esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 3D Modelling and Cooling Performance Indicators
### Cooling Performance Indicators and Evaluation Criteria  
In this study, the maximum temperature (T\({}_{\rm max}\)), minimum temperature (T\({}_{\rm min}\)), and maximum temperature difference (\(\Delta\)T) were used to evaluate the overall cooling performances. The maximum pressure difference (\(\Delta\)P) was used to indicate the power consumption of the air-cooling BTMS. T\({}_{\rm max}\) and T\({}_{\rm min}\) refer to the maximum and minimum temperatures of all cells and busbars surfaces during the discharging process. \(\Delta\)T is the maximum difference between T\({}_{\rm max}\) and T\({}_{\rm min}\) at any time during discharging. According to the general operating characteristics of LIB cells, the battery heat generating rate usually peaks at the end of the discharging, accompanied by the highest cell temperature, so the \(\Delta\)T values were collected at the last simulation time step. \(\Delta\)P is the difference between the average pressures of the inlet and outlet. Since \(\Delta\)P is generally a constant value during the discharging only dependent on the inlet velocity and cooling channel structures, the \(\Delta\)P values were also collected at the last simulation time step. Theoretically, the desired LIB cell operating temperature range would be from 25 \({}^{\circ}\)C to 40 \({}^{\circ}\)C [16; 17; 18; 19; 20; 21]. The thermal hazards (such as ignition, internal short circuit, combustion, explosion, toxic gases leakage, and thermal runaway, etc.) caused by high temperatures could lead to the early failure of the LIB cells [22; 23; 24]. The maximum \(\Delta\)T is usually set as 5 \({}^{\circ}\)C, which is widely elucidated by much relevant research [25; 26; 27; 28; 29; 30; 31]. In this study, two major cooling performance evaluation criteria are T\({}_{\rm max}\)\(\leq\) 35 \({}^{\circ}\)C (308.15 K) and \(\Delta\)T \(\leq\) 5 \({}^{\circ}\)C (5 K).
###### Abstract  
Human movement is vital for life, with active engagement affording function, limiting disease, and improving quality; with loss resulting in disability; and the treatment and training leading to restoration and enhancement. To foster these endeavors a need exists for a simple and reliable method for the quantitation of movement, favorable for widespread user availability. We developed a Mobile Motion Capture system (MO\({}^{2}\)CA) employing a smart-phone and colored markers (2, 5, 10 mm) and here define its operating envelope in terms of: (1) the functional distance of marker detection (range), (2) the inter-target resolution and discrimination, (3) the mobile target detection, and (4) the impact of ambient illumination intensity. MO\({}^{2}\)CA was able to detect and discriminate: (1) single targets over a range of 1 to 18 ft, (2) multiple targets from 1 ft to 11 ft, with inter-target discrimination improving with an increasing target size, (3) moving targets, with minimal errors from 2 ft to 8 ft, and (4) targets within 1 to 18 ft, with an illumination of 100-300 lux. We then evaluated the utility of motion capture in quantitating regional-finger abduction/adduction and whole body-lateral flex motion, demonstrating a quantitative discrimination between normal and abnormal motion. Overall, our results demonstrate that MO\({}^{2}\)CA has a wide operating envelope with utility for the detection of human movements large and small, encompassing the whole body, body region, and extremity and digit movements. The definition of the effective operating envelope and utility of smart phone-based motion capture as described herein will afford accuracy and appropriate use for future application studies and serve as a general approach for defining the operational bounds of future video capture technologies that arise for potential clinical use.  
motion analysis; image capture; mobile health; digital health; smart phone; range of motion +
Footnote †: journal: Applied Sciences
# An SQP Algorithm for Structural Topology Optimization Based on Majorization-Minimization Method
## 4 Numerical Examples
### Cantilever Beam  
To demonstrate the effectiveness of the surrogate function under multiple constraints, a cantilever beam problem is provided in this section. The design domain of the cantilever beam is illustrated in Figure 6. It was fully constrained at the left end and subjected to a downward unit load at the right center. To set multiple constraints, a fixed value constraint was added, and the virtual densities corresponding to the first four columns of finite elements in the design area were set to 1, expressed as follows:  
\[\sum_{i=1}^{4*nely}x_{i}=4\times nely. \tag{20}\]  
In terms of the other parameters, \(move=1,q=2,f=0.5\). The design domain was discretized with 60 \(\times\) 30, 80 \(\times\) 40, and 120 \(\times\) 60 square elements. Both the IPC and MMA methods were applied to this problem, and the optimization results are shown in Figure 7.  
As shown in Figure 7, some checkerboard and porous elements appeared around the structure optimized using the MMA method, and the same phenomenon occurred in the literature [30]. Compared with the MMA method, the optimized structure of the IPC method was clearer, and the boundary contours were more distinct, with no checkerboard phenomenon. These benefits suggest that the optimized structure of the IPC method was superior to that of the MMA method in terms of manufacturing.  
Table 2 shows the optimization data of both methods for different sizes of design domains. The IPC method performed better than the MMA method. In the design domain, from small to large, the compliance of the IPC method was about 18.8%, 18.9%, and 13% smaller than that of MMA, respectively, while the number of iterations was 76%, 76%, and 42% smaller, respectively, and the overall calculation time was about 60%, 74%, and 4.2% smaller, respectively. It is worth noting that, in Figure 8a, the compliance curve of the MMA method showed a peak value, while the volume ratio conve  
Figure 6: Design domain of the cantilever beam.  
Figure 7: Optimized designs of the cantilever beam using IPC and MMA methods.  
in Figure 8b showed a corresponding valley value. The decrease in volume ratio was the reason for the sudden increase of compliance, which also indicates that the MMA method was not as capable of maintaining constraints as the IPC method.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 6 Discussion
Second, the other parameters of an Ornstein-Uhlenbeck process give clear insight into the noise characteristics: \(\theta\) describes how the rate measurements revert towards the mean \(\mu\), while \(\sigma\) indicates the amount of noise present between each measurement. [PERSON] and [PERSON] (2017) analysed the effect of time-correlated noise on the applicability of classical Kalman Filtering, and proposed new filter equations. The noise models we proposed here can thus be incorporated into a Kalman Filter or Bayesian inference algorithms and provide both an estimate for the location along with quantified uncertainty. In addition, recent work ([PERSON] _et al._, 2018) has shown that there are practical algorithms for sensor fusion in multi-dimensional systems that allow arbitrary noise distributions and approach the computational efficiency of Kalman Filtering. Conceptualisation: A.W.R.S., T.M., P.B., and D.S. ; Methodology: A.W.R.S., A.M., and D.S. ; Software and validation: A.W.R.S., A.M., and T.M. ; Formal analysis: A.W.R.S., A.M., and D.S. ; Resources, B.J.P., P.B., and T.C.A.M. ; Writing: A.W.R.S., A.M., and D.S. ; Review and editing: A.M., T.M., and D.S. ; Visualisation: D.S. ; Supervision: D.S. and T.C.A.M. ; All authors have read and agreed to the published version of the manuscript. This research was funded by the New Zealand Ministry of Business, Innovation, and Employment under grant number UOOX-1208. [PERSON] is grateful for the financial support from the Dodd-Walls Centre for Photonic and Quantum Technologies. The authors are grateful for the reviewers for their recommendations, which helped to clarify some of our earlier discussions. We are also indebted for one of their suggestions, which will provide an alternative measure of goodness for the noise models presented in this study. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 4 Deep Learning-Based Detectors
### Network Architecture Design
#### 4.2.3 Hierarchical MLP with 1 DCNN-BiLSTM  
This DNN model is built using a CSS method based on a hierarchical multilayer perceptron network. It integrates a 1 DCNN with an end-to-end bidirectional LSTM (1 DCNN-BiLSTM) at the SU. The complete network architecture, which includes the 1 DCNN-BiLSTM-MLP structure, is shown in Figure 6.  
Figure 5: Proposed architecture 2: hierarchical MLP with 1 DCNN-LSTM (CNN_LSTM_MLP).  
Figure 6: Proposed architecture 3: hierarchical MLP with 1 DCNN-BiLSTM (CNN_BLSTM_MLP).  
The current model follows the 1 DCNN-LSTM-MLP architecture, except for the replacement of the LSTM model with BiLSTM in this case. We use a BiLSTM to capture long-term temporal connections between the input data and the hidden connections among the features extracted, according to the timing regularity of the input data. Unlike the standard LSTM network, which can only analyze previous data, the BiLSTM network scans the data in both directions, using both pre- and post-data simultaneously. This improves the global correlation extraction process compared to LSTM. The remaining steps of the process are identical to the previous DNN model.
## 3 Results  
The ratio of males to females in the study is nearly equal, with 49% being male and 51% being female. The study recorded an average age of 20.75 \(\pm\) 2.36 years (19-31 years old).  
Regarding tooth heights, there was a statistically significant difference in one measurement (tooth 34) between the plaster and digital models, with an average difference of 0.01 mm (8.40 \(\pm\) 0.75 mm and 8.39 \(\pm\) 0.75 mm, respectively; \(p\) = 0.025, n = 63) (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Measurements**} & \multicolumn{2}{c}{**Plaster Models**} & \multicolumn{2}{c}{**Digital Models**} & \multirow{2}{*}{\(p\)**-Values**} \\ \cline{2-2} \cline{4-6}  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline H11 & 9.87 & 0.99 & 9.88 & 0.99 & 0.318 \\ \hline H12 & 8.30 & 0.87 & 8.29 & 0.8  
Regarding tooth widths, there was a statistically significant difference in one measurement (tooth 15) between the plaster and digital models, with an average difference of 0.03 mm (7.23 \(\pm\) 0.39 mm and 7.20 \(\pm\) 0.39 mm, respectively; \(p\) = 0.033, n = 63) (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Measurements**} & \multicolumn{2}{c}{**Plaster Models**} & \multicolumn{2}{c}{**Digital Models**} & \multirow{2}{*}{\(p\)**-Values**} \\ \cline{2-2} \cline{4-4}  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline H43 & 9.30 & 1.19 & 9.29 & 1.20 & 0.201 \\ \hline H44 & 8.42 & 0.67 & 12.89 & 35.27 & 0.265 \\ \hline H45 & 7.30 & 0.68 & 7.30 & 0.69 & 0.293 \\ \hline H46 & 6.73 & 0.68 & 6.72 & 0.69 & 0.366 \\ \hline \end{tabular} H: heights; SD, standard deviation; \(p\)-values were obtained from paired \(t\)-tests.  
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Measurements**} & \multicolumn{2}{c}{**Plaster Models**} & \multicolumn{2}{c}{**Digital Models**} & \multirow{2}{*}{\(p\)**-Values**} \\ \cline{2-2} \cline{4-4}  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline W11 & 8.65 & 0.44 & 8.64 & 0.44 & 0.498 \\ \hline W12 & 7.  
The Bland-Altman plots showed that almost all of measurements of the tooth heights and widths between the plaster models and intraoral scans were within the limits of agreement (Figure 1). There was no statistically significant difference in measurements of overjet, overbite, arch width, and depth between both models (Table 4).
## 3 Results and Discussion
### Carbon and Nitrogen Removal Study
#### 3.1.3 pH  
The average influent pH for Phases 1, 2, and 3 was 4.62, 4.97, and 5.30, respectively. These values are consistent with those found in literature, as shown in Table 1. There was not a significant difference in the effluent pH between treatment systems, with and without oyster shells as a pH buffer. The effluent from Column 1 averaged 7.10, 7.17, and 7.00 in each phase. This wastewater was recirculated and mixed with raw winery wastewater, which helps to neutralize the pH before distribution into  
Figure 5: Total nitrogen removals and nitrogen transformations in the treatment systems.  
Column 1. The average effluent from the third column for each phase was 8.04, 8.09, and 8.17. The increase in pH with both nitrification and denitrification aligns with the findings by [PERSON] et al. (2003) ([PERSON] et al., 2003), who found that pH increased with a decrease in COD by metabolic processes of microbes naturally occurring within winery wastewater. While oyster shells were not needed in this study, oyster shells might be useful as a source of alkalinity in the nitrification stage of biofilters treating low alkalinity wastewaters, because nitrification consumes alkalinity.
###### Abstract  
Utilizing well logging data, outcrop profiles, and previous research, this study analyzes the sedimentary and tectonic evolution of the Yan'an Formation in the Ordos Basin, correlating the resulting sedimentary facies with hydrocarbon reservoirs to establish the necessary connections. The study reveals that: (1) Vertically, the sediment grain size shows a pattern of coarser grains at the bottom and top, with finer grains in the middle. Horizontally, the grain size tends to become finer from the northern, western, and southern parts of the basin toward the central-western region. (2) Tectonic movements during the Yan'an period controlled the sedimentary environment. These tectonic activities, through uplift and subsidence, caused the Yan'an Formation to experience four stages of sedimentary environments: braided river, lake, delta, and meandering river. (3) The Yan'an Formation exhibits four types of reservoir sandbody stacking patterns--continuous superposition, intermittent superposition, interbedded sand-mud, and single sandbody types--with continuous and intermittent stacking being the most common. (4) The hydrocarbons in the Yan'an Formation originated from the Chang 7 Member of the Yanchang Formation and migrated into the Yan'an reservoirs. The oil is characterized by its low density, low viscosity, and low pour point, indicating it is a high-maturity, high-quality crude oil.  
Ordos Basin; Yan'an formation; tectonic-sedimentary evolution; petroleum geological significance
## 1 Introduction
### Input Data
#### 2.2.2 Harmonized Landsat Sentinel-2  
The Harmonized Landsat and Sentinel-2 (HLS) product suite is a combined surface reflectance dataset consisting of observations from both the Landsat 8 Operational Land Imager and Sentinel-2 Multi-Spectral Instrument (MSI) [30]. We used two products from HLS version 1.4: (1) S30--SR derived from Sentinel-2 MSI L1C data and resampled to 30m and (2) L30--30 m SR derived from Landsat-8 OLI L1T data. Both S30 and L30 products provide nadir BRDF-adjusted (Bidirectional Reflectance Distribution Function) reflectance (NBAR) data gridded with the Sentinel-2 tiling system in Universal Transverse Mercator (UTM) projection and World Geodetic System 1984 datum (WGS84). The Sentinel-2 MSI radiometry is adjusted to mimic the spectral bandpasses of Landsat 8 OLI for visible, near infrared, and shortwave infrared bands. On average, there are 20.2 and 18.3 HLS observations per pixel for 2016 and 2017, respectively (Figure 2).
## 2 Materials and Methods
### Experimental Set-Up and Operation  
#### 2.2.1 Filter Press Backwash Process  
The whole process of the \"filter press backwash-chemical precipitation-gaseous membrane absorption\" method is illustrated in Figure 1. The cyanide tailings were transported to the mixing barrel in front of the filter press in the form of slurry and were driven into the countercurrent washing machine through the slurry pump. The feeding pressure was about 0.6 MPa. When there was no obvious filtrate outflow in the filtrate pipeline, the filter chamber was judged to be basically full. The feeding time was 5-10 min. After the feeding was complete, an air compressor blew air for 0.5-3 min (primary air-drying), and the filter press filtrate and the primary air-dried blow-off liquid were returned to the cyanide process together as the filter barren solution. Then, the backwash pump was turned on, the backwash water parameters were adjusted, and a backwash pressure of 0.8 MPa was used for backwashing the cyanide tailings. After washing, secondary air-drying was conducted for 3-25 min. After air-drying, the filter cake was removed and a leaching toxicity test carried; the washing solution and the secondary air-dried blowout solution were used as eluent to enter the chemical treatment process. The influences of primary air-drying time, secondary air-drying time, and backwash water parameters (pH value, total cyanide content, multiple (ratio of the amount of backwash water to the mass of primary air-dried cyanide tailings)) on the harmless effect of cyanide tailings were studied in the filter press backwash process.
# Plant-Derived Bioactive Compounds for Rhabdomyosarcoma Therapy In Vitro: A Systematic Review
## 3 Results
### Plant Extracts and Isolated Bioactive Compounds That Induce Cell Death by Blocking Proteins  
In total 5% of the plant extracts or bioactive compounds induce cell death by blocking proteins [36; 49; 50], each with a different antitumor mechanism (Table 5).  
_Choosropondias axillaris_ is a tree whose extracts were obtained using methanol. It is known to have beneficial effects at the cardiovascular level. In the research study, it was demonstrated, in different cell lines, that each of its components could inhibit proliferation and causing toxicity in malignant cells. Five compounds (Choerosponols A-E) were isolated from its fruit, with compound two (Choerosponols B) being the most effective in SJCRH30 (RMS cells) with an IC\({}_{50}\) value of 0.12 \(\pm\) 0.02 \(\mu\)M, while compound A is more effective in D283 and A-673 (RMS cell lines). Compound one (Choerosponols A) had antitumor action by inhibiting the monocarboxylate transporter type 1 (MCT1), especially in Ewing sarcoma and medulloblastoma [49].  
Another extract with bioactive compounds with antitumor activity against RMS was the methanol extract from the branches and leaves of _Choerospondias axillaris_. The isolated phytochemicals were eight types of limonoids studied in RMS and hepatocarcinoma cells. The IC\({}_{50}\) value was similar in both cell lines, with a result of 10.08 \(\mu\)g/mL in RMS. Limonoids form a gamma-lactone ring at carbons C4-C7 as an antitumor mechanism; however, more studies are needed to confirm this hypothesis [50].  
Finally, in the _Morus alba_ root methanol extract, a specific type of flavonoid called Sangennon G1 is highlighted among its components [36]. This flavonoid inhibits the XIAP protein, which is an apoptosis inhibitor. The inhibition of XIAP induces apoptosis in cancer cells by activating caspase 9. It is known that the extract alone had less effect than the combination with etoposide. This action was verified in vitro in RMS, T-cell lymphoblastic leukemia, and neuroblastoma cells.
# Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
## 3 Laboratory Equipment  
The Velodyne LiDAR PUCK VLP-16 used in this study, as shown in Figure 2, allows full-time data transmission and reception, 360-degree full coverage, 3D distance measurement, and calibration reflection measurement. The farthest effective range of the VLP-16 sensor is 100 m, and the nearest effective range is 30 cm. It has low power consumption, is light weight, and has a small footprint and dual return functions. The structure of the LiDAR includes the LiDAR host, central processing system, power supply, and computer, as shown in Figure 3. Figure 4 illustrates the process of LiDAR signal transmission. An EFG-1400 wireless remote-control fog machine was used to simulate thick smoke; see Figure 4. This model has a remote-control distance of 8-10 m. The power is 1500 cu ft/min.  
Figure 1: Principle sketch map of the radius filter.  
Figure 3: Processing architecture of the LiDAR (light detection and ranging).  
Figure 2: Velodyne LiDAR PUCK VLP-16.
# Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing
## 7 Experimental Results
### Discussion  
The deployment of the Machine Learning Reference Architecture described above demonstrates the many benefits of the proposed solution as well as highlighting certain limitations. This use case exemplifies the three broad aims of Reference Architectures introduced in Section 3:  
1. The ISVs and Software Developers are supported in that they need only provide a rich set of metadata describing their services, without needing to invest effort in learning the underlying tools used for deployment and execution.
2. The generated deployment descriptor (MiCADO ADT) is automatically deployed and the described containers are orchestrated by MiCADO across cloud and edge.
3. The SACs for Kafka, Zookeeper, and AKHQ can be imagined as generic tools that support connecting data with computation. Reused, these SACs can reduce time and effort involved in future developments.  
The focus of the proposed solution on concrete, software architectures reduces the likelihood of errors in the deployment expects since the Docker container prepared by the ISV or Developer beforehand is deployed as-is by MiCADO. This also raises a potential limitation--higher-level RAs may be able to offer more opportunity for optimization of certain software if a particular orchestrator were able to optimize for different container run-times or image formats. Another limitation exists in that, while there is no hard restriction on the cloud provider imposed by MiCADO, support for a given cloud provider must be developedin MiCADO in the form of plugins. While many clouds are supported already, MiCADO does not support every available cloud.  
Applying a Reference Architecture to facilitate the deployment and execution of the Machine Learning component of the DIGITbrain Digital Twin empowers the use case owners with a flat, cohesive interface for describing a complex application. Once deployed, their application is afforded all the benefits provided by a cloud orchestrator such as MiCADO--smart scheduling and self-healing of containers, distributed computations across cloud and edge, enhanced security, and easy migrations--none of which would be available to a more traditional local or otherwise non-orchestrated deployment.  
One of the greatest benefits of Reference Architectures is their potential for reuse. Because of this, communities should naturally gravitate towards RAs. As more ISVs and Software Developers in a given community embrace a Reference-Architecture-oriented approach to designing their applications, more and more examples will become available to guide the rest of the community. At the same token, this can limit initial growth, since an initial effort is required by a community to describe the first SACs, SAFs, and RAs that will appear again and again as examples.