esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Wideband Splitter for a Four-Element Antenna Array
## 4 Achieved Results and Discussion
The parameters of the antennas are summarized in \[V_{f2A}=\frac{V_{f1}\times A}{2\times(Z_{Ca}+0.5\times Z_{f1}+A)}, \tag{2}\]2 The measured results of the realized prototype of the proposed splitter under these conditions are shown in Figures 9 and 10. In this case, the measurements were carried out on a calibrated measuring system using a spectrum analyzer (SA) R&S ZVL6 and a personal computer with a GPIB interface. Since the beamwidth of a single antenna is \(\pm 25^{\circ}\), it could be assumed that the beamwidth of a four-antenna array is reduced to \(\pm 12.5^{\circ}\) in both the E and H planes. Figure 9 shows the 5G NR band n14 spectrum measured using a single antenna plotted using a dotted line. On the other hand, the solid line represents the received spectrum using a four-element antenna array with the proposed splitter. This plot shows that the realized gain of the antenna array with the proposed splitter has increased by approximately 5 dB compared to the realized gain of a single antenna. The estimate of gain increase is shown in \[V_{f2A}=\frac{V_{f1}\times A}{2\times(Z_{Ca}+0.5\times Z_{f1}+A)}, \tag{2}\]3 The increase in realized gain ranges from 4.5 dB to 5.1 dB, which is fully in accordance with the previous measurements in the laboratory conditions. Table 2 shows the performance characteristics of the proposed solution with RF power dividers and splitters from the literature. Compared with current structures of RF splitters and dividers, the proposed antenna array splitter is characterized by excellent phase accuracy within a relatively high bandwidth, extremely flat transmission and reflection responses, low insertion loss, and excellent impedance matching. It is important to note here that the authors of the cited papers did not evaluate and present all the parameters compared in the table. Therefore, some of the values presented in the table are only estimates based on previously published results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Number of** & **Frequency** & **Fractional** & **Insertion Loss** & **Reflection** & **Reflection** \\  & **Ports** & **Band (GHz)** & **Bandwidth** & **(dB)** & **Coefficient (dB)** & **Flatness (dB)** \\ \hline
[9] & 4 & 2.2 & 89.1\% & \(\geq\)3 & \(\leq\)\(-\)13 & \(\pm\)25 \\
[10] & 3 & 6.85 & 110\% & \(\geq\)0.4 & \(\leq\)\(-\)12 & \(\pm\)25 \\
[12] & 3
## 1 Introduction
Small drone detection requires wide surveillance coverage and high accuracy under diverse environmental conditions. The noise and computational overhead associated with resolution enhancement processes pose significant constraints on real-time processing. NSSRD addresses these issues by integrating image partitioning, noise suppression, high-resolution conversion, and efficient detection processes. NSSRD first partitions the surveillance area into N grids, and each grid image is passed as input to the YOLO object detection model. To address the potential degradation of detection performance caused by amplified noise near small drones during image scaling, NSSRD applies bilateral filtering as a preprocessing step to effectively reduce the noise before feeding the images into YOLO. Furthermore, to improve the detection speed and reduce unnecessary computations, NSSRD selectively focuses on a single region of interest (ROI) instead of processing all N-grid images. The selected ROI image is then enhanced to high resolution using SRCNN, providing clearer details about the drone. The enhanced image is subsequently processed by YOLO, achieving both high performance and real-time detection. The proposed NSSRD was evaluated through comparative experiments with various state-of-the-art detection models under different drone altitude and drone-to-sensor distance conditions (10-50 m and 10-70 m, respectively). The results demonstrated an improvement of up to 24% in true positive (TP) rates and a 25% increase in recall at a 40 m altitude compared to existing models, validating the performance and practicality of the proposed framework. The main contributions of this study can be summarized as follows:A detection model integrating image partitioning, noise suppression, and super-resolution image enhancement was developed to improve detection performance for small drones, while preserving as much of the critical silhouette information of the drone as possible for accurate detection. - A preprocessing method using image partitioning and bilateral filtering was proposed to suppress noise and distortion occurring during image magnification, thereby improving the reliability of drone detection. - An ROI exploration process based on Laplacian filtering was introduced to reduce unnecessary computations in image partitioning, thereby enhancing overall computational efficiency and detection speed, and meeting real-time processing requirements. - The performance and adaptability of the proposed model were validated in real-world scenarios with various drone altitudes, drone-to-sensor distance conditions, and drone sizes. The organization of the paper is as follows: Section 1 provides an overview of the proposed small drone detection architecture. Section 2 presents a detailed step-by-step description of the architecture. In Section 3, we discuss the experimental process and present the results. Section 4 concludes the paper with a summary of findings and outlines potential directions for future research.
## 3 Parameter Influence and Mesh Sensitivity of the Stress Integration Approach
### Mesh Sensitivity  
Figure 6 shows mesh sizes of deck-rib welding details with different fineness values. The mesh sizes are 1, 2, 4, 8, and 16 mm, respectively.  
Figure 4: The multi-scale finite element model (FEM) of the deck-rib component (units: mm).  
Figure 5: Variation of structural stress with the parameters \(\delta\) and \(w\). (**a**) The parameter \(\delta\) (\(w=2\) mm). (**b**) The parameter \(w\) (\(\delta=0\)).  
Figure 7 shows the variation of the structural stress at the weld toe with the mesh size, calculated by the extrapolated hot spot stress approach and the stress integration approach, respectively. The calculation formula of the extrapolated hot spot stress approach is shown in Equation (11) [12].  
\[\sigma_{hs}=1.67\cdot\sigma_{0.4t}-0.67\cdot\sigma_{1.0t} \tag{11}\]  
where \(\sigma_{hs}\) is the structural hot spot stress, and \(\sigma_{0.4t}\) and \(\sigma_{1.0t}\) are the extrapolation reference stress at 0.4t and 1.0t, respectively, from the weld toe. \(t\) is the plate thickness.  
Figure 8 shows the error \(\sigma_{error}\) comparison of two different calculation approaches under different mesh sizes. The error calculation is shown in Equation (12).  
\[\sigma_{error}=\left(\frac{\sigma_{s}-\sigma_{s1}}{\sigma_{s1}}\right)\times 100\% \tag{12}\]  
\(\sigma_{s1}\) is the structural stress value under different approaches when the mesh size is 1 mm and the 20 node reduced integration element is adopted. For the extrapolated hot spot stress approach and stress integration approach, \(\sigma_{s1}\) are 267.42 MPa and 266.27 MPa, respectively.  
Figure 6: Mesh sizes of deck-rib welding details.  
Figure 7: Variation of structural stress with mesh size.  
Figures 7 and 8 show that: (1) When the mesh size is 1 mm, the structural stress results of the extrapolated hot spot stress approach and stress integration approach are similar, with a difference of only 1.15 MPa. (2) The calculation errors of the different approaches increase with the increase of the mesh size. When the mesh size is no more than 8 mm, which equals 0.5 times the deck thickness, the error of the stress integration approach is significantly lower than that of the extrapolated hot spot stress approach. When the mesh size is 16 mm, the error of the stress integration approach is larger. (3) When the mesh size is 4 mm (i.e., 0.25 times the deck thickness), the error of the extrapolated hot spot stress approach reaches approximately 9%, and the error of the stress integration approach is only approximately 2%. (4) The error of the stress integration approach is no more than 5% when the mesh size is no more than 8 mm, so this approach is considered mesh insensitive. Therefore, considering the calculation accuracy and efficiency comprehensively, the stress integration approach is better, and the mesh size should be 0.25 times the deck thickness with a calculation error of approximately 2%.
## 4 Harmonic Emission Profiles
### Characteristic Emission Profiles  
The analysis of the weekly and daily time series for harmonic currents allows for an automated determination of characteristic emission profiles [8], similar to the classification of electric loads and load patterns [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. The application of the method proposed in [22] for measurements with a duration of at least one year leads to four different emission profiles of harmonic currents, with respect to the dominating consumer configuration (residential, commercial, office, and mixed areas). Two typical emission profiles for the 3 rd harmonic current of an office building and an electronic market are displayed in Figures 2 and 3.  
Due to the consumer behavior for an office building the results in Figure 2 represent the typical emission of harmonic currents during one year. Group 1 (c.f. Figure 1(a)) mainly represents the weekend days and holidays, whereas group 2 (c.f. Figure 1(b)) represents the working days from Monday to Friday. The two emission profiles for the electronic market in Figure 3 reveal that it is usually closed on Sundays and holidays (group 1 in Figure 2(a)) and open from Monday to Saturday (group 2 in Figure 2(b)). The analysis of the 40 different sites of the measurement campaign showed that the emission profiles are split into three groups, which are influenced by different types of day. These three groups are similar to the VDEW load profiles [23]: Monday to Friday (G1), Saturday (G2), and Sunday and holidays (G3). The 50 th percentile is calculated for each time point of all available daily time series within a group. The resulting 50 th percentile time series are used to represent the typical emission profile for the harmonic currents of the 40 measurement sites. Typical examples for the emission behavior of the 3 rd harmonic current for different consumer configurations are displayed in Figure 4.  
Figure 3: Profile of the 3 rd harmonic current for an electronic market; median (red), 5 th and 95 th percentile (blue), daily time series (grey); aggregation interval: 10 min; measurement duration: 353 days from January to December 2012.  
Figure 2: Profile of the 3 rd harmonic current for an office building; median (red), 5 th and 95 th percentile (blue), daily time series (grey); aggregation interval: 10 min; measurement duration: 320 days from January to December 2012.
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 5 Experimental Results and Performance Evaluation
### Thresholding Approaches and Metrics  
**Fixed Thresholding**: This approach uses a static threshold value for detecting anomalies. While it is straightforward to implement, it lacks the adaptability required for datasets where anomaly magnitudes vary over time.  
**Sliding Window Thresholding**: This dynamic approach computes the threshold as a moving average with a deviation factor. It adapts to variations in anomaly magnitude and enhances the detection of extended anomaly regions.  
**\(F_{1 CPA}\)**: In addition to the traditional \(F_{1}\) score and pointwise precision-adjusted score (\(F_{1 PA}\)), we adopt \(F_{1 CPA}\) (Coverage-based Precision-Adjusted \(F_{1}\)) for evaluation. This metric accounts for the temporal continuity of anomalies, rewarding models that detect anomalies consistently across their entire duration. Unlike \(F_{1}\), which measures pointwise accuracy,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Threshold**} & \multirow{2}{*}{**Metric**} & **ChaMTeC** & **ChaMTeC** & **LMS** & **iTransformer** & **TimeMixer** & **PatchTST** & **Anomaly Trans** \\  & & **(Ours)** & **(Ours)** & **AutoTSF** & **iTransformer** & **TimeMixer** & **PatchTST** & **Anomaly Trans** \\ \hline \multirow{2}{*}{Fixed} & \(F_{1}\) & 0.1942 & 0.3674 & 0.1844 & 0.1785 & 0.2593 & 0.2228 & 0.2617 \\  & \(F_{1 PA}\) & 0.2725 & 0.3804 & 0.2308 & 0.2599 & 0.2803 & 0.2695 & 0.2894 \\  & \(F_{1 CPA}\) & 0.2725 & 0.3804 & 0.2308 & 0.2599 & 0.2803 & 0.2695 & 0.2894 \\ \hline \multirow{2}{*}{Sliding window} & \(F_{1}\) & 0.2635 & 0.3246 & 0.1287 & 0.2210 & 0.2190 & 0.1632 & 0.5125 \\  & \(F_{1 PA}\) & 0.3861 & 0.4635 & 0.1915 & 0.3284 & 0.3309 & 0.2405 & 0.5999 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the evaluation metrics on the WaterLog\({}^{*}\) dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Threshold**} & \multirow{2}{*}{**Metric**} & **ChaMTeC** & **ChaMTeC** & **ChaMTeC** & **LMS** & **iTransformer** & **TimeMixer** & **PatchTST** & **Anomaly Trans** \\  & & **(Ours)** & **(Ours)** & **AutoTSF** & **iTransformer** & **TimeMixer** & **PatchTST** & **Anomaly Trans** \\  & & **w/o Attention** & & & & & & & \\ \hline \multirow{2}{*}{Fixed} & \(F_{1}\) & 0.0529 & 0.0528 & 0.0073 & 0.0601 & 0.0630 & 0.0664 & 0.0649 \\  & \(F_{1 PA}\) & 0.8017 & 0.7638 & 0.7423 & 0.7987 & 0.7945 & 0.7926 & 0.8184 \\  & \(F_{1 CPA}\) & 0.1590 & 0.1460 & 0.1634 & 0.1363 & 0.1446 & 0.1484 & 0.1340 \\ \hline \multirow{2}{*}{Sliding window} & \(F_{1}\) & 0.0503 & 0.0486 & 0.0614 & 0.0473 & 0.0521 & 0.0473 & 0.0440 \\  & \(F_{1 PA}\) & 0.829 & 0.8299 & 0.8079 & 0.8248 & 0.8449 & 0.8370 & 0.8249 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the evaluation metrics on the MSL dataset.  
\(F_{1 CPA}\) ensures that anomalies are treated as coherent regions, making it more suited for real-world applications where anomalies often span multiple time steps.
## 2 Related Works and Overview
### Advanced Metering Infrastructure (AMI)  
As already mentioned, for a SG to exist, it is important to have a standardized two-way communication infrastructure between the EEC and final consumers such as homes, buildings, businesses and industries. For this, an AMI has been developed and studied, as presented in [60]. An AMI is responsible for standardizing the communication infrastructure as well as the infrastructure of services provided by the EPC to end consumers [11]. That is, it is responsible for organizing and standardizing how the technologies involved will communicate as well as where the services will be executed. A standardized AMI, can result in a standardized model for all cities to implement in PES, as well as companies and universities can develop solutions and implementations of new technologies and services for DSM in the context of SG [61; 62; 63; 64].  
In the Figure 2, is the implementation of an AMI in a context of SG. It is possible to observe, mainly, that in addition to the distribution of electricity to final consumers, the EPC has a two-way communication with them. With this, some technologies arise to propose new implementations either of technology as well as services. Among these technologies are the SM, responsible for receiving energy and distribution to all household appliances, in addition, it will be responsible for communicating with the EPC through a communication infrastructure used bidirectional [13]. In short, the SM is the most important component for the SG. Then there will be antennas for collecting and aggregating information, these antennas are part of the communication infrastructure to be implemented, and may be a gateway or just a router [65]. Inside the house, has the Home Energy Management (HEM), this will be an optional item for end consumers, however, will be of great importance because it will be responsible for managing and controlling appliances  
Figure 2: Advanced Metering Infrastructure Architecture.  
and enabling home automation in an intelligent way and integrated with web systems and mobile applications, this on account of services in the Home Cloud (HC) [66; 67; 68].  
Within the context of AMI, are the end customers, and among them the largest number of consumers are households. The residential consumers are present in the SH scenarios that are intelligent homes that are located in rural and urban areas of cities, in addition to SH, there are the condomiums of homes which in turn is a scenario where are located a quantity that can be reduced or extensive of homes or buildings present in an infrastructure deployed and centralized only for them. This means that a small energy substation is implanted for this set of residences, which makes the control and management of a certain quantity of houses that have a more similar consumption profile less complex. In addition, PES is composed of public buildings such as hospitals and universities, commercial points and industries.
# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
## 2 Materials and Methods
### Semantic-Based SGM
#### 2.4.1 Computation of Matching Costs  
Several methods exist for computing matching costs in SGM. The original study employed a matching-cost computation based on Mutual Information (MI) [32]. Mutual Information is a correlation measure that is insensitive to variations in image brightness and contrast. However, due to its complex nature and the need for iterative computation, it is inefficient. Therefore, in practical applications, a simpler method like the census transform is often used, followed by calculation of the Hamming distance (the number of corresponding bits that differ between two-bit strings).  
Figure 3: General overview of the semantic-based SGM.  
Figure 2: Feature matching filtering based on semantic consistency. The purple points represent the feature points of dynamic targets in the scene.  
This study utilized weighted zero-mean normalized cross correlation (WZNCC) as a consistency measure [33], which is derived from the ZNCC by applying a weighting calculation to each pixel. The formula for the ZNCC calculation is as follows:  
\[ZNCC\big{(}x,x^{\prime}\big{)}=\frac{\sum_{i}\big{(}I(x+i)-\overline{I}(x)\big{)} \left(I^{\prime}(x^{\prime}+i)-\overline{I^{\prime}}(x^{\prime})\right)}{\sqrt {\sum_{i}\big{(}I(x+i)-\overline{I}(x)\big{)}^{2}\sum_{i}\big{(}I^{\prime}(x^{ \prime}+i)-\overline{I^{\prime}}(x^{\prime})\big{)}^{2}}} \tag{5}\]  
The WZNCC with the inclusion of weighting factors is defined as follows:  
\[WZNCC\big{(}x,x^{\prime}\big{)}=\frac{\sum_{i}w(x+i)w^{\prime}(x^{\prime}+i) \big{(}I(x+i)-\overline{I}(x)\big{)}\left(I^{\prime}(x^{\prime}+i)-\overline {I^{\prime}}(x^{\prime})\right)}{\sqrt{\sum_{i}w^{2}(x+i)\big{(}I(x+i)- \overline{I}(x)\big{)}^{2}\sum_{i}w^{\prime 2}(x^{\prime}+i)\big{(}I^{\prime}(x^{ \prime}+i)-\overline{I^{\prime}}(x^{\prime})\big{)}^{2}}} \tag{6}\]  
The weight \(w\) in this context is composed of three components: color, distance, and orientation.  
\[w(x+i)=w_{c}(x+i)\times w_{s}(x+i)\times w_{r}(x+i) \tag{7}\]  
\[w^{\prime}\big{(}x^{\prime}+i\big{)}=w_{c}\big{(}x^{\prime}+i\big{)}\times w_{ s}(x+i)\times w_{r}\big{(}x^{\prime}+i\big{)} \tag{8}\]  
Considering the weights in the left image, the three weight components include the color weight component, which is defined as follows:  
\[w_{c}(x+i)=exp(-\frac{\sqrt{\sum_{j\in R,G,B}\big{(}c_{j}(x+i)-c_{j}(x)\big{)} ^{2}}}{\max_{i}\sqrt{\sum_{j\in R,G,B}\big{(}c_{j}(x+i)-c_{j}(x)\big{)}^{2}}}) \tag{9}\]  
The distance component is defined as follows:  
\[w_{s}(x+i)=exp(-\frac{\|s(x+i)-S(x)\|}{t/2}) \tag{10}\]  
The orientation component is defined as follows:  
\[w_{r}(x+i)=exp(-\frac{r(\psi(x+i),\psi(x))}{\sigma_{r}}) \tag{11}\]
## 6 Results and Discussions
### Scenario 5: Rayleigh--\(\eta-\lambda-\mu\) Fading  
The outage probability vs. \(\bar{\gamma}\) for scenario 5, i.e., Rayleigh-\(\eta-\lambda-\mu\) fading, for \((\gamma_{th})=10\) dB, is shown in Figure 6. The results are provided for the channel parameters \(\eta=0.5\), \(1\), \(\lambda=0.5\), \(1\), and \(\mu=0.5\), \(1\). In this analysis, \(\delta_{x}^{2}\), which is the average power of the in-phase component, was set at one. We observed that, out of these cases, the best outage performance was obtained at \(\eta=\mu=0.5\) and \(\lambda=1\). This shows that the more the propagation environment became non-homogeneous, the better the performance. We observed that increasing \(\eta\) from 0.5 to 1 while keeping other parameters constant improved the performance of the system because as \(\eta\) approached \(1\), the \(\eta-\lambda-\mu\) fading approached the Rayleigh fading scenario.  
Figure 5: Outage Probability under Nakagami-mNakagami-q fading for (**a**) fading parameter \(m=0.5\), \(1\), \(2\), \(2.5\) and (**b**) Fading parameter \(q=0.1\), \(0.5\); \(\gamma_{th}=1\) dB, \(5\) dB, \(15\) dB.
## 3 Results and Discussion
### Thermodynamic Study  
Thermodynamic parameters such as Gibbs energy (\(\rm\Delta G\)), enthalpy (\(\rm\Delta H\)), and entropy (\(\rm\Delta S\)) are important parameters for understanding the adsorption process [59]:  
\[\rm\Delta G=-RTLnK_{d} \tag{16}\]  
\[\rm LnK_{d}=\frac{\rm\Delta S}{R}-\frac{\rm\Delta H}{RT} \tag{17}\]  
where R is the constant of perfect gases (8.31 j/mol\(\cdot\)K), \(\rm K_{d}\) represents the thermodynamic equilibrium constant, and T represents the absolute temperature in (K\({}^{\circ}\)). Figure 12 shows the variation of \(\rm lnK_{d}\) as a function of 1/T. The determined slope and intercept are used to calculate \(\rm\Delta H\) and \(\rm\Delta S\).  
The standard exchange enthalpy was calculated from the Van\({}^{\prime}\)t Hoff equation (see Table 5), and the negative value \(\rm\Delta H=-41.16\ kJ/mol\), which indicates that this is an exothermic adsorption process [60]. The negative value of entropy (\(\rm\Delta S^{\circ}=-0.144\ kJ/mol\cdot^{\circ}K\)) suggests a decrease in disorder at the solid-liquid interface [61]. The negative Gibbs values (\(\rm\Delta G\) from \(-6.87\) to \(-1.26\ kJ/mol\)) indicate that the process of biosorption of DR-81 on cockleshells is spontaneous in nature and feasible [62; 63]. The decrease in \(\rm\Delta G^{\circ}\) values with increased temperature indicates highly efficient adsorption at low temperatures [64; 65].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Parameters**} \\ \hline
**T** (\({}^{\circ}\)**K**) & \(\rm\Delta G^{\circ}\)**(kJ/mol)** & \(\rm\Delta H\) **(kJ/mol)** & \(\rm\Delta S\) **(kJ/mol\(\cdot^{\circ}K\))** & \(\rm\bf R^{2}\) \\ \hline
295 & \(-1.32\) & & & \\
304 & \(-2.62\) & \(-41.16\) & \(-0.144\) & 0.96 \\
334 & \(-6.94\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermodynamic parameters for biosorption of DR-81 onto cockleshells.  
Figure 12: Plot of ln (Kd) vs. \(\frac{1}{\rm 1}\) for removal of DR-81 by CS.