esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Our objective is to enhance the understanding of ISA estimation with medium spatial resolution data using object-based classification, and to assess the impact of ISA on the urban thermal environment. Therefore, this study began with applying an object-based algorithm in ISA estimation, with a particular interest in comparing the impact of medium resolution data characteristics (including spectral, textural information) on ISA estimation. Then, in order to test the feasibility and effectiveness of object-based impervious surface mapping, this study compared further the pixel-wise and object-based ISA estimation in analyzing the relationship with population density, human settlement density, LST and night-time light density maps. HJ-1B satellite data in the summer season in three consecutive years (2009-2011) under real and varying atmospheric conditions in southern China were used in this study.
## 3 Experiments and Results
### Performance Comparison of the REI Module with Different Occupancy Ratios
After balancing the inference speed and accuracy, from shallow to deep layers, we chose a configuration where regular convolution occupied 2/3 and the REI module occupied 1/3 of the overall network to build our encoder structure.
# Phase Balancing and Reactive Power Support Services for Microgrids
## 3 Advanced PV/ESS Controller
### Proposed PQ Controller with Sharing Strategy for Ancillary Services
Thus, the PQ controller calculates the positive and negative sequence reference currents (\(\hat{\mathbf{i}}_{dq}^{+1\star}\)) and (\(\hat{\mathbf{i}}_{dq}^{-1\star}\)) according to Equation (8) and Equation (9),  
\[\hat{\mathbf{i}}_{dq}^{+1\star}\ =\ \left[ \begin{array}{c}i_{d}^{+1\star}\\ i_{q}^{+1\star}\end{array} \right]\ =\ \frac{2}{3}\cdot\frac{1}{\left(v_{d}^{+1} \right)^{2}+\left(v_{q}^{+1}\right)^{2}}\left[ \begin{array}{cc}v_{d}^{+1}&v _{q}^{+1}\\ v_{q}^{+1}&-v_{d}^{+1}\end{array} \right]\ \left[ \begin{array}{c}P_{inv}^{+1\star}\\ Q_{inv}^{+1\star}\end{array} \right] \tag{8}\]  
\[\hat{\mathbf{i}}_{dq}^{-1\star}\ =\ \left[ \begin{array}{c}i_{d}^{-1\star}\\ i_{q}^{-1\star}\end{array} \right]\ =\ \left[ \begin{array}{c}i_{d-inv}^{-1\star}\\ i_{q-inv}^{-1\star}\end{array} \right]\ =\ \hat{\mathbf{i}}_{dq-inv}^{-1\star} \tag{9}\]  
where \(P_{inv}^{+1\star}\) is the active power reference as generated by the MPPT or ESS charging controller. \(Q_{inv}^{+1\star}\) and \(\hat{\mathbf{i}}_{dq-inv}^{-1\star}\) corresponds to the reactive power compensation reference and the negative sequence reference currents that are provided by the MGCC to enable the reactive support and phase balancing service. It is noted that the MGCC will generate a reactive support reference (\(Q_{inv-n}^{+1\star}\)) and a negative sequence current reference (\(\hat{\mathbf{i}}_{dq-inv-n}^{-1\star}\)) for each inverter able to provide ancillary services, where \(n\) corresponds to the \(n^{\text{th}}\) inverter participating in the control scheme. Furthermore, the available current capacity for phase balancing support and reactive power compensation calculated by Equation (7) and Equation (6) respectively are used to limit the current injection in order to avoid violation of the inverter nominal current that can overload the inverter operation causing catastrophic failures. The overall structure of the proposed inverter among with the new PQ controller for enabling the fair provision of ancillary services is demonstrated in Figure 3.
## 5 Insights and Future Directions
### Big Data Issues and Challenges--Regarding Routing-Related Surveys in WSNs
#### 5.1.2 Data Analysis and Processing Challenge  
Due to ongoing research in various fields of technology, data volume is continuously increasing day-by-day which requires more and more space for data storage. If such a big data is properly analyzed, then it can be a source of utility and knowledge resource for future decision making. Otherwise, it is simply a storage burden which may continuously consume system resources. Therefore, there is a need to devise novel data analysis frameworks, so that the real benefits of data storage may be ascertained. Performing proper data analysis may in turn raise some thought-provoking questions such as the definition of proper/improper analysis in a given scenario, relevancy/irrelevancy of data for analysis, choice of post-analysis decision making approaches and so on. To the best of our knowledge, we have not found any data analysis model for organizing routing related surveys in WSNs.  
Since each routing related survey for WSNs discusses a variety of relevant protocols, therefore analyzing and categorizing a large amount of such routing-related surveys is a tedious and time consuming problem. To address this issue, two frameworks are proposed herein. The first one is the general survey design framework as discussed in Section 3 that may help to analyze the design soundness of routing-related surveys in WSNs. The second framework may help to examine the routing-related surveys on the basis of frequency of surveys publication as considered in Section 4. Such analysis may help to clearly categorize and index the routing-related survey literature into various categories and uncover those areas which are either least-focused, neglected-altogether or unsatisfactorily-managed and require further attention for bringing about technological advancements in WSNs. For example:  
* Least focused areas of routing surveys publication in WSNs may include in-network fusion [49], DHT based routing[57; 89], UWSNs oriented routing [71; 102; 108; 110] and opportunistic based routing [101].
* The neglected-altogether areas include those that are NOT in-depth reviewed at all. One such example is multichannel routing in WSNs where to the best of our knowledge, only one survey [114] is written very recently.
* The unsatisfactorily-managed areas include those where, as per proposed GSDF in Section 3, the surveys of low quality are published as illustrated in Tables 2 and 3.  
Therefore, more research and brain storming is immensely required in the above mentioned areas of routing in WSNs. It may result into devising new solutions and exploring novel dimensions for dealing with the outstanding challenges regarding different areas of routing in WSNs and bringing forth further technological developments in those fields.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### Rfid
For instance, in [4] a two-step process for localization of objects using a hand-held RFID reader and passive tags was used. In fact, they employed some reference tags for localization of RFID reader and then the location of the tags determined by using a path loss regression model and trilateration technique. Unlike barcodes, RFID tags can withstand harsh conditions with a much longer reading range which facilitates the automated material tracking on site. Having said that, its performance is degraded in vicinity of metal and liquid at high frequencies (e.g., UHF and microwave) [21].
## 5 Results
### The Proposed Mechanisms
#### 5.3.2 The Impact of the Content Request Rate  
We compared our mechanisms with the standard NDN and LFBL in a scenario with a high number of consumers with different content request rates. We considered 30 consumers randomly chosen at each simulation run, requesting contents available in two producers, and we varied the content request rate from 10 to 50 int/s.  
Figure 8 compares the default NDN, LFBL, and our mechanisms. First, we conclude that the proposed mechanisms outperformed the default NDN and LFBL regarding the number of content packets delivered. Less restrictive mechanisms, such as ReCIF and ReCIF+PIF with \(p=0.2\) (RECIF02 for the sake of brevity), provided higher delivery rates. With an increase in the content request rate, the nearest nodes of producers were more likely to receive contents. In the more saturated scenario, with 50 int/s, the difference from LFBL to ReCIF, regarding content delivery, 263.4% more contents used ReCIF. The reason for this behavior lies in the fact that LFBL uses multiple independent flows to request contents. Therefore, LFBL did not use the shared cache among neighboring nodes.  
The medium competition also impacts the delivery delay. For all mechanisms, the delivery delay increased as the content request rate increases, as expected. LFBL produced the highest delay compared with our mechanisms and the default NDN. The reason for this higher delay is related to LFBL characteristic of using independent flows for the request and delivery of contents. This requirement of independent flow overloads nodes placed at the regions closest to producers. These nodes have to forward more interest and content packets than others. The consequence is the high delivery delay experienced by content packets. The overhead of interest packets on the network center creates overloading near to the destination nodes on LFBL. This occurs because nodes on the network center  
Figure 7: Content packets delivered, average delivery delay, and overhead for 30 consumers requesting popular contents with PIF considering different interest forwarding probabilities.  
present more neighbors than the peripheral nodes. This overload increased the packets' hop counts by forcing part of the flows to use peripheral nodes to deliver the content packets.  
The proposed mechanism, PIF with \(p=0.2\) (PIF02 for the sake of brevity), presented the highest performance in terms of overhead. With PIF02, high-centrality nodes only forward 20% of interest packets received, as explained in Section 4. This mechanism produces a lower level of broadcast of interest packets, thus reducing the competition for the wireless channel. Therefore, PIF02 is more suitable for this scenario.
## 3 Mathematical Modelling
### Tammann-Tait Equation for Density Estimation of Blends Versus Temperature  
The density of liquid mixes, such as blends of diesel and biodiesel, is frequently predicted using the modified Tammann-Tait equation under specific conditions. The densities of diesel, biodiesel, and their blends at a specific pressure and temperature can be related by this equation. Therefore, the modified Tammann-Tait equation [24; 43] was used to relate the densities of diesel, biodiesel, and their mixtures at a pressure of 0.1 MPa and in the temperature range 278-298 K.  
\[\rho(T,p)=\frac{\rho^{ref}(T)}{1-C*\ln\left(\frac{B(T)+p}{B(T)+p^{ref}}\right)} \tag{3}\]  
where \(C\) is a temperature-independent parameter and \(B(T)\) is a temperature-dependent parameter, which can be calculated from the following second-order polynomial:  
\[B(T)=\sum_{i=0}^{2}b_{i}T^{i} \tag{4}\]  
\(\rho^{ref}\) is the density of the examinated sample at reference pressure (\(p^{ref}=\)1 MPa), which can be calculated using the second-order polynomial:  
\[\rho^{ref}(T)=\sum_{i=0}^{2}a_{i}T^{i} \tag{5}\]  
where \(a_{i}\), \(b_{i}\), and \(C\) are parameters that can be adjusted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Method—Reference** & **Value** & **Method** & **Value** & **Method** & **Value** \\ \hline \multicolumn{5}{c}{**Diesel**} & \multicolumn{3}{c}{**Biodiesel**} & \multicolumn{3}{c}{**Biodiesel**} \\  & & \multicolumn{3}{c}{**Vegetable (Sunflower)**} & \multicolumn{3}{c}{**Animal Fat**} \\ \hline Density 15\({}^{\circ}\)C (g/mL) & ISO 12185 [32] & 0.821 & ISO 12185 [32] & 0.884 & D1298 & 0.870 \\ \hline Ignition point (\({}^{\circ}\)C) & ISO 2719 [33] & 56.0 & ISO 3679 [34] & 167 & D93 & 172 \\ \hline Kinematic Viscosity (\(\xi\)St) & ISO 3104 [35] & 2.393 & ISO 3104 [35] & 6.0 & D445 & 6.0 \\ \hline Cetane index & ISO 4264 [36] & 55.1 & ISO 5165 [37] & 52.3 & D613 & 52.5 \\ \hline Water K-F in products (mg/kg) & ISO 12937 [38] & 105 & ISO 14214 [39] & 187 & D6304 & 287 \\ \hline Conductivity (pS/cm) & ISO 6297 [40] & 2 & ISO 6297 [40] & 4 & D2624 & 122 \\ \hline Acidity (mgKOH/gr) & ISO 660 [41] & 0.03 & EN 14104 [42] & 0.24 & D1980 & 0.92 \\ \hline \end{tabular}
\end{table}
Table 1: Physicochemical properties of diesel/biodiesel fuels based on quality certificates.
## 2 Materials and Methods  
The cloud model is utilized for the appropriateness evaluation of urban engineering construction, which establishes a grade standard for suitability evaluation using the cloud model. The digital features of the second-level index cloud are generated through utilizing the reverse cloud generator, and then obtaining the digital feat. Firstly, the suitability evaluation system for engineering construction is established through identifying single influencing factors. Then, the index weight is determined through a combination of the analytic hierarchy process and entropy weight method. Subsequently, the complete cloud map is generated using a forward cloud generator and compared with a standard cloud map to obtain the results of the suitability evaluation. The overall method flow is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Methodology** & **Advantages** & **Disadvantages** \\ \hline Spatial data superposition analysis of GIS & The operation is simple and the & The research of secondary development of software is insufficient, and the \\  & portability is strong & analysis of complex geological \\  & & environment is still lacking \\ \hline Artificial neural network method & Nonlinear correlation function, intelligent processing ability for wrong results; It has & For local minimization problem, the \\  & the ability to react quickly to different & convergence speed of the algorithm is \\  & indicators & slow; the choice of network structure is \\  & & different, and there is no unified and \\  & & perfect theoretical guidance \\ \hline Analytic hierarchy process (AHP) & Simple application, hierarchical division system, few quantitative parameters; It is & Less quantitative data, more qualitative \\  & suitable for complex problems with & components, and more subjective factors; \\  & multiple indicators & when there are too many evaluation \\  & & factors, the weight is difficult to \\  & & determine \\ \hline Fuzzy comprehensive evaluation method & The combination of qualitative and & The calculation is complicated and it is \\  & quantitative analysis of the selected index & difficult to collect each factor \\  & system; the evaluation accuracy is more & quantitatively. There are subjective \\  & accurate, and the evaluation result is & factors in determining the index weight \\  & close to the reality & \\ \hline Composite index method & The method is simple, easy to understand & When selecting indicators, we should pay \\  & and easy to calculate; reliable, practical, & attention to select the same direction \\  & clear economic meaning & indicators; difficult to quantify accurately \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of advantages and disadvantages of suitability evaluation methods.  
Figure 1: Flow chart of cloud model method for suitability evaluation of engineering construction.
## 3 Results
### Measuring Comprehension
#### 3.4.1 Multiple-Choice Questions
Using an iPad Pro for eye tracking, the study found that less frequent, longer fixations and saccades were associated with better comprehension. Participants who answered all comprehension questions correctly exhibited shorter mean fixation durations (274 ms) compared to those with no correct answers (318 ms), with differences statistically significant (\(p<0.05\)). A linear mixed-effects model further confirmed that fixation duration negatively correlated with comprehension (\(p<0.001\)), while faster reading speed positively influenced performance (\(p<0.001\)). The study by [PERSON] et al. extended this work by assessing comprehension 30 min after reading a lengthy 6500-word text, as well as shorter 1000-word texts [31]. Across three studies involving 381 participants, the results indicated that shorter, more frequent fixations correlated with higher comprehension. Regression models using eye-tracking data achieved significant correlations between predicted and observed comprehension scores, ranging from \(r=0.362\) to \(r=0.384\) (\(p<0.001\)), demonstrating robust predictive accuracy across different text lengths and contexts. Finally, [PERSON] et al. explored the predictive capability of deep learning models in reading comprehension using eye-tracking data [32]. The study examined overall and passage-level comprehension, perceived reading difficulty, and first language through a CNN model (using features including fixation location, fixation duration, and pupil size) that achieved 65% accuracy in predicting overall comprehension, outperforming the null model by 11%. Although the CNN model generalised well across passages, its accuracy diminished when applied to new readers. An RNN model used in the same study demonstrated lower accuracy compared to the CNN. Data were collected from 95 undergraduate students who read four published SAT passages followed by corresponding multiple-choice SAT questions and self-evaluation questionnaires.
## 2 Problem Formulation
\(\forall\)\(t\geq 0\), if the inputs satisfy \(|\dot{\alpha}_{i}|\leq\zeta_{1}\), \(|\dot{\alpha}_{i}|\leq\zeta_{2}\), where \(\zeta_{1}>0\), \(\zeta_{2}>0\), then there exist \(\zeta\in(0,1]\), \(\omega_{\rm n}>0\) and \(\mu>0\), such that \(|z_{i}-\alpha_{i}|\leq\mu\), \(|z_{\rm id}-\dot{\alpha}_{i}|\leq\mu\), and \(|\dot{z}_{i}|\), \(|\dot{z}_{i}|\), \(|\dot{z}_{i}|\) are bounded. Theoretically, the filtering errors of the SCFs can be made arbitrarily small by increasing \(\omega_{\rm n}\)._  
The structure of an SCF is shown in Figure 2, from which it is clearly seen that the derivative of \(\alpha_{i}\) is obtained through an integrator rather than a differentiator. This can reduce the measurement noise caused by differential operation and improve the control accuracy. Figure 2: Structure of a second-order command filter. **Lemma 2** ([19; 49]). : _For \(V:[0,+\infty)\in\mathbb{R}\), the solutions of inequality equations of \(\dot{V}(t)\leq-aV(t)+f\) are_  
\[V(t)\leq e^{-a(t-t_{0})}V(t_{0})+\int_{t_{0}}^{t}e^{-a(t-h)}f(h)dt,\;\forall t \geq t_{0}\geq 0 \tag{5}\]  
_where \(a\) is any constant._  
The control problem of this paper is formulated as following. Consider the single-link FJM (2) in the presence of lumped mismatched and matched disturbances. Design a proper controller by integrating the techniques of RESO and SCFB such that the output \(y\) tracks a desired trajectory \(y_{\text{d}}\) quickly and precisely.
# Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs
## 2 Materials and Methods
### Grid Voltage Support Capability of PV Systems  
#### 2.2.1 Analysis of PV Generation Profiles  
As the grid voltage support capability of PV systems is directly affected by the solar irradiance absorbed by the PV panel, it is necessary to investigate the PV generation profile with the environmental factors considered. Typically, daily solar irradiation follows a Beta distribution as represented by (5), while the power injected into the grid is represented by (6). It can be concluded that the operating power of the PV system almost linearly increases with the increasing solar irradiance, assuming the PV system is operating at its maximum power point. Due to the intermittent features of solar irradiation, the power output (and hence the grid voltage support capability) of the PV system will fluctuate with the varying weather conditions. Figure 4 illustrates the typical PV generation profiles of a PV system operating on a sunny day and a cloudy day.  
\[f(r_{t})=\frac{\Gamma(\alpha_{t}+\beta_{t})}{\Gamma(\alpha_{t})+\Gamma(\beta_{t })}(\frac{r_{t}}{r_{max}})^{\alpha_{t}-1}(1-\frac{r_{t}}{r_{max}})^{\beta_{t} -1} \tag{5}\]  
\[P_{t}=P_{std}\frac{r_{t}}{r_{std}}\eta_{PCE}\eta_{PVI} \tag{6}\]  
where \(r_{t}\) (W/m\({}^{2}\)) is the solar irradiance at time instance \(t\) while \(r_{max}\) (W/m\({}^{2}\)) refers to its maximum value, \(\alpha_{t}\) and \(\beta_{t}\) are the coefficients of the Beta distribution at time instance \(t\), \(P_{t}\) (W) is the active power injected into the grid at time instance \(t\) while its standard value is denoted \(P_{std}\) (W), \(r_{std}\) is the standard solar irradiance, and \(\eta_{PCE}\) and \(\eta_{PVI}\) refer to the photoelectric conversion efficiency and the efficiency of the PV inverter.
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## Appendix D Multi-Scale Residual Networks (ResNets) Solution  
Residual Networks (ResNets) [37] are introduced to cope with the degradation problem in deep convolutional neural networks. The identity shortcut connections embedded in the ResNets can aid gradient backpropagation through the network and allow the training of very deep models, as shown in Figure 14. Currently, ResNets are a common backbone architecture in computer vision and also have been adapted to time series classification [39] where 2D convolutions through the spatial dimensions are replaced by 1D convolutions through time [5]. They consist of residual blocks of convolution layers and batch normalization layers (optionally), followed by a Rectified Linear Unit (ReLU) activation function.  
Multiscale ResNets architecture, constructed by multiple residual CNN blocks with different convolutional kernel sizes that can capture complex features at multiple scales, has shown good results on time series benchmark datasets [5; 39]. In this work, we utilize the ResNets model proposed by [PERSON] et al. [39] where the input time series are processed in three independent streams, each stream has three residual blocks, and the convolution kernel sizes are 3, 5, and 7, respectively. After the residual blocks are in each stream, the features are then average pooled with pooling kernel sizes of 16, 11, 6; thus, yielding multiple-scale feature representations. Because their architecture is well-designed, we adopted their architecture. One difference from the architecture [39] is that we added a LayerNorm1d layer after the original input and before the FC layer. In addition, we replaced the feature concentration operation with a simple element-wise feature addition operation after the three residual blocks. If we denote the feature representations in the three streams as \(\chi_{c1}\), \(\chi_{c2}\), \(\chi_{c3}\), then this operation can be expressed as \(\chi_{c}=\chi_{c1}+\chi_{c2}+\chi_{c2}\). Another commonly used operation for the features fusion is to perform a weighted element-wise feature addition, which can be expressed as \(\chi_{c}=\alpha\chi_{c1}+\beta\chi_{c2}+\gamma\chi_{c2}\), where \(\alpha\), \(\beta\), \(\gamma\) are learnable parameters during training Usually learning these three parameters will greatly slow down the training process and need more training samples.