esa-sceva/satcom-chunk-collection huggingface dataset:
# Creating Audio Object-Focused Acoustic Environments for Room-Scale Virtual Reality
## 2 Background
### 6-DoF Audio  
Object-based and sound-field rendering approaches have been used to implement passive player participation (\"6-DoF audio\") [6]. For example, 30 time-aligned third-order Ambisonic recordings of an orchestra performance are interpolated based on the listener's position and rotation in their room-scale VR music experience _Zylia Concert Hall_ using the sound-field rendering technique [19; 20]. [PERSON]. [21] uses an object-based rendering method of multiple mono-recordings of an orchestra performance. [PERSON] et al. use both rendering methods in their experience depending on the source material [6]. In contrast to sound-field rendering, object-based rendering also considers a sound source's radiation pattern and interaction with the \"virtual\" acoustic environment during the rendering process [6]. This approach gives the sound designer deep control over the acoustic appearance of a sound source in space.  
The cited experiences, however, differ from our work in that they focus on the recreation of existing music performance and its performance space, rather than arbitrary (imaginary) acoustic environments or active player interaction. As the corresponding standard for 6-DoF audio (MPEG-I) is currently being developed [22], Refs. [20; 21; 22] use their own custom toolchain, while [PERSON] et al. use game technologies for both visuals and audio [6].
## 2 Materials and Methods  
### Network Model Structure  
In the field of IRASs, a new network model (CNN-transformer-BiGRU) that is applied to the task of water pump fault detection is thus proposed. Its overall structure is illustrated in Figure 1.  
To detect the water pump's faults, its vibration signal data were collected via sensors, and then processed by normalization and resampling. To convert these one-dimensional vibration signals into image data that integrate the time and frequency domains, the continuous wavelet transform was used; a CNN then preprocessed the input data and extracted local features; after that, the data were fed into the transformer for global feature modeling. This output served as input into the bidirectional gated network model for sequence modeling and processing. Finally, the fully connected layer and Softmax function output the classification results.
## 6 Discussion and Future Research Directions
### Optimized Sensing and Localization  
ML-empowered THz, VLC, and IRS systems have shown effectiveness in enabling sensing and localization solutions with very high accuracy in the cm ranges. The positioning accuracy of both indoor and outdoor applications have been effectively improved by integrating ML with VLC techniques [187, 188, 189]. In 6G networks, accurate localizationcan enhance optimal power and resource allocation, which will thereby lead to efficient resource management. ML-assisted 6G system will perform with optimal data transmission route, experience lower delays, and possess high capabilities.
# Onboard Processing in Satellite Communications Using AI Accelerators
## 4 AI-Capable Commercial Chipsets  
So far, complex onboard AI applications can only be performed with very expensive custom-designed ASICs [30]. The increase in performance requirements for onboard processing to support higher data rates and autonomy has made the existing space-graded CPUs obsolete. New technologies, including non-qualified commercial off-the-shelf (COTS) devices from other critical domains, are currently being explored [31]. Finding a device with adequate computer capacity, proper power consumption, and compliance with standards specifications for onboard and standalone applications \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Input** & **Output** & **Total Scores** \\ \hline Interference & Baseband & Binary & 13 \\ Detection & digital signal & detection flag & \\ \hline FEC in & Modulated or & & \\ Regenerative & demodulated & Corrected & \\ Payload & codeword & codeword & 10 \\ \hline Link Adaptation/ & SNR time & & \\ ACM Optimization & series & & \\ \hline Flexible & Demand & Configuration & 11 \\ Payload & Information & of the RF & \\ \hline Antenna & User locations & Beamforming & \\ Beamforming & within antenna & parameters & 12 \\ FOV & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different scenarios comparison. Figure 10: Use cases evaluation. is one of the most critical points for future implementations. This section compares non-qualified COTS AI-capable chipsets, and provides guidelines for chip selection. The current market for commercial AI-capable chipsets is divided into graphics processing units (GPUs), mostly used for the training steps, and dedicated devices for hardware co-processors or stand-alone embedded systems. Focusing on this last classification, several devices have been launched on the market, led mainly by NVIDIA, AMD, Intel, and Qualcomm [32]. Table 3 summarizes current COTS AI-capable chipsets [33; 34; 35; 36; 37]. AI-capable chipsets include a central processing unit (CPU) and an on-chip accelerator, which define their capabilities and performance in AI and ML tasks. The number of CPUs and cores depends on the manufacturer and the family, and they vary from one to two CPUs, and two to twelve cores. Most remarkable are the multicore ARM Cortex-R5F2 on the Versal chips, already on the market, and the 12 cores on the Jetson Orin AGX high-end chip, partially available since late 2022 (according to the information
## 6 Reviewed Papers on Machine-Learning-Based Path Loss Models
### Assessment of Machine Learning Path Loss Models in Outdoor Urban Environments  
In [81], among the four machine learning models that were examined for their suitability in path loss prediction at 2.1 GHz, the RF model outperformed the rest of the models with minimum RMSE, MAE, and MAPE values of 4.662 dB, 3.19 dB, and 2.96%, respectively. Similarly, in [88], the RF model outperformed the two other models such as the CNN and MLP at 5.9 GHz, having the minimum RMSE and MAE values of 1.44 dB and 2.19 dB, respectively, followed by the CNN model with an RMSE value of 2.03 dB, as shown in Figure 6.  
Also, the RF model outperformed the KNN model at 3.5 GHz in [95], with a minimum RMSE value of 3.42 dB. Among the other works that considered a comparison of machine learning models at 3.5 GHz is that of [91], involving the DNN, NN, and K-NN. The DNN model outperformed the rest of the models with the minimum RMSE value. Meanwhile, the supremacy of the machine learning model over empirical models is showcased in [84], where the DNN model outperformed the ABG and CI models at 3.5 GHz with the minimum RMSE value of 4.84 dB, and in [93], where the NN model outperformed the FI and CI empirical models with an RMSE value of 4.3 dB at 7 GHz.  
The XGBoost model was not left out when it competed with the CNN model in [83] to show its excellent performance over the CNN model with a minimum RMSE value of 2.4 dB at 1.5 GHz.
## 2 Materials and Methods
### Data Processing  
#### 2.2.1 Meteorological Data  
Meteorological data for 2001-2020 were acquired from the China Meteorological Science Data Sharing Service Network ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))), which provides daily surface meteorological data for Zhejiang Province and its neighboring regions. To ensure spatial continuity, data from surrounding provinces and cities were interpolated using an inverse distance-weighted algorithm. The Hangzhou data was then extracted to generate daily meteorological raster data with the same resolution as the MODIS EVI. Temperature data were adjusted for elevation using digital elevation model (DEM) data, assuming that the temperature decreases with elevation at a decreasing rate of 6.5 \({}^{\circ}\)C km\({}^{-1}\)[35]. The total solar radiance was modeled according to Ju [36], and utilizes the sunshine time of each station's measurements to simulate solar radiation. Annual-scale meteorological data including Pre, Tmax, Tmin, Rad, and relative humidity (Rhu) were obtained by averaging the daily-scale data.
## 3 Engineering Overview and Rock Mass Temperature Variation Characteristics with Depth
### The Relationship Between the Temperature of the Rock Mass and Its Burial Depth  
As shown in Figure 3a (the temperature data were acquired from the monitoring boreholes at the 645 m bench platform), the temperature variation curves at different depths within the Wushan Open\(-\)Pit Mine slope reveal that the rock mass temperature follows a sinusoidal pattern in response to seasonal cycles. However, distinct thermal response characteristics exist between shallow and deeper layers: Shallow rock layers exhibit higher sensitivity to atmospheric temperature variations due to their proximity to the atmospheric environment, manifesting as larger temperature fluctuation amplitudes and faster change rates. In contrast, deeper rock layers experience less influence from atmospheric temperature, resulting in smaller temperature variation ranges and slower change rates.  
The relationship between temperature extremes and depth plotted from the temperature variation curves is shown in Figure 3b. As depth increases, positive and negative temperature extremes progressively converge with decreasing convergence rates, reaching a depth where the negative temperature extreme equals 0 \({}^{\circ}\)C (maximum freezing depth) at approximately 7.5 m.
# From Trees to Barcodes and Back Again: Theoretical and Statistical Perspectives
## 2 Mathematical Background  
Precisely what a mathematician means by the terms \"tree\" and \"barcode\" can vary depending on context. First, we specify what these terms mean in this article. We then recall biologically motivated algorithms for generating barcodes from trees [13] and trees from barcodes [21], the relation between which will be made clear in the following sections.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 5 Simulation Analysis
### Lateral Motion Simulation Condition
#### 5.2.3 Condition 3  
From Figure 23a, we can see that, when the speed is raised to 36 km/h, the designed obstacle avoidance function algorithm can avoid obstacles because of the high speed of the driverless ferry vehicle when the prediction time domain, \(N_{p}=7\), of the planning module is too high, while the other three cases can avoid obstacles smoothly. From Figure 23b,c, we can see that the tracking error of the driverless ferry vehicle to the expected yaw angle is minimum when \(N_{p}=10\). As can be seen from Figure 23d, except for \(N_{p}=7\), the tracking error of the driverless ferry vehicle is the smallest, and the tracking effect is the best when \(N_{p}=10\) is used. Therefore, when the speed of the driverless ferry vehicle is increased to 36 km/h, the predicted time domain, \(N_{p}\), value in the planning module is set to 10.  
To sum up, although the designed single-lateral motion controller with obstacle avoidance function has a certain ability for obstacle avoidance replanning and trajectory tracking, the tracking error is still more than 1 m when turning. It shows that the single-lateral motion control cannot enable the driverless ferry vehicle to track the reference trajectory stably. Therefore, on this basis, the coordinated control of horizontal and longitudinal motion should be designed.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 3 Evolving Computing Paradigms and Related Concepts
### Fog Computing
#### 3.12.2 Architecture
With huge data produced by intelligent transportation systems (ITS), processing using centralized model results in large delays. In this sense, fog nodes at particular intersections could be utilized to analyze data locally and brief people on current situations, substantially lessening the delay [117]. Augmented reality (AR) and virtual reality (VR)  
Augmented reality overlays digital and virtual content into a physical environment. It is highly time-critical, warranting responses in real-time. Moreover, it is extremely latency-intolerant, as even a minor delay may impact user experience, effectuating a negative response [116]. For this reason, fog computing has the potential to become a key player in the augmented reality domain, as computer-intensive jobs can be offloaded to nearby fog devices. This also holds true for the virtual reality (VR) field, which offers real-world experience through a simulated environment and is generated by computer technology. Smart healthcare system  
When distant cloud servers are used to process and store enormous healthcare data generated from sensors, the huge data transmission, defining of location, and access latency pose critical challenges [118; 119]. As healthcare datasets increase, there is a higher possibility of error occurrence during processing as well as transmission. Even a minute data analysis error may instigate the administering of an inappropriate treatment that could cost a human life. The patient health data is sensitive; hence, security and privacy preservation are vital. The integration of fog computing into healthcare enhances efficiency and quality as the computer and storage are provided nearer to end devices, which permits aggregation, processing, local storage, and real-time analytics. It further displayed a low latency, mobility support, privacy, and location awareness, and experiments demonstrated an enhanced system response time along with an improvement in energy consumption [120]. As agriculture caters to the food supply chain, it plays a prime role in smart city schemes [121]. With smart agriculture, sensors installed in field vehicles gather data on plant growth and field climate conditions. Moreover, the field can be sensed from the sky using air balloons. These sensing activities can be effectively accomplished by fog computing, and agricultural lands can be managed and tracked through sensor nodes' alarm notifications. As far as sustainable smart cities are concerned, smart water management is crucial. It supervises the quantity of water consumed, transported and anticipates the use of water in the future. Above all, it enhances the water system of the city to be more reliable, sustainable, and efficient, as it assists in mitigating water loss using sensors that collect and analyze data of the water system [121].
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 4 Solution Concept
### Mosaic of Measured Points  
For every captured image, the point cloud of the laser location is achieved in the camera coordinate. As the displacement platform moved, a series of point clouds is obtained. Though the point cloud obtained from different image is all in the camera coordinate, it is independent actually because the camera is fixed. In order to generate the point cloud for the whole ROI, it is significant to combine the point cloud obtained from each image together, which is the mosaic procedure.  
Suppose \(d_{i}\) is the movement distance of the displacement platform when the \(i\)-th image is captured. As the unit motor direction of the platform \(n_{d}\) is calibrated, the translation of the platform during the time from the first image captured to the \(i\)-th image captured in camera coordinate is \(d_{i}\cdot n_{d}\). The intersection point in the \(i\)-th image is adjusted as Equation (17).  
\[P_{w}=P_{c-i}-d_{i}\cdot n_{d}. \tag{17}\]  
\(P_{c-i}(x_{c-i},y_{c-i},z_{c-i})^{T}\) is the intersection result in the \(i\)-th image, while \(P_{w}(x_{w},y_{w},z_{w})^{T}\) is the final mosaic result of the corresponding point in the camera coordinate system of the first capture.
# A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain
## 4 Specific Implementation
### Build the Network  
Multiple entities, including data subjects, data controllers, and data processors, are registered as consortium chain nodes. Blockchain will assign a blockchain address based on participating roles and grant different permissions. As displayed in Figure 4, nodes are divided into light nodes and full nodes: Light nodes are all ordinary nodes, and full nodes include ordinary nodes, service nodes, and regulatory nodes. The data subjects are all user nodes (nodes that do not participate in commit and endorsement).  
Light Node: Light nodes only store block headers. By storing block headers, it can be demonstrated that certain transactions have not been changed without using most of the memory to store the blockchain. Additionally, light nodes can also access the specific data they want. For the purpose of saving storage resources, the data subject can choose to become a light node.  
Full Node: Full nodes need to save every transaction and block in the blockchain to store complete blockchain information. User nodes can also apply to become full nodes. The service node is composed of service providers (that is, data controllers and data processors) that comply with GDPR. The Committer is selected to verify the block generation, while the regulatory node is composed of regulatory authorities (such as the Data Protection Agency) that operates and endorses the blocks generated by the Committer.
## 1 Introduction
This \"fingerprint\" has to be robust enough to be unfeasible to clone. For a system to be considered a UNO, it must have the following properties [5]:  
* **Disorder--**The generated fingerprint should only be based on the unique disorder of the object in study;
* **Operability--**It is imperative that the fingerprint is calculated in a timely manner, can be detected with different measurements, and is robust to ageing and environmental conditions. Simultaneously, it should be economically viable to manufacture several instances of the measurement equipment;
* **Unclonable--**It should be overly expensive or impossible for any entity to reproduce an object with the same features of the extracted fingerprint of the physical object. Summarily, a UNO should be unique and unclonable, and the measuring equipment should be mass produced. From this work, two main contributions are highlighted:  
* Proposing cork as a UNO, part of the Physical Unclonability and Disorder systems approach;* Proposing a low cost hand-held illumination device for wine anti-counterfeiting applications;  
According to a report published in 2016 by the European Union Intellectual Property Office, roughly 21% of the value of Geographical Indication (GI) infringing products in the European Union in 2014 were in spirits and wines [6]. Additionally, from the same report, the \"consumer loss, defined as the price premium unjustly paid by consumers in the belief that they are buying a genuine GI product, is estimated at up to \(\copyright\)2.3 billion.\" Within this scenario, another contribution can be identified in a social context: proposing a device and method to prevent wine counterfeiting. The remainder of this document is organized as follows: Section 2 presents a literature review focusing on image-based approaches for individual object recognition, targetting anti-counterfeiting systems; the illumination device and RIOTA's implementation are detailed in Section 3; the evaluation of the performance including the databases used, evaluation metrics, testing procedures, results and discussion are shown in Section 4; in Section 5 the conclusions for this work are drawn; finally, in Section 6 a description of the related patent for this work is presented.
## 3 Results and Discussion
### Influence of Unsupported Height  
For experimental validation of the unsupported height effects on the crash response of the specimen, tests with four different free heights, namely 5, 10, 20, and 35 mm, were performed on glass fiber specimens. Figure 11 shows snapshots of specimens under impact in each of the four conditions. In the extreme case of 35 mm of unsupported height, huge plate bending appears and the specimen crush failure is far from the expected one. In the case of 20 mm of unsupported height, the resulting dominant failure modes are delamination and splaying, which are not as effective as fragmentation in energy absorption [2,3].  
With 10 mm of free height, we see the mixed mode of crushing, i.e., splaying of the outer layers and fragmentation of the inner ones; this is the expected failure mode. Finally, in the case of 5 mm unsupported height, the specimen is overly constrained by the fixture such that splaying  
Figure 10: Representative experimental results: (**a**) typical force–displacement graph obtained using the designed fixture, with different areas shown on the graph; (**b**) specimen at the end of the test process with total crushed length visible.  
The second diagram reports the values of SEA as a function of the considered variables. The 10 mm solution is characterized by a high value of SEA and a small spread. Meanwhile, the results for 20 mm and, much more, for 35 mm are characterized by lower values of SEA (meaning that the developed failure mode is not the right one) and higher values of spread (indicating higher uncertainty in the obtained values). Both diagrams prove the effects of unsupported height on the energy absorption of the specimens. Similar trends were also observed in the past by researchers who found that, in particular, even if an over-constrained specimen leads to higher energy absorption capabilities [11; 14], it results in a failure mode that is not representative of the reality, i.e., of the failure mode exhibited by real parts during impact. Since 10 mm free height allows the occurrence of the main absorption mechanisms characterized by [PERSON] and [PERSON] [2; 3] during the tests, keeping the cylindrical supporting bars at 10 mm distance from the base was deemed appropriate.
## 2 Construction of the Contact Group Contact Model
### Morphological Feature Extraction of Contact Groups
#### 2.2.1 Image Denoising Preprocessing  
Contact images are easily being affected by environmental lighting and reflections from metallic surfaces during the capture process, which necessitates noise reduction preprocessing of the images.  
First, the Gaussian function is used to extract the illumination component of the contact surface morphology image [24], with the expression of the Gaussian function as follows:  
\[B(x,y)=\lambda\left(-\frac{x^{2}+y^{2}}{c^{2}}\right) \tag{1}\]  
In which, \(\lambda\) is the normalization constant, and \(c\) is the scale factor.  
Use \(P(x,y)\) to represent the HSV value of the contact surface morphology image, and by convolving with a multi-scale Gauss function and \(P(x,y)\), the expression of the illumination component \(C(x,y)\) in \(A(x,y)\) is as follows:  
\[C(x,y)=P(x,y)B(x,y) \tag{2}\]  
Secondly, adjust the parameters of the 2D-Gamma function based on the distribution characteristics of the illumination components to achieve uniform illumination processing of the 3D morphology image of the contact point, the expression of a brightness value is as follows:  
\[D(x,y)=255\left(\frac{P(x,y)}{255}\right)^{\gamma} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Contact Morphology & Reconstructed Morphology \\ \hline Unablated area (local) & & \\ Ablated area (local) & & \\ Ablated area (local) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface morphology map of the contact.  
In which, the magnitude of \(\gamma\) is determined by both the characteristics of the lighting component and the average brightness of the lighting component, and its expression is as follows:  
\[\gamma=\left(\frac{1}{2}\right)^{\frac{k-C\left(\omega g\right)}{2}} \tag{4}\]  
In which, \(k\) is the mean brightness of the illumination component.  
Equations (1)-(4) are from references [24; 25].  
The contact group morphology image captured by the non-contact 3D profiler is a mirror image of the actual contact image, so a mirror transformation is required. Normalized intensity contact group morphology mirror image as shown in Figure 2. Contact group gradient direction degree image as shown in Figure 3.