esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Testing  
All the machine learning models are supervised. Thus, we compare the network prediction with the known ground truth values. As previously mentioned, the dataset was split into 70% for training, 15% for validation (during training), and 15% for testing. For the SBP, DBP, and MAP, the ground truth values are the pressures extracted from the ABP waveform, while for the waveform prediction, we compare with the ABP signal itself. The metrics used for the networks differ. For the discrete blood pressure prediction, the mean average error and coefficient of determination are calculated. Additionally, the percentage of predictions with an error less than 10 mmHg is specified. For the waveform prediction, the Pearson \(r\) value is compared between architectures. A further description of these metrics is found in Section 4.
###### Abstract
STSGCN [13] proposes the dynamic learning of spatial information over time by constructing local spatio-temporal graphs but overlooks the capture of temporal information. AGCRN [14] and DGCRN [15] use adaptive graph generation with gated cyclic units to capture spatio-temporal heterogeneity but lack attention to global information. Although these methods improve traffic prediction performance from various perspectives, they often capture temporal and spatial correlations independently, which may weaken the captured spatio-temporal correlations or even amplify irrelevant features. Recent studies such as AFDGCN [16] aim to model temporal and spatial relationships synchronously via spatial module embeddings, making a positive contribution to the model. Nevertheless, these approaches are constrained in effectively capturing the interactive learning of spatial-temporal information, which affects the models' ability to make predictions. In order to overcome the shortcomings of the current approaches, we provide an approach based on the combination of a feature-augmented down-sampling dynamic graph convolutional network and multi-head attention mechanism for predicting traffic flow. The down-sampling dynamic graph convolution network and multi-head attention mechanism based on feature augmentation are used to learn complex traffic change relationships  
Figure 1: The intricate spatio-temporal relationship of the traffic road network. at a multi-scale spatio-temporal level. The following are the principal contributions of this study:  
* A new spatio-temporal forecasting model ADDGCN is put forward. This model introduces a feature augmentation mechanism to fuse features of different scales, and embeds dynamic graph convolution into the down-sampling convolution network so that the model can simultaneously capture time and spatial correlation. Through the down-sampling dynamic graph convolution network based on feature augmentation, the spatio-temporal dependency is accurately captured by the model and, combined with the multi-head temporal attention mechanism, achieves long-term prediction of traffic flow. * A down-sampling dynamic graph convolution module (DS-DGC) is designed. Among them, the down-sampling convolution network can enhance the information interaction of spatio-temporal data, and the dynamic graph convolution network can use the generated graph structure to better simulate the dynamic correlation among nodes, which is essential to improve the model's ability to depict spatial heterogeneity. * Extensive tests are conducted on two authentic traffic datasets and compared with 11 baseline models. The experimental findings demonstrate that our proposed approach surpasses these baseline techniques across three standard evaluation metrics. This paper provides a summary of related work on traffic flow forecasting in Section 2, followed by the framework of the model and problem descriptions in Section 3. Section 4 provides a detailed explanation of our proposed model, ADDGCN. Section 5 showcases the analytical and experimental findings. Lastly, Section 6 offers conclusions and prospects for further research.
## 3 Proposed Dynamic Graph Processing Scheme
### Operation Reduction Method  
The Operation Reduction Module implements techniques to minimize computational overhead before transmitting graph snapshots to the GPU. These techniques account for the dynamic nature of graphs by analyzing the characteristics of snapshots that evolve over time. As snapshots are generated at different time points, the module efficiently preprocesses them to reduce redundant operations before execution on the GPU. This preprocessing step optimizes GPU resource utilization and improves overall processing efficiency.  
The core of the operation reduction method lies in efficiently tracking changes in the graph at each time step and eliminating redundant computations on identical edges or vertices. To achieve this, the Operation Reduction Module stores graph snapshots and compares them with previous states, identifying unchanged partitions and skipping their processing. This significantly reduces the overall computational load. This approach is particularly beneficial in scenarios where edges are frequently added or removed, as it prevents unnecessary operations on unaffected vertices. Additionally, only the modified portions of the graph are transmitted to the GPU, rather than reprocessing the entire dataset. By focusing solely on the necessary updates, this method minimizes memory transfer costs, reducing both computational overhead and resource waste during data transmission. Therefore, the proposed optimization enhances the overall efficiency and performance of dynamic graph processing.  
Figure 5 illustrates an example of operation reduction for redundant computations on the same edge. As shown in the figure, dynamic graph snapshots are generated at time points t1, t2, and t3. At t2, an edge is added between V2 and V3, and at t3, the same edge is removed. Consequently, the snapshot at t1 becomes identical to the snapshot at t3. In such scenarios, the module avoids unnecessary update operations, thereby reducing the computational workload on the GPU. This optimization minimizes redundant operations, conserves resources, and enhances the efficiency of dynamic graph processing.  
Figure 4: Active vertices and candidate active vertices in dynamic graphs. (**a**) An edge between vertices V2 and V3 in partition P3 is deleted. (**b**) V2 and V3 become active vertices, and their connected vertices (V1, V3, V7) are identified as potentially active vertices (highlighted in blue). (**c**) Previously active vertices are processed, and the potentially active vertices (V1, V4, V7) transition into active vertices (highlighted in red) for the next step.  
Figure 5: Example of operation reduction for the same edge.
## 3 Simulation and Performance Analysis
### Optimized Sonar Broadband Focused Beamforming Algorithm  
First, the 21 sub-band signals were focused on the optimal focusing frequency 2760 Hz by the focusing matrix corrected by the RCB algorithm. Then, the narrowband signals were beamformed by the SOCP algorithm. The time complexity of this algorithm is \(O(n^{3})\).  
Figure 7 shows the main lobe width of the broadband focusing beam is 19.3\({}^{\circ}\), and the sidelobe level of the broadband focusing beam is 41.7 dB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Focusing Frequency of Broadband.** & **Variation of Main Lobe Width** & **Variation of Sidelobe Level** \\
**(Hz)** & **(\({}^{\circ}\))** & **(dB)** \\ \hline
2250 & 0.2 & 0.7 \\
2450 & 0.7 & 2 \\
2760 & 2.4 & 0.17 \\
2900 & 3.1 & 0.72 \\
3000 & 3.1 & 1.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of beamforming parameter change between narrowband and broadband signals.  
Figure 6: Broadband beam pattern focused on a frequency of 2760 Hz after robust Capon beamforming (RCB) correction.  
Figure 7: Broadband focused beam pattern with an optimized algorithm.  
Compared to the above beam pattern, the broadband focused beam pattern with an optimized algorithm performs better. Water pool experiments were carried out to further verify the validity of the algorithm.
## 3 Methodology
### Quaternion Space Rotation  
Figure 6 shows the specific construction of the Quaternion space rotation module. We combined the outputs \(CLS_{0}^{\prime}\) and \(CLS_{1}^{\prime}\) with er to obtain a relation instantiation matrix. We performed a Hamilton product of \(e_{s}\) with each of the three vectors in this matrix, resulting in a quaternion rotation of the source entity from three different angles and producing a quaternion matrix \(M_{q}\) as  
\[M_{q}=concat(e_{s}\otimes e_{r},e_{s}\otimes CLS_{0}^{\prime},e_{s}\otimes CLS _{1}^{\prime}) \tag{4}\]  
\(M_{q}\) represents multi-hop neighbor information of the source entity, capturing a coarse-grained contextual situation. Meanwhile, fine-grained neighbor information is derived from the first-hop neighbors of the source entity. To integrate and unify these different levels  
Figure 5: Architecture of relation instantiation module.  
of contextual information, we link the first-hop neighbor information with \(M_{q}\). The first-hop neighbors serve as intermediate information that are used to explicitly establish the association between the source entity and its context, thus creating hierarchical contextual information. Inspired by [PERSON] [22], we utilize a multi-head Transformer \(T_{n}\) to integrate the first-hop neighbor entities \(e_{fs}\) and the relations \(e_{fr}\). The source entity and relation, \(e_{s}\) and \(e_{r}\), respectively, are also used as input to ensure that the information of the triple can be fully processed. Thus, each input group consists of the flag vector \(CLS_{n}\), together with \(e_{s}\) or \(e_{fs}\) and \(e_{r}\) or \(e_{fr}\), which are sequentially fed into \(T_{n}\). The first-hop neighbor information is combined through \(T_{n}\) using \(CLS_{f}\), and their corresponding outputs \(CLS_{f}^{\prime}\) are concatenated and stored as the neighbor matrix \(M_{n}\) as  
\[M_{n}=concat(\sum_{i=1}^{f+1}CLS_{fi}^{\prime}) \tag{5}\]  
where \(f\) is the number of first-hop neighbors. To ensure that the source entity information is not overwhelmed by excessive neighbor information, \(e_{s}\) in \(T_{n}\) is replaced or masked by a random entity with a certain probability, encouraging the model to recover the source entity later.  
\(M_{n}\) conveys the source entity information in a fine-grained environment, while \(M_{q}\) conveys the target entity information produced by rotation in a coarse-grained environment. We combine \(M_{n}\) with \(M_{q}\) to produce a mixed matrix \(M_{m}\) as  
\[M_{m}=concat(CLS_{out},M_{n},M_{q}) \tag{6}\]  
Consequently, it covers the complete process of quaternion rotation under the influence of contextual relation. We insert a flag vector \(CLS_{out}\) onto the first line of \(M_{m}\), which is subsequently employed in geometric decoding to resolve the representation of the target entity.
## 3 Data Modeling Methodology
### Differential Evolution (DE)
#### 3.2.3 Population Selection  
In the DE algorithm, the current population will generate the intermediate population after completing the variation and crossover operations. The selection operation is to compare the fitness function values of the current population individuals with those of the intermediate population individuals, and to select the one with the best fitness value as the next-generation population individuals, which is formulated as follows:  
\[X_{i}(g+1)=\begin{cases}U_
# IoT-Based Small Scale Anomaly Detection Using [PERSON]'s Q Test for e-Health Data
## 5 Conclusions  
This paper presents a novel IoTDixon methodology that can work on small-size data packets obtained from the given IoT dataset. As the Q test only provides a single anomaly from a small data packet, it can be useful for sensor data gathering wherein a few repetitions of averaging are performed. The proposed techniques uses Q-test to detect point anomalies. We find that value 76.37 is statistically significant where \(P=0.012<\alpha=0.05\), thus rejecting the null hypothesis for a test data packet. The IoTDixon algorithm can be useful in real-life applications with a small fragment of data analytics scenario, for example gathering small number of health data by an IoT sensor and identifying any anomaly present therein. Thus, anomaly detection can be imposed at the IoT-edge devices to detect possible point anomalies from a small set of data instead of searching them from a very large dataset that can be difficult in terms of power and processing capacity utilization by the resource-constrained IoT devices.  
[PERSON] conceptualized, investigated and written the paper, [PERSON] supported with expert advice in this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Q** & **P** & **POS** & **Anomaly** \\ \hline x1 & 0.477 & 0.1343 & 5 & 75.69 \\ \hline x2 & 0.300 & 0.533 & 5 & 67.56 \\ \hline x3 & 0.378 & 0.311 & 2 & 68.86 \\ \hline x4 & 0.365 & 0.344 & 6 & 75.58 \\ \hline x5 & 0.090 & 1 & 7 & 73.85 \\ \hline x6 & 0.665 & 0.012 *** & 3 & 76.37 *** \\ \hline \end{tabular}
\end{table}
Table 1: IoTDixon aware anomaly detection.  
Figure 4: IoTDixon cumulative distribution function of x1, x2, x3, x4, x5, and x6 dataset (from top-left row-wise).
## 4 Materials and Methods
### Non-Anthocyanin Phenolic Compounds  
The analysis of the non-anthocyanin polyphenols (phenolic acids, flavan-3-ols, stilbenes) was performed through a UHPLC-DAD-HRMS system (Agilent 1290 Infinity, Les Ulis, France) equipped with a UV-Vis diode array detector (DAD) (1290 Infinity) connected to the quadrupole time of a flight mass spectrometer (QToF/MS) with an electrospray ionization (ESI) source (Agilent 6530 Agilent Accurate Mass). The UHPLC-DAD-HRMS analyses were carried out on a C18 reversed-phase column (\(2.1\times 100\) mm, \(1.8\) um, Agilent). Samples were directly filtered on a 0.45-um-membrane filter before the injection. The temperature of the column was 25 \({}^{\circ}\)C and the flow rate was set to 0.3 mL/min, with the injection volume set at 2.0 \(\upmu\)L. The mobile phases consisted of (A) aqueous 0.1% formic acid and (B) methanol with an addition of 0.1% formic acid. The gradient of solvent B was as follows: 6% for 0.5 min, 6% to 40% for 29.5 min, 40% to 100% for 8 min, 100% for 5 min and back to 6% in 2 min, followed by washing and re-equilibration for 3 min. The mass spectrometer was operated in the extended dynamic  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wines** & **Control** & **Conventional Stoppers** \\ \hline Lagrein red & Blend a & One-piece natural cork \\ Lagrein Rosé & Blend & Technical cork 1+1 \\ Merlot & Blend & Technical cork 1+1 \\ St Magdalener & Blend & Agglomerated natural cork \\ \hline \hline \end{tabular}  
* Sanitized cork micro-granule blend of natural cork with polymers and without the addition of glue to the mix.  
\end{table}
Table 6: Types of closures used for the wine bottles.  
range of 2 GHz (_m/z_ 3200). The nebulizer pressure and flow rate were set at 25 psi and 9 L min\({}^{-1}\), respectively. Its drying gas temperature was 300 \({}^{\circ}\)C. The sheath gas flow and temperature were set at 11 L min\({}^{-1}\) and 350 \({}^{\circ}\)C. The fragmentation, skimmer, OCT and capillary voltage were set at 150, 65, 750 and 4000 V, respectively. The analyses were performed in negative ionization mode. The data analysis was performed on Mass Hunter Qualitative Analysis software (version B.06.00). Phenolic compounds were identified by comparing their chromatographic retention times and accurate masses with those of pure standard compounds. The calibration curves of pure standard substances were established through the DAD and were used to quantify the phenolic concentrations. When reference compounds were not available, a calibration with structurally related standard substances was used (gallic acid for protocatechuric acid and syringe acid; caffe acid for caftaric acid and glutathionyl caftaric acid (GRP); (+)-catechin for (-)-epicatechin). The integrated peaks were allowed to obtain the concentrations of the identified compounds. Concentrations were expressed in mg L\({}^{-1}\) of standard or of the structurally related standard.
## 4 Protocol Description
### EAR Performance Compared with No Power Adaptation and No Fire Situations  
We evaluate EAR routing when using 3 power levels adaptation and no power adaptation situations. Figure 9 illustrates the end-to-end delay with power adaptation and without power adaptation situations. We get the results with \(\delta\) sink and 3 sinks respectively. It is obviously the end-to-end delay decreases a lot when we use power adaptation by the benefit of power level adaptation, we could increase the network connectivity in the case and also help to find lower delay route path to guarantee a real-time packet delivery and the given delay bound.  
Figure 8: Average node energy when delay bound = 70 ms.  
Figure 9: End-to-end delay with/without power adaptation.  
Figure 10 shows the miss ratio with/without power adaptation. The miss ratio increases greatly if we adapt power level in the network to increase the probability of real-time packet delivery.  
We then evaluate energy efficiency of RAR routing when fire happens and no fire happens situations. Figure 11 illustrates the average node energy in the simulation time when delay bound is set to 50 ms. From the results, it is obvious that average node energy decreases in fire situation. But until 250 s of simulation, the average node energy is larger than 0. For delay bound chosen as 50 ms, the network is still effective until close to the end of the simulation in building fire situations.  
Figure 11: Average node energy when delay bound = 50 m.  
Figure 10: Miss ratio with/without power adaptation.
# Experimental Investigation of Clearance Influences on Cage Motion and Wear in Ball Bearings
## 4 Effect of Cage Pocket Clearance  
### Radial Motions of the Bearing Cage  
The vibration waveforms, spectra of the cage motion in the vertical direction, and normalized trajectories of the cage with three different cage pocket clearances at the rotating speed 6000 rpm and constant preload (axial load 1000 N and radial load 500 N) and same guiding clearance 0.45 mm are illustrated in Figure 8.  
It is can be seen from Figure 8 that the cage radial motion is always periodic with main frequency components \(f_{c}\), \(2f_{c}\), and \(f_{i}\). With the increment of cage pocket clearance, the whirl trajectory of cage are gradually transformed from well-defined patterns to complicated ones, but it does not cause obvious instability of the cage, which is consistent with the simulations in reference [5].  
The cage with small pocket clearance (\(C_{p}\) = 0.1 mm) moves in a stable state, and its whirl trajectory is regular. When the pocket clearance reaches 0.25 mm and more, the amplitudes of \(f_{c}\) and \(f_{i}\) decreases, but a random broadband near \(f_{c}\) appears in the spectrum of its cage motion. Because larger pocket clearance gives more free space for the rolling balls, the impact of the cage and balls will be undermined, and more space for the cage independent of the inner ring and balls will be obtained, which makes the cage motion pattern more irregular.
# The Summertime Diurnal Cycle of Precipitation Derived from IMERG
## 3 Results  
The summertime diurnal cycles of precipitation occurrence, intensity and accumulation according to IMERG are analysed globally at \(2^{\circ}\times 2^{\circ}\) resolution across a four-year period. Case studies of the diurnal cycles are investigated first, then results are presented for the diurnal mean and the amplitude and phase of the 24 h and 12 h harmonics. Fitting errors on the summertime amplitudes and phases are then analysed, prior to an investigation into the variations in the diurnal cycle of accumulation across the seasons.  
A \(2^{\circ}\times 2^{\circ}\) spatial resolution has been selected in all following results. This is a compromise between coarsening the spatial resolution, which reduces the sampling errors, and keeping the finest  
Figure 2: Same as for Figure 1 except for the North Atlantic Ocean near the coast of North and South Carolina, US (Latitude: 32\({}^{\circ}\)–34\({}^{\circ}\)N; Longitude: 74\({}^{\circ}\)–72\({}^{\circ}\)W).  
\(0.1^{\circ}\times 0.1^{\circ}\) resolution in order to capture the regional details of the diurnal cycle. In particular, it has been previously shown that there is improvement in the detection of precipitation occurrences and the random errors of IMERG precipitation estimates when passing from \(0.1^{\circ}\times 0.1^{\circ}\) to \(2^{\circ}\times 2^{\circ}\) at half-hour resolution [21] (i.e., the probability of detection increases from 0.63 to 0.69, the false alarm ratio reduces from 0.28 to 0.24, the Heidke skill score increases from 0.61 to 0.63, the normalised mean absolute error reduces from 0.68 to 0.6, and the normalised root mean square error reduces from 1.23 to 1.1). The \(2^{\circ}\times 2^{\circ}\) spatial resolution was also selected by [14] for analysis of the diurnal cycle using the TRMM TMPA, CMORPH (Climate Prediction Center morphing) and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) products. Due to an expected spatial correlation between precipitation estimates and random errors at \(0.1^{\circ}\times 0.1^{\circ}\) within a \(2^{\circ}\times 2^{\circ}\) grid box, random error estimates of accumulation and intensity for a specific half hour and grid box (bracketed term in Equation (2)) are increased by a factor of 5 to account for an estimated correlation length of \(0.5^{\circ}\). This estimated correlation length is compatible with passive microwave sensor footprints.  
Note that references to morning, afternoon and evening refer to 0-12 LST, 12-18 LST, and 18-24 LST, respectively.