esa-sceva/satcom-chunk-collection huggingface dataset:
# On Flexural Performance of Girder-To-Girder Wet Joint for Lightweight Steel-UHPC Composite Bridge
## 4 Experimental Test of the Second Generation T-Shaped Joint
### Test Results  
#### 4.2.1 Load-Deflection Curve and Crack Development  
As shown in Figure 11a, the loading process can be divided into the same three stages: (1) elastic stage, (2) crack propagation stage, and (3) yield stage. The elastic stage is defined as an applied load less than 400 kN; a crack could be found in the joint interface. The crack propagation stage refers to an applied load less than 950 kN; the small and dense cracks propagated in the specimen, and the main crack (the crack which was larger and developed faster than the rest) gradually appeared at the joint  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Part Item** & **Curing Condition** & **Compressive Strength** & **Flexural Strength** & **Young’s Modulus** \\ \hline Prefabricated part & Steam curing & 162.38 & 28.74 & 49,030 \\ Post-cast Joint part & Natural curing & 135.01 & 32.19 & 45,820 \\ \hline \end{tabular}
\end{table}
Table 4: Mechanical properties of UHPC (MPa).  
Figure 10: Loading and measuring device.  
Figure 9: Diagram of the second generation T-shaped joint model test specimen.  
interface. Furthermore, the stiffness of the specimen decreased slightly. The specimen develops to the yield stage when the applied load exceeds 950 kN; the deflection increased rapidly and the stiffness of the specimen decreased significantly--simultaneously, the width of the main crack increased drastically. At 1033.9 kN, the I-shaped steel girder bucked close to the loading point and the test terminated.  
The main crack development is shown in the Figure 11b. When the crack (main crack, Interface C-1 and C-2 in Figure 11b) width reached 0.05 mm, the applied load was in the range of 143.5-196.6 kN; while for the prefabricated parts, the range was 336.5-424.5 kN. The cracks were found below the loading point, and the crack in the mid-span was extremely small (\(\leq\)0.05 mm) during the entire loading process.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## Guidelines and Recommendations  
Our review and case studies show that the impacts of TD error on EO applications can vary, as can the procedures for assessing those impacts. Nevertheless, several best practices and guidelines can be discerned from this work. Below we synthesize a set of suggested steps for minimizing and accounting for TD error, within the context of undertaking and assessing the accuracy of a typical ML-based mapping project.
## 4 Results
### Dataset X2  
#### 4.2.1.  
In Figure 10, the scree plot generated from the PCA results provides insights into the variance explained by each principal component.  
Figure 10 illustrates the scree plot, providing insights into the variance explained by each principal component. Key observations for Dataset X2 include:  
* Gradual Decline: Variance explained by principal components exhibits a gradual descent, indicating an even distribution of information.
* No Clear Elbow: Unlike Dataset X1, Dataset X2 lacks a distinct \"elbow\" in the scree plot, making the optimal number of components challenging.  
Figure 9: Absolute loadings of FRs (Dataset X1).
## 3 Results and Discussion
### Variation in Urea Hydrolysis Completion Efficiency, Ca2+ Immobilization Percentage, and pH
Figure 3: Variation in urea hydrolysis completion efficiency over time: (**a**) GT-Zn0/0.25/0.5; (**b**) GT-Ni0/0.25/0.5; (**c**) GT-Cr0/0.25/0.5; (**d**) GT-multiple0/0.25/0.5. The variation in pH was closely related to urea hydrolysis completion efficiency and the Ca\({}^{2+}\) immobilization process. The increase in pH of GT-Zn0, GT-Ni0, GT-Cr0, and GT-multiple0 was attributed to the continuous decomposition of urea. In GT-Zn0.5 and GT-multiple0.5, the pH initially decreased and then increased in the first 9 h, which is attributed to the combined effects of calcium carbonate precipitation and urea hydrolysis. In the first 3 h, urea hydrolysis completion efficiency was less than 40%. During this period, the pH decreased because the increase in pH from urea hydrolysis was offset by a greater decrease from calcium carbonate precipitation. Later on, as the urea hydrolysis progressed, the rise in pH caused by urea hydrolysis exceeded the decrease in pH caused by calcium carbonate precipitation. Finally, a trend of pH decreasing first and then increasing was observed. However, in GT-Zn0.25, GT-Ni0.25/0.5, GT-Cr0.25/0.5, and GT-multiple0.25, a continuous increase in pH was observed. This occurred because, in these groups, the urea hydrolysis completion efficiency could reach 60 to 90% in the first 3 h. Consequently, the increase in pH due to urea hydrolysis far exceeded the reduction in pH caused by calcium carbonate precipitation.
## 2 Design Concept of New Composite Tall-Pier Bridge  
The design concepts of the new composite tall-pier bridge are as follows:  
* Under normal service conditions, the new composite tall pier provides sufficient flexural and torsional stiffness. Meanwhile, the I-shape steel beams are installed along the height of the pier to ensure the overall stability of the structure.
* Under the service level earthquakes (SLEs), the whole bridge is intentionally designed to remain essentially elastic.  
Figure 1: Design concept of the new composite tall pier. (**a**) longitudinal view, (**b**) transverse view, (**c**) cross section. CFST: column: concrete-filled steel tubular column.  
* Under the design-based earthquakes (DBEs), the EDMSPs are intentionally designed for energy dissipation while CFST columns are designed to remain elastic. The seismic input energy is dissipated by the hysteretic behavior of the EDMSPs, which in return reduces the stiffness of the pier and mitigates the seismic damage. Only slight damage or inelastic states are allowed for I-shape steel beams.  
In terms of load-carrying capacity design, the lateral and vertical load transferred from the superstructure are mainly resisted by the four-limb CFST columns under the basic load combination. Under the seismic load combination, both the lateral and vertical loads are mainly resisted by the four-limb CFST columns, while the EDMSPs and the I-shape steel beams participate in resisting part of the lateral load. Compared with the conventional RC tall-pier bridge, the advantages of the new composite tall-pier bridge are readily visible:  
* The new composite tall piers are supposed to perform as well as the conventional RC box section tall piers subject to the static loads. Meanwhile, due to the vertical axial loads are mainly resisted by the CFST columns, the problem of local bulking in conventional steel pier can be avoided.
* During the earthquakes, the CFST columns are designed to remain elastic with the help of the energy dissipating devices. The EDMSPs yield and dissipate seismic energy and can be replaced quickly. The design concept of seismic resilience for tall-pier bridges can be achieved by proper design.
* The new composite tall piers have the same characteristic of rapid construction as the CFST lattice columns, such as segmental prefabrication, less volume of concrete, easy hoisting and installation. Compared with the construction of traditional RC tall piers, the construction equipment is simpler, and the construction speed can be significantly improved during the turnover and climbing formwork.  
A conventional RC continuous rigid-frame bridge with box section tall piers located in the highway of the mountainous area is taken as the prototype. Then, the prototype tall piers are replaced by the new composite tall piers. Meanwhile, the other parts of the bridge remain the same as the prototype. Both static and time history analyses of the new and conventional tall-pier bridges are conducted in Midas2019 and ABAQUS for comparing their seismic performances.
# Step 12: Compile the grid search model: compile_model(grid_search_model)
## 4 Conclusions  
In conclusion, this paper introduces the deep location routing problem (DLRP) model as an innovation in route planning within the context of disaster logistics. The DLRP model is specifically designed to address the challenges associated with the location routing problem (LRP), which is a key issue in distributing humanitarian aid after disasters. DLRP utilizes the K-means algorithm to determine optimal facility locations, which is a critical step in ensuring efficient distribution centers closely linked to post-disaster rescue and recovery efforts. Furthermore, DLRP harnesses artificial intelligence in the form of artificial neural networks (ANN) within the deep learning model for route prediction and planning. This allows for responsive and adaptive route planning, even in uncertain or changing conditions. One of the major advantages of the DLRP model is its ability to efficiently tackle NP-hard problems, yielding optimal solutions with polynomial time complexity. This means that disaster relief distribution can be carried out more effectively and responsively, with more efficient resource utilization. In the context of disaster logistics, where every second is precious, the DLRP model holds significant potential to enhance preparedness and disaster response capabilities. With the existence of this model, disaster logistics management can become more efficient, timely, and adaptive, thereby helping to reduce the impact on human suffering and property loss in emergency situations. As a result, this research opens the door to further improvements in disaster management and humanitarian aid distribution worldwide.  
Further research in this topic could explore several areas. One area of focus could be the development of more complex and realistic models that consider a wider range of variables and constraints that may arise in disaster logistics management. Additionally, further research could investigate the practical implementation of the deep location routing problem (DLRP) model in real-world scenarios and assess its impact on operational efficiency and responsiveness in actual disaster situations. Furthermore, research into the integration of emerging technologies such as the Internet of Things (IoT) and big data processing in the context of disaster logistics management could also be an intriguing area to explore. By continuing to emphasize research and development in this field, we can enhance our preparedness and response to disasters and reduce their impact on affected communities.  
Conceptualization, H.M.; Methodology, H.M.; Writing-original draft, L.T.; Supervision, S.E. and M.S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. There is no need for data statement. The authors declare no conflict of interest.
# Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples
## 3 GFormer  
### Overview  
**Problem Definition.** Widely used episodic meta-training [22] is adopted in few-shot segmentation. Specifically, we denote the training set as \(D_{train}\) and testing set as \(D_{test}\), the categories of the two sets \(C_{train}\) and \(C_{test}\) are disjointed (\(C_{train}\cap C_{test}=\varnothing\)). During training, we randomly select a set of sets from the training set; each set of images has the same category and is divided into a support set \(S=\left\{\left(I_{s}^{k},M_{s}^{k}\right)\right\}_{k=1}^{K}\) and a query set \(Q=\left(I_{q},M_{q}\right)\), where \(K\) represents the number of support set images, and \(I\) and \(M\) are the images and their corresponding ground-truth binary mask. For each target category, we select an additional category from the training set that is similar to the target category, which is called the interference category. We name \(S\) from the target category as the positive support set \(S_{p}\), and \(S\) from the interference category as the negative support set \(S_{n}\). During testing, we perform the same for the test set. Note that both support masks \(M_{s}\) and query masks \(M_{q}\) are available for training, and only support mask \(M_{s}\) is accessible during testing.  
**Model Architecture.** As shown in Figure 2, the GFormer proposed in this paper has four components: encoder, prototype extractor, adversarial prototype generator, and decoder. Each module and training strategy will be described in detail next.
# Spatio-Temporal Distribution of Ground Deformation Due to 2018 Lombok Earthquake Series
## 3 Results  
### Magnitude of Displacement in Northern Part of Lombok Island  
This study focused on earthquakes occurring on four dates: 29 July, 5 August, 9 August, and 19 August 2018 on northern Lombok Island. The spatial pattern of the ground displacement due to these earthquakes was visualized in an interferogram with radian units \(-\pi\) to \(\pi\) (Figure 4) and a displacement map with metric values (Figure 5). The interferogram of the foreshock earthquake of 6.4 Mw on 29 July 2018 was produced based on radar imageries on 27 July 2018 (master) and on 2 August 2018 (slave). Based on interferogram, the observed ground movement was focused on the northern shoreline. This involved a very light uplift of less than 0.155 m. The uplift was also found on the north-eastern shore (0.074 m). The subsidence was concentrated on the Rinjani crater rim, reaching \(-\)0.144 m.  
The interferogram of the mainshock earthquake of 7.0 Mw on 5 August 2018 was derived from radar imageries on 2 August 2018 (master) and on 8 August 2018 (slave). Clear uplift was found from the north-western (0.574 m) to the north shore (0.452 m). Rinjani caldera was also uplifted less than 0.186 m. The uplift in the eastern shore was measured at 0.072 m. Light subsidence was observed at the western part of the island, near Mataram city.  
The interferogram of the aftershock earthquake of 6.2 Mw on 9 August 2018 was processed based on radar imageries on 8 August 2018 (master) and 14 August 2018 (slave). The subsidence was observed throughout the study area. It reached \(-0.217\) m on the western shore and \(-0.389\) m at the three touristic islands in front of the western shore (Gili Trawangan, Gili Meno and Gili Air Island). Another subsidence of \(-0.130\) m was also found at the Rinjani volcano.  
The interferogram of the earthquake of 6.9 Mw on 19 August 2018 was produced based on radar imageries on 14 August 2018 (master) and 20 August 2018 (slave). This earthquake caused the uplift in almost the whole study area. The north-eastern shore has shown a unique characteristic because it contains an uplift of 0.329, but if we went a bit further to the eastern shore, it had a subsidence of \(-0.101\) m. The highest uplift was measured 0.450 m at small islands at the northwest (Gili Trawangan, Gili Meno and Gili Air island) and at the northeast (Lawang and Sulat island). Light uplift of 0.036 m was measured at the Rinjani volcano.  
Figure 4: Interferogram of earthquakes on 29 July, 5 August, 9 August, and 19 August 2018.
## 5 Applications
### Ambient Assisted Living  
HAR-based systems have important applications in the ambient assisted living (AAL). These systems are used as healthcare systems to understand and analyse the patients' activities, to facilitate health workers in treatment, diagnosis, and general healthcare of the patients. In addition to this, these systems are also useful for smart homes such as monitoring daily life activities of elderly people, meant to provide a safe, independent and comfortable stay for elderly people. Usually, these systems capture the continuous movement of elderly people, automatically recognize their activities and detect any abnormality as it happens, such as falling down, having a stroke or respiration issues.  
Among these abnormal activities, 'fall' is a major cause for fatal injury, especially for elderly people and is considered a major hindrance for independent living. Different methods have been proposed in the literature for daily life monitoring of elderly people [184]. The fall detection approaches are divided into three types; ambience-device-based, wearable-device-based, and vision-based. Among these, vision-based approaches are more popular and offer multiple advantages as compared to ambience-device-based and wearable-device-based approaches. In [184], a method for fall detection was proposed using combination of integrated time motion image (ITMI) and Eigen space method. The ITMI is a spatio-temporal database which contains motion information and its time stamp and Eigen space technique was used for feature reduction. Then, the reduced feature vector was passed to the neural network for activity recognition. A classification method for fall detection using deformation of the human shape was proposed in [185]. In this method, edge points from the silhouettes wereextracted using Canny edge operator for matching two shapes. The Procrustes analysis and mean matching cost were applied for shape analysis. Finally, the fall is characterised by the peak of the smoothed mean matching curve or Procrustes curve. A detailed survey on fall detection approaches can be found in [186].  
Monitoring patients' activities remotely is very important for assessing their well-being when they are shifted from hospital to their homes. The homes which are equipped with monitoring facilities are known as smart homes. Different techniques have been proposed in literature for smart homes [187]. Beside, vision-based techniques, a number of sensor-based techniques have also been presented in the literature [188]. In [189], a genetic programming-based classifier was proposed for activity recognition in a smart home environment. It combined the measurement level decisions of the four different classifiers Artificial Neural Network (ANN), Hidden Markov Model (HMM), and Support Vector Machine (SVM) with respect to the assigned weights to each activity class. The weights are optimized using genetic programming for each classifier, where the weights are represented as chromosomes in the form of strings of real values. The results indicate the better performance of the ensemble classifier as compared to a single classifier.
## 5 Results and Discussion
### Selection of Activation Function  
Next, we investigate the selection of the activation function after the routing layer. To normalize the routing probability values, we convert the routing score to the routing probability by the activation function. There are two functions to choose from:  
\[Softmax(x)=\frac{e^{x_{i}}}{\sum_{j=1}^{n}e^{x_{j}}} \tag{3}\]  
\[Sigmoid(x)=\frac{1}{1+e^{-x}} \tag{4}\]  
As illustrated in Table 2, it is obvious that choosing the Softmax function is the best option. This is because Softmax combines the normalization of all the input values, and the output result is the correlation between the probabilities. For the sigmoid function, it cannot reflect the correlation between different probabilities, and because the gradient of the part far away from 0 is small, it is prone to gradient disappearance.  
Figure 4: The confusion matrix for our best LGNet model (accuracy = 82.71%).
## 3 Enhanced LMS Algorithm  
### Sliding Mode Control  
SMC is one of the most popular nonlinear control methodologies in use today. The strength of this controller is that it is robust, insensitive to plant dynamics, and able to decouple higher-order systems, regardless of their nonlinearity and uncertainty [48]. Once a sliding surface is defined, the state trajectory moves between both sides of the surface by high frequency switching control with discontinuous inputs, and it eventually arrives on the pre-defined sliding surface. The motion of a system is not associated with its original dynamics, and it depends on only the sliding mode equation. The main advantage of SMC lies in its robustness and insensitivity to noise, since the system prescribes the dynamics associated with desirable performance characteristics and the system response is insensitive to disturbances or uncertainties in the system parameters [49].  
The SMC design procedure includes the following two parts: (1) a discontinuity surface called the sliding surface is chosen to have dynamics related to desirable performance properties; and (2) a high-frequency switching control input is designed to drive the initial system states to the sliding surface. Because the discontinuous input leads the system states towards the sliding surface, the states eventually reach the surface and move along it, and this condition is referred to as \"sliding mode.\" The sliding surface is defined for linear systems:\[s=G_{\rm s}x, \tag{16}\]  
where \(x\) is a state vector, and \(G_{\rm s}\) is a row vector that decides the dynamics of the sliding mode. The discontinuous inputs \(u_{\rm s}^{+}(x,\,t)\) and \(u_{\rm s}^{-}(x,\,t)\) are then chosen to fulfill the requirements for the existence and the reachability of the sliding mode. A common model of the control input for linear system is:  
\[u_{\rm s}=-Msign(s), \tag{17}\]  
where \(M\) is a positive gain that is fairly large to insure \(s\dot{s}<0\) so that the state trajectory will reach the sliding surface.  
Figure 3 shows conceptual figures of an ideal sliding mode and a real sliding mode. The system trajectory in the state space \((x,\dot{x})\) arrives at the sliding surface defined by \(s=0\) and slides towards the origin along the surface. To accomplish ideal sliding mode control, discontinuous control inputs should be able to switch at a fairly high frequency (theoretically infinite), as shown in the figure. However, most mechanical actuators cannot realistically replicate this condition, which results in chattering problems that could possibly make the overall system unstable when combined with unmodeled dynamics.  
The SMC should also be considered in the discrete time domain since controllers are usually implemented on modern digital computers. For traditional discrete time sliding mode control (DSMC), the sliding mode is forced in the very next sampling moment [50], the point of which may cause controller saturation. The control system will then not work as expected and can even destabilize the target structure. Therefore, there is a need for a novel control technique that can solve these issues.
## 3 Methodology
### Geostatistical Methods
#### 3.2.3 Ordinary Cokriging (COK)  
The last methodology applied in the present study is ordinary cokriging (COK). It offers additional advantages over ordinary kriging. It involves the use of a secondary variable (covariate) that is cross-correlated with the primary or sample variable of interest. The secondary variable is usually sampled more frequently and regularly, thus allowing estimation of unknown points using both variables 'globally' for the mean of all estimates but also 'conditionally' for the estimates within individually specified grade categories. This can aid in minimizing the error variance of the estimation.