGS) reaction, and methane cracking (MC) requiring heat (350–450 \({}^{\circ}\)C with steam flooding); for catalytic MC, Ni-salt-based solutions are used. & [13; 14; 15] \\ \hline \multirow{4}{*}{H\({}_{2}\) through in situ combustion (DORs)} & Converting in situ hydrocarbons of DORs into H\({}_{2}\) using reforming techniques. Besides hydrocarbon, the other reactants can involve steam (steam reforming) or oxygen (partial oxidation, with up to 1000 \({}^{\circ}\)C using in situ combustion) or both (auto-thermal). & [15; 16; 17] \\ \cline{1-1} \cline{2-3}  & & \\ \cline{1-1} \cline{2-3}  & & \\ \cline{1-1} \cline{2-3}  & & \\ \cline{1-1} \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the proposed technologies using DHRs (DORs and DGRs) for generating H\({}_{2}\) in situ. Figure 1: Schematic description of the H\({}_{2}\) production from the depleted hydrocarbon reservoirs (HC for hydrocarbons). the production of accompanying gases, like CO and CO\({}_{2}\). It is claimed that lighter H\({}_{2}\) separates rapidly from the other gases and moves upwards, accumulating at the top of the reservoir. CO\({}_{2}\) or other gaseous components like methane (CH\({}_{4}\)) can dissolve in water and oil. In a gas reservoir, CO\({}_{2}\) tends to move downwards as its density is higher than that of NG. It is certain that more expensive horizontal wells can increase the performance of the processes as the energy injected is better spread throughout the reservoir. It is argued that the membrane separators can be used to capture H\({}_{2}\) from the gas streams already downhole [18,19]. For higher amounts of H\({}_{2}\) and/or in a case where such separation is not possible, the separation of the H\({}_{2}\) and other gases can be performed using capture units installed at the surface, which, however, involves higher investment costs. The related literature lacks an in-depth engineering analysis of these processes. Are the technologies mature enough and are the processes technically and economically viable? What are the main drawbacks and risks of H\({}_{2}\) generation in the reservoir? Is in situ H\({}_{2}\) generation, production, and separation at the surface competitive with conventional H\({}_{2}\) generation methods? In the following sections, we describe these technologies and assess their technical and economic feasibility.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
###### Abstract  
Optical Coherence Tomography Angiography (OCTA) refers to a powerful class of OCT scanning protocols and algorithms that selectively enhance the imaging of blood vessel lumens, based mainly on the motion and scattering of red blood cells (RBCs). Though OCTA is widely used in clinical and basic science applications for visualization of perfused blood vessels, OCTA is still primarily a _qualitative_ tool. However, more quantitative hemodynamic information would better delineate disease mechanisms, and potentially improve the sensitivity for detecting early stages of disease. Here, we take a broader view of OCTA in the context of microvascular hemodynamics and light scattering. Paying particular attention to the unique challenges presented by capillaries versus larger supplying and draining vessels, we critically assess opportunities and challenges in making OCTA a _quantitative_ tool.  
optical coherence tomography; angiography; scattering; red blood cells; rheology; imaging; hemodynamics; blood flow +
Footnote †: journal: Applied Sciences
## 3 Signal Performance
Figure 3 shows scatter plots of MP for BeiDou-2 IGSO and MEO, BeiDou-3e, and GPS satellites as a function of the azimuth and elevation from the aforementioned tracking stations, where Figure 3a-c represent the B1I pseudorange, Figure 3d represents the GPS L1 signal, and Figure 3e-g represent the B3I pseudorange, and (h) represents the GPS L2 signal. It can be observed that the MP is independent of the azimuth for both BeiDou-2 and BeiDou-3e satellites and is also independent of the elevation for BeiDou-3e satellites, in the same way as for GPS satellites. Figure 2: Multipath (MP) combinations for BeiDou-2 (C10, C14), BeiDou-3e (C31, C34), and GPS (G32) satellites with respect to elevation. For BeiDou satellites, MP1 and MP3 represent the B1 and B3 frequency MPs, respectively, whereas for global positioning satellites (GPS) (right column), MP1 and MP2 represent the L1 and L2 frequency MPs. Blue dots represent MP1 and red dots represent MP3/MP2. The average and RMS values of the MP combinations are listed in Table 2. The data used in Table 2 include values for elevation angles ranging from 0 to 90\({}^{\circ}\). As seen in Table 2, there is an average bias of more than 1 cm for the B1I and B3I signals in BeiDou-2 IGSOs and MEOs, but this bias is absent for BeiDou-3e and GPS satellites. In addition, the RMS values for BeiDou-3e satellites are on the same level as those for GPS satellites, which represents a remarkable improvement in comparison with BeiDou-2 satellites.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Applications and Implications
#### 3.3.1 Urban Planning and Environmental Management  
First and foremost, the predictive capabilities of these models can be used to inform proactive strategies in urban planning and environmental management. They can help determine when and where interventions might be necessary to preserve or improve water quality. For example, when the model predicts a degradation in water quality, it could signal the need for immediate action, such as additional water treatment, astricter regulation of residential sewage discharge, or enhanced stormwater management [16; 55; 56].  
The identification of BOD and Turbidity as influential factors has particular relevance. Urban and environmental planners can focus on implementing strategies that target these specific issues. For instance, increasing the treatment efficiency of sewage before it is released can reduce BOD. Similarly, implementing green infrastructure like rain gardens or permeable pavements can help control runoff and reduce turbidity [57; 58; 59].
## 1 Introduction
### Major Contributions  
The key contributions of this paper are summarized as:  
1. Data aggregation, multi-objective-based cluster head selection and optimization-based path selection for transmitting data are the three primary processes in the framework network for smart-energy-routing algorithms that are developed.
2. The model for a multi-path network is deployed where the distance D between the transmitter and receiver is more than the threshold value, which is the distance square for the dissipation of radio energy for the multipath model.
3. The cluster head selection method is chosen for every round to achieve network longevity and energy efficiency.
4. The performance is evaluated based on the metrics and compared with baseline algorithms (PSO, GA, ALO) with the proposed methodology.
# High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector
Therefore, we propose using a Kalman filter to distinguish the signals from the noise. However, when the output photocurrents of QD are sent to the amplifiers, some DC offsets are brought into the signal, affecting the values of the useful signals. In order to solve this problem, we suggest using a modulation method, where we use a sine signal to modulate the intensity of the beacon light at the transmitting end, after which we can give an inverse gain to move the center of signals to zero to eliminate the DC offsets before the data are sent to the Kalman filter. After the signals are run through a ADC, we use the peak values of the output signals in every period as the elements of the measurement matrix, which already contains measurement noise. Using the Kalman method can help us filter out the white noise and improve the detecting accuracy. What is more, the method requires few steps of iteration, which helps us to realize real-time processing. In the following sections of the paper, we first introduce the description of QD characteristics in order to get the output signal form of QD. Then, we compare the working process of the direct detection method with the improved detection method. Finally, we set up an experimental platform to verify the effects of the Kalman filtering method.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 3 Problem Formulation and Existing Challenges
### Overview of WSN and IoT Communication Systems
#### 3.1.1 Basic Clustering in WSNs  
A mechanism known as clustering is used to segment the entire topology of WSN into sub-regions for efficient data transmission and prolonging of network duration. Typically, every cluster consists of a Cluster Head, which collects data already sensed by the member nodes inside the cluster and returns the collected data to the central station. The communication between sensor nodes within a cluster is described as intra-cluster routing, while the communication between separate clusters is known as inter-cluster routing. In WSNs, to collect the data and to pass it on to the central station, single-or multi-hop communication is implemented as a routing paradigm. The Cluster Head responsibility is logically rotated among each member within a cluster to achieve longer network lifetime and to reduce the depletion of energy for the WSN.