esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
CuO was synthesized free of crystalline impurities using ultrasound, microwave, or combined mode (microwave-ultrasound).  
The crystallinity of the CuO is modified during the transformation of Cu\({}_{\text{x}}\)O\({}_{\text{y}}\)H\({}_{\text{z}}\) to CuO due to the different treatments, the lattice parameters do not change. The CuO MW sample has the smallest crystal size, 3.3 times smaller than the one obtained with conduction heating (CuO CH). The combined treatment presents an intermediate crystal size between the one obtained with microwaves or ultrasound.  
Figure 10: SEM micrographs using LED at different magnifications: CuO MW (**a**) 10,000\(\times\); (**b**) 20,000\(\times\); (**c**) 50,000\(\times\); CuO US (**d**) 10,000\(\times\); (**e**) 20,000\(\times\); (**f**) 50,000\(\times\), and carbon-CuO US mixture (**g**) 10,000\(\times\); (**h**) 20,000\(\times\); (**i**) 20,000\(\times\) (using backscatter electron detector (BED)).  
The treatment modified the textural characteristics (specific surface area, diameter, and pore volume) in the CuO during the synthesis. The ultrasound irradiation promotes larger pore diameter in comparison with microwave and combined treatments.  
The reaction time is reduced during the synthesis of materials and reactions such as pyrolysis using microwave and ultrasound irradiation, the time could be reduced from hours to minutes.  
The texture and morphology depend on the irradiation method during the nucleation stage for the synthesized materials. The synthesis method when obtaining CuO modifies the morphology due to the topotactic effect, resulting in irregular faceted monoliths using ultrasound and nanoleaves in the case of microwaves.  
The best conditions to obtain bio-oil (14.5%) by the pyrolysis of sugarcane bagasse using a multimodal microwave reactor and CuO as a microwave absorber were: a power of 1160 Watts, 4 min of irradiation time, CuO US as an absorber and ps \(<\) 0.5 mm of the biomass.  
Using CuO as a microwave absorber, the amount of the obtained liquid fraction is 2.7 times larger than the pyrolysis without absorber.  
During the pyrolysis of cane bagasse using CuO as a microwave absorber, 87% of the CuO is reduced to Cu\({}_{2}\)O and Cu after 4 min of irradiation, slowing the reaction progress because CuO is a better microwave absorber than Cu\({}_{2}\)O and Cu.  
Conceptualization, E.J.P.-G., S.P.-C., S.O.F.-V; methodology, [PERSON]-G., R.M.P.-G.; investigation E.J.P.-G., I.S.R.-C.; formal analysis E.J.P.-G., S.P.P.-C., S.O.F.-V; writing--original draft E.J.P.-G., S.P.P.-C., S.O.F.-V; writing--review and editing [PERSON]-G., S.P.P.-C., J.C.S.-O.  
This research was funding by Instituto Politecnico Nacional (IPN): SIP 20196290, SIP 20196381, SIP 20181685 projects.  
We want to thank Instituto Politecnico Nacional (IPN) and Laboratorio de Nanomateriales Sistemables IPN-ESIQIE. E.J.P.-G. thanks the scholarship support 565252 from CONACYT.  
The authors declare no conflict of interest.
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 2 Materials and Methods  
### Image Data Acquisition  
The kiwifruit images were acquired at the orchard base of Hunan Academy of Agricultural Sciences at different times of the day. The camera used for acquisition was a RGB-D camera, RealSense D435i, which is manufactured by Intel (Santa Clara, CA, USA). The camera was positioned 20-90 cm away from the kiwifruit fruit for the acquisition, as shown in Figure 1. To simulate the vision system of a picking robot, different elevation angles were used for the shots, and a total of 2325 raw kiwifruit images were collected. To avoid overfitting phenomena due to insufficient diversity of sample data acquisition, the presence of branches and leaves shading, fruit adjacency and denseness of scaffolded kiwifruit were differentiated to increase sample diversity. To enhance the generalization ability of the training model results, the collected images were randomly enhanced to get 6890 kiwifruit images, and later divided into training, validation and test sets according to the ratio of 9:1:1. The final data set is shown in Table 1. The LabelImg tool, an opensource software from Github ([[https://github.com/heartexlabs/labelImg](https://github.com/heartexlabs/labelImg)]([https://github.com/heartexlabs/labelImg](https://github.com/heartexlabs/labelImg)), accessed on 29 August 2022), was used to label the data, and the smallest outer rectangle of the fruit was used as the real frame to avoid the interference of useless pixels; the part of the kiwifruit fruit exposed in the image that was obscured or overlapped was labeled to generate the dataset file in XML format.
## 3 Results and Discussion  
The results regarding antioxidant activities in lupine and kale sprouts as well as their bioactive compounds are summarized in Tables 1 and 2, respectively, while the cytotoxic activity is presented in Figures 1 and 2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Tested Groups** & **Biochanin** & **Dalazin** & **Genistein** & **Genistein** & **Glycicien** & **Chlorogenic** & **Protocatchuic** & **Single Acid** \\
**of Sporouts** & (**ug/100 g dw**) & (**ug/100 g dw**) & (**ug/100 g dw**) & (**ug/100 g dw**) & (**ug/100 g dw**) & (**ug/100 g dw**) & (**ug/100 g dw**) \\ \hline L1 & \(43\pm 8\) & \(264\pm 4\) \({}_{*}\) & \(238\pm 8\) \({}_{*}\) & \(1.82\pm 0.02\) \({}_{*}\) & \(271\pm 3\) \({}_{*}\) & \(679\pm 12\) \({}_{*}\) & ND & ND \\ L2 & \(43\pm 7\) & \(151\pm 14\) \({}_{*}\) & \(513\pm 3\) \({}_{*}\) & \(1.22\pm 0.01\)\({}_{*}\) & \(216\pm 34\) \({}_{*}\) & \(803\pm 68\) & ND & ND \\ L3 & \(89\pm 19\) & \(203\pm 13\) & \(396\pm 26\) & \(1.38\pm 0.02\) & \(359\pm 13\) \({}_{*}\) & \(1465\pm 11\) \({}_{*}\) & ND & ND \\ L4 & \(37\pm 12\) \({}_{*}\) & \(140\pm 17\) \({}_{*}\) & \(224\pm 6\) \({}_{*}\) & \(2.26\pm 0.03\) & \(124\pm 6\) \({}_{*}\) & \(755\pm 6\) \({}_{*}\) & ND & ND \\ L5 & \(34\pm 6\) \({}_{*}\) & \(108\pm 6\) \({}_{*}\) & \(512\pm 6\) \({}_{*}\) & \(1.46\pm 20\) & \(165\pm 12\) \({}_{*}\) & \(664\pm 5\) \({}_{*}\) & ND & ND \\ L6 & \(49\pm 7\) & \(151\pm 65\) & \(411\pm 7\) & \(0.84\pm 0.02\) & \(256\pm 7\) & \(870\pm 20\) \({}_{*}\) & ND & ND \\ L7 & \(58\pm 4\) \({}_{*}\) & \(302\pm 4\) \({}_{*}\) & \(272\pm 18\) \({}_{*}\) & \(2.93\pm 0.02\) \({}_{*}\) & \(259\pm 13\) \({}_{*}\) & \(937\pm 13\) \({}_{*}\) & ND & ND \\ L8 & \(79\pm 6\) \({}_{*}\) & \(270\pm 6\) \({}_{*}\) & \(422\pm 11\) \({}_{*}\) & \(2.33\pm 0.02\)\({}_{*}\) & \(299\pm 11\) \({}_{*}\) & \(1216\pm 6\) \({}_{*}\) & ND & ND \\ L9 & \(68\pm 12\) & \(136\pm 12\) \({}_{*}\) & \(374\pm 59\) & \(0.85\pm 0.12\)\({}_{*}\) & \(354\pm 6\) \({}_{*}\) & \(1058\pm 16\) & ND & ND \\ K1 & ND & ND & ND & ND & ND & \(685\pm 12\) & \(204\pm 21\) \({}_{*}\) & \(745\pm 21\) \({}_{*}\) \\ K2 & ND & ND & ND & ND & ND & \(786\pm 30\) \({}_{*}\) & \(154\pm 8\) \({}_{*}\) & \(1403\pm 29\) \({}_{*}\) \\ K3 & ND & ND & ND & ND & ND & \(771\pm 15\) \({}_{*}\) & \(155\pm 8\) & \(1207\pm 15\) \\ K4 & ND & ND & ND & ND & ND & \(522\pm 45\) \({}_{*}\) & \(115\pm 8\) & \(937\pm 8\) \\ K5 & ND & ND & ND & ND & ND & \(545\pm 18\) & \(120\pm 9\) \({}_{*}\) & \(916\pm 9\) \({}_{*}\) \\ K6 & ND & ND & ND & ND & ND & \(898\pm 40\) \({}_{*}\) & \(197\pm 12\) \({}_{*}\) & \(1458\pm 12\) \({}_{*}\) \\ K7 & ND & ND & ND & ND & ND & \(510\pm 28\) \({}_{*}\) & \(131\pm 13\) \({}_{*}\) & \(1080\pm 23\) \({}_{*}\) \\ K8 & ND & ND & ND & ND & ND & \(395\pm 6\) \({}_{*}\) & \(235\pm 13\) \({}_{*}\) & \(757\pm 17\) \({}_{*}\) \\ K9 & ND & ND & ND & ND & ND & \(797\pm 132\) \({}_{*}\) & \(277\pm 18\) \({}^{*}\) & \(1050\pm 16\) \({}^{\dagger}\) \\ \hline \hline \end{tabular}
\end{table}
## 2 Materials and Methods  
### Preparation and Determination of Pb-Contaminated Soil  
As illustrated in Figure 1, the clean soil sample (RS) was collected from Dalian Maritime University, exhibiting a pH of 8.0 and a Pb concentration of 0.02 mg/kg, significantly below the thresholds set by the \"Risk Control Standards for Soil Pollution in Development Land\" (GB 36600-2018) [23] issued by China's Ministry of Ecology and Environment and State Administration for Market Regulation. The samples labeled PS2.5, PS5, and PS10 were prepared by mixing RS with Pb(NO3)2 solutions at concentrations of 0.036 mol/L, 0.072 mol/L, and 0.144 mol/L, respectively, for one month. This resulted in Pb concentrations of 2.5 g/kg, 5 g/kg, and 10 g/kg, representing onefold, twofold, and threefold the Soil Environmental Quality Risk Control Standard, respectively. The soil pH was measured using a potentiometric method. Air-dried soil was mixed with distilled water at a 1: 2.5 ratio of soil mass to water volume and analyzed with a pH meter (PHS-3C, LeiCi, Sichuan, China). Pb concentration in the soil was determined using inductively coupled plasma mass spectrometry (ICP-MS) (XSeries 2, Thermo, Waltham, MA, USA).  
Figure 1: Flow chart of different soil remediation methods (The yellow box indicated the groups without BES, while the blue box represented the treatment groups with the composite remediation agent combined with BES).
## 2 Mathematical Model of Active Power Filter  
Figure 1 illustrates the fundamental circuit configuration of single-phase active power filter (APF) system control. The working principle of the APF is as follows: The APF collects current signal \(i_{s}\), voltage signal \(u_{s}\), DC-side voltage signal \(U_{dc}\), and load current signal \(i_{L}\) through sensors, extracting the harmonic signals from them. The controller generates corresponding control signals based on the extracted harmonic signals. These signals are  
modulated through PWM and control the conduction and turn-off of power semiconductor devices (IGBTs are used as an example in this paper) via a driving circuit, thereby generating compensating current \(i_{c}\) to counteract the adverse components in the original grid current. In addition, the equivalent inductance and resistance of the APF circuit are \(L\) and \(R\).  
With reference to [15], the practical system can be simplified as follows:  
\[\dot{x}=f+bu \tag{1}\]  
where \(f=f(x)+g(t)=-\frac{R}{L}i_{c}-\frac{u_{s}}{L}-\frac{u_{dc}}{L}+g(t)\), and \(g(t)\) represents the overall disturbance, encompassing both parameter perturbation and external disturbance.
## 3 Results and Discussion
### Rain Detection Ability Assessment  
The precipitation products under study showed an overall good performance with the PERSIANN-CDR showing the best performance with POD of 0.91, followed by the APHRODITE (POD = 0.88), GPCP-1 DD (POD = 0.84), CMORPH (POD = 0.79), 3B42 RT (POD = 0.76), and 3B42V7 (POD = 0.76) (Table 5). This result is similar to that found by [PERSON] _et al._[62] over Iran, where PERSIANN had a POD value of 0.93. The APHRODITE showed a good performance, most likely because of the use of field  
Figure 5: Coefficient of determination (R\({}^{2}\)) of monthly precipitation between rain gauges and **(a)** 3B42 RT, **(b)** 3B42V7, **(c)** GPCP-1 DD, **(d)** PERSIANN-CDR, **(e)** CMORPH and **(f)** APHRODITE over Malaysia (red circle in Figure 5a represents the locations of two mountainous stations).  
observations from the local government agency, namely the Malaysia Meteorological Department (MMD), in the interpolation process. The regional POD analysis of precipitation products against 38 principal rain gauges is shown in Figure 6. The PERSIANN-CDR showed the best performance with POD ranging from 0.81 to 0.98. As seen in Figure 6, major SPPs obtained better PODs in the western PM and southern EM regions, but showed a poor POD value in the eastern PM. This is because the POD value was higher in drier regions and lower in wetter regions [63].  
In general, the SPPs had high performance of ACC, with values ranging from 0.58 to 0.72. The capability of SPPs to correctly estimate overall rain and no-rain events was quite high, especially in the northern PM. TRMM products (3B42 RT and 3B42V7) were marked by higher ACC values compared to the other products. The 3B42V7 showed the lowest FAR compared to other precipitation products by 0.41, indicating about 40% of estimated precipitation, but actually there is no rainfall from rain gauges. All precipitation products had a moderate CSI ranging from 0.46 to 0.49, indicating that about half of the precipitations were correctly estimated. The HSS analysis showed a moderate performance of SPPs on precipitation detection over Malaysia, although better HSS performance was found in the northern PM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **3B42 RT** & **3B42V7** & **GPCP-1 DD** & **PERSIANN-CDR** & **CMORPH** & **APHRODITE** \\ \hline ACC & 0.67 & 0.68 & 0.61 & 0.55 & 0.66 & 0.60 \\ POD & 0.76 & 0.76 & 0.84 & 0.91 & 0.79 & 0.88 \\ FAR & 0.42 & 0.41 & 0.48 & 0.52 & 0.43 & 0.49 \\ CSI & 0.49 & 0.49 & 0.47 & 0.46 & 0.49 & 0.48 \\ HSS & 0.36 & 0.36 & 0.27 & 0.19 & 0.35 & 0.25 \\ \hline \end{tabular}
\end{table}
Table 5: Overall rain-detection capability of each precipitation product over Malaysia.  
Figure 6: The probability of detection (POD) of daily precipitation between rain gauges and (**a**) 3B42 RT, (**b**) 3B42V7, (**c**) GPCP-1 DD, (**d**) PERSIANN-CDR, (**e**) CMORPH and (**f**) APHRODITE over Malaysia.
## 2 Materials and Methods
### Biological Determinations
#### 2.5.4 Cytogenetic Analysis  
Sister Chromatid Exchange  
The frequency of sister chromatid exchanges (SCE), as a genotoxicity biomarker, was determined by analysing 50 s division metaphases for each treatment (Figure S1, Supplementary Material). The assessment of SCE scores in lymphocytes involved recording the total number of exchanges in the analysed cells for each treatment to establish their frequency (Y\({}_{\text{SCE}}\)). Additionally, we recorded the number of chromosomes with one (Y\({}_{\text{1 SCE}}\)), two (Y\({}_{\text{2 SCE}}\)), or three (Y\({}_{\text{3 SCE}}\)) chromatid exchanges in the total number of analysed cells for each treatment.  
Evaluation of MI and PI  
For each concentration tested, MI and PI as cytotoxicity biomarkers were analysed. According to [PERSON] et al. (1992) [PERSON] et al. (1992), the mitotic index was determined by exclusively considering the proportion of metaphases in 500 cells during the first-division metaphases.  
Equation (3) was used to evaluate the relative mitotic index (RMI) [PERSON] et al. (1992):  
\[\text{RMI}=\frac{\text{MI treated}}{\text{MI control}}\times 100 \tag{3}\]  
To evaluate the PI, the proportion of cells in the first (M1), second (M2), and third (M3) division was obtained from 100 consecutive metaphases for each concentration (Figure 1). The proliferation index (PI) was calculated using Equation (4), and the relative proliferation index (RPI) was evaluated using Equation (5) [PERSON] et al. (1992).  
\[\text{PI}=\frac{\text{M1}+2\text{M2}+3\text{M3}}{100} \tag{4}\]  
\[\text{RPI}=\frac{\text{PI treated}}{\text{PI control}}\times 100 \tag{5}\]  
Chromosomal Aberrations Assays  
The analysis of CAs for assessing radioprotective capacity was carried out via the dicentric chromosomes assay since this biomarker is the gold standard technique for this purpose [PERSON] et al. (1992).  
Chromosomal analysis was performed on first-division metaphases, which consisted of 46 centromeres in a total of two hundred cells (Figure S1, Supplementary Material). Chromosomal aberrations were categorized into dicentric chromosomes (Dic) and rings, with the latter being considered only when an acentric fragment was present. Extra acentric fragments were classified as acentric fragments that were not associated with dicentric and ring chromosomes. Other abnormalities such as chromatid breaks and gaps were also taken into account.
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 3 Parameter Identification Method for the Hyper-Viscoelastic Model
### Results and Discussions  
#### 3.5.1 Uniaxial Relaxation Tests  
The identified parameters by each group of relaxation test are presented in Table 3. The original values for the undetermined parameters \(t_{1}\), \(u_{1}\), \(u_{2}\), and \(u_{3}\) are respectively 10, 0.4, 0.1, and 0.  
As shown in Table 3, all the groups with different loading histories and initial values are able to identify the parameters in the hyper-viscoelastic models. The identified weight in each group is nearly identical with its original one, while the weights \(u_{2}\), and \(u_{3}\) in some groups, are a bit poorly determined. For the identified relaxation time \(t_{1}\), it becomes more accurate with the increasing of the loading time \(\mathrm{T}_{\mathrm{load}}\), and in the group of the \(\mathrm{T}_{\mathrm{load}}\) being 30 s, the identified relaxation time is about 3.6% lower than its true value. Subsequently, the deviation \(\mathrm{S}_{2}\) of each group decreases as the \(\mathrm{T}_{\mathrm{load}}\) increases with the least one being 2.87 \(\times\)\(10^{-5}\). Besides, for the results with the same loading time, the identified parameters are little affected by the initial setting value and the number of terms for the Ogden model, which indicates a strong reliability of the PSA.
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 1 Introduction
### Dynamic Drop Formation  
This is the phenomena that is explained in almost all atomization processes. In this the liquid is flowing at a definite flow rate to cause an uninterrupted stream of droplets. Researchers did a study to understand the mechanism of atomization and explained two mechanisms which are superficial interruption of thin waves at the exterior of the liquid and cavitation [7]. Later, the observations concluded that atomization is a result of the combination of these two mechanisms [8; 9]. The series of steps involved in atomization are [1]:  
* The liquid is allowed to flow through a nozzle to accelerate its velocity to a required level causing the liquid to lengthen into jets or sheets;
* Liquid surface is initiated with external or internal disturbances like pressure, airflow, waves, etc.;
* Short liquid ligaments are shaped on the surface as a result of these disturbances;
* The surface tension of the liquid causes the ligaments to break into small droplets;
* These droplets will be further broken down into fine and uniform drops as they travel across the gassy channel.  
Figure 1 represents the schematic of the atomization process. The atomization process is ordered into three stages: the first stage is the liquid ligament development on the surface, the second stage is the breakdown of the ligaments into droplets and third stage is the added breakdown of the droplets into small drops.
## 4 Experimentation
### Results and Discussion
#### 4.2.2 Effect of Progressive Image Resizing  
The progressive image resizing technique has been discussed in Section 3.3. During experimentation, the preprocessed face region \(\mathcal{F}\in\mathbb{R}^{200\times 200}\) was further down-sampled into \(n_{1}\times n_{1}\times 3\), \(2n_{1}\times 2n_{1}\times 3\), and \(4n_{1}\times 4n_{1}\times 3\) size images. Here, we already considered \(n_{1}=48\) in the above experiments. Hence, in progressive image resizing, we down-sampled \(\mathcal{F}\in\mathbb{R}^{200\times 200}\) to \(\mathcal{F}\in\mathbb{R}^{48\times 48}\), \(\mathcal{F}\in\mathbb{R}^{96\times 96}\), and \(\mathcal{F}\in\mathbb{R}^{192\times 192}\). In other words, the \(CNN_{1}\) architecture (Figure 4) was trained with \(\mathcal{F}_{48\times 48\times 3}\) images. Then, \(\mathcal{F}_{96\times 96\times 3}\) images were used to train \(CNN_{2}\) architecture (Figure 5). Lastly, the \(CNN_{3}\) architecture was trained with \(\mathcal{F}_{192\times 192\times 3}\) images. The purpose behind learning these architectures with the increasing image sizes concern the fact that (i) the high-resolution images are trained in the network; (ii) the effect of multi-resolution approaches can be introduced in the network such that the texture patterns at the higher level of abstraction will be reflected during the learning of parameters; and (iii) the system will provide deeper information that would be beneficial for the hierarchical representations of features. Hence, the use of progressive image resizing not only increases the performance of the recognition system but also reduces the overfitting problems. The effect of progressive image resizing on the performance of the proposed system is reported in Table 5 and from this table, we can observe that for the KDEF, GENKI, CK+, and SFEW databases, the proposed FERS exhibits a better performance for \(\mathcal{F}_{192\times 192\times 3}\) images than for both \(\mathcal{F}_{96\times 96\times 3}\) and \(\mathcal{F}_{48\times 48\times 3}\) images. Moreover, it is evident that both progressive image resizing and data augmentation techniques together are very effective for the proposed CNN models for recognizing facial expression on facial regions.
# Probe: A Framework for Serious Games in Telecrabilitation
## 2 Materials and Methods  
Several libraries and frameworks have been proposed to facilitate the programming of visual/artistic applications (e.g., openFrameworks.cc) and games (e.g., stencyl.com accessed on 11 May 2021, defold.com accessed on 11 May 2021, www.yoyogames.com/en/gamemaker, accessed on 11 June 2021, [[https://gdevelop-app.com/](https://gdevelop-app.com/)]([https://gdevelop-app.com/](https://gdevelop-app.com/)), accessed on 11 June 2021). Libraries such as openFrameworks can benefit from using the C++ language, which has various modules that are specifically designed for artistic purposes with computer vision support, a crucial tool when dealing with serious games. However, openFrameworks is intended for C++ developers, which can be a barrier to those who have no programming skills. On the other hand, applications and libraries for game development (even with a simplified block programming interface) are missing significant tools necessary in the field of telerehabilitation, such as specific computer vision tools aimed at pose estimation, face detection, facial expression recognition etc.  
The aim of the proposed platform, Proteo, is hence to give therapists and researchers without specific skills in software development, the capability of defining serious game customizations starting from game templates. Given a game template, which defines the game characteristics, Proteo enables therapists/researchers to define the game logic and the user interaction in a simplified manner, focusing only on what she/he wants to analyze, overcoming technological barriers and concentrating only on defining tasks and collecting data.  
Moreover, the framework can be applied in rehabilitation or research centers with software development skills that could easily develop suites of remotely administered serious games, focusing only on a simplified game implementation, exploiting the framework modules for client/server communication, user management, a database for collecting data, human-computer interaction, or deep learning techniques, thus potentially gathering a large amount of data that can enable the exploitation of machine learning techniques [27].  
In the following, we present Proteo, an open-source and cross-platform scriptable platform that is highly modular with built-in support for many useful modules for serious game implementation. We analyze two use cases testing Protee with two different kinds of users: therapist/researchers and end users. In the first case, we ask the therapist/researcher group to customize a serious game after being given a game template through the platform. In the latter, we ask a group of end users to play a serious game developed with Protee. In both cases we collected data regarding user satisfaction, which will be discussed in the Results section. Moreover, the technical details of the platform are presented in Appendix A.