esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Simulation Model
#### 6.1.3 Performance Metric  
We analyze the average energy consumption (AEC) in terms of joules per node. The average energy consumption is the total network energy consumption divided by the number of sensor nodes in a network. We also observe the energy efficiency rate (EER) defined by the rate of the average energy consumption without merging to that with merging. For example, if the overall energy consumption was 6 kJ without the merging process and 2 kJ with the merging process, the EER is 3.  
We also examine network lifetime and network lifetime ratio (NLR), defined by the rate of the network lifetime without the merging and with it. The network lifetime is the time duration until any one node in a network (which will be the sink node in our case since all data is transmitted through the sink node) dies out due to energy depletion.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 3 Wastewater Treatment System Optimization and Control
### Measurement and Implementation  
The measurement system is mainly composed of sensors and online instruments and is a module for data measurement and input. The development of sensors, instruments, and communication systems enables real-time data collection from the entire system [118]. The measurement and implementation of WWTPs depend on the development of instrumentation, control, and automation (ICA) [119; 120]. The purposes of using ICA are to keep treatment systems running efficiently in the presence of large fluctuations and to achieve the desired performance at an affordable cost. Studies show that the nutrient removal rate of wastewater can be increased by 10-30% by using ICA technology in WWTPs [121]. With improvement of ICA, smart sensors with multiple detectors and transmission abilities will be widely used [122]. Sensors and monitoring systems are used to collect and transmit data, provide early warning and analysis data, and provide information to managers, which helps managers operate, control and make decisions [123; 124].  
The implementation system corrects the action of the system according to the process control algorithm to achieve the desired change when the controller monitors the state of a system. This kind of execution is usually realized by feedback and feedforward. Ireland's Ringsend WWTPs uses a variable frequency driver to change the speed of the pump to match the flow conditions, reducing the energy consumption of the pump by up to 50% [125]. Upgrading of the equipment itself can also bring about a reduction in energy consumption. It is easy to save more than 35% energy by replacing the traditional blower technology with the direct drive turbine blower [126]. Wastewater pumps operational life ranges between 15 and 20 years. However, they are not directly addressed in \"Waste electrical and electronic equipment\" Directive (2012/19/EU). Therefore, wastewater pumps have the possibility of renovation or upgrading. The research of [PERSON] et al. shows that sewage pump has high recovery potential, which may bring huge environmental and economic benefits [127]. Therefore, upgrading the sewage pump can improve energy efficiency without worrying about the disposal of the equipment. Although the European Commission's previous standards for wastewater pumps focused on energy efficiency, many recent studies have focused on reliability and delivery characteristics [128].
# Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
## 5 Image Dehazing Method Based on Full Polarization Information Inversion and Image Fusion
### Validation Based on Celestial Images and Real Space Target Images  
In order to further verify the effectiveness of the proposed method, the space target polarization detection system established in this paper was used to obtain images of celestial targets and real space targets, including the moon, the International Space Station (ISS), and the Space Debris (SDS). The HMIRCA method was also used to obtain the original polarization information of the target before degradation. The method proposed in our paper is compared with the other three dehazing methods introduced in Section 6.1, and the subjective comparison results are shown in Figure 9.  
As shown in Figure 9, compared with other methods, by applying the method proposed to the detection of celestial targets and space targets, the detailed features can be better highlighted so as to achieve a better dehazing effect. In the dehazing images of the moon, more texture features and crater features can be identified. In the dehazing image of the ISS, the solar panel of the target can be better distinguished from the main structure. The contour features of space debris are also more clearly distinguished in the dehazing image of SDS. In terms of objective evaluation, the four methods described in Section 6.1 were still used for comparison, and the results are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **PSNR** & **SSIM** & **AG/10\({}^{-5}\)** & **MV** & **SD** \\ \hline \multirow{4}{*}{Scene 1} & PDD & 57.1244 & 0.9884 & 1.8601 & 0.1305 & 0.0635 \\  & Retinex & 60.4686 & 0.9907 & 3.0990 & 0.4438 & 0.1150 \\  & DCP & 55.7116 & 0.9870 & 0.9307 & 0.1152 & 0.0573 \\  & Proposed & 63.4158 & 0.9934 & 12.4111 & 0.1817 & 0.1474 \\ \hline \multirow{4}{*}{Scene 2} & PDD & 61.3964 & 0.9936 & 2.1373 & 0.1566 & 0.0778 \\  & Retinex & 61.7181 & 0.9934 & 2.7208 & 0.4768 & 0.1219 \\  & DCP & 62.6160 & 0.9916 & 0.9991 & 0.1107 & 0.0446 \\  & Proposed & 66.7844 & 0.9955 & 13.5778 & 0.1920 & 0.1548 \\ \hline \multirow{4}{*}{Scene 3} & PDD & 61.4414 & 0.9941 & 3.9280 & 0.1800 & 0.0893 \\  & Retinex & 62.2443 & 0.9948 & 3.4650 & 0.5174 & 0.1449 \\ \cline{1-1}  & DCP & 62.9835 & 0.9934 & 1.0521 & 0.1383 & 0.0615 \\ \cline{1-1}  & Proposed & 69.0211 & 0.9977 & 15.0112 & 0.2100 & 0.1813 \\ \hline \end{tabular}
\end{table} 
Table 2: Objective comparison results of the dehazing effects of simulated space targets. It can be seen in Table 3 that when the PDD method is used to restore the images, part of the detailed information about the target is lost due to the assumption that there are no polarization characteristics in the scene light. Although the contrast of the image is improved by using the multiscale Retinex method, color distortion is caused, and much detailed information is submerged. The target dehazing image obtained by using the DCP method is darker, and the image quality is not significantly improved. However, the
# An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute
## 5 Discussion  
Troubleshooting and failure localization is an actively studied area in SDN. In this paper, we showcased a framework that builds on existing network monitoring tools and a strong formalism that can specify network behavior by leveraging topology and policy information. Our framework is able to automatically test network paths and localize failures affecting them, though it has some limitations. It can create a full coverage of network paths to be tested, but the applied selection method does not take into account already available test information. By modifying the selection algorithm, we can focus on failures much quicker. With the current proof-of-concept implementation, only those paths are tested that are deemed to be able to relay traffic based on the specification. With slight modification of the test paths generation and selection methods, the framework might be able to test paths that are not supposed to carry traffic. This would make failure localization much stronger. The current implementation is based on the assumption that if we do not get monitoring information from a target host, then the path is considered to be faulty even if this information is lost on the reverse path. Another assumption that we made was that packets use the same path independently of the used protocol, which might not be true for all networks. This again can be overcome by a slight modification in our test generation methods. As our sample use-cases show, our framework can find switch- or host-related issues in the network and seriously reduce the number of network nodes that can cause the issue. Since by nature, our framework is extensible, other tools can be incorporated that can further filter out switches from our suspicious list. This extensibility can also help in adapting the tool to different networks. The current implementation supports only OpenFlow-centric SDN-enabled networks, but other SDN implementations or traditional networks can be covered as well. More specifically, only SDN traceroute needs to be replaced in such cases, since on the highest level, NetKAT (thus, Felix and its measurement service) can handle different network types.
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 3 Multi-Strategy Hybrid Particle Swarm Optimization Algorithm (MSPSO)
### Multi-Strategy Hybrid Particle Swarm Optimization Algorithm
#### 3.2.4 Improved Handling Method of Boundary-Crossing Particles  
Usually, at the end of each iteration, it is necessary to determine whether the particle is out of bounds. And particles out of bounds need to be adjusted. There are four methods to deal with particles out of bounds: absorbing, reflecting, invisible, and damping. Among them, absorbing and reflecting are more commonly used. However, these existing methods discard the potentially excellent search direction of the particles, which is not conducive to the search of the boundary region and decrease the diversity of the populations [33,34].  
In this regard, we propose an improved boundary treatment method: When the particle generated by Formulas (1) and (2) is beyond the \(j_{th}\) dimensional decision variableboundary in the decision space, the inertia weight \(\omega\) or one of the learning factors \(c_{1}\), \(c_{2}\) (depending on which one contributes more to the particle velocity in the \(j_{th}\) dimensional component) is reduced to half. Then the position of the particle is recalculated, and if the new particle is still beyond the boundary, the above steps continue to be repeated until the particle can effectively fall within the boundary.  
Figure 7 shows that when a particle crosses the boundary of a decision variable, \(\omega\) is reduced by half so that the particle falls within the boundary. The improved boundary handling method shortens the particle search step by decreasing the values of \(\omega\), \(c_{1}\) or \(c_{2}\), and induces the particles to seek optimum in a smaller region close to the boundary, improving the algorithm's convergence and accuracy.
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 4 Discussion
### Future Work  
Future research should include repeated analyses in subsequent cultivation periods to further validate the findings of this study, as well as an expansion of the study to incorporate a larger number of fields for the cross-validation of results. Additionally, evaluating the model's applicability across different geographical regions with varying soil properties and climatic conditions is essential to assess its sensitivity to these factors. Another important direction for future research is extending the analysis to include other crops, such as maize and sunflowers, to evaluate the model's adaptability and predictive capabilities.  
Since the application is designed to assist small-scale farmers and agronomists with limited knowledge of geospatial technology, future work could focus on further automation. This includes estimating phenological stages based on sowing dates provided by farmers combined with historical climate data--such as regional climatic zones--to determine the necessary imagery for each stage. These advancements will enhance the application's ability to deliver more precise and actionable insights to farmers. Additionally, integrating Natural Language Processing (NLP) [67] into the web application will further enhance usability. By leveraging NLP, the system will minimize the time and effort required for farmers to interact with the application, allowing for intuitive communication with minimal input--such as sowing dates--while improving accessibility and ease of use. Moreover, the application possesses significant potential for future advancements beyond its existing functionalities, especially in the realm of incorporating artificial intelligence (AI) to augment the analysis of geospatial data. The use of innovative machine intelligence frameworks, featuring both machine learning and deep learning approaches, offers a remarkable opportunity to enrich the functionality of systems in exploring massive datasets, uncovering essential correlations and crafting accurate prediction models. For example, incorporating AI-driven methods for disease detection, pest management, and multi-crop monitoring would further enhance its utility and relevance. Such technological advancements would facilitate the application's capacity to tackle a wider array of challenges inherent in precision agriculture, thereby augmenting global initiatives directed towards the enhancement of food security and the promotion of environmental sustainability.
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 3 Arabic Dialect Identification Model
### The Classifier System
#### 3.2.1 Logistic Regression (LR)  
Logistic regression is one of the classification algorithms. It can be used to estimate output based on a number of independent parameters representing the used features set. For the LR classifier, we tested the TFIDF features.
## 1 Introduction  
Passive microwave remote sensing of Soil Moisture (SM) and Sea Surface Salinity (SSS) has been at the center of attention of many research programs for several decades. Various airborne and _in situ_ radiometers have been developed, showing the high potential of L-band measurements for the estimation of surface parameters [1-6].  
The SMOS satellite mission is based on an aperture synthesis L-band radiometer [7,8], designed and developed by the European Space Agency (ESA). In the context of the validation activity for the SMOS mission, the authors proposed to design, build and operate the CAROLS L-Band radiometer from an aircraft. Because the sensitivity of L-band brightness temperature to salinity is very small (\(-0.45\) K/psu at a physical temperature equal to 105 K), it was necessary to build a very accurate, sensitive, and stable system. The radiometer was installed in the French research ATR-42 aircraft. In coordination with _in situ_ field campaigns, it is used together with a suite of other airborne instruments: a scatterometer--or a C-Band radar--used to measure ocean roughness and winds, a GPS receiver to estimate ocean surface roughness, an infrared radiometer to measure sea surface temperature, and a visible wavelength wide angle camera to provide a qualitative description of the surface.  
The first campaign was conducted in September 2007, for the qualification and certification of the aforementioned instruments. Following various improvements to the CAROLS instrument, a second campaign was carried out in November 2008, in order to validate the CAROLS data quality. In spring 2009, a scientific campaign was organized, to acquire different types of brightness measurements over oceanic and land surfaces. The main objective of this third campaign was to validate the direct and inversion models used to estimate Soil moisture and Sea Surface Salinity.  
This paper presents a detailed description of the CAROLS instrument, of its refined calibration system, and of the first results obtained from the campaigns carried out in 2007, 2008 and 2009.  
In Section 2, we present the instruments used in our campaigns, in particular the CAROLS radiometer, and in Section 3 we describe the campaigns. The CAROLS data quality is discussed in Section 4. Our conclusions are given in Section 5.
## 5 Conclusions  
This article confirmed that EgLiCl-mGO is a qualified adsorbent to simultaneously preconcentrate ultratrace amounts of Pb(II) and Cd(II) in legumes. In additional to acceptable analytical results, this adsorbent is easy to prepare without consumption of dangerous and harmful reagents. The EgLiCl-mGO is an energy efficient and environmentally friendly adsorbent.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Pb(II)**} & \multicolumn{3}{c}{**Cd(II)**} \\ \cline{2-7}
**T** & \(\mathbf{C_{c}}\) & \(\mathbf{q_{e}}\) & \(\mathbf{\Delta G}\) & \(\mathbf{C_{e}}\) & \(\mathbf{q_{e}}\) & \(\mathbf{\Delta G}\) \\  & **(mg L\({}^{-1}\))** & **(mg g\({}^{-1}\))** & **(J mol\({}^{-1}\))** & **(mg L\({}^{-1}\))** & **(mg g\({}^{-1}\))** & **(J mol\({}^{-1}\))** \\ \hline
273 & 28.65 & 6.75 & 3581 & 25.43 & 22.85 & 265 \\
283 & 26.43 & 17.85 & 972 & 23.45 & 32.75 & \(-827\) \\
298 & 22.91 & 35.45 & \(-1081\) & 20.85 & 45.75 & \(-1946\) \\
318 & 14.65 & 76.75 & \(-4103\) & 17.76 & 61.2 & \(-3065\) \\
343 & 7.13 & 114.35 & \(-6875\) & 12.21 & 88.95 & \(-4920\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Thermodynamic parameters for the Pb(II) and Cd(II) adsorption onto EgLiCl-mGO.  
Supplementary Materials: The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13095702/s1](https://www.mdpi.com/article/10.3390/app13095702/s1)]([https://www.mdpi.com/article/10.3390/app13095702/s1](https://www.mdpi.com/article/10.3390/app13095702/s1)), This article includes Electronic Supplementary Materials (ESMs). FT-IR analysis, Effect of initial concentrations of **a** Pb(II) and **b** Cd(II) on adsorption capacity of EgLiCl-mGO, [PERSON] model of **a** Pb(II) and **b** Cd(II) adsorptions onto EgLiCl-mGO, [PERSON] model of **a** Pb(II) and **b** Cd(II) adsorptions onto EgLiCl-mGO, [PERSON] model of **a** Pb(II) and **b** Cd(II) adsorptions onto EgLiCl-mGO, [PERSON] model of **a** Pb(II) and **b** Cd(II) adsorptions onto EgLiCl-mGO, [PERSON] model of **a** Pb(II) and **b** Cd(II) adsorptions onto EgLiCl-mGO, the first-order kinetic model of **a** Pb(II) and **b** Cd(II) adsorptions onto EgLiCl-mGO, the first-order kinetic model of **a** Pb(II) and **b** Cd(II) adsorptions onto EgLiCl-mGO and thermodynamic parameters, enthalpy and entropy for the adsorption of **a** Pb(II) and **b** Cd(II) onto EgLiCl-mGO are provided in ESMs.  
Conceptualization: P.J. and M.H.; Methodology: P.J. and M.H.; Validation; P.J.; Formal Analysis: P.J. and M.H.; Investigation: P.J.; Resource; P.J. and M.H.; Data Curation: P.J. Writing--Original Draft Preparation: P.J. and M.H.; Writing--Review and Editing: P.J. and M.H.; Supervision: P.J.; Project Administrtion: P.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Support for this study by the Research Council of the University of Tehran through grants is gratefully acknowledged.  
The authors declare no conflict of interest.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Filtering Sensitivity Function Example
\]  
_From \(F^{3}\), we have the sensitivity function_  
\[p(x_{1}^{3},y_{e}^{1:3})(\theta_{a})=0.0254+0.0984\cdot\theta_{a}-0.054\cdot \theta_{a}^{2},\]  
_and from \(F^{2}\),_  
\[p(x_{2}^{2},y_{e}^{1:2})(\theta_{a})=0.0743-0.072\cdot\theta_{a}\]  
The sensitivity functions for probability of evidence by summing column entries of the forward matrices become:  
\[p(y_{e}^{1:3})(\theta_{a})=(F_{1,1}^{3}+F_{2,1}^{3})+(F_{1,2}^{3}+F_{2,2}^{3}) \cdot\theta_{a}+(F_{1,3}^{3}+F_{2,3}^{3})\cdot\theta_{a}^{2},\]  
and  
\[p(y_{e}^{1:2})(\theta_{a})=(F_{1,1}^{2}+F_{2,1}^{2})+(F_{1,2}^{2}+F_{2,2}^{2}) \cdot\theta_{a}\]  
The sensitivity functions for the two filtering tasks become:  
\[p(x_{1}^{3}|y_{e}^{1:3})(\theta_{a})=\frac{-0.054\cdot\theta_{a}^{2}+0.0984 \cdot\theta_{a}+0.0254}{0.0108\cdot\theta_{a}^{2}-0.0305\cdot\theta_{a}+0.09 38},\]  
and  
\[p(x_{2}^{2}|y_{e}^{1:2})(\theta_{a})=\frac{-0.072\cdot\theta_{a}+0.0743}{-0.0 12\cdot\theta_{a}+0.1099},\]  
these sensitivity functions are displayed in Figure 2.  
The derivative of these sensitivity functions can be used to analyze the sensitive of the filtering tasks for the change in parameter \(\theta_{a}\). \[\frac{\mathbf{d}p(x_{1}^{3}|y_{e}^{1:3})(\theta_{a})}{\mathbf{d}\theta_{a}}=\frac{-0. 006\cdot\theta_{a}^{2}-0.0107\cdot\theta_{a}-0.0094}{(0.0108\cdot\theta_{a}^{2 }-0.0305\cdot\theta_{a}+0.0938)^{2}},\]and  
\[\frac{\mathbf{d}p(x_{2}^{2}|\mathbf{y}_{\mathbf{e}}^{1:2})(\theta_{a})}{\mathbf{d}\theta_{a}}= \frac{-0.0070}{(-0.012\cdot\theta_{a}+0.1099)^{2}}\]
# MIDOM--A DICOM-Based Medical Image Communication System
## 2 Background
### Cbpc  
Data compression is a process defined by storing information in a more compact form with an ultimate goal of lowering necessary space needed to store the data, or lowering time needed to transfer the data over a communication channel. CBPC [7; 8] is a lossless image compression method based on error prediction coding. A phenomena that the aforementioned coder is based on is the fact that in natural images neighbouring image elements correlate, which alternatively means that the difference between two neighbouring elements is relatively small. If we turn this fact around, we can freely conclude that the value of a single element can be predicted by values of a few of the previous elements. The CBPC method, instead of encoding actual values of image elements, first tries to predict their values, followed by encoding the difference between the real and predicted value in entropy coder. By approaching the problem in such a manner, and assuming relatively high quality of prediction, model of the prediction errors will have smaller entropy than image elements values model whose larger entropy is mostly caused by the irregular distribution of their values. With fairly good prediction, most of the prediction errors will converge towards zero (0), with the small amount of cases with large prediction errors, e.g., borders between two different regions in image. Such a distribution is generally considered better for entropy coding, as entropy coders encode the most frequent values with the shortest code words, and the least frequent values with the longest code words. Out of that follows that such distribution of prediction errors yields a better compression level.  
CBPC has also been implemented for heterogeneous systems featuring graphics processing units, as presented in [9]. The method itself can be divided in two steps:* Predictive model,
* Entropy coder.