esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### Evaluation Protocol  
To identify the most efficient and effective solution among the platforms previously described, the following evaluation protocol has been put together: a USB camera is used  
Figure 4: (**Left**) Raspberry Pi with a Pijuice Hat battery on top of it, (**Right**) Pijuice Solar panel (6 Watt model). More details available at [[https://uk.pi-supply.com/products/piuice-standard](https://uk.pi-supply.com/products/piuice-standard)]([https://uk.pi-supply.com/products/piuice-standard](https://uk.pi-supply.com/products/piuice-standard)) (accessed on 28 July 2021).  
to capture images at 1430 \(\times\) 1080 resolution with each Raspberry Pi. The camera is an Aukey FHD 1080p Webcam PC-LM1E. The computing platform is remotely controlled via the WiFi network and a Bluetooth multimeter is used to log voltage and amperage consumption three times per second. The complete setup is described in Figure 5. To further minimize energy consumption, unused modules (HDMI and Bluetooth) have been turned off; the Ethernet port and USB hub are handled by the same device, thus they are both turned on only when the camera is framing the scene. The WiFi module can not be disabled during testing to monitor and control the device; however, it will be turned off at in situ deployment. Finally, it is worth noticing that, alternatively to a USB camera, a camera connected directly to the Raspberry Pi dedicated connector could also be used.  
As proposed in [3], to speed up computation and reduce energy requirements, we consider efficient computing paradigms and resources available for the Raspberry Pi-integrated ARM processors. Specifically, we run optimized versions of OTV that take advantage of ARM Single Instruction Multiple Data (SIMD) and the four-core CPUs (Multicore) capabilities available in all the Raspberry devices considered. Furthermore, we run the original algorithm on a single core of the CPU to serve as baseline. Concerning SIMD instructions, we exploit the NEON instruction set [13], while, for Multicore, we rely on the TBB framework [14]. The OpenCV library [15] (version 4.5 in our experiments) seamlessly allows for exploiting both computing facilities. As already mentioned, we do not use the computing resources available in the ARM GPU since, according to our experiments with Raspberry model 3B/3B+, they did not provide any evident advantage and are not even currently supported in model 4.  
Regarding the parameters of the OTV algorithm, we use the same proposed in [3], and evaluate its performance at three resolutions: Full (1430 \(\times\) 1080), Half (715 \(\times\) 540), and Quarter (357 \(\times\) 270). Specifically, we skip one out of two frames, tracking at most 15,000 FAST features. Moreover, concerning the Lukas-Kanade algorithm, we set a search area of radius 4, deploying three pyramidal levels. To evaluate the feasibility of running OTV on the embedded setups, we apply the algorithm on on a 20-s sequence acquired on the Brenta river at 25 Hz and previously studied in [1,3]. Furthermore, considerations on power consumption are drawn by using the USB camera to capture a 20-s video indoor.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 2 Materials and Methods
### Color Features  
In the next phase, color feature vectors were extracted from the dataset. This approach allowed us to capture a comprehensive range of information from the image colors, enhancing the efficacy of weed detection and classification algorithms.  
For the color-based features, color histograms were utilized. Initially, we analyzed the original RGB images, which were composed of three bands: red, green, and blue. Furthermore, we transformed the images into the HSV color space, where the three bands represented hue, saturation, and value. From these six channels, histograms comprising 32 bins each for the R, G, B, H, S, and V bands were constructed, yielding a total of 192 parameters. These histograms provided valuable insights into the distribution of colors within the images, thereby facilitating weed detection and classification efforts. The HSV channel histograms are displayed in Figure 6.  
Figure 5: The distance transformation method: (**a**) the weed distance transformation map; (**b**) the local maximums and the values as radius.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 4 Discussion
### Effect of Initial pH of Fermentation Medium on Cellulase and Xylanase Production  
Many enzyme systems and transport of several enzyme species across the cell membrane are influenced by the initial pH of cultivation medium [36]. In addition, synthesis and expression of certain genes, as well as microbial metabolic activities, are thought to be influenced by the internal pH, in response to that of the external environment [37]. Consequently, bacterial synthesis and activity of proteins associated with various processes are adjusted in response to internal and external pH levels [37]. The findings of this study were in accordance with an earlier report by [PERSON] et al. [38], but contrary to the optimal pH 4 for xylanase production by _Streptomyces albidoflavus_ PSM-3n reported by [PERSON] et al. [26]. Optimal initial pH for cellulase and xylanase production varies between bacterial groups, due to differences in interactions of media compositions and microbial specificities [39]. For instance, optimum pH 6.5 and 8 for cellulase production by _Streptomyces_ strain C188 and _Streptomyces_ sp., respectively, have been reported [28; 40], while [PERSON] and [PERSON] [41] reported pH 6.5 as optimal for CMCase production by both _B. licheniformis_ MVS1 and _Bacillus_ sp. MVS3. In addition, other authors reported optimal initial pH 7 for xylanase and endocillusic production by _Geobacillus_ sp. WSUCF1 and _Cellulomonas_ spp., respectively [42; 43]. In general, _S. albidoflavus_ strain SAMRC-UFH5 showed alkaline tolerance in the production of cellulase and xylanase, as suggested by the result of this study.
## 4 The Research Method
### The Experiment Setup
**Activity 6 (see Figure 2)--Implementing selected requirements while tracking time--took place in two separate dedicated timeslots, each approximately 2 h long. During that time, the development teams were asked to implement the tasks of the selected user stories. They were also required to track the time they needed to fulfil a single development task, which was carried out by GitLab--tracking time of moving issues on the kanban board (from \"to do\" to \"in-progress\" and from \"in-progress\" to \"done\"). **  
After the implementation finished, all the participants were asked to **fill the post-test questionnaire**. At the same time, we reviewed, checked and tested all the delivered user stories from all the development teams to accept the user stories that were claimed to be finished. In the data analysis (regarding the effort estimation accuracy), we only included the tasks and user stories that were accepted as completed correctly and according to requirements. Partially or fautilly implemented increments were considered not to be implemented. This is how our research method and controlled experiment design supported gathering the empirical data that were crucial to answer the research questions. The challenged variable was a method of task generation and their effort estimation, while the dependent variables included estimates (tasks, effort estimates, chatbot prompts, chatbot responses, set of delivered functionalities, actual development efforts) and questionnaire responses.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 4 Results and Discussion
### Other Future Developments  
Quality of the predicted maps: We plan to cross each map of predictions with a skin mask in order to assess the relevance of the detection. Metrics like precision, which is expected to be high, and recall could be used for this purpose.  
Only the class (pulse rate value) that presents the highest score was saved and presented in the prediction maps. The network, however, attributes to each class a score, and we believe that this information could be exploited to improve prediction performance.  
Currently, the method accepts as input only a single channel. We therefore did not consider color as we processed only the green channel. We plan to enhance the network and compare the impact of color on performance, especially in terms of pulse rate accuracy and artifact removal.
# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 3 Prediction Model for Dementia Diagnosis
### Classifiers
#### Support Vector Machine  
The machine learning method of Support Vector Machine (SVM) involves a mapping model for analyzing data and recognizing patterns. Classification and regression analysis are its primary uses. When provided with a set of data falling into one of two categories, an SVM algorithm constructs a non-probabilistic binary linear classification model by which it can ascertain from the given data the correct category in which to place new data. The classification model it produces is conveyed as a boundary within the space of the mapped data. An SVM algorithm establishes the widest boundary. It is useful for both linear and nonlinear classification. For non-linear classification, it is necessary to map given data onto a high-dimensional feature space. In order to do this efficiently, a kernel trick may be used [20; 21].
## 4 Determination of the Mean Value of \(G_{ic}\)  
In Section 2, it was mentioned that in order to calculate the standard deviation of \(G_{IC}\), it was essential to know its mean value. However, as discussed in Section 3, obtaining an intrinsic value of \(G_{IC}\) through direct testing was challenging and required large-scale fracture mechanics tests, which were time-consuming and costly. Therefore, to address this issue, it is crucial to develop a strategy for determining the intrinsic value of \(G_{IC}\) using simpler tests and establishing relationships between \(G_{IC}\) and other readily measurable mechanical characteristics of concrete. A recent work was performed to determine this intrinsic value of \(G_{IC}\) from the knowledge of the compressive strength, \(f_{c}\), or the tensile splitting strength, \(f_{ls}\)[42]. From this work, the following relations were proposed:  
\[G_{IC}=9.5f_{ls}+90, \tag{15}\]  
\[G_{IC}=0.4f_{c}+110, \tag{16}\]  
in Equations (15) and (16), the unit of \(G_{IC}\) is in J/m\({}^{2}\) and \(f_{ls}\) and \(f_{c}\) are in MPa. It is important to recall that these relations were determined for concretes with the following: \(4\leq f_{ls}\leq 6.5\) MPa and \(50\leq f_{c}\leq 105\) MPa. They could be considered valid only when the compressive and tensile splitting tests were conducted on cylindrical specimens with dimensions of \(16\times 32\) cm\({}^{2}\) (standard tests). The direct link between toughness and compressive or tensile strength may be considered overly simplistic. However, this link was both possible and relevant due to the similar underlying physical mechanisms governing these mechanical characteristics.  
The transition from diffuse microcracking to localized macrocracking is responsible for the development of compressive and tensile strengths. In the case of \(G_{IC}\), it is associated with the existence of a process zone at the front tip of the macrocrack. The macrocrack can propagate only when the total dissipative energy in this process zone (microcracked zone) was reached, indicating a process of cracking localization and, therefore, macrocrack propagation.  
There are no inherent physical or mechanical limitations that restrict the applicability of Equations (15) and (16) to concretes with lower compressive and tensile strengths. However, it should be noted that these relations are not valid for fiber-reinforced concretes [30]. Additionally, considering that Equations (4)-(7) were established for concretes with a maximum aggregate diameter greater than or equal to 10 mm, and Equations (15) and (16) were established for concretes with maximum aggregate diameters of 12 mm and 20 mm, the presented equations were satisfactorily applicable to concrete mixtures with maximum aggregate diameters between 10 mm and 20 mm. In the case of larger aggregates, it is necessary to verify their applicability.
## 3 Experimental Methods and Results
### The Influence of the Segment Number for Power Spectrum  
In this section, the spectrum information entropy of the power spectrum estimated by the AR model is calculated and the influence of the segment number for power spectrum is analyzed. The segmented range for power spectrum is from 2 to 40 for analysis and comparison.  
This experiment used this method to judge 188 groups of data, including 90 groups of the HIFU echo signals and 98 groups of the noise signals. The power spectra of these sampled signals were estimated by the AR model, and these power spectra were segmented from 2 to 40 to calculate spectral information entropy under each segment number. Then, the appropriate threshold under each segment number was determined to judge the types of these sampled signals. We analyzed the consistency between the results of this method and empirical judgments, and the Accuracy, Sensitivity and Specificity under each segment number were calculated using Equations (11)-(13). The fitting curve of the comparison results is shown in Figure 5.  
Figure 4: Two methods of power spectrum estimation for the HIFU echo signal.  
Figure 3: AIC value of the HIFU echo signal.  
Figure 5: The influence of the segment number. (**a**)Accuracy; (**b**) Sensitivity; (**c**) Specificity.  
As shown in Figure 5a, when the segment number is 2 to 20, the Accuracy starts to increase from 0.932. In addition, when the segment number is between 14 and 17, it has the highest value. When the segment number is greater than 20, the Accuracy decreases slowly. Figure 5b is a fitting curve of the Sensitivity, when the segment number is 2 to 40, the Sensitivity keeps increasing and always stays above 95%. Figure 5c shows the Specificity, when the segment number is from 2 to 20, the Specificity starts to increase, and when the segment number is greater than 20, it starts to decrease slowly. In the application environment of this experiment, it is recommended to select the segment number from 14 to 17, because when the segment number is 14 to 17, this method has better Accuracy, Sensitivity and Specificity. Moreover, the single average running time of this method is only 1.2 s, which is easy to calculate.
# Application of MMC-RPC in High-Speed Railway Traction Power Supply System Based on Energy Storage
## 3 VSG Control
### Small Signal Analysis of VSG
As shown in Figure 12, the open-loop transfer function under separate PI control is [26]:  
\[G(s)=\frac{KG_{\rm I}(s)G_{\rm V}(s)}{LCs^{2}+KG_{\rm I}(s)Cs+1}, \tag{22}\]  
Combined with the above equation, the open-loop transfer function after adding QPR control is:  
\[G(s)=\frac{KG_{\rm I}(s)[G_{\rm V}(s)+G_{\rm PR}(s)]}{LCs^{2}+KG_{\rm I}(s)Cs+1}, \tag{23}\]  
According to Equation (23), the Bode diagram of the open-loop transfer function corresponding to the PI controller and the PI and QPR composite controller can be drawn. As shown in Figure 13, under PI control, the dq axis output component of VSG is DC component. And the open-loop amplitude characteristic has infinite gain, which can realize the error-free control of dc component in dq axis. However, it cannot realize the error-free control of the double frequency circulating current component. With the QPR regulator, when the voltage component in the dq axis has double frequency fluctuation, the open-loop amplitude characteristic has infinite gain, which can realize the error-free control of the double frequency component in VSG. Therefore, the double frequency circulation is suppressed inside the VSG. Figure 12: Block diagram of double closed-loop control. Figure 13: Frequency characteristics of double closed-loop control.
## 2 Materials and Methods
### MM/PBSA-Based Protein-Ligand Interaction Energy Calculation  
In order to calculate the binding energy for predicted complexes, the MM/PBSA method was applied. The simulation-based end point methods, such as molecular mechanics with Poisson-Boltzmann (MM/PBSA) and molecular mechanics with generalized Born and surface area (MM-GBSA) due to their computational efficiency [39; 40; 41; 42]. For all complexes, binding free energy calculations was carried out using the g_mmpbsa tool [40]. The binding free energy is given by:  
\[\Delta G_{bind}=\ \langle\Delta E_{MM}\rangle-T\Delta S+\langle\Delta G_{ solvation}\rangle \tag{1}\]  
\[\Delta E_{MM}=\Delta E_{bonded}+\Delta E_{\mathrm{pd}W}+\Delta E_{elc} \tag{2}\]\[\Delta G_{solvation}=\Delta G_{polar}+\Delta G_{nonpolar} \tag{3}\]  
Here, \(\Delta E_{MM}\) represents the energy of bonded and non-bonded terms and is calculated on the basis of molecular mechanics force-field parameters. In addition, in the single trajectory approach, the protein ligand conformation in bound and unbound form is identical and therefore is assumed to be zero [43]. The solvation free energy term includes polar and non-polar terms. The polar solvation energy is solved using the Poisson-Boltzmann equation [44] while non-polar solvation energy is calculated by attractive and repulsive forces between the solute and solvent, generated through cavity formation and Van der Waals interactions [45; 46]. For the current study, MM/PBSA calculations were done on the last 5 ns segment of the trajectory. For each system, 100 snapshots were extracted at the interval of 50 ps along the trajectory.
# O-Band and C/L-Band III-V Quantum Dot Lasers Monolithically Grown on Ge and Si Substrate
###### Abstract  
Direct epitaxial growth of III-V heterostructure on CMOS-compatible silicon wafer offers substantial manufacturing cost and scalability advantages. Quantum dot (QD) devices are less sensitive to defect and temperature, which makes epitaxially grown III-V QD lasers on Si one of the most promising technologies for achieving low-cost, scalable integration with silicon photonics. The major challenges are that heteroepitaxial growth of III-V materials on Si normally encounters high densities of mismatch dislocations, antiphase boundaries and thermal cracks, which limit the device performance and lifetime. This paper reviews some of the recent developments on hybrid InAs/GaAs QD growth on Ge substrates and highly uniform (111)-faceted hollow Si (001) substrates by molecular beam epitaxy (MBE). By implementing step-graded epitaxial growth techniques, the emission wavelength can be tuned into either an O band or C/L band. Furthermore, microcavity QD laser devices are fabricated and characterized. The epitaxially grown III-V/IV hybrid platform paves the way to provide a promising approach for future on-chip silicon photonic integration.  
Keywords:III-V quantum dot; silicon photonics; epitaxial growth +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
1. There are still many droplets leaving the canopy and drifting loss, which results in pesticide waste and environmental pollution. A V-shaped anti-drift spray device in multi-airflow synergy was designed according to modern orchards with low root stock in a high-density planting. The two auxiliary air ducts are arranged in a V shape in the horizontal direction. The droplet's spatial motion model was constructed based on the particle dynamics analysis, and the anti-drift mechanism of multi-airflow synergy was clarified. The influences of spray pressure and V-shaped wind speed on the droplets' movement were illuminated by Matlab. When the V-shaped wind speed ranges from 15 m/s to 25 m/s, the anti-drift effect of the machine is better;  
Figure 16: Droplet spatial distribution and the droplet concentration in the calibration line.  
2. A simulation model of the flow field between the spray device and the fruit trees canopy was constructed by the method of CFD. By considering crosswind speed, V-shaped wind speed, and spray pressure and using partial multivariate orthogonal regression, three-level simulation experiments of the droplets' deposition were designed. The influence of V-shaped wind speed on the spatial distribution of droplets was analyzed, which indicated that the three factors had a significant influence on the droplet deposition characteristics, and the degree from big to small was V-shaped wind speed, crosswind speed, spray pressure. The prediction model of the drift distance of the droplets deposition center was constructed, its fitting degree is high, and the correlation coefficient is 0.998;
3. The experiments on the machine were carried out. The results show that when the crosswind speed, the spray pressure, and V-shaped wind speed are 2.2 m/s, 0.52 MPa, and 20.8 m/s, respectively, the droplet drifting rate was 29.2% lower than that of single-airflow. The drifting distance of the droplet deposition center is 5.0 cm, which is consistent with the prediction model. The research can provide a basis for the design and parameters optimization of the similar sprayers used in modern orchards with low root stock in a high-density planting.  
Conceptualization, G.F. and C.N.; methodology, G.F. and P.B.; validation, D.W. and C.N.; data curation, W.S.; writing--original draft preparation, G.F. and C.N.; writing--review and editing, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Shandong Provincial Natural Science Foundation (ZR2018 MC017), Major Agricultural Application Technology Innovation Project of Shandong Province (SD2019 NJ001), and Shandong Key Laboratory of Horticultural Machinery and Equipment Project (YYJX-2019-07).  
Not applicable.  
The authors declare no conflict of interest.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 3 Algorithm Design
### Comprehensive Evaluation Model
#### 3.2.1 Establish the Hierarchical Structure of Evaluation of Network Application Business  
In this paper, we establish a three-layer evaluation model for network applications. We select the evaluation key performance index (KPI) set for delay, jitter, and packet loss rate for the QoS evaluation criteria. Thus, it overcome the one-sidedness of a single evaluation index and achieve multi-index fusion. The layer of the business scheme is divided into three network applications G = {web interactive service, voice communications service, streaming media service}. Figure 3 shows the hierarchical structure of the network performance evaluation model based on application service.
## 3 Dynamic CH Selection and Energy-Efficient Routing Design
### Network Model  
Figure 1 displays the WSN schematic structure. Depending on the following conditions, the model of the network is formulated:  
* In terms of processing time and initial energy, the entire nodes are the same as every other in WSN.
* Depending on the Euclidean distance, the distance of the sensor is evaluated.
* The node's position is constant after the deployment, and the nodes are casually positioned in the sensing situation.
* From the nodes, the BS obtains the distance and residual energy information. CHs are selected by an effective CH selection algorithm based on this information. Hence, the route among the CHs to the BS is obtained by the routing process.