esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] has suggested that it is a difficult task to separately estimate the effects of _Hrms_ and correlation length \(l\) on the backscattering behavior of rough surfaces [33]. Furthermore, [PERSON] has indicated that when IEM/AIEM is used to simulate the backscattering characteristics, using only _Hrms_ as a roughness parameter in the simulations is not sufficient to derive correct results because the effect of correlation length \(l\) on the backscattering coefficient is neglected [40]. In addition, [PERSON] et al. (2008) suggested that conducting field measurement of roughness may become impractical and expensive when large areas must be covered [22]. Therefore, many researchers [40; 41; 42; 43] introduced a roughness parameter _Zs_ (which is equal to _Hrms2/l_) expressed as the surface roughness to invert soil moisture. However, roughness parameter _Zs_ adopted correlation length \(l\) in their studies. One study showed that the measurement error of correlation length \(l\) was much larger than that of _Hrms_ in the roughness measurement of the fieldwork [35]. Therefore, how to reduce the error of the correlation length when the soil surface roughness is parameterized has become the major question to answer. In this paper, the surface roughness defined by _Hrms_ and the empirical correlation length _lopt_ as proposed by [PERSON] were used to introduce analytical equations of backscattering coefficient using the CIEM. When the backscatter database was built to propose analytical equations of the backscattering coefficient, many researchers mostly used the IEM and AIEM, whereas CIEM was rarely used [40; 41; 44; 45]. To compare the findings concerning the surface roughness parameterization and model, the related studies were summarized in Table 1.  
The paper developed an empirical model through numerical simulation and regression analysis based on CIEM. First, we discussed the effects of surface roughness defined by _Hrms_, empirical correlation length _lopt_ and soil moisture _mv_ on the backscattering coefficient simulations. Then, the relationship of the backscattering coefficient, _Hrms_, and soil moisture was established. In building the soil moisture inversion model, the combined roughness parameter was not used; only _Hrms_ and the empirical correlation length _lopt_ were used. Finally, this empirical model was developed based on the analytical equations to invert the soil moisture and surface roughness. The experimental results show that using _Hrms_ and the empirical correlation length _lopt_ as the roughness parameters in the simulations is sufficient for the inversion.
# A Hybrid Energy Equating Game for Energy Management in the Internet of Underwater Things
## 4 Hybrid Energy Efficient Clustering Game
### Cluster Head Selection Game
#### 4.3.2 Final CHs Selection  
The network lifetime depends upon the energy management, controlled cluster size, and optimal number of CHs. If we control the cluster size and number of clusters in the network, the energy distribution will automatically optimize. To attain this energy goal, we optimize the number of TCHs through the \(EX_{prob}\) and now we optimize the cluster size to reduce the redundant energy consumption. From the \(EX_{pro}\) equation, we can note that each TCH is communicating at radius \(r\) with the SMNs. Consequently, every TCH and ECHs are covering an area equal to \(\pi R^{2}\) in the NSF is \(\frac{F}{\pi(\varepsilon R)^{2}N}\). So, here we need to verify that the selected number of expected TCHs and ECHs are completely covering the sensing field. For verification purposes, we used the \(EQ_{pro}\) that helps us to select the number of TCHs and ECHs to meet the optimality criteria for cluster size as shown in Figure 5. Each TCH ensures its rank as a FCH in the existing round by comparing selected member probability \(P_{SCM}\) with \(EQ_{pro}\). Each TCH and ECH will compute the \(P_{SCM}\) using the following expression:  
\[P_{SCM}=1-(w_{i})^{\frac{1}{|SCM(i)|}} \tag{23}\]  
On comparing the \(P_{SCM}\) with \(EQ_{pro}\), we have the following three cases:  
* **Case 1:** If \(P_{SCM}<EQ_{pro}\), the TCHs will add more CMs in the cluster until reaching the \(EQ_{pro}\). The ECHs also repeat the same process and if they cannot attain the \(EQ_{pro}\) state, these ECHs quit from the current status and become the FNs.
* **Case 2:** If \(P_{SCM}>EQ_{pro}\), the TCHs will cancel some SCMs in the cluster until reaching the \(EQ_{pro}\). The ECHs also repeat the same process and if they cannot attain the \(EQ_{pro}\) state, these ECHs quit from the current status and become the FNs.
* **Case 3:** If \(P_{SCM}=EQ_{pro}\), the TCH will declare its status as an FCH for the existing round.  
After the successful completion of the FCH selection phase, the TDMA slot allocation starts. The FCH selects nodes from a different direction and assigns them the TDMA slots. For this selection, only those nodes are chosen that are not in overlapping range of other nodes. The nodes that do not perform the sensing activity remain in sleep mode to save the available resources. While the chosen nodes start sensing their fields, at the arrival of their time slots these nodes send their data to their corresponding FCHs. There is no need for time scheduling to forward the data to the SS. FCHs can forward the data to their corresponding SS anytime during a round. The SCH collects the data from the SMNs in the cluster and forwards it to the next FCHs toward the SS. The relay selection criteria are discussed in the next subsection.
## 3 Assessment of Precise MGEX Orbit and Clock Products
### Orbit Comparisons
#### 3.1.3 QZSS  
The qualities of QZSS orbit products were assessed by individual satellites. The orbits of three IGSO satellites (i.e., J01, J02 and J03) were compared with respect to ESM due to its best completeness among the three IGSO satellites. The orbits of the GEO satellite J07 are included by GFZ, WUM, and TUM. However, the J07 products of TUM were only available for 36 days, due to a long interruption. As a result, J07 orbits were compared only between GFZ and WUM.  
Figure 5 shows the RMS values of QZSS orbit differences in along-track, cross-track and radial components. For J01, the periods with ON mode are marked as the grey area in the top left subfigure. It is clear that the RMS values increased significantly during and after switching the attitude mode to ON mode. As a result, only the RMS values in the yaw-steering (YS) period account for J01 in Table 5. Furthermore, the J01 RMS values of GFZ showed a clear dependency on the beta angle (the sun elevation above the orbital plane) during YS mode in 2018. The RMS value gradually increased as the absolute value of the beta angle increased. Similar dependency can also be seen in the GFZ products for J02 (top right subfigure) and J03 (bottom left subfigure) in 2018, especially in the radial component. This may have been because GFZ was the only AC applying the ECOM model during the QZSS POD process.  
Figure 5: Daily RMS of QZSS orbit differences in along-track, cross-track and radial components with regard to (w.r.t.) ESM/GFZ. The grey-shaded area in the top left subfigure represents the ON period.  
Comparisons of the 3D RMS values among the three components of orbit are summarized in Table 5 for individual QZSS satellites. J01, COD, ESM, JAX and WUM showed good agreement with each other at the 10-20 cm level, while GFZ and TUM exhibited comparatively large orbit differences to other ACs. For J02 and J03, COD and ESM also showed the best agreements at the 10-20 cm level, while the orbit differences among GFZ, TUM and WUM were mostly between 30 and 50 cm. The mean RMS values of J07 orbit comparisons between GFZ and WUM were 256.4, 5.7 and 6.0 cm in the along-track, cross-track and radial components, respectively.
## 2 Materials and Methods
### Calibration with a McSL Video  
The relative poses of multi-cameras in McSL change every time a surgeon manipulates the McSL position. This mechanism prevents photographing a calibration target and calibrating the cameras before the surgery. Instead, we propose to use surgical video frames for calibration. However, surgical video frames are not feature-rich (see an example in Figure 1a). Therefore, to collect more features, we utilize feature points and their matching results that appear on every available time frame.  
The key idea is to treat the scene geometry that changes over time as a whole structure observed at a time since such structure changes are irrelevant from the inter-camera poses. Assuming that \(M\) consecutive frames are recorded under a fixed McSL location, we randomly select \(N\) (\(\leq\)M) multi-camera frames. At a time frame, we extract feature points [30] at every camera image and run matching [31] between McSL cameras. We obtain \(N\) such sets of inter-camera feature correspondences and use all correspondences as if all features were detected at a time frame (Figure 2). Finally, we perform structure from motion (SfM), followed by bundle adjustment, for which we rely on the COLMAP framework [32; 33].
## 5 Establishment of 3D Geological Model
### Reservoir Parameter Model
The high-value parts correspond to areas with thicker sand bodies, and they are concentrated in  
Figure 12: Reservoir attribute model. the south-central and west-central parts of the study area, but the distribution area is small and relatively dispersed. According to the data distribution of the permeability model (Figure 12b), 30% are smaller than 0.1 mD, and 70% are larger than 0.1 mD. According to this, the shale reservoirs of Chang 7,12, 712, Chang 721, Chang 722, and Chang 73 sands in the study area belong to extremely low-permeability reservoirs. The permeability model reflects that, under the cooperative constraints of the porosity model, the two attribute models have a good correlation (Figure 12c). Both models are in concordance with the sandstone distribution in the distributary channel of the petrographic model, thereby reflecting the phase-controlled characteristics of the reservoir's physical properties. In conjunction with the structural and sedimentary environmental attributes of the Chang 7 member of the Yanchang Formation in the southern Heshui area, the single-well lithofacies interpretation data derived from logging were discretized and coarsened into longitudinal lithofacies data. Following this transformation, the parameters of the variation function were calculated. The geological probability surface was delineated by comparing the thickness map of the sand and mud bodies with the stratum thickness map of the target layer, employing this comparison as the constraint condition. According to sequential indication and sequential Gaussian stochastic simulation, a reservoir attribute model based on the lithofacies model and phase-controlled constraints was established, providing a stable and reliable static model. The resulting 3D geological model of the reservoir provides a more precise depiction of the spatial distribution characteristics of each lithofacies. This enhanced precision enables the utilization of sophisticated data analysis and simulation algorithms to uncover the macroscopic statistical patterns of various parameters. Consequently, lithofacies modeling transitions from an approach grounded in \"mathematical reality\" to one that is anchored in \"geological reality\", taking into account a multitude of geological conditions. This transition significantly augments the precision of predictions made between wells. Furthermore, the model effectively diminishes the uncertainty and randomness associated with inter-well interpolation to a considerable extent.
## 3 Results and Discussions
### Flows Characterization
#### 3.1.2 Free Sugars Content of Flours  
The free sugars determined in the present study were: glucose, sucrose, maltose and fructose, as showed in Table 4. The main sugar from the analyzed flours was glucose (4.80%) which was identified in higher extent amount in SF flour, meanwhile WF and BW flours reached smaller values such as 0.07% and 0.03%, respectively. Fructose, was the second major sugar identified in a bigger amount in SF flour (2.5%), followed by sucrose which was identified mainly in BW flour (0.74%). The presence of glucose and fructose in SF flour could be justified by the germination process which lead to the degradation of disaccharides, trisaccharides and tetrasaccharides into simple sugars in order to provide the energy needed for the sprouts grow [34; 46; 47].  
Sucrose was the major compound in buckwheat (0.74%) and belongs to the category of low-molecular-weight sugars; [PERSON] and [PERSON] reported also that sucrose has the highest amount among the low-molecular-weight sugars in buckwheat flour [48].  
With respect to WF sugars content, sucrose was the mainly simple sugar identified (0.21%), followed by glucose (0.07%), maltose (0.07%) and fructose (0.05%). The results are similar to those reported by [PERSON] et al. [49] who identified for WF values of 0.05% for glucose, maltose and fructose and 0.2% for sucrose, respectively. [PERSON] et al. [50] analyzed flours from six wheat cultivars and obtained values between 0.04-0.05% for glucose, 0.188-0.246% for sucrose, 0.44-0.063% for maltose, but a higher value for fructose 0.79-1.04%.  
Therefore, composite flours with BW had higher values for sucrose and maltose, meanwhile flours with added SF obtained higher levels of glucose and fructose as shown in Table 4.
## 5 Discussion and Conclusions
It may not always be possible to stabilize permanent targets suitable for control geodetic measurement on the surface of these objects, so it is necessary to pay extra attention to the configuration of the camera network during non-contact photogrammetric measurement. Moreover, considering only the results of the bundle adjustment can be misleading. Reprojection errors can reach ideal values in tenths of a pixel (0.18 pixel in this case study), but the overall camera network can suffer from significant deformations, which, without the use of check points or comparison with a reference measurement, e.g., using TLS, may not be detectable. Of course, the overall deformations of the image blocks can be minimized using well-distributed control points, but the goal of this experiment was to test the configurations of the camera network without the influence of the control points. The comparison of the photogrammetric processing with all available images (using all openings) relatively to more reliable TLS showed that more than 85% of the photogrammetric points were evaluated with a relative accuracy of 1:2400 or better. Therefore, increasing the robustness of the camera network using image strips through all available openings is justified, despite the increase in the number of images, especially in cases where TLS or control geodetic measurement are not available. Conceptualization, M.M. and M.F. ; methodology, M.M. ; software, M.M. and M.F. ; validation, M.M., M.F., L.K., and P.B. ; formal analysis, M.F. ; investigation, L.K. ; resources, M.F. ; data curation, M.M. and M.F. ; writing--original draft preparation, M.M. ; writing--review and editing, M.F., L.K., and P.B. ; visualization, M.M. and M.F. ; supervision, M.F. ; project administration, L.K. ; funding acquisition, M.F. and P.B. All authors have read and agreed to the published version of the manuscript. This research work was supported by the Slovak Research and Development Agency under contract No. APVV-18-0472. This article was written thanks to the generous support of the Operational Program Integrated Infrastructure for the project: \"Support of research activities of Excellence laboratories STU in Bratislava, Project no. 313021 BXZ1, co-financed by the European Regional Development Fund.\" This work was supported by grant No. 055 TUKE-4/2021, funded by the Cultural and Educational Agency of The Ministry of Education, Science, Research and Sport of the Slovak Republic (KEGA). Not applicable. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 3 On-Board Processing Unit Design
### On-Board Processing Pipeline  
Processing pipeline refers to the software-hardware co-design for capturing and processing hyperspectral data consisting of a predefined set of processing stages. At launch only a minimal set of on-board processing features were implemented. This minimal set of processing features, the Minimal On-Board Image Processing (MOBIP) pipeline, is shown in Figure 5. Many other processing features are in development, as well as an application implementing a reconfigurable on-board processing pipeline framework which is separate from the main application software, see Figure 6. This framework is described in Section 3.4.2.  
Figure 5: Illustration of the data flow across the subsystems and components of the HYPSO-1 satellite in the MOBIP pipeline. 1. Low-level camera control and data transfer by the aveye driver. 2. Online binning of individual lines/frames and storage of the binned cube data into a buffer in main memory. 3. Transfer via cubeDirect Memory Access (DMA) and CCSDS123 compression on FPGA. 4. Storage of compressed cube on SD Card. 5. After some time, buffering followed by downlink.  
Figure 6: Illustration of the data flow across the subsystems and components of the HYPSO-1 satellite using the general pipeline framework. Differences to Figure 5 are: Storage of the online binned cube data to SD Card (2) and Loading of binned cube data to main memory for processing in software or on FPGA (3). The arrow from Timestamping to Main App. is omitted in this figure, for simplicity.
## 3 Proposed Scheme
### Head Component  
The head component is the watermark block, encompassing four key functions: optimizing the feature maps, generating image identifiers, encryption, and merging with copyright information. The intricate structure is illustrated in Figure 7.  
Within the watermark block, the input comprises feature maps generated by the neck component. These feature maps undergo processing through an exceptionally large depthwise convolutional layer, utilizing a 21-unit kernel. Subsequently, they are subjected to adaptive max pooling to maintain the size of the output feature map, fixed at \(16\times 16\times 1\). This \(16\times 16\times 1\) feature map can be viewed as the robust features of the input host image.  
The feature map is then divided into two branches. The first branch is directed through a linear layer to produce an image identifier. This image identifier serves as a unique code differentiating the input image from others, which will be further elucidated in Section 3.5. The second branch is funneled through an encrypt-conv layer within a loop function. This loop function emulates the encryption process of the Arnold transformation, where the encrypt-conv layer, abbreviated as the encryption-convolution layer, executes a single permutation of the Arnold transformation. Key1 represents the secret key of the Arnold transformation, which is fed into the loop function. Post-encryption, the feature map undergoes quantization based on a threshold, T, resulting in the conversion of the  
Figure 6: ZWNet’s neck details.  
feature map into a binary sequence. This binary sequence is then merged with copyright information through an XOR operation.
## 2 Design Rules of Small Cell Enhancement Systems
### Channelization of Link Budget
#### 2.2.2 Link Budget between eNB and Cellular Phone  
The necessary factors for the calculation of the link budget parameter are shown in Table 2.  
eNB and cellular phone sensitivity (\(S\)) is the sum of the minimum noise floor (\(N_{min}\)), noise figure (\(NF\)), and minimum signal-to-noise ratio\(\left(\frac{S}{N}\right)_{min}\). The sum can be expressed as Equation (4):  
\[S=N_{min}+NF+\left(\frac{S}{N}\right)min \tag{4}\]  
For work in the telecommunications field, the maximum radio frequency uplink path loss signal can be written as Equation (5):  
\[PL_{UL,max}=PO_{cellular\ phone}-S_{eNB} \tag{5}\]  
In this equation, \(PL_{UL,max}\) is the maximum radio frequency uplink path loss signal, \(PO_{cellular\ phone}\) is the output power of the Cellular Phone, and \(S_{eNB}\) is the sensitivity of BTS.  
The maximum radio frequency downlink path loss signal can be written as Equation (6):  
\[PL_{DL,max}=PO_{eNB}-S_{cellular\ phone} \tag{6}\]  
In this equation, \(PL_{DL,max}\) is the maximum downlink path loss, \(PO_{eNB}\) is the eNB output power, and \(S_{cellular\ phone}\) is the cellular phone sensitivity.  
Therefore, the highest path loss discrepancy of the bi-direction imbalance (\(Imb\)) can be expressed as Equation (7):  
\[Imb=PL_{DL,max}-PL_{UL,max} \tag{7}\]  
According to the standard of 3 GPP Technical Specification, the calculation of link budget processing can be divided as follows:  
* Determine the sensitivity of eNB and cellular phone: \(S_{eNB}=(-120\ \mathrm{dBm})+10\ \mathrm{dB}+9\ \mathrm{dB}=-101\ \mathrm{dBm}\). \(S_{cellular\ phone}=(-121\ \mathrm{dBm})+12\ \mathrm{dB}+9\ \mathrm{dB}=-100\ \mathrm{dBm}\).
* Find the maximum radio frequency uplink path loss signal, \(PL_{UL,max}\): \(PL_{UL,max}=PO_{cellular\ phone}-S_{eNB}=30\ \mathrm{dBm}-(-101\ \mathrm{dBm})=131\ \mathrm{dB}\).
* Find the maximum radio frequency downlink path loss signal, \(PL_{DL,max}\): \(PL_{DL,max}=PO_{eNB}-S_{cellular\ phone}=44\ \mathrm{dBm}-(-102\ \mathrm{dBm})=146\ \mathrm{dB}\).
* Find the highest path loss discrepancy of the bi-direction imbalance, \(Imb\): \(Imb=PL_{DL,max}-PL_{UL,max}=146\ \mathrm{dB}-131\ \mathrm{dB}=15\ \mathrm{dB}\).  
In the greatest path loss of two directions, there is a clear unbalance. If the downlink output power of the higher signal is adjusted, there will be a very noticeable increase in unbalanced production. Therefore, increasing the downlink output power does not greatly help the system equilibrium.
# On the Evaluation of Complex Networks Designs for an Energy-Efficient IP/WDM Core Network
## 3 Complex Network Designs
### SFNs  
The scale-free networks (SFNs), modeled by [PERSON] and [PERSON], are described as inhomogeneous networks and characterized by a power-law degree distribution [8]. In SFNs, few nodes exist with a high number of links (hubs), and a high number of nodes with few links exist. The emergence of the SFN property is governed by growth and preferential attachment. When a new node is added into the network, the probability of the node's attachment to other nodes will be higher to those with a higher degree. As a result, hubs will have higher rates of connecting new nodes than other nodes; hence, a skewed degree of distribution with a heavy tail is produced (power-law behavior). Networks with power-law behavior are said to be SFNs.  
In SFNs, more than 60% of nodes can be reached through heavily connected hubs, demonstrating a fruitful property of SFNs, in addition to the low diameter property of such models. The larger the network or the number of added nodes, the smaller the diameter and the shorter the path is between any two nodes [10].