esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measured Parameters
### Geomagnetic Parameters  
Geomagnetic observatory practice is a relatively complex task requiring skilled staff, both from a technical and a scientific point of view [17]. The fundamental reason of this complexity is the fact that the physical magnitude to be measured is a vector, needing an accurate determination with respect to a fixed reference frame. The geomagnetic vector can be given in either of three coordinate systems linked to a geographic local reference frame: (a) a Cartesian one where X, Y, Z are the (geographic) northward, eastward and downward projections, respectively; (b) a cylindrical one where H and Z are the horizontal and downward projection, respectively, and D is the declination or angle between the (geographic) north and the magnetic field (positive towards east); or (c) in a spherical coordinate system, where F is the total vector magnitude, D is declination, and I is inclination or angle between the horizontal projection and the magnetic vector itself (positive downward).
## 1 Introduction
Except that, a forward GPP estimation approach based on canopy SIF measurement and leaf ChlF parameter was proven useful for the estimation of GPP dynamics [26], which implies whether we can use a backward way from measured GPP as a new alternative approach for canopy SIF estimation. An evaluation of these two approaches is needed for better interpreting the temporal dynamics of the SIF signal, especially for the diurnal dynamics. Subtropical forests, a widely distributed ecosystem in southern China, are crucial to the regional and even global carbon cycle because of their high carbon sequestration capacity. It was estimated that the total annual net ecosystem production (NEP) of monsoon subtropical forests in East Asia accounted for 8% of the global forest NEP [27]. A recent study reported that the rapid increase of afforestation in the past 30 years resulted in the large carbon sink in mainland China, especially in Southwest China, in which the carbon uptake accounted for approximately one-third of the carbon uptake in mainlandChina [28]. Due to the highly significant correlation of chlorophyll fluorescence and photosynthesis, accurately quantifying the SIF dynamics of this vital ecosystem is essential both for verifying satellite observation data and interpreting the underlying driving forces in the global terrestrial carbon cycle. Therefore, the practical issue is how we can accurately estimate SIF for the subtropical evergreen coniferous forests. We evaluated the performances of two approaches, i.e., (1) the CCC approach and (2) the backward approach from GPP, for diurnal SIF estimation using measured and simulated data for a typical subtropical evergreen coniferous forest site in eastern China. In this study, our objectives are: (1) interpreting the diurnal dynamics of chlorophyll fluorescence (F\({}_{\text{s}}\) and SIF) of a subtropical evergreen coniferous forest; (2) quantitatively evaluating the performance of the two approaches for estimating SIF by comparing with the timulation of the SCOPE model.
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 2 Configuration Design of Reconfigurable Modular Space Robot
### Unit Module Design  
In order to better verify the expansion, shift, and reconstruction missions of the module, and to reduce the complexity of the system, the unit module only sets a single degree of freedom.  
Modules are connected through universal docking surfaces to achieve system expansion. Meanwhile, in order to increase the connection reliability of the docking surfaces, the docking mechanism only completes the locking function without setting additional degrees of freedom of motion. Thus, the module motion joints can only be set inside the module and based on the built-in requirements of this joint, the entire module can be divided into two parts that can move with each other. Meanwhile, due to the complex space environment, the functional load and basic load of the module cannot be exposed outside of the module, which needs to set internal installation loads.  
The unit module is composed of two relatively rotatable sub-modules. In order to ensure the scalability of the system, the two sub-modules are identical and have a symmetrical structure.  
1. The two sub-modules have a common rotation surface that is connected and perpendicular to the rotation axis.
2. Modules can be connected through the connection surfaces opened on the sub-modules.  
As shown in Figure 4, once the sub-module adopts a prism configuration, when the two sub-modules are rotated relative to each other, their rotation surfaces cannot overlap,  
Figure 3: Reconstruction changes of the robot from single-armed to multi-armed.  
and the external contour of the module will be destroyed. Once it is in a space environment, the internal load will also be exposed. Thus, in order to ensure that the module envelope shape remains uniform during the rotation of the sub-module, i.e., the outer contour of the rotation surface always coincides, it is appropriate to design the rotation surface as a circle.  
During the rotation of the entire module, the outer contours of the two sub-cavities always coincide with each other, making the structure simpler and more compact. The module sub-cavity can be regarded as two symmetrical planes cut out of a hemisphere, and the attitude angle between the planes is \(90^{\circ}\). When the structural sections in Figure 4C are increased to three, the three faces are made perpendicular to each other, and the following special geometry is obtained, called a quasi-tetrahedron. The resulting configuration becomes a configuration design based on the quasi-tetrahedron.
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 2 Materials and Methods  
### Data Source of Organ  
The tomographic abdominal organ dataset Chaos [38] is a medical imaging dataset focusing on abdominal organs such as liver, kidney, and spleen structures. This dataset is prominent in the adjunctive treatment of diseases such as acute liver failure, impaired renal function, and abdominal aneurysms. Abdominal imaging is an essential subfield of diagnostic radiology, which plays a vital role in daily control and postoperative follow-up. Imaging modalities of Chaos dataset images are mainly composed of electron computed tomography (CT) and magnetic resonance imaging (MRI). The CT data images were used in 40 cases, with the task of single-organ segmentation of the abdominal liver, and the MRI data images are used in 80 points, with the mission of multi-organ segmentation of the abdomen. Imaging modalities of the MRI data images are divided into longitudinal relaxation imaging (T1) and transverse relaxation imaging (T2), with T2 imaging being practical in showing lesion areas, edema, and tumors. T2 imaging effectively shows lesion areas, edema, and tumors and usually provides better soft tissue contrast than T1 imaging. For the above reasons, this experiment selected T2 imaging MRI medical image maps for abdominal 4-organ image segmentation; the organs are the spleen, left kidney, right kidney, and liver. Partial T2 dataset, partial T2 dataset corresponding labels, and labeled organ location schematics are shown in Figure 1a-c, respectively.
## 3 Underwater Routing Protocols
### LF-Iehm  
A localization-free interference and energy holeminimization (LF-IEFHM) routing protocol is proposed in [71]. It uses a variable transmission range of the sensor nodes to avoid the condition when a sensor node has no neighbor for data forwarding. In addition, a unique packet holding time is defined for every sensor node to reduce the probability of two or more nodes forwarding the packets at the same time. The protocol achieves high throughput at the cost of high energy consumption.
###### Abstract  
Conventional protection schemes in the distribution system are liable to suffer from high penetration of renewable energy source-based distributed generation (RES-DG). The characteristics of RES-DG, such as wind turbine generators (WTGs), are stochastic due to the intermittent behavior of wind dynamics (WD). It can fluctuate the fault current level, which in turn creates the overcurrent relay coordination (ORC) problem. In this paper, the effects of WD such as wind speed and direction on the short-circuit current contribution from a WTG is investigated, and a robust adaptive overcurrent relay coordination scheme is proposed by forecasting the WD. The seasonal autoregression integrated moving average (SARIMA) and artificial neuro-fuzzy inference system (ANFIS) are implemented for forecasting periodic and nonperiodic WD, respectively, and the fault current level is calculated in advance. Furthermore, the ORC problem is optimized using hybrid Harris hawks optimization and linear programming (HHO-LP) to minimize the operating times of relays. The proposed algorithm is tested on the modified IEEE-8 bus system with wind farms, and the overcurrent relay (OCR) miscoordination caused by WD is eliminated. To further prove the effectiveness of the algorithm, it is also tested in a typical wind-farm-integrated substation. Compared to conventional protection schemes, the results of the proposed scheme were found to be promising in fault isolation with a remarkable reduction in the total operation time of relays and zero miscoordination.  
protection coordination; wind dynamics; wind-speed forecasting; seasonal autoregression integrated moving average (SARIMA); adaptive neural network-based fuzzy inference system (ANFIS); smart energy systems; system stability
# CFD Estimation of a Resistance Coefficient for an Egg-Shaped Geometric Dome
## 1 Introduction
In other attempts, [12; 13] performed a CFD analysis and got a good agreement between numerical and experimental results. In a similar case to this study topic, [PERSON] et al. in [14] showed that the drag coefficient of an elliptical section is 51.3% smaller than for a circular section using the finite volume method. CFD simulations for partially submerged objects are often carried out for a transient state using the subgrid-scale model (SGS) and Large Eddy Simulation (LES) [15] with the free surface approach. However, for special cases where the Froude number is small, the free surface may be replaced by a surface with zero shear stress [8]. This study aims to develop a CFD numerical model to estimate the resistance and coefficients on an egg-shaped dome (tank). The tank is a combination of cylindrical and ellipsoidal shapes, which makes it difficult to compare the hydrodynamic loads with available research. The developed CFD model utilizes the Volume of Fluid (VOF) technique and Reynolds averaged equations, which allows for the simulation of free surface flow over a partially submerged dome. The developed model may be an alternative to the large computational effort transient SGS model. The numerical results are validated with experimental data. A reliable numerical model can be utilized for full-scale cases with any changes in the geometry and environmental conditions.
## 2 Materials and Methods
### Remote Sensing Data  
SAR data were collected from Sentinel-1, which operates in the C-band at a frequency of 5.405 GHz, corresponding to a wavelength of approximately 5.55 cm. The SAR data were acquired at the GRD (Ground Range Detected) level in IW (Interferometric Wide Swath) mode to obtain backscatter coefficients. Additionally, SAR data were acquired at the SLC (Single Look Complex) level 1 in the same IW mode for polarimetric decomposition processing. The spatial resolution is 5 \(\times\) 20 m for SLC data and 10 m for GRD data. The  
Figure 1: Experimental area with field boundaries marked in red and soybean yield data points in each harvest.  
mode used provided dual-polarization data: _VH_ (vertically polarized waves emitted and horizontally polarized waves received) and _VV_ (vertically polarized waves emitted and vertically polarized waves received).  
Optical images were used from the Sentinel-2A/B satellite constellation, captured by the Multispectral Instrument (MSI). Sentinel-2 is a satellite developed under the European Space Agency's Environmental Monitoring and Security Program. Sentinel-2, a medium-resolution multispectral imaging satellite, equipped with a multispectral imager, has a total of 13 spectral bands, with spatial resolution ranging from 10 to 60 m and a revisit time of approximately 5 days for the same area. The bands utilized in this study, which have the highest spatial resolution (10 m), include blue (\(\approx\)465 nm), red (\(\approx\)665 nm), and near-infrared (NIR) (\(\approx\)842 nm). The data were acquired through the Google Earth Engine platform, using the collection \"Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A\"([[https://developers.google.com/earth-engine/datasets/catalog/COERNICUS_S2_SR_HARMONIZED](https://developers.google.com/earth-engine/datasets/catalog/COERNICUS_S2_SR_HARMONIZED)]([https://developers.google.com/earth-engine/datasets/catalog/COERNICUS_S2_SR_HARMONIZED](https://developers.google.com/earth-engine/datasets/catalog/COERNICUS_S2_SR_HARMONIZED)), accessed on 7 August 2024). Level-2A processing includes atmospheric correction, which eliminates atmospheric effects such as scattering and absorption by gases and aerosols, allowing for the recovery of surface reflectance values and ensuring consistent information for studies of terrestrial targets.
## Abbreviations  
Notations and description.  
\begin{tabular}{l l}
**Notation** & **Definition** \\ \(V_{in}(t)\) & Input video feed \\ \(F_{p},F_{p}\) & People detection and tracking \\ \(F_{d}\) & Distance estimation \\ \(F_{g},F_{g}\) & Group identification and tracking \\ \(F_{h}\) & Identifying and localizing physical interaction (handshakes) \\ \(F_{m},F_{m}\) & Mask detection and tracking \\ \(J_{i}(t)\) & Output of model \(F_{i}\) \\ \(S_{i}(t)\) & State information \\ \(bb_{pk}(t),bbi_{pk}(t)\) & Bounding box encompassing person \(k\) at time \(t\) and bounding box encompassing person \(k\) at time \(t\) which is being tracked with their unique index \\ \(bb_{hk}(t),bbi_{hk}(t)\) & Bounding box encompassing handshake interaction \(k\) at time \(t\) and bounding box encompassing handshake interaction \(k\) at time \(t\) which is being tracked with their unique index. \\ \(bb_{mk}(t),bbi_{mk}(t)\) & Bounding box encompassing the face of person \(k\) at time \(t\) and bounding box encompassing face of person \(k\) at time \(t\) which is being tracked with their unique index \\ \(u,v\) & The 2D coordinates of the center of the bounding box \\ \(h,r\) & The height and aspect ratio of the bounding box \\ \(R,R^{\prime}\) & The coordinates of the reference points in the video frame and two-dimensional floor plane, respectively \\ \(M_{T}\) & Transformation matrix for the perspective transform from CCTV perspective to \\  & floor plane \\ \(s_{(i)}\) & Standing location of person \(i\) at time \(t\) in the CCTV perspective \\ \(floorLocation_{(i)}\) & Standing location of person \(i\) at time \(t\) in the floor plane \\ \(dist_{(i,j)}\) & Distance between a pair of people \(i\) and \(j\) at time \(t\) \\ \(P_{i}\) & Person \(i\) in the frame \\ \(G(t)\) & Graph at time \(t\) \\ \(V(t)\) & Vertices of graph \(G\) at time \(t\) given by \(\{v_{1}(t),v_{2}(t),\ldots,v_{n}(t)\}\), each vertex corresponding to person \(P_{i}\) with the vertex parameters embedded \\ \(E(t)\) & Edges of graph \(G\) at time \(t\) given by \(\{e_{1,1}(t),e_{1,2}(t),\ldots,e_{i,j}(t),\ldots,e_{n,n}(t)\}\), where \\  & \(e_{ij}\) is the edge between person(vertex) \(i\) and
## 2 Related Work  
Super resolution was initially introduced and applied to natural images before being adapted and employed for remote sensing images. Over time, various super-resolution techniques based on convolutional neural networks, generative adversarial networks, and Transformer structures were developed. Due to the ill-posedness of super resolution, increased attention has been given to this aspect as the scale factor of the proposed super-resolution methods increased. One effective approach to reduce the ill-posedness of the super-resolution problem is to increase supervision in the network training process.  
Consequently, several super-resolution methods aimed at increasing the number of supervisions were proposed, with some employing progressive structures, and others using loops or other structures. All these approaches enhanced the performance of super-resolution networks.
# Inter-UAV Routing Scheme Testbeds
## 4 Findings and Discussion
### Standalone Aerial Mode  
In standalone aerial mode, mesh modules are directly attached to each UAV comprising a FANET. The main concept behind the design is the compartmentalization of task  
Figure 4: Rendering of proposed testbed module.  
handling so as to avoid unnecessary computational burden of each component. This design idea is directly translated to hardware design implementation. The Raspberry Pi Zero is attached directly to the main board with an exposed header configured to match its pinout. As seen in Figure 5, one of the PC/104 holes and two extra nonplated 3.2 mm \(\diameter\)mounting holes offer extra mechanical stability to the interconnection of the main components. As the Raspberry Pi is placed onto the main board, it is internally powered by the main board's regulator. The module has full support for an amplitude of commercially available GNSS modules. Node location can therefore be used as a route selection metric, in addition to node velocity and acceleration as previously mentioned. Thus, in addition to predictive routing schemes, evaluation/implementation of location aware or location-based routing is made possible.  
Figure 6 illustrates the interfacing between the Raspberry Pi and the ESP32 as well as tasks each module handles.  
The Raspberry Pi Zero shall only implement route-related computations. After a route has been established, the packet shall be serially transmitted to the ESP32, which shall in turn implement transmission handling. In turn, upon packet reception, the ESP32 shall serially transmit the data to the Raspberry Pi. Packet transmission/reception handling shall constitute a distinct nonblocking RTOS task, so that the ESP32 can simultaneously implement the flight control logic. It becomes obvious that the ESP32 now has two functions, which are defined as distinct RTOS tasks handled by the two cores of the Xtensa LX6 microprocessor. All packet transmission and reception is done via the embedded WiFi antenna which ensures a low power consumption as well as the possibility of interfacing with conventional 802.11 networks. The Raspberry Pi is thus successfully only occupied with route handling.  
Figure 5: Layout of the module’s main board.  
Table 4 showcases the serial interface between said modules in standalone aerial mode. The mentioned gyroscope/accelerometer processed data shall only be transmitted to the Raspberry Pi if the evaluated routing protocol requires them as a metric. Similarly, RSSI (measured between) shall be shared if link quality or power-awareness is desired and considered by the respective protocol. GNSS data is again, optional and shall be transmitted in the case geolocation-based routing is implemented.
## 5 Performance Evaluation
### Simulation Results
#### 5.2.7 Traffic Load vs. Energy Consumption  
The average energy consumption per node for various packet rates and traffic loads is shown in Figure 13. Packet rates of 0.1 to 0.5 imply that one to five packets were sent every 10 s during each simulation. The performances of the protocols for 100 to 900 nodes  
Figure 11: Comparison of the average energy consumption per node with varying duty cycles and non-uniform densities.  
are shown in Figure 13a-d. In each setup, the FCM-OR outperformed the ORW and ORR protocols. The energy consumption per node increased for ORW and ORR with increasing packet rate, traffic load, and network density. In contrast, FCM-OR consumed less energy and the energy load was balanced with varying traffic loads and densities.  
Figure 12: Comparison of the average number of forwarders per hop with varying duty cycles and non-uniform densities.  
Figure 13: Comparison of average energy consumption per node with varying packet rates and non-uniform densities.
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 2 Literature Overview
Furthermore, in [25], it is optimized the 3D locations of UAVs for wireless powered NB-IoT. Another work worth to mention is [26]. The authors propose a NB-IoT model to collect underground soil parameters in potato crops using a UAV-aided network. The analysis in this case is mostly application-dependent, and therefore differs from our general evaluation with different metrics. This activity can be considered an extension of [1]. To the best of the authors' knowledge, the literature still lacks the detailed model and protocol analysis of similar scenarios and setups. Therefore, the focus of this work is to extend and further discuss the system dynamics of NB-IoT networks served by an UAV, rather than compare our approach with other research activities. This study helps us to extract the major impacts of the overall protocol stack of the NB-IoT technology on UAV-aided networks.
## 4 Analysis
### Overhead Analysis
Next we evaluate the overhead of our mechanism in terms of communication, computation and storage compared with some existing schemes. The comparison is showed in Table 1. As LD-1 is local detection, nodes only need communication with their neighbors and update their private keys when needed. Hence the communication and computation overhead are O(1). Nodes do not need to store other information except for their related key materials, thus the storage cost of LD-1 is O(1). LD-2 is also a local detection, so the communication and computation overhead are O(1). Neighbor lists need to be exchanged between every node and their neighbors, so the storage overhead is O(N), that is because in the extreme case, each node need save the information about other N-1 nodes. The overall detection OD is executed by the sink node. Since the sink node usually has higher computation capability and larger storage space [1, 2, 3, 19], some detecting tasks could be transferred to the sink node to ensure better energy balance. When node u applies for updating its private key at a certain position in the monitory region, sink node will receive the authentication messages from t witness nodes. The sink node will also send the new key to node u after ensuring the messages are legitimate by checking the authentication messages. In the extreme case, there are 0.41 nodes need update their private keys. There are t + 1 messages which will be transmitted when private keys are updated, including receiving t authentication messages and sending one updated key. Hence the communication overhead is O (0.41(t + 1) N). The computation cost is O(1) because the sink node needs to store information from  
Figure 9: The upper limit of the expected numbers of key-updating nodes. all nodes. It is important to note that the communication overhead in our mechanism OD is global, but in other papers it usually means the cost of one single node. For example, the overall communication overhead of TDD is O(N\(\sqrt{\text{N}}\)). By contrast, our mechanism has less overhead.