esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Cascaded U-Net Network  
Traditional methods, such as [PERSON] et al. [5], employ 2D convolutional networks for feature extraction, however, this approach can only perceiv image textures within a fixed field of view. In contrast, [PERSON] et al. [33] utilize an improved U-Net network for feature extraction, achieving favorable results. In this section, an enhanced cascaded U-Net feature extraction module is designed. The first part of the structure is illustrated in Figure 2. The network selectively handles low-texture regions to preserve more intricate details.  
The given reference image \(I_{i=0}\) and adjacent source images \(\{I_{i}\}_{i=1}^{N-1}\) are fed into the network to construct image features at different scales. In this cascaded U-Net network, the front-end feature encoder utilizes successive convolution and pooling operations, increasing the channel dimensions while reducing the size to extract deep features from the images. However, as the network depth increases, more feature information tends to be lost. The back-end decoder functions inversely to the encoder, performing upsampling to not only restore the original size but also connecting with feature maps from earlier stages. This facilitates better reconstruction of target details. The key to this process lies in fusing high-level and low-level features to enrich the detailed information in the feature maps. Subsequently, the cascaded network repeats this process, and the second part of the cascaded structure appends convolution operations at the output ports, obtaining features \(F_{i=0,\dots,N-1}^{k}\epsilon R^{\frac{H}{2^{k}}\times\frac{N}{2^{k}}\times 2^{(3 +k)}}\), where \(k=0,\ 1,\ 2\) denotes the three different stages of the model, omitted for simplicity in the following discussion. This cascaded U-Net feature extraction module aids in preserving richer detailed features, providing more accurate information for depth estimation.  
Figure 1: The network architecture of our proposed model. Initially, the multi-scale 2D features are extracted using the cascaded U-Net module. Subsequently, various operations, including homography warping, cost volume aggregation, 3D regularization, and multi-depth classification, are employed to obtain depth estimations at different scales. In Stage 1, the uncertainty module dynamically adjusts the sampling range for the subsequent stage.  
Figure 2: Enhanced cascade U-Net feature extraction module.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 5 Measurement-Based Performance Metrics with Wirepas IoT Platform  
This section presents experimental results from an IoT positioning system built with Wirepas IoT mesh solution. The results presented in this section that were obtained are Wirepas' offices and are based on power measurements.  
The environment where measurements took place, with a total area of 180 m\({}^{2}\) (10 by 18 meters), is a typical work environment with few small rooms and a large open areas (see Figure 8). Several battery powered operated devices were placed across the floor extending the network coverage in and outside the rooms. Some of these devices acted as known reference points while others as tracked devices. The reference points are identified in Figure 8 as routing devices (blue squares) and the measurement devices as yellow dots. All the devices were operating in the 2.4 GHz using Nordic's NRF51 as the radio chipset.  
In this setup, the measurement devices were statically collecting information about network beacons' broadcast periodically by the routing devices. The information about the routing devices' beacons, as seen by the measurement devices, was sent regularly towards the network sink. In turn, the network sink and gateway communicated the measurements to a positioning engine running on a local computer. The position engine provided a location estimate based on the known location of the routing devices and the RSS observed by the measurement devices. A locatio  
Figure 7: Example of LoRa performance at various numbers of access nodes.  
by one-shot runs of a weighted centroid algorithm, meaning that no average or filtering were applied to the location estimates. However, the RSS measurements were averaged over a window of time to mitigate the channel propagation effects.  
In addition to a location estimate on a global or local reference frame, the position engine also provided an area-based location. The area-based location consists of matching the location estimate to a set of geographical areas of interest (shaded areas in Figure 8). For a device to be in such area, it meant that its location estimate was found to be inside the geographic area defined by the four coordinate points of each area.  
The results on Table 3 show the probability of correctly classifying the measurement devices in the areas of interest. The percentage is calculated by summing the amount of location estimates in the node's correct area versus the total amount of location estimates in any other area of interest.  
The results on Table 3 show that, during a day, the devices were correctly located inside the logical area where they were known to be at more than 90% of the time.  
Figure 8: Office environment where the measurements were acquired.
# Calibration of a Rotating or Revolving Platform with a LiDAR Sensor
## 4 Evaluation
### Discussion
As the data in the table on the left side in Table 1 suggest, most parts of the optimisation process have more or less constant runtime. Only the time taken by the grid search follows a cubic curve (shown in Figure 9), as the search space has three dimensions yaw, pitch and roll. However, working parallel reduced the duration by a factor of \(\frac{1}{c}\), where \(c\) is the number of available hardware threads the used CPU can provide. The experimental runs discussed above resulting in the best extrinsic parameter configuration had a resolution of 0.00025 and took about 43 minutes for the full configuration run excluding the time to perceive the target center points. This is a long time, but is still tractable, as the calibration is an offline process. However, runs with a doubled grid resolution will take 7.41 times as long and thus will be hard compromise for a higher calibration accuracy. Figure 8: Point clouds sampling the excerpt of the test scene, the quality measure was applied to. The images (**1.a,1.b**) in the first row show the records done with the initial extrinsic parameter sets, used to study the influence of the initial pose the optimiser starts with. The cloud in (**1.a**) results from using the sensor pose parameters measured on the SWAP platform by hand. The one in (**1.b**) results from applying the transformation resulting from a deterioration of the measured transformation. Images (**2.a,2.b**) in the second row depict the resulting clouds after the parameter optimisation from the respective initial poses. The red and blue point colouring in the first two rows result from separating the two redundant measurements. The images (**3.a,3.b**) in the last row show the distances between the two clouds above. The intensity encodes the magnitude of changes with blue representing strong changes and reddish colors representing small changes.
# Terahertz Wideband Filter Based on Sub-Wavelength Binary Simple Periodic Structure
## 3 Redundancy Analysis
As shown in Figure 5c, when the groove depth was less than 30.379 \(\upmu\)m, the 3-dB bandwidth of the filter dropped sharply to less than 1.7 THz, while when the groove depth was shifted 1.4 \(\upmu\)m more than the design value, the 3-dB bandwidth of the filter dropped under 2 THz. It can be seen from Figure 5d that the average transmission efficiency in the main lobe remained at nearly 40%, and the graph shows a relatively gentle curve. One can conclude that groove depth shift had a significant effect on the 3-dB bandwidth of the filter, but it did not affect the average transmission efficiency in the main lobe of the filter significantly. The slot depth was in the range of 1.4 \(\upmu\)m, the 3-dB bandwidth was above 2 THz, the error from the ideal design value of 3-dB bandwidth 2.33 THz was less than 15%, and the average transmission efficiency in the filter main lobe was kept at around 40%. In order to discuss the influence of a single parameter on the filtering effect, the above control variables method was performed by fixing other parameters. However, all structural parameters in the actual production will deviate from the expected ideal design value. Therefore, this paper conducts a comprehensive error analysis on the design of the terahertz wideband filter so that the structural parameters (_f, d, h_) can be chosen in certain ranges, allowing the structural parameters to make the deviance as large as possible in order to reduce difficulties in the actual production. After calculation, the variation ranges of each structural parameter for obtaining the best filtering performance were: grating period from 125.4 to 130.5 \(\upmu\)m (relative offset 127.9 \(\pm\) 2.5 \(\upmu\)m); grating duty ratio from 0.412 to 0.417 (relative offset 0.414 \(\pm\) 0.002); minimum line width redundancy \(\pm\)0.4 \(\upmu\)m; and grating groove depth from 28.9 \(\upmu\)m to 30.3 \(\upmu\)m (relative offset was 29.6 \(\pm\) 0.7 \(\upmu\)m). Note that the structural parameters satisfy current fabrication technology. The terahertz wideband filter had a 3-dB bandwidth above 2 THz and an average transmission efficiency of around 40% in the main lobe. The best fabrication structure parameters were selected as follows: grating period of 127.9 \(\upmu\)m; duty ratio of 0.414 (the minimum line width was 53 \(\upmu\)m); and grating groove depth of 29.6 \(\upmu\)m. The current mature micro-nano manufacturing technology meets the manufacturing requirements of the structure.
# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 4 Method
### ROI Localization
#### 4.3.4 ROI Model Training  
Training an ROI model was done by computing the target ROIs using the boxes from the ground truth masks. Empirical observations show that the ROI boxes obtained by the Standard model are larger and usually do not respect the ratio of the true ROI box. In an attempt to account for the mistakes of the Standard model, we trained the ROI models to successfully perform segmentation from larger than perfect ROIs.  
To achieve this, we specifically optimize the training of the ROI model, by perturbing the ground truth ROI boxes randomly to reflect the shapes and sizes of dynamically computed ROI boxes. Perturbation was performed both on width and height starting from a minimum value up to a certain threshold (usually a fifth of the size of the box). Moreover, we also imposed a minimum size of the ROI box, to avoid artifacts produced by aggressive bilinear upsampling afterwards. In order to avoid randomness in validation, the perturbation was set to a fixed size at time of evaluation (half the value of the max threshold). All ROI models had the same input resolution, namely \(224\times 224\).  
Accordingly, the perturbation during training is done as follows: we sampled an individual perturbation value \(pi\) within the range \((min_{p},max_{p})\), for each of the four coordinates of the bounding box: \((x1,y1)\) top left and \((x2,y2)\) bottom right, respectively. Then, the perturbation operations are:  
\[x1=x1-p1;\ y1=y1-p2\] \[x2=x2+p3;\ y2=y2+p4.\]
## 2 Materials and Methods
### Literature Review and Analysis on HRC Modeling Methods
#### 2.1.6 Research Gap  
To summarize, UML and SysML are more well-adapted for high-level system modeling, requirement, and system integration. They are more geared for technical people instead of systems' end users. Behavior trees then mainly focus on automation, predefined actions, and decision sequences, which are not suitable for humans nor HRC. Petri nets lack expressiveness for decision making and communication. Although BPMN notations have been found to be well-accepted by its intuitive modeling notation in terms of usability, the standard BPMN focuses on business process modeling and does not have separate notations for robots (robot tasks, robot skills, robot primitives, and HRC tasks). Although BPMN can be applied to robotics, the process model becomes too complicated for non-experts in BPMN. In general, there is a need to model HRC, but these aforementioned modeling tools are not suitable for this purpose. They are all well-established tools in their application areas. However, they are not developed specifically for robotics in the HRC domain. In the current versions of these tools, it is not possible to separate the HRC task types, specify the HRC modes, verify safety standards, nor monitor human factors.  
Therefore, we have created a new modeling language, RTMN, for robotic processes that is based on BPMN. It uses the well-known basic notation shapes and builds robotic notations upon them. After our first RTMN version, we continued our research with the aim of improving RTMN, especially in the human-robot collaboration area. RTMN 2.0 is an extension of RTMN.
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 5 Simulation Results
### Performance Analysis for the Dynamic UE Scenario
Figure 12 plots the optimized total transmit power versus the SINR threshold under different schemes, where each simulation point is averaged over 200 random initializations. As demonstrated in the figure, by adapting to the proposed DDPG-based algorithm, the source BS can transmit the minimum power compared to other schemes, which indicates the superiority of our algorithm and its applicability to reach an optimal beamforming configuration. Similar to Figure 6, under the dynamic UE scenario, a system without aid from the IRS leads the source to transmit the highest power consumption. Moreover, while choosing random phases for the reflected elements shows less averaged power than without any deployed IRS, its power is still constrained by the passive beamforming gains obtained by these random phases. Other approaches show sub-optimal solutions, i.e., iterative, AO, and AO heuristic, which depend on the iterative convergence level. Figure 13 plots the optimized total transmit power versus the number of units per each reflecting IRS under different schemes. Each simulated point is averaged through 200 random initializations with an SINR threshold of 0 dB for each UE. It is observed that the required minimum transmit power for the considered schemes decreases with the increasing number of reflecting units, except for the system without IRSs. This is because increasing the number of reflecting elements on the deployed surfaces with well-optimized phases will increase the gains brought by these surfaces, which helps the source BS to transmit less power to satisfy the UEs' constraints. Moreover, the random phase scheme has the second-worst performance compared to those without IRSs. This implies the gain brought by IRSs through the indirect channel paths. Other approaches show sub-optimal solutions, i.e., iterative, AO, and AO heuristic, which depend on the iterative convergence level and imply that the phases of IRS elements should be optimized rather than randomly chosen, to achieve the maximum gain by increasing the number of IRS elements. Again, as shown in the figure, the proposed DDPG-based algorithm can achieve the lowest transmit power compared to other benchmark schemes. Figure 11: Average total transmit power of BS versus learning episodes.
# Non-Cannabinoid Metabolites of _Cannabis sativa_ L. with Therapeutic Potential
## 2 Non-Cannabinoid Compounds  
Cannabinoid and non-cannabinoid phytochemicals possess bioactive and protective properties that are beneficial to human health. In addition to cannabinoids, the cannabis plant also produces hundreds of non-cannabinoid secondary metabolites including approximately 120 terpenoids (61 monoterpenes, 52 sesquiterpenoids, and 5 triterpenoids [2], essential oils, over 26 flavonoids [2] lignans, stilbenoid derivatives, alkaloids, amino acids, spiroindans, polyphenols, 20 steroids [2]), dihydrophenanthrenes, glycoproteins (such as galactose, glucose, mannose and xylose), and dibenzyls [5,6]. This list also includes a-cannabispiranol, chrysoerol, 6-premylagienin, cannflavin A and b-acetyl cannabispiranol [13].  
Flavonoids and terpenoids in particular have a wide therapeutic window, including but not limited to cancer, inflammation, excessive reactive oxygen species in cells, and enhancement of neuroprotection. In addition to the phytochemicals mentioned below, a 2008 study by [PERSON] and colleagues identified and isolated six new non-cannabinoid phytochemicals from a high potency _Cannabis sativa_ L. strain. These were 5-acetoxy-6-geranyl-3-n-pentyl-1,4-benzoquinone, 4,5-dihydroxy-2,3,6-trimethoxy-9,10-dihydrophenanthrene, 4,7-dimethoxy-1,2,5-trihydroxyphenanthrene, cannflavin C, c-sitosterolyl-3-O-b-D-glucopyranoside-20-O-palmitate, and 4-hydroxy-2,3,6,7-tetramethyl-9,10-dihydrophenanthrene [13]. The review below focuses on non-cannabinoid phytochemicals with therapeutic potential.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 5 Classification of Efforts for Evolving the Control Plane
### The Criteria
#### 5.1.4 Explore Path Diversity  
The current inter-domain routing protocol follows the BGP's destination-based forwarding paradigm, which indicates that all forwarding decisions about IP packets will rely exclusively on the IP destination header field. Hence, BGP will only select one \"best\" route per prefix when it is constructing the RIB. Thus, even when there are multiple paths to reach a given network, only one of them will be selected [15; 53]. Unlocking the full potential of the Internet path diversity (such as resilience or QoS) is a goal of various previous works. This work analyzes the following criteria to explore path diversity:  
* Overlay: The control plane creates networks that run independently on top of another network.
* Sourcing routing: The sender has the possibility of specifying the paths that the packet will take through the network.
* Based on flows: This is the set of network rules that the control plane defines and includes the sequences of nodes that a given packet has to pass between source and destination.
* Inter-domain negotiations: The solution provides mechanisms to allow each path to negotiate to explore the path diversity of the network.
* Alternative routes: Instead of using one single best path per prefix, alternative routes try to achieve resilience, security or optimize bandwidth utilization for inter-domain interconnections exploring the availability of multiple paths.
* Not Applied (N/A): This is when the work does not provide direct evidence of how it explores the path diversity. It can occur, for example, when the research is just an architecture description or other high-level abstraction.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 5 Outlook  
The linewidth characterization technology of narrow-linewidth lasers is gradually maturing and can characterize linewidth on the order of 100 Hz. The manuscript proposes several prospects in response to the current situation. First, for the most widely used delayed self-heterodyne technology, the noise caused by double Rayleigh scattering in delay fibers can affect the shape of spectral lines [139; 140], leading to center frequency offsets and significant frequency noise, which can introduce inaccuracy into the linewidth measurement. The introduction of \(1/f\) noise caused by excessively long delay fibers in the heterodyne method can lead to phase noise jitter and beat frequency spectrum broadening, which is a key issue to consider. Second, the characterization techniques for the linewidth enhancement factor need to be improved [141; 142]. One of the key parameters of semiconductor optical amplifiers is the linewidth enhancement factor [143], which is defined as the ratio of changes in the real and imaginary parts of the complex refractive index and is an important parameter affecting the output characteristics of semiconductor lasers. The free-space interference method in the measurement of linewidth factor [144] needs to be carried out on an optical platform, and laser alignment and environmental fluctuations can cause instability factors. The spectrum analyzer in RF modulation measurement methods requires direct use of high-frequency-modulated laser sources [145], and further improvements to corresponding characterization techniques are needed to avoid spatial interference and improve measurement accuracy. Finally, the constraints of environmental noise and measurement system noise on the testing process should be reduced or eliminated [146; 147]. The detection range and accuracy of linewidth characterization can be improved by controlling precise and stable experimental testing conditions and using ultra-low-noise linewidth characterization testing devices.
## 1 Introduction
A critical pore radius \(r_{cr}\) is often used, which is determined during the injection of a nonwetting liquid into rock samples under laboratory conditions [24; 25; 26]. [PERSON] and [PERSON] considered \(l\) as the size corresponding to the percolation threshold of the electrical conductivity of porous media [30]. [PERSON] and [PERSON] studied 17 sandstone samples and 1 limestone sample, which were subjected to air displacement of water to determine \(r_{cr}\)[25]. As a result of this, relationships were built between \(k\), \(\phi\), transport porosity \(\phi_{tra}\), and factor \(\phi_{cr}^{2}\). Their results indicated that the permeability of the studied samples correlated better with \(\phi_{cr}^{2}\) than with porosity \(\phi\). This relationship was expressed as \(k=0.012\phi_{tra}r_{cr}^{2}\), where \(\phi_{tra}\) means transport porosity, defined as the total porosity without dead-end pores [25]. They examined the applicability of this relationship to describe the permeability of other porous media, and found that this relationship works well for these media as well. They also found that by fitting the hydraulic tortuosity from the K-C equation, a better correlation can be achieved, which is described with the equation \(k=8.5\big{(}\phi r_{cr}^{2}\big{)}^{1.3}\). A review of the previous studies on the subject of this article proved the following: (1) the relationship between \(k\) and \(l\) requires comprehensive study; (2) most studies available have been conducted using sandstone, volcanic rock, or artificial or ideal porous materials; (3) each study was conducted on a small number of samples; and (4) the effect of rock dissolution on the relationships between permeability, porosity, tortuosity, and specific surface area has been little studied. This paper studies the relationship between permeability, total and connected porosity, hydraulic tortuosity, specific surface area, and mean pore radius based on data from 408 cubic sub-volumes selected from heterogeneous and naturally fractured cylindrical carbonate samples, before and after injection of hydrochloric acid (HCI) solutions. In addition, the percolation threshold porosity for all samples was determined based on the relationship between the connected and total porosity. The microscopic and macroscopic characteristics of the sub-volumes were obtained through pore-scale modeling.