esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors
## 4 Materials and Methods
Then, the user can examine the signal and process it by applying the fast Fourier transform to the induction motor current signature to obtain the frequency spectrum and to locate the fault-related frequency components. Additionally, the users can export the raw data at any time after capture to apply further processing to the data provided in this virtual tool. With the processed signal, the user can learn in the left side panel to detect indications in the signal that reveal the presence of the fault, as can be seen in Figure 6D. Raw data were taken from real benches and experimental data [42; 43] from a real experimental bench, used to evaluate different failure conditions in the induction motor and gearing. Each fault condition was individually evaluated under different frequency values that are programmed in the frequency inverter (12 Hz, 30 Hz, 60 Hz). The user has a frequency inverter available to adjust the frequency, where the frequency to be simulated can be selected at any time. The duration of the data is 30 s, where the first 10 s correspond to the start-up transient (acceleration ramp from 0 to 10 s), and the remaining 20 s belong to the steady state of the motor. The users can remain at this level as long as desired and perform the necessary tests and comparisons between different types of failure to consolidate their knowledge. * _Detect induction motor faults:_ At this level, the user will have to apply the knowledge acquired and should be able to detect which of the possible failures (broken bars, gradual wear, misalignments and unbalances) are randomly generated in the motor arrangement. The process is the same as the one used in practice, but this time without the detailed help panels. The process starts with data acquisition (Figure 7A)and its processing, and according to this data, the user will have to decide what type of failure the system is simulating by comparing it to a signal from a normal component (Figure 7B). Figure 5: Introductory tutorial. (**A**) Device for the measurement of electrical signals. (**B**) Clamp accessory. Figure 6: Analyzing induction motor faults. (**A**) Panel for fault selection. (**B**) Exploded view of the object. (**C**) Device for the measurement of electrical signals. (**D**) Panels displaying measured signals to diagnose the type of fault. Figure 7: Analyzing induction motor faults. (**A**) Device for data acquisition. (**B**) Panel showing the processed data.
## 4 Technologies to Support Contextualization  
To support contextualization as summarized in the requirements above, information modelling and formal semantics play a central role. In the following sections, we argue that technologies are needed to build and interconnect formal models. These models form the basis for sampling and integrating process data, as well as for connecting the current situation with historical information. Figure 1 provides an overview of the technical concepts and technologies, their links to the functional requirements, and their interconnections.  
Before we delve into the technologies, two clarifications are due. First, the present article does not focus on the contents of formal models. If technologies are to enable an automated use of models, a prerequisite is that these models exist and their contents allow operators to answer the right questions. Therefore, a first step is to understand the details of the production process. This knowledge is often insufficient, either because it is unavailable in principle or because its elicitation from domain experts is a major effort. However, instead of discussing the contents or elicitation methods of formal models, we ask how they can be worked with once they are available. Second, it is important to clarify that most technologies presented in this article are not handled by operators. Instead, they support programmers by enabling an automated use of plant and process knowledge that needed to be looked up and connected manually in the past. Due to the effort associated with that, many promising forms of operator support have not been implemented. Thus, the technologies introduced here do affect operators, but indirectly, by ensuring that the information they need can be provided in a cost-efficient and flexible way.
## 3 Mother's Day Storm: 10-11 May 2024  
### Observed Solar Wind and Geomagnetic Indices  
An exceptionally strong geomagnetic storm occurred during the night from 10 to 11 May 2024, with auroras observed around the world [2; 3; 4]. This was the strongest storm of the last twenty years with a minimum Disturbance Storm Time index of Dst \(<-400\) nT (see bottom panel of Figure 3). Due to the varying speeds and quick succession of different coronal mass ejections (CMEs) coming from the active region NOAA 13664, several of them merged and interacted as they travelled through the interplanetary medium, leading to enhanced effects in the Earth's space environment [1]. These CMEs were associated with the X2.2 flare on 9 May and three X1 flares on 8 May.  
The characteristics of the solar wind when it reached 1 AU, obtained from OMNI, are shown in Figure 2. The bulk velocity of the solar particles reached more than 1000 km/s (see Figure 2, 2 nd panel), and peaks of high temperature \(>10^{6}\) K were observed (panel 4). The high-density peak (panel 1) at the arrival of the hot plasma shows the apparition of a shock.  
The pressure (panel 3) combines the density and velocity effects and determines the position of the magnetopause. The downward peak of the Z component of the Interplanetary Magnetic Field Bz (panel 5) shows a southward direction that can explain the strong answer of the terrestrial magnetic field [35].  
The arrival time of enhanced solar plasma at 1 AU with regard to density, velocity, and pressure is 10 May 2024 at 17:07 UT. It directly caused the sudden commencement of the strong geomagnetic storm (see Figure 3), followed by the main phase, when the Dst index decreased down to a minimum value of \(-412\) nT (see panel 3) in the night of 11 May, and the planetary geomagnetic activity index Kp increased to the maximum value of 9 (panel 1). The geomagnetic activity indices Kp, Ap (in nanotesla) that reached 400 nT (see panel 2), and Dst (also in nT) are illustrated in Figure 3, together with the F10.7 solar _radio flux_ at 10.7 cm (panel 4) and the intensity of the Lyman alpha line in the solar spectrum (panel 5) that are daily solar activity indices.  
The bottom panel of Figure 3 illustrates the Dst index from 1 January 2000 to 31 January 2025. The two analyzed events are indicated by the red vertical lines. This panel clearly shows that the last event with Dst \(<-400\) nT appeared in November 2003 (known as the Halloween Day storm [1]) and that the intensity of the geomagnetic storm of October 2024 was not seen since November 2004.  
Figure 2: Solar wind density n (**top**), bulk velocity v (**2 nd panel**), pressure P (**3 rd panel**), proton temperature T (**4 th panel**), and the southward component of the Interplanetary Magnetic Field Bz (**bottom panel**) from 1 to 24 May 2024, observed by OMNI at 1 AU.
## 3 Results and Discussion
### Response Surface Plot  
Response surface plots were generated for pairs of factors, keeping the third factor constant at its zero level. Thus, the effects of the factors influencing the flavonoid extraction yield in the ATPS are indicated by the response surface plots (Figure 4). These plots provide a method of visualizing the relationship between a response and test variable. Figure 4a shows a contour plot of the flavonoid extraction yield at different PEG (\(X_{1}\)) and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) concentrations (\(X_{2}\)) while the ATPS solvent/solid ratio (\(X_{3}\)) is held at zero. The extraction yield increased with an increase in the concentration of PEG1500 (\(X_{1}\)), and the same trend is observed in Figure 4b. An increase in (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) resulted in a decrease in the flavonoid yield (Figure 4a,c), and the mutual relationship between the ATPS solvent/solid ratio (\(X_{3}\)) and the concentration of PEG1500 (\(X_{1}\)) can be seen in Figure 4b. In the studied concentration range, the extraction yield was influenced by the ATPS solvent/solid ratio and PEG1500, with increases in both these factors leading to an increase in the flavonoid yield. The maximum flavonoid extraction yield was obtained with a high concentration of PEG1500, and ATPS solvent/solid ratio factors corresponding to an intermediate (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) concentration. According to the gradients of the response surface plots (Figure 4) and the value of Prob \(>F\) in Table 3, the significances of the independent variables on the extraction yield decreased in the following order: ATPS solvent volume/leaf mass ratio (\(X_{3}\)) \(>\) PEG concentration (\(X_{1}\)) \(>\) (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) concentration (\(X_{2}\)) [27].
# Effect of Slot Opening Width on the Air-Gap Magnetic Field of a Direct Drive Permanent Magnet Motor
## 2 Analytical Model
### Air-Gap Flux Density Analysis of DDPMM with 24 Poles and 27 Slots  
Figure 11 shows the air-gap magnetic field distribution of the DDPMM with 24 poles and 27 slots and its harmonic components at different slot opening widths. The harmonic contents and their air-gap magnetic field amplitudes were reduced. The air-gap magnetic field harmonics in position 1 mainly included the third harmonic. The value of the fifth harmonic was very small and the amplitudes of the other harmonics were almost zero. Figure 11d shows that the second, third, and fifth harmonics are the main factors that affected the magnetic field distribution. In position 3, the air-gap flux density was only affected by the second harmonic. The harmonics in Figure 11h mainly consisted of the third, fifth, and seventh harmonics.  
Figure 10: Air-gap magnetic field analysis of the DDPMM with 16 poles and 18 slots at different positions: (**a**) magnetic field distribution at position 1; (**b**) harmonics at position 1; (**c**) magnetic field distribution at position 2; (**d**) harmonics at position 2; (**e**) magnetic field distribution at position 3; (**f**) harmonics at position 3; (**g**) magnetic field distribution at position 4; (**h**) harmonics at position 4.
## 3 Results
### Services Provided by the Novel PHR Application for Emergency Situations: FirstER
#### 3.1.2 Data Provided by FirstER  
Patients' ED records (name, gender, age, diagnosis, vital signs, hospital visited, visiting date and time, etc.) coded by the NEDIS method were integrated among the three data-providing tertiary hospitals. Medication information coded by the EDI mapped with different drug codes from each hospital was successfully extracted. Twenty-nine items most frequently referred to by emergency physicians were selected for the most frequent test categories (Table 2). These data extracted from the hospital information system constituted a \"core dataset\" to be sent to the private cloud.  
Figure 2: The functional structure of FirstER. The main functions for patients were categorized into registration, management of health records and emergency ID, download and view, disclosure of the PHR-tethered EMR of the hospital, and setting of the degree of information disclosure. (ID: identification, PHR: personal health record)
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 3 Congestion-Aware Routing Algorithm  
Wireless sensor networks have the advantages of having a large number of nodes, low power consumption and cost requirements, and self-organization and scalability features. Therefore, they can be used at the end of local networks as an effective complement to other networking methods [27]. In an information acquisition system, due to the continuous power supply, the WSN's sensor node will not die of energy failure. However, it has specific requirements for packet loss and delay, both of which will affect business performance. Therefore, the design of efficient routing algorithms to meet the relevant provisions is critical for WSN applications. In this paper we take network congestion control as the primary purpose, consider reducing network energy consumption and delay, and design an active congestion control routing algorithm.  
To simplify the WSN model, we make the following reasonable assumptions:  
* Many sensor nodes are randomly placed in a certain area, and all nodes are immovable after deployment;
* All sensor nodes are homogeneous, have routing and data acquisition function;* A sensor node can estimate the distance to the source node based on the strength of the received signal;
* The cache queue length is null. Therefore, packets enter the cache queue using the mean of the first-in-first-out (FIFO).
# Boundary-Aware Concurrent Queue: A Fast and Scalable Concurrent FIFO Queue on GPU Environments
## 3 BACQ Proposal
### Smooth Progress Guarantee Based upon Available Space  
According to [PERSON]'s definition [8], an implementation is considered _progressing_ or _non-blocking_, if it guarantees that some thread will complete an operation within a finite number of steps, even though an individual operation may starve. In other words, while a specific operation might not progress, the system ensures that some operations will. In the context of concurrent queues, an enqueue(x) operation might fail to progress at the tail due to insufficient available space, causing the operation to either starve or perform an empty transition. When this happens, the thread may unnecessarily retry the fetch-and-add (FAA) operation, further increasing contention on the head and tail pointers. To address  
Figure 4: Leader thread is selcted from only active threads (threads in gray box).  
Figure 5: Throughput of CAS and FAA operations in a contended scenario (8 billion simultaneous requests).  
this issue, BACQ checks the available space before incrementing the tail pointer and ensures the other enqueue requests are complete within a finite number of steps.  
Since BACQ operates at the warp level, ensuring sufficient space exists for all active enqueue requests is crucial before incrementing the tail pointer. As shown in Listing 2, Line 10 checks whether there is sufficient available by evaluating the condition:  
\[(tail-head)+ae>N\]  
where \(ae\) represents the size of the active enqueue request group, and N is the total size of the queue. This condition is true when there is not enough space to accommodate all active enqueue requests, and we must calculate a safe size for the group of threads attempting to enqueue. This is carried out by computing:  
\[guaranteeSpace=N-(tail-head).\]  
If there is enough space, we proceed with the size of the active thread group. In Line 16, any thread within that active group whose rank exceeds the available space is skipped to avoid unnecessary contention and failed enqueue operations. This approach ensures that we only allow threads with guaranteed space to proceed.  
On the other hand, a dequeue:x operation can fail to make progress if it encounters an invalid state where the head pointer exceeds the tail pointer (i.e., head > tail). In this scenario, the dequeuer will perform an empty transition and return an EMPTY_QUEUE result, which leads to wasted throughput. To avoid this inefficiency, BACQ checks whether the following condition is met:  
\[head+ad>=tail\]  
where \"ad\" is the active dequeue request group size (as shown in Listing 3 Line 10). When this condition is true, there are not enough elements in the queue to fulfill all active dequeue requests. We compute the guaranteed space for the dequeue operation as:  
\[guaranteeSpace=(head+ad)-(tail+1).\]  
This ensures that only valid dequeue operations proceed, avoiding unnecessary transitions and optimizing throughput with an overall finite number of steps.
## 3 Model Training and Testing
### ReUnet Model  
In various deep learning feature extraction networks, whether for classification, detection, instance segmentation, or semantic segmentation, the primary objective is to deeply extract features. Therefore, the encoder section's primary framework is a deep neural network, designed to extract image features. Building upon the UNet architecture, this study incorporates residual blocks to further enhance the network's representation capabilities and training efficiency [38].  
The core idea of the residual block is to introduce shortcut connections, allowing the input to be directly passed to the output, thereby forming a \"residual connection\". Specifically, the output of the residual block is further expressed as follows:  
\[y=F(x)+x \tag{1}\]  
In this context, \(x\) represents the input and \(F(x)\) denotes the output obtained after passing through two \(3\times 3\) convolutional layers followed by the ReLU activation function.  
In this study, residual blocks have been primarily incorporated into the encoding and decoding sections of the Unet model. Each downsampling unit, composed of two convolutional layers and one pooling layer, has its convolutional layers replaced by residual blocks, as indicated by the blue arrows in the figure. The same procedure is applied to the upsampling operations (transposed convolutions). The integration of residual blocks not only preserves critical information from the images but also improves the efficiency of network training. Moreover, it enhances the model's performance when dealing with high-dimensional and complex features.
## 2 Materials and Methods
### ECG Databases
#### 2.1.2 PTB Diagnostic ECG Database  
For the test purposes, clinical ECG signals included in the PTB Diagnostic ECG Database [42] from the PhysioNet archive [43] were used. Originally, the database contained 549 conventional 12-lead resting ECGs, which were digitized with a sampling rate of 1000 Hz, resolution of 0.5 \(\upmu\)V/LSB, and 16-bit ADC, including healthy controls and various pathologies for myocardial infarctions (MI), arrhythmias, heart blocks, myocardial hypertrophy, etc. Most of the recordings, however, contained noises induced during the primary ECG acquisition stage and, therefore, were unusable for benchmark testing of filters. In such testing, the filter could potentially suppress the genuine noisy components, which would be read as generating distortions. Therefore, before the study, the noise content of the PTB Diagnostic ECG Database was automatically estimated in the low-frequency band (selective for baseline wander) and high-frequency band (selective for powerline and muscle noises), and only low-noise recordings were considered eligible for general benchmark testing of filters. The low-frequency band was estimated at the output of a linear pass-band filter (0.3-0.5 Hz) to not exceed 25 \(\upmu\)V in all 12 ECG leads. The high-frequency band was estimated at the output of a linear-slope finite impulse response (FIR) filter (30-48 Hz)--order 182, equiripple (negligible in pass and stop bands)--that does not produce ringing as an effect after steep QRS. The maximal tolerated high-frequency content outside QRS complexes was limited to 12.5 \(\upmu\)V in all 12 ECG leads. The eligible best quality records numbered 40, all used in the filter benchmark study, including: s00641 re (diagnosis: anterior MI), s01271 re (inferior MI), s02191 re (inferior MI), s01371 re (antero-septal MI), s03131 re (antero-lateral MI), s04111 re (inferior MI), s02911 re (healthy control), s02921 re (healthy control), s0006_re (diagnosis: n/a), s0154_re (palpitation, coronary heart disease, arterial hypertension), s03011 re (healthy control), s03251 re (healthy control), s03661 re (congenital complete AV block), s03741 re (healthy control), s0490_re (healthy control), s03081 re (healthy control), s0433_re (anterior MI), s0449_re (infero-lateral MI), s0452_re (healthy control), s0455_re (inferior-lateral MI), s0458_re (healthy control), s0483_re (healthy control), s0460_re (healthy control), s0462_re (healthy control), s0465_re (healthy control), s0466_re (healthy control), s0471_re (healthy control), s0478_re (healthy control), s0485_re (hybrid-phy, arterial hypertension), s0487_re (healthy control), s0491_re (healthy control), s0495_re (inferior MI), s0502_re (healthy control), s0504_re (healthy control), s0529_re (diagnosis: n/a), s0531_re (healthy control), s0533_re (healthy control), s0534_re (healthy control), s0552_re (healthy control), s0546_re (dysrhythmia).
# Forewarning Model for Glacial Lake Outburst in Southeast Tibet
## 3 Results
### Establishment of an Assessment Model for Glacial Lake Susceptibility  
In this study, the susceptibility evaluation model was established using MATLAB with X\({}_{1}\), \(X_{2}\), \(X_{3}\), \(X_{4}\), \(X_{5}\), \(X_{6}\), and \(X_{7}\) as independent variables and C as the dependent variable. Before establishing a logistic regression model, the evaluation factors need to be analyzed for collinearity (Table 2). Two commonly used important diagnostic methods were employed, namely, the variance inflation factor (VIF) and tolerance (TOL). When the variance inflation factor (VIF) is less than 10 and tolerance (TOL) is greater than 0.10, the independent variables do not exhibit collinearity. In other words, the linear correlation between the independent variables is not strong.  
Samples of glacial lake outburst in southeast Tibet are limited, and the threshold value was set according to the Sigma principle. The specific calculation method is shown in the literature [28]. The independent variables were iteratively calculated using the forward step-by-step conditional likelihood ratio test. The dam backwater slope (\(X_{7}\)) of the terminal moraine dams was eliminated because its Sig value was larger than 0.05 at 0.174. The Sig values of the other six evaluation factors were all less than 0.05. Therefore, the constructed moraine lake outburst susceptibility evaluation model is as follows:  
\[\mathrm{P}=\frac{1}{1+\mathrm{e}^{-(-0.872-2.589\mathrm{x}_{1}-0.066\mathrm{x }_{2}+0.994\mathrm{x}_{3}-0.955\mathrm{x}_{4}+0.099\mathrm{x}_{5}+0.731\mathrm{ x}_{6})}}\]In the formula: \(X_{1}\) is the supply glacier area, \(X_{2}\) is the glacial lake area, \(X_{3}\) is the distance from the glacial lake to the glacier, \(X_{4}\) is the glacier slope, \(X_{5}\) is the glacier tongue slope, and \(X_{6}\) is the ratio of the height of the glacial lake from the water surface to the dam crest and the height of the dam body.