esa-sceva/satcom-chunk-collection huggingface dataset:
# Greenhouse Gas Impact of Algal Bio-Crude Production for a Range of CO2 Supply Scenarios
## 2 Methodology
### Electricity Production and Night-Time Emissions  
In scenarios 1 and 2, the electricity requirement of the biorefinery is met through a relatively small onsite NGCC plant. This NGCC plant is sized according to the electricity requirement of the biorefinery as well as that of the CO\({}_{2}\) compression and scrubbing units. The daytime CO\({}_{2}\) emissions from the NGCC plant in scenarios 1 and 2, combined with CO\({}_{2}\) from off-site power plant flue gas, are utilized for algal growth. The night-time emissions, however, are vented to the atmosphere. The extra electricity from the onsite NGCC plant during the night is exported to the grid. This is different from scenarios 3, 4, and 5, where the NGCC plant and the biomass combustion/gasification unit, respectively, are sized based on the CO\({}_{2}\) requirement of the biorefinery. Thus, scenarios 3, 4, and 5 produce more electricity than is required by the biorefinery, and this extra electricity is exported to the grid. The exported electricity is larger during night-time operation. The night-time emissions are captured and refrigerated for scenario 3. The night-time CO\({}_{2}\) emissions for scenarios 4 and 5 are vented to the atmosphere, as they come from a biogenic source. In scenario 6, a CHP unit producing both electricity and steam is proposed. The CHP system is sized to meet the heat and electricity demands of both the DAC unit and the biorefinery. A significant part of the CO\({}_{2}\) emissions from the CHP unit are captured by the DAC system during daytime operations. The night-time emissions, however, would be vented to the atmosphere.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **CO\({}_{2}\) Source** & **Transport Distance (mi)** & **Infrastructure Required** & **Excess Electricity** \\ \hline
1 & Coal based power plant & 2 & Pipeline & mostly at night \\ \hline
2 & Natural gas power plant & 2 & CO\({}_{2}\) capture facility, natural gas & mostly at night \\ \hline
3 & NGCC plant with carbon capture & 0 & CO\({}_{2}\) capture facility, natural gas boiler, & day and night \\  & and refrigeration & 0 & refrigeration system, pipeline & day and night \\ \hline
4 & Biomass combustion plant & 0 & Pipeline & day and night \\ \hline
5 & Biomass gasification plant & 0 & Pipeline & day and night \\ \hline
6 & Direct air capture plant & 0 & Pipeline & day and night \\ \hline \hline \end{tabular}
\end{table}
Table 1: Systems considered for providing CO\({}_{2}\) to biorefinery.  
Figure 1: Process flowsheet for refined bio-crude production from algae in base-case scenario.  
Additionally, the night-time extra electricity production would be exported to the grid. In reality, there would be a provision to ramp down the power plants due to reduced electricity requirements. However, this option is not considered in the present study.
## 2 Related Works
Current methods attempt to expand the local receptive field through techniques like dilated convolutions, but these strategies do not fundamentally resolve the issue of global receptive field deficiency, leading to performance bottlenecks for CNN-based models in semantic segmentation tasks [34]. In summary, the primary technical challenges associated with the high-precision extraction of shade houses from high-resolution remote sensing imagery are as follows:  
1. The lack of publicly available datasets for shade houses, despite the critical need for high-quality data to train deep learning models. 2. The limited receptive fields of traditional CNNs, which restrict their ability to capture sufficient contextual information, thereby reducing extraction accuracy. 3. The poor generalization ability of existing models, particularly when applied to diverse backgrounds, with suboptimal extraction accuracy in non-farmland environments. 4. The high misclassification rate in complex scenarios, where shade houses are frequently confused with visually similar objects such as roads, building shadows, dark-colored vegetation, and dark-colored water bodies. To address the issue of a limited receptive field in CNNs, the Transformer architecture, based on self-attention mechanisms, has been proposed in recent years [35], achieving significant results in remote sensing image analysis [34]. In this study, we selected the Swin Transformer [17] as the backbone network for feature extraction. Its hierarchical structure and sliding window attention mechanism effectively expand the receptive field and improve the ability to capture multi-scale features. To address the issue of insufficient extraction accuracy in complex backgrounds, we chose the Mask2 Former decoder [18], which performs more precise extraction through mask generation rather than pixel-by-pixel classification, thus effectively handling complex backgrounds. This study is the first to apply the combination of Swin Transformer [17] and Mask2 Former [18] to the remote sensing extraction task of APCs, demonstrating the potential of this combination for remote sensing object extraction tasks in complex backgrounds. To mitigate the high misclassification rate, we introduced a Global Channel and Local Spatial Attention module (GCLSA) to strengthen the model's understanding of inter-channel relationships and spatial dependencies, improving its ability to capture global features and comprehensively analyze contextual information. Since no public semantic segmentation dataset for shade houses currently exists, the proposed ShadeNet is tested on a self-labeled shade house dataset.
## 2 CFD Simulations
### Grid Strategy
#### 2.1.3 Mesh Parameters
Similarly, the values at a far-field distance of 16 m are (9.96%, 6.03%, 8.35%), (6.23%, 3.25%, 4.74%), and (9.36%, 4.36%, 6.86%), and those of 20 m far-field distance are (6.78%, 6.84%, 7.97%), (3.15%, 4.14%, 3.65%), and (5.64%, 5.38%, 5.51%). Compared with the total mean error ratios of 588,000, the calculation accuracies of 418,000 and 828,000 of 12 m far-field distance are dropping and the decline degrees are \(-\)175% and \(-\)126%. Similarly,  
Figure 4: (a) Close view of meshes near the WALL region; (b) Close view of meshes near the blunt trailing edge. Figure 3: (a) Close view of meshes near the WALL region; (b) Close view of meshes near the sharp trailing edge. the calculation accuracies of 418,000 and 828,000 of far-field distances of 16 m and 20 m are also dropped and the decline degrees are \(-\)76%, \(-\)45%, \(-\)118%, and \(-\)51%. Under x/L locations from 0 m to 0.7 m, the pressure coefficient \(C_{p}\), entropy, and the error ratios compared to the reference data are shown in Tables 4-6. At 12 m far-field distance, the mean error ratios of (\(C_{p}\), entropy) of three-level meshes are (12.41%, 4.79%), (8.31%, 3.85%), and (11.2%, 4.4%). Similarly, the values at a far-field distances of 16 m are (11.9%, 4.2%), (5.5%, 3.14%), and (8.79%, 4.06%), and those of 20 m far-field distance are (12.29%, 4.86%), (8.86%, 4.15%), and (11.51%, 4.51%). Hence, the performances of the 588,000 are preferred. Meanwhile, considering the limited computing resources, the mesh with 588,000 is adopted.
## 3 Computation of the principal components
### Distributed estimation of the principal components
#### 3.4.1 Power iteration method  
Let \(\mathbf{C}\) be a covariance matrix. The power iteration method starts with a random vector \(\mathbf{v}_{0}\). At the \(t\)-th iteration, the vector \(\mathbf{v}_{t+1}\) is obtained by multiplying \(\mathbf{C}\) by \(\mathbf{v}_{t}\), and by normalizing it. It can be easily shown [21] that \(\mathbf{v}_{t}\) converges to the principal eigenvector \(\mathbf{w}_{1}\) of \(\mathbf{C}\), under the mild condition that \(\mathbf{v}_{0}\) is not strictly orthogonal to the principal eigenvector. The convergence rate is exponential, its base being the squared ratio of the two principal eigenvalues. The convergence criteria can be defined either as a minimum variation \(\delta\) for \(\mathbf{v}_{t}\), or as a highest number of iterations \(t_{\max}\)[21, 22]. Algorithm 3.4.1 outlines the different steps.  
Standard power iteration algorithm \(\mathbf{v}_{0}\leftarrow\mathrm{arbitrary\ initialization}\ t\gets 0\ \mathbf{v}_{t}\leftarrow\mathbf{C}\mathbf{v}_{t}\ \mathbf{v}_{t+1}\leftarrow\frac{\mathbf{v}_{t}}{\| \mathbf{v}_{t}\|}\)\(t\gets t+1\ \ t>t_{max}\ \mathrm{and}/\mathrm{or}\ \|\mathbf{v}_{t+1}-\mathbf{v}_{t}\|\leq\delta\ \mathrm{return}\ \mathbf{v}_{t}\)  
Note that as the method converges to the principal eigenvector \(\mathbf{w}_{1}\), the normalizing factor \(\|\mathbf{v}_{t}\|\) converges to the associated eigenvalue \(\lambda_{1}\), as by definition :  
\[\mathbf{C}\mathbf{w}_{1}=\lambda_{1}\mathbf{w}_{1} \tag{11}\]  
In practical settings, the power method quickly converges to a linear combination of eigenvectors whose eigenvalues are close, or to the eigenvector whose eigenvalue is the highest if eigenvalues are well separated. As our purpose here is to find the subspace that contains the signal, eigenvectors with close eigenvalues can be thought of as generating a subspace where similar amount of information are retained along any vector of the subspace. The convergence to a linear combination of eigenvectors with close eigenvalues is therefore deemed acceptable as far as principal component aggregation is concerned.
## 2 Results and Discussion
### Profiling of Volatile Compounds in Sweet Potato
#### 2.4.5 Terpenes  
Terpenes are another important aroma contributor for plants since it constitutes the largest and most diverse class of secondary metabolites. Terpenes, containing monoterenes, diterpenes, and sesquiterpenes, are derived from isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) [47; 48; 49]. In the present study, eight terpen were identified. Camphene with a typical camphoraceous flavor was abundant in all cultivars and particularly in Anna and Ayamurasaki, which contained four or five times that of the other cultivars. Nerol was identified as a common compound in all cultivars and has been reported as common aroma compounds in baked sweet potato. Anna had the highest level of nerol, which provided a pleasant sweet and lemon flavor, followed by Shuangzai, while Jieshu95-16 reported the lowest content. Geraniol, one of the most potent odorants in baked sweet potato [24], had a high level in Ayamurasaki, but was not identified in Shuangzai. Its odor was characterized as rose and geranium. Two woody flavor compounds, \(\alpha\)-huaiene and humulene, were characteristic sesquiterpenes of Anna; both were first reported in sweet potato. \(\beta\)-Selinene with a herbal flavor appeared only in Jieshu95-16. Most of the terpen are present in trace amounts, however, in some cases, low-abundance terpen stand out as important flavor active compounds since most terpen have low odor threshold values [50].
###### Abstract
[16] compared the extraction efficiency of maceration, ultrasound-assisted extraction (UAE), and matrix solid phase extraction (MSPD). Figure 1: Chemical structures of pyrethrin I and II, cinerin I and II, and jasmolin I and II [10]. [PERSON] et al. [15] compared Soxtec, RP-HPLC, and ultrasound extraction with three different solvents (hexane, ethanol, and petroleum ether). [PERSON] et al. [21] compared three extraction methods, including traditional maceration in n-hexane and ethyl alcohol, supercritical CO\({}_{2}\) extraction (SC-CO\({}_{2}\)), and cyclic pressure extraction, known as rapid solid-liquid dynamic extraction (RSLDE). Traditional methods, such as maceration, Soxhlet extraction, and solvent extraction, have notable limitations, including prolonged extraction times, high solvent consumption, and the risk of thermal degradation of pyrethrins due to extended heat exposure. These approaches often yield lower extraction efficiencies compared to more advanced techniques like ultrasound-assisted extraction or supercritical CO\({}_{2}\) extraction. Furthermore, the reliance on large volumes of organic solvents raises environmental and safety concerns, making the development of more efficient and sustainable alternatives increasingly important [22]. To promote sustainable practices, novel green extraction techniques were used to maximize extraction efficiency for the target compound while minimizing the use of solvents, shortening the amount of time, and lowering the temperature to prevent thermal degradation of pyrethrins from different species [23; 24]. However, some unconventional extraction techniques have not been tested for pyrethrin extraction, including microwave-assisted extraction (MAE), subcritical water extraction (SWE), high-voltage electric discharge (HVED) extraction, and deep eutectic solvent (DES) extraction. Against this background, the innovative, non-conventional extraction techniques of microwave-assisted extraction (MAE), high-voltage electric discharge (HVED) extraction, deep eutectic solvent (DES) extraction, and subcritical water extraction (SWE) were evaluated for the extraction of pyrethrins from Dalmatian pyrethrum (_Tanacetum cinerarifolium_), together with previously investigated supercritical CO\({}_{2}\) extraction (SC-CO\({}_{2}\)) and ultrasound-assisted extraction (UAE). A key novelty of this research lies in the application of extraction methods that have not yet been explored for pyrethrin extraction. The objective was to evaluate the most effective green extraction techniques and to optimize the process conditions in order to obtain pyrethrin from the Dalmatian pyrethrum plant (_Tanacetum cinerarifolium_) as a natural alternative to synthetic chemotherapeutics.
# A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors
## 4 Experimental Results and Verification
### Implementation of Simulated and Experimental Platforms
To verify the effectiveness of the proposed multi-physics modeling-based method, a 1.5 kW SRM prototype with the same geometric parameters as the simulation model is utilized to build the experimental platform, as shown in Figure 8. The control of SRM is implemented in a Digital Signal Processor (DSP) TMS320F2812 from Texas Instruments (Dallas, TX, USA), which work at a frequency of 150 MHZ. The current and voltage signals are measured in real time through sensors and data acquisition card. The rotor position is obtained by an incremental encoder with resolution of 1024. Meanwhile, the vibration is measured using a piezoelectric accelerometer attached to the housing surface of the stator core on the radial direction of the A-phase tooth as shown in Figure 8. Therefore, the measured shell surface vibration can reflect the actual vibration of the motor to the greatest extent. A 1.5 kW DC motor is used as the mechanical load and can provide a loading ranging from 0 to 20 Nm.
## 3 Results
### Sensitivity to the Sampling Frequency Variation
#### 3.2.2 Autocorrelation  
Although the values for the Autocorrelation did not vary with the sampling frequency, a comparative analysis was made between the three sampling frequencies for the 90 control and pathological audios. In Figure 8, it is possible to observe the scatter plot for 25 and 12.5 kHz of the 90 Autocorrelation measurements over 10 control subjects (left-hand side), and 90 measurements for 10 pathological subjects (right-hand side). Each subject has nine Autocorrelation measurements for three vowels, and each vowel has three tones.  
From Figure 8, it is possible to observe that the Autocorrelation is just slightly influenced by the frequency variation, as previously confirmed.
## 2 Materials and Methods
### The Methodology
#### 2.1.1 Time Synchronization  
The TLS must be able to provide a timestamp, in order to have the acquisition time of each point and assign a time to each surveyed line. The use of the GNSS receiver provided with the TLS, can allow us to express the timestamp as GPS time; in this way the acquisitions obtained with different instruments (videos, point sensors, etc.) can be synchronized and, above all, by positioning a receiver on a mobile load, its positions can be correlated to the acquired deformations.  
The TLS are able to scan many lines per second (the instrument used, for example, can scan up to 120 lines per second), so it is possible to assign to a line the time derived from the timestamp of a point of the central zone with the precision of about 0.01 s.  
As for the TLS/Video synchronization, we need to compose the approximation of the time assigned to the lines with the frame rate which, typically, is 30 fps. Therefore, we can consider conservatively a mean synchronization error equal to \((0.0167^{2}+0.01^{2})^{1/2}=0.0194\) sec., and a 95% confidence interval of 0.039 sec. With regard to the TLS/GNSS synchronization, given that the time given by the GNSS receiver can be easily obtained with a precision of 0.001 sec, we can assume a mean error equal to \((0.01^{2}+0.001^{2})^{1/2}=0.010\) sec., and a 95% confidence interval of 0.020 sec. The synchronization error involves a positioning error proportional to the speed of the mobile load. For the TLS/GNSS combination, by taking into account a speed of 20 m/sec, an error of \(\pm 0.20\) m is obtained, with a maximum of \(\pm 0.40\) m. For the TLS/video pair, an error of \(\pm 0.39\) m is obtained, with a maximum of \(\pm 0.78\) m. This error, that can be dramatically reduced by using higher frame rates (60 to 120 fps), should be composed with the error due to the approximation in the positioning of the mobile load in the image, depending on the Ground Sample Distance (GSD). This last error is negligible (a few cm) even for frames taken from a distance of 100 m, given the high resolution of the recent cameras.  
Ultimately we can say that the error due to imperfect synchronization of acquisitions produces an error in the positioning of the load on average equal to a few decimetres. This implies a variation of the displacement from 1 to 3 percent for medium-span bridges (from 20 to 50 m) and lower for larger spans.
# Research on Lazy Theta+
## Route Planning Modeling
### Threat Modeling
#### 3.2.1 Terrain Threat  
Terrain threat mainly refers to the threat posed to a flight by basic topography and a mountainous environment. The basic terrain model can be expressed as:  
\[z_{1}(x,y)=\sin(y+a)+b\cdot\sin(x)+c\cdot\cos(d\cdot\sqrt{x^{2}+y^{2}})+e\cdot \cos(y)+f\cdot\sin(g\cdot\sqrt{x^{2}+y^{2}}) \tag{7}\]  
where \(x\) and \(y\) are the coordinates of the model points projected onto the horizontal plane, \(z_{1}\) is the elevation value that corresponds to the horizontal plane, and \(a,b,c,d,e,f,g\) are the constant factors that control the undulation of the reference terrain.  
The mountain environment model mainly refers to higher natural mountains in the flight route, represented by an exponential function. The mathematical model is described as:  
\[z_{2}(x,y)=\sum_{i=1}^{m}h_{i}\exp[-(\frac{x-x_{i}}{x_{st}})^{2}-(\frac{y-y_{i }}{y_{st}})^{2}] \tag{8}\]  
where \(z_{2}(x,y)\) is the peak elevation value, \((x_{i},y_{i})\) is the center coordinate of the \(i\) peak, \(h_{i}\) is the terrain parameter that controls the peak's height, and \(x_{si}\),\(y_{si}\) are the attenuation of the peak along the \(x\) and \(y\) axes, respectively, to control the slope. \(n\) represents the total number of peaks.  
Figure 5: Schematic diagram of turning radius constraints.  
The safety threshold requirement should be met between the flight height and the terrain height, and the terrain constraint can be expressed as:  
\[\min(z_{i}-h_{p_{i}})-h_{safe}\geq 0 \tag{9}\]  
where \(z_{i}\) is the height of the current waypoint, \(h_{p_{i}}\) is the terrain height at the current waypoint, and \(h_{safe}\) is the safe height threshold.  
The terrain threat probability is presented below:  
\[P_{G}=\begin{cases}0&\min(z_{i}-h_{p_{i}})-h_{safe}\leq 0\\ 1&\min(z_{i}-h_{p_{i}})-h_{safe}>0\end{cases} \tag{10}\]
## 4 Results
### Tomato Ripeness Classification: Deep Learning vs. HSV Colour Space Models  
Regarding the classification problem, Table 5 presents the results for the different metrics used in the evaluation of the two DL models and the proposed HSV colour space model. All models were able to distinguish the Red tomato class very well, with Precision rates above 80%, with the HSV colour space and the YOLOv4 models standing out with around 89%. However, they still had much more struggle to classify all relevant objects of this class, unlike the SSD MobileNet v2 model, which achieved a Recall of 84%.  
On the other hand, the complexity of detecting and subsequently classifying green tomatoes affected the SSD MobileNet v2 model much more. The YOLOv4 and HSV colour space models obtained excellent results, both in Precision and Recall, with the proposed model standing out with rates higher than 98% in both metrics.  
It is noticeable that there was considerable difficulty in distinguishing Turning and Light Red class tomatoes by all models, especially the MobileNet v2 SSD and HSV colour space which could not detect even half of the relevant objects of the two classes, with poor Recall rates of around 43%.  
Macro F1-Score and Balanced Accuracy metrics provides a general and better understanding of the results obtained. YOLOv4 model outperformed the SSD MobileNet v2 and HSV colour space models with a Macro F1-Score of 74.16%. The low value obtained by the SSD MobileNet v2 model was highly affected by its weak ability to distinguish between Turning and Light Red tomatoes.  
Regarding the Balanced Accuracy, the YOLOv4 model was again better at classifying the fruits, with 68.87% of Balanced Accuracy. However, the HSV colour space model achieved a very similar result (68.10%) mainly due to its strong ability to classify Green and Red class tomatoes.  
Figure 11: Detection results comparison between the two DL models in situations of lesser or greater complexity. Blue bounding boxes represents groundtruth annotations; Green, Yellow, Orange and Red bounding boxes represents Green, Turning, Light Red and Red tomatoes predictions, respectively.  
Figure 12 illustrates cases of poor classifications due to the difficulty of the models in distinguishing some tomato classes. An interesting aspect is how the DL models dealt with these situations. For some fruits, the models drew two bounding boxes of different classes. The tomatoes are correctly detected, but at least one of the class predictions is incorrect, which ultimately affects the classification results obtained.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 1 Introduction
### Related Work
In [28], the outage probability was minimized in a two-hop source-relay-destination network and the efficient usage of a DF relay for SWIPT toward the energy-constrained destination was proposed in Rician fading. In [29], the outage performance of hybrid protocol based AF relay networks with SWIPT was studied over asymmetric fading channels, where the source-relay link and the relay-destination link are subjected to Rician fading and Rayleigh fading, respectively. In [30], the outage probability of the achievable secrecy rate in the presence of multiple eavesdroppers that employ EH and information decoding (ID) was investigated in Rician fading.