esa-sceva/satcom-chunk-collection huggingface dataset:
# The Potential for the Use of Hydrogen Storage in Energy Cooperatives
## 3 Materials and Methods
### Optimization Model  
The results presented in the paper were obtained on the basis of data from a simulation of a dedicated mathematical model. The mixed-integer programming technique [56] was used for modeling. The GLPK software was used for modeling, in particular the available high level GMPL language [57] (this software is available on an open-source basis). The COIN-OR/CBC software [58] (also available on an open-source basis) was used to solve the individual optimization tasks. Below we present the more important elements of the model in the variant with hydrogen storage, when minimizing its capacity.  
In this model, in hourly granularity, for h from the set Hours = [1,2,..., H], we minimize the value of the variable: _minimize objective: MSL_; where: _subject to def_max_storage_level[h in Hours]:_ _SL[h] <= MSL;_ SL[h] is a non-negative real variable describing the level of filling of the hydrogen storage at the end of interval h. Assuming that REH is a parameter equal to the efficiency of electricity-to-hydrogen conversion, SS[h] is a non-negative real variable describing the amount of electricity transferred to the storage at hour h, and RS[h] is a non-negative real variable expressing the amount of electricity withdrawn from the storage at hour h, the filling level of the hydrogen storage is modeled by the formula: _subject to def_storage_level[h in Hours]:_ _SL[h] == SL[h - 1] + REH : SS[h] - RS[h];_ In the above, we assume that SL[0] = 0, so the storage was initially empty.  
In the optimization task, specific generation profiles of unit photovoltaic and wind sources are assumed. The corresponding generation values are represented in the model by the vectors SRC[pv,h] and SRC[wind,h]. Taking the non-negative variables SRCM[pv] and SRCM[wind] as the size of the sources, the total generation from renewable sources at hour h is represented by the vector G[h] given by the formula: _subject to def_production_level[h in Hours]:_ _G[h] == SRCM[pv] \(\times\) SRC[pv,h] + SRCM[wind] \(\times\) SRC[wind,h];_ Depending on the simulation variant, SRCM[pv] or SRCM[wind] were upper bounded by 0.  
Let D[h] be the given energy demand at hour h, F[h] be a non-negative real variable representing the amount of electricity purchased from the grid at hour h, and RHE be a parameter equal to the efficiency of hydrogen-to-electricity conversion, then the formula describing the energy balance is modeled as follows: _subject to constr_demand[h in Hours]:_ _D[h] == F[h] + G[h] - SS[h] + RHE : RS[h];_ Only selected parts of the optimization model for the case with hydrogen storage are presented above. Full descriptions of both models--for networked virtual storage and hydrogen storage--can be found here: [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) AiWk9]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) AiWk9) clyaSFS3 LZNJ2-3 XGMVFeVtmdD6?usp=sharing (accessed on 2 September 2024).
# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
## 2 Materials and Methods  
### Basic Principle of STIV  
STIV mainly includes three steps: synthesizing STIs, detecting the MOT, and calculating the velocity vector in the world coordinate system [10]. As shown in Figure 1, the steps of synthesizing STIs include, first, collecting \(m\) consecutive frame images and then setting a set of single-pixel wide and \(l\) pixels long testing lines along the flow direction in the image. Finally, the \(l\) \(\ast\)\(m\)-size STI is synthesized, with \(x-t\) as the rectangular coordinate system for each testing line. The synthetic STI has significant texture features, which are manifested as bright and dark bands with a specific orientation. The angle \(\delta\) between the main orientation of the texture and the vertical axis is defined as the MOT, which reflects the time-averaged water surface velocity.  
In the world coordinate system, surface flow tracers move a distance \(D\) along the search line within the time \(T\), while in the image coordinate system, they move \(d\) pixels within \(\tau\) frames. Therefore, the corresponding surface velocity \(V\) can be expressed as  
\[V=\frac{D}{T}=\frac{d\cdot\Delta s}{\tau\cdot\Delta t}=\tan\delta\cdot\frac{ \Delta s}{\Delta t}=v\cdot\Delta s \tag{1}\]  
where \(v\) is the optical flow motion vector, \(\Delta s\) is the spatial resolution of the testing line, and \(\Delta t\) is the time interval of the image sequence.
## 4 Discussion
Utilizing a laser with a rep-rate of 200 Hz with 20 rotation steps and 10 frame acquisition per step for averaging will allow for 1 Hz frame rate full-volume imaging which will satisfy the hemodynamic imaging requirement [33]. We demonstrated (see Figure 1c) that the implementation of a more sophisticated reconstruction algorithm improved the quality of the reconstructed image [50]. This is because reconstruction methods such as DAS, UBP, and filtered back projection follow a general delay-and-sum procedure. These algorithms are based on an ideal point transducer element with an infinite angular view. Whereas more sophisticated algorithms such as the ones presented in [70; 71; 72; 73; 74] consider a more realistic acoustic field for the transducers in the image reconstruction process. Further, it is possible to utilize a speed of sound map in the reconstruction of the imaging targets, especially in biological tissue, and use iterative image reconstruction algorithms [69] for further improvement of the reconstructed images. With regards to optimization, utilizing optimization methods such as the simulated annealing algorithm or Genetic algorithm along with an effective cost function will allow an automated and more effective calibration [60; 75; 76; 77; 78; 79; 80; 81].
## 2 Experiment Details
### Parametric Effect Study  
The experimental factors (pH grade, dye initial concentration, dosage and temperature) influence was studied using batch experiments. For preparing a stock solution of CV (1000 mg L\({}^{-1}\)), double distilled water was used. Preparation of solution concentrations ranging from 25 mg L\({}^{-1}\) to 300 mg L\({}^{-1}\), was performed using the bulk stock solution. A 250 mL Erlenmeyer flask was combined with a dye solution dose of 50 mL. The calculated quantity of NIFGS was added to every flask. Parametrical evaluations were performed for various parameter ranges stated, pH (2, 4, 6, 7, 8, 10 and 12); initial concentration of CV dye solution (25-300 mg L\({}^{-1}\)) and adsorbent quantity (0.025-0.200 g) in 50 mL (0.500-4.000 g L\({}^{-1}\)). Thermal influence on adsorption was investigated for a dye concentration of 200 mg L\({}^{-1}\) at three selected temperatures. The data were fitted into Equations (1)-(3), observed in Table 1. Solution stirring at 165 rpm was carried out for 3 h in an orbital shaker under constant thermal conditions. Samples were withdrawn at predetermined equilibrium time. The unadsorbed CV dye in the solution phase was separated from NIFGS by centrifugation with 3000 rpm for five minutes. If the solution was unclear, the centrifugation was repeated for an additional 5 min. The CV dye concentration at an equilibrium pertaining to the supernatant centrifuged solution was determined using a spectrophotometer. To study the effect in a range of pH 2-12, batch experiments were performed. The pH regulation within a desired range was achieved by concentrating the solution with 0.01-1.00 M HCl or NaOH solution. For the adsorption kinetic studies of CV dye solution (200 mg L\({}^{-1}\)), three temperatures (303, 313 and 323 K) were selected, and experiments carried out with time as independent variables. Experiments were replicated thrice, and the mean values were considered.
# Performance Analysis of Tunnel Boring Machines for
## 4 Case Study: The Moncenisio Base Tunnel
### Application of Correlation Models between the Mechanical Parameters of TBMs to the Case Study
#### 4.1.2 \"Federica\" Single Shield TBM  
The equations that correlate the diameter D to the three mechanical parameters power (P), torque (C), and thrust (S) in case of single shield TBM are shown in Table 5. Through these equations, by entering a diameter of 11.26 m, the values of power, torque, and thrust for single shield TBM are estimated. The results are compared with the real machine values (Table 6).  
By analyzing the results of Table 5, the differences between estimated values and real values are noticeable. As for the comparison of references, only the data of [42] are available. In particular, he highest gap between the forecasted and the real values can be spotted for the thrust. The proposed prediction model provides a value about 75% lower than the real one, but this discrepancy is halved compared to [42]. The cutterhead power is also underestimated (44%) while for the torque an overestimation of more than 20% is obtained both in the proposed and in the reference model. These divergences are due to the peculiarity of the geological formation (carboniferous) where the tunnel is built. During the excavation of the winze with the conventional method, strong convergences were found, up to 1 m in 12 h, which required an appropriate study of the TBM to be used. Specifically, there was no demand for high torque due to the low strength of the material. On the other hand, in order to avoid the shield clamping due to the strong convergences, it was preferred to equip the machine with a thrust capacity able to reduce the risk. Therefore, the atypical real values, compared to the experimental theoretical models, are linked to the peculiar characteristics of the geological formation.
## 3 Evaluation  
### Dataset  
The methodology underwent three evaluations: one comparing the method on VPIs against EIs, another evaluating the method across multiple resolutions, and a third evaluating the method on the same dataset but against two other deep learning methods. This study utilised two Holoscopic datasets to determine the effectiveness of the methodology. The first is a synthetic dataset [21] that was specifically created to replicate the features of Brunel's Holoscopic full-frame camera sensor (Figure 12), which has a sensor size of \(35\times 24\) mm and a resolution of 40 megapixels, resulting in image dimensions of \(7900\times 5300\) pixels. This dataset has five EI resolutions: \(20\times 20\), \(40\times 40\), \(60\times 60\), \(80\times 80\), and \(100\times 100\) pixels. The simulated images were used to evaluate different resolutions and compare them with deep learning techniques. The second dataset was acquired using Brunel's Holoscopic camera. This dataset is utilised because the synthetic one provides flawless VPI and EI pixel mapping, resulting in perfect VPIs that are free from lens effects, distortion, and artefacts. Hence, it is not feasible to directly compare the disparity outcomes between EIs and VPIs derived from the synthetic images.  
Figure 11: Background EIs’ disparity values are corrected using the foreground EIs’ background disparity values. **Left**: Holoscopic image showing background and foreground EIs. **Right**: Holoscopic disparity map showing incorrect background EIs being corrected by the background information of the correct disparity of the foreground EIs.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 5 Computational Experiments  
Since there is no standard calculation example for the VRPDTWDD problem studied in this paper, the R, C, and RC series test data for the VRPTW problem given by [PERSON] are selected as the benchmark data. The data are downloaded from the VRP Web link (dorronsoro.es, [[https://www.bernabe.dorronsoro.es/vrp/](https://www.bernabe.dorronsoro.es/vrp/)]([https://www.bernabe.dorronsoro.es/vrp/](https://www.bernabe.dorronsoro.es/vrp/)) 7 November 2023). The customers and distribution center in the R series test sets are randomly distributed. The C series test sets present aggregated characteristics. The RC series test sets show mixed delivery characteristics of randomness and aggregation, and the three types of test sets are distributed in the 100 by 100 coordinate system. To meet the characteristics of the problem in this study, the unit delivery distance cost ratio of the truck and drone is set to 5:1. The speed ratio of the truck and drone is 1:1.1. The loading capacity of the truck and drone is 200 kg and 12 kg, respectively. The maximum flying distance of the drone is 20 km. The test environment is Matlab 2018b, Intel (R) Core (TM) i5\(-\)10200H CPU @ 2.40 GHz processor, Windows 10 operating system. The parameters for the algorithm are set as follows: population size, 100; maximum iterations, 200; probability of crossing, 0.9; probability of mutating, 0.5; generation gap, 0.9; upper and lower bounds of the nonlinear learning factor, [1, 0.2]; chaotic vector, \(chaos(3;1;1)\); annealing coefficient, 0.99; and initial annealing temperature, 100.
# Electrocoagulation Based Chromium Removal Efficiency Classification Using Logistic Regression
## 3 Materials and Methods
### Building Logistic Regression Model
#### 3.2.2 Logistic Model Building  
Let us consider a logistic model with inputs-named predictors, \(x_{pH}\), \(x_{CD}\), and \(x_{CC}\) that represent pH variable, Conductivity, and Concentration, respectively. The output is the probability that the experimentation will give attempted results with good efficiency which is represented by the event \(E=1\). Let's denote this possibility \(p(E=1/X,\Theta)\) with \(X=(x_{pH},\ldots,x_{CC})\) and \(\Theta=(\beta_{0},\ldots,\beta_{3})\) that represent the parameters set efficiency. We assume a linear relationship between the predictor variables and the log-odds of the event \((E=1)\). This linear relationship can be written in the following mathematical form (where \(l\) is the log-odds, \(b\) is the base of the logarithm, and \(\beta_{i}\) are parameters of the model): \(l=\log_{b}(\frac{p}{1-p})=\beta_{0}+\beta_{1}x_{pH}+\beta_{2}x_{CD}+\beta_{3}x _{CC}\). We can recover the odds by exponentiating the log-odds: \(\frac{p}{1-p}=b^{\beta_{0}+\beta_{1}x_{pH}+\beta_{2}x_{CD}+\beta_{3}x_{CC}}\). By simple algebraic manipulation, the probability of the event \((E=1)\) is given by Equation (3):  
\[p=\frac{b^{\beta_{0}+\beta_{1}x_{pH}+\beta_{2}x_{CD}+\beta_{3}x_{CC}}}{b^{ \beta_{0}+\beta_{1}x_{pH}+\beta_{2}x_{CD}+\beta_{3}x_{CC}+1}}=\frac{1}{1+b^{ \beta_{0}+\beta_{1}x_{pH}+\beta_{2}x_{CD}+\beta_{3}x_{CC}}} \tag{3}\]  
The above formula shows that once \(\beta_{i}\) are fixed, we can easily compute either the log-odds that \(E=1\) for a given observation, or the probability that \(E=1\) for a given observation. The main use-case of a logistic model is to be given an observation and estimate the probability \(p\) that \((E=1)\) will occurs. In most applications, the base \(b\) of the logarithm is usually taken to be \(e\approx 2.77\).
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 3 Results
### Differential Expression Analysis  
Another important goal of this study was to identify genes in the insects in response to their parasitic hosts, which had difference fruit scents. Using edgeR, we identified a total of 2650 transcripts differentially expressed in the Ashmead insects in response to the fruit flies with different scents (Table S4), and the number of differentially expressed transcripts can be seen in Figure 4A. Compared to G, there were 1466 upregulated and 53 downregulated transcripts identified in both C and M (Figure 4B). Some transcripts were specifically expressed in the insects when they were parasitic to the fruit flies with one fruit scent (Figure 4B). We next analyzed the differentially expressed transcripts encoding olfactory proteins in the Ashmead insects in response to the three fruit flies. A total of 58 transcripts encoding olfactory proteins were found, including 9 OBPs, 13 CSPs, 3 GRs, 4 IRs, 25 ORs, and 4 SNMPs (Figure 4C). This evidence further supports the existence of multiple pathways of Ashmead insects in response to different fruits scents. In addition to olfactory proteins, some other protein families were differentially expressed in the wasps maintained with fruit flies supplied with different fruits, such as 2 LOC107047718 (Putative 7 transmembrane sweet-taste receptor of 3 gcpr), 88 ribosomal proteins, 109 transcription factors, 99 histones, 11 heat shock proteins, and 43 G-protein  
Figure 2: Expression levels of olfactory transcripts in the Ashmead insects. (**A**) Number of transcripts identified in the insects stimulated by three fruits. (**B**) Heat maps of the expression levels of OBP, CSP, and OR transcripts. (**C**) Heat maps showing the expression levels of transcripts encoding GRs, IRs, and SNMPs in the Ashmead insects. coupled receptors/regulators (Table S4). The differential expression of transcripts from different families indicated the complicated regulation mechanisms of parasitoid wasps in response to their hosts with different fruit scents. More experiments are required to explore their functions in this process.
## 2 Data and Methods
### Satellite Data
#### 2.2.3 Landsat 8  
Landsat 8, launched in 2013, is equipped with two main payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) (Table 3). The TIRS sensor is specifically designed to measure thermal radiation from the Earth's surface, providing crucial data for understanding surface temperature dynamics [26].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & **Band No.** & **Spectral Range/\(\mu\)m** & **Spatial Resolution/m** & **Width/km** & **Orbit Period/min** \\ \hline \multirow{4}{*}{PMS} & 1 & 0.45–0.90 & 2 & & \\  & 2 & 0.45–0.52 & & & \\ \cline{1-1}  & 3 & 0.52–0.59 & & & \\ \cline{1-1}  & 4 & 0.63–0.69 & & & \\ \cline{1-1}  & 5 & 0.77–0.89 & & & \\ \hline \multirow{4}{*}{WFV} & 1 & 0.45–0.52 & & & \\ \cline{1-1}  & 2 & 0.52–0.59 & & & \\ \cline{1-1}  & 3 & 0.63–0.69 & & & \\ \cline{1-1}  & 4 & 0.77–0.89 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: GF-1 and GF-6 satellite payload parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & **Band No.** & **Spectral Range/\(\mu\)m** & **Spatial Resolution/m** & **Width/km** & **Orbit Period/min** \\ \hline \multirow{4}{*}{CZI} & 1 & 0.42–0.50 & & & \\  & 2 & 0.52–0.60 & & & \\ \cline{1-1}  & 3 & 0.61–0.69 & 50 & 950 & 100.34 \\ \cline{1-1}  & 4 & 0.76–0.89 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: HY-1C/D CZI satellite payload parameters.  
TIRS consists of two thermal infrared bands with a spatial resolution of 100 m, operating in the wavelength range of 10.60-11.19 \(\upmu\)m (Band 10) and 11.50-12.51 \(\upmu\)m (Band 11). These bands are capable of sensing thermal radiation from targets, making them ideal for applications such as surface temperature retrieval, fire detection, soil moisture assessment, and nocturnal imaging [27].  
Furthermore, the application of an improved radiative transfer equation algorithm for SST retrieval from Landsat 8 has shown significant improvements in both atmospheric transmittance and SST deviation [28], with biases and root mean square errors reduced to negligible levels in nearshore areas. This indicates that Landsat 8 data, through refined algorithms, can provide reliable SST information, which is crucial for various oceanographic and environmental studies. In conclusion, Landsat 8's TIRS sensor offers a robust tool for SST retrieval, particularly in coastal regions where detailed thermal dynamics are essential. The high resolution and quality of Landsat 8 data, combined with advanced retrieval algorithms, make it a valuable resource for monitoring and studying sea surface temperatures.
# A Cross-Layer Optimization QoS Scheme in Wireless Multimedia Sensor Networks
## 1 Introduction
Firstly, the characteristic that the lifetimes of nodes in the area surrounding the sink node determine the network lifetime is not considered, which causes the trade-off between data transmission and the energy consumption of nodes in the area surrounding the sink node to be ignored. Secondly, how to set weights of data queues and energy queues in the optimization framework is not taken account. The above two problems may cause a reduction in the network lifetime. Thus, existing QoS schemes for wireless multimedia sensor networks do not achieve a good trade-off between QoS performances and network lifetime. In addition, the scheduling schemes for data packets of different types of applications also need improvement. To solve the above problems, this paper proposes (based on a Lyapunov optimization framework) a joint flow control, routing, scheduling, and power control algorithm in wireless multimedia sensor networks. The key contributions of this paper can be summarized as follows:  
* Considering the characteristics of wireless sensor networks, distance coefficients are introduced into the Lyapunov function to increase network lifetime while maintaining QoS performances. * To unify the dimension, different types of queues are normalized in the Lyapunov optimization framework. * A DQS scheme and delay coefficients are utilized to adaptively allocate scheduling priorities to packets belonging to different services. The remainder of this paper is organized as follows. Section 2 introduces the system model and queue structures. In Section 3, the algorithm is designed based on Lyapunov optimization. The simulation results are given in Section 4. Finally, the conclusions are provided in Section 5.
## 1 Introduction
### ORC System
When R113 replaced R600, the thermal efficiency and exergy efficiency were increased by 0.59% and 3.18%, respectively, and the levelized electrical cost was reduced by about 11%. Some other scholars [27; 28; 29; 30] have performed detailed design and modeling calculations on the core components of the ORC system, which contains an evaporator, a condenser, a pump, and an expander. Analysis was also conducted on the primary thermal factors that impacted the functionality of core components and how those factors impacted other factors like the system efficiency. Those research results offer a useful point of reference for resolving the coupling issue between system and component models.
## 3 The Proposed Encoder-Decoder Network
### Decoding Search Space Reduction  
One drawback of original CTC is that Equation (2) weights all paths equally, causing the sum in Equation (4) to include visually inconsistent paths \(\mathbf{\pi}\) that deteriorate the performance.  
We incorporate visual similarity into by Equation (2) rewarding paths:  
\[\begin{split} P(\mathbf{\pi}|\mathbf{x})&\propto\prod_{t=1} ^{T}\phi_{t}\cdot\psi_{t}^{t+1}\\ \text{where:}&\phi_{t}=y_{t}(\pi_{t}),\\ &\psi_{t}^{t+1}=\left\{\begin{array}{ll}\max(\theta,s_{t}^{t+1} )&\quad\pi_{t}=\pi_{t+1}\\ \theta&\quad\pi_{t}\
eq\pi_{t+1}\end{array}\right.\\ &s_{t}^{t+1}=f_{sim}(x_{t},x_{t+1})\end{split} \tag{6}\]  
where \(\phi_{t}=y_{t}(\pi_{t})\) represents the original Equation (2) formulation, and \(\theta\) is a minimum threshold of the frame similarity function \(s_{t}^{t+1}=f_{sim}(x_{t},x_{t+1})\).  
* When \(\pi_{t}=\pi_{t+1}\) and \(s_{t}^{t+1}>\theta\) (high similarity), \(\psi_{t}^{t+1}=s_{t}^{t+1}\) reward path to stay at the same prediction.
* When \(\pi_{t}=\pi_{t+1}\) and \(s_{t}^{t+1}<\theta\) (low similarity), \(\psi_{t}^{t+1}=\theta\) means no intervention after normalization.