esa-sceva/satcom-chunk-collection huggingface dataset:
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 6 Quantitative Evaluation  
The quantitative results for each object are summarized in Table 1. We calculate the average error of the entire 10 scenes in our dataset and summarized per primitive-shaped object. We exclude the frames that failed to match the point cloud (Section 3.4) in order to purely compare the object pose estimation accuracy. It can be seen that the proposed method outperforms the conventional methods [7; 37; 38] and the baseline method for all of the objects. Even though the RANSAC-based method [37] outperformed the baseline for cylindrical objects, introducing the coefficient \(\beta\) improves the performance. The quantitative results for each scene are summarized in Table 2. It can be seen that our method outperformed the conventional and baseline methods for most of the scenes. For the scenes in which the baseline outperforms the proposed method, our robust pose estimation method failed to track the objects. The example failure is visualized in Figure 8. As our method implicitly exclude the 3D  
Figure 7: Comparison of the pose estimation results. (**a**) geometric segmentation result, pose estimation result by (**b**) Iterative Closest Point (ICP) tracking, (**c**) Normal Distributions Transform (NDT) algorithm, (**d**) RANSAC-based feature matching method, (**e**) the baseline method, and (**f**) the proposed method. Superquadric surfaces were rendered onto a 2D image. points which are distant from the superquadric surface, the point clouds of fast-moving objects are not considered for pose estimation. In the bottom figure, the pose of the sphere was not updated due to the fast movement of the object. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**3D Error [cm] \(\downarrow\)**} \\ \hline
**Object** & **ICP [7]** & **NDT [38]** & **RANSAC [37]** & **Baseline** & **Ours** \\ \hline Sphere & 4.213 & 8.645 & 0.642 & 0.072 & **0.070** \\ Tall cylinder & 3.194 & 193.7 & 2.001 & 2.738 & **0.502** \\ Wide cylinder & 2.104 & 28.65 & 2.013 & 2.140 & **0.808** \\ Cube & 5.212 & 41.46 & 1.829 & 0.867 & **0.420** \\ \hline \multicolumn{7}{c}{**2D Error [px] \(\downarrow\)**} \\ \hline
**Object** & **ICP [7]** & **NDT [38]** & **RANSAC [37]** & **Baseline** & **Ours** \\ \hline Sphere & 24.03 & 44.81 & 14.50 & 4.232 & **4.012** \\ Tall cylinder & 19.52 & 45.76 & 15.46 & 29.78 & **7.418** \\ Wide cylinder & 11.83 & 71.25 & 19.97 & 31.26 & **3.478** \\ Cube & 24.52 & 72.59 & 27.10 & 7.423 & **5.010** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error of primitive shape pose estimation (per object). It is evident that our proposed method outperforms the baseline and the other methods.
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 4 Evaluation
### A Quantitative Comparison with Other Datasets  
To accurately demonstrate the advantages and novelty of the TYCOS dataset, quantitative comparisons were made with SPEED and SPEED+. SPEED and SPEED+ are currently publicly available unified datasets for satellite detection and key component segmentation. SPEED is constructed from synthetic images obtained from the internet, while SPEED+ is constructed from synthetic images obtained from the internet augmented with real-shot data from a semi-physical environment. To compare the challenges of the three datasets, the performance of some detection models was compared using all three datasets. The best-performing detection models (Faster R-CNN, YOLOv3, YOLOv5, YOLOv8) were trained on the SPEED, SPEED+, and TYCOS datasets. For a fair comparison, 3404 images were randomly selected from the SPEED and SPEED+ datasets, and divided into training, validation, and testing sets in an 8:1:1 ratio. Since the SPEED and SPEED+ datasets can only detect solar panels and satellite bodies, we found (as illustrated in Table 6) that the solar panel AP was lower than that of the satellite bodies and radar, indicating that the solar panels of the satellite were most affected by external interference. Therefore, we chose the solar panel AP value from the TYCOS dataset as the evaluation metric. The detection results are shown in Table 7. Due to the challenges of the dataset, the performance of all models using the TYCOS dataset was relatively low. However, since extreme lighting conditions, satellite motion states, and occlusion states in space were not considered, the SPEED and SPEED+ datasets were simpler datasets. Therefore, all models achieved the highest performance using SPEED, followed by SPEED+. This indicates that the current detection models have lower detection rates using the TYCOS dataset compared to synthetic image datasets, demonstrating that the TYCOS dataset more realistically reflects the authenticity of the space environment and is more suitable for validating modified detection models.
## 5 Description of Simulator Parameters and Network Architecture  
These experiments are based on a grid of 49 nodes, where 48 of them send data with a rate of 1%-4% packets/s to a coordinator node (in the upper-left corner) functioning as a sink node. The grid is shown in Figure 3. The network consists of a MAC layer under the CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) protocol, with a maximum of three retransmissions per packet and maximum of five CSMA retries, while the nodes are waiting for the channel to be idle before transmission. The packet rate of the jammer node varies according to the type of jamming attack; random (\(\rho=50\) packets/s), random (\(\rho=80\) packets/s) or reactive.  
Figure 2: Self-configuring WSNs: AODV, DSR and MPH under a reactive jamming attack.  
Let us consider using a transmission power of 0 dBm [17] for active transmission mode according to the CC2530 chip [18] and a receiving power of \(-85\) dBm according to the IEEE standard 802.15.4 [19]. With these power values, we obtain an average coverage radio of 50 m for sensors following Equation (1) for outdoors [19]:  
\[d=8*10^{\frac{P_{r}-P_{r}-58.5}{33}} \tag{1}\]  
where \(d\) is the coverage radio, \(P_{t}\) is the transmission power in dB and \(P_{r}\) is the receiving power in dB.  
Table 2 shows the physical, MAC and network layer parameters that were used in the simulator.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline \multicolumn{2}{c}{**Physical layer parameters**} \\ \hline Sensitivity thresholds & \(-78\) to \(-94\) dBm \\ Transmission power & \(0\) dBm \\ \hline \multicolumn{2}{c}{**MAC layer parameters**} \\ \hline Maximum retransmission number & 3 \\ Maximum retry number & 5 \\ Maximum number of tries to reach a node from the coordinator & 9 \\ Packet error rate & 1\%-4\% \\ Average frame length & 22 bytes \\ Maximum number of backoffs & 4 \\ MAC protocol & IEEE 802.15.4 \\ MAC layer & CSMA/CA \\ \hline \multicolumn{2}{c}{**Network layer parameters**} \\ \hline Number of nodes & 49 \\ Discovery neighbor time & 10 s \\ Maximum data rate & 250 kbps \\ Scenario & Static nodes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters. CSMA, carrier sense multiple access.  
Figure 3: Network topology.
## 3 Results
### Effects of Heatwaves on Vegetation
The seasonal cycles used in the GOSIF and NDVI calculations correspond to those used in frost estimation. Figure 7: Temporal trends in average GOSIF and NDVI values over the Third Pole were analyzed for periods of different lengths (\(\geq\)8 years) and starting years: (**a**,**e**) spring trends; (**b**,**f**) summer trends; (**c**,**g**) autumn trends, (**d**,**h**) the growing season trends. For each starting year, trends were examined over periods with a minimum duration of 8 years. For instance, trends for the 2001 starting year were evaluated for periods ending between 2008 and 2018, while for 2009, trends were assessed for periods ending in 2016, 2017, and 2018. The Mann–Kendall trend test was used for each period. Positive trends reflect increasing GOSIF and NDVI values, while negative trends indicate decreases. * (\(p<0.05\)), ** (\(p<0.01\)) and *** (\(p<0.001\)) denote statistical significance. To compare the spatial distribution of the GOSIF and NDVI trends in different seasonal cycles, the GOSIF and NDVI trends in the Third Pole during 2001-2018 are shown in Figure 8. Overall, the GOSIF and NDVI between the periods of 2001-2009 and 2010-2018 showed the largest area with increasing trends and the smallest area with decreasing trends. The seasonal GOSIF and NDVI trends exhibited similar spatial patterns, but the spatial differences were still apparent. The spatial patterns of the GOSIF values were obviously clearer than those of the NDVI. The explanation for this is that the GOSIF product can monitor seasonal physiology and capture seasonal plant photosynthetic responses in Figure 7. Therefore, understanding the seasonal dynamics of alpine vegetation is a key step in comprehending the seasonal vegetation response to environmental factors. Moreover, our results suggest that both the GOSIF and NDVI can help clarify the differences among and the information provided by the seasonal variations in the vegetation structure, greenness, and physiology at regional scales in the Third Pole.
## 3 Results and Discussion
### Trimmed Results Realized by Two Methods at Same Time  
The flapping frequency of a seagull in cruising flight is usually in a fixed range, therefore this paper managed to find the trim state under a certain flapping frequency, such as 3 Hz. By changing the wash-out angle and the forward-sweep angle at the same time, we can obtain the trim state at different angles of attack. It can be seen from Figure 10 that with the increase in the angle of attack, the weight gradually increases. The wash-out angle gradually increases, while the forward-sweep angle first decreases and then increases. The above results show that in order to achieve equilibrium at 3 Hz, both the forward-sweep angle and the outer-sweep angle are indispensable, and the forward-sweep angle has a minimum value of about 11 degrees.  
We further studied the longitudinal stability at three angles of attack (3 deg, 6 deg, and 9 deg). As can be seen from the eigenvalues \(\lambda\) in Table 5, there are two pairs of complex eigenvalues with a negative real part, representing two stable oscillatory motions. This means the seagull-based model is dynamically stable in the longitudinal direction at the three angles of attack. The disturbed motion is a linear combination of natural modes. When disturbed in balanced flight, the disturbed motion is a linear superposition of a pair of stable oscillatory modes.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Wash-Out** & **Forward-Sweep** \\ \hline AoA & 2 & 6.3 \\ \hline f & 4.4 & 3.7 \\ \hline Forward-angle/deg & 0 & 28 \\ \hline Maximum nose-down twisting angle at the tips/deg & \(-\)13 & 0 \\ \hline Weight/N & 3.1 & 5.5 \\ \hline \end{tabular}
\end{table}
Table 4: The trimmed results by two methods independently.  
Figure 10: The variation of wash-out angle and forward-sweep angle that meet the requirement of drag equals thrust and pitching moment is zero.  
As the angle of attack is increased from 3 degrees to 9 degrees, the modal characteristics do not significantly change. It is shown that there is a short period mode and long period stable mode. It is noticeable that such flight stability is very similar to that of fixed-wing aircraft, whose longitudinal equations of motion also have two pairs of complex conjugate roots: the short period motion, and a much longer period known as phugoid mode. The period of the phugoid mode increased from approximately 4.25 s to 4.55 s, and the time for a damped motion to halve in amplitude decreased from 45.9 s to 9.57 s. The period of the short-term mode decreased from 0.283 s to 0.166 s, and the time for a damped motion to halve in amplitude first increased from 0.167 s to 0.230 s and then decreased to 0.173 s.
# HBIM: Background, Current Trends, and Future Prospects
## 4 Data Collection and Modeling
### Data Collection Techniques
#### 4.2.2 Photogrammetry  
Photogrammetry is a more affordable alternative to laser scanners for accurately acquiring geometrical data from historical buildings. This technique uses photographs combined with reference site measurements, allowing different lenses to be employed to capture various scales of detail with the same camera. In contrast, different laser scannersare needed for varying ranges, significantly increasing the cost of data collection from monumental complexes, especially when the surrounding area must also be documented [54]. Similar to laser scanning, the results obtained through photogrammetry are transformed into a point cloud, and in most cases, post-processing is necessary to optimize the graphical data [63].  
One advantage of photogrammetry is that it generally requires less expertise for data acquisition compared to laser scanners. However, there are limitations when collecting data in outdoor sites due to changing ambient lighting conditions [54]. Additionally, photogrammetry can be time-consuming, especially when high accuracy is required, compared to the time spent using laser scanners for the same level of precision [55].
## 1 Introduction
Their study found that MCDO-NN outperformed the seasonal autoregressive integrated moving average (SARIMA) model, achieving a lower root mean squared error (RMSE) and producing more reliable uncertainty estimates. Additionally, [PERSON] et al. [20] emphasized the significance of prediction intervals in uncertain forecasting, leveraging Bayesian and delta methods to assess interval reliability through coverage probability and width. Recent work has also explored ensemble-based regression approaches, such as random kernel k-nearest neighbors, to improve prediction robustness and model reliability in nonparametric forecasting contexts [21]. Although MCDO has achieved success in various areas such as computer vision, medical imaging, and general time series forecasting, its application to sinusoidal time series has not been thoroughly investigated. Two main reasons likely explain this gap in literature. First, the adoption of MCDO in deep learning is relatively recent, with initial research focusing on broader applications before narrowing to specific forecasting problems. Second, while sinusoidal time series are common in scientific and engineering disciplines,they have only recently been recognized as a distinct category for MCDO-based forecasting, prompting the need for targeted investigations. This study aims to bridge this gap by applying MCDO-NNs to sinusoidal time series and rigorously evaluating their performance in generating accurate forecast intervals under diverse conditions. The study compares MCDO-NNs with SARIMA, a widely used benchmark model for handling autocorrelated errors in time series analysis. The evaluation focuses on forecast accuracy, interval reliability, and forecast interval width, offering a comprehensive assessment of MCDO's utility for sinusoidal time series forecasting.
## 4 Application of Methodology for Satellite Images  
This section briefly discusses the dataset and the initial decision-making matrix creation. It displays the final ranking of segmentation algorithms produced by PROMETHEE-IFS that demonstrates the optimal method for each test image. Finally, the verification step is performed by comparing the average expert rank with the average rank by the PROMETHEE-IFS method. In addition, a rank sensitivity test has been conducted.
# A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature
## 2 Materials and Methods
### Anomaly Detection Methods  
#### 2.2.1 Autoencoder  
An autoencoder (AE) is a neural network including two sub-network parts, namely an encoder and a decoder. AEs belong to unsupervised learning and their training does not require a labeled dataset. The encoder part maps the input vectors by extracting the meaningful features into the compressed (hidden) representations, which are called codes, while the decoder part maps the codes back onto the original input space and generates  
Figure 1: DC motor experimental equipment.  
Figure 2: Monitoring the data values (temperature, speed, and current).  
reconstructed vectors. AE learning aims to minimize the reconstruction error between the input vectors and the reconstructed vectors [17; 18; 19; 20]. The architecture of a typical AE is given in Figure 4.  
The input vectors, codes, and reconstructed vectors are represented as \(\mathbf{x}\), \(\mathbf{h}\), and \(\hat{\mathbf{x}}\), respectively, and the dataset has \(N\) input vectors.  
The input vectors \(\mathbf{x}_{t}\in R^{n}\), \(\mathbf{x}_{t}=\left[x_{1}^{t},x_{2}^{t},\ldots,x_{n}^{t}\right]\), \(t=1,2,\ldots,N\), are mapped onto the hidden vectors (codes) \(\mathbf{h}\in R^{m}\), \(\mathbf{h}=\left[h_{1},h_{2\ldots}h_{m}\right]\), and \(n>m\).  
The output of the \(p_{th}\) neuron of the hidden layer is computed by using Equation (1)  
\[h_{p}=af\left(\sum_{j=1}^{n}w_{pj}^{\text{c}}x_{j}^{t}+b_{p}^{E}\right) \tag{1}\]  
Figure 4: Typical AE architecture.  
Figure 3: Visualization of normal and abnormal data samples.  
where \(af\) is an activation function, \(w_{pj}^{E}\) represents the encoder weight coefficient between the \(j_{th}\) input layer neuron and the \(p_{th}\) hidden layer neuron, and \(b_{p}^{E}\) is a bias term for the \(p_{th}\) hidden layer neuron.  
The hidden vectors are mapped onto the reconstructed vectors \(\hat{\textbf{x}}_{t}\in R^{n},\hat{\textbf{x}}_{t}=\left[\hat{x}_{1}^{t},\hat{x }_{2}^{t},\ldots,\hat{x}_{n}^{t}\right]\), and their size is equal to the input space.  
The output of the \(i_{th}\) neuron of the output layer is computed by using Equation (2)  
\[\hat{x}_{i}^{t}=af\Big{(}\sum_{p=1}^{m}w_{ip}^{D}h_{p}+b_{i}^{D}\Big{)} \tag{2}\]  
where \(af\) is the activation function, \(w_{ip}^{D}\) represents the decoder weight coefficient between the \(p_{th}\) hidden layer neuron and the \(i_{th}\) output layer neuron, and \(b_{i}^{D}\) is a bias term for the \(i_{th}\) output layer neuron.  
The reconstruction error between the input vectors and reconstructed vectors is computed by using Equation (3)  
\[Error=\sum_{t=1}^{N}\sum_{i=1}^{n}\left(x_{i}^{t}-\hat{x}_{i}^{t}\right)^{2} \tag{3}\]
## 4 Results
As depicted in Figure 9, illustrating the boxplot of processing times across the various tested scenarios, there is greater variability in processing times during indoor scenarios in the simplification phase. This is attributed to the luminosity and reflections within the indoor setting. Similarly, the processing times for the correlation phase exhibit increased variability in daytime scenarios overall, attributed to the higher number of transmitter candidates, which is due to a greater presence of brightness. Finally, concerning SNR and BER values, better results were obtained in lower ambient light conditions due to the reduced noise effect. This can be observed in Table 4, which displays a summary of the average SNR and BER for each scenario and wavelength used. These values were obtained while operating with a voltage of 5 volts and a current of 5 mA. Likewise, lower ambient light conditions resulted in less variation in these parameters over time for the same transmitter as well as a smaller difference in the parameter values between transmitters. The transmitter that achieved the best results was the green one, which was followed by the blue, white, and finally the red. The best results, associated with the green transmitter, reached SNR values around 32 dB. On the other hand, the worst case presented an SNR around 11 dB. Nonetheless, both boundaries correspond to BER values far below the Forward Error Correction limit (usually defined as 3.2 \(\times\) 10\({}^{-3}\)) according to Equation (6). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **LED Color** & **Average SNR (dB)** & **Average BER** \\ \hline \multirow{4}{*}{Daytime Indoor} & Red & 20.6 & \(2\times 10^{-20}\) \\  & Blue & 21 & 7.6\(\times 10^{-22}\) \\  & Green & 26.1 & \(2.5\times 10^{-90}\) \\  & White & 20.8 & \(6\times 10^{-21}\) \\ \hline \multirow{4}{*}{Daytime Outdoor} & Red & 19 & \(7\times 10^{-16}\) \\  & Blue & 24 & \(2\times 10^{-30}\) \\ \cline{1-1}  & Green & - & - \\ \cline{1-1}  & White & - & - \\ \hline \multirow{4}{*}{Nighttime Outdoor} & Red & - & - \\ \cline{1-1}  & Blue & 27.2 & \(3\times 10^{-110}\) \\ \cline{1-1}  & Green & - & - \\ \cline{1-1}  & White & 21.5 & \(6.3\times 10^{-33}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average SNR and BER in the different scenarios. Figure 9: Boxplot of processing times by scenario for simplification (**a**) and correlation (**b**) phases.
## 3 IEC 62253:2011: Current Procedure for Measuring the Performance of Isolated PVPSs  
IEC 62253:2011 [26] establishes procedures to assess the performance of isolated photovoltaic pumping systems (PVPSs), focusing primarily on steady-state operating conditions rather than transient or dynamic regimes. This standard outlines methods for indoor laboratory testing, utilizing actual PV generators or PV simulators, as well as simplified field-testing procedures. In the laboratory, essential system performance curves such as the Power-Flow (P-Q) and Head-Flow (H-Q) characteristics are obtained by varying input power at constant pumping heads and varying heads at constant pump speeds, respectively.  
The P-Q tests involve adjusting the system's input power in increments from high to low at a fixed head, recording the corresponding water flow rates to generate characteristic curves [27]. Field testing, a simplified approach, involves measuring operational parameters such as pump head, flow rate, voltage and current directly in situ to estimate system efficiency [28]. The H-Q tests involve varying the head at predetermined speeds by adjusting valve positions, generating characteristic curves for various operating speeds.  
However, the IEC 62253:2011 [26] testing procedures notably exclude considerations of transient regimes, such as dynamic start-up conditions or rapid fluctuations in power input. Although a specific test for starting power is mentioned, it primarily evaluates the minimum power required for initiation rather than transient operational dynamics. Therefore, the standard is limited to characterizing steady-state behaviors and does not address dynamic performance scenarios crucial for comprehensive PV pumping system assessments.
## 3 Numerical Results
### Speed-Up Evaluation  
In this section, we estimate the potential speed-up of the reduced-precision version of the code in terms of the bit complexity. Figure 4 shows that simulations could be done with an acceptable degradation of the accuracy using 15-bit instead of 52-bit precision for the LU algorithm Table 1. The algorithm performed \(\frac{n^{3}}{3}+\frac{n^{2}}{2}-\frac{5n}{6}\) multiplications and \(\frac{n(n+1)}{2}\) divisions, where \(n=9\) was the rank of matrix \(A\). In our implementation, the reduced-precision code computed the divisions using double-precision arithmetic. Hence, we evaluated the speed-up accounting for the multiplications only, which was about 86% of the floating-point operations. Table 1 shows the gain in the bit complexity of the multiplication function that uses the operands of the reduced precision compared to the double precision. As can be seen, one could reach a 4\(\times\) to 12\(\times\) gain in the bit complexity depending on the multiplication algorithm. For example, for the Karatsuba method, the potential speed-up of our algorithm was about six times. Moreover, the use of the reduced precision required four times less inner iterations than that for 52 bits. As a result, the reduced-precision version of the code could potentially be accelerated by about 24 times.  
Figure 4: Reduced-to-full precision discrepancy of power, fuel temperature and coolant temperature.