esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing a Universal Mirror-mirror Laser Mapping System for Single Event Effect Research
## 2 Materials and Methods
### Verification of the Beam Scan Module (BSM)
#### 2.2.2 Temporal Accuracy of the BSM  
The voltage-driven mirror-mirror BSM system moves a certain number of \"steps\" inside a specific ROI drawn by the user. The number of \"steps\" is defined by the following equation.  
\[Number\_steps=Round\_upwards\left[\frac{Width\_ROI}{step\_size}\right]\times Round\_upwards \left[\frac{Height\_ROI}{step\_size}\right] \tag{1}\]  
where \"_round_upwards_\" means take the closest upwards integer value and the \"_step_size_\" is defined by Equation (2)  
\[step\_size=size\ per\ pixel\times pixel\_number \tag{2}\]  
The \"_size per pixel_\" is determined by the optical limitation of the objective lens and the \"_pixel_number_\" is set by the user.  
After the ROI is drawn and the _step_size_ is set, it is necessary to set the \"_dwell-time_\", the duration time that a laser pulse projected at each step. The \"_dwell-time_\" is mostly set to a value according to the repetition of the pulsed laser source. For example, the repetition of the pulsed laser in use is 10 kHz. Correspondingly, the \"_dwell-time_\" is set to be 100 us, which should project only one laser pulse to the current step in the ROI.  
In the verification testing, a ROI was drawn with the dimensions of \(32.75\times 60.32\) (um); the size per pixel was 1.4 um; one step contains 5 pixels, leading to a step_size of 7 um. As mentioned above, the number of steps in horizontal direction was 5 and 9 in the vertical. Consequently, the total number of steps was 45. With the dwell-time set to be 100 us, there should be 45 pulses observed through an oscilloscope. This process is shown in Figure 6.  
Figure 6: Schematic of the beam scan module (BSM) scanning movement.  
Figure 7 shows the pulses generated in the ROI, where exactly 45 pulses can be observed by an oscilloscope.  
In Figure 7a, exactly 45 pulses can be observed during the entire scanning period in the ROI. Furthermore, due to the time division of 1 ms and dwell-time of 100 us, exactly 10 pulses can be observed inside a single time division. In Figure 7b, after zooming in with a time division of 50 us, a single pulse can be observed for that dwell-time.  
This verification confirms that the BSM has high temporal accuracy. Once a certain ROI was drawn, the number of steps, the track of movement, the number of pulses could be confirmed to be accurate.
## 3 Results and Discussion  
### Evaluation Metrics  
We performed several experiments to train and evaluate our models. In each case, the classification performance was assessed using the following metrics:  
* accuracy: \[ACC=\frac{TP+TN}{TP+FP+TN+FN}\cdot 100\%,\] (1)
* sensitivity (recall, true positive rate): \[TPR=\frac{TP}{TP+FN}\cdot 100\%,\] (2)
* specificity (true negative rate): \[TNR=\frac{TN}{TN+FP}\cdot 100\%,\] (3)
* F1 score (Dice index): \[F1=\frac{2\cdot TP}{2\cdot TP+FP+FN}\cdot 100\%,\] (4)  
where \(TP\), \(TN\), \(FP\), and \(FN\) denote the number of true positive, true negative, false positive, and false negative classifications, respectively.
## 4 Results and Discussion
### Aerodynamic Heat
#### 4.3.1 Aerodynamic Heat Field Distribution at Different Altitudes  
The results of the analysis of the aerodynamic heat field distribution of the radio soundings at different heights are shown in Figure 12, where the blank is the radiosonde, from which it can be seen that the temperature rise in front of the radiosonde is the largest  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Altitude** & **Falling Speed** & **Ambient Temperature** & **Air Pressure** & **Time** \\
**(m)** & **(m/s)** & **(K)** & **(Pa)** & **(s)** \\ \hline
70,000 & 100 & 217.452 & 5.01699 & 0 \\
61,724 & 310.35 & 240.63 & 15.94 & 35.41 \\
60,000 & 304.34 & 245.45 & 20.31 & 40.97 \\
50,000 & 166.31 & 270.65 & 75.95 & 84.23 \\
40,000 & 77.76 & 251.05 & 277.55 & 174.27 \\
30,000 & 35.19 & 226.65 & 1171.95 & 370.02 \\ \hline \end{tabular}
\end{table}
Table 1: State parameters during the fall of the sounding device.  
Figure 11: Velocity curve of the falling radiosonde.  
due to aerodynamic heat. For example, in Figure 12a, the temperature in front of the radiosonde is 290.71 K with an error of +45.264 K. The temperature of the outer wall behind and around the radiosonde is 248.21 K with an error value of +2.76 K. Meanwhile, the temperature of some areas outside the side decreases to 199.92 K, resulting in a significant temperature error of \(-\)45.53 K. At the same time, aerodynamic heat analysis was carried out for the temperature sensor. As shown in Figure 13a, the blank area is the sensor, and the temperature around the sensor is different (Point 1, 292.11 K; Point 2, 274.42 K; Point 3, 281.32 K; Point 4, 281.35 K). In other words, the aerodynamic heat exerted on the temperature sensor is minimal. Therefore, we positioned the temperature sensor at the front of the radiosonde with the Pt thin film end facing backward and symmetrically arranged two temperature sensors to ensure uniform force distribution on the radiosonde.
# Structural Modifications in Epitaxial Graphene on SiC Following 10 keV Nitrogen Ion Implantation
###### Abstract  
Modification of epitaxial graphene on silicon carbide (EG/SiC) was explored by ion implantation using 10 keV nitrogen ions. Fragments of monolayer graphene along with nanostructures were observed following nitrogen ion implantation. At the initial fluence, sp\({}^{3}\) defects appeared in EG; higher fluences resulted in vacancy defects as well as in an increased defect density. The increased fluence created a decrease in the intensity of the prominent peak of SiC as well as of the overall relative Raman intensity. The X-ray photoelectron spectroscopy (XPS) showed a reduction of the peak intensity of graphitic carbon and silicon carbide as a result of ion implantation. The dopant concentration and level of defects could be controlled both in EG and SiC by the fluence. This provided an opportunity to explore EG/SiC as a platform using ion implantation to control defects, and to be applied for fabricating sensitive sensors and nanoelectronics devices with high performance.  
ion implantation; Raman; AFM; XPS; graphene +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
Driven by a strong commitment to improving the reliability and efficiency of IIoT communications, this work explores the adaptation of LoRa technology to the particular requirements of industrial applications. At the heart of our study is a practical experiment involving a Fixed Transmitter (TX) and a Mobile Receiver (RX). The primary focus of this investigation is a comprehensive assessment of LoRa's communication performance in challenging NLoS scenarios, which addresses a notable knowledge gap. By simulating the challenging conditions of industrial landscapes characterized by significant obstacles such as buildings and plants, our experiment aims to bridge the gap in understanding LoRa's real-world performance in challenging environments, where reliable communication is paramount. The contributions of this paper can be summarized as follows:  
* Development and analysis of an open-source prototype of a standard-compliant LoRa Physical Layer (PHY) Software-Defined Radio (SDR) based on GNU Radio. * Simulation and evaluation of LoRa's performance in NLoS conditions, in a noisy mobile environment, using key metrics such as BER, SINR, and data rate, demonstrating its high communication reliability for IIoT. * Exploration and integration of the LoRa protocol into the Universal Software Radio Peripheral (USRP) for IIoT applications, leveraging the versatility of the USRP for communication system development and prototyping to provide a reliable and energy-efficient communication system capable of transmitting and receiving data over varying distances, making it suitable for industrial applications. * A comprehensive evaluation of LoRa technology combining an open-source LoRa PHY SDR prototype, mobility, and performance metrics analysis to contribute to the knowledge base and help operators, system designers, and researchers make informed decisions about deploying wireless communication systems in industrial applications. The rest of this paper is organized as follows: Section 2 discusses various works using LPWAN and LoRa communication technologies. Section 3 provides a detailed exploration of LoRa's physical layer, modulation techniques, use of GNU Radio software version 3.10.7.0, and hardware implementation using USRP N321. Section 4 describes the setup designed to simulate industrial conditions. Section 5 provides a comprehensive insight into the performance of LoRa. The discussion that follows contextualizes our results and provides relevant comparisons and insights. Section 6 highlights the importance of LoRa technology in improving the reliability of IIoT communications in challenging industrial scenarios, in line with the broader context of the IIoT landscape.
## 2 Methodology  
### Measurement Mechanism of LiDAR  
LiDAR is a technology that uses eye-safe laser beams to produce a 3D map of the surveyed environment. LiDAR is applied in a wide variety of industries, such as the automotive industry, infrastructure, robotics, the trucking industry, the industrial industry, mapping, and many more [16; 17]. LiDAR releases pulsed light waves into the surrounding area, and these pulses return to the sensor after bouncing off nearby objects. The sensor determines the distance traveled by each pulse by measuring the time it takes for it to return to the sensor. A real-time 3D map of the environment is produced by repeating this procedure millions of times per second. In this study, an Intel(r) Realsense(tm) LiDAR Camera L515 was used as the LiDAR device (Figure 1). The device has the advantage of being able to simultaneously measure both point cloud and RGB data because it includes both an image camera and an IR camera. The IR camera section comprises an IR transmitter, which fires an infrared laser with a wavelength of 860 nm at the target, and an IR receiver, which detects the reflected laser. The built-in depth camera can collect 1024 \(\times\) 768 point cloud data points and measure distances from 0.25 m up to a maximum of 9 m. The image camera can capture 1280 \(\times\) 720 high-definition RGB data points, and its detailed specifications are shown in Table 1.  
Figure 1: LiDAR equipment used in the experiment.
## 3 Performance Evaluations
### Performance Evaluation Metrics
RMSE is particularly valuable in this context as it captures the model's robustness against large deviations, reflecting the overall predictive accuracy of the KAM prediction model. \[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{t=1}^{n}(A.K_{t}-P.K_{t})^{2}} \tag{9}\]  
By combining these metrics, we achieve a comprehensive view of the model's performance: Accuracy and F1 Score evaluate classification reliability, while MAPE and RMSE assess regression accuracy for KAM prediction. Additionally, a Confusion Matrix visualizesclassification accuracy across the five gait patterns, enabling intuitive interpretation of the per-pattern model performance.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 6 Antibacterial Effect of Chitosan Nanofibers
### Effect of Surface Topography on the Antibacterial Activity  
Using electrospinning and film casting techniques, two samples of chitosan/PVA with the same volume ratio and concentration were created [147]. For the film formation, a solution of 4 wt% hydrolyzed chitosan in acetic acid at 60 \({}^{\circ}\)C was produced. The 10 wt% PVA solution was then produced using a chitosan/PVA ratio of 70:30 [147].  
To enhance chitosan electrospinningning, the crude powder was hydrolyzed in a 50% NaOH solution to increase its DDA and decrease its molecular weight. Because of its non-toxicity, biocompatibility, biodegradability, solubility in acetic acid, and ability to form hydrogen bonds with chitosan through amino or hydroxyl groups, PVA was added to chitosan to improve the solution mixing and formation of stable jets. A voltage of 21 kV and a distance of 15 cm were the optimum electrospinningning parameters for the generation of a beadless structure. The goal of the performed antibacterial assay was to determine the effects of surface nanotopography on the antibacterial activity of materials using two distinct bacterium strains, _E. coli_ and _S. aureus_[147].  
The initial step of colonization and biofilm development is bacterial adhesion to surfaces. Thus, the impact of the morphology on the bacterial adhesion was tested using an identical composition for a film-cast sample (8a) and electrospun sample (8b). Images of the samples are shown in Figure 8. For both bacterium strains, sample (8b) had greater antibacterial activity than sample (8a), indicating that bacteria adhere better to the sample (8b).  
The amount of bacterial attachment has been demonstrated to be influenced by surface roughness, as bacterial cells may detect roughness changes of one micrometer or less [148]. The nanofibrous membrane's enhanced roughness relative to the film (Figure 8) seems to have a favorable impact on antibacterial activity, which is substantially higher in both Gram-positive and Gram-negative strains for sample (8b). Given the micron size of bacteria, it is known that the attachment of rod-shaped bacteria such as _E. coli_ to nanofiber structures with diameters less than the bacterial length causes conformational changes in bacteria as they wrap around each fiber [149]. Consequently, the surface's overall antibacterial action may be enhanced.  
Figure 7: Antibacterial activity of electrospun chitosan/PEO (80:20) nanofibers with different molecular weight against _E. coli_, _S. Typhimuriumium_, _L. innocua_, and _S. aureus_. Reprinted from [146], originally published under a CC-BY 4.0 license.
## 2 Analysis of Forces Acting on Strands during Traveling
### Analysis of Forces on Guide Holes  
The movement of the strand between the cylinder bodies is a complex and multi-span composite motion, making it difficult to model and analyze the strand over the entire span range. At the same time, different strands follow different paths during the twisting process,  
Figure 1: Strand-twisting production schematic diagram.  
and they pass through a different number of guide holes. Therefore, it is a prerequisite and basic for the strand in the overall feeding process to analyze the force between the strand and guide hole under a single span. The arrow of w stands for center axis rotation, as shown in Figure 2. The figure was drawn in the visio.  
When the strand rotates radially along the cylinder, it is subject to an outward movement tendency and is compressed against the inner wall of the guide hole under the action of centrifugal force \(F_{c}\). By dividing the wire strand into small elements, the force analysis can be carried out as follows:  
\[dF_{c}=2 dT\sin(\frac{ds}{2R_{\rm s}}) \tag{1}\]  
where \(R_{\rm s}\) is the radius of rotation, \(ds\) is the length of the strand element, \(dT\) is the tension of the strand element, and \(dF_{c}\) is the centrifugal force. The magnitude of \(dF_{c}\) satisfies the following relationship:  
\[dF_{c}=\rho dsR_{s}(\omega_{r})^{2} \tag{2}\]  
where \(\rho\) is the linear density of the strand element, and \(\omega_{r}\) is the rotational angular velocity. Combining (1) and (2) together:  
\[2 dT\sin(\frac{ds}{2R_{\rm s}})=\rho dsR_{s}(\omega_{r})^{2} \tag{3}\]  
Expanding \(\sin(\frac{ds}{2R_{\rm s}})\) using the Taylor series and omitting higher-order terms yields:  
\[\sin(\frac{ds}{2R_{s}})=ds/(2R_{s})+o((ds/2R_{s})^{3}) \tag{4}\]  
Combining Equations (3) and (4), \(dT\) can be integrated and simplified within the single-span range:  
\[\int\limits_{\varrho}^{T}dT=T=\rho(R_{S}\omega_{r})^{2} \tag{5}\]  
Figure 2: Analysis of the force on strands and guide holes under single span.  
The strand travels through multiple guide holes and spans, and the motion of the strand in each span is assumed to be superimposed on the next one [15]. The initial tension of the strand at the \(\mathrm{J^{th}}\) span can be obtained:  
\[T_{j}=T_{0}+\sum_{j=0}^{j-1}T_{j-1} \tag{6}\]  
where \(T_{0}\) is the pre-tensioning force applied to the strand by the hysteresis brake at the unwinding end. Under the action of centrifugal force, the strand element in a single span presents a certain curvature, but its degree of bending can be neglected compared to the rotation radius \(R_{S}\), approximately as a straight line. The centrifugal force \(F_{c}\) is uniformly distributed within a single span. The friction force between the strand and the guide hole can be calculated according to the following formula:  
\[F_{f}=\mu mR_{s}(\omega_{r})^{2}+(T_{0}+\sum_{j=0}^{j-1}T_{j-1})\cos\frac{ \theta_{r}}{2} \tag{7}\]  
where \(\mu\) is the sliding friction factor between the strand and the guide hole, and m is the strand mass within the full span.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 3 Computational Platforms
Due to the explosion in architectures and systems, cross-tool chain development is also becoming a commonplace practice. Popular tools such as crosstool-NG [153] and Yocto project [154] can develop complete development systems for custom hardware or can modify Linux distribution for the target board. The topic of operating systems and tool chains for embedded development is too vast to be covered here and lies at the heart of smart systemsdevelopment. Another popular approach is to use tools for optimizing the neural networks for embedded systems such as TensorFlow Lite [155], PyTorch Mobile [156], Android Neural Network API [157], Apple OS Core ML [158], Qualcomm Neural Processing SDK [159], and HUAWEI HiAI [160]; these are some examples of the APIs to run and train neural networks on embedded, edge, and mobile platforms. There are several projects, such as uTensor [161], deepC [162], Glow [163], openVINO [164], nncase [165], X-CUBE-AI [166], ONNC [167], TVM [168], and elQ [169], that convert standard neural network models such as the Open Neural Exchange [170] (ONNX) compatible format to C/C++ code for direct deployment on microcontrollers. The myriad of options available to add flexibility to the development life cycle comes at the cost of complexity and compatibility, which tools such as oneAPI [171] aim to address by defining a hardware-independent and vendor-neutral framework for computing intensive tasks. In short, the computing domain has progressed by leap and bounds in multiple dimensions, and a brief review pertinent to the physical node in the smart system is presented, which covers the hardware and software from the development and operational point of view. The final piece of the hardware node is the communication protocols, which will be discussed in another section, but a brief overview is presented to discuss the smart meters' state of the art as a physical node. From the perspective of IoT, the communication protocols could be divided into personal area, local area and wide area networks (PAN, LAN and WAN, respectively). PANs and LANs include ZigBee, 6 LoWPAN, LoRa, 802.11, 802.15.4, RFID, BLE, NFC, Z-Wave, and Sigfox, whereas WAN technologies include cellular, terrestrial, and satellite communications, such as NB-IoT, LTE, 5G, etc. [172; 173]. The protocols are utilized to transfer the data across different semantic layers and for different applications using MQTT, AMQP, CoAP, DNS-SD, and JSON-LD [174].These IoT protocols are mapped to the smart metering infrastructure for the home area network (HAN) and different levels of grid communications. The review of relevant communication protocols is given in Table 8.
###### Abstract  
In this paper, we investigate traffic bottlenecks, a primary cause of congestion that significantly impacts the overall efficiency of traffic networks. To address this challenge, a multi-segment variable speed limit control strategy is proposed to mitigate moving bottlenecks, particularly those on super-long-span bridges. First, an extended macro-traffic flow model, built upon the classic MetaNet framework, is proposed as a state-space model to capture the critical characteristics of long segments, which is a key contribution of this paper. Next, a fast prediction model is developed to forecast traffic flow states in lane-drop bottlenecks with restricted passing capacity over long road segments. Then, a controller leveraging the state-compensation flow model is designed to regulate the future evolution of bottleneck density. Finally, the multi-segment variable speed predictive control (MVSPC) strategy is validated on a simulation platform integrating PYTHON and SUMO, and its performance is compared with both traditional and advanced methods. The results demonstrate that under varying traffic flow levels, particularly in high-demand scenarios, the strategy achieves significant improvements in efficiency, safety, and environmental metrics. These include a 62.44% reduction in waiting time, a 95.32% decrease in potential collisions, and reductions in emissions: 26.4% in CO\({}_{2}\), 14.11% in CO, 26.53% in NO, and 32.90% in NOX. The proposed strategy is particularly effective for long segments, such as super-long-span bridges.  
connected automated vehicles (CAVs); variable speed limit; model predictive control +
Footnote †: journal: Applied sciences  
[PERSON], [PERSON], P.  
Multi-Segment Variable Speed Predictive Control Strategy for Eliminating Traffic Bottlenecks in Emergency Cases on Super-Long-Span Bridges.