esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Multiobjective Collaborative Optimization Model  
The random forest rate of the penetration prediction model and drag calculation model are used as the optimization functions of NSGA-II, and the maximum ROP and minimum drag are used as the optimization objectives. Giving the optimization boundary of drilling parameters and setting the hyperparameters of NSGA-II, the maximum ROP and minimum drag within the parameter boundary can be obtained using NSGA-II, as well as their corresponding parameter combinations (WOB, RPM, and GPM). The hyperparameters are selected in Table 5.  
We used the model to provide drilling parameters for another neighboring well under drilling. Table 6 shows the drilling parameters before and after the optimization of the well's five test points, as well as the resulting ROP increase and drag reduction. Compared with the neighboring well, the average ROP in the horizontal section of the new well is increased by 10.3% on average, and the average drag of the drill string is reduced by 4.5%. The final test results show that the optimized parameters achieve the purpose of increasing ROP and reducing drag.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Depth** & **Values** & **WOB** & **RPM** & **GPM** & **ROP** & **Improve** & **Drag (kN)** & **Reduce** \\
**(m)** & **Values** & **(kN)** & **(r/min)** & **(l/min)** & **(m/h)** & **Improve** & **(kN)** & **Reduce** \\ \hline \multirow{3}{*}{1181} & Initial & 206.3 & 60.5 & 25.0 & 18.26 & \multirow{3}{*}{16.31\%} & 47.2 & \multirow{3}{*}{4.0\%} \\  & Recommended & 188.2 & 66.8 & 27.0 & 21.24 & & 45.3 & \\ \multirow{3}{*}{1591} & Initial & 95.4 & 61.2 & 34.0 & 20.08 & & 53.5 & \\  & Recommended & 89.5 & 68.0 & 37.0 & 22.59 & & 51.9 & \\ \multirow{3}{*}{2304} & Initial & 85.2 & 52.5 & 38.0 & 15.11 & & 104.7 & \\  & Recommended & 80.0 & 59.2 & 42.0 & 16.98 & & 103.6 & \\ \multirow{3}{*}{3004} & Initial & 144.9 & 56.0 & 43.0 & 10.23 & & 118.2 & \\  & Recommended & 133.2 & 63.0 & 46.0 & 12.22 & & 109.66\% & \\ \multirow{3}{*}{3636} & Initial & 170.0 & 48.2 & 48.0 & 3.26 & & 120.6 & \\  & Recommended & 163.0 & 56.8 & 52.0 & 3.9 & 19.63\% & 114.6 & 5.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The ROP increase and drag reduction effect at test points.  
Figure 11: The calculated drag distribution on the drill string of well A.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 6 Risk Analysis
### Critical-Risk-Level Threats and Mitigation Strategies
#### 6.1.1 Spoofing of Radar  
**Security Risk**. Spoofing radar poses a critical security vulnerability to AEB systems; it allows attackers to manipulate radar networks by creating phantom objects or masking real ones. This defeats the correct judgment of radars about the environment and hence may lead to hazardous driving conditions, wrong decisions, and accidents. The consequences include damage to vehicles and infrastructure, the disruption of operational vehicles, and safety hazards. Although personal data are not directly impacted, the high potential impact and medium feasibility of such attacks make radar spoofing a critical threat to the integrity and reliability of AEB systems. **Security Measures**. Key security measures to mitigate radar spoofing risks include performing a risk assessment every month to identify emerging threats and the effectiveness of controls, using strong authentication to block unauthorized access, and checking signal integrity and encryption to verify and protect radar communications. These steps improve the reliability and accuracy of radar systems, ensuring vehicle safety and preventing accidents caused by spoofed data. Together, these strategies greatly reduce the risks of radar spoofing and protect the integrity of AEB systems.  
Figure 4: Distribution of risk levels.  
Figure 5: Discovered threats in our architecture according to the STRIDE threat model.
## 5 Scene-Driven Remote Sensing Image Object Detection
### Taking the Scene as the Primary Unit to Interpret Objects from Remote Sensing Images  
For scene-based detection, the primary unit is the image block, and its semantic scene category is determined not only by the contained objects, but also their relative positions. Hence, scene-based classification methods are able to better distinguish complex categories, and can simultaneously consider the relationships between objects.  
[PERSON] et al. [88] first utilized multi-kernel learning to incorporate multiple features to implement the block-level image interpretation. Then, multi-field integrating was proposed to obtain the block-level result. Subsequently, multi-hypothesis voting was finally utilized for the built-up area detection result through multi-hypothesis super-pixel representation and graph smoothing. Compared with pixel-based Grey-Level Co-occurrence Matrix (GLCM) (89.9%), [PERSON]'s method can reach 94.3%. In addition, [PERSON] [89] presented an unsupervised deep neural network (UDNN), which is composed of an unsupervised deep convolutional neural network (UDCNN) with UDCNN3's accuracy reaching \(97.08\pm 1.76\%\) and UDNN3-1-2's accuracy reaching \(98.55\pm 0.87\%\), as well as an unsupervised deep fully connected neural network (UDFNN). The former can hierarchically extract the local features, from simple to complex. UDFNN attaches importance to the merits of fully connected layers by stacking the RBMs, and works to further abstract the feature representation from a global perspective.  
Figure 6: Flowchart of scene-driven object detection: take urban village (UV) detection as an example. The scene trained for deep networks is gained through sliding scenes. By comparing the scene and scenes which have been labeled “UVs”, the training scenes can be labeled UVs or non-UVs, and the object detection result can be achieved.  
In our previous work [90], a double-stream convolutional neural network (DSCNN) model based on Inception V3 was proposed to extract the built-up area automatically. The network consists of two branches: the upper branches manipulate the panchromatic image providing primary information for object classification, and the lower branch attaches more importance to the multispectral image, providing auxiliary information for improvement in accuracy (with the overall accuracy reaching 98.39%).
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 5 Discussion
Method development and optimization of techniques to extract and analyze toxins from different matrices rely on spiking the matrix with the target analytes, processing these samples through the optimized workflow, and reporting recoveries of the spiked compounds and precision as a metric of method performance. While this process may work in the case of simple matrices, such as water or algal cells, it may only partially mimic toxin-containing environmental samples from more complex matrices, such as zooplankton or tissue, as noted in [71]. For example, in spike recovery studies involving tissue, either the sample is spiked with the target toxins using a syringe needle inserted into the tissue prior to homogenization or the homogenate is spiked with the target toxins, followed by mixing prior to the processing of these samples through the optimized workflow. However, these spiked toxins may not entirely mimic the way that natural toxins are bound in tissue samples in the environment. This may lead to biases in estimated recoveries, especially if the spiked toxins are more readily amenable to the extraction process than the natural toxins, leading to incorrect quantifications of these toxins in complex matrices. While spike recovery studies are a good first step for developing and optimizing methods for toxin detection, method validation with different types of samples collected from HAB sites will provide an insight into the reliability of the methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Toxins Measured** & **Matrix** & **Extraction/Sample Preparation Procedure** & **Analysis Method** & **Toxin Detection** \\ \hline \multirow{4}{*}{[100]} & \multirow{4}{*}{MCs} & \multirow{4}{*}{Fish tissue} & Tissues were homogenized and extracted with 75\% methanol. Extracts were centrifuged, the supernatant was removed, and the solids were resuspended in 75% methanol for two more extractions. The supernatant from all extractions was pooled and diluted to one-quarter strength with deionized water. The resulting solution was concentrated/cleaned with SPEC (18 column) and eluted with 5 mL of 100% methanol. The sample was then diluted to 5% methanol and analyzed. & & \\ \hline \multirow{4}{*}{[78]} & \multirow{4}{*}{MCs} & \multirow{4}{*}{Fish tissue} & \multirow{4}{*}{Lemiacu oxidation reactions were performed to convert MCs to MMPR. After treatment of reactions, the samples were centrifuged at 300 rpm for 5 min to remove tissue. Aliquost of oxidation products in the supernatant were dried and dissolved in a 5% HCl methanol solution, followed by heating and neutralization with silver carbonate. Total MC content was measured by headspace by polychloritysiloxane/driylebenzene (PDMS-DVB) solid-phase microextraction (SPME) GC/MS/MS analysis. & & \\ \hline \multirow{4}{*}{[51]} & \multirow{4}{*}{MCs} & \multirow{4}{*}{Rainbow trout tissue, liver, kidney} & \multirow{4}{*}{MCs were oxidized to MMPR.
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
## 3 Results
### \(S_{4}\) Statistics  
Another analysis conducted, is the study of the probability to find points with certain \(S_{4}\) at a particular hour in LT, averaging the data over long periods of time. This way, it can be studied if there is any correlation between the \(S_{4}\) obtained with this technique and the LT, as previous models and experimental evidence shows. The set of histograms in Figure 18 shows all the data points from 0 h to 24 h (UTC) during selected days in the year 2017.  
The horizontal axis of the histograms represents the local time in hourly bins, and the vertical scale is the \(S_{4}\) value in bins of 0.1. The color represents the number of counts per each of the two-dimensional bins. The color scale is adjusted up to 500 counts to increase the contrast for \(S_{4}\) values larger than 0.1. This means that all the bins in red on the bottom side of the histogram are saturated.  
The data plotted in the histograms correspond to all the specular reflections over open oceans and seas (further away than 50 km from any coastline), within the latitudes of CYGNSS coverage  
Figure 16: Histogram of number of events wrt its LT.  
Figure 17: Histogram of number of events wrt the maximum \(S_{4}\) value reached.  
(40\({}^{\circ}\)N, 40\({}^{\circ}\)S). In total, every day there are approximately \(1.9\times 10^{6}\) samples. The registered values include all reflections on the cited areas without any other filter (i.e., no sea surface roughness filter).  
In these histograms, a small dependency with LT can be observed, that is more visible in April and August, with a slightly higher probability of higher \(S_{4}\) during sunrise and sunset, around 6 h and 17 h during 21 st August, and 9 h and 21 h during both days in April. Also, a similar correlation can be observed on 20 th November.
# User Experience of Older People While Using Digital Health Technologies: A Systematic Review
## 3 Results  
The comprehensive search of the PubMed, Google Scholar, and Ichushi Web databases initially yielded 184 papers. A thorough screening of the titles and abstracts led to the exclusion of 141 papers. Subsequently, the full texts of the remaining 43 papers underwent meticulous review. In the final analysis, 17 papers were considered suitable for data extraction [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50] (Figure 1). The System Usability Scale (SUS) emerged as the most frequently used standardized questionnaire for assessing digital health technologies among older people (\(n\) = 9, 52.9%) [35; 38; 39; 42; 44; 45; 48; 49; 50]. Other commonly utilized questionnaires included the User Experience Questionnaire (UEQ) and its short version (\(n\) = 5, 29.4%) [35; 37; 42; 44; 45],  
Figure 1: Flowchart of search strategy. the Post-Study System Usability Questionnaire (PSSUQ; \(n\) = 2, 11.8%) [34; 42], the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST; \(n\) = 2, 11.8%) [46; 48], the Facilitators and Barriers Survey/Mobility (FABS/M; \(n\) = 1, 5.9%) [46], the Psychosocial Impact of Assistive Devices Scale (PIADS; \(n\) = 1, 5.9%) [46], the Telehealth Satisfaction Questionnaire for Wearable Technology (TSQ-WT; \(n\) = 1, 5.9%) [47], and the User Satisfaction Evaluation Questionnaire (USEQ; \(n\) = 1, 5.9%) [38]. Eight studies utilized multiple standardized questionnaires [35; 38; 39; 42; 44; 45; 46; 48] (Table 3).
# Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
## 2 Collection and Splicing of Side Edges of End Mills  
### Acquisition Systems and Devices  
According to the characteristics of the end mill, a hardware device for collecting the side edge is designed, as shown in Figure 1. The machine tool is a three-axis vertical machining center (model: VDL-1000E, Dalian Machine Tool, Dalian, China). The acquisition system is fixed on one side of the milling machine table and consists of an industrial camera (model: MER-125-30 UC, DaHeng IMAGING, Beijing, China), a double telecentric lens (model: BT-2336, DaHeng IMAGING, Beijing, China), and a ring light source (model: TY63 HW, DaHeng IMAGING, Beijing, China), a multifunctional adjustment bracket and a notebook. The acquisition frame rate of the camera is 30 frames/s, and the acquisition frame rate during the test is unchanged. The composition of the computer is shown in Figure 2. The end mills with helical edges are used for side milling. After processing, a digital microscope (model: VPIX-1000, Keyence, Osaka, Japan) is used to verify the images obtained by the detection system. The visual collection device is installed on the side of the workbench. After processing according to the cutting speed of the test, the device is moved to the acquisition position by moving the workbench. After the spindle speed is reduced to the acquisition speed, the image is captured according to the image collection requirements.  
Figure 1: The experimental system.
## Notations  
Abbreviations  
5G  
AfG  
Aydcial Graph  
AMM  
Advanced Metering Monitor  
AO  
Aerial Optical Pathways  
API  
Application Programming Interface  
AT  
Attack Trees  
BE  
Basic Event  
BDD  
Binary Decision Diagram  
BO  
Subterranean Optical Counterpart  
BTS  
Base Transceiver Station  
CI  
Critical Infrastructure  
CM  
Corrective Maintenance  
COS  
Core Optical Switch  
Cyped-Physical System  
CFT  
Conventional Fault Tree  
DER  
Distributed Energy Resources  
\begin{tabular}{l l} DFT & Dynamic Fault Tree \\ DSO & Distribution System Operator \\ DT & Distribution Transformer \\ DTS & Distribution Transformer Station \\ EFT & Extended Fault Tree \\ ER & Edge Router \\ ET & Event Trees \\ FTA & Fault Tree Analysis \\ FT & Fault Tree \\ GSM & Global System for Mobile Communications \\ HV & High Voltage \\ I/O & Input/Output \\ KPI & Key Performance Indicator \\ LBS & Load Break Switch \\ LTE & Long-Term Evolution \\ LV & Low Voltage \\ MM & Markov Modeling \\ MDE & Model-Driven Engineering \\ MBF & Mean Time Between Failures \\ MTTF & Mean Time To Failure \\ MTT & Mean Time To Incident \\ MTTK & Mean Time To Known issue \\ MTTRep & Mean Time To Replicate \\ MTTRes & Mean Time To Repairable event \\ MTTV & Mean Time To Validate \\ MV & Medium Voltage \\ OLT & Optical Line Terminal \\ PDMP & Piecewise Deterministic Markov Process \\ PN & Petri Nets \\ POD & Point of Delivery \\ POS & Passive Optical Splitter \\ PDF & Probability Density Function \\ RFT & Repairable Fault Tree \\ RAMS & Reliability, Availability, Maintainability, and Safety/Security \\ RTU & Remote Terminal Unit \\ SAIDI & System Average Interruption Duration Index \\ SAIFI & System Average Interruption Frequency Index \\ SCADA & Supervisory Control And Data Acquisition \\ VPN & Virtual Private Network \\ W & Wireless Linkage \\ WAN & Wide Area Network \\ Variables & \\ \(X\) & time to failure (the lifetime) \\ \(Y\) & repair (recovery) time after a failure occurs \\ \(T_{rep}\) & cumulative time allocated for repairs \\ \(T_{res}\) & cumulative time allocated to restore \\ Indices & \\ \(F(t)\) & distribution function of a random variable \(X\) \\ \(f(t)\) & probability density function of a random variable \(X\) \\ \(U(t)\) & instantaneous time-dependent unavailability function \\ \(A(t)=1-U(t)\) & instantaneous availability function \\ \(h(x)\) & renewal density \\ Parameters & \\ \(\beta\) & shape parameter \\ \(\theta\) & scale parameter \\ \end{tabular}
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 3 Results
### Water Segmentation
#### 3.1.2 Visual Analysis of the Segmentation Results  
To visualize the segmentation results more clearly, this work represents the outcomes of the testing images. The original and the ground truth images are also presented to compare with the segmentation outcomes. Figure 9 shows the outcomes of DeepLabv3+ and SegNet models for the two sample images of the test dataset. Both networks could identify water regions in most of the images, even though the color of water regions vary from one another. However, it can be seen from Figure 9c,d that some of the water pixels were misidentified as background pixels due to similar color features between the water region and objects. Overall, the segmentation results from the DeepLabv3+ network yielded more accurate predictions as it could segment small objects such as signboards from water regions while SegNet failed to do so.  
To see the errors more distinctly, the segmentation results were compared with the expected ground truth. The magenta and green regions highlight FN and FP areas, the areas of segmented results that differed from the expected ground truth as shown in Figure 10. The visual inspection shows that semantic segmentation results overlapped quite well for water class especially for DeepLabv3+, but the boundary of the segmented regions is not as accurate, which led to low BF scores. Besides, the figure also demonstrates that segmentation results using DeepLabv3+ obtained fewer errors as compared to SegNet.  
Figure 9: Visualization and comparison of two representative results on test data. (**a**) Original image. (**b**) Ground truth. (**c**) DeepLabv3+ prediction. (**d**) SegNet prediction. Pink and green colored regions represent water and background, respectively.
## 3 Results and Discussion
### Quality of Flour-Bran Blends Using SRC  
The SRC analysis results of flour-bran blends with different blending ratios are shown in Table 2. The water SRC values were 65.2-81.7%, and the sodium carbonate SRC values were 88.4-102.5%. Both SRC values increased proportionally as the blending ratio of bran increased. As an advantage of SRC, for flours produced from blends of wheat or flour, each SRC value equates to the mathematical, linearly proportional, weighted average of the corresponding SRC values for the 100% flour from each individual wheat in the blend [18]. The SRC values in water and sodium carbonate solutions are indicators of water absorption and the contribution of damaged starch among the quality characteristics of wheat flour, respectively [18]. These results of the SRC analysis suggest that an increased amount of water is needed for dough formation during the bread-making process for the breads made from flour-bran blends. [PERSON] et al. [22] reported that the water retention capacity of bran is negatively correlated with bread baking properties. The increased sodium carbonate SRC values indicated an increase in the contribution of damaged starch. Damaged starch in wheat flour also contributes to an increase in water absorption, and excessively damaged starch delays the water absorption of gluten protein, lowers gluten  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Moisture** & **Ash** & \multicolumn{2}{c}{**SRC (\%)**} \\ \cline{3-5}  & **(\%)** & **(\%)** & **Water** & **Sodium Carbonate** \\ \hline F100/B0 & 12.1 \(\pm\) 0.0 a (1) & 0.48 \(\pm\) 0.00 a & 65.2 \(\pm\) 0.6 a & 88.4 \(\pm\) 0.0 a \\ F90/B10 & 12.4 \(\pm\) 0.1 b & 0.81 \(\pm\) 0.00 b & 70.5 \(\pm\) 0.3 b & 92.1 \(\pm\) 0.4 b \\ F80/B20 & 12.7 \(\pm\) 0.0 c & 1.14 \(\pm\) 0.01 c & 76.3 \(\pm\) 0.6 c & 97.8 \(\pm\) 0.5 c \\ F70/B30 & 12.9 \(\pm\) 0.0 d & 1.46 \(\pm\) 0.01 d & 81.7 \(\pm\) 0.3 d & 102.5 \(\pm\) 0.1 d \\ \hline \hline \end{tabular} \({}^{(1)}\) Results are expressed as mean \(\pm\) SD. Values with the same letter within the same column are not significantly different (\(p<0.05\)) according to [PERSON]’s HSD test.  
\end{table}
Table 2: Moisture and ash contents, and SRC values of the flour–bran blends at different ratios.  
formation, and negatively affects bread quality [23]. [PERSON] [24] reported that if flour with the same protein content increased the damaged starch content, the moisture absorption capacity of flour increased to a certain degree of homogeneity during dough mixing. However, when the starch granules are damaged excessively, the interface between air and dough becomes unstable during gelatinization, which results in the adhesion of the internal gas cell during baking, a decrease in bread volume, and a negative impact on bread crumb texture [25; 26; 27]. Thus, the water and sodium carbonate SRC results of the blends can predict the impact of blending ratio on the bread-baking performance of the blends.
# Microsphere Polymers in Molecular Imprinting: Current and Future Perspectives
## 1 Introduction  
Molecularly imprinted polymers (MIPs) are specific crosslinked polymers that exhibit binding sites for template molecules. MIPs are widely used because of their high selectivity and affinity for template molecules [1; 2]. MIPs have good thermal and chemical stability under high or low pH and temperature [3]. MIPs have advantages over their biological counterparts as they are inexpensive, simple, stable, reproducible, induce strong chemical bonds, and demonstrate stability to heat and pressure [4]. MIP formation requires monomers, molecular templates, initiators, crosslinkers and solvents [5; 6]. The complex between the monomer and the template molecule requires either non-covalent interactions or covalent interactions [7]. In recent studies, organic solvents have shown better specific binding ability than aqueous solvents [8].  
MIPs have been developed in various application areas of biology and chemistry, such as in selective separation and sample preparation [9; 10; 11; 12], nanocomposite materials [13], biological assays [14; 15; 16], catalysis [17], drug delivery [18; 19], sensors [20; 21; 22; 23], bioimaging and optical devices [24], food safety and environmental applications [25; 26], enantioseparation [27; 28], natural product extraction [29; 30] and rare element purification [31]. MIPs can be obtained by various methods, including bulk polymerisation, precipitation polymerisation [32; 33], suspension polymerisation, emulsion polymerisation, two-step swelling polymerisation [34], electropolymerisation [35], electrospinning [36], sol-gel imprinting [37] and phase inversion [38]. However, according to [PERSON] et al. [39], these methods have some problems, such as an irregular material shape (which is not ideal for chromatographic purposes [40; 41] when used as the stationary phase, causing irreproducible results and asymmetrical peaks), low affinity,weak binding forces and incomplete removal of template molecules, which limits extraction efficiency and imprinting ability. This review provides recent and future perspectives of microsphere polymers in molecular imprinting, including new polymer synthesis routes that overcome the drawbacks of conventional methods.