esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Results  
### Overview of Spread-Spectrum Coding Systems  
The communication schematic of a spread-spectrum coding system is shown in Figure 5, where bits of information are first coded by the channel and redundant check bits are added to obtain a code word for transmission. The length of the information bit before coding is given as \(k\), the length of the code word after coding is given as \(n\) and the code rate is given as \(R=k/n\), indicating the amount of information carried by each channel bit. To reduce the effects of ISI, the baseband signal is waveformed. The baseband signal is then spread and carrier-modulated to be transmitted via the transducer to the underwater acoustic channel. After the receiver has received the acoustic signal, the signal is demodulated by the local carrier, filtered by low-pass filtering to remove out-of-band noise and then recovered by matched filtering and de-spreading.  
The input to the decoder varies according to the spreading method used. For the direct spread system, the information is fed into the decoder without judgment, which allows the decoding performance of the soft judgment decoder to be fully utilized. For the M-ary spread-spectrum system, the judgment rule of detecting the maximum energy of the correlated peaks means that the information obtained after decoding is in the form of symbols, and is sent to the decoder as a 0.1 bit stream. This  
Figure 4: Spread-spectrum sequence correlation characteristics. (**a**) Seventh-order M-series correlation properties; (**b**) Cyclic shift sequence correlation properties for seventh-order M-sequences.  
Figure 5: Communication schematic for spread-spectrum coding systems.  
information. Overall, the spread-spectrum coding system using the soft spread-spectrum combined with LDPC codes obtains a limited coding gain, and is inferior to the direct spread coding system.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 5 Some Prospects of the Microseismic Monitoring Technique in Coal Mines  
Though coal demand and output will fall in almost all advanced economies, for example, Poland will stop coal mining by 2049 and the coal production of the United States will decline to below 400 million tons in 2026 [1], coal will still play an important role in developed economies in the following decades due to the subdued hydropower and nuclear electricity generation. The coal mining industry has no choice but to exploit deep-seated coal resources due to the depletion of shallow coal resources. The mechanical properties of coal/rock mass in deep coal mines and their engineering responses present significant differences compared to those in a shallow mining environment. As a coal mine goes deeper, the influences of high ground stress, high temperature, high gas pressure, and mining disturbances become serious, which increases the frequency of mining accidents and makes the disaster mechanism more complicated. Though microseismic monitoring has been commonly employed as a practical tool for safety management in many Chinese coal mines, some problems should be solved to make the technique more sophisticated and efficient for the monitoring and warning of mining-induced disasters.
# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 3 Method
### Experimental Setting  
The target robot data set is first created. Each image has a dimension of 4160 \(\times\) 3120 pixels. All images are collected from different distances and angles of the target robot. For example, we take photos at a certain distance from the robot, and then move around to ensure that photos from different angles of the robot are collected. The distance between the robot and the camera varies under mobility. The photos are taken accordingly. The images also contain the ones taken under bad lighting conditions or noisy conditions. When the distance or angle changes, the background of the image may change as well.  
The operating workspace environment includes Windows 10, CUDA 11.4, cuDNN 7.6.5, and Visual Studio 2022. The hardware used in the experiment is an NVIDIA GeForce GTX 1650 with 4096 MiB (Santa Clara, CA, USA).  
The Faster RCNN, SSD and YOLOv5 models are all fast deep learning methods. To make the comparison between them as fair as possible, much attention was paid to the settings of these three models.  
For the Faster RCNN model, we adopt non-maximum suppression (NMS) on the proposal regions in the RPN network and set the IoU threshold for NMS as 0.7. In the Region of Interest (ROI) pooling layer, both the numbers of the positive and negative samples are balanced to 128. Due to the different sizes of the proposed anchors, the ROI pooling obtains the output of a fixed size via the method of mapping and max pooling. For YOLOv5, there are three main steps to set up the model. First, the file train.py is called. Then in the voc_ball.yaml file, there is only one target in our experiment. The target name  
Figure 1: Manual annotation of the object in Labeling.  
is called robot. The training data set and the verification data set are in the local location. Finally, settings in the yolov5s_ball.yaml file are responsible for most of the parameters. It contains a number of categories of objects in the data set, the coefficient controlling network depth, the coefficient controlling network width and the batch size. All these parameters are separately well set based on the specific experiments. For the SSD model, the steps of the matching strategy are as follows: each default anchor should be sorted according to the confidence score. The confidence threshold is set to be 0.5, which means that default anchors that have scores higher than 0.5 will be retained. After that, one object may have several default anchors, and box position and score for non-maximal suppression are used to filter anchors. The IoU for non-maximal suppression is set to 0.7. The training is divided into freeze training and unfreeze training. In freezing training, the network backbone is frozen, and more resources are used to train the posterior network parameters. For more efficient use of the GPU memory, the batch size of freeze training is 16. In the unfreeze training, the backbone network is unfrozen, and all parameters are trained at the same time. The batch size of the part is set to 4.
# Evaluating the Reliability of Powder Bed Fusion for Biomedical Materials: An Experimental Approach
## 3 Results
### Manufacturing the SLM Specimens from Ti6 Al7 Nb Powder  
The laser power used for manufacturing the specimens ranged from 50 to 160 W. As shown in Table 6, the highest manufacturability rank (4) was achieved at low laser power values (50-70 W). However, the mechanical characteristics corresponding to 50 W were not acceptable. A laser power of 70 W is recommended to achieve both superior manufacturability and acceptable mechanical properties.  
Additionally, a laser power of 100 W is recommended to obtain significantly improved mechanical properties (almost three times higher) compared to lower power settings, even though the PBF process has a slightly reduced success rate (rank 3). The Poisson's ratio was determined for all laser power values used in these tests.  
The highest roughness values were measured at the lower laser power settings (50-70 W). This can be attributed to the reduced energy input, which affects the surface quality of the manufactured parts.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Characteristics** & **100 W** & **120 W** & **140 W** & **160 W** \\ \hline Processability rank & 3 & 4 & 3 & 3 \\ \hline Ultimate tensile stress, R\({}_{\mathrm{m}}\) [MPa] & 461 & 441 & 430 & 401 \\ \hline [PERSON]’s modulus, E [GPa] & 75 & 103 & 79 & 80 \\ \hline Poisson’s ratio, v & - & 0.30 & - & - \\ \hline Maximum elongation, A5 [\%] & 8.4 & 6.5 & 4 & 4.5 \\ \hline Vickers hardness [HV1] & 234.2 & 260.4 & - & - \\ \hline Roughness Ra [\(\mu\)m] & 8.4 & 8.2 & - & - \\ \hline Roughness Rz [\(\mu\)m] & 50.5 & 42.1 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Processability index and physical–mechanical characteristics of the pure Ti specimens.
## 3 Noise Measurement and Reduction in Mode-Locked Lasers
### Carrier-Envelope Phase Noise
#### 3.2.2 Carrier-Envelope Phase Noise Stabilization Methods
demonstrated a passively carrier-envelope phase stabilized Er-doped fiber laser system [116]. After the output pulse of an Er-doped fiber laser system is spectrally broadened, the obtained soliton wave and the dispersion wave are sent to a different frequency generation crystal. Since the soliton wave and the dispersive wave originate from the same laser source, the \(f_{\text{ceo}}\) of the difference frequency generated pulse at 1550 nm is self-stabilized. The carrier-envelope phase noise of this system is only 250 mrad (integrated from 10 uHz to 50 MHz) [117]. Figure 6 summarizes the performance of the carrier-envelope phase noise reduction methods mentioned above.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G
### SON for Backhaul Management in 5G  
As already mentioned, the authors of [79] identified the backhaul restrictions and proposed a SON load balancing MRO algorithm considering the backhaul state. Since the application of SON in RAN proved to be very beneficial, academy and industry started working on the application of it in wireless backhaul so that self-configuring, self-optimizing, and self-healing capabilities are adopted in this domain as well. Even if radio access and backhaul technologies impose different handlings and have different characteristics, the operational challenges are closely related [106; 107].  
As an example, a self-optimization use case might enable cooperation with neighboring backhaul radios and interference mitigation, while the self-healing use case can adjust a link's transmission parameters in order to overcome a possible failed link or a new link addition [107; 108]. In terms of self-healing, the key point is to automatically identify proactive rerouting needs in cases of performance degradation due to bottlenecks in the backhaul network. Inputs to backhaul SON might include QoS KPIs collected both from radio and backhaul networks [106].  
Finally, the authors of [109] demonstrated a SON solution for mobile backhaul networks, leading to a 31% increase in traffic in case microwave link capacity degradation is detected and a 12% increase in case a link failure is detected.
###### Abstract  
This paper presents the initial steps taken in analysing the benefits of connected autonomous vehicles (CAVs), especially Modular Autonomous Vehicles (MAVs), in search of sustainable solutions for reducing energy consumption per passenger in air transport. For this particular case, a Mobility-as-a-Service (MaaS) solution is proposed, correlating airside transport with landslide transport, as an urban mobility alternative. To better understand the benefits of the proposal, this paper studies the impact on energy consumption conceptual differences between a conventional public transport fleet using Battery Electric Buses (BEBs) and an MAV fleet. Simulations for simple public transport tasks are performed to highlight the advantages of the modular vehicle concept, in which routes are assigned dynamically based on the requested passenger carrying capacity and travel distance, aiming to optimize the efficiency of the entire system. With a proven reduction in energy consumption due to better use of available passenger capacity and reduced travel times in which the vehicle is driving with a number of passengers less than half of its capacity, the concept can be addressed further in developing a predictive system that processes public transport data and delivers an optimized schedule for the entire fleet. The main goal being to improve overall operational efficiency and total cost of ownership, the second part of the paper studies the impact of weight distribution on efficiency parameters such as energy consumption, range, and overall performance of an electric bus. The impact on dynamic elements such as acceleration, braking, and comerring performance is analyzed, to assess the viability and safety of all types of electric bus operations.  
modular autonomous vehicle fleet; public transport; energy efficiency; weight distribution; electric bus +
Footnote †: journal: Applied Sciences
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 2 Materials and Methods
### Design of the Grid Array Antenna for Radar Sensing
#### 2.3.2 Radiation Characteristics of the GAA  
The simulation results regarding peak realized gain (G), peak directivity (D), radiation efficiency (\(\eta\)) and the front-to-back ratio (FTBR) of the GAA, for the center and end frequencies of the target band, are indicated in Table 2. High levels of G (\(\geq\)13.7 dBi) and D (\(\geq\)14 dB) are achieved, endorsing the suitability of the GAA as short- to medium-range radar sensor. Furthermore, \(\eta\) and FTBR are key for wearable applications and the obtained levels, \(\eta\geq\) 89% and FTBR \(\geq\) 22.9 dB, are optimal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Frequency Band**} \\ \hline \multicolumn{2}{c}{**Freq (GHz)**} & \multicolumn{2}{c}{**BW**} \\ \hline
**f\({}_{\text{Low}}\)** & **f\({}_{\text{Up}}\)** & **Total (MHz)** & **\%** \\ \hline
23.60 & 24.51 & 910 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frequency band and bandwidth of the array antenna on PP.  
Figure 4: Geometry of the GAA with parameterized dimensions. Visible metallic parts (radiating grid and coaxial outer conductor) appear as dark.  
Figure 5: Reflection coefficient results, S\({}_{11}\)(dB), in simulation for the GAA on PP.  
Broadside radiation, with low side lobe level (SLL) and a half-power beam width (HPBW) \(\geq\) 30\({}^{\circ}\) for Phi = 0\({}^{\circ}\) and Phi = 90\({}^{\circ}\) pattern cuts (see Figure 6) is achieved at 24.15 GHz (band center frequency), as indicated in Table 2, which is suitable for the aimed ETA application. Furthermore, broad 3 dB gain-drop bandwidth (11%) is obtained.  
The above results account for both the finite conductivity and thickness (33 \(\upmu\)m) of the Al, which reduces 0.4 dB the G and 5% the \(\upeta\) with respect to ideal simulation using a perfect electric conductor (PEC).
# Comments on the Physics of Microwave-Undulators
## 1 Introduction
The paradigmatic assumptions underlying the hypothesis of an inverse WW-A are the definition of the microwave undulator (MU) equivalent period \(\lambda_{u}\) and magnetic field \(B_{u}\). As we will see in the following, they should be respectively specified as (see ref. [5; 10] and the forthcoming sections for the appropriate mathematical analysis)  
\[ \begin{split}\lambda_{u}&=\frac{\lambda}{1+\sqrt{1- \left(\frac{\omega_{c}}{\omega}\right)^{2}}},\\ B_{u}&=\frac{E_{0}}{c}\Bigg{(}1+\sqrt{1-\left( \frac{\omega_{c}}{\omega}\right)^{2}}\Bigg{)},\end{split}  \tag{1}\]  
where \(E_{0}\) is the intensity of the microwave peak electric field, \(\omega_{c}=\pi c/a\) is the cutoff frequency, with \(a\) the waveguide dimension (see Figure 1), \(c\) is the speed of light, and \(\omega\) and \(\lambda\) are the operating angular frequency and wavelength, respectively. Accordingly we can define the corresponding K-strength as [25]  
\[K_{u}=\frac{eB_{u}\lambda_{u}}{2\pi m_{e}c}=\frac{e\,E_{0}\lambda}{2\,\pi\,m_ {e}c^{2}}, \tag{2}\]  
with \(m_{e}\) being the electron mass. It's worth noting that, from Equation (2), the relation between the electric field peak intensity of the microwave and the associated K value is given by  
\[E[V/m]\sim\sqrt{3}10^{-3}\omega[Hz]K. \tag{3}\]  
Assuming \(\lambda/\lambda_{u}=2\) and using the parameters of the European X-FEL [26] undulators, we derive from Equation (3) a peak electric field around 300 MV/m, which is hard to manage in a microwave waveguide. Accordingly, \(\omega\) being fixed by the waveguide geometrical constraints, the only way to reduce the peak electric field value is to operate at lower K values. Together, Equations (1)-(3) offer the key relations for a quick design of an MU dedicated to FEL operation [10]. These elements, however, are not sufficient to avoid design and conceptual pitfalls. For example, a problem of not secondary importance, such as the evaluation of the detrimental effects of the microwave field inhomogeneities, has been only superficially included in the MU-FEL design. This paper is aimed at filling a gap. Even though undulators based on the use of electromagnetic waves for FEL operation are often \"invoked\", a thorough analysis of how the design parameters should be embedded is still lacking. Therefore, a more adequate approach is necessary to stay on the safe side. In the forthcoming sections, we address the following points:  
1. Write and integrate the equation of motion of electrons in the MU-field. 2. Discuss the properties of the radiation emitted by relativistic electrons moving inside the microwave. 3. Specify the conditions under which the MU can be exploited to drive an FEL either in the oscillator or SASE configuration. We will, in particular, discuss how the inhomogeneities associated with the electron-MU interaction should be included to evaluate the gain reduction effects and state the limits of safe operation.
# Matrix Adaptation Evolution Strategy with Multi-Objective Optimization for Multimodal Optimization
## 3 Variants of CMA-ES and MA-ES Algorithm
### Variants of CMA-ES  
In recent years, many CMA-ES variants have been developed to solve single-objective and multi-objective optimization problems. For single-objective optimization problems, a DE variant with covariance matrix self-adaptation (DECMSA) [31] combines different features of DE and CMA-ES instead of hybridizing these two algorithms simply. In DECMSA, the individuals are sampled from a Gaussian distribution to guide the search direction in the DE. In addition, an enhanced local search strategy based on CMA-ES is designed. In order to improve the classical optimization algorithm's ability of rotational invariance, differential evolution based on covariance matrix learning and the bimodal distribution parameter setting (CoBiDE) [32] utilizes the covariance matrix learning to construct an appropriate coordinate system for the crossover operator, while biogeography-based optimization (BBO) with covariance matrix-based migration (CMM-BBO) [33] utilizes the covariance matrix migration to reduce BBO's dependence on the coordinate system. The differential covariance matrix adaptation evolutionary algorithm (DCMA-EA) [34] incorporated the mutation, crossover, and selection operators of the DE into CMA-ES to compose a hybrid algorithm in order to improve the performance of CMA-ES for solving the optimization problems with complicated fitness landscapes. The bilevel covariance matrix adaptation evolution strategy (BL-CMA-ES) [35] employs CMA-ES at the upper level and lower level to extract the priori knowledge: the search distribution, which significantly reduces the number of function evaluations and improves the efficiency of the algorithm. The Differential Crossover Strategy based on Covariance Matrix Learning with Euclidean Neighborhood (L-covnSHADE) [36] employs covariance matrix learning to establish a coordinate system for the better crossover operator.  
For multi-objective optimization problems (MOPs), multi-objective differential evolution (MODE) with dynamic covariance matrix learning (MODEs + DCML) [37] utilizes the dynamic covariance matrix learning (DCML) to establish a proper coordinate system for the binomial crossover operator. Therefore, the poor performance of MOPs solved by MODE can be significantly improved. Decomposition-based multi-objective evolutionary algorithms (MOEA/D)-CMA-ES [38] integrates CMA-ES into the decomposition-based multi-objective evolutionary algorithms (MOEA/D) to solve the multi-objective optimization problems. The hybrid algorithm MOEA/D-CMA-ES takes advantage of the MOEA/D in multi-objective optimization and CMA-ES in complex numerical optimization. The experimental results show that MOEA/D-CMAES is an effective algorithm for solving complex multi-objective optimization problems.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## 3 Results and Discussion
### Impact of Mediator Addition on TrOC Degradation  
As noted in Section 3.2, of the 30 TrOCs tested, MD-EMBR achieved high degradation (85-99%) for 14 compounds (10 phenolic and 4 non-phenolic compounds) but the degradation efficiency varied widely (40-70%) for the rest of the compounds. To improve the degradation of the latter group,three redox-mediators, namely SA, VA and HBT, were added at 0.25 and 0.5 mM concentrations each in separate runs. Depending on the redox-mediator type and concentration, degradation of phenolic compounds and non-phenolic compounds by the MD-EMBR was improved by 20-30% and 10-50%, respectively (Figure 3) as explained in the following sections.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 4 Results
### Accuracy Assessment  
For the year 2014, we compared the classification results of OCSVM\({}_{2014}\) to our own reference dataset and calculated a confusion matrix (Table 2). We used 25% of the _rice_ and 50% of the _no_rice_ polygons from the reference dataset. From the confusion matrix, we can calculate an overall accuracy of 0.90 and a user's accuracy of 0.90 for the _no_rice_ and 0.89 for the _rice_ class. The producer's accuracy is 0.89 for the _no_rice_ and 0.79 for the _rice_ class. Precision and recall for the _rice_ class are 0.89 and 0.79, respectively, with an f1-score of 0.84. For the _no_rice_ class, precision is 0.90 with a recall of 0.95 and an f1-score of 0.93. The average f1-score for both classes is 0.90, and Matthews' correlation coefficient and the kappa coefficient are both 0.77.  
For the years 2010, 2005 and 2002, we compared the paddy rice area per province derived from our classifications OCSVM\({}_{2010}\), OCSVM\({}_{2005}\) and OCSVM\({}_{2002}\) to the _Sown Area_ values published per year, season and province in China's Statistical Yearbooks. We compared our classified paddy rice areas to the seasonal area with the largest extent for each province. This comparison yields coefficients of determination of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002 (Figure 6a). We also compared our classified paddy rice area to the total sown area of provinces where only one season of rice sowing was reported or one season accounted for more than 95% of the _Total Sown Area_, which is the case for all provinces, except Anhui, Fujian, Guangdong, Guangxi, Hubei, Hunan, Jiangxi and Zhejiang [50; 51; 52]. Coefficients of determination for this comparison are 0.92 for 2010, 0.93 for 2005 and 0.91 for 2002 (Figure 6b).  
For the last accuracy assessment group, we extracted the classified pixel values from OCSVM\({}_{2002}\) at the geographic locations of the agricultural meteorological stations. From a total of 126 AMS point measurements, 80 are located inside OCSVM\({}_{2002}\) pixels classified as _rice_ and 46 are located inside _no_rice_ pixels. From this, we can calculate an overall accuracy of 0.64 for our classification for the year 2002 compared to the AMS point dataset.