esa-sceva/satcom-chunk-collection huggingface dataset:
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 5 Experimental Results and Performance Evaluation
### Point-Adjusted F1-Score (F1\({}_{pa}\))  
A predicted anomaly is deemed a true positive if it falls within any ground-truth anomaly segment according to the _Point-Adjusted F1-Score (F1\({}_{pa}\))_, which introduces a heuristic modification to address the drawbacks of the standard F1-score in time-series  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Batch Size & 128 \\ Input Sequence Length & 100 \\ Input Features & 16 \\ Embedding Dimension & 32 \\ Number of Encoders & 3 \\ RNN Hidden Size & 8 \\ RNN Layers & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameter settings of ChaMTeC for the WaterLog\({}^{*}\) dataset. For other datasets, the settings remain the same, except for the input feature dimension, which varies accordingly.  
settings. The following definitions of true positives, false positives, and false negatives are altered in this method:  
* \(TP_{PA}\) (Point-Adjusted True Positive): A predicted anomaly point is counted as a true positive if it overlaps with any ground-truth anomaly segment.
* \(FP_{PA}\) (Point-Adjusted False Positive): A predicted anomaly that does not correspond to any ground-truth anomaly.
* \(FN_{PA}\) (Point-Adjusted False Negative): A ground-truth anomaly with no overlapping predicted anomaly points.  
Using these adjusted values, the point-adjusted F1-score is calculated as follows:  
\[F1_{PA}=2\times\frac{\text{Precision}_{PA}\times\text{Recall}_{PA}}{\text{ Precision}_{PA}+\text{Recall}_{PA}} \tag{23}\]  
where  
\[\text{Precision}_{PA}=\frac{TP_{PA}}{TP_{PA}+FP_{PA}} \tag{24}\]  
\[\text{Recall}_{PA}=\frac{TP_{PA}}{TP_{PA}+FN_{PA}} \tag{25}\]  
By taking time-continuous anomalies into account, \(F1_{PA}\) enhances the standard F1-score; however, it introduces a bias towards true positive detection, which may lead to an overestimation of model performance. This problem occurs because, even in cases where the model is unable to identify the majority of the anomaly duration, a single correctly detected anomaly point within an interval is enough to count the entire interval as correctly detected.
## 2 Materials and Methods
### Carotenoid Analysis
#### 2.6.4 Determination of Carotenoid Profiles  
The carotenoid profiles before and after saponification were determined using the HPLC-DAD method, adapted from that described by [PERSON] et al. [23]. The carotenoids were separated using a 250 \(\times\) 4.6 mm and 5 \(\upmu\)m C\({}_{30}\) column (YMC, Kyoto, Japan) connected to a guard column. The system used for separation was an Agilent 1100 series liquid chromatograph (Agilent, USA) equipped with a cooled automatic injector. The column temperature was maintained at 25 \({}^{\circ}\)C. The mobile phase flow rate was 1 mL\(\cdot\)min\({}^{-1}\) and the injection volume was 20 \(\upmu\)L. The mobile phase was a mixture of eluent A\(-\)H\({}_{2}\)O Millipore, eluent B\(-\)methanol, and eluent C\(-\)MTBE. The solvent gradient was set as follows:0-2 min, 40% A and 60% B (initial conditions); 2-5 min, 20% A and 80% B; 5-10 min, 4% A, 81% B, and 15% C; 10-60 min, 4% A, 11% B, and 85% C; 60-70 min, 4% A, 11% B, and 85% C; 70-71 min, 100% C; 71-72 min, return to initial re-equilibration conditions. The DAD was set at 450 and 470 nm, with full spectrum acquisition between 190 and 600 nm.  
To identify the carotenoids, specific retention times and spectral absorption characteristics (% III/II) were used. These parameters were compared with known carotenoid molecules reported in the literature to ensure accurate identification, and expressed in \(\beta\)-carotene.
# Role of Restored Underwater Images in Underwater Imaging Applications
## 2 Proposed Restoration Method
### Transmission Map Estimation
#### 2.2.1 Transmission Map of Direct Signal  
The transmission map of direct signal is estimated using spectral attenuation coefficients calculated for red, green and blue channels, together with the calculated depth map \(\widetilde{d}(x)\). The transmission map for the red channel can be calculated using,  
\[t_{D}^{r}(x)=e^{-\beta_{D}^{r}\widetilde{d}(x)} \tag{15}\]  
Restoration results are not sensitive to spectral attenuation coefficient \(\beta_{D}^{r}\) of the red channel [17], with values between [0.125,0.20] for oceanic water type I [21], and hence, spectral coefficient value \(\beta_{D}^{r}\) of the red channel is set to 0.142.  
The transmission map for the green and blue channels due to direct signal can be found by utilizing the transmission and attenuation coefficient of the red channel [22],  
\[t_{D}^{k}(x)=t_{D}^{r}(x)^{\frac{\beta_{D}^{k}}{\beta_{D}^{r}}},k\in\{g,b\} \tag{16}\]  
where the linear relationship between the attenuation coefficients of the green, blue, and red channels is given by Equation (17), with values \(m=-0.00113\), and \(i=1.62517\)[23]. Wavelengths for the red, green and blue light are taken to be 620 nm, 540 nm, and 450 nm, respectively [17].  
\[\frac{\beta_{D}^{k}}{\beta_{D}^{r}}=\frac{\widetilde{B}^{r}\Big{(}m\lambda^{k }+i\Big{)}}{\widetilde{B}^{k}(m\lambda^{r}+i)},\;\;k\in\{g,b\} \tag{17}\]  
where \(\widetilde{B}^{k}\) is the background light estimated using Equation (4) for the respective channel \(k\in\{g,b\}\).
## 3 Influence of Inertia on Frequency Regulation
### Inertia Coefficient Analysis  
In order to fully understand the influence of inertia coefficient \(H\) on the performances of frequency regulation, the expressions of several important performance indices such as the steady-state frequency, rate-of-change-of-frequency (ROCOF), frequency nadir (peak), frequency overshoot, and settling time will be derived in this subsection based on (1). Since these expressions take different forms for different values of \(\zeta\), three cases with different ranges of \(\zeta\), i.e., \(\zeta<1\), \(\zeta=1\), and \(\zeta>1\), will separately be discussed.
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 3 Dataset
### Sensor Data Acquisition  
A thorough understanding of sensor operations is fundamental for accurate data collection. An accelerometer measures the acceleration of a device in various directions, whereas a gyroscope quantifies the rotational velocity about its three principal axes [40]. On the Android platform, these sensor readings are accessed via dedicated Application Programming Interfaces (APIs) [41].  
Initially, our application declares the necessary sensor permissions. Subsequently, access to the accelerometer and gyroscope is obtained through the Android SensorManager service (note that magnetometer data are not utilized in this study). By registering a sensor listener, our system is capable of receiving real-time sensor data. As shown in Figure 1, the accelerometer reports the device's acceleration along the three sensor coordinate axes, capturing both physical acceleration (change in velocity) and gravitational acceleration. The data are provided in the x, y, and z fields of sensor_event_t.acceleration in SI units (m/s\({}^{2}\)), representing the net acceleration after subtracting gravitational acceleration. Similarly, the gyroscope reports the device's rotational velocity around the three axes. Following the standard mathematical definition, counterclockwise rotation is considered positive (right-hand rule), and the results, measured in radians per second (rad/s), are stored in the x, y, and z fields of sensor_event_t.gyro.  
To balance data accuracy and processing efficiency, the collection frequency is carefully optimized through empirical testing. A high frequency can produce excessive data volume and computational overhead, particularly relevant for resource-constrained IoT devices, while a low frequency may miss critical motion details essential for activity recognition. Through systematic evaluation of different sampling rates, we determined that a moderate frequency of 20 Hz (implemented as private final long interval = 50) provides optimal balance between temporal resolution and processing efficiency. This sampling rate captures the fundamental frequencies of human activities (typically below 10 Hz) while maintaining reasonable data volumes. Our sensitivity analysis confirmed that increasing the sampling rate beyond 20 Hz offered diminishing returns in classification accuracy while substantially increasing computational demands. This careful calibration, backed by empirical testing rather than arbitrary selection, represents an important methodological contribution of our dataset acquisition strategy, optimizing both accuracy and efficiency for real-world IoT deployments.
## 3 Implementation of pdABC for TT-WSN Relay Node Deployment
### Introduction of pdABC into TT-WSN Relay Node Deployment
#### 3.4.2 Feasible Solution Formation  
The WSN used in this study has two layers, with the relay nodes forming the upper layer backbone network. Such a set of relay node coordinates is called a feasible solution of the network. In the process of randomly deploying nodes, due to the limited communication distance of relay nodes, a certain relay node may be too far away from other relay nodes to communicate with them. To solve this problem, when the coordinates of a relay node are determined, a feasible solution of the algorithm can be formed. The following steps are specifically used to form the backbone network of the relay node: Step 1. \(S_{r}=\{\mathit{All relay nodes}\}\), \(S=\{\mathit{The sink node}\}\); Step 2. Find all nodes in \(S_{r}\) that can communicate with at least one node in \(S\) to form a set \(S_{r}^{\prime}\); Step 3. If \(S_{r}^{\prime}\) is empty, find the two closest points \(r_{2}\) and \(r_{1}\) in \(S_{r}\) and \(S\), respectively; \(r_{2}\) moves toward \(r_{1}\) straightly until the distance between them is equal to or less than \(r_{c}\). If there are two or more nodes in \(S_{r}\), which are at the same distance with respect to a node in \(S\), choose any one of them; Step 4. \(S_{r}^{\prime}=S_{r}^{\prime}\cup\{r_{2}\}\); Step 5. \(S=S\cup S_{r}^{\prime}\); Step 6. \(S_{r}=S_{r}-S\); Step 7. If \(S_{r}\) is empty, the iteration stops; otherwise, go to step 2. Through the above virtual movement process of the relay nodes, the formation of the TT-WSN backbone network is ensured.
## 5 Discussion
### Implications for Indoor-Outdoor Campus Navigation App Design
#### 5.4.5 Notifications and Alerts  
Navigation systems should provide notifications and alerts to guide users in finding their destination [85; 86; 87]. Previous research has shown that a lack of visual or audio notification upon arrival at a destination can lead to failure in recognizing the target location by participants [88]. Therefore, one useful feature of wayfinding technology is providing notification when the user is approaching the destination [87; 88]. Moreover, notification of obstacles or road closures in the path can lead to further improvement in wayfinding aid [89].  
However, sudden notifications can disturb users in uncertain situations [90]. According to [PERSON] et al. [77], the responsiveness of the notification system is important to create a better user experience. It is useless to notify the user when they are not in the area or notify when they have already passed the area. [PERSON] and [PERSON] [86] highlighted the importance of the accuracy of displayed notifications. They developed an AR-based indoor navigation system, \"INSAR\", that issues a 2 s notification vibration when it finds the destination and displays a message telling the user that they have arrived at their destination [86]. [PERSON] [91] used a map feature to support users in campus navigation which is enabled by sending notifications when they are within 60 m of the target destination.  
The qualitative data also highlighted the importance of notifications and alerts. Many participants suggested that the navigation app must provide relevant notifications about destinations and warnings for wrong directions for further improvement as a campus wayfinding aid. Some comments include: \"More obvious notification when a room is inside another room, is behind a locked/special access door, etc.\", \"Pop-up notification when you are taking the wrong direction\", \"Warning if you are turning towards wrong directions\", \"I think a beep or a sound to inform me when I need to go upstairs or downstairs of the building\", \"A ring when the location is closer.\"
# Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
## 3 Results and Discussions
### Decomposition of Flow Field
#### 3.2.1 DMD Analysis of Basic Case  
In this section, DMD analysis is carried out for \(x\)-direction velocity \(U\), \(y\)-direction velocity \(V\) and vorticity \(\omega\).  
Firstly, the \(x\)-direction velocity \(U\) is analyzed and as shown in Figure 7, the singular value arrangement, growth rate, frequency diagram, eigenvalue distribution diagram and part of the DMD modal contours (showing only the physical computational domain (0\(\sim\)0.0254 m)) is obtained.  
Figure 7a shows the order of singular values. The DMD modes are arranged in the order of singular values. As can be seen from Figure 7a, the first-order singular value is more than one order of magnitude higher than other singular values. In Table 5, the second column gives the singular values of the first 10 modes. The first- order singular value is utterly dominant, implying that after flow field decomposition, the first-order mode is the most important mode. From Figure 7a, it can be seen that the first mode is consistent with the average flow field.  
Figure 7b shows the relationship between the modal growth rate  
Figure 7: DMD analysis of \(x\)-direction velocity \(U\). (**a**) Singular value distribution; (**b**) modal growth rate versus frequency; (**c**) modal eigenvalues distribution; (**d**) modal amplitude versus frequency; (**e**) first 10 modes.  
It can be seen from Figure 8e that the first 10 modes are identical, indicating that the corresponding modes are conjugate modes, and the flow field structure presents an alternating circular structure.  
The results of DMD analysis of vorticity \(\omega\) are shown in Figure 9. The DMD result of vorticity \(\omega\) is similar to that of velocity \(U\) in \(x\)-direction. It is visible from Figure 9a that the dominant singular value of vorticity \(\omega\) exists, and the first singular value is greater than the other singular values by more than one order of magnitude. It can be seen from Table 7 that except for the first mode, the others are conjugate modes. The growth rates of the first 10 modes are less than 0, indicating that they are stable modes. In Figure 9b, the first mode's growth rate and frequency are both equal to zero. It shows that it is stable and the primary mode of flow field. Figure 9c shows that the eigenvalue basically falls near the unit circle, and the first mode falls at point (1, 0). Figure 9d shows the amplitude of the first mode is the largest. Figure 9e shows the first 10 modes, which is similar to the first 10 modes of velocity \(U\). A slender and large-scale structure is shown by low-frequency modes 2-9.
## 3 Governing Equations  
In this experiment, air supplied by an electrical blower serves as the working fluid and is circulated through the heat exchanger. In a steady-state instance, the net heat energy transmitted from the electrical heater to the air through the heat exchanger wall was calculated as Equation (1), whereas real heat was specified as follows [31,32]:  
\[Q_{net}=V_{input}\cdot I_{input}-Q_{ls}; \tag{1}\]  
\[Q_{actual}=\frac{Q_{net}+Q_{con.}}{2}; \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** \\ \hline Position-x-(mm) & 10 & 20 & 30 & 50 & 70 & 90 & 130 & 170 & 230 & 290 & 380 & 470 & 570 & 670 & 770 & 900 & 1040 & 1180 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermocouple’s location.  
Figure 3: Geometric configurations of twisted tapes.  
where:  
\[Q_{con.}=\dot{m}_{a}\cdot C_{p,a}\cdot(T_{b,\rho}-T_{b,i}); \tag{3}\]  
Based on Equation (2), the mean rate of heat transfer is denoted as:  
\[\overline{h}=\frac{Q_{actual}}{A_{tt}\cdot(T_{av,w}-T_{b})}; \tag{4}\]  
where \(T_{av,w}\), \(T_{b}\) are the air bulk temperature and average wall temperature, respectively, which are obtained as follows [32]:  
\[T_{b}=\frac{(T_{b,\rho}+T_{b,i})}{2}; \tag{5}\]  
\[T_{av,w}=\frac{\sum_{i=1}^{18}T_{wi}}{18}; \tag{6}\]  
The (\(Nu\)) mean is defined as below [29]:  
\[Nu_{d}=\frac{h\cdot D_{h}}{k_{a}}; \tag{7}\]  
The (\(Re\)) can be expected as below:  
\[Re=\frac{U\cdot D_{h}}{\
u_{a}}; \tag{8}\]  
Friction factor (\(f\)) is estimated as follows:  
\[f=\frac{\Delta P}{\left(\frac{L}{D}\right)\left(\frac{\rho L^{2}}{2}\right)}; \tag{9}\]  
Enhancement efficiency (\(\xi\)) was calculated as follows for a constant pumping power [33]:  
\[\xi=\frac{h}{h_{0}}=\frac{Nu}{Nu_{0}}=\left(\frac{Nu}{Nu_{0}}\right)\left( \frac{f}{f_{0}}\right)^{-1/3} \tag{10}\]
## 3 Results and Discussion
### Photocatalytic Reaction
#### 3.2.5 Reaction Pathway of Ethofencarb Degradation
Likewise, cyclization routes in ethiofencarb degradation have been documented in the presence of isopropanol and cyclohexane [49]. Route **B** involves the cleavage of the carbamate group, resulting in compound **4** (2-[(ethylsulfonyl)methyl]phenol) and compound **5** (2-methylbenzo[e][1,3]oxathane). Compound **4** can be further photooxidized to compound **1** (2-[(ethylsulfonyl)methyl]phenol). Another alternative degradation route of compound **4** is also proposed in Figure 10b. This route is initiated by oxidation of intermediate **4** by the attack of the positive holes and the interfacial transfer of a single electron from the sulfur atom, which generates the 2-[(ethylsulfonyl)methyl]phenol cation radical. Different reports have described the formation of such cation radicals in the degradation of other thiocarbamates [53], organophosphorus pesticides [54], and thioethers [55]. Cleavage of the C-S bond in the generated cation radical produces (2-hydroxyphenyl)methylium ion and C\({}_{2}\)H\({}_{5}\)S\({}^{\bullet}\) radical. The formed ion is unstable and can be rapidly hydrolyzed into compound **6** (2-hydroxybenzyl alcohol). Compound **6** can be subsequently oxidized to compound **7** (2-hydroxybenzaldehyde). In turn, the produced C\({}_{2}\)H\({}_{5}\)S\({}^{\bullet}\) radical transforms to compound **9** (ethanethiol), upon reacting with an H atom. The H atom can originate from proton reduction by photogenerated electrons (H\({}^{+}+\) e\({}^{-}\)\(\rightarrow\) H\({}^{\bullet}\)), which was previously observed in degradation of fenitrothtion [56]. Lastly, two C\({}_{2}\)H\({}_{5}\)S\({}^{\bullet}\) radicals can be dimerized to form compound **8** (diethyl disulfide), which was also observed in photodegradations of other thiocarbamates [57]. Disulfide **8** could also be formed via oxidation of thiol **9** as reported elsewhere [58].
# A Molecularly Imprinted Polymer Based SPR Sensor for 2-Furaldehyde Determination in Oil Matrices
## 3 Results
### Effect of Extended Washing and Conditioning  
A sensor (SPR-POF-A2 MIP2) was washed by flushing five times with ethanol, which is the usual procedure, and then further washed alternately with ethanol and the same oil used for the successive measurements. The idea is that while ethanol is able to extract 2-FAL and other hydrophilic components of the polymer, oil can extract hydrophobic impurities, which could cause a large irreproducibility during the successive measurements in oil if not completely extracted. This could also contribute to making stronger combination sites free. The spectra in oil with different concentrations of 2-FAL were then registered. The washing procedure of the sensor between successive standards consisted of five repeated flushing with ethanol. All the spectra were normalized on the same spectrum of the bare sensor in air. An example of the spectrum of a sensor with thicker MIP film in oil after extended extraction and by washing with ethanol between successive standards is reported in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(K_{\text{aff}}\) (L M\({}^{-1}\)) & **Sens (nm mg\({}^{-1}\) L)** & **SEy (nm)** & \(\Delta\lambda_{\text{max}}\) (nm)** & **LOD (ug L\({}^{-1}\))** \\ \hline SPR-POF-A2 MIP2 (N = 6) stand & \multirow{2}{*}{8.32 \(\times\) 10\({}^{7}\)} & \multirow{2}{*}{8.18 \(\times\) 10\({}^{7}\)} & \multirow{2}{*}{0.13} & \multirow{2}{*}{0.98} & \multirow{2}{*}{0.3} \\ with washing in water & & & & & \\ SPR-POF-A2 MIP2 (N = 5) stand & \multirow{2}{*}{1.77 \(\times\) 10\({}^{8}\)} & \multirow{2}{*}{2.70 \(\times\) 10\({}^{8}\)} & \multirow{2}{*}{0.19} & \multirow{2}{*}{1.52} & \multirow{2}{*}{0.14} \\ with washing in ethanol & & & & & \\ \hline \hline \end{tabular} SEy is the error of the response, \(\Delta\lambda\); N indicates the number of standards considered.  
\end{table}
Table 2: Parameters of the dose–response curve for 2-FAL in vegetable oil, at an SPR platform extensively cleaned, for different washing procedures between measurements.  
Figure 9: Spectra of SPR-POF-A2 MIP2 (with extended cleaning) in oil.  
Notice the presence of the peak at 500 nm, with two counter dips at 460 nm and 530 nm, which could be due to the particular spectrum used for normalization, the same for all the reported curves.  
Table 2 shows the parameters obtained from two dose-response curves, one obtained by washing in ethanol between successive standards and the other with water. The results are substantially similar.  
The more relevant point is that the affinity constant of the sites revealed after extended washing is about two magnitude orders higher than that with rapid flushing with ethanol. As a consequence, the LOD was also much lower.  
Evidently, only by this washing procedure, the 2-FAL molecules linked to the stronger interaction sites can be extracted.
## 1 Introduction
used multi-temporal Landsat 8 OLI images, combined with spectral Angle mapping and decision tree classification, to extract the distribution of main crops in the study area [17]. These studies show that the spectral angle Mapper is less affected by terrain in the classification process, which reduces the influence of factors, such as \"foreign objects in the same spectrum\", to a certain extent. However, image classification tasks with numerous samples and input features incur significant labor and time costs. Google Earth Engine (GEE), with its powerful data processing and analysis capabilities, can directly invoke preprocessed mass images and multiple algorithms and effectively address these problems [18; 19]. Several studies have recently been conducted on image classification using GEE [20; 21; 22; 23]. These studies show that GEE can provide massive data and cloud-computing support for remote sensing research. To effectively identify apple orchards in Zhaotong City, an improved spectral Angle mapper method is proposed in this paper. Based on the GEE platform and Sentinel-1 and Sentinel-2 images, apple orchards, cultivated land, artificial surface, water body, and other land types were selected, and the improved spectral angle features and other features were integrated to achieve accurate identification of apple orchards in the Ganhe Reservoir area of Zhaoyang District, Zhaotong City, by the random forest (RF) classification method. The structure of this article is as follows: Section 2 details the study area and data sources, including remote sensing and sample data. Section 3 details the research methods for apple orchard identification, including the sample selection methods, principles of the spectral angle mapper, improved spectral angle features, input feature selection, classification experimental design, RF classification methods, and accuracy evaluation. Section 4 describes the apple orchard extraction results and accuracy evaluation. Section 5 summarizes the discussion, and Section 6 summarizes the conclusions.