esa-sceva/satcom-chunk-collection huggingface dataset:
# Prevent Workers from Injuries in the Brewing Company via Using Digital Human Modelling Technology
## 3 Results  
### Pose#1 in Task#1  
For the posture analysis, two reference joint centers were used to define the movement of the trunk and hips in the digital human model: (1) Spine#1: the center of two posterior superior iliac spines (PSIS); (2) Root: the center of two greater trochanters. Hence, the trunk angle is defined as the relative angular displacement between vector #1, (from Spine#1 to the Root) and vector #2 (from the Acromion to Spine#1). The hip angle is defined as the relative angular displacement between vector #1 (from Spine#1 to the Root) and vector #2 (from the Root to the Knee), as shown in Figure 4. The joint angle distributions of the trunk, hips, and knees among the three subjects are listed below. Assuming that the trunk and hip angles are 0\({}^{\circ}\) at the neural pose (when the person stands erected), the positive and negative values for the trunk, hips, and knees indicate flexion and extension, respectively.  
Between subjects, the range of compressive load on the lower back ranged between 2485.2 N and 2869.2 N (2654.4 \(\pm\) 196.0 N), while the A/P shear forces ranged from 517.8 N to 674.0 N (613.8 \(\pm\) 84.1 N), as shown in Figure 5. The predicted maximum hand load to prevent workers from injury was approximately 200 N for the spinal forces to reach the recommended safety threshold.  
* Trunk: range from 6.5\({}^{\circ}\) to 27.6\({}^{\circ}\) (15.9 \(\pm\) 10.7\({}^{\circ}\)) in Figure 5b;
* Right hip: range from 11.7\({}^{\circ}\) to 50.2\({}^{\circ}\) (32.1 \(\pm\) 19.3\({}^{\circ}\)) in Figure 5c;
* Left hip: range from 25.8\({}^{\circ}\) to 47.2\({}^{\circ}\) (39.9 \(\pm\) 12.2\({}^{\circ}\)) in Figure 5c;
* Right knee: range from \(-\)1.1\({}^{\circ}\) to 20.4\({}^{\circ}\) (7.4 \(\pm\) 11.5\({}^{\circ}\)) in Figure 5d;
* Left knee: range from 15.5\({}^{\circ}\) to 23.8\({}^{\circ}\) (19.5 \(\pm\) 4.2\({}^{\circ}\)) in Figure 5d.  
For the correlation coefficient analysis, the A/P shear force and trunk flexion have a strong correlation with \(R\) = 0.81. The other variables do not show a strong correlation at this specific pose.  
Figure 4: The definition of trunk and hip angles in the digital human model.
## Materials and Methods  
### Materials  
Lipase B from _Candida antarctica_ (CALB) (3.8 U/mg by activity assay) was kindly provided by Novozymes (Bagsvaerd, Denmark). The substrate _p_-nitrophenyl palmitate (_p_-NPP) of analytical grade was purchased from Sigma-Aldrich (St. Louis, MO, USA). The standard _p_-nitrophenol (_p_-NP) of analytical grade was purchased from Sinopharm Chemical Reagent Beijing Co., Ltd. (Beijing, China). The PST chips were purchased from Biolin Scientific AB (Goteborg, Sweden). The giga-/meso-porous PST microspheres were prepared by National Engineering Research Center of Biotechnology (Beijing, China). All chemicals were of analytical grade and used without further purification unless otherwise described.  
The scanning electron microscope (SEM, JEOL, JSM-6700F, Tokyo, Japan) images of the microspheres were shown as Figure 1. The structural data of the microspheres measured by mercury porosimetry (Micromerities, AutoPore IV 9500, Norcross, GA, USA) were shown in Table 1. The gigaporous PST microspheres were recorded as PST-300, and the mesoporous ones were PST-14.  
Figure 1: SEM images of PST-300 (**a**) and PST-14 (**b**) microspheres.
## 3 Materials and Methods
### Dynamic Attestation
#### 3.2.2 Developed Dynamic Attestation  
Let \(\mathcal{S}\) denote the set of attestable subsystems. These represent a series of components, which can be attested by leveraging a collection of control and observable variables. Let \(u^{s}\), and \(y^{s}\) denote a control variable, and an observable variable, respectively, associated with subsystem \(s\in\mathcal{S}\). Let \(\mathcal{B}^{s}\) denote a set of attestable behavioural scenarios associated with a particular subsystem \(s\). An attestable behavioural scenario consists of a series of interventions that are performed on several control variables, which then trigger a certain expected behaviour measurable via observable variables.  
Let \(Y^{0}_{s}(t)\) denote the intervention-free measurement performed on \(y^{s}\) at time \(t\), and \(Y^{b}_{s}(t)\) the measurement performed on \(y^{s}\) at time \(t\) in scenario \(b\in\mathcal{B}^{s}\).  
Then, the intervention-free mean value for \(y^{s}\), denoted by \(\bar{Y}^{0}_{s}\), performed in a time window \(T^{0}\) is defined according to Equation (1):  
\[\bar{Y}^{0}_{s}=\frac{1}{n}\sum_{t\in T^{0}}Y^{0}_{s}(t),n=|T^{0}|. \tag{1}\]  
Next, for each behaviour scenario \(b\in\mathcal{B}^{s}\) applied during time window \(T^{b}\), the mean value for \(y^{s}\), denoted by \(\bar{Y}^{b}_{s}\), is computed according to Equation (2):  
\[\bar{Y}^{b}_{s}=\frac{1}{n}\sum_{t\in T^{0}}Y^{b}_{s}(t),n=|T^{b}|. \tag{2}\]  
According to these definitions, each regular agent calculates the cross co-variance for each \(y^{s}\) between the intervention-free, and the intervention scenario \(b\) according to Equation (3):\[C(y^{s},b)=\frac{1}{n}\sum_{t\in T^{b}}\frac{(Y_{s}^{b}(t)-\bar{Y}_{s}^{b})(Y_{s}^ {0}(t)-\bar{Y}_{s}^{0})}{\bar{Y}_{s}^{0}\bar{Y}_{s}^{b}},n=|T^{b}|. \tag{3}\]  
For all \(y^{s}\) and scenarios \(b\), \(C\) is hereinafter called the _impact matrix_. This quantifies the relation between the magnitude of interventions and their measured impact on the physical process.
###### Abstract  
Within this work, the radial Proportional Derivative (PD-) controller along with the eight-poles electro-magnetic actuator are introduced as a novel control strategy to suppress the lateral oscillations of a non-linear Jeffcott-rotor system. The proposed control strategy has been designed such that each pole of the magnetic actuator generates an attractive magnetic force proportional to the radial displacement and radial velocity of the rotating shaft in the direction of that pole. According to the proposed control mechanism, the mathematical model that governs the non-linear interactions between the Jeffcott system and the magnetic actuator has been established. Then, an analytical solution for the obtained non-linear dynamic model has been derived using perturbation analysis. Based on the extracted analytical solution, the motion bifurcation of the Jeffcott system has been investigated before and after control via plotting the different response curves. The obtained results illustrate that the uncontrolled Jeffcott-rotor behaves like a hard-spring duffing oscillator and responds with bi-stable periodic oscillation when the rotor angular speed is higher than the system's natural frequency. It is alsomfound that the system, before control, can exhibit stable symmetric motion with high vibration amplitudes in both the horizontal and vertical directions, regardless of the eccentricity magnitude. In addition, the acquired results demonstrate that the introduced control technique can eliminate catastrophic bifurcation behaviors and undesired vibration of the system when the control parameters are designed properly. However, it is reported that the improper design of the controller gains may destabilize the Jeffcott system and force it to perform either chaotic or quasi-periodic motions depending on the magnitudes of both the shaft eccentricity and the control parameters. Finally, to validate the accuracy of the obtained results, numerical simulations for all response curves have been introduced which have been in excellent agreement with the analytical investigations.  
Jeffcott-rotor; radial controller; electro-magnetic actuator; non-linear vibrations; quasi-periodic motion; chaotic motions; Poincare-map; frequency spectrum +
Footnote †: journal: Applied Sciences
## 4 Experimental Analysis
### Datasets  
In this study, two publicly available datasets are utilized to evaluate the performance of the proposed RNN-based epilepsy classification model. The primary difference between the two datasets is the electrode type which has a direct impact on the spatial coverage and quality of the recorded EEG signals. The first one is BONN dataset, which is a smaller dataset containing single-channel intracranial EEG signals. while the second one, CHB-MIT is a larger dataset consisting of 23 channels of surface EEG recordings. Since the spatial coverage in intracranial EEG is more localized and focused on specific brain regions, it provides a higher spatial resolution as compared to scalp EEG. Therefore, the BONN dataset offers enhanced data quality as compared with CHB-MIT in terms of spatial resolution as intracranial EEG electrodes provide more direct measurements of brain activity allowing for more accurate and detailed capture of neural signals [47]. Our proposed method was first tested for classification on the smaller BONN dataset to adjust the model parameters. Following this, the proposed method was used to classify the larger CHB-MIT dataset.
# A Comparison Study on the Metabolites in PC-3, RWPE-1, and Chrysin-Treated PC-3 Cells
## 2 Materials and Methods
### Cell Culture and GC-MS  
PC-3 cells (human castration-resistant cancer cell line No. 21435, Korean Cell Line Bank, Seoul, Republic of Korea) and RWPE-1 cells (benign prostate cell line; ATCC, Manassas, VA, USA) were cultured in RPMI 1640 (Cat: LM 011-01, Welgene, Daegu, Republic of Korea) medium supplemented with 10% fetal bovine serum (FBS) (Cat: S101-07, Welgene, Daegu, Republic of Korea) and 1% antibiotics (Cat: LS203-01, Welgene, Daegu, Republic of Korea) in a 5% CO\({}_{2}\) incubator at 37 \({}^{\circ}\)C. PC-3 and RWPE-1 cells (3 \(\times\) 10\({}^{5}\) cells) were cultured in 6 wells for 24 h. PC-3 cells (3 \(\times\) 10\({}^{5}\) cells) were treated with 10 \(\upmu\)M chrysin for 24 h. The cell supernatants were collected for metabolomic profiling using untargeted GC-MS. Untargeted GC-MS-based metabolomic profiling was performed by the National Instrumentation Center for Environmental Management in Seoul National University (Seoul, Republic of Korea). The details of this method are as follows: Approximately 20 mg of the dried cell culture medium was mixed with 1 mL of 70% MeOH by sonication for 30 min. After filtration with 0.2 \(\upmu\)m microporous membranes, 150 \(\upmu\)L of the collected supernatant was transferred into a GC vial and dried using a speed vac. For the analysis of compounds by GC-MS, dried extract samples were redissolved and derivatized by addition of 50 \(\upmu\)L of 20 mg/mL methoxyamine hydrochloride in pyridine and incubated at 30 \({}^{\circ}\)C for 90 min for oximation. Then, 50 \(\upmu\)L of BSTFA and 30 \(\upmu\)L of fluoranthene (1000 ug/mL in pyridine as an internal standard) were added to each sample for trimethylsilylation derivatization, and the mixture was heated at 60 \({}^{\circ}\)C for 30 min. The samples were then subjected to GC-MS. Each 1.0 \(\upmu\)L aliquot of derivatized sample was injected in a 30:1 split ratio into an Thermo Scientific (Trace 1310/ISQ LT, Seoul, Republic of Korea) GC-MS equipped with DB-5 MS capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent J\(\&\)W Scientific, Santa Clara, CA, USA). Helium was used as the carrier gas at a constant flow rate of 1.5 mL/min. The temperature program was as follows: The initial temperature was 50 \({}^{\circ}\)C held for 2 min, raised to 180 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and held for 8 min, increased to 210 \({}^{\circ}\)C at a rate of 2.5 \({}^{\circ}\)C/min, elevated to 325 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and maintained for 10 min. The temperature of the injector, transfer line, and ion source were set to 300, 320, and 270 \({}^{\circ}\)C, respectively. A mass range (35-680 m/z) in full-scan mode for electron impact ionization (70 eV) was applied. The solvent delay time was set to 14 min.
# Consumer Characterization of Wet- and Dry-Aged Mutton Flavor Profile Using Check-All-That-Apply
## 4 Discussion
While at first glance, these results may seem to indicate that the dry-aged sample is likely to have a more metallic flavor, it should be noted that the odor impact of 1-octen-3-one is very high and it has an odor threshold an order of magnitude higher than the other compounds (ppt vs. ppb). Therefore, it is proposed that this compound is contributing to the metallic flavor associated with the wet-aged parties [30; 42]. In addition to the compounds discussed above, sheep meat has a number of unique species-specific volatile compounds that can influence its flavor and aroma and impact the consumer acceptance of sheep meat [42; 61]. The effect of dry-ageing on these compounds has not been previously described. In the present study, a \"sheepy\" flavor was more associated with the wet-aged patties than dry-aged and the increased \"sheepy\" flavor in wet-aged patties could be attributed to two compounds: hexanoic acid was present at higher levels in the wet-aged samples and is associated with a \"goaty\" aroma [38], and skatole (3-methylindole) was also found in the wet-aged treatment, but not the dry-aged, and is associated with \"barnyard\", \"fecal\" and \"animal\" odor [42; 62]. Para-cresol (p-cresol), associated with a \"stable\" and \"animal\" odor [61], was found at very low concentrations in only the dry-aged samples and does not appear to have influenced consumer characterization of the dry-aged mutton flavor. Other potentially \"problematic\" compounds did not appear to be affected by ageing method; the concentration of the branched chain fatty acids 4-methyloctanoic (MOA), 4-ethyloctanoic (EOA) and 4-methylnonanoic (MNA), which are implicated in \"mutton\" flavor and consumer acceptance [62; 63], were not influenced by ageing method, suggesting that dry-ageing has no impact on the background level of these compounds.
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
## 3 Compression-Assisted Adaptive ECC and RAID Scattering  
This Section describes, first, the adaptive ECC and RAID-scattering scheme, followed by the metadata structure and operation of FTL for RAID parity management as an implementation issue.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BER & Bit Error Rate \\ CDP & Constructed data pilots \\ DFT & Discrete Fourier Transform \\ DPS & Generalized Discrete Prolate Spheroidal \\ DSRC & Dedicated Short Range Communications \\ GI & Guard Interval \\ iCDP & Improved constructed data piloted \\ LS & Least square estimation \\ LTS & Long Training Symbols \\ MBCE & The Midamble Based Channel Estimation Scheme \\ MCS & Modulation Coding Scheme \\ NLOS & Non line of sight \\ OFDM & Orthogonal frequency division multiplexing \\ PHY & Physical \\ PLCP & Physical layer convergence procedure \\ STS & Short Training Symbols \\ STA & Spatial temporal Averaging \\ TDL & Tapped Delay Line \\ TDLSE & Time Domain Least Square Estimation Scheme \\ VTV & Vehicle to vehicle \\ WSUS & Wide sense stationary uncorrelated scattering \\ \end{tabular}
## 4 Wind Retrieval Testing
### ASCAT Comparisons  
The remaining 20% of the aligned data were applied by the FDS GMF as described in Section 3.3.2. After the quality control filters, the maximum wind speed was 25 m/s and the number of validation data became 34,029. The performance of the ASCAT-trained GMF has an unbiased RMSE of 2.04 m/s, and the coefficient of determination is 0.61, as shown in Figure 7.  
Figure 6: Scatter density plots of GMF-derived wind speeds against ECMWF reanalysis dataset. The colors (from cool to warm) represent the percentage of the maximum number of the densest bin. The GMF-derived winds are on the horizontal axis and the ECMWF reanalysis data are on the vertical. **(a)** With the time distance of 1 h, the unbiased RMSE is 2.72 m/s, and the coefficient of determination is 0.5. The ECMWF reanalysis wind speed range is 0.6–36.7 m/s, and the retrieved result is between 0.6 and 36.1 m/s. **(b)** For 10 minus the alignment criteria, the unbiased RMSE is 2.63 m/s with the coefficient of determination of 0.55. The reanalysis wind speed range is 0.9–32.7 m/s, while the derived winds are 0-34.3 m/s.  
Figure 7: Scatter density plot of GMF-derived wind speeds against ASCAT observations. The colors (from cool to warm) represent the percentage of the maximum number of the densest bin. The GMF-derived winds are on the horizontal axis and the ASCAT wind speeds are on the vertical. The unbiased RMSE is 2.04 m/s at the ASCAT wind speed range of 0–25 m/s. Accordingly, the retrieved wind speed is 0–25.8 m/s.
###### Abstract  
Mobile Ad hoc Networks (MANETs) are characterized by highly dynamic phenomena and volatility. These features have a significant impact on network performance and should be present in the scenarios of experiments for the assessment of MANET-related technologies. However, the currently available experimentation approaches suffer from limitations, either employing overly abstract simulation-based models that cannot capture real-world imperfections or drawing upon \"monolithic\" testbeds suited only to a narrow set of predetermined technologies, operational scenarios, or environmental conditions. Toward addressing these limitations, this work proposes a versatile platform that can accommodate many of the complexities present in real-world scenarios while still remaining highly flexible and customizable to enable a wide variety of MANET-related experiments. The platform is characterized by a modular architecture with clearly defined modules for the signaling between peer mobile nodes, the tracking of each node's location and motion, the routing protocol functionality, and the management of communication messages at each node. The relevant software runs on inexpensive Raspberry Pi-based commodity hardware, which can be readily attached to robotic devices for moving the network nodes in accordance with controlled mobility patterns. Moreover, through an appropriate tuning of certain modules, a number of important operational conditions can be precisely controlled through software, e.g., restricting the communications range (thus reducing the network density) or for emulating the mobility patterns of nodes. The effectiveness and versatility of the proposed platform are demonstrated through the realization of a series of experiments on the performance comparison of selected routing protocols under diverse network density conditions.  
mobile ad hoc networks; UxV-based networks; experimentation platform; routing protocols; performance evaluation; diverse density conditions +
Footnote †: journal: Future Internet  
0000-0001-5400-0000-00001-54
# Road Safety Management of Uncontrolled Access Points:
## 3 Results and Discussion
### Analysis of Access Points Configuration
#### 3.1.1 Transition Entry Shape  
Table 2 shows data related to the entry manoeuvre; the same analysis is reported in Table 3 for both rural and urban areas.  
As shown in Tables 2 and 3, T\({}_{\text{2,j}}\) is the most widespread type (48% of the analysed access points have this configuration); T\({}_{\text{2,j}}\) is particularly common in the urban area (268 access points) whereas T\({}_{\text{1,j}}\) is the most common geometry in the rural area (112 access points).  
Although the highest number of accidents is recorded for the T\({}_{\text{2,j}}\) class, the value of \(F\) (\(F=0.49\)) highlights that the T\({}_{\text{4,j}}\) type shows the highest safety issues. The T\({}_{\text{4,j}}\) geometry is characterised by a higher interface area between the main roadway and the side access; this scenario can facilitate the formation of conflict points. This last finding is confirmed for the rural environment (\(F=0.71\)); in the rural area, this result appears to be related to the higher operating speeds that are a contributing factor to the increase in risk. The danger related to T\({}_{\text{2,j}}\) type is strictly related to the absence of a well-defined and univocal geometry which can be the main cause of the correct perception of the lateral access and the intersection area (driveway visibility, position of drivers, and pedestrians). On the contrary, the lowest \(F\) values are recorded for the T\({}_{\text{3,j}}\) class both for the aggregate analysis (\(F=0.35\)) and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Transition** & **N (T\({}_{\text{i,j}}\))** & **\% \({}^{*}\)** & **Total Number of Accidents** & **F** \\ \hline T\({}_{\text{1,j}}\) & 195 & 28\% & 74 & 0.38 \\ T\({}_{\text{2,j}}\) & 337 & 48\% & 138 & 0.41 \\ T\({}_{\text{3,j}}\) & 111 & 16\% & 39 & 0.35 \\ T\({}_{\text{4,j}}\) & 57 & 8\% & 28 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Aggregate analysis (rural and urban areas) of data related to the transition entry shape. % \({}^{*}\) is the percentage of access points with the same type of transition entry shape compared to the total number of accesses that were noted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Rural Area**} & \multicolumn{3}{c}{**Urban Area**} \\ \hline
**Type of** & \multicolumn{3}{c}{**Total**} & \multicolumn{3}{c}{**Total**} \\
**Transition** & **N (T\({}_{\text{i,j}}\))** & **Number of Accidents** & **F** & **N (T\({}_{\text{i,j}}\))** & **Number of Accidents** & **F** \\ \hline T\({}_{\text{1,j}}\) & 112 & 40 & 0.36 & 83 & 34 & 0.41 \\ T\({}_{\text{2,j}}\) & 69 & 29 & 0.42 & 268 & 109 & 0.41 \\ T\({}_{\text{3,j}}\) & 21 & 8 & 0.38 & 90 & 31 & 0.34 \\ T\({}_{\text{4,j}}\) & 21 & 15 & 0.71 & 36 & 13 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis related to the transition entry shape for rural and urban areas.  
urban area (\(F=0.34\)); the reason is probably attributable to a clear delimitation of the access entry which allows a better perception of the turning path.
## 4 Methodology  
### 3-Parameter Transformation  
This transformation (often called Molodensky 3-parameter transformation) belongs to the group of similarity transformations and is based only on translations along three coordinate axes (Figure 2).  
General expression for the 3-parameter transformation is [3]  
\[\mathbf{X}_{o}=\mathbf{t}+\mathbf{X}_{i} \tag{1}\]  
or, in matrix form,  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{o}=\begin{bmatrix}t_{x}\\ t_{y}\\ t_{z}\end{bmatrix}+\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{i} \tag{2}\]  
where \(\mathbf{X}_{o}\) is the position vector in the output coordinate system, \(\mathbf{t}\) is the translation vector from the input to the output coordinate system, and \(\mathbf{X}_{i}\) is the position vector in the input coordinate system, all in meters.