esa-sceva/satcom-chunk-collection huggingface dataset:
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 2 Problem Statement and System Overview
### Background and Problem Statement
By assigning each moving object \(o\) (i) \(VR(o)\) and (ii) query segments inside \(VR(o)\), \(o\) can locally monitor whether it may affect the results of nearby queries based on the following two lemmas. **Lemma 1**. : _Given a query \(q=(q.p,q.d)\), a moving object \(o\in O\), and one of the query segments \(qs\) of \(q\) in the road network \(G\), let \(o.p\) and \(o.p\) be the point of \(o\)'s last known location at time \(t\) and the point of \(o\)'s current location at time \(t\), respectively (\(t<t\)). Suppose that \(o.p\
otin qs\) and \(o.p\in qs\), i.e., \(o\) enters \(qs\) from outside. Then, there exists a case such that \(o\) affects the current result of \(q\)._  
Proof. : To prove this lemma, it suffices to show that such a case exists. Without loss of generality, let us assume that \(o.p\
otin QR(q)\), and thus \(dist_{N}(q.p,o.p)>q.d\), meaning that \(o\) is not a result object at the last known time \(t\). From Definitions 4, 5, 6, and the description of the query segment, we know that \(o.p\in QR(q)\), meaning that \(o\) is a result object at the current time \(t\). This immediately implies that \(o\) becomes a new result object, and therefore \(o\) affects the current result of \(q\). **Lemma 2**. : _Given the same setting and notation as Lemma 1, suppose that \(o.p\in qs\) and \(o.p\
otin qs\), i.e., \(o\) leaves \(qs\). Then, there also exists a case such that \(o\) affects the current result of \(q\)._Proof. : This lemma can be proved similarly as Lemma 1, and thus we omit the proof. Only when each moving object \(o\) (i) leaves \(VR(o)\) or (ii) enters or leaves any of the assigned query segments, does it send a location-update to the server in order to (i) receive a new vicinity region together with new query segments or (ii) let the server update the result of a nearby query \(q\) if necessary. In this paper, we focus on how to efficiently search the appropriate vicinity regions for moving objects, and propose a new spatial index structure, namely the SSP-tree. We also present the details of the vicinity region-based communication strategy between the server and moving objects for processing range monitoring queries in the road network. To conclude Section 2.1, we summarize the frequently used notations in Table 1.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems
### Stable Latency Applications
#### 12.2.3 Wide Area Multi-Antenna Systems  
Because of HCF stability of latency vs. temperature (Section 7.3), it seemed likely that the space sector, specifically radio-astronomy and space radars, would become a customer installing 1000 s of km of outdoor NANF cables to build \"phase stable\" fiber links to antenna sites in distributed arrays. However, this prospect has probably vanished following the development of electronic latency compensation--see Section 12.2.1.
## 5 Experiments
### In-Depth Study  
To further explore the influence of contrastive learning on model performance, we conduct a deep investigation into the effect of varying the contrastive loss weight. As shown in Figure 13, we compare different configurations of contrastive loss (\(\lambda_{CL}\)) and the fusion coefficient (\(\lambda_{UL}\)) for user and location views, across two datasets: (a) NYC and (b) TKY. The Recall@5 scores under different parameter settings reveal key insights into how these components contribute to the overall recommendation performance.  
From the results, it is evident that contrastive learning significantly enhances the model's ability to capture useful representations, particularly in the NYC dataset. As \(\lambda_{CL}\) increases, Recall@5 consistently improves across most \(\lambda_{UL}\) settings, confirming the importance of contrastive objectives in aligning representations under different contexts. This trend persists, albeit less prominently, in the TKY dataset, suggesting contrastive learning also plays a beneficial role in sparser or differently distributed spatial environments.  
In addition to the contrastive loss weight, the fusion weight \(\lambda_{UL}\)--which balances the contributions of the user view and the location view--also shows a noticeable effect. For the NYC dataset, the best performance is achieved when \(\lambda_{UL}=0.6\), indicating that the user-centric view is slightly more influential than the location-centric view. In contrast, the TKY dataset achieves optimal performance around \(\lambda_{UL}=0.5\), suggesting both views contribute equally and should be treated with the same level of importance.  
These findings suggest that, in denser urban environments like NYC, where users frequently interact with a diverse set of locations, the personalization aspect (user view) is more critical. Conversely, in less dense or differently structured environments like Tokyo, user preferences and location characteristics are more balanced in importance. This nuanced understanding supports the necessity of adaptively weighting contrastive signals based on regional characteristics and data density.  
To further validate the effectiveness of our geospatial modeling, Figures 14 and 15 visualize the learned geographical view hypergraph \(\mathcal{G}_{G}\) on the NYC and TKY dataset. The left panel overlays the correlation of different locations on an actual city map, revealing highly dense and complex spatial dependence. The right panel presents a structural abstraction of the hypergraph, illustrating the connectivity among locations and the spatial relationships captured by our MPHCL model. The hypergraph constructed for the NYC dataset contains 3835 nodes and 2160 edges, with an average degree of 0.5632. This relatively low average degree, despite the large number of nodes, highlights the sparsity and diversity of user interactions across the spatial landscape. Such complexity underscores the need for robust context modeling, which our contrastive framework effectively captures.
## 5 Discussion on the Application of PSVC  
It could be pointed out that the maximum efficiency enhancement was obtained under the excitation with the frequency of shedding vortex from Figure 13. However, the frequency of unsteady excitation must be altered along with mass-flow coefficients to achieve the positive effects under vortex shedding control. As indicated in Figure 8, the vortex shedding frequency was sensitive to the mass-flow rate. The traditional separation control method, which was based on VSC, demanded the variation of frequency at excitation, altering with the working points. The effect of separation control was mainly dependent on the frequency of excitation. So, it was very difficult to apply in an industry application, which required alternating the frequency of wake shedding vortices needed at all operating points.  
Meanwhile, the variations of the other two vortex separation flow frequencies \(F_{ps}\) and \(F_{ss}\) were relatively much smaller than the variation of the vortex shedding frequency to the change of working  
Figure 16: Instantaneous vorticity contour with excitation at 7 times the RF.  
Figure 17: Instantaneous vorticity contour with excitation at 28 times the RF.  
conditions, as shown in Figure 6. Though the effective range of PSVC was less than that of VSC, it was highly possible to obtain a performance enhancement at the whole working range under a certain excitation frequency that equaled the effect under excitation of frequency \(F_{ps}\).  
To verify this conjecture, the computations with the excitation at the frequency of 28 times the RF were conducted with DES. And the effects on the stage performance under excitation were presented in Figure 18. It was shown that under the type of separation control of PSVC, separation positions on the suction side of blade were delayed almost in the whole working range. And the cascade performance was also enhanced. As a less investigated kind of separation control method, the PSVC was indeed able to obtain stage performance improvement at the whole working range with a certain frequency. This result turned out to be a case that it was easier to apply in industries by the PSVC than that of the traditional unsteady separation control method.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)  
By integrating spatial feature extraction, time series modeling, and an attention-weighting mechanism, the CNN-LSTM-SAM-Attention model presented in this paper aims to increase the accuracy of landslide prediction. It consists of a convolutional neural network (CNN), a long short-term memory network (LSTM), and a spatial attention mechanism (SAM). With the help of the spatial attention mechanism, the model can better forecast outcomes by highlighting significant aspects and accurately capturing the intricate spatial and temporal connections found in landslide data.
# Underground Mine Road Detection Using Deep Learning Technique
## 3 Experiment and Results
### Dataset Construction  
The front vehicle condition camera installed on the mine car was used to collect video data, and obtain the image of the underground mine slope by extracting video frames. The data included curves, dust and sand effects, and road images under different lighting conditions. Images under different environmental conditions enhance the robustness of the network and improve the recognition ability of the network. the extracted image data were screened to remove image data that seriously affect network training, such as repetition and ambiguity. Due to the particularity of the underground mine environment, data collection is more difficult, the video data collected are limited, less image data are extracted, and the dataset required for neural network training is larger, so the method of data enhancement was used to increase the amount of data. Transformations such as horizontal mirroring, vertical flipping, vertical mirroring, brightness enhancement, and contrast enhancement were used during training. Figure 10 shows before and after transformation; the enhanced data can make up for the shortcomings of less data. The image resolution size was uniformly processed as 1280 \(\times\) 720, and the data were divided randomly into training set and verification set according to the ratio of 8:2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Value** & **Description** \\ \hline Batch size & 4 & Number of samples deposited each time \\ Momentum & 0.9 & Gradient descent momentum \\ Learning rate & 0.01 & Initial learning rate \\ Weight decay & 0.0005 & Prevent model overfitting \\ Iterations & 20,000 & Iterative training times \\ \hline \end{tabular}
\end{table}
Table 1: Experimental parameter table.
## 3 Results and Mechanism Analysis
### Uniformity Analysis of Remolded Specimens
#### 3.1.4 Analysis of Sample Uniformity via the Discrete Element Method  
To characterize the uniformity of samples, the variance of the dry density of samples was introduced for analysis. Variance is the degree of deviation between a set of measurements and its average, which represents a measure of sample uniformity. The following formula was used in this study:  
\[\sigma^{2}=\frac{1}{n}\sum\left\{\ \left[\ \rho_{d}(h_{x})-\rho_{d}(h_{\overline{ \tau}})\ \right]\ ^{2}\right\} \tag{2}\]  
where \(\sigma^{2}\) represents the sample variance, \(\rho_{d}(h_{x})\) is the dry density value of a certain layer (such as the dry density of the first layer), where \(x=1,2,3\ldots n,n\) is the number of layers in the sample (for example, \(n=16\) in the 8 cm sample), and \(\rho_{d}(h_{\overline{\tau}})\) is the average dry density of the sample (i.e., the designed dry density).  
As can be seen from the results reported in Table 3, all 15 samples exhibited a certain degree of inconsistency (\(\sigma^{2}>0\)). The sample variance was the highest when \(\rho_{d}(h_{\overline{\tau}})=1.5\ \mathrm{g/cm^{3}}\), and it was the lowest when \(\rho_{d}(h_{\overline{\tau}})=1.3\ \mathrm{g/cm^{3}}\). At the same height, the higher the dry density, the greater the variance, and the lower the uniformity of the sample. Based on the sample variance diagram presented in Figure 7, it can be concluded  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Designed Dry Density of**} & \multicolumn{4}{c}{**Overall Height \(h\) of the Prepared Sample (cm)**} \\ \cline{2-6}  & **8** & **10** & **12** & **14** & **16** \\ \hline
1.5 & 0.134450 & 0.110383 & 0.087022 & 0.059050 & 0.032733 \\
1.4 & 0.107363 & 0.097991 & 0.053462 & 0.039350 & 0.028700 \\
1.3 & 0.080375 & 0.073550 & 0.036197 & 0.029375 & 0.018952 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The range of the dry densities of the stratified samples.  
Figure 6: The range of the dry densities of the stratified samples.  
that under the same dry density, the greater the height, the smaller the variance, and the lower the dispersion of the sample, that is, the more stable the variation in the sample difference, and the better the uniformity of the sample. This demonstrates that the sample variance reflects the dispersion and uniformity of the sample; the variance has a certain positive correlation with the dry density of the sample and a stable inverse correlation with the height of the specimen.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
###### Abstract  
Change detection in remote sensing images is a critical task that requires effectively capturing both global and differential information between bitemporal or more images. Recent progress in foundational vision models, like the Segment Anything Model (SAM), has led to significant improvements in feature extraction. However, these models do not have specific mechanisms designed to effectively utilize global and differential information for change detection tasks. To address this limitation, we propose Siamese-SAM, a novel Siamese network incorporating SAM as the encoder for each input image. To enhance feature representations, we introduce three specialized modules: the Global Information Enhancement Module (GIEM) to refine global representations, the Differential Information Enhancement Module (DIEM) to emphasize differential features, and the Differential Global Information Fusion Module (DGF) to integrate global and differential information effectively. Our model is evaluated on three benchmark datasets: LEVIR-CD, SYSU-CD, and GZ-CD, achieving state-of-the-art performance. Specifically, Siamese-SAM attains F1 scores of 92.67%, 82.61%, and 88.79% and IoU scores of 86.34%, 70.17%, and 79.83%, respectively, outperforming conventional approaches.  
Keywords:remote sensing change detection; Siamese network; Segment Anything Model; global and differential information fusion
## 2 Material and Methods
### UHPLC-DAD Quantitative Determination of Polyphenols in Plant Extract  
The five parts, 5 mg each, of crude green gum residue from plant extract were dissolved in 1 mL of methanol, filtered through 0.22 \(\upmu\)m syringe filters and quantitatively analysed by UHPLC method for polyphenols identified in this study. UHPLC analyses were performed in Agilent Technologies 1290 Infinity apparatus equipped with diode array detector (DAD), a binary solvent delivery pump, vacuum degasser, an autosampler and a thermostated column compartment. The separation was performed on a Zorbax Eclipse Plus C\({}_{18}\) column (100 \(\times\) 2.1 mm id; 1.8 \(\upmu\)m Agilent Technologies) and pre-column (100 \(\times\) 2.1 mm id; 1.8 \(\upmu\)m; Agilent Technologies) at 40 \({}^{\circ}\)C. The mobile phase consisted of acetonitrile (_v/v_) (solvent A) and water (solvent B), both acidified in 0.1% formic acid. A gradient program used for the separation of the constituents was applied as follows: 0-10 min 3% A; 10-45 min 3-21% A; 45-60 min 21-40% A. The column was equilibrated with 3% A for 3 min between injections. The flow rate was 0.4 mL min\({}^{-1}\) and the injection volume was 5 \(\upmu\)L. The detection wavelength was set at 325 nm. The identity of the determined phytocompounds with those detected by the UHPLC-DAD-ESI-MS method was confirmed based on the UV (\(\uplambda_{\text{max}}\)) spectra data. The content of identified polyphenols was calculated based on the calibration curves created by UHPLC analysis and expressed as mg g\({}^{-1}\) of dry extract. To determine the calibration curve for quantification of polyphenols in extracts, the luteolin commercial standard (Roth) and non-commercial caffeoylquinic and dicaffeoylquinic acids (isolated by preparative-HPLC in this study) were used. The content of all caffeoylquinic acids was determined by using calibration curves of caffeoylquinic and dicaffeoylquinic acids, earlier isolated by preparative HPLC.
## 3 Materials and Methods
### Ecosystem Service Assessment  
#### 3.1.1 Water Yield Module  
In the context of the InVEST model, water yield is a basic rainfall-runoff module that incorporates local environmental conditions and LULC data as inputs and calculates net water balance at the watershed scale [45]. This module does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a given pixel reaches the point of interest via one of these alternative pathways. Thus, the model calculates the amount of water yield as the difference between precipitation and actual evapotranspiration (Equation (1)), as follows:  
\[Y_{xj}=\left(1-\frac{AET_{xj}}{P_{x}}\right)\times P_{x} \tag{1}\]  
Figure 4: Maps to show the geographical distribution of converted land within the TRHR in 2000 (**a**) and 2012 (**b**).  
In this expression, \(Y_{xj}\) denotes the water yield (mm) for LULC type \(j\) in a given grid cell, \(x\), while \(P_{x}\) refers to annual precipitation (mm) of this cell and \(AET_{xj}\) is the actual annual evapotranspiration of LULC type \(j\) in the cell.  
We adopted the output of this module, i.e., water yield (\(Y_{xj}\)), to assess the water provision service. The inputs of this module include precipitation, potential evapotranspiration, the depth to root-restricting layer, plant available water fraction, LULC, watershed and biophysical table (the details are shown in Appendix A.1).
## 2 Theoretical Background  
### Bearing Capacity of Aggregate Pier Reinforced Clay  
The configuration of footings that rest on aggregate pier-reinforced soil is categorized into four general designations, as illustrated in Figure 1.  
Aggregate piers under a vertical compressive load transmit the load to the surrounding soil through side friction or lateral confinement. Previous research on the bearing capacity of aggregate pier ground improvements has largely focused on the failure modes of a single pier, and the failure modes of a pier are different under loading and pier conditions. The failure modes of homogeneous soft soil reinforced with aggregate pier are classified into three modes and are exhibited in Figure 2.  
Bulging failure occurs when the pile length is greater than two-three times the pile diameter, as displayed in Figure 2a, and shear failure likely occurs with short piles, which are supported by firm-bearing strata as presented in Figure 2b. In particular, floating aggregate piers with slenderness ratios of less than 3 fail by plunging, as illustrated in Figure 2c. Various models have been proposed to predict the ultimate bearing capacity in accordance with the failure modes: these models were  
Figure 1: Common aggregate pier configurations for spread footings: (**a**) single isolated pier, (**b**) intermediate single pier, (**c**) intermediate group of piers, and (**d**) group of piers ([PERSON] and [PERSON] [18]).  
Figure 2: Failure Modes of individual columns: (**a**) bulging failure, (**b**) shear failure, and (**c**) punching failure (after [PERSON] and [PERSON] [6]; [PERSON] and [PERSON] [9]).  
summarized in [9; 24]. As another approach to predict bearing capacity, [PERSON] and [PERSON] [18] performed MLR modeling to estimate the ultimate bearing capacity of aggregate pier-reinforced clay expressed as  
\[\ln(q_{ult})=4.756+0.013S_{rp}+1.914a_{r}+0.07d_{f}S_{rp}-13.71\tau_{mp}^{-1}+0. 005\tau_{mp} \tag{1}\]  
where \(S_{rp}\) is the slenderness ratio of an aggregate pier, \(a_{r}\) is the area replacement ratio, \(d_{f}\) is the depth of footing embedment, and \(\tau_{mp}\) is the matrix soil shear mass participation factor defined as the ratio of \(s_{u}\) to \(a_{r}\).
## 3 Experimental and Numerical Procedures
### Experimental and Numerical Programs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Investigation** & \begin{tabular}{c} **Reinforcement** \\ **Layout** \\ **Configurations** \\ \end{tabular} & **Test Series** & **Soil Ground** & **Filled Soil** & \begin{tabular}{c} **Reinforcement** \\ **Layout** \\ **Configurations** \\ \end{tabular} & **Test Series** \\ \hline \hline \multirow{8}{*}{\
\begin{tabular}{c} Experimental and numerical investigations \\ \end{tabular} } & Unreinforced soil & A & B0, B20, B40, B60, B80, B100 & - & - & - \\  & & B-1 & & - & N = 1, W/B = 6 & U/B = 0.1, 0.3, \\  & Horizontal planar & & B0 & - & N = 1, U/B = 0.3 & 0.5, 1 and 2 \\  & reinforced soils & B-2 & & B0 & - & W/B = 2, 3, 4, 5 \\  & & & & - & N = 1, U/B = 0.3 & and 6 \\  & & & & & N = 2, U/B = 0.3, & H/B = 0.1, 0.25, \\  & & B-3 & & - & W/B = 3 & 0.5, 0.7 \\  & & & & U/B = 0.3, & \\  & & B-4 & & - & W/B = 3, & N = 1, 2, 3 and 4 \\  & & C-1 & B0 & & \\  & & C-2 & B20 & & \\  & Horizontal planar & C-3 & B40 & - & U/B = 0.3, & \\  & reinforced soils & C-4 & B60 & & - & W/B = 3, & R\({}_{\rm{inter}}\)= 0 to 1.0 \\  & & C-5 & B80 & & & N = 1, 2, 3 and 4 \\  & & C-6 & B100 & & & \\ \hline \hline \multirow{8}{*}{
\begin{tabular}{c} Numerical investigations \\ \end{tabular} } & D-1 & B0 & B0 & & \\  & D-2 & B20 & B20 & & U/B = 0.3, & \\ \cline{1-1}  & Full wraparound & D-3 & B40 & B40 & W/B = 3, & N = 2, 3 and 4 \\ \cline{1-1}  & ends of geotextile & D-4 & B60 & B60 & W/B = 3, & N = 2, 3 and 4 \\ \cline{1-1}  & & D-5 & B80 & B80 & H/B = 0.5 & \\ \cline{1-1}  & & D-6 & B100 & B100 & & \\ \cline{1-1}  & & E-1 & B20 & & & \\ \cline{1-1}  & Full wraparound & E-2 & B40 & & U/B = 0.3, & \\ \cline{1-1} investigations & ends of geotextile & E-3 & B60 & B0 & W/B = 3, & N = 2, 3 and 4 \\ \cline{1-1}  & with filled-in sand & E-4 & B80 & & H/B = 0.5 & \\ \cline{1-1}  & & E-5 & B100 & & & \\ \cline{1-1}  & Full wraparound & F-1 & B20 & & & \\ \cline{1-1}  & ends of geotextile & F-2 & B40 & & U/B = 0.3, & \\ \cline{1-1}  & reinforcement with & F-3 & B60 & B0 & W/B = 3, & N = 2, 3 and 4 \\ \cline{1-1}  & filled-in sand and & F-4 & B80 & & H/B = 0.5 & \\ \cline{1-1}  & sand backfill & F-5 & B100 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of experimental and numerical programs. The numerical results of Series C, D, E and F would illustrate the improvement of bearing capacity of RSF with different reinforcement configurations.