esa-sceva/satcom-chunk-collection huggingface dataset:
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 4 Numerical Simulation
### Influence of Intercepted Codewords  
Recognition rate curves under different numbers of codewords were simulated with code rate \(1/2\), code length \(64\), and SNR range \(-2\)-\(4\) dB. The recognition rate was output once every \(0.5\) dB and \(1000\) Monte Carlo calculations performed. The results are shown in Figure 9.  
Figure 8: Recognition performance under different code rates with code length \(512\).  
Figure 9: Performance under different number of codewords.  
As shown in the figure, with the increasing number of codewords, the recognition performance of the algorithm continues to improve, but the degree of improvement is increasingly lower. This is because the continuous increase in the number of codewords makes the statistic approach the theoretical value gradually; the closer to the theoretical value, the less the performance improvement.
###### Abstract  
The existence of several gridded precipitation products (GPP) has facilitated studies related to climate change, climate modeling, as well as a better understanding of the physical processes underpinning this key variable. Due to complexities in estimating rainfall, gridded datasets exhibit different levels of accuracy across regions, even when they are developed at relatively high resolution or using sophisticated procedures. The performance of 16 GPP are evaluated over the Caribbean region, which includes the Caribbean Islands, and portions of Central South America. Monthly data for sixty weather stations are used as a reference for the period 1983-2010. The 16 GPP include six products based on station data only, two that combine ground station and satellite information, two merging station and reanalysis information, four based on reanalysis, and two using multisource information. The temporal resolution of the GPP ranged between daily and monthly and spatial resolution from 0.033\({}^{\circ}\) to 0.5\({}^{\circ}\). The methodological approach employed combined a comparison of regional and sub-regional precipitation annual cycles, the Kling-Gupta efficiency (KGE) index, as well as several metrics derived from the standardized precipitation index (SPI). Overall, the best performances were obtained from GPCC025 and MSWEP2, likely reflecting the positive impact of the large number of station data utilized in their development. It is also demonstrated that a higher spatial resolution does not always mean better accuracy. There is a need for this kind of assessment when undertaking climate studies in regions like the Caribbean where resolution is a significant consideration. ERA5 performed best among the reanalyses analyzed and has the potential to be used to develop regionally based GPP by applying bias correction or downscaling techniques. The methodological approach employed provides a comprehensive and robust evaluation of the relative strengths and weaknesses of GPP in the Caribbean region.  
precipitation; precipitation datasets; Caribbean region; precipitation dataset performance evaluation
## 2 Materials and Methods
### SIF Retrievals and Downscaling  
The upwelling radiance includes solar-induced chlorophyll fluorescence in addition to radiance reflected by foliage and soil background. Because of the specific absorption by atmospheric molecules, the SIF emitted by leaves contributes significantly more to the reflected radiance in the atmospheric absorption regions than the atmospheric window does [30; 31]. Therefore, it provides us with an approach to retrieve SIF in the telluric O\({}_{2}\) absorption bands. Based on the presumption that the fluorescence and reflectance in the retrieval bands obey a specific law of change, numerous methods for SIF retrieval have been proposed [32]. In this study, we used the retrieved SIF results based on the spectral fitting approach (SFM) with the assumption that fluorescence and reflectance exhibit a nonlinear pattern with wavelength, which can better utilize the features in fitting the window [17; 33]. The specific equation for SFM is as follows:  
\[L(\lambda)=f(m,\;\Delta\lambda)\times\frac{E(\lambda)}{\pi}+f(n,\;\Delta\lambda) \tag{4}\]  
where \(L(\lambda)\) is the reflected radiance by canopy; \(E(\lambda)\) represents the downwelling irradiance from sky; \(f\) is the fitted function based on the assumption that fluorescence and reflectance obey a certain law for change; \(\Delta\lambda\) is the width of the fitting window; and \(m\) and \(n\) represent the reflectance shape and fluorescence shape, respectively.  
The fPAR can be calculated using observations of the four PAR components or estimated using radiative transfer models or vegetation indices [34]. The measured fPAR contains a significant amount of missing and incorrect data since the instrument calibration was not completed in a timely manner, the mounting location was not typical of the entire canopy, and the instrument malfunctioned due to adverse weather. Therefore, this study used the fPAR calculated by the red-edge normalized difference vegetation index [21]. Then, the \(f_{esc}\) at the near-infrared band can be approximately equal to \(\rho_{NIR}\) multiplied by the normalized vegetation index (NDVI) divided by fPAR:  
\[f_{esc}(NIR)\approx\frac{\rho_{NIR}\times NDVI}{fPAR} \tag{5}\]  
where \(f_{esc}(NIR)\) represents the \(f_{esc}\) at the near-infrared band. Finally, the \(SIF_{t}\) can be obtained by the \(SIF_{c}\) divided by \(f_{esc}\) (Equation (3)). For better handling of the missing or incorrect data, the linear interpolation approach was utilized to obtain the gap filling of the fPAR and other variables derived from it.
## 2 Related Work
### Rank of Domain (ROD)  
The primary focus of domain selection is to minimize EEG variations between the TD and SDs, mainly to minimize the effect of NT, which has a significant impact on classification performance across individual TDs [14; 15]. ROD iterates through domains to identify less-beneficial sources, and, in each iteration, a single less-beneficial domain is discarded, while the remaining domains are considered SDs, and a single domain is considered TD when transfer mapping is applied. To achieve this goal, ROD makes use of _KL_ divergence to calculate the adaptation level across both source and target domains; as such, similarly distributed domains are denoted by a lower _KL_ divergence [13].  
\[\mathcal{R}(S,T)=\frac{1}{\mathrm{d}^{*}}\!\!\sum_{i}^{\mathrm{d}^{*}}\!\! \theta_{i}[KL(S_{i}||T_{i})+KL(T_{i}||S_{i})] \tag{1}\]  
ROD makes use of three steps to rank domains that are closely related and computed using Equation (1). The first step identifies the best dimensionality for the subspaces, with the dimensionality being denoted by \(\mathrm{d}^{*}\). The second step calculates the symmetrized _KL_ divergences across data distributions, after having estimated the data distributions of the two domains with two one-dimensional Gaussian functions. The third step measures the _KL_ divergence-weighted average of the principal angles. In this case, the two Gaussian distributions are denoted by \(S_{i}\) and \(T_{i}\), whereby the principal angle denoted by \(i\) is correlated with the principal vectors utilized to acquire the two Gaussian distributions from the projected data. A combination of sources consisting of low principal angles and _KL_ divergences will results in a low value of \(\mathcal{R}(S,T)\), which simply implies that the sources are closely related [13].
## 7 Physical Synthesis of Reverse Conversion Methods  
Physical synthesis of methods for translating from a residue number system to a positional number system was performed on an ASIC in the RTL and physical synthesis Cadence Genus Synthesis Solution environment using the NangateOpenCell.library and computation time optimization. The parameters measured were the signal propagation time across the circuit (picoseconds, ps) and the area used (square micrometer, \(\upmu\)m\({}^{2}\)). The Chinese residue theorem-based method (Formula (1)), the approximated CRT-based method (Formulas (5) and (6)), the mixed-radix conversion-based method (Formula (2)), a method based on special moduli \(\{2^{n}-1,2^{n},2^{n}+1\}\)[8], the reference points method proposed by the authors and discussed in Section 5, the modified MRC-based method (Algorithm 3), and the introduced method based on reference points and modified MRC (Section 6). Simulation results for the reference points method, CRT, approximate CRT, modified MRC, MRC, special moduli \(\{2^{n}-1,2^{n},2^{n}+1\}\) and the proposed method are shown in Figures 4-6 for 8, 16 and 32 bits, respectively. In addition, Tables 2-4 are given to compare the proposed modification with the previously developed methods. In these tables, the reference points based method is denoted as I, the modified mixed-radix conversion based method--II, the proposed modification--III. The configuration of the layers for the reference points method is given in the second column. The analysis of the Figure 4 shows that the reference point based method has the smallest area in the 8-bit domain and is only slightly inferior to the special type moduli in terms of time. The MRC and approximate CRT-based methods have complex computational algorithms and perform poorly in terms of both area and time. In the 16-bit domain (Figure 5), the method using special moduli has a clear advantage, and the method based on reference points has a low execution time, but at the same time significant hardware costs. The proposed method allows to reduce the used area, but slightly increases the computation time. \begin{table}
\begin{tabular}{l c c c} \hline \hline \(X_{4}\) & **Reference Point Value** & \(X_{4}\) & **Reference Point Value** \\ \hline
0 & 0 & 4 & 420 \\
1 & 105 & 5 & 525 \\
2 & 210 & 6 & 630 \\
3 & 315 & 7 & 735 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: LUT values for the example at hand. In the 32-bit domain (Figure 6), the reference points method shows both some of the best and some of the worst results in terms of time and area, and thus a strong dependence on the set of moduli and the configuration of layer over which the residues are grouped for the LUT. The MRC-based method performs worse than the CRT and the approximate CRT in most cases, but has less area as the dimensionality increases. Figure 4: Reverse conversion for 8 bits. Figure 5: Reverse conversion for 16 bits.
## 5 The COGNIFOG Project
### Use Cases
#### 5.4.3 Trial 3--Smart Manufacturing  
The third trial planned within COGNIFOG is focused on Smart Manufacturing in a modern factory setting. This trial is developed by the Laboratory for Manufacturing Systems & Automation, University of Patras, Greece. The manufacturing scenario involves the collaboration of a mobile robot equipped with a collaborative arm alongside two additional robots tasked with transportation. The mobile robot supports an operator in the assembly process, while the other two robots handle the transportation of materials to the workstation. Each robot operates with its own ROS (Robot Operating System) application, collaborating to complete the assembly on time. Task scheduling plays a vital role in ensuring the timely transportation of parts, and in case of failure of one transportation robot, the other must take over both tasks to maintain the workflow. While the assembly robot is a real mobile robot, the transportation robots are simulated.  
The smart manufacturing scenario presents several challenges that could be addressed through the integration of COGNIFOG technologies. Given the scenario's requirement for seamless interaction between multiple devices, efficient cooperation, and control of both hardware and software components is crucial. Additionally, dynamic deployment across all devices and applications, which rely on ROS channels for data exchange, is necessary. Security for data and networks is another key concern. Furthermore, the increased connection of devices to a single network introduces challenges related to latency and data storage capacity, which need to be addressed.  
From a technical perspective, intelligent orchestration, dynamic deployment, and high-level coordination are challenges that can be addressed using COGNIFOG components. In simple manufacturing scenarios, each robot uses its own ROS infrastructure, typically utilizing edge resources to minimize delays, though cloud deployment may be necessary for more complex coordination tasks. Centralized coordination also introduces a requirement for innovative and dynamic deployment setups, which can be managed through Kubernetes(K8s) cluster deployments. Robots must first be implemented as ROS containers, deployed on K8s clusters, and then managed by COGNIFOG components. However, deploying multiple robots under the same infrastructure could pose challenges related to network bandwidth or storage capacity, requiring careful consideration.  
In summary, the benefits of this use case (and the other two detailed above) are shown in Table 2.
## 3 Results and Discussion
### Pipe  
In this subsection, finite element analyses for circumferentially cracked pipes with full internal cracks having two different _a/t ratios_ (_a/t_ = 0.2 and 0.5) are conducted. Figure 9 displays the ductile fracture resistance curves for pipes with different mismatch ratios. It can be seen that, throughout the crack growth process considered in this study, pipes with higher \(M_{y}\) yield remarkably higher resistance curves for both the shallow and the deep cracked cases, which is still consistent with that of the SENB and SENT specimens.  
The quantity of \(3\sigma_{\rm m}/2\sigma_{\rm e}\) (where \(\sigma_{\rm m}\) and \(\sigma_{\rm e}\) are the mean stress and effective stress, respectively) defines a convenient measure of triaxiality linked to the growth rate of micro-scale voids consistent with the subsequently introduced damage measures [42]. In the following, Figure 10 presents the stress triaxiality distributions along the ligament ahead of the current crack tip for shallow-cracked pipes (_a/t_ = 0.2) at different crack growth (\(\Delta a\) = 0.1 mm, 0.5 mm, and 1.0 mm). What is interesting to observe is that the reduction of the stress triaxiality with the increase of \(M_{y}\) applies to the whole range of interest, 0 \(<\) r \(<\) 8 CTOD. At \(\Delta a\) = 0.1 mm (see Figure 10a), the pipe with the highest mismatch ratio \(M_{y}\) exhibits the lowest stress triaxiality value, therefore producing the highest resistance curve, which corresponds to our above findings (see Figure 9a). For a propagating crack, the similar results can also be obtained, as shown in Figure 9b,c. Similar observations have also been obtained for the deep-cracked pipe (_a/W_ = 0.5) with different mismatch ratios, while the results are not included here in order to avoid repetition.  
Figure 8: Crack tip opening stress distributions ahead of current crack tip, a/W = 0.5, (**a**) \(\Delta\)a = 0.1 mm; (**b**) \(\Delta\)a = 0.5 mm; (**c**) \(\Delta\)a = 1.0 mm.  
Figure 9: CTOD–\(\Delta a\) curves for pipes with different mismatch ratios, (**a**) _a/t_ = 0.2; and, (**b**) _a/t_ = 0.5.
## 3 Result and Discussion
### Capillary Rise Properties  
The capillary rise results of the geopolymer paste samples are shown in Figure 3. Equation (1) has been employed.  
\[I=m_{t}/(ad)\]  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Materials**}} & \multicolumn{4}{c}{**Properties \& Performance**} & \multirow{2}{*}{**Reference**} \\ \cline{2-2} \cline{4-6}  & \multicolumn{1}{c}{**Mechanical Strength**} & & & & \\ \hline Porous geopolymer: & & & & & \\ Metakaolin + sodium silicate & Bending strength: & & & & \\ solution + polyacetic acid & 36 MPa & Capillary rise: 1125 mm & - & & \\ (PIA) & & & & & \\ \hline Porous geopolymer: & & Compressive strength: 14.2 MPa & 27\% & & \\ Metakaolin + sodium silicate & (H\({}_{2}\)O/AL\({}_{2}\)O\({}_{3}\)) to \(\sim\)5 MPa & (H\({}_{2}\)O/AL\({}_{2}\)O\({}_{3}\))-51.1\% & Improving & Passive & \\ solution + NaOH & (H\({}_{2}\)O/AL\({}_{2}\)O\({}_{3}\)) & (H\({}_{2}\)O/AL\({}_{2}\)O\({}_{3}\)) & & & \\ \hline Porous geopolymer: & & & & & \\ fly ash + sodium silicate & - & - & & & \\ solution + NaOH & & & & & \\ \hline Porous geopolymer: & & & & & \\ fly ash + metalskin sodium & - & 50.44\% & 72.5\% & Building & [99] \\ hydrogen peroxide (H\({}_{2}\)O) & & & & & \\ \hline Porous geopolymer: & & & & & \\ fly ash + metalskin sodium & (Al powder additional) Compressive & & & & \\ silicate solution + NaOH + & & & & & \\ hydrogen peroxide (H\({}_{2}\)O) & & & & & \\ AI powder & & & & & \\ \hline Geopolymer resin + & & & & & \\ metalskin + carbon fiber + & & & & & \\ potassium silicate solution & & & & & \\ \hline Geopolymer paste: & & & & & \\ rice husk + rice husk ash + & & & & & \\ metalskin + palm oil fuel & Compressive strength: \(\sim\)5 MPa & \(\sim\)38\% & -33\% & - & Current \\ ash + slag NaOH + & & & & & \\ sodium silicate solution & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison of different investigated geopolymers for evaporative cooling applications.  
where \(I\) = the absorption, \(m_{t}\) = the change in specimen mass in grams at the time \(t\), \(a\) = the exposed area of the specimen, in mm\({}^{2}\), and \(d\) = the density of the water in g/mm\({}^{3}\).
## 2 Background and Related Work
### Active Queue Management
#### 2.1.1 Controlled Delay (CoDel)  
CoDel is an active queue management (AQM) algorithm first proposed by [PERSON] in 2012 [13] as an advancement over the traditional drop-tail algorithm and is a variant of the RED algorithm. CoDel primarily utilizes packet-sojourn time (or queue delay in simpler terms) as the primary congestion indicator. Once the queuing delay has exceeded a certain target for at least a certain interval, a packet is discarded, and the subsequent drop time is determined by a control law proportional to the square root of the number of drops since entering the dropping state. Conversely, the queue ceases discarding packets if the delay falls short of the target.  
Several variants of CoDel have been proposed, including FQ-CoDel, which uses fair queuing to improve flow-level fairness, and PIE-CoDel, which incorporates the PIE algorithm to improve queue management.
## 3 Gated Recurrent Unit-Based Prediction  
In order to maximize the training performance while minimizing the energy consumption cost of GPU clusters, we need to accurately predict the renewable generation even under its irregularity. In this paper, we adopt the GRU [15], a modified version of the LSTM model. The GRU combines the forget and input gates into a single update gate and merges the cell state with the hidden state, making it well suited for predicting the sequence-ordered data of energy generation from renewable sources.  
Figure 2 illustrates the structure of our defined GRU network model for prediction. Let \(v(k)\) represent the historical input data and \(\bar{v}(k)\) denote the predicted output data at time slot \(k\). Based on [15], we present the associated functions for training the GRU network model as follows:  
Figure 1: The Framework Structure of the proposed Renewable-Aware Frequency Scaling Approach.  
\[q(k) =\sigma(W_{z}\cdot[h(k-1),v(k)]), \tag{1}\] \[u(k) =\sigma(W_{r}\cdot[h(k-1),v(k)]),\] \[\tilde{h}(k) =\tanh(W\cdot[u(k)*h(k-1),v(k)]),\] \[h(k) =(1-q(k))\cdot h(k-1)+q(k)\cdot\tilde{h}(k).\]  
Here, \(q(k)\) and \(u(k)\) denote the update gate vector and reset gate vector at time slot \(k\), respectively. \(W\) denotes the model weight parameter matrix. The terms \(\tilde{h}(k)\) and \(h(k)\) represent the associated output vector and candidate activation vector at time slot \(k\), respectively. The hyperbolic tangent function \(\tanh(\cdot)=\frac{\sinh(\cdot)}{\cosh(\cdot)}\) is employed for calculating the candidate activation vector \(\tilde{h}(k)\), where \(\sinh(\cdot)\) and \(\cosh(\cdot)\) represent the hyperbolic sine cosine functions, respectively. Through the feedback-based forward and back-propagation within the GRU network model, the model parameters for each block are updated in response to the discrepancies between the historical input sequences and actual output sequences.  
Based on historical input sequences of renewable energy, the defined GRU network model is capable of inferring the corresponding output sequences. Let \(l\) denote the length of both the input and output sequences. Then, we present our GRU-based prediction model as follows:  
\[(\tilde{v}_{i}(k+1),\cdots,\tilde{v}_{i}(k+l)) \tag{2}\] \[=\text{GRU}((v_{i}(k-l),\cdots,v_{i}(k)),W)\]  
Figure 2: Gated recurrent unit-based many-to-many neural network model for renewable generation sequence prediction.  
Different types of loss functions, such as mean squared error loss and cross-entropy loss, can be considered for updating the model parameters \(W\). In this work, we use the MSE loss function due to its simplicity and compatibility with the PyTorch framework.
## 2 Materials and Methods
### Model Evaluation  
The model accuracy test is based on the standard regression and error-index evaluation, which reflects the degree of fit between the model modelling and the validation set and includes R\({}^{2}\), RMSE, and relative analytical error (RPD). The calculation formulae are, respectively:  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}\hat{y_{i}}-\overline{y}^{2}}{\sum\limits_{i=1 }^{n}y_{i}-\overline{y}^{2}} \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}}} \tag{3}\]  
\[RPD=\frac{SD}{RMSE} \tag{4}\]  
where \(\hat{y_{i}}\) is the predicted soil texture, \(y_{i}\) is the measured soil texture, \(\overline{y}\) is the mean soil texture, \(n\) is the number of samples, and \(SD\) is the standard deviation [41].  
R\({}^{2}\) determines the degree of explanation of the corresponding variable of the independent variable, ranging from 0 to 1, whereby the closer to 1, the better the model fits; RMSE measures the deviation between predicted and observed values, with a smaller RMSE indicating a smaller prediction error.  
RPD is the ratio of the standard deviation of the sample SD to the standard error of the prediction, and RPD is used as a comprehensive measure of the predictive power of the model. The model is considered to have excellent quantitative predictive ability when RPD > 2, predicting the sample content when 1.4 < RPD < 2, and failing to predict the sample when RPD < 1.4.