esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation-Based Optimization of Transport Efficiency of an Urban Rail Transit Network
## 1 Introduction
### Literature Review and Innovation of This Paper
This paper provides an idea for the calculation and optimization of network transport efficiency by shadow capacity and simulation, which can provide systematic and scientific guidance for improving efficiency. Other scholars can refer to the ideas of this paper and conduct theoretical research on facilities coordination and train operation plans inside the hub, as well as study the connection and coordination between urban rail transit and land transport. It also has certain theoretical significance for the future research of comprehensive transportation systems. It is very limited and unscientific to evaluate the final effect of efficiency only by the index system. No matter the total index or the average load index, it can't show the actual transport effect of the network system. However, the evaluation method based on global optimization is idealized, especially when considering the sharing relationship of the urban rail transit system in the whole transport system. This paper puts forward a theoretical framework of transport efficiency and the shadow efficiency method, explaining the relationship among capacity, passenger flow and efficiency. Shadow efficiency also reflects the different values of the same resource input in different parts of the network and can be used to analyze the rational allocation of resources. A quantitative calculation method of system utility based on \"double time penalty\" is proposed for the first time. Simulation research on the optimization strategy of network transport efficiency is proposed to fully reflect the interaction among various influencing factors, and simultaneously to study dynamic systems from time and space. The feasibility of the simulation experiment method has been proved by the actual case of Beijing subway network. This paper is organized in the following way. Section 2 introduces the calculation method of transport efficiency. Section 3 presents the efficiency estimation method based on simulation. Section 4 discusses the experimental analysis method based on DEA. Section 5 is the conclusion of this paper and suggestions for future research.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 4 Experiment Analysis and Results
### Influence of Flood on L4 for GLONASS Satellites  
#### 4.2.1 Ground Track Repeat Period of GLONASS Satellite  
Different from GPS satellites, the orbital period of GLONASS satellites is approximately 11 h and 16 min. Thus, based on the simplest fraction method [17; 29], the ground track repeat period of GLONASS satellites for the stationary receiver is approximately 8 solar days. In order to demonstrate this phenomenon, the ground track of the GLONASS R24 satellite on DOY 193, DOY 201, and DOY 194, DOY 202 are presented in the view of the sky plot. The width of lines on DOY 201 and DOY 202 is narrower than that of DOY 193 and DOY 194 to make it clear that these two lines overlap almost exactly.  
From Figure 11, we can find that the satellite elevation angle and the azimuth of R24 satellite on DOY 194 perform consistently to that of DOY 202. In addition, it is obvious that the ground track of DOY 193 and DOY 201 is totally different between DOY 194 and DOY 202. This is different from GPS satellites, in which the ground track repeat period is approximately one solar day. Thus, to analyze the influence of flood on L4 for GLONASS satellites, data sets collected on DOY 194, 2021 should be used to compare and analyze.
# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
## 4 Data and Evaluation  
### Dataset and Training Settings  
We construct a specific dataset to evaluate the detection effect of the designed detector. In order to ensure the diversity of the data, the dataset contains 6670 images of 300 pixels \(\times\) 300 pixels actually taken at the working site and in the laboratory. We marked five class: gangue, bolt, stick, iron sheet, iron chain, as a total of 9461 objects. The mean area ratio (MAR) can be calculated by dividing the pixel value of the object in the image by the total pixels of the original image. In order to investigate the detection effect of the TMRC-SSD network on different scale objects, the MAR of each class in our dataset was calculated, as shown in Table 2. By observing Table 2, it can be concluded that the proportion of iron chains is significantly smaller. The r mean area ratio is only 5.06%, which brings great challenges to the detection of small objects. Due to the differences in shooting angle and distance, the same type of foreign object may have a huge difference in area ratio in different images. Therefore, we classify each object according to the area ratio in the image where it is located. Those less than 10% are small objects, greater than 10% and less than 40% are medium objects, and greater than 40% are large objects, the statistical results are shown in Table 3. We used 80% of the images in the dataset for network training and the other 20% for detector testing.  
This dataset is used for network training, which is similar to the end-to-end training strategy in SSD. We set the batch size to 16, and used a stochastic gradient descent strategy to optimize our network. In the first 10k iterations, we set the initial learning rate to 0.001, and reduced it by 0.1 times at 20k and 30k iterations.
## 2 Materials and Methods
### 2-DE Analysis of Saliva  
For 2-DE analysis, a sub-sample of 6 participants (5 women and 1 man) was selected, based on the amount of salivary protein available to allow 2-DE gel analysis. A previously published protocol optimised for saliva samples was used [16]. Briefly, a volume of each saliva sample, corresponding to 250 ug of total protein was aliquotted, desalted, and concentrated using 3 kDa cut-off ultra-filtration microfuge tubes (Nanosep 3K omega, PALL Corporation, New York, NY, USA). The recovered volume (always inferior to 50 uL) from the fraction higher than 3 kDa was mixed with rehydration buffer to a final volume of 250 uL. Thirteen-centimetre pH 3-10 NL IPG strips (Cytiva Europe GmbH--Freiburg im Breisgau, Germany) were passively in-gel rehydrated overnight in a Multiphor Reswelling Tray (GE Healthcare, Chicago, IL, USA) and subsequently focused using the previously described focusing program [22], using a Multiphor II isoelectric focusing system (GE Healthcare). Following focusing, gel strips were equilibrated and then applied on the top of a 12% SDS-PAGE gel (1 \(\times\) 160 \(\times\) 160 mm) for vertical separation, using a Protean II xi cell (Bio-Rad, Hercules, CA, USA) at a constant voltage of 150 V until the end of the run. Gels were stained with CBB-R250 (0.1% in 40% methanol (VWR), 10% acetic acid (VWR)) and de-stained with several washes of 10% acetic acid. The de-stained gels were scanned using the ImageScanner III scanner (Epson, Suwa, Japan) and Labscan software version 6.0. (GE Healthcare, Tokyo, Japan), and the protein profiles were analysed using SameSpots software 4.5 (TotalLab, Newcastle upon Tyne, UK). A first manual alignment of each gel with respect to the reference gel was made, which served as the basis for the subsequent automatic alignment. The detection of spots according to the aligned gels was corrected and those spots in which the labelling was wrong were edited.
## 4 Multichannel PAM4 Amplification Results
Experimentally, increasing the number of co-propagating channels did not change the BER performance at low-input power levels [15], where the BER of the central channel remained the same with no other co-propagating channels and with two other co-propagating channels. In our simulations, for the case of amplifying at higher input powers with significant gain saturation the BER worsens with increasing number of channels. There is a 3 dB reduction in the IPDR for the multi-section SOA when comparing with the amplification of four channels as opposed to amplifying a single channel; and a 2 dB reduction in the IPDR for the single-section SOA when amplifying four channels as opposed to amplifying a single channel. Nonetheless, the performance of the multi-section SOA remains better due to the larger effective saturation power of the multi-section SOA. Also from Figure 9 we notice that there seems to be a cross-over point in the BER performance when the multi-channel BER outperforms the single channel BER; for the single section SOA this occurs at \(-4\) dBm, and for the multi-section SOA this occurs above 0 dBm. The crossover point occurs because the power fluctuations are smoothed out due to the increased number of channels that results in reduced intensity fluctuations on individual channels. Though in comparison with the experimental results for amplifying PAM4 signals in SOA [15] for three channel amplification: the BER performance in [15] is worse than presented here. The channel spacings used in [15] were much greater than those considered here and the discrepancies could be due to the inevitable wavelength dependent gain and gain saturation characteristics of SOAs. Without knowledge of the exact SOA gain saturation characteristics, the channels could be closer to the gain-peak of the SOA and therefore the saturation power would be lower such that different channels will experience a different saturation power. An experimental study for amplifying a single wavelength channel and two wavelength channel systems with only 150 GHz channel separation for various modulation formats (though PAM4 as not one of them) [9] showed one or two dB power penalty when amplifying two channels compared to the single channel scenario, and the simulation results here are in qualitative agreement those multi-channel penalties. Figure 9: _Cont._
# Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network
## 2 Material and Methods  
Before they are fed at the input of the neural network, the data must be subjected to preliminary processing. Images fed in the form of values given to successive pixels are mainly performed with deep neural networks, which require a large base of input data in their learning process. An image 480 \(\times\) 600 px in size fed in a classic pixel-by-pixel version requires a network with a structure of 240,000 neurons in the input layer. However, this problem may be solved, and the learning results may be satisfactory even with a small training database. In the case of this research into damage detection in a conveyor belt, the Python programming language was used to define areas located in close vicinity to each other. Figure 8 shows the borders of such areas.  
Figure 7: Two-layer network.  
In order to avoid providing the network with the entire set of data vectors, the detected areas were used to identify three values describing the surface areas of sub-areas forming the detected area (the surface of the red area, the surface of the green area, the surface of the red area or the surface of the green area, the surface of the red area, the surface of the green area) and three values identifying the number of channels on which the signal related to a given sub-area has been read. In a situation when the detected area consists of one or two sub-areas, the missing sub-area lub liczba kanadow has the surface area or the number of channels on which the failure was detected equal to 0. In effect, a large input vector was replaced with six values. The detectable types of damage to the conveyor belt include: one cord missing, two cords missing, three cords missing, strand/wire cut, one cord partially cut, one cord cut, two cords cut, three cords cut, and belt splice.  
For the so-prepared input data and possible network outputs, the structure of the neural network must comprise 6 neurons in the input layer and 9 neurons in the output layer.
# A 28 GHz 5G Phased Array Antenna with Air-Hole Slots for Beam Width Enhancement
## 2 Design of the Two-Element Antenna Array
This also increases the total magnetic field around the slot which is the left hand side of the Ampere's Law. Since the H field is time-varying, the right-hand side of the Faraday's Law increases and causally induces stronger total E field which is the left-hand side of the Faraday's Law. Eventually, the induced stronger E field around the slot enables the radiation of the complementary dipole to be enhanced. Because of the pattern summing mechanism illustrated in Figure 4b, the HPBW of the proposed array antenna in the elevation plane is improved. This is verified through observing the difference of the intensity of the surface current around the slot with and without the air hole. Figure 6b shows the plot of the surface current (\(y\)-axis) versus the distance around the slot (\(x\)-axis) when it is applied alone and along with the air hole. Figure 5: Realized gain for various values of the slot width (W). Figure 7 shows the radiation patterns of two-element arrays for three cases (i.e., without the slot, with the slot, and with the air-hole slot in the ground) in the YZ plane at 28 GHz. As we can see from the radiation pattern plot, the HPBW of the two-element array applied with the ground slot is increased from 190 degree to 203 degree compared with that without the ground slot. The HPBW is further increased to 207 degree in the YZ plane by introducing an air-hole slot instead of a ground slot.
## 5 Experiments and Results
### 5G Vertical Applications Platform  
The specific details about the 5G vertical applications platform, such as the exact source of their network data (data access interfaces) and the detailed characteristics of various application scenarios (e.g., the number of base stations per scenario), vary depending on the specific implementation, standards, and configurations defined by different operators and technology providers. Generally speaking, network data from 5G dedicated networks can come from several sources, including:  
* **Network equipment:** This includes data collected from base stations, switches, routers, and other hardware elements that make up the network infrastructure.
* **Network management systems:** These systems monitor and manage the operation of the network, and can provide data about network traffic, faults, performance, and security.
* **User equipment:** This includes data from user devices connected to the network, which can provide data on usage patterns, performance, and service quality.  
As for the specific scenarios and the number of base stations involved, this would greatly depend on the needs of the application being supported by the dedicated network. For example, a dedicated network supporting an industrial automation application in a factory might be quite dense with a large number of base stations to provide high-capacity,low-latency communication within a small area. Conversely, a dedicated network for a rural telecommunication service might have a much lower density of base stations covering a larger geographic area.  
To obtain the most accurate and up-to-date information, one may refer to the technical specifications and guidelines provided by the relevant standards bodies (like 3 GPP for 5G), technology providers, and network operators, or consulting with experts in the field.
## 2 Extension of Dynamic Relational Network for Spatiotemporal Interpolation
### Dynamic Relational Network Model for Space Weather Forecasting
In STEREO mission, STEREO-A and B were launched in 2006 into orbits around the Sun that cause them, respectively, to pull further ahead of and fall gradually behind the Earth. The STEREOs are moving on their orbits, so that the distance between two satellites are longer and longer. On the other hand, ACE were launched in 1997 and is placed at near \(L_{1}\) Lagrangian point. The angular separation for STEREO-A, STEREO-B, and ACE, are within \(\pm 25^{\circ}\) in 2008. The angular separation of these three satellites are related to prediction performance of the high-energy electron flux. Figure 1 shows the sensor network based on the dynamic relational network. The sensor network consists of the five sensors: four real sensors and one virtual sensor. The sensors of \(V_{1}\) (STEREO-A), \(V_{2}\) (ACE), and \(V_{3}\) (STEREO-B), which represent the solar wind speed. The sensor \(E\) (GOES-10) represents high-energy electron flux at geostationary orbit. The virtual sensor \(E_{24}\) represents the high-energy electron flux 24 h in advance. The real sensors diagnose its state each other. However, the diagnosis by virtual sensors is different. The virtual sensor for prediction does not diagnose other sensors, while the virtual sensors for interpolation do it each other. In the earlier study [3], the sensor network for space weather forecasting aiming to predict condition on high-energy electrons had been proposed. The proposed model with seven real sensors and single virtual sensor: Three sensors for the solar wind speed, three sensors for north-south component of interplanetary magnetic field (IMF) and the high-energy electron flux and its flux 24 h ahead. However, this paper chooses the four real sensors and the single virtual sensor for space weather forecasting: Three sensors for the solar wind speed, the single sensor for the high-energy electron flux and its flux 24 h ahead. The north-south component of IMF is also the crucial parameter for detecting precursor of changing of the space environment around the Earth. The proposed model does not involve the three real sensors of IMF, because that complicates the model. Therefore, this paper keeps the simplicity of the proposed model so that we are able to consider the main aim of possibility for spatiotemporal interpolation in sensor networks.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 1 Introduction
We then theoretically established the physical model of the interelectrode gap \(d\) and the flat-plate capacitor charging time constant \(\tau\), and designed a minimum-variance self-tuning controller to control the interelectrode gap.
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
## 6 Conclusions  
The future of vehicular networks will probably be driven by increasing demand for capacity for safety and entertainment applications. The next evolutionary step in this direction is to enable V2V communication at 28 GHz. Thus, in this work, the interference analysis of vehicular networks has been performed for the 28 GHz band for V2V communications. We have derived closed-form expressions of PEP and ergodic capacity and validated them by performing extensive simulations. The diversity analysis shows that PEP depends on the number of multipath clusters and channel non-linearity. This work presents guidelines for quantifying the PEP and ergodic capacity of V2V communication at 28 GHz.  
In the future, we aim to look at the integration of sub-6-GHz and mmWave for efficient vehicular communications. In this context, it would be interesting to quantify the impact of channel uncertainties on V2V communications. We anticipate that in the presence of incomplete knowledge of the channel, the performance of vehicular networks would degrade resulting in higher values of PEP. These challenging yet interesting studies would probably be conducted in the future.  
Conceptualization, O.A.S., A.A. and I.K.; Data curation, K.R., A.A.; Formal analysis, O.A.S.; Funding acquisition, K.R.; Investigation, O.A.S.; Methodology, I.K.; Project administration, K.R.; Software, I.K. and A.A.; Writing--original draft, I.K.; Writing--review & editing, O.A.S. and K.R. All authors have read and agreed to the published version of the manuscript.  
This research work is funded by the Manchester Metropolitan University, UK.  
The authors would like to say thanks for the reviewers and editors for reviewing this manuscript.  
The authors declared no conflict of interest.  
Figure 3: Ergodic capacity as a function of \(\bar{\lambda}_{c}\).
###### Abstract  
Peripheral neural interfaces are used to connect the peripheral nervous system to high-tech robotic devices and computer interfaces. Soft materials are nowadays used to build the main structural part of these interfaces because they are able to mimic the mechanical properties of peripheral nerves. However, if on the one hand soft materials provide effective connections, reducing mechanical mismatch with nervous tissues and creating a close contact between active sites and neural fibers, on the other hand, most of them are not mechanically stable during implantation. As a consequence, tungsten (W) microneedles are used to insert soft neural interfaces, because they are able to pierce the peripheral nervous tissue because of their high stiffness. Nevertheless, this stiffness cannot prevent microneedles from local microscopic structural damage, even after successful insertions. In addition, the nature of this damage is not totally clear. Therefore, this work aimed at quantitatively investigating the phenomenological changes of the microneedles' tip shape after insertion into the in vivo peripheral nerves. In particular, a quantification of the interactions between peripheral nerves and W microneedles was proposed through the Oliver-Pharr formula, and the interaction force was found to be directly proportional to the power \(<m>=2.124\) of the normalized indentation depth. Moreover, an experimental correlation between insertion force and the opening tip angle was described together with an assessment of the minimum diameter to effectively puncture the peripheral nervous tissue. Finally, a computational framework was presented to describe the local changes affecting the microneedles' tip shape. This approach was able to detect a bulging phenomenon along with the microneedle tips with a characteristic amplitude of approximately 100 \(\upmu\)m, and a folding phenomenon, with a characteristic mean amplitude of less than 20 \(\upmu\)m, affecting the extreme ending sections of the microneedle tips. These geometrical changes were related to the synergistic action of interaction forces likely resulting in compression and elastic instability of the tip.  
tungsten microneedles; neural interfaces; indentation; peripheral nerves; bulging; folding; Oliver-Pharr formula; compression; elastic instability +
Footnote †: journal: Applied Sciences
# Thermo-Mechanical Properties of PLA/Short Flax Fiber Biocomposites
###### Abstract  
In this work, biocomposites based on poly(lactic acid) (PLA) and short flax fibers (10-40 wt.%) were produced by extrusion and characterized in terms of thermal, mechanical, morphological, and thermo-mechanical properties. Analytical models were adopted to predict the tensile properties (stress at break and elastic modulus) of the composites, and to assess the matrix/fiber interface adhesion. The resulting composites were easily processable by extrusion and injection molding up to 40 wt.% of flax fibers. It was observed that despite any superficial treatment of fibers, the matrix/fiber adhesion was found to be sufficiently strong to ensure an efficient load transfer between the two components obtaining composites with good mechanical properties. The best mechanical performance, in terms of break stress (66 MPa), was obtained with 20 wt.% of flax fibers. The flax fiber acted also as nucleating agent for PLA, leading to an increment of the composite stiffness and, at 40 wt.% of flax fibers, improving the elastic modulus decay near the PLA glass transition temperature.  
poly(lactic acid); flax fibers; biocomposites; predictive analytical model; mechanical properties Article Thermo-Mechanical Properties of PLA/Short Flax Fiber Biocomposites