esa-sceva/satcom-chunk-collection huggingface dataset:
# An ARCore Based User Centric Assistive Navigation System for Visually Impaired People
## 1 Introduction  
According to statistics presented by the World Health Organization in October 2017, there are more than 253 million visually impaired people worldwide. Compared to normally sighted people, they are unable to access sufficient visual clues of the surroundings due to weakness in visual perception. Consequently, visually impaired people face challenges in numerous aspects of daily life, including when traveling, learning, entertaining, socializing, and working.  
Visually impaired people have a strong dependency on travel aids. Self-driving vehicles have achieved SAE (Society of Automotive Engineers) Level 3 long back, which allows the vehicle to makedecisions autonomously regarding the machine cognition. Autonomous robots and drones have also been dispatched for unmanned tasks. Obviously, the advances in robotics, computer vision, GIS (Geographic Information System), and sensors allow for integrated smart systems to perform mapping, positioning, and decision-making while executing in urban areas.  
Human beings have the ability to interpret the surrounding environment using sensory organs. Over 90% of the information transmitted to the brain is visual, and the brain processes images tens of thousands of times faster than texts. This explains why human beings are called visual creatures; when traveling, visually impaired people have to face difficulties imposed by their visual impairment [1].  
Traditional assistive solutions for visually impaired people include white canes, guide dogs, and volunteers. However, each of these solutions has its own restrictions. They either work only under certain situations, with limited capability, or are expensive in terms of extra manpower.  
Modern assistive solutions for visually impaired people borrow power from mobile computing, robotics, and autonomous technology. They are implemented in various forms such as mobile terminals, portable computers, wearable sensor stations, and indispensable accessories. Most of these devices use computer vision or GIS/GPS to understand the surroundings, acquire a real-time location, and use turn-by-turn commands to guide the user. However, turn-by-turn commands are difficult for users to follow.  
In this work, we propose an assistive navigation system for visually impaired people (ANSVIP, see Figure 1) using ARCore area learning; we introduce an adaptive artificial potential field path planning mechanism that generates smooth and safe paths; we design a user-centric dual channel interaction that uses haptic sensors to deliver real-time traction information to the user. To verify the design of the system, we have the proposed system prototype implemented with full functionality, and have the prototype tested with blind folded and blind subjects.
## 2 Background and Related Work
### Machine Learning-Based AQM Schemes  
In recent years, researchers have proposed several machine learning-based schemes in active queue management (AQM). [PERSON] et al. [21] proposed a unique approach using supervised convolutional neural networks to replicate the behavior of the AQM PI mechanism. However, it is constrained by its dependence on previous training data, limiting its efficiency in handling unique scenarios.  
Researchers started using deep reinforcement learning (RL) to overcome these limitations. RL algorithms can learn through exploration, meaning they do not solely rely on historical data but actively interact with the environment to discover the optimal action. This exploratory nature of RL allows it to push the network towards better performance by continuously analyzing and learning from the outcomes of its actions.  
Both papers, Refs. [22; 23], propose a DRL-based AQM implementation that optimizes the maximum dropping probability (\(max_{p}\)) of the RED AQM scheme using a Q-learning algorithm. [PERSON] et al. [23] especially use a traffic predictor with Q-learning to optimize their drop probability. Though the way they optimize their models and gather data is quite different, both show promising results, especially in scenarios where the network behaves erratically.  
The paper [24] proposes an AQM algorithm that uses explicit congestion notification (ECN) to regulate network congestion. They build a congestion predictor using an LSTM-based ML model and Q-learning to find optimal drop probability.  
Similarly, other studies [25; 26; 27] all utilize a DRL approach to learn the optimal packet drop probability based on the observed network state and to regulate the queue length and congestion level.  
We can see the constant rate of improvement and development of technology, especially with the advent of machine learning, and the performance of AQM can be increased significantly. The above literature summarizes the latest developments in AQM using machine learning. We have found out that all these papers primarily implement them in Linux-based operating systems, and all these papers are based on single queue-based AQMs, which are not suitable for L4S architecture, whereas our solution entails that we develop A3C-L4S for the FreeBSD OS. FreeBSD is an essential operating system for several reasons, including its high reliability, stability, security, and customizability. However, the main reason is that it is widely utilized in servers and networking devices where stability is critical. Many major IT-service-providing companies like Apple, Microsoft, and Netflix use FreeBSD in their production services. These are the few we mentioned among the many others, as we utilize these services heavily, making it easier to understand their significance.
## 2 Materials and Methods  
### Measurement Equipment and Methodology  
During the in situ measurements, an SRM-3006 basic unit (Narda Safety Test Solutions GmbH, Pfullingen, Germany) was used. This device, which covers a frequency range from 9 kHz to 6 GHz, was directly connected to two isotropic tri-axial E-field antennas, also from Narda Safety Test Solutions. These antennas have distinct frequency ranges (27 MHz to 3 GHz and 420 MHz to 6 GHz) and dynamic measurement ranges (0.2 mV/m to 200 V/m and 0.14 mV/m to 160 V/m, respectively). Two service tables were created: the first table included frequencies from 27 MHz to 3 GHz, measured with the first antenna, while the second table encompassed frequencies from 3 GHz to 6 GHz, measured with the second antenna. The SRM-3006 was configured in the \"safety evaluation\" mode, and E-field measurements for each frequency range in the service tables were taken. The resolution bandwidth (RBW) during the measurements was set to the device's automatic setting. According to the SRM-3006's internal configuration, this automatic mode ensures that the RBW is less than or equal to one-quarter of the frequency span of the narrowest service band being measured. For each band, the device then selects the closest smaller available RBW value based on this criterion, as described in [10]. It is important to note that each frequency band is measured with its own dedicated RBW, adapted according to the specific span of that band.  
At each measurement location, measurements were conducted at three distinct heights: 110 cm, 150 cm, and 170 cm, according to the guidelines of the IEC 62232:2022 standard [5]. The E-field value at each height and frequency band f corresponding to the chosen service were recorded. The electric field for each frequency band f was calculated using the data gathered at the three different heights at each measurement site as follows:\[E_{f}=\sqrt{\frac{\sum_{i=1}^{3}E_{i,f}^{2}}{3}} \tag{1}\]  
The total E-field value for that particular location was subsequently determined as follows:  
\[E_{total}=\sqrt{\sum_{f}E_{f}^{2}} \tag{2}\]  
It is important to mention that all in situ measurements took place on weekdays, between 09:00 and 15:00. By reviewing the data gathered by the National Observatory of Electromagnetic Fields (NOEF) [11], we confirmed that the measurements were taken during periods of high telecommunications traffic.  
To avoid generating downlink traffic that could trigger beamforming towards the measurement device and increase the BS antenna output power, all mobile devices held by operators were set to flight mode. This approach was intentionally selected to assess exposure levels in public areas accessible to everyone, such as squares, parks, and high-traffic locations such as bus and transportation stations, without inducing any traffic load by the operator.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 5 Experiments and Results
### ProPART Use Case  
The PRoPART use case is to perform an automated lane change to the left on the highway that allows vehicles to enter on the right, as Figure 6 shows. That is why the UWB anchor nodes have been installed on one side of the road close to the RSUs. Each RSU integrates a GNSS base station, a radar and V2X communications device. To complete the system, an UWB tag node is placed on-board the heavy vehicle (a real truck) with the aim of providing a precise positioning when the GNSS signal is lost. Note that, the antenna of the UWB tag node is located outside the heavy vehicle to avoid the interferences generated by the vehicle's structure. Whereas the electronic is kept within the vehicle. Thus a flexible cable is used to connect both parts.  
From this use case, the PRoPART project imposes the following challenges to the UWB measurements: high accuracy in the centimetre level, range of hundreds of meters and vehicle speeds up to 40 km/h. The PRoPART concept is explained in detail in [20].
## 3 Cluster Formation
### Failure Management
#### 3.3.1 Permanent-Fault Management
Using this model, each CH first transmits its experience about its connectivity with other CHs and after receiving the experience of the rest of the CHs, if the network is fully fault-free, no _status update_ forwarding happens, otherwise it forwards the updates. Opposite to the comprehensive fault detection technique, the proposed recovery phase by the approach is that the cluster members of the faulty CH simply join the best alternative neighbouring CHs. There are some drawbacks concerning the applicability of the algorithm. First, although the paper applies some techniques to reduce the overhead of the algorithm, periodical exchange of the table of cluster members on a network scale is not an energy-efficient approach for large-scale networks having large-size tables. Another main drawback of the algorithm is that it is primarily designed for scenarios having all CHs in direct communication range with each other. However, this is not the prevailing condition in typical WSNs. Finally, the proposed recovery method is not very efficient and may lead to the creation of isolated regions consisting of orphan cluster members not in the range of any other CHs.
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 3 Evaluation Methods
### Barton-Bandis Parameter Mapping
#### 3.4.1. Hoek-Brown Model Parameter Calculation  
\[\text{RMR}=100-5\text{J}_{\text{d}}-10\text{log}(\text{J}_{\text{s}})-\text{ Adjustment Factor} \tag{15}\]  
where  
\(\text{J}_{\text{d}}\): Joint density;  
\(\text{J}_{\text{s}}\): Joint Spacing.  
The adjustment factor in the Hoek-Brown model considers joint surface roughness, infill material properties, and hydrogeological conditions. These factors affect how the rock mass behaves, including its strength and stability. Adding the adjustment factor makes the model better at showing rock mass properties under different conditions.  
The GSI value can be calculated using the formula below:  
\[\text{GSI}=\text{RMR}-5 \tag{16}\]  
The Hoek-Brown model explains rock mass strength under different confining pressures. The main equation is shown below:  
\[\sigma_{1}=\sigma_{3}+\sigma_{\text{c}}(\text{m}_{\text{b}}\frac{\sigma_{3}}{ \sigma_{\text{c}}}+\text{s})^{\text{a}} \tag{17}\]  
where  
\(\sigma_{1}\): Maximum principal stress;  
\(\sigma_{3}\): Minimum principal stress (confining pressure);  
\(\sigma_{\text{c}}\): Uniaxial compressive strength of the intact rock;  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multirow{2}{*}{**Density** & **Cohesion** & **Friction** & **Elastic** & **Shear** & **Bulk** & **Tensile** & **Poisson’s** \\  & & **(g/cm\({}^{3}\))** & **(MPa)** & **Angle (\({}^{\circ}\))** & **Modulus** & **Modulus** & **Modulus** & **Strength** & **Ratio** \\  & & & & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** & **(MPa)** & **(MPa)** & **Ratio** \\ \hline
1 & 2.39 & 1.02 & 26.22 & 38.98 & 11.47 & 82.67 & 2.1 & 0.26 \\
2 & 2.27 & 4.26 & 2  
\(\rm m_{b}\): Rock mass strength parameter, representing the shear characteristics of the rock mass;  
s: Rock mass integrity parameter;  
a: Empirical parameter, describing the nonlinear behavior of the rock mass.  
Calculation of Hoek-Brown Parameters:  
The parameters \(\rm m_{b}\),s, and a can be calculated using the following equations:  
\[\rm m_{b}=m_{i}\cdot exp\ (\frac{GSI-100}{28-14D}) \tag{18}\]  
\[\rm s=exp\ (\frac{GSI-100}{9-3D}) \tag{19}\]  
\[\rm\alpha=0.5+\frac{1}{6}(exp\ (-\frac{GSI}{15})-exp\ (-20)) \tag{20}\]  
where  
\(\rm m_{i}\): Rock material constant, determined experimentally;  
D: Disturbance factor, reflecting the degree of engineering disturbance to the rock mass.  
The Hoek-Brown model was adopted in this study because of its ability to describe nonlinear rock strength properties, especially for jointed rock bodies. Compared to the Mohr-Coulomb model, the Hoek-Brown model can better accommodate the mechanical properties of fractured rock bodies, especially the stress changes before and after rock rupture. In addition, combined with the joint parameters extracted from the Barton-Bandis model, the Hoek-Brown model is able to provide a more reasonable estimation of the mechanical parameters of the rock mass, which improves the accuracy of the stability analysis of the surrounding rock.
## 3 Simulation of Thermal Power Characteristics
### Simulation Results and Analysis  
#### 3.2.1 Simulation Analysis of Mechanical Losses Under Rated Operating Conditions  
As shown in Figure 7, the mechanical losses of the internal gear pump exhibit positive correlations with both rotational speed and pressure yet demonstrate significantly higher sensitivity to speed variations compared to pressure. Under a constant speed of 2000 r/min, the mechanical losses increased by 0.69 kW as the pressure rose from 0 to 21 MPa. Conversely, at a constant pressure of 21 MPa, the mechanical losses escalated by 0.92 kW when the rotational speed increased from 0 to 2000 r/min. Comparative analysis with piston pumps revealed distinct mechanical loss characteristics: piston pumps exhibit reduced mechanical losses under high-pressure conditions due to improved lubrication  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Physical Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Hydraulic oil kinematic viscosity & \(\mu\) & 43 & mm\({}^{2}\)/s \\ \multicolumn{4}{c}{Gear tooth root radius} & rf\({}_{1}\) & 21.5 & mm \\ Ring gear tooth root radius & rf\({}_{2}\) & 38 & mm \\ Number of contacting teeth (gear-crescent plate) & \(z_{1}\) & 4 & - \\ Number of contacting teeth (ring gear-crescent plate) & \(z_{2}\) & 7 & - \\ \multicolumn{4}{c}{Gear pair thickness} & B & 27 & mm \\ \multicolumn{4}{c}{Gear tip circle radius} & ra\({}_{1}\) & 29.8 & mm \\ Ring gear tip circle radius & ra\({}_{2}\) & 30.38 & mm \\ Ring gear outer wall radius & R & 46 & mm \\ \multicolumn{4}{c}{Gear shaft radius} & r & 14 & mm \\ \multicolumn{4}{c}{Gear tip-crescent plate clearance} & \(\delta_{1}\) & 70 & \(\mu\)m \\ Ring gear tip-crescent plate clearance & \(\delta_{2}\) & 70 & \(\mu\)m \\ \multicolumn{4}{c}{Ring gear-housing radial clearance} & \(\delta_{3}\) & 46 & \(\mu\)m \\ \multicolumn{4}{c}{Gear shaft-sliding vane clearance} & \(\delta_{4}\) & 64 & \(\mu\)m \\ \multicolumn{4}{c}{Gear-housing end face clearance} & \(\delta_{5}\) & 20 & \(\mu\)m \\ \multicolumn{4}{c}{Gear shaft-sheet contact length} & 1 & 28 & mm \\ \multicolumn{4}{c}{Ring gear-housing leakage clearance length} & L & 144.44 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Heat tracing simulation parameter table of internal gear pump.  
in friction pairs, whereas gear pumps show continuous mechanical loss growth with increasing pressure. This phenomenon is primarily attributed to the deteriorated lubrication and intensified frictional heating at the ring housing annular interface caused by amplified radial unbalanced forces. These findings conclusively identify rotational speed variation as the dominant factor influencing mechanical losses.
## 3 Materials and Methods
### Models Used  
Five deep learning models have been selected to use architectures that are diverse in size and performance. We have not tried to make an exhaustive analysis of which models to select, but rather, we have taken models that normally show good performance in image classification tasks and whose architectures are varied in the number of parameters and layers. Thus, the models used were _VGG16_ and _VGG19_[61], _MobileNet_[62], _ResNet50_[63] and _DenseNet121_[64]. These five models have been widely used in the bibliography [65; 66; 67; 68; 69; 70; 71].  
1. **VGG16** and **VGG19**. [PERSON] and [PERSON] proposed the idea of the VGG network in 2013 and presented the current model based on this idea at the 2014 ImageNet Challenge [61]. VGG is a special convolutional network designed for classification. They named it VGG after the department of the Visual Geometry Group at Oxford College to which they belonged, and because it is a convolutional neural network that is either 16 layers deep (VGG-16) or 19 layers deep (VGG-19). The VGG16 and VGG19 architecture in Deep Learning is a rather simple architecture that uses only blocks consisting of an increasing number of convolutional layers with filters of size \(3\times 3\). Moreover, to reduce the size of the activation maps obtained, max-pooling blocks are inserted between the convolutional layers, reducing the size of these activation maps by half. Finally, a classification block is used, consisting of two dense layers of 4096 neurons each and a final layer, the output layer, of 1000 neurons.
2. **MobileNet**. This model was proposed in 2017 and it is based on a simplified architecture with depth-separable convolutions. The model was originally developed for mobile imaging applications. The architecture of MobileNet initially includes a convolutional layer with 32 filters, followed by 19 layers. MobileNet uses \(3\times 3\) filters, dropout (an operation in which neurons generate partial learning of the network to avoid overtraining), and normalization during training [72; 73]
3. **ResNet**. This deep learning model is a deep convolutional neural network with 50 layers. The architecture of ResNet-50 starts with a convolution with a kernel size of \(7\times 7\) and 64 different kernels, all with a step size of 2, so we get one layer. Then there are different convolutional layers with different kernels [74; 75].
4. **DenseNet-121**. The denseNet-121 model of densely connected convolutional networks, proposed in late 2017, consists of networks with blocks where each block has direct access to the gradient of the cost function, the function that provides the error, and the initial input of the block. Of course, there are also convolutional layers and pooling layers [76].  
Table 3 summarizes different deep learning models that were used in this paper alongside pre-trained weights (using information from Keras Applications). Depth refers to the topological depth of the network (including activation layers, batch normalization layers, etc).
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 4 Experiments
### Performance Comparison
#### 4.3.1 Object Detection  
As shown in Table 6, detectors incorporating MSBA consistently achieve substantial enhancements in comparison to conventional methods, encompassing both single-stage and multi-stage detectors. Our proposed MSBA demonstrates improvements of 1.5 and 2.1 points when integrated with RetinaNet and Faster R-CNN utilizing ResNet 50, respectively. Leveraging the ample coarse-grained information at lower levels, multi-stage detectors exhibit a more pronounced accuracy enhancement. Moreover, when combined with diverse backbones in conjunction with more sophisticated detectors, our approach attains superior outcomes, attributable to the reinforced multi-scale representation. Additionally, as depicted in Figure 4, MSBA effectively captures substantial spatial information through ample interaction, while mitigating the impact of erroneous and overlooked detections.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & \(AP\) & \(AP_{\mathbf{50}}\) & \(AP_{\mathbf{75}}\) & \(AP_{\mathbf{S}}\) & \(AP_{M}\) & \(AP_{\mathbf{L}}\) \\ \hline baseline & 37.4 & 58.1 & 40.4 & 21.2 & 41.0 & 48.1 \\ \hline PLF & 38.3 & 59.4 & 41.4 & 21.8 & 41.5 & 48.6 \\ CWP & 38.4 & 59.0 & 41.4 & 22.0 & 41.9 & 48.4 \\ PLF with CWP & **38.8** & **59.7** & **42.4** & **22.6** & **42.4** & **50.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the effect of each component in BFI on COCO _val_2017. PLF: pixel-level filter, CWP: channel-wise prompt.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & \(AP\) & \(AP_{\mathbf{50}}\) & \(AP_{\mathbf{75}}\) & \(AP_{\mathbf{S}}\) & \(AP_{M}\) & \(AP_{\mathbf{L}}\) \\ \hline baseline & 37.4 & 58.1 & 40.4 & 21.2 & 41.0 & 48.1 \\ \hline CWP \(\bigoplus\) PLF & 38.6 & 59.1 & 42.0 & 21.5 & 42.1 & 50.2 \\ PLF \(\bigoplus\) CWP & **38.8** & **59.7** & **42.4** & **22.6** & **42.4** & **50.7** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparsion of interaction orders in BFI on COCO _val_2017.