esa-sceva/satcom-chunk-collection huggingface dataset:
# Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids
## 6 Conclusions  
The randomness and variability of the DERs bring a stochastic nature to the distribution grid operation. In order to solve voltage fluctuations while decreasing the LCCs of the ESSs at the same time, a two-stage optimal sitting and sizing method was proposed for the distribution networks. The first stage optimized the installation site and the scheduling of the ESS, and the second stage made further economic optimization for capacity, by prolonging the ESSs' lifetime. The proposed method could be applied in low-voltage distribution systems with a high penetration of the DERs, and the simulation results demonstrated the feasibility and effectiveness of the proposed planning method. The work of this study can be summarized as follows.  
1. The intermittence of renewables integrated in the distribution grid led to the risk of voltage fluctuations, which could be effectively solved by a suitable ESS configuration in low-voltage distribution networks.
2. A two-stage optimization model was built to optimize the installation site and the capacity of the ESS, which consider the coupling effects between the sizing and sitting of the ESS and the optimal operation of the system, in the first stage, and consider the LCC of the ESS in the second stage. Through the two-stage optimization, the overall cost of the ESS in the project cycle could be effectively reduced. It is worth noting that the second-stage optimization reduces costs by extending the ESSs' service life, the life-cycle cost is, therefore, reduced due to the significant deduction of the replacement and the disposal cost.  
Figure 13: The relationship of the capacity, lifetime, and average annual cost of the ESS.  
3. The two-stage solution method reduced the computation scale and the difficulty of optimization, with a higher computation efficiency.
4. The effectiveness of this method was verified by the case study. The results showed that the costs per kW h of battery 0 and battery 2 had reduced by 32.4% and 58.3%, respectively, after the second-stage optimization. The voltage profiles were regulated within the pre-defined limits, the economy of the ESS was greatly improved through the optimization.
5. The optimization method used in this study was mainly verified for low-voltage AC distribution networks, and could be extended to the AC/DC hybrid distribution grids, in the near future.
6. The revenue in a competitive market environment, with various types of participants, could also be considered into the ESS planning, to maximize the benefits of the storage and the entire distribution grids.  
[PERSON], [PERSON] and [PERSON] conceived the idea of the study, conducted the research and wrote the most of the paper; [PERSON] [PERSON] studied the characteristics of various types of ESSs; [PERSON], [PERSON] and [PERSON] analyzed the case study network and contributed to finalizing this paper.  
This research was funded by the China National Key Research & Development Program under grant numbers [2016 YFB0900500 and 2016 YFB0900503].  
The authors declare no conflict of interest.
## 3 Results
### Optimization of the Method
#### 3.2.5 Effect of Desorption Time  
A completion of the desorption step depends on the desorption time. Insufficient desorption time does not let the eluent elute the adsorbed ions, resulting in poor analytical efficiency of the method. At the same time, it has a direct effect on the speed of preconc  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Recovery (\%)** & **Eluent** \\ \hline
**Cd(II)** & **Pb(II)** & \\ \hline
60.52 \(\pm\) 0.03 & 41.23 \(\pm\) 0.01 & HNO\({}_{3}\) (1 mol L\({}^{-1}\)) \\
98.14 \(\pm\) 0.05 & 99.34 \(\pm\) 0.07 & HNO\({}_{3}\) (2 mol L\({}^{-1}\)) \\
84.48 \(\pm\) 0.11 & 76.15 \(\pm\) 0.13 & HNO\({}_{3}\) (5 mol L\({}^{-1}\)) \\
14.96 \(\pm\) 0.10 & 24.94 \(\pm\) 0.14 & HCl (1 mol L\({}^{-1}\)) \\
22.46 \(\pm\) 0.15 & 11.16 \(\pm\) 0.11 & HCl (2 mol L\({}^{-1}\)) \\
5.63 \(\pm\) 0.08 & 4.63 \(\pm\) 0.11 & HCl (5 mol L\({}^{-1}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of different eluents (1 mL) on the preconcentration of Pb(II) and Cd(II) (n = 3).  
tration. Therefore, it is necessary to find the optimum desorption time. In the present study, the effect of desorption time on the method was studied in the range of 1-5 min and 1 min of shaking is sufficient for desorption of Pb(II) and Cd(II).
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 1 Introduction
* We model the user-perceived capacity-oriented system as a two-dimensional Markov chain with the queue length and time index as the system state. Based on this, we rigorously analyze the power consumption and show conditions for ensuring a target user-perceived capacity. * Under this Markov chain, we formulate a dual problem of maximizing the user-perceived capacity over fading channels, i.e., minimizing the average power while achieving a constraint on the user-perceived capacity. To address the non-convexity of this dual problem, we convert it into a linear programming problem, equivalently, via a variable combination method. * Next, we obtain an optimal cross-layer scheduling policy by solving the derived linear programming problem. Subsequently, a binary search method is developed to acquire the maximum user-perceived capacity under an arbitrary average power constraint. * Furthermore, we represent the single user link as a finite horizon Markov decision process, and demonstrate a series of threshold structures of the optimal policy in terms of queue length, time-slot, and channel state. The rest of this paper is organized as follows. Section 2 initially provides a detailed definition of user-perceived capacity. Section 3 illustrates the system model. The BT problem with the cross-layer approach is investigated in Section 4. In Section 5, we prove the structure of the optimal policy. Numerical results and concluding remarks are presented in Sections 6 and 7, respectively.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 2 Additional Attenuation of Vegetation
### Statistical Models  
#### 2.3.1 Exponential model with Maximum Attenuation (MA)  
Foliage attenuation can be calculated with the exponential model with maximum attenuation, known as the MA model [11; 17]. In that model, the distance \(d_{in}\) is the distance in a homogeneous environment, as presented in Figure 5. The foliage is assumed to be homogeneous from the edge of the forest to the point of the receiving antenna. Then, the excess foliage loss \(A_{F}\) is given as  
\[A_{F}=A_{\text{m}}\Big{(}1+e^{-d_{in}\gamma_{v}/A_{\text{m}}}\Big{)}, \tag{23}\]  
where \(A_{m}\) and \(\gamma_{v}\) are two crucial parameters which are foliage-specific and are determined by measurements. \(A_{m}\) is the maximum attenuation per given depth in the foliage, and \(\gamma_{v}\) is the species-specific attenuation coefficient per single meter of foliage given in \(\left[\text{dB/m}\right]\). In the model, it is assumed that both Tx and Rx are of the same height; therefore, the signal path is parallel to the ground.  
Figure 4: Diffraction model for double edges.  
Figure 5: Homogeneous woodland of length \(d_{\text{in}}\) at the link between transmitter (TX) and receiver (RX). The woodland in the link is represented by a gray color, while yellow represents the position of the Rx.
###### Abstract  
An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two bottom bars), design B (with two bottom bars), and design C (with one bottom bar). The concrete beam had a size of 100 mm \(\times\) 100 mm \(\times\) 400 mm in length with a plain reinforcement bar of 9 mm in diameter. An ultrasonic test with pitch-catch configuration was conducted at each loading with the transducers oriented in direct transmission across the beams' length with recordings of 68 datasets per beam specimen. Recordings of ultrasonic test results and strains at the top and bottom surfaces subjected to multiple step loads in the experiment were done. After the collection of the data, feed-forward backpropagation artificial neural network (ANN) was used to investigate the sensitivity of the ultrasonic parameters to the mechanical load applied. Five input parameters were examined, as follows: neutral axis (NA), fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), and peak-to-peak amplitude (PPA), while the output parameter was the percentage of ultimate load. Optimum models were chosen after training, validating, and testing 60 ANN models. The optimum model was chosen on the basis of the highest Pearson's Correlation Coefficient (\(R\)) and soundness, confirming that it exhibited good behavior in agreement with theories. A classification of sensitivity was performed using simulations based on the developed optimum models. It was found that A2 and NA were sensitive to all WC and reinforcements used in the ANN simulation. In addition, the range of sensitivity of A2 and NA was inversely and directly proportional to the reinforcing bars, respectively. This study can be used as a guide in the selection of ultrasonic parameters to assess damage in concrete with low or high WC and varying reinforcement content.  
non-destructive test; concrete; ultrasonic; bending; neural network +
Footnote †: journal: Applied Soft. 2018, 8, 405; doi:10.3390/app8030405
# NMR Analysis of Pulegone in Food Products
## Materials and Methods
### Sample Preparation  
An amount of 100 mg of the essential oils was dissolved in an appropriate deuterated solvent (1 mL) and could be measured directly after the addition of 0.1% TMS. For the extraction of pulegone from teas and herbs using ultrasonic-assisted liquid extraction, 200 mg of the sample was first dissolved in 1.5 mL of CDCl\({}_{3}\) or MeOD. The samples were then subjected to an ultrasonic bath for 30 min. In the second experiment, a 400 mg sample was dissolved in 2 mL of deuterated solvent and again subjected to an ultrasonic bath for 30 min. In both cases, sample components were filtered out using a syringe attachment before measurement.  
Hydrodistillation was conducted in accordance with and modified from ISO 6571 [28]. For peppermint, a 25 g sample was used, while for spearmint and pennyroyal mint, a 20 g sample was used. For apple mint, a 25 g sample was used. To all four mint varieties, 300 mL of distilled water was added. For peppermint, spearmint, and apple mint, 200 mL of xylene-d\({}_{10}\) was employed, while for pennyroyal mint, 100 mL of xylene-d\({}_{10}\) was used.  
The distillation period for all samples was 3 h. After hydrodistillation, the produced oils were collected and stored under nitrogen in a refrigerator until measurement.  
Before hydrodistillation, the pastilles (25 g) were first dissolved in 25 mL of distilled water. The dissolved sample was then transferred to a round-bottom flask and filled up with 275 mL of distilled water. In this case, 200 \(\mu\)L of xylene-\(d_{10}\) was used, and the distillation time was 4 h.  
For the dragees, 40 g of sample material was used, while for the other confectioneries, a 40-50 g sample was used. Subsequent processing and hydrodistillation followed the aforementioned methodology for pastilles (i.e., dissolving in 24 mL of distilled water and filling up with 275 mL distilled water). The produced oil was also stored under nitrogen in a refrigerator. For NMR analysis, 50 \(\mu\)L aliquots were dissolved in 1 mL of CDCl\({}_{3}\).
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 3 Monitoring of Solar Radiation  
The maximum solar radiation in a clear day is generally considered to provide the maximum sunshine effect on the temperature distribution in bridge structures. In this paper, the hourly global solar radiation and hourly diffuse solar radiation for eight cities in Fujian were measured using an automated meteorological station, as shown in Figure 1. The cities (from north to south) and the corresponding measurement dates are shown in Figure 2: Fu'an (10 August 2016), Jian'ou (7 August 2016), Fuzhou (2 August 2010), Ninghua (4 August 2016), Zhangping (27 July 2016). Anxi (2 August 2015), Hua'an (30 July 2016) and Zhangzhou (31 July 2014).  
Figure 1: Automated meteorological station.  
Figure 2: Map of eight cities in Fujian.  
The measured hourly global and diffuse solar radiation curves for the eight cities on a clear day are illustrated in Figure 3a,b, respectively. The hourly beam solar radiation can be calculated by subtracting the measured hourly diffuse solar radiation from the corresponding hourly global solar radiation. The calculated hourly beam solar radiation curves are shown in Figure 3c. The variation trend for all hourly solar radiation curves are similar, with the solar radiation appearing at about 06:00, reaching the peak value at about 12:00, and disappearing at about 18:00.  
The global solar radiation is related to parameters such as latitude, altitude and atmospheric quality. The influence of latitude on the global solar radiation in Fujian is negligible, because all cities are located between 23\({}^{\circ}\) and 28\({}^{\circ}\) northern latitude. The maximum measured global solar radiation for Ninghua (about 1100 W/m\({}^{2}\)) was the highest among all cities, because this city has the highest altitude (as shown in Figure 3a). However, the difference between maximum global solar radiation values for different cities was not large (about 100 W/m\({}^{2}\)). The maximum measured diffuse solar radiation and the calculated beam solar radiation were in Fu'an (about 250 W/m\({}^{2}\)) and Ninghua (about 900 W/m\({}^{2}\)), respectively, as illustrated in Figure 3b,c. The difference between maximum diffuse or beam solar radiation values for different cities was about 150 W/m\({}^{2}\).
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 2 Materials and Methods
### Datasets  
Three distinct datasets are utilized for the experiments: the CamVid dataset [4,18], the FloodNet dataset [19], and the RIT-18 dataset [20]. Each of these datasets offers unique characteristics and challenges that enhance the analysis of vegetation detection. The CamVid dataset, or Cambridge-driving Labeled Video Database, is a pioneering resource for semantic segmentation in urban driving environments. It comprises 701 labeled frames extracted from video footage captured by a camera mounted on a vehicle, providing a unique perspective of real-world driving scenarios. The dataset features a resolution of 720 \(\times\) 960 pixels and includes 32 distinct semantic classes, such as cars, pedestrians, trees, and roads, which are essential for understanding complex urban scenes. Each pixel in the images is annotated to facilitate precise object classification, making it a valuable tool for researchers developing and evaluating algorithms in computer vision.  
In contrast, the FloodNet dataset is a high-resolution aerial imagery dataset designed for post-flood scene understanding, providing critical resources for damage assessment and disaster response. Collected using small unmanned aerial systems (UAS), specifically the DJI Mavic Pro quadcopter, the dataset consists of 2343 images captured after Hurricane Harvey [19]. These images are annotated with detailed semantic labels across 10 distinct classes, including flooded and non-flooded buildings, roads, water bodies, and vegetation. The dataset is divided into training (approximately 60%), validation (20%), and test (20%) sets, facilitating various tasks such as image classification and semantic segmentation.  
Additionally, The RIT-18 dataset is a high-resolution multispectral dataset specifically designed for semantic segmentation in remote sensing applications. It was gathered using a Tetracam Micro-MCA6 multispectral imaging sensor mounted on a DJI-1000 octocopter. This dataset features very high-resolution imagery that captures detailed environmental features. It includes six spectral bands in the visible and near-infrared (VNIR) range. The RIT-18 dataset comprises 18 distinct object classes, plus a background category, which facilitates comprehensive training and evaluation of machine learning models.  
Together, these datasets enable a comprehensive evaluation of the proposed approach across diverse contexts and conditions. To ensure the consistency of the analysis, all datasets are divided into training (60%), validation (20%), and test (20%) sets.
## 6 Numerical Example
### Step 6: Reconstruct the Original Message  
The subscriber reconstructs the original message as follows:  
\[M=P_{1}\oplus P_{2}\oplus P_{3}\]  
\[M=00101101\oplus 11010011\oplus 10110110=01001000\]  
Converting 01001000 back to ASCII, we obtain the letter \"H\".
## 5 Experimental Results Analysis
### Performance Comparison of Yolo Models  
This section describe the comparisons between the proposed Quantized YoloV4-Tiny model with prior YOLO models, which are also trained and evaluated with the BDSL 49 [35] dataset. The models are compared based on their flash occupancy, average mAP, average inference or response time, and average Frames Per Second (FPS) after 100 trials. Additionally, some previous works are recreated to be trained on the BDSL 49 [35] dataset and to evaluate the results. Thus, a comparison of other models to the proposed model is presented in Table 3.  
The comparison of previous research endeavors is limited to the BDSL 49 dataset, as other currently accessible datasets lack annotated images. The sole dataset that currently offers annotated images is BDSL 49, which is notable for its comprehensive and equitable distribution of data. From Table 3, it is observed that when the model is trained on the BDSL 49 dataset, the proposed quantized YoloV4-tiny model achieves an accuracy of 99.7%, which outperforms other methods from prior work.  
The comparison of detection models is illustrated in Figure 10. YoloV4, YoloV4-Tiny, and YoloV7 were trained and evaluated utilizing the BDSL 49 dataset. After quantization, the quantized YoloV4-Tiny model's results were generated. YoloV4 has the highest flash occupancy, whereas the proposed Quantized YoloV4-Tiny has the lowest of only 12 MB. Concurrently, the proposed Quantized YoloV4-Tiny has the quickest response time of 30 ms. As a result, it can work in real-time with an average FPS of 33. This means that the model can predict 33 frames in one second. The proposed Quantized YoloV4-Tiny has superior real-time performance compared to other Yolo models despite the fact that these models have comparable accuracy, with YoloV7 being marginally less accurate.  
Figure 10: Performance comparison between proposed detection model and other Yolo models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research** & **Year** & **Dataset** & **Detection Model** & **mAP** \\ \hline Talukder [13] & 2020 & BDSL 49 & YoloV4 & 96.95\% \\ Hoque [10] & 2020 & BDSL 49 & YoloV3 & 55.3\% \\ D. Talukder [19] & 2021 & BDSL 49 & YoloV5 & 97\% \\ Dima [5] & 2021 & BDSL 49 & YoloV5 & 97.8\% \\
**Proposed model** & **2023** & **BDSL 49** & **Quantized YoloV4-Tiny** & **99.7 \%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between proposed and other sign-detection models.
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
## 2 Optical Second Harmonic Generation for Investigation of Ferroic Materials
### SHG in Bulk Ferroic Systems
The effective polarization components of the SHG wave are given by  
\[P_{x}(2\omega)\propto\ \chi_{xxxx}e^{i\Psi_{xxx}}\cos^{2}\theta \sin^{2}\varphi+2\chi_{xyxy}e^{i\Psi_{xyy}}\cos\theta\cos\varphi\sin\varphi+ \chi_{xyy}e^{i\Psi_{xyy}}\cos^{2}  
On the other hand, such parasitic SHG provides a reference light wave which can be used in interference experiments to achieve domain contrast in imaging experiments [79; 86; 88]. The substrate-induced SHG response in the case of a DSO substrate measured in transmission is shown in Figure 7a. The surface-related SHG signal emerging from a centrosymmetric SrRuO\({}_{3}\) (SRO) metallic oxide measured in reflection is shown in Figure 7b. For full access to the possible orientations of polar axes in a thin film material, it is often desirable to generate SHG in transmission where surface contributions can be avoided in normal incidence configuration. However, while thin films of only a few tens of nanometers in thickness have negligible absorption throughout the visible and near-infrared spectrum, the absorption spectrum and possible birefringence effects of some substrates might affect the thin film SHG detection [16; 80]. To minimize the substrate SHG contributions in thin film measurements, it may be advantageous to use an experimental setup in reflection geometry. There is therefore no standard SHG setup for thin films analysis and the balance between substrate, surface, and thin film contribution must be optimized in each measurement. Finally, in contrast to many bulk systems, the expected domain sizes in thin films are usually below the optical SHG resolution limit. While certain sub-resolution features can be detected using SHG under the proper conditions, the efficiency to probe a ferroelectric state in thin films with SHG truly comes into its own when single domain or macroscopically-ordered domain systems are selected, i.e., when a macroscopic net polarization is present.
## 3 Results and Discussion
### Evaluation of Implementation in Additional Sites  
Based on the validated plant density identification and NDVI calculation methods, additional GSI sites, including the green roof of the library (ESRL) and the green roofs of the business school (GSBM) and Tyler Hall (CTTH) were evaluated and are presented in Figures 6 and 7. The results indicated that plant density of the green roof in the library building was 72.76%, slightly lower than the bioretention of the CBEIS building (ranging from 75.30% to 74.51%). This variance can be attributed to the age of the GSIs; the green roof of the library was constructed in 2007, while the bioretention of CBEIS was constructed in 2012. In contrast, the green roof of the business school was built later than that of the CBEIS (in 2015). However, the plant density value (63.63%) was lower than the CBEIS bioretention, likely due to limited maintenance since its construction. Typically, the green roofs require extra maintenance, including watering, fertilizing, and weeding, to maintain proper plant coverage and functionality (e.g., thermal performance, environment, and air quality). Compared with the NDVI map of CBEIS (in Figure 5), MSU Tyler Hall (Figure 7) exhibits dark green and dense green areas. This observation leads to the conclusion that the MSU Tyler Hall (built in 2020) has more healthy plants. However, there was a significant area of yellow which likely represents soil or sideways. In Figure 7, this suggests the need to add additional plants in sections 2-5 compared with sections 1 and 6, and to increase the plant coverage areas to reduce the volume of stormwater discharges, reduce urban heat island effects, and improve air quality [42]. These results underscore that the age of a building, as well as proper maintenance, are crucial factors for achieving higher plant density and better plant health in the GSIs.  
Figure 5: RGB, NIR, and NDVI maps of bioretention at CBEIS. (**a**) 27 May 2021 and (**b**) 28 June 2021.