esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cell Adhesion Analysis  
MG-63 cells were stained with PI dye to explore the relationship between the surface texture parameters and cell attachment. We used the 40\(\times\) objective lens in the absence and presence of 1.5\(\times\) intermediate magnification to visualize MG-63 cells attached to the untreated and heat-treated Ti64 specimens. Fluorescence images of cell nuclei were applied to identify the vertical distance (\(\Delta\)H) between the highest and lowest position of MG-63 cells attached to the specimens, where values of \(\Delta\)H were identified via adjusting the objective lens in the vertical direction (see also Movie S1). Here, ten sets of fluorescence images were selected on each Ti64 specimen, and three sets of these images are shown in Figure 8. Figure 8a-l respectively show the highest/lowest positions of MG-63 cells attached to untreated and heat-treated Ti64 specimens. As can clearly be seen in Figure 8, MG-63 cells preferred lying in concave hollows. In addition, the present results showed that for heat-treated specimens, values of \(\Delta\)H statistically significantly decreased from 31.6 \(\pm\) 4.2 to 8.8 \(\pm\) 2.8 \(\upmu\)m (\(n\) = 10, \(p\) < 0.01) when S\({}_{\text{ku}}\) decreased from 3.3 \(\pm\) 0.6 (greater than 3) to 2.6 \(\pm\) 0.3 (less than 3), indicating a strong influence of S\({}_{\text{ku}}\) on cell attachment.  
Figure 7: Determination of cell viability by an MTT assay. The MTT assay was performed to evaluate cell viability of osteoblast-like human MG-63 cells attached to untreated and heat-treated Ti64 substrates after 4, 24, and 72 h of incubation (\(n\) = 5, *, \(p\) < 0.05; **, \(p\) < 0.01).  
This can simply be understood as the untreated specimen (S\({}_{\rm{ku}}\) > 3) having relatively many high peaks and low valleys compared to the heat-treated specimen, such that the spatial distribution of cells in the vertical direction had a greater degree of change. This finding suggests that 3D surface texture parameters can be employed to provide greater insights into relationships between surface roughness features and cell attachment.  
We adopted a microscopy-based approach to separately map 3D surface topographies of untreated and heat-treated Ti64 specimens and visualizing the highest/lowest MG-63 cells attached to Ti64 specimens, which allowed indirect probing of the relationship between the surface texture parameters and cell attachment in the present work. We are now trying to integrate fluorescence detection into the laser confocal 3D profiler that allows direct probing of both 3D surface topographies and the corresponding MG-63 cell attachment simultaneously.
## 1 Introduction
However, these proposals are complex and time-consuming, and always require a polar catalyst, which results in the easy composition of PeQDs. Furthermore, one micro-sized SiO\({}_{2}\) particle contains more than one CsPbI\({}_{3}\) PeQD particle, which inevitably drops the PLQY and causes low dispersibility of the PeQD solution. Therefore, it remains highly necessary to propose a one-step method to in situ synthesize single SiO\({}_{2}\)-coated CsPbI\({}_{3}\) PeQDs free of the necessity of a catalyst. In this study, we report a one-step in situ synthesis of single SiO\({}_{2}\)-coated CsPbI\({}_{3}\) PeQDs, which is achieved through a modified hot injection method using APTES as the sole SiO\({}_{2}\) precursor without any catalyst. The obtained SiO\({}_{2}\)-coated CsPbI\({}_{3}\) (short for SiO\({}_{2}\)-CsPbI\({}_{3}\) hereafter) PeQDs are cubic in shape with an average size of 10.07 \(\pm\) 0.93 nm and a more uniform size distribution. They have near unit PLQY of up to 97.5%, higher than that of OAm-CsPbI\({}_{3}\) PeQDs (89.6%). Moreover, the SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs exhibit a much-improved stability against storage, heat, and ethanol. Finally, the SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs were used as down-conversion emitters of GaN LED chips, which also showed significantly enhanced luminous performance and stability.
# Influence of Cryogenic Grinding on the Nutritional and Antinutritional Components of Rapeseed Cake
## 1 Introduction
Great efforts have been made to reduce undesirable components in rapeseed meal and cake and to reduce their antinutritional effects, which would allow their use in human nutrition. In recent decades, new varieties with lower glucosinolate and hull content (and thus insoluble dietary fibre--IDF) have been developed. Phytates are removed by enzymatic methods, while various extraction methods are used for phenolic compounds. New techniques are also being explored, such as ultrasound, pulsed electric field, and micromization, but so far none of these techniques are being used commercially [2]. Micromization technology is developing rapidly as it not only improves the functional and sensory properties of the ground material but also increases the bioavailability of nutrients and the rate of biological and chemical reactions. Various methods can be used to reduce particle size [12]. It has been reported that cryogenic grinding increases the production of fine particles and reduces the energy consumption for grinding the materials by increasing their brittleness. This could be very important for oily materials such as cake and meal residues after oil extraction, as the extremely low temperatures in the grinder solidify the oils, causing the materials to crumble easily [13]. In our research group, experiments were performed on millet bran [14] and pumpkin seed cake [15] to investigate the effects of cryogenic grinding on the composition, functional properties, and extractability of nutritive compounds. The aim of this study was to determine the effects of cryogenic grinding on the content and composition of the nutritional and antinutritional components of rapeseed cake and its fitness as a treatment or pre-treatment for further use of rapeseed by-products in food formulations.
## 3 Results and Discussion
### Validation of Vaporisation Enthalpies Using Structure-Property Relationships
#### 3.3.3 Empirical Correlations: Vaporisation Enthalpies versus Solvent-Accessible Surfaces  
The vaporisation enthalpies for the most interesting extremely strained di-tert-butyl-, tri-tert-butyl- and tetra-tert-butylbenzenes were estimated using the correlation of vaporisation enthalpies versus solvent-accessible surfaces, \(S_{\rm x}\). The solvent-accessible surface area is the surface area of a molecule calculated using the \"rolling ball\" algorithm suggested by [PERSON] [53]. This algorithm uses a sphere of a suitable radius to develop the surface, \(S_{\rm x}\), of the molecule. In our earlier work, we propose \(S_{\rm x}\) as a measure of the intermolecular forces related to the enthalpy of vaporisation [54]. As a test, these solvent-accessible surface areas were calculated with the Connolly program [55] for a range of aromatics and alkyl-substituted aromatics using a 250 pm probe sphere. A good linear correlation between \(\Delta_{\rm I}^{\rm g}H_{\rm m}^{\rm o}(298.15\;{\rm K})\) and \(S_{\rm x}\) was found. In this work, we calculated the surface areas of di-tert-butyl, tri-tert-butyl, and tetra-tert-butylbenzenes (see Table 7) using the same algorithm.  
The vaporisation enthalpies, \(\Delta_{1}^{\rm g}H_{\rm m}^{\rm o}(298.15\;{\rm K})\), and surfaces, \(S_{x}\), correlate according to the following equation:  
\[\Delta_{1}^{\rm g}H_{\rm m}^{\rm o}(298.15\;{\rm K})/\left({\rm kJ\cdot mol^{-1}} \right)=21.3+0.1752\times S_{x}\;{\rm with}\;\left(R^{2}=0.993\right) \tag{10}\]  
The 'empirical' vaporisation enthalpies of alkylbenzenes according to Equation (10) are denoted as \(S_{x}\) in Table 2. The vaporisation enthalpies of 1,3-di-tert-butyl-benzene, 1,4-di-tert-butyl-benzene, and 1,3,5-tri-tert-butyl-benzene estimated with Equation (10) are indistinguishable from the experimental values obtained using conventional experimental methods, providing confidence in the estimates for 1,2-di-tert-butylbenzene, 1,2,4-tri-tert-butylbenzene, 1,2,3-tri-tert-butylbenzene and 1,2,4,5-tetra-tert-butylbenzene (see Table 7).
## 3 Results
### Parameter Sensitivity for GPP and NEE  
For different model outputs, the importance of a given parameter may vary. This experiment examined differences in parameters' SIs for NEE and GPP, and the results were shown in Figure 2.  
As the model output was the GPP, the most sensitive parameters were \(\alpha_{q}\), \(f_{ci}\), \(V_{\text{m}}^{25}\), and \(E_{Vm}\). However, if the model output changed to NEE, ecosystem respiration (\(Q_{10}\)), whose TSI was 0.40, was the most sensitive parameters and was followed by the temperature dependence of ecosystem respiration at 0 \({}^{\circ}\)C (\(ER_{0}\)) and \(E_{Vm}\). Both \(f_{ci}\) and \(E_{Vm}\) were sensitive to GPP and NEE. \(\alpha_{q}\) and \(V_{\text{m}}^{25}\) were sensitive to the GPP but not to the NEE, and \(ER_{0}\) and \(Q_{10}\) were only sensitive to the NEE. The results indicated that the sensitive parameters and sensitive degree varied for different model outputs.
# Deadlocks Detection in Multithreaded Applications Based on Source Code Analysis
## 5 Deadlock
### DL2 Application Model  
The reason for the deadlock phenomenon in the DL2 application is very similar to that in the DL1 application. Namely, the locking operation is in the loop body block, while the unlocking operation is outside the loop. As a result, no more than one thread is required for a resource conflict that causes a deadlock phenomenon, because the thread is waiting for itself due to a programmer error. Figure 5  
Figure 4: DL1 application operation graph.  
presents a graph of the DL2 application operation, the model of which is presented in a further part of the article.  
According to the model in Section 3, the source code of DL2 application is as follows:  
\[\begin{array}{ll}T_{DL2}=(t_{0},t_{1}),&F_{DL2}=\{(o_{0,1},o_{0,2}),(r_{1},o_{ 0,2}),\\ U_{DL2}=(\{t_{0}\},\{t_{1}\},\{t_{0}\}),&(o_{0,2},o_{0,3}),(o_{0,3},o_{0,4}),(r_{ 1},o_{0,4}),\\ R_{DL2}=\{\{counter\},\{i,t2 arg\}\},&(o_{0,4},o_{0,5}),(o_{1,1},o_{1,2}),(r_{2}, o_{1,2}),\\ O_{DL2}=\{o_{0,1},o_{0,2},o_{0,3},o_{0,4},o_{0,5},o_{1,1},&(o_{1,2},o_{1,3}),(q_ {1},o_{1,3}),(o_{1,3},o_{1,4}),\\ o_{1,2},o_{1,3},o_{1,4},o_{1,5},o_{1,6}\},&(o_{1,4},o_{1,2}),(o_{1,4},r_{1}), (o_{1,4},o_{1,5}),\\ Q_{DL2}=\{(m,PMD)\},&(o_{1,2},o_{1,5}),(o_{1,5},q_{1}),(o_{1,5},o_{1,6})\}. \end{array}\]  
Resource conflict in the DL2 application will occur when the appropriate input parameter is given. The parameter value will cause the while loop to be executed more than once, and an attempt will be made to re-lock, as a result of which the thread will stop its operation and begin to wait indefinitely for itself. In the DL2 application, the resource conflict causing the deadlock phenomenon is manifested by attempting to re-establish the existing lock. Detection of this conflict in the graphic representation boils down to finding such a cycle in which locking operation is present, while it does not contain the unlocking operation.  
The next lemmas, therefore, read as follows and apply to both DL1 and DL2 applications.  
**Lemma 2**: _If in the \(\sides{{}_{i}}{G_{P}}\) subgraph of \({G_{P}}\) graph of \({P}\) application (subgraph for thread \(t_{i}\) and \(q_{s}\) lock), there is a path that starts with \(q_{s}\) lock and is not the cycle, then the phenomenon of deadlock type S2 and S3 occurs in \({P}\) application._  
**Proof.** The necessary condition for the occurrence of deadlocks of type S2 and S3 in thread \(t_{i}\) is the existence of a operations sequence, resulting in the failure to unlock a pre-established \(q_{s}\) lock. This condition is met when there is a path in the \((\sides{{}_{i}}{)}{G_{P}}\) subgraph that starts with \(q_{s}\) (establish a lock) and unrecurrent to \(q_{s}\) (release of lock)--a path that is not a cycle. \(\square\)
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Provide Transportation in Areas Where Ground Transportation Is Limited  
Flying taxis have the potential to provide transportation in areas where ground transportation is limited or unavailable, such as remote or mountainous regions, islands, or areas with poor road infrastructure. Since flying taxis can take off and land vertically, they do not require a runway or other extensive ground infrastructure, which can make them a more flexible transportation option in certain locations. Additionally, flying taxis are able to travel faster than traditional ground transportation, which could help reduce travel time and improve accessibility for people in these areas. However, it is worth noting that the availability and affordability of flying taxi services in these areas will depend on a number of factors, including regulatory approvals, operator interest, and the development of appropriate infrastructure [156].
# Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers
## 3 Principal Component Analysis  
The PCA is a method of linearly combining the optimal weights for a data distribution [8] and employs the covariance matrix and eigenvector of the data. The eigenvector of the covariance matrix shows the direction of data distribution. Thus, the eigenvectors determine the influence of the principal component (PC). The direction of the eigenvector cannot be changed; however, it is possible to change its magnitude. Therefore, the eigenvector, as well as the PC, can be regarded as a reference axis with increasing or decreasing size. This allows one to determine the reference axis, which comprises the PC that is calculated using the covariance matrix and the eigenvector. The number of PC axes depends on the data dimension. The first PC axis is the maximum variance, which is the longest and widest extent of data distribution. The second PC axis is perpendicular to the first one, and it is the second-widest and longest extent of data distribution. In summary, PCA deals with the decomposition of the eigenvalue calculated using the input data covariance. When the PCA algorithm is adapted to image fusion, the column vectors of the input images must be produced as the first step.  
\[E(X)=1/M\sum_{k=1}^{M}X_{k}, \tag{5}\]  
\[cov(X,\ Y)=\frac{\sum_{i=1}^{n}(X_{i}-E(X))(Y_{i}-E(Y))}{(n-1)}, \tag{6}\]  
where \(E(\cdot)\) is the average of the input data. \(X\) is an input image, and cov(\(\cdot\)) is the covariance matrix of the data pairs \(X\) and \(Y\).  
\[Ax=\ \lambda x,\ det(A-\lambda I)=0,\ A=cov(x,y), \tag{7}\]  
\[P_{1}=\frac{Ev_{1}}{\sum Ev},\ P_{2}=\frac{Ev_{2}}{\sum Ev}, \tag{8}\]  
where \(Ev\) is a unique vector and the sum of the PCA weights \(P_{1}\) and \(P_{2}\) is 1.  
PCA synthesis is accomplished using the mean weights of \(P_{1}\) and \(P_{2}\), which are calculated by multiplying the weights of each input image in Equation (9).  
\[I_{fused}=P_{1}\times I_{VIS}+\ P_{2}\times I_{NIR}, \tag{9}\]  
where \(I_{fused}\) is the result of image synthesis calculated using the PCA algorithm [9], and \(I_{VIS}\) and \(I_{NIR}\) are the input VIS and NIR images, respectively. Therefore, the proposed method assigns the weight obtained from the PCA algorithm to fuse the VIS and NIR input images, as shown in Figure 2:
## 6 Conclusions  
In this paper, a design of a blockchain-based system, namely UniChain, for managing EARs is proposed. UniChain is designed to be compatible with the existing EARs' databases and to provide interoperable, secure, and efficient access to EARs by universities, students and third parties, while maintaining the students' privacy. In UniChain, the blockchain maintenance including creation, verification and appending of new blocks is the responsibility of universities, while allowing students to securely control accesses their EARs. Privacy is maintained in the UniChain by employing timed-based smart contracts for governing transactions and monitoring the computations performed on the EARs through the enforcement of the acceptable usage policies. The adoption of hashing techniques ensures the integrity of data. Security and access control are maintained by the adoption of advanced encryption and authentication techniques throughout the blockchain. Interoperability, auditability, and accessibility are provided by the use of comprehensive logs. The proposal is independent of any specific system, and its variations can potentially accommodate other similar systems with multiple access for electronic records. This work proposes a new incentive mechanism integrated with the PoA for mining. It leverages the degree of universities regarding their efforts on maintaining academic records and creating new blocks. The proposed mechanism rewards the \"block's creator\" an incentive to be added to its degree and accordingly decreasing its probability of recreating the next block instead of just creating a digital currency, thus achieving the fairness and the equality among universities and ensuring the sustainability of the system. Extensive experiments were conducted to evaluate the UniChain performance on different aspects, including response time, throughput, and communication overhead. The results indicate the efficiency of the proposal in handling a large dataset at low latency.  
Conceptualization, E.-Y.D. and Y.-A.D.; methodology, E.-Y.D.; formal analysis, E.-Y.D. and Y.-A.D.; writing--original draft preparation, E.-Y.D., Y.-A.D. and S.-M.Y.; writing--review and editing, Y.-A.D. and S.-M.Y.; supervision, S.-M.Y; and project administration, S.-M.Y.  
This research received no external fund.  
We thank Palestine Technical University--Kadoori, Bank of Palestine, and Taawon association for their support.  
The authors declare no conflict of interest.  
The entity relation diagrams of the university registration office, student marks, and examination scheduling are shown in Figures 11-13.
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
###### Abstract  
As lower-limb exoskeleton and prostheses are developed to become smarter and to deploy man-machine collaboration, accurate gait recognition is crucial, as it contributes to the realization of real-time control. Many researchers choose surface electromyogram (sEMG) signals to recognize the gait and control the lower-limb exoskeleton (or prostheses). However, several factors still affect its applicability, of which variation in the loads is an essential one. This study aims to (1) investigate the effect of load variation on gait recognition; and to (2) discuss whether a lower-limb exoskeleton control system trained by sEMG from different loads works well in multi-load applications. In our experiment, 10 male college students were selected to walk on a treadmill at three different speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h) with four different loads (L0 = 0, L20 = 20%, L30 = 30%, L40 = 40% of body weight, respectively), and 50 gait cycles were performed. Back propagation neural networks (BPNNs) were used for gait recognition, and a support vector machine (SVM) and k-nearest neighbor (k-NN) were used for comparison. The result showed that (1) load variation has significant effects on the accuracy of gait recognition (\(p\) < 0.05) under the three speeds when the loads range in L0, L20, L30, or L40, but no significant impact is found when the loads range in L0, L20, or L30. The least significant difference (LSD) post hoc, which can explore all possible pair-wise comparisons of means that comprise a factor using the equivalent of multiple \(t\)-tests, reveals that there is a significant difference between the L40 load and the other three loads (L0, L20, L30), but no significant difference was found among the L0, L20, and L30 loads. The total mean accuracy of gait recognition of the intra-loads and inter-loads was 91.81%, and 69.42%, respectively. (2) When the training data was taken from more types of loads, a higher accuracy in gait recognition was obtained at each speed, and the statistical analysis shows that there was a substantial influence for the kinds of loads in the training set on the gait recognition accuracy (\(p\) < 0.001). It can be concluded that an exoskeleton (or prosthesis) control system that is trained in a single load or the parts of loads is insufficient in the face of multi-load applications.  
sEMG; load variation; gait recognition; lower-limb exoskeletons
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 3 Results
### Series of Values of the Spectral Indices Estimated from the Synthetic Images of High Resolution  
In this section, we show another example of the application of this work. The remapping of pixel values using the blending algorithm applies to the entire Sentinel 2 image series, as indicated in Section 2.3. In this way, a temporal sequence of fused images can be created that allows to simulate the evolution of the spectral indices in these synthetic images.  
Figure 4 shows the simulation of the time evolution of the NVDI, and GNDVI indices that are generated from the high-resolution merged images (dashed lines) and from the original Sentinel 2 images (continuous lines). Figure 5 shows the simulation of the temporal evolution of the GCI index.  
Figure 4: Evolution of the NDVI and GNDVI spectral indices of a point associated with vegetation (green striped lines: ndvi-veg, gndvi-veg), of a point associated with soil (brown dotted lines: ndvi-soil gndvi-ground) and of the pixel in which these points are located in the Sentinel 2 images (gray continuous lines: ndvi-S2, gndvi-S2). Period: 1 May to 31 October 2019.  
In both figures, the green lines of stripes (ndvi-veg, gndvi-veg and gci-veg) represent the values obtained from a point in the merged images (high resolution) associated with vegetation. The brown dotted lines (ndvi-soil gndvi-soil and gci-soil) represent the values obtained from a point in the fused (high resolution) images associated with soil. The continuous gray lines ndvi-S2, gndvi-S2 and gci-S2 correspond to the homologous pixel in the Sentinel 2 images.  
It is verified that after fusion, the indices are higher at the point that corresponds to the vegetation and lower at the point that corresponds to the ground, while the area that covers a pixel of the original Sentinel 2 images (10 * 10 m in the bands considered) maintains intermediate values.
# Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors
## 4 Testbed Setup of Heart DT
### ECG Data Acquisition and Results  
As previously mentioned, in order to validate the model described in Section 4.2, real ECG recordings were acquired using two identical smartwatches with an ECG signal sampling frequency of 500 samples per second. The recording campaign was carried out on 2 healthy subjects (normal heartbeats) for a total of 4 recordings, each lasting 30 s.  
For each patient, an instance of heart DT is executed. Once the data is acquired, the data acquisition block performs a filtering and preprocessing phase of the data. In particular, a resampling of the ECG signal is performed at a sampling frequency of  
Figure 4: ECG signal related to: (**a**) normal sinus rhythm and different type of diseases such as (**b**) paced rhythm; (**c**) atrial fibrillation; (**d**) ventricular bigemy; (**e**) sinus bradycardia; (**f**) atrial flutter; (**g**) junctional rhythm.  
\(f_{s}=1000\) Hz to match the recorded signal to the signals that constitute the network model (introduced in Section 4.2). Subsequently, each recording is divided into 2 s long sequences.  
The two heart DTs, one for each patient, perform the inference process in the AI process block with data from the smartwatches of the two patients.  
The trends in Figure 5 show that the classification accuracy in the \"Normal\" class for Patient A and Patient B improves as the analysis window (\(W_{a}\) [s]) changes. In fact, this window is obtained using a post-processing filter called the \"Recurrence Filter\" [38]. Using the latter, the obtained accuracy reaches 100% after about 24 s.