esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Dataset Pre-Processing  
The sEMG can be disturbed by various types of noise, such as noise from electronic devices (from 0 Hz to thousands of Hz) and noise from motion artifacts [34]. Therefore, filtering operations are needed to remove the noise information in order to preserve the original signal's characteristic information as much as possible. The same filtering method is used for the six public datasets to filter the original EMG signals. Wavelet denoising is used to filter the original EMG signals, and the third-level mother wavelet \"db7\" is selected for wavelet filtering. The filtered data are then normalized using min-max normalization to ensure that all data are distributed between [0,1]. The filtered and normalized EMG signal segments are shown in Figure 3. The formula for min-max normalization is as follows:  
\[\overline{x}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{10}\]  
where \(x_{min}\) represents the minimum value of the signal vector \(x\), and \(x_{max}\) represents the maximum value of the signal vector \(x\). Finally, the sEMG signal is split into a collection of signal segments with a window size of 150 ms and a sliding step of 25 ms [35].
## 3 Results and Discussion
### Measurements of Main Physical Properties of Products
#### 3.2.2 Water Absorption (%) and Apparent Porosity (%) Parameters  
The water absorption (WA%) and the apparent porosity (AP%) results of fired products with the addition of hematite, and considering the thermal treatment at 1300 \({}^{\circ}\)C for 30 min are shown in Figure 4 for all the examined samples with or without the applied thermal pre-treatment. Forsterite bricks require apparent porosity values < 20% while increased apparent porosity levels constitute a negative factor, as the target of their production is to achieve the maximum bulk density which maximizes when apparent porosity is minimized [16]. The respective values of WA% and AP% with the addition of hematite show a slight decrease, with the highest decrease found for the sample W6, i.e., from 6.2% to 4.7% and from 16.2% to 13.3%, respectively, which is attributed to the higher presence of magnesite in the W6 sample (see Table S1, Supplementary Material) which can react with hematite to form magnesiofferite spinel. It is noticeable that the lower porosity levels are reported for the pre-treated samples with the highest decrease shown for the sample W8, i.e., from 4.7% to 3.6% for the WA% parameter and from 13.2% to 10.4% for the AP% parameter, which can be attributed to the highest presence of pyroxenes in the thermally pre-treated sample (W8 PT, see also see Table S1, Supplementary Material). The lower porosity levels correspond to higher bulk density values, whereas the higher porosity levels result in the opposite effect, indicating that the increase of hematite nanoparticles' addition (from 5 to 10 wt.%) and the pre-treatment at 850 \({}^{\circ}\)C can decrease both the WA(%) and AP(%) parameters.  
Figure 3: The firing shrinkage values (FS%) of different samples: (**a**) with the addition of 5 wt.% hematite; (**b**) thermally pre-treated samples with the addition also of 5 wt.% hematite; (**c**) with the addition of 10 wt.% hematite; (**d**) thermally pre-treated samples with the addition of 10 wt.% hematite.
## 5 Conclusions  
In this work, we investigated the outage and error performance of a parallel-UAV-relay-based FSO communication system. By considering the atmospheric loss, atmospheric turbulence, pointing error, and AoA fluctuation, we derived the PDF of the overall channel gain and closed-form expressions of the OP and the ABER. Moreover, we also analyzed the asymptotic bounds of the OP and ABER. Our numerical results verified the accuracy of the theoretical expressions we derived. Moreover, the results indicated that the system's OP is related to the number of relay nodes, the FoV, and the standard deviation of the direction deviation, while the ABER is only related to the FoV and the standard deviation of the direction deviation.  
Conceptualization, S.-H.L.; methodology, J.-Y.W.; software, S.-H.L.; validation, X.H. and [PERSON]-Y.W.; investigation, X.H.; resources, J.-Y.W.; writing--original draft preparation, S.-H.L. and J.-Y.W.; writing--review and editing, S.-H.L., X.H. and [PERSON]-Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, under Grant 62472233, and the Natural Science Foundation of Jiangsu Province, under Grant BK20221328.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank the anonymous reviewers and the Editor for their helpful comments and review of this article.  
Author [PERSON] was employed by the Jiangsu Province Special Equipment Safety Supervision Inspection Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 14: System ABER versus average transmitted optical power for different \(\sigma_{0}\) when \(M=2\) and \(Z_{m,j}=0.5\) km.
Several sterilization methods, including ethylene oxide (EtO), beta radiation, and gamma radiation, have been employed for the sterilization of collagen-based materials. EtO sterilization is a widely used method that relies on the alkylating properties of ethylene oxide gas to inactivate microorganisms [8]. Beta radiation, which utilizes high-energy electron beams, and gamma radiation, which employs radioactive isotopes like cobalt-60, are also effective sterilization techniques. However, these sterilization methods may potentially alter the structural and functional properties of collagen hydrolysates, necessitating a comprehensive evaluation of their effects [9; 10; 11].  
Identifying the changes in collagen hydrolysates after sterilization can be performed via many techniques. Nuclear magnetic resonance (NMR) spectroscopy is an analytical technique that can be used for the investigation of chemical structure, enabling the detection of modifications or degradation caused by the sterilization processes [12]. Additionally, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) can be employed to analyze the molecular weight distribution in collagen hydrolysates [13]. Differential scanning calorimetry (DSC) is a thermal analysis technique that can provide information about the thermal stability and denaturation behavior of collagen hydrolysates by monitoring the heat flow associated with phase transitions or structural changes, which can reveal alterations in the thermal properties of the hydrolysates caused by the sterilization processes [14]. This study aims to investigate the effects of the ethylene oxide, beta radiation, and gamma radiation sterilization methods on the collagen hydrolysates obtained from tuna tendons, utilizing NMR, MALDI-TOF MS, and DSC analyses. The findings can contribute to a better understanding of the impact of these sterilization methods on the structural and functional properties of collagen hydrolysates.
## 2 Materials and Methods
### Dataset Generation and Problem Description  
#### 2.3.1 Dataset Generation  
According to the signal model outlined in Section 2 and the construction method of the C/N\({}_{0}\) distribution map described in Section 3.1, a dataset was generated. This dataset comprised 20,000 C/N\({}_{0}\) distribution maps, each corresponding to a single interference localization experiment. For each interference localization experiment, the following parameters were configured:  
(1) The monitoring area was set to 60 \(\times\) 60 km. (2) The number of receivers was randomly set between 400 to 600 per square kilometer, with their positions following a random distribution. (3) The maximum number of interference sources was set to 16. In each trial, the total number of interference sources (denoted as _N_all_) was randomly selected as an integer between 1 and 16. Subsequently, an integer _N_Od_ between 1 and _N_all_ was randomly chosen to represent the number of omnidirectional interference sources. The number of directional interference sources for that trial was then calculated as _N_all - N_Od_. For instance, in one trial, a random integer between 1 and 16 was selected, resulting in 16 jamming sources. Among these, 7 were randomly designated as omnidirectional jammers, leaving 9 as directional jammers. (4) The antenna type of each jamming source was randomly assigned using typical models provided by MATLAB R2024a's Antenna Toolbox [30]. Figure 2 illustrates the radiation patterns of different antenna models at the navigation frequency. Among them, the typical omnidirectional antenna is the dipole antenna, while typical directional antennas include Yagi-Uda antennas, horn antennas, Vivaldi antennas, helical antennas, and patch microstrip antennas. Simultaneously, the power of each jammer and the jammer direction were randomly set sequentially. The jammer power was an arbitrary integer between \(-10\) dBm and \(10\) dBm. The jammer direction was randomly set to an arbitrary angle within the range of \(1^{\circ}\) to \(360^{\circ}\), with an adjustment accuracy of \(1^{\circ}\). The jamming transmission was narrowband jamming, corresponding to a Q value of 1.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 2 Geological Background
### Stratigraphy and Sedimentology  
Due to the influence of strong tectonic activities, the Huizhou Sag developed the Paleocene Shenhu Formation (SH), the Eocene Wenchang (WC) and Enping Formations (EP), and Oligocene Zhuhai Formation (ZH) during the continental rift stage. Among them, the Eocene Wenchang and Enping Formations are the most important source rocks, which consist of lacustrine mudstone, fluvial sandstone, and coal in an interbedded manner [40]. The Oligocene Zhuhai Formation consists of deltaic coastal siltstone and is the main reservoir in the Huizhou Sag. During the passive continental margin stage, the Miocene marine siltstone, mudstone, and carbonate Zhujiang (ZJ) and Hanjiang (HJ) Formations were developed in the Huizhou Sag. The Miocene Yuehai (YH) Formation, Pliocene Wanshan Formation (WS), and Quaternary sediments were deposited in a marine environment during the neotectonic stage (Figure 2). From the Paleocene to the Neogene, there were three sets of oil source rock, reservoir, and caprock. Influenced by regional extensional forces, the Huizhou Sag was a lake basin during the Eocene Wenchang Formation deposition period, and high-quality lacustrine hydrocarbon source rocks were widely developed during this period. Subsequently, the paleolake basin gradually shrank during the Eocene Enping deposition period, and fluvial deltas were developed in most areas (Figure 1C) [39; 40].  
Figure 2: Comprehensive column chart of strata in Huizhou Sag, Pearl River Estuary Basin [41; 42].  
The formation studied in this paper is the Enping Formation, which has a maximum thickness in the range of 2062.7-4184.8 m, with an average burial depth of 3259.2 m [38; 39]. An analysis of the Enping Formation revealed that the Enping Formation is a product of the transitional stage. The sedimentary facies were dominated by subaqueous distributancy channels and mouth bars and consisted of a depositional environment characterized as deltaic and lacustrine deposits. The Enping Formation is a sandstone-dominated stratum of generally thick sand conglomerate and fine-medium sandstone, with occasional layers of siltstone and mud [38; 43; 44]. The target interval lies within an underwater low rise, which is characterized by a shallow water depth, a relatively flat terrain, and strong lake wave movement conducive to beach bar development (Figure 1C). Therefore, a sensitivity investigation of the deep layers is vital for deep oil and gas exploration in this area.
# A Novel Plug-in Board for Remote Insect Monitoring
## 2 Materials and Methods
### Camera Unit  
On the market, there are more RGB (red, green, blue color space) camera units that are compatible with Raspberry Pi devices. The most popular official camera is probably the Raspberry Pi camera v2. Another popular camera type is the HQ (high-quality) 12 MP Raspberry Pi camera. As the name indicates, the HQ camera can capture images at a rather higher resolution (4056 \(\times\) 3040 pixels). In addition to the official Raspberry Pi cameras, some other third-party camera modules also exist, such as the most advanced cameras from Arducam [20].  
Although the image quality is higher for the HQ or Arducam cameras than the quality of the v2 camera, our experiences show that images taken with the v2 camera can be sharp enough to detect small insects. Moreover, higher-resolution cameras can be twice as expensive as the Raspberry Pi camera v2. For these reasons, image capturing is performed by a Raspberry Pi camera v2 in our sensing device, which is connected to the Raspberry Pi Zero W via the dedicated CSI (Camera Serial Interface) port. This fixed-focus 8 MP RGB camera is positioned at the center of the plug-in board, as shown in Figure 1.  
From the software point of view, the developer has several options for fine-tuning the camera. The most widely used Pi camera package provides a Python interface with the camera unit and significantly facilitates the writing of customized image-capturing functions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Raspberry Pi Zero W** & **Arduino Uno (ATmega32 BP)** \\ \hline Type & Single-board computer & Microcontroller \\ Operating system & Raspberry Pi OS & None \\ Processor & 32-bit & 8-bit \\ Memory & 512 MB & 32 kb \\ Clock frequency & 1 GHz & 16 MHz \\ Camera port & Yes & No \\ Input voltage & 5 V & 7-12 V \\ GPIO pins & 40 (PWR, GND, digital) & 20 (PWR, GND, digital, analog) \\ Background storage & MicroSD card (up to 1 TB) & Flash memory (32 KB) \\ Power consumption (in idle state) & 750 mW & \textless{}250 mW \\ Sleep mode & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the Raspberry Pi Zero and Arduino Uno.
## 4 Conclusions and Future Works  
Predictive detection of machine anomalies is an important concern in many industries, which can be performed by the analysis of machine sounds. In this paper, we introduce a novel two-stage self-supervised anomalous sound detection method named the Regularized Contrastive Masked Autoencoder (RC-MAE) model. In the first stage, we employ a diffusion-based audio augmentation method called AudioLDM for pre-training. By applying our proposed method to both the augmented and real sounds, we aim to achieve a higher-quality audio representation from unlabeled data. To further enhance the model's performance, we then fine-tune the pre-trained model using supervised Contrastive learning. Our approach is pre-trained and fine-tuned using the MIMI and ToyADMOS2 datasets. Specifically, we assessed the model's performance on the MIMI test dataset. The obtained results demonstrate that our model exhibits impressive average Area Under Curve (AUC) and partial Area Under Curve (pAUC) values of 94.39% and 87.93%, respectively. We perform qualitative analyses on different visualizations of the learned representations generated by the RC-MAE encoder. Our observations revealed that fine-tuning the model with metadata, such as machine type and machine ID, enhances the ability to distinguish between normal and anomalous sounds. The t-SNE visualization of the learned representations encodes meaningful information associated with the underlying structures in the pre-trained stage. In this study, the quantitative findings have demonstrated the efficacy of RC-MAE, while the qualitative observations have indicated valuable insights within the acquired representations. To the best of our knowledge, no studies have been conducted with a mixture of Contrastive and Generative self-supervised learning methods; also, there has been no prior research undertaken on diffusion-based data augmentation in the area of anomalous sound detection.  
Our research on this topic is ongoing. Our future work will aim to improve the model's ability to generalize different machine types and explore this ability with other datasets and metadata. Furthermore, other types of self-supervised models and their incorporation, and other data augmentation methods can be studied.  
Conceptualization, E.Z., M.S., F.S.M. and M.Y.; methodology, E.Z., M.S. and F.S.M.; software, F.S.M. and M.Y.; validation, E.Z., M.S. and F.S.M.; formal analysis, E.Z., F.S.M. and M.Y.; investigation, E.Z., M.S. and F.S.M.; resources, E.Z., M.S., F.S.M. and M.Y.; data curation, F.S.M. and M.Y.; writing--original draft preparation, E.Z., M.S. and F.S.M.; writing--review and editing, E.Z., M.S., F.S.M. and M.Y.; visualization, F.S.M. and M.Y.; supervision, M.S.; project administration, E.Z. and M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 Materials and Methods
### User Profiling Based on Frequent Subgraphs
#### 2.2.4 User Profiling  
The frequent subgraph of a user expresses their preference. In other words, it represents the major attributes that the user considers when selecting an item and the strength of the preference the user has for that attribute. Each user has different frequent subgraphs; our proposed user profiling method makes these subgraphs into vectors of the same dimension.  
Table 1 describes the notations of the main sets used in this section.  
First, we collected a set of frequent subgraphs extracted for all the users of the user-item interaction. If the subgraph of each user \(u\) is denoted by \(g_{u}\), and the triples included in \(g_{u}\) are denoted by \(t(g_{u})\), then the frequent subgraph set \(G\) of the users can be denoted as follows:  
\[G=\{g_{u}|\,\exists g_{u}:\!|t(g_{u})|\!>0,u\in U\}. \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(U\) & Users that exist in the user–item interaction data \\ \(G\) & Frequent subgraphs of \(U\) \\ \(T\) & Instance triples in \(KG\) \\ \(T_{G}\) & Instance triples in \(G\) \\ \(I\) & Items that exist in the user–item interaction data \\ \(I_{u}\) & Items that user \(u\) has interacted with \\ \(S\) & Samples in the training set \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation of Sets.  
Figure 4: Example of frequent subgraph.  
That is, if the frequent subgraph obtained for each user includes one or more triples, these subgraphs are collected for set \(G\). Additionally, the triple set \(T_{G}\) can be defined as follows:  
\[T_{G}=\bigcup_{g^{u}\in G}t(g_{u}). \tag{4}\]  
\(T_{G}\) can be viewed as a set of item attributes that all users consider important when selecting an item. Not all the elements of \(T_{G}\) are included in \(T\) due to abstraction, but they at least have a form like that of the triples included in \(T\).  
Finally, we define the user profile vector. The user profile vector \(v_{u}\) of user \(u\) can be expressed as follows:  
\[v_{u}=\left(e_{1},e_{2},\cdots,e_{i}\cdots,e_{|T_{G}|}\right),\]  
\[e_{i}=\left\{\begin{array}{ll}0&(ift_{i}\
otin t(g_{u}))\\ frequency\ of\ t_{i}&(ift_{i}\in t(g_{u}))\end{array}\right.. \tag{5}\]  
If user \(u\)'s subgraph contains the \(i\)-th triple \(t_{i}\), which belongs to \(T_{G}\), the value of element \(e_{i}\) is the frequency of the \(t_{i}\) that appears in user \(u\)'s frequent subgraph. The user profile vector represents the subgraph extracted from the user graph in the dimensional space of \(|T_{G}|\). In other words, it is the preference of a specific user \(u\) in the space of attributes that the user set \(U\) considers important in item selection.
## 5 Experiments and Results
### Ablation Study
#### 5.4.1 Effectiveness of Traffic Rules Features  
In practice, the experiment is conducted by changing the model settings in terms of input, where traffic rules are included or excluded. As shown in Figure 7, we report the RMSE values at each time step to compare the model results. It is worth noting that the prediction errors in considering traffic rules are always significantly lower than those after removing the traffic rules condition in all prediction horizons. On average, considering traffic rules improves model performance by approximately 11%. In addition, with the prediction step growing, the deviation of the two group experiments increases, indicating that the influence of traffic rules on driving decision making increases. This is because when approaching the intersection, the drivers focus not only on the surrounding agents but also the traffic light phase status, leading to continual interactions. Consequently, it can be concluded that the traffic rules features are a benefit of the proposed scheme and ought to be meticulously weighted in vehicle trajectory anticipation issues.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Time Consumption (s)**} \\ \hline
**CV** & **GRU** & **V-LSTM** & **GAIL-GRU** & **TPASTGCN** \\ \hline
0.02864 & 0.87395 & 0.08742 & 0.99733 & 0.07945 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model calculation time consumption.
# Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures
## 3 Establishing IRT Database  
### Creation of Specimens  
To increase the accuracy of the measurement results, a total of 54 specimens were created, including 3 specimens that were created for each of the 3 cover thicknesses. The six corrosion ratios, determined by the results of several preliminary tests performed beforehand to create the IRT database, and the three cover thicknesses are given below.  
1. Corrosion ratios: 0%, 1%, 3%, 5%, 7%, and 10%.
2. Cover thicknesses: 10 mm, 20 mm, and 30 mm.  
The specimens are shown in Figure 3. To examine the impact of the cover thickness, the experiment variables were set at cover thicknesses of 10, 20, and 30 mm and rebar corrosion ratios of 0%, 1%, 3%, 5%, 7%, and 10%. The corrosion ratio determined in this study was calculated the corrosion rate, which would be able to occur when the concrete is generally exposed to the outside. Moreover, the corrosion ratio 0% was added to the experiment under the assumption that corrosion would notoccur. Along with this assumption, the maximum corrosion rate was established to 10% based on reviewing the previous studies in corrosion of concrete. In addition, the cover thickness of this study (10, 20, and 30 mm) is most commonly applied in the reinforced concrete structures. Each variable was tested and a total of 60 specimens were created (54 specimens and 6 preliminary specimens). Temperature sensors were installed at the center of the rebar within the concrete such that the rebar's internal temperature changes could be determined. The numbers of and variables related to the specimens are listed in Table 2.  
The concrete that was used to create the specimens was poured under ready-mix conditions with a slump of 15 cm and a design standard strength of 21 MPa, as shown in Table 3. For the rebar sample, commonly used D13 rebar was embedded. Table 4 shows the strength of the concrete and rebar that were used.