esa-sceva/satcom-chunk-collection huggingface dataset:
# Setting Up and Operating Electric City Buses in Harsh Winter Conditions
## 1 Introduction  
Several cities across the world have sustainable mobility plans to reduce carbon dioxide (CO2) emissions, pollution and traffic jams [1; 2; 3]. For example, the city of Tampere in Finland aims to be carbon-neutral in 2030 and wants to find out how the electrification of public transport would help achieve the climate goal. Public transportation, especially in the form of green solutions, such as electrification, walking, and cycling, can have an enormous effect on reducing CO2 emissions [4]. The European Commission's 2016 strategy towards low emission mobility includes zero-emission vehicles, such as fully electric cars [5]. Research shows that electric buses produce up to 75% fewer emissions than conventional diesel buses [6]. However, fewer emissions are determined by the grid emissions of the used electricity [7]. Electric buses can also decrease the city transport noise [2; 8]. Some studies have also mentioned that electric buses are more comfortable than buses with combustion engines [9].  
Electric city buses are still a fairly new phenomenon in city transport. Less research has been conducted on testing electric city buses in various climatic zones. For example, in Finland, the temperature can vary from +35 \({}^{\circ}\)C to -35 \({}^{\circ}\)C [8]. It is of utmost importance for city traffic planners to understand how electric buses perform in different ambient temperatures [10]. This understanding forms the basis for making other crucial decisions related to electric city buses, such as investment costs, the number of buses, chargingstations and routes [11; 12]. It needs to be noted that the electric buses' procurement cost is still higher than buses with conventional combustion engines [13].  
A research gap exists as less is known about the effects of ambient temperature change on the efficiency (vehicle fuel economy) and range of the city buses in cold climatic zones. In addition, less is known how to operate city buses in hard winter conditions. Therefore, this study aims to understand how to operate battery-electric buses in a city located at latitude 61.3 North, where temperature varies between \(+32\ ^{\circ}\)C and \(-32\ ^{\circ}\)C, and buses need to operate on snowy and icy street conditions. In addition, the study aims to understand how electric buses perform in such conditions.  
This study collected and analysed weather and electric city bus data to understand the effects of ambient temperature, driving conditions and weather on the efficiency (vehicle energy economy) and range of the city buses in the city of Tampere, Finland.
## 1 Introduction
Well-known real-time capable operating strategies are the Equivalent Consumption Minimization Strategy (ECMS) [36; 37; 35] and various Model Predictive Control (MPC) approaches [38; 39; 40], while the optimization-based implementation of the ECMS is equivalent to PMP [41]. In this contribution, different implementations of the optimization-based EMCS algorithm are analyzed in terms of the theoretically achievable fuel economy. These implementations are based on different approaches for predicting the future driving cycle, while the accuracy of the predictions increases with the prediction effort and the quality of the predicted information. For the analysis, the hybrid electric powertrain given by the basic concept shown in Figure 1 is used. All implementations of the ECMS are evaluated by means of corresponding powertrain simulations, which are carried out with representative real-world driving data as the input. Therefore, in Section 2, the powertrain model used for implementing and evaluating the optimization-based ECMS is presented. The control of the hybrid electric powertrain by means of the ECMS requires the definition of an optimization problem and a method for its solution. Both are described based on the previously defined powertrain model in Section 3 and are applied in Section 4, comprising the ECMS algorithm and the different prediction methods. Finally, in Section 5, the results of the powertrain simulations considering various ECMS implementations are evaluated and discussed.
## 3 Illumination Systems' Performance Comparisons
### Bandwidth and Short Pulse Capabilities  
At the flow speed and imaging system's resolution, the objective for the illumination sources is to generate light pulses of approximately 50 ns duration carrying the maximum amount of energy to the probe volume and later collected by the receiving optics. We used a high-speed photo-detector to measure the temporal pulse profile emitted by the VCSEL and LED systems, and recorded the signal with a computer-based 200 MHz oscilloscope. The photo-detector rise and fall times are on the order of 1.5 ns, which is sufficient to track the ramp up and decay of the light pulses. The results of the pulse profiles for a 50 ns duration for both VCSEL and LED-based illumination sources are presented in Figure 4.  
The profiles of Figure 4 show that both the VCSEL and the LED systems offer high-speed capabilities when driven by the respective electronics developed in our laboratories. As expected, the two light pulses coincide in duration at the half intensity mark, both producing 50 ns light pulses, based on the full width at half maximum measurement. Surprisingly, and despite the differences being minor, the LED system's bandwidth appears to be superior to that of the VCSEL illumination system.  
Figure 4: Measured pulse profiles for a 50 ns pulse duration with both LED and vertical-cavity surface-emitting laser (VCSEL) illumination systems developed in our laboratories.  
Both systems present fast ramp-up profiles of approximately 10 ns, but the decay is noticeably faster for the LED system.  
It is important to note that under these conditions, the VCSEL and LED emitters are driven at much higher current than their nominal specifications, much beyond their respective specified maximum current. The result of driving these two illumination sources at higher current levels than the specifications is much higher radiation flux than specified, with a factor of eight improvement for the VCSEL and seven for the LED. Despite the level of performance with respect to short pulse capabilities, these two systems are configured for maximum output, rather than bandwidth. Other configurations showed higher bandwidth, with both systems being capable of pulse widths comfortably below 10 ns. Such short pulse durations are still achieved at higher driving current and radiation intensities than the nominal specifications of the respective VCSEL or LED emitters.
## 3 Results and Discussion
### Value Evaluation and Improvement Direction of the Application Prototype  
The application prototype was developed based on the model proposed in this study to demonstrate the potential of the model in practical applications. The prototype is able to identify the user's emotional state based on subjective text input and visualize the emotions in various graphical forms, which can help the user understand their emotional state in real time and adjust their emotions in time. The application prototype shows the application potential of text sentiment analysis technology in the field of mental health care.  
At present, the function of this application prototype is relatively simple, and the user's emotional state derived from text sentiment analysis is not detailed enough. In the future, the model will be improved so that the application will have more detailed emotion recognition capabilities. At the same time, the form of emotion visualization can be richer, for example, using animation to show the user's emotional state. In addition, the application will be augmented with a real-time comment function, which will help users to regulate emotions through giving users corresponding suggestions based on their emotional state.
# Fusing Appearance and Prior Cues for Road Detection
## 2 Proposed Algorithm
### Preprocessing of the Input Image
#### 2.1.2 Lane Markings Removal by Morphological Processing  
Lane markings, which always exist on urban roads in the form of white lines, are a disadvantage for appearance based road detection algorithms. Specifically, to estimate the road model, pixels placed at the bottom part of road images are usually selected as seeds. In this case, algorithms will fail if the pixels of the lane markings are chosen to estimate the road model. It is important to mention that even though we have selected seed pixels and estimated the road model correctly, the lane markings, which actually show drivable area, will be detected as a non-road region due to their different appearance from the road surface.  
To tackle this problem, we propose a novel method to remove most of the lane markings by way of morphological image processing. In this paper, the opening operation is carried out for the lane markings which have higher brightness properties than the road surface. As described in [34], opening generally breaks narrow isthmuses, smoothes contours, and eliminates thin protrusions. The opening of image \(A\) by structuring element \(B\), denoted \(A\circ B\), is defined as follows:  
\[A\circ B=(A\ \Theta B)\oplus B \tag{1}\]  
where the structuring element is actually a shape in the form of a small binary matrix, used to probe or interact with a given image. Thus, the opening is the erosion of \(A\) by \(B\), followed by a dilation of the result by \(B\). The erosion of \(A\) by \(B\) is denoted as \(A\ \Theta\ B\), and the dilation of \(A\) by \(B\) is denoted as \(A\oplus B\). Respectively, for the input RGB image, the value of the output pixel via erosion is the minimum value of all the pixels in the input pixel's neighborhood, with that neighborhood defined by the structuring element; the value of the output pixel via dilation is the maximum value of all the pixels in the input pixel's neighborhood, with that neighborhood defined by the structuring element. Note that since a characteristic of the lane markings is that the horizontal length is much smaller than the vertical length, we use a line-shaped structuring element with a length of 15 pixels at an angle of 0 degrees to carry out the opening operation. Results of the morphological processing are illustrated in Figure 1.
# A 77 GHz Transmit Array for In-Package Automotive Radar Applications
## 1 Introduction
The design procedure is based on an analytical model of a transmit array to optimize several antenna parameters like gain, but also the focal distance over aperture size (\(f/D\)). The final structure has been integrated into a 12 \(\times\) 12 mm\({}^{2}\) QFN [13; 15; 16] open-cavity package. The proposed design approach allows us to place the transmit array elements very close to the feed (\(f/D\) = 0.5), allowing for a highly integrated structure compatible with QFN technology. The high gain and integration properties of the proposed transmit array are promising for automotive radar applications. The preliminary results were presented by the authors in [17].
## 4 Experiments and Discussion
### Effects of Components and Parameters  
We compared the proposed multi-association graph generation and multi-stage fusion methods, verified the effectiveness of each component in MFDGCN, and compared the selection of important model parameters. We removed the spatial similarity graph in the multi-association graph and named the model No-simg. We also removed the gate in the multi-stage fusion and named the model No-gate. Comparing them with the MFDGCN model, the MAE values of the prediction results at 15 min are shown in Figure 10. The results show that stronger features can be obtained by using the multi-association graph and the spatio-temporal multimodal fusion module shows better performance.  
We used \(L\) to represent the number of layers of FDGCN, fixed each component in FDGCN, and tested the model prediction results when \(L\) was set to 2, 4, 6, 8, and 10. The results are the average of all MAE values within an hour. As shown in Figure 11a, the model works best when \(L\) = 8. The results show that the model needs a certain depth to obtain richer spatio-temporal information, but too much depth will influence the model effect, and parameter selection needs to be made according to different data and models.  
Figure 8: Comparison of prediction performance at each time step on the PeMS_BAY data set. (**a**) MAE values comparison; (**b**) RMSE values comparison; (**c**) MAPE values comparison.  
Figure 10: Comparison of MAE values of prediction results of MGSTCN, No-simg, and No-gate models at 15, 30, and 60 min on the PeMS_BAY data set.  
Figure 9: Comparison of the truth value and the predicted value.  
We fixed the number of FDGN layers at \(L=8\) and tested the model prediction results when the graph convolutional network neighborhood order was set to 1, 2, 3, and 4. The results are the average of all MAE values within an hour. As shown in Figure 11b, the model works best when order = 2. The results show that graph convolution is not as large as possible when selecting neighborhoods and appropriate parameters should be selected according to the different data and models.
## 6 Simulation Result  
In this section, evaluations are conducted to present the performance of the scheme proposed in this paper. _Pytorch_ is applied to build the deep learning framework. In the simulation scenario, we consider a circular area consisting of four UAVs and a number of parked vehicles, which number varies from 100 to 500 [36]. Suppose the coverage radius of each UAV is 200 m, and the UAVs are located at a fixed height of 10 m [37]. Each UAV randomly generates tasks with data size from 2 Mbit to 4 Mbit. The maximum delay tolerance of the tasks ranges from 4 s to 8 s. The size of the final result is considered as 0.1 Mbit. The transmission range of the vehicle is denoted as 300 m. The local default CPU frequency of the vehicle is set as 0.4 GHz. The communication radius between two neighboring UAVs is 300 m. Other parameter values in evaluation are described in Table 1. Moreover, two hidden layers are constructed in the neural network used in the proposed DRL model. The numbers of neurons in these two hidden layers are 512 and 256, respectively. The activation function used is Rectified Linear Activation Function(Relu).  
For comparisons, other two task offloading schemes are presented to validate the performance of the scheme described in our paper, where local computing (LC) places all the tasks in the UAVs for computing and in random offloading scheme (RF), each UAV randomly selects parked vehicles for task uploading, while the computational resources are allocated based on the strategy presented in Section 5.1.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
###### Abstract  
The increasing amount of space debris poses a major threat to the security of space assets. The timely acquisition of space debris orbital data through observations is essential. We established a mathematical model of optical satellite constellations for space debris observation, designed a high-quality constellation configuration, and designed a space debris tracking observation scheduling algorithm. These tools can realize the efficient networking of space debris from a large number of optical satellite observation facilities. We designed a constellation consisting of more than 20 low-Earth orbit (LEO) satellites, mainly dedicated to the observation of LEO space objects. According to the observation scheduling method, the satellite constellation can track and observe more than 93% of the targets every day, increase the frequency of orbital data updates, and provide support for the realization of orbital space debris cataloguing. Designing optical satellite constellations to observe space debris can help realize the advance perception of dangerous collisions, timely detect dangerous space events, make key observations about high-risk targets, greatly reduce the false alarm rate of collisions, and provide observational data support for space collisions.  
constellation; satellite; observation; space debris; scheduling Article Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations [PERSON] _et al._ (2023); 13, 4127. [[https://doi.org/10.3309/app13074127](https://doi.org/10.3309/app13074127)]([https://doi.org/10.3309/app13074127](https://doi.org/10.3309/app13074127))
# Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed
## 2 Methodology and Data Source
### Forecast Validation  
In order to analyze the accuracy of the models, among the most commonly used parameters for estimating wind speed predictions is root mean square error (RMSE) [20]. It expressed by  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(y_{a_{i}}-y_{f_{i}}\right)^{2}}{N}} \tag{8}\]  
where, \(N\) the number of data, \(y_{ai}\) is the actual value and \(y_{fi}\) is the forecast value.  
Figure 2: RNN structure.
# Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres
###### Abstract  
Fault propagation behaviour analysis is the basis of fault diagnosis and health maintenance. Traditional fault propagation studies are mostly based on a priori knowledge of a causality model combined with rule-based reasoning, disregarding the limitations of experience and the dynamic characteristics of the system that cause deviations in the identification of critical fault sources. Thus, this paper proposes a dynamic analysis method for fault propagation behaviour of machining centres that combines fault propagation mechanisms with model structure characteristics. This paper uses the design structure matrix (DSM) to establish the fault propagation hierarchy structure model. Considering the correlation of fault time, the fault probability function of a component is obtained and the fault influence degree of nodes are calculated. By introducing the Copula and Coupling degree functions, the fault influence degree of the edges between the same level and different levels are calculated, respectively. This paper constructs a fault propagation intensity model by integrating the edge betweenness and uses it as an index to analyze real-time fault propagation behaviour. Finally, a certain type of machining centre is taken as an example for specific application. This study can provide as a reference for the fault maintenance and reliability growth of a machining centre.  
machining centre; DSM; Copula function; fault propagation intensity; fault propagation behaviour +
Footnote †: journal: Applied Sciences
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### Application of Digital Watermarking
#### Covert Communication  
The following application pertaining to the text watermarking technique represents the transferring process related to the private data (e.g., images or plain texts). Hidden communication represents the addition of a covert message into an unacceptable looking text based on a particular method, which has not yet been discovered in reality, its meaning except for the intended receivers. Further, the related algorithms pertaining to text watermarking techniques are applied for secret communications [72].
# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
## 2 Proposed Internet of Things (IoT) Glucose Monitoring System
### Data Acquisition  
It contemplates the incorporation of two elements: the RPi camera and the 650 nm laser both embedded in a 3D-printed case and attached to the index fingertip of the glove, as told before. The laser-beam is fixed internally to the case, facing to the camera lens, with enough space in between for enclosing the fingertip of a person adequately.  
This setup is intended for data acquisition but also to characterise how the incident laser-beam interacts with the finger. To this end, there are some things to take into consideration:1. The laser-beam is used as the energy source to pass throughout the medium.
2. The finger is the medium through which the light will be transmitted.
3. The camera acts as the sensor responsible to capture the transmitted light and how it is scattered along the finger.  
In this stage, fingertip images of size \(640\times 480\) px are taken, in time intervals of \(8\) s for allowing the camera to perform a precise focus. This process is carried out in a period of \(2\) min, thus acquiring a total of \(14\) images. Caused by the act of putting and removing the finger, the first and last images may not be captured properly, entailing errors in future stages. For this reason, the system only takes into consideration the central \(12\) images, discarding the first and last.  
The camera configuration used in this proposal was set to night exposure mode, with camera exposure compensation of \(25\), ISO sensitivity of \(800\) and values for brightness and contrast of \(70\).
# Relay Selection Schemes for FSO Communications over Turbulent Channels
## 4 Simulation Results
Figure 6.0 ABER vs. SNR of FSO over G-G channel for strong turbulence with distance 500–500 m, the number of relays =3, and w\({}_{\rm z}\) = 2 m.  
Figure 6.1 ABER vs. SNR of FSO over the Gamma-Gamma (G-G) channel for strong turbulence with distance 400–600 m, the number of relays = 3 and w\({}_{\rm z}\) = 2 m.  
Figure 12 shows that both the S-R-D and R-D schemes have the same SNR gain and are higher and better than the S-R scheme by 15.56 dB. This SNR gain is due to the selection of the best relay depending on the long distance, S-R-D and R-D, which have the same dominant effect. Like Figure 12, Figure 13 shows that both the S-R-D and S-R schemes have the same SNR gain and they are higher and better than R-D scheme by 13.44 dB. This SNR gain is due to the selection of the best relay depending on the long distance, S-R-D and S-R, which have the same dominant effect. Figure 14 shows that the S-R-D scheme has higher SNR gain than R-D and S-R schemes by 5 dB and 9.9 dB, respectively, because of higher pointing error. Figure 15 shows that the S-R-D scheme has higher SNR gain than the S-R and R-D schemes by 5 dB and 10.6 dB, respectively. This SNR gain of S-R-D as compared with S-R and R-D is due to the pointing error in this condition, which is higher than the effect of turbulence, and pointing error is not a function of distance, and hence it has the same effect on the two hops. Figure 6.2 ABER vs. SNR of FSO over G-G channel for strong turbulence with distance 400–600 m, the number of relays = 3, and w\({}_{\rm z}\) = 0.5 m.  
Figure 6.3 ABER vs. SNR of FSO over the G-G channel for strong turbulence with distance 600–400 m, the number of relays = 3 and w\({}_{\rm z}\) = 2 m.  
Figure 16 displays the effect of moderate turbulence and weak pointing error. Clearly, the R-D scheme has higher SNR gain than S-R-D and S-R schemes by 0.8 dB and 5.3 dB, respectively. Unlike Figure 16, Figure 17 considers strong turbulence with strong pointing error for the best relay selection of two relays. The obtained results show that the S-R-D scheme has higher SNR gain when compared with the other two schemes. Figure 6.4 ABER vs. SNR of FSO over L-N channel for moderate turbulence with distance 400–600 m, the number of relays = 2, and \(\rm w_{z}\) = 2 m.  
Figure 6.5 ABER vs. SNR of FSO over the G-G channel for strong turbulence with distance 600–400 m, the number of relays = 3, and \(\rm w_{z}\) = 0.5 m.