esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Model Validation  
To ensure the accuracy of the constructed prediction model, most commonly used error measures such as the coefficient of determination (\(R^{2}\)), root mean square error (RMSE), and normalized root mean square error (NRMSE) are used. \(R^{2}\) shows the relative errors of the model fitness and RMSE shows the absolute error of the predicted model. In addition, the NRMSE gives the scale-free RMSE result. The details of model accuracy are provided as follows.  
data. Using the obtained data, this study aims to evaluate the performance of data-driven regression models in the prediction of SOG.  
For the transparency of the built-in studies which are shown in Figure 1, the particular procedure followed to obtain the results was as follows:  
1. The acquired dataset was loaded.
2. Unnecessary features such as static information in the AIS data were rejected.
3. Data where the ship is moored and anchored were identified and discarded.
4. Data where the ship has an SOG value of less than 5 knots were discarded.
5. The missed data were identified in the AIS data and discarded.
6. The outliers were discarded for some of the features based on the z-scores.
7. The key features were selected by applying feature selection methods such as a high correlation filter.
8. The dataset was subjected to sampling (splitting) into a training and test set.
9. The models which can potentially estimate the target were listed down.
10. k-fold cross-validation was implemented for each model: * Each model was trained using hyperparameter optimization by specifying the range of the search space for each hyperparameter, Bayesian optimization was executed over the specified search space, and the results were assessed. * The model was trained using the whole training set after the optimal hyperparameters were obtained. * The results of the constructed model results were evaluated using a test set, and the performance metrics were calculated.
11. The constructed models were evaluated using three accuracy measures (R\({}^{2}\), RMSE, NRMSE) and overall conclusions were drawn.
## 2 Three-Dimensional Imaging Theory  
### System Structure  
In this study, the non-scanning 3D imaging system with a single-pixel detector is based on the traditional compressed sensing two-dimensional imaging. Meanwhile, modulation information is added to the laser light intensity. We can obtain the distance information about the target area by phase detection to complete the 3D imaging. The system structure is shown in Figure 1.  
The 3D imaging process of the system works as follows. The laser emits a continuous modulated beam with a sine wave modulation. The continuous laser enters the mirror area of a digital micro-mirror device (DMD) after collimation. The DMD's micro-mirror array flips according to the elements of the first row in the measurement matrix, which are input in advance. The laser beam reflected by the DMD is irradiated onto the target through an expanded beam. The reflected laser from the target surface is  
Figure 1: System structure.  
focused by the condensing lens on the photo-sensitive element of the single-pixel detector, which then receives an electrical signal after photo-electric conversion. The signal is collected by the capture card, and a measured value is obtained after the phase detection process.  
The remaining elements of the measurement matrix are entered in sequence, and the above operation is repeated to obtain a number of measured values that correspond to the required sampling rate. After performing the compressed sensing reconstruction processing on the obtained measurement values, the delay phases of the modulated laser at various points in the target area are obtained. Finally, according to the phase method ranging principle, the distance of each point in the target area is calculated, and the 3D information about the target can be obtained.
## 2 System Model and Atmospheric Error Analysis  
### System Configuration  
The configuration of the GNSS-based bistatic SAR (GNSS-BSAR) is illustrated in Figure 1. The system mainly relies on the motion of GNSS satellites to form the synthetic aperture. Taking GPS satellites as an example, the satellite orbit is in the Medium Earth Orbit (MEO) with an orbital altitude of 20,200 km. Since GNSS satellites are far away from the imaging area and their velocities are slower than low-orbit satellites, the velocity relative to the stationary ground scene is very small. When the receiver is placed on the ground, to obtain fine azimuth resolution, the required synthetic aperture time is very large, even on the order of hundreds of seconds.  
The studied GNSS-BSAR system works in dual-channel receiving mode. The receiver is placed on a fixed platform and serves as the origin of the coordinate to generate a two-dimensional SAR image by receiving the echo of the target area.  
Figure 1: The system configuration of the studied GNSS-BSAR system.  
equipped with an omnidirectional GNSS antenna, which is used to receive direct wave signals from GNSS satellites for system synchronization (blue path in Figure 1) and is called the reference channel. The other channel is equipped with a narrow-beam antenna pointing at the target area to receive the echo for SAR imaging (red path in Figure 1), which is called the imaging channel. Since the polarization characteristic of the signal transmitted by navigation satellite is right-handed circular polarization, it can be decomposed into two linear polarization components, vertical and horizontal. After reflection from the Earth's surface, the polarization characteristics of some electromagnetic waves become left-handed circular polarization. The specific proportion of each polarization component is related to the altitude angle of the satellite. In this paper, the receiving antenna of the imaging channel adopts left-hand circular polarization mode.
# Utilizing Energy Storage for Operational Adequacy of Wind-Integrated Bulk Power Systems
###### Abstract
A Monte Carlo simulation approach is used in Reference [16] to evaluate the spinning reserve requirements for power system operation considering virtual power plants, while Reference [17] also incorporates the transmission network in a similar study. A coordinated operational dispatch scheme for a wind farm with a battery energy storage is presented in Reference [18]. Similarly, a rule-based simulation tool has been developed in Reference [19] to simulate the cooperation of ESS and a wind farm to minimize errors in wind generation forecast. Reference [20] demonstrates a predictive operation scheme for optimal sizing of battery energy storage to enhance the operation of wind and battery systems to meet the commitment. Moreover, reliability standards set under operations planning (TOP-002-4) and reliability coordinator operational analyses and real-time assessments (IRO-008-2) [21] require system operators under the North American Reliability Corporation (NERC) jurisdictions to conduct the next day and current day operational planning. While these studies suggest innovative approaches to wind and energy storage operation, these standards try to maintain uniformity among jurisdictions. In this regard, a standard operational adequacy measurement mechanism or criteria is yet to be established. This work presents a dynamic system state probability-based operational adequacy assessment framework for a bulk power system considering wind generation and ESS integration. The proposed framework is then used to quantify the operational adequacy and economic benefits for different operational strategies driven by differing objectives and ownership of wind farm and ESS facilities. The reliability benefit is quantified in terms of the improvement in operational adequacy. The economic benefits are evaluated in terms of the reduction in the operating costs. Different operating scenarios and strategies of wind generation and ESS operation have been simulated using the proposed framework on a test system to illustrate the results.
## 4 E-Waste Management in Serbia--Current Situation
### Obstacles and Potential Solutions in the E-Waste Management System in SERBIA
#### 4.1.1 Legal Obstacles  
From a legal point of view, a major problem is the inconsistency of the legislation governing e-waste management in Serbia with the legislation of the European Union (EU). According to data from 2021, each EU resident collected an average of 11 kg of e-waste, and the EU recycled nearly 40% of this waste, clearly indicating Serbia's significant lag [38]. The European Directive 2012/19/EU, the umbrella law for regulating the conditions of e-waste management at the EU level, has not updated the national legislation concerning waste management. The umbrella law that regulates waste management in the Republic  
Figure 2: Schedule and number of e-waste recycling and collection centers in Serbia [35].  
of Serbia is the Law on Waste Management (\"Official Gazette of the Republic of Serbia\", no. 36/2009, 88/2010, 14/2016, 95/2018--other law and 35/2023) [5], while the umbrella legal act regulating the management of waste from electrical and electronic devices is the Rulebook on the list of electrical and electronic products, measures prohibiting and limiting the use of electrical and electronic equipment containing hazardous substances, methods and procedures for managing waste from electrical and electronic products (\"Official Gazette of the Republic of Serbia\", No. 99/2010) [3]. Some of the observed non-conformities are as follows [38; 39; 40; 41]:  
* The absence of a legal framework for the establishment of collective and individual schemes according to the principle of waste management, \"producer responsibility\" in Serbian legal acts, as prescribed by Article 5 of Directive 2012/19/EU.
* The absence of a legal framework for establishing a National Register for manufacturers or importers of electrical and electronic equipment in Serbian legal acts, as prescribed by Article 16 of Directive 2012/19/EU.
* The absence of prescribed obligations of separate collection, treatment, reuse, and disposal of e-waste in Serbian legal acts, as provided for in Articles 5, 12, and 13 of Directive 2012/19/EU.
* The absence of a prescribed financial guarantee by the manufacturer or importer of electrical and electronic equipment that they will finance the responsible management of e-waste in Serbian legal acts, as prescribed in Article 12 of Directive 2012/19/EU.
* The inconsistency of prescribed national goals for the collection and recycling of e-waste with European goals, prescribed by Article 7 of Directive 2012/19/EU. Moreover, Serbian legal acts do not define who is in charge of implementing the goals.  
Given that Serbia has been a candidate country for EU accession since 2004, a deadline for harmonizing national regulations with European goals concerning e-waste was established as 1 January 2022 [42]. Apparently, that deadline was not met.
## 4 Data Analysis  
The reliability and validity of the measured indicators were then assessed in PLS-SEM to determine whether the indicators should be included or excluded in the final model. In PLS-SEM, this process is commonly known as assessing the outer model. The last step for this SEM model analysis assesses the inner model, also known as the structural model. In this analysis, the hypothesized relationships between constructs were evaluated to determine whether the null hypothesis could be rejected, indicating significance between constructs. Additional analysis, including coefficient of determination (R\({}^{2}\)) and predictive accuracy (Q\({}^{2}\)), were also assessed to provide an in-depth analysis of the model.
## 2 Results and Discussion
### Odor Analysis
#### 2.4.3 Sweet and Fruity Odor  
In the _Rbloodendron_ flowers, the compounds contributing to the sweet and fruity odor mainly included aldehydes and alkenes. Among the aldehydes, citronellal (sweet, citrus), decanal (sweet, orange), and undecanal ( floral, citrus) all provide a sweet and fruity odor, especially citronellal with its high contents in the four flower species (4.178% \(\pm\) 0.654% to 7.944% \(\pm\) 0.225%). Among the alkenes, limonene is a typical sweet and citrus-like odor compound which was previously identified in lychee [32]. \(\alpha\)-Ocimene with a fruity aroma was also reported in a previous study [17]. Some alcohols like major compound 2-nonen-1-ol also have a sweet and melon odor. Besides this, it has been previously reported that \(\alpha\)-terpineol is one of the major components providing fruity and floral notes in Pu-erh tea [38]. Other compounds, for example, 2-pentyl-furan, reported to have a fruity, green, and earthy odor [39], accounted for a relatively high proportion in \(R\). _irroratum_ (up to 1.643% \(\pm\) 0.290%) among the four flower species studied.
## 4 Case Studies
### Case Study 4--Cabbage  
For the last case study, \(a_{\rm w}=0.990\), 0.991 and 0.992 were considered, originating, in turn, with relative humidity scenarios of \(\varphi=99\%\), 99.1% and 99.2%, respectively. For the study of this product, the bacterial growth of _Aeromonas hydrophilia_, _Bacillus cereus_ and _Salmonella_ were considered. The analysis of cabbage begins at a storage temperature of \(T=2\)\({}^{\circ}\)C, at which the onset of proliferation of _Aeromonas hydrophilia_ is verified. The study interval is also delimited by _Aeromonas hydrophilia_, at \(T=17\)\({}^{\circ}\)C. In terms of acidity, the study range includes pH values between \(5.2\leq\) pH \(\leq 6.9\). The sampling of the results obtained in the study of cabbage concerning remaining shelf life is shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(T\) (\({}^{\circ}\)C) & pH & \(a_{\rm w}\) &
\begin{tabular}{c} **Remaining Shelf** \\ **Life (\(t\), in Hours)** \\ \end{tabular} & **Contamination by** \\ \hline \multirow{4}{*}{2} & & 0.983 & 1321.6 & _Aeromonas hydrophilla_ \\  & 4.9 & 0.988 & 829.0 & _Aeromonas hydrophilla_ \\  & & 0.993 & 541.2 & _Aeromonas hydrophilla_ \\ \cline{2-5}  & & 0.983 & 1096.2 & _Aeromonas hydrophilla_ \\  & 5.0 & 0.988 & 689.0 & _Aeromonas hydrophilla_ \\  & & 0.993 & 451.8 & _Aeromonas hydrophilla_ \\ \hline \multirow{4}{*}{10} & & 0.983 & 152.4 & _Bacillus cereus_ \\  & 4.9 & 0.988 & 126.4 & _Bacillus cereus_ \\  & & 0.993 & 101.4 & _Aeromonas hydrophilla_ \\ \cline{2-5}  & & 0.983 & 148.0 & _Bacillus cereus_ \\  & 5.0 & 0.988 & 123.0 & _Bacillus cereus_ \\  & & 0.993 & 84.6 & _Aeromonas hydrophilla_ \\ \hline \multirow{4}{*}{19} & & 0.983 & 13.6 & _Aeromonas hydrophilla_ \\  & 6.3 & 0.988 & 9.0 & _Aeromonas hydrophilla_ \\  & & 0.993 & 6.2 & _Aeromonas hydrophilla_ \\ \cline{2-5}  & & 0.983 & 13.1 & _Aeromonas hydrophilla_ \\  & 6.4 & 0.988 & 8.6 & _Aeromonas hydrophilla_ \\  & & 0.993 & 6.0 & _Aeromonas hydrophilla_ \\ \hline \hline \end{tabular}
\end{table}
Table 8: Sampling of results of DSS—Remaining shelf life of carrot.  
The sample analysed in cabbage study is strongly marked by contamination by _Aerononas hydrophilla_, given that, for the combinations shown, there is no contamination by any of the remaining bacteria considered.  
At the most unfavourable scenario for bacterial growth: water activity, storage temperature and pH at minimum values (\(a_{\rm w}=0.990\); \(T=2\)\({}^{\circ}\)C; pH = 5.2), the shelf life is limited to \(t=413.4\) h after the entrance of the batch of the studied horticultural product into storage is established at the threshold of contamination by the only bacteria capable of replicate under those conditions, _Aerononas hydrophilla_. At the inverse end of the study range, when the selected values for water activity, temperature and pH are maximum, given the study range (\(a_{\rm w}=0.992\); \(T=17\)\({}^{\circ}\)C; pH = 6.9), shelf life is dictated when food safety threshold is reached, after \(t=7.7\) h of storage. At an intermediate storage scenario (\(a_{\rm w}=0.991\); \(T=9\)\({}^{\circ}\)C; pH = 6), a remaining shelf life of \(t=34\) h is estimated.
## 4 Analysis of Influencing Factors
### Influence of the Supporting Pile Wall Size
#### 4.3.1 Tunnel Horizontal Displacement  
Figure 14a illustrates the tunnel's maximum horizontal displacement when the excavation of the foundation pit is completed with different supporting pile wall sizes. The displacement of the tunnel decreases by 5.52%, 6.84%, 7.73%, 12.58% and 14.79% as the equivalent wall thickness increases from 600 mm to 1200 mm. The tunnel's horizontal displacement gradually decreases with increasing equivalent wall thickness (i.e., the size of the supporting pile wall). Taking the tunnel deformation control standard in the existing specifications as a reference, when the thickness of the equivalent ground wall reaches more than 1100 mm, the tunnel's horizontal displacement falls below the warning value. Increasing the wall thickness significantly reduces the tunnel's horizontal displacement.  
Figure 13: Maximum structural stress of the tunnel for various horizontal spacings between the foundation pit and tunnel.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 5 Simulation and Results
### Simulation Setup  
The parameters used to evaluate the proposed mechanism are listed in Table 2. The parameter names, values, and ranges follow the 3 GPP specification [18]. The eNB scheduler is designed to select a small value to minimize the active time as much as possible. For the same reason, we assumed that the eNB and MME support release assistance indicator functionality, which means that the eNB can release the radio bearer immediately after the last packet transmission without monitoring the packet activity.  
According to the 3 GPP specification [19], cells classify the UEs into three coverage-enhancement levels depending on the radio condition. We classified the channel condition into good, medium, and bad conditions according to the MCL. Each channel condition is designed to have a 10 dB difference in MCL. The simulation metrics for each channel condition are listed in the Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline ul\_K0\_for\_DCI\_formatN0 & 8 ms \\ ul\_K0\_for\_DCI\_formatN0\_for\_random-access response & 12 ms \\ dl\_K0\_for\_DCI\_formatN1 & 0 \\ k0\_for\_ack\_nack\_pusch & 13 \\ NPRACH cyclic prefix0 & 267 \(\upmu\)s \\ Number of uplink subcarrier & 6, fixed \\ Scheduling size for MSG3 & 88 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 12: Network command and UE response scenario: (**a**) existing NB-IoT; (**b**) proposed PBESM.
## 2 Method
### Theoretical Basis of EFSDAF
#### 2.2.2 Temporal Prediction (\(F^{TP}\) ) for No Land Cover Change from \(T_{B}\) to \(T_{p}\)
\begin{array}{c}A_{C}^{B}(x_{1},y_{1},1)\ A_{C}^{B}(x_{1},y_{1},2)\ \cdots\ A_{C}^{B}(x_{1},y_{1},m)\\ \vdots\\ \vdots\\ A_{C}^{B}(x_{i},y_{i},1)\ A_{C}^{B}(x_{i},y_{i},2)\ \cdots\ A_{C}^{B}(x_{i},y_{i},m)\\ \vdots\\ A_{C}^{B}(x_{n},y_{n},1)\ A_{C}^{B}(x_{n},y_{n},2)\ \cdots\ A_{C}^{B}(x_{n},y_{n},m)\\ \end{array} \right\}\left\{ \begin{array}{c}\Delta R_{C}(1,b)\\ \vdots\\ \Delta R_{C}(1,b)\\ \vdots\\ \Delta R_{C}(m,b)\\ \end{array} \right\} \tag{6}\]  
where \(\Delta R_{C}(m,b)\) can be calculated by computing a least squares best fit solution. Selecting k of the purest MODIS pixels of each endmember in the sliding window for least squares best fit aims to reduce the errors by collinearity [32]. The final determined MODIS pixels for least squares best fit should be smaller than the size of the sliding window and larger than the number of endmembers. * Adjustment of the differences between the Landsat images and MODIS images Theoretically speaking, the reflectance values of the Landsat pixels corresponding to the MODIS pixels are the same. However, due to the physical differences of the sensors, the effects of bandwidth, weather conditions, and atmospheric correction, differences between MODIS images and Landsat images are inevitable. As with previous studies [20; 41], a linear model is introduced in this study to adjust the differences between Landsat images and MODIS images. \[R_{F}^{B}(m,b)=a_{1}(b)\times R_{C}^{B}(m,b)+b_{1} \tag{7}\]  
\[R_{F}^{P}(m,b)=a_{1}(b)\times R_{C}^{P}(m,b)+b_{1} \tag{8}\]  
where \(a_{1}(b)\) and \(b_{1}\) are the slope and interception of the linear model, respectively. From Equations (7) and (8), the differences between \(\Delta R_{C}(m,b)\) and \(\Delta R_{F}(m,b)\) can be calculated as the following Equation (9). \[\Delta R_{F}(m,b)=a_{1}(b)\times\Delta R_{C}(m,b) \tag{9}\]  
\(\Delta R_{F}(m,b)\) can be obtained from Equations (5)-(9) in this study. However, the \(\Delta R_{F}(m,b)\) of FSDAF were calculated by assigning the temporal changes of MODIS images to Landsat images without considering the differences of Landsat and MODIS images. * Temporal prediction for the fine resolution image at \(T_{P}\)  
\(\Delta F\) is linearly mixed with \(A_{F}^{B}\) from Equation (3), and the final temporal prediction can be calculated by Equation (10) as follows:  
\[F^{TP}\Big{(}x_{ij},y_{ij},b\Big{)}=F_{B}\Big{(}x_{ij},y_{ij},b\Big{)}+ \Delta F\Big{(}x_{ij},y_{ij},b\Big{)} \tag{10}\]  
Although the final temporal prediction can be calculated by Equation (10), all of the above calculations are based on the assumption of no land cover change from \(T_{B}\) to \(T_{P}\). Hence, the temporal prediction cannot accurately predict abrupt change events from \(T_{B}\) to \(T_{P}\). In addition, because this prediction only uses the information of the temporal changes between \(T_{B}\) and \(T_{P}\), we call Section 2.2.2 the temporal prediction.
## 2 Extraction Techniques
### Solid-Phase Microextraction (SPME)
#### 2.1.1.2 Coating Fibers  
Several types of stationary phases, of different thicknesses and polarities, are commercially available (Supelco, Gland, Switzerland ), showing great selectivity for different analytes, namely three poly(dimethylsiloxane) (PDMS) films of different thicknesses (seven, 30 and 100 \(\upmu\)m), 85 \(\upmu\)m polyacrylate (PA), the 60 and 65 \(\upmu\)m polydimethylsiloxane/divinylbenzene (PDMS/DVB) mixed phases, 75 \(\upmu\)m carboxen/polydimethylsiloxane (CAR/PDMS), 60 \(\upmu\)m polyethylene glycol (PEG), 50 \(\upmu\)m carboxax/templated resin (CW/TPR) and 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). Stationary phases are immobilized by non-bonding, bonding, partial crosslinking or high crosslinking (Figure 2). Non-bonded phases are stable with some water-miscible organic solvents, although some swelling may occur when used with non-polar solvents. Bonded phases are stable with all organic solvents, except for some non-polar solvents. Finally, partially crosslinked phases are stable in most water-miscible organic solvents and some polar solvents, while highly crosslinked phases are similar to the latter, except that some bonding to the core may occur (reviewed in [22]).  
Regarding fiber polarity, polar fibers are effective for extracting polar analytes, and nonpolar fibers are effective for extracting the nonpolar analytes from different matrices. Fibers with different polarities provide high extraction selectivities and reduce the possibility of extracting interferences. For example, PDMS/DVB, PDMS/CAR and PEG fiber coatings are more polar than those containing PA, and for this reason, they are more often used to extract highly polar compounds, like alcohols and carboxylic acids; on the other hand, CAR is responsible for the CAR/PDMS coating's greater specific surface area, and this results in a very efficient extraction of volatile organic compound (VOC) analytes [25].