esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Increase the Upper Limit of PV Capacity: Reactive Power Control
### The Relationship Between Reactive Power Control and Upper Limit of Node Voltage  
As the PV power supply increases, the voltage VL may exceed the upper voltage limit. To alleviate voltage rise, PV power sources can take one of the following two actions:  
* Reduce its actual power generation (actual power rationing) [28];
* Absorb reactive power.  
For the convenience of analyzing and absorbing the impact of reactive power, consider a simple case of integrating with the power grid through \(R\)-\(L\) lines, as shown in Figure 16. Concentrate the impedance of the circuit and transformer into one resistor \(R_{tx}\) and one reactance \(\mathrm{j}X_{lc}\). Assuming the grid voltage is \(V_{g}\), the PV voltage is \(V_{\mathrm{PV}}\), the actual power generated by PV power generation is \(P_{\mathrm{PV}}\), and the absorbed reactive power is \(Q_{\mathrm{PV}}\). By considering the voltage \(V_{x}\) at the node between \(R_{lc}\) and \(\mathrm{j}X_{lc}\), the system can be easily analyzed. Due to the purely resistive impedance between PV point and \(x\) point, the actual power transfer between them is very close to the following:  
\[P_{\mathrm{PV}}=\frac{V_{\mathrm{PV}}(V_{\mathrm{PV}}-V_{x})}{R_{lc}}\to V _{x}=V_{\mathrm{PV}}-\frac{P_{\mathrm{PV}}R_{lc}}{V_{\mathrm{PV}}} \tag{18}\]  
\[Q_{\mathrm{PV}}=\frac{V_{g}\big{(}V_{g}-V_{x}\big{)}}{X_{lc}}\to V_{x}=V_{g}- \frac{Q_{\mathrm{PV}}X_{lc}}{V_{g}} \tag{19}\]  
\[Q_{\mathrm{PV}}=\frac{V_{g}\big{(}V_{g}-V_{\mathrm{PV}}\big{)}}{X_{lc}}+\frac {V_{g}}{V_{\mathrm{PV}}}\frac{R_{lc}}{X_{lc}}P_{\mathrm{PV}} \tag{20}\]  
In reality, \(Q_{\mathrm{PV}}\) represents the reactive power generated by the power source, but considering the practical situation, \(\Delta Q_{\mathrm{PV}}\) can be approximately equivalent to the reactive power absorption of PV.  
As shown in (20), when \(Q_{\mathrm{PV}}\) increases, \(V_{\mathrm{PV}}\) will be affected and decrease. However, the increase in \(Q_{\mathrm{PV}}\) will also increase \(P_{loss}\). Below is the calculation of \(Q_{\mathrm{PV}}\):  
\[P_{\mathrm{PV}}=I_{\mathrm{PV}}V_{\mathrm{PV}}\cos\Delta \tag{21}\]  
Figure 15: Simplify system theory/simulation \(P_{loss}\) curve.  
\[\Delta=\cos^{-1}\left(\frac{P_{\text{PV}}}{I_{\text{PV}}V_{\text{PV}}}\right) \tag{22}\]  
\(\Delta\) is the phase difference between the PV output current and the access point voltage:  
\[Q_{\text{PV}}=I_{\text{PV}}V_{\text{PV}}\sin\Delta \tag{23}\]
# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
###### Abstract  
The small scale of transmission towers and the environmental diversity around their situations make their detection in Synthetic Aperture Radar (SAR) images a challenging task. This paper presents a new hierarchical detection algorithm for transmission towers. First, Signal-to-Clutter Ratios (SCRs) of pixels are calculated. Afterwards, a SCR threshold is set. Since transmission towers possess strong scattering characteristics, pixels with SCRs above the threshold are considered as potential transmission tower pixels. Second, spatial densities of potential transmission tower pixels are calculated. According to the aggregation characteristics of transmission tower pixels, some potential transmission tower pixels with small spatial densities are removed. The remained potential transmission tower pixels are considered as candidate transmission tower pixels. The candidate transmission tower pixels are grouped by the nearest neighbour scheme such that in each group the distance between pixels is under a given threshold. Thus, each of the groups is viewed as a quasi-transmission tower. Convex-hulls of quasi-transmission towers are built, and then Minimum Bounding Rectangle (MBR) for each convex-hull is generated. According to the rectangle aspect ratios of MBRs, the real transmission towers are extracted. C-band HH-polarization GaoFen-3 (GF-3) amplitude images are used for experiments and four of the most popular transmission tower detection algorithms are selected as comparing algorithms to validate the proposed algorithms. The detection performance of transmission towers is evaluated with detection rate and quality factor. Experimental results verify that the proposed algorithm can efficiently and accurately detect transmission towers while maintaining the transmission tower geometry to a certain extent, which indicates that the proposed algorithm is efficient and promising.  
signal-to-clutter ratio (SCR); transmission tower detection; synthetic aperture radar (SAR); spatial density; minimum bounding rectangle (MBR) +
Footnote †: journal: Remote Sensing  
0202210 January 2022  
Hierarchical Transmission Tower Detection from High-Resolution SAR Image. _Romte Sens._**2022**, 14, 625. [[https://doi.org/10.3390/rs14030625](https://doi.org/10.3390/rs14030625)]([https://doi.org/10.3390/rs14030625](https://doi.org/10.3390/rs14030625))
## 2 Input Design
### Wall Distance Field  
In order to avoid the problem of data heterogeneity, this paper draws on [PERSON]'s [22] idea to express the shape in the form of images, that is, assigning different values to the internal and external spatial points of the shape. In [PERSON]'s original design, the inside of the shape is expressed by 1, and the outside of the shape is expressed by 0. Although this design can express any shape, the efficiency of conveying shape information is not high, and it cannot provide the distance information of spatial points and walls. Therefore, this paper proposes the wall distance field as a description of the aerodynamic shape, which can provide the relative positional relationship between the spatial point and the wall for the limited receptive field of the CNN and directly introduce the shape pair into the input of the neural network, influencing the factors of aerostatic performance. In the wall distance field \(I_{Dist}\), which is one of the model inputs (\(I\)), the assignment of the i spatial point is shown in Equation (1):  
\[I_{Dist,i}=\beta\times e^{-1\times\frac{\min(R_{i,\Gamma})}{\beta}} \tag{1}\]  
In the equation, \(R_{i,\Gamma}\) represents the set of distances between the i spatial point and the set of aerodynamic shape boundary points \(\Gamma\), and \(\min\left(R_{i,\Gamma}\right)\) refers to the final minimum value at all distances. For the consideration of dimensionless shapes, B is introduced to represent the characteristic length of the shape. Because the space near the wall has a greater impact on the pressure field, we use a negative exponential form to increase the weight near the boundary layer while limiting the input to 1 to reduce the hidden danger of gradient explosion. \(\beta\) expresses the coefficient inside and outside the shape. When the i spatial point is located on the wall or inside the shape, it is set to 0, and otherwise, it is set to 1. The wall distance field \(I_{Dist}\) of an example ellipse with an aspect ratio of 4 is shown in Figure 1.
## 3 Results
### Induction of Apoptosis in SCC-25 Cells by the Treatment of PD-L1 ab + GNP + NTP  
The Western blot analysis showed that the different treatment modalities displayed proteins that were typical apoptosis-inducing elements. Compared to the nontreatment group, the experimental groups showed higher levels of expression for apoptosis-related proteins, such as cleaved caspase-3 and cleaved PARP. Elevated expression levels of cleaved caspase-3 and cleaved PARP were observed in the PD-L1 ab + GNP + NTP group. \(\beta\)-actin served as the loading control. The experiments were repeated 3 times with similar results. The expression of cleaved caspase-3 and cleaved PARP was quantified by using ImageJ J freeware from the National Institute of Health (Figure 6).  
Figure 4: Effect of PD-L1 ab + GNP + NTP on the cell viability of SCC-25 cells and HaCaT cells. (**A**) According to the SRB assay, SCC-25 cells overall showed lower cell viability compared to HaCaT cells. Least number of cells were observed after PD-L1 ab + GNP + NTP treatment. (**B**) Data shown represent three independent experiments. * \(p<0.05\), statistical difference using a paired \(t\)-test. Abbreviations: NT, nontreatment; ab, antibody; GNP, gold nanoparticle; NTP, nonthermal plasma.  
Figure 5: Comparison of the cell viability of SCC-25 and HaCaT cells following treatments using the LIVE/DEAD(tm) Viability/Cytotoxicity Kit. Green, live cells; red, dead cells. Compared to HaCaT cells, more dead cells were identified in SCC-25 cells among the experimental groups. Abbreviations: NT, nontreatment; ab, antibody; GNP, gold nanoparticle; NTP, nonthermal plasma.  
Figure 6: (**A**) Microscopic images. (**B**) Western blot analysis of cleaved caspase-3 and cleaved PARP. (**C**) Relative expression levels of cleaved caspase-3 and cleaved PARP protein in SCC-25 cells with NTP, PD-L1 ab + GNP + NTP and PD-L1 ab + GNP treatments. Abbreviations: NT, nontreatment; ab, antibody; GNP, gold nanoparticle; NTP, nonthermal plasma.  
After the PD-L1 ab + GNP + NTP treatment, the mitochondrial apoptosis-related factors were assessed to verify the subcellular localization as evidence of apoptosis at the molecular level. The apoptosis-inducing factor (AIF) and cytochrome C (cyt C) in the control were clustered in punctuate distribution forms. On the contrary, in treated cells, those proteins were disseminated diffusely at immunofluorescence microscopy, demonstrating their release from the mitochondria. Especially, in the control cells, AIF was distributed mostly in the cytosol. On the other hand, AIF was detected in the nuclei of the treated cells (Figure 7).
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 7 Evaluation
### Modeling for Penalty Cost \(\epsilon\) of Energy  
The energy model determines the consumption at the transmission stage in the fog layer and the waiting/execution stage in the cloud layer. The model presents that the energy consumed to execute a fog job \(J_{i}\) in a cloud resource \(R_{m}\) is higher than the energy required to hold a fog job \(J_{i}\) in a cloud queue \(Q_{m}\) waiting for execution in the cloud resource \(R_{m}\). Yet, the bandwidth energy consumed at the fog layer required for transmitting a fog job \(J_{i}\) to be executed at the cloud layer is higher than the energy consumed to execute such a job in a cloud resource \(R_{m}\).  
At the fog layer, the energy consumption per bit \(g\) (measured in Joule per bit) is modeled by the rate \(\lambda_{\Gamma}=0.3\), and hence, \(g=\exp(0.3)\), according to Equation (25). The communication bit-rate (measured in bits per second) according to Equation (26) is modeled by \(q=\text{uniform}()\). As a result, the energy cost per time unit of bandwidth allocation \(\bar{\Gamma}\) is modeled by \(e_{\gamma,\bar{\Gamma}}=\exp(0.3)\times\text{uniform}()\), as in Equation (27).  
At the cloud layer, the energy cost per time unit of waiting \(t\omega_{i}^{\beta}\) in a resource queue \(Q_{m}\) is modeled by Equation (29) to become \(e_{m,\omega_{i}}=\exp(1.0)\) with the rate \(\lambda_{\omega}=1.0\). For a fog job \(J_{i}\) being serviced in a cloud resource \(R_{m}\), the energy cost per time unit of service \(\mathcal{E}_{i}\) in the cloud resource \(R_{m}\) is modeled using Equation (31) by \(e_{m,\mathcal{E}_{i}}=\exp(0.2)\) with the rate \(\lambda_{\mathcal{E}}=0.2\). Similarly, the energy cost developed per time unit of SLA violation \(\alpha_{i}^{\beta}\) with the cloud service provider is modeled by \(e_{m,\alpha_{i}}=\exp(0.2)\) using Equation (33) with the rate \(\lambda_{a}=0.2\). It is shown that rates of execution \(\lambda_{\mathcal{E}}\) and SLA violation \(\lambda_{a}\) are modeled to be higher than the rate of waiting \(\lambda_{\omega}\), but to be lower than the rate of data communication \(\lambda_{\Gamma}\).
## 3 Case Studies
### Old Crafts Virtual Museum
#### 3.2.3 Workflow  
The work on the Virtual Museum started with the design of the application concept. Based on information provided by historians from the Museum of Sarajevo, we created the application structure in such manner that the users can learn about crafts from Bascarsija in an attractive and amusing way, as well as get a feeling of life in the place during the Ottoman period. According to that concept, the writer created three stories for the actor, with literary descriptions of craftsmen's life in past times. We selected the actor who was suitable for this performance and the costume designer prepared three different costumes. A famous Bosnian illustrator created the drawing that would be placed in the background, enjoying the opportunity to \"enter into his own illustration\".  
In the production phase we filmed the interviews with real craftsmen, as well as their work processes. In VR, we recorded their workshops and three locations in Bascarsija. The actor was recorded on a green screen. The music was composed by a Bosnian rock star, who also created his original song from that music.  
We recorded the actor on a green screen with a DSLR camera (Canon 5D Mark IV) and inserted him into a spherical background image. When seen with a VR device, the viewer is  
Figure 3: Old Crafts Virtual Museum application structure.  
placed in the middle of the picture, getting the feeling that the picture is spread 360 degrees around him, and the actor is placed at different positions inside the picture. Both actor and camera are static again, since we did not experiment with camera movement. Further tests are needed before we decide to use it in a final product.  
In post-production we edited the interviews with the craftsmen, inserting some historical photos that they provided. We created a composite of the background image drawing and the actor performances and programmed all those assets into a Virtual Reality application. Considering that not many users possess the VR headset, we also created a desktop version of the application and posted it online on a landing page of the project [32]. The workflow of the project's creation is shown in Figure 4.  
Motivated by the quality of the song composed for the project, we also created a music video for that song. This is the first VR music video in Bosnia and Herzegovina. The video is uploaded on YouTube and can be watched in VR on all devices [33].  
The Virtual Museum should be installed in the Info points offices of Sarajevo Tourist Organisation to introduce visitors to the intangible crafts heritage. We also created posters with QR codes linked to craftsmen videos. As they cannot afford expensive displays, they will put those posters in their shop windows so that visitors can access their videos and the Virtual Museum application (Figure 5).
# Biochar Improved Sugarcane Growth and Physiology under Salinity Stress
## 2 Materials and Methods
### Experimental Procedure  
A pot experiment was laid out following a split-plot design with biochar as a main factor consisting of three application rates (0, 5, and 10 tons\(\cdot\)ha\({}^{-1}\)) and saline conditions as a sub-factor (non-saline and saline conditions). Non-saline conditions are achieved by irrigating with full tap water during the growth of sugarcane plants. The saline condition was carried out by irrigating with saline solution after 4 weeks of transplanting to plastic pots. Specifically, the sugarcane seedling in each pot was watered with 300 mL of 100 mM NaCl every two days for 2 weeks with a total of 2100 mL NaCl solution. Each of the 180 plastic pots (300 mm diameter \(\times\) 250 mm height, a surface area of 0.071 m\({}^{2}\)) was filled with an old alluvial soil obtained from the experimental field at the Vietnam National University of Agriculture, Hanoi, Vietnam. The initial chemical properties of the experimental soil used in this study are shown in Table 1. Each pot contained 10 kg of dry soil. The biochar was applied at 0, 5, and 10 tons ha\({}^{-1}\), respectively (equivalent to 0; 35.33 and 70.65 g, respectively, per pot) before transplanting. One week after transplanting, plants in each pot were fertilized weekly with 200 mL of a modified Hoagland's nutrient solution with a composition of 0.3125 mM KNO\({}_{3}\), 0.45 mM Ca(NO\({}_{3}\)), 0.0625 mM KH\({}_{2}\)PO\({}_{4}\), 0.125 mM MgSO\({}_{4}\).7H\({}_{2}\)O, 11.92 uM H\({}_{3}\)BO\({}_{3}\), 4.57 uM MnCl\({}_{2}\).4H\({}_{2}\)O, 0.191 uM ZnSO\({}_{4}\).7H\({}_{2}\)O, 0.08 uM CuSO\({}_{4}\).5H\({}_{2}\)O, 0.024 uM (NH\({}_{4}\))\({}_{6}\)MO\({}_{7}\)O\({}_{24}\).4H\({}_{2}\)O, 15.02 uM FeSO\({}_{4}\).7H\({}_{2}\)O, and 23.04 uM Na\({}_{2}\)EDTA 5H\({}_{2}\)O.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Total N (\%) & 0.09 \\ \hline Total P (\%) & 0.18 \\ \hline Total K (\%) & 1.33 \\ \hline Exchangeable N (mg/100 g) & 4.25 \\ \hline Exchangeable P (mg/100 g) & 50.05 \\ \hline Exchangeable K (mg/100 g) & 11.75 \\ \hline Organic matter (\%) & 1.65 \\ pH & 6.35 \\ \hline \end{tabular}
\end{table}
Table 1: The initial chemical properties of the experimental soil.
# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 4 Experimental Results
### Datasets
#### 4.1.3 UTD-MHAD Dataset  
The UTD-MHAD dataset was captured by the members of the embedded systems and signal processing laboratory at the University of Texas at Dallas. This dataset contains a total of 861 sequences which are performed by 8 different subjects. For better generalization and variability, both male and female subjects are considered while capturing the action sequences. It has a total of 27 classes of actions (_ArmCross_, _ArmCurl_, _BaseballSwing_, _'BasketballShoot_', _'Bowling_', _'Boxing_', _'Catch_', _'Clap_', _'DrawCircle_ (CLW)_', _'DrawViracle_ (CCLW)_', _'DrawVirangle_', _'DrawX', _'Jog_', _'Knock_', _'Lunge_', _'PickIplandThrow', _'Push', _'SitToStand_', _'Squat_', _'StandToSit_', _'SwipeLeft', _'SuipeRight', _'TemisServe', _TemisSwing', _Throw', 'Walk', _'Wave_'). Figure 7c shows an example of the _'BaseballSwing_' action in the UTD-MHAD dataset.
## 1 Introduction
### Contribution of the Study  
Taking all of the above into consideration, it is clear that there are very few studies relating to the implementation of VR or even digital escape rooms in the EFL context, with the majority relying either on students' and teachers' perceptions to report on their effects, or not having undergone empirical testing. Additionally, while both collaborative and non-collaborative VRERs can promote engagement and learning in the educational context, there are no studies that compare the benefits of one approach over the other. In fact, given that the majority of VR escape rooms are usually designed for individual players [44] and taking into account the positive effects of collaboration in education [46], it is interesting to examine whether it can also affect outcomes in a VRER environment.  
Therefore, this study aims to investigate the role of collaboration within a VR escape room environment in the Metaverse that was specifically designed for adult EFL learners. The focus was placed on vocabulary, as VR environments can positively affect vocabulary learning and retention in the EFL context [47], while escape rooms in EFL settings foster contextualised learning [48]. The intervention was implemented in a non-formal educational context. Non-formal education refers to any planned educational activity that is not part of a formal educational setting and aims to improve users' skills by addressing their needs [49]. Moreover, it was tested online since immersive technologies are commonly used in distance foreign language- education and can enhance learning achievement, attention,motivation, engagement and satisfaction, as well as support experiential and collaborative learning [3].  
The goal of the current paper is to examine possible effects on learners' academic performance and learning experience when manipulating a variable, i.e., collaboration. In this regard, the following research questions (RQs) were formed:  
RQ1: What is the effect of the incorporation of collaboration in a Metaverse VR escape room on EFL learners' academic performance compared to when they play individually?  
RQ2: How does collaboration in a Metaverse VRER influence EFL learners' perceived learning effectiveness?  
RQ3: What is the role of collaboration in a Metaverse VRER on EFL learners' perceived cognitive benefits?  
RQ4: To what extent does a collaborative VRER in the Metaverse affect EFL learners' active engagement and control over the learning process?  
RQ5: What is the effect of collaboration in a Metaverse VRER on EFL learners' motivation and satisfaction compared to when they play individually?
## 2 Literature Search  
A literature search was performed on PubMed, Embase, CENTRAL (Cochrane Library), and Google Scholar, up to September 2024. For Section 3, which discusses current risk stratification strategies, the search terms were \"specific risk stratification,\" \"meta-analysis\", and \"stroke\". This yielded 17 meta-analyses and systematic reviews related to the stroke and bleeding risk stratifications detailed in Tables 1 and 2, respectively (see Appendix A for details). For Section 4.2, which discusses stroke-related hemodynamic indexes, the search terms were \"left atrial appendage\", \"hemodynamic indexes\", and \"stroke risk\". The screening process involved title review, abstract review, and finally, full-text review to identify novel LAA hemodynamic indexes associated with stroke, as summarized in Table 3.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Stroke Risk Stratification** & & \multicolumn{2}{c}{**HADS\({}_{2}\)**} & \multicolumn{2}{c}{**Framingham**} & \multicolumn{2}{c}{
\begin{tabular}{c} **CHA\({}_{2}\)DS\({}_{2}\)-** \\ **VASc** \\ \end{tabular} } & \multicolumn{1}{c}{**ABC**} \\ \hline \multirow{4}{*}{Risk factors} & Patient & Age & ✓ & & ✓ & ✓ \\ \cline{2-6}  & Characteristics & Sex & & & ✓ & \\ \cline{2-6}  & & Diabetes & ✓ & ✓ & ✓ & \\ \cline{2-6}  & & Hypertension & ✓ & & ✓ & \\ \cline{2-6}  & & Vascular Diseases & & ✓ & ✓ & \\ \cline{2-6}  & Heart Failure & ✓ & & ✓ & & \\ \cline{2-6}  & & Prior Stroke/TIA & ✓ & ✓ & ✓ & ✓ \\ \cline{2-6}  & & Prior Thromboembolism & & & ✓ & \\ \cline{2-6}  & & NT-ProBNP & & & & ✓ \\ \cline{2-6}  & Biomarkers & & Hs-CTnI & & & & ✓ \\ \hline \multirow{2}{*}{C-statistic range [mean value]} & 0.61–0.67 & 0.633 & 0.61–0.71 & 0.6–0.8 \\  & & [0.645] & [0.633] & [0.644] & [0.642] \\ \hline References & & [22; 23; 24; 25] & [25] & [11; 22; 24; 25; 12; 25; 22; 26; 27; 28; 29] & [11; 23; 25] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description and performance of several stroke risk stratifications.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Stroke Risk Stratification** & & \multicolumn{2}{c}{**HAS-BLED**} & \multicolumn{2}{c}{**ATRIA**} & \multicolumn{2}{c}{**ORBIT**} & \multicolumn{2}{c}{
\begin{tabular}{c} **ABS-** \\ **Bleeding** \\ \end{tabular} } \\ \hline \multirow{4}{*}{Risk factors} & Patient & \multirow{2}{*}{Age} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} \\  & Characteristics & & & & & \\ \cline{2-6}  & & Drug/ Alcohol Use & ✓ & & & \\ \cline{2-6}  & \multirow{2}{*}{Comorbidities} & Hypertension & ✓ & ✓ & & \\ \cline{2-6}  & & Abnormal renal of & & & & \\ \cline{2-6}  & & liver function & ✓ & ✓ & ✓ & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description and performance of several bleeding risk stratifications.