esa-sceva/satcom-chunk-collection huggingface dataset:
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 3 Methodology
### Fusion Module  
Given two images \(A\) and \(B\) with timestamp and camera ID, as described in Section 3.1 for the visual feature stream and Section 3.2 for the STPro stream, we can obtain their global features \(f_{g}^{A}\), \(f_{g}^{B}\), and spatial-temporal probability (STPro) \(p\). We then calculate the Euclidean distance \(d=||f_{g}^{A}-f_{g}^{B}||_{2}\) between \(f_{g}^{A}\) and \(f_{g}^{B}\), which is used for inference when only considering visual features.  
Visual feature distance \(d\) and STPro \(p\) show opposite trends in measuring vehicle similarity. A smaller \(d\) means vehicle images are more similar, while a smaller \(p\) indicates they are less similar. Furthermore, \(d\) and \(p\) have different ranges: \(d\in(0,\infty)\) and \(p\in(0,1)\). Therefore, we adopted two nonlinear transforms based on the exponential function to scale \(d\) and \(p\) from 0 to 1 before fusing them, respectively. Concretely, the visual feature distance \(d\) and STPro \(p\) are scaled according to Equation (16) and Equation (17), respectively.  
\[d^{\prime}=2-\frac{2}{\mathrm{e}^{-d}+1}, \tag{16}\]  
where \(d^{\prime}\in(0,1)\) represents the scaled visual feature distance, and \(\mathrm{e}\) is the Euler number.  
\[p^{\prime}=\mathrm{e}^{p-1}, \tag{17}\]  
where \(p^{\prime}\in(0,1)\) represents the scaled STPro.  
Finally, according to Equation (18), the result of \(d^{\prime}\) multiplied by \(p^{\prime}\) represents the final similarity \(S\) between \(A\) and \(B\) for vehicle re-identification. The multiplication operation is inspired by the union of independent probabilities, under the assumption that visual features of vehicle images and their spatial-temporal cues are quite independent.  
\[S=d^{\prime}\times p^{\prime}. \tag{18}\]
## 3 Results and Discussion
The accuracy of the hybrid ANFIS-PSO with input combination (Model-7) was reasonably superior to the other models (i.e., ANFIS-ACO, ANFIS-GA, ANFIS-DE, and ANFIS) with different combinations of inputs (Tables 2-6), demonstrating that the ANFIS-PSO was a well-designed algorithm to extract pertinent features for _Ss_ prediction. The precision of ANFIS-PSO with other algorithms revealed that the different input combinations were also advantageous in indicating the pertinent features making the model parsimonious. Since the fundamental operations of the AI models of machine learning are significantly contingent upon the patterns in historical datasets that can substantially disturb the learning strategy, the results here assured the suitability of input combinations to sort out the best combination capturing minimum pertinent features and characteristics. Prior to the prediction process, several input combinations are constructed using related physical properties. The _Ss_ of the HSC slender beams was predicted using two different modeling scenarios based on (i) non-processed (initial) dataset (NP) (i.e., Model-1, Model-2,..., Model-6) and (ii) pre-processed dataset (PP) (i.e., Model-7, Model-8,..., Model-18). This was to examine the influence of the non-homogeneity of the dataset on model prediction accuracy. Apparently, the PP data were excellent data cleaning prior to the model's construction. This is due to the fact that some redundant measures associated with some error can influence the learning process, which leads to poor predictability capacity. Based on the attained modeling results, a couple limitations are observed, which are worth to be highlighted for future research. The investigated computer aid model's performance can be inspected based on the changes in the type of aggregated consideration and evaluating the weight of the interlock strength of the total shear strength. Besides, this is clear, noting that the impact of high strength beam was reported successfully. However, the impact of normal beam shear strength could be a  
Figure 3:0 Boxplot of data’s uncertainty based on indices over (**a**) Training stage and (**b**) Testing stage. prospective objective. Further, the lateral stability of the slender beam can be investigated, which is totally dependent upon the data availability of the experiments.
# Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
## 3 Interferometric Data Processing
### Baseline Measurement and Elevation Maps  
In our study, the vertical baseline of the interferometric pair was 598 m, the slant range was 990 km, the viewing angle was 43.36\({}^{\circ}\), and the radar operating wavelength of Gaofen-3 was 5.55 cm. Therefore, the elevation ambiguity was as follows:  
\[\Delta h_{2\pi}=\frac{\lambda R_{1}\sin\theta}{2B_{\perp}}\simeq 32.0m. \tag{1}\]  
The elevation was obtained in two ways. The first involved estimating the baseline using orbital parameters, and using the residual fringe frequency of the flattened interferogram to estimate the trend system error of the baseline parameters and refining the baseline. The second was to obtain high-accuracy baseline parameters through the ground control points and phase unwrapping and then solving the elevation. These two methods were consequently employed to measure the evaluation accuracy of InSAR technology for Gaofen-3 data with or without ground control points. Figure 11a,b shows the obtained vertical baselines with or without ground control points, respectively.  
As can be seen from Figure 11a, the time variation of the vertical baseline was small due to the high degree of parallelism of the orbits. Limited by the orbital accuracy, the vertical baselines calculated by the orbital parameters could not reflect the small number of changes in azimuth. Therefore, the flat-earth phase calculated by this baseline will bring about systematic errors, and the residual regular fringes will be shown in the flattened interferogram. After estimating the phase residuals by the means of least squares adjustment and refining the baseline, the obtained vertical baseline was compared to that calculated by the ground control points, as shown in Figure 11b. There existed a nearly linear difference in the baseline parameters obtained by the orbital parameters combined with the means of least squares adjustment and ground control points, ranging from 0 to 4 cm, mainly varying in azimuth, with small changes in range. The system difference was due to the limited accuracy of the estimation of the baseline parameters using residual phase frequency, which will finally be reflected by the accuracy differences in evaluation calculated with and without ground control points. The DEM calculated and inverted with control points is shown in Figure 12a, and the DEM with baselines through the orbital parameters, refined and inverted, is shown in Figure 12b.  
Figure 10: Phase unwrapping results with the minimum cost flow method.  
Figure 11: Distribution of vertical baselines (B) obtained by ground control points (**a**) and the difference distribution of vertical baselines (\(\Delta\)B) obtained through ground control points, orbits, and least-squares adjustment method (**b**).  
Figure 12: The digital elevation models acquired with ground control points (GCPs) (**a**) and without GCPs (**b**).
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 3 Methodology
### Image Dataset Generation  
Remote sensing images divided by urban functional units are usually irregular and of varying sizes, which are not suitable as input for deep learning models. Therefore, we have designed a central window cropping strategy, selecting the center of the urban functional unit as the window center and cropping the 1.3 m image with a size of 227 \(\times\) 227 pixels. These cropped images are used to generate the dataset. To address the imbalance in the number of samples for different UFZs, we employ strategies of diagonal offset cropping and image flipping for data augmentation on UFZs with a small number of samples. As shown in Figure 6, the red-dotted box represents the new window obtained by diagonally moving the central window toward the upper left corner and moving 20 pixels in both horizontal and vertical directions. The yellow-, green-, and blue-dotted boxes indicate the new windows resulting from translating the central window in the other three diagonal directions. The detailed composition of the dataset is shown in Table 3, the training data have 4240 images and the test data have 1006 images. Through image augmentation, the  
Figure 5: Urban functional unit division.  
number of training samples increases by more than 4 times, and the number of samples in each category is balanced effectively.
## 3 Results and Discussion
### Pozzolanic Properties of Raw Materials  
Figure 2a shows the percentages of the lime fixation of each of the residues studied at curing ages of 1, 7, 14, 21 and 28 days. After the first day of reaction, all the residues show similar results, but after seven days, clear differences are observed since this is when the chemical and mineralogical compositions present a greater influence on the reaction kinetics. It can be seen that ceramic wastes, both porous and porcelain, have a high capacity to fix calcium hydroxide from relatively short reaction times [13]. After 14 days of reaction, both materials show values around 90%. These values are also very high compared to the fixation percentages presented by both fly ash and slag. Slag particularly has the lowest lime fixation, reaching 50% after 28 days of reaction. Observing the chemical compositions (Table 1), it is concluded that higher contents of acidic oxides, such as SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\), lead to greater pozzolanic activity [11; 13; 23].  
Figure 1: (**a**) FTIR spectra region between 2000 and 400 cm\({}^{-1}\) and (**b**) XRD diffractogram of different materials used (Portland cement (CEM I), slag, fly ash, porous waste, and porcelain waste). The diffractogram corresponding to CEM I presents different crystalline phases (G = gypsum; CA = C\({}_{3}\)A; An = anhyde; A = C\({}_{3}\)S; B = C\({}_{2}\)S; FF = C\({}_{4}\)AF); the diffractogram corresponding to porous and porcelain waste presents different crystalline phases (Q = quartz; M = mullite; D = CaMg(SiO\({}_{3}\))\({}_{2}\); A = NaAlSi\({}_{3}\)O\({}_{8}\).  
According to the results obtained from the pozzolanic activity of each of the used raw materials, different mixtures of hybrid systems (without Na\({}_{2}\)SO\({}_{4}\)) were prepared with 80 wt % of slag, fly ash, porous waste or porcelain waste and 20 wt % of CEM I. The pozzolanic activity of these systems was evaluated using the Fratini test. According to European Standard 197-1, for a cement type to be considered a pozzolanic material, it must comply with the CaO solubility point below the calcium oxide saturation concentration curve for a period of 8 days or after 15 days. In all cases, the hybrid cement compositions comply with it at seven days, indicating pozzolanic behavior (Figure 2b). Therefore, regardless of the type, it can be concluded that all of the residues can be used as suitable cementitious materials.  
Figure 2: (**a**) Time fixed to different wastes used in this study over time (1, 7, 14, 21 and 28 days); (**b**) pozzolanicity test for studied cement paste compositions at 8 days (filled symbols) and 15 days (hollow symbols).
## 3 Results and Discussion  
### Physical Characteristics of Fortified Wheat Breads with SWAP  
The color of the crust and crumb of the wheat bread was influenced by the inclusion of SWAP materials (Figure 1). A darker crust and crumb color (lower L* values) were observed for all SWAP-supplemented breads, compared to those of control. A similar trend was also reported in previous studies on breads fortified with aromatic plants [9; 31], which was attributed to the light brown color of the used aromatic plant powders [31]. In most cases, breads supplemented with 2% of SWAP showed reduced crust redness and yellowness (lower \(\alpha\)* and b* values, respectively), as shown in Figure 1b. On the other hand, a crumb of breads with LEB2% and SPE2% exhibited higher b* values compared to control bread, while ORE and ROS wastes resulted in increased yellowness at both 1 and 2% supplementation levels (Figure 1a). Additionally, the crumb a* values did not show a clear pattern, as the inclusion of ROS, LEB, and SPE solid wastes at both 1 and 2% levels resulted in a more greenish crumb, compared to the control, whereas only the breads fortified with the ORE solid residues exhibited less intensity of the green hue than the control; the highest crumb green hue was observed for the ROS waste residues containing breads.  
Loaf specific volume was affected slightly only by the inclusion of LEB2% in the fortified breads (Figure 2). This could be attributed to the relatively low level of wheat substitution by SWAP as it is well known that the substitution of wheat flour with other components can result in a decrease in the specific volume of the loaf due to interference or/and dilution of the gluten network [32]. Thus, [PERSON] et al. [17] have noted that the incorporation of lavender waste only at 5% concentration and melissa waste at both 2.5 and 5.0% levels into wheat bread formulations resulted in decreased loaf specific volume, indicating that this quality attribute can be depended on the concentration of the added by-product and the plant species from which is originated.  
Figure 1: Color parameters of crumb (**a**) and crust (**b**) of wheat breads fortified with 1 and 2% oregano (ORE), roesamy (ROS), lemon balm (LEB), and spearmint (SPE) post-distillation solid waste residues; mean values with a different letter for the same parameter are significantly different (Tukey’s test, \(p\) < 0.05).
# Validation of the Implant Stability Test for Implant Provisional Crowns: An In Vitro Study
## 1 Introduction  
In recent years, implant treatment has become widely used in dentistry. Technological improvements have enabled the treatment of various patient conditions [1; 2; 3]. In dental implant treatment, particular surface properties can help shorten the unloading period and specific morphological characteristics can promote initial fixation [4; 5; 6]. Development of these technologies has helped shorten the treatment period, allowing for immediate implant placement with immediate or conventional loading. In terms of the osseointegration period in conventional loading, the period recommended by the manufacturer is used as an index. However, the duration required for osseointegration in cases with poor primary stability or for immediate loading is not precisely known and is chosen largely based on the experience of the surgeon.  
Instruments, such as the Oostell(r) and AnyCheck(r), were developed to measure implant stability [7; 8; 9]. Oostell(r) has been applied in implant therapy for several years; it measures implant stability via a jig called a smart peg that is attached to the implant body [10]. The optimal time for superstructure placement for implants that receive a provisional crown immediately after placement has not been defined. Therefore, we thought that these devices could be used after the placement of the provisional crowns to shorten the treatment time. [PERSON] et al. [11] reported that bone resorption at 6 months was 0.28 mm in the group in which healing abutments were removed twice before superstructure placement usingthe conventional method compared to the 0.13 mm in the group in which abutments were placed at the time of implant surgery. The removal of healing abutments or provisional crowns before osseointegration is obtained may lead to bone resorption. With Oostell, when a provisional crown is placed immediately after implant placement, the provisional crown must be removed in order to measure subsequent implant stability.  
AnyCheck(r) measures the percussion response to the healing abutment during the recovery period and quantifies bone and implant stability. Therefore, we wondered if the stability of an implant with a provisional crown could be measured using AnyCheck(r). Thus, we believe that being able to measure implant stability with provisional crowns can help reduce treatment time. The purpose of this study was to compare the stability of implants with those of healing abutments and provisional crowns using AnyCheck(r).
# A Scheduling Algorithm for Multi-Workshop Production Based on BOM and Process Route
## 3 Process Network Diagram Model Based on BOM and Process Route
\begin{table}
\begin{tabular}{c c} \hline
**Symbol** & **Description** \\ \hline \(P\) & Set of all products \\ \(P_{i}\) & Product \(i\), \(i=1,\dots,n\) \\ \(P_{i}^{p}\) & Parent node of \(P_{i}\) \\ \(P_{i}^{c}\) & Set of all \(P_{i}\) components \\ \(Q\) & Product process set \\ \(Q_{i}\) & Process set for product \(P_{i}\) \\ \(Q_{ik}\) & The \(k\)-th process of product \(P_{i}\), \(k=1,\dots,l\) \\ \(LQ\) & Number of processes in the product, \(l=LQ_{i}\) \\ \(F\) & Workshop set \\ \(F_{w}\) & The \(w\)-th workshop \\ \(M\) & Set of device resources \\ \(M_{wr}\) & The \(r\)-th device of workshop \(w\), \(r=1,\dots,m\) \\ \(MQ_{ik}\) & Processing device, if process \(Q_{ik}\) is processed on device r \\  & in workshop w, then \(MQ_{ik}=M_{wr}\) \\ \(T\) & Set of product processing time \\ \(T_{i}^{s}\) & Start time of product \(P_{i}\) \\ \(T_{i}^{e}\) & Completion time of product \(P_{i}\) \\ \(T_{i}^{f}\) & Required completion time product \(P_{i}\) \\ \(TQ\) & Set of processing time \\ \(TQ_{ik}\) & The processing time of the \(k\)-th process of product \(P_{i}\)—that is, the processing time on the device \(k\) in workshop \(w\) \\ \(TQ_{ik}^{e}\) & Start time of the \(k\)-th process of product \(P_{i}\) \\ \(TQ_{ik}^{e}\) & End time of the \(k\)-th process of product \(P_{i}\) \\ \(TQ_{il}\) & End time of product \(P_{i}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Notations. \[TQ_{ik}^{e}=TQ_{ik}^{s}+TQ_{ik} \tag{16}\]  
\[TQ_{i1}^{s}\geq\max(TQ_{jl}^{e}),\forall P_{j}\in P_{i}^{c} \tag{17}\]  
\[TQ_{il}^{e}\leq T_{i}^{f} \tag{18}\]  
Equation (14) represents the process route constraint of the product; Equation (15) provides the exclusive constraint of the device; Equation (16) represents the consistency constraint of a single processing; Equation (17) gives the BOM structure constraint of the product; Equation (18) represents the processing cycle constraint of the product.
# Synchronous Grouting Analysis of Shield Tunneling through High Water Pressure Fault Fracture Zone
## 3 Analysis of Synchronous Grouting Results in Fault Fracture Zones  
By establishing a fault fracture zone model, numerical simulation of the shield tunneling process is conducted, with a focus on analyzing the disturbance of rock and soil during the shield tunneling process, including analysis of surrounding rock deformation, effective stress of rock and soil, and changes in pore pressure. This article takes the model simulation results of grouting pressure of 450 kPa, top support pressure of 300 kPa, burial depth of 25 m, and water cover depth of 25 m as examples for analysis.  
Perform geostress balance on the fault fracture zone formation model to restore the true geostress state. Firstly, calculate the initial geostress of the rock and soil, select a fixed increment type, and then apply the calculated geostress file as a prestressing condition to the model for secondary balance. If the accuracy of the balance result still does not meet \(10^{-5}\) m, multiple balances need to be performed.  
From Figures 5-7, it can be seen from the analysis of the results of the geostress balance that after the geostress balance, there is a significant distortion of vertical stress at the fault structure without excavation. The vertical stress inside the fault is significantly lower than that of the adjacent normal surrounding rock strata, which is related to the formation process of the fault fracture zone. The fault area is actually formed after the formation of strata movement and fracture, with many fillers combined in the middle, and the physical and mechanical properties such as gravity and strength are significantly reduced, the strata have large deformation and belong to the stress release zone, so the stress in the fault zone is relatively low. From Figures 8 and 9, it can be seen that after the second balance, the initial displacement field of the geostress balance is very small, and the balance effect is good.  
After the ground stress is balanced, the soil elements of the excavation surface are gradually removed, and the shield tail lining and grouting layer elements are activated  
Figure 8: Stress field after equilibrium.  
Figure 6: Shield machine casing.  
Figure 7: Grouting layer.  
Figure 9: Displacement field after equilibrium (amplified by 107 times).  
simultaneously to simulate tunnel excavation construction. Use software post-processing tools to obtain the displacement and stress fields of the model rock and soil, extract the output data of each field during the excavation process, select monitoring points in the ordinary surrounding rock, crack zone, and fractured zone rock and soil, and analyze the changes in stress, displacement settlement, and pore pressure of the soil units at 0.8 m above and below the tunnel top and bottom. The Schematic diagram of longitudinal section monitoring points of the model is shown in Figure 10.
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 1 Introduction
The embedded depth ratio is an important factor that affects the stability of foundation pit, and average values range from 0.44 to 0.96. It has been suggested in numerical analyses and design charts that movements are related to the stiffness of the support system [8,10,20]. These studies form a series of databases composed of figures and diagrams, and they have enriched our comprehension of performance with regard to deep excavations. They offer valuable resources for engineers and researchers to aid in verifying the accuracy of numerical analyses or estimating the scale of deep excavation movements. Fewer investigations have focused on heterogeneous soils [2,12,14]. Owing to distinct regional characteristics, variations are observed in terms of the size and deformation patterns of foundation pits across diverse areas. Numerous engineering projects concerning foundation pits have been implemented in Beijing, but systemic research and comprehensive summaries concerning ground deformation that mention characteristics of surface deformation, are currently lacking. To elucidate the principles of ground surface deformation resulting from deep excavations in Beijing, field data from 88 excavation cases concerning high-quality construction were chosen. These data were utilized to analyze ground behavior and to determine the relationship between deformation and excavation in Beijing's heterogeneous soils. The magnitude and location of the ground surface settlement, linkages between \(\delta vm\) and excavation depth (\(H\)), the embedded depth ratio (EDR), length width ratio, and stiffness were evaluated. And the evolution process, which is apparent in the influence zone, and the pattern are portrayed. Some factors affecting deformation were discussed. In addition, comparisons with worldwide cases are made. We conducted a comprehensive study for this region in this paper. The results are of great significance, they enrich databases which comprise similar engineering papers, and they improve design theory, which can provide experiences for reference in Beijing and other areas in the future.