esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Assessment of Satellite Rainfall Data
This discrepancy suggests lower accuracy in the CHIRPS precipitation estimates for the mountainous regions. Studies in various regions of Morocco, including the Tensiff watershed [60; 62; 63], the High Atlas mountains [37], the Souss Massa and upper Draa watershed [38], and the Oum Er-Rbia watershed [64], yielded similar results regarding CHIRPS performance in estimating precipitation. The CHIRPS data strongly correlated with the rain gauge measurements in the low-to medium-altitude areas characterized by low rainfall and arid conditions. However, this correlation diminished in the high-altitude regions due to challenges distinguishing between non-rainy and rainy thermal infrared clouds, especially the relatively warm clouds prevalent at higher elevations [65]. Furthermore, hot and dry conditions can reduce the CHIRPS accuracy due to precipitation evaporation below the clouds [66] and the algorithm's reliance on cloud-top temperature for precipitation estimation [38]. Despite limitations in detecting snowfall events [39], the CHIRPS remains a valuable resource for obtaining monthly rainfall data and accurately capturing spatial and temporal precipitation variability, enhancing its applicability for hydro-meteorological studies, particularly in regions with scarce rainfall observed data [38]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Stations** & **BIAS** & \(\boldsymbol{r}\) & **RMSE** & **MAE** & \(\boldsymbol{R^{2}}\) & \(\boldsymbol{p}\)**-Values** \\ \hline
11 & Marrakech & 1.66 & 0.77 & 15.64 & 10.22 & 0.60 & \(2.01\times 10^{-92}\) \\
12 & Nkouris & \(-29.60\) & 0.66 & 47.49 & 32.56 & 0.44 & \(2.15\times 10^{-59}\) \\
13 & Sidi Bou-Othman & \(-6.32\) & 0.65 & 29.53 & 21.08 & 0.42 & \(1.06\times 10^{-43}\) \\
14 & Sidi Rahal & 2.20 & 0.75 & 21.62 & 14.43 & 0.56 & \(9.65\times 10^{-84}\) \\
15 & [PERSON] & \(-2.78\) & 0.87 & 15.16 & 10.35 & 0.75 & \(3.38\times 10^{-12}\) \\
16 & Taferiat & 1.60 & 0.75 & 24.61 & 15.51 & 0.56 & \(4.00\times 10^{-74}\) \\
17 & Tahanout & \(-18.66\) & 0.67 & 44.35 & 28.15 & 0.45 & \(4.02\times 10^{-60}\) \\
18 & Talmest & \(-2.08\) & 0.85 & 20.00 & 12.07 & 0.72 & \(8.95\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Autonomy
#### 2.4.1 Decentralized Power Control  
An inverter, as an interface for a DG, is required to be equipped with control schemes to control its active and reactive power flow autonomously. One method is using droop control schemes, which can control active and reactive power sharing effectively for parallel inverters only by using local measurements. These methods have been conventionally used for large power systems based on synchronous generators and gradually became a well-established method for microgrids. Droop control methods and more comprehensive proposed structures based on these methods, such as hierarchical control, have been extensively reviewed and presented in the literature [8; 9; 12; 13; 14; 16; 17; 19; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. By implementing droop control accurate power sharing between parallel connected inverters can be achieved with regard to their power ratings. In an AC microgrid \(P\)-\(\omega\) and \(Q\)-\(V\) droops are being used for this purpose. The active and reactive output power can be calculated by local voltage-current measurements from low-pass filters. Then by using the droop Equations (1) and (2) and using the no-load values for \(\omega_{0}\) and \(V_{0}\), grid values for V and \(\omega\) can be worked out:  
\[\omega=\omega_{0}-mP, \tag{1}\]  
\[V=V_{0}-nQ, \tag{2}\]  
It shall be noted that in Equations (1) and (2) \(P\) and \(Q\) are the active and reactive delivered powers of each inverter, so the total power consumed by the loads is \(P=P_{1}+P_{2}+\cdots\) and the same for reactive power. \(m\) and \(n\) are droop coefficients, and in simple droop, they are calculated in a way that they take the power rating of each component into effect:  
\[m_{1}S_{1}=m_{2}S_{2}=\cdots, \tag{3}\]  
\[n_{1}S_{1}=n_{2}S_{2}=\cdots, \tag{4}\]  
In Equations (3) and (4) \(S\) values are the apparent power ratings of each DGs. By solving Equations (1) and (2) the reference voltage can be extracted that will be used by the inverters inner control loops to regulate its power flow. In other words, the droop control provides references for the inner control loops of the inverter. In more complex control schemes, which consist of more than one layer of control, the secondary control compensates for the frequency and voltage deviation resulted from the droop control. This means that it transmits voltage and frequency references for the primary level (droop). The droop coefficients are also influenced by energy management consideration of higher levels of control, _tertiary_ level of hierarchical control [13]. Figure 5 illustrates the droop control concept for active power including the secondary control effect.  
Figure 5: Illustration of active power sharing between two parallel inverters using conventional droop method. The secondary response of the hierarchical control is also included.
## 8. Lunar Missions--Support to Artemis
Small satellites have given crucial hope for supporting the operations of Artemis not only to fulfill the need for communication and ground tracking, but forming one of the essential support systems to the mission itself. Figure 22: Orientation analysis of the satellites constellating the moon. This will add significantly to the process of implementing this mission from logistics from LEO to the moon, the operation on the ground, and on lunar orbit with continuous and enhanced communication capabilities. Not only for the communications and tracking, small satellites will be an asset for deep-space data relay from the moon to Earth and bring back information/samples to the scientists for breakthrough research for the understanding of the moon and beyond. This in fact adds to the value of Artemis mission.
# Robust Backstepping Control Applied to UAVs for Pest Recognition in Maize Crops
## 2 Problem Formulation and Main Results
### System Description  
The following perturbed nonlinear system is addressed:  
\[\dot{\phi} = f_{0}(\vartheta)+f_{1}(\vartheta)\zeta \tag{1}\] \[\dot{\zeta} = u+\delta(t,\vartheta), \tag{2}\]  
where \(\left[\vartheta^{T},\zeta\right]^{T}\in\mathbb{R}^{n+1}\) is the state, and \(u\in\mathbb{R}\) is the control input. It is considered that the next assumptions are fulfilled:  
1. The known functions \(f_{0}:D\to\mathbb{R}^{n}\) and \(f_{1}:D\to\mathbb{R}^{n}\) are continuously differentiable in a domain \(D\subset\mathbb{R}^{n}\) that contains the origin \(\vartheta=0\) and \(f_{0}(0)=0\).
2. The Equation (1) of the system can be stabilized with a state feedback \(\zeta=\mu(\vartheta)\), with \(\mu(0)=0\), then, there is a Lyapunov function that satisfies the following equation: \[\frac{\partial V(\vartheta)}{\partial\vartheta}[f_{0}(\vartheta)+f_{1}( \vartheta)\mu(\vartheta)]\leq-W(\vartheta),\] where \(W(\vartheta)\) is a positive definite function, \(\forall\vartheta\in D\).
3. Function \(\delta(t,\vartheta)\) is a bounded matched vanishing perturbation, i.e., \[\delta(t,0)=0,\ |\delta(t,\vartheta)|\leq\Delta,\text{ and }\Delta>0.\] (3)  
**Problem statement:** Design a control \(u\) that guarantees the closed-loop robust stability of the origin (\(\vartheta=0\), \(\zeta=0\)) of system (1) and (2) in the presence of unstructured matched disturbance \(\delta(t,\vartheta)\). Then, compute the control laws for every subsystem of the quadrotor, guaranteeing its robust stabilization.
# Effect of Mineral Additives on the Behavior of an Expansive Soil for Use in Highway Subgrade Soils
## 4 Results and Discussion
### Mineralogical Changes of Stabilized BCS  
#### 4.2.1 Evolution of pH and EC  
The variation of pH with stabilizer dosage and curing time is shown in Figure 6. The increase in the pH of the stabilized BCS indicates that the solubility of aluminate and silicate in soil increases, which can accelerate the pozzolanic reaction between the soil and stabilizers. The pH decreases with increasing curing time due to the consumption of stabilizers.  
The variation of electrical conductivity (EC) with stabilizer dosage and curing time is shown in Figure 7. The increase in EC of the stabilized BCS indicates the increase of exchangeable cations in the stabilizers-soil mixtures due to the dissolution of VA and lime in the stabilized soil, which can accelerate cation exchange between the calcium cation of the stabilizers and the cations absorbed on the surfaces of BCS particles. The effect of this process results in the closeness of soil particles and the improvement of the physical-mechanical properties of the BCS. The EC of stabilized BCS suffers a sharp reduction during the initial stage due to cation exchange, followed by a slow decrease due to the consumption of cations and hydroxide ions for the pozzolanic reaction.  
Figure 5: Influence of (**a**) lime, (**b**) VA, and (**c**) mixed stabilizer content on the UCS of BCS.  
Figure 6: Variation of pH in stabilized BCS with curing time.
## 3 Results
### Photodegradation Mechanisms  
The hydroxyl radical scavenger test was conducted to assess the influence of OH\({}^{\bullet}\) species on the photodegradation of amoxicillin by the green photocatalyst. In the absence of the scavenger, the photodegradation efficiency was 81% \(\pm\) 0.2054, whereas, in the presence of isopropyl alcohol, the efficiency decreased to 48% \(\pm\) 0.2254. This significant reduction indicates that hydroxyl radicals play a crucial role in the photocatalytic degradation process. Similar results were found by [PERSON] et al. (2024) [PERSON] et al. (2024). These hydroxyl radicals are generated when the incident light is absorbed by the surface of the iron nanocatalyst, promoting electron excitation and hole (h\({}^{+}\)) generation. These holes react with water molecules adsorbed on the catalyst surface, forming highly reactive hydroxyl radicals [PERSON] et al. (2024); [PERSON] et al. (2024). When the nanocatalyst is synthesized through green processes, biomolecules present in the synthesis medium can influence its catalytic properties. According to previous studies [PERSON] et al. (2024); [PERSON] et al. (2024), the presence of biomolecules in the plant extract and on the surface of the nanoparticles can enhance photocatalytic activity--an effect already observed in dye degradation and potentially extendable to the photodegradation of antibiotics.  
The proposed reaction mechanism for the photodegradation of amoxicillin using the green photocatalyst has been adapted and can be seen in Equations (2)-(6). However, the influence of other radicals on the photodegradation of amoxicillin should also be considered, even if their effect is less significant in the reaction (Equations (5) and (6)) [PERSON] et al. (2024); [PERSON] et al. (2024); [PERSON] et al. (2024).  
\[Green\ photodatalyst\stackrel{{ h_{\gamma}}}{{ \rightarrow}}h_{VB}^{+}+e_{CB}^{-} \tag{2}\] \[Water\ (H_{2}O_{2})+h_{VB}^{+}\longrightarrow OH^{\bullet}+H^{+}\] (3) \[OH^{\bullet}+Amoxicillin\longrightarrow by\text{-products}\] (4) \[Oxygen(O_{2})+e_{CB}^{-}\longrightarrow O_{2}^{-\bullet}\] (5) \[O_{2}^{-\bullet}+Amoxicillin\longrightarrow\text{by-products} \tag{6}\]
###### Abstract  
Extensive numerical simulations were performed in MATLAB R2020b based on the classical nonlinear Thomson scattering theory and single-electron model, to systematically examine the influence of initial phase in tightly focused linearly polarized laser pulses on the radiation characteristics of multi-energy-level electrons. Through our research, we have found that phase variation from 0 to \(2\pi\) induces an angular bifurcation of peak radiation intensity, generating polarization-aligned symmetric lobes with azimuthal invariance. Furthermore, the bimodal polar angle decreases with the increase of the initial energy. This phase-controllable bimodal distribution provides a new solution for far-field beam shaping. Significantly, high-harmonic intensity demonstrates \(\pi\)-periodic phase-dependent modulation. Meanwhile, the time-domain pulse width also exhibits \(2\pi\)-cycle modulation, which is synchronized with the laser electric field period. Notably, electron energy increase enhances laser pulse peak intensity while compressing its duration. The above findings demonstrate that the precise control of the driving laser's initial phase enables effective manipulation of the radiation's spatial characteristics.  
laser physics; tightly focused laser; line polarization; initial phase; numerical simulation +
Footnote †: journal: Applied sciences
# Large-Scale Multimodal Piano Music Identification Using Marketplace Fingerprinting +
## 4 Discussion
### Effect of Runtime Budget  
The first analysis is to understand the effect of the runtime budget. Figure 6 shows the effect of runtime budget on retrieval accuracy (top) and average runtime (bottom) for the camera-based retrieval task. Each point shows the retrieval accuracy and average runtime on the test set with a selected runtime budget, where we considered runtime budgets ranging from 63 to \(10^{6}\). The lower plot shows the average and standard deviation of the query runtimes. Note that the x-axis is on a log scale.  
There are a few things to notice about Figure 6. First, the retrieval accuracy increases substantially until runtime budget reaches 1000, at which point the MRR plateaus at roughly 0.9. It is quite surprising how small the runtime budget is when performance plateaus. For reference, the dynamic n-gram system described in [33] allows for up to 10,000 matches per fingerprint, meaning that the entire budget would likely be used up after a single fingerprint lookup! This suggests that the system is finding fingerprints that are very discriminative and whose database matches require very little runtime to process. Second, the average runtime grows with the runtime budget, but remains at roughly 0.6 seconds until a runtime budget of 5000. Combining the above two observations, we see that the runtime budget that achieves optimal speed and accuracy is between 1000 and 5000.  
The biggest takeaway from our first analysis is this: it is not necessary to process a lot of database matches in order to achieve high retrieval accuracy. We do not see an active tradeoff between retrieval accuracy and runtime. Instead, there is a broad range of runtime budgets that achieve approximately optimal retrieval accuracy and runtime.
# Analytical Investigation of Vertical Force Control in In-Wheel Motors for Enhanced Ride Comfort
## 2 Dynamics Models and Methods  
To investigate how vertical force control in IWMs can enhance ride comfort, a dynamic vehicle model in state-space form is developed. A proportional ride-blending controller is then implemented, transforming the model into a multiple-input single-output (MISO) system. The state-space model is converted into a transfer function to analyze the system's frequency response. To evaluate ride comfort, road profiles are generated according to ISO 8608 standards [29] and converted into Power Spectral Density (PSD) inputs for the vehicle model. Both the open-loop and closed-loop systems of the vehicle are simulated, and the results are collected. Finally, frequency-weighted acceleration (\(\mathbf{a_{w}}\)) is calculated according to ISO 2631 standards [30] to assess ride comfort improvements.
## 3 Case Study
### Results and Analysis
#### 3.2.2 Route Planning Results  
The routes of the two sections are planned using our route planning model in Section 2.2. The route from the Singapore shipyard to Sri Lanka is recorded as IBA*routeA, and the route from Sri Lanka to Djibouti is recorded as IBA*routeB. The whole route from the Singapore shipyard to Djibouti is recorded as IBA*route. The route planning results are shown in Figures 9 and 10.  
According to the result in Figure 9, IBA*routeA basically coincides with TMrouteA. The average risk of TMrouteA is 0.3499, while the average risk of IBA*routeA is only 0.3388, which shows that IBA*routeA can avoid risk more effectively than TMrouteA.  
According to the result in Figure 10, IBA*routeB coincides with TMrouteB for the most part, but it is better at avoiding high risks in the open sea than TMrouteB. The average risk of TMrouteB is 0.4424, while the average risk of IBA*routeB is only 0.3929, which shows that IBA*routeB can avoid risk more effectively overall.  
Figure 10: IBA*routeB from 16 June to 7 July 2022.  
Figure 9: IBA*routeA from 1 to 12 May 2022.
# Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction
## 5 Experimental Results
### Additional Analysis

Table 4: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**\# Shots**} \\ \cline{2-11}
**Type** & \multicolumn{2}{c}{**0**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**8**} & \multicolumn{2}{c}{**16**} \\ \cline{2-11}
**BLEU** & **GLEU** & **BLEU** & **BLEU** & **BLEU** & **BLEU** & **BLEU** & **BLEU** & **BLEU** & **BLEU** \\ \hline  & \multicolumn{10}{c}{_temperature = 0.8_} \\ \hline spacing & 44.92 & 44.73 & 63.36 & 61.68 & 76.93 & 74.69 & 79.84 & 77.39 & 80.13 & 77.50 \\ \hline punctuation & 45.56 & 45.36 & 64.73 & 63.18 & 80.01 & 78.34 & 83.66 & 81.94 & 88.60 & 87.12 \\ \hline numerical & 42.73 & 39.10 & 51.99 & 46.43 & 64.14 & 57.95 & 68.72 & 63.73 & 72.20 & 66.47 \\ \hline remove & 43.34 & 42.50 & 52.79 & 50.71 & 57.63 & 55.56 & 65.14 & 63.06 & 67.59 & 64.70 \\ \hline addition & 50.90 & 48.75 & 59.00 & 56.89 & 71.32 & 68.45 & 76.88 & 74.15 & 76.53 & 73.61 \\ \hline word\_replace & 45.94 & 44.90 & 51.01 & 49.61 & 67.32 & 65.18 & 70.07 & 67.99 & 71.96 & 70.35 \\ \hline rotation\_replace & 46.89 & 45.79 & 62.89 & 59.35 & 70.81 & 66.73 & 76.71 & 73.22 & 77.82 & 74.64 \\ \hline separation & 59.41 & 61.52 & 77.39 & 76.83 & 84.46 & 84.31 & 86.49 & 86.31 & 86.33 & 85.78 \\ \hline typing\_language & 20.93 & 20.93 & 61.87 & 61.22 & - & - & - & - & - \\ \hline foreign\_and\_conversion & 45.36 & 43.96 & 59.78 & 57.35 & 74.87 & 71.93 & 77.86 & 74.21 & 80.34 & 76.86 \\ \hline consonant\_vowel\_conversion & 52.20 & 50.91 & 60.84 & 57.45 & 72.11 & 70.07 & 73.66 & 71.75 & 77.74 & 75.16 \\ \hline G2P & 46.70 & 44.75 & 58.45 & 55.62 & 71.74 & 70.47 & 79.01 & 76.23 & 79.11 & 76.27 \\ \hline element & 39.82 & 40.17 & 46.21 & 46.43 & 54.56 & 53.89 & 58.73 & 57.24 & 58.17 & 57.43 \\ \hline tense & 44.35 & 37.55 & 50.76 & 44.61 & 61.49 & 51.29 & 65.87 & 54.93 & 68.02 & 59.35 \\ \hline postposition & 41.25 & 39.24 & 65.25 & 61.75 & 72.38 & 70.61 & 76.11 & 72.90 & 75.71 & 73.52 \\ \hline suffix & 45.92 & 45.43 & 64.13 & 63.58 & 74.10 & 74.97 & 75.66 & 75.58 & 80.13 & 8 For more subtle errors, more complex outcomes are observed. For instance, in the case of polite speech errors, as the number of shots increases, the influence of temperature diminishes. In zero-shot scenarios, there is a performance variation of up to 4.24 compared to temperatures set at 0.2, whereas in 16-shot scenarios, the difference is at most 2.04. Additionally, concerning behavior errors, providing shots proves effective; however, it is observed that temperature tends to decrease performance as the number of shots increases. This underscores the necessity for a nuanced evaluation, segmenting by specific error types, to acquire a more comprehensive understanding of the capabilities of LLMs, including ChatGPT.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 2 Materials and Methods
### Finite Element Modelling and Analysis
#### 2.1.2 Response Analysis of Spar Platform  
In our current investigation, a completely coupled analysis of the mooring line and spar hull was performed as a single and integrated prototype. At each phase, this tactic maintained a dynamic steadiness among the forces operating on the mooring lines and spar hull. This accurately depicted the damping and showed the genuine behavior of the spar hull and mooring line arrangement. A completely integrated analysis method was used to determine the ability to forecast the platform's response more correctly. The structural behavior of the spar platform was examined after it was simulated and studied under hydrodynamic loadings.  
The wave data were considered when analyzing responses in Morrison's regime, and it was assumed that these measures provided statistical stability in the structure and accuracy in the solutions for short periods [23]. The results of the response analysis of the platform and the detailed responses of the mooring lines are presented. Dynamic and static loads were applied depending on the analysis. The model automatically incorporated the stress and stiffness related with the mean curvature. Hence, it was an accurate approach. The wave loading of the mooring line was carried out at each stage.
## 2 Measurement Setup  
The E-Band transceivers for this measurement campaign are the Mini Link ML-6352 manufactured by Ericsson, specification details can be found in [19]. To study the rain effects on full-duplex wireless back-haul link, the 73.5 and 83.5 GHz E-band links were launched in September 2018. The links used a bandwidth of 750 MHz and adaptive modulation of up to 256 QAM and 32 QAM which supports up to 4.532 Gbps and 2.832 Gbps along a 1.8-km and 300-m path length, respectively. The link parameters including site, directional antenna specification, and link budgets parameters are listed in Table 1. The antennas were covered with acrylonitrile styrene acrylate (ASA) radomes to reduce the wet antenna effects. A complete test of the antennas and the calculation of their specifications were conducted after the dielectric coating was applied and has been shown to fulfill the ETSI radiation pattern envelope (RPE) class 4 [20]. The received signal power was recorded using the built-in data logger in the Ericsson E-Band Mini Link equipment with accuracy of 0.1 dBm. The maximum and minimum received signal level (RSL) values were provided every 15 min using the data logger of the ML-6352 at the Rx. To provide the 1.8-km and 300-m E-band links, the transceivers are located in three different sites--Site A, B, and C--as shown in Figure 1. The stations' names and geographic coordinates are provided in Table 1. Based on the measurement setup and configurations, there are four E-band links that support full-duplex wireless back-haul: the 73.5-GHz 1.8-km link from Site A to Site B (A-B link), the 83.5-GHz 1.8-km link from Site B to Site A (B-A link), the 73.5-GHz 300-m link from Site C to Site B (C-B link), and the 83.5-GHz 300-m link from Site B to Site C (B-C link). Considering the spatial inhomogeneity of rain rate, the rain distribution is not uniform along the link, especially for the 1.8-km path length. We used three rain gauges along the path of 1.8 km to collect the rainfall intensity every minute, two rain gauges were installed at Site B and Site C, and the third one was installed at Site A, as shown in Figure 1. The data-logging rain gauge system is battery powered and includes data logger with a tipping-bucket rain gauge of 0.2-mm sensitivity, and the rain gauge specifications are given in [21]. The rain gauges record the rainfall that occurs every minute--i.e., at an integral multiple of 12 mm/h--and the average rain rate throughout the link was calculated using the inverse distance weighting (IDW) method [17,22]. IDW has been effectively utilized as one of the standard spatial interpolation procedures [23; 24]. In IDW, the estimated value \(R_{E}\) is a weighted sum of the rain gauge values \(R_{l}\) given by  
\[R_{E}=\sum_{l=1}^{M}w_{l}R_{l}, \tag{1}\]  
where \(M=3\) is number of rain gauges. The weight of each rain gauge value \(w_{l}\) on the estimated location \(E\) depends on the distance \(d_{l}\) in between, and is given by [25; 26]  
\[w_{l}=\frac{d_{l}^{-2}}{\sum_{l=1}^{M}d_{l}^{-2}}. \tag{2}\]  
From these estimated values, the average rain rate was determined and used in this analysis.