esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 1 Introduction  
Earth observation satellites (EOS) are playing an increasingly important role in economic development, disaster countering, surveillance, and many other fields for both military and civilian purposes [1; 2; 3]. Satellites take pictures by their optical devices and send the data back to the ground for various purposes. With the development of technology, the resolution of the satellite camera is getting higher and higher. The massive image data impose a heavy burden on the transmission of data from satellites to the ground. Therefore, it is important to consider data transmission when managing satellite imaging activities.  
Satellites need more time to send the data back to the ground, and data transmission management is certainly an important part of the satellite scheduling. In reality, the scheduling of EOSs with separate considerations for imaging and data transmission is quite difficult because the two operations are highly coupled [4]. There are many studies on the scheduling of satellites, but few have considered the data transmission activities integrated with the imaging ones. Most studies consider data transmission and imaging activities individually. The data transmission plan is usually created after the plan of imaging activities is determined. The satellite scheduling problem is actually divided into two subproblems: imaging scheduling given a set of user requests and data transmission scheduling given an imaging plan. Such a scheme will be easily trapped in a local optimum and therefore influences the final outcome of the algorithm.  
In this paper, we consider the earth observation satellite integrated scheduling problem (EOSIS), whose output is an imaging plan as well as a data transmission plan. We first provide an integer programming model to formulate the problem. As EOSIS is a very complicated problem and an exactapproach can only solve problem instances of very limited size, we propose an efficient local search heuristic (ELSH) to solve the problem of realistic size. We also present numerical studies to illustrate the excellent performance of the proposed ELSH.  
The rest of the paper is organized as follows. Section 2 presents a detailed literature review on relative topics. Section 3 is the problem description. The proposed ELSH is introduced in Section 4 and experiments are presented in Section 5.
## 2 Materials and Methods  
### Type of Problem the N-Way Partial Least Squares Aims to Address in Remote Sensing  
The type of problem that the proposed methodology of this study addresses is the detection of a rapid extreme climate event (frost, hail, heat wave, etc.) that may affect production over a large area (e.g., a regional scale). It is assumed that the extreme climate event has an effect on production by affecting either the pigment content of the leaves, the biomass itself (growth limitation) or the physical architecture of the plants [23]. Therefore, a time-series of multispectral satellite image should provide relevant information to detect the date of occurrence, the severity and also the spatial footprint of the extreme event under study. However, monitoring only a single variable, or only a few variables, is problematic as many variables are correlated and interrelated and have an effect on one another. Consequently, the use of a multidirectional (spectral and temporal) unfolding method (N-PLS) for a given extreme weather event (heat wave) that simultaneously examines all spectral information at different points in time should be well adapted to the challenge of anomaly and event detection in temporal data.  
Considering imagery time-series, a fixed classical object/variable scenario, i.e., an agricultural field, can be observed several times under different conditions, yielding a separate data table for each condition [16]. Another option would be to pool all the object/variable conditions together taking into account the time-series of images, which leads to a consideration of a cubic or a three-dimensional data array as shown in Figure 1. Such an array has N images containing the corresponding objects (O) at T\({}_{n}\) different dates, with each image being constituted of M\({}_{m}\) spectral bands or wavelengths (variables). Given the data dimensionality, multiway arrays have been proven to be a natural and efficient representation of the data, in particular, tensor subspace learning methods have been shown to outperform their corresponding vector subspace methods, especially for small sample size problems [24]. Therefore, the N-PLS algorithm should constitute a relevant approach to retrieve the information contained in the spectral bands of satellite imagery to highlight the target phenomenon taking into account its effect on the temporal evolution of the imagery.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### Node Structure  
The node running the MASU platform can play one or more of the roles defined in the framework. Figure 3 represents the role composition structure of a MASU node.  
A node can also have various instances of the same role activated at the same time, but in different sensing sessions. These roles are dynamic and can evolve over time according to the characteristics of the network infrastructure, the mobility of the nodes, or changes in the number of nodes involved in the activity.  
In order to conduct a collaborative sensing activity, it is necessary that at least two nodes are willing to collaborate, and such collaboration implies some benefit for the participating nodes. Otherwise, the MASU units would be working independently, in autonomous mode. A node performing sensing activities independently in a stand-alone fashion, would be at the same time Producer and Consumer of its own sensing services. It would, therefore, only require the activation of particular Producer roles for the required services and the corresponding Consumers of such services. By contrast, in a scenario where a set of nodes is working collaboratively to perform a sensing activity, there can be diverse combinations on the number and type of roles that must be activated in different nodes, depending on the requirements of the activity and on the characteristics and capabilities of the participating devices. However, this collaborative sensing activity would require that at least one Manager, one Monitor, one Producer and one Consumer roles be activated in the activity.  
Typically, most nodes collaborating in a sensing activity will have at least a Consumer role activated, because we assume that they will be interested in at least part of the sensed data. Nevertheless, if some of the MASU units participating in the activity are Internet of Things (IoT)-based devices, there can be cases where they would require the activation of a Consumer role (e.g., a shared display receiving an image), but in other cases, they would only provide sensing services and would not have any Consumer role active (e.g., sensors in smart buildings).  
There can be two different kinds of Producer roles: Collector and Processor, depending on the type of information that they generate or in the method used by them to obtain it. Moreover, Relay and Storage roles can be activated depending on the network conditions and on the characteristics of the sensing activity and of the participating nodes.
# Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
## 2 Materials and Methods
### Evaluation Metric
#### 2.3.1 Metrics for Point Forecasting  
Several methods have been used to assess the efficacy of prediction models in the literature. Commonly used metrics to evaluate deterministic/point forecast accuracy are the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient value (R\({}^{2}\)) [56]. A lower RMSE and MAE imply a more accurate forecast, which evaluates the difference between actual and predicted values. The R\({}^{2}\) value determines the correlation between actual and predicted values, which is between 0 and 1 (where 0 means no correlation and 1 means the model has no error). The three error measures are defined as follows:  
\[\text{RMSE }(\text{X},\text{h})=\sqrt{\frac{1}{\text{m}}\sum_{\text{i=1}}^{ \text{m}}\left(\text{h}(\text{x}^{(i)})-\text{y}^{(i)}\right)^{2}} \tag{15}\]  
\[\text{MAE}(\text{X},\text{h})=\frac{1}{\text{m}}\sum_{\text{i=1}}^{\text{m}} \left|\text{h}\left(\text{x}^{(i)}\right)-\text{y}^{(i)}\right| \tag{16}\]  
\[R=\frac{\sum_{i=1}^{m}(\text{x}_{i}-\overline{x})(\text{y}_{i}-\overline{y}) }{\sqrt{\sum_{i=1}^{m}(\text{x}_{i}-\overline{x})^{2}}\,\sqrt{\sum_{i=1}^{m} \left(\text{y}_{i}-\overline{y}\right))^{2}}} \tag{17}\]  
where x is considered to be the actual values, y defines the predicted values, \(\overline{x}\) defines the mean of x, \(\overline{y}\) defines the mean of y, X is a matrix containing all the features value, h is the prediction function, and m is the total number of instances in the test set.
## 1 Introduction
### Pore Formation Mechanism  
The general mechanism of how porogens control the resin surface area, pore-size, pore-size distribution (PSD), pore-volume, and morphology is illustrated in Figure 1[18]. The period of phase separation, shown in Figure 1, is the main rate determining step. As a general rule, phase separation will occur at a much later stage if the porogen is a \"good\" solvent or a solvating diluent [24], _i.e._, miscible with both the monomer and its resulting polymer. On the other hand, the \"bad\" solvent or non-solvating diluent has poor miscibility at the polymer level. If a non-solvating diluent is used (A in Figure 1), microgels [2; 18] will form, fuse together, and aggregate into larger clusters. This results in an increased volume of the voids (pores) forming larger pores (_i.e._, macropores) and reduced polymer surface area. Unlike non-solvating diluents, a solvating diluent (B in Figure 1) delays phase separation; hence, ensuring that microgels retain their individuality even though they undergo fusion. This leads to a reduced distance between voids or clusters; hence, the pore-sizes are reduced (mesopores and micropores) and the polymer surface area increases. The mixture of A and B in Figure 1, offers two modes of phase separation, _i.e._, early and late. Thus, bimodal pores are formed as follows: micropores/mesopores or mesopores/macropores or micropores/macropores. It is noteworthy that many authors use the term \"_good thermodynamic solvent_\" instead of \"_solvating diluent_\". In this review, the term _solvating diluent_ is preferred because the thermodynamic parameters of these solvents are not adequately characterized in the context of PCR materials; therefore, the term \"_good thermodynamic solvent_\" was not used in view of the existing knowledge gaps (_vide infra_), and to avoid misleading the reader that a thermodynamic analysis of solvation was reviewed herein.
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 2 Review of the MSC Zoe Accident  
### Description of the Event  
The information pertaining to the MSC Zoe accident is derived from the investigation report [4], unless otherwise specified. On the night of January 1 to 2, 2019, the containership MSC Zoe lost a total of 342 containers in the North Sea. The incident occurred in the vicinity of the Dutch and German coastlines, resulting in a significant pollution with debris being dispersed along the coastline.  
The meteorological conditions during the night of the incident were characterized by strong winds and a significant wave height of 5.5 m. The wind approached approximately from north-northwest and reached 35 knots (equivalent to 8 Beaufort scale units, Bft). Gusts reached probably 10 Bft, two Bft over the mean wind. These conditions are met or exceeded on one or two occasions per year and thus cannot be considered as extreme or exceptional [4] (p. 60).  
The wave direction was from abear. The investigation following the accident revealed, that this was the least favorable direction under the prevailing conditions, resulting in extreme ship motions and accelerations [4] (pp. 74-76). Other potential contributing factors to the loss of containers included contact with the seabed, green water, and slamming.  
The investigation revealed that the initial loss of containers occurred at approximately 8 p.m. local time, during the storm, and in darkness. Most of the containers on the MSC Zoe were stowed behind the bridge, as the accommodation on large containerships is typically situated closer to the bow. Until 1 a.m., the following day, several container stacks collapsed and were lost overboard. At this juncture, it was observed that several containers were missing. Another container loss was witnessed by the navigation crew 34 min later. Subsequently, the decision was made to alter the ship's course upwind, and the vessel traffic service (VTS) was duly informed. Approximately 12 h later, the ship continued its voyage to Bremerhaven, where most of the damaged containers were discharged. Subsequently, the MSC Zoe sailed to Gdansk for the complete discharge of its cargo and underwent repairs in a shipyard.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 4 Challenges and Open Research Issues
### Software-Defined Networking and Network Function Virtualization  
Software-defined networking (SDN) makes a separation between control and data planes, in which network switches are considered as dummy packet forwarding devices logically controlled by a centralized entity [5]. Network management, security policy deployment, scaling up or down, and troubleshooting are some of the major advantages of this network paradigm compared to traditional networking system [78]. In C-RAN architecture, SDN can be a suitable solution for dynamic resource allocation, balancing traffic load among BBUs and automatic recovery in case of hardware failure [5]. However, the data forwarding flow in SDN is mainly at the IP layer, and combining the functions of medium access control and the physical layer for C-RAN is a challenging task. For proper rollout of C-RAN with SDN, this issue challenge needs to be overcome.  
Network Function Virtualization (NFV) enables the transferring of network functionalities from dedicated hardware to software-based applications, which can be shared in a flexible and dynamic way [5]. It is essential to design the technique to virtualize the SDN controller to run on the cloud server, which can be migrated to a location according to network needs. Implementation of a real-time processing algorithm, virtualization of the BBU pool for C-RAN, and dynamic signal processing for dynamic traffic load are challenging, and researchers are still working to find the optimal virtualization technique for C-RAN.
## 4 Analysis Results  
This study uses a two-step method of structural equation modeling (SEM) to evaluate measurements and the structural model. The first step checked the reliability and validity of the measurement model using confirmatory factor analysis (CFA). Then, the second step examined the path effects of the structural model. By using maximum likelihood estimation (MLE), the measurement model was evaluated factor loading, measurement reliability, convergence validity, and discriminant validity, respectively.
###### Abstract  
This paper discusses the effect of doping of electro-insulating liquids with nanoparticle materials on the thermal properties of the obtained nanoliquids and heat transport in the transformer. Mineral oil, synthetic ester, and natural ester were used as base liquids. The effectiveness of doping base liquids with nanoparticles was supported by ultraviolet-visible (UV/VIS) measurements. In turn, Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) confirmed the absence of intermolecular interactions (i.e., hydrogen bonding). The influence of modification of electro-insulating liquids with fullerene C\({}_{60}\) and titanium dioxide TiO\({}_{2}\) nanoparticles on such thermal properties as thermal conductivity, specific heat, kinematic viscosity, density, and thermal expansion was investigated. Based on these properties and the theory of similarity, the cooling efficiency of the transformer filled with the analyzed nanofluids was determined. Nanofluids' cooling effectiveness was compared with the cooling effectiveness of the base liquids. This comparison was supported by an analysis of Grashof, Prandtl, and Nusselt numbers. It has been shown that the modification of electro-insulating liquids with nanoparticles widely used in order to improve their dielectric properties, such as C\({}_{60}\) and TiO\({}_{2}\), does not have a significant influence on their thermal properties. The addition of fullerene C\({}_{60}\) caused an increase in kinematic viscosity, which was compensated by the increase in specific heat. In the case of TiO\({}_{2}\), the addition of this nanoparticle resulted in an increase in kinematic viscosity and a decrease in specific heat, which were balanced out by the increase in thermal conductivity. In summary, the heat exchange-capacity of liquids did not change due to doping with nanoparticles.  
nanoparticles; heat transfer; dielectric liquid; mineral oil; synthetic ester; natural ester; thermal properties; attenuated total reflection; Nusselt, Grashof, and Prandtl numbers 1
## 6 Performance Evaluation  
The proposed schemes were evaluated through an extensive set of simulations to measure their performance. The evaluation was based on several metrics, including position estimation accuracy, packet delivery ratio, and network overhead.  
Simulations were implemented in OMNeT++ (OMNeT++ ([[https://omnetpp.org/accessed](https://omnetpp.org/accessed)]([https://omnetpp.org/accessed](https://omnetpp.org/accessed)) on 1 January 2023) is an extensible, modular, component-based C++ simulation library and framework, primarily for building network simulators) and parameters can be found in Table 3. The network size is 1000 \(\times\) 1000 m\({}^{2}\). The number of sensor nodes varies from 50 to 150 in order to evaluate the schemes in sparse and dense networks. The mobile sink starts at the center of the area, i.e., (500, 500), and its average speed is 10 m/s, e.g., a drone, following the random waypoint as its mobility model [35]. The sink sends its position to nodes every 5 s. The transmission range of the sink and nodes is 185 m using breakpoint path loss [36]. The threshold for the position distance algorithm is 50 m, which presented the best performance. Simulations were run 10 times following the Student's t-distribution [37]. We compare our algorithms with trilateration without packet validation in order to show performance improvement.  
Figure 10: The closer the nodes are to the sink, the higher the value of K. In this way, the network will be more dense when close to the sink, where there are more packet transmissions.
###### Abstract  
A new dual-band balanced bandpass filter based on magnetically coupled open-loop resonators in multilayer technology is proposed in this paper. The lower differential passband, centered at the Global Positioning System (GPS) L1 frequency, 1.575 GHz, was created by means of two coupled resonators etched in the middle layer of the structure, while the upper differential passband, centered at a Wi-Fi frequency of 2.4 GHz, was generated by coupling two resonators on the top layer. Magnetic coupling was used to design both passbands, leading to an intrinsic common-mode rejection of 39 dB within the lower passband and 33 dB within the upper passband. Simulation and measurement results are provided to verify the usefulness of the proposed dual-band differential bandpass filter.  
Keywords:dual-band differential filter; common-mode suppression; magnetic coupling; multilayer structure +
Footnote †: journal: Applied Sciences
## 4 Discussion
The outcomes of the comparison between the \(\sigma^{0}\) mean values obtained by Sentinel-1 and ALOS-2/PALSAR-2 time series showed that the qualitative analysis is adequate to describe surface and subsurface salar dynamics over the Andes Highland areas. Lastly, the differences observed for the same day at different sites were due to the large area of study, different altitude locations, and incidence angles as presented before [3]. It is important to highlight that there are significant limitations in comparing data retrieved by weather and optical satellite sensors and Socize ground weather station due to differences in spatial and temporal resolutions.
## 4 Computational Study
### Discussion  
The results shown in Table 2 reveal that the _KNN_ model outperformed the other proposed regression models trained with the lowest _MAE_ value (_MAE_ = 0.166) and the highest \(R^{2}\) score (\(R^{2}\) = 89.8%). The stacking model, on the other hand, outperformed the _KNN_ model with the highest \(R^{2}\) score (\(R^{2}\) = 91.0%) and the second-lowest _MAE_ value (_MAE_ = 0.204), proving to be the model with the best results for predicting the optimal period to send email marketing communications.  
Although the _LSTM_ model did not perform as poorly as the _SVR_ model, it still has the second-lowest \(R^{2}\) score (\(R^{2}\) = 27.1%) and the second-highest _MAE_ value (_MAE_ = 1.702). The low performance is due to the dataset used in the context of the problem. When working with recurrent neural networks, dealing with continuous data is preferred, whereas the data provided in the dataset was sequential. The negative \(R^{2}\) score of the model _SVR_ means that the model does not follow the trend of the data, leading to a worse fit than the rest of the models.  
Given the poor performance of the _LSTM_ model, the ML & DL ensemble will also have a poor performance compared to the ML ensemble, because it weights equally all  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{R}^{2}\) & **MAE** & **MSE** & **RMSE** \\ \hline Random Forest & 0.840 & 0.328 & 1.572 & 1.254 \\ Linear Regression & 0.312 & 1.621 & 6.748 & 2.598 \\ KNN & **0.898** & **0.166** & **1.171** & **1.082** \\ SVR & \(-\)0.048 & 2.052 & 8.786 & 2.964 \\ LSTM & 0.271 & 1.702 & 6.831 & 2.614 \\ \hline ML Ensemble (stacking) & **0.91** & **0.204** & **1.051** & **1.025** \\ ML \& DL Ensemble & 0.640 & 1.159 & 3.430 & 1.852 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational Results.  
Figure 7: Hour of the day with the highest subscriber action.  
models. Because _RNNs_ require three-dimensional input data whereas ML models only require two-dimensional data, simple averaging (another parallel ensemble technique) was the only technique able to overcome this incompatibility. As a result, the results from the ML & DL ensemble were obtained using simple averaging.  
The stacking strategy is the best approach for predicting the ideal period of time to send marketing communications of all the approaches implemented, based on the best \(R^{2}\) score and the lowest _MAE_ value.
## 3 Results and Discussion
### Thermodynamic Study  
The thermodynamic parameters can be calculated according to Equations (6) and (7). The \(\Delta\)G\({}^{0}\) values were calculated to be negative at the investigated temperatures (Table 3), suggesting the spontaneous nature of MB adsorption on MPC. As shown in Figure 5c, the values of \(\Delta\)H\({}^{0}\) and \(\Delta\)S\({}^{0}\) can be obtained by the slope and intercept of the fitting line, in which both positive values are obtained (Table 3). The positive value of \(\Delta\)H\({}^{0}\) implies that the endothermic property of the present adsorption reaction, indicating that adsorption consumes energy. Therefore, MB adsorption increases with increasing temperature [40]. The positive value of \(\Delta\)S\({}^{0}\) indicates an increment in the randomness at the solid-liquid interface through MB adsorption onto the MPC composite [41].  
\[\Delta\)G\({}^{0}=-\mathrm{RTlnK}_{d} \tag{6}\]  
\[\ln K_{d}=\frac{\Delta\)S\({}^{0}}{\mathrm{R}}-\frac{\Delta\)H\({}^{0}}{\mathrm{RT}} \tag{7}\]