esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed DanRETwin Solution
### Technical Approach
#### 4.2.2. Clamp-On IoT Sensors and the ICT Communication Infrastructure  
As part of the DanRETwin digital twin platform design, a cloud-based solution, developed by ReMoni, is seamlessly and wirelessly connected and integrated with low-cost, clamp-on IoT sensors and meters. The unique aspect of this technology lies in its sensor system, which allows users to effortlessly monitor power, flow, heating, and cooling by simply attaching sensor units onto cables, pipes, etc., without the need for technical expertise or system disconnection. This is achieved through the implementation of advanced mathematical AI models, eliminating the need for costly and intricate hardware. By utilizing the same IoT sensors for multiple solutions, ReMoni also established a scalable technology that offers cost advantages for individual solutions. In the context of this project, these sensors play a vital role in the developed solution and will undergo further refinement and customization to seamlessly integrate into an interoperable platform. The wireless connectivity ensures seamless two-way communication between the sensors and the internet, enabling online reconfigurations and over-the-air firmware updates. Despite sampling data every second and transmitting every 5 min, the sensors are designed to operate on low power, ensuring a long battery life of over 15 years. The solution developed by ReMoni distinguishes itself from competitors through its comprehensive clamp-on cloud AI solution, which costs only 20% of alternative offerings in the market for a fully integrated solution.  
As part of the proposed solution development, a collaborative ICT communication infrastructure will be established to facilitate secure data exchange between the physical building and its digital twin, as well as with cloud systems and models. To achieve this, data acquisition and storage systems will be constructed on the foundation of an existing operational cloud infrastructure within the ReMoni ReCalc Building Energy Analytics tool. The communication infrastructure will have a crucial feature: it will enable both real-time online operation and simulations of the building, with the simulation speed typically being faster than real-time. This flexibility is essential to accommodating the specific requirements of operating a digital twin. The ICT communication infrastructure will leverage the existing ReMoni cloud platform, which provides open APIs, and will be expanded to incorporate integration with existing HVAC sensors, actuators, and other devices. All sensors can be consolidated into a single unit or device, wirelessly transmitting data through a gateway to the cloud. The cloud will host the artificial intelligence (AI) components, user interfaces, and integration with other systems, enabling scalable cloud operation and ensuring the solution's robustness.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 5 Comparison of Online SOC Estimation before and after Model Parameter Optimization
### Convergence Comparison of the Initial Error of the AUKF before and after Parameter Optimization  
The Kalman filter has a strong convergence effect on the initial error of the SOC. This section verifies the effect of the reasonable optimization of the model parameters on the initial error convergence ability of the AUKF. The initial value of the system state SOC in the AUKF is set to the same value, and the output of the AUKF estimation before and after model parameter optimization is compared. The initial SOC value is 0.9, and the simulation lasts for one cycle. A waveform comparison between the SOC value of the simulation model and the theoretical value is shown in Figure 16. Figure 17 shows the convergence of the initial error before and after the model parameter optimization of the first 400 s. The SOC estimation values before and after optimization at 200 s are given.  
As seen from Figure 16, before and after model parameter optimization, the SOC values estimated by the AUKF converge to the theoretical value quickly. In Figure 17, at 200 s, the estimated value before optimization is 0.967, which is closer to the theoretical value than the optimized estimated value of 0.964, but the difference is only 0.003; that is, the difference is only 0.3%, which can be ignored. This shows that the convergence ability of the AUKF algorithm to the initial error before and after optimization of the model parameters is approximately the same, and optimization of the model parameters has no effect on the convergence of the algorithm.
## 3 Path Loss Simulation and Illuminated Field Intensity Observation in an Air-to-Ground Scenario
Figure 6: Path loss simulation results between Pohang (S4) and Heuksando (S6) as a function of the range and altitude: (**a**) the standard atmospheric conditions; (**b**) the observation-based atmospheric condition on 15 February 2019; and (**c**) the path loss comparison among the simulation tools. Figure 7 illustrates the two-dimensional illuminated electromagnetic field intensities on the terrain according to the atmospheric conditions based on the air-to-ground scenario in the SAR application. We assumed that the aircraft was located at the center between Pohang (S4) and Heuksando (S6) at an altitude of 5000 m. The observing diameter was up to 387.4 km for the long-range SAR application, so it included coastal, sea, mountain, and urban terrains. Figure 7a shows the path loss on the ground with standard atmospheric conditions (\(\
abla M=118\)), while Figure 7b shows the results using the same observation-based atmospheric conditions (15 February 2019). The path loss with the observation-based atmospheric conditions was greater than that in the standard atmospheric conditions. In the case of the sea surface (in the direction of Heuksando), concentric circles (the red part in the figure) repeatedly appeared due to the periodic characteristics of the field strength. For example, the distances from the center to the concentric circles were about 170 km, 145 km, and 125 km. In free space, the theoretical distances at the maximum electric fields would be 171 km, 133 km, and 109 km, which are similar to the results in our scenario [33]. That is, the concentric circles occurred due to the ground effect of the sea surface. In the case of the mountainous area (in the direction of Pohang), the blue blur spots are due to the shadow of the high-elevation mountains and urban areas. The white region is the area below the aircraft where the wave propagation could not reach due to the limit of the steering angle, causing the high path loss levels.
## 3 SSF-EDF Schedulability Analysis
### SSF-EDF Schedulability Test  
**Theorem 1**.: _Sporadic task set \(\Lambda\) is SSF-EDF schedulable on a performance asymmetric multiprocessor platform \(\pi\), provided that_  
\[\mathrm{LOAD}(\Lambda)\leq\mu-\beta\delta_{\max}(\Lambda), \tag{34}\]  
_where \(\mu\) and \(\beta\) are defined in (13) and (31), respectively._  
Proof.: A sufficient condition for SSF-EDF schedulability arises by negating the necessary condition for \(\tau_{i,j}\) missing the deadline.  
From the previous discussion, the bounds for \(Q\big{(}t_{0},A_{i,j}+D_{i}\big{)}\) are as follows:  
\[Q\big{(}t_{0},A_{i,j}+D_{i}\big{)} >\mu\Delta, \tag{35}\] \[Q\big{(}t_{0},A_{i,j}+D_{i}\big{)} \leq\mathrm{LOAD}(\Lambda)\Delta+\beta\Delta\delta_{\max}( \Lambda).\]  
From this, the necessary conditions for \(\tau_{i,j}\) to miss the deadline are as follows:  
\[\mathrm{LOAD}(\Lambda)\Delta+\beta\delta_{\max}(\Lambda)\Delta >\mu\Delta, \tag{36}\] \[\mathrm{LOAD}(\Lambda) >\mu-\beta\delta_{\max}(\Lambda).\]  
Therefore, negation of (36) gives  
\[\mathrm{LOAD}(\Lambda)\leq\mu-\beta\delta_{\max}(\Lambda). \tag{37}\]  
This is Theorem 1.
## 4 Numerical Result and Geometric Reconstruction
### Geometry Reconstruction  
Although the existing commercial software can automatically fit the topology optimization results with surfaces to generate solid geometry files, the fitting accuracy cannot satisfy the assembly requirements of the fatigue tester fixture. So the post-processing result is required to be reconstructed by CAD software and the reconstructed geometry is shown in Figure 10. In the process of geometric reconstruction, bars are added to some regions to improve the stability of the structure, based on engineering experience, and also to lighten several places where the stresses are concentrated, from which some inessential regions are deleted, thus reducing the mass of the structure. A comparison of the reconstructed geometry and topology optimization result is shown in Figure 11. The numerical simulation results of the reconstructed geometry with the same boundary conditions and external load are shown in Figure 12. The mesh is shown in Figure 13 and quadratic elements are applied in the FEM analysis. The number of elements are 249,195 and the number of nodes are 403,126. The stress singularity is neglected in fixed boundary condition regions. The main region's von-Mises stress is about 470 MPa. The mass of the reconstructed geometry is 285.7 g and the weight is reduced about 37% compared to the original 480 g. The application of topology optimization technology significantly changed the force transmission path of the structure, reducing the weight of the structure while making the stress distribution more reasonable, thus improving the mechanical properties of the structure. However, it is also because of the use of topology optimization that the new aerospace bracket is difficult to manufacture by traditional mechanical processing methods whereas the use of 3D-printing makes it relatively easy to manufacture parts of such a complex configuration, such as the new bracket. The new bracket is fabricated by EOS M290, which allows a fast, flexible, and cost-effective production of metal parts directly from CAD data. EOS Titanium Ti64 is used as metal powder in 3D-printing and more information about which  
Figure 8: Discrete mesh diagram.  
Figure 9: (**a**) Represents the isometric view of the result; (**b**) represents the front view of the result; (**c**) represents the side view of result; (**d**) represents the top view of the result.
## 2 Structure and Mathematic Model  
### Structure of the BPEH and IBPEH  
Figure 1a is a structural model of the BPEH. BPEH is mainly composed of a base, a magnet fixing plate, a linear-arch composite beam, a magnet, piezoelectric material PVDF, and an external resistor \(R\). One end of the composite beam is fixed on the top of the base, the other end is glued with magnet A, and magnet B is placed on the fixing plate. In particular, magnet A and magnet B maintain a mutually repelling relationship. The total length of the composite beam is \(L\), the height is \(h_{g}\), the width is \(b_{s}\), the arc-shaped radius is \(R_{s}\), and the distance between the centers of magnet A and magnet B is \(d\). Based on this structural model, magnets C and D are added, and magnets C and D are placed symmetrically concerning magnet B, thereby developing the IBPEH, as shown in Figure 1b. All magnets are made of NdFeB. The vertical distance between the centers of magnet B and magnet C is s, and the horizontal distance is \(d_{g}\). Adjusting the positions of magnet C and magnet D properly can change the magnetic force received by magnet A, effectively affecting the nonlinearity of the system.
# Spam Review Detection Techniques: A Systematic Literature Review
## 3 Transformation  
Document term matrix is used to represent tokens generated by the n-gram model in the form of a sparse matrix. A sparse matrix defines the frequency of terms or tokens in the collection of the reviews. It was observed by the literature review that most of the researchers use the following two techniques for transformation.  
* **Simple Count**  
In simple count technique, the value of the term in the matrix is determined by the number of times it occurs in a document, e.g., if a token occurs 2 times its value will be 2, or 0 if it does not exist in the document. Moreover, the occurrence frequency of each term in a review is considered [31; 32]. Simple count approach uses review text of the review to determine the number of times a term appears in the review text of a single review. Term frequency according to their structure is shown in Table 7.  
**Review 1:** The car is very comfortable to drive and fuel consumption is good  
**Review 2:** The interior is very comfortable  
**Review 3:** The fuel consumption is good  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline
**Review** & **the** & **Car** & **Is** & **Very** & **Comfortable** & **to** & **Drive** & **Its** & **Performance** & **Good** & **Interior** & **Fuel** & **Consumption** & **and** \\ \hline Review 1 & 1 & 1 & 2 & 1 & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 1 & 1 & 1 \\ Review 2 & 1 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ Review 3 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Example of text features frequencies dataset structure, for Review 1, 2, and 3.  
* **Term frequency and Inverse document frequency (TF-IDF)**  
TF-IDF technique is intended to consider how significant a word or token is to a document in a collection of the corpus. The TF-IDF value increases proportionally to the number of times a token occurs in the document but is often decreases by the occurrence of the word in the corpus, which helps to adjust for the fact that few words occur more often generally. Nowadays, TF-IDF is one of the most popular term-weighting schemes and provides better results than a simple count technique. TF-IDF can be mathematically represented as,  
\[\text{TF}(t,d)=\ \ \frac{f_{t,d}}{\sum_{t^{\prime}}f_{t^{\prime},d}}\]  
In the above equation, \(f_{t,d}\) is frequency of term \(t\) in document \(d\) and \(\sum\limits_{t^{\prime}}f_{t^{\prime},d}\) is number of terms in document \(d\).  
\[IDF(t,D)=log\frac{N}{|deD:tcd|}\]  
where \(N\) is total number of documents and \(|deD:ted|\) is number of documents with Term \(t\) in them. TF-IDF according to their structure is shown in Table 8.  
**Review 4:** This is a very good car. A good car indeed.  
**Review 5:** This book is very awesome, I loved it very much.  
After removing Stop words review 4 and review 5 looks like \"awesome\", \"book\", \"car\", \"good\", \"loved\".
## 4 Analysis of Test Results  
### Load Displacement Relationship of Ulplift Pile and Compressive Pile  
Figure 5 shows the load-displacement curves (\(Q\)-\(s\) curve) of the uplift pile and compressive pile. We can see from Figure 5, the \(Q\)-\(s\) curves of the uplift pile consist of two sections, which have the characteristics of sudden destroy, the \(Q\)-\(s\) curves of the compressive pile consist of three sections, and have good ductility. Figure 6 shows the displacement-time logarithmic curves (\(s\)-\(lst\) curve) of the uplift pile and compressive pile. In Figure 5, the \(Q\)-\(s\) curve of the uplift pile rises steeply at a load of 1400 N and the \(Q\)-\(s\) curve of the compressive pile falls steeply at a load of 1700 N. The load at the start of the steep rise or fall is taken as the ultimate bearing capacity [24]. In Figure 6, the \(s\)-\(lst\) curve of uplift piles does not change linearly when the load is 1500 N, and the \(s\)-\(lst\) curve of compressive piles does not change linearly when the load is 1800 N, and the first level load before the nonlinear change is taken as the ultimate bearing capacity [24]. From Figures 5 and 6, the ultimate bearing capacities of the uplift pile and compressive pile are determined to be 1400 N and 1700 N, respectively. Both the uplift pile and compressive pile are pure friction piles. The uplift pile produces a negative shaft resistance of 1400 N on the pile side, while the compressive pile produces a positive shaft resistance of 1700 N on the pile side, and the positive shaft resistance is greater than the negative shaft resistance. The uplift coefficient is defined as the shaft resistance of the uplift pile divided by the shaft resistance of the compressive pile, so the uplift coefficient obtained from this test is approximately 0.82.  
Figure 4: Compressive pile test diagram.  
Figure 5: \(Q\)-\(s\) curves of the uplift pile and compressive pile.
## 5 Discussion  
Droughs are a big part of the US Southwest, they reshape both land and people and are projected to intensify [13]. As they become more severe and longer, their impacts will become more profound, especially on the already vulnerable landscapes. In this work, we looked at the interannual variability of vegetation productivity, estimated by seasonal cumulative NDVI, a well-established proxy of gross primary production, in the Hopi and Navajo Nation Native American reservation lands in the context of a dryer and hotter climate. We looked at the strength of the relationships between vegetation productivity variability and the key environmental drivers. We used multi-sensor NDVI time series, long term climate data, and ancillary parameters and multiple linear regression analysis to assess the responses to the ongoing drought.  
The spatial distribution of the interannual changes in vegetation productivity suggests that the highest variability is in the middle and low elevations, a key ecosystem service area to the inhabitants of this region. This is driven by a combination of limited and vulnerable vegetative cover, highly irregular and low precipitation, and hotter temperatures. An indication that this elevation zone has undergone greater change over the last two and a half decades compared to the rest of the region. On the other hand, the well-established high elevation forest cover seems to be less sensitive to seasonal variability and subsequently more resilient to droughs, a likely result of the deeper rooting system that permit access to deeper soil moisture [16] during peak dry and hot season.  
Using the spatial regression model, we identified the main drivers of these spatial and temporal patterns of vegetation productivity change. Stepwise regression model analysis of the spatial variation in vegetation productivity indicates climate drivers exert more control (\(p<0.05\)) than topography drivers. In general, spring precipitation and temperature, and fall temperature variability seem to be the most significant factors controlling vegetation productivity. Other explanatory drivers were found to be either insignificant or even anti-correlated with the interannual variability.  
The strong relationship between interannual vegetation productivity of lower elevation and the spring and fall seasons is supported by earlier findings that suggest prolonged high evaporative demand can result in carbon starvation and tree mortality [39]. This will be exacerbated further by the projected decrease in winter and spring precipitation in the region [13; 40]. At higher elevation, temperature and snowmelt dynamics are mitigating the impacts of drier and hotter than normal years, and consequently explain the weak relationship between change in vegetation productivity and seasonal variability of the climate drivers [16]. Snowmelt provides moisture throughout the early parts of the growing season, while hotter temperature, associated with the drier years, may also induce more favorable conditions for growth considering the usually cold and freezing conditions of higher elevation [4].
## 4 Experiments and Results
### Segmentation Results of the Proposed Model  
The segmentation results of the proposed model over different datasets with various metrics are shown in Table 2. As presented in the table, these metrics demonstrate the high performance of our proposed framework in liver segmentation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & **DSC** & **VOE** & **RAVD** & **ASSD** & **MSSD** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline LiTS & 0.9738 & 0.0509 & 0.0201 & 0.64 & 12.97 & 0.9986 & 0.9744 & 0.9736 & 0.9738 \\ Sliver07 & 0.9732 & 0.0520 & 0.0141 & 0.76 & 18.34 & 0.9971 & 0.9789 & 0.9678 & 0.9733 \\
3D-IRCADb & 0.9750 & 0.0488 & 0.0206 & 0.59 & 12.29 & 0.9975 & 0.9768 & 0.9735 & 0.9750 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the proposed G-UNETR++ for liver segmentation.  
To further validate our model, we assessed the areas under the curve (AUC) derived from the precision-recall curves on the proposed G-UNETR++ network. As shown in Figure 7, the AUC for the most challenging dataset, Sliver07, ranged from 0.99158 to 0.998752, which demonstrates the high effectiveness of the proposed model.
## 4 Discussions of Engineering Applicability of Theoretical Solutions
### The Influence of the Story Height of the Metro Station  
Based on parameters of the example in Section 3.1, only the story height of the station is changed. As height differences between two adjacent layers cannot exceed 2 m in subway  
Figure 14: Three-dimensional global deformation (unit: mm).  
Figure 15: The bending moment of the wall column under three-dimensional simulation model (unit: kN\(\cdot\)m).  
station, height difference values of 2 m, 1 m, 0 m, \(-\)1 m, and \(-\)2 m are considered. In addition, the control-bending moments at the bottom of column under these height differences are obtained by three-dimensional simulation via software. Meanwhile, the analytical solution is still computed on the assumption of 0m height difference via Equation (38) (i.e. the upper and the lower story heights are equal). Bending moments at the bottom of the column under different conditions are shown in Figure 16. The comparison between simulation and analytical solution is shown in Table 2.  
It can be seen from Figure 16 and Table 2 that the error between analytical solutions and the simulation results ranges from 2.0% to 6.6%, which indicates that the slight changes in the height difference of the metro station cannot play a decisive role in the control bending moment at the column bottom. In addition, it can be certified that the assumption of the same story height of the upper and lower layers of the metro station is rational in theoretical analysis.