esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Texture and Total Color Difference
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Sample**} \\ \cline{2-5}  & **BMC** & **BMC/WPC** & **BMC/SPWP** & **BMC/DPWP** \\ \hline Firmness (g) & 5469.50 \(\pm\) 423.05 \({}^{\text{a}}\) & 20,083.36 \(\pm\) 2928.53 \({}^{\text{b}}\) & 15,774.46 \(\pm\) 83.99 \({}^{\text{b}}\) & 25,330.61 \(\pm\) 4064.40 \({}^{\text{b}}\) \\ Work of Shear (g-s) & 5593.25 \(\pm\) 601.48 \({}^{\text{a}}\) & 14,130.35 \(\pm\) 1357.68 \({}^{\text{a}}\) & 16,079.89 \(\pm\) 954.08 \({}^{\text{ab}}\) & 26,885.77 \(\pm\) 5078.48 \({}^{\text{b}}\) \\ Stickiness (g) & 474.07 \(\pm\) 20.91 \({}^{\text{a}}\) & 435.86 \(\pm\) 298.90 \({}^{\text{a}}\) & 1258.43 \(\pm\) 7.27 \({}^{\text{b}}\) & 1582.00 \(\pm\) 23.84 \({}^{\text{b}}\) \\ Work of Adhesion (g-s) & 30.62 \(\pm\) 13.21 \({}^{\text{a}}\) & 155.54 \(\pm\) 44.51 \({}^{\text{b}}\) & 105.65 \(\pm\) 3.73 \({}^{\text{ab}}\) & 115.12 \(\pm\) 7.50 \({}^{\text{ab}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Texture of buttermilk cheeses with polymerized whey protein. Figure 2: Texture of buttermilk cheeses with polymerized whey protein. BMC, BMC/WPC, BMC/SPWP, BMP/DPWP: the same at Table 1.  
proteins (BMC/WPC) was darker than the other samples. Calculated \(\Delta\)E for BMC/WPC, BMC/SPWP, and BMC/DPWP cheese samples were 9.33, 1.72, and 2.91, respectively (Figure 3). As suggested by [PERSON] et al. [50] and [PERSON] et al. [51], if \(\Delta\)E is above the value of 3.3, color is considered unacceptable. Texture and color may correlate with consumer ratings of overall desirability [52]. [PERSON] et al. [53] report that \(\Delta\)E values higher than 3 are in the threshold for a clearly discernible color difference. The use of polymerized whey proteins in cheese production leads to innovative of dairy product, while simultaneously having a chance to be sensory accepted. This is indicated by the degree of water binding, and thus no leakage of whey from the product and an increased proportion of proteins. In addition, texture parameters indicate the correctness of slicing cheese and biting in the mouth. Therefore, in the future, it is advisable to conduct a sensory profile assessment with the use of descriptors and consumer tests.
# Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine +
## 2 Materials
### Construction of the Remote Sensing Index of Ecological Quality
#### 2.2.2 Ecological Stability Index  
The ecosystem stability can be expressed by the deviation from the average value of an ecosystem indicator, GPP was chosen as the evaluation indicator for ESI calculation, and the formula was detailed in Equation (2).  
\[ESI=1-A\times\sqrt{\left(GPP-GPP_{ref}\right)^{2}} \tag{2}\]  
\(ESI\) is the Ecosystem Stability Index. \(A\) is the normalization coefficient. \(GPP\) is the annual average GPP. In this study, \(GPP_{ref}\) is the mean value of GPP from 2001 to 2020.
## 2 Materials and Methods
### Optical Absorption Coefficient of BC  
This study measured BC in PM\({}_{2.5}\) at one-minute intervals under a flow rate condition of 5.0 L/min using a 7-wavelength dual-spot aethalometer (AE33, Aerosol Corp., Berkeley, CA, USA) equipped with a PM\({}_{2.5}\) impactor. Measurements were conducted across different wavelengths (370, 470, 520, 590, 660, 880, 950 nm). The BC instrument improved measurement accuracy by compensating for measurement errors caused by the loading effect of aerosol particles on the quartz filter. The BC concentration calculation utilized the attenuation of light (\(\Delta\)ATN1) at the first spot of the high-flow-rate filter, and detailed information about the measurement method is given in [2]. Briefly, the optical absorption coefficients of aerosol particles in the measurement device were simultaneously measured at seven wavelengths. The mass absorption cross-sections used in calculating BC(\(\lambda\)) mass concentrations were 18.47, 14.54, 13.14, 11.58, 10.35, 7.77, and 7.19 m\({}^{2}\) g\({}^{-1}\) at each respective wavelength. Furthermore, the aerosol particle's light absorption coefficient, measured using the aethalometer, is expressed as the sum of contributions from light absorption due to automobile emissions, biomass combustion, and inorganic dust emissions.  
Figure 1: Sampling site on the rooftop of a 4-story building at the Mokpo University, Republic of Korea.
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 3 Line Matching for Image Pair on the Rectified Image Space
### LJL Descriptor and Similarity Matching  
LJL first uses the assumption of the two coplanar adjacent line segments to find the intersection point, after which the LJL structural unit is formed imitating the scale invariant feature transform  
Figure 4: Line matching process using the line-juncture-line (LJL) method.  
Figure 3: Perspective distortion and rectification of the rectangular buildings: (**a**) the regular rectangle taken by down-looking camera; (**b**) the deformation under a back-looking camera; (**c**) the rectified rectangle of the deformed image.  
(SIFT) descriptor and a gradient direction histogram in the local neighborhood of the structural unit is generated, thereby constructing an LJL descriptor. The LJL descriptor takes the juncture at the center and draws two circles with a radius of \(r\) and \(2r\) pixels (\(r=10\) in this paper). These concentric circles are divided into four regions by two line segments. Each zone contains a sector and a ring, and the ring is divided into three sub-regions. In this way, a LJL structure has a total of 16 sub-regions and gradient orientation histograms with 8 main directions established in each sub-region. So, a 128-dimensional LJL descriptor vector is constructed, as shown in Figure 5.  
As shown in Figure 5, two line segments \(L_{1}{}^{(1)}\), \(L_{2}{}^{(1)}\) and juncture \(J^{(1)}\) in the left image constructs a descriptor \(L_{1}{}^{(1)}J^{(1)}L_{2}{}^{(1)}\). The two line segments \(L_{1}{}^{(2)}\), \(L_{2}{}^{(2)}\) and juncture \(J^{(2)}\) in the right image construct another descriptor \(L_{1}{}^{(2)}J^{(2)}L_{2}{}^{(2)}\). Using \(L_{1}{}^{(1)}I^{(1)}L_{2}{}^{(1)}\) as reference, \(L_{1}{}^{(2)}J^{(2)}L_{2}{}^{(2)}\) attempts to match it.  
Before two descriptor vectors to be compared, LJL firstly uses the crossing angles to discard many false candidates in advance. Suppose \(\theta_{1}\) and \(\theta_{2}\) are the crossing angle of two line segments of the two LJLs, respectively, the absolute difference \(\theta=|\theta_{1}-\theta_{2}|\) which should be smaller than a threshold is used as the constraint to decide whether they are a correctly matched (\(\theta=30^{\circ}\) in this paper). Then, the distance between the descriptor vector on the right and on the left is compared. Now, supposing \(D_{i}^{1}\) and \(D_{j}^{2}\) are the \(i\)th and \(j\)th descriptor vector in the first image and second image, respectively, \(\|D_{i}^{1}-D_{j}^{2}\|\leq d_{v}\) is the criteria to accept the two descriptor vectors as an LJL match; if less than a certain threshold (\(d_{v}=0.5\) in this paper), it is identified as a match. After all the LJLs are matched, the planar homograpy can be estimated from existing matches. Therefore, matching for the remaining individual line segments can be conducted with this planar homograpy.
## 3 Results and Discussion
### Automated Column Sorption System
#### 3.2.4 Heavy Metallic Ion Concentration Analysis  
Table 6 provides the results of the HMI concentrations (C\({}_{\rm t}\)) in both sorption columns based on triplicate readings obtained by the ICP-AES software. The percent removal (%R) values are presented with respect to the 2.0 meq/L influent concentrations of each HMI. During the experimental sequence of sample collection, C2-11 is lost due to improper handling when transferring from the sampling port to filtration at 169:45 mins. However, this sample is of a lower HMI concentration and the overall removal trend has been well-established by the time this sample is collected. The first major observation is that throughout the analysis period, Pb\({}^{2+}\) is not detected in both column effluents as well as the total waste, indicating a complete removal of the ion. The C1-13 sample for Cu\({}^{2+}\), Fe\({}^{3+}\), and Zn\({}^{2+}\) reports a removal of 18.09%, 82.54%, and 10.71%, respectively, from the first column. The dual-column configuration provides a substantial improvement on the removal as observed with the second pass in sample C2-13, to achieve a final removal of Cu\({}^{2+}\), Fe\({}^{3+}\), and Zn\({}^{2+}\) of 80.07%, 99.98%, and 51.53%, respectively. This improvement is also attributed to the unique feeding rate and design of the second column C2; the additional EBCT of approximately 10-minutes is available for the sorption process to occur as well as the slightly higher pH levels (and therefore lower presence of competitive H\({}^{+}\) ions). The final total waste (TW2) effluent concentrations report very good removal for all ions except for Ni\({}^{2+}\), with a removal of 48.97%. This removal trend is also consistent with the batch analyses conducted by [PERSON] and [PERSON] [32; 33]. This is significant, as it proves that results from complex experimental batch studies, which are in high abundance, are useful in providing information on the sorption performance (i.e., removal efficiency, selectivity, and kinetics) in industrial applications where the process is run in a continuous flow-feeding configuration. In summary, the results demonstrate for the first time the effectiveness of multiple HMIs sorption by zeolite in a dual-column system with continuous flow.
# DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
## 5 Experiments
### Comparison Experiment  
In order to fully evaluate the performance of DScanNet, we compare DScanNet with the current mainstream detection algorithms under the same experimental conditions. Specifically, YOLOv6-v12, SSD, and Faster R-CNN are selected for the following reasons: YOLOv6-v12 are all models that belong to the same series, and by comparing them with the same series of models, it is ensured that the experimental variables focus on the improvement points themselves, rather than the differences at the architectural level. In addition to the comparison of the same series, we also selected two models, Faster R-CNN and SSD, for comparative testing. Faster R-CNN and SSD are representative algorithms for dual-stage object detection and single-stage object detection, and the two algorithms are widely used in different scenarios with high-quality detection results and efficient inference  
Figure 10: DScanNet detection results.  
Figure 9: YOLOv5 detection results.  
speed, respectively. By comparing it with them, you can better evaluate the performance of the model. The specific experimental results are shown in Table 3.  
As shown in Table 3, DScanNet has the best accuracy (96.8%) and [EMAIL_ADDRESS] (95.1%), and the highest detection accuracy. With an FPS of 97, the fastest speed, and a parameter volume of only 3.68 M and a computational volume of 9.4 B, the model is the lightest, and it performs the best among YOLO models in the same series. The SSD has an accuracy of 84.98% and a [EMAIL_ADDRESS] of 86.1%, which is low, but the FPS is 90 and the speed is fast, with 62.74 M parameters and 26.29 B calculations. Faster R-CNN accuracy is 93.66%, [EMAIL_ADDRESS] is 91.73%, the accuracy is good, but the FPS is only 40, the speed is slow, the number of parameters is 137 M, the amount of computation is 370.21 B, and the model is complex. Overall, DScanNet performs well in terms of recognition accuracy, with more accurate judgment of the target category and fewer false positives, while SSD and Faster R-CNN may be more suitable for scenarios with less stringent performance requirements. The following figure shows the inspection results of each model.
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 5 Substrates of Ypk1
### Control of Sphingolipid Biosynthesis
Furthermore, we found that an \(orm1\Delta\)\(orm2\Delta\) double mutant was a more robust suppression of the temperature-sensitive growth of the \(ypk1^{ts}\)\(ypk2\Delta\) cells than the original transposon insertion in \(ORM2\) alone, confirming that a primary physiological role of Ypk1-mediated phosphorylation is to negatively regulate their function. Moreover, we documented that Ypk1 activation upon sphingolipid depletion required TORC2-mediated phosphorylation of Thr62 in its hydrophobic motif [22]. Further support for the conclusion that regulation of sphingolipid biosynthesis is one of the most physiological important functions under TORC2 control was the findings that viable \(tor2\Delta\) spores could be recovered if they also carried \(orm1\Delta\)\(orm2\Delta\) mutations [34]. Thus, when under the stress of sphingolipid limitation, the cell responds by increasing metabolic flux into the sphingolipid biosynthetic pathway through the increase in SPT activity that is brought about via the TORC2-Ypk1-mediated phosphorylation and inhibition of the SPT inhibitors, Orm1 and Orm2. In a subsequent global screen for additional candidate Ypk1 substrates using a combined bioinformatic, genetic, and biochemical screening procedure [23], we identified the two catalytic subunits (Lac1 and its paralog Lag1) of the ceramide synthase complex, another ER-localized pacemaker enzyme in sphingolipid synthesis, as direct substrates of Ypk1. In this instance, however, phosphorylation by Ypk1 stimulates the function of this enzyme. Furthermore, we showed that the TORC2-Ypk1-driven increase in ceramide synthase activity ensures that the long-chain base precursors made by the TORC2-Ypk1-driven increase in SPT activity are efficiently channeled into the production of complex sphingolipid end-products of the yeast sphingolipid biosynthetic pathway [23]. Exerting control at this step of the pathway also prevents accumulation of pathway intermediates that would otherwise compromise cell growth by stimulating autophagy [164].
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 1 Introduction  
Groundwater is a critical resource for agriculture, industry, and domestic needs worldwide, supporting ecosystems and socioeconomic stability [1]. Its variability, driven by natural and human-induced factors, significantly affects water availability, making effective monitoring essential for sustainable water resource management. As pressures from population growth and climate change intensify, understanding GWS dynamics is increasingly vital to ensure long-term water security.
# Risk Assessment of Landfalling Tropical Cyclones in China Based on Hazard Risk Theory
## 3 Results and Discussion
### Analysis of the Characteristics of Tropical Cyclone Hazards in China
#### 3.1.2 Distribution Characteristics of the Impact Intensity of Landfalling Tropical Cyclones  
Statistical analysis of the landfall intensity of tropical cyclones affecting China from 1949 indicated that, as shown in Table 3, over the past 75 years, severe tropical storms and typhoons were the most frequent categories, accounting for 50.6% of the total. The stronger categories, including severe typhoons and super typhoons, made up 10.6% of the total. Although super typhoons occurred only 18 times, each event caused significant damage from formation to dissipation. For example, Typhoon Lekima (No. 1909), which made landfall along the coastal area of Wenling, Zhejiang in 2019, was the strongest tropical cyclone to hit China since the founding of the People's Republic. The maximum wind speed near its center reached 52 m/s (super typhoon), and it remained over land for 44 h, affecting regions including Zhejiang, Fujian, Jiangsu, Shanghai, Anhui, Shandong, Henan, Hebei, Tianjin, and Liaoning, resulting in extensive damage [34].  
Figure 4: Temporal variation of landfall tropical cyclones in China from 1949 to 2023 (**a**) Annual Variations. (**b**) Monthly Variations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strength Level** & \begin{tabular}{c} **Tropical** \\ **Depression** \\ \end{tabular} & \begin{tabular}{c} **Tropical Storm** \\ \end{tabular} & \begin{tabular}{c} **Severe Tropical** \\ **Storm** \\ \end{tabular} & \begin{tabular}{c} **Typhoon** \\ \end{tabular} & \
\begin{tabular}{c} **Severe Typhoon** \\ \end{tabular} &
\begin{tabular}{c} **Super Typhoon** \\ \end{tabular} \\ \hline Quantity & 116 & 128 & 158 & 166 & 54 & 18 \\ Proportion/\% & 18.1 & 20.0 & 24.7 & 25.9 & 8.4 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical Count of Tropical cyclones of Various Intensities Landing in China from 1949 to 2023.  
Considering the temporal trends of tropical cyclones, although the number of landfalling tropical cyclones has been decreasing, their intensity is increasing. Furthermore, the occurrence of severe typhoons has progressively moved earlier in the year. Specifically, since 1980, the intensity of landfalling tropical cyclones in China has increased by 12.4% to 14.7%, and severe typhoons have shifted from autumn to summer. This shift has led to a higher frequency of summer typhoons, posing a more severe challenge to China's hazard prevention and mitigation capabilities.
## 4 APSP and Routing Tables
### Final Steps of the Triangle Finding  
We continue here to complete step 3.2 of the ComputePairs procedure, armed with Theorem 4. We need simultaneous searches to be executed by each node \(v_{(i,j,k)}\) to determine the triangles in \(U_{i}\times U_{j}\times U_{k}^{\prime}\). We provide a short lemma first that ensures the conditions for the quantum searches:  
**Lemma 2**.: _The following statements hold with a probability of at least \(1-2/n^{2}\):_  
1. \(|\Delta(i,j,k)|\leq 2n\)__
2. \(|\Lambda_{k}(U_{i},U_{j})\cap\Delta(i,j,k)|\leq 100\cdot\sqrt{n}\log n\) _for_ \(i,j\in[n^{1/4}]\)_._  
The proofs of these statements are technical but straightforward, making use of a Chernoff bound and union bounds; hence, we skip them here. To invoke Theorem 4, we describe a classical procedure first, beginning with an evaluation step, EvaluationA, implementable in \(\hat{\mathcal{O}}(1)\) rounds.  
EvaluationA  
Input: Every node \(v_{(i,j,k)}\) receives \(m\) elements \((u_{1}^{i,j,k},\ldots,u_{m}^{i,j,k})\) of \(\mathcal{U}^{\prime}\)  
Promise: For every node \(v_{i,j,k}\) and every \(\mathbf{w}\in\mathcal{U}^{\prime},|L_{\mathbf{w}}^{i,j,k}|\leq 800\sqrt{n}\log n\).  
Output: Each node outputs a list of exactly those \(u_{l}^{i,j,k}\), such that there is a negative triangle in \(U_{i}\times U_{j}\times u_{l}^{i,j,k}\).  
1. Every node \(v_{(i,j,k)}\) for each \(r\in\sqrt{n}\) routes the list \(L_{\mathbf{w}}^{i,j,t}\) to node \(v_{(i,j,t)}\).
2. Every node \(v_{(i,j,k)}\) for each \(vu\) it received in step \(1\) sends the truth value of the inequality  
\[\min_{w\in U_{k}^{\prime}}\{W_{uw}+W_{wv}\}\leq W_{vu} \tag{3}\]  
to the node that sent \(vu\).  
Each node is the source and destination of up to \(800n\log n\) messages in step 1, meaning that this step can be implemented in \(1600\log n\) rounds. The same goes for step 2, noting that the number of messages is the same, but they need to only be single-bit messages (the truth values of the inequalities). Hence, the evaluations for Theorem 4 can be implemented in \(3200\log n\) rounds. Now, applying the theorem with \(X=\mathcal{U}^{\prime}\), \(\beta=800\sqrt{n}\log n\), noting that then the assumptions of the theorem hold with a probability of at least \(1-2/n^{2}\) due to Lemma 2, implies that step 3.2 is implementable in \(\hat{\mathcal{O}}(n^{1/4})\) rounds, with a success probability of at least \(1-2/m^{2}\).  
For the general case in which we do not assume \(\alpha=0\) for all \(i,j,k\) in IdentifyClass, covered in the appendix, one needs to modify the EvaluationA procedure in order to implement load balancing and information duplication to avoid congestion in the simultaneous searches. These details can be found in the appendix, where a new labeling scheme and a different evaluation procedure EvaluationB are described; more information can also be found in [7].
# Fake Base Station Detection and Link Routing Defense +
## 7 Implementation and Experimental Results
### Link Routing Performance Analysis  
We implement a proof of concept of our link routing scheme, and it works correctly, e.g., the next base station is chosen among those that have been seen and used before, i.e., which has provided connectivity before. Unlike the current practice, which has the victim UE stay with the fake base station, our link routing scheme enables recovery from the fake base station by dropping the connection to it and rerouting the RRC connection to a legitimate base station.  
In this section, we focus on the performance overheads of our link routing scheme. For the performance overhead, we measure the switching time duration overhead, i.e., time required for the user equipment to recover from the fake base station after detection. We also measure the overhead of storing and looking up a base station in the reputation vector (\(\overrightarrow{R}\)) with 150 known base stations within the vector. Figure 8 shows the link routing overheads. We use a 95% confidence interval for \"switching\" time duration overhead only because we measure it for 600 samples. For \"storing\" and \"lookup\", we take the average of \(100,000\) instances of storing and looking up. For the proof-of-concept implementation of our link routing, the user equipment requires 2029.11 ms, i.e., 2.03 s to redirect its RRC connection from the fake base station to the legitimate base station. It is \(\frac{2029.11}{897}=2.26\) times more than base station detection. The time duration for storing the base station in \(\overrightarrow{R}\) and looking up in \(\overrightarrow{R}\) to check if any base station is within the list are 0.06 ms and 0.04 ms, respectively, which are of a magnitude of five times lower than the switching time duration.  
Figure 8: Link routing overhead in the time duration of storing, looking up the base station in the reputation vector, and switching connectivity to the legitimate base station.