esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
The orthogonal Sym4 wavelet is utilized to decompose the electrochemical emission data in depth of 8-level, and the approximation signal in 8-level is removed as the harmful trend. Then, the decomposed signal in each level is reconstructed and the energy distribution plots are obtained based on the equation (3). Figure 8a,b illustrates the energy ratio on each level of the potential and current emission data, respectively. According to Figure 8a, the energy of potential emission data collected by SN0 at passive sate accumulates on the level 1-3. The frequency of these parts between 1 and 2-3 Hz is much higher than that of other parts. However, the energy transfers to level 5-8 as the pitting corrosion appears. The  
Figure 8: Energy distribution plots of the electrochemical emission signals in wavelet domain. (**a**) and (**b**) are the energy distribution plots of the electrochemical potential emission and electrochemical current emission, respectively. energy condensed on these levels is no less than 80% of the total energy. The frequency of the levels from 5 to 8 is no more than 2-5 Hz. The condition of electrochemical current emission in Figure 8b is similar with that of the electrochemical potential emission in Figure 8a. According to the change of the energy distribution on the different crystal, the pitting corrosion is verified. The energy which is distributed on the level 5-8 is the intrinsic characteristics of pitting corrosion of the reinforcing steel in concrete. The energy distribution plot of the electrochemical emission signal can be applied as the benchmark to qualitatively identify the presence of the pitting corrosion. Actually, the _in-situ_, real-time and on-line corrosion monitoring information of the electrochemical emission spectrum could be applied to drive the 3-D cellular automata to quantitatively predict the development of the corrosion pit. Considering the aim of this paper, we do not discuss this point in detail here.
## 4 Analysis and Discussion
### Stability Analysis of SR of Cavern Group  
According to the failure process, crack propagation depth and key point displacement of SR during cavern excavation under the three schemes, the stability of SR in the cavern can be analyzed as follows:  
* From the SR failure process, after the excavation began of the three schemes, small cracks appeared in the surrounding rocks of the arch's right side of the MP cavern and the arch's left side and the bottom floor lower right corner of the TST cavern. In the first few steps in the process of excavation, the cracks did not grow and expand until the excavation steps were more than halfway completed (for Scheme 1 at the end of the seventh step of excavation, and for Schemes 2 and 3 at the end of the third step of excavation) the cracks around the cavern group began to increase and expand. It can be seen that in the early half stage of excavation, the stability of SR mainly focused on the arch SR of the MP cavern and the arch and bottom floor SR of the TST cavern.
* According to the failure process of the SR, in the latter half of the excavation period, the cracks increased rapidly. In this period, the cracks in the surrounding rocks of the three caverns increased and expanded, and after the excavation was completed, cracks appeared in the surrounding rocks of the arch, left wall, right wall and bottom floor of the MT cavern, in the surrounding rocks of the arch, left wall and bottom floor of the TST cavern. According to the depth of the crack propagation, the crack propagation depth of the arch, left wall, right wall and bottom floor of the MP cavern, the arch, left wall and bottom floor of the MTC cavern, and the arch, right wall and bottom floor of the TST cavern are bigger. From the perspective of the displacement of the key points, the displacements of the arches and the bottom floors of the three caverns are relatively obvious. In summary, after the completion of the excavation of the cavern group, the stability problems of the cavern group mainly appeared in the surrounding rocks of the arch, left wall, right wall and bottom floor of the MP cavern, in the surrounding rocks of the arch, left wall and bottom floor of the MTC cavern, and in the surrounding rocks of the arch, right wall and bottom floor of the TST cavern.
* After the completion of the excavation, the cracks in the SR of the bottom floor of the TST cavern expanded and connected with fault f2. Therefore, fault f2 has an influence on the stability of SR of the TST cavern, while fault f1 has no influence on the stability of the cave group.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 3 The Proposed e-RLRP Algorithm  
In this section, the proposed e-RLRP algorithm is explained. Firstly, the RL technique in the routing protocol is implemented according to Reference [40]. Later, the proposed method to reduce the overhead is detailed.
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 5 Control Strategies for the Mitigation of the Impacts Induced by Induction Motor Loads
### Fault Ride Through Strategy for Grid-Following Units
#### 5.1.2 Robustness Analysis  
The purpose of the analysis performed in this subsection is to validate the robustness of the proposed CI-RES FRT strategy (presented in Figure 15) and the control tuning proposed in Case 3 (\(V_{1}=0.5\) p.u., \(V_{2}=0.9\) p.u. and \(V_{3}=1.06\) p.u.). Thus, the corresponding solution was tested in several conditions contemplating different percentages of the IM load while considering the same operational scenario (Table 1). In this sense, four distinct cases were compared regarding the IM load percentage: (Case A) 11% of IM load (L\({}_{5}\) load); (Case B) 15.5% of IM load (L\({}_{3}\) load); (Case C) 26.5% of IM load (L\({}_{3}+\) L\({}_{5}\) load); (Case D) 27% of IM load (L\({}_{1}\) load).  
The obtained results are depicted in Figures 24-26. In the test cases with high shares of IM loads (Cases C and D), an increased reactive current contribution by the CI-RES during the post-fault period is verified, since more reactive power will be consumed by the IM loads, which decreases the network voltage profiles (as verified in Figures 25 and 26). Nonetheless, the recovery of all the IM loads is ensured for all the test cases.  
Figure 23: L3 small and large industrial motors’ speeds and terminal voltages: influence resulting from voltage/reactive current hysteresis tuning.  
Figure 24: BESS power converter synthetic frequency and terminal voltage with different IM load percentages.
## 3 Use Case. CEDER-CIEMAT Microgrid
### Identification of Elements
#### 3.1.3 Energy Storage Systems  
CEDER microgrid incorporates several distributed energy storage systems, including the following:  
* Pumping system associated with the Pelton turbine, with a capacity of 30 kW.
* Lead-acid battery PEPAI: 120 cells of 2 V each, capacity of 1080 Ah per cell.
* Lead-acid battery PEPAII: 120 cells of 2 V each, capacity of 765 Ah per cell.
* LFP batteries: Two racks with 196 cells of 3.2 V each, with a capacity of 50 Ah
## 4 Case Study
### Selection of Ground Motion Records  
A key step in evaluating the seismic vulnerability of structures is the selection of input earthquake ground motions. For this study, a suite of earthquake ground motions was carefully chosen to represent a wide range of seismic intensity levels and spectral characteristics. These records were used in incremental dynamic analysis (IDA) to evaluate the seismic response of the tank under varying intensity measures (\(IM\)). The ground motion records were selected based on the following criteria: (1) To capture a wide range of seismic effects, the records included both pulse-like and non-pulse-like motions. Pulse-like motions, often associated with near-fault effects, typically induce higher demands on structures due to long-period velocity pulses and permanent ground displacements. (2) Records were chosen to represent earthquakes of varying magnitudes and distances from the fault rupture to account for different seismic sources. (3) The records correspond to rock-like site conditions, consistent with the assumption of a rock foundation for the tank.  
A total of 28 ground motion records were selected from the Next Generation Attenuation-West2 (NGA-West2) database [43], maintained by the Pacific Earthquake Engineering  
Figure 7: (**a**) Color map of deformed shell (unit: m); (**b**) top view (deviation magnification \(\times\)35); (**c**) side profile showing dents and out-of-plane displacements (deviation magnification \(\times\)38).
# Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation
## 3 Discussion
### Deep Learning Applications in Solutions
Nevertheless, limitations of using a deep learning model for solid-content detection in concrete wastewater also exist. Deep learning models require a large amount of annotated data for training, including concrete wastewater samples with different solid-content levels. Without sufficient data collection methods and accurate labeling, the trained model may not accurately infer the amount of solid content. Moreover, the model's accuracy and reliability under unknown circumstances may be compromised if the dataset used is limited to specific construction projects or a specific area's concrete wastewater samples, as the model may not be generalized well in regard to other construction projects or buildings of different scales. Furthermore, the generation of solid content in concrete wastewater may vary based on different construction scenarios, building scales, and construction processes, restricting the performance of the model to be less effective in various practical applications. Hence, before using a deep learning model to detect the wastewater solid content, a prerequisite regulation for exploring diverse construction scenarios is crucial to secure recognition accuracy.
## 2 Materials and Methods
### Validation of the UHPLC-MS/MS Method for AFB1 Analysis  
The selectivity of the AFB1 analysis method was studied by running (a) pure extraction solvent without the internal standard, caffeine and (b) FBS extract, without AFB1 but with caffeine. No caffeine signal was detected in the acetonitrile. No AFB1 signal was detected in the acetonitrile and FBS extract. The specificity of the method was composed of the characteristic multiple reaction monitoring (MRM) values and correct retention times for both caffeine and AFB1 [32]. The correlation coefficients (r\({}^{2}\)) of both the solvent and matrix-matched calibration curves were at least 0.99, which indicated good linearity. The matrix effect of the UHPLC-MS/MS method was studied by comparing the slopes of matrix-matched calibration curve to the solvent curve and was calculated as the ratio of signal suppression and enhancement: SSE% = 100 \(\times\) (slope of matrix-matched curve/slope of solvent curve). If SSE% = 100%, no matrix effect exists; if SSE% > 100%, it means an enhanced matrix effect, and if SSE% < 100% it means a suppressed matrix effect. A minor enhanced matrix effect was detected during the first binding test UHPLC-MS/MS run (SSE% = 106%), but during the second test the enhancing effect was significant (SSE% = 157%). Because the matrix-matched calibration was used for quantification, the repeatability in and the reproducibility between tests 1 and 2 was excellent (Figure 1). There was no background MS signal of AFB1 (RT 6.7 \(\pm\) 0.2 min) in the lowest calibration level (0 ng/mL AFB1 in FBS) or in the AFB1-free FBS samples, which were normally used for the calculation of the LOD (which equals the average + 3 x standard deviation) and the LOQ (which equals the average + 6 \(\times\) standard deviation). So, the lowest quantitative calibration level (0.2 ng/mL AFB1 in FBS) was used as the LOQ (=0.2 ug/kg), and the following findings were made: (1) the RT was correct, (2) both quantitative (241.2) and qualifier (285.0) ions existed and (3) their ratio (1.0 \(\pm\) 0.2) was correct. (4) In addition, the average signal to noise (S/N) value of AFB1 in this level was 44 (varying between 34 to 54), which was adequate for quantification. The concentrations of AFB1 in all the spiked FBS + LAB samples were higher than the LOQ, and lower concentrations of AFB1 were not detected in any of the AFB1-free samples. The calculated LOD (=LOQ/2) was 0.1 ng/mL.
## 3 Proposed Method  
### Intelligent Equipment Management System  
The proposed PQUM-DNS was integrated into an intelligent equipment management system for practical field applications. Signals were initially collected from the device sensors within the system before feature extraction transformation and data preprocessing, as shown in Figure 8. The feature extraction transformation involved converting raw vibration feature values into multiple key parameters related to machine health. Data preprocessing involved eliminating unnecessary empty and abnormal values to retain only the normal values required for unsupervised learning. The processed data were stored in a database (PostgreSQL) until there were sufficient accumulated data (e.g., 3000 records, adjustable). Subsequently, both AE and Holt-Winters algorithms were used to train the anomaly detection and prediction models, respectively. An inference was performed using anomaly detection and prediction models to generate results that indicated the degree of equipment or process degradation. The inference results were stored in a database and abnormal detection and prediction outcomes were sent to the visual dashboard of the intelligent management system, providing users with insights into the machine conditions. The system issued alert notifications to relevant personnel if the results of the abnormal detection and prediction exceeded the threshold of the AI equipment or process degradation. When retraining the model with a small amount of data using PQUM-DNS, the anomaly detection model can adapt after the first training of the Pretrain and Metatrain models, and then use a small amount of data to fine-tune retraining to obtain the new model.  
Figure 8: Flowchart of the proposed intelligent equipment management system.  
Figure 7: The results obtained from negative sample (left curve) overlap with the results obtained from positive samples (right curve). By moving (green arrow) the result cutoff value (vertical bar), the rate of false positives (FP) can be decreased, at the cost of raising the number of false negatives (FN), or vice versa (TP = True Positives, TPR = True Positive Rate, FPR = False Positive Rate, TN = True Negatives).
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 4 FPGA Implementation
### Circular Buffer  
The acquired samples reach the circular buffer, where they are temporarily stored (see Figure 9). The heart of the buffer is a dual-port memory capable of holding 512 data. The memory is used like a clock-domain bridge as well: it is written from the 10 MHz side and read from the 100 MHz side. The write side is managed as a typical circular buffer: the writing address pointer (Wrp) is generated by a counter modulo 512 that increments every input sample. The position of the writing pointer is necessary also in the read-domain. A cross-bounding logic protects against possible metastable events that can occur when the pointer crosses the clock-domain.  
Figure 8: “Bird’s eye” view of the decoder architecture implemented in FPGA.  
In the read-side of the buffer, the read-pointer (Rdp) is generated by a counter modulo 512 managed by a specific logic, detailed in this section. The values of write and read pointers are monitored to detect possible overflow of the buffer, which occurs when input samples are written in positions where previous data have not been read yet. In addition, the values of the pointers are used to calculate the number of samples available in the buffer, i.e., the number of sample not yet read, but ready to be read. The number of available samples and the information about overflow are mapped in the address space of the \(\mu\)C that reaches them through its bus.  
Let us go back to the logic that manages the read-pointer. The \(\mu\)C, again through its bus, writes a Read Offset. When this operation occurs, the Rdp is updated with the operation: Rdp = Rdp-ROff, where ROff is the Read Offset set by the \(\mu\)C. In addition, the streaming of 100-samples data towards the next stage is automatically triggered. At the sample time of 1/10 MHz, the 100-sample corresponds to \(T_{b}\)= 10 \(\mu\)s. In other words, the data corresponding to a bit with the starting point set by the \(\mu\)C, is moved at the maximum velocity of one sample per clock cycle towards the demodulator. During the streaming, Rrp is incremented, and the data valid to the next block is generated as needed.  
It should be noted that, although the logic that governs this circular buffer resembles that of a First-In-First-Out (FIFO) memory, it is not exactly the same. In particular, while the write-side corresponds to the logic of a FIFO, the read-side does not. In fact, in FIFO memory, each sample must necessarily be read, and it should be read only once. In the proposed architecture, according to the Read Offset set by the \(\mu\)C, it can happen that some sample is not read at all, or some sample is read multiple times, as required for the decoder and detailed in Section 2.3.  
The memory is composed by 512-sample, and thus it can buffer about \(5\cdot T_{b}\) of data.
## Result and Discussion
### Scanning Electron Microscope (SEM)  
The specimens cured for 7 and 28 days were analyzed using SEM. Figure 14 shows the water-repellent's active ingredient extracted from the water-based water repellent. Figure 15 shows the results of analyzing the images of the specimens at 7 curing days.  
Figure 14: Water repellent active ingredient.  
Figure 13: Results of the mercury intrusion porosimetry (MIP) test after 28 days.  
At the OPC at 7 day of curing days, the crystals of monosulfate and Ca(OH)\({}_{2}\), which are plate-type hydration products, could be confirmed. In the Sand-Co specimen, transparent and white silane/siloxane crystals were observed. The Silica-Co and FA-Co specimens had no pores, and the particles had a smooth and spherical surface and, thus, most of the water-repellent's active ingredient was separated and attached only to some of the surface. Some of the water-repellent's active ingredient appears to have been released due to the bleeding phenomenon during condensation caused by the difference in specific gravity. In addition, ettringite, a hydration product of some cement types, was observed. When the water-based water repellent was directly added to mortar, the transparent white water repellent was not evenly dispersed and appears to be aggregated at one position. In addition, the formation of ettringite was confirmed in areas without water repellency. The ZWR specimen showed natural zeolite in the form of hexagonal cubes, which was bonded with the water-repellent's active ingredient. In addition, it appears that the pores were filled as with stacked Ca(OH)\({}_{2}\).  
In the case of OPC at 28 curing days, calcium silicate hydrate (CSH) gel and Ca(OH)\({}_{2}\), which can be generally found in the hydration reaction, were confirmed. In the Sand-Co specimen, some hydration products, such as ettringite, were found and the presence of the white water-repellent active ingredient around the sand particles and pores was confirmed. This appears to have delayed the supply of water. In the case of Silica-Co and FA-Co, the formation of some hydration products around the spherical particles was observed, but they were not widely distributed. It appears that hydration products were partially formed due to the separation of the water-repellent's active ingredient. This is the phenomenon that generally occurs because the water-repellent's active ingredient is not fixed to powder. In the case of WR-In, more ettringite was formed compared to the Sand-Co, Silica-Co, and FA-Co specimens. As the hydration product that can be seen in the early stage of the hydration reaction was observed, it appears that the hydration reaction was significantly delayed. In the ZWR specimen, the water-repellent's active ingredient was not separated and most of it was fixed to natural zeolite, and it was confirmed that some ZWR samples were bonded with hydration products and filled the pores. Figure 16 shows the results of analyzing the images of the specimens at 28 curing days.  
Figure 15: Electron scanning microscope analysis after curing for 7 days.
## 2 Materials and Methods
### Data Processing and Quality Control  
Raw data time series were recorded at 10 Hz, as well as corrected fluxes of CO\({}_{2}\). Latent heat and sensible heat were calculated on a half-hourly basis, using the EasyFlux(r) CRBasic software (Campbell Scientific, Inc., Logan, UT, USA), installed in the CR1000X datalogger. The corrections and procedures applied to the data were despike, filter high-frequency time series data, coordinate rotation using planar fit method [22], frequency corrections using co-spectra [23], correction for air density fluctuations using [24], data quality classifications (QC) [20], and calculation of footprint characteristics [25; 26].  
Further postprocessing of fluxes included fetch filter (removing records in which fetch 90 was larger than the upwind distance from the tower to the edge of the area of study), outlier detection and removal was done using the MAD method [27], and a QC filter, where only the records with QC < 6 were kept [20].
## 2 Methodology
### Prediction Block
#### 2.2.1 Spatial Feature Extraction
(3) Physical Topology  
The physical topology is used to capture the topological information among the stations based on GCN for each type of station. Since the physical location of the stations is fixed, it is easy to construct an adjacent matrix \(\text{A}\in\mathbb{R}^{\mathbb{S}_{i}\times\mathbb{S}_{i}}\), which is shown in Equations (24) and (25). We only consider the passenger flow in the real-time pattern (\(X^{R}_{S_{i}T}\)) because the network topology does not change. The input of physical topology is defined as \(I_{Topology}\) shown in Equation (26). \[A=\left\{  
(4) Spatial Feature Fusion  
The extracted spatial features from inflow, outflow, and physical topology will be weighted fused as \(O_{SF}\) based on Equation (27).  
\[O_{SF}=W_{1}\circ O_{P\_{i}}+W_{2}\circ O_{P\_{O}}+W_{3}\circ O_{P\_{Topo}} \tag{27}\]  
where \(O_{P\_{I}}\), \(O_{P\_{O}}\), and \(O_{P\_{Topo}}\) are the outputs of inflow, outflow, and physical topology, respectively. \(W_{1}\), \(W_{2}\), and \(W_{3}\) are the trainable weights, respectively. The term \"\(\circ\)\" denotes the Hadamard product.