esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation and Results
Even so, the OUD technique is seen to improve schedulability, and it can be maximized when the number of task sets is not loaded. Figure 8: Schedulability for the different number of tasks. Figure 7: The average throughput for the techniques. Lastly, the scheduling techniques are also evaluated based on the percentage of jobs executed when set at different priority levels in different scenarios. The jobs submitted to the schedulers are assigned to two different priority levels, in which PL1 is a lower priority that can be assigned to a job when compared to a higher priority level PL2. Figure 9a considers a scenario in which there is an equal job distribution and displays the jobs that have been executed at priority levels (PL) 1 and 2. It is observed that jobs executed based on a higher priority yield more than 50% of total job executed for the various techniques. Since there is an equal number of jobs requests, the various techniques at PL1 processes less jobs due to the fact that there is less capability in differentiating these jobs into priorities and more overhead in determining which job to execute in the queue. From Figure 9b, the percentage of jobs executed in the two priority levels when there is a random job distribution is displayed. In this scenario, there is no prior knowledge of the rate of arrival of jobs. The DSC scheduling does not schedule jobs in a fair manner; hence, it selects the first job request and executes it regardless of its main priority, thereby having more jobs to execute but not taking into consideration its optimal priority before  
Figure 9: (**a**) Scenario 1—Percentage of jobs executed based on priority level. (**b**) Scenario 2—Percentage of jobs executed based on priority level. execution. Most of the techniques perform almost equally with a minimal difference owing to the fair manner in which the jobs are being processed. Although more jobs are being processed in priority level 2, most of the processed jobs might not be the optimal ones in the queue. In summary, the OUD technique has shown itself to be the best implementation choice in all the performance indexes considered and hence recommended to be suitable for implementation in 5G-based smart grid communications.
# Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy
###### Abstract  
This study investigates the potential of spaceborne imaging spectroscopy to support the analysis of the status of two major Estonian lakes, i.e., Lake Peipsi and Lake Vortsjarv, using data from the PRISMA and EnMAP missions. The study encompasses nine specific applications across 12 satellite scenes, including the validation of remote sensing reflectance (Rrs), optical water type classification, estimation of phycocyanin concentration, detection of macrophytes, and characterization of reflectance for lake ice/snow coverage. Rrs validation, which was performed using in situ measurements and Sentinel-2 and Sentinel-3 as references, showed a level of agreement with Spectral Angle \(<16^{\circ}\). Hyperspectral imagery successfully captured fine-scale spatial and spectral features not detectable by multispectral sensors, in particular it was possible to identify cyanobacterial pigments and optical variations driven by seasonal and meteorological dynamics. Through the combined use of in situ observations, the study can serve as a starting point for the use of hyperspectral data in northern freshwater systems, offering new insights into ecological processes. Given the increasing global concern over freshwater ecosystem health, this work provides a transferable framework for leveraging new-generation hyperspectral missions to enhance water quality monitoring on a global scale.  
Rrs; PRISMA; EnMAP; water quality; remote sensing; Lake Peipsi; Lake Vortsjarv
## 3 Results
### Results of the Economic Analyses and Their Discussion
The calculation results showed that the value of cash inflows (denoted as \(\Delta CF\) in Equation (2) is greater than the costs only in the case of Variant I (NPV = 2.95 EUR/m\({}^{2}\)), i.e., when double-glazed windows were replaced with triple-glazed ones in the case of  
Figure 8: Dependence of LCC value on discount rate over 30 years of life cycle: (**left**)—Bialystok, (right)—Kiruna. Figure 9: Dependence of LCC value on discount rate over 30 years of life cycle after taking into account 2% increase in energy prices: (**left**)—Bialystok, (**right**)—Kiruna. a house located in Bialystok. The negative net present value indicates that Variant II (NPV = \(-\)19.1 EUR/m\({}^{2}\)) and Variant III (NPV = \(-\)22.08 EUR/m\({}^{2}\)) are unprofitable. Assuming a 2% increase in energy prices, the NPV value increases in each case considered and amounts to NPV = 15.9 EUR/m\({}^{2}\) for Variant I, NPV = 1.31 EUR/m\({}^{2}\) for Variant II and NPV = \(-\)14.58 EUR/m\({}^{2}\) for Variant III. Therefore, only replacing a triple-glazed window with a quadruple-glazed window can probably not be economically justified. As expected, the NPV values for the Kiruna site show that projects involving the use of energy-efficient windows are more cost-effective. The values of this indicator are 29.35 EUR/m\({}^{2}\), 20.45 EUR/m\({}^{2}\) and \(-\)8.89 EUR/m\({}^{2}\) for Variant I, Variant II and Variant III, respectively. So, simply replacing a triple-glazed window with a quadruple-glazed window will not provide a profit. Window replacement should be assessed as a low-risk project. In this case, it is usually assumed that the rate of return should be of around 5% to 10%. Figure 10 shows the change in the IRR value in individual years of the life cycle for the three options described above. As we can see, only Variant I meets the low-risk criterion, because after 26 years the IRR value exceeded 5% in the case of a house located in Bialystok. The project of replacing a double-glazed window with a triple-glazed one (Variant I) becomes even more profitable with a 2% increase in energy prices (Figure 11). In this case, the time to achieve a 5% discount rate is reduced by 6 years. When considering the Kiruna location, Variant I and II meet the criterion of \(r>5\%\) after only 14 and 21 years, respectively. The situation is even better if we assume an increase in energy prices, because all variants can be treated as low-risk projects. Variant I meets the criterion after 12 years, Variant II after 18 years and Variant III after 29 years. To sum up the above analysis, it can be concluded that triple glazing is the optimal solution in terms of economic assessment in the climatic conditions of Bialystok. In the case of a more severe climate and at a low discount rate, both triple- and quadruple-glazed windows are a cost-effective solution. Figure 10: Dependence of IRR values in the considered time period: (**left**)—Bialystok, (**right**)—Kiruna.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 2 Methods
### Image Matching and Outlier Removal  
Image matching in the overlap area is carried out to extract tie points used for discrepancy compensation. This study uses a template matching based on NCC (Normalized Cross-Correlation) as Equation (3). The similarity between reference and projected images is measured using NCC. A matching with NCC larger than 0.5 is typically considered similar, but a higher threshold such as 0.7 is preferred to reduce matching outliers.  
\[NCC=\frac{\sum\limits_{i=1}^{w}\sum\limits_{j=1}^{w}\left(R_{ij}-\overline{R} \right)\left(P_{ij}-\overline{P}\right)}{\sqrt{\left[\sum\limits_{i=1}^{w} \sum\limits_{j=1}^{w}\left(R_{ij}-\overline{R}\right)^{2}\right]\left[\sum \limits_{i=1}^{w}\sum\limits_{j=1}^{w}\left(P_{ij}-\overline{P}\right)^{2} \right]}} \tag{3}\]  
where \(R\) is a patch in the reference image and \(P\) is a patch within the established search region in the projected image, both are in the size of \(w\times w\). \(\overline{R}\), \(\overline{P}\) are averages of all intensity value in the patches.  
These automated image matchings often produce a lot of mismatches that should be detected and removed. RANSAC (Random Sample Consensus) is a popular outlier detection method [17] because it iteratively estimates established modeling parameters from a set of data that includes outliers.
# Time-Series Forecasting of Seasonal Data Using Machine Learning Methods
## 3 Development of the Models
### Facebook Prophet  
Prophet is a procedure for forecasting time series data based on an additive regression model, where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and it typically handles outliers well [43]. The main idea of the method is presented in [31]. The Prophet is an open-source library that is distributed in R and Python. The selection of the required model parameters is fully automated, so to build a model, it is sufficient to pass the input dataset for training. The _Prophet_ function of the prophet library is used to create the model. Predicted parameter values can be found using the prediction model method, with a data frame of time stamps corresponding to the prediction horizon as input. The result of forecasting with the Prophet model is shown in Figure A4 (see Appendix A).
## 5 The Experiment and Analysis
### Fault Data from QPZZ-T
#### 5.2.3 Experimental Results under Variable Working Condition  
In order to verify the effectiveness of KMVMD-PGMCKD under variable working conditions, the experiment is carried out again after the rotating speed is changed to 1000 r/min. According to the changed conditions, the fault frequency is about 83.5 Hz, and the rotating frequency is about 16.7 Hz. The experimental results are given in Figure 12.  
Figure 11: Power spectrum of the reconstructed signal by MCKD based on Shannon entropy.  
Figure 12: _Cont._  
From Figure 12, fault frequency \(f_{0}\) (82.03 Hz) and its third harmonic (246.1 Hz) under the condition of variable working conditions can still be extracted from the fault signal of the rolling element by KMVMD-PGMCKD, which further proves that this method has strong generalization ability under the variable working condition.
# Behavior of Circular Hollow Steel-Reinforced Concrete Columns under Axial Compression
## 2 Experimental Procedures and Results  
### Experimental Design  
Six circular HSRC columns were designed and tested under axial compression. The outer diameter (\(D\)) of the specimens was 200 mm, and the diameter (\(d\)) and thickness (\(t_{\text{s}}\)) of the inner hollow steel tube were 80 and 2.78 mm, respectively. Table 1 presents the details of the specimens. The circular HSRC columns were reinforced with eight longitudinal rebars with a diameter of 12.0 mm and stirrups with a diameter of 6.4 mm and spacing of 100 mm. The labels of circular HSRC columns were detailed in Table 1. The first number represents the parameter as the column height (\(H\)), where \"1\" represents \(H\) = 1600 mm and \"2\" represents \(H\) = 3200 mm; the second number represents the parameter as the diameter (\(d\)) of the steel tube in the circular hollow steel tube part, where \"1\" represents \(d\) = 80 mm and \"2\" represents \(d\) = 100 mm; the last number represents two comparative specimens with the same parameters. Taking specimen 1-1-1 as an example, it represents the first circular HSRC specimen with a column height of 1600 mm and a steel tube diameter of 80 mm.
## 2 Backgrounds and Related Works
### Mainchain  
The mainchain (MC) of HMBS has the same blockchain structure as explained in Section 2.1. Here, for future use, we give the following three equations with explanations:  
* Miners in MC network updates MC blockchain \(\mathbb{MC}^{[n-1]}\) to \(\mathbb{MC}^{[n]}\) by connecting MC block \(B^{[n]}\). \[\mathbb{MC}^{[n]}=\mathbb{MC}^{[n-1]}||B^{[n]}.\] (15)
* The MC block \(B^{[n]}\) consists of \(h^{[n-1]},t^{[n]}_{1},\dots,t^{[n]}_{c^{[n]}},r\), where \(h^{[n-1]}\) is the hash value of the preceding MC block \(B^{[n-1]}\), \(t^{[n]}_{1},\dots,t^{[n]}_{c^{[n]}}\) are transactions to be recorded in MC through \(B^{[n]}\), and \(r\) is a nonce that proves \[H(h^{[n-1]}||t^{[n]}_{1}||\dots||t^{[n]}_{c^{[n]}}||r)\in\mathcal{C}_{\text{ MC}},\] (16) where \(\mathcal{C}_{\text{MC}}\) is a condition which every MC block must satisfy.
* The hash value of MC block is defined by \[h^{[n]}=H(h^{[n-1]}||t^{[n]}_{1}||\dots||t^{[n]}_{c^{[n]}}||r^{[n]}),\] (17)with a nonce \(r^{[n]}\) which satisfies (16).  
HMBS uses MC to (1) merge the blockchain state changes made in each shard, (2) impose the current blockchain state to each shard, (3) transfer the value stored in one UC to another UC, and (4) execute state-dependent stateful smart contracts. The third objective of MC follows the same approach as (12) while the fourth objective of MC follows the one in (14). The first two objectives of MC will be explained in Section 3.4.
## 6 Experimental Evaluation
### The Distributed Mapping Service
#### 6.2.2 Quality of the Reconstructed Maps  
Figure 10 show the maps constructed by the centralised and the distributed mapping services respectively. The two maps were generated assuming no packet loss. By comparing the two maps, it is obvious that they are visually very similar to each other as well as to the original map. This demonstrates that the distributed mapping service offers accuracy level comparable to that of the centralised service. Quantitatively, the difference between the high-resolution source data and the re-constructed data is measured as the root-mean-square error (RMSE) difference of the pixels. Using this measurement, there is just a \(1.2\%\) difference between the distributed and centralised mapping algorithms. This is because the two mapping services utilise the full network data, which naturally leads almost equal mapping fidelity. One particular side-effect of the distributed method can be beneficial. The enforced exclusion of distant points in local mapping can produce a final map that is visually much closer to the original for some features. The influence of irreverent distant points in the centralised mapping (Figure 11b) causes less of a similarity with Figure 11a than the distributed method (Figure 11c). Note the complete absence of the \"loop\" shapes under the main dark region in Figure 11b.  
Figure 10: Maps generated from \(2000\) nodes randomly distributed on the surface from Figure 8 using the centralised and distributed mapping services respectively.  
Figure 12 shows the isopleth maps reconstructed by using the three studied mapping schemes. Without imposing any measurement noise, all maps were constructed for a \(116\) units and a threshold of \(1\). The distributed and suppression-based contour maps deviate from the centralised contour map by different degrees. By comparing the two maps, the distributed mapping service outperforms the suppression-based mapping. This is because the used mineral map exhibits low spatial correlation. In all studied mapping schemes, the limited capability of the underlying wireless communications contributes to the degradation of the mapping fidelity.
## 2 Materials and Methods
### Characterization of the Imaging Performance
#### 2.5.1 FWHM Resolutions  
The 3-D resolution was assessed with phantom Ph1. To determine the FWHM resolution, the image of each microsphere was fit with a 3-D Gaussian blob using a nonlinear least-squares minimization. The three main axes of the blob were the axes of the Cartesian coordinate system, and the following equation was used:  
\[g=A\cdot exp\left\{-\left[\frac{\left(x-x_{0}\right)^{2}}{2\sigma_{x}^{2}}+ \frac{\left(y-y_{0}\right)^{2}}{2\sigma_{y}^{2}}+\frac{\left(z-z_{0}\right)^{ 2}}{2\sigma_{z}^{2}}\right]\right\}, \tag{4}\]  
where \(x_{0}\), \(y_{0}\), and \(z_{0}\) are the coordinates of the center of the blob, \(A\) is the amplitude, and \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{z}\) are the Gaussian root mean square widths along the x, y, and z axes, respectively. The FWHM resolutions were computed using the equation: \(FWHM_{i}=2\sqrt{2 ln(2)}\sigma_{i}\) with \(i=x\), \(y\) or \(z\).  
The expected FWHM resolution along the x-axis was on the order of \(\lambda_{c}\), because of the large synthetic angular aperture. The wavelength \(\lambda_{c}\) = 0.3 mm was the wavelength at the center frequency of the array. Given the apodization along the y-axis (Appendix A), we expected a resolution on the order of 2 \(\times\)\(\lambda_{c}\). The resolution along the z-axis was limited by the ultrasound pulse width and was expected to be on the order of \(\lambda_{c}\). Accordingly, the fits with the Gaussian blob were performed in volumes of 1.5 \(\lambda_{c}\)\(\times\) 4 \(\lambda_{c}\)\(\times\) 1.5 \(\lambda_{c}\), each centered on an individual microsphere.  
To compare the spatial resolution for different angular sampling periods, we used statistical quantifiers, such as the median and interquartile range (IQR), because of their robustness to outliers and reliability for various data distributions. The resolution comparisons are presented in boxplot displays.
## 3 Spectrum Auction Structures
### Dutch Auction  
The Dutch auction is characterized by distinct rounds and a descending price trend.  
Especially practical for perishable items, the initial phase of this structure is characterized with a high reserve price denoted as \(p^{0}=Y\). Participants must decide whether to bid or abstain from the round. If all participants choose to abstain, a new round begins with a reduced price, \(p^{1}<p^{0}\), until the first player expresses interest in acquiring the auctioned item at the price \(p^{*}\), which is announced at the beginning of the \(n\)th round.  
Descending price auctions can vary in their approach to defining rounds. One, popular in Japan, is the Clock Auction. In this format, rounds progress after a predetermined amount of time, eliminating the need for the auctioneer to announce each round and wait for its conclusion.  
Another variant that is popular in spectrum auctions worldwide, including Brazil (2021) and Argentina (2014), is the Sealed Bid variant. This method follows the same logic as the Dutch auction, where each participant submits a sealed bid indicating the price \(p^{*}\) they would have announced in a traditional Dutch auction. Once all bids are submitted, they are revealed, and the highest bidder is awarded the contract.  
The primary objective of this auction format is to place players in a vulnerable position, as sealed bids prevent them from understanding their opponents' behavior or pricing ranges. This compels each player to bid \(p_{i}=V_{i}\). Strategically, this format is equivalent to the English auction.  
As highlighted by [13], two auctions with the same players and the same strategic space are said to be strategically equivalent if the expected payoff of each player is identical in both cases.  
This is indeed the case with the Sealed Bid auction, where each player's utility equals \((V_{i}-p_{i})\) for the winner and \(U_{i\
eq i}=0\) for all others.  
The structure reversal allows the auction to close in a single round while transferring the decision process to the players themselves. However, assuming rationality by each player, the bids may be compressed given their expectations of other players' behavior, resulting in inefficiencies. Each player \(i\) can try to evaluate their opponents \(V_{j}\) and try to win the dispute bidding \(b_{i}=\mathbb{E}_{i}[V_{j}]+\epsilon\), \(\epsilon\in\mathbb{R}_{+}\).  
This phenomenon occurs due to each player's incentive to maximize their payoff, that is, attempting to win the auction while expending as little as possible. The difference between the actual price paid and the second-highest bid is known as the \"winner's curse\".  
Suppose an auction for a pen where player 1 bids USD 10 while player 2 bids USD 7. The item is awarded to the former, who honors its bid of USD 10. The difference \(10-7=3\) equals the curse of the winner incurred by player 1.
## 4 Discussion
### Complexity Analysis  
To provide a quantitative assessment of the computational complexity of Hybrid-DeepSCS, particularly relevant for IoT and edge device applications, we conducted a detailed analysis of the model's floating-point operations (FLOPs). The following sections present the complexity analysis and FLOP count for each segment of our model. In this study, we analyze the computational cost of our model by estimating the FLOPs for each of its main components. The results are summarized in Table 3.  
From the table, we observe that the semantic interpreter requires the highest computational workload and model size. On the other hand, the semantic compressor has the lowest computational requirement as well as the model size, reflecting its role in reducing data dimensionality before transmission. Future work could explore model compression techniques, such as pruning or quantization, as mentioned earlier, to improve computational efficiency without significant performance loss. Based on the computational power of a given edge device, the latency incurred by the semantic model can be estimated. Specifically, latency can be calculated as the ratio of the FLOP value to the peak floating-point operations per second (FLOPS) capability of the target edge device when the processing unit is operating at maximum utilization [30]. We denote FLOPs as the number of floating-point operations and FLOPS (note the uppercase 'S'), as a measure of hardware performance. Mathematically, this can be expressed as follows [31]:  
\[\text{Latency}\,\cong\,\frac{\text{FLOPs}}{\text{FLOPS}} \tag{16}\]  
This approach allows for a device-specific estimation of latency, providing a practical understanding of the system's performance in resource-constrained environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subcomponent** & **Total Parameters** & **Size (MB)** & **FLOPs** \\ \hline Semantic Extractor & 4,386,112 & 17.54 MB & \(5.73\times 10^{11}\) \\ Semantic Compressor & 221,440 & 0.88 MB & \(4.83\times 10^{9}\) \\ Semantic Expander & 295,168 & 1.18 MB & \(1.21\times 10^{10}\) \\ Semantic Interpreter & 9,086,112 & 36.34 MB & \(1.22\times 10^{12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model total parameters, size, and FLOPs for each subcomponent.