esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 4 Results
### Validation of the Hectometric and Kilometric LAI Products  
Figure 7 shows the comparisons between the hectometric/kilometric LAI products and the reference LAI. There are differences in the performance of different LAI products due to different input reflectances and retrieval algorithms, but they all show an overall high consistency with the reference LAI (\(R^{2}\) > 0.9) except EPS LAI. All the retrieved LAI meet the GCOS uncertainty requirements (i.e., max (0.5, 20%)) except EPS and C3S V2 in few samples (Figure 7). MODIS, GEOV3 and GEOV2 LAI products perform the best among the validated LAI products in our study, with \(RMSE\) of 0.21, 0.22 and 0.29, respectively, small bias (\(BIAS\) of 0.05, 0.14 and \(-\)0.01, respectively) and a high coefficient of determination (\(R^{2}\) of 0.97, 0.98 and 0.96, respectively). The GLASS product also performs well in our study area (\(R^{2}\) = 0.94, \(RMSE\) = 0.34 and \(BIAS\) = 0.10). EPS shows the weakest consistency with reference LAI in our study area (\(R^{2}\) = 0.69, \(RMSE\) = 0.68) and a saturation when LAI is higher than 2.5 (Figure 7d). The C3S product shows high coefficient of determination with the reference LAI (\(R^{2}\) = 0.96), whereas it systematically underestimates the reference LAI values (\(BIAS\) = \(-\)0.29, Figure 7f).  
Figure 8 shows the time series of MODIS, GEOV2, GEOV3, EPS, GLASS, C3S V2 and reference LAI during 2019. The gray area in Figure 8 represents the standard deviation calculated from reference LAI. The Copernicus Global Service products GEOV2 and GEOV3 show a high temporal consistency between them (and with reference LAI). MODIS also agrees with reference LAI in terms of seasonality, but it shows some gaps close to the maximum LAI. GLASS LAI is over-smoothed and shows a temporal shift with a delay in the timing of the peak of the growing season. EPS LAI also shows a temporal delay in its seasonality and a high discrepancy compared to reference LAI: the latter half part of EPS time series after the peak of the growing season is outside the confidence interval. The C3S V2 shows an advance in its phenology compared to reference LAI and it provides some values which are out of the confidence interval.  
Figure 6: Cross-validation between LAINet LAI and (**a**) the recalibrated SL2P-based LAI, (**b**) the empirically based LAI for the green-up season (from day of year (DOY) 152 to 211). The black dashed line indicates the 1:1 line, the solid line indicates the linear regression, and the gray dashed lines show the Global Climate Observing System uncertainty requirements for LAI (max (0.5, 20%)).
## 2 Results and Discussion  
### Optimization of the Aroma Extraction Method for cooked sorghum  
Solid-phase microextraction (SPME) is widely used for the analysis of aroma compounds due to its relatively simple sample preparation and fully automated operation [12]. In this study, SPME was used to analyze the aroma compounds in cooked sorghum. Before analysis, the SPME conditions were optimized. Firstly, four SPME fibers with different coatings, PDMS, PDMS/DVB, CAR/PDMS, and DVB/CAR/PDMS were evaluated using GC-MS. Among them, the DVB/CAR/PDMS fiber, with a bipolar coating, had the highest extraction yield of aroma compounds. As a result, this fiber was selected to further optimize the extraction temperature and time of the SPME procedure. GC-MS analysis revealed that the volatile compound content of cooked sorghum increases significantly (\(p<0.05\)) with increases in the extraction temperature (40-80 \({}^{\circ}\)C) (Figure 1A) and time (30-120 min) (Figure 1B). Therefore, 80 \({}^{\circ}\)C (upper temperature limit of the instrument) and 120 min were determined to be the optimal conditions for aroma extraction.  
Figure 1: (**A**) extraction temperatures, and (**B**) extraction times of solid-phase microextraction (SPME) with corresponding gas chromatography-mass spectrometry (GC-MS) peak areas. Different letters indicate significant differences in values (\(p<0.5\)).
## 3 ABER Analysis
### Qam  
When the modulation mode of the system is QAM, from [35], it can be obtained that the ABER is as follows:  
\[P_{e-QAM}\approx\frac{4}{\log_{2}M}\bigg{(}1-\frac{1}{\sqrt{M}}\bigg{)}\int_{0 }^{\infty}Q\Bigg{(}\sqrt{\frac{3\log_{2}M}{M-1}\cdot x}\bigg{)}f_{\gamma_{SIR} }(x)dx \tag{11}\]  
Substituting Equations (7) and (8) into Equation (11), and by using Gauss-Laguerre to carry out integration, the following can be obtained:  
\[P_{e-QAM}=\frac{4\rho\bigg{(}1-\frac{1}{\sqrt{M}}\bigg{)}}{D\overline{\gamma} \log_{2}M}\frac{\bigg{(}\frac{g_{S}}{b_{S}}\bigg{)}^{SS}\bigg{(}\frac{g_{I}}{ b_{I}}\bigg{)}^{S_{I}}}{B(g_{S},g_{I})}\sum_{i=1}^{n}\frac{w_{i}e^{x_{i}} \bigg{(}\frac{\rho x_{i}}{D\overline{\gamma}}\bigg{)}^{SS-1}Q\bigg{(}\sqrt{ \frac{3\log_{2}M}{2(M-1)}x_{i}}\bigg{)}}{\bigg{(}\frac{g_{S}}{b_{S}}\bigg{(} \frac{\rho x_{i}}{D\overline{\gamma}}\bigg{)}+\frac{g_{I}}{b_{I}}\bigg{)}^{S +S_{I}}} \tag{12}\]  
Proof.: Refer to Appendix B for the proof. \(\square\)  
**Remark 2**.: _The Gauss-Laguerre approximation used in Equations (10) and (12) converges to the exact integral values in Equations (9) and (11), respectively, when \(n\) is finite. In the simulations, we considered \(n=60\)._  
**Remark 3**.: _From Equations (10) and (12), it can be observed that the ABER in both Equations (10) and (12) increases with the probability of jamming \(\rho\). As \(\overline{\gamma}\) increases, the ABER decreases._
## 5 Discussion
### Algorithms  
Of the algorithms that we discussed in Section 4, the data say that the A3C algorithm was the best compared to DQN, DDQN, and A2C, but since these comparations, algorithms such as PPO and SAC have appeared. PPO has the advantage of being a more stable algorithm than A3C, which can be sensitive to hyperparameter settings and requires careful tuning but, unfortunately, there have not been any comparisons between A3C and these in FPS games and, as such, it is difficult to say which is the better one.
# A Comparison of Households' Energy Balance in Residential Smart Grid Pilots in the Netherlands
## 1 Introduction
Residual load is the load that the grid operators observe, so grid load is important for matters, such as voltage fluctuations and the sizing of connections, cables, and transformers, related to the design of grids. Therefore, residual load is employed as an important parameter in EU reports, such as [31]. The same holds for peak import and export of electricity, which are, respectively, the maximum (positive) and minimum (negative, i.e., electricity feed-in) values of the residual load. These are essential metrics for dimensioning at the household level. Cables and transformers are not dimensioned by summing all the maximum import and export values of individual households, but by the aggregated values of multiple households. Therefore, on an aggregated level (e.g., a cable and a transformer), the simultaneity of both electricity import and PV feed-in are essential metrics [32]. Next, self-sufficiency quantifies how well a household can rely on its renewable energy production and independently operate from the local grid, and self-consumption figures are relevant for sizing distributed energy systems with respect to household demand [33; 34; 35]. The aforementioned indicators are often studied individually, whereas this study presents a more comprehensive overview. Adding to this novelty, normally only one data set is studied [36], while this study compares multiple data sets, increasing the robustness of the results. Furthermore, in this research, a distinction is made between the aforementioned indicators at a household level [35] and a community level. The reliability of the grid depends significantly on the aggregated level instead of the individual level [31; 37; 38]. In this paper, we analyze and compare the monitored time series of energy data from three different residential smart grid pilot projects and one set of smart meter data, namely, the Power Matching City (22) in Groningen [39; 40; 41], the Jouw Energie Moment (117) in Breda [42], and the Rendement voorIedereen (79) in Amersfoort [43]. Further, Zornedael (70), an open access dataset of smart meter data provided by a Dutch distributed system operator (DSO) [44], is used as a reference and for comparison purposes. A total of 287 households were analyzed in this study. In Section 2, the pilots, the employed technologies, the data format, and the processing methods are described. In Section 3, the results are presented, and the pilots are compared according to seven predefined key performance indicators (KPIs). The paper is concluded with a discussion and recommendations for future studies.
# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
## 3 Methodology
### Sentence Representation
#### 3.1.3 Knowledge Attention Based GCN
Furthermore, previous GCN-based methods for sentence encoding treat each node of the dependency tree equally without distinguishing the importance of them. In this paper, we introduce the prior knowledge obtained from FrameNet to highlight the key words and cue phrases of a sentence for relation extraction. During the graph convolution operation, we assign each word \(w_{i}\) a knowledge attention score \(\alpha_{i}\) calculated by using the pseudo self-attention mechanism described in previous section. Then, we modify the calculation of each layer, and the knowledge attention guided graph convolution operation of node \(i\) at \(l\)-th layer is expressed as  
\[\mathbf{h}_{i}^{(l)}=ReLU(\sum_{j=1}^{k}\alpha_{j}(\mathbf{\bar{A}}_{i,j}\mathbf{W}^{l} \mathbf{h}_{j}^{(l-1)}+\mathbf{b}^{l})), \tag{6}\]  
where \(\alpha_{j}\) is the knowledge attention score of node \(j\). Using knowledge attention to selectively obtain information from neighboring nodes can effectively alleviate the negative impact of noisy nodes. As a result, the key words will contribute more to the sentence encoding in the graph convolution operation. After conducting a \(L\) layers knowledge-guided GCN for the word vectors, we obtain the hidden representations of each word that directly integrated information from neighbors no more than \(L\) edges apart in the dependency tree. For each sentence in a instance bag, all the original word embeddings of the sentence are feed into the GCN model to obtain the representation of the whole sentence. The hidden representation of the sentence is obtained as follows:  
\[\mathbf{h}^{(L)}=GCN(\mathbf{h}^{(0)})=GCN(\mathbf{S}). \tag{7}\]  
Finally, a max-pooling layer is used to capture the most important and relevant features from generated sequence and address the issue of variable sentence lengths. \[\mathbf{s}=Max(\mathbf{h}^{(L)})=Max\{\mathbf{h}_{1}^{(L)},\mathbf{h}_{2}^{(L)},...,\mathbf{h}_{m }^{(L)}\}. \tag{8}\]  
where \(\mathbf{s}\in\mathbb{R}^{d}\) is the final representation of the input sentence, \(Max()\) is the max-pooling operation that maps \(m\) output vectors to the final sentence vector.
# Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
## 2 Background
### Evaluation Metrics for Imbalanced Data Classification  
Literature analysis suggests that most malware family classification studies work with imbalanced datasets. Their main performance evaluation metrics are precision, recall, ACC, F1-score, ROC, etc. [16; 17; 18; 41]. In binary classification, ACC and F1-score calculated on confusion matrices have been the most popular metrics. A confusion matrix is a useful analytical tool to obtain the detail about what happened in the evaluation test and is the basis for calculating other performance measurements [42]. However, the two metrics can show excessively optimistic inflated results, particularly on imbalanced datasets [43]. It is also known that the ROC is inappropriate especially when we are interested in minority sets [17; 18]. The interpretability of ROC plots from the viewpoint of imbalanceddatasets can be misleading with respect to conclusions about the credibility of classification performance, due to an intuitional, but incorrect interpretation of specificity [18].  
Balanced accuracy (BAC) has some notional merits over the conventional accuracy while maintaining its simplicity. Accuracy treats each data point equally. Therefore, it has the effect of assigning different weights for each class. In the case of an imbalanced dataset, the overall performance can be distorted by classes with a large number of data points. [PERSON] and [PERSON] [15] used balanced accuracy as the metric. They said that BAC reweights the class with respect to the number of data points in each class and emphasizes the importance of learning low-frequency classes. The definition of BAC is given in Section 4.3.  
The Matthews correlation coefficient (MCC) is said to explain the confusion matrix better than ACC, F1-score, and BAC [43]. The MCC is a balanced measure that yields a high value if the prediction achieves good results in all categories (true positives, false negatives, true negatives, and false positives). The definition of the MCC is shown in Section 4.3. If the MCC is one, this indicates that a classifier always forecasts correctly. A value of \(-1\) indicates that a classifier always forecasts incorrectly. If the MCC is zero, it has the equivalent predictive ability as random prediction.
# A Study on Particulate Matter from an Area with High Traffic Intensity
## 4 Discussion  
The purpose of this study was to show the concentrations of PM near a highly trafficked road section. The novelty of this research lies in the utilization of two distinct measurement methodologies to ascertain the levels of particle concentrations. The authors, drawing upon their experiential insights, deem the incorporation of complementary measurement devices and methods as highly significant in this context.  
Under Romanian Law, the annual limit for PM\({}_{10}\) concentration is set at 40 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\), with a daily limit of 50 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) that cannot be exceeded for more than 35 days within a given year. However, for PM\({}_{2.5}\), Romanian Law does not cover any limits compared to the European Union Directive 2008/50/EC, which states an annual average limit for PM\({}_{2.5}\) of 20 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)[37, 38]. For PM\({}_{1}\), neither Romanian Law nor European Union Directives state any limits.  
Comparing the data obtained over the 23-day period using gravimetric impactors to that obtained over the 1-day parallel measurement using both gravimetric impactors and a spectrometer, it is evident that the PM\({}_{10}\) concentrations exceeded the daily limit of 50 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) on 13 out of the 23 days with the highest recorded value of 57.71 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) recorded on 23 July 2022. While these fall short of the annual limit of 35 days, it is worth noting that exceeding the limit on 13 out of 23 days suggests that the limit is likely to be breached in the longer term, thereby contravening relevant legislation. Although current legislation does not address PM\({}_{2.5}\) and PM\({}_{1}\) concentrations, these particles are known to pose a greater threat to human health than PM\({}_{10}\), as explained in the article. Therefore, it is essential to consider these finer particulate matter sizes when assessing air quality and associated health risks, even if they are not explicitly regulated by existing laws.  
With regard to the chemical analysis conducted on PM filters, it is evident that carbon emerges as the prevailing chemical element in terms of abundance. This outcome is to be anticipated, considering carbon's role as a byproduct of combustion processes inherent in internal combustion engines. Hence, the observed proportion of carbon is in line with expectations.
# Low-Frequency Square Kilometer Array Pattern Optimization via Convex Programming
## 3 Materials and Methods
### Full-Wave Simulation of the Radiating Element (FEKO)  
The SKALA-4.1 antenna, a dual-polarized log-periodic dipole array manufactured by Sirio Antenne, was modeled in FEKO [23,24]. The antenna consists of 19 triangular dipoles and a bow-tie dipole, mounted on a dual-conductor boom above a metallic ground mesh. To simplify simulations, the ground mesh was modeled as an infinite perfect electric conductor, thereby implementing the image theorem and consequently reducing meshing complexity.  
Given the antenna's dual-polarization characteristics, simulations were performed for both polarizations. The outcomes demonstrate minimal cross-coupling between the polarizations. The design and simulation processes are based on established techniques detailed in references [22,25], while the construction of the prototype aligns with the specifications provided in reference [6].  
To optimize computational efficiency, non-essential structural elements, such as screws and plastic fittings, were omitted from the model. These adjustments maintained the antenna's electrical performance while significantly reducing the mesh complexity from an initial 29,000 unknowns to approximately 10,000, as noted in [22].  
The feed structure was represented by a square-shaped port with an area equivalent to the original circular connector. A differential voltage source was applied at the intersection of the booms to emulate balanced excitation, ensuring accurate impedance behavior  
Figure 4: SKALA-4.1 Antenna: (**a**) electromagnetic model implemented in FEKO; (**b**) On-site inspection of a deployed SKALA-4.1 antenna by one of the authors ([PERSON]), from [22].  
with simplified geometry. Detailed modeling guidelines for reproducibility are available in [22; 25].  
After validating the single-element model, a full station array was constructed (Figure 3) and its simulated power pattern (Figure 5), consisting of 256 SKALA-4.1 antennas arranged in a configuration based on Vogel's spiral distribution. The simulated array spans a circular aperture of approximately 35 m in diameter, with inter-element spacing constrained by SKA-Low mechanical design rules. The Vogel spiral configuration ensures high packing density with quasi-random spacing, and all antennas were modelled at a reference frequency of 350 MHz. Mutual coupling effects are inherently accounted for through the use of EEPs computed by the full-wave electromagnetic simulator. The array configuration and mesh parameters were selected to reproduce, as closely as possible, the physical SKA-Low station layout, ensuring both realism and reproducibility.  
The radiation patterns were evaluated over a spherical grid with 0.5\({}^{\circ}\) angular resolution, ensuring sufficient spatial sampling for pattern reconstruction [26].
## 4 Discussion
### Experimental Mobile Network Infrastructure  
The cellular network developed for the flight tests remained stable up to flight altitudes of 100 m. The three COTS USB-modems and the two MMTs were able to establish a connection to the base station and successfully register with the core network. After authentication, all network subscribers were able to exchange user traffic with each other via the Wireguard VPN. The high SNR allowed the UEs to operate primarily in 16-QAM MODCOD. All network subscribers were also able to access content from the Internet via an external LTE router connected to Deutsche Telekom's commercial mobile network.
## 2 Multi-Scenario Generation Based on Wind Power Forecasting Errors
### Objective Function  
For a single wind power output scenario, the objective function comprehensively considers the electricity sales revenue from the reported wind power schedule, the operation and maintenance cost of the energy storage system, the cost of the initial \(SOC\), the penalty cost for scheduling deviations, and the arbitrage revenue from peak-valley electricity prices. This objective function is used to guide the wind/storage scheduling system in  
Figure 3: Framework of multi-scenario-based wind power and energy storage scheduling optimization.  
balancing revenue and cost, thereby achieving optimal decision-making for wind power reporting, energy storage charge/discharge strategies, and management of scheduling deviations. The mathematical expression of the objective function for the \(k\)-th scenario is given as follows:  
\[C_{k}=\sum_{t=1}^{T}\left[C^{sell}(t)-C_{k}^{cf}(t)-C^{b}(t)-C^{soc}(t)+C^{fg}( t)\right] \tag{13}\]  
\[\left\{\begin{array}{l}C^{sell}(t)=p_{sell}(t)\cdot p_{j}(t)\cdot\Delta t\\ C_{k}^{cf}(t)=\lambda_{k}(t)\cdot\Delta_{k}(t)\\ C^{b}(t)=C_{b}\cdot(p^{c}(t)+p^{d}(t))\cdot\Delta t\\ C^{soc}(t)=C_{soc}\cdot E_{b}\cdot SOC_{0}\\ C^{fg}(t)=\gamma\cdot(\alpha^{f}(t)\cdot p^{d}(t)\cdot p_{sell}(t)-\alpha^{g}( t)\cdot p^{c}(t)\cdot p_{buy}(t))\end{array}\right. \tag{14}\]  
where \(C_{k}\) is the total revenue of the wind farm under the \(k\)-th scenario. \(C^{sell}(t)\) denotes the electricity sales revenue. \(C_{k}^{cf}(t)\) is the penalty cost for scheduling deviation. \(C^{b}(t)\) is the operation and maintenance cost of the energy storage system. \(C^{soc}(t)\) is the cost associated with the initial SOC. \(C^{fg}(t)\) represents the arbitrage revenue from peak-valley electricity price differences. \(p_{sell}(t)\) is the electricity selling price. \(p_{j}(t)\) is the scheduled wind power, and \(\Delta t\) is the time interval. \(\Delta_{k}(t)\) is the scheduled deviation energy under scenario \(k\). \(\lambda_{k}(t)\) is the unit penalty cost for scheduling deviation, and \(C_{b}\) is the cost coefficient of energy storage. \(p^{c}(t)\) and \(p^{d}(t)\) are the charging and discharging power of the energy storage system. \(C_{soc}\) is the cost coefficient of the initial _SOC_. \(E_{b}\) is the rated capacity of the energy storage system, and \(SOC_{0}\) is the initial _SOC_. \(\gamma\) is the arbitrage incentive coefficient. \(\alpha^{f}(t)\) and \(\alpha^{g}(t)\) are binary variables indicating peak and valley price periods, respectively (0-1). \(p_{buy}(t)\) is the electricity purchase price from the grid.  
Therefore, based on the \(K\) clustered wind power output scenarios, the objective function for the wind farm's multi-scenario expected revenue is defined as follows:  
\[\max C=\sum_{k=1}^{K}w_{k}\cdot C_{k} \tag{15}\]  
where \(w_{k}\) is the weight of the \(k\)-th wind power output scenario, \(C_{k}\) is the total revenue of the wind farm under this scenario, and \(K\) is the total number of clustered scenarios.