esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Approach
### Procedure  
The flowchart in Figure 2 summarizes the complete methodology. Starting from the base radial configuration, the approach identifies buses with demand and generation flexibility, along with alternative branches for potential reconfiguration. The reconfiguration algorithm (highlighted in blue) systematically generates all possible radial configurations by incorporating these additional branches. Each configuration is then evaluated by the optimization framework (in grey), which adjusts \(P\) and \(Q\) at the flexible buses, taking into account constraints such as power limits and voltage levels across the system. The objective function aims to minimize both power loss costs and the costs associated with using flexible power. Ultimately, the configuration with the lowest objective function value is selected as the most efficient topology.  
The flowchart highlights two key components: the reconfiguration algorithm and the optimization framework, each distinguished by colour for clarity. The reconfiguration algorithm focuses on generating potential configurations by exploring different combinations of branches and flexible buses, ensuring all configurations maintain radiality. Meanwhile, the optimization framework evaluates these configurations by determining the optimal \(P\) and \(Q\) settings at the flexible buses. Within this framework, load flow analysis plays a critical role, involving network modeling and the calculation of various electrical parameters, such as the voltage levels, power flows, and compliance with operational constraints. This comprehensive approach ensures that the system can dynamically adapt to changes in demand and generation, leading to a more efficient and resilient distribution network.  
Figure 1: Electric distribution system with flexible buses, \(\upmu\)grids, and additional branches.  
Figure 2: Flowchart illustrating the complete methodology for the reconfiguration and optimization of the radial distribution network.
## 4 Simulation Results  
MATLAB/SIMULINK software has been widely used to simulate all types of DFIG and PV systems; it was also used here to evaluate the performance of the proposed system in a hybrid system comprising PV, wind turbine, and battery components. The proposed design consists of three steps. The first step includes the wind turbine, DFIG, and ANFIS; the second is a PV-INC MPPT and Z-source converter; and the last part is the MPPT controller with shadow prediction.
# Monitoring Rainfed Alfafa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
## 3 Results and Discussion
### Temporal Analysis
Thus, the use of remote sensing data for detecting moving or grazing practices [59; 60; 61; 62] represents a great opportunity for crop management compared to cattle feeding, especially in scenarios where it is difficult to access the plots, which is the case in some areas of Bardenas Reales.
## 3 Methodology
### Estimation Model  
We first use the foundational cost behavior model, which establishes a relationship between annual variations in operating expenses (OC) and simultaneous fluctuations in sales revenue (_SALE_). This model draws upon the seminal work of [PERSON] et al. (2016).  
The theoretical underpinning behind the model proposed by [PERSON] et al. focuses on how managerial decisions and the pre-existing cost structure influence the asymmetric cost response to changes in activity levels, challenging the traditional view that costs adjust mechanically and symmetrically [1]. This model suggests that in the short term, managers make decisions leading to an asymmetric cost response to activity changes, primarily due to deliberate managerial adjustments and associated adjustment costs.  
The equation is as follows:  
\[\begin{split}&\Delta InOC_{it}=\alpha_{0}+\alpha_{1}\Delta InSALE _{it}+\alpha_{2}DEC_{it}\times\Delta InSALE_{it}+\alpha_{3}SD_{it}\times\\ & DEC_{it}\times\Delta InSALE_{it}+\alpha_{4}lnDEBT_{it}\times DEC _{it}\times\Delta lnSALE_{it}+\alpha_{5}SD_{it}+\\ &\alpha_{6}lnDEBT_{it}+Y_{t}+\varepsilon_{it}\end{split} \tag{1}\]  
\(\Delta InOC_{it}\) is the logarithmic change in operating costs for firm \(i\) in year \(t\), \(\Delta InSALE_{it}\) denotes the logarithmic change in sales for firm \(i\) in year \(t\), and \(DEC_{it}\) represents a binary variable that equals one if the firm observes a decline in sales in year \(t\), and zero otherwise. \(Y_{t}\) represents the annual effect in the panel data.  
Within this equation, the coefficient \(\alpha_{1}\) illustrates the percentage adjustment in costs associated with a 1% increase in sales. Conversely, the combination of \(\alpha_{1}+\alpha_{2}\) represents the cost adjustment percentage for a 1% decrease in sales. Thus, \(\alpha_{2}\) is aimed at capturing the asymmetrical nature of cost behavior. Specifically, a negative value of \(\alpha_{2}\) indicates that costs decrease by a smaller percentage than they increase for a 1% change in sales, highlighting the concept of cost stickiness. To perform a comparative analysis of cost stickiness between Nordic countries and Spain, we performed regression analysis for the two regions.  
Following previous research, we incorporate specific control variables into our analysis to distinguish the effects of sales fluctuations on cost behavior from other potential influencing factors [2; 16; 26; 81]. First, we examine the pattern of consecutive sales declines, using a dummy variable (\(SD_{it}\)) that equals 1 when sales declined in the previous period, and 0 otherwise. Second, we explore the role of debt, calculated as the natural log of the ratio of total debt to total assets (\(lnDEBT_{it}\)). By integrating these variables, we isolate the impact of sales changes on cost dynamics from different aspects of financial performance, specifically financial downturns and the company's leverage.
## 3 Methodology
### Small Multi-Model Guidance  
LLMs suffer from severe hallucination or overgeneration issues [39], so the usage of large models alone cannot achieve good performance on subtasks. Small model guidance not only provides relation category confidence scores for the LLM to refer to, but also compensates for the LLM's uncertainties. Both visual and textual modules use pure Transformer models [40], with cross-attention for modality interaction. Here, we choose  
Figure 2: The EMRE2 llm framework mainly consists of two stages: small model guidance and LLM knowledge enhancement. In the small model guidance stage, we enhance the small model through multimodal relation data augmentation (MRDA) and flexible threshold loss (FTL). In the LLM knowledge enhancement stage, first, the context guidance from small model outputs and small model guidance information are obtained; then, caption guidance information is generated by processing images with a caption generation model; finally, the task description guidance is combined to be fed into the LLM. The LLM knowledge enhancement stage can process data that the small model stage fails to handle, which requires additional knowledge bases for supplementation and further reasoning.  
MKGformer [22] as the baseline. For the MRE task, we propose MRDA and FTL to further enhance the small model, aiming to address insufficient datasets and imbalanced relations.  
The small model uses Transformer-based architecture for both visual and textual modules. Specifically, the Image Encoder and Text Encoder use the encoder architectures from BERT [27] and the first K layers of CLIP [25], respectively, while the Modality Interaction uses their last L-K layers containing cross-attention interactions. Based on the Transformer architecture with L stacked layers, each layer contains multi-head self-attention (MHA), feed forward network (FFN), layer normalization (LN) and residual connections. We adopt the M-Encoder architecture from MKGformer [22] to implement the Modality Interaction part, reducing modality heterogeneity by incorporating the prefix-guided interaction module (PGI) into self-attention. In the self-attention layer, visual queries and textual keys/values are operated to aggregate textual information into the visual Transformer side. MHA performs the attention function in parallel over \(N_{h}\) heads, where each head is separately parameterized by \(W_{q}\), \(W_{k}\), \(W_{v}\in R^{d\times d_{h}}\) to project inputs to queries, keys, and values. Given the input sequence vectors \(x\in R^{n\times d}\), the attention heads \(head^{M_{t}}\) and \(head^{M_{v}}\) for text and vision are formulated as  
\[head^{M_{t}}=Attn(x^{t}W_{q}^{t},x^{t}W_{k}^{t},x^{t}W_{v}^{t}), \tag{1}\]  
\[head^{M_{v}}=Attn(x^{v}W_{q}^{v},[x^{v}W_{k}^{v},x^{t}W_{k}^{t}],[x^{v}W_{v}^ {v},x^{t}W_{v}^{t}]). \tag{2}\]
## 3 Results  
### Vegetative  
#### 3.1.1 Vegetative Stage Plant Growth Metrics (Data Collection 1)  
The first group of data was collected 4 weeks after transplant. Given that the plants were still young and developing, no differences were observed in plant height or plant diameter regarding P concentration (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**P Fertility**} & \multicolumn{4}{c}{**Four Weeks of Growth**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Height \(\gamma\)**} & \multicolumn{2}{c}{**Diameter \(\gamma\)**} \\ \cline{2-5}  & **Mean** & **SD \({}^{\text{x}}\)** & **Mean** & **SD** \\ \hline
3.75 & 40.49 A & 7.29 & 32.28 A & 8.91 \\ \hline
7.50 & 41.63 A & 10.19 & 30.21 A & 10.30 \\ \hline
11.25 & 42.32 A & 6.31 & 34.00 A & 9.15 \\ \hline
15.0 & 45.60 A & 3.50 & 39.22 A & 5.64 \\ \hline
22.50 & 48.09 A & 5.63 & 38.35 A & 6.59 \\ \hline
30.0 & 47.31 A & 4.26 & 36.32 A & 5.29 \\ \hline \multicolumn{5}{c}{Significance w} & NS & NS \\ \hline \hline \end{tabular} \({}^{\text{z}}\) P fertility concentrations based on mg:L\({}^{-1}\) (ppm). \({}^{\text{y}}\) All height and diameter measurements based on cm. Diameter was calculated by taking the widest two points on a plant taken 90\({}^{\text{o}}\) from each other. These numbers were then added together and divided by 2 to obtain the diameter measurement. \({}^{\text{x}}\) All standard deviation values assumed to be \(\pm\) of the given value. \({}^{\text{w}}\)\({}^{\text{x}}\), \({}^{\text{w}}\), or \({}^{\text{w}}\) indicate statistically significant differences between sample means based on \(F\)-test (proc GLM) at \(p\leq 0.05\), \(p\leq 0.01\), or \(p\leq 0.001\), respectively. NS (not significant) indicates the \(F\)-test difference between sample means was \(p>0.05\). Values with the same letter indicate a lack of statistical significance while values with different letters indicate statistically significant results.  
\end{table}
Table 1: The impacts of phosphorus (P) fertility on plant growth metrics of _C. sativa_ “BaOx” after four weeks of vegetative growth.
# GNSS Positioning Using Mobile Devices with the Android Operating System
## 2 GNSS Positioning Technique with Smartphones  
Only by measuring the distances (pseudo ranges) between the user's receiver and the position of at least four satellites of the same constellation, is it possible to obtain a GNSS solution [10; 11]. The distance between receiver and satellite is proportional to the signal propagation time if the transmitter and receiver clock are perfectly synchronized. As is widely known, if this condition is not satisfied, then the satellites' and receivers' clock biases have to be estimated. Moreover, other biases affect GNSS signals, such as thermal noise, uncompensated biases, multipath, and other propagation effects. Nevertheless, the main error source is given by two atmospheric propagation effects: the ionospheric and tropospheric delays and sometimes the ionospheric scintillations (only in particular conditions). If you do not estimate or remove these biases, the positioning error can be greater than 30 m, making the GNSS positioning useless for most applications. For solving these issues, one of these two principal approaches can be adopted, as widely described in the literature: the post-processing or real-time techniques. This last method can be used when the required accuracy has to be 5 cm at least [12], a condition that cannot be generally satisfied and obtained especially considering smart devices, such as smartphones or tablets [13; 14].  
If the previous level of accuracy is not enough or if it is not possible to estimate some biases in real-time in an accurate way, the post-processing approach is the only method that allows reaching better performances, exploiting, for example, the use of two or more frequencies, as in case of geodetic or handled receivers [15]. The use of these kinds of devices was really expensive, and only research institutes and few private companies were able to buy and use them. But starting from the last decade, with the advent of mass-market receivers, GNSS positioning has become more common because the cost of GNSS receivers and antennas has decreased to few US dollars, and many open source applications have increased the diffusion of these devices and techniques.  
Most GNSS receivers available inside smartphones are only single-frequency (and not multi-frequency [16]) receivers, so all collected measurements are referred to the L1 frequency (L1 band). In this case, it is not possible to apply neither the most common differencing methods, also known as double or triple differences [12; 15], nor to combine different observations [17]. Therefore, the only two possible solutions are represented by the single difference approach (considering one receiver and a reference satellite) or to model most of the GNSS biases (e.g., ionospheric and tropospheric delays, satellite and receiver clock drifts) using mathematical models [18].
## 5 Conclusions  
Predicting odor descriptors is an important research area in olfactory perception, with a wide range of applications in virtual reality, food inspection, air quality, etc. In this paper, we propose an olfactory perception prediction model, noted as HGAFMN, to classify and predict the odor descriptors of chemical molecules. It is a hypergraph neural network-based model considering the feature-semantic interaction of molecular structure-odor descriptors, combined with a deep attentional factorization machine. One part is the molecular structural feature extraction part. To extract structural features from the molecular structure graph, the features of the molecular structure graph are decomposed into feature vectors corresponding to each odor descriptor, and hypergraph neural networks are used to correlate these vectors to explore higher-order semantic interactions. It also adds a lateral  
Figure 11: (**a**) Performance comparison with respect to dropout rate; (**b**) performance comparison with respect to embedding size.  
Figure 10: (**a**) Performance comparison with respect to layers of HGNNs on F1-score; (**b**) performance comparison with respect to layers of HGNNs on embedding size.  
inhibition-inspired attention mechanism into the convolutional neural network that extracts the feature maps of molecular structure graphs to improve sharpening and anti-interference ability. The other part is the molecular descriptive feature extraction part, that is, adding an attentional factorization machine into the deep neural network. Through weighted deep feature combination, feature interactions that are important for odor classification are selected from the numerous and complex interactions of molecular feature descriptors so as to obtain molecular descriptive features.  
In synthetic fragrances and sensory neuroscience, the chemical characterization of odor molecules is the key to designing new products. The development of the quantitative structure-odor relationship prediction will help to understand the general mechanisms underlying the relationship between the chemical structure and physicochemical properties of odor molecules and olfactory perception. In the future, we will introduce natural language processing techniques to predict the odor descriptors that are necessary for constituting the odor perceptual space.  
Conceptualization, Y.W.; investigation, Y.W.; methodology, Y.W.; resources, Q.Z.; software, Y.W. and Q.Z.; supervision, M.M. and J.X.; writing--original draft, Y.W.; writing--review and editing, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under Grant 2019 YFA0706401, and in part by the National Natural Science Foundation of China under Grant 62172014, Grant 62172015, Grant 61872166 and Grant 62002002.  
Not applicable.  
The authors declare no conflict of interest.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Pressure Signals
#### 3.1.1 Acquisition of In-Cylinder Pressure Signals  
Several efforts have been made to reduce the cost and intrusive nature of pressure transducers in order to allow them to be used in production automobiles. [PERSON] et al. [20] introduced a low-cost engine control system utilizing a non-intrusive cylinder pressure sensor in the spark plug. Their study found that by measuring pressure variations through a pressure transducer that was mounted in the spark plug, they could more accurately detect knock onset, allowing the engine control unit to better control the ignition timing. A similar study by [PERSON] et al. [21] also used a spark plug-based pressure transducer. While their results were similar to those reported by [PERSON] et al. [20], they noted challenges associated with thermal and vibration issues affecting the sensor readings.  
[PERSON] et al. [22] introduced a low-cost, series-produced in-cylinder pressure sensor, which enabled the engine control unit to detect knock. Comparisons with structure-borne vibration sensors showed the feasibility of implementing cylinder-pressure-based knock control in the engine control unit, resulting in a 0.5-0.7 percent increase in engine efficiency. This increase in engine efficiency comes from more optimal spark timing, which results in higher pressures and a higher overall thermal efficiency of the engine. Although more accurate, this technique is more expensive than conventional knock sensors and requires the sensor to have access to the combustion chamber, which could lead to durability and cooling issues for the engine.
## 4 Results and Discussion
### Parametric Study on Breakthrough Curves of AC/Zeolite 13X Layered Bed
#### 4.1.2 Effect of Feed Flow Rate on Breakthrough Curves  
The breakthrough curve simulation of the layered bed was conducted under different feed flow rates. Figure 4 shows that as the feed flow rate increases, the breakthrough time of each impurity gas decreases. The increase in the feed flow rate shortens the residence time of the gas in the adsorption bed, reducing contact between the adsorbent and impurity gases. This limits mass transfer, causes the adsorbent to reach saturation more quickly, and results in an earlier breakthrough of the impurity gas. Notably, the difference in the breakthrough time between 2 LSTP/min and 3 LSTP/min is significantly greater than the difference between 3 LSTP/min and 4 LSTP/min. This phenomenon can be attributed to the lack of a linear relationship between the feed flow rate and breakthrough time at higher flow rates, which is due to the non-constant mass transfer resistance of the adsorbent.
## 3 Simulation of Typhoon Lekima
### Simulated Tracks and Intensity  
Figure 5 shows the result of the simulated track and CMA best track records [41; 42] ([[https://tcdata.typhoon.org.cn](https://tcdata.typhoon.org.cn)]([https://tcdata.typhoon.org.cn](https://tcdata.typhoon.org.cn)), accessed on 12 May 2024). As we can see, the ARW model fully reproduces the movement trajectory of the typhoon's first landfall, reentry into the sea, and subsequent landfall and the simulated track closely matches the observation. Figure 6 presents the time series of the minimum sea level pressure (SLP) and the maximum sustained wind (MSW) velocity derived from simulation and CMA observation. Before making landfall in Zhejiang, the simulation underestimates the intensity of Lekima with lower MSW and higher minimum SLP, while the intensity results are in good agreement with observations after landfall with maximum deviation of MSW less than 20% and 3% for SLP. The misalignment before landfall might be due to the sea surface flux scheme, assigned by parameter \"isftcflx\" in WRF model. In this paper, the default sea surface flux  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nested Domain** & **Mesh Size (m)** & **Horizontal Scale (km)** & **Time Step (s)** \\ \hline d01 & 12,000 & 211 \(\times\) 229 & 60 \\ d02 & 4000 & 187 \(\times\) 172 & 20 \\ d03 & 1333.3 & 235 \(\times\) 193 & 6.67 \\ d04 & 444.4 & 220 \(\times\) 178 & 2.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spatial and temporal resolutions of WRF domains.  
Figure 4: Nested domains from d01 to d04.  
scheme is applied, leading to the underestimation of latent heat flux and sensible heat flux, and is not conducive to the rapid intensification of Lekima near the coast.
## 4 Experimental Results  
### S-Parameter Measurements  
Measured S-parameters with different digitally controlled bias settings are presented in Figure 6. At the lowest gate bias setting of M1, the drain current was I4Q = 17.7 mA (deep class AB) and the maximum was I4Q = 48.7 mA (class A). Gate bias voltages for M2, M3 and M4 were kept equal. The Cascade calibration substrate P/N 101-190 was used for S-parameter calibration, which set the reference plane at the tips of the probes. It can be seen from Figure 6 that the measured maximum gain of 13.1 dB occurred at 26 GHz by using the maximum bias voltage. Based on S21 curves, the frequency range of the the proposed design ranged from 20 GHz to 30 GHz, matching 3 GPP/NR FR2 bands n257 and n258 [17]. It can also be seen that the frequency did not shift as a function of the bias current. The gain difference between the lowest and the highest bias current was only 1.3 dB (from 11.8 dB up to 13.1 dB). The input matching (S11) was below 10 dB from 21 GHz to 30 GHz, and S22 was well matched at the center of the band regardless of the bias settings.  
The measured and simulated S-parameters are compared in Figure 7 by using the maximum Vgl bias (i.e., I4Q = 48.7 mA). It can be seen that the measured peak gain 13.1 dB at 26 GHz matched the simulated gain within 0.5 dB at the center of the band, representing a very good match. This was achieved by using a parasitic extracted transistor core and careful EM modeling; i.e., all the passive components and routing placed outside the PA (see Figure 3 ) were included in a single EM simulation block. The measured input and output matches at 26 GHz were \(-\)18.5 dB and \(-\)11.5 dB, respectively. The simulation  
Figure 6: Measured S-parameters of the stacked PA with different bias settings.  
results of S11 and S22 were not as accurate as S21, however. It can also be seen that the measured S11 and S22 curves showed better matching, which in fact was mainly due to the higher losses compared to simulations. In addition, simulations estimated lower input matching (see the blue curve in Figure 7), while the S22 simulations estimated roughly 2 GHz lower matching compared to the measured S22 curve (red curve in Figure 7).