esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography
## 3 Results
### Tibiofemoral Contact Force  
The JRFs extracted from the musculoskeletal model are shown in Figure 6, and statistical analysis of peak values are listed in Table 5. The differences of JRFs caused by the FPA modification were not statistically significant based on the \(p\)-values (\(p>0.05\)). In other words, the TF joint contact forces were not changed by the toe-in and toe-out gaits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Joint Moment (\%BW*Hb)** & **Natural** & **Toe-In** & **Toe-Out** & \(p\)**-Value** \\ \hline Max hip flexion & 3.21 (0.94) & 3.54 (0.86) & 3.34 (0.77) & 0.19 \\ Max hip extension & 3.16 (0.43) & 2.89 (0.57) & 3.22 (0.51) & 0.43 \\
1 st hip abduction & 4.43 (0.66) & 3.97 (0.85) & 4.46 (0.70) & 0.12 \\
2 nd hip abduction & 4.77 (0.95) & 4.51 (1.05) & 4.65 (0.85) & 0.55 \\ Peak hip rotation & 0.79 (0.39) & 0.74 (0.35) & 0.90 (0.37) & 0.07 \\
1 st peak knee flexion & 2.35 (0.41) & 2.29 (0.55) & 3.23 (0.65) & 0.22 \\
2 nd peak knee flexion & 1.63 (1.12) & 1.45 (0.94) & 1.60 (0.99) & 0.50 \\
1 st knee abduction & 2.23 (0.35) & 1.97 (0.43) * & 2.25 (0.29) * & 0.04 \\
2 nd peak abduction & 2.45 (0.54) & 2.06 (0.72) & 2.31 (0.57) * & 0.03 \\ Peak ankle plantar flexion & 8.48 (0.45) & 8.17 (0.59) * & 8.34 (0.24) * & 0.04 \\ \hline \end{tabular} BW: body weight, Ht: height, * significantly different from natural gait, # significantly different from toe-in gait.  
\end{table}
Table 4: Joint moments of hip, knee, and ankle (mean \(\pm\) SD).  
Figure 5: Joint moments (mean and standard deviation) calculated from the musculoskeletal model for the natural, toe-in, and toe-out gait patterns (12 participants and 5 trials for each gait patterns).
## 5 Conclusions  
The mutational analysis of mRNA secondary structures in the regulatory region of the _ilvBNC_ operon of _C. glutamicum_ allows us to propose a model of the regulation of its transcription with the participation of three mRNA secondary structures, which essentially play the roles of an antiantiterminator, antiterminator, and terminator, and demonstrates the effectiveness of the influence of the terminator structure on the regulation of _ilvBNC_ operon expression, AHAS enzyme activity, and valine production.  
Computer calculations of the formation of secondary structures in the attenuator region of the _C. glutamicum ilvBNC_ operon have shown that the bases of the two thermodynamically stable structures--the transcription terminator hairpin and the antiterminator hairpin--are adjacent to each other and overlap at a trinucleotide GCC. The choice between the termination and antitermination of transcription comes down to competition between hairpin bases for binding to the boundary trinucleotide GCC at positions 76-78, which is complementary to the GGC trinucleotides at positions 111-113 and GGC at positions 36-38 (Figure 1). The presence of a boundary trinucleotide, which is important for the formation of both hairpins, is a hallmark of the _ilvBNC_ operon. Because of this feature, the attenuator becomes capable of rapid, low-energy transitions from one state to another without the complete disruption of base pairing. This is a new type of attenuator that can be called \"fast\". Our model is supported by experimental data and has high predictive value. All mutations located at the base of the terminator hairpin GGGC (positions 110-113) disrupt complementarity with the tetranucleotide GGCC (positions 76-79) and lead to the activation of the expression of _ilvBN_ genes, an increase in the activity of the key enzyme of valine synthesis--AHAS--and an increase in the production of valine. These results expand our knowledge of the transcriptional regulation of the _ilvBNC_ operon and may facilitate the development of valine-producing strains.
## 2 Estimation of Interruption Costs Arising from Transmission System Originated Failures  
This section describes the methodology to estimate the cost of interruptions incurred at the bulk load points due to forced outages of components of a bulk power system. As earlier mentioned, the customer interruption costs obtained from previous works [29; 30; 31; 32; 33; 34; 35; 36] are mainly applicable to the determination of optimal investment in generation capacity reserves in a power system. It should be noted that power utilities decide investment in generation based on the capacity reserve requirement, or a generation adequacy criterion, such as NERC's loss of load expectation (LOLE) criterion. The peak demand dictates the capacity reserve and is most sensitive to the LOLE index. Value-based investment in generation resources, therefore, utilizes customer outage costs during the peak demand. Existing customer damage functions available to the power industry are functions of the peak-demand-normalized costs with outage durations. A new approach is proposed in this paper that considers the fact that power outages arising from the failures of bulk system components, such as transmission lines or devices connected to the bulk power grid, can occur at any time of the day or year with a probability that can be estimated from past performance data. The new approach also recognizes the fact that the energy consumption patterns vary significantly depending on the type of electricity customer and so do the interruption costs. For example, a residential customer load is characterized by a specific diurnal and seasonal variation pattern, whereas an industrial customer load is more or less constant throughout the year. The bulk system customers are divided into different sectors, such as industrial, residential, commercial, farm, etc. The proposed methodology consists of (1) developing a sector periodic demand model, and (2) developing an integrated interruption cost model, in order to obtain the cost of interruption originating from HL-II components.
## 3 Results and Discussion
### Linearity and Slope  
The linearity was evaluated using correlation coefficients (R2). Regression is considered to be linear if R2 \(\geq\) 0.995 [50]. R2 values were higher than or equal to 0.98 for all elements of this work. The linearity was calculated based on five analyte concentrations. Mean values of intensity from three repetitions were taken for each calibration point. Table 5 shows the linearity, slope and the validation range of our method.  
A linear regression can be assumed for all analyzed elements except for Al, Co, Zn, Se and Pb--where the R2 is slightly lower than the recommended value [50].
## 3 Methods
### EU-SENSE Case Study
#### 3.1.3 Chemical Agents to Be Detected  
As mentioned above, two groups of hazardous chemical agents can be distinguished: chemical warfare agents (CWA) and toxic industrial chemicals (TICs). There are also various classes of chemical agents. Bland (2014) indicates nerve agents, blistering agents (vesicants), cyanides (blood agents), pulmonary agents (choking or lung-damaging agents), incapacitants (both mental and physical), toxic industrial chemicals (TICs), riot control agents (RCAs), and pharmaceuticals [33]. There are three types of agents: non-persistent (gaseous); persistent; and thickened, non-persistent. Non-persistent agents are disseminated mainly as vapour or may leave unevaporated liquid in shell or bomb craters. Persistent agents are disseminated as liquid and constitute a vapour hazard as well as a contact hazard. Thickened, non-persistent agents can be treated as persistent, ground contaminating agents [34].  
With the EU-SENSE sensor node, the following chemical warfare agents (CWA) will be detected: tabun (GA) (nerve agent, persistent), sarin (GB) (nerve agent, non-persistent), soman (GD) (nerve agent, non-persistent/persistent), cyclosarin (GF) (nerve agent, persistent), VX (methylphosphonothioic acid) (nerve agent, persistent), sulphur mustard (HD, Yperite) (blister agent, persistent), and lewisite 1 (L) (blister agent, persistent). Regarding toxic industrial chemicals (TICs), these are: ammonia (NH\({}_{3}\)), chlorine (Cl\({}_{2}\)), sulfur dioxide (SO\({}_{2}\)), hydrogen cyanide (HCN) and hydrogen sulfide (H\({}_{2}\)S), all non-persistent gases [35].
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
## 4 Estimation of QoS Thresholds on a Per-Service Basis
### Step 2: Estimation of Minimum Qos Thresholds
Thus, the minimum QoS threshold, \(\tau_{max}\), for VoLTE is the value of average DL packet delay above which the mean connection duration starts to decrease. It is envisaged that, in real networks, some services may not be fully represented by the three above cases. For instance, web service or social networks might show different behaviors depending on the size of their objects. Likewise, live streaming may have strict latency requirements.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
###### Abstract  
The reliability, scalability, and stability of routing schemes are open challenges in highly evolving vehicular ad hoc networks (VANETs). Cluster-based routing is an efficient solution to cope with the dynamic and inconsistent structure of VANETs. In this paper, we propose a cluster-based routing scheme (hereinafter referred to as connectivity-based clustering), where link connectivity is used as a metric for cluster formation and cluster head (CH) selection. Link connectivity is a function of vehicle density and transmission range in the proposed connectivity-based clustering scheme. Moreover, we used a heuristic approach of spectral clustering for the optimal number of cluster formation. Lastly, an appropriate vehicle is selected as a CH based on the maximum Eigen-centrality score. The simulation results show that the suggested connectivity-based clustering scheme performs well in the optimal number of cluster selections, strongly connected (STC) route selection, and route request messages (RRMs) in the discovery of a particular path to the destination. Thus, we conclude that link connectivity and the heuristic approach of spectral clustering are valuable additions to existing routing schemes for high evolving networks.  
clustering; connectivity; VANETs; scalability; stability +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### AM Fabrication  
Scalmalloy(r) feedstock was sourced from Toyal America, Inc. (Lockport, IL, USA). The chemical composition of Scalmalloy(r) is given in Table 1. The powder, shown in Figure 2a, exhibited a high degree of sphericity with minimal agglomerates. As seen in the schematic Figure 2b, DED was used to fabricate 25 mm \(\times\) 25 mm \(\times\) 4 mm Scalmalloy(r) coupons. The deposition was performed in a shielded argon environment using the parameters given in Table 2. Parameter optimization was performed and is detailed in [PERSON] et al. [31]. These optimized parameters displayed minimal defect formation and strong deposit-substrate adhesion. Furthermore, the DED Scalmalloy(r) deposits had densities that matched the theoretical, indicating that the chosen parameters can produce nearly fully dense parts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **Specification (wt\%)** & **Actual (wt\%)** \\ \hline Magnesium & 4.5–4.9 & 4.7 \\ Scandium & 0.68–0.78 & 0.73 \\ Zirconium & 0.2–0.4 & 0.33 \\ Manganese & 0.2–0.7 & 0.56 \\ Iron & \(\leq\)0.2 & 0.09 \\ Silicon & \(\leq\)0.2 & 0.08 \\ Titanium & \(\leq\)0.15 & 0.08 \\ Aluminum & Balance & Balance \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of Scalmalloy®.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Power (W) & 1625 \\ Scan Speed (mm/s) & 15 \\ Powder Feed Rate (g/min) & 2.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deposition parameters for this study. Reprinted with permission from Ref. [31], 2024, MDPI. Refer to [PERSON] et al. [31] for full DOE for parameter optimization.  
Figure 2: (**a**) Scalmalloy® feedstock sourced from Toyal America Inc. Reprinted with permission from Ref. [31], 2024, MDPI; (**b**) Schematic of directed energy deposition (DED) process. Reprinted with permission from Ref. [44], 2021, MDPI.
## 4 Discussion  
The effects of over-fertilization and ensuing nitrogen saturation have repeatedly delayed the successful rehabilitation of degraded grasslands [13]. As such, the development of a framework to identify grasslands at the brink of nitrogen saturation is fundamental in assisting rangeland management protocols and thus, safeguarding the well-being  
Figure 5: Classification results for discriminating nitrogen-saturated vs. -unsaturated grassland plots, where RF = Random Forest, and CART = Classification and Regression Tree analysis.  
Figure 6: The relative importance of each spectral variable in classifying nitrogen-saturated vs. nitrogen-non-saturated grassland plots.  
of South African grassland ecosystems. To this end, this study set out to characterize tropical grassland plots within the preliminary stages of nitrogen saturation using 2 m 8-band Worldview-3 satellite imagery (400-1040 nm) and decision tree techniques. The results produced from this study demonstrated that Worldview-3 spectral variables and the RF algorithm enabled the successful identification of nitrogen-saturated grassland plots. Furthermore, the Red-Edge (705-745 nm), Coastal (400-450 nm), and Near-Infrared (838-950 nm) parts of the electromagnetic spectrum, as well as the SAVI and the NDVI indices, were all proven to be highly influential spectral variables for identifying nitrogen-saturated grassland plots.
# Development and Application of MAGIC-f Gel in Cancer Research and Medical Imaging
## 8 Conclusions  
The development of MAGIC-f gel is a milestone in polymer gel application for three-dimensional dose measurement, due to its high thermal stability, low dependence of varying dose rate, energy, and fractionation. The MAGIC-f gel presents a prominent method for evaluating simple-to-complex dose-distribution applications, such as tomotherapy devices, intensity-modulated radiation therapy (IMRT) treatments, clinical brachytherapy, electron beam therapy, and conformal radiation therapy. From many studies, our understanding of different physical and chemical processes inside the polymer gel dosimeter has been extended. Very few attempts have been made so far to optimize the composition of MAGIC-f gel to obtain a highly efficient dosimeter that satisfies the dosimetric criteria and can achieve high dose sensitivity. Although Monte Carlo methods and comparison with TPS calculation are present gold standards for comparing the 3D dosimeter so far, future studies should focus on optimizing MAGIC-f gel capacity to give high spatial dose accuracy by optimizing its composition and developing more accurate evaluation-method parameters.  
**Author Contributions:** Conceptualization, R.D. and H.M.; methodology, R.D.; investigation, R.D. and M.Y.; resources, H.M.; writing--original draft preparation, R.D. and M.Y.; writing--review and editing, R.D.; visualization, M.Y. and H.M.; supervision, H.M.; project administration, R.D.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is supported by proposal assisted program funding of Texas Tech University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** We would like to acknowledge and extend our gratitude to the Fiber and Biopolymer Research Institute (FBRI) at Texas Tech University, for preparing MAGIC-f gel.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Applying a Genetic Algorithm to Elect a Path  
Since TSP is an NP-hard problem [10,11,38] we used a genetic algorithm to find the optimum sequence of checkpoints to be visited by the ferry. Genetic Algorithms are heuristic approaches which can be used to solve the TSP. They use simple chromosomes to encode solutions of data and apply crossover and mutation operators to these chromosomes to find an optimum solution. Good solutions will be selected by the fitness function and reproduced to produce a better solution and the bad ones will be removed. After several generations the genetic algorithms will produce an optimum solution to the problem.  
In this work, we represented the ferry's path as a list of genes or chromosomes. Where each checkpoint will have a number to identify it. e.g., \((1,2,3,4~{}....\,CPm)\) and the path or the solutions will be represented by the ordered sequence of checkpoints. Zero is used to represent the Base Station. The path will start and stop at the Base Station, so each path will contain 0 at the start and end of its sequence. Example of path representation will be (0,3,1,2,4,0). Below is the Pseudo code for the used genetic algorithm, the input to this pseudo code will be taken from the previous pseudocode that ordered the checkpoints into sequence according to their weights to be visit by the ferry.  
* **Input:** p(t) and c(t) are parents paths and offspring candidate paths in current generation t.
* // Input will be taken from the previous pseudocode
* **Output:** The optimum solution TSP.
## 5 Conclusions  
By analysing the relationships between the climate factor (rainfall) and the NDVI time series over the 30 years, we used the RESTREND method to distinguish the areas of productivity change impacted by human activities from those affected by climate dynamics. The results indicated that LD in the GSDM is mostly influenced by climatic drivers (41.41%), rainfall in this case, and less so by anthropogenic ones (11.59%). With about 50% of the district dominated by pastoralism and grazing, 50% of the district can be addressed by controlling over grazing. Other interventions include home gardens, cross-slope measures, minimal soil disturbance, area closure, etc. SLM technologies and approaches such as technical and material support and stakeholder engagements have to be implemented to address LD. The increase in vegetation biomass (9.52%) in other parts of the district could be due to bush encroachment caused by rainfall and/or human activities. Bush encroachment is a challenging factor as it can be attributed to a range of factors. It is recommended that further studies on bush encroachment be conducted to curb land degradation as it affects livestock grazing and thus livelihoods and has negative impacts on the ecosystem. Providing spatial data on how vegetation has changed over the last 30 years and spatial information on where LD is occurring using RESTREND, particularly due to human activities, is critical. This is important because it serves as the basis for district-level policy decisions that help identify SLM practices best suited to rehabilitate the landscape and address LD in a semi-arid region.  
M.J.K.: drafting the article and revising, conception and design, acquisition of data, analysis and interpretation of data; A.R., J.O., J.-M.M.K. and A.S.: substantial contributions to revising the draft of the article, concept design, analysis and interpretation of data, revising the article critically for crucial intellectual content. All authors have read and agreed to the published version of the manuscript.  
This study was co-funded by the Global Environment Facility (with the Grant number 00088758) and Department of Science and Innovation, Republic of South Africa (with the Grant number P1 AHS00).  
Ethics approval was obtained from the Council for Scientific and Industrial Research (CSIR) Ethics Committee. The declaration and procedures used in this study were adhered to.  
Informed Consent Statement: Informed consent was obtained from all individual participants included in the study.  
Data Availability Statement: Data available upon request from the corresponding author. The data are not available publicly as a result of privacy or ethical considerations.  
This research was made possible by the UNDP-GEF5 Sustainable Land Management Project through the CSIR and the CSIR Parliamentary Grant. Further acknowledgments go to the local community and the officials of the Limpopo Department of Rural and Agriculture Development (LDARD).  
The authors declare no conflict of interest.
# Cross Sensor Simulation of Tomographic SAR Stacks
## 5 Application to BIOMASS: Impact of Orbital Control
Many scatterplots as shown by the top panel of Figure 12 were computed and for each one the linear fit estimated. The effectiveness of the tomographic power at being a good proxy for AGB has been assessed by evaluating the correlation coefficient and the sensitivity to AGB; this latter expressed in dB/(Mg/Ha-100) so that a high value suggests more robust estimations. The behavior of the correlation and the sensitivity varying the orbital irregularities is shown in the bottom panels of Figure 12; again, the error bars express \(\pm 1\sigma\). As expected, the correlation between tomographic power and AGB drops by moving toward an irregular configuration, at the same time the dispersion around the average value increases. The same happens for the sensitivity, reaching a plateau after an error of about 4% of the critical baseline. Figure 10: Sensor positions in the zero Doppler plane of the simulated spaceborne system; regularly spaced (nominal) positions are shown as blue circles, irregular positions as red asterisks. The irregular coordinates have been obtained by modifying the nominal positions with a Gaussian perturbation, the elevation above the Earth surface being kept constant. The amount of perturbation is defined by the standard deviation of the Gaussian noise as a percentage of the total baseline aperture. In this work, 11 perturbations have been tested from 0% to 10%. The corresponding sampling of the total baseline aperture is shown in the 11 panels here shown. Figure 11: Tomographic SAR systems offer the possibility of focusing on specific depths by attenuating the radar echoes coming from surrounding elevations; the vertical discrimination can be appreciated by analyzing the impulse response functions (IRFs) here shown. The elevation of interest is here set around 0 m (with respect to a given reference height) that is, where the IRFs reach the maximum value. The attenuation of the echoes coming from other elevations is shown in dB in the vertical axis, being the horizontal axis the distance from the desired elevation in meters. Irregularities in the baseline distribution modify the IRF and worsen the capability of the tomographic system of rejecting undesired radar echoes. The panels here shown correspond to the sensor positions shown in Figure 10. 10 outcomes of the Gaussian noise perturbing the baselines have been generated for each perturbation intensity (going from 0% to 10% of the critical baseline); the average IRF, together with the \(\pm 1\sigma\) errorbars are here shown.