esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Isothermal Static Adsorption  
Static adsorption tests of NPs on a calcite surface were conducted with DIW and SSW. Figure 3a shows that the adsorption of the NPs on the calcite surface increases with the NP concentration. Figure 3a also shows that at a lower concentration of NPs prepared in SSW, adsorption is higher than in DIW (\(\approx\)40%). At higher NP concentrations, however, adsorption in SSW and DIW was almost the same. [PERSON], et al. [34] have reported an increase in adsorption of unmodified silica NPs on a calcite surface with increasing salinity (0-0.2 M) of single salt (NaCl) brine at low NP concentrations (0.4 and 0.6 g/L).  
Zeta potential of the NPs becomes less negative in the presence of SSW ions (Figure 2) owing to the compression of the double layer. This would lower the electrostatic repulsion and enhance the adsorption. Figure 3b shows an SEM image of a chalk core which was vacuum-treated with 1 g/L NF prepared in DIW. The image was taken along the injection plane. In DIW, the average size of NPs is about 38 nm (at 25 \({}^{\circ}\)C). However, some of the NPs may have been agglomerated during handling. In general, NPs are shown to be spread on the chalk surface in a similar way to the observations made by [PERSON], et al. [34]. No pore throat blockage was observed from SEM imaging. The SEM images were done on spots along horizontally cut core. However, they are small fractions of the whole core.  
Figure 3: (**a**) Adsorption of silica NPs on Calcite surface. (**b**) SEM image of NPs adsorbed on chalk core with DP 9711 nanofluid at 1 g/L concentration prepared in DIW.  
Figure 2: (**a**) Average particle size of the NPs dispersed in deionized water (DIW), low salinity water (LSW) and synthetic seawater (SSW) measured at varying temperatures. (**b**) Zeta potential measurements (25 \({}^{\circ}\)C) of the NPs in DIW, LSW and SSW.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 3 Methodology
### Inertial Sensor Signals  
According to a recent survey conducted by [2], inertial sensors are the most used for HAR systems. This set of sensors, compared to the video cameras, captures body movements effectively without posing any privacy issues. The accelerometer, gyroscope, and magnetometer are the most popular examples of inertial sensors, which are now freely integrated into smartphones, smartwatches, and clothes. In this work, we utilized sensing signals from the 3 inertial sensors to train the baseline model, just like many other studies [39, 65]. The raw signals from the inertial sensors were preprocessed and fed into the convolutional layers for feature extraction.  
The accelerometer, for example, is used to measure acceleration, the same as the rate of change of velocity of an object. The unit of measurement is meters per second squared (m/s\({}^{2}\)) along the 3 axes (x, y, and z). Therefore, each sample is made up of a tri-variate time series. The gyroscope is another motion sensor used for measuring orientation and angular velocity. The unit of measurement is degree per second (\({}^{\circ}\)/s). In contrast to the accelerometer that measures changes in linear velocity, the gyroscope is a more advanced sensor that captures angular and lateral orientations of an object. Lastly, the magnetometer is a motion sensor often installed together with an accelerometer and gyroscope into an inertial unit. It helps measure changes in the magnetic field at specific locations. Recently, the magnetometer has become common sensing equipment in mobile devices to detect orientation relative to the Earth's magnetic north [2]. Similarly, the magnetometer captures signals in three axes as the accelerometer and gyroscope.
## 2 Materials and Methods  
### Study Area  
Kumejima Island (59 km\({}^{2}\)) is about 100 km west of the main Okinawa Island (Figure 1) and is located in a subtropical climate [36]. The mean annual air temperature is 23.0 \({}^{\circ}\)C, annual rainfall is 2111.8 mm, 44% of which falls in the rainy season from May to June or is associated with typhoons in August and September (Japan Meteorological Agency, [[http://www.data.jma.go.jp/obd/stats/etrn/](http://www.data.jma.go.jp/obd/stats/etrn/)]([http://www.data.jma.go.jp/obd/stats/etrn/](http://www.data.jma.go.jp/obd/stats/etrn/))). The central part of the island is composed of volcanic rock and reaches a maximum altitude of 310 m above sea level [36; 47].  
The study area was an agricultural area of 17.1 km\({}^{2}\) (Ministry of Agriculture, Forestry and Fisheries, Japan, MAFF; [[https://www.maff.go.jp/j/tokei/kouhyou/sakumotu/mensekj/#c](https://www.maff.go.jp/j/tokei/kouhyou/sakumotu/mensekj/#c)]([https://www.maff.go.jp/j/tokei/kouhyou/sakumotu/mensekj/#c](https://www.maff.go.jp/j/tokei/kouhyou/sakumotu/mensekj/#c))), which is covered by sugarcane, pasture, purple yam, pineapple, agricultural facilities such as greenhouses, and other crops such as squash, okra, and rice. Sugarcane is the predominant crop [36]. On Kumejima Island, there are three key sugarcane management practices (Table 1): (1) Ratooning, in which sugarcane grows from sprouts of underground stubble left in the field after harvest of the main crop (i.e., ratoon), and is harvested about every 12 months, usually for at least 4 years, and then the crop is renewed due to decreasing yield; (2) spring planting of seedlings (between February and May, just after harvesting); and (3) summer planting of seedlings (between July and October). The sugar industry equipment usually operates from January to March. In ratooning, sugarcane is harvested 12 months after harvesting in previous year, in spring planting it is harvested 12 months after planting, and in summer planting it is harvested in the sugar-production period 18 months after planting. About 63% of farmers manage a sugarcane area of less than 1 ha [48].  
Figure 1: Regional location map (inset) and Planet Dove image of Kumejima Island, Okinawa, Japan.  
Cultivation of purple yam for confectionery products and as feed for meat calf husbandry is also active, and new pastures are being developed in some parts of the island. Pineapples were cultivated extensively on the island in the 1960s and 1970s, but are now grown only by some farmers and in private gardens. Flowers, bitter gourd, mango, and banana are cultivated in agricultural facilities such as greenhouses. Precise classification is required for various crop type fields on the island; this need also provides us an opportunity to evaluate the performance of classification of crop types and practices using high-frequency and high-spatial imagery from a satellite constellation.
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 4 Comparison and Analysis of Calculation Results and Monitoring Data
### Support Axial Force Analysis  
The calculated results of the maximum axial force of the support in the cases of prestress loss rates of 0% and 10% in the numerical calculation are extracted and compared with the measured axial force value, as shown in Figure 14. It can be seen that the measured axial force of the support in the excavation process is in good agreement with the numerical simulation results, and the axial force of the support when the excavation is completed (358.6 kN) is reduced by about 7.6% compared with the calculated axial force without prestress loss (387.9 kN).  
Figure 13: Comparison and analysis of the measured and calculated horizontal displacement of the pile body.
## 2 Graph Neural Networks
### Graph Convolution Networks  
The graph convolutional network (GCN) architecture serves as a fundamental paradigm for graph neural networks (GNNs), which was introduced in [3]. The primary objective of this architecture is to develop a computationally efficient version of convolutional neural networks (CNNs) for graph-based data. Specifically, it aims to approximate the graph convolution operation in graph signal processing. The GCN has emerged as a widely used and flexible architecture in various scientific domains, and it is frequently employed as a benchmark for graph data analysis. In comparison to tabular or image data, graph data exhibit varying numbers of neighbors for each node, rendering traditional graph neural networks inadequate. This discrepancy in neighborhood size poses a significant challenge that must be addressed. One solution involves dividing node embeddings by their respective degrees, i.e., the number of edges incident to each node. This process, known as degree normalization, ensures a fair comparison of nodes despite their differing numbers of neighbors. Degree normalization is accomplished via matrix multiplication, whereby each node embedding in the graph is multiplied by the degree matrix raised to the power of \(-1/2\) as can be seen in Figure 3. The resulting normalized graph representation accounts for the variability in neighborhood size and enables effective analysis. The mathematical relation for computing vector representations for the nodes of a graph is now as follows:  
\[H=\tilde{D}^{-1/2}\tilde{A}^{T}\tilde{D}^{-1/2}XW^{T}\]  
where \(\tilde{D}=D+I\) with \(D\) being the degree matrix for each node.  
Figure 2: Illustration of the input graph (**left**) and the corresponding computation graph (**right**) depicting the process by which a graph neural network (GNN) computes the vector representation of node E by aggregating information from its neighboring nodes.
## 4 Experiments
### Experimental Setup Details
#### 4.1.2 Datasets  
We utilize the widely recognized public dataset Flare7k++ [39] for training and testing. Additionally, to evaluate the generalization capability of our glare removal model, we construct a nighttime glare image dataset named NRGD, with example images illustrated in Figures 5 and 6. NRGD is collected by capturing videos in real nighttime road and street scenarios using a Vivo S19 smartphone with a 0.6\(\times\) ultra-wide-angle setting to capture glare regions caused by strong light sources such as headlights and streetlights, ensuring diversity and realism in the captured scenes. The videos are recorded at a resolution of \(1920\times 1080\) to maintain clarity while facilitating subsequent processing and frame extraction. To obtain a uniform and diverse frame distribution, frames are extracted at 3-s intervals, resulting in approximately 500 images for model training and evaluation. Among them, images without obvious glare are paired with synthetically generated glare counterparts using the method described in Section 3.2, forming paired glare/non-glare data, as shown in Figure 5, where (a), (b), (c), and (d) are four pairs of clean images and their corresponding synthetic glare images. Meanwhile, images with noticeable glare are used as unpaired data, as shown in Figure 6, where (a), (b), (c), and (d) are four examples of unpaired images from the NRGD.
## 5 Results and Discussion
### MDDA with LC Integration Scenario  
In the current scenario, the LC algorithm was employed to improved the position's RMSE. The MDDA-LC algorithm improves the 2D and 3D position RMSE by 25% and 66.8%, respectively, over the solution from all visible satellites based on the Google weighting matrix in the case of using only the receiver and about 8% improvement in both against the solution from all satellites with LC integration. Additionally, it improved the limited receiver with only 10 channels 10 MDDA-LC by 24% in 2D and 40% in 3D position RMSE over 10 MDDA with a receiver only, shown in Table 2, and about 18% and 4% in 3D position RMSE over 10 EL-LC and 10 CNR-LC, respectively. The results are shown in Table 3.  
In Figures 9 and 10, the 2D and 3D position RMSE values are plotted, with data points sampled at 30-second intervals. The results clearly demonstrate the superior performance of the MDDA-LC algorithm compared to other algorithms, as it consistently achieved the lowest position RMSE.  
Additionally, Figure 11 includes an illustration of the cumulative distribution function (CDF) curve with the 3D position RMSE for LC integration. The comparison indicates that the MDDA, when compared with all satellites on the horizon, exhibited the highest accuracy. Furthermore, when confronted with reducing the number of satellites to match the maximum receiver channels, the MDDA outperformed other techniques, demonstrating superior performance.
## 3 Design of IPFC Controller
### Predictive Functional Control (PFC) Algorithm
According to Equation (21), the expression of the model state at the time \((k+i)\) can be derived as:  
\[ \begin{array}{l}X_{m}(k+i)=A_{m}^{i}X_{m}(k)+A_{m}^{i-1}B_{m}u(k)+A_{m}^{i-2 }B_{m}u(k-1)+\cdots\\ \hskip 14.226378 pt+A_{m}B_{m}u(k+i-2)+B_{m}u(k+i-1)\end{array}  \tag{22}\]  
According to Equations (18), (19), and (22), the output of the model at the time \((k+i)\) can be obtained by recursion as follows:  
\[y_{m}(k+i)=C_{m}A_{m}^{i}X_{m}(k)+\mu^{T}(k)g_{k}(i) \tag{23}\]  
where,  
\[\left\{ \begin{array}{l}\mu(k)=\left[\mu_{1}(k)\mu_{2}(k)\cdots\mu_{N}(k) \right]^{T}\\ g_{k}(i)=\left[g_{k1}(i)g_{k2}(i)\cdots g_{kN}(i)\right]\\ g_{kj}(i)=C_{m}A_{m}^{i-1}B_{m}f_{j}(0)+C_{m}A_{m}^{i-2}B_{m}f_{j}(1)+\cdots+ C_{m}B_{m}f_{j}(i-1)\end{array} \right. \tag{24}\]  
For a stable system, the first-order exponential form is generally chosen to describe the reference trajectory, namely:  
\[y_{r}(k+i)=y_{c}(k+i)-\beta^{i}(y_{c}(k)-y(k))\ \ \ i=1,2,\cdots,h-1 \tag{25}\]  
where, \(y_{r}(k+i)\) is the reference trajectory value at the sampling time of \((k+i)\), \(y_{c}(k+i)\) is the quasi-tracking set value of the system at the sampling time of \((k+i)\), \(y(k)\) is the actual output response value of the system at the sampling time of \(k\). \(\beta\) is the softening coefficient of the reference trajectory and satisfies \(\beta=e^{-3T_{s}/T_{r}}\), \(\beta\in(0,1)\). Where, \(T_{s}\) is the sampling period and \(T_{r}\) is the desired system closed-loop response time. \(\beta\) is the rate at which the predicted output of the regulation system tracks the setpoint approach. The optimized performance index of predictive functional control involves the future behavior of the system. The performance index can be used to obtain the control input. Generally, the size of the current and future control inputs of the controlled object can be determined based on the performance index. These control inputs will make the predictive model output of the controlled object follow the reference trajectory to achieve the ultimate goal of making the actual output of the system track the set value. The usual optimization criterion is to minimize the sum of squares of the reference trajectory at the optimization point and the prediction error output of the prediction process. The expression of the optimization objective function in this paper is shown in Equation (26). \[J(k)=\text{min}\underset{i=1}{\overset{H}{\sum}}[y(k+i)-y_{r}(k+i)]^{2} \tag{26}\]  
where: \(H\) refers to the predictive time-domain length, which mainly affects the stability and robustness of the control. \[y(k+i)=y_{m}(k+i)+e(k+i) \tag{27}\]  
where, \(y(k+i)\) is the system output; \(y_{m}(k+i)\) is the output of the generalized predictive model; \(e(k+i)\) is a systematic error.
## 4 Analysis  
### Test Data  
The \(E_{m}\) values determined by the PMTs were between 7.8 and 864.2 MPa and generally increased with depth at each test site. The \(E_{m}\) at site A dramatically increased and those at site B decreased with depth below 29 m (Figure 4a). This tendency was also found for \(P_{L}\) (Figure 4b). The SPT-\(N_{60}\) determined by linear extrapolation conversion for a penetration of 30 cm corresponded to blows of between 61 and 644 at the three sites. Overall, SPT-\(N_{60}\) tended to increase with increasing depth at each test site, but those at site B decreased with depth below 29 m (Figure 5). As shown in Figures 4 and 5, the measured geotechnical properties showed different distribution due to different site conditions. Site A is near the riverside, so that it is rarely affected by the geological forces making fold and fissure. On the other hand, sites B and C are located in mountain areas that experienced complex geological forces (Figure 1).  
The \(VR\) values, indicating the degree of weathering generally decreased with increasing depth at each test site. Weathering was affected by the vertical effective stress. Therefore, to exclude the effects resulted from the different depth and thickness at which the weathered granite layer was developed, \(VR\) was normalized using vertical effective stress and atmospheric pressure (Figure 6b); the vertical effective stress was calculated based on the results of in-situ density logging conducted at each test site. This is consistent with the results suggested in many studiesand the theoretical trend of \(VR\), that is, the increase of \(VR\) with greaterweathering [25; 26; 27; 28]. In this study, nonlinear multiple regression was used to estimate \(E_{m}\) and \(P_{L}\) based on SPT-\(N_{60}\) and the _normalized VR_. The details of the analysis and the results are presented in the next section.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \(E_{m}\) & \(P_{L}\) & \(\text{SPT-}N_{60}\) & \multirow{2}{*}{_VR_} & _Normalized_ \\  & **(MPa)** & **(MPa)** & **(Blows)** & & _VR_* \\ \hline  
\hline \hline \end{tabular}  
* \(\frac{VR}{\sigma_{v}^{\prime}/\text{P}_{\text{\boldmath$\beta$}}}\), \(\sigma_{v}^{\prime}\) = vertical effective stress, \(Pa\) = 1 atm.  
\end{table}
Table 1: Data for nonlinear multiple regression analysis.  
Figure 4: Variations of the (**a**) pressuremeter modulus and (**b**) limit pressure with depth.  
Figure 5: Variation of SPT-\(N_{60}\) with depth.  
Figure 6: Variations of (**a**) \(VR\) and (**b**) _normalized VR_ with depth.
## 2 Introduction to the Concept with Milestone Questions
### What Is the Nature of the Bone-to-Implant Connection?  
In the original experiment related to osseointegration, [PERSON] et al. [16; 17] found that the bone's surface had grown towards the titanium device which had been placed inside the bone. Both surfaces were congruent and their device was polished. However, it was very easy to detach them from each other as the surfaces were not glued. Later, the endosseous surfaces of oral implants started being roughened with the intention to increase the surface contact area and to thereby increase the load transmission possibilities. What actually is increased with these surfaces is the removal torque [18; 19; 20]. [PERSON] and [PERSON] reviewed existing paradigms in implant dentistry, and on the basis of their findings concluded that bone-to-implant contact is greater for implants with rougher surfaces and/or they require greater forces to be separated from the bone than implants with smoother surfaces [21]. With the older polished implants (without macro-retention like bone-bays) used at that time (the 1970s/1980s), it was possible to remove the implant, while the peri-implant cortical layer remained intact and in the bone, and likewise, the same implant could have been screwed back in and put back into function [20; 22]. This is not possible with the present roughened implant devices, but this should not lead to the assumption  
Figure 2: Schematic presentation of a cyst (yellow circle) and an integrated implant (grey circle) inside the bone. The cyst is a foreign body which is surrounded by a cortical bone because it disrupts the continuity of the bone structure. Likewise, the endosseous implant represents such a disruption. Both foreign bodies are extra-territorialized by a cortical bone.  
that the bone bonds to such implant surfaces. This is clearly visible during the process of preparing histology specimens and how easily the \"well and long-term integrated\" implant body detaches from the surrounding bone, i.e., the \"osseointegarted\" metallic part just falls off the bone as soon as macro-retention is taken away [22]. Between 1982 and 1987, [PERSON] et al. conducted a series of experiments, in which they demonstrated that it is possible to osseointegrate commercially pure titanium, alloys of titanium with zirconium and niobium metals, whereas it was not possible for gold or stainless steel [23]. Today, we put more interest in the type and stability of the oxide layer created at the implant surface and its interaction with bone.
# Understanding Balance Control in the Context of Riding a Personal Mobility Device
## 2 Methods
### Measures
#### 2.5.1 Weight-Shifting Task  
In the weight-shifting task, the COP movement was classified into two phases, namely primary submovement (PSM) and secondary submovement (SSM), to investigate the fluency and accuracy of the movement, respectively (Figure 3). The PSM is defined as the COP movement until the COP reaches a goal target for the first time. The SSM is the COP movement following the PSM until the trial is completed (i.e., when the COP remains in a goal target for 0.5 s). Ten outcome measures were derived from the COP movement data.  
Time measures  
In the perspective of the COP movement time, the time taken to complete a trial is referred to as the movement time (MT). Moreover, the time taken to reach a goal target is called the movement time of the PSM (MT\({}_{\text{PMS}}\)).  
Fluency measures  
In terms of fluency, four outcome measures were derived based on the COP trajectory of the PSM. The total excursion of the PSM (TE\({}_{\text{PMS}}\)) is defined as the COP trajectory length until the COP reaches a goal target. The maximal deviation (MD) indicates the farthest  
Figure 3: Schematic overview of the COP movement trajectory.  
distance in the off-axis of the PSM. The directional control (DC), which expresses the degree of COP control of the PSM, is the difference between the on-axis COP movement length and the off-axis COP movement length divided by the on-axis movement length in the PSM expressed as a percentage [35]. The non-minimum phase (NMP) behavior, which explains the COP movement in the direction opposite to the intended direction at the initiation of the weight-shifting trial based on control theory [36], was applied to derive the magnitude of the NMP (d\({}_{\text{NMP}}\)), which is the distance from the initial COP point to the farthest point in the NMP.
* Accuracy measures  
Four outcome measures were derived from the SSM in terms of accuracy of the COP movement. The total excursion of the SSM (TE\({}_{\text{SSM}}\)) is defined as the COP trajectory length until a trial is completed in the SSM. The dwelling time (DT) is the time taken until a trial is completed in the SSM. The maximum COP excursion (ME) indicates the farthest distance along the on-axis from the target COP point during the COP movement in each trial. The target area (TA) was derived from the 95% confidence ellipse area of the COP movements in the SSM.
* Subjective measures  
The following four items were subjectively evaluated for each trial based on a 5-point Likert scale (Appendix A): perceived performance (PP), ease of task (ET), perceived stability (PS), and physical comfort (PC). A higher value indicates that the participant felt strongly agree to the four question items respectively when shifting the participant's weights to the target COP location.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Fuzzy Logic
#### 3.1.3 Leach-Fl  
LEACH-FL is an improved version of LEACH using FL [33]. It has a similar mechanism with LCH but the variables (parameters) used are battery level, distance from sink, and the node density. Election of CH is centralized on the sink, which has to calculate the chance of nodes becoming the CH like in LCH. LEACH-FL has three main parts: fuzzification module (four functions), an inference engine, and a defuzzification module. The authors of [33] compare LEACH-FL and LEACH, the experimentations have shown that the proposed algorithm has a lower energy consumption rate than LEACH. The network lifetime of the network using LEACH-FL outperforms the network lifetime when using LEACH.