esa-sceva/satcom-chunk-collection huggingface dataset:
# Verification of Industrial Worker Walking Efficiency with Wearable Hip Exoskeleton
## 3 Results
### Treadmill Walking
#### 3.1.2 Energy Expenditure  
The results of energy consumption measured during six-minute treadmill walking are shown in Table 3.  
When wearing a WIM compared with no WIM, oxygen uptake, EEM, total EE, and net EEM were significantly reduced by 7.93%, 7.39%, 7.26%, and 10.59%, respectively (all \(p\) < 0.001). However, there was no significant difference in the heart rate between the conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **No WIM** & **WIM** & **\% Difference** & **t-Value** \\ \hline \multirow{4}{*}{RF} & Muscle activity (\%MVC) & \multirow{2}{*}{18.42 \(\pm\) 9.34 \({}^{\lx@sectionsign}\)} & \multirow{2}{*}{16.91 \(\pm\) 8.95} & \multirow{2}{*}{\(-\)8.20\%} & \multirow{2}{*}{0.591} \\ \cline{2-2} \cline{5-6}  & Muscle fatigue (Hz) & & & & \\ \hline \multirow{4}{*}{ST} & Muscle activity (\%MVC) & \multirow{2}{*}{30.60 \(\pm\) 12.06} & \multirow{2}{*}{29.02 \(\pm\) 15.16} & \multirow{2}{*}{\(-\)5.16\%} & \multirow{2}{*}{0.334} \\ \cline{2-2} \cline{5-6}  & Muscle fatigue (Hz) & & & & \\ \hline \multirow{4}{*}{TA} & Muscle activity (\%MVC) & \multirow{2}{*}{11.05 \(\pm\) 2.82} & \multirow{2}{*}{11.58 \(\pm\) 3.12} & \multirow{2}{*}{4.80\%} & \multirow{2}{*}{\(-\)0.708} \\ \cline{2-2} \cline{5-6}  & Muscle fatigue (Hz) & & & & \\ \hline \multirow{4}{*}{GCM} & Muscle activity (\%MVC) & \multirow{2}{*}{17.29 \(\pm\) 2.64} & \multirow{2}{*}{17.42 \(\pm\) 4.83} & \multirow{2}{*}{0.74\%} & \multirow{2}{*}{\(-\)0.106} \\ \cline{2-2} \cline{5-6}  & Muscle fatigue (Hz) & & & & \\ \hline \multicolumn{6}{l}{RF, rectus femoris; ST, semitendinos; TA, tibialis anterior; GCM, gastrocnemius; \({}^{\lx@sectionsign}\) mean \(\pm\) standard deviation; \({}^{**}\)\(p\) < 0.01.} \\ \hline \multirow{4}{*}{RF, rectus femoris; ST, semitendinos; TA, tibialis anterior; GCM, gastrocnemius; \({}^{\lx@sectionsign}\) mean \(\pm\) standard deviation; \({}^{**}\)\(p\) < 0.01.} \\ \cline{2-2} \cline{5-6}  & \multicolumn{1}{c}{} & \multirow{2}{*}{17.29 \(\pm\) 2.64} & \multirow{2}{*}{17.42 \(\pm\) 4.83} & \multirow{2}{*}{0.74\%} & \multirow{2}{*}{\(-\)0.106} \\ \cline{2-2} \cline{5-6}  & Muscle fatigue (Hz) & & & & \\ \hline \multicolumn{6}{l}{RF, rectus femoris; ST, semitendinos; TA, tibialis anterior; GCM, gastrocnemius; \({}^{\lx@sectionsign}\) mean \(\pm\) standard deviation; \({}^{**}\)\(p\) < 0.01.} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of average muscle activity and muscle fatigue of lower-extremity muscles during six-minute treadmill walking.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 5 Point-of-Care Clinical Diagnostics in HF
### MMPs, TIMP and Collagen Degradation Products  
Matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) are responsible for maintenance of the equilibrium between collagen types I and II in the cardiac extracellular matrix. Certain causes of HF are the result of or are accompanied by changes in the cardiac extracellular structure, such as myocardial fibrosis in dilated cardiomyopathy-opathy [199; 200]. Additionally, MMPs are an integral part of the atherosclerotic plaque extracellular matrix, and MMP plasma levels are elevated after myocardial infarction as a result of plaque rupture [201]. Thus, the MMP/TIMP system can be used as a possible target of HF treatment and possibly may serve as a future indicator of imminent myocardial infarction [202; 203].  
There are MMP-8-specific immunomodromagnographic dip-stick tests available, which can be used as a bedside test and deliver rapid information about MMP-8 activity. However, this activity can be a result of various pathologic conditions, such as periodontitis, peri-implantitis, and CVD [204; 205]. Therefore, use as an early detection method of atherosclerotic plaque rupture cannot be proven yet. There are an abundance of point-of-care collagen degradation assays, which could be useful in the early detection of cardiac remodeling. However, a relevant clinical use for this application is still to be discovered in the future [206].
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 5 Experimental Results and Analysis
Even under the low SNR condition  
Figure 14: Look angle estimation errors in the experimental arc segments of LowEleArc. LowEleArc: low elevation arc. Figure 15: Look angle estimation errors in the experimental arc segments of HighEleArc. HighEleArc: High elevation arc. of 10 dB, the mean value of the look angle estimation errors using the LowEleArc arc segment is 0.0156\({}^{\circ}\), and the variance is 0.1487\({}^{\circ}\); the mean value of the look angle estimation errors using the HighEleArc arc segment is 0.0031\({}^{\circ}\), and the variance is 0.0337\({}^{\circ}\). Therefore, the performance of the proposed method is still good. Based on the above experimental results, the method proposed in this article has good robustness. Therefore, the robustness of the proposed method in this article is validated. The proposed method has good robustness because the coplanarity of the scatterers on the antenna component of the spaceborne SAR is easily satisfied, and the normal vector of the selected antenna datum plane is easy to obtain and very accurate. In addition, the normal vector of the datum plane of the selected spaceborne SAR antenna is not necessarily along the positive direction of the X-axis, as chosen in this article. It can be  
Figure 16: The look angle estimation errors with the LowEleArc segment under the Gaussian noise conditions of 10 dB, 15 dB, and 20 dB. LowEleArc: low elevation arc. Figure 17: The look angle estimation errors with the HighEleArc segment under the Gaussian noise conditions of 10 dB, 15 dB, and 20 dB. HighEleArc: high elevation arc. selected as needed according to the definition of the satellite body coordinate system and the component body coordinate system.
## 4 Materials and Methods
### Methodology  
Figure 2 summarizes the methodology used in the study. The process began with the collection of ERA5 hourly fractional cloud cover data [43], i.e., the average portion of the sky covered by clouds when observed from a specific location per each study station from 2014 to 2021. The hourly data were summed to produce daily fractional cloud cover data for each site. A clear-sky day occurred when the daily fractional cloud cover for each station was zero. Following the identification of clear-sky days, the hourly McClear clear sky model data timesteps were compared to modelled clear-sky data. Each model's clear-sky data were generated by solving the algorithms in Section 3 and entering parameters in Table 3. The matched timesteps of data were further filtered by considering only the hours when the zenith angle was less than 90 degrees, i.e., only the hours between sunrise and sunset, during the validation process. Each model's performance was benchmarked using statistical metrics.
## 3 Proposed MFA-AOA Algorithm  
CHs are chosen using a formula based on the node's various properties in traditional protocols. Even though these approaches are easy and simple to apply, they do not consider suitable criteria for choosing CHs. Although metaheuristic-based methods are more efficient in picking optimal CHs than classical techniques, they suffer from high time and computational difficulties since an iterative algorithm must be used to identify CHs at each round. The three categories mentioned above all have the disadvantage of not being application-specific. Therefore, the controllable parameters cannot be adaptively altered to meet the application's needs. Though these procedures may provide acceptable results in certain situations, their effectiveness may be compromised in others. Furthermore, most present protocols do not use adequate criteria for selecting CHs (during the clustering phase) and forwarders (during the multi-hop routing phase). The proposed MFA-AOA is offered to address the problems mentioned earlier.  
By altering the components, an optimization strategy is utilized to reduce or maximize a function's output. All viable options for this problem are referred to as possible solutions, and the best is referred to as the optimal solution. All swarm intelligence algorithms are population-based, which means that their iterative approach improves the position of individuals in the population and, as a result, their progress toward better positions. The network model, energy model, Mayfly optimization description, and Aquila optimization technique are all covered in this section. Figure 1 depicts the construction of the WSN. The following factors are used to develop the network model:  
* All SNs starting energy and process time in a WSN are comparable.
* The Euclidean distance formula is used to compute the distance between the sensors
* The SNs are placed in the sensing region at random, and their position remains constant after installation
* The SNs send information to BS regarding the reserve energy and distance. An efficient CH selection method is used to choose CHs for all SNs based on that data. The routing procedure is then employed to find the optimal path between the CHs and the BS [29].
## 2 Materials & Methods
### CT Analysis  
All patients underwent CT examination including the abdomen. For the quantification of total adipose tissue (TAT), VAT, subcutaneous adipose tissue (SAT) and total abdominal muscle (TAM), an application of Horos v.4.0.0 RC2 software (Annapolis, MD, USA) was used to yield semiautomatic quantification by selecting the attenuation values of the adipose tissue and skeletal muscle (i.e., respectively about \(-190/-30\) and \(+10/+40\) Hounsfield Unit). The area was calculated on the axial image located 3 cm above the lower margin of L3 vertebra as described above [21]. The TAM area was measured on the same slice using a region of interest (ROI) on the following muscles: rectus abdominis,transversus abdominis, external oblique, quadratus lumborum, ilicostalis lumborum, longissimus thoracis, spinalis thoracis and psoas major [22]. All the ROIs were measured by the consensus of two experienced radiologists ([PERSON] and C.A.M.), who were blinded to clinical data.  
Female patients were not included for body composition analysis due to lack or unclear information about menopausal status, which is known to influence fat distribution [23]. Only Caucasian patients were included due known differences of body compositions according to different ethnicity [24; 25].
## 3 LCI System Design Framework
### Motion Artefacts and Fringe Washout
#### 3.3.1 Axial Motion Effects  
Motion in the axial plane may occur due to vibrations of the measured sample or sensor leading to SNR reduction. This manifests as fringe washout due to the continuous phase changes of fringes over the integration period of the measurement. Equation (20) represents the detected number of electrons per wavenumber, \(N(k)\):  
\[N\left(k\right)=N_{0}sinc\left(k\Delta z\right), \tag{20}\]  
where \(N_{0}\) is the number of electrons obtained when the sample and sensor are stationary and \(\Delta z=\left|v_{z}T\right|\) is the axial displacement of the sample during the integration time \(\left(T\right)\) with axial velocity \(\left(v_{z}\right)\). Performing a Fourier transform on Equation (20) also allows for the approximation of the factor \(sinc(k\Delta z)\) to be seen as \(sinc(k_{0}\Delta z)\) for the case \(\left|\Delta z\right|<<\left|z-z_{b}\right|\), where \(z_{b}\) is the zero path length location. Thus, the axial motion of the sample or sensor gives rise to an SNR penalty of \(sinc^{2}(k_{0}\Delta z)\). For \(k\Delta z>>1\), broadening of the axial resolution occurs, due to the Sinc function in Equation (20) limiting the effecting spectral bandwidth. This can be seen as a convolution of the original image with a Rect function given by the Fourier transform of the Sinc function [54].
# Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study
## 3 Methodology
### Bulding Damage Assessment Relative Stiffness Method
In this paper we investigate how MT-InSAR measurements can be used, in combination with the relative stiffness method, to rapidly evaluate potential tunneling-induced building damage by providing information on the actual soil-structure mechanism which would be otherwise inaccessible. In particular our procedure and validation can be summarized in the following steps: (a) analytical calculation of the greenfield displacement fields (i.e., zero height) for the Crossrail project, (b) comparison and validation of the analytical greenfield settlement profile with persistent scattering (PS) points located on the ground, (c) calculation of the PS based deflection ratio for the selected buildings and modification factor calculation as in Equation (1), (d) derivation of the building strains and their comparison with the greenfield strains.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
###### Abstract  
This paper examines the performance of Bluetooth Low Energy signal reception for indoor localization by analyzing the interactions between gateways, beacons, and receiver placements. The study investigates the effect of different BLE beacon placements on signal strength and localization accuracy. It evaluates ten receiver ceiling-mounted and wall-mounted configurations, as well as five beacon body positions: shoulder, front pocket, back pocket, and wrist. A dataset comprising 2700 data points was collected and localization accuracy was assessed using a Radial Basis Function-based methodology. The results demonstrate that ceiling-mounted gateways offer more stable signal strength and enhanced localization accuracy compared to wall-mounted gateways. The findings highlight the significance of optimizing both gateway positioning and body placement to improve the performance of BLE-based indoor positioning systems.  
Keywords:bluetooth low energy signal reception; indoor localization; gateway and beacon interactions; open-source dataset +
Footnote †: journal:
# Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs
## 3 Results
### Magnetic Dataset  
In Campos Basin, there is a large number of wells where borehole imaging logs and, consequently, magnetic field data from their orientation tools were acquired. Our choice for well 1-RJS-755-RJ is due to the fact that 58 core samples were collected and analyzed in laboratory for magnetic susceptibility along about 550 m of Pre-Salt rocks. The sampled interval includes carbonates on the top of an igneous sequence composed by basalts, diabases, hyalocalites and breccias. From the total, 47 samples were identified as igneous rocks and 11 as carbonates, all showing high alteration degree [22]. This large number of laboratory measurements provides the necessary control to validate our susceptibility estimates, but since samples were not oriented, the directions of the remanent magnetization were not determined, which makes comparison impossible.  
Figure 7: Simplified stratigraphic and tectonic framework of Campos Basin. (**a**) The main unconformities are: BSU (Base-Salt Unconformity), PRU (Post-Rift Unconformity), tRift (top Rift), tI/A (top Itabapoana/Atafona fms.), tBas (top basement), modified from [28]. (**b**) Geomagnetic polarity time scale for Cretaceous, modified from Concise Geologic Time Scale [29].
## 4 Conclusions  
In this study, a novel 2-vinylpyridine functionalized magnetic nanoparticle (Mag-PVP) was successfully prepared for the decontamination of BPA from aqueous solutions. The prepared Mag-PVP exhibited excellent thermal stability and saturation magnetization, and could be easily separated from the suspension by an external magnetic field. The maximum adsorption capacity (\(Q_{m}\)) of Mag-PVP for BPA obtained from the Langmuir isotherm was 115.87 mg/g at 20 \({}^{\circ}\)C, which was more than most other adsorbents. Mag-PVP reached its adsorption equilibrium in approximately 10 min. In the presence of NaCl, the amount of BPA adsorbed at the equilibrium on Mag-PVP improved because of the screening effect of Mag-PVP surface charge and the salting-out effect. In the presence of CaCl2 and HA, the amount of BPA adsorbed at the equilibrium decreased due to competitive adsorption. The adsorption of BPA by Mag-PVP increased slightly with an increase in pH from 3.0 to 5.0 and obtained the largest adsorption amount at pH 5.0, which was attributed to hydrogen bonding interactions in the adsorption process. Mag-PVP showed excellent adsorption performance in removing BPA from environmental water samples. Furthermore, Mag-PVP magnetic composites could be regenerated and reused for adsorption of BPA.  
[PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
We gratefully acknowledge the generous support provided by the \"Program for Changjiang Scholars and Innovative Research Team\" in the University, the \"National Natural Science Foundation of China (51438008 and 51378251),\" the \"Support Program from the Central Government for the Local Science and Technology Development of Hubei Province (2018 ZYYD024)\", and the \"Natural Science Foundation of Hubei Province (2018 CFB397),\" China.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of Sorbent** & **pH** & **Temp. (\({}^{\circ}\)C)** & \(Q_{m}\) **(mg/g)** & **Reference** \\ \hline Mag-PVP & 6–7 & 20 & 115.9 & Our study \\ Porous carbon produced at 1000 from Moso bamboo & - & 23 & 41.8 & [41] \\ AC purchased from Wako & - & 23 & 56.5 & [41] \\ Modified CNTs & 6.0 & 7 & 70.0 & [42] \\ Magnetic chitosan fly-ash-cenospheres & 7.0 & 20 & 31.9 & [18] \\ Magnetic carbon nanotube & 6.2 & 25 & 45.3 & [17] \\ Multi-walled carbon nanotube & 5.2 & 25 & 88.27 & [43] \\ Magnetic particles MP\({}_{\text{DVB}}\) & 8.0 & 20 & 90.9 & [20] \\ MAP-GBM & 7.0 & 20 & 324.0 & [44] \\ Vc-GBM & 7.0 & 25 & 133.1 & [45] \\ Graphene oxide & 6.0 & 25 & 87.8 & [46] \\ KOH-activated tyre pyrolysis char & 6.5–7.5 & 25 & 123.0 & [47] \\ \hline \hline \end{tabular}  
* AC = activated carbon. CNT = carbon nanotubes. MP\({}_{\text{DVB}}\) = magnetic polydivinylbenzene. MAP-GBM= magnesium ascorbyl phosphate-graphene-based monoliths. Vc-GBM = three-dimensional-graphene-based monoliths.  
\end{table}
Table 3: Comparison of BPA adsorption on Mag-PVP with other adsorbents.
# Quantitative Testing and Analysis of Non-Standard AEB Scenarios Extracted from Corner Cases
## 1 Introduction
To assess the danger level of the following car scenarios more accurately, it is necessary to consider additional factors, such as preceding vehicle speed, acceleration, and deceleration. Recent research has proposed new evaluation indicators like the Safety Distance Index (SDI) and Safety Time Index (STI), which comprehensively consider various factors in the following scenarios and have been validated in practical scenarios. To address the limitations of TTC and THW, [PERSON] et al. [20] proposed a method that calculates a danger perception coefficient by applying a reciprocal weighting approach. Furthermore, with the advancements in autonomous driving and artificial intelligence technology, various hazard assessment algorithms for hazardous driving conditions have been proposed. For example, [PERSON] et al. [21] quantified the risk level using metrics such as maximum deceleration, average deceleration, and percentage decrease in vehicle kinetic energy. They also utilized classification and regression trees to study the relationship between the risk level and driver characteristics, surrounding vehicles, and road environment. [PERSON] [22] comprehensively evaluated the risk level between the host vehicle and the preceding vehicle during lane-changing processes by using the ratio of relative distance to safe distance. [PERSON] et al. [23] developed a graded hazard assessment model for multiple key points during the vehicle braking process, utilizing an adaptive cuckoo search algorithm and BP neural network. The Responsibility-Sensitive Safety (RSS) model [24] proposed a minimum safe distance between the target vehicle and the following vehicle, assuming the worst-case scenario of the target vehicle suddenly applying the brakes. However, the RSS model's conservative approach limits its use in risk assessment. Professor [PERSON]'s team from Tongji University proposed the use of time margin as an indicator for evaluating potential risks in the following scenarios [25]. Time margin enables the partitioning of the hazard area and earlier detection of rear-end collision risks, making it suitable for describing risks in stable following processes. In summary, this article aims to extract typical hazardous scenarios from the KITTI dataset using a natural driving data extraction method. Inspired by the use of the five-point method for camera relative motion estimation [26], the five-point sampling method will be employed to simulate and reconstruct these typical hazardous scenarios in PRESCAN (8.5.0) and SIMULINK (R2020a). Our proposed boundary collision evaluation model will be used to quantitatively evaluate the performance of automatic emergency braking systems.