esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Numerical Approach
#### 2.2.5 Boundary Conditions and Grid  
The accuracy of the numerical simulations strongly depends on the quality of the computational mesh. Choosing the right domain size has a significant impact on the accuracy of the solution and the computational cost. Figure 3 illustrates the computational domain and mesh structure for the vertical flow around the model. The upper surface of the computational domain acted as the inlet boundary with fluid velocity, located at a distance of \(L\) away from the hull, whereas the lower surface was the outlet boundary with fluid pressure, located at a distance of \(3L\) from the hull. The left, right, front, and rear surfaces were considered solid walls, all of which were located at a distance of \(2L\) from the hull.  
The computational mesh adopted a shearing-type structure, with five mesh layers assigned to the hull boundary layer. The size of the first layer, \(y+\), was approximately set as 1, and the mesh size of the hull body was considered to be 8 mm. The entire area around the hull was refined with a mesh size of 16 mm, while protruding areas, such as the hull and stern rudder, were locally refined with a mesh size of 16 mm. The initial number of meshes was taken as 1.06 million.
# Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
## 4 Anomaly Detection and Identification of Satellite Telemetry Data Based on Pseudo-Period  
Phase-plane analysis can obtain the shape characteristics of the data in the time domain from a global perspective, avoiding the influence of data values. This approach also has a high computational speed and is robust with respect to different data types. Based on the principle of phase-plane analysis, the length of the analyzed data directly affected the phase-plane trajectory analysis results. Therefore, in the anomaly identification of the satellite telemetry data, the pseudo-period was taken as the basic analytical unit, and the anomaly identification was achieved by comparing the differences between the data in each pseudo-period and the sample data, as mentioned in Section 1. In this paper, an identification method for the satellite anomalous telemetry data was proposed based on the pseudo-period. Figure 9 shows the flow chart of the proposed method.
## 2 Method
### Setup  
The setup for the impulse response measurements is illustrated in Figure 3 for a piano under construction. In the presented approach the piano is replaced by a harpsichord. An acoustic vibrator excites the instrument at the termination point between the bridge and a string for each key. Successive microphone array recordings are carried out in the near field to sample the sound field at \(M=1500\) points parallel to the sound board.  
In addition to the near field recordings, the microphone array samples the _listening region_. The head of the instrumentalist will be located in this region during the performance (ear channel distance to keyboard y \(\approx\) 0.37 m, ear channel distance to ground z \(\approx\) 1.31 m for a grown person). The location is indicated by black dots in Figure 4.  
A lightweight piezoelectric accelerometer measures the vertical polarization of the transverse string acceleration \(h(\kappa_{a},t)\) at the intersection point between string and bridge for each of the \(A=62\) keys. This is not illustrated in Figure 3 but indicated as brown dots in Figure 5. The acceleration measured by the sensor is proportional to the force acting on the bridge. Details on the setup can be found in [32; 33]. Alternatively, \(h(\kappa_{a},t)\) can be recorded optically, using a high speed camera and the setup described in [34; 35], or it can be synthesized from a physical spectrum-string model [36]. The string recording represents the _source signal_ that excites the harpsichord.  
Figure 4: Depiction of the radiation keyboard. A regular grid of loudspeakers is installed on a board. The board has the shape of the harpsichord sound board. Microphones (black dots) sample the listening region in front of the keyboard.  
Figure 3: Measurement setup including a movable microphone array (in the front) above a soundboard, and an acoustic vibrator (in the rear) installed in an anechoic chamber. After the nearfield recordings parallel to the sound board, the microphone array samples the listening region of the instrumentalist in playing position.  
Measuring string vibrations isolated from the impulse response measurements of the sound board adds a lot of flexibility to the radiation keyboard. We derive impulse responses for the loudspeaker array of the radiation keyboard. This radiates all frequencies the same way the harpsichord would do. Consequently, any arbitrary source signal can serve as an input for the radiation keyboard. In addition to the measured harpsichord string acceleration \(h(\mathbf{\kappa}_{a},t)\), the radiation keyboard can load any sound sample, such as alternative harpsichord tunings, alternative instrument recordings, or arbitrary test signals.  
Figure 4 illustrates the radiation keyboard. A rigid board in the shape of the harpsichord sound board serves as a loudspeaker chassis. A regular grid of loudspeakers is arranged on this chassis. The radiated sound field created by each single loudspeaker is recorded in the listening region.
# A Data Processing and Distribution System Based on Apache NiFi
## 5 Research and Results
Furthermore, within the module group responsible for data loading, the maximum queue size was reduced from 1 GB to 200 MB to save RAM. On the other hand, within the group responsible for data processing, the limit of cached FlowFiles was increased from the default of 10,000 to 30,000. The proposed improvements are an original contribution of this publication. Individual settings were chosen experimentally and iteratively to achieve error-free system operation with the highest possible performance. A similar optimization process was performed in the aforementioned commercial 5G environment on the components of the system that handled the relations between objects. This was required due to the following properties of the source data in the production environment. Typically, one server had four optical transceivers and six twisted-pair ports that generated events for the system. Furthermore, a single virtualization hypervisor could run multiple virtual machines, and each of the virtual machines could host multiple containers. As a result, one FlowFile could even generate up to 20 new FlowFiles. This became an issue during full resynchronization of the data because the entire Kafka topic was loaded in a matter of seconds, which would flood the processing path. As a countermeasure, we increased the size of queues from the default of 10,000 to 100,000 or 200,000 depending on the particular case. Figure 8: The logical scheme of the experiment. All tests were run on virtual machines hosted by the type 2 hypervisor in Oracle VirtualBox version 6.1.26. Each was assigned two AMD Ryzen 9 5900X processor cores at an average clock speed of 4.4 GHz, 4256 MB of RAM at 3600 MHz, and 60 GB of storage space (NVMe PCIe 3.0 drive). The test and result sets were stored in a folder shared among all hosts (NVMe PCIe 4.0 drive). We used Ubuntu Linux 20.04 LTS as an operating system. We monitored the following parameters: the load average (average load on the Linux system), CPU utilization percentage, RAM occupancy, and processing time for both a single file and the entire dataset. In consecutive scenarios, the clusters consisted of a number of nodes varying from 1 to 5 (with a step of 1). The rationale was that scalability is a critical issue for making Apache NiFi usable in optical networks that support 5G/6G networks due to the possible deployments of new sites that may be composed of numerous optical devices reporting operational parameters. Adding nodes can be the only way to handle the additional load. Detailed results are presented for scenarios with one, three, and five nodes in a cluster to analyze the performance as a function of cluster size. We repeated every experiment eight times to ensure statistical credibility. In general, the results are presented in graphs, and the numerical values in Table 3 are presented with 98% confidence intervals. However, due to the high variability of the system load parameters, the confidence intervals for experiments with more than one node in a cluster were extended to 68%.
## 7 Results and Discussion
### Static PM Approaches on the Friction Welding Use Case  
The PM scheduling approaches described in Section 6 offer a significant advantage in some use cases, as they only rely on the data on the time between failures. In Figure 5, we illustrate these data for both training and testing instances of the friction welding use case. During the training phase, we use the distribution of the time between failures to define a replacement strategy, which is then applied to the testing data to evaluate the associated costs. It is noteworthy that the distribution of training instances varies from that of testing instances. This suggests that using a fixed value for the time between interventions may not be as cost-effective for the testing data as it was for the training data.  
Figure 6 displays the costs obtained when considering various static maintenance intervals for each case. It is important to note that the minimum time between failures in the training data is 119, while the MTBF is 608.82. Setting the time between interventions \(T_{R}\) to values lower than 119 results in a conservative PM approach (PMC), which avoids all failures but may under-exploit the useful life of friction welding tools, leading to economic inefficiency. On the other hand, using the MTBF as the replacement interval allows for higher exploitation of the tools' life cycle, but it may not necessarily be the best interval since a considerable number of failures may occur, resulting in higher costs. It is noteworthy that setting \(T_{R}\) to a value lower than 119, which is the minimum observed in the training set, does not guarantee that all failures are avoided for the new instances. For this reason, in cases where safety is a major concern, the adopted value may be even more conservative to avoid all failures. Since these are not concerns for the friction welding use case, we focus solely on the economic costs of maintenance.  
In Case A, the optimal \(T_{R}\) is closer to the minimum time between failures compared with Case B, as shown in Figure 5(a). Here, a conservative approach yields more interesting results, as \(C_{c}\) significantly outweighs \(C_{p}\). However, in Case B, where the costs are more balanced, the optimal \(T_{R}\) is closer to the MTBF, as can be seen in Figure 5(b). The most widely used PM approaches, such as PMC or PMMTBF do not represent the optimal solution economically for different scenarios, which highlights the necessity of having a more flexible PM scheduling strategy.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Case A** & **Case B** \\ \hline \(C_{c}\) & 500 & 200 \\ \(C_{p}\) & 100 & 100 \\ \(C_{os}\) & 10 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Costs for two different cases.  
Figure 5: Box plot of the performed cycles until failure for the friction welding use case. Note that outliers are not plotted.
## 2 Materials and Methods  
### Experiment Site and Design  
The field experiment was carried out at Xiaomeng research field (35\({}^{\circ}\)40\({}^{\prime}\) N, 116\({}^{\circ}\)41\({}^{\prime}\) E), Shandong province, China and National Key Laboratory of Wheat Improvement from 2022 to 2024. The study area is characterized by the representative environment of the NCP, where experiences a temperate, semi-humid, and continental monsoon climate. It has an annual average temperature of 12.9-13.6 \({}^{\circ}\)C and average annual precipitation of 500-800 mm. The soil properties (0-20 cm) prior to the start of the experiment were 18.65 g kg\({}^{-1}\) soil organic matter (SOM), 1.31 g kg\({}^{-1}\) of total nitrogen (TN), 110.00 mg kg\({}^{-1}\) hydrolysable N,48.57 mg kg\({}^{-1}\) Olsen phosphorus (P), and 207.30 mg kg\({}^{-1}\) available potassium (K). The meteorological conditions in the region are shown in Figure 1.  
The experiment was a split-plot design with four N input levels as main plot factor and two N management practices as sub-plot factor (Figure 2). The main plot treatments had four application rates of N (0, 150, 210, 270 kg N ha\({}^{-1}\)), named N0, N1, N2, and N3, respectively. The sub-plot treatments comprised two N management practices, including traditional fertilization method of farmers (T) and fertigation (F). Manual surface broadcast (the common farmer practice) was adopted as the traditional fertilization method. The other was to inject the dissolved urea solution into micro-sprinkling hose equipment. Before planting, phosphorus pentoxide and potassium oxide were applied at 150 kg ha\({}^{-1}\) each, along with 50% of the pure N, topdressing other N fertilizers at jointing stage. Urea, calcium superphosphate, and potassium sulfate were selected for N, phosphorus, and potassium fertilizers, respectively. Seeds of wheat cultivar Yannong 1212 were planted. The irrigation measures were carried out during the jointing and antisens stages, and the irrigation amount for each treatment was 60 mm. Among them, furrow irrigation was adopted by traditional fertilization treatment, and the specific N application and irrigation practices are shown in Figure 2. The irrigation, fertilizer, cultivation, and tillage were kept essentially the same operations during all experimental years.  
Figure 1: Meteorological conditions during the growth stage of wheat.  
Figure 2: The design of field plots and N management practices.
## 4 Conclusions  
Having an environmentally friendly and green technology are an important aspect in overall development these days. In line with this, an approach of using coir fibre treated in marine clay soil stabilization was investigated. A positive finding of this research study has proven that existence of combination action among the lime and alkaline activation on soft clay soil has overcome the problems in stabilizing. From the experiment work, the summarize detailed conclusions are as follows:  
* Inclusion of fibre in treated soil for lime and alkaline stabilized soil increases the compressive strength, tensile strength and indirect tensile strength test for both chemical agents.
* The inclusion of fibre in the treated soil enhances the post-peak behaviour at the different curing period of 7, 28 and 90 days.
* There is an important pozzolanic reaction that occurred after 28 days curing period for treated soil with lime and alkaline activation compared to the untreated clay soil.
* Stabilization on soil using reinforce alkali activation shows higher strength compared the utilization by lime for all curing periods. The noticeable is clearly seen after 90 days of curing periods. The interfacial interlocking of treated soil and fibre shows a good bonding compared to the untreated soil.
* Analysis of FESEM and EDX shows that, reaction occurs between the treated soil with lime and alkaline activation contributes to denser soil. The reaction also seems to bind the soil particles around and strengthen the soil.
* Cementitious reaction products of calcium- silicate hydrate (C-S-H) and calcium aluminium silicate hydrate (C-A-S-H) for lime and formation of SI-O-Al and Si-O-Si bonds for alkaline activator shows that interaction between fibre and treated soil contribute in enhance the behaviour of reinforced mixtures.  
Conceptualization, F.A.K.; Data curation, F.A.K.; Formal analysis, F.A.K.; Investigation, F.A.K.; Methodology, F.A.K.; Project administration, F.A.K.; Resources, F.A.K. and H.N.; Supervision, H.N., B.K.H. and V.A.; Validation, H.N.; Writing--original draft, F.A.K.; Writing--review & editing, H.N., B.K.H. and V.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Energy, Science, Technology, Environment and Climate Change (MESTECC) under project number no 06-01-04- SF 2387.  
The author would like to acknowledge financial support from Ministry of Energy, Science, Technology, Environment and Climate Change (MESTECC) under project number no 06-01-04- SF 2387.  
The authors would like to declare that no conflict interest.
# Cryobiotechnology of Plants: A Hot Topic Not Only for Gene Banks
## 3 Pathogen-Free Plant Material
### Cryotherapy
#### 3.2.2 Merits and Demerits  
Compared to other methods for pathogen eradication in plants, cryotherapy of shoot tips has the following advantages: (i) easier handling with meristens due to the lack of correlation between the size of the shoot tip and pathogen eradication rate [49; 70]; (ii) high efficiency of pathogen eradication [49; 71]; (iii) no need for special and expensive equipment, and (iv) it is easy to handle a large number of samples [36; 49]. The major limitation of the wider application of the cryotherapy to produce pathogen-free plant material is the genotype- or often also cultivar-dependent response of plants to cryo-treatment. This means that each cryoprotocol needs to be developed and optimized for every single plant species or cultivar [68; 72]. Although the risk of somaclonal variation during short-term cryotherapy is minimal, it is important to verify the genetic stability of regenerated plants [72].  
Figure 3: Schematic diagram of shoot tip cryotherapy. HC = healthy cells, IC = infected cells, SC = survived cells, KC = killed cells.
## 2 Baseline Algorithm, SAR-SIFT: A SIFT-like Algorithm for SAR Images  
Synthetic Aperture Radar (SAR) images exhibit inherent speckle noise due to their unique imaging mechanism, rendering conventional SIFT feature algorithms unsuitable for SAR images. The SAR-SIFT feature algorithm is specifically proposed to address this issue, encompassing the detection of key feature points and improved computation of local descriptors. The SAR-SIFT feature algorithm introduces a novel gradient calculation method to overcome the robustness challenges posed by speckle noise. This gradient is employed in several steps as the SIFT algorithm to adapt to SAR image processing.  
SAR-SIFT is a processing framework tailored for SAR image characteristics, primarily encompassing the following steps:
## 4 Results
### Model Performance Metrics
#### 4.2.2 Regression Analysis of G3  
To complement the binary classification results, we performed an additional analysis using the final grade (G3) as a continuous outcome variable. We applied linear regression, Random Forest regression, and XGBoost regression to predict the exact final grade. Table X presents the performance of these models in terms of the mean squared error (MSE) and R2 score. The regression results confirm that G1, G2, absences, and failures remain the most influential predictors of final performance. While regression models allow for more personalized interventions by identifying students across a spectrum of performance, we observe that the relative predictive importance of variables is consistent across both tasks. The choice between binary and continuous outcome modeling thus depends on the intended practical application: binary for early risk flagging; regression for detailed academic advising.
# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 3 Classification of IR Radiation Intensity Sequence Based on IRRNN
### Structure of IndRNN
#### 3.1.1 Analysis of IndRNN Structure  
This section mainly explains the gradient back propagation of IndRNN and how it solves the problem of gradient vanishing and explosion. For gradient back propagation of each layer, the gradient of the IndRNN can be calculated independently for each neuron because there is no interaction between the neurons in one hidden layer [19].  
The output of the \(j\)th neuron is calculated without ignoring the deviation, \(h_{j,t}=f\Big{(}\mathbf{W}_{j}^{HI}\mathbf{x}_{t}+w_{j}h_{j,t-1}\Big{)}\). Assuming that the objective function to be minimized at time \(t\)' is \(V_{j}\), when the gradient is propagated back to time step t, we have:  
\[\begin{array}{ll}\frac{\partial V_{j}}  
RNNs. In order to solve the gradient explosion and vanishing problem over time, we only need to adjust the exponential term \(w_{j}^{t^{\prime}-t}\prod\limits_{k=t}^{t^{\prime}-1}f_{j,k+1}^{\prime}\) to the appropriate range.  
In order to maintain long-term memory in the network, the current state (time step t) can still effectively influence the future state (time step t') after a large time interval, so the gradient at time t' should also be effectively propagated to the time step t. By assuming a minimum effective gradient \(\epsilon\), a range of recurrent weights of IndRNN neurons can be obtained to maintain long-term memory. Specifically, in order to maintain the memory of the t'-t time step, it can be obtained according to Equation (10).  
\[\left|w_{j}\right|\in\left[\sqrt[t^{\prime}-t]{\frac{\epsilon}{\prod_{k=t}^{t ^{\prime}-1}f_{j,k+1}^{\prime}}},+\infty\right]. \tag{13}\]  
In order to avoid the vanishing of the gradient of the neurons, the above constraints should be satisfied. To avoid gradient explosion problems, the scope needs to be further constrained to  
\[\left|w_{j}\right|\in\left[\sqrt[t^{\prime}-t]{\frac{\epsilon}{\prod_{k=t}^{t ^{\prime}-1}f_{j,k+1}^{\prime}}},\sqrt[t^{\prime}-t]{\frac{\gamma}{\prod_{k=t}^ {t^{\prime}-1}f_{j,k+1}^{\prime}}}\right], \tag{14}\]  
where \(\gamma\) is the maximum gradient value without explosion. For commonly used activation functions, such as ReLU and tanh, their derivatives are not greater than 1, i.e., Especially for ReLU, the gradient is 0 or 1. Considering that short-term memory is important for network performance, the constraints on recurrent weight ranges using the ReLU activation function can be relaxed to \(\left|w_{j}\right|\in\left[0,\sqrt[t^{\prime}-t]{\gamma}\right]\). When the recurrent weight is 0, the neuron uses only information from the current input without retaining any memory information in the past. In this way, different neurons can learn to keep memories of different lengths.
# Detection of Welding Defects Tracked by YOLOv4 Algorithm
## 3 Algorithm Improvement  
### k-Means++ Clustering  
The original YOLOv4 anchor boxes are well suited for the Visual Object Classes (VOC) dataset; however, aluminum alloy weld defects represent small target objects. To enhance both the training efficiency and prediction accuracy of the entire model, we employ the k-means++ algorithm to re-cluster the X-ray weld defect dataset, using the resulting clusters as the initial anchor boxes. Compared with traditional k-means [23], k-means++ [24] optimizes the initial cluster center selection method to ensure that the distance between initial cluster centers is maximized, thereby achieving a superior clustering performance. The input image resolution is set to 416 \(\times\) 416 pixels. Considering the varying sizes of different types of defects, we determine nine sets of anchor box dimensions: (300,29), (15,160), (135,24), (64,74), (41,39), (28,38), (51,20), (26,25), and (15,19). These anchor boxes are assigned to three feature layers of different scales: (13 \(\times\) 13), (26 \(\times\) 26), and (52 \(\times\) 52).