esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## Appendix A Delay Correlation
### Delay-Doppler Correlation  
To improve conceptual clarity and understanding, we have separately analyzed fading selectivity in the time (or space) and frequency domains. In the time domain, with no frequency variations assumed, the signal is represented as \(y(t)=\sqrt{G}h(t)x(t)\). In the frequency domain, with no time variations considered, the signal is represented as \(y(f)=\sqrt{G}h(f)x(f)\), which is equivalent to \(y(t)=\sqrt{G}(h*x)(t)=\sqrt{G}\int h(\tau)x(t-\tau)d\tau\).  
When both types of selectivity are combined, as is common in real wireless channels, small-scale fading is most accurately modeled as a time-varying process:  
\[y(t)=\sqrt{G}\int_{-\infty}^{\infty}h(t,\tau)x(t-\tau)d\tau, \tag{10}\]  
where \(h(t,\tau)\) represents the channel response at time \(t\) due to an impulse delayed by \(\tau\). By transforming delay into frequency or time into Doppler shift, \(h(t,\tau)\) can be expressed as a time-varying transfer function:  
\[\tilde{h}(t,f)=\int_{-\infty}^{\infty}h(t,\tau)e^{-j2\pi f\tau}d\tau, \tag{11}\]  
or as the Doppler-delay spreading function:  
\[\tilde{h}(\
u,\tau)=\int_{-\infty}^{\infty}h(t,\tau)e^{-j2\pi\
u t}dt. \tag{12}\]  
Both representations offer different views on expressing the noiseless received signal in terms of the transmitted signal:  
\[y(t)=\sqrt{G}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\tilde{h}(\
u,\tau )x(t-\tau)e^{j2\pi\
u t}d\tau d\
u, \tag{13}\]  
highlighting that \(y(t)\) consists of replicas of \(x(t)\), each delayed by \(\tau\) and influenced by Doppler shift \(\
u\) as described by \(\tilde{h}(\
u,\tau)\). This formulation provides a comprehensive description of multipath fading phenomena.  
From \(\tilde{h}(t,f)\), the joint time-frequency correlation can be derived, which depends on \(\Delta t\) and \(\Delta f\) due to stationarity:  
\[R_{\tilde{h}}(\Delta t,\Delta f)=\mathbb{E}\Big{[}\tilde{h}(t,f)\tilde{h}^{*} (t+\Delta t,f+\Delta f)\Big{]}. \tag{14}\]
## 3 Results and Discussion
### Predictions  
#### 3.4.1 Considering All Stages Together  
Initially, the scenario using only SAR data was not viable due to the low correlations with soybean yield (Figure 5). Using scenarios with data from all growth stages, the integration of EVI and all SAR variables showed superior performance in predicting soybean yield compared to the model using only EVI (Figure 6). However, this improvement in prediction performance was less than 2% in terms of R\({}^{2}\) and 3% in RMSE, not exceeding the second decimal place (Table 8). Despite the observed improvement by including SAR indices, the high computational cost associated with processing SAR images must be considered. When optical images are available for all three development stages of soybean, the performance using only these optical images is like the performance with the inclusion of SAR. Therefore, if optical images are available for all three stages, it is more economical and efficient to use only these optical images rather than incorporating SAR data.  
Observing the harvests, there was a difference in prediction performance for harvest 3 compared to the other harvests. This can be seen in the actual yield maps (Figure 1), where there were significant spatial differences as well as differences in yield values. In the first and second harvests, the most productive area is concentrated in the south, while in the third harvest, this pattern is reversed. Additionally, the change in cultivars in harvest 3 compared to harvests 1 and 2 may have affected the SAR sensor's response: in harvest 3, a mix of cultivars was used, one of which has a high tendency for branching, unlike the other harvests. This high branching alters the plant's structure in terms of leaf density, leaf and branch arrangement, and moisture, which directly influences how radar  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Difference in \%** & \\ \hline  & **R\({}^{2}\)** & **RMSE** \\ \hline Harvest 1 & 1.51 & \(-\)2.89 \\ Harvest 2 & 0.73 & \(-\)1.76 \\ Harvest 3 & 2.81 & \(-\)0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Percentage difference in performance of R\({}^{2}\) and RMSE when comparing scenarios that include SAR data from all stages across different harvests with scenarios using SAR and EVI, versus using only EVI.  
Figure 6: R\({}^{2}\) and RMSE values of predictions for each harvest individually with all stages of image collection, using only optical data (EVI) compared to using optical data together with all SAR variables.  
electromagnetic energy interacts with the target [13; 14]. This can be observed in Figure 7, where different patterns in DPSVI are noticeable, especially in stage 3 of harvest 3 in the area with different cultivars.
## 8 Inference on \(R\) with All Different Parameters  
In the sections above, we assume that the shape parameters are taken to be equal. In order to expand the paper, in this section, we study the inference of \(R\) with all different parameters. We consider the problem of estimating \(R=P(Y<X)\) under the assumption that \(X\sim IW(\alpha_{1},\theta_{1})\) and \(Y\sim IW(\alpha_{2},\theta_{2})\). Then, it can be easily calculated that  
\[R=P(Y<X)=1-\int_{0}^{\infty}\frac{\alpha_{2}}{\theta_{2}}y^{-\alpha_{2}-1}e^{ -\frac{y^{-\alpha_{2}}}{\theta_{2}}}e^{-\frac{y^{-\alpha_{1}}}{\theta_{1}}}dy. \tag{39}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & **Scale Parameter** & **Shape Parameter** & **Log-Likelihood** & **K-S** & \(p\)**-Value** \\ \hline
1 & 4.9497 & 12.6152 & 71.8967 & 0.0417 & 0.9997 \\
2 & 19.0814 & 13.6228 & 79.4095 & 0.0846 & 0.7572 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Scale parameter, shape parameter, log-likelihood, K-S distances and \(p\)-values of the fitted inverse Weibull models to Data Sets 1 and 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Intervals** & **Expected Frequencies** & **Observed Frequencies** \\ \hline
0.00–0.19 & 0.01 & 0 \\
0.19–0.30 & 21.06 & 19 \\
0.30–0.40 & 29.48 & 32 \\
0.40–0.50 & 9.23 & 11 \\
0.50–\(\infty\) & 3.22 & 1; \\ \hline \hline \end{tabular}
\end{table}
Table 8: Expected frequencies and observed frequencies for modified Data Set 1 when fitting the inverse Weibull model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Intervals** & **Expected Frequencies** & **Observed Frequencies** \\ \hline
0.00–0.19 & 0.01 & 0 \\
0.19–0.30 & 21.06 & 19 \\
0.30–0.40 & 29.48 & 32 \\
0.40–0.50 & 9.23 & 11 \\
0.50–\(\infty\) & 3.22 & 1; \\ \hline \hline \end{tabular}
\end{table}
Table 9: Expected frequencies and observed frequencies for modified Data Set 2 when fitting the inverse Weibull model.  
To computer the MLE of R, we first obtain the MLEs of \(\theta_{1}\) and \(\theta_{2}\). Suppose \(X_{1},X_{2},\cdots,X_{n}\) is a random sample from \(IW(\alpha_{1},\theta_{1})\) and \(Y_{1},Y_{2},...,Y_{m}\) is a random sample from \(IW(\alpha_{2},\theta_{2})\). The joint likelihood function is:  
\[l(\alpha_{1},\theta_{1},\alpha_{2},\theta_{2})=\frac{\alpha_{1}^{n}}{\theta_{1 }^{n}}\frac{\alpha_{2}^{m}}{\theta_{2}^{m}}(\prod_{i=1}^{n}x_{i}^{-\alpha_{1}- 1})(\prod_{j=1}^{m}y_{j}^{-\alpha_{2}-1})e^{-\sum_{i=1}^{n}\frac{x_{i}^{-\alpha_ {1}}}{\theta_{1}}}e^{-\sum_{j=1}^{m}\frac{y_{j}^{-\alpha_{2}}}{\theta_{2}}}.\]  
Then, the log-likelihood function is  
\[\begin{split} L(\alpha_{1},\theta_{1},\alpha_{2},\theta_{2})=& n\ln\alpha_{1}+m\ln\alpha_{2}-n\ln\theta_{1}-m\ln\theta_{2}-(\alpha_{1}+1) \sum_{i=1}^{n}\ln x_{i}-(\alpha_{2}+1)\sum_{j=1}^{m}\ln y_{j}\\ &-\frac{1}{\theta_{1}}\sum_{i=1}^{n}x_{i}^{-\alpha_{1}}-\frac{1 }{\theta_{2}}\sum_{j=1}^{m}y_{j}^{-\alpha_{2}}.\end{split} \tag{40}\]  
Then, similar to the previous approaches, we can obtain the point estimates and interval estimates of \(R\) by using the MLE, AMLE and Bayesian method. Furthermore, we also can get bootstrap confidence intervals of \(R\).
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
## 3 Methodology  
As shown in Figure 1, the user first generates n subsecret messages through the secret sharing method, and the subsecret messages are embedded into n original images through the Direct Bitmap Substitution (DBS) algorithm after AMBTC compression. The encoded bitmap is then uploaded to multiple servers for separate storage, rather than having all the covert images in one server. This method can prevent malicious attackers from obtaining all the shadow images at the same time, effectively ensuring the security of secret messages. Since the AMBTC used is a compression method with a high compression rate, it can greatly reduce the space required for transmission and storage. Figure 2 shows the extraction process of the secret message. The user downloads at least k shadow images from different servers, then extracts the subsecret messages of the secret images, respectively, and finally restores the secret message through the secret reconstruction algorithm. Even if a small number of secret images are damaged during storage or transmission, as long as the number of secret images available to the user is at least \(k\), the secret message can be recovered, thus improving the fault tolerance of the method.
## 2 Materials and Methods  
### Materials  
Insulation materials, including expanded polystyrene from two different manufacturers (EPS-S and EPS-C), mineral wool (MW), and expanded cork agglomerate (Co), were tested (Figure 1), using maritime pine (_Pinus pinnster_ Aiton) as a control (C). In this study, the mineral wool (MW) used consisted of either rock wool or glass wool. Glass wool is obtained by mixing natural sand and glass at a temperature between 1300 \({}^{\circ}\)C and 1450 \({}^{\circ}\)C, where the heated mass is forced through rotating nozzles, often via centrifugal force, thus creating fibers. On the other hand, rock wool is produced by melting different types of rocks (e.g., basalt, dolostone, and diabase) at 1600 \({}^{\circ}\)C, thus obtaining fibers that are bound together using resins (often terpolymeric mixtures) and oil (to reduce dust release). Both glass wool and rock wool are commercialized as panels, felt pipe sections, or rolls. Expanded polystyrene (EPS) is composed of small spheres of polystyrene, including an expansion agent (e.g., aliphatic alkanes, such as pentane). This method allows for the production of a rigid closed-cell foam made of approximately 98% air and 2% plastic. Insulation cork board (Co) is a natural, renewable, and recyclable material composed of natural cork that can be applied on the facade of buildings, improving their energy performance. No chemical adhesives or additives are used in the production of insulation cork boards.  
For termite resistance testing, wood plus glass fibers (WGFs) and coconut fibers were also used. The WGF was of commercial origin, and the coconut fiber used was made of unprocessed coconut fibers, directly from the producer. These materials were selected due to their widespread use in Europe [25], where they are not only applied as a form of interior insulation, but also as a form of exterior insulation (i.e., ETICS). All samples were placed in a climatic room at a temperature of 20 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C and a relative humidity (RH) of 65% \(\pm\) 5% and were maintained in these conditions until they were required for testing. The samples that were inoculated with molds were previously steam sterilized in an autoclave at 100 \({}^{\circ}\)C for 20 min.  
Different samples were cut from the insulation panels: a set of nine samples of each studied insulation material, with dimensions of 40 \(\times\) 40 \(\times\) 30 mm\({}^{3}\), was used to assess fungal growth; cylindrical pieces of each insulation material that were later compacted into  
Figure 1: Detailed images of the insulation materials EPS-S (**a**), MW (**b**), and Co (**c**).  
glass tubes (see Section 2.3) were used to evaluate subterranean termites' ability to cross the materials.
## 4 The Design of GeneDiff
### Intermediate Representation  
Intermediate representation (IR) is a type of abstract code designed to provide an unambiguous representation of the binary. IR can convert binaries from different ISAs into the same abstract representation, which makes it easier to analyze cross-architecture binary code. Our work uses PyVEX [27] as an intermediate representation.  
PyVEX exposes the VEX module from Valgrind [25], which provides a series of python application programming interface (API) interfaces. It is a side-effect-free representation of different ISAs (such as MIPS, ARM, and AMD). To solve the problem of the differences between different architectures, VEX possesses the following features:  
Consistent registers. Although the quantity and names of registers vary according to the architecture, each type of architecture contains several general-purpose registers. VEX provides a consistent abstraction interface with memory integer offset for the registers of different architectures.  
Memory operation. Different architectures also access memory differently. Some architectures support memory segmentation using special segment registers. VEX abstracts these differences as well.  
Side-effects representation. Many instructions have side effects. For example, on the AMD64 architecture, an add instruction may update the condition flags (OF or CF). For instruction side effects, VEX updates only certain operations, such as the stack pointer. Other side effects, such as condition flags are not tracked.  
VEX possesses four main abstract object classes: (i) _expressions_ represent a calculated or constant value, (ii) _operations_ describe a modification of VEX expressions, (iii) statements describe model changes in the state of the target architectures, and (iv) _temporary variables_ are used to store the temporary results of _expressions_. Figure 3 shows a snippet that was compiled into AArch64 assembly and AMD64 assembly. The different ISAs and different registers show differences between the two snippets of assembly code. These differences increase the difficulty of binary similarity comparisons. After being converted into VEX code, the snippets have the same format and similar content. In Figure 3, the similar parts are highlighted by same-colour boxes. This converts the problem of binary similarity comparison on different architectures into a similar comparison problem of the same IR, which effectively reduces the analysis difficulty.  
Figure 3: C source of a snippet from deflate.c in zlib and the assembly code for two architectures. Except for the registers, the others are almost identical.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Surface Model Generation
#### 2.2.1 Epipolar Rectification  
Epipolar rectification for push-broom images can be seen as a solved problem [53; 54]. However, in contrast to perspective geometry, where analytic solutions exist for epipolar rectification (cf. References [55; 56]), such rectification can only be approximated for push-broom images. The epipolar curves can be estimated by using the modified collinearity equations [57] and are hyperbola-like non-linear curves [58]. Some authors ignore the specific mathematical formulation of the push-broom collinear equations and treat the images as being perspective. These solutions work on a local level, that is, rectifying small parts of the images separately as shown in References [59; 60]. In reality, it is sufficient if the location differences orthogonal to the epipolar direction (e.g., the column direction of the epipolar images) are below half a pixel [21]. Recently, it was also shown that an epipolar rectification with small residual errors can be achieved for high-resolution SAR satellite images [54]. Alternatively, epipolar rectification might be skipped if the subsequent stereo matching algorithm searches along the (non-linear) epipolar curves such as in Reference [61]. The drawbacks of this approach are that the image matching algorithm has to be general and has to consider the geometric constraints of the underlying sensor model. Additionally, for each location in the search space along the epipolar curve the local search window has to be resampled during matching.
# A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
## 3 Proposed Algorithm  
### Overall Framwork  
To make the FCN model achieve a more accurate and detailed description of the target edge, this paper integrates the superpixel edge of the image into the FCN model. The block diagram of our proposed algorithm that is based on FCN and BSLIC superpixel is shown in Figure 3. First, the FCN model based on VGG-16 network is trained on the dataset PASCAL VOC 2012 in order to develop the FCN semantic segmentation model. Then, the trained FCN semantic segmentation model is used to segment the image to obtain the image pixel-level classification map. At the same time, BSLIC superpixel segmentation is performed on the segmented image, generating a superpixel segmentation map. Finally, the results of FCN and BSLIC are combined based on superpixel semantic annotation. BSLIC superpixel is semantically annotated by the pixel-level classification map, and the classification of the image at the pixel level is obtained, which is the revised semantic segmentation result. The pixel-level classification map combines the advantages of high-level semantic information and good edge information.  
Figure 2: The segmentation results of BSLIC algorithm under different input parameters. The image resolution is \(481\times 321\). From the first row to the third row, the parameter superpixels number \(k\) is 100, 500, and 1000. From the first column to the third column, the parameter weight factor \(m\) is 5, 15, and 30.
# A Jug-Shaped CPW-Fed Ultra-Wideband Printed Monopole Antenna for Wireless Communications Networks
## 4 Conclusions  
A simple jug-shaped ultra-wideband (UWB) antenna is presented in this work. The presented design is printed, and measured results are also taken. The simulated results are verified by a measured result of the ultra-wideband antenna. The designed UWB antenna is printed on a low-cost FR-4 substrate with relative permittivity of 4.3, loss tangent 0.025, and a standard thickness 1.6 mm, sized at 25 mm \(\times\) 22 mm \(\times\) 1.6 mm, suitable for wireless communication system. The designed UWB antenna works with maximum gain (peak gain of 4.1 dB) across the whole UWB spectrum of 3-11 GHz. The simulated and measured reflection coefficients and radiation pattern are in close agreement. The designed antenna is a good applicant for wireless communication systems portable devices.  
Conceptualization, S.A., S.N., M.A.Q. and F.A.; methodology, U.I., A.G. and C.H.S.; software, A.G., S.N. and U.I.; validation, M.A.Q., F.A., U.I. and S.N.; formal analysis, S.A. and A.G.; investigation, A.G., N.O.P. and S.N.; resources, A.G., U.I. and S.A.; data curation, S.A., M.A.Q. and A.G.; writing--original draft preparation, S.A. and A.G.; writing--review and editing, S.N. and F.A.; supervision, N.O.P., C.H.S. and R.A.-A.; funding acquisition, N.O.P., C.H.S. and R.A.-A. All authors have read and agreed to the published version of the manuscript.  
This work received no funding.  
The authors declare no conflict of interest.
## 3 Results
### Analysis of Extraction Results by D-InSAR
#### 3.1.3 Mining Subsidence Depth Classification  
This research aims to provide a more scientific and intuitive understanding of the subsidence in mining areas. To do this, the subsidence region is classified based on the depth of surface deformation in the mining area. The study monitors the subsidence for a duration of five years. The subsidence level is classified based on the degree of surface collapse in the mining area over this period. There are four levels of subsidence: Level I (>300 cm), Level II (200-300 cm), Level III (100-200 cm), and Level IV (0-100 cm). This research classifies the collapse grade of the permanently waterlogged region in collapse basin No. 5 as Level II. Figure 6 shows the collapse classification diagram.  
Figure 4: Collapse depth of study area.  
Figure 5: The extent of the subsidence of the study area.  
To quantitatively assess the extent of each level of subsidence, the surface area of each level was measured and recorded in Table 3. The data in this table reveal that the mining area has the highest share of Level I sinking area, followed by Level III and II. The fraction of the Level IV subsidence area is the smallest. The Level I subsidence area covers 544.26 \(\mathrm{h}\mathrm{m}^{2}\), representing 28.94% of the total subsidence area. The Level II subsidence area covers 433.83 \(\mathrm{h}\mathrm{m}^{2}\), accounting for 23.06% of the total. The Level III subsidence area covers 475.02 \(\mathrm{h}\mathrm{m}^{2}\), representing 25.25% of the total. Lastly, the Level IV subsidence area covers 427.88 \(\mathrm{h}\mathrm{m}^{2}\), accounting for 22.75% of the total.  
Table 4 presents the statistics regarding the area of each subsidence level in Zhaogu No.1 Mine and Zhaogu No.2 Mine. Overall, the subsidence area and depth of subsidence in Zhaogu No.1 Mine are greater than those in Zhaogu No.2 Mine. The reason for this is twofold. Firstly, the Zhaogu No.1 Mine was completed and commissioned in May 2009, which is earlier than the completion date of the Zhaogu No.2 Mine in April 2011. Secondly, the Zhaogu No.1 Mine has a larger annual design production capacity of 2.4 million tons per annum, compared to the Zhaogu No.2 Mine's capacity of 1.8 million tons per annum.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Collapse Grade** & **Collapse Depth (cm)** & **Area (\(\mathrm{h}\mathrm{m}^{2}\))** & **Total (\(\mathrm{h}\mathrm{m}^{2}\))** & **Scale (\%) 1** \\ \hline Level I & \(>\)300 & 387.61 & 544.26 & 28.94 \\  & Perennial waterlogged areas & 156.65 & & \\ \hline Level II & 200–300 & 433.83 & 433.83 & 23.06 \\ \hline Level III & 100–200 & 475.02 & 475.02 & 25.25 \\ \hline Level IV & 0–100 & 427.88 & 427.88 & 22.75 \\ \hline \end{tabular}  
* The ratio being referred to here is the measurement of the area of the cave-in grade divided by the size of the cave-in land specifically in the Zhaogu Mine.  
\end{table}
Table 3: Study area subsidence grading table.  
Figure 6: Collapse depth classification in the study area.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 3 Results  
NPP estimated using the MOD17 algorithm has the advantage of providing spatial- and temporal-continuous NPP estimates across Europe on a 1-km resolution and Figure 2 illustrates this by showing MODIS EURO for the years 2000 to 2012. Note that MODIS EURO also covers not-forest land cover types such as crops, shrub- or grassland.  
Terrestrial NFI NPP is driven by forest information collected by field crews. Thus it provides NPP and the carbon accumulation by forest stands during a certain time period. Table 1 gives a summary of the forest inventory results by country, by region and the whole dataset, with the terrestrial NFI NPP at the right side.  
Figure 2: MODIS EURO NPP on 1-km resolution representing average NPP for the period 2000–2012 using European daily climate data (available under [[ftp://palantir.boku.ac.at/Public/MODIS_EURO](ftp://palantir.boku.ac.at/Public/MODIS_EURO)]([ftp://palantir.boku.ac.at/Public/MODIS_EURO](ftp://palantir.boku.ac.at/Public/MODIS_EURO))).  
Our NFI dataset covers the full elevational and latitudinal range of forest conditions in Europe including different site conditions, tree species, development stages or management practices. For most countries we have more than 5000 inventory plots (exception: Belgium with 512 plots) and in most cases a plot spacing of at least 4 by 4 km (Table S1). This dataset also provides information on forest properties such as tree age, carbon stocks or stand density and Table 2 indicates that these characteristics vary across Europe.