esa-sceva/satcom-chunk-collection huggingface dataset:
# Closed-Form Solution of the Bending Two-Phase Integral Model of Euler-Bernoulli Nanobeams
## 3 Closed-Form Solution of [PERSON]'s Two-Phase Integral Model Equations
### Clamped Beam (CC)  
Here, we study the behavior of a clamped beam, i.e., a beam subject to boundary conditions  
\[w(0)=w^{\prime}(0)=0,\quad w(L)=w^{\prime}(L)=0. \tag{42}\]  
We define the operator \(B:X\to X\) by  
\[Bw(x) = \frac{d^{2}}{dx^{2}}\Big{(}\frac{d^{2}w(x)}{dx^{2}}+\frac{\xi_{2} }{\xi_{1}}\int_{0}^{L}k(x,t)\frac{d^{2}w(t)}{dt^{2}}dt\Big{)},\] \[\mathcal{D}(B) = \Big{\{}w(x)\in C^{4}[0,L]:w(0)=w^{\prime}(0)=0,\,w(L)=w^{\prime} (L)=0\Big{\}},\]and write the integro-differential boundary value problem (16), (42) compactly as  
\[Bw(x)=\frac{1}{EI\xi_{1}^{*}}q(x),\quad 0<x<L,\quad q(x)\in X. \tag{43}\]  
We take the Fredholm integro-differential operator \(B_{2}:X\to X\) as in (22) in Section 3.2, viz.  
\[B_{2}w(x) = \frac{d^{2}w(x)}{dx^{2}}+\frac{\xi_{2}}{\xi_{1}}\int_{0}^{L}k(x,t )\frac{d^{2}w(t)}{dt^{2}}dt,\] \[\mathcal{D}(B_{2}) = \Big{\{}w(x)\in C^{2}[0,L]:w(0)=w^{\prime}(0)=0\Big{\}},\]  
and the differential operator \(B_{1}:X\to X\) as  
\[B_{1}y(x)=\frac{d^{2}y(x)}{dx^{2}},\quad\mathcal{D}(B_{1})=\Big{\{}y(x)\in C^{ 2}[0,L]\Big{\}},\]  
where \(y(x)=B_{2}w(x)\).  
Then, the operator \(B_{1}B_{2}:X\to X\) is defined on  
\[\mathcal{D}(B_{1}B_{2}) = \Big{\{}w(x)\in C^{2}[0,L]:w(0)=w^{\prime}(0)=0,\,y(x)=B_{2}w(x) \in\mathcal{D}(B_{1})\Big{\}} \tag{44}\] \[= \Big{\{}w(x)\in C^{2}[0,L]:w(0)=w^{\prime}(0)=0,\,\,,\,y(x)\in C ^{2}[0,L]\Big{\}}\] \[= \Big{\{}w(x)\in C^{4}[0,L]:w(0)=w^{\prime}(0)=0\Big{\}},\]  
and the boundary value problem (43) may be written in the form  
\[Bw(x) = B_{1}B_{2}w(x)=\frac{1}{EI\xi_{1}^{*}}q(x),\quad 0<x<L,\quad q(x) \in X,\] \[\mathcal{D}(B) = \big{\{}w(x)\in\mathcal{D}(B_{1}B_{2}):w(L)=w^{\prime}(L)=0\big{\}}. \tag{45}\]  
The solution of the problem  
\[\text{DBVP:}\quad B_{1}y(x)=\frac{1}{EI\xi_{1}^{*}}q(x),\quad 0<x<L,\quad q(x) \in X,\]  
for any \(q(x)\in X\) is given by  
\[y(x)=\frac{1}{EI\xi_{1}}\bigg{[}\int_{0}^{x}(x-t)q(t)dt+C_{1}x+C_{2}\bigg{]}, \tag{46}\]  
where \(C_{i},\,i=1,2\), are arbitrary constants.  
By using \(y(x)\) in (46), we solve the problem  
\[\text{FIDBVP:}\quad B_{2}w(x)=y(x),\quad 0<x<L,\]  
or in the case of a kernel function \(k(x,t)\) of the type (15), the problem  
\[\text{VFIDBVP:}\quad B_{2}w(x)=y(x),\quad 0<x<L,\]  
where operator \(B_{2}\) assumes the form (27). As before, we follow the procedure in Section 3.2 except that now \(f(x)=y(x)\) as in (46) to get the solution  
\[w(x)=D^{-1}f(x)+D^{-1}g(x)W^{-1}\Psi\Big{(}AD^{-1}f(x)\Big{)}, \tag{47}\]  
which depends linearly on the arbitrary constants \(C_{i},\,i=1,2\).  
By enforcing the boundary conditions \(w(L)=0\) and \(w^{\prime}(L)=0\), we can calculate \(C_{i}\), \(i=1,2\), which when replaced at (46) gives the solution of the boundary value problem (44) or the nonlocal Euler-Bernulli Equation (16) subject to the boundary conditions (42).
# Conformal Titanyl Phosphate Surface Passivation for Enhancing Photocatalytic Activity
## 1 Introduction  
Titanium dioxide (TiO\({}_{2}\)) is the most extensively studied metal oxide semiconductor used for diverse solar energy conversions including photovoltaics, photodegradation of organic pollutants, and photocatalytic hydrogen production [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. As for the promising heterogeneous photocatalyst, numerous efforts have been conducted to enhance the photocatalytic activity of TiO\({}_{2}\) nanoparticles by modifying their surface properties [12]. Despite numerous advantages of TiO\({}_{2}\), the tremendous intrinsic defect states in TiO\({}_{2}\) can deteriorate the charge transfer performance of photocatalysis. The deeply localized defects in TiO\({}_{2}\) can act as undesired charge trap centers and promote the rapid charge recombination [13; 14; 15]. These defect states and deep traps induce the crucial charge losses [16; 17].  
In recent years, numerous efforts have been conducted to enhance the photocatalytic activity of TiO\({}_{2}\) nanoparticles by modifying their surface properties with phosphate anion (PO\({}_{4}\)-) [18; 19; 20; 21; 22]. The surface modification using the phosphate species can generate the negative electrostatic field at the interface. This preferred surface electrostatic field facilitates the efficient charge separation and prolongs the photo-induced charge migration [20; 22]. In addition, the negative electrostatic field can activate the formation of hydrogen bonds between phosphate anions and water molecules. Thereby, the phosphate species bound on TiO\({}_{2}\) can promote the generation of hydroxyl radicals, resulting in the highly efficient photocatalytic degradation of organic pollutants [23]. However, the photochemical conversion efficiency can be weakened since the direct hole oxidation pathway can be hindered by the hydroxyl radicals. The adsorbed phosphate anion on the surface of TiO\({}_{2}\) also can increase theresistance of electron transfer at the interface [24; 25]. Therefore, a rational design of the phosphate modification is strongly required for efficient TiO\({}_{2}\) photocatalysis.  
In this study, the TiO\({}_{2}\) nanoparticles were conformally modified by the titanyl phosphate passivation layer with a thickness of ca. 5 nm. The use of a facile surfactant-assisted sol-gel process with titanium isopropoxide, phosphoric acid, and polyethylene glycol as a surfactant facilitated the formation of titanyl phosphate because the phosphate anion can strongly bind the surface Ti atom with the oxygen bridge, inducing the Ti-O-P-O bonds [26]. The titanyl phosphate passivated the surface defects in TiO\({}_{2}\) and reduced the deep traps associated with Ti\({}^{3+}\). Consequently, the titanyl phosphate modified TiO\({}_{2}\) (TP-TiO\({}_{2}\)) shows significantly enhanced photocatalytic activities through the photocatalytic degradation of organic pollutant (rhodamine B) and hydrogen production experiments.
## 2 Materials and Methods
### Data-Collecting Sensors Used in the Project: GNSS, Meteorrones, In Situ Sensors, Sentinel  
The observation system developed within the MAGDA project integrates GNSS receivers, Meteorrones, in situ meteorological and soil sensors, and Copernicus Sentinel satellite data, forming a multi-platform, high-resolution monitoring network. This system is designed to improve the accuracy of numerical weather prediction (NWP) models and hydrological simulations by providing continuous, multi-source observational data.  
In addition to leveraging pre-existing observation networks--including weather radars, meteorological stations, and permanent GNSS networks--MAGDA has deployed new observational assets directly within agricultural fields of interest. The installation of low-cost GNSS receivers, Meteorrones, and in situ metIS sensor hubs enables localized, high-resolution environmental monitoring, improving the characterization of key meteorological and hydrological variables. The newly deployed sensors are integrated with existing data sources through dedicated assimilation and processing frameworks, ensuring a synergistic use of satellite, airborne, and ground-based observations.
## 2 Materials and Methods
### Computational Details  
As mentioned in the Introduction, two applications are here included to better illustrate the potentials of Tree2C. In both cases, the utilized datasets are retrieved as lists of SMILES strings from the reference papers [11; 12]. By using the features implemented in the VEGA environment [25], all SMILES strings were automatically converted into 3D structures and then optimized in their neutral form by PM7 semi-empirical calculations [26], as implemented in MOPAC2016 (keywords: PM7 PRECISE GEO-OK SUPER). Based on the prepared molecules, 129 properties/attributes were calculated by both VEGA and MOPAC2016 and their values were exported into the ARFF file format. By using the Weka program, the most significant attributes were selected according to both the BestFirst search algorithm (direction = Forward; lookupCacheSize = 1; searchTermination = 5) and the WrapperSubsetEval attribute evaluator (classifier = RandomForest with default settings; doNotCheckCapabilities = False; evaluationMeasure = accuracy, RMSE; folds = 5; seed = 1; threshold = 0.01).  
The final model was developed by the random forest machine learning algorithm as implemented in Weka with default parameters (bagging with 100 iterations and base learner) and performing a 10-fold cross-validation. The Weka output files along with the input ARFF files are then utilized by the Tree2C tool to generate the source code which deploys the developed models to be used for new predictions. Tree2C can be run either outside the VEGA environment by command line according to a set of options, which can be seen in the manual, or within the VEGA environment by using a specific graphical interface. To better illustrate this second option, Figure 1 shows the Tree2C graphical interface as implemented in the VEGA environment. The user has to define the required input files (the ARFF file is required only to determine the applicability domain) and the output source code file. The user can assign a name to the model and can select if the output code should be a VEGA C-script or should be compiled as a standalone tool.
## 5 Performance Analysis
### Static Environment Sensor Deployment  
Three sets of experiments were implemented in a three-dimensional monitoring area of 200 m \(\times\) 200 m \(\times\) 200 m: (1) six sensor nodes and 40 events were unevenly distributed in a T shape; (2) six sensor nodes and 40 events were randomly distributed; and (3) six sensor nodes and 40 events were unevenly distributed in a line.  
Figure 6 shows the results of the DSFSOA algorithm for self-organizing deployment of nodes. The light blue sphere represents the three-dimensional sensing range of the sensor node (the red center of the sphere is the position of the node), and the blue star represents the event. It can be seen that the DHFSOA algorithm is capable of achieving a final state in which all events covered by nodes and there is a good match between node distribution density and event distribution density.  
The PSSD algorithm and the DHFSOA were used to deploy the sensor nodes. Figure 7 shows the evolution of the total moving distance and event coverage for the two algorithms in the three experiments. It should be noted that the final result for each set of experiments here is the average of 30 experiments. It can be seen in Figure 7a,c,e that the DHFSOA algorithm not only achieved high coverage of the event, but indeed achieved optimal coverage after just a few moves of the node, demonstrating faster convergence speed than the PSSD. More critically, the DHFSOA algorithm overcame the node blindness found in the traditional heuristic random search algorithm, while the PSSD algorithm exhibited significant instability and a poor final result. Figure 7b,d,f, is a comparison of the trend of the total moving distance of the node with the change of the number of iterations of the DSFSOA algorithm and the PSSD. It is clear that the DHFSOA algorithm greatly decreases the total moving distance of nodes during deployment compared with the PSSD algorithm. This is mainly due to the fact that the nodes in the PSSD algorithm make blind movements. The DHFSOA algorithm utilizes information sharing between the nodes based on the information pool. This improves the global sensing ability of the distributed fish swarm algorithm and thus avoids the blind movement of nodes, thereby reducing the total moving distance of nodes during deployment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Node’s radius of perception \(r^{s}\) & 50 m & Maximum number of iterations \(T_{max}\) & 50 \\ Node’s radius of communication \(r^{c}\) & 100 m & Constant \(N_{max}\) & 5 \\ Length of moving step \(l\) & 15 m & Constant \(\psi\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 6: Achievement of self-organized Deployment of Nodes using the DHFSOA. (**a**) events unevenly distributed in a T shape; (**b**) 40 events randomly distributed; (**c**) 40 events unevenly distributed linearly.  
Figure 7: _Cont._
## 4 Results and Comparison
### RUL Estimation for Test Instance Bearings
#### 4.1.3. RUL Estimation for Test Instance Bearing1-6  
The similarity evaluation for RUL estimation was made based on the pair of 2 nd PC feature sequences between the test instance Bearing1-6 and the learning instance Bearing1-2. Initially, we specify the comparing segment as the monotonic segment in the range of \([0,1590]\) (time cycles) within the degradation stage of Bearing1-6. Using Bearing1-2, the segment of \([0,825]\) (time cycles) is specified to generate nine synthetic sequences with scaling factors of 3.40, 3.60, 3.80, 4.00, 4.20, 4.40, 4.60, 4.80, and 5.00, as shown in Figure 13. The duration \(\Delta\) is equal to 45 (time cycles) in this case. Table 4 presents the results of similarity evaluation for the RUL estimation. Based on the seven synthetic sequences of \(\alpha\) = 3.60, 3.80, 4.00, 4.20, 4.40, 4.60, and 4.80, the final RUL estimate is computed as 2654 (time cycles) when the degradation process reached the time cycle 1590. As the degradation process has lasted 710-time cycles from the time cycle 1590 to the current time cycles 2300, thus \(RUL_{e}\) = 2654 \(-\) 710 = 1944 (time cycles).  
Figure 11. Similarity evaluation for RUL estimation of Bearing1-4 using Bearing1-2.  
Figure 12. Similarity evaluation for RUL estimation of Bearing1-5 using Bearing1-2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\alpha\) & 2.80 & 2.90 & 3.00 & 3.10 & 3.20 & 3.30 & 3.40 & 3.50 & 3.60 \\ \(d_{min}(\alpha)\) & 0.8149 & 0.4430 & 0.3562 & 0.3527 & 0.3750 & 0.3967 & 0.4166 & 0.4347 & 0.4495 \\ \(RUL(\alpha)\) & 46 & 46 & 46 & 70 & 141 & 211 & 284 & 355 & 430 \\ \hline \hline \end{tabular}
\end{table}
Table 3. Similarity evaluation for RUL estimation of Bearing1-5 using Bearing1-2.  
However, from the 2 nd PC feature sequence, we can observe that sudden and abrupt interruptions occurred during the range of [1590, 2300] (time cycles) of the test instance. Thus, we may consider that the degradation process has entered the close-to-failure stage after the time cycle 1590. Reasonably, the RUL of Bearing1-6 can be estimated as \(RUL_{e}=\Delta=45\) (time cycles) at the last time cycle 2300. As the actual RUL is known as \(RUL_{a}=146\) (time cycles), the relative error of estimation can be calculated as \(E_{r}=69.2\%\).
## 3 Mathematical Formulation
### Array Far-Field Calculation
#### 3.3.1 Array Excitations  
Complex array excitations \(\underline{w}=\{w_{n}\}_{n=1,\ldots,N}\in\mathbb{C}\) are computed with a uniform amplitude \(\alpha_{n=1,\ldots,N}=1\). Only a cluster of elements is used, while some elements are electronically deactivated (e.g., amplitude zero). This occurs when the steering angle deviates by more than \(90^{\circ}\) from the element's broadside. The concept is illustrated in Figure 6.  
The phases of array \(\beta_{n}\) are instead defined analytically as  
\[\beta_{n}=-k(x_{n}\sin\theta_{s}\cos\varphi_{s}+y_{n}\sin\theta_{s}\sin\varphi_ {s}+z_{n}\cos\theta_{s})-\psi_{n}, \tag{12}\]  
where \(\left(\theta,\varphi\right)_{s}\) are the steering angles, respectively, in elevation and azimuth, and \(\psi_{n}\) is the azimuthal rotation of the element, performed to ensure symmetrical steering along both axes. For example, a linear array is depicted in Figure 7, showing how the antennas are rotated to compensate for the asymmetry of the element pattern when dealing with non-ideal sources.  
Figure 6: Example representing the cluster of elements activated while the array is steering: (**a**) all the elements are activated, (**b**) only a cluster of elements is turned on.
## 4 Main Results
### Result for Increasing Self-Mapping  
**Theorem 2**. : _Let \((P,\mathbf{d}_{CA})\) be a complete cone \(\mathbf{A}\)-metric space along with a cone \(\mathcal{C}\subset E\) and \(g:P\to P\) be a self-mapping. Let \(S\in B(E,E)\) so that \(\|S\|_{1}<1\), \(I:E\to E\) be an identity operator. If for all comparable elements \(\ell,\kappa\in P\), \(\digamma\in\mathcal{H}\), \(g\) satisfies the following conditions:_  
\[(I-S)\mathbf{d}_{CA}(\zeta_{\ell}^{g(\ell)})\preceq_{e}\mathbf{d}_{CA}(\zeta _{\ell}^{\kappa})\text{ implies }\]  
\[\digamma\Big{(}\ \mathbf{d}_{CA}(\zeta_{g(\ell)}^{g(\kappa)}),\mathbf{d}_{CA}( \zeta_{\ell}^{\kappa}),\mathbf{d}_{CA}(\zeta_{\ell}^{g(\ell)}),\mathbf{d}_{CA} (\zeta_{\ell}^{g(\kappa)}),\mathbf{d}_{CA}(\zeta_{\ell}^{g(\kappa)}),\mathbf{ d}_{CA}(\zeta_{\ell}^{g(\ell)})\ \Big{)}\preceq_{e}\mathbf{0}, \tag{3}\]  
_and_  
_there is_ \(\ell_{0}\in P\) _such that_ \(\ell_{0}\mathcal{R}g(\ell_{0})\)_;_  
_for all_ \(\ell,\kappa\in P\)_,_ \(\ell\mathcal{R}\kappa\) _implies_ \(g(\ell)\mathcal{R}g(\kappa)\)_; and_  
_for each sequence_ \(\{\ell_{n}\}\)_, with comparable sequential terms, converging to_ \(c^{*}\)_, we have_ \(\ell_{n}\mathcal{R}c^{*}\) _for all_ \(n\in\mathbb{N}\)_._  
_Then, there exists \(c^{*}\in P\) such that \(c^{*}=g(c^{*})\)._  
Proof. : We construct a sequence \(\{\ell_{n}\}\) such that \(g(\ell_{n-1})=\ell_{n}\). By (1), there exists \(\ell_{0}\in P\) such that \(\ell_{0}\mathcal{R}g(\ell_{0})\), so, taking \(\ell=\ell_{0}\) in (3), we have  
\[(I-S)\mathbf{d}_{CA}\Big{(}\zeta_{\ell_{0}}^{g(\ell_{0})}\Big{)}=(I-S)\mathbf{d }_{CA}(\zeta_{\ell_{0}}^{\ell_{1}})\preceq_{e}\mathbf{d}_{CA}(\zeta_{\ell_{0}}^ {\ell_{1}})\text{ implies }\]  
\[\digamma\Big{(}\ \mathbf{d}_{CA}\Big{(}\zeta_{g(\ell_{0})}^{g(\ell_{1})}), \mathbf{d}_{CA}(\zeta_{\ell_{0}}^{\ell_{1}}),\mathbf{d}_{CA}\Big{(}\zeta_{\ell _{0}}^{g(\ell_{0})}\Big{)},\mathbf{d}_{CA}\Big{(}\zeta_{\ell_{1}}^{g(\ell_{1})} \Big{)},\mathbf{d}_{CA}\Big{(}\zeta_{\ell_{0}}^{g(\ell_{1})}\Big{)},\mathbf{d}_ {CA}\Big{(}\zeta_{\ell_{1}}^{g(\ell_{0})}\Big{)}\ \Big{)}\preceq_{e}\mathbf{0},\]  
that is,  
\[\digamma\Big{(}\ \mathbf{d}_{CA}(\zeta_{\ell_{1}}^{\ell_{2}}),\mathbf{d}_{CA}( \zeta_{\ell_{0}}^{\ell_{1}}),\mathbf{d}_{CA}(\zeta_{\ell_{0}}^{\ell_{1}}), \mathbf{d}_{CA}(\zeta_{\ell_{1}}^{\ell_{2}}),\mathbf{d}_{CA}(\zeta_{\ell_{0}}^{ \ell_{2}}),\mathbf{d}_{CA}(\zeta_{\ell_{1}}^{\ell_{1}})\ \Big{)}\preceq_{e}\mathbf{0}. \tag{4}\]  
By (4), we have  
\[\mathbf{d}_{CA}(\zeta_{\ell_{0}}^{\ell_{2}})\preceq_{e}(n-1)\mathbf{d}_{CA}( \zeta_{\ell_{0}}^{\ell_{1}})+\mathbf{d}_{CA}(\zeta_{\ell_{1}}^{\ell_{2}}),\]and by rewriting (4) and applying (\(F_{1}\)), we obtain  
\[ \begin{split}\digamma&\big{(}\ \mathbf{d}_{CA}(\zeta_{\ell_{1}}^{ \ell_{2}}),\mathbf{d}_{CA}(\zeta_{\ell_{0}}^{\ell_{1}}),\mathbf{d}_{CA}(\zeta_{ \ell_{0}}^{\ell_{1}}),\mathbf{d}_{CA}(\zeta_{\ell_{1}}^{\ell_{2}}),(n-1) \mathbf{d}_{CA}(\zeta_{\ell_{0}}^{\ell_{1}})+\mathbf{d}_{CA}(\zeta_{\ell_{1}}^ {\ell_{2}}),\mathbf{0}\ \ \Big{)}\preceq_{\epsilon}\mathbf{0}.\end{split}
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 2 Calibration Requirements
### Calibration Methods
For the beam characterization, Jupiter provides an unpolarized thermal signal allowing a noise floor (NF) level of about \(-\)50 dB at 100 GHz [40; 42], taking into account the overall contributions of this particular detectors band during the complete observation period of a typical mission. However, for future missions with thousands of detectors the level of accuracy reported by these sources seems clearly insufficient to extract all the potential scientific results expected from them.
# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 3 Data for Study
### 110 Call Records  
The data consist of all the 110 Call records in Chaoyang District, Beijing, for the year 2019. Any record lacking essential details like the incident's address was excluded, as these cannot be accurately used in the research. For repeated alarms, those triggered for the same incident within a short time (typically within tens of minutes) were combined into a single record. After these steps, the dataset was reduced to 377,260 records, which retained 95.2% of the original records. The records encompass various types of incidents, which have been classified into three categories: crime-related (148,208 incidents), security-related (172,087 incidents), and assistance-related (56,965 incidents). The crime-related incidents primarily include personal assault, robbery, theft, and fraud. The security-related incidents encompass economic disputes, neighborhood conflicts, noise complaints, harassment, and traffic accidents. The assistance-related incidents involve situations such as missing persons, lost items and requests for assistance. The main content includes the time of the call and the address. The address information is in the form of the latitude and longitude. A distance-based mapping algorithm was employed to calculate the Euclidean distance between each incident location and the nearest street segment. The nearest neighbor analysis tool in ArcGIS 10.6 was used to match each incident to its closest street. To ensure accuracy, we adhered to geocoding standards, achieving a hit rate above the 85% reliability threshold recommended by [PERSON] [45]. The distribution of all the incidents across the street network is depicted in Figure 2.  
Figure 1: Map of Chaoyang District: (**a**) location of Chaoyang District in Beijing; and (**b**) street network of Chaoyang District.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 4 Discussion  
The time stretching experiment demonstrated that stretching within the range [0.88, 1.12] produced the best results, while excessive or insufficient stretching could reduce accuracy, weakening the salient features of the original audio signal. Spectrogram masking techniques can further enhance the diversity of audio signals, resulting in a higher recognition performance for indoor abnormal sound events. Pitch Shifting experiments have shown that MFCC features are not sensitive to pitch changes, but the combining spectrogram masking techniques can improve the sensitivity of the Pitch Shifting technique. The energy levels of the background noise can affect the feature representation of the original audio signal. However, the combination of spectrogram masking and background noise is unstable under high background noise conditions, where the high background noise masks the characteristics of the raw audio. This indicates that data with high background noise are not suitable for abnormal sound detection and recognition. The proposed TS [0.88, 1.12] and BN [10 dB] and Online Aug combined data augmentation method can significantly improve the accuracy of indoor abnormal sound event recognition. Its overall accuracy was 10.6% higher than the baseline model.  
The confusion matrix provides a clear view of the confusion between different types of abnormal sound events. Figure 7 shows the confusion matrix results generated by our method on the test dataset. It can be observed that the number of false negatives was very low and mainly concentrated in pairs such as glass breaking and gunshot, coughing, and sneezing. Gunshot is generally loud and sharp, depending on the type of gun, and large glass breaking is often loud and sharp. Both coughing and sneezing come from throat sounds. This indicates that these pairs of events share similar time-frequency characteristics. We found that the screams had excellent performance, which may be related to its characteristic higher frequency and sustained vocalization. This result demonstrates that EANN combined with the combined data augmentation technique achieved superior performance and outperforms the baseline classification methods.  
Table 8 shows the performance comparison of various data augmentation techniques, and their results were all improved compared to the original no data augmentation model. This highlights the effectiveness of applying data augmentation techniques to acoustic event recognition. Our method had a classification accuracy of 0.974 for indoor anomalous sound datasets.  
Figure 7: The confusion matrix results from combined augmentation.