esa-sceva/satcom-chunk-collection huggingface dataset:
applied sciences  
The Effect of Load Carrying on Gait Kinetic and Kinematic Variables in Soldiers with Patellofemoral Pain Syndrome [PERSON] 1,2,*(), [PERSON] 1, [PERSON] 3,4() and [PERSON] 5()  
Individuals suffering from patello-femoral pain syndrome (PFPS) exhibit altered gait parameters compared with healthy individuals. As soldiers carry heavy equipment, the extra load might further alter gait pattern. The aim of this study was to investigate the effect of load carrying on kinetic and kinematic variables in soldiers with PFPS compared with controls. The sample comprised 23 active-duty infantry soldiers (10 with bilateral/13 without bilateral PFPS, mean age: 20.4 (\(\pm\)0.5) years, height 179 (\(\pm\)5.8) cm, weight 76 (\(\pm\)6.9) kg). The participants walked barefoot on a 10 m walkway with and without loading equipment. The equipment added 50% to each participant's body mass. Gait kinematic and kinetic variables were assessed by the VICON three-dimensional motion analysis system and two force plates. Weight carrying increased joint maximal angles, mean peak moments and double support and decreased single support and walk speed in both groups, without differences between groups (\(p>0.05\)). The only difference between groups was in the hip adduction angle without a load (\(p<0.05\)); no difference was observed while carrying the load. Kinematic and kinetic differences in gait were detected between weight and non-weight conditions, yet there was no effect of PFPS. Further studies with subjects performing different tasks are essential to examining the effect of PFPS and load among soldiers.  
Keywords:walking; knee pain; weight; load +
Footnote †: journal: Applied Sciences
# Research on the Processing Method of Acoustic Focusing Cavities Based on the Temperature Gradient
## 4 Conclusions  
On the basis of the relationship between the speed of sound and temperature of air, combined with the generalized Snell law, a new thermoacoustic phase control unit was designed in this paper by combining the metasurfaces and discrete temperature. The acoustic focusing theory, based on the combination of the metasurfaces and discrete temperature, was subsequently verified. The numerical simulation results showed that the acoustic wave was focused on a wide frequency range from 20 kHz to 55 kHz, thereby providing a wide frequency band for the internal processing of acoustic focusing. The whole 2\(\pi\) interval was covered by the transmission and reflection phase delay of the acoustic wave. The results showed that the focusing lens designed in this paper has the advantages of wideband, high focusing, and higher robustness properties. It is of guiding significance for acoustic focusing internal processing.  
Conceptualization, L.W.; methodology, L.W., H.W. and L.Z.; software, Y.F.; validation, Y.S., Y.F. and Y.W. ([PERSON]); formal analysis, L.W.; investigation, Y.F.; resources, L.W. and Y.W. ([PERSON]); data curation, H.W. and L.Z.; writing--original draft preparation, L.W. and Y.F.; writing--review and editing, Y.F. and Y.S.; supervision, Y.W. ([PERSON]); funding acquisition, H.W., L.W. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Natural Science Foundation of China under Grant No. 51775154 and 11902107 and Zhejiang Provincial Natural Science Foundation of China under Grant No. LY21F040005.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 20: Spatial distributions of acoustic intensity field through focusing lens at (**a**) 28 kHz and (**b**) 35 kHz.
## 4 Experiment and Result Analysis
### Unsupervised Feature Selection
#### 4.1.3 Comparison Schemes  
For convenience, the incomplete data set was divided into two parts, i.e., the incomplete set (IS), containing all incomplete instances, and the observed set (OS), containing all observed instances. To verify the effectiveness of the proposed UFS-ID method, we compared it with the following competing UFS methods for incomplete data sets:  
* For the baseline, we used the k-means clustering algorithm with all features of OS.
* Lapscore [25] is a filter method that evaluates the importance of each feature based on its Laplacian score. It selects features of the OS.
* GSR [28] is a general framework which unifies a sparse embedding model and feature selection together. It selects features of the OS.
* RFS [29] is another typical embedded model of feature selection which has proven its effectiveness in reducing the influence of outliers. It selects features of the OS.
* GSR_mean is an imputation feature selection framework, which uses the mean-value imputation method [15] on the IS, and selects feature from the union of the IS and OS using GSR.
* GSR_KNN is imputation feature selection framework, which uses KNN imputation method [12] on the IS, and selects features from the union of the IS and OS using GSR.
* GSR_missForest [18] is an iterative imputation framework based on random forest on the IS, which selects features from the union of the IS and OS using GSR.
* GSR_DGM [20] is a probabilistic framework based on deep generative models for missing value imputation on the IS, which selects features from the union of the IS and OS using GSR.
* GSR_MIVAE [21] is an importance-weighted autoencoder framework, which maximizes a potentially tight lower bound of the log-likelihood on the IS, and selects features from the union of the IS and OS using GSR.
* HQ-UFS [23] is a framework for incomplete data sets, in which the half-quadratic minimization technique is used to make the weight of outliers more negligible or even zero and reducing the influence of outliers. It selects features directly from the incomplete data set.
###### Abstract  
This study investigates the surface modification of Ti-6 Al-4V alloy through the electrical discharge machining (EDM) process to improve its suitability for orthopedic and dental implant applications. The analysis focused on evaluating the morphological, wettability, roughness, hardness, and biocompatibility properties of the modified surfaces. Samples were subjected to different dielectric fluids and polarities during EDM. Subsequently, optical microscopy, roughness measurements, Vickers microhardness, contact angle tests, and in vitro cytotoxicity assays were performed. The results demonstrated that EDM processing led to the formation of distinct layers on the sample surfaces, with surface roughness increasing under negative polarity by up to \(\sim\)304% in Ra and 305% in Rz. Additionally, wettability measurements indicated that the modified surfaces presented a lower water contact angle, which suggests enhanced hydrophilicity. Moreover, the modified samples showed a significant increase in Vickers microhardness, with the highest value reaching 1520 HV in the recast layer, indicating improvements in the mechanical properties. According to ISO 10993-5, all treated samples were classified as non-cytotoxic, presenting RGR values above 75%, similar to the untreated Ti-6 Al-4V alloy. Therefore, it is concluded that surface modification through the EDM process has the potential to enhance the properties and safety of biomedical implants made with this alloy.  
Ti-6 Al-4V alloy; electric discharge machining; surface enrichment; surface modification; cytotoxicity
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 3 Trust Management in the Internet of Vehicles
### Classification of Trust
#### 3.1.1 Direct Trust (DT)  
Direct trust is the cognition formed by the accumulation of direct historical interactions between entities. The degree of trust between the trustor and the trustee is related to the degree of satisfaction with their historical interactions. Through the direct interaction between entities, credibility can be flexibly adjusted to reflect the dynamic change in trust. At present, there are many methods to calculate direct trust. [PERSON] [25] used the similarity, family and packet delivery ratio; in [26], the quality of received messages and the ability of nodes to disambiguate messages were used, as represented by Equation (1); and in [27], familiarity and package delivery rate were used to calculate direct trust, taking timeliness and interaction frequency as the weight of the trust calculation. [PERSON] [28] calculated the trust value by using the forwarding rate of messages in time interval \(t\), as shown in Equation (2); [PERSON] [29] calculated direct trust by Bayesian inference and revised the trust value by penalty factor, as shown in Equation (3); [PERSON] [30] used \(H^{t}_{(j,k)}\) to represent the legal behavior of node \(j\) to node \(k\) in a specific time period \(t\); and [PERSON] [31] used inter-vehicle subordinate trust weight and the original trust of the vehicle.  
\[DTR=\frac{1}{2}\sum_{i=1}^{n}(\frac{M_{Quality}\times MDR}{M_{Quality}+MDR}) \tag{1}\]  
where \(DTR\) is the direct trust, \(M_{Quality}\) is the quality of the received message, and \(MDR\) is the message dissemination ratio.  
\[T^{(t)}_{d_{(i)}}=\frac{Successful-Interactions}{Total-Interactions} \tag{2}\]  
where Successful-Interactions is the number of successful interactions between nodes, and Total-Interactions is the number of total interactions between nodes.  
\[TR_{D_{ij}}(N+1)=\frac{N_{\mathrm{s}}+1}{N_{\mathrm{s}}+N_{f}+1}(1-\mu)^{N}f \tag{3}\]  
where \(N_{\mathrm{s}}\) is successful forwarding times, \(N_{f}\) is failed forwarding times, and \(\mu\) indicates the sensitivity of forwarding failure.
## 3 Results and Discussion
### Results of Used Approaches Comparison  
The approach based on VIs shows slightly better results in terms of extracting the olive tree canopies compared to the GEOBIA approach, according to five measures of accuracy. In the method of testing the accuracy with the use of regularly distributed points within the RT and FT, the VIs showed better results in four of the five measures used. According to the measures of AUC (Figure 15), _PA_, _UA_, and _KC_, the approach based on VIs is more accurate, while the GEOBIA approach showed slightly better results only in the measure of _OA_ (Table 3).  
The second accuracy check method based on a layer of one thousand randomly distributed points confirms the results obtained by the first method. The results of all five used accuracy measures indicate the selection of the approach based on VIs was more accurate in the extraction of the olive tree canopies than the GEOBIA approach (Table 4) (Figure 16).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm/Measure** & _UA_ & _PA_ & _OA_ & _KC_ \\ \hline \multirow{2}{*}{VIs (NDRE)} & 0.9194 & 0.9893 & \multirow{2}{*}{0.9180} & \multirow{2}{*}{0.6311} \\  & 0.9043 & 0.5380 & & \\ \hline \multirow{2}{*}{GEOBIA (RTC)} & 0.9634 & 0.8135 & \multirow{2}{*}{0.8170} & \multirow{2}{*}{0.4855} \\  & 0.4567 & 0.8354 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy assessment results for 1000 points comparison.  
Figure 15: ROC curves and AUC values for RT and FT polygons comparison.
# Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques
## 2 Literature Review
[38] showcased recent advancements in hyperspectral image processing techniques, focusing on algorithms for handling high-dimensional data and integrating spatial and spectral information, highlighting their potential for innovation across various industries. [PERSON] et al. [39] created MOLASS, an analytical software that automates the processing of matrix data from SAXS, UV-visible spectroscopy, and SEC-SAXS, using matrix optimisation with low-rank factorisation. Building on advancements in educational tools, [PERSON] et al. [40] introduced a method for teaching UV-Vis spectroscopy using a 3D-printable smartphone spectrophotometer, thus addressing accessibility issues associated with traditional laboratory equipment in educational settings. [PERSON] et al. 's [41] study on imaging spectroscopy and the Air-borne Visible/Infrared Imaging Spectrometer (AVIRIS) provides a comprehensive analysis of its diverse applications in Earth remote sensing research, including environmental monitoring and Earth observation. [PERSON] et al. [42] introduced a visible light spectroscopy method using plasmonic colour filter arrays, which offers a spatial resolution of 1.4 mm and a video rate of up to 30 frames per second, demonstrating its feasibility and potential in hyper-spectral imaging systems for improved spectral resolution and accuracy. [PERSON] et al. [43] developed a real-time terahertz imaging system with a spatial resolution of 1.4 mm and a video rate of up to 30 frames per second for materials science and imaging applications. [PERSON] et al. 's [44] study utilised reflectance imaging spectroscopy for pigment mapping in paintings, demonstrating the effectiveness of hyperspectral imaging cameras in identifying and mapping artist pigments. [PERSON] et al. [45] used UV-Vis/NIR spectroscopy and hyperspectral imaging to identify raw cotton types, revealing proteins, hydrocarbons, and hy-droxyl groups as dominant structures. [PERSON] et al. [46] developed a UV-Vis spectroscopy method for measuring transformer oil's interfacial tension, offering a cost-effective and rapid alternative for transformer maintenance. [PERSON] et al. [47] developed VIS-X, a high-resolution spectrograph for the Magellan Clay 6.5 m telescope, enhancing sensitivity to protoplanetary systems and providing insights into planet formation processes in the visible spectrum. [PERSON] et al. [48] conducted a study on the use of hue-angle products in optical satellite sensors for water quality monitoring, revealing accurate colour properties from ocean colour satellite instruments since 1997. The identified literature gap revealed a lack of processes utilising image processing for visible spectroscopy. This research addressed this gap by conducting a visible spectroscopy of chemical element vapours through the integration of machine learning and image processing techniques.
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
###### Abstract  
The complex backgrounds of satellite videos and serious interference from noise and pseudo-motion targets make it difficult to detect and track moving vehicles. Recently, researchers have proposed road-based constraints to remove background interference and achieve highly accurate detection and tracking. However, existing methods for constructing road constraints suffer from poor stability, low arithmetic performance, leakage, and error detection. In response, this study proposes a method for detecting and tracking moving vehicles in satellite videos based on the constraints from spatiotemporal characteristics (DTSTC), fusing road masks from the spatial domain with motion heat maps from the temporal domain. The detection precision is enhanced by increasing the contrast in the constrained area to accurately detect moving vehicles. Vehicle tracking is achieved by completing an inter-frame vehicle association using position and historical movement information. The method was tested at various stages, and the results show that the proposed method outperformed the traditional method in constructing constraints, correct detection rate, false detection rate, and missed detection rate. The tracking phase performed well in identity retention capability and tracking accuracy. Therefore, DTSTC is robust for detecting moving vehicles in satellite videos.  
spatiotemporal characteristics constraint; satellite video; moving vehicle detection; data association
## 3 Related Work
Compared to the proposed MLMS-ABM model, these works considered only the conventional ABM and did model an entire complex system using multiple layers of modeling while simultaneously incorporating the agent-based and cellular automata computing approaches. By re-conceptualizing an existing agent-based modeling approach, the modeling levels and layers (scales) have been integrated to ensure the ability and robustness of the proposed model to test all scenarios for traffic complex networks and capture all traffic phenomenon during risk situations. Additionally, dissimilar from the surveyed previous researches, the proposed versatile agent-based framework combines the dynamic agents' status updates, relying on behavioral factors, with the complex system modeling. This combination offers significant contributions to existing surveyed works, which specify the agents' roles prior to the simulation beginning without considering dynamic environmental events that could occur after the simulation begins. Furthermore, in contrast to existing models, the proposed transition decision-making sub-model takes into account all factors that impact the transition of agents through the environment, such as distance to the goal, need to avoid collisions and obstacles, and density. Implementing this theoretical principle uses an algorithm for finding the best path to the goal that considers not only the shortness of distance but also the obstacle-repulsion natural behavior. Moreover, in contrast to the conventional ABM, the MLMS-ABM provides a new solution to the problem of trustworthiness by incorporating a trustworthiness evaluation tool in modeling the communication and information exchange between agents. This assessment of the trustworthiness among agents promotes an accuracy of decisions taken by individuals in the complex system, thereby improving the system's overall performance. Overall, the presented model is distinct from all previous works in its novelty and effectiveness in simulating large-size heterogeneous complex traffic networks based on both CA and hybrid, multi-component ABM techniques.
###### Abstract  
[PERSON]'s K function was developed to analyze the spatial distribution characteristics in point pattern analysis, including geography, economics and biomedical research. In biomedical applications, it is popularly used to analyze the clusters of proteins on the cell plasma membrane in single molecule localization microscopy (SMLM), such as photo activated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), universal point accumulation imaging in nanoscale topography (uPAINT), etc. Here, by varying the parameters of the simulated clusters on a modeled SMLM image, the effects of cluster size, cluster separation and protein ratio inside/outside the cluster on the accuracy of cluster analysis by analyzing [PERSON]'s K function were studied. Although the predicted radius of clusters by analyzing [PERSON]'s K function did not exactly correspond to the actual radius, we suggest the cluster radius could be estimated within a factor of 1.3. Employing peak analysis methods to analyze the experimental epidermal growth factor receptor (EGFR) clusters at fibroblast-like cell lines derived from monkey kidney tissue - [PERSON] cell surface observed by uPAINT method, the cluster properties were characterized with errors. Our results present quantification of clusters and can be used to enhance the understanding of clusters in SMLM.  
Ripley's K function; peak analysis method; cluster; EGFR; COS7 +
Footnote †: journal: Applied Sciences
## 4 Results and Discussions
### Validation of Proposed Model  
The estimation of errors resulting from the difference between experiments and model of \(u_{2}\) is qualitatively described in Figure 7. From the Normal Probability Plot, it is observed that the contribution of errors is similar to normal distribution. The Versus fits graph discloses that the relation between residual and fitted value of model is random. Moreover, the Versus Order also exhibits the random relationship between residual and order of data point. The identical tendency is also noted when comparing experiments and proposed model in case of \(u_{3}\) response. The observed phenomena given by the graphs one more time show the reliability of the proposed model which is highly fitted for the experiments.  
Another way to validate the approximation of data is probability plot exhibited in Figure 8. The Anderson-Darling test in Minitab@19 which is a statistical test to validate the data set come from a specific distribution, e.g., the normal distribution or not. In this way, the data set is representative by blue points. There are three straight lines in the plot where the middle line presents the probability of normal distribution, while two lines in the left and the right refer to limiting boundary with significance of 95%. It is observed that all data set for both case of \(u_{2}\) and \(u_{3}\) are sited inside two limiting line when the \(p\)-value of 0.289 and 0.097 are greater than \(\alpha\) value of 0.05. This indicates that the data set follows the distribution.  
Figure 7: Graphs estimating errors between experiments and model of \(u_{2}\).
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 2 System Model and Problem Formulation
### Requirements of Different Network Slices  
As mentioned before, each device belongs to one typical scenario. To provide the transmission service required, three corresponding network slices are defined. That is,  
* **eMBB slice**: The devices served by this network slice require a high transmission rate, such as the device with real-time streaming of high-resolution 4K or 3D video [24]. That is, the transmission rate achieved by these devices has a minimum requirement: \[R_{n}\geq R_{0},\ \ \forall n\in\mathcal{N}_{\text{eM}},\] (4) where \(R_{0}\) denotes the rate threshold.
* **URLLC slice**: The devices served by this network slice have a strict requirement on delay, which include the transmission delay, queuing delay, propagation delay, and routing delay [25]. Denoted them by \(T_{1},T_{2},T_{3},T_{4}\), respectively, the end-to-end delay can be calculated as \(T_{1}+T_{2}+T_{3}+T_{4}\). The minimum requirement for wireless transmission delay is as follows: \[\frac{L}{R_{n}}\leq T_{0},\ \ \forall n\in\mathcal{N}_{\text{uR}},\] (5) where \(L\) denotes the packet length, and \(T_{0}\) denotes the delay threshold. As mentioned in [26], the achievable rate of a URLLC wireless link, i.e., Equation (4) in [26], can be approximated by the Shannon capacity when the block length is large. For this reason, in this work, we use the Shannon capacity to calculate the link rate and focus on the transmission time independent of the other delay components [27].
* **mMTC slice**: The devices served by this network slice have no strict rate or latency requirements [27]. Hence, to ensure the basic wireless connection, a minimum bandwidth \(B_{0}\) should be allocated to support the connection. That is, \[\sum_{m\in\mathcal{M}}b_{nm}\geq B_{0},\ \ \forall n\in\mathcal{N}_{\text{mM}}.\] (6)