esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
###### Abstract  
The fast depletion of oil reserves has steered the petroleum industry towards developing novel and cost-effective enhanced oil recovery (EOR) techniques in order to get the most out of reservoirs. Engineered water-polymer flooding (EWPF) is an emerging hybrid EOR technology that uses the synergetic effects of engineered water (EW) and polymers to enhance both the microscopic and macroscopic sweep efficiencies, which mainly results from: (1) the low-salinity effect and the presence of active ions in EW, which help in detachment of carboxylic oil material from the rock surface, wettability alteration, and reduction in the residual oil saturation; (2) the favorable mobility ratio resulting from the use of a polymer; and (3) the improved thermal and salinity resistance of polymers in EW. Various underlying mechanisms have been proposed in the literature for EW EOR effects in carbonates, but the main driving factors still need to be understood properly. Both polymer flooding (PF) and EW have associated merits and demerits. However, the demerits of each can be overcome by combining the two methods, known as hybrid EWPF. This hybrid technique has been experimentally investigated for both sandstone and carbonate reservoirs by various researchers. Most of the studies have shown the synergistic benefits of the hybrid method in terms of two- to four-fold decreases in the polymer adsorption, leading to 30-50% reductions in polymer consumption, making the project economically viable for carbonates. EWPF has resulted in 20-30% extra oil recovery in various carbonate corefhood experiments compared to high-salinity water flooding. This review presents insights into the use of hybrid EWPF for carbonates, the main recovery driving factors in the hybrid process, the advantages and limitations of this method, and some areas requiring further work.  
hybrid engineered water; low salinity; polymer; surfactant; wettability; interfacial tension; mobility ratio; carbonates +
Footnote †: journal: Applied sciences
## 3 Routing
### Routing in CRSN: Rising Issues
#### 3.2.7 Cross-Layer Framework  
Although this concept has been widely utilized, there is no generalized framework for cross-layer interactions that shows the limitations and opportunities. Figure 1 presents a cross-layer framework in the context of CRSNs that can easily be generalized to other areas.  
Depending on the task at hand, a QoS route selector/controller (QRC) is proposed to form the bidirectional link between the required layers to achieve optimum cross-layering. The operation can be explained as the QRC obtaining the application QoS demand from the application layer. This result arises because various QoS demands expect minimum requirements to be met regarding channel characteristics, node buffer state and latency. However, some of these requirements can be traded off for others to cushion the limitation of one of the factors. In this regard, differentiated service can easily be characterized. Thus, based on the spectrum opportunities (SOP) available to the QRC, the MAC layer can perfectly allocate channels that appropriately suit the various local demands by carrying out a rule-based contention. This idea can prove very effective in multimedia networks.  
It is expected that nodes that are qualified to take part in routing will send the SOP and piggyback other information such as the buffer state, sending rate, _etc._, depending on the routing protocol being deployed. Furthermore, based on the SOP of neighboring nodes, network coding opportunities can easily be identified and utilized when network coding is implemented on the network. Although some researchers have proposed adding a separate coding plane to the communication layers [24], this  
Figure 1: Cross-layer framework communications.  
method perfectly solves the case of network coding-aware schemes. Such information is used for both power and sending rate control in the physical layer.  
A mobility manager that handles spectrum sensing, spectrum sharing and spectrum handoff will also need information from the QRC to efficiently perform these responsibilities. This module is hosted by the network layer with the network coding module. The QRC is expected to perform optimization based on its needs and requirements. Determining the most appropriate scheme, which can prove to be a non-trivial task, is left to the discretion of the designer based on the purpose of the design.  
To justify the need to consider the various factors presented in this model, we must evaluate the performance of WSN routing strategies with respect to DSA.
# An Overview of Infrared Remote Sensing of Volcanic Activity
## 2 Infrared Remote Sensing: Theory
Comparable functionality has now passed to the largely under-investigated SWIR bands of Landsat-8 [20]. The advantage of SWIR volcanic observations is that, in the absence of sunlight, terrestrial SWIR signals recorded from space will be purely a product of the temperature of a hot emitting surface. In contrast, MIR and TIR detections from space will be the product of emissions from both ambient and hot surface components, the former of which must therefore be removed. The advantage that MIR and TIR observations have over SWIR observations, however, is that they will also detect cooler volcanic surfaces, thereby negating the requirement of incandescent or fresh lava to be present while facilitating the monitoring of a greater range of volcanic activity.
# A Novel LOS Decision Technique Reflecting 3D DTED for Modeling an Electronic Warfare Environment
###### Abstract  
A new line-of-sight (LOS) decision algorithm applicable to simulation of electronic warfare (EW) is developed. For accurate simulation, the digital terrain elevation data (DTED) of the region to be analyzed must be reflected in the simulation, and millions of datasets are necessary in the EW environment. In order to obtain real-time results in such an environment, a technology that determines line-of-sight (LOS) quickly and accurately is very important. In this paper, a novel algorithm is introduced for determining LOS that can be applied in an EW environment with three-dimensional (3D) DTED. The proposed method shows superior performance as compared with the simplest point-to-point distance calculation method and it is also 50% faster than the conventional interpolation method. The DTED used in this paper is the data applied as level 0 for the Republic of Korea, and the decision of the LOS at approximately 1.8 million locations viewed by a reconnaissance plane flying 10 km above the ground is determined within 0.026 s.  
electronic warfare; line-of-sight; digital terrain elevation data; EW simulation; LOS decision algorithm; sign counting method +
Footnote †: journal: Medical  
the line-of-sight (LOS) path. However, if the jamming signal is obscured by obstacles, such as mountains, it experiences complicated physical phenomena, such as diffraction, reflection, etc. The non-LOS calculation is performed in separate subroutines, and the depth and complexity of the modeling depends on the modeling purpose. Thus, in this scenario, the priority is to check whether the obstacle exists on the LOS path of the jamming signal [8; 9; 10].  
This study proposes a novel LOS decision technique that is based on the scenario shown in Figure 1. In the proposed algorithm, a virtual triangular plane including an aircraft and a threatening object is created, and it is rapidly determined whether the signal path is LOS based on sign counting, which is elaborated in the following sections. The efficiency of the proposed algorithm is validated in a space reflecting actual three-dimensional (3D) terrain data.  
The rest of the paper is organized in the following order: In Section 2, we introduce the commonly used terrain data format and the LOS decision algorithm in the literature; in Section 3, we present the proposed algorithm, which determines LOS faster than the existing methods; in Section 4, we validate the performance of the proposed algorithm by comparing the LOS decision times of the proposed algorithm and the existing methods.
## 3 Proposed Algorithm
### Modifications of the CIM Computation  
As shown in (2) and (3), the CIM in CAEAC uses a common bandwidth \(\sigma\) for all attributes. Thus, a specific attribute may have a large impact on the value of the CIM if the common bandwidth \(\sigma\) is not appropriate for the attribute.  
In this paper, two modifications of the CIM computation [48] are integrated into CAEAC in order to mitigate the above-mentioned effects: (1) One is to compute the CIM by using each individual attribute separately, and the average CIM value is used for similarity measurement, and (2) the other is to apply a clustering algorithm to attribute values, then attributes with similar value ranges are grouped. The CIM is computed by using each attribute group, and the average CIM value is used for similarity measurement.
# Bias and Variance Analysis of Contemporary Symbolic Regression Methods
## 1 Introduction  
Symbolic regression (SR) is a task where we aim to find a closed-form mathematical equation which describes linear and nonlinear dependencies in data without making prior assumptions. The goal is to make predictions for unseen data by training a mathematical expression with a finite set of existing observations. While there is a huge variety of methods for solving regression problems, starting from linear models over decision trees [1] up to neural networks [2], SR methods deliver non-linear and human-readable closed-form expressions as models with smooth and differentiable outputs. Therefore, SR is commonly applied in situations where we cannot rely on black-box models like neural networks, as they require comprehensible and traceable results for throughout model verification.  
Bias and variance are properties of a machine learning algorithm that affect the interpretability of its models. The variance of an algorithm describes, how the outputs of its models change when there are differences in the used training data. SR algorithms are considered as high-variance algorithms, which means that even slightly different training data can lead to very dissimilar models [3]. However, high variance of an algorithm implies that it is capable to fit a model, to a certain extent, perfectly to training data. It is often necessary to limit the variance in order to perform well on unseen data, without restricting it too much, which would prevent highly accurate models and cause so-called bias. Balancing both properties is called the bias/variance trade-off [1].  
From a practitioner's point of view, perturbations caused by variance do not spark trustworthiness for practitioners, as we would expect small changes in training data to have little effect on the overall results. Therefore, many algorithms use, e.g., statistical tools to gracefully deal with bias and variance or ignore this property as the primary focus is accuracy regardless of the model structure [4]. However, bias and variance have specific implications to SR. SR is known for its human-readable and potentially interpretable white-box model structure, but high variance limits this feature. On the other hand, the complexity of models is limited for the sake of interpretability, which causes bias. Algorithmic aspects of SR amplify these effects: First, the stochastic nature of a Genetic Programming (GP)-based [5] SR algorithm results in differences in models between multiple SR runs even when the training data do not change at all. Second, since there is no guarantee for optimality in an SR search space [6], models trained in different SR runs might even provide very similar accuracy despite being completely different mathematical expressions due to, e.g., bloat [5] or over-parameterization [7] that increase the size of a model without affecting its accuracy.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 2 Experimental Site and Data
### Experimental SAR Data  
The data used in the experiment were obtained on 19 June 2023, and were quad-polarized QPSI mode data from the C-SAR/02 satellite with an 8 m resolution. The data are single-look complex (SLC) data that have undergone antenna pattern correction. For detailed data information, refer to Table 3. The experimental image is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Antenna Status**} & \multicolumn{2}{c}{**Theoretical Scattering Matrix**} \\ \hline
**Transmitting Antenna Angle \(\mathbf{\theta_{1}}\)** & **Receiving Antenna Angle \(\mathbf{\theta_{2}}\)** & **L-Band and S-Band** & **C-Band** \\ \hline \(0^{\circ}\) & \(0^{\circ}\) & \(\begin{bmatrix}0&0\\ 0&1\\ 0&1\\ 0&1\\ 0&1\\ 0&0\\ \end{bmatrix}\) & \(\begin{bmatrix}1&0\\ 0&0\\ 1&0\\ 0&1\\ 0&0\\ 1&0\\ \end{bmatrix}\) \\ \(45^{\circ}\) & \(0^{\circ}\) & \(\begin{bmatrix}0&0\\ 1&1\\ 1&1\\ 0&0\\ \end{bmatrix}\) & \(\begin{bmatrix}1&0\\ 0&0\\ 1&0\\ 0&0\\ 1&1\\ 0&0\\ \end{bmatrix}\) \\ \(45^{\circ}\) & \(90^{\circ}\) & \(\begin{bmatrix}1&1\\ 0&0\\ 0&0\\ 0&0\\ 1&0\\ \end{bmatrix}\) & \(\begin{bmatrix}-1&-1\\ 1&1\\ 1&1\\ 0&0\\ \end{bmatrix}\) \\ \(90^{\circ}\) & \(0^{\circ}\) & \(\begin{bmatrix}1&1\\ 0&0\\ 0&0\\ 0&0\\ \end{bmatrix}\) & \(\begin{bmatrix}0&0\\ 0&1\\ 0&1\\ 0&0\\ 0&0\\ \end{bmatrix}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: ARC polarization matrix.  
Figure 3: Photograph of the ARC antenna structure.
## 3 Field Trialed System
### Remote Controlled Tractor
#### 3.2.4 Network Quality Measurements  
This section describes how we conducted a performance analysis of the available networks. Network quality must be measured to verify reliable remote control. In this trial, adequate remote control required at least two high-definition videos. Furthermore, these videos require a total throughput of 5 Mbps in the uplink direction. An uplink throughput of 5 Mbps was then obtained with the 2.3 GHz time division duplex van mounted base station when the reference signal received power (RSRP) was over \(-98\) dBm. Figure 6 shows the RSRP measurements of the base station. From this figure, it is possible to roughly estimate the remote driving range of this base station configuration. Note that the uplink-downlink frame configuration had two subframes for uplink and six for downlink due to the obtained frequency license.  
The round-trip time (RTT) of the same base station is shown in Figure 7. Note that the one-way delay is approximately half of the RTT. Thus, the control messages reaches the tractor faster. Whenever a network is good enough for videos, the control messages are fast enough with the local network. We also measured commercial network round-trip times. The RTT was approximately 30-40 ms faster with a local base station than with a commercial network. The delays were sufficient for the use case.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Device** & **Name** \\ \hline Camera & AXIS P3935-LR \\ Camera & Hikvision DS-2 CD2326G2-ISU/SL \\ Camera & Dahua IPC-HDW4231 EM-ASE \\ Camera & Dahua IPC-BI820P (\(\times 2\)) \\ Switch & Tenda TEF110 SP PoE-switch \\ Router & Teltonika RU7955 \\ Router & Goodmill w24h-S \\ PC & HFP ProDesk 600 G3 DM Mini PC Core i5-7500T 2.7 GHz 8/256 SSD (SATA) Win 10 Pro \\ PC & Lenovo C24-25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Devices used to provide remote views from the tractor.  
Figure 6: Standalone private LTE 2.3 base station RSRP measurement. The red X marks the van-mounted base station with a transmit antenna height of 10 m.
# Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
## 2 Related Work
### Two Improvements of SAX Distance Measure for Time Series
#### 2.2.2 Sax-Bd  
Reference [17] and others suggest adding boundary distance as a new consideration instead of trend distance and propose the algorithm SAX-BD. This algorithm considers that, for each segmented time series fragment, there are maximum and minimum points, and their distances to the mean value are referred to as the boundary distance. The average of the segment's boundary distances contributes to a more accurate measurement of the different trends in the time series data. Details are provided below.  
From Figure 3, we can observe that the maximum and minimum values within each time segment serve as the boundaries. The boundary distance of a is denoted as \(\Delta q(t)_{max}\) and \(\Delta q(t)_{min}\), as shown in Equations (5) and (6):  
\[\Delta q(t)_{max}=q(t)_{max}-\overline{q} \tag{5}\]  
\[\Delta q(t)_{min}=q(t)_{min}-\overline{q} \tag{6}\]  
In fact, the SAX-BD algorithm computes the trend changes (i.e., the boundary distance) of a as \(\Delta q(t_{min})\) and \(\Delta q(t_{max})\), and these values are equivalent to \(\Delta q(t_{s})\) and \(\Delta q(t_{e})\). Therefore, it is evident that SAX-BD can also effectively distinguish between them. For cases a and b, the distance calculated using SAX-TD is 0. However, in the SAX-BD approach, the distance calculated using SAX-BD is not equal to 0, indicating the potential for differentiation between these two sequences. Regarding the situations c and d, according to the TD and BD methods, it is as follows:  
for case c,  
\[\Delta q(t_{s})=\Delta q(t)_{max}\;and\;\Delta q(t_{e})=\Delta q(t)_{min} \tag{7}\]  
for case d,  
\[\Delta q(t)_{min}-\Delta q(t_{e})\
eq 0\;and\;\Delta q(t)_{max}-\Delta q(t_{s})\
eq 0 \tag{8}\]Similar to the SAX-TD distance measurement concept, the following SAX-BD distance measurement formula can be used for temporal data \(Q\) and \(C\):  
\[BDIST(\hat{Q},\hat{C})=\sqrt{\frac{n}{w}}\sqrt{\sum_{i=1}^{w}{((dist(\hat{q}, \hat{c}))^{2}+\frac{w}{n}(bd(q_{i},c_{i}))^{2})}} \tag{9}\]
## 3 Results and Discussion
### Effect of KKD-NT and KKD-WA on ADH and ALDH Activities  
Alcohol-induced mechanisms are closely related to the metabolism of ethanol, and the main enzymatic pathway is the ADH pathway. In the present study, we examined whether the KKD-NT and KKD-WA extracts could enhance the ADH/ALDH-mediated ethanol metabolism in liver cells. ADH/ALDH activities were evaluated from the enzymatic assays using HepG2 liver cells. ADH and ALDH activities increased dose-dependently when treated with KKD-NT and KKD-WA (Figure 6).  
Figure 5: Effect of KKD-NT and KKD-WA on intracellular ROS levels in EtOH-induced HepG2 cells. Cells were treated with different concentrations of KKD-NT and KKD-WA in medium containing 4% EtOH. Values are expressed as mean \(\pm\) SE (\(\mu\) = 3). ## \(p\) < 0.01, ## \(p\) < 0.001, compared with the EtOH control group.  
Figure 6: Activities of ADH (**a**) and ALDH (**b**). The activities of both of the enzymes were determined using the methods described in the Materials and Methods section. * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001 compared to control (CTL).  
ROS are generated by the excess ethanol in the metabolic pathways, and the ADH-dependent removal of alcohol is a primary mechanism for the protection of liver cells from oxidative stress. Our results proposed that KKD-NT and KKD-WA increase ADH and ALDH enzymatic activity. As our samples contain Vit-C along with gallic acid, it is already proven that gallic acid can increase ADH and ALDH activities [56]. In addition, Vit-c has a protective effect against ascorbic acid equivalents, acetaldehyde, in alcoholism [34]. According to our results, KKD-NT and KKD-WA show a strong antioxidant and a potent catalytic factor to stimulate the alcohol metabolism-related enzymes.
# Enrichment of Oesophageal Speech: Voice Conversion with Duration-Matched Synthetic Speech as Target
## 4 Evaluations and Results
### STOI Scores  
STOI [47] is an intrusive objective intelligibility measure which is known to be correlated with subjective intelligibility scores for noisy speech. An intrusive intelligibility measurement requires a degraded signal and an aligned reference signal. We calculated STOI for unprocessed OS samples and converted BLSTMSS samples for the four OS speakers using the already aligned duration-matched SS (target signal) as the reference signal. We used the SS as reference because they were the best possible clean aligned signals available. Calculating STOI with aligned healthy laryngeal speech would have resulted in alignment errors and hence, in an inaccurate STOI measurement. The STOI results are shown in Figure 6.  
We can observe that the STOI scores have improved considerably (at least 15 percentage points) from OS to BLSTMSS for all four speakers. A high STOI score of over 60% was observed for all the BLSTMSS samples with intelligible synthetic speech (>70% ASR accuracy) as reference.  
Figure 5: WER scores for Unprocessed OS, previous systems (PPG and BLSTMHS) and the proposed BLSTMSS system as calculated by ASR 3 for speaker 02M3. Error bars show standard errors.  
Figure 6: STOI scores for the four OS speakers and the enriched versions. Reference signal for STOI is duration–matched SS. Error bars show standard errors.  
Like the ASR, we compared the STOI scores of the proposed system with those of our previous methods (see Figure 7). The references used to calculate STOI were the same duration-matched SS signals. The proposed system has higher STOI scores (about 5%) compared to previous systems and unprocessed OS.
## 3 Results  
### Identification of Predominant Fungi by High-Throughput Sequencing  
The fungal community composition was assessed by amplicon sequencing of three samples taken from different locations using the Illumina HiSeq2500 PE250 platform. Figure 1 shows the distribution of the predominant fungi in the three samples. The dominant phylum in the three samples was Ascomycota. Ascomycota was present in all three samples and accounted for 96.73%, 82.4% and 65.45% of the communities (Figure 2A). Basidiomycota also existed in three samples and accounted for 2.2%, 15.14%, and 30.85% of the communities. Zygomycota accounted for 0.82%, 1.28% and 1.55% of the communities in the three samples. Neocallimastigomycta (0.03%) was only detected in sample DMZ-11.  
Figure 2: Fungal community analysis by high-throughput sequencing. Distribution patterns of fungal phyla and relative abundances of the fungal genera among samples (**A**); relative abundances of the fungal genera among samples (**B**).  
The three samples presented large differences in the diversity and distribution of fungal genera (Figure 2B). _Penicillium_ (36.63%) and _Chaetonium_ (23.99%) accounted for a large proportion of DMZ-9, while DMZ-10 consisted largely of the genus _Exophila_ (29.15%). The DMZ-11 sample presented a relatively uniform distribution of the genera _Cladosporium_ (26.4%), _Cryptococcus_ (22.16%) and _Aspergillus_ (20.84%). In addition, _Talaronyces_, _Alternaria_, _Humicola_, _Pestalotiopsis_, and _Trichosporon_ were detected in the three samples, with prevalence values ranging from 0.04% to 10.9%.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 10 Seed Biotechnologies for Sustainable Agriculture
### Innovations in Seed Technology  
Key advancements highlighted in the review are as follows:  
Synthetic seeds: Encapsulation technologies enable the propagation of elite plant varieties, ensuring genetic uniformity and long-term storage. Synthetic seeds also facilitate the conservation of rare and endangered plant species.  
Nano-enabled priming: Nanoparticles enhance seed germination, vigor, and stress tolerance, offering eco-friendly solutions for sustainable agriculture. For example, multi-walled carbon nanotubes (MWCNTs) improve water uptake and germination rates.  
CRISPR and genome editing: Advanced genetic tools are being used to enhance seed traits, such as pest resistance, nutrient content, and shelf life, addressing key challenges in modern agriculture.