esa-sceva/satcom-chunk-collection huggingface dataset:
# Modifications to ArduSub That Improve BlueROV SITL Accuracy and Design of Hybrid Autopilot
## 2 ArduSub Alterations
### Hardware and Software Limitations  
Out of the box, the R1, R2, and R3 versions of the BROV2 come equipped with a Pixhawk and the R4 version (those sold after 7 June 2023) comes with a Navigator [26], both of which are flight management units (FMUs). Regarding hardware connections, onboard the FMU are input and output serial ports, sensors, and pulse width modulation (PWM) output connections for accessories and motor electronic speed controllers (ESCs). Onboard the R1-R3 version, the Pixhawk FMU handles the pilot inputs, sensor processing, and state estimation on a STM32F427 system on a chip (SoC). The Pixhawk SoC has a 180 MHz CPU, 256 KB of RAM, and a 2 MB flash drive which the ArduSub firmware is stored on [27].  
Navigator FMUs on R4 versions act as a hardware-on-top augmentation of the Raspberry Pi 4 companion computer, giving FMU computations direct access to the much more powerful single-board computer.  
Perhaps in consideration of the lean technical specifications of the Pixhawk and other lightweight FMUs that may be utilized on marine vehicles, the ArduSub source code was written with efficiency in mind [17]. That is to say, certain software limitations stem from hardware limitations. The autopilot libraries indicated with the 'AP_' filename prefix are built using custom math functions and primitive data types, performing the bulk of computations with three element vectors, three-by-three matrices, and a limited selection of linear algebra operations. While this implements an Extended Kalman Filter on a Pixhawk, it must do so in roundabout ways to circumvent a more extensive matrix math library that would require larger processing overhead. In the ArduSub source code, this manifests as frequently breaking apart calculations into smaller pieces, like separating linear and angular dimensions into two three-dimension vectors, using program loops that iterate over the elements, and then recombining pertinent values with summation and scaling.  
The key challenge in developing ArduSub is operating within the existing software framework because external libraries cannot be used.
# Analysis of Grinding Flow Field under Minimum Quantity Lubrication Condition
## 4 Experimental Verification
### Grinding Temperature Measurement  
In this study, the machine tool used for grinding was the Carver S600A. Its maximum spindle speed is 18,000 rpm. The maximum working travels of the x, y and z axes were 600 mm, 500 mm, and 300 mm, respectively. The workpiece size was 15 mm \(\times\) 15 mm \(\times\) 70 mm and its material was maraging steel 3J33. The experiment used CBN grinding wheels and the UNI-MAX MQL equipment. The type K thermocouple is connected to the LAB view signal acquisition system with an NI-9351 thermometer. The diameter of the two wire cores of the thermocouple is 0.255 mm. But they are different materials with one core made of nickel chromium and the other made of nickel silicon. It could obtain the grinding  
Figure 16: Droplet size test equipment.  
Figure 15: Droplet trajectories at the different compressed gas pressures. (**a**) P = 2 bar; (**b**) P = 4 bar; (c) P = 6 bar.  
temperature of the workpiece surface. The detailed experiment equipment is illustrated in Figure 18.  
As displayed in Figure 19, the workpiece was divided into two parts named workpiece 1 and workpiece 2. They were connected by screws. At the end of the workpiece, there is a groove, where the thermocouple was installed. The thermocouple wire was clamped between workpiece 1 and workpiece 2. In addition, polyimide film was placed between the thermocouple and the workpiece. It was used for insulation and isolation between the two poles of the thermocouple. During grinding, there would generate high temperature in the grinding area. At the same time, there would be severe squeezing between the grinding wheel and the workpiece. Then, the thermocouples are connected and welded to form thermocouple nodes through insulation and isolation, thus forming a closed loop. Ultimately, the grinding temperature could be measured.  
Figure 19: Schematic diagram of thermocouple installation.  
Figure 17: The diameters of droplet SMD.  
When the MQL nozzle is positioned at an angle toward the wheel (at approximately 10-20\({}^{\circ}\) to the workpiece surface), the effect of the cooling and lubrication of MQL would be better [22]. So, the value of nozzle angle is set as 15\({}^{\circ}\) and the value of droplet deposition distance is set as 40 \(\upmu\)m.  
The cooling and lubrication effect of MQL mainly depends on how well the grinding fluid penetrates the air boundary and enter the grinding zone. That is, it depends mainly on the compressed gas pressure P, the flow rate of the grinding fluid Q and the grinding wheel velocity \(v_{s}\). The grinding depth \(\alpha_{e}\) and the workpiece feed rate \(v_{w}\) mainly influence the heat of the grinding fluid passing through the air boundary layer. Hence, the grinding depth and the feed rate are set to the constant values of 0.01 m/s and 8 \(\upmu\)m, respectively. The other detailed parameters are listed in Table 3.
## 5 Plasma Precursors
### Other Noble Gases  
Like argon, other noble gases are chemically inert, making them primarily suitable for purely physical plasma cleaning processes where chemical reactions are not desired. While xenon and krypton can be used, they are significantly more expensive than argon or helium; xenon, in particular, also suffers from limited availability [113].  
Recombination mechanisms, which neutralize charged species, play an important role in plasma decay. The rate of recombination increases drastically as atoms grow larger and possess more electron shells. For example, the recombination rate of helium is approximately 100 times lower than those of krypton and xenon [19].  
Regarding neon, stable plasmas have been observed, such as those generated at pressures of 100 Torr in microwave discharge systems [115]. Neon is also commonly used in lighting (neon tubes) and other glow discharge systems [113]. However, despite its availability and ability to form plasmas, no evidence of neon being utilized in plasma cleaning processes was identified in the reviewed literature.  
Table 15 presents the grading of the parameters relevant to noble gas plasmas to outline noble gases' suitability for use in cleaning plasmas.  
All noble gases are non-reactive and good for physical bombardment plasmas. Xe and Kr should be better-suited than argon for kinetic bombardment processes, since their ions are heavier, with Xe\({}^{+}\) (131 amu) > Kr\({}^{+}\) (84 amu) > Ar\({}^{+}\) (40 amu) > Ne\({}^{+}\) (20 amu). Xenon therefore has the best parameters. However, it is important to note that Ne, Kr, and Xe are rare and very costly when compared with Ar. This means that they would only be applicable for specialist applications, such as hard material etching and precision milling. They are not used for general-purpose plasma cleaning. Neon provides less efficient physical sputtering than argon because of its lower atomic mass, which is why argon is favoured for noble gas plasma cleaning. The score is with respect to xenon and krypton [116].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Summary** & **Parametric Score** \\ \hline Removal of Organic Contaminants Through Chemical Reduction and Oxidation & Noble gas particles do not react chemically with organic contaminants, especially at the low energy levels used in plasma cleaning systems. & \\ \hline Removal of Oxides Through Chemical Reduction & Noble gas particles do not react chemically with metal oxides. & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Grading of parameters for other noble gas plasmas.  
Neon could potentially be used for physical plasma cleaning, but argon is significantly more effective (due to higher mass leading to better sputtering) and cheaper. There is no compelling technical or economic advantage to using neon over argon for the standard physical plasma cleaning applications where noble gases are employed. Krypton or xenon might be chosen for specific, niche applications where the absolute highest bombardment is critical, the added cost is acceptable, and potential lead frame damage is managed or tolerated.
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
## 2 Materials and Methods
### ADR Techniques
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Setting Number**} & **Modulation** & **Setting** & \multirow{2}{*}{**Master Timeout**} & \multirow{2}{*}{**Device Timeout**} \\  & **Type** & **Configuration** & & \\ \hline
0 & FSK & 300 kbps & 0.1 s & 0.4 s \\
1 & FSK & 200 kbps & 0.1 s & 0.4 s \\
2 & FSK & 115.2 kbps & 0.1 s & 0.4 s \\
3 & FSK & 57.6 kbps & 0.1 s & 0.4 s \\
4 & FSK & 19.2 kbps & 0.1 s & 0.4 s \\
5 & FSK & 9.6 kbps & 0.1 s & 0.4 s \\
6 & LoRa & SF = 6 & 0.1 s & 0.4 s \\
7 & LoRa & SF = 7 & 0.2 s & 0.4 s \\
8 & LoRa & SF = 8 & 0.25 s & 0.8 s \\
9 & LoRa & SF = 9 & 0.4 s & 1.6 s \\
10 & LoRa & SF = 10 & 0.7 s & 2.8 s \\
11 & LoRa & SF = 11 & 1 s & 4 s \\
12 & LoRa & SF = 12 & 3 s & 12 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The settings and timeout values used for adaptive data rate (ADR). mode, (2) Go to setting, (3) Exit ADR, (4) Ping, and (5) Acknowledgment (Ack), or No Acknowledgement (Nack). \[S_{next}=ceil(-0.0002(RSSI)^{3}-0.0751(RSSI)^{2}-8.7614(RSSI)-343.76). \tag{3}\]  
Binary search is the most efficient technique when converging to lower settings but struggles at the higher settings. This is true because the device must fail more often at the higher settings in order to converge there. Incremental search is more efficient when a higher setting is required because it only fails once, but is slower to converge to a lower setting because it must communicate on every setting. The RSSI optimal search method improves upon incremental search and is best overall at higher settings because the search process is eliminated. However, the additional overhead of obtaining an accurate RSSI value reduces its performance. Further analysis of the ADR method including acknowledgement techniques, starting parameter settings, and the breakdown of transmit and receive time are assessed in detail in [19].
## 4 Application
### Clinical Application
#### 4.1.2 Assessing Patients with Chronic Pain  
[PERSON] et al. [124] used imaging photoplethysmography to assess patients with chronic pain, particularly neuropathic pain, using a monochrome camera. Another study by [PERSON] et al. [125] also employed the iPPG method to find new biomarkers of migraine patients using a monochrome camera.
# The Impact of Incorporated Nonwovens on the Surface Roughness of Sport Pitches
## 1 Introduction
Noncontact measurements of soil surfaces are performed by photometers, infrared sensors, ultrasonic sensors, laser technics and satellite radar [13]. The major disadvantage of the first-mentioned contacting instruments is the deformation of the soil surface during the measurement. Noncontact measurements are widely used, but they require more technical and expensive equipment. Furthermore, the resolution accuracy of the height in airborne measurement methods is not sufficient, or measurement errors occur due to the turf [15]. In the present study, the surface of an experimental sport pitch needs to be evaluated. Because a standard soccer field has a size of 4000 to 10,000 m\({}^{2}\), the measurement would be time-consuming if using a pin meter. Therefore, in the present study, the measurements were performed by a measurement frame. It carries contact and noncontact sensors. A feeler wheel, an ultrasonic sensor and a laser pointer were attached to the measurement frame. The use of laser measurement methods with static apparatus for determining soil surfaces is widely known, but the use on turf-covered areas by using continuous measurement methods is a new approach [16]. There are many indexes or methods to compare the roughness of a soil surface. Following [PERSON] et al. [17], the indices are divided into amplitude, spacing and hybrid parameters. The amplitude parameter describes the vertical characteristics and the spacing parameters measure the horizontal characteristics of the surface deviations. The hybrid parameters are a combination of both [17]. Amplitude parameters are the most common. The surface roughness of turf sports fields is described by the parameter of maximum roughness. This value is based on the depth gauge. It is the maximum deviation at one point from a straight line formed by two maximum points of the measured profile. If several measurements are taken on one sports field, the arithmetic mean of this maximum roughness is used to describe the evenness [2]. Therefore, the root mean square was used in the presented study to describe the changes of roughness on the experimental sport pitch. The main purpose of the present study was to evaluate the impact of incorporating a nonwoven into a sport pitch in relation to the surface roughness.
# Wind Field Characteristics of Complex Terrain Based on Experimental and Numerical Investigation
## 4 Numerical Simulation Results
### Comparison between Wind Tunnel Test and Numerical Simulation  
Based on the numerical simulation, the flow conditions in three control directions were carried out in the wind tunnel test, and the wind speeds of five measuring points were monitored. The positions of the measuring points are as shown in Figure 2. These measuring points are distributed in areas with different topographic characteristics on the island. P1 and P3 are located in a relatively flat area of the model, and the distance is relatively close. P2 and P5 are located on the hillside, P4 is located in the internal valley. Among them, the elevation of P5 is the largest and the elevation of measuring point P1 is the smallest.  
Figure 10 is the streamline diagram of each measuring point under three control wind directions. It can be seen from the figure that the state of air flow under different inflows is quite different. Compared with 90\({}^{\circ}\) and 270\({}^{\circ}\), the air flow fluctuates the most after entering  
at 40\({}^{\circ}\). At the same time, there are also great differences in the air flow at the measuring points in different areas.  
The profile of flow velocity U is normalized by the average velocity U\({}_{\rm r}\) of the inflow to reduce the influence of simulation and test errors: U/U\({}_{\rm r}\). Figure 11 shows the velocity results of the numerical simulation and wind tunnel test at the height of 104 m at each measuring point. It can be seen from the figure that the errors of the two results are different, but the values are mainly within 20%. Under this error, it is feasible to use numerical simulation for comprehensive qualitative analysis. The accurate analysis depends on the wind tunnel test, which greatly improves the accuracy of the research [45].  
Figure 10: Streamline diagram at measurement locations. (**a**) 40\({}^{\circ}\) (**b**) 90\({}^{\circ}\) (**c**) 270\({}^{\circ}\).
## 4 Experiments and Analysis
### Experiment IV: Comparisons with Other Domain Adaptation Methods  
To further evaluate the performance of our newly proposed framework, two other open access datasets of ISPRS Postdam semantic segmentation dataset (abbreviated as Postdam) [45] and the other WHU aerial building dataset are chosen as the source and target domains, respectively. The Postdam dataset consists of aerial orthorectified color imagery with a spatial resolution of 0.3 m. The other WHU aerial building dataset includes aerial images with a spatial resolution of 0.075 m covering in Christchurch, New Zealand. Four recently proposed adversarial learning-based domain adaptation methods of the DAugNet, DATA (Domain Adaptive Transfer Attack) [29], JPRNet (Joint Pixel and Representation level Network) [18], and FDANet (full-level domain adaptation network) [10] are selected for the comparisons. In the experiment, we divide the Postdam dataset into the training and validation sets in a ratio of 8:2 and use them to train the UNet pre-trained model in our AD-DAF framework. A total of 50 images are randomly selected from the WHU aerial dataset for cross-domain building extraction. In addition, F1 and IoU are chosen as the evaluation metrics.  
Table 4 provides the comparison results from the source domain of the Postdam datasets to the target domain of the WHU aerial building dataset by different methods. As can be seen from this table, our F1 values are lower than the FDANet and DATA methods and higher than the DAugNet and JPRNet methods, while our IoU values are only lower  
Figure 7: Some building extraction results on WHU Satellite Dataset I. The white area indicates the building footprint. (**a**,**d**) are the remote sensing image. (**b**,**e**) are the extraction results of our framework with the UNet model. (**c**,**f**) are the ground truth.  
than the FDANet method and better than the other three methods. The experimental results show that our AD-DF framework is comparable to some of the recent domain adaptation methods based on adversarial learning, but they also have some shortcomings with individual methods. However, the adversarial learning-based domain adaptation methods require a large amount of unlabeled data in the target domain to be retrained in concert with the labeled data in the source domain, which makes it necessary to retrain every time when the target domain changes. In contrast, our AD-DF framework only needs to be trained once based on the labeled data in the source domain to satisfy the requirements of multiple target domains and does not require a large amount of unlabeled data in the target domain. This again demonstrates the effectiveness of our anomaly detection and reclassification process.
# Metamorphic Testing of Relation Extraction Models
## 3 Approach
### Applying Metamorphic Testing to RE  
RE is a fundamental task of NLP which aims to identify the relation between two entities in a given context. Formally, let \(\mathcal{R}\) be an RE model. Suppose that \(s\) is a sentence and \(e_{h}\) and \(e_{t}\) are two entities (the head and tail entities, respectively) involved in \(s\). \(\mathcal{R}\) takes a triplet of \((s,e_{h},e_{t})\) as an input and yields the prediction relation \(r\) based on a predefined relation set; that is, \(r=\mathcal{R}(s,e_{h},e_{t})\). For example, consider the sentence \"[PERSON] was born in Honolulu\", with a head entity \"[PERSON]\" and a tail entity \"Honolulu\". An RE model predicts the relation between them as \"per:city_of_birth\". Note that the prediction of  
Figure 1: A motivating example. The head entity is marked in blue, and the tail entity is marked in red.  
requires the understanding of \(s\) as well as the roles of \(e_{h}\) and \(e_{t}\) in the context described by \(s\), which is thus non-trivial.  
In this paper, we apply MT to evaluate RE models. An overview of the approach is presented in Figure 2. We first identified a list of MRs based on the necessary and expected properties of the RE task. Each MR was defined as \(MR_{i}=(t_{i},r_{i})\), where \(t_{i}\) is a transformation operation to be conducted on the source input and \(r_{i}\) is the corresponding output relation. Then, we constructed a set of MRs based on MRs; that is, for a source input \(e_{i}\) satisfying the \(MR_{i}\) condition, we used the transformation \(t_{i}\) to obtain the follow-up input \(e_{i}^{\prime}\). After that, we ran each MG (\(e_{i}\), \(e_{i}^{\prime}\)) on RE models and collected the prediction results as source and follow-up outputs. Finally, we checked the source and follow-up outputs against relevant \(r_{i}\) values. If \(r_{i}\) is violated, then failures of the RE model are revealed. Specifically, in this study, three categories of transformation operations were employed in our method, namely replacement, swap, and combination. We present the concrete transformation operation and corresponding output relationship of individual MRs in Table 1.
# Neural-Network-Based Quark-Gluon Plasma Trigger for the CBM Experiment at FAIR
###### Abstract  
Algorithms optimized for high-performance computing, which ensure both speed and accuracy, are crucial for real-time data analysis in heavy-ion physics experiments. The application of neural networks and other machine learning methodologies, which are fast and have high accuracy, in physics experiments has become increasingly popular over recent years. This paper introduces a fast neural network package named ANNAFLES developed in C++, which has been optimized for use on a high-performance computing cluster for the future Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR, Darmstadt, Germany). The use of neural networks for classifying events during heavy-ion collisions in the CBM experiment is under investigation. This paper provides a detailed description of the application of ANNAFLES in identifying collisions where a quark-gluon plasma (QGP) was produced. The methodology detailed here will be used in the development of a QGP trigger for event selection within the First Level Event Selection (FLES) package for the CBM experiment. Fully-connected and convolutional neural networks have been created for the identification of events containing QGP, which are simulated with the Parton-Hadron-String Dynamics (PHSD) microscopic off-shell transport approach, for central Au + Au collisions at an energy of 31.2 A GeV. The results show that the convolutional neural network outperforms the fully-connected networks and achieves over 95% accuracy on the testing dataset.  
artificial neural network; multi-layer perceptron; convolutional neural network; heavy-ion experiment; compressed baryonic matter experiment; quark-gluon plasma +
Footnote †: journal: Physics Letters B  
1 Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany 12 Institute of Computer Science, J. W. Goethe University, 60325 Frankfurt am Main, Germany 2