esa-sceva/satcom-chunk-collection huggingface dataset:
# A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs
## 3 Results
### Datasets  
The compared methods have been evaluated on four underwater datasets taken under different conditions, including a recently published dataset [40]. Within these four datasets we have included a pair of datasets obtained from the same place while a diver and a robot were exploring the coral reef.  
The first dataset [40] includes several trajectories of a simulated underwater robot navigating above an static coral reef mosaic. From that dataset, we have taken a trajectory suggested in [40] to evaluate place recognition algorithms. In this paper, we have dubbed this collection of images the _FURG_ dataset. We refer to the second dataset as _Expo1_ (from the name of the diving site) and it was extracted from a video recorded by a robot exploring a coral reef in real-life conditions. The underwater robot that we utilized to record the video is from the Aqua family of amphibious robots [41; 42] manufactured by Independent Robotics ([[http://www.independentrobotics.com/](http://www.independentrobotics.com/)]([http://www.independentrobotics.com/](http://www.independentrobotics.com/))). These two datasets are utilized to evaluate the performance of the proposed method to recognize places from images taken during the same exploration.  
The next datasets are composed of two groups of images that were extracted from videos of the same coral reef during different explorations. We will refer to the first of these two datasets as _Pearls_. The images in this dataset were recorded by two divers using different cameras moving above a coral reef at different depths. The second dataset is composed of images captured by a diver and our robotic platform navigating above the same coral reef. We will refer to this dataset as _Expo2_. For both pairs of datasets, the different agents tried to follow the same path during the exploration, however, they were not instructed to follow it exactly. To avoid using all of the images from the recorded videos, we extracted 1 frame per second of each video. Since the exploration was performed at a low speed this sampling rate is sufficient for our experiments. Some examples of the images contained in each of those datasets are shown in Figure 8. Also, we have summarized the important information about each dataset in Table 1. The number of images per dataset in Table 1 is the value obtained after the sampling.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Frames** & **Size (w \(\times\) h)** \\ \hline _FURG_[40] & 238 & 320 \(\times\) 240 \\ _Expo1_ & 164 & 480 \(\times\) 270 \\ _Expo2 (Diver)_ & 600 & 640 \(\times\) 360 \\ _Expo2 (Robot)_ & 300 & 420 \(\times\) 262 \\ _Pearls (Diver 1)_ & 442 & 640 \(\times\) 360 \\ _Pearls (Diver 2)_ & 300 & 640 \(\times\) 360 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the datasets utilized for evaluation.  
Figure 8: Examples of the images contained in each dataset. In the case of Pearls and Expo2 dataset we show images from the same place taken by the different agents (in the same row).
# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 1 Introduction
For example, one of the fundamental challenges of hand pose estimation, namely hand localization, has been alleviated by the utilization of DNNs [38]. Additionally, DNNs also enabled the implementation of 3D hand pose estimation methods from RGB images and videos, and recent methods are one step further in being applied on real world scenarios, whereas older methods were limited on depth data and required sub-optimal rearrangements for each specific dataset. Different from existing review papers that are solely focused on depth-based methods [39; 40], in this paper, we conduct a comprehensive study on the recent DNN-based 3D hand pose estimation methods, taking into account depth-based, RGB-based, and multimodal methods. More specifically, our contributions can be summarized as follows:  
* We provide a comprehensive overview of the modern deep learning methods on 3D hand pose estimation, along with a brief overview of earlier machine learning methods for context. For each presented work, we describe the proposed method and the network architecture. * We propose a new taxonomy for a better categorization and presentation of deep-learning-based 3D hand pose estimation methods. More specifically, in this paper, the following three categories are used: RGB-based, depth-based, and multimodal approaches. * We present the most significant datasets employed by the research community, along with state-of-the-art results on each one. * We draw conclusions and suggest possible future directions in terms of datasets and model architectures. In order to explore the literature, we utilized the following digital libraries: IEEEXplore, Springer(link), arxiv.org, and the Google scholar search engine. The screening criteria for the selection of the works presented in this paper are: (a) literature works that address 3D hand pose estimation (2D methods are excluded), (b) literature works based on single-view camera setups (multi-view camera setups are excluded), (c) recent deep-learning-based methods published from 2014 until July 2020, (d) methods utilizing standalone visual marker-less input (i.e., no specialized equipment, such as gloves, markers, or specialized sensing equipment), and e) works that achieve state-of-the-art results on well-known and highly experimented datasets, such as Imperial College Vision Lab (ICVL) [41], NYU Hand Pose Dataset [42], Rendered Handpose Dataset (RHD) [43], Stereo Hand Pose Tracking Benchmark (STB) [44], and HANDS 2017 [45]. The rest of this survey is organized as follows. In Section 2, previous categorization methods and early machine learning methods are described. DNN-based 3D hand pose estimation methods are classified and reviewed explicitly in Section 3. Since training data play an important role in DNN-based methods, the existing 3D hand pose estimation datasets, the utilized evaluation metrics, and state-of-the-art results in each dataset are summarized in Section 4. Finally, conclusions and potential future research directions are highlighted in Section 5.
# A Novel Acoustic Ventilation Barrier Based on the Block-Shaped Space-Coiling Metamaterial
## 1 Introduction
Yet the noise reduction of this block-shaped meta-unit is not as good as that of space-coiling meta-units due to the insufficient use of the space in the thickness direction. In addition, none of the above-mentioned acoustic barriers has been optimized to achieve better performances. To this end, this paper proposes a novel acoustic ventilation barrier based on the block-shaped space-coiling metamaterial. In this metamaterial, the meta-unit contains several squared acoustic channels distributing in the thickness direction, forming multiple Fano-like resonances and achieving desirable noise reduction effects. The rectangular hole in the center of the meta-unit allows air to pass through, resulting in good ventilation performance. It is noted that the thickness of the proposed acoustic barrier is merely 50 mm. On the other hand, for the proposed metamaterial, it is also easy to form a wall-like acoustic barrier by stacking the proposed block-shaped meta-units together. Furthermore, the proposed meta-unit can be adjusted to different working frequency ranges through modifying the design parameters. Hence, an optimization method based on a three-layer enumeration algorithm is proposed, which yields the optimal design of the proposed meta-unit subjected to a given working frequency range. After such an optimization, the proposed acoustic ventilation barrier exhibits broadband TL above 10 dB across the frequency range of 495-1063 Hz and an averaged TL over 14 dB when the working frequency range is set to 500-1000 Hz. Lastly, a composite acoustic barrier created by stacking two layers of different proposedmetamaterials is proposed, which achieves multiband noise reduction performance across the ultra-broad frequency range of 479-1758 Hz.
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 3 The Scheme Design
### Establishment of Session Keys
#### 3.3.2 Key Establishment Stage  
The Establishment of Cluster Keys  
The MS launches the request to establish a session key, and then cluster head \(C_{i}\) will reply to the MS with the message: \(C_{i}\)\(\rightarrow\)\(MS\):\(\{ID_{Ci}\,  
* In a similar way, the MS calculates the session key \(K_{MSi-Ci}\) with the cluster head after receiving the session key factor from the cluster head. \[\begin{array}{ll}K_{MS-C_{i}}&=R_{MS}\times M_{C_{i}}\times S_{MS}^{-1}\\ &=R_{MS}\times R_{C_{i}}\times P_{MS}\times S_{MS}^{-1}\\ &=R_{MS}\times R_{C_{i}}\times S_{MS}\times G\times S_{MS}^{-1}\\ &=R_{MS}\times R_{C_{i}}\times G\end{array}\] (13)  
Due to the fact \(K_{Ci-MS}=K_{MSi-Ci}\), the session key establishment between the cluster head \(C_{i}\) and the MS is completed.  
Session Keys Establishment Between a Cluster Head and the Member Nodes  
Due to the limitations of member nodes' resources, the cluster head establishing a session key with member nodes has to adopt the binary symmetric function, and the specific process is as follows:  
* The cluster head sends the request to establish a session key with the member node and disseminates the digital signature (\(W_{Ci}\),\(w_{Ci}\)). The member node replies with the digital signature (\(W_{Ni}\),\(w_{Ni}\)), after validating the legality of the cluster head. In a similar way, after validating the legality of member nodes, the cluster head establishes a session key with the member nodes: \[K_{C_{i}-N_{i}}=F(ID_{C_{i}}\oplus K_{cluster},ID_{N_{i}}\oplus K_{cluster})\] (14)
* Member nodes validate the legality of the cluster head, and then set up a session key with the cluster head: \[K_{N_{i}-C_{i}}=F(ID_{N_{i}}\oplus K_{cluster},ID_{C_{i}}\oplus K_{cluster})\] (15) Due to the symmetry of binary function, we can get the expression: \(K_{C_{i}-N_{i}}=K_{N_{i}-C_{i}}\)
* A session key between a base station and a member node is also established by the binary symmetric function.  
Traversal to Next Cluster Head for Session Key Establishment  
A session key will be established in accordance with the above method when the MS visits the next cluster head. After the MS collects all the data from cluster heads and comes back to the base station, we have a complete data collection cycle.
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 4 Results and Findings
### Sustainability Analysis
#### 4.5.3 Social Criteria  
From the set of selected papers, only a limited number directly address social aspects within the scope of optimization problems. However, those that do tend to focus on important human-centered criteria, particularly when dealing with scheduling problems. For instance, one factor is the minimization of repetitive occupational actions, which aims to reduce worker fatigue and the risk of long-term injuries associated with repetitive tasks [82]. Another social criterion that is incorporated is the level of worker satisfaction, which reflects the importance of employee well-being in operational decision making and seeks to balance productivity with a positive and sustainable work environment.  
In the realm of network design and distribution optimization, several social aspects come into play. One notable criterion is the creation of fixed job opportunities, which highlights the potential for new facilities and infrastructure to provide stable employment in the surrounding community [64; 84]. This is often linked to broader goals of fostering regional economic development, ensuring that the expansion of logistics and supply chain networks has a positive socio-economic impact. Moreover, worker safety is an important concern, particularly during both the establishment of new facilities and the initial stages of operation [64]. Research in this area often emphasizes the need to design processes and facilities that minimize hazards and promote a safe working environment.  
In the context of vehicle routing problems, route balance in terms of time or distance can be considered as a social criterion, since drivers feel a fair distribution of work [38]. Another critical social consideration is the reduction in accident risks [35]. This is particularly relevant in urban logistics and last-mile delivery, where the higher density of traffic increases the likelihood of accidents. By optimizing routes not only for efficiency but also for safety, these studies aim to minimize the exposure of drivers and other road users to  
Figure 7: Number of publications per sustainability field.  
potentially dangerous situations. This approach highlights the importance of integrating social and safety considerations into operational planning, ensuring that logistic systems do not compromise the well-being of those involved or impacted by processes.  
Overall, while the number of papers that address these social dimensions is relatively small compared to environmental and economic factors, the integration of social criteria, such as worker well-being, safety, job creation, and accident risk reduction, reflects an important shift towards more holistic and sustainable decision making in logistics and supply chain management.
## 2 Unit Cell Design Configurations
### Modified Jerusalem Crossed Loop (MJCL) FSS  
The MJCL-FSS geometry is principally evolved through a three-step process, as illustrated in Figure 2. Initially, a diagonally placed Jerusalem cross is considered, and a square loop is incorporated at each leg end of it. Further, rectangular slots are etched from the geometry. This helps to miniaturize the unit cell size and achieve a resonant length in a confined space. Finally, the edges at the design center are chamfered at radii of 'r' and 'R'. These radii are further optimized to accomplish a stopband in the Ku-band.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 5 Conclusions and Outlook
### Comparison of the Methods in This Paper  
Both analog and digital methods have specific benefits and challenges. Analog methods for comb regeneration inevitably add hardware complexity, but the digital parts of the receivers can be operated independently. Digital methods require all subchannels to be processed in the same DSP application-specific integrated circuit (ASIC) or have ASICs synchronized, which could be challenging for a large number of subchannels. On the other hand, for analog methods, the added complexity for joint processing does not scale with the number of subchannels. Therefore, analog methods are more suitable for massive superchannels with a large number of subchannels, while digital methods are more suitable for smaller superchannels. This is further accentuated by the fact that analog methods require pilot tones, which often reduces the spectral efficiency.  
Nevertheless, there are scenarios where digital methods can be implemented essentially for free. This is the case in superchannel systems that already rely on some joint DSP, such as multiple-input multiple-output (MIMO) equalization for crosstalk mitigation [103]. Then, the subchannel receivers already need to be synchronized, and joint carrier recovery can be readily implemented.
## 3 Advantages, Characteristics and Types of Digital Twin
### Classification of Digital Twin
#### 3.3.5 Level of Maturity/Sophistication  
Based on the sophistication level of DT, i.e., the quantity and quality of data obtained from the physical twin and its environment, DTs can be grouped into [81]:  
* _Partial DT:_ It contains a small number of data points, e.g., pressure, temperature, humidity, etc., which is useful in determining the connectivity and functionality of DT.  
Figure 6: Hierarchical levels of DT in manufacturing. (Adapted from [75].)  
* _Clone DT:_ It contains all significant and relevant data from the product/system that can be used for making prototypes and categorizing development phases.
* _Augmented DT:_ It utilizes data from the asset along with its historical data and at the same time derives and correlates the useful data using algorithms and analytics. The sophistication level of DT can be improved with the accumulation of bigger sets of data over times of operation. For [PERSON] et al. (2018), the level of maturity of DT is not just limited to data but it also encompasses the sophistication level of the virtual representation/model. On this basis, DT is divided into four levels:
* _Pre-Digital Twin:_ This is level 1, where DT is created prior to the physical asset for the purpose of making decisions on prototype designs to reduce any technical risk and resolve issues upfront by using a generic system model.
* _Digital Twin:_ Level 2 incorporates the data from the physical asset related to its performance, health, and maintenance. The virtual system model uses these data to assist high-level decision-making in the design and development of the asset, along with scheduling maintenance. The data transfer at this level is bidirectional.
* _Adaptive Digital Twin:_ This level 3 provides an adaptive user interface between physical and Digital Twin, and has the capability to learn from the preferences and priorities of human operators using supervised machine learning. Using this DT, real-time planning and decision-making during operations is possible.
* _Intelligent Digital Twin:_ In addition to the features from level 3, level 4 has unsupervised machine-learning capability, making it more autonomous than level 3. It can recognize patterns in the operational environment and using that along with reinforced learning allows for a more precise and efficient analysis of the system.
## 4 Case Study  
### Experimental Scheme  
To assess the energy-saving efficiency and processing performance of the proposed system, the system was tested on a 13 MN hydraulic press machine. The experimental setup (see Figure 5) consisted of a liquid-providing tank (volume and flow rate of 23 m\({}^{3}\) and 5800 L/min, respectively, Huawei, Yancheng, China), a buffering tank (volume of 6 m\({}^{3}\), Huawei, Yancheng, China), a hydraulic power unit containing an asynchronous motor (Y315S-4; rated power and speed of 110 kW and 1480 rpm, respectively, YXP, Botou, China), an oil pump (A2F250R2P2; rated speed and flow rate of 1480 rpm and 360 L/min, respectively, Rexroth, Shanghai, China), coolers, and filters. In addition, a DT-8850 noise meter is employed to measure the noise generated during the machining process, and body vibration is measured by the technical operator at the processing site.  
To test the operating stability of the system and the dynamic responsiveness during the rehydration process, two common forging methods, fast forging and constant forging, were selected and performed both on the proposed hydraulic press and a traditional hydraulic press. The forging frequency of constant forging and fast forging was set at 20 times per minute and 90 times per minute, respectively. The maximum forming pressure was 35 MPa. The installed power of the proposed forging press was set at 810 kW, while the traditional hydraulic press was 1050 kW.  
Table 1 details the experimental parameters of this experiment. The pressure and flow rate of the prefill system (PFS) and the traditional power units (TPU) were recorded by a pressure gauge and a flow sensor and then uploaded to a computer.
## 3 Experimental Results  
### Experimental Setup  
3D point cloud data is measured in the barn environment using D435 from intel as shown in Figure 3a. D435 has a maximum resolution 1920 \(\times\) 1080 and a maximum frame rate of 30 FPS. It can be measured up to 10 m and 90\({}^{\circ}\) about the depth diagonal field of view. Our robot has intel core i5 4300 U processor and 8 GB RAM. This experiment has been performed using the robot equipped with a D435 shown in Figure 3b. When networks are learned, the CPU Intel core i9-9900 K and GPU Nvidia GeForce RTX 2080 Ti are used.  
Figure 2: Applying the collision grid map to the convolutional neural network.  
The experimented barn is 54.9 m in width and 6.05 m in height. Each section is 4 m wide and consists of a total of 10 sections. We experimented by setting the robot in a barn environment, as shown in Figure 4.  
The data in the case of non-collision situation, when there is a fence only, has been measured using 2000 data. Similarly, the data in the case of the collision situation, when cows stick their head out of the fence, has been measured using 2000 data. The data is divided into 1800 training data and 200 test data each. Figure 5a shows the example of 3D point cloud data about the collision situation. Furthermore, Figure 5b shows the example of 3D point cloud data about the non-collision situation.  
Figure 4: Appearance of the experimented barn environment.  
Figure 5: (**a**) Example of 3D point cloud data about the collision situation. (**b**) Example of 3D point cloud data about the non-collision situation.  
Figure 3: (**a**) D435 form Intel. (**b**) Robot equipped with a D435.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 3 Isolation Mechanisms
### Decoupling Structure for Across Radiators  
The mutual coupling reduction between antennas placed across is achieved by a customized parasitic decoupling structure. The structure consists of several resonant features combined to form a horizontal strip placed in the middle of the substrate's rear-side. The decoupling strip consists of three  
Figure 3: Fabricated UWB-MIMO antenna: (**a**) front view; (**b**) rear view.  
Figure 2: Equivalent circuit: (**a**) real impedance of UWB antenna; (**b**) comparison of reflection coefficients.  
distinct sections which are joined together horizontally. Two circles and a hash sign slot are etched in the central section whereas squares and rectangular slots are etched in the outer section. These distinct structures combine to form a wide-band multi-resonant structure. It provides isolation better than 20 dB between antennas placed across. This customized structure is shown in Figure 1b.