esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Effects of Fire on Mechanical Properties
#### 4.2.1 Compressive Strength Development
The possible reason for the higher reduction in strength in the fiber-reinforced concrete after exposure to fire could be; fibers would have become soften after exposure to high temperatures such as 400 \({}^{\circ}\)C or above. The softening of fibers would have resulted in a reduction in stiffness; hence, the strength reduction factor is high in FRC. Because with a reduction in stiffness of fibers, their crack arresting capability is reduced. The test was conducted using a Universal Testing Machine, UTM, and the load was applied at a rate of 0.94 kN/s [25; 26; 27]. Figure 13 shows the splitting cylindrical tensile strength of all concrete core samples, which were and were not exposed to fire. In this case-controlled concrete samples showed the lowest strength than the fiber-reinforced sample; the reason is evident that the addition of fibers arrested the cracks. Hence the tensile strength is increased. Table 4 showed a reduction in the split tensile strength of all concrete mixes. In this case, the reduction in strength was reported between 17%  
Figure 12: Effects of 120 min of fire exposure on the compressive strength. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mix Type**} & \multicolumn{2}{c}{**Core Compressive Strength (Mpa) at 90 Days Curing**} & \multirow{2}{*}{
\begin{tabular}{c} **Reduction in Strength (\%)** \\ **(After 120 min of Fire** \\ **Exposure)** \\ \end{tabular} } \\ \cline{2-3}  & \multicolumn{1}{c}{**Unfired Samples**} & & **Fixed Samples** \\ \hline M1-0F & 68.7 & 54.2 & 21.1 \\ M2-1.0 BF & 54.4 & 36.9 & 32.2 \\ M3-1.5 BF & 51.9 & 33.4 & 35.6 \\ M4-2 BF & 45.3 & 32.9 & 27.4 \\ M5-1 PF & 73.3 & 54.8 & 25.2 \\ M6-1.5 PF & 67.1 & 52.3 & 22.1 \\ M7-2.0 PF & 67.8 & 48.2 & 28.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reduction in compressive strength after 120 min of fire exposure. and 27%. As compared to the compressive strength test, strength reduction factor in fiber reinforced concrete is reduced. The reason could be the post elastic characteristics of the fibers has improved even after being softened upon exposure to high temperature. (2) Effects of 120 min of fire exposure on the split cylindrical tensile strength  
Split cylindrical tensile strength is an indirect tensile strength of concrete for this study 100 mm diameter and 200 mm long cores taken from the slabs that were and were not exposed to fire. The test was carried out by ASTM C496M-04 [25].
###### Abstract  
Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granite complex in Mardan District (Khyber Pakhtunkhu) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., \(2\times 17\) components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region's precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhu Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.  
remote sensing; geological mapping; feature-oriented principal component analysis; product-level image fusion; Google Earth Engine; [PERSON]; Ambela; Malakand; Nagarparkar 1111486. [[https://doi.org/10.3390/app112311486](https://doi.org/10.3390/app112311486) [https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://doi.org/10.3390/app112311486](https://doi.org/10.3390/app112311486) [https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 3 Results
### User's and Producer's Accuracies
The multi-band DTW30 for TA2 had 1.5% lower OA than the single-band DTW45. Although it increased the UAs for double crop with 18.1% by reducing the confusion with corn and for grass/pasture with 6.7% by reducing the confusions with cotton and fallow, it decreased the UAs for cotton with 16.1% by increasing the confusion with corn and for fallow with 10.8% by increasing the confusion with wheat class (Table 7). Regarding PAs, multi-band DTW30 increased the PAs for cotton with 9.8% by reducing the confusion with grass/pasture and for fallow/idle cropland class with 9.7%, by reducing the confusion with grass/pasture and wheat. However, PAs decreased for double crop with 11.6% by slightly increasing confusion with multiple classes, for corn with 5.6% by increasing confusion with cotton crops, and for winter wheat with 3.5% by increasing the confusion with fallow class (Table 7). Overall, the lowest OA for multi-band DTW30 compared with single-band DTW45 for TA2 was attributable to two main increases in confusion between classes: between what we classified as cotton and was corn and what we classified as fallow that was winter wheat in the validation dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Crop**} & \multicolumn{2}{c}{**Single-Band DTW30**} & \multicolumn{2}{c}{**Multi-Band DTW30**} \\ \cline{2-5}  & **UA** & **PA** & **UA** & **PA** \\ \hline Wheat & 56.5 & 81.3 & 82.0 & 85.4 \\ Alfalfa & 98.2 & 77.7 & 98.8 & 85.4 \\ Other hay/non-alfalfa & 65.9 & 78.4 & 74.4 & 75.0 \\ Sugarbeets & 69.6 & 91.4 & 77.3 & 97.1 \\ Onions & 88.4 & 85.7 & 91.8 & 90.8 \\ Sod/grass seed & 82.4 & 51.2 & 77.9 & 64.6 \\ Fallow/idle cropland & 86.4 & 100 & 92.7 & 100 \\ Vegetables & 33.8 & 97.8 & 39.1 & 97.8 \\ Water & 100 & 63.3 & 100 & 86.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: User’s (UA) and producer’s accuracy (PA) for most accurate single-band (DTW30) and multi-band (DTW30) object-based time-constrained DTWs for TA1, in California.
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 3 Hybrid Methodology EMD-VMD
### Step 4: Post-Processing with Hilbert Transform  
After the application of VMD, the Hilbert Transform is applied to each mode to extract the instantaneous amplitude \(A(t)\) and frequency \(\omega(t)\). The Hilbert Transform \(\mathcal{H}\{u_{k}(t)\}\) of each mode \(u_{k}(t)\) is given by  
\[\mathcal{H}\{u_{k}(t)\}=\frac{1}{\pi}\text{P.V.}\int_{-\infty}^{\infty}\frac{ u_{k}(\tau)}{t-\tau}d\tau\]  
where P.V. denotes the Cauchy principal value.  
The analytical signal is then formed as  
\[z_{k}(t)=u_{k}(t)+j\mathcal{H}\{u_{k}(t)\}\]  
The instantaneous amplitude \(A(t)\) and frequency \(\omega(t)\) are derived as  
\[A(t)=|z_{k}(t)|=\sqrt{u_{k}^{2}(t)+\mathcal{H}^{2}\{u_{k}(t)\}}\]  
\[\omega(t)=\frac{d}{dt}\arg(z_{k}(t))\]
## 3 Materials and Methods
### Orbitrap SPMESH-DART-HRMS/MS: Optimization of Conditions and Determining Figures of Merit  
Optimization experiments were performed using spiked grape macetate for SPMESH-DART-MS analyses. To optimize scan density, Q Exactive MS analytical cycle time was varied by changing the maximum injection time from 250 ms to 100 ms in 50 ms increments. The effect of automatic gain control (AGC) was evaluated at 10\({}^{6}\) and 10\({}^{5}\). The effect of scan resolution was evaluated at 70,000 and 140,000. After optimization, the MS was operated in positive ion mode at a scan resolution of 70,000, 1 microscan, 100 maximum injection time, automatic gain control (AGC) of 10\({}^{5}\) over a mass range of _m/z_ 75-180. IBMP and d\({}_{3}\)-IBMP were measured by MS/MS in parallel reaction monitoring mode (PRM) with mass tolerance of 5 ppm. The MS/MS transitions were identified in previous work [12]. Precursor ion for d\({}_{3}\)-IBMP was identified by dropping 10 uL of standard solutions in the DART reaction zone while operating the MS in full scan mode over mass range of _m/z_ 75-180. Product ion scans were performed in a similar manner; however, the MS was operated in PRM mode. Transitions monitored were: _m/z_ 167.1427\(\rightarrow\)124.0632 at CE 17 eV for IBMP and _m/z_ 170.1351\(\rightarrow\)128.0896 at CE 17 eV for d\({}_{3}\)-IBMP. For method calibration and determining linearity, repeatability, and limits of detection, six calibration standards (range = 0.5-100 ng/kg IBMP; 100 ng/kg d\({}_{3}\)-IBMP) were prepared in Barbera grape macetate, as described above, and analyzed in replicate. To determine recovery, samples were spiked with 5 ng/kg IBMP. LOD and LOQ were calculated using calibration data based on S:N = 3 and S:N = 10, respectively.
## 4 Results and Discussion  
In this study, the simulations are analyzed under three conditions. Firstly, a reliability study is performed where heat transfer models are subject to identical initial conditions to CFD simulations. By doing such an operation, the robustness of the proposed heat transfer model is shown to capture in-cylinder temperature and trapped mass. Secondly, only Woschni and the proposed heat transfer models are studied to evaluate the impact on engine performance, in which combustion and heat transfer settings are restrained. Finally, a sensitivity analysis depicting the impact of the correction coefficients of the correlations is studied.
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Unsupervised Method
#### 3.2.1 Autoencoder  
Encoder and decoder are the two core parts of the self-encoder. Among these two, the encoder corresponds to the hidden layer in the network model and is used to learn the low-dimensional features of the input signal; the decoder corresponds to the output layer in the model, used to reproduce the input signal as much as possible. Therefore, enabling the encoder to learn the good low-dimensional characteristics of the input signal and reconstructing the input signal are the ultimate goals of the self-encoder.  
Literature [57] used the SSIM index in traditional image processing as the reconstruction loss and introduced it into the AE-based image reconstruction; tests were carried out on the woven texture data set and the nanofibrous material dataset, both of which obtained a significant difference compared with the L2 loss. In order to solve the shortcoming that AE has good reconstruction of abnormal samples. Literature [58] converts anomaly detection into patch-sequence inpainting [59] problem; meanwhile, to make up for the shortcomings of this type of method that it is difficult to cover larger abnormal regions, transformer network is proposed to reconstruct covered patches only, and local and global embedding methods were designed for different situations. Literature [60] designed a full-convolution AE (MS-FCAE) with multi-scale feature clustering, used multiple FCAE sub-networks at different scales to reconstruct the texture image background and then subtract the texture background from the input image to obtain the residual images, and finally merged them to obtain a defect image, where each FCAE sub-network uses a fully convolutional neural network to directly obtain the original feature image from the input image and perform feature clustering. Literature [61] proposed a multi-scale convolutional denoising autoencoder (MSCDAE), which uses a multi-modal strategy to synthesize the results of multiple pyramid levels and tests on LCD panels, tiles, and textiles; the experiment proves that this method obtains high accuracy and robustness. Literature [62] uses the convolutional autoencoder (CAE) to detect the logo image of mobile phones, extracts the difference between the template image generated by CAE and the input image, and then processes it through mathematical morphology to achieve the purpose of anomaly detection. Literature [63] proposed a convolutional autoencoder (CAE) for unsupervised feature learning. Each CAE uses traditional online gradient descent training without additional regularization items. Good results are obtained on MNIST and CIFAR10.
## 3 Implementation and Software  
### Implementation of the Stability Measures  
The PCE method is implemented using the function _confEllipse()_ and the corresponding _plot()_ function of the _smacof_ package in R. The _plot()_ function has been modified to format the ellipses and give their parameters. The method is applied only with the mMDS method implemented using the _mds()_ function with _type=\"ratio\"_ of the _smacof_ package [4].  
The jackknife method (JACmds) is implemented using the _jackmds()_ function and the corresponding _plot()_ function of the _smacof_ package. The method has been modified to present not only the plot with stars but also the convex hulls of the jackknife solutions, their areas, and the percentage of overlapping area among the polygons of the convex hulls. In this method, the uncertainty in a MDS plot was assessed based on the area of these polygons. The method is applied using the _mds()_ function of the _smacof_ package [4].  
The BOOTres method uses the following steps: For a certain MDS method, we first calculate the original distances, D; the Euclidean distances between the MDS points, Dmds; and the residuals, R = D - Dmds. Then, we use random sampling or permutations of the residuals via the _sample()_ function to create bootstrap samples of residuals, RB, from which we calculate the bootstrap distances, DB = RB + Dmds. Finally, bootstrap MDS maps/points are estimated using DB as the input matrix for the MDS method used. Note that when the MDS method is the PCoA, the above steps can be implemented via the _BootstrapDistance()_ function of the _MultBiplotR_ package of R. Moreover, when the MDS method is implemented using the _mds()_ function of the _smacof_ package, the matrices D and Dmds can be obtained from the matrices _dhat_ and _conflist_, respectively, of the output of the _mds()_ function. It is evident that the point configuration (map) obtained from a MDS solution may be scaled, reflected, rotated, and translated without affecting its correlation to the input distance matrix. Therefore, before the superimposition of all bootstrap MDS maps, these maps must be properly reflected, rotated, translated, and scaled to achieve optimal matching. This procedure can be done using Ordinary Procrustes Analysis [23; 24] and, in particular, the _procOPA()_ function of the _shapes_ package. In the final step, confidence regions (ellipses) for the MDS solutions are constructed from the superimposed data using the _dataEllipse()_ function of the _car_ library.  
Finally, the steps needed for the application of the _MAPsov_ method were implemented as follows: The generation of simulated distances based on the original dataset was carried out using the Monte-Carlo method, or bootstrapping, as described in [15]. For each artificial dataset of pairwise distances, the first two MDS dimensions are computed, and they are scaled, reflected, rotated, translated, and superimposed using the _procOPA()_ function of the _shapes_ package, as in the previous method. Similarly, confidence ellipses are constructed from the superimposed data using the _dataEllipse()_ function of the _car_ library.
## 6 Conclusions and Future Directions  
This paper provides a comprehensive analysis of the most recently proposed multipath routing protocols for wireless sensor networks. Nowadays, multipath routing techniques are considered an efficient approach to improve network capacity and resource utilization under heavy traffic conditions. With respect to the recent advances in the development of multipath routing protocols for wireless sensor networks, there is a need to investigate the significance as well as the detailed operation and classification of the proposed approaches. To fill this gap, in this paper we have attempted to identify the challenges pertaining to the design of multipath routing protocols for wireless sensor networks. In addition, we have highlighted the main advantages of using multipath routing approach to satisfy the performance requirements of different applications. This paper also introduces a new taxonomy on the multipath routing protocols designed for wireless sensor networks. The provided classification is performed based on the employed path utilization methods that can be used by multipath routing protocols to achieve various performance benefits. Tables 1-3 demonstrate detailed operational characteristic of the existing multipath routing protocols related to the different categories. Finally, all the presented multipath routing protocols in this paper are summarized in Table 4 to provide a fast overview of the main motivations behind their design and the methods employed to achieve the desired goals.  
Although in the past years multipath routing has been researched through numerous studies, nevertheless, there are several important research issues that should be further investigated. These possible areas can be summarized as follows: first, cross-layer principles can be used to improve multipath routing protocols. For instance, the channel access delay and per-hop latency perceived at the MAC layer can be used at the network layer to achieve more accurate path quality estimation and rate assignment. Second, when multiple paths are in use, packet reception order at the sink node may be different from the packet transmission order at the source node. This issue affects the performance of applications such as multimedia streaming and wastes network resources. Finally, the development of multi-constrained QoS multipath routing protocols to guarantee the QoS demands of different applications is an open area. To this aim, different multipath routing approaches (such as using backup paths, sending multiple copies of data packets, and concurrent path utilization) should be integrated efficiently.
# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 5 Conclusions and Future Work  
This paper proposes a HAR recognition model called BSTCA-HAR based on an LSTM neural network. In addition, the proposed model integrates an attention mechanism and TCN network. The model inputs data collected by mobile sensors into the BiLSTM layer; the bidirectional structure of the LSTM considers both past and future information, providing a better understanding of the context within sequential data. Subsequently, a TCN is utilized to process long time series information and an attention mechanism is introduced to learn the associations and dependencies between different time steps in the action sequences, effectively improving the model's recognition accuracy. After feature extraction, GAP and BN layers are added to significantly reduce the number of model parameters and accelerate the model's convergence speed. We tested the proposed model's efficacy and generalizability using three publicly available datasets (WISDM, UCI-HAR, and IELD), each with varying data features. With accuracy rates of 93.2%, 91.9%, and 96.1%, respectively, in human activity recognition, the model demonstrated strong performance. Multiple baseline models were used in comparison studies, which showed that the BSTCA-HAR model has higher training efficiency and improved generalization ability. Our model possesses the capability to dynamically extract salient activity features while exhibiting the benefits of a reduced parameter count and enhanced prediction precision.  
Figure 6: Effect of batch size on model performance.  
Lastly, more investigation into the cooperative learning of visual and non-visual modalities may be conducted in future studies. This includes using multimodal data fusion techniques to further increase the accuracy and resilience of action identification by merging visual modalities such video frames and skeletal data with non-visual modalities such as inertial sensor data.  
Conceptualization, S.Y.; methodology, Y.Y.; validation, Y.Y. and F.Y.; formal analysis, X.T.; investigation, S.Y.; data curation, L.H. and F.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, L.H. and F.Y.; visualization, L.H. and Y.Y.; supervision, Y.Y.; funding acquisition, L.H. and X.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (Nos. 11361104 and 12261104), the Youth Talent Program of Xingdian Talent Support Plan (No. XDYC-QNRC-2022-0514), and the Yunnan Provincial Basic Research Program Project (Nos. 202301 AT070016 and 202401 AT070036).