esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Relationship between Slip Rate and Skidding Damage  
The slip rate s can be obtained from Equation (7),  
\[\mathrm{s}=1-\frac{\pi\mathrm{D}_{\mathrm{g}}\mathrm{n}_{\mathrm{g}}}{\pi\mathrm{ d}\mathrm{n}_{\mathrm{n}}}. \tag{7}\]  
Under the experimental conditions, the inner ring speed was 2250 r/min. The slip rate changes the curve of the roller and the inner ring is shown in Figure 12. The slip rate first decreases and then tends to be stable within a certain range and then gradually increases. The large slip rate at the beginning is due to the higher inner ring speed, and the relative speed of the roller and the inner ring is also high. With the increase in the roller speed, the relative speed between the two gradually decreased, and then the slip gradually decreased. It can be revealed from the figure that at the beginning, the slip curve presents a slow and gentle state of decline, and this stage is the normal contact process between the roller and the inner ring. With the increase in the roller speed, slip rates change after 8.5 s. At this stage, the roller and the inner ring may occur skidding damage, the roller speed is still increasing, and the inner ring speed decreases, hence the slip rate decreases. During the experiment, after producing an abnormal noise, the machine stopped at about 10.65 s. The inner ring speed decreases rapidly after the halt, resulting in a rapid decrease in the slip. After 13 s, the rotation speed of the two becomes the same, and the slip rate is close to 0. At the final stage, the inner ring is not able to drive the roller, resulting in a rapid reduction in the roller speed and a gradual increase in the slip. In the start-stop stage, the slip rate is very high, which may lead to the rolling bearing skidding damage. Therefore, the process of the rolling bearing should be reasonably planned.  
Figure 11: Time-varying curve of temperature.  
Figure 12: Time-varying curve of slip rate.
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 4 Wideband Spectrum-Sensing Based on RFB
### Selection of RFB Sub-Bands Number
It is known from Equation (14) that the a reduced value of \(M\) results in the value of \(\lambda\) to be decreased, and \(P_{f}^{(m)}\) may come down to reduce \(\hat{M}_{10}\). Therefore, the \(M\) value is reduced to half of the original at the next sensing time, and the current sensing time ends. If the change of two detections with two adjacent \(M\) is greater than \(10\%\), that is \(\left|P_{d}^{(m)}-P_{d}^{(m)}\right|\geq 10\%\), then the \(M\) would be unchanged at the next sensing time, for the detection performance could be more deteriorated after the \(M\) is adjusted. If the detection results are not the above values, the current sensing ends, and the value of \(M\) remains unchanged at the next sensing time.
###### Abstract  
A mathematical model for an electric field induced by an angularly oscillating magnetic dipole was proposed with magnetic vector potential to analyze the characteristics of the electric field induced by a rotating and angularly oscillating magnetic object. This mathematical model was constructed for the electric field induced by a magnetic object oscillating at a certain angle. On this basis, the phase relationship among the three components of the induced electric field was analyzed (defining the right-hand Cartesian coordinate system). Evidently, a phase difference of \(\pi/2\) always existed between the horizontal components of the electric field induced by a magnetic dipole rotating around the \(z\)-axis. The phase difference between the vertical and transverse components in the \(x\)-\(z\) plane was also \(\pi/2\). A phase difference of \(\pi\) was observed in the \(y\)-\(z\) plane. The above theoretical analysis was verified through simulation and experiment. The results showed that the frequency of the induced electric field was related to the angular velocity and angle of rotation. The amplitude was associated with the magnetic moment and the angular velocity and angle of oscillation. The maximum amplitude did not exceed the amplitude of the electric field induced by a magnetic object angularly oscillating at the same velocity. With regard to the amplitude and phase relationship, the three components of the induced electric field measured in the experiment were consistent with the results of the theoretical analysis.  
magnetic object; angularly oscillating; magnetic vector potential; induced electric fields; phase relationship +
Footnote †: journal: Physics Letters A
# Energy-Efficient Wireless Hopping Sensor Relocation Based on Prediction of Terrain Conditions
###### Abstract  
It is inevitable for data collection that IoT sensors are distributed to interested areas. However, not only the proper placement of sensors, but also the replacement of sensors that have run out of energy is very difficult. As a remedy, wireless charging systems for IoT sensors have been researched recently, but it is apparent that the availability of charging system is limited especially for IoT sensors scattered in rugged terrain. Thus, it is important that the sensor relocation models to recover sensing holes employ energy-efficient scheme. While there are various methods in the mobile model of wireless sensors, well-known wheel-based movements in rough areas are hard to achieve. Thus, research is ongoing in various areas of the hopping mobile model in which wireless sensors jump. Many past studies about hopping sensor relocation assume that all sensor nodes are aware of entire network information throughout the network. These assumptions do not fit well to the actual environment, and they are nothing but classical theoretical research. In addition, the physical environment (sand, mud, etc.) of the area in which the sensor is deployed can change from time to time. In this paper, we overcome the theoretical-based problems of the past researches and propose a new realistic hopping sensor relocation protocol considering terrain conditions. Since the status of obstacles around the sensing hole is unknown, the success rate of the hopping sensor relocation is used to predict the condition of the surrounding environment. Also, we are confident that our team is uniquely implementing OMNeT++ (Objective Modular Network Testbed in C++) simulation in the hopping sensor relocation protocol to reflect the actual communication environment. Simulations have been performed on various obstacles for performance evaluation and analysis, and we are confident that better energy efficiency with later appearance of sensing holes can be achieved compared to well-known relocation protocols.  
mobile sensor; hopping sensor; relocation protocol; energy efficient protocol; internet of things (IoTs); wireless sensor networks (WSNs); simulation +
Footnote †: journal: Accepted: 26 December 2019; Published: 28 December 2019
# Finding Bottlenecks in Message Passing Interface Programs by Scalable Critical Path Analysis
## 5 Profiling MPI Applications  
### Logging MPI Calls  
In standard-compliant MPI libraries, every exported MPI_XXX function is in fact a weak symbol, and each such symbol has a strong counterpart named PMPI_XXX. When the symbol  
Figure 12: Topological sorting time dependence on the no. of MPI calls.  
is weak, we can replace it with a strong symbol possibly from another library during link time or during run time by dynamically pre-loading the library. The latter approach is used to implement MPI profilers--programs that wrap MPI calls and collect statistics from their arguments. Inside the wrapped MPI function, they usually call its PMPI counterpart but may perform any other calls when necessary.  
We followed this approach to implement the _mpi-graph_ library that is preloaded by the application using the \(LD\_PRELOAD\) environment variable. In order to minimize the impact of our profiler on the execution time of the program, we perform a minimal number of PMPI calls inside each wrapped MPI function. Usually, it is only one call, but more than one MPI call is performed when we have a non-MPI_COMM_WORLD communicator and in some other corner cases.  
For each MPI call, we record start time, end time, call number, and other relevant information, which are unique for each call (tag, source, destination, communicator, etc.). The log of all MPI calls is stored in the memory of each MPI process. We allocate memory directly from the operating system kernel using the \(mmap(2)\) system call and do not use pointers inside log record structures. This allows us to gradually move old log records to a disk if the log consumes too much memory.  
When the program calls MPI_Finalize, we collect all of the logs inside the first MPI process, convert them to the graph, and find a critical path. When we switch to the parallel path-finding algorithm, we will no longer collect the logs but process them inside each process in parallel.
# Sensor Micro and Nanoparticles for Microfluidic Application
## 4 Microfluidic Systems for Chemosensing
### Detection of Hydrogen peroxide  
[PERSON] et al. [65] reported the incorporation of silver nanoparticles on the pencil graphite electrodes (PGEs) for electrochemical sensing of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). The silver nanoparticles were formed by applying a green synthesis method using an extract of marigold flowers. For the synthesis, aqueous solution of silver nitrate was mixed with the flower extract, which consisted of 95% lutein. After storing at room temperature overnight in a dark place, color change of the mixture was observed due to the Ag\({}^{+}\) ions reduction to Ag nanoparticles of 20-50 nm size. After, solution of washed AgNPs was drop-casted on PGE with chitosan (CS) matrix in order to form a hybrid PGE/AgNPs/CS electrode. Figure 12 shows a schematic illustration of the PGE/ AgNPs/CS electrode for effective H\({}_{2}\)O\({}_{2}\) sensing. Formed electrodes were integrated in a 3D microfluidics platform and provided a significant cathodic reduction peak for reduction of H\({}_{2}\)O\({}_{2}\). The electron transfer activity was enhanced by AgNPs on the electrode and caused electrocatalysis of H\({}_{2}\)O\({}_{2}\). A cyclic voltammograms (CV) technique in the range of \(-\)0.7 to 0.2 V was applied for quantitative analysis. Linear response of the current on the H\({}_{2}\)O\({}_{2}\) concentration was found in the concentration range of 1-10 uM with limit of detection of 0.52 uM. As a result, the detection of H\({}_{2}\)O\({}_{2}\) concentration was performed in medical and cosmetic samples accurately and with high selectivity.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 4 Reflected Field Strength Calculation  
To simplify the model calculations, a relay node is placed at the center of the roadway. This node is usually equipped with an amplifier that is capable of receiving weaker electromagnetic waves and amplifying them close to the original intensity through enhancement processing so that the antenna can be viewed as re-emitting electromagnetic waves at the relay node. According to the above theory, the maximum number of reflections from the relay node to the receiving point is seven.
## 3 Analysis of Original Research Articles
### Deep Learning
[55] proposed a hybrid SARIMA-LSTM (HySALS) model to forecast the global prices of five key commodities. The approach achieved MAPEs of 5.37 for wheat, 7.80 for corn, and 6.87 for rice on test data, showing strong predictive performance. A study by [PERSON] et al. [56] proposed four hybrid models (BPNN optimized by the particle swarm optimization (PSO) algorithm and four decomposition methods: empirical mode decomposition (EMD), wavelet packet transform (WPT), intrinsic time-scale decomposition (ITD), and variational mode decomposition (VMD)) to forecast futures prices of wheat, corn, and soybean. VMD-PSO-BPNN yielded the lowest MAPE across all commodities, confirming superior accuracy. [PERSON] et al. [57] proposed a BP-LSTM hybrid model for short-term wheat price forecasting in China. The model incorporated multiple external factors and achieved high accuracy, with the lowest MSE (0.00026) compared to LSTM-only and BP-only approaches. [PERSON] et al. [58] proposed a deep learning model combining ChineseBERT, text CNNs, and ensemble empirical mode decomposition to predict corn futures prices using Weibo sentiment analysis, achieving high accuracy for 30- and 60-day horizons. Collectively, these studies underline the versatility and potential of deep learning in agricultural commodity price forecasting.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Online Learning Direct-to-Satellite Packet Scheduling  
Transmitting to the LEO satellites can be unreliable due to minute changes in equipment setup or environmental factors. For example, severely cloudy days lead to too high RF background noise (i.e., higher than \(-93\) dBm). Further, unshielded microcontroller within 10 to 20 cm of the antenna could increase measured RF background noise by as much as 5 to 10 dB. Further, slightly angling the antenna towards or away from a cell tower  
Figure 5: Activity diagram for the host device with two processes  
a few kilometers away could vary the RF background noise by several dB. With all these factors, creating a generalizable pass model is intractable.  
Previous work with indirect-to-satellite scheduling shows that online learning is a successful strategy [11]. Thus, each individual sensor should learn for itself and for its exact site conditions and hardware setup via online learning. Previous work in indirect-to-satellite scheduling uses a Lyupanov optimization problem for network queuing to avoid making assumptions about when new data would become available [11] while the perfect knowledge of satellite overpasses is assumed. However, the data production rate is constant in our case, so rather than treating it as a network queuing problem, we ought to predict the uplink availability. Thus, a novel approach will be used.
# Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks +
###### Abstract  
Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be performed in polynomial time. Therefore, approximate inference is required. In approximate inference, it is often necessary to trade off accuracy against solution time. This paper presents an extension to the Hybrid Message Passing inference algorithm for general CG networks and an algorithm for optimizing its accuracy given a bound on computation time. The extended algorithm uses Gaussian mixture reduction to prevent an exponential increase in the number of Gaussian mixture components. The trade-off algorithm performs pre-processing to find optimal run-time settings for the extended algorithm. Experimental results for four CG networks compare performance of the extended algorithm with existing algorithms and show the optimal settings for these CG networks.  
artificial intelligence; Bayesian decision theory; hybrid Bayesian network; message passing algorithm; Gaussian mixture reduction; time-constrained inference
## 2 Materials and Methods
### Outcomes
#### 2.3.2 Quality of Life Questionnaires  
The 36-Item Short Form Survey (SF-36; RAND Corporation) was used to assess quality of life (QoL) [39]. The participants self-reported how limited they were in performing everyday activities due to physical functioning (10 items), role functioning (7 items), energy/fatigue (4 items), emotional wellbeing (5 items), social functioning (2 items), pain (2 items), and general health (5 items) [39].  
The Epworth Sleepiness Scale was used to measure the participants' sleepiness throughout the day during 8 low-energy activities [40]. The participants were instructed to rate, from 0 (would never doze or sleep) to 3 (high chance of dozing or sleeping), how likely they are to doze off or fall asleep during eight low-energy activities.  
The Patient Health Questionnaire (PHQ-9) was used to measure the frequency and severity of a depressed mood or depression [41]. The participants were instructed to provide ratings from 0 (not at all) to 3 (nearly every day) for how often they felt each of the nine statements applied to them.  
The Life Habits Checklist 2.0 (used with permission from Dr. [PERSON]) evaluated quantity and quality of sleep, exercise habits, eating habits, mindfulness, and interpersonal relationship habits. The questions were phrased as \"I\" statements, such as \"I sleep in complete, or near complete darkness\". The participants were instructed to respond to each question on a 5-point scale, from 1 (\"daily\" doing the activity) to 5 (\"never and have no intention of doing\" the activity). The scores were summarized in six domains: neurodegenerative prevention score; brain health promotion score; healthy body rhythm score; food quality and dietary habits score; healthy shape and weight promotion score; and an overall aggregate score. This scale is part of the CNV-VS platform for which it can be used as an accompanying tool to CNV-VS in both research and clinical settings.
## 4 Discussion
### Statistical Results
This narrow focus reduces sensitivity to small but consistent performance differences across the entire dataset, leading to fewer significant results compared to tests that consider all observations. [PERSON] (1998) notes that [PERSON]'s test has lower statistical power in scenarios where performance differences are subtle but widespread [18]. The paired 5 \(\times\) 2 CV \(t\)-test uses a limited number of folds (5 repeats of 2-fold splits) to estimate variance, which can lead to overly conservative error estimates. [PERSON] (1999) showed that this test's strict variance correction reduces Type I error rates (false positives) but also decreases power, making it harder to detect significant differences unless the effect size is large [45]. In contrast, tests like corrected repeated CV or Wilcoxon may overestimate significance due to less rigorous variance handling. Regarding the elevated Type I error in resampled and repeated cross-validation tests, [PERSON] (1998) highlights that the resampled \(t\)-test is prone to increased Type I error due to the non-independence of training sets within cross-validation. (same logic can be applied to repeated k-fold cross-validation), making it unreliable. The classical resampled \(t\)-test's inflated error arises because it ignores dependencies between training folds, leading to overly optimistic significance estimates [18]. The corrected random resampled CV paired \(t\)-test, proposed by [PERSON] and [PERSON] (2003), adjusts the variance estimate to account for overlapping training data, mitigating this issue and providing valid Type I error control [20]. Additionally, the corrected repeated 10-fold cross-validation method was employed in this study to mitigate Type I error.
###### Abstract  
In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data.  
angle effect correction; side-view imaging; bidirectional reflectance distribution function (BRDF); Jilin-1; crop parameters retrieval; leaf area index (LAI); fraction of vegetation cover (FVC); leaf chlorophyll concentration
# Spatial Sound Rendering Using Intensity Impulse Response and Cardioid Masking Function
## 3 Recording of Test Signals  
Modern listening systems can ensure that the listener is surrounded or even immersed by sound by arranging speakers around the listener. The task of the channels placed at the back of the listener is, with the essential presence of the sound source from the front channels, to reproduce the interior acoustics more faithfully and improve the depth of the sound scene. One of the most widespread surround sound systems is the 5.1 system, with five speakers (Center; Front Left and Front Right sets, respectively, in a circle at an angle of \(\pm 30^{\circ}\) to the center; and Rear Right and Rear Left sets in a circle at an angle of \(\pm 110^{\circ}\) to the center) responsible for surround effects. With such a listening system in mind, microphone systems are available on the market so that the recorded signal of any sound event can be directly played on a 5.1 surround system. Therefore, to confirm the suitability of the proposed algorithm, its effect was compared with that of the classical approach, and all necessary test signals were acquired. The experiment consisted of recording impulse responses and speech in a medium-sized auditorium at five source positions. The signals were recorded simultaneously using a Schoeps MK5 omnidirectional microphone, a mechatronic probe, a commercial SPL ATMOS 5.1 Surround Mixing System with an array of Brauner large-diaphrag condenser cardioid microphones in an INA 5 configuration, and a Neumann KU100 dummy head (Figure 5). The capsules of all the microphones were placed at near-equal heights of 2 m from the floor. All elements of the recording system were placed as close as possible to each other.  
Figure 6 shows the positions of the sound sources around the microphone system. A Genelec 8040 active loudspeaker was used as the sound source, which emitted a logarithmic sweep signal in the frequency range of 20 Hz to 20 kHz with a duration of 15 s. The sweep signal was emitted four times at each of the five speaker positions to determine the pressure and intensity impulse responses of the speaker signals. After each emission, the mechatronic probe changed the position of the microphone by 90\({}^{\circ}\) so as to move the microphone onto another axis. The speaker was also located in the same place, and a short speech was recorded. A digital audio workstation with Protocols 12 software, RME Digiface USB interface, and Focusrite Clarett OctoPre ADAT preamplifier were used to broadcast the sweep signal and record the microphone signals. The sampling rate of the system was 48 kHz.  
Figure 4: The 2D intensity impulse response with a vertical time axis.