esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
A psi-shaped circularly polarized antenna with high gain and bandwidth was investigated and found to be best suited for detection of the blind spots of a vehicle in V2V and V2R communications [17]. An elliptical-shaped fractal antenna in a tree-structured configuration for super-wideband applications was proposed. The antenna has a size of 170 \(\times\) 150 mm\({}^{2}\) and provides an IBW of 0.65 to 35.61 GHz, a ratio bandwidth of 54.78:1, a peak realized gain of 6.51 dB, and an average gain of 3.24 dB [18]. An octagonal, ring-shaped, 40 \(\times\) 40 \(\times\) 1.01 mm\({}^{3}\) sized monopole antenna with a stub placed on the top of the right corner was developed to enhance the impedance bandwidth over a range of 2.59 to 31.14 GHz at 1511 \(\leq-\) 10 dB [19]. A triple-notch super-wideband antenna with a bandwidth of 2.34 GHz to 20 GHz was designed and fabricated for Bluetooth and LTE 2600 bands, and achieved a gain of 4.98 dBi and radiation efficiency of 89% [20]. An offset elliptical ring patch antenna with a tapered feed line was developed. The proposed antenna has a ratio bandwidth of 34.63:1, a bandwidth dimension ratio of 1732, and a peak gain of 5.81 dBi over the functional frequency range of 2.31-40.0 GHz [21]. A compact bulb-model planar super-wideband SWB antenna was designed with the dimensions of 35 \(\times\) 30 \(\times\) 0.8 mm\({}^{3}\) and works in the frequency range of 2.8-40 GHz with a RBW of 14.28:1. The antenna has a FBW of 173.8% and a BDR of 1904. A FSS was placed on top of the radiating patch with an air gap of 19 mm to enhance gain over the specified SWB range [22]. A Sierpinski fractal antenna was designed with a patch in the shape of a hexagon and coplanar waveguide feeding. The bandwidth ratio of 11:1 was achieved over an impedance bandwidth of 3.4-37.4 GHz [23]. A star-triangular fractal monopole antenna feed with microstrip-fed and semielliptical ground plane was investigated with a footprint of 20 mm \(\times\) 20 mm \(\times\) 1 mm\({}^{3}\) and exhibited a super-wideband range of 1-30 GHz with VSWR \(<\) 2 [24]. The SWB antennas reported in [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24] have limited FBW, BDR, and radiation performance. Therefore, the major issues in the design of SWB antennas are miniaturization, wide impedance bandwidth, and a high BDR. A compact super-wideband antenna was presented and analyzed in this article. It consists of an octagonal radiating patch fed by a linearly tapered microstrip line and the partial ground plane. The proposed antenna achieved a super-wideband frequency range of 3.71-337.88 GHz, a fractional bandwidth of 195.65%, and a very high bandwidth dimension ratio of 6057.27, with an overall size of 14 mm \(\times\) 16 mm. VSWR, efficiency, and radiation patterns were also investigated to support SWB applications.
## 6 Simulation Result
### Evaluation Results of Modifying the Generated Tasks  
This subsection discusses the effect of the number of tasks to be processed on the performance of these schemes. When the computational complexity is set as 1000 rounds/bit, the evaluation results of each offloading scheme are presented in Figure 6a,b as the number of tasks is gradually increased from 40 to 80. The successful rates for these three offloading methods decline as we increase the number of tasks. It could be expounded by the fact that, once the number of compute-intensive tasks from the UAVs is small, parking resources are sufficient for the assignment. However, since the more tasks there are, the less CPU resources per task can be obtained with a fixed amount of deployed resources. The successful rates of the three schemes reduce significantly with a large number of tasks. Because of the advantages of the DRL model, the proposed learning-based scheme shows a higher successful rate than other two schemes.  
Figure 6b shows our scheme greatly decreases the task execution cost. The reason is that the proposed DRL-based scheme continuously adjusts the offloading strategy through multiple training processes to achieve the lowest task execution cost, with a relatively high task successful rate, among the three schemes.
## 2 Architecture of Dima System  
Understanding system architecture of DIMA is essential for optimizing the system. Figure 1 illustrates the overall architecture of DIMA system, with the consideration of the hardware and software mapping and its quality measures as well as constraints. It has a hierarchical layer design for allocation, which includes system layer, hardware layer, and installation layer. The subsystems in the system layer are supported by the hardware in the hardware layer, with functions mapped into devices and signals into links. The hardware in the hardware layer is installed into the installation layer, by placing devices into mount locations and connecting cables via networks.
## 2 Methodology
### Direct Conditioning  
In [2], the authors proposed a method of directly conditioning the latent representation of each predictive branch through a conditioning layer. The conditioning element is part of an auxiliary classification network, one that is aimed at predicting whether the given sample belongs to the daytime distribution or the nighttime distribution. The latent representation used for this auxiliary prediction is derived from an intermediate representation of the entire image. Thus, the representation must be able to extract a notion of day and night, which can make the network 'aware' and adapt accordingly.  
In task-conditioned YOLO [2], the overall mAP does not improve significantly at higher IoUs; however, the weather-conditioned network shows a reduction in object Miss-Rate (MR). By directly conditioning the intermediate representation, the network is forced to directly incorporate the weather information in its semantically rich representation. We employ the original implementation of the YOLOv5 model. As shown in Figure 5a, the standard YOLO architecture is extended with an auxiliary branch, and it is extracted from one of the early stages of the feature extractor. Subsequently, a series of fully connected layers condense the representation to feed it to a prediction head. In addition, the prediction head produces a single value that is regressed following the exponential \(L1\) loss described in Section 2.2. Individual fully connected layers feed the representation to the conditioning layer in the different stages of the network, and this occurs prior to the given stage's prediction head. The conditioning layer (shown in Section 2.3) takes in the set of feature maps, as well as an element-wise multiplication and summation with separated auxiliary representations \(\alpha_{n}\) and \(\beta_{n}\), respectively. Thus, the latent representation used for predicting the weather-related variable is directly enforced into the intermediate representation used by the object detector, which forces it to adjust accordingly.
## 2 Dynamic Nuclear Polarization in Semiconductor Nano-and Microstructures
### Optically Induced DNP in Semiconductors
#### 2.1.4 Optical Spin Orientation in Various Semiconductors
The successful optical generation of a net spin polarization of electrons in the strained Ge\({}_{1\text{-}}\)Sn\({}_{\text{x}}\) alloy, which manifests hybridization of the spin properties of Ge with the peculiar electronic states offered by \(\alpha\)-Sn, was demonstrated by recording PL spectra, the optical spin orientation being robust over a wide temperature range up to room temperature [45]. The spin coherence time of photoexcited spin-polarized electrons was found to decay from 12 ns to 6 ns when increasing the magnetic field from 0 to 53.5 mT owing to \(g\)-factor fluctuations as long as spin dephasing was triggered by the cooling process, during which optically generated electrons underwent momentum relaxation under random effective magnetic fields due to local changes in the alloy composition and strain [45].
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs  
In this section, the future direction of spaceborne LiDARs is described. Starting with planned missions that are planned to hold a LiDAR as a scientific instrument, we continue with the currently developed concept.
# Investigation of the Pressure Coefficients Correlation Field for Low-Rise Building Roofs
## 3 Experimental Wind-Tunnel Results
### Mean Wind Direction 0\({}^{\circ}\)
This effect was evidently due to the lower curvature and therefore the shape of the roof, because a flatter roof minimizes the detachment of vortices at 0\({}^{\circ}\), as also confirmed by the slightly lower coefficients for suction pressure [29]. Comparing the results for wind direction 0\({}^{\circ}\) for higher models with large curvature (RHC) and small curvature (RHF), shown in Figures 7 and 8, respectively, the differences found were in many ways similar to those observed comparing the results between flatter and more curved roofs on lower models, so confirming a general trend. In these cases, the correlations between a given pressure tap and points in the surrounding area were significantly larger for pressure taps at the inner part of the roof, compared with pressuretaps closer to the edges of the model. For taller models, however, the detachment of vortices from the edge that was directly hit by the wind was larger. Unlike the lower models, this implies a faster decrease of correlation for the perimeter pressure taps on flatter models, which was only partially regained with subsequent reattachment of the flow (e.g., see Figure 8 Ac). Slower decrease of correlation was instead observed for the more curved roof, for which the correlation between the pressure taps on the leading edge and the surrounding area was larger (e.g., see Figure 7 Ac), because the flow reattached sooner due to the higher downward curvature in the direction of the wind. An opposite trend as a function of the roof curvature, analogous to the trend observed for lower models, was observed with increased distance between the leading edge and the pressure tap under consideration. This is shown, for example, by the larger correlation of the pressure taps Ba and Be in the flatter model (Figure 8) compared with the analogous pressure taps Ba and Be of the more curved model (Figure 7). On the other hand, with the same curvature, greater model height reduced the differences between the correlation relative to the pressure taps of internal regions and the correlation relative to the pressure taps along the edges. This was especially the case for the models with larger curvature; for example, the pressure taps Ac and Cc of the model RLC (Figure 5) compared with the same pressure taps Ac and Cc of the model RHC (Figure 7). In conclusion, for a wind angle of 0\({}^{\circ}\) the correlation between the pressure at a point on the roof and the surrounding points was mainly influenced by the curvature of the roof, with the exception of the areas near the leading edge, where the correlation was also significantly influenced by the height of the model.
## 3 Three-Dimensional Joint Inversion Algorithm
### Process of Three-Dimensional Joint Inversion  
The typical routine of the conventional TDIP inversion is to first invert the conductivity, and then the inversion of polarizability is fulfilled based on the conductivity model, which usually leads to the excessive dependence of the polarizability result on the conductivity result. In this study, the alternating iteration method to invert the conductivity and polarizability models was applied in the algorithm, and the inversion results of the two parameters were mutually constrained by means of the cross gradient. Thus, on the basis of minimizing the data misfit of separate inversions, a model in which the conductivity and polarizability share a structurally similar distribution can be found. The process of joint inversion is shown in Figure 1, which can be briefly summarized in the following steps:  
1. Input the initial conductivity and polarizability model.
2. Run the separate conductivity inversion. After several iterations, the conductivity model with a certain abnormal form is obtained, and then separate TDIP inversion is performed based on the conductivity model to update the polarizability model. The cross-gradient term calculated by the models derived from a separate inversion, which is substituted into (11).
3. Start joint inversion, and the conductivity and polarizability models with cross-gradient constraints are calculated using several iterations through joint inversion.
4. Repeat step (3) several times and update the conductivity and polarizability models iteratively, in which the cross-gradient term is calculated by the latest models that derived from joint inversion. Check whether the iteration termination criteria are met. If so, the iteration is terminated; if not, the alternating iteration continues.  
(5) The termination criteria of inversion are that the data misfit is less than a predefined threshold or the number of iterations reaches a set value. The formula for calculating the misfit (root mean square, \(RMS\)) is the following: \[RMS=\sqrt{\frac{[d_{\text{obs}}-F(m)]^{T}V^{-1}[d_{\text{obs}}-F(m)]}{N}},\] (17) where \(N\) is the number of data points. (6) If the convergence criterion established for one of the properties is reached, then the renewal of this property is stopped, while the other property continues to be updated until both properties meet the convergence condition.
# Consistency Models of NoSQL Databases
## 5 NoSQL Consistency Implementations
### Cassandra
#### 5.2.4. ONE Read Consistency Level and ONE Write Consistency Level  
In Figure 8, we have the strongest form of eventual consistency configuration in Cassandra. We need just one node with the updated data to acknowledge the write operation. For the reads, the first node the coordinator node chooses will retrieve the requested data. This may or may not be the most updated version of the data object. Eventually, the most updated version will be returned on all requests. The time needed to get to a 100% probability of consistency will depend on the _Read Repair Chance_ and the _Replication Factor_. The higher the probability of the _Read Repair Chance_, the shorter the time to get to full consistency. The lower the _Replication Factor_, the shorter the time to get to full consistency. Modifying the _Read Repair Chance_ and the _Replication Factor_ to reach consistency faster will result in higher latencies because more copies and nodes are involved in the read and write operations for each client request.
## 3 Results
### McSL Calibration Accuracy  
We prepared McSL videos with five cameras of three different types of surgeries: polysyndactyly surgery, cleft lip surgery, and accessory auricle surgery (Figure 3). To investigate the impact of frame counts used in calibration, we randomly selected \(N=1\), 10, 20,..., 100 frames 10 times in each video and performed the calibration. We calculated the mean and standard deviation values of reprojection errors for each number of frames and the success ratios. For the success ratios, we calculated the number of fails of COLMAP bundle adjustment over all trials.  
Figure 4 summarizes the results. A one-frame input is not enough for the calibration because of the lack of features in the frames. Increasing the number of inputs increases the success ratio. However, no improvement is observed over the 20-frame input. The use of more than 70 frames shows a slight decrease in success ratio, perhaps due to the increased interference of erratic feature matching. The reprojection errors stay very similar regardless of the number of images.  
In 3D reconstruction, in general, COLMAP [32; 33] is the de facto standard and state-of-the-art method. Although the approach is extended with a graph-based neural network approach (i.e., SuperPoint [30] and SuperGlue [31]) instead of SIFT features [35] and heuristic matching, the result is summarized as 1-frame (the leftmost result) in Figure 4 in our experiment.  
Figure 4: Reprojection errors and success ratios over various numbers of images for McSL calibration. A 1-frame input is apparently not enough for calibrating the system. Almost identical mean errors and standard deviations are observed. However, the success ratio reaches 1.0 in the 20-frame input. Notably, with more than 70 frames, the success ratio is slightly lower, which suggests that including a significantly larger number of images would introduce erratic and less beneficial images for calibration.  
Figure 3: Examples of none-feature-rich frames. Each shows one of five view images in McSL in three different surgeries: polysyndactyly, cleft lip, and accessory auricle surgery (from **left** to **right**).  
Figure 5 presents a qualitative result of our calibration method with a 20-frame input. The red cones represent the camera view frustum and their heights indicate the focal lengths. The camera views are posed with extrinsic parameters. The colored points represent a 3D point cloud reconstruction. These results show our method provides qualitatively reasonable performance.
## 2 Clinical Background
### Heart Wall Structure and Terminology  
The human heart is surrounded and protected by a sac of tissue called the pericardium. The pericardium consists of a thin outer layer of connective tissue called the fibrous pericardium. The fibrous pericardium envelops the serous pericardium, a tissue consisting of two layers. The outer layer of the serous pericardium, called the parietal pericardium, is fused to the fibrous pericardium. On the other hand, the inner-most layer of the pericardium called the visceral pericardium or epicardium [10] immediately surrounds the heart muscle (myocardium). These tissues can be seen in Figure 3.  
Figure 2: A summary of paper frequency and used methods.  
Epicardial adipose tissue is most loosely defined as any adipose tissue within the pericardial sac. The fat immediately outside the pericardial sac is called paracardial fat, and together they form pericardial fat [6,21]. Epicardial fat is mostly concentrated in the atrioventricular and interventricular grooves and along the major branches of the coronary arteries and around the atria Sacks and Fain [10]. Paracardial fat is concentrated on the outside of the parietal pericardium within the mediastum.  
It should be noted that there are disagreements in the medical literature on the terminology and anatomical definitions of cardiac adipose tissues. Namely, pericardial fat is sometimes referred to as intrathoracic fat [6] and EAT and PAT are sometimes separated as two disjoint tissues and not a subset of each other [16,21]. It can be difficult to compare methods of EAT and PAT quantification since the used definitions can vary the quantified volume. In this review, we took that into account and analyzed the definitions used by each mentioned paper to make sure a valid comparison is made. In the rest of the paper we adopt the following definitions, according to the definitions most commonly used in the analyzed literature:  
* Epicardial adipose tissue is all adipose tissue enclosed by the pericardium.
* Paracardial adipose tissue lies just outside the pericardium.
* The union of these tissues is pericardial adipose tissue.  
Moreover, note that in this review, the acronym PAT is used to denote pericardial adipose tissue, not to be confused with paracardial adipose tissue.
Due to the scarcity of this type of study, the idea arose to write an article in which a comparison is offered of several of these useful tools for the creation of DTs using 3D point clouds obtained from photogrammetric techniques and oriented toward digital agriculture. Here, the ideas of the generation and use of DTs help us to elaborate the 3D models with photogrammetric techniques. These models serve as a framework to enable the collection of agricultural information with the IoT and detect problems with AI techniques. The goal of this paper is twofold: firstly, to evaluate the efficacy of point clouds in the creation of digital twins for agricultural applications, utilizing photogrammetric techniques with both RGB and multispectral images. Secondly, to conduct a thorough review and comparative analysis of tools dedicated to developing DTs for Agriculture 5.0 applications, leveraging 3D point clouds. Furthermore, the paper explores a forward-looking concept, Agriculture 6.0, aiming to integrate the advancements from Agriculture 5.0. This new paradigm seeks to harness the collective innovations in the field, contemplating how they can synergistically enhance various social and economic aspects. The discussion on Agriculture 6.0 serves as a forward-thinking exploration into the potential evolution of agricultural practices and technologies. To accomplish this aim, after introducing the concepts of Agriculture 5.0 and DT, along with articulating the core objective of this article, the subsequent content is systematically organized as follows. In Section 2, an outline of the potential advantages inherent in utilizing this technology within the context of Agriculture 5.0 is presented to describe the methodological approach. Also, an exposition of contemporary tools applicable to DT development is provided to describe the materials, elucidating their respective contributions to 3D modeling. Subsequently, these tools are subjected to testing and a comparative analysis in Section 3, culminating in the selection of the optimal candidate through the determination of a synthetic quality metric. Lastly, Section 4 engages in a discourse on the conclusions drawn from the study and delineates potential pathways for prospective research endeavors, including a new concept called Agriculture 6.0, which is introduced in order to take a step beyond Agriculture 5.0 as we know it today.