esa-sceva/satcom-chunk-collection huggingface dataset:
## Data and Data Preparation  
### Satellite Data  
In total, 63 Landsat scenes (path/row: 193/027) acquired from 1985 to 2016 (between 1 st July and mid-October in each year) were selected for this study (Table 1, full list in the supplementary document). Landsat scenes provide the longest time series and their spatial and spectral resolution allows a robust quality assessment in terms of SC, cloud cover and impact of shadows. However, a clear sky image from the end of the ablation period is not available in every year and the difference between the image acquisition date and the date of the highest snow line in each year varies randomly. All Landsat data were orthorectified (level 1T corrected), have less than 40% cloud cover, and were downloaded from Earth Explorer ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))) and the Earth Observation Link (EOLi) of the European Space Agency (ESA).  
All Landsat satellite missions have a revisiting time of 16 days but vary in specific characteristics, with spatial resolution and available spectral bands changing through time (Table 2). This presents a challenge for homogenized and automated processing when working with the complete time series. For example, Landsat MultiSpectral Scanner (MSS) has four image bands and three metadata files (no thermal band), Landsat 4 and 5 Thematic Mapper (TM) have three metadata files, whereas Landsat 7 Enhanced Thematic Mapper plus (ETM+) has nine bands and two metadata files. The Landsat 7 scan line corrector (SLC)-off scenes additionally have an extra folder with the gap mask. The Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) has 11 image bands and one metadata file and instead of 8-bit, a 12-bit radiometric resolution in each band. Hence, each Landsat sensor is processed individually.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Scene** & \multirow{2}{*}{**Year**} & **Scene** & \multirow{2}{*}{**Year**} & **Scene** & \multirow{2}{*}{**Year**} & **Scene** & \multirow{2}{*}{**Year**} & **Scene** & \multirow{2}{*}{**Year**} & **Scene** \\  & **Count** & & & **Count** & & **Count** & & **Count** & & **Count** \\ \hline
1985 & 3 & 1991 & 1 & 1996 & 1 & 2002 & 1 & 2007 & 2 & 2012 & 1 \\
1986 & 2 & 1992 & 2 & 1997 & 2 & 2003 & 2 & 2008 & 1 & 2013 & 3 \\
1988 & 3 & 1993 & 1 & 1999 & 1 & 2004 & 3 & 2009 & 6 & 2015 & 3 \\
1989 & 2 & 1994 & 1 & 2000 & 2 & 2005 & 3 & 2010 & 2 & 2016 & 2 \\
1990 & 3 & 1995 & 3 & 2001 & 2 & 2006 & 1 & 2011 & 4 & Total: & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of Landsat scenes for each year as used in this study (total 63).
###### Abstract  
This paper proposes a multimodal deep learning method for forecasting the daily power generation of small hydropower stations that considers the temporal and spatial distribution of precipitation, which compensates for the shortcomings of traditional forecasting methods that do not consider differences in the spatial distribution of precipitation. First, the actual precipitation values measured by ground weather stations and the spatial distribution of precipitation observed by meteorological satellite remote sensing are used to complete the missing precipitation data through linear interpolation, and the gridded precipitation data covering a group of small hydropower stations are constructed. Then, considering the time lag between changes in the daily power generation of the group of small hydropower stations and precipitation, the partial mutual information method is used to estimate the \"time difference\" between the two, and combined with the precipitation grid data, a data set of the temporal and spatial distribution of precipitation is generated. Finally, using only the temporal and spatial distribution of precipitation and historical power generation data, a multimodal deep learning network based on a convolutional neural network (CNN) and multilayer perceptron (MLP) is constructed, and a highly accurate prediction model for the daily power generation of small hydropower stations is obtained. Taking the real power generation data of a group of small hydropower stations in southern China as an example, after considering the temporal and spatial distribution of precipitation, the prediction accuracy of the proposed method is as high as 93%, which is approximately 5.8% higher than before considering the temporal and spatial distribution of precipitation. In addition, compared with mainstream methods such as support vector regression (SVR) and the long-short-term memory network (LSTM) (the average accuracy is about 87%), and the average accuracy improvement of the proposed method is approximately 6%.  
small hydropower stations; daily power generation forecasting; temporal and spatial distribution of precipitation; multimodal deep learning +
Footnote †: journal: Applied Computer Science  
1
Footnote 1: [[https://www.mdpi.com/journal/energies](https://www.mdpi.com/journal/energies)]([https://www.mdpi.com/journal/energies](https://www.mdpi.com/journal/energies))
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 3 Iterative Positioning Algorithm for the Target Node Based on Distance Correction
### The Iterative Positioning Algorithm
#### 3.3.1 The Calculation of the Distance Deviation Coefficient  
The distance between the given anchor node and unknown target node, which is calculated by the log-distance distribution model, is denoted \(d_{bi}\). The distance between the coordinates of the anchor node and the coordinates calculated by the triangle centroid positioning algorithm is denoted \(d_{ci}\). To indicate the deviation of the two distances, the distance deviation coefficient \(C_{dev}\) is defined by Equation (13).  
\[c_{dev}=\frac{d_{bi}}{d_{ci}}(i=1,2,3), \tag{13}\]  
As distance deviation coefficients can be solved by Equation (13), it is essential to determine a characteristic quantity to represent the degree of deviation of the overall node measurement distance. The two statistical parameters, the average value and the median of distance deviation coefficients, can be closely used to express the overall distance deviation characteristics.  
The average value of the distance deviation coefficients can express the average level of the overall measurement distance deviation. However, its fatal disadvantage is that if the extremum at both ends is too low or too high, the final calculation result will significantly deviate from the real situation.  
Therefore, the median distance deviation coefficients are usually used to express the overall distance deviation characteristics. The median is not affected by the extreme values of both ends and can better reflect the overall distance deviation characteristics, making the final calculation result closer to the real situation.  
The distance deviation coefficients are calculated by Equation (13). Then, the values of \(C_{dev}\) are sorted to obtain the median named \(C_{m-dev}\).  
The distance between the given anchor node and unknown target node can be recalculated based on the median \(C_{m\text{-}dev}\), as shown in Equation (14).  
\[d_{ni}=\frac{d_{bi}}{C_{m-dev}}(i=1,2,3), \tag{14}\]  
The new distance \(d_{ni}\) is obtained by Equation (14) and the triangular centroid positioning algorithm is iteratively conducted to obtain the positioning result (\(x_{\text{G}},y_{\text{G}}\)).
# Improving the Design of MEMS INS-Aided PLLs for GNSS Carrier Phase Measurement under High Dynamics
## 4 Testing and Verification
### Navigation Results Comparison  
The test results in Section 4.2 show that stand-alone PLLs should use short integration time and wide bandwidth to keep lock under dynamics. Hence, position and velocity results are compared, shown in Figure 14, based on stand-alone second-order PLLs with an integration time of 1 ms and a bandwidth of 18 Hz, third-order PLLs with an integration time of 1 ms and a bandwidth of 18 Hz, MEMS INS-aided second-order PLLs with an integration time of 1 ms and a bandwidth of 18 Hz, and MEMS INS-aided second-order PLLs with an integration time of 10 ms and a bandwidth of 10 Hz. The position errors based on the four kinds of PLLs did not have significant differences. This is because the position results were calculated by pseudo-ranges, generated by code tracking loops, not obviously affected by PLLs, except for losing lock.  
Velocity results were calculated by Doppler, generated by PLLs. As is shown in Figure 14a, when the platform acceleration increased to the maximum, the velocity error was extremely large. This was caused by losing lock of second-order PLLs. The velocity error in Figure 14b did not increase obviously  
Figure 14: Position and velocity errors based on different PLLs. (**a**) Stand-alone second-order PLLs, (**b**) stand-alone third-order PLLs, (**c**) INS aided PLL with an integration time of 1 ms and a bandwidth of 18 Hz and (**d**) INS aided PLL with an integration time of 1 ms and a bandwidth of 18 Hz.  
under dynamics. This is because a phase error of 50\({}^{\circ}\) equates to a Doppler error of 0.14 Hz, only leading to a velocity error of 0.03 m/s. If the carrier phase was used for positioning, the position error would increase obviously under dynamics. Since stand-alone PLLs use short integration times and wide bandwidths under dynamics, the velocity noise was large. The root mean square (RMS) of horizontal velocity error was about 0.25 m/s, and the RMS of vertical velocity error was about 0.70 m/s. The velocity errors were compared, based on MEMS INS-aided second-order PLLs, with an integration time of 1 ms and a bandwidth of 18 Hz, and that with an integration time of 10 ms and a bandwidth of 10 Hz, shown in Figure 14c,d, respectively. When the integration time was lengthened and the bandwidth was narrowed, the velocity noise reduced obviously. The RMS of horizontal velocity error was about 0.11 m/s, and the RMS of vertical velocity error was about 0.20 m/s. Therefore, the velocity accuracy can be improved by MEMS INS-aided PLLs. The position based carrier phase may be realized through testing in future work.
## 5 Experimental Investigations
### Set Up  
This paper uses the self-labeled corpus CADS as an example to validate the effectiveness of the Auto-HPIF framework for the joint task of multi-intent detection in the automotive domain. In addition, two public datasets, MixATIS [4; 29] and MixSNIPS [30; 31], are introduced to evaluate the generalization of the model to other domains. The MixATIS dataset is a multi-intention dataset that contains conversation data targeting the air travel reservation domain and involves different types of intentions, such as checking flights, booking tickets, and canceling orders. MixATIS includes 13,162 discourses for training, 756 for validation, and 828 for testing. The MixSNIPS dataset contains conversation data for multiple domains (e.g., music, weather, movies, etc.), each with multiple different types of intents and slots. The MixSNIPS dataset includes 39,776 discourses for training, 2198 for validation, and 2199 for testing.  
As per previous research, the evaluation of intent detection and slot-filling tasks involved accuracy (Acc) and F1 scores, respectively. To assess the model's performance in the sentence-level semantic frame parsing task, the overall accuracy was employed. Sentence accuracy represents the percentage of correctly predicted intentions and slots across the entire corpus, indicating the overall performance of both tasks. Conversely, overall accuracy denotes the percentage of all sentences where both intentions and slots are correctly predicted.  
The experiments were conducted on a Linux server equipped with an NVIDIA GeForce RTX 1080 GPU, utilizing the Python 3.7 and the PyTorch 1.12.1 framework. The model in this paper is built upon Chinese BERT, comprising 12 layers of transformer blocks, 768 hidden states, and 12 attention heads. The BERT base has a total of 110 million parameters. For the MixATIS and MixSNIPS datasets, a batch size of 16 was used, and the initial learning rate was set to \(1\times 10^{-5}\).
## 3 Materials and Methods
### Self-Normalizing Neural Network (SNN)  
The SNN is a type of neural network designed to keep the activation of neurons within a desired range without the need for external normalization techniques such as batch normalization. Each layer is normalized and used as input to the next layer. SNNs are self-normalizing neural activations that automatically converge towards mean zero and unit variance. This is achieved by using the SELU (scaled exponential linear unit) activation function. This accelerates convergence in the training process. The SELU learns faster and better than other trigger functions without additional processing. It can be expressed mathematically as follows:  
\[\begin{array}{ll}\text{f}(\text{x})=\lambda\times\text{x}&\text{if x}>0\\ \text{f}(\text{x})=\lambda\times\alpha(\text{ex}-1)&\text{if x}\leq 0\end{array}\]  
Graphically, it is illustrated in Figure 2.  
The SELU (scaled exponential linear unit) function is continuous and differentiable across its entire domain, including the source. This characteristic ensures that there are no jumps or discontinuities in the curve, which is beneficial for optimization during the training of neural networks.  
In the positive region (X > 0), the SELU function behaves linearly. This means that the output increases proportionally to the input. The scale parameter, approximately equal to 1.0507, determines the slope of the curve in this region.  
In the negative region (X < 0), the SELU function applies an exponential transformation. This transformation keeps the output close to zero, but not exactly zero, which helps mitigate the \"dying ReLU\" problem, where neurons cease to update during training. The alpha parameter, approximately 1.67326, influences the curve in this region.  
The convergence property of SELU networks allows them to trend toward a mean of zero and a unit variance, enabling the training of deep networks with many layers and making the learning process highly robust.  
The configuration of the network is illustrated in Figure 3.  
The network is structured as follows:  
1. Input Layer * Dense Layer: This layer consists of 64 units (neurons) and employs the scaled exponential linear unit (SELU) activation function. The SELU function is designed to maintain the self-normalization of data throughout the network. * AlphaDropout: This layer applies dropout with a rate of 0.1. AlphaDropout is a variant of standard dropout that preserves the auto-normalization properties of the SELU function.
2. Hidden Layers * Dense Layer: this layer has 32 units and utilizes the SELU activation function. * AlphaDropout: this layer applies dropout with a rate of 0.1. * Dense Layer: this layer has 16 units and also uses the SELU activation function. * AlphaDropout: this layer applies dropout with a rate of 0.1.
3. Output Layer * Dense Layer: this layer has 1 unit, utilizes a linear activation function, and is responsible for generating the final output of the network.
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 3 Road Management Center Information Platform
**Pavement Condition Index (PCI)**: PCI is a comprehensive index for evaluating road pavement conditions, covering a variety of road damage situations such as cracks,  
Figure 8: Road Management Center Information Platform. potholes, and surface deterioration. The data provided by PCI help to determine the road's lifespan and immediate maintenance needs, assisting management personnel in prioritizing areas that require urgent attention, thus extending the road's lifespan and reducing maintenance costs. By integrating these inspection data, the information platform provides comprehensive data analysis for the entire road network, allowing management units to effectively monitor road conditions and identify potential issues. Through discussions and analysis of these indicators, management units can more accurately determine which sections need priority repair, thereby enhancing the effectiveness of subsequent road maintenance and ensuring a safer and more comfortable driving environment (Figure 9).
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 2 Common Methods for Detecting Underwater Cracks in the Surface of Concrete Dams
### Intelligent Monitoring Techniques  
#### 2.2.1 Acoustic and Vibration Methods  
It was found that the acoustic signal (energy attenuation, travel time of diffracted wave, or the scattered wave field, etc.) and vibration characteristics (frequency, mode shape, modal damping, etc.) of the structure correspond to its mechanical properties [24,39,40,41], so acoustic and vibration methods can be used to detect underwater dam cracks. The acoustic and vibration methods mainly include ultrasonic detection, vibration-based technology, and impact-based technology. Ultrasonic waves are applied to underwater concrete crack detection because of their strong penetrating force, simple operation, and the absence of the need to consider water resistance [42, 43, 44]. The cracking of a structure implies a change in the mechanical properties of the structure, and the acoustic signal and vibration properties are closely related to the mechanical properties of the structure. Therefore, it is feasible to use impact-based and vibration-based techniques to detect underwater concrete cracks. All the above methods belong to non-destructive methods, which can ensure the normal operation of the structure while testing. However, the aforementioned methods are difficult to implement, the survey depth is shallow, and the localization error is large, which leads to the low efficiency of such methods in practice. Moreover, these methods are not suitable for large-scale control due to their high detection cost, low level of automation, susceptibility to interference from various factors, and the need to predict crack locations in advance [45,46].
## 4 Experimental Results and Analysis
### Synchronization with Mailbox System  
In Section 3.1, we introduce an efficient way to implement synchronization with the asymmetric mailbox system. With the inbound mailbox, the cores can synchronize at each decoding steps and can share values in the pivot column at once. We have compared decoding speed of the two synchronization methods based on inbound mailbox and outbound mailbox respectively in Figure 13. For the three kinds of computation approaches, COMPUTE, TL, and VECTOR, the decoding procedure is tested with varying the synchronization method and simply divided workload for each thread.  
In experimental results, the synchronization method, which combines synchronization and the data transfer, reduces more than 10% of decoding time. COMPUTE and TL show remarkable reduced results since the three methods already have severe synchronization overhead by unfairness of workload distribution which does not consider the computation capability different types of cores. Consequently, the synchronization with inbound mailbox systems reduces performance degradation by inefficient synchronization methods and the performance degradation caused by absence of proper workload distribution. The performance improvement by well balanced workload is tested in the next subsection.
# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 3 Approach
### Proposed Method  
The architecture of our model consists of an image encoder and a detection decoder. The image encoder processes the original input image \(x\) to extract high-level features using a backbone network with an FPN (Feature Pyramid Network). The detection decoder takes a set of random proposal boxes as input to crop RoI features from the feature map generated by the image encoder. The RoI features are then sent to the detection head to refine the predicted boxes generated from the noisy boxes. During training, we first construct the diffusion process from ground-truth boxes to noisy boxes and then train the model to reverse this process. The noise scale is controlled by \(\alpha_{t}\) (in Equation (1)), which adopts the monotonically decreasing cosine schedule for \(\alpha_{t}\) in different time step \(t\). During inference, the OrientedDiffDet performs a denoising sampling process to generate object boxes. The model starts with boxes sampled from a Gaussian distribution and progressively refines its predictions. Within the image encoder, we use oriented boxes instead of horizontal boxes to accurately identify objects with arbitrary orientations. To achieve rotation-invariant features for RoI classification and bounding box (bbox) regression, we employ a feature alignment method.  
Please refer to Figure 1 for an illustration of the architecture.
## 5 Experimental Evaluation
### Discussion  
The results offer practical insights into the trade-offs of deploying Edge-FLGuard+ in real-world smart home scenarios. Specifically, our findings highlight the following:  
* Scalability: Edge-FLGuard+ supports asynchronous and partial participation. It performed consis  
Figure 4: Heatmap summarizing ablation results across different configurations of Edge-FLGuard+, including model pruning and quantization. The trade-offs between efficiency and performance are clearly observable.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Variant** & **Accuracy** & **Comm. Overhead** & **Convergence Rounds** \\ \hline Full Model (160 K) & 96.4\% & 2.1 MB & \(\sim\)100 \\ Pruned Model (90 K) & 95.6\% & 1.3 MB & \(\sim\)80 \\
8-bit Quantized & 94.9\% & 0.7 MB & \(\sim\)120 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results for Edge-FLGuard+ across different model configurations. The results show how model pruning and 8-bit quantization impact accuracy, communication overhead, and training convergence in a 5G-enabled smart home FL setup.  
tently with 5-10 clients, and the system scales to dozens of nodes without performance collapse.
* Privacy Trade-offs: Incorporating differential privacy noise (\(\varepsilon\) = 5) resulted in a \(\sim\)1.4% drop in F1-score, but significantly enhanced privacy guarantees.
* Real-World Applicability: The low inference latency (<7 ms) and small memory footprint (<1.2 MB) confirm its suitability for smart home routers and IoT gateways. All experiments were simulated on Raspberry Pi-like edge nodes.  
To better understand the trade-off between communication efficiency and detection performance, Figure 5 plots accuracy against communication overhead across different configurations.