esa-sceva/satcom-chunk-collection huggingface dataset:
# Configuration of Non-Pumping Reactive Wells Considering Minimum Well Spacing
## 2 Materials and Methods  
### Configurations of NPRW  
A NPRW is an array of non-pumping reactive wells for remediating contaminants in groundwater. A NPRW was proposed for remediation of a deep aquifer, whose depth precluded a trench-type PRB. With the development of drilling technology, NPRWs are easier to construct than the existing trench-type PRBs for use in deep subsurface environments. In previous studies, various configurations of NPRWs have been developed, such as those employing a single transect, a 45-degree structure transect [31], and multi-transects in the shape of a zigzag [28] (Figure 1). Each non-pumping well filled with reactive media had a capture width larger than its diameter when the hydraulic conductivity of the reactive material was higher than that of the surrounding geologic media. The contrast in hydraulic conductivity induced groundwater flow into the well from the vicinity of the well [28]. Due to the induced groundwater flow, the spaced wells also capture the contaminant plume.  
A single transect configuration is an array of wells arranged perpendicular to the groundwater flow direction (Figure 1a). Unlike the trench-type PRB, which has a horizontally continuous structure, the reactive wells in NPRWs are spaced apart. This allows the contaminated groundwater to flow downgradient through the gap between the wells. The multi-transect configuration can be constructed by placing two or three transects of the reactive wells slightly diagonally along the groundwater flow direction (Figure 1b,c). The center of the reactive well located in the downgradient transect is located in the middle of two reactive wells in the upgradient area. The multi-transect configuration can capture the contaminant plume completely by repeated treatment in the reactive wells on the second and third transects. However, the performance of NPRWs in preventing pollution  
Figure 1: Schematic diagram of NPRW configurations to capture contaminants in ground water in **(a)** single transect, **(b)** double transects, **(c)** conventional triple transects, **(d)** triple transects in zigzag array, and **(e,f)** sawtooth array in three- and four-transects. \(\text{S}_{\text{w}}\) is the plume width.  
is affected by several configuration factors such as well spacing, distance between transects, and number of wells and transects [28; 31].  
A diagonal arrangement of reactive wells (zigzag array) was proposed as a more efficient well configuration (Figure 1d) in which the zigzag array transect consists of linear segments arranged at a 45-degree angle with respect to the direction of groundwater flow. This configuration performed effectively in terms of reducing the maximum concentrations, as well as the residual plume area [31].
# Monitoring of Surgically Treated Upper Arm Fracture by Implanted Antenna at 402 MHz
## 3 The Method
### Fracture Monitoring  
The monitoring application records the transmitted power density at the far field of an implanted antenna in the human humerus. The antenna is implanted at the boundary between the fractured bone and the muscle layer. In the case of a fractured humerus, an additional layer represents the fracture in the upper arm model shown in Figure 1b. The bleeding in the fractured bone area leads to inflammation, followed by the clotting of blood at the fracture site. Bone production begins when the clotted blood is replaced with cartilage and fibrous tissues. As healing grows, the fibrous tissue is replaced with new hard bone tissue. The electrical properties of the fracture layer change from blood properties at the beginning of fracture time to the new normal bone after the healing completeness. This change affects the amount of loss from the radiated power of the antenna and then the transmitted power density outside the body. As the bone heals, the transmitted APD from the implanted antenna to outside the body increases gradually until a normal value is reached when the bone fracture is completely recovered. The bone status during the monitoring period can be expressed by the difference in transmitted APD between the normal and fractured bone. The transmitted power is received by the monitoring device at home which, in turn, retransmits it again to the clinical laboratory or hospital for following up, adjustment, or changing the healthcare plan if needed.
## 2 Development of Sensor Module
### Multiple-Sensor Orientation and Coordinate System
The parameter \(d_{s}\) corresponds to the measured distance from the center of the sensor to its surrounding object, which ranges from 0 to 400 cm. These transformations convert each sensor's distance data into a unified 3D point cloud. The complete transformation is expressed in Equations (2)-(4), where each matrix converts the multi-zone array distance sensor outputs into the sensor module position frame relative to the drone:  
\[\mathbf{M}=\mathbf{R}_{z}(\alpha)\mathbf{T}_{x^{\prime}}(r)\mathbf{R}_{z^{\prime}}(\beta)\mathbf{ R}_{y^{\prime\prime}}(\gamma)\mathbf{T}_{x^{\prime\prime}}(d) \tag{2}\]  
\[\mathbf{M}= \begin{bmatrix}cos\alpha&-sin\alpha&0&0\\ sin\alpha&cos\alpha&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}1&0&0&r\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}cos\beta&-sin\beta&0&0\\ sin\beta&cos\beta&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}cos\gamma&0&sin\gamma&0\\ 0&1&0&0\\ -sin\gamma&0&cos\gamma&0\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}1&0&0&d_{s}\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}  \tag{3}\]  
\[\mathbf{M}= \begin{bmatrix}cos(\alpha+\beta)cos\gamma&-sin(\alpha+\beta)&cos(\alpha +\beta)sin\gamma&d_{s}cos(\alpha+\beta)cos\gamma+ros\alpha\\ sin(\alpha+\beta)cos\gamma&cos(\alpha+\beta)&sin(\alpha+\beta)sin\gamma&d_{s} sin(\alpha+\beta)cos\gamma+ris\alpha\\ -sin\gamma&0&cos\gamma&-dsin\gamma\\ 0&0&0&1\end{bmatrix}  \tag{4}\]  
Using the final transformation matrix \(\mathbf{M}\), each distance measurement is projected into the sensor module coordinate frame, namely the drone's frame. The result is a complete 3D point cloud of obstacles detected in the environment. Each sensor is assigned a specific angular index relative to the front of the drone (sensor \(i_{0}\) at \(0^{\circ}\)), allowing for the accurate mapping of direction and distance. This allows the drone to localize and respond to obstacles from all directions in real-time, enabling smooth and accurate collision avoidance.
## 3 Results
### Accuracy Analysis  
Accuracy verification aims to compare the optimal prediction map of two different simulation methods with the set of sample pixels used for accuracy verification. Type matching means that the simulation results are correct, and the type inconsistency indicates that the simulation results are wrong. The results are shown in Table 2.  
The overall accuracy of Globeland30 products was high [21], but accuracy was spatially varied in Southeast Asia in part or some land cover types. The proportion of matching the pre-classified image with the sample pixels was 75.34%. The accuracy of the MCRF simulation results was 81.52%, which was 6.18% higher than that of the GlobeLand30-2015 products. The accuracy of matching the co-simulation results with the verification points was 86.48%, which was 11.14% higher than that of the GlobeLand30 products. The simulation results also revealed where the accuracy was relatively low. The area with a low accuracy can be used as the warning area of false classification, which provides a reference for subsequent product improvement.
## 4 Case Studies
### Benchmark Test Function  
In order to verify the validity of the algorithm, three different types of functions were selected for numerical experiments. The number of iteration steps was set to 100.  
The Sphere function is a simpler unimodal function that examines the convergence speed of the algorithm, where the mathematical model can be written as follows:  
\[\min f(x)=\sum_{i=1}^{n}{x_{i}}^{2},n=2,|x_{i}|\leq 5.12 \tag{39}\]  
The global advantage is \(x_{i}=0,f(x)=0\). The comparison of the fitness convergence curves in the unimodal function is shown in the Figure 6. It can be seen that the MSO can converge to the global optimum with a fastest convergence rate compared to the other nine algorithms.  
The step function is a discontinuous step function that can be used to verify the validity of the algorithm, which can be described as follows:  
\[\min f(x)=\sum_{i=1}^{n}{(x_{i}+0,5)}^{2},n=5,|x_{i}|\leq 10 \tag{40}\]  
The global best advantage is \(x_{i}=0,f(x)=0\). The comparison results of the fitness convergence curves of the discontinuous step function are shown in the Figure 7. Similarly, the proposed MSO also had the fastest convergence rate among all of the algorithms, while a global optimum could be guaranteed.  
The Rastrigin function is a multimodal function that can be used to test the algorithm's ability to jump out of local optimum, which can be described as follows:  
\[\min f(x)=10n+\sum_{i=1}^{n}{x_{i}}^{2}-10\cos(2\pi x_{i}),n=2,|x_{i}|\leq 5.12 \tag{41}\]  
The global best advantage is \(x_{i}=0,f(x)=0\). The comparison results of the fitness convergence curves of the discontinuous step function are shown in the Figure 8. It can be seen that the global optimization ability of the MSO was more obvious, and other comparison algorithms were trapped in a low-quality local optimum.  
Figure 6: Comparison of convergence curves of ten algorithms in single peak test function.  
In the figures (like Figure 6), the MSO seems to have an advantage immediately in iteration 0 or 1. The main reason for this is that the MSO adopts chaotic mapping for the population initialization, which can reach every state of a certain scale due to its regularity and ergodicity. On the other hand, the other algorithms only adopt a simple random search-based initialization, such that they easily lead to a low optimization efficiency during the initialization process. Hence, the MSO can effectively obtain a higher quality initial best solution compared to other algorithms.
## 2 Related Work
These data are useful for authorities to supervise beach areas in real-time and make decisions regarding social distancing, use of masks, and other measures to contain the pandemic. Beach attendance is also an essential factor to plan different beach services in terms of security, rescue, health, and environmental assistance. Beach access conditions, number and size of parking areas, and other services (restaurants, leisure activities, public restrooms, etc.) are affected as well. The emergence of COVID-19 has brought new implications for beach access. There is a need to implement and enforce additional mitigation strategies (physical distancing, limiting gatherings, supporting hygiene, etc.) so that there is no widespread community transmission of the virus. Beach attendance estimation is essential to plan the reopening of beaches and offer beach services safely. Real-time occupation was analyzed in 14 beaches of the coast of Guipuzcoa (Basque Country, Northern Spain) [24]. Video images from 12 stations located along 50 km of coastline were processed. A machine learning algorithm (AdaBoost, SVM and Quadratic Regression) was used to count beach users. The occupancy level (full, high, medium, or low) of every beach was sent to local authorities through a web/mobile app as well as special warnings under particular circumstances to allow them to take action in cases where carrying capacity limits were about to be reached [25]. In this paper we describe a deployment to predict beach attendance using machine learning at Castelldefels beach (Barcelona, Spain).
## 2 Changes in the Statements Corresponding to the Tables
For Group B, similar results can be seen in Tables 2 and 3 that ASLM-LHS and ASLM-AP outperform all the baselines under all measurements in 1/10 Reddit, 1/80 Last.fm, and Tmall datasets. For the Tmall dataset, the evaluation result of ASLM-LHS shown in Table 3 improves 70.86%, 49.06%, and 114.50% in R@10, R@20, and MRR@20 scores compared with the SWIWO-I method, respectively. Please note that, for the Tmall dataset, as shown in Table 1 in the original paper, there is scarcely enough sessions for each user(namely, 3.99 per user) and number of average session length (2.99, per user) compared with those in Reddit and Last.fm. For 1/10 Reddit and 1/80 Last.fm, since the user history information is reduced dramatically compared with the full Reddit and Last.fm datasets, most of the models' performances significantly decrease accordingly. However, both ASLM models still outperform the strongest baselines, as shown in Table 2. It demonstrates that ASLM can reach better performance even when it is severely short of both long-term and short-term user behavior. When \(K\) changes from 5 to 20, for the Reddit and 1/10 Reddit datasets, there is a downward trend in the relative scores of ASLM-LHS and ASLM-AP. For the Last.fm dataset, there is an upward trend in those of ASLM-LHS. The reason for the upward trend is that there is more abundant user history information in the Last.fm dataset, and, with the increase of \(K\), ASLM-LHS still has the potential to further capture the context information implied by the Last.fm dataset, which results in some space to improve its performance. In terms of the two versions of ASLM (ASLM-AP using average pooling and ASLM-LHS using the last hidden state), Tables 1-3 show that, for the Reddit dataset, the test performance of ASLM-LHS has an obvious advantage compared to that of ASLM-AP, while they are at the same level for the other four datasets. As mentioned above, the model's subsequent training process benefits from the last hidden state method by receiving the
## 3 System Design
### Algorithm  
In our proposed solution, the user-side ICN routers bound with UBSID function as backup nodes for ICN network transmission, thereby enhancing the scalability of ICN transmission. To fully utilize the idle bandwidth resources on the user side, it is crucial to select the user-side ICN routers with optimal states to assist in packet forwarding. To address this, we introduce a node selection strategy based on the resources and network status at the user side. Considering the dynamic nature of user-side nodes, we have integrated a blockchain consensus algorithm into our design to incentivize users and extend their online time by encouraging them to contribute network resources. This incentive mechanism operates similarly to Filecoin's storage \"mining\" mechanism, allowing users to \"mine\" by forwarding data packets.
## 3 Mobility-Aware Duty Cycling
### Significant Nodes  
The first step in this study is to identify the significant nodes with which the MSN shall communicate directly as it travels along its path. These nodes now take on the role of hotspot,with responsibility for communication with the sink node spread amongst them. We aim to reduce energy consumption amongst these significant nodes the MSN communicates with via one-hop, while also improving the number of frames delivered to the sink node, or at least keeping that figure within a reasonable boundary.
# The Generalized Mohr-Coulomb Failure Criterion
## 6 Comparisons with Commonly Used Failure Criteria
### Comparisons of Shear Strength  
Now let us compare the capability for the four criteria of M-C, L-D, SMP, and GMC-L, respectively, to predict the shear strength.  
Figure 7 illustrates the images of \(\phi_{b}\)-\(b\) given by the four criteria, respectively, suggesting that:  
1. By and large, the friction angle \(\phi_{b}\) from the test increases with the increase in \(b\);
2. The M-C criterion is accurate only under the conventional triaxial condition, underestimating the friction angle to a bigger \(b\);
3. The L-D criterion overestimates the contribution of \(b\) to the friction angle, particularly for \(b\approx 0.4\);
4. If \(0\leq b<0.5\), the SMP criterion is relatively consistent with the test data, but if \(0.5\leq b\leq 1\), it underestimates the contribution of \(b\) to soil strength;
5. The GMC-L criterion matches the test data best even if the simplest linear interpolation is employed to the data of \((c_{0},f_{0})\) at \(b=0\) and \((c_{1},f_{1})\) at \(b=1\). The two groups of data can be directly measured with conventional triaxial tests.  
Figure 7: _Cont._  
Figures 8 and 9 display more images of \(\phi_{b}\) by the four failure criteria, implicating that while GMC-L is not very good at fitting experimental data, GMC-Q is always able to approximate them very well.  
For cohesive soil, Figure 10 shows the prediction results of GMC-L for the strength parameters of loess with different moisture contents (w), while Figure 11 shows the images of \(\phi_{b}\) and \(c_{b}\) by using GMC-Q to some true triaxial tests from [PERSON] [49]. From the comparisons with the experimental data, it is observed that GMC is suitable for the prediction of the cohesion as well as the friction angle.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 4 Interfaces and Protocols in QKD Networks Enabled by SDN  
### Related Interfaces  
As shown in Figure 4, the main interfaces in controller layer are listed at below.  
(a) QKD Control Interface (Q-CI): It is between the controller and the QKD devices for control and management. The controller can manage QKD resources in QKD layer through this interface to connect with multiple QKD devices.  
(b) Controller Interaction Interface (C-II): It is used for the interaction between controllers. Since one controller can provide multiple I-CPI interfaces, allowing simultaneous communication with multiple controllers.  
(c) Application Control Interface (A-CI): It serves the interactions from application demands to the controller.
## 2 Image Processing in Remote Sensing  
There exist numerous methods and algorithms available for satellite image processing. The satellite image processing techniques discussed are enhancement, feature extraction, segmentation, fusion, change detection, compression, classification and feature detection which is represented in Figure 1.  
Figure 1: Image processing framework.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## Appendix A  
In this section, we present a detailed flowchart of Algorithm 1 for extracting the damping ratio from multiple impulse response signals. The accompanying flowchart (Figure A1) illustrates the sequential steps involved, beginning with the acquisition of multiple signals and their FFT-based processing, and continuing through the application of the continuous time wavelet transform. Both algorithms utilize the maximum energy time point in the wavelet domain to align and normalize the signals. However, the CTWT-1 method employs complex-valued wavelet coefficients, while the CTWT-2 method uses absolute values.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 4 Sensor Deployment Files
### Sensor Instance  
The Sensor Instance models a specific sensor by inheriting a Sensor Model and expanding it with static metadata related to a particular instance (such as unique ID) alongside with dynamic deployment information (such as position). A sensor and its host observation platform may be deployed in remote regions with low bandwidth communication, or no communication link at all. Therefore, a Sensor Instance cannot reference an on-line resource containing its Sensor Model description. Instead it expands a sensor model, resulting in a single file containing all the sensor's metadata. Its model is shown in Figure 8.  
Figure 8: Sensor Instance UML diagram. It inherits a sensor model and expands it with information related to a sensor instance alongside with information related to a specific deployment. The gray elements represent all the required information by the SWE Bridge (compulsory ) while the blue elements represent optional metadata (used to register the sensor to a SOS Instance).  
Figure 7: UML diagram of the Sensor Model. The gray elements represent all required information by the SWE Bridge (compulsory) while the blue elements represent optional metadata (used to register the sensor to a Sensor Observation Service (SOS) Instance).  
From the operational point of view, the more important part of the Sensor Instance is the _DataInterface_. It expands the generic definition of the Sensor Model, defining the parameters required to establish a communication link from the observation platform to the sensor.  
In the Sensor Instance model it is possible to include different elements that may be useful to register, discover and exploit the generated data (blue elements). Some of them are the _UniquelD_, _attachedTo_ (reference to the observation platform where the sensor is deployed), position (spatial position where the sensor is deployed), _FeatureOfInterest_ (a computational representation of the real world phenomenon being observed by the sensor).
## 4 Discussion
### Performance Analysis
#### 4.2.2 Comparative Experiment of DSEF-DenseNet-DT
is slightly below that of the SD-DT method, the other three performance metrics are superior to those of the CTD-DT and SD-DT methods. This is sufficient evidence that the deep learning network has an excellent capability for extracting detailed picture features, and the deep learning network can adaptively extract the features required by the classification model to improve the recognition accuracy of the classification model. The effectiveness and superiority of the proposed method are shown by comparing CTD-DT, SD-DT, DenseNet-DT and shallow-feature-fusion-based DT (SFF-DT) methods with DSFF-DenseNet-DT. The comparison outcomes are shown in Table 8. It is easy to find that the DenseNet-DT method is superior to the CTD-DT and SD-DT methods. This is primarily due to the fact that deep neural networks are more effective at extracting certain features that help classifiers. We also found that the accuracy, precision and recall of the SFF-DT method all reached 84%, which was at least 5.3%, 3.9% and 7.35% higher than those of the other two methods, respectively. The F1 score reached 0.847, an improvement of at least 0.058. The SFF-DT method performs significantly better than CTD-DT and SD-DT. This is because when color textures and size features are fused, the fused features have more information that is useful for classifier classification. Finally, it can be found that DSFF-DenseNet-DT outperforms the other methods in four evaluation metrics: the accuracy, precision and recall are improved by at least 6.67%, 9.2% and 9.6%, and the F1 score is improved by at least 0.094. On the one hand, deep features can provide more detailed information to the classifier. On the other hand, multi-feature fusion can make each feature undergo complementary fusion and provide the classifier with more valuable data. Therefore, the proposed method DSFF-DenseNet-DT is effective for mineral flotation process working condition recognition and is superior in performance to other existing methods.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers  
In this section, the proposed pseudolite-based positioning system will be described.