esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Acid Oil Characterization  
Acid oil is a byproduct of the oil refining process. Acid oil is considered highly rich in free fatty acid, and hence, its utilization as a newer substrate for MEL production was tested both to reduce the load in waste treatment as well as to reduce the production cost of MEL. The physicochemical properties of the acid oil were analyzed as per the American Oil Chemists' Society (AOCS)'s official methods and are shown in Table 2. The iodine value indicates the presence of unsaturation in the acid oil.  
Further analysis of acid oil methyl ester by GC revealed the presence of different fatty acids at variable concentrations [32]. As acid oil is a complex waste, its fatty acid content depends on the crude oil and the refining process itself. The chromatogram detected both the presence of saturated as well as unsaturated fatty acids ranging from C10 to C18 in chain length. The main constituents were unsaturated fatty acids like oleic and linoleic acid, which constituted about 78% of the total fatty acid content. These fatty acids present in acid oil are of high interest for the production of both acylated MEL as well as nonacylated MEL [15; 16]. Many vegetable oils such as soybean oil, safflower oil, rapeseed oil, and olive oil serve as suitable carbon sources for MEL production [17; 18]. Accordingly, the acid oil containing a fatty acid profile similar to that of vegetable oil is considered here as a hydrophobic carbon substrate for the production of MEL. Very recently, we reported the use of acid oil for the production of glycolipid surfactant sophorolipid and rhamnipid [4; 19].
## 5 Results and Discussion
### Comparison of Traffic Conditions in the Merge Bottleneck Area
This is because SPSC-LCC only monitors flow in the local on-ramp merge areas and controls traffic at the merge bottlenecks only after congestion occurs, resulting in poorer performance on overall metrics. On the other hand, the FM-ITC and FM-ITC-Darea control strategies show improvements in both TTS and total vehicle turnover (TVT) metrics. Compared to integrated control in a static control area, the FM-ITC-Darea strategy, by dynamically adjusting the control area, can more effectively reduce the overall traffic travel time. Figure 11: Changes of mainline flow in ramp merging areas under different integrated control strategies, where (**a**) denotes the no-control scenario, (**b**) denotes the SPSC-LCC strategy scenario, (**c**) denotes the FM-ITC strategy scenario, and (**d**) denotes the FM-ITC-Darea strategy scenario.
# Study of Precipitation Forecast Based on Deep Belief Networks
## 2 Related Work
[PERSON] used DBN (Deep belief networks) to improve precipitation accuracy with the past precipitation, temperature, and parameters of the sun and moon's motion in Seoul. Their experiment proved that the DBN performed better than the MLP (Multi-Layer Perceptron) for forecasting precipitation [18]. Under the background of meteorological big data, deep learning technology can use massive multi-source meteorological data and take sufficient observation data as training samples to ensure the accuracy of the weather forecasting model. The deep learning model can explore the inherent data relationship between meteorological elements in depth, and establish a more accurate proxy model of complex mechanism models between weather conditions and meteorological elements. Based on the above reasons, this study proposes an effective rainfall forecasting model based on deep belief networks. The purpose of our survey is to explore the potential of deep learning technologies for weather forecasting. We will explore several deep learning models for weather forecasting, such as support vector machine (SVM), support vector machines based on particle swarm optimization (PSO-SVM), and deep belief network models (DBN). The remainder of this paper is organized as follows: In the following section an introduction to PSO-SVM and DBN are presented. In the Simulation and Discussion sections, we describe the features of the data used for predictions of precipitation, as well as the comparative results among these prediction models. The last part is the Results section of this paper, and it presents some discussions of our model and the last section states our conclusions.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 2 Security Challenges in 5G Network
### Policies
As for the future directions, various potential future research can be conducted to mitigate the data privacy issues since 5G technologies can be designed to offer privacy protection for a data source or privacy embedded for application, device, or service. Privacy-by-Design (PbD) is one of the general architectures for 5G privacy that future researchers can define and focus on. Other wide areas can be investigated, such as location-privacy based on multi-access edge computing (MEC). Data processing for MEC occurs at the edge nodes, where the operators will monitor and control the nodes. Moreover, some examples of 5G applications that will directly impact the privacy solutions are healthcare, IoT, transportation, and smart cities. Furthermore, other methods such as software-defined privacy (SDP) also can be used in the 5G network [17]. SDP allows privacy officers to define and implement an IaaS Cloud Customer privacy policy [59]. The methods in managing and storing data under different policies are still at an early stage. The author in [60] proposed PADRES, an open-source tool to examine web applications and aid in the compliance process for securing data, privacy, and security. The proposed tools can be extensible by adding questions related to general data protection regulation and more cookie and vulnerability analysis tools. However, the tools' limitations include that the breadth and scope of the questions must be expanded, as well as more case studies and end-user input. Since 5G network contain various stakeholders (such as ISP, mobile network, CSP) and different connected verticals (such as smart cities, data centres, power distribution) that may have different objectives, effective regulation on the privacy policies for various entities such as user, government, and other industries level are needed. To summarize, 5G is the iteration of cellular technology and is designed to improve the speed and quality of services of wireless networks. 5G technology plays an important part in securing the communications. Thankfully, as technology advances, so do the tools available to protect it. Nowadays, secure communications are widely utilized; they are incorporated into any web browser that communicates over the Internet, and they allow users to connect physically mobile devices or different corporate networks to private networks. Quantum keys can make 5G communications significantly more secure by physically transferring secret key sharing between two entities. To generate the shared key, QKD can be used instead of asymmetric key agreement systems, which is subsequently employed in symmetric schemes to safeguard messages.
## 4 Experiment and Results  
### UVI Data Set  
The data set includes 200 auroral oval images which were captured by ultraviolet imager (UVI) aboard on National Aeronautics and Space Administration (NASA) Polar satellite on 10 January 1997, 31 July 1997, 15 January 1998, and 25 January 1998. The Polar satellite was launched in February 1996 into a highly elliptical orbit of \(2\times 9R_{E}\) (Earth radius) with \(86^{\circ}\) inclination, and it views the north polar region for about 9 hours out of every 18-hour orbit [38]. Auroral oval images used in this study were obtained using the Lyman-Birge-Hopfield \"long\" (LBHL) filter of the UVI on the Polar satellite, and the auroral radiance in this wavelength band originates from \(N_{2}\) (Nitrogen molecules) emissions at altitudes of \(\sim\)120 km [39]. The corresponding benchmark images which show the auroral oval region were annotated and cross-verified by several aurora experts manually. An example of the auroral oval image and the corresponding benchmark image is shown in Figure 8.  
Among the UVI images, 50 typical images are used to extract training samples for CNN, and the rest 150 images are used as test images for the validation of the proposed method. As abovementioned, 45,600 sub-images with the size of \(11\times 11\) can be obtained from each UVI image, in which 4000 sub-images are used for training CNN. Therefore, there are 200,000 (4000 \(\times\) 50) sub-images are selected as training samples for CNN. The process of training samples extraction is shown in Figure 9. In the first step, 2000 auroral oval pixels and 2000 background pixels are selected randomly from each image. In the second step, centered on these pixels, sub-images with the size of \(11\times 11\) are available. In the third step, the center pixels' values in the benchmark image are taken as the labels of the corresponding sub-images. With the 200,000 training samples, the prediction accuracy reaches 93.2% when the CNN was trained for 50,000 epochs.  
The 150 test images that do not participate in the training process are used for validating the performance of the DFALS method. The experimental results are also compared with other auroral oval segmentation models and recently developed level set methods, i.e., SIIALSM [25], LIDLSM [26], RLSF [23], and SPFLIF-IS [24]. The parameters of the DFALS are empirically set as follows: \(\gamma_{1}=1,\gamma_{2}=2\), \(\gamma_{3}=1\). Optimal parameters of other methods are chosen to guarantee the fairness of the comparison.
## 2 System Modeling
### Power Consumption Modeling for Data Processing  
The simulations have considered a simple scenario, including different data processing algorithms using IoT device hardware which applies data compression techniques [17; 18], to collect the data. Estimating the number of operations performed to do a specific task to calculate energy consumption is necessary to create a realistic simulation model. To estimate the consumed energy for all collected data, we need to calculate the number of operations by the required number of clock cycles to perform that operation using a particular hardware and processing unit.
## 3 Materials and Methods
### Methodology
#### 3.2.1 Preprocessing  
Data analysis applications require data preprocessing to remove the superfluous information to increase the learning process of classification models for increased accuracy. Superfluous information refers to any data that contribute very little or no contribution at all to predicting the target class; however, it increases the size of the feature vector and thus introduces unnecessary computational complexity. Consequently, the performance of classification models is degraded if no or improper preprocessing is carried out. Thus, data cleaning or preprocessing are performed before encoding [32]. Python's NLP toolkit has been used for preprocessing tweets data in this study. Initially, the text is converted into lower case, followed by the removal of links, HTML (HyperText Markup Language) tags, and punctuation. Then, stemming and lemmatization methods are performed to clean the text, and stopwords are removed in the end.  
* Convert to lowercase: Converting the text to lowercase reduces the complexity of the feature set as, 'go' and 'Go' are taken as different features by machine learning models, so converting to lowercase both terms will be 'go'. Models consider upper and lower case words as different words which affect the training process and classification performance.
* URL links, tags, punctuation, and number removal: URL links, tags, punctuation, and numbers do not contribute to improving the classification performance because they provide no additional meaning for learning models and increase the complexity of feature space, so removing them helps to reduce the feature space.
* Stemming and Lemmatization: The goal of both stemming and lemmatization is to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form [33]. For example, 'walks, 'walking', and 'walked' are converted to the root word 'walk' in this process.
* Stopwords removal: Stop words are frequently used words that give no useful information for analysis. Stop words such as 'the', 'is', 'a', and 'an' are removed [34]. Table 4 shows samples of raw text from tweets and cleaned text after applying the preprocessing steps.
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 6 Experimentation
### Discussion on Generalization
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Methods** & **BPA** & **E-SEAS** & **RR** & **W-Rand** & **W-RR** & **PPO** \\ \hline
1500 jobs & 0.87 & 0.85 & **0.89** & 0.84 & **0.89** & 0.88 \\
2500 jobs & 0.76 & 0.74 & **0.80** & 0.74 & **0.80** & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage of job completed in the fixed job environments with 120 devices. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **Train Set** & \multicolumn{5}{c}{**Upward Generalization**} \\ \hline Jobs: & 1500 j & 2500 j & 1500 j & 2500 j & 3500 j & 4500 j & 5500 j & 6500 j \\ \hline BPA & 0.53 & 1.00 & 0.79 & 0.91 & 1.00 & 1.13 & **1.23** & **1.32** \\ E-SEAS & 0.52 & 0.85 & 0.74 & 0.82 & 0.86 & 0.94 & 1.02 & 1.09 \\ RR & 0.75 & 0.78 & 0.67 & 0.76 & 0.86 & 0.93 & 0.97 & 1.04 \\ W-Rand & 0.81 & 1.10 & 0.72 & 0.80 & 0.86 & 0.94 & 1.01 & 1.07 \\ W-RR & 1.04 & 1.28 & 0.68 & 0.77 & 0.84 & 0.90 & 0.97 & 1.01 \\ \hline PPO-1500 & **1.73** & **—** & **1.35** & **1.46** & **1.35** & **1.25** & 1.18 & 1.14 \\ PPO-2500 & — & **1.60** & 1.06 & 1.23 & 1.23 & 1.21 & 1.19 & 1.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Upward generalization of policies that were learned over the fixed job environments with 120 devices and 1500 (or 2500) jobs measured in GIPS. There are two (quite positive) unexpected results from Table 2. First, even though we trained using only one training instance, PPO still dominates in the test sets with 1500 to 4500 jobs. The second unexpected result is that the test performance of PPO-1500 with 2500 jobs was better than the test performance of PPO-2500 with 2500 jobs. As we discussed in Section 6.2.3, training PPO-2500 was considerably harder than training PPO-1500. However, the results from Table 2 suggest that it might be a better strategy to train the RL agents using short sequences of jobs and then gradually increase the length of the training sequences. The reason is that the agent is generalizing well to longer sequences and, hence, gradually increasing the size of the training sequence might allow us to train RL agents in Dew environments more effectively.
## 3 The New Approach Framework
### Phase 1--Engineering Characteristics Definition: Evaluation of Enterprise Capabilities  
In Phase 1, the production capabilities of an enterprise are evaluated. Engineering characteristics (ECs) are defined and selection pools are constructed based on the information collected from the service provider. Each EC has a selection pool to contain the parts that could be chosen in the configuration. Each part has a normalized EC fulfillment score and configuration constraint with other parts stated by the domain experts.  
A product typically consists of multiple parts, modules, or subassembly. The ECs of a product refer to the types of parts that it has. In this study, the product configuration system focuses on only discrete ECs, i.e., the selection of parts. Technical experts help to define ECs to be involved in product configuration and define the selection pool for each EC, i.e., the set of parts can be chosen for the EC. For an EC, there could be more than one score to evaluate the performance of parts. These scores must be normalized for further analysis. In addition, configuration constraints must be known by the configuration system to avoid the recommendation of infeasible solutions.
## 2 Methodological and Operational Framework for CDTs
### Enablers for CDTs in Agile Supply Chains
#### 2.3.5 Communication  
Is the ability of a CDT to communicate with its physical asset and with other CDTs as part of the network it belongs (e.g., Workstation A with Workstation B, which belong to the same production line). Communication services are supported through a messaging layer which is responsible for the interoperation and information exchange. Through the communication services we can model and operate networks of CDT, for instance:  
* A production line: Network of interconnected machine CDTs.
* A factory: Network of interconnected operational entities that illustrate the main manufacturing operations in the entire value chain.
* A supply chain network: A supply chain network will be a network of interconnected CDTs each one representing the entities participating in the whole network (factories, suppliers, customers, warehouses, etc.).
# Injection Molding Process Control of Servo-Hydraulic System
## 2 Materials and Methods
### Control Strategy  
It is well known that the PID controller has been widely accepted for controlling the industrial system.  
Figure 3 shows the control strategy developed in the present study to control the servo-hydraulic system in the constant velocity and constant pressure modes. In the filling (constant velocity) stage, the controller takes the feedback signal provided by a flow meter as the input and adjusts the servo-motor speed in accordance with a prescribed control law such that the velocity remains at the required value. Once the hydraulic stage reaches the target switchover position, the controller reduces the voltage output in order to reduce the motor speed and enters the pressure control stage.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Components** & **Specifications** \\ \hline Hydraulic cylinder & Diameter: 100 mm \\  & Max. of stroke: 170 mm \\  & Max. working pressure: 140 bar \\ Servo proportional valve & Max. flow rate: 90 L/min \\  & Max. working pressure: 350 bar \\  & Step response: 3.5 ms \\ Micro control unit & OS: Linux kernel 2.6.33 \\ Position transducer & Resolution (Analog): 12 bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main components’ specifications of the experiement system.  
Figure 2: The outfit of the experimental system.  
Subsequently, the controller maintains a constant pressure by advancing the hydraulic stage slowly in accordance with the signals received from a pressure transducer and torque sensor.  
Figure 4 indicates how the servo proportional valve acts in the experiment. The servo proportional valve is set to fully open in the initial from node P to B (see Figure 1) by applying the voltage \(-10\) V. Once the position of the hydraulic cylinder reached to the first target position, set the opening size to \(0.5\%\) (applied the voltage of \(-0.05\) V). Next, switch the node P to A (applied the voltage of \(0.25\) V to the valve) in order to perform the pull-back motion.
## Data and Methods
### Data
#### 2.2.2 Meteorological Forcing and Ancillary Data  
Atmospheric forcing datasets were acquired for modeling purposes from the European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological reanalysis data (ERA-Interim) available at 6-h intervals at 0.703\({}^{\circ}\)[47]. These data include air temperature, air pressure, wind velocity, specific humidity, and downward shortwave and long wave radiation. Precipitation accumulation data were provided at 3-h and \(\sim\)25 km resolution by the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 and instantaneous vertical rainfall profiles from TRMM 2A25 at 5 km horizontal and 250 m vertical resolution [48]. All forcing data were interpolated to the 5 km and 1-h model resolution using nearest neighbor.  
Temporally invariant (ancillary) data are used to determine regional soil characteristics. Soil texture for the study area was extracted from the Harmonized World Soil Database at 30 arc sec spatial resolution [49]. The soil texture map was spatially interpolated using nearest neighbor to the 5 km model resolution. Soil parameters corresponding to saturated hydraulic conductivity, porosity, field capacity, and wilting point were determined from a look-up table based on the soil texture data [50; 51; 52].
## 2 Moon Exploration Mission Trajectory  
The mission trajectory of the spacecraft for the exploration of the Moon is presented and analyzed in this section. The overall mission trajectory in the earth-centered, earth-fixed (ECEF) coordinate is presented in Figure 1. The Earth is located at the origin point of the coordinate, and the Moon exploration spacecraft maneuvers toward the Moon. The spacecraft is assumed to be pointing to Earth during the maneuver (i.e., no attitude maneuver), indicating that the GNSS antenna is always facing the Earth. The mission trajectory has a gradually increasing circle-like shape. The distance, speed, acceleration, and jerk values of the spacecraft during the Moon exploration mission are presented in Figure 2. From this figure, the distance of the spacecraft is depicted as several arc shapes with respect to time, while the height of the arc gradually increases. Additionally, large acceleration and jerk occur when the distance becomes close to Earth due to the strong propulsion power of the spacecraft, which is used for advancing further away.  
LOS information, such as the C/N\({}_{0}\) and jerk value for each visible satellite, is calculated for the global positioning system (GPS) using the GPS orbit information and presented in Figures 3 and 4, respectively. For the calculation of the LOS information, the side-lobe signal of the transmitter antenna pattern is utilized. Each color in the figures indicates a different GPS satellite. As expected, the C/N\({}_{0}\) information in Figure 3 has a reversed arc shape because the free space loss is increased when the distance between the spacecraft and the GPS satellites increases. The C/N\({}_{0}\) has a range from 1.48 to 56.58 dB-Hz. The calculated overall LOS jerk information and the enlarged version of the maximum jerk region are presented in Figure 4. The jerks occur periodically due to the propulsion power of the spacecraft, and the magnitude of each jerk gradually decreases. The maximum jerk occurs at the beginning of the trajectory, and its magnitude is approximately 411 g/s, remaining for only 1-2 s.  
Figures 3 and 4 show that the low SNR circumstance and high dynamics condition do not coexist during the mission trajectory. In other words, when the receiver suffers low signal power, there is negligible dynamic stress to the receiver, and when high dynamic stress exists, the signal power is sufficiently high. These analysis results are taken into account later when configu  
Figure 1: Overall Moon exploration mission trajectory in the earth-centered, earth-fixed (ECEF) coordinate.  
Figure 2: Overall information on the Moon exploration spacecraft during the mission trajectory.
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 4 Experimental Setup
### Loss Functions and Training Models  
This paper chooses three loss functions including MSE in (4), Time-MSE-based loss (TMSE) [35] and recently proposed SI-SDR-based loss [35] as baselines. As a T-F domain-based network is used, an additional iSTFT layer is needed to transform the estimated T-F spectrum back into time domain for TMSE- and SI-SDR-based loss [38]. The iSTFT layer is a type of specific deconvolutional layer, whose basis function corresponds to the iSTFT coefficient matrix. The baselines are compare with the proposed generalized loss function given in (18) with (20) and (21).