esa-sceva/satcom-chunk-collection huggingface dataset:
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 8 Results
### Ablation Studies
#### 8.2.2 TTA on Satellite Hardware: DUA  
Of the two proposed TTA methods, DUA [71] was first applied to CloudScout-3-S2 and evaluated for different numbers of samples used for adaptation. The effect of data augmentation on DUA was also evaluated by generating batches of augmented samples--one batch from each test sample--and using the generated batches for adaptation.  
**Number of samples**: Figure 7(a) shows the effects of varying the number of samples used to update CloudScout-3-S2 to Landsat 9. We found that ACC saturates at 16 samples; this trend is consistent with [PERSON] et al.'s observations [71]. The optimal number of samples corresponds to the point of diminishing returns for ACC, because past this point, the FP rate continues to rise. The ordering of the incoming samples holds little significance as it has negligible impact on model performance; this again confirms [PERSON] et al.'s observations [71]. These results demonstrate the memory-efficiency of DUA,  
Figure 7: Effects of FISH Mask on cloud detectors for different mask sparsity levels. SOURCE ONLY refers to training cloud detectors only on source data. FC ONLY means that only the weights in the FC layers were updated.  
as peak performance is achievable using a small amount of unlabelled target data. This efficiency can be attributed to these key factors:  
* DUA utilises models pretrained on source-domain data. This pre-training helps a model learn general features that are often shared across different domains. As a result, the model requires only minor adjustments to adapt to the target domain, avoiding the need for a large amount of new data.
* The statistics of the source and target domain, such as the mean and variance, may exhibit a high degree of similarity. When these statistics are closely aligned, a model is  
Figure 8: Effects of DUA on CloudScout-3-S2. SOURCE ONLY refers to CloudScout-3-S2 trained only on source data. Error bars represent standard deviations over 10 runs with different random seeds. (**a**) Model performance for different numbers of test samples without data augmentation. (**b**) Model performance for different augmentation batch sizes and a fixed number of 16 batches (one batch for each test sample).  
efficiently transferable across domains, i.e., the model parameters can be adapted to a new domain using few new samples.  
**Data augmentations**: Each sample is augmented by random horizontal flipping, cropping and rotation. The term \"augmentation batch size\" refers to the number of augmented samples in a batch. Figure 8b shows the effects of varying the augmentation batch size. Contrary to [71], we found that increasing the augmentation batch size does not provide further improvements in model performance. This is good news because it eliminates the need for data augmentation and thus reduces the time and computational efforts of DUA to perform adaptation.
# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
## 1 Introduction
Additionally, in order to enable navigation in a complex environment, there are situations where a robot needs to follow a path that initially moves away from the emitter to avoid large obstacles. This is achieved by integrating a heuristic A* path planning algorithm with our motion planning algorithm and SLAM to construct ideal paths to the target, while avoiding deadlock scenarios where the robot is trapped in a loop or stops navigation early before reaching the target destination. The rest of the paper is organised as follows: Section 2 describes a target motivating scenario in the context of industrial automation in our work. Section 3 describes the proposed architecture of our navigation system and presents the Pioneer platform used for this research in more detail. Section 4 discusses works related to existing RF-based localisation, explains our unique approach that utilises a single emitter, and explores the use of filtering of the raw radio input data to improve target localisation. Section 5 covers our motion control algorithm that enables robots to manoeuvre without hitting obstacles. Section 6 covers the use of mapping and path planning for improved navigation in complex environments, and Section 7 presents the experiments and results along with full descriptions of the test cases and experimental settings. Section 8 concludes the paper and suggests future work for this research.
## 3 Case Study
### Effects of Applying an Energy-Dissipation Support  
#### 3.3.1. Support Application Status  
The construction of the energy-dissipation support was carried out in the large-scale deformation section of the tunnel, and the effect of the supporting work is shown in Figure 15a. According to the figure, once the initial support deformation had stabilized, the steel plate composite structure was still not fully compacted, and according to the compression curve, the steel plate composite structure was at the constant resistance stage. This indicated that the surrounding rock pressure had fallen to a level that the existing support structures could bear and that the characteristic curve of the surrounding rock intersected that of the support, signifying that the supporting structure had reached a stable state. No safety problems arose during construction, and the support effect is shown in Figure 15.  
It was found that, compared to conventional support sections, the experimental pressure type support could effectively alleviate the phenomena of initial support cracking and concrete block dropping. The steel plate composite structure incurred significant deformation as part of the tunnel excavation support, but the overall state of the support structure remained sound, which indicated that the release of deformation pressure from the surrounding rock via controlled deformation in the highland rock stress area was conducive to the stability of the structure and the convergence of deformation.
# Options for Performing DNN-Based Causal Speech Denoising Using the U-Net Architecture +
## 4 Experiment and Performance Evaluation
### Performance Evaluation  
We evaluated the performance of the proposed U-Nets across different domains, focusing on the speech quality and intelligibility of the denoised output. The speech enhancement metrics used included CSIG, CBAK, COVL (proposed by [PERSON] and [PERSON] [35]),and the Perceptual Evaluation of Speech Quality (PESQ) [36]. CSIG rates speech signal quality, CBAK assesses background noise distortion, and COVL evaluates overall quality--all on a scale from \(1\) (poor) to \(5\) (excellent). The PESQ metric ranges from \(-0.5\) to \(4.5\), with higher scores indicating better speech quality. Additionally, we used the short-time objective intelligibility (STOI) metric [37], ranging from \(0\) to \(1\) in terms of percentage, to assess speech intelligibility.  
Our tests involved corrupting clean speech with \(18\) different noise types at initial SNRs of \(-2.5\), \(2.5\), \(7.5\), and \(12.5\) dB. We repeated each test ten times to minimize variation and averaged the results for consistency. According to the results presented in Table 1, the U-Net's performance in enhancing SNR and improving speech quality and intelligibility was robust across all tested domains, with slight variations. For the classical U-Net constructed using CCABs, the SNR improvement was noteworthy for low SNR conditions where the SNR level jumps from \(-2.5\) dB to above \(13\) dB. By contrast, the improvement is less than \(8\) dB when the initial SNR is \(12.5\) dB. Notably, the advanced U-Net model, equipped with GLFBs, generally outperformed the classical model with CCABs. Improvements of approximately \(0.48\) dB in SNR and \(0.033\) in PESQ scores were observed for the input taken from the STFT domain, and the gains were \(0.42\) dB in SNR and \(0.041\) in PESQ for the input taken from the STDCT domain.  
Our findings suggest that the U-Net's performance was comparably strong in both the STFT and STDCT domains, with marginal superiority to that attained directly using time sequences in the temporal domain. Furthermore, metrics sensitive to the Fourier spectra, like CSIG, CBAK, and COVL, also demonstrated an apparent preference for the STFT and STDCT domains over the temporal domain. This adaptability and performance consistency underline the potential of U-Net architectures for a broad range of audio processing applications.
## 3 Results
### Albis (Near Zurich) Radar at an Altitude of 938 m  
The Albis radar is located near Zurich (lat.: 46.2843\({}^{\circ}\); long.: 8.5120\({}^{\circ}\)) at an altitude of 938 m. Six Sun-tracking observations (see Table 4) are available so far for the absolute calibration of the horizontal and vertical receivers. On a logarithmic scale, the average solar flux from DRAO measurements is 21.97 \(\pm\) 0.58 dBsfu. As expected, the dispersion associated with the radar-retrieved values is larger (\(\pm\)0.65 dB, approximately). In absolute terms, the average obtained from the radar H (V) channel is \(-\)1.13 (\(-\)0.75) dB smaller; this corresponds to an average mutual bias between H and V equal to \(-\)0.38 dB. The issue of differential reflectivity calibration is crucial for the successful application of polarimetric weather radar. Whether a small average offset such as +0.09 dB observed for Lema, or +0.17 dB observed for WEI, should be compensated or not, is debatable. For sure, one has to investigate and eventually correct the \(-\)0.38 dB mutual bias of Albis, especially considering the small standard deviation of \(\pm\)0.04 dB among the seven measurements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **DRAO** & \multirow{2}{*}{**I\({}_{5.5}\) Rx H**} & \multirow{2}{*}{**I\({}_{5.5}\) Rx V**} & **10 log** & **10 log** & **10 log** \\  & **Reference** & & & **(H/V)** & **(H/DRAO)** & **(V/DRAO)** \\ \hline
2 November 2013 & 22.34 \(\pm\) 0.079 & 22.32 & 22.17 & 0.15 dB & \(-\)0.02 dB & \(-\)0.17 dB \\
29 April 2014 & 21.88 \(\pm\) 0.037 & 21.52 & 21.37 & 0.15 dB & \(-\)0.36 dB & \(-\)0.51 dB \\
7 August 2014 & 22.17 \(\pm\) 0.039 & 21.97 & 21.87 & 0.10 dB & \(-\)0.20 dB & \(-\)0.30 dB \\
28 January 2015 & 22.56 \(\pm\) 0.039 & 22.32 & 22.37 & \(-\)0.05 dB & \(-\)0.24 dB & \(-\)0.19 dB \\
7 April 2015 & 21.76 \(\pm\) 0.125 & 21.67 & 21.57 & 0.10 dB & \(-\)0.09 dB & \(-\)0.19 dB \\
21 March 2016 & 21.16 \(\pm\) 0.017 & 20.98 & 20.90 & 0.08 dB & \(-\)0.18 dB & \(-\)0.26 dB \\ \hline Average & 21.98 & 21.80 & 21.71 & +0.09 & \(-\)0.18 & \(-\)0.27 \\ Standard dev. & \(\pm\)0.50 & \(\pm\)0.52 & \(\pm\)0.54 & \(\pm\)0.07 & \(\pm\)0.12 & \(\pm\)0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Solar flux values from six Sun-tracking observations at the _Monte Lema_ site (near Lugano) and corresponding nearest-in-time reference values by the DRAO in Canada.
## 1 Introduction
The main contribution of this paper is the design and test of a practical technique for the initial (coarse) synchronization of parallel converters referred to in [20], using identical microcontroller units (MCUs) connected through a CAN bus interface to a master unit managing the entire system. Although the authors estimated that the initial (coarse) synchronization is required to yield a <1% synchronization error in order for the secondary (fine) synchronization to do its job, experiments show that the proposed solution guarantees a <0.1% error on the starting times of the modulating signals of three parallel units.
## 2 Related Work
### Endoscopic Image Augmentation  
GANs have been increasingly used particularly in endoscopy. Endoscopy is a medical procedure that utilizes specialized equipment to examine internal organs or perform interventional procedures using real-time imaging. It is most commonly used to investigate diseases of the digestive tract.  
In the work [23], the authors proposed a Swin Transformer encoder-based StyleGAN (STE-StyleGAN) for unbalanced endoscopic image enhancement, which consists of an adversarial learning encoder and a generator. The encoder extracted multi-scale features layer by layer from endoscopic images. After that, a self-attention mechanism was applied to the generator, which adds detailed information to the image layer by layer through the encoded features. Experimental investigation with real data showed that the images generated by STE-StyleGAN achieved a Frechet Inception Distance (FID) value of 100.4, and the model achieved an accuracy of 86%  
Endoscopic images are difficult to analyze quickly and thoroughly because they often rely on human vision. In the work [12], the authors aimed to create a classification system for digestive system diseases. During the study, a total of six models were trained: VGG-16, ResNet-50, DenseNet-121, InceptionNet-V3, EfficientNet-B7, and ViT. To increase the amount of medical data, data augmentation was applied using two adversarial generative neural network-based models. The authors' experimental studies demonstrated that InceptionNet-V3 showed the best performance improvement with augmentation based on a StarGAN.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Single-Channel MAC Protocols
#### 4.1.1 eRTS-SA  
The request transmission split-slotted ALOHA-based MAC protocol provides fair channel access with high throughput and interference avoidance using a GPS-embedded micro-cell unit.  
Every vehicle reports their location information to micro base stations (mBS) by GPS, and MEC is used in the contention access phase to provide cloud capabilities. The road between mBS is divided into the segment and allocated with segment number to detect location information to reduce overheads. The vehicles are arranged for time slot allocation  
Figure 3: Classification of MAC protocols.  
in increasing order of their segment number. To minimize channel interference, the same segment number is allocated to vehicles geographically apart. The methodology includes dividing the frame into three phases: contention access phase (CAP), broadcast feedback phase (BFP), and contention-free phase (CFP). In CAP, the vehicles send a request signal to RSUs. After the signal is received by RSU, successive interference cancellation (SIC) is implemented; then, the vehicle occupies the time slots to enable transmission without interference. To prevent interference, the BFP is divided into two divisions assigned to adjacent mBSs. The mBS are equipped with the MEC server to serve vehicles within their micro-cells, and vehicles move in a bi-directional highway with no \"on-and-off-ramp\" [69].  
The physical interference model based on signal-to-interference-plus-noise ratio (SINR) is calculated to keep track of successful transmission. Vehicles and mBSs are time-synchronized; the time-slot allocation mechanism avoids the hidden terminal problem. This protocol is better in throughput (4.8 percent lower than the theoretical maximum throughput) and transmission efficiency. The protocol does not prioritize safety-based applications or non-safety-based applications, energy efficiency is not analyzed, and the scalability issue is not addressed.
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 2 Technology Enablers for the Internet of Verticals
### Non-Terrestial Networks  
From 3 GPP Release 17 onwards, the 5G network architecture includes the Non-Terrestrial Networks (NTN), which include GEO, LEO and HAPS. The focus is currently on non-transparent architecture where satellites or HAPS act as \"dumb\" relays in the space/sky for 5G, but from Release 19 (2025) onwards, we can expect a full integration, where a satellite or HAPS will have a full gNB on board. In the 6G era, this will bring a massive change to the architecture of mobile networks with new \"mobile infrastructure in the space\" carried by satellites and HAPS. This will open up the extension of mobile Internet to space in the 6G era, built on a multi-tier network infrastructure architecture in the space reminiscent of Macro (GEO), Small (LEO, Femto (HAPS) networks. Besides providing ubiquitous coverage for a host of vertical applications, e.g., in areas such as transportation, supply-chain and logistics, 6G NTN will enable new \"Internet of Space\" applications, such as the remote command and control of spacecrafts, space robots and the Internet of Space Things, e.g., for assets in space, health and security monitoring in space and space engineering.
# Review on Soft Mobility Infrastructure Design Codes
## 2 Codes for Soft Mobility Infrastructure
### Road and Roadside Facilities
#### 2.4.4 Traffic Counters or Monitors  
Traffic counters or monitors are extensively used to measure car volume, occupancy and speed, to manage traffic on a real-time basis or perform off-line analyses. The same applies to statistics on soft mobility traffic. However, few countries have bicycle and pedestrian counting devices. And not all countries with counting devices make the data they obtain public. Thus, in this part, we will take Paris, France as a best practice example for bicycle monitoring devices. Markedly, traffic counters are not part of design codes but are here addressed because the lack of evidence on SMI traffic is undoubtedly a major barrier to establishing evidence-based recommendations and regulations.  
In 2021, Paris launched its Plan Velo 2021-2026 (Cycling Plan 2021-2026), hoping to make Paris a \"100% bikeable city\". Therefore, the city of Paris has been deploying permanent bicycle counters to assess the growth of cycling. The system Comptage velo--Donnees compteurs (Bike counting--Counter data) displays and counts bicycle traffic through Eco-DISPLAY Light, a large counter developed by Eco-counter, the technology provider of the program, which can display bicycle traffic in real time (as shown in Figures 10 and 11). The system can distinguish different vehicles and count different directions separately to avoid interference from other traffic participants. However, the system also has many shortcomings. For example, the system only counts bicycle traffic based on time and cannot calculate other data such as speed. At the same time, if the target lane is occupied by other traffic participants, the system will be paralyzed. Therefore, other cities should consider related issues when introducing the same project and propose solutions for these.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Standard** & **HDB’s * Designs** & **High** & **Rest Area Along** & **Sheltered Pedestrian Waiting Area** \\ \hline Cross-Section & & & & 70–100 m interval & \\ Data & 2.4 m \(\times\) 2.4 m & 3.0 m \(\times\) 2.4 m & 5.7 m \(\times\) 5.0 m & 0.8 m \(\times\) 3.25 m (1.2 m \(\times\) 0.9 m for wheelchair) & Provided at intersections and road crosses \\ \hline Purpose and Requirements & - & Columns on one side & For cross minor roads or vehicle crossing & Keep distance to intersections & \\ \hline \hline \end{tabular}  
* HDB means Housing and Development Board of Singapore.  
\end{table}
Table 13: Requirements of covered linkway in Singapore.
# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
###### Abstract
In the second stage, comparing to the solutions based on reconstruction or generation, our system locates the anomalies by using multiple multivariate Gaussian models to predict the anomaly scores of each receptive field. Besides that, each multivariate Gaussian model is fitted to the corresponding feature representations, which means that the receptive fields at different loactions have their own Gaussian models. Futhermore, according to the principle of VAE, our feature vectors are more independent and decoupled than those extracted by original auto-encoders and convolutional auto-encoders. Then, an averaging operation is used to handle the overlapping parts of the receptive fields. The proposed anomaly detection system is evaluated on the challenging large-scale surveillance scene datasets and compared with several methods. The experiments show that our method outperforms most previous methods and yields competitive performance comparing with two state-of-the-art methods. Our main contributions are as follows:  
* We propose a new baseline of the anomaly detection and location framework using the variational auto-encoder to learn the discriminative feature representations of appearance and motion patterns. * We extract the feature representations of all receptive fields at one time and model a unique Gaussian for each feature. * The variational auto-encoder is used to decouple the components of the feature from each receptive field as much as possible so that it is easier and more accurate to model the Gaussian for the features. The remainder of this paper is organized as follows. Section 2 reviews the related work of anomaly detection and localization. A detailed description of our proposed method is given in Section 3: first the overall framework, then the variational auto-encoder with convolution kernels for feature extraction and the anomaly estimation at last. Section 4 presents experimental results and comparisons. The conclusion is finally summarized in Section 5.
# Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders
## 5 The Hierarchical-Sparse Modelling Representative Selection  
A hierarchical implementation of the Sparse Modelling Representative Selection (SMRS) algorithm, say H-SMRS [6], is adopted in this paper for key-frame extraction. The H-SMRS is applied on the compressed transformed signals, \(\vec{h}_{k}^{(n)}\) of the encoding mode of SAEs instead of our previous works where this algorithm has been applied directly on the 3D attributes. This way, we discard redundant information existing in the data samples, a process which is very important especially in case where multiple humans are dancing in a sequence.  
The proposed hierarchical scheme is based on the Sparse Modelling for Representative Selection (SMRS) algorithm [12] which reconstructs the \(N\) total frames of the dance sequence from \(K\) representatives. The optimization of the algorithm is achieved using the Alternative Direction Method of Multipliers (ADMM) [31]. Actually, this method comprises of iterative steps, taking into consideration the Lagrange multipliers.  
The traditional SMRS algorithm is sensitive to temporal redundancies. Therefore, it fails to model the temporal dependencies of a choreography. To overcome this difficulty, we have introduced in Reference [6] a hierarchical decomposition scheme of the SMRS algorithm which first detects time intervals on which further decomposition takes place so as to create hierarchies of the key frame representatives. Thus, hierarchical SMRS segments the initial feature space into suitable sub-spaces that better model the choreography. The proposed H-SMRS is able to efficiently describe more complicated choreographic patterns, since the feature fluctuation within a sub-time interval (sub-space) is less than the fluctuation of the entire feature space of the sequence. Figure 5 presents an example of the hierarchical decomposition framework (H-SMRS). At the first layer, three representatives are extracted to model the whole video sequence (marked in green). Therefore, the initial video sequence is decomposed into four further sub-sequences (intervals), since the first and the last frame are also considered as representatives. Then, we assume that the third out of the fourth video sub-sequences. that is the interval \(\Delta\tau(1,2)\), is further decomposed. \(\Delta\tau(1,2)\) expresses the first layer at the second sub-sequence (interval). For this reason, the SMRS algorithm is applied within the interval \(\Delta\tau(1,2)\) for extracting representatives that best fit the frames of this interval. In this example, two representatives are identified, again marked in blue color at layer 1. Therefore, the video segment of \(\Delta\tau(1,2)\) is further decomposed into three more sub-segments. This procedure is iteratively applied until the decomposition criterion identifies that no further decomposition is required.