esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparative Analysis of PFS-XGB vs. PFS-RF vs. PFS-NN  
In general, the fluctuation of the predicted production capacity data of these three pipelines is smaller than that of the three pipelines PS-XGB, PS-RF, and PS-NN. Simultaneously, the differences in production prediction trends among the three pipelines are minimal, but there are significant differences in the production prediction of some wells in the pipeline. For example, between 1717 and 1720 m in Well 3, the PFS-XGB and PFS-RF  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **PCC** & **RMSE** & **SD** \\ \hline \multirow{8}{*}{\(\bigstar\)} & Reference & 1 & 0 & 192.2 \\  & PS-XGB & 0.89 & 88.51 & 159.77 \\  & PS-RF & 0.861 & 101.78 & 137.29 \\  & PS-NN & 0.94 & 66.22 & 173.24 \\  & PFS-XGB & 0.933 & 70.32 & 168.11 \\  & PFS-RF & 0.916 & 81.86 & 148.87 \\  & PFS-NN & 0.979 & 39.57 & 181.52 \\  & PR-XGB & 0.875 & 93.24 & 164.66 \\  & PR-RF & 0.86 & 102.93 & 134.86 \\  & PR-NN & 0.927 & 74.79 & 156.18 \\  & PFR-XGB & 0.929 & 74.67 & 156.25 \\  & PFR-RF & 0.908 & 84.63 & 148.45 \\  & PFR-NN & 0.974 & 44.47 & 177.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Taylor diagram evaluation index list.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **R2** & **MAE** & **MSE** \\ \hline \multirow{8}{*}{\(\bigstar\)} & PS-XGB & 0.79 & 42.43 & 7834.73 \\  & PS-RF & 0.72 & 41.33 & 10,360.13 \\  & PS-NN & 0.88 & 17.44 & 4385.2 \\  & PFS-XGB & 0.87 & 29.99 & 4944.32 \\  & PFS-RF & 0.82 & 29.39 & 6700.64 \\  & PFS-NN & 0.96 & 9.03 & 1565.52 \\  & PR-XGB & 0.76 & 50.17 & 8693.67 \\  & PR-RF & 0.71 & 42.96 & 10,594.91 \\  & PR-NN & 0.85 & 22.21 & 5672.91 \\  & PFR-XGB & 0.85 & 31.21 & 5575.96 \\  & PFR-RF & 0.81 & 30.57 & 7162.82 \\  & PFR-NN & 0.95 & 10.8 & 1978 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pipelines standard evaluation index list.  
forecasts are good. Still, in PFS-NN, the productivity forecast surges to 2000 m\({}^{3}\)/d, which may be related to the strategic optimization of the neural network parameters. In Well 25, an elevated predicted productivity value emerges at shallower depths, yet the predictions from all three pipelines align closely at greater depths. The shallow depth predictions for Well 30 significantly deviate from the real values. An observable production fluctuation between depths of 1690 and 1692 m in Well 4 eludes the predictive capabilities of all three pipelines. In Well 21, the predicted values substantially exceed the real production. Conversely, the trio of pipelines demonstrates commendable accuracy in forecasting the productivity for Wells 20 and 27. This could be attributed to a more straightforward relationship between logging parameters and productivity for this particular well.
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 2 Theoretical Analysis of Displacement-Dependent Damping Inerter System (DDIS)
### Energy Balance Analysis  
From the perspective of energy balance evaluation, a part of the excitation-induced input vibrational energy is dissipated by the primary structure, and the rest is dissipated by the DDIS via the DDE. Equation (7) can be used to calculate the energy balance in the SDOF structure equippedwith the DDIS by premultiplying with \(\mathbf{u}^{T}\) and integrating over the time domain. The energy balance equation in the time domain is given by:  
\[e_{total}(t)=e_{k}(t)+e_{e,s}(t)+e_{d}(t)+e_{DDIS}(t) \tag{13}\]  
where the total input energy \(e_{total}(t)\) is composed of structural kinetic energy \(e_{k}(t)\), structural elastic strain energy \(e_{e,s}(t)\), structural inherent damping dissipated energy \(e_{d}(t)\), and DDIS-dissipated energy \(e_{HDIS}(t)\). With particular regard to the three elements of the DDIS, \(e_{DDIS}(t)\) is the result of kinetic energy \(e_{k,DDIS}(t)\), elastic strain energy \(e_{e,DDIS}(t)\), and DDE-dissipated energy \(e_{d,DDIS}(t)\). Because the input excitation is assumed to be a stochastic process, the energy response should be evaluated stochastically by applying the expectation operator to Equation (13). On the basis of the stationarity hypothesis, the expected values of \(e_{e,DDIS}(t)\), \(e_{k,DDIS}(t)\), \(e_{k}(t)\), and \(e_{e,s}(t)\) are all zero. Hence, the stochastically expected value of Equation (13) is given by:  
\[\mathrm{E}[e_{total}(t)]=\mathrm{E}[e_{d}(t)]+\mathrm{E}\Big{[}e_{d,DDIS}(t) \Big{]}=c\sigma_{ii}^{2}+(1-\alpha)k_{d}\sigma_{z_{ii}\dot{u}_{d}} \tag{14}\]  
where \(\sigma_{ii}^{2}\) and \(\sigma_{z_{ii}\dot{u}_{d}}\) are respectively the variance of the structural velocity and the cross-variance of \(z_{h}\) and \(\dot{u}_{d}\).
## 4 Experiments and Analysis  
### Experimental Setup  
The physical setup of the polarized image acquisition system is shown in Figure 6. The experimental system mainly consisted of a light source, a polarization camera, maritime targets, and a computer. The light source illuminated the target's surface, and the reflected light was captured by the polarization camera. The acquired digital images were subsequently transmitted to a computer for processing. The sun served as the light source, and the polarization camera was a division of the focal plane polarized imaging system, model OR-250 CNC-P (Milltronics, Waconia, MN, USA). Figure 6b shows the lens, which has a diameter of 50 mm and a relative aperture of 1:2.8; Figure 6c shows the camera and lens assembly; Figure 6d displays the camera's imaging interface; and Figure 7 illustrates the pixel arrangement in the polarization detector.  
To obtain experimental data, the polarization camera captured images of the seaside on a sunny day. To ensure high-quality image data, the camera was stabilized on a tripod with a gimbal. The resolution of the images is 2448 \(\times\) 2048, with the adjacent 4 pixels arranged in a 2 \(\times\) 2 grid corresponding to the polarization directions of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\), respectively. The raw image was split into four polarized images--0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\)--each with a resolution of 1224 \(\times\) 1024. The code of the proposed 3D reconstruction algorithm was implemented in Python and executed on a laptop with a 12 th Gen Intel (R) Core (TM) i7-12700H 2.30 GHz (Intel Corporation, Santa Clara, CA, USA).  
Figure 5: The degree of polarization of the mixed reflected light in relation to the refractive index and the angle of incidence.
# Fall Recognition Based on Time-Level Decision Fusion Classification
## 2 Related Work
### Vision-Based Methods  
Advancements in deep learning have resulted in significant progress in computer vision technology. Consequently, there has been a notable increase in research aimed at utilizing RGB and RGB-D cameras to recognize human actions and detect falls [14; 15; 16; 17; 18; 19; 20; 21; 22]. These studies utilized the capabilities of deep learning algorithms to process visual data, facilitating the recognition of human behavior and detection of falls with enhanced accuracy and efficiency. In [14], human behavior was recognized by utilizing estimated human poses from an RGB-D sensor. In [15], fall detection was performed by extracting human silhouettes from RGB-D images through background removal. In [16], fall detection was performed by obtaining 3D skeleton values from RGB-D images. With the advancement of pose estimation algorithms for 2D images, the authors of [17; 18; 19; 20; 21] employed RGB images for fall detection. They estimated human poses from RGB images and used these estimations to detect falls. In [17], a recurrent neural network (RNN) utilizing a pose vector as the input was employed for fall detection. In [18], fall detection was conducted using random forest, SVM, and multilayer perceptron (MLP), and their respective performances were compared. The authors of [19; 20] employed a spatial temporal graph convolutional network (ST-GCN) for fall detection. Especially in [20], a pre-trained ST-GCN on action recognition data, not fall data, was utilized to extract features for action. Subsequently, the extracted features were used as input to the encoder-decoder model, and if the restored features from the decoder did not exceed a specified threshold, they were recognized as a fall. Furthermore, refs. [21; 22] detected falls using feature-level fusion and a 3D-CNN. Vision-based fall detection can utilize the global posture of a person in an image to detect falls. Long-distance fall detection using a vision sensor is advantageous for user convenience, and the integration with existing surveillance cameras allows for cost reduction by minimizing the need for additional equipment.
## 4 Proposal for an Ontology Building Process
### Evaluation Stage
#### 4.3.1 Data Sample Mapping and Ingestion  
The data sample mapping and ingestion stage is the first step in the ontology evaluation. This phase involves loading a small sample of the available data to validate the ontology's structure. The primary objective is to ensure that the concepts within the ontology are accurately mapped and that all attributes of interest are appropriately assigned to relevant classes, along with their corresponding relationships.  
During this stage, a subset of data must be ingested into the implemented ontology. These sample data act as a litmus test, allowing developers to verify that the ontology can accommodate the full scope of data attributes intended for future use. If the ontology is correctly structured, all significant attributes and relationships within the sample should be representable within the ontology's defined classes and relationships.  
The success of this stage is crucial, as it provides confidence that the ontology is robust and flexible enough to handle the actual data in production. Any discrepancies or gaps identified during this phase can be addressed before full-scale data integration, thereby ensuring the ontology's effectiveness in representing the domain knowledge accurately.
## 6 Numerical Simulation of Monitoring Equipment in Non-Cohesive Soil
### Analysis of Computational Results
This suggests that both iron-based and PVC-based linear heat sources exhibit good thermal conductivity with cohesive and non-cohesive soils, but the difference in the temperature field's impact range between the two material types was relatively small. This conclusion provides data support for the interference-free distance in subsequent experiments and field deployments of erosion and sedimentation monitoring equipment on bank slopes. This study employed numerical simulation to analyze the temperature rise and propagation range in eight types of clay and six types of non-clay soils due to linear heat sources with two different materials. It verified the feasibility of identifying the water-soil interface of dock slopes based on heat transfer principles, providing a foundation for the design of subsequent monitoring sensors. It is noteworthy that when applying the research findings in engineering, one should first sample and test the soil parameters at the engineering site. After obtaining accurate parameters, the temperature difference threshold of the linear heat source sensor at the water-soil interface can be determined based on the relevant research results, thereby enabling the monitoring of sedimentation changes. Conceptualization, J.Y. and H.Z. ; methodology, J.Y. ; software, H.Z. ; validation, M.L., Y.W. and X.Y. ; formal analysis, P.Z. ; investigation, P.Z. ; resources, X.Y. ; data curation, M.L. ; writing--original draft preparation, J.Y. ; writing--review and editing, H.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China (2022 YFB2603000), the Zhejiang Provincial Science and Technology Plan Project (2022C01004) and the Fundamental Research Funds for the Central Research Institutes (TKS20220105 and TKS20230601). **Infrared Consent Statement**: Not applicable. **Data Availability Statement**: The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the fact that the dataset production is part of the innovation point of this paper, so the dataset will not initially be open source; we will continue to explore new possibilities in the subsequent experimental research, and then the dataset will be made public along with the related code.
## 3 Results and Discussion
### Identified Key Technologies of IoT for Energy-Efficient 6G in Smart Cities
This 6G environment comprises three layers, including an (i) application layer, (ii) communication layer and (iii) blockchain layer. Similarly, [PERSON] et al. [17] discussed the blockchain as a powerful technology that can support and ensure the smooth working of 6G. The authors posited that the current telecommunication infrastructure could not support virtual reality massive Machine Type Communication (mMTC). These technologies require blockchain and 6G to perform smart city operations effectively. Blockchain is significantly essential to IoT security. Therefore, the amalgamation of blockchain, IoT and 6G can allow smart cities to take advantage of the faster and safer connectivity. Moreover, [PERSON] et al. [18] discussed the use of VLC in 6G. The authors propounded that the use VLC in 6G facilitates high transmission service through offline and real-time processing. The two types of VLC devices include PureLiFi and ByteLight and two light sources: LED and laser diode (LD). However, the main disadvantage of LED VLC is its limited bandwidth, which is compensated by LD. Hence, blockchain and VLC, as IoT devices, are related to 6G's implication in smart cities. The emergence of COVID-19 transformed the lives of human beings. [PERSON] et al. [26] articulated that COVID-19 brought a new normal, working from the home, for people around the globe [30]. However, the 5G wireless communication network is unable to fulfill people's connectivity demands. Therefore, the authors discussed RIS, Ambient Backscatter Communication (ABC) system, UAVs, CubeSats, and others, which can be used as IoT key technologies for 6G in smart cities to fulfill the post-pandemic connectivity needs. [PERSON] et al. [25] shed light on the ABC system and declared it to be an efficient communication tool. The device improves overall transmission because it forms a backscattering link, forming an effective and advanced communication system. In addition, [PERSON] et al. [24] posited that 6G BCI is an emerging device used to control or support sensible wearable headsets and embedded devices. Moreover, [PERSON] et al. [31] discussed the use of RIS technologies. The systematic research analysis depicts that RIS technologies with AI, IoT, and blockchain can revolutionize communication network dynamics. Hence, IoT devices with 6G help people to fulfill their connectivity needs after humanitarian crises, i.e., COVID-19. The idea that using such devices in smart cities can benefit humanity is elaborated in the subheading below.
## 2 Experimental Study
### Fatigue Test Program  
Before the fatigue test, a static preloading was applied to the specimens to check the normal working state of test instruments and fatigue testing machine and to remove the nonlinear reading of test instruments caused by the insufficient compression of the test specimens. When the specimen was statically preloaded, it was loaded to the maximum load \(P_{\text{max}}\) by ten stages. After the static preloading, the fatigue test with constant amplitude load range was carried out. When the cyclic loading reached a certain number, the fatigue damage of shear studs was detected to explore whether there were fatigue cracks in studs  
Figure 4: Push-out test setup and test specimen layout.  
Figure 3: Test instruments and the layout.  
and crack details. After the specimens were failed with fatigue damage, the fatigue loading cycles and failure modes were recorded. During the fatigue test, the relative slip and the local strain were measured. The whole loading procedure of specimens is shown in Figure 5.  
The three specimens (S-1, S-2 and S-3) with the same dimensions were tested under fatigue loading. According to Eurocode 4 [38], the design shear capacity of single headed stud \(P_{Rd}\) was 101.4 kN. Thus, the static shear strength (\(P_{u}\)) of testing specimens was 810.9 kN (eight studs). To keep a steady fatigue crack growth process with appropriate loading cycles, the cyclic load ranges \(\Delta P\) (\(\Delta P=P_{\text{max}}-P_{\text{min}}\)) of 37%, 42% and 44.4% of the static shear strength \(P_{u}\) were applied on three testing specimens. Considering the load capacity of the actuator (500 kN), the maximum cyclic loads \(P_{\text{max}}\) of the three testing specimens varied from 43.2% to 49.3% of the static shear strength \(P_{u}\), respectively. For each specimen in the fatigue test, the load range \(\Delta P\) and load ratio \(R\) (\(P_{\text{min}}/P_{\text{max}}\)) were also kept constant in testing, where the \(P_{\text{min}}\) were the minimum fatigue loads. The load ratio \(R\) of the three specimens was all kept at about 0.1 to ensure that the fatigue machine would not separate from the specimen during the fatigue test. The nominal shear stress range of studs was derived by dividing the load range \(\Delta P\) by all stud section areas. The maximum and minimum loads and the loading amplitude are presented in Table 2. In the fatigue test, the constant amplitude cyclic force of sine wave was adopted with the loading frequency of 4 Hz according to the target load range of specimens. The vertical load was applied using an actuator under force control.
# Geo-Electrical Detection of Impermeable Membranes in the Subsurface
###### Abstract  
In this paper, we have investigate looking ahead capabilities of electrical resistivity tomography (ERT) for a soil penetrating tool.  
In this paper, we have investigate looking ahead capabilities of electrical resistivity tomography (ERT) for a soil penetrating tool with electrodes. In our case study, the desired detection resolution (10 to 20 cm at a depth of at least 6 m) was much higher than what can be achieved from classical surface ERI measurements. Therefore, we designed a logging-type tool, that can be pushed into the ground. Our target was a buried PVC membrane which acts as an electrical insulator. In this phase, we performed numerical simulations and laboratory measurements. The methodology is based on a two-step approach. First, we calculate the background resistivity of the tool's path is determined by inversion of near-looking electrode configurations. Next, the theoretical response (kernel) of the far-looking configurations is calculated for different membrane positions. The root mean square (RMS) error between the kernel and the measurements is minimized to detect the membrane. If the membrane is within sensing reach, the RMS has a minimum for the kernel corresponding to the true membrane position. If no minimum in RMS is found, the membrane is not within sensing reach and the tool can be pushed closer to the membrane. The laboratory tests comprised measurements in a tank filled with either water or saturated sand or saturated sand with clay slabs and chunks. The laboratory results were successful in pinpointing the position of the membrane with an accuracy of 10 to 20 cm, depending on the dimension of the tool and the distance from the membrane.  
looks-ahead; detection of impermeable membranes; electrical resistivity tomography; geophysics +
Footnote †: journal: Applied Sciences
## 4 Antenna Prototype Fabrication and Test Results
### OCROP Antenna Prototype
As can be seen, the impedance bandwidth of the operation is the same as the simulation. The measured S\({}_{\text{dd11}}\) and S\({}_{\text{dd22}}\) would be slightly different if a different feeding network were to be used, and therefore, in practice, the feeding network must be carefully selected and designed. Radiation patterns were measured in a Compact Antenna Test Range. The simulated and measured results of the fabricated antenna are compared in Figure 11. Figure 11a,b compare the simulated and measured radiation patterns at 1.5 GHz in different \(\phi\)-cut planes, namely principal planes of \(\phi\) = 0\({}^{\circ}\) and 90\({}^{\circ}\) and diagonal planes of \(\phi\) = 45\({}^{\circ}\) and 135\({}^{\circ}\). Figure 10: OCROP differential reflection coefficient simulation and measurement: (**a**) Without feeding network; (**b**) With feeding network. Figure 9: OCROP prototype: (**a**) Situated on mass scale; (**b**) In the test chamber for radiation patterns. The measured gain (8.6 dBi), accounting for the insertion loss from the feed network, is 0.4 dB lower than the simulated gain (9.0 dBi). Compared to the simulated X-pols, the measured ones are higher in all four planes. More specifically, the measured X-pols in the \(\phi=0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\) planes are \(-36.5\) dB, \(-25.0\) dB, \(-37.0\) dB, and \(-26.5\) dB, respectively, while the simulated X-pols accounting for the amplitude and phase errors are \(-44.8\) dB, \(-31.2\) dB, \(-53.0\) dB, and \(-31.1\) dB in the four corresponding planes, respectively. In the measurement, there are two sources of errors for cross-polarization, i.e., the Compact Range reflector and the antenna mount. The Compact Range reflector is an offset reflector, and its cross-polarization is limited to \(-35\) dB. The scattering from the antenna mount on the tower and the tower itself contributes to the cross-polarization. Despite the differences, the measured X-pols at 1.5 GHz still met the X-pol requirement of \(\leq-25\) dB in both principal and diagonal planes. In addition to the excellent electrical performance, the fabricated antenna is also lightweight (197.0 g) and compact (0.825 \(\uplambda\times 0.825\)\(\uplambda\times 0.4\)\(\uplambda\) at the center frequency of 1.5 GHz).