esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing
### Data Preparation  
Each dataset is divided into data segments with the length of half a second, 50,000 samples respectively, which are transformed into the time-frequency domain and depicted as spectrograms. The spectrograms are scaled logarithmic and the color scale is normed with the minimal and maximal values over the whole dataset. The spectrograms are saved as pictures without an axis because they are normed. The pictures are then used as three-dimensional input for a CNN. In total, the dataset consists of 9800 spectrograms, which are evenly distributed over all seven classes. An example of the generated spectrograms for a rotational speed of 2700 rpm is shown in Figure 2. For the same operating conditions, the resulting spectrograms of three damage cases, respectively _healthy_, _dist 2_ and _local 0_, are shown. Slight differences can be detected. In relation to the healthy condition, the signal amplitude in the lower frequency range increases in the case of distributed damage, while the middle frequency range obtains higher amplitudes only within a clearly definable frequency band. For local damage, on the other hand, the lower frequency band has higher amplitude than for the healthy state and is larger. In addition, this damage exhibits higher amplitude-frequency bands in the middle-frequency range. In the high-frequency range, all three damage phenomena behave similarly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subclass** & **Class** & **Damaged Components** \\ \hline healthy & healthy & \\ \hline \multirow{3}{*}{distributed} & dist 0 & severe pittings on outer race and tapper rollers \\  & dist 1 & severe pittings on outer race and tapper rollers \\  & dist 2 & severe pittings on outer race and tapper rollers \\ \hline \multirow{3}{*}{localized} & local 0 & severe scoring on outer race \\  & local 1 & severe and light scoring on outer race \\  & local 2 & severe and light scoring on outer race, severe scoring on tapper roller \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of the considered damaged bearings.
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Tiling  
Tiling is one of the typical solutions proposed by various researchers in order to overcome the bandwidth issues of 360-degree videos by projecting and splitting video frames into numerous sections known as tiles. In general, this technique divides a frame into several sections known as tiles, focusing on the quality of the Region of interest (RoI)/Quality Emphasis Region (QER)/Field of View (FoV) while reducing the others to overcome the bandwidth issue. Most of the solutions are based on the DASH framework as discussed in Section 3.1.  
Figure 5 illustrates the small region of FoV in an equirectangular mapped 2K picture. Following that, the most popular HMDs have a small FoV. For example, Google Cardboard [31] and Samsung Gear VR [32] have an FoV of 100 degrees whereas Oculus Rift and HTC Vive [33] have wider 110 degrees of FoV as demonstrated in Figure 6.  
Figure 5: FoV in a full 360-degree video frame.
# Study on the Optimization of Pile Length of Micropiles in Soil Landslides
###### Abstract  
This study summarizes the engineering design and calculation methods of micropiles and proposes a pile length optimization model based on numerical simulation software. Based on the proposed micropile calculation method and optimization method, a specific analysis of a project example was carried out, and a series of calculations, such as micropile design calculation and pile length optimization for the project, was completed. The results show that the miniature pile length optimization model based on numerical simulation finite difference method improves the previous method by automating the optimization process through fast modeling, automatic creation of optimization commands, and output and analysis of optimization results, and realizes the optimization of pile length using numerical simulation, which improves the efficiency of the optimization of the pile length under the premise of guaranteeing accuracy, and achieves the unity of both efficiency and accuracy. The feasibility of this optimization process is proved by engineering examples. The engineering practicability of the micropile design calculation method and optimization method proposed in this study is proved through practice. It provides a reference value for the initial fast and flexible management of small landslides.  
micropiles; design calculation methods; numerical simulation; pile length optimization; engineering practicality +
Footnote †: journal: Applied sciences
# A Cross-Modal Hash Retrieval Method with Fused Triples
## 4 A Cross-Modal Hash Retrieval Method with Fused Triples
### Hash Learning
#### 4.3.1 Hamming Distance Loss  
Assume there are n sample pairs in the data set, each with features in both image and text modalities. In this paper, we use \(X=\{x_{i}\}_{i=1}^{n}\) to define modal image features and \(Y=\{y_{j}\}_{j=1}^{n}\) modal text features, where \(x_{i}\) is the raw pixels of the image \(i\) and \(y_{j}\) is the text information associated with the image \(i\). In addition, define the cross-modal similarity matrix \(S\). If the image \(x_{i}\) is similar to the text \(y_{j}\), then \(S_{ij}=1\); otherwise, \(S_{ij}=0\). The similarity is defined based on category labels with similar semantic information. If image \(x_{i}\) and text \(y_{j}\) have the same category label, then \(x_{i}\) and \(y_{j}\) are considered similar, and vice versa.  
The cross-modal hash retrieval approach focuses on learning the hash function, noting the imaging modality as \(h^{(\mathcal{N})}(x)\in\{-1,+1\}^{c}\) and the text modality as \(h^{(\mathcal{N})}(y)\in\{-1,+1\}^{c}\), where \(c\) is the length of the binary hash code. Mapping the feature vectors of the two-modal data into a Hamming space ensures that the two modalities' hash functions remain semantically similar \(S\) while maintaining semantic similarity within and across modalities. If \(S_{ij}=1\), the Hamming distance between the binary hash codes \(B^{(x)}=h^{(x)}(x_{i})\) and \(B^{(y)}=h^{(y)}(y_{j})\) should be very small. Conversely, if \(S_{ij}=0\), the Hamming distance between the two binary codes should be prominent. \(G=\{g_{y_{i}}|\ i=1,2,\ldots\ldots,N\}\subseteq R\) denotes the hash representation learned in the imaging modality, and \(F=\{f_{x_{i}}|\ i=1,2,\ldots\ldots,N\}\subseteq R\) denotes the representation learned in the text modality. \(G^{T}\) denotes the transpose of \(G\), \(||\bullet||_{F}^{2}\) denotes the F-norm, and \(sign(\bullet)\) denotes the symbolic function defined as:  
\[sign(x)=\left\{\begin{array}{cc}1&x\geq 0\\ -1&x<0\end{array}\right. \tag{2}\]Given the hash code pair \(\{F_{i*},G_{j*}\}\), define the logical functions \(\sigma\big{(}\Theta_{ij}\big{)}=1/\left(1+e^{-\Theta_{ij}}\right)\), \(\Theta_{i}=\frac{\delta}{\xi}F_{i*}G_{j*}^{T}\), \(\delta>0\) as a hyperparameter indicating the tuning scale factor. Define the likelihood value of the cross-modal similarity \(S\) as  
\[p(S|F,G)=\prod_{i,j=1}^{n}p(S_{ij}|F,G) \tag{3}\]  
\[\text{where }p(S_{ij}|F,G)=\left\{\begin{array}{l}\frac{1}{1+e^{-\Theta_{ ij}}},\quad S_{ij}=1\\ 1-\frac{1}{1+e^{-\Theta_{ij}}},S_{ij}\
eq 1\end{array}\right..\]  
Then, the log-likelihood values of \(F\) and \(G\) are as follows:  
\[L_{H}=\log p(S|F,G)=\sum_{i,j=1}^{n}\Big{(}S_{ij}\Theta_{ij}-\log\Big{(}1+e^{ \Theta_{ij}}\Big{)}\Big{)} \tag{4}\]
## 6 Challenges and Future Research Directions
### Adaptive Threshold and Feature Selection  
It became evident that different threshold and feature selection methods can produce diverse results, even with the same attack detection dataset. The performance of entropy- and statistical-based methods strongly depends on the choice of threshold and feature selection. Since traffic features are rapidly changing due to new attack structures and  
Figure 15: Detection accuracies observed from different research studies: (**a**) conventional machine learning models such as SVM [127], DT [135], XGBoost with ANOVA [140], LR with RF [141], NB [149], LGMB [150], and RF [158], (**b**) deep learning models such as DBN [11], RBF with PSO [154], LSTM [166], AE [170], SAE with DNN [176], and GRU with RNN [178].  
growing attack intensities, the threshold should be self-adaptive, based on the new features. However, it is expected that more computational overhead will be added due to the adaptive threshold and feature selection algorithms integrated, so it is crucial to strike a balance between adaptive threshold and computational overhead while maintaining reasonable detection speed and accuracy. This is currently lacking in the literature and would be a good focus for future research.
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
[22] introduced the ResU-Net deep learning network model for the automatic segmentation of landslide areas. They conducted tests comparing this method with the baseline model (U-Net) for the automated mapping of landslide areas in Tian Shui city, Gansu Province. The ResU-Net model demonstrated an improved accuracy compared to the U-Net. Despite the successful achievements in landslide segmentation in the aforementioned studies, the task of semantic segmentation in landslide areas still presents three significant challenges due to the intricate nature and variability of landslides, as follows: (1) the current landslide dataset directly used for deep learning training is limited; (2) the old landslide areas in a landslide image are similar to the background landscape features so that the general semantic segmentation network cannot segment the boundaries of the old landslides accurately; and (3) redundant information in landslide images can lead to the omission of small-scale landslide areas. To address these issues, this paper, firstly, uses image enhancement techniques such as rotation and deflation. Secondly, the paper introduces fractional order image enhancement [23]. These methods can enrich the dataset and enhance the generalization performance of the network. In addition, this paper adopts the method of manual annotation to produce more accurate image labels. Finally, an improved landslide segmentation network model, the Deepened High-Resolution-ECA Network (DHRECA-Net), is proposed. The main contributions of this paper are listed as follows:  
(1) This paper introduces ECARes-Net and ECA-ASPP for acquiring broader contextual information, enhancing the network's ability to detect old landslides and small-scale landslides. ECARes-Net is incorporated into the feature extraction network, while ECARSP is added after the feature extraction network. (2) This paper presents a deepened high-resolution semantic segmentation network specifically designed for landslide scenes. By deepening the network's backbone, a stacked feature module is constructed. This module effectively captures deeper semantic information, enhancing the network's performance in semantic segmentation of landslide scenes. To address the issue of imbalanced positive and negative samples and improve the segmentation performance, this paper employed a combined loss function that included BCE loss and Dice loss [24] during the network training. To assess the effectiveness of the proposed model, this paper performed ablation experiments using a landslide dataset collected from the Loess Plateau. For comparative analysis, the results were compared with the performances of the U-Net [25], Deeplabv3+ [26], and PspNet [27] semantic segmentation networks. The experimental findings unequivocally demonstrate that the proposed method significantly enhances the accuracy.
# Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation
## 4 Experimental Analyses
### Monotonicity of the Similarity Degree for Generalized Decision Preservation  
In this subsection, we will verify the monotonicity of the similarity degree (as proposed in Section 3.1). The eight data sets shown in Table 2 were used for this experiment. Figure 1 shows the change trends of the similarity degree for generalized decision preservation with the number of the attributes increasing. In Figure 1, the x-axis denotes number of the attributes, while the y-axis denotes the similarity degree for generalized decision preservation. The similarity degree increased with an increasing number of attributes, and the relationship between the number of attributes and similarity degree for generalized decision preservation was strictly monotonic.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Data Sets** & **Objects** & **Attributes** & **Data Types** & **Classes** \\ \hline
1 & Blood Transfusion Service Center & 748 & 4 & Numerical & 2 \\
2 & Breast Cancer Wisconsin & 699 & 9 & Numerical & 2 \\
3 & Diabetic Retinopathy Debrecen & 1151 & 19 & Numerical & 2 \\
4 & House & 506 & 13 & Numerical & 4 \\
5 & Liver Disorders & 345 & 6 & Numerical, Nominal & 2 \\
6 & Seismic Bumps & 2584 & 15 & Numerical, Nominal & 2 \\
7 & Tic-Tac-Toe Endgame & 958 & 9 & Nominal & 2 \\
8 & Wilt & 4339 & 5 & Numerical & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data sets.  
Based on discernibility matrix-based attribute reduction, we can obtain all reducts from a decision system. Therefore, a reduct calculated by heuristic attribute reduction must be one of the reducts calculated by discernibility matrix-based attribute reduction. In other words, the reducts calculated by discernibility matrix-based attribute reduction must include a reduct calculated by heuristic attribute reduction. Under this consideration, by reduction results calculated by the three algorithms in this paper, we can verify the correctness of the proposed Algorithms 2 and 3. As shown in Table 3, it is obvious to see that a reduct calculated by FGRAG or BGRAG is one of the reducts calculated by DMRAG. For example, if the set of the reducts calculated by DMRAG on the data set Breast Cancer Wisconsin (Number 2) is denoted by Set_1, a reduct calculated by FGRAG was {1, 3, 5, 6} and a reduct calculated by BGRAG was {1, 3, 6, 8}. From Table 3, it is easy to see that {1, 3, 5, 6} \(\in\) Set_1 and {1, 3, 6, 8} \(\in\) Set_1. Therefore, on the data set Breast Cancer Wisconsin, any reduct calculated by FGRAG (or BGRAG) corresponded to one of the reducts calculated by DMRAG. Similarly, let the set of the reducts calculated by DMRAG on data set Tic-Tac-Toe Endgame (Number 7) be denoted by Set_2. The reducts calculated by FGRAG and BGRAG were {1, 2, 3, 4, 5, 7, 8, 9} and {2, 3, 4, 5, 6, 7, 8, 9}, respectively. It is obvious that {1, 2, 3, 4, 5, 7, 8, 9} and {2, 3, 4, 5, 6, 7, 8, 9} belong to Set_2. Thus, on data set Tic-Tac-Toe Endgame, a reduct calculated by FGRAG (or BGRAG) was also one of the reducts calculated by DMRAG.
## 4 Results of Probabilistic Stability and Performance Assessment
### Inverter Power Level Impact on System Stability and Performance
#### 4.2.3 Time-Domain Simulations  
Time-domain simulations were also prepared to validate the above discussions. Two different SCRs were considered, and the system stability and performance were studied under a grid voltage sag. The PCC voltage under a 10% grid voltage sag and two different power levels are depicted in Figure 9.  
Figure 8: Probability density functions (PDFs) of stability indices for different grid SCRs and power levels. **(a)** PDFs of the maximum damping factor; **(b)** PDFs of the minimum damping ratio. Colors correspond to different grid SCRs in Figure 6a.  
Figure 9: Time-domain simulations showing the system response for 10% voltage sag at grid voltage under different power levels (\(P=0.25P_{n}\) (orange) and \(P=0.75P_{n}\), (blue)). **(a)** SCR = 5; **(b)** SCR = 1.67.  
As expected due to the analytical results, the system response was the same under different power levels at higher SCRs. However, under lower SCRs, the system response was affected by the inverter power level such that under a lower power level, higher oscillations existed.
# Preliminary Field Results of a Dedicated 5G Positioning Network for Enhanced Hybrid Positioning +
## 4 Field Results  
The cumulative density function (CDF) of the pseudorange variation over 1 s and the CDF of the RSRP is computed for each of the UAV hovering points grouped per distance to the UE and UAV height from ground. As it is shown in Figure 3, there is a correlation between ranging precision and distance between flying BS and UE, due to the favorable LoS propagation. A remarkable ranging precision is obtained with the flying BS and the optimal receiver conditions. As it is summarized in Table 1, the static UE achieves a ranging variation below 1 m in 95% of cases, under the operational conditions of this experiment. Furthermore, the outlier rate (or amount of non-available measurements per UAV hovering point) is kept to around or below 1% depending on the experimentation point. Indeed, the first point (i.e., P1) shows the worse degradation, even with respect to the fifth point (i.e., P5). This degradation is assumed to be associated with the multipath errors due to ground reflections, although further experimentation and analyses are required as part of future work, e.g., by comparing ground and flying BSs ranging errors to fully characterize the UAV hovering impact. Future field results of the HOP-5G testbed are expected to further demonstrate the positioning capabilities of dedicated 5G networks.
## 3 Methodology
### Continuity  
Captured data often deviate from ground truth due to factors such as external infrared light sources, reflections, particulates, and sensor imperfections. These issues result in invalid data and outliers that disrupt the coherence of depth images.  
Outliers are particularly problematic, as the soil does not exhibit sharp, isolated points distant from other points in the point cloud. Thus, points that significantly deviate from their neighbors are identified as outliers and filtered out. Enhancing the continuity of data is essential for accurately reconstructing the excavation area. Due to the inherent characteristics of soil, its surface tends to be smooth with minimal sharp edges. Therefore, enforcing continuity in the reconstruction process allows the simulated soil surface to better retain its original shape, leading to a more accurate representation of real-world data.
## 2 Related Work
In [43], the research presents an SCRN model, a combination of CNN and LSTM neural networks, to extract spatial and temporal relations to predict the traffic speeds of 278 road links. In [44], the researcher proposes a PCNN model, which uses vehicle passage records from the surveillance cameras on roads, to predict the short-term traffic congestion. In [45], the authors propose a spatial-temporal complex graph convolution network (ST-CGCN) to predict traffic. The authors first generate a complexcorrelation matrix for spatial and temporal feature and then feed it to a 3D convolution operator followed by LSTM. In [46], the author presents a traffic congestion prediction with bilateral alternation of a spatiality and temporality (TCP-BAST) algorithm. The algorithm first captures spatial correlation based on graph attention networks, then captures temporal correlation using masked attention networks, and finally predicts traffic congestion on multiple road sections. In [47], the author presents a city-wide traffic congestion prediction based on data from TOPIS using a hybrid neural network based on CNN, LSTM and Transposed CNN which incorporates both spatial and temporal features, in which the CNN operation learns the spatial information and LSTM learns the temporal information. In contrast to [42; 43; 44; 45; 46], where the authors had to go through a complex process to incorporate spatial-temporal features of the dataset into the model, in [47], the author presents a simple and straightforward algorithm without any complex data-preprocessing for traffic congestion prediction. Building on the simplistic design presented in [47], in this study, we replace the initial CNN blocks with the HRNet architecture to learn multi-scale feature representation of the input dataset and replace the LSTM layer by Convolutional LSTM layer for learning spatial and temporal information.
## 1 Introduction
Some studies use CNNs and generative adversarial networks (GANs) to directly learn fusion rules for multi-focus images through end-to-end training, achieving remarkable results [16]. Deep learning-based methods convert multi-focus fusion into a classification task that distinguishes focused from defocused regions, and extract and fuse clear regions from source images based on the classification results. [PERSON] et al. [17] introduced deep CNNs into multi-focus image fusion by constructing a training dataset containing sharp image patches and their corresponding blurred versions. A classification model is trained to distinguish between focused and defocused patches. This model accepts a pair of source images as input and generates a fusion decision map through dense block prediction. In addition to CNNs, GANs have also been used in the field of multi-focus image fusion to generate decision maps [18]. However, deep learning-based fusion methods generally rely on large amounts of training data, may suffer from limited generalization ability, and involve complex models with high computational costs [19]. We improve upon spatial domain methods by innovatively combining Gaussian difference images and image pyramids to quickly and comprehensively extract the focused regions of the image. At the same time, considering the characteristics of embedded devices, region-based interpolation down-sampling is applied at the input stage, and nearest-neighbor interpolation up-sampling is applied at the output stage of the decision map to reduce the computational data volume. Ultimately, real-time multi-focus image fusion is achieved on embedded devices for images with a resolution of 3840 \(\times\) 2160.
# Speech Recognition for Task Domains with Sparse Matched Training Data
## 4 Experimental Settings
### Detailed Architecture of the End-to-End ASR  
Each utterance of the training speech data was converted into 80-dimensional mel filter bank (MFB) features using the Kaldi toolkit [41]. Each frame of the feature vectors was computed with a 25 ms window size and 10 ms shift. One segment of the proposed method consists of five consecutive frames. For an end-to-end ASR system, we used ESPnet, an open source platform [23] to train the end-to-end model parameters in all of the experiments. The encoder network was represented by 2 blocks of VGG layer [42], followed by 5 layers with 1024 units of bidirectional long short-term memory (BLSTM). The decoder network consisted of 2 layers with 1024 units of long short-term memory (LSTM). A location-aware attention mechanism was used. Learning was performed so that cross entropy and connectionist temporal classification (CTC) loss was optimized.