esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Parameter** & **Sensor Hardware** & **IoT** & **Remark** \\ \hline \multirow{3}{*}{[24]} & Soil temperature, moisture, pH, and NPK & D518B20; FC28; No & \multirow{3}{*}{Amazon Web Services (AWS)} & Sensors are connected to \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{[25]} & Soil temperature, moisture, humidity, and NPK & No information provided & Thing5 peak & Sensors are connected to \\  & & & & \\  & & & & Arduino for IoT communication \\ \hline \multirow{3}{*}{[26]} & Soil temperature, electric & \multirow{3}{*}{JXCT} & \multirow{3}{*}{Authors developed (mobile application)} & Sensors are connected to \\  & conductivity, moisture, pH, and NPK & & & \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{[27]} & Soil NPK & Authors developed NPK & \multirow{3}{*}{Google cloud (sensor)} & Sensors are connected to \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{[28]} & Soil NPK & No information provided for & \multirow{3}{*}{Thing5 peak} & Sensors are connected to \\  & & & & Arduino for IoT communication \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. Thinger.IO. The IoT-based SCADA in [46] is used for migration of the existing SCADA, an open source SCADA called EclipseSCADA, to the cloud. From Tables 1 and 2, it can be seen that the application of SCADA (IoT-based SCADA) for plant monitoring is rare. Most works are implemented using the common IoT platforms or developing web applications for these. The drawbacks of these approaches are that (a) compared with the SCADA HMI, the display in the dashboard of the typical IoT platform is usually limited in appearance; (b) developing a custom dashboard requires a specialist web programmer. The previous discussions motivated us to develop an IoT-based SCADA for use in a plant monitoring system, where the main objectives are to provide a low-cost instrument, easy and fast development, and multi-parameter measurements. The main contributions of our proposed systems are as follows:  
1. Our proposed system uses a low-cost IoT-based SCADA system produced by Xiamen Haiwell Technology [48]. We use the Haiwell IoT Cloud HMI, a small-sized SCADA server equipped with an HMI display that is remotely accessible via the Haiwell cloud. 2. The monitored parameters combine the leaf temperature, air, and soil parameters. Since our proposed system provides more parameters than the existing works in Table 1, the plant condition can be analyzed in more detail.
# Communication Infrastructure Design for Reliable UAV Operations in Air Mobility Corridors
## 4 Discussion  
### Comparative Analysis of Communication Availability in the Air Mobility Corridor  
A key performance criterion for reliable UAV operation within an air mobility corridor is the availability, \(A\), of the communication service at every point along the flight path. To evaluate this, we analyze the channel unavailability, \(U=1-A\), across all four proposed deployment strategies, which correspond to different levels of redundancy.  
The results of this comparative analysis are presented in Figure 21, which shows unavailability as a function of the communication channel load parameter, \(\rho\), using a logarithmic scale.  
This figure reveals a systematic exponential improvement in reliability as redundancy increases. Specifically, the following patterns are observed.  
With \(N\)=1 (non-redundant deployment), unavailability ranges between 10% and 35%, depending on the traffic load. This level is inadequate for any application requiring consistent or safe UAV operation.  
Increasing to \(N\) = 2 already brings about a 10-fold reduction in unavailability, making the system moderately reliable but still sensitive to congestion under high load conditions.  
For N = 3, the system achieves high reliability with unavailability values in the order of \(10^{-3}\), corresponding to 99.9% availability at low loads and 99.5% at higher loads--suitable for demanding UAV missions.  
Finally, N = 4 achieves unavailability below \(10^{-4}\), approaching \"five nines\" reliability. This makes it a strong candidate for mission-critical or autonomous UAV operations where service disruption is unacceptable.  
The near-linear behavior in the logarithmic domain highlights an important engineering insight. Each additional radio station at a point of coverage provides approximately one order of magnitude improvement in communication availability.  
This principle is instrumental for system designers when balancing between infrastructure costs and reliability requirements. While Strategy 4 offers the best performance, Strategy 3 may be optimal in many cases, offering excellent reliability with significantly lower resource demands.
# Fast Object Detection in Light Field Imaging by Integrating Deep Learning with Defocusing
## 2 Methodology  
Figure 1 shows the framework of the proposed method. First of all, the scene that is captured by camera arrays or micro lens arrays are calibrated and parameterized by two-plane parametrization to form a light field. The parameterization of the light field in given in detail in Section 2.1. The captured light field is then used to generate three different kinds of images, including multi-view images, all-in-focus image, and a certain number of focal stacks by digital refocusing that can be processed with highly parallel computation on Graphics Processing Unit (GPU) or even Field Programmable Gate Array (FPGA), so as to achieve high efficient processing and imaging. Details of the digital refocusing is given in Section 2.2. Convolutional neural network (CNN) based detection and defocusing are then conducted to perform object detection and localization in 3D space. It is performed in three steps. In the first step, CNN is used to detect the objects on the all-in-focus image so that the positions of the objects in 2D space are determined. Occasionally, the detected objects may be hindered by something, such as tree. In such case, the detection of the objects may be fail on all in focus image. In such scenario, CNN should be conducted on all the focal stacks to enhance the capability of the detection. In the second step, the depths of the detected objects are evaluated by defocus response that is based the focal stacks. It is emphasized that, instead of estimating the depth of each pixel of the objects, only a depth is estimated for each object in this step since it is sufficient in most of scenarios in object detection and tracking. The details of the used CNN and defocus response are given in Section 2.3. In the third step, if it is necessary, the depths of the detected objects are further optimized via multi-baseline stereo matching, based on the multi-view images. Since the region and a depth are already assigned to each  
object by CNN and defocus response, the efficiency of the stereo matching can be improved from two aspects. Firstly, instead of the dense stereo matching, only the objects that are detected by the CNN are matched. Secondly, the searching space of the disparity is compressed by using the assigned depths in defocus response as initial value.
## 1 Introduction  
Electro fuels (plasma torch, hydrogen) are considered as a possible replacement for fossil fuels in heavy process industries' combustion processes in Sweden, due to a large amount of renewable electricity production from hydro and wind power. It is important to ensure that the replacement of fossil fuels would not worsen both the combustion/heating performance of the sustainable system and pollutant emission, in particular NOx. Due to the complexity of the practical processes, it is often difficult to predict how changes in fuel and process parameters will influence the performance of the plant. Therefore, accurate experimental data, especially from inside the furnace, that provide reliable information on the temperature and concentrations of the major species and pollutants are of great value. At present, optical and laser-based absorption spectroscopy is one of the most promising and rapidly developing technologies applied in thermochemical conversion systems for online and in situ diagnostics [1; 2; 3]. The most important advantages of the optical and laser-based methods are that they are non-invasive, rapid (the millisecond temporal resolution is routinely attainable), low-cost, calibration-free, and capable of detecting in high-temperature and dusty environments with limited optical access. The optical ultraviolet (UV) absorption near 226 nm was successfully applied for NO diagnostics at high-temperature conditions, see for example [3; 4; 5; 6; 7].  
This paper describes the application of the UV absorption (NO concentration and T) and TDL (H\({}_{2}\)O concentration and T) measurement set-ups in trials performed in a 400-kilowatt pilot-scale test facility-ECF designed to mimic a burner port in one of LKAB's straight grate iron-ore maturation machines [8]. The campaign goal was to investigate the effects of the replacement of fossil oil (FO) by electro fuels (plasma torch and H\({}_{2}\)) on energy efficiency and emissions. This paper focuses on demonstrating the performance of non-intrusive set-ups for the characterization of the process parameters in long-term tests and to discuss the benefits and shortcomings of the methods for a better understanding and control of the process. The optical and laser-based sensors were developed earlier by RISE ETC [9; 10; 11] and applied for diagnostics of lab-sale and pilot-scale, up to 150 kW, high-temperature processes. The optical measurements are compared with those from the traditional equipment. The work extends a range of the applicability of the NO UV absorption method for simultaneous diagnostics of NO and T for both T and the NO column density. The maximal temperature in ECF experiments was 200 K higher than that reported in lab-scale research [9] \(\sim\)1350 K, while the maximum NO column density was increased by a factor of 1.8 in the ECF experiments.
## 4 Results and Discussion
### Varying \(C_{\rm nw}\) Coefficient  
The \"near-wall\" coefficient \(C_{\rm nw}\) is designed to control the inner part of BL flows while not affecting free shear flows [21]. Specifically, both the wall shear-stress and wall heat transfer rate (for non-adiabatic walls) increase as this parameter increases (in non-equilibrium flows). Therefore, the surface skin-friction exhibits a larger sensitivity to this parameter than the surface pressure, as is confirmed by looking at the coefficient distributions reported in Figure 13, corresponding to GEKO solutions with \(C_{\rm sep}=1\) and various levels of \(C_{\rm nw}\). According to these results, fixing the default value that is \(C_{\rm nw}=0.5\) represents a good choice for this particular application.  
However, in order to determine whether the previous conclusion about the optimal choice of \(C_{\rm sep}\) was affected by the prescribed \(C_{\rm nw}\) coefficient, a further analysis was conducted by setting \(C_{\rm sep}\) at the suggested default value--that is, \(1.75\)--while varying \(C_{\rm nw}\). By looking at the sectional pressure and skin-friction coefficient distributions drawn in Figure 14, the choice of the default value for \(C_{\rm sep}\) turns out not to be optimal regardless of the \(C_{\rm nw}\) level.  
Figure 12: Normalized sectional pressure (**left**) and skin-friction (**right**) coefficients at \(\eta=0.40\) for different values of \(C_{\rm mix}\) compared to SBES.  
Figure 14: Normalized sectional pressure (**top**) and skin-friction (**bottom**) coefficients at \(\eta=0.40\) (**left**) and \(0.63\) (**right**) for \(C_{\rm sep}=1.75\) and different values of \(C_{\rm nw}\) compared to SBES.  
Figure 13: Normalized sectional pressure (**top**) and skin-friction (**bottom**) coefficients at \(\eta=0.40\) (**left**) and \(0.63\) (**right**) for \(C_{\rm sep}=1\) and different values of \(C_{\rm nw}\) compared to SBES.
## 4 Evaluation
### QKD Runtime Variability  
Figure 11 shows the runtime variability of the QKD key generation phase across 25 independent BB84 protocol sessions. The \(x\)-axis represents the session number, the \(y\)-axis indicates the runtime in seconds, and the values inside each bar indicate the retry loop count to generate a valid key under the QBER threshold. Each session generates a 128-bit key using an identical quantum circuit structure and performs post-processing under the condition that the quantum bit error rate (QBER) remains below 11%. As shown in the figure, the runtime remains relatively stable across sessions. The minimum and maximum runtimes are 3.35 and 6.79 s, respectively, with an average of 4.49 s and a standard deviation of 0.88 s. Most sessions fall between 4 and 5 s, resulting in only a 2.03\(\times\) difference between the fastest and slowest cases. QKD sessions with runtimes of 3 to 4, 4 to 5, and over 6 s completed in 4-5, 6-8, and up to 12 loops, respectively.  
This consistent runtime shows that the QKD key generation latency is not significantly affected by the inherent randomness of the BB84 protocol. Within the full encryption routine of our proposed scheme, the QKD Key generation phase incurs negligible overhead and shows low variability, validating its practicality for repeated use in cloud systems. In addition, to ensure QBER compliance, the key generation loop is retried until the extracted key satisfies the <\(11\%\) threshold. Despite this retry mechanism, the total runtime remains low due to parallel execution across multiple thread workers. This enables fast key agreement with minimal latency, making the QKD Key generation phase suitable for secure large-scale encryption without incurring significant performance overhead.
# A Survey on Malleability Solutions for High-Performance Distributed Computing
## 3 Fault Tolerance for Malleability
### SRS Library and RSS
* MPIX_Comm_shrink: Creates a new communicator with the same characteristics as a revoked communicator. The new one includes only all non-failed processes. * int MPIX_Comm_failure_ack(): Local function to acknowledge all errors in a communicator. If there are errors, from this moment all collective operations will return the error MPIX_ERR_PROC_FAILED. Authors in [16] analyze this approach and show its effectiveness with hardly any degradation in performance in their experiments. These functions can also be used to implement malleability in users' codes. Although ULFM does not provide any specific mechanism other than those found in the native MPI Standard [18] to expand a job; however, shrinking is enabled with the function MPI_Comm_shrink, which terminates a defined number of processes intentionally. In short, if a job has to be shrunk from \(n\) to \(m\) processes, \(n-m\) processes are forced to fail. Then, invoking MPI_Comm_shrink, the processes are removed from the communicator. Listing 2 shows how ULFM can be used to achieve malleability. Notice in the listing that there is no data redistribution helper provided by ULFM, as it is not intended for this task; therefore, the application itself has to include the necessary code to carry out this redistribution. ```
1 intmain(intargc,char**argv){
2 intnump;
3 MPI_Commcomm;
4...//Initializationcode
5 MPI_Comm_size(comm,&numP);
6...//Initializationcode
7 for(..){
8 nodList=get_new_nodelist_somehow();
9//Performresizeonlyifthereisanewmodellist
10 if(nodeList!=NULL){
11 MPI_Comm=aux.com;
12 if(nump>=nodeList.maxProcs){//Expandoperation
13 MPI_Comm_spawn(myapp.bin,nodeList.maxProcs,
14 modeList.names,&aux.comm);
15...//Datredistributioncode
16 exit(0);
17}else{//Shrinkoperation-ULFM
18...//Datredistributioncode
19 kill_NProcess_somehow(numP-nodeList.maxProcs);
20 MPIX_Comm_shrink(comm,&aux_comm);
21 MPI_Comm_free(comm);
22 comm=aux_comm;
23}
24}
25...//ComputeandMPIcode
In [14], the authors also demonstrate the superiority of ULFM malleable applications in front of SCR malleable counterparts (see Section 3.3), as a reconfiguration is performed in less time with ULFM when expanding and shrinking a job.
## 5 Application: IoT Temperature Time Series
### Results  
Once again, we apply the TP-NARX and TP-RLARX models to each alert event in our dataset and then compare the results to the GP-NARX and GP-RLARX models. For each model, we use one of four covariance kernels: radial basis function (RBF), Matern  
Figure 1: Example of an alert event. The black dotted line denotes the time of an alert.  
3/2, Matern 5/2, or Ornstein-Uhlenbeck (OU). _Outdoor (external) temperature_ is the only exogenous predictor used in the experiment.  
Figures 2 and 3 depict the forecasts for the GP-RLARX and TP-RLARX models with RBF kernel on the same alert event. As expected, the point estimates (red lines) are similar for both models; however, the predictive interval (red shaded areas) for TP-RLARX is slightly wider. For the results depicted in Figures 2 and 3, the average difference between the 0.025 quantile and 0.975 quantile for GPRLARX is \(\approx\) 11.76, whereas the average difference between the 0.025 quantile and 0.975 quantile for TPRLARX is \(\approx\) 15.703. The wider predictive interval of TP-RLARX means that our decision on whether a customer has taken appreciable action will be more conservative. Firms wishing to be more conservative in assessing customer behavior or those with noisier time series data might prefer the TP-RLARX model.  
Furthermore, Table 3 shows the root mean squared error (RMSE), symmetric mean absolute percentage error (sMAPE), and continuous ranked probability score (CRPS) [33]  
Figure 3: Forecasts for TP-RLARX.  
Figure 2: Forecasts for GP-RLARX.  
for each combination of model and kernel. Each metric is calculated by averaging the metrics for each alert event within each model combination. In addition, Table 3 also gives CPU times for each model. Overall, we find that the TP-RLARX model using the Ornstein-Uhlenbeck kernel provides the best average RMSE, followed by the TP-NARX using the Matern 5/2 kernel. Furthermore, we find that the GP-RLARX models give considerably worse performance than both TP-NARX and TP-RLARX models, regardless of kernel.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 4 Discussion  
In the current study, we successfully introduce PCCAU-Net, an advanced road extraction model, and subject it to comprehensive performance evaluation using two distinct road datasets from Massachusetts and DeepGlobe. When juxtaposed with leading algorithms like U-Net, ResU-Net, DeepLab V3+, and SDUNet, PCCAU-Net consistently outperforms them in key performance metrics such as precision, recall, and Intersection-over-Union (IoU). The efficacy of individual model components is investigated deeply through a series of ablation experiments. Notably, the Pyramid Pathway Input and DCA modules emerge as decisive factors in performance enhancement. Intriguingly, even a DCA module devoid of function attention contributes to model improvement, albeit less so than the fully equipped DCA, underscoring the critical role of function attention within the DCA framework. In real-world scenarios marked by spectral variations, shadow occlusions, and intricate road structures, PCCAU-Net demonstrates robust generalization capabilities. These claims are substantiated by qualitative results where the model reliably extracts roads, even in settings marred by complex details or significant occlusions.  
In summary, this paper not only advances an efficient and robust algorithm for road extraction, but also corroborates its superiority and practicality through comparative and ablation studies. It thus offers a new, reliable toolset for automated road extraction and potentially for a broader range of remote sensing image analyses. However, the computational complexity of the model affects real-time applications, and in future research we will focus on optimizing the computational requirements while maintaining performance.
# Visual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environments
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Image** & \multicolumn{3}{c}{**Pose Estimation**} & \multicolumn{3}{c}{**CMSSS**} & \multicolumn{3}{c}{**Pose Estimation**} & \multicolumn{3}{c}{**IMU**} & \multicolumn{3}{c}{**Ref.**} & \multicolumn{3}{c}{**Camera Localization (Model Results)**} & \multicolumn{3}{c}{**Distance**} \\
**Number** & **X (m)** & **Y (m)** & **Z (m)** & **X (m)** & **Y (m)** & **Y (m)** & **Y (m)** & **Y (m)** & **Points** (m)** & **X (m)** & **Y (m)** & **Z (m)** & **to (m)** \\ \hline
1 & 486,125.13 & 4,287,166.37 & 154.61 & 486,125.52 & 4,297,164.70 & 1.94 & \(-\)89.9 & \(-\)42 & 1,2 & 486,125.58 & 4,297,165.14 & 153.71 & 1.58 & 0.45 \\ \hline
2 & 486,123.42 & 4,287,203.41 & 154.18 & 486,123.13 & 4,297,207.77 & 3.09 & -90.0 & 1.7 & 3.4 & 486,121.77 & 4,287,203.17 & 152.60 & 2.31 & 3.31 \\ \hline
3 & 486,002.01 & 4,287,136.52 & 153.54 & 486,002.03 & 4,297,134.66 & 2.75 & \(-\)90.0 & -0.5 & 5.6 & 486,002.95 & 4,297,135.90 & 1.18 & 3.57 \\ \hline
4 & 489,096.49 & 4,287,136.24 & 135.38 & 486,051.36 & 4,297,134.20 & 2.63 & -89.9 & \(-\)40.0 & 6.7 & 486,048.50 & 4,287,164.81 & 153.39 & 1.22 & 3.70 \\ \hline
5 & 486,056.71 & 4,287,148.51 & 153.63 & 486,058.61 & 4,297,146.63 & 2.88 & -89.9 & \(-\)02 & 7.1 & 486,055.67 & 4,287,148.86 & 152.57 & 1.52 & 3.68 \\ \hline
6 & 486,121.00 & 4,287,165.43 & 154.28 & 486,120.44 & 4,297,162.25 & 3.24 & -89.9 & \(-\)18 & 1,2 & 486,121.96 & 4,280,165.51 & 154.01 & 1.00 & 3.60 \\ \hline
7 & 486,056.05 & 4,287,112.84 & 154.96 & 4,085,053.9 & 4,297,100.25 & 2.79 & -90.0 & 0.1 & 8.9 & 486,004.86 & 4,237,121.82 & 154.48 & 1.42 & 3.05 \\ \hline
8 & 486,072.06 & 4,287,111.81 & 154.62 & 486,003.75 & 4,297,100.12 & 2.89 & -89.9 & \(-\)19 & 8.9 & 486,004.26 & 4,297,111.86 & 15194.02 & 1.52 & 2.78 \\ \hline
9 & 486,007.32 & 4,287,201.44 & 154.35 & 486,007.58 & 2,457,089.95 & 2.66 & -89.9 & 1.2 & 8.6 & 486,007.11 & 4,297,102.29 & 153.46 & 1.25 & 3.38 \\ \hline
10 & 486,003.14 & 4,287,134.81 & 152.60 & 469,044.35 & 4,297,137.0 & 2.69 & \(-\)89.9 & \(-\)02 & 6.7 & 486,001.54 & 4,287,133.95 & 151.89 & 1.96 & 4.23 \\ \hline
11 & 485,995.03 & 4,287,110.77 & 152.12 & 485,994.08 & 4,297,113.46 & 2.90 & \(-\)89.9 & \(-\)22 & 6.8 & 485,995.96 & 4,287,109.37 & 151.60 & 1.76 & 4.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the tests.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 2 Magnetic Properties
### Magnetic Properties under DC Conditions  
For electrical steel, permeability is a measure of the resistance of a material in opposition to the formation of a magnetic field, while coercivity is a measure of the ability for a ferromagnetic material to withstand an external magnetic field without becoming demagnetized.  
Permeability is defined by the instantaneous slope of the \(B\)-\(H\) curve, and thus is sensitive to induction [1]. For applications under low induction conditions (1 T), at 50 Hz, the grain size of NGO electrical steels has a strong influence on the corresponding permeability [10; 11]. Grain growth during recrystallization affects the amount and distribution of desirable magnetic textures. Under high induction conditions (1.5 T), the effect of texture on permeability and magnetic flux density is more significant than that of grain size.  
Furthermore, coercivity and permeability are inversely proportional [12; 13]. It is widely known that a larger grain size results in higher permeability and lower coercivity, which is also the cause for a decrease in hysteresis loss. To elaborate, grain boundaries may delay and impede the movement of the domain wall. However, a larger grain size also leads to a larger domain size, which in turn increases the core loss, especially anomalous loss [14]. Thus, an optimum grain size results in a minimum core loss. [PERSON] [15] described the optimum grain size (\(G_{\text{sOP}}\)) as follows:  
\[G_{\text{sOP}}=(\frac{c_{2}\rho}{B^{2-q}t^{2}f^{1/2}})^{2/3}, \tag{7}\]  
where \(c_{2}\) is an experimentally determined constant, \(B\) is the magnetic induction, \(\rho\) is the resistivity, \(t\) is the sample thickness, and \(f\) is the operating frequency. [PERSON] experimentally determined that \(q=1.6\)[16].