esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results (Study 1)
### Phase 2: Classification and Ranking of the FOD--A Combinations Based upon Simulator Performance
Discriminant scores calculated using the first discriminant function revealed that FOD-A combinations 1 and 2 were grouped together while combinations 3 and 4 were grouped together, which suggests that performance on FOD-A combinations 1 and 2 was different than performance on FOD-A combinations 3 and 4. A second discriminant function was calculated as shown below:  
\[\text{D}= (-0.31)Z_{1}+(0.16)Z_{2}+(0.83)Z_{3}+(0.30)Z_{4}+(-0.50)Z_{5} \tag{2}\]  
where D = Discriminant score or classification, \(Z_{1}=\) Average target E reaction time, \(Z_{2}=\) Percent of target Es detected, \(Z_{3}=\) erage brake reaction time, \(Z_{4}=\) Number of target E extra presses, and \(Z_{5}=\) Average speed. Although the second discriminant function accounted for only 6.88% of variance explained, the relative contribution of this linear combination was not considered. However, to further understand the classification and order of the FOD-A combinations, scores on the second discriminant function (y-axis) were plotted with scores on the first discriminant function (x-axis) to reveal that FOD-A combination 1 was grouped separately from combination 2. Based on the first and second discriminant functions, the FOD-A combinations can be ranked from easy to hard in terms of task performance as: combination 1, combination 2, combinations 3 and 4. As part of the LDA process, a classification analysis [46] was conducted to assess the accuracy of the predictive classification model developed (i.e., using the first discriminant function only). The apparent classification rate was 70.90%. Because the classification rate will be positively biased when making predictions using the original data, cross-validation is preferred for a more accurate assessment of predictive accuracy [54]. To assess prediction accuracy, 70% of observations were randomly selected for the training sample and the coefficients from the first discriminant function were calculated. Using these coefficients, the remaining 30% of observations (i.e., the test sample) was classified and the classification rate was calculated. This process was repeated 1000 times. The average classification rate across the 1000 iterations provides a less biased estimate of the classification rate. The cross-validated classification rate was 71.48%. This means that if a clinician only had the results of these five simulator variables (average target E reaction time, percent of target Es detected, average brake reaction time, number of target E extra presses, and average speed), approximately 71.5% of the time that clinician would be able to classify the FOD-A combination of their healthy older adult (when using Equation (1)).
# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
## 3 Methodology
### Mock-Up Crime Scene Setup  
An indoor mock-up crime scene was prepared at Naif Arab University for Security Sciences (NAUSS), featuring elements like a dummy, imitation evidence markers, and blood spatters, as previously shown in Figure 1. Two AprilTag markers were strategically positioned facing each other at the crime scene as reference markers on the wall, with 4.18 m between them, for the 3D reconstruction scaling procedure when using the Recon 3D application. Moreover, five markers were strategically placed near areas with evidence. The distance between these markers is detailed in Table 3, which was accurately measured using a laser ruler, as shown in Figure 2.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Ice
#### 2.1.2 Lake Ice Cover
In addition to the radar-based investigations, lake surface (ice/snow) temperature observations from MODIS have also been evaluated for estimating lake ice thickness [91]. Using heat balance terms derived from the Canadian Lake Ice Model [92], the authors retrieved ice thicknesses up to 1.7 m from MODIS with RMSE of 17 cm and MBE of 7 cm when compared to field measurements acquired on GSL and Baker Lake, Canada. Work on the estimation of ice thickness from satellite remote sensing is still in its infancy. Biases in retrievals are relatively large from some spaceborne instruments, while the remote sensing time series are generally too short to analyze climate trends in satellite-derived ice thickness. Bedfast ice--Radar remote sensing allows for distinguishing lakes with bedfast (grounded) ice due to the difference in backscatter intensities between floating ice (generally higher backscatter return) and bedfast ice (lower return) [93]. Recent analyses of polarimetric SAR (X-, C-, and L-band) satellite and ground-based scatterometer (Ku- and X-band) measurements, supported by radiative transfer modeling experiments, have revealed that the high backscatter of floating ice on shallow Arctic lakes is from the ice-water interface (due to appreciable surface roughness or preferentially oriented ice facets), dominated by single-bounce scattering [94; 95]. Areas of bedfast and floating lake ice are monitored/mapped from SAR using image thresholding [73] or region-based segmentation approaches [96]. Analyses of C-band SAR time series (ERS-1/2, RADARSAT-1/2, ENVISAT ASAR, and Sentinel-1) have been used to document trends and variability in bedfast ice across Alaska, over the last 20-25 years [73; 88]. [PERSON] et al. [97] have also shown the potential of a unique time series of three-year repeat-pass TerraSAR-X imagery with higher temporal (11 days) and spatial (10 m) resolutions than available in past studies for monitoring both bedfast ice and lake ice phenology in the Lena River Delta, Siberia. The authors also analyzed an 11-day sequential interferometric coherence time series from TerraSAR-X as a supplementary approach for bedfast ice monitoring. Coherence time series have been found to detect most areas of bedfast ice as well as spring snow/ice melt onset.
## 4 Discussion
### Content Validity  
To establish content validity, the correlations between the spatiotemporal metrics obtained during the acceleration and steady-state phases were examined. The \"moderate\" correlations (i.e., ranging from 17.9 to 37.8% of the shared variance) suggest that the observed spatiotemporal variables provide independent measures based on the skating phase in which they were acquired. These findings are in line with the idea that spatiotemporal parameters of sprint skating are task dependent, that is to say the skating kinematics are changed with an increase in skating speed from a running-like technique at acceleration to a gliding technique during the steady-state phase [15]. For instance, [PERSON] et al. [14] showed that accelerometer-derived metrics could be sensitive to the skating phase, with a shorter contact and stride time during the acceleration phase. The results from the current study corroborate this concept, showing a significantly shorter gliding time (367 vs. 433 ms), length (2.31 vs. 4.35 m), double support time (63 vs. 82 ms), and length (0.41 vs. 0.84 m), and step length (1.89 vs. 3.51 m) in the acceleration phase than in the steady-state phase. This was accompanied by a higher step frequency (3.32 vs. 2.89 step/s) and a lower velocity (6.18 vs. 10.03 m/s) during the acceleration phase compared to the steady-state phase.  
Based on previous studies, phase-to-phase changes are the result of greater hip rotation and abduction, and knee flexion at ice contact, during the acceleration phase than the steady-state phase [5; 9]. These specific joint actions enable a larger stride propulsion and push-off force, which are accompanied by a shorter contact and stride time and higher muscle activation (e.g., the gastrocnemius) in the acceleration phase than in the steady-state phase [14; 15; 44]. Moreover, the players' center of mass has a larger vertical and a smaller side-to-side oscillation during the acceleration phase than in the steady-state phase [9], which is followed by a decreased step width and an increased glide length as a player transit from the running-like to the gliding phase [9; 45; 46].  
All of these biomechanical and technique adaptations, when transiting from acceleration to maximal steady-state skating, are logical when considering the mechanical properties of the ice surface. Specifically, due to its low friction, players must set their skate blades in an optimal and fixed push-off on-ice position (e.g., with the blade on edge and perpendicular to the movement line) to maximize traction and the reactive force, which in turn enables fast forward propulsion and skating velocities [5; 45; 46]. After transition ing from running to gliding, the low friction of the ice becomes an advantage because it allows for longer strides, resulting in increased speed [45; 46; 47]. Consequently, it is essential to analyze the acceleration phase and steady-state phase separately to gain a complete understanding of skating kinematics and techniques, since the maximum skating speed and acceleration are critical components of bands.
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 4 Evaluation Results and Discussion
### Simulation Results and Discussion
#### 4.3.2 Energy Efficiency  
We run the simulation in RPL-collect network (the sink collect environment data from every other nodes) with and without our IDS integration and obtain the energy and power consumption as calculated in Formulas (1) and (2) in Section 4.2. In normal RPL network, the energy is around 190J, while in the RPL with IDS integration, the average energy consumption is about 202J, which represents an increase of 6.3%. This is a lot more saving to compare with the IDS in [6], where the overhead is 30% for a setup of 64 nodes (the overhead would be larger if they setup for the same 100 nodes like our setup). The reason for this saving is that we only limit the IDS work locally, where monitoring nodes only need to deal with 8-12 nodes in average, instead of sending all the IDS data to one IDS center. In this paper, we implement between 8 and 12 nodes in each cluster for the experiment set-up. In other scenarios with denser network, the number of nodes in each cluster can be higher, which leads to slightly more energy consumption. Besides, the average power consumption in each node increases slightly 6.3% with 1.2 mW in IDS integration scenario to compare with 1.05 mW in normal scenario. This indicates that the network lifetime will not be affected much once implementing the IDS.
## 2 Model and Method  
The cyclopentadienyl metalloporphyrin (PTMCp, TM = transition metal) was first synthesized in 1990 [31] where it was stable and solid at room temperature, and was followed by the synthesis of similar compounds [32; 33; 34]. Their characteristic sandwich-structure has aroused our interest. In this article, we consider PTMCp complexes without side groups for transition metal elements from Sc to Co in the periodic table. We analyze their geometry structures and spin crossover properties, as well as the electron transport when connected to Ni electrodes.  
The optimized molecular geometries and the electronic properties are calculated using GAMESS at the B3 LYP/6-311G** level. To describe the procedure of the geometric transition for PTMCp clusters from one spin configuration to another, the ATK package via nudged elastic band (NEB) method is adopted [35; 36], which is a method for seeking saddle points and minimum-energy paths between two structures.  
To calculate the MR of these metal porphyrin complexes, we connect molecules with two coaxial Ni nanowires pointing at the center of a porphyrin and Cp ring. The junctions are then structurally optimized and relaxed. The supercell optimization and transport simulations in this work are executed using the Atomistix Toolkit (ATK) [35; 36], which is based on the density functional theory (DFT) combined with the non-equilibrium Green's function method. The local spin density approximation with the Perdew-Zunger parameterization (LSDA-PZ) is adopted for the exchange-correlation function, and the basis set of double zeta-polarized (DZP) atomic orbits is used [37; 38; 39]. Before calculating the transport properties, the structures are geometrically optimized until the force on any atom is less than 0.04 eV/A. The cutoff energy is set as 150 Hartree and the k-space mesh grid is set to be \(1\times 1\times 100\). In the simulation, the electronic temperature of 300 K is assumed in the real axis integration for the non-equilibrium Green's functions and in the electrodes for transport simulation. The current through the junction is calculated from the corresponding Green's function and self-energies using the Landauer-Buttiker [40; 41]  
\[I(V)=\frac{2e}{h}\int_{-\infty}^{+\infty}T_{\sigma}(E,V)[f_{L}(E-\mu_{L})-f_{R }(E-\mu_{R})]dE,\]where \(\mu_{L}\) and \(\mu_{R}\) are the electrochemical potentials of the left and right electrodes and \(f_{L}\) and \(f_{R}\) are the corresponding electron distributions of two electrodes. The spin-resolved transmission function \(T_{\sigma}(E,V)\) with the spin index \(\sigma=\uparrow,\downarrow\) is obtained by  
\[T_{\sigma}(E,V)=\text{Tr}\big{[}\Gamma_{L}G_{\sigma}\Gamma_{R}G_{\sigma}^{+} \big{]}.\]  
Here, \(G_{\sigma}\) is the spin-dependent retarded Green's function of the molecule and \(\Gamma_{\text{L/R}}\) is the coupling matrix between the molecule and the left/right electrode.
## 2 Materials and Methods  
### Amplification, Cloning and Sequencing  
Yeast wild type proteins (Tsa1, Tsa2, Trx1, and TrxR1) were obtained as previously described [3; 51]. Bacterial AhpC and the corresponding thiol reductive systems were amplified by PCR from the genomic DNA of the following species/strains: _Escherichia coli_/ATCC25922, and _Pseudomonas aeruginosa_/ATCC29853. The _alrbc_ gene from _Staphylococcus epidermidis_/ATCC12228 was synthesized by GenScript, based on the sequence SE_2357 from KEGG database, with optimized codons for expression in _E. coli_, and flanked with _Nde_ I and _Bam_ HI restriction sites. Amplified DNA fragments and synthetic gene were digested with _Nde_ I and _Bam_ HI and cloned into pET15b vector previously digested with the same enzymes. The resulting recombinant vectors were sequenced in an ABI 3730 DNA  
Figure 1: Structural and functional transitions of 2-Cys Prx. (**A**) In reduced (C\({}_{\text{P}}\)-S\({}^{-}\)) and in the fully folded (FF) state, 2-Cys Prx are decamers formed by association of five homodimers (\(\alpha_{2}[5]\)). The red trapeze denotes one of the homodimers that compose the decamer. The monomers of each homodimer are depicted in dark blue and light orange, represented in cartoon. At low hydroperoxides level, C\({}_{\text{P}}\) is oxidized to sulfenic acid (C\({}_{\text{P}}\)-SOH) and then the condensation with C\({}_{\text{R}}\) occurs, resulting in the formation of an intermolecular disulfide bond and locking the 2-Cys Prx in the locally unfolded (LU) state. (**B**) In 2-Cys Prx containing Thr (Thr-Prx), the disulfide formation results in decamer dissociation, while in 2-Cys Prx containing Ser (Ser-Prx), the decamers are stable even in the disulfide form. (**C**) At high levels of hydroperoxides, C\({}_{\text{P}}\)-SOH can react with another(s) hydroperoxide molecule(s) resulting in hyperoxidized species (C\({}_{\text{P}}\)-SO\({}_{2}\)H or C\({}_{\text{P}}\)-SO\({}_{3}\)H) and in the functional switch from peroxidase to chaperone. This functional switch may be accompanied by the appearance of species with molecular weight higher than the decamer. This process can be reversed by Srx, with ATP hydrolysis. The functional transition from peroxidase to chaperone can also be triggered by high temperatures and the peroxidase activity is restored at physiological temperatures. The 2-Cys Prx representations were made using the crystallographic coordinates of _Saccharomyces cerevisiae_ enzyme (PDB = 3 SBC).  
Analyzer (Applied Biosystems) using the Big Dye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) and then were used to transform _E. coli_ BL21 (_DE3_) or _E. coli_ Tuner (_DE3_) strain (Sigma-Aldrich, Taufkirchen, Germany).
# Novel Chitosan Derivatives and Their Multifaceted Biological Applications
## 3 Biological Applications of ChDs
### Miscellaneous Bio-Applications
A novel chitosan amphiphile (PEI-ss-HECS-ss-OA, HSPO) with glutathione (GSH)-reversible cationization and hydrophobicization by polyethyleneimine (PEI) and octylamine (OA), respectively, was developed. Overall, their study validated the great promise of HSPO as an efficient site-specific rapid co-trafficking vehicle of siRNA and chemotherapeutics for synergistic tumor inhibition [127]. A reactive antibacterial compound (4-(2,5-dioxo-pyrrolidin-1-yloxycarbonyl)-benzyl)-triphenyl-phosphonium bromide (NHS-QPS) was synthesized for chemical modification of CS, and a series of antibacterial N quaternary phosphonium ChDs were obtained. The water solubility and antibacterial activities of N-QPCSxy against _Escherichia coli_ and _Staphylococcus aureus_ were evaluated and confirmed along with its low cytotoxicity [67]. New novel chitosan-sulfonamide derivatives have been designed to develop new wound dressing biomaterials. The antimicrobial assay evidenced that chitosan-based sulfadiazine, sulfadimethoxine and sulfamethoxazole derivatives were the most active. The MTT assay showed that some of the ChDs are nontoxic. Additionally, in vivo studies on a burn wound model induced in Wistar rats demonstrated an improved healing effect and enhanced epithelialization of chitosan-sulfonamide derivatives. The obtained results strongly recommend the use of the developed ChDs as antimicrobial wound dressing biomaterials [109]. In terms of ChD-assisted anticancer therapy, an excellent review by [PERSON] et al. [128] is available. Table 3 gives the list of anticancer milestones achieved using ChDs \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Novel ChDs** & **Cancer Type** & **Anticancer Application** & **References** \\ \hline \hline \multirow{4}{*}{Chitosan–thymine conjugate} & \multirow{4}{*}{Liver cancer} & Antiproliferative activity towards HepG2 liver & \multirow{4}{*}{[59]} \\  & & carcinoma cells, no cytotoxic activity on normal & \\  & & mouse fibroblast cells, confirming selective targeting & \\  & & of cancer cells & \\ \hline Polypyrrole-chitosan & \multirow{2}{*}{Ehrlich ascites carcinoma, breast cancer} & Active against Ehrlich ascites carcinoma (EAC) cells & \multirow{2}{*}{[129; 130]} \\  & & and MCF-7 breast cancer cells & \\ \hline Sulfated chitosan & Breast cancer & Blocks cell cycle and FGF-2 mediated & \\  & & phosphorylation ERK & \\ \hline Sulfated benzaldehyde chitosan & Breast cancer & Induces apoptosis and blocks FGF-2 mediated & \\  & & phosphorylation ERK & \\ \hline Carboxymethyl chitosan & Liver cancer & Anitangiogenic activity decreasing VEGF and & \\  & & stimulating immune activity & \\ \hline Quatermized amino & \multirow{2}{*}{Cervical cancer, colon cancer} & Induces necrosis & [133] \\ \cline{1-1}  & & & \\ \hline Sulfated chitologlossaccharides: & Cervical cancer, colon cancer & Induces necrosis & [133] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the anticancer achievements of ChDs.
## 2 Methodology
### Coal Mine Roadway Water Intrusion Prediction and Assessment Procedure  
Building upon the theoretical foundation, Figure 11 presents the coal mine water intrusion prediction and assessment flowchart for excavation roadway roofs, structured in three consecutive modules.  
1. Water-induced fracture zone quantification Site-specific rock mechanics parameterization assesses hydraulic overpressure conditions to analyze and compute the hydraulically fractured zone's extent. Chemically degraded parameters define the softened fracture zone boundaries, ultimately delineating the water-induced fracture zone.
2. Excavation-induced fracture zone quantification The transient energy shock characteristics were verified through excavation methods. In mechanized excavation without transient energy, a fracture zone can be calculated via roadway parameters and geomechanical properties. In drilling-blasting excavation with blast-induced transient energy shock, extents of loosened, secondary, and crack initiation zones are analyzed and quantified using blasting and rock mass parameters.
3. Prediction evaluation and refinement loop The water-resisting zone extent is determined based on working face advance water detection data. Peridynamics local damage modeling computes the stability coefficient \(T_{JS}\), establishes discriminant criteria for prediction evaluation, and terminates or refines the process per validity assessment.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Concrete Circumstances** & **Corresponding Conditions** & **Form of Effective Judgment Function of Water Diversion Channel** & **Principle of Effective Judgment of Water Conduction Channel** \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Parts of the \\ water-resisting zone \\ were damaged \\ \end{tabular} } & \(y_{g}<0\) & \(T_{JS}=\sqrt{\text{e}^{\frac{(y+\gamma_{j})}{\eta_{S}}-1)(\frac{y+\gamma_{j}}{ \eta_{S}}-1)}+\kappa}\) & \(T_{JS}<1\): The closer \(T_{JS}\) is to 0, the lower the effectiveness of the water conduction channel; it can be considered that the sudden water will not occur temporarily, but there is the possibility that lagging-type sudden water cannot be ruled out; the closer \(T_{JS}\) is to 1, the higher the effectiveness of the water conduction channel. It can be considered that the lagging-type sudden water or instantaneous-type sudden water may occur. When \(T_{JS}>1\), the water conduction channel is highly effective, and instantaneous water intrusion may occur. When \(T_{JS}>1+\kappa(\text{y}_{S}\text{-}\text{z}_{S}<0)\) or \(1+\sqrt{\kappa}\) (\(y_{g}\text{-}\text{z}_{S}=0\)), the water conduction channel is effective, and instantaneous water intrusion can be considered to occur. \\ \hline \multirow{6}{*}{
\begin{tabular}{c} The water-resisting \\ zone was completely \\ destroyed \\ \end{tabular} } & \(y_{g}<0\) & \(T_{JS}=\sqrt{\text{e}^{\frac{(y+\gamma_{j})}{\eta_{S}}-1)(\frac{y+\gamma_{j}}{ \eta_{S}}-1)}}\) & When \(T_{JS}>1\), the larger the \(T_{JS}\), the more dangerous it is. The water conduction channel is effective, and instantaneous water intrusion can be considered to occur. \\ \hline \end{tabular}
\end{table}
Table 1: Cont.
# Developing a Novel Design for a Tubular Solid Oxide Fuel Cell Current Collector
###### Abstract  
This study presents a novel current collector design for a tubular SOFC and numerically investigates its performance. The new current collector design has a flow channel with a trapezoidal shape. Several channels, such as four, eight, and twelve, are investigated, and their effects on cell performance are reported and compared. Additionally, a traditional tubular SOFC and the newly developed design are presented. The equations of mass conservation, momentum, charge transport, and energy were considered in the numerical model, and the ANSYS Fluent SOFC module was used to solve the numerical model. The results show that the developed design performed better than the traditional design. The new design with twelve channels collected 0.384 A, higher than the other designs. Although the design with twelve channels gave a high concentration of hydrogen at the outlet compared to the designs with four and eight channels, it gave higher performance than the designs with four and eight channels. Increasing the number of channels in the developed design enhanced the cell performance significantly due to the increased contact area, leading to the efficient collection of the generated current.