esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods  
### Photogrammetric Digital Survey  
The production of the three-dimensional model of the mosaic surface was carried out through two photogrammetric surveys carried out in 2018 and 2020. The frames were acquired along horizontal bands (ensuring an average overlap of 80% both horizontally and vertically) at a distance of about 0.5 m from the mosaic surface using a Nikon D80 reflex camera (CCD sensor (23.6 \(\times\) 15.8 mm) with 12.2 million pixels and a fixed focal length of 24 mm). The camera was set in aperture priority mode (f/9 value) and with an ISO sensitivity equal to 100. In order to reference the 3D model in Cartesian space in real metric units, a survey with a total station was also created using 163 clearly recognizable elements on the mosaic surface as markers. Therefore, a reference system was set up with an origin (P = 0,0,0) located in the lower-left corner of the mosaic and all the project data were implemented in an information system managed with Quantum GIS software (Version 3.16.11 Hannover, Open Source Geospatial Fundation, Beaverton, OR, USA).  
Data processing was carried out using the Photoscan software (Agisoft Metashape Pro, Version 1.6.4, Agisoft LLC, St. Petersburg, Russia) through the following operations:  
1. Alignment of frames using the structure-from-motion (SfM) technique [30]. Three datasets were generated: a discrete point cloud describing the object's starting geometry, the positions of the camera at the time of the acquisition of the frames and the internal calibration parameters of the camera (focal length and three radial and two tangential distortion coefficients relative to the main point).
2. Geometry construction through the generation of a dense cloud.
3. Positioning of the 3D model in Cartesian space. The points detected with the total station were entered as ground control points (GCPs) within the software. The result was the processing of a dense metric and georeferenced cloud, obtaining an average registration error of 0.007 m for the 2018 survey and 0.005 m for the 2020 survey.
4. Mesh generation by transforming the three-dimensional model from a point cloud to the surface of triangulated points. In order to recognize and adapt the discontinuities in the model, a \"multi-resolution model\" routine based on automated algorithms was used. Furthermore, an \"optimization method\" and a \"decimation filter\" were applied to reorganize and smooth the nodes of the triangles and to simplify the model and generate a multi-resolution model, respectively.
5. Construction of the texture through the application of digital images to the model and the creation of metric and geo-referenced orthophotos exported in the GeoTIFF format.
6. Generation and export in GeoTIFF format of the digital elevation model (DEM).
# Higher-Order Belief Propagation Correction Decoder for Polar Codes
## 2 Preliminaries
### Belief Propagation Decoder  
BP decoding for polar codes is an iterative decoding algorithm based on the \((n+1)\)-stage factor-graph realization of \(G_{N}\). Two types of LLR messages are involved: right-propagating \(R_{i,j}\) and left-propagating \(L_{i,j}\), where \(i\) denotes the stage and \(j\) denotes the row index. The four nodes connecting two stages constitute the processing elements (PEs). The architecture of a PE is shown in Figure 1. Thus, the iteration rule can be represented by Equation (2):  
\[\left\{\begin{array}{l}L_{i,j}=f(L_{i+1,j},L_{i+1,j+N/2^{i}}+R_{i,j+N/2^{i}}) \\ L_{i,j+N/2^{i}}=f(L_{i+1,j},R_{i,j})+L_{i+1,j+N/2^{i}}\\ R_{i+1,j}=f(R_{i,j},L_{i+1,j+N/2^{i}}+R_{i,j+N/2^{i}})\\ R_{i+1,j+N/2^{i}}=f(L_{i+1,j},R_{i,j})+R_{i,j+N/2^{i}}\end{array}\right., \tag{2}\]  
where the function \(f(x,y)=\ln(1+e^{x+y})/(e^{x}+e^{y})\). The \(L_{n+1,j}\) messages at the rightmost stage are obtained by the channel output \(\text{LLR}(y_{j})\). Additionally, the \(R_{1,j}\) messages at the leftmost stage are initialized by a priori known information from the information bits and frozen bits, as shown in Equations (3) and (4).  
\[L_{n+1,j}=\text{LLR}(y_{j}), \tag{3}\]  
\[R_{1,j}=\left\{\begin{array}{cc}0,&\text{if }j\in\mathcal{I}\\ +\infty,&\text{if }j\in\mathcal{I}^{C}\end{array}\right.. \tag{4}\]  
Conventional flooding scheduling is applied [23]. The messages, starting from the rightmost stage, are iteratively updated column by column from right to left and then from left to right until a maximum iteration number \(I_{max}\) is reached or a predefined stopping  
Figure 1: The processing element (PE) in BP decoder.  
condition is valid. The BP decoder outputs the estimated vector \(\hat{u}_{1}^{N}=(\hat{u}_{1},\hat{u}_{2},...,\hat{u}_{N})\) through \(\hat{u}_{j}=\text{sgn}(L_{1,j}+R_{1,j})\), where \(\text{sgn}(x)=0\) when \(x\geq 0\), and \(\text{sgn}(x)=1\) otherwise.
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
## 3 Proposed Approach Towards the Definition of a Space Sustainability Index
### Performance-Related Efficiency
#### 3.3.1 Coverage Efficiency  
The coverage efficiency \(e_{comp}\) is defined as follows:  
\[e_{cov}=\begin{cases}ratio_{sat}&\text{if }ratio_{sat}\leq 1\\ 0&\text{if }ratio_{sat}>1\end{cases} \tag{10}\]  
where \(ratio_{sat}\) is equal to:  
\[ratio_{sat}=\frac{min_{sat}}{n_{sat}} \tag{11}\]  
The minimum number of satellites \(min_{sat}\) needed to cover the desired area is defined as follows:  
\[min_{sat}=A_{cov}/A_{hex} \tag{12}\]  
where \(A_{cov}\) refers to the area on Earth that a certain constellation covers with its satellites and it is calculated using the Maximum North Latitude, \(\phi_{1}\), and the Maximum South Latitude, \(\phi_{2}\), at which it can operate and Earth's radius \(R_{e}\):  
\[A_{cov}=2\pi R_{e}^{2}[sin(\phi_{1})-sin(-\phi_{2})] \tag{13}\]  
To calculate the hexagonal area of a cell we first find the radius of the field of view of a single satellite, \(r_{FoV}\), given its altitude, \(h\), the Earth's radius, \(R_{e}\), and the value of its minimum elevation of the user terminal, \(\varepsilon_{\text{UT}}\):  
\[r_{FoV}=R_{e}\Bigg{(}\frac{\pi}{2}-\varepsilon_{\text{UT}}-\arcsin\Bigg{(} \frac{R_{e}\big{(}\sin(\frac{\pi}{2}+\varepsilon_{\text{UT}})\big{)}}{R_{e}+h }\Bigg{)}\Bigg{)} \tag{14}\]  
Therefore, the area of the hexagonal cell is:  
\[A_{hex}=\frac{3\sqrt{3}}{2}r_{FoV}^{2} \tag{15}\]
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 3 Overview of Current Channel Estimation Schemes
### Scenario 2: Remain the Structure of the IEEE 802.11p
#### 3.2.5 DFT Based Scheme  
To obtain a trade-off between performance and complexity in vehicular communication, a Discrete Fourier Transform (DFT) based channel estimation technique was introduced in [41]. In this scheme, the channel response obtained by LS estimation is converted back to the time domain by taking its inverse Fourier transform. In this scheme, the coefficients for the maximum channel delay are selected after ignoring the noise-containing coefficients of the time domain channel response. These selected components are then returned back to the frequency domain and the equalization is performed with the simulation results better than LS estimation. This scheme performs better than LS, STA, and CDP under the conditions when the maximum Doppler shift is under 10 Hz, however, as the Doppler shift increases above 10 Hz, its performance degrades severely.
## 4 System Model Based on Power Splitting and NOMA
### Outage Probability, Throughput and Sum-Throughput
: The detailed proof is formulated in Appendix D.  
Combining Equations (37) and (39), we finally get the analytical equation for the sum-throughput of the proposed system using PS and NOMA. To find out optimal time switching factor \(\alpha^{*}\) and power splitting factor \(\varepsilon^{*}\) that gives the best performance for sum-throughput maximization for our proposed system using TS, PS and NOMA, we evaluate \(\left(\frac{d\tilde{Thr}(\varepsilon)}{da}\right)_{TS}=0\) and \(\left(\frac{d\tilde{Thr}(\varepsilon)}{de}\right)_{PS}=0\), where \(\tilde{Thr}(\alpha)\) is the sum-throughput function with respect to time switching factor \(\alpha\) and \(\tilde{Thr}(\varepsilon)\) is the sum-throughput function with respect to power splitting factor \(\varepsilon\) respectively. By analyzing the sum-throughput function for source and IoT node versus \(\alpha\) and \(\varepsilon\), we determine that this is concave function which has a unique maxima \(\alpha^{*}\), \(\varepsilon^{*}\) on the interval \([0,1]\). Therefore, we resort to Golden section search method [35] which is simple yet compelling iterative process to find out the optimal \(\alpha^{*}\) and \(\varepsilon^{*}\) that maximizes the sum-throughput of the proposed system using TS and PS respectively. The Golden section search method for determining optimal \(\alpha^{*}\) and \(\varepsilon^{*}\) is shown in Algorithm 1. ```
1:\(\eta\), \(\delta\), \(R\), \(\phi_{1}\), \(\phi_{2}\)
2:Initialization: Set the start interval \(a=0.001\), end interval \(b=0.99\), golden proportion coefficient \(\tau=0.618\), the iteration index, accuracy value \(\mu=0.000001\), choose starting points \(x_{1}=a+(1-\tau)*(b-a)\) and \(x_{2}=a+\tau*(b-a)\)
3:OutputOptimal \(\alpha^{*}\) and \(\varepsilon^{*}\)
4:do function evaluation for respective TS and PS protocol i.e., \(\left(\frac{d\tilde{Thr}(\varepsilon)}{da}\right)_{TS}\), \(\left(\frac{d\tilde{Thr}(\varepsilon)}{de}\right)_{PS}\) at point \(x_{1}\) and \(x_{2}\)
5:repeat
6:if evaluated function \(\left(\frac{d\tilde{Thr}(\varepsilon)}{da}\right)_{x_{1}}<\left(\frac{d\tilde {Thr}(\varepsilon)}{da}\right)_{x_{2}},\left(\frac{d\tilde{Thr}(\varepsilon)} {de}\right)_{x_{1}}<\left(\frac{d\tilde{Thr}(\varepsilon)}{de}\right)_{x_{2}}\) then
7:choose \(b=x_{2}\), \(x_{2}=x_{1}\) and find new point \(x_{1}\) for both TS and PS
8:do function evaluation as step 1
9:else
10:\(a=x_{1}\), \(x_{1}=x_{2}\) and find new point \(x_{2}\) for both TS and PS
11:do function evaluation as step 1
12:end if
13:until\(|b-a|>\mu\) and iteration index = max
14:Choosing Optimal \(\alpha^{*}\) for TS
15:\(if\) evaluated function \(\left(\frac{d\tilde{Thr}(\varepsilon)}{da}\right)_{x_{1}}<\left(\frac{d\tilde{Thr }(\varepsilon)}{da}\right)_{x_{2}}\) then
16:\(\varepsilon^{*}=x_{1}\)
17:else
18:\(\varepsilon^{*}=x_{2}\)
19:end of Algorithm 1
```  
**Algorithm 1** Golden Section Search Method for Finding Optimal Time Switching Factor \(\alpha^{*}\) and Optimal Power Splitting Factor \(\varepsilon^{*}\)
## 1 Introduction  
Volatile organic compounds in different honeys often provide a unique aroma profile that constitutes a characteristic fingerprint that may be used for distinguishing honeys as a function of their botanical origin [1, 2]. Phenolic compounds, norisoprenoids and terpenoids, as well as aliphatic dicarboxylic acids, have been proposed as strong botanical origin markers of honeys [3, 4]. In continuation of the research on honey flavor composition within the scope of chemical characterization (organic analysis) and identification of potential marker compounds, this paper is focused on a Croatian map (_Acer_ spp.) honey sample. _Acer_ spp. (such as _Acer campestre_ L. _Acer platanoides_ L. or _Acer tataricum_ L.) are mentioned among the species of Croatian honey-rich plants [5], but in general _Acer_ spp. honey is rare in Croatia.  
Maple syrup is most widely known as a natural sweetener that is produced by the evaporative concentration of the sap of certain maple species (mainly _Acer saccharum_ L. native to the hardwood forests of northeastern North America). The initial maple sap represents a solution in which sucrose is the major component, with minor quantities of reducing carbohydrates and organic acids as well as minerals, nitrogenous and phenolic compounds [6, 7, 8]. The flavour of maple syrup develops during evaporation. In principle two types of flavour bearing constituents occur: thermal carbohydrate degradation compounds and derivatives of lignin precursor such as coniferyl, dihydroconiferyl, and dihydroosinapyl alcohols. In particular, the lignin derivatives vanillin and syringaldehyde are known to be flavour-bearing constituents of the maple syrup. The concentration of the lignin derivatives does vary considerably (in contrast to the carbohydrates degradation compounds) depending on the provenance and processing [7, 8].  
No chemical characterization data are currently available for the rare Croatian _Acer_ spp. honey, therefore we investigated the volatile compounds of the honey sample in order to identify the most striking ones from the GC and GC/MS results obtained after ultrasonic solvent extraction (USE) with solvents of different polarity and headspace solid-phase microextraction (HS-SPME). The results obtained were compared to other single Croatian tree honey samples as well as literature data.
# IDSMatch: A Novel Deployment Method for IDS Chains in SDNs +
## 5 Evaluation
### Detection Rate under Varying Topology  
In Figure 14, the detection rate of the IDS is visually presented, showcasing the performance under various deployment methods. The three deployment strategies examined are as follows:  
* Centered IDS: This method involves placing an IDS in the SDN architecture. The NIDS monitors all switches and subsequently sends a comprehensive security report to the SDN controller.
* IDS App: In this scenario, an IDS application is integrated into the control plane of the SDN. Collaborating with other monitoring applications, it analyzes network traffic and assists the controller in establishing appropriate rules on switches for enhanced security.
* IDSMatch: This represents the proposed method, which deploys a set of IDS chains on the data plane of the SDN. IDSMatch aims to optimize intrusion detection by strategically distributing IDSs in the data plane to efficiently analyze and mitigate potential security threats.  
The results indicate that, under different topologies and even in the case of a large-scale traffic scenario, IDSMatch outperforms other methods in terms of detection rate. This superior performance underscores the effectiveness of strategically deploying IDS chains on the data plane, providing enhanced capabilities for identifying and mitigating security threats within the SDN environment. These findings contribute valuable insights for selecting an optimal intrusion detection deployment strategy tailored to specific network requirements.  
Figure 12: Network delay for different flow weights. (**a**) Number of flows = 100. (**b**) Number of flows = 300. (**c**) Number of flows = 500.  
Figure 13: Network delay for the different number of flows. (**a**) Weight of flows = 100. (**b**) Weight of flows = 200. (**c**) Weight of flows = 200.
###### Abstract  
The remote sensing community increasingly demands precise ecosystem monitoring, environmental change detection, and natural resource management, particularly in forestry. Key metrics such as biomass and total area index require accurate estimation, necessitating extensive experiments and reliable scattering models. Recent advances in radar interferometry introduce two essential parameters--interferogram phase and correlation coefficient--containing crucial target information. Understanding their relationship to forest biophysical parameters requires analyzing wave interactions with vegetation particles. This study presents a discrete interferometric model for a random medium layer, establishing the link between radar interferometry and forest biophysical properties. Correlation analysis plays a vital role in estimating one variable based on another, reducing uncertainty in random media. The research introduces a novel modeling approach that enhances theoretical foundations and supports empirical studies in the literature. Bridging theoretical analysis and practical observations, this work enhances the precision and applicability of radar interferometry for vegetation monitoring. The findings contribute to improving remote sensing methodologies and expanding their potential in ecological and environmental research. Ultimately, this study advances the use of interferometric models in extracting critical forest parameters with greater accuracy.  
e +
Footnote †: journal: Applied sciences  
Article
## 3 Factors Influencing U-WSN Design
### Architectures of U-WSN
#### 3.1.3 UAV-Smart Phone Link (U-SP)  
In some applications, the UAV is connected to smart-phone in order to collect data from the phone. The commutation range between them is medium. Thus, the U-SP link basically uses the medium range protocols such as IEEE 802.11 ah (WiFi) and IEEE 802.16p (WiMAX), which are adapted for machine to machine communication. Adaptations of LTE-Advanced (LTE-A) are also experimented [40].
## 4 Discussion  
The results showed that for all sites and all satellite products, the relationship between EVI2 and the flux tower modelled GPP was generally strong (Table 2). This means that seasonal GPP variations can be estimated at different resolutions, from 500 down to 10 m spatial resolution, implying that remotely sensed data has the potential to provide highly detailed spatial estimates of carbon uptake dynamics across northern boreal landscapes. This tool can contribute to future work on carbon-friendly forest management and climate mitigation.  
The model selection using climatological flux footprint led to a linear regression model, driven by \(\mathrm{T_{a}}\), in combination with satellite reconstructed daily EVI2 (Equation (6)). However, the selected model is not always optimal for each site (Table A1). The agricultural sites and deciduous forest sites had a larger seasonal variation of greening in comparison with the evergreen coniferous forest sites, which helps to explain the strong relationship between EVI2 and GPP in SE-Lnn and DK-Sor. Air temperature was the most important environmental variable for estimating GPP at the northern sites (SE-Sto, SE-Svb, SE-Deg and FI-Hyy) whereas at the southern sites (SE-Nor, SE-Lnn, SE-Htm and DK-Sor) the differences between models were small. This is explained by the fact that in the northern part of our study area the growing season begins when the temperature exceeds the temperature limit, which occurs well after the amount of light has reached sufficient levels for photosynthesis [63; 64]. This finding indicates that, although EVI2 is the most important driving variable overall, choosing the correct model formulation is important for GPP estimation across a study area traversing such a large latitudinal range.  
The method of estimating GPP used in this study is similar to the method proposed by [PERSON] et al. [27] regarding the methodology, model design and study area, but we used finer spatial and temporal resolution satellite data, dynamic flux footprint, and ground-based measured environmental variables. In comparison with the study by [PERSON] et al. [27], we used four common sites: SE-Sto, FI-Hyy, SE-Nor and DK-Sor. The estimation of GPP has small RMSE and higher \(\mathrm{R^{2}}\) at site SE-Sto, but it has larger RMSE and lower \(\mathrm{R^{2}}\) at site FI-Hyy. The RMSEs are larger but \(\mathrm{R^{2}}\) are lower at sites SE-Nor and DK-Sor. One of the reasons for this inconsistency is that we used a different time period in the study.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 3 Proposed Architecture  
An architecture for the condition monitoring of the rolling bearing is shown in Figure 2, which is mainly divided into four submodules (VMD decomposition, ARMA-ANN prediction, T-F features extraction and SVM classification).  
The specific details are as follows.  
1. The VMD algorithm decomposes the multi-component vibration signal of rolling bearing into several IMFs. The descriptions of the VMD algorithm are presented in Section 2.1;
2. Based on the established ARMA-ANN prediction model, the prediction of each IMF is conducted, and the sensitive IMFs are selected;
3. The multi-domain features set, i.e., T-F features set including time domain and frequency domain, are extracted as characteristic parameters from the sensitive IMFs. The specific features are presented in Section 2.3;
4. The classification of the condition is performed by a SVM classifier based on T-F features.
# Enhancing Knowledge on Energy Refurbishment of Buildings and Green Procurement through Living Labs
## 3 Results
### Level of Knowledge Gain in LLs Specialized in Green Public Procurement  
As seen from the left part of Figure 6, LLs focusing on electronic green public procurement (eGPP) highlight a pronounced improvement in knowledge following their participation.  
As illustrated in Figure 6 (the right part indicating average values \((\overline{x}_{B},\overline{x}_{A})\) and differences \((\Delta\overline{x}_{A-B})\)), the lowest level of knowledge before participating in the LLs was observed for the topic of \"Implementation of energy refurbishment of public buildings (tenders' implementation) \((\overline{x}_{B}=1.78)\), while the highest level of knowledge was noted for \"Environmental questions considering green procurement and energy refurbishment of public buildings\" \((\overline{x}_{B}=2.51)\). This indicates that stakeholders generally had a lower initial knowledge regarding the practical implementation of energy refurbishment projects using specific tools, compared to environmental considerations. After participating in LLs, the highest level of knowledge was observed for the topic of \"Green public procurement criteria for energy refurbishment of public buildings\" \((\overline{x}_{A}=3.89)\). In relative terms, knowledge levels improved significantly across all areas, with the highest relative improvement being observed for the field of \"Implementation of energy refurbishment of public buildings with eGPP tool\" (\(\Delta\overline{x}_{A-B}=1.82\)), followed by \"Use of green public procurement criteria for energy refurbishment of public buildings\" \((\Delta\overline{x}_{A-B}=1.66)\) and \"Green public procurement criteria for energy refurbishment of public buildings\" \((\Delta\overline{x}_{A-B}=1.62)\). This demonstrates a substantial increase in stakeholders' knowledge after participating in LLs, particularly in the practical aspects of green public procurement and energy refurbishment projects.  
Figure 5: Level of knowledge regarding mentoring among stakeholders before and after participating in LL (%) (\(n=21\)). Likert scale: 1—Poor, 2—Fair, 3—Good, 4—Very good, and 5—Excellent.
# Semantic Image Segmentation Using Scant Pixel Annotations
## 4 Evaluation of the SSPA Approach
### Network Architecture  
The U-Net [20] is a convolutional network architecture for pixel-based image segmentation. It consists of an encoder (contracting path) and decoder (expansive path) designed specifically to perform segmentation tasks on medical images. The contracting path is a stack of convolutional and max-pooling layers that encodes high-level semantic information at each layer into feature representations at different levels. The decoder projects the discriminative features learned by the encoder onto the pixel space by recovering spatial information at each layer using transposed convolutions. Bottleneck layers combine high resolution features from the encoder and upsampled features from the decoder by concatenation, resulting in a symmetrical network in contrast to traditional FCNs. The U-Net architecture accepts a set of pairs--unlabeled images and their corresponding binary masks (labels)--as its input for training a segmentation model. The unlabeled images and their masks are both \(512\times 512\) pixels in height and width.