esa-sceva/satcom-chunk-collection huggingface dataset:
# Dispensing of Prescribed Medicines in Swiss Community Pharmacies-Observed Counselling Activities
## 3 Results  
Forty-nine community pharmacies were invited and 18 (37%) took part in the study. Reasons for participation refusal were no interest (n = 7), lack of staff resources (n = 4) or time (n = 4), holidays (n = 2), or unknown (n = 14). All pharmacies were located in the urban area of Basel. Thirteen were independent pharmacies (72.2%), while five belonged to a pharmacy chain (27.8%). They were on average open for 10.25 \(\pm\) 1.5 h per day, and were observed during 8 \(\pm\) 0.6 h (covering 78.0% of opening hours) per day, per pharmacy. The mean number of working staff per pharmacy at the observation day was 5.8 \(\pm\) 2.6 (1.7 \(\pm\) 0.9 pharmacists, 2.8 \(\pm\) 1.7 pharmacy technicians, 1.0 \(\pm\) 0.3 apprentices, and 0.2 \(\pm\) 0.7 pharmacists in training).  
During the total observation time of 145.5 h (18 observation days), 571 PEs (mean 31.2 \(\pm\) 6.4 per pharmacy, range 22-45) were documented. Fifteen PEs had to be excluded because no medicines were dispensed (n = 9, e.g., drug not in stock), spoken language was foreign (n = 3), ordered medicines were picked-up (n = 1), physician-ordered medication (n = 1), or no documentation about the dispensed medicines was available (n = 1). A total of 556 PEs (269 first PEs and 287 refill PEs) constituted the sample for statistical analysis (each PE involved one customer).  
Table 1 illustrates the characteristics of the patient, prescription, and provider. The number of medicines on a prescription varied from 1 to 25 (mean 3.2 \(\pm\) 3.2).
# Polyisocyanurate Foam Pyrolysis and Flame Spread Modeling
## 3 Results and Discussion
### TGA and DSC Experimental Results and Modeling
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reaction \#** & **Reaction Equation** & \(A_{r}\) (s\({}^{-1}\)) & \(E_{r}\) (J mol\({}^{-1}\)) & \(\Delta H_{r}\) (J kg\({}^{-1}\)) \\ \hline
1 & PIR\(\rightarrow\) 0.992 PIR\_Int\_1 + 0.008 Gas\_1 & 2.42 \(\times\) 10\({}^{8}\) & 8.09 \(\times\) 10\({}^{4}\) & 0 \\
2 & PIR\_Int\_1 \(\rightarrow\) 0.96 PIR\_Int\_2 + 0.04 Gas\_2 & 2.27 \(\times\) 10\({}^{7}\) & 8.96 \(\times\) 10\({}^{4}\) & 2.16 \(\times\) 10\({}^{4}\) \\
3 & PIR\_Int\_2 \(\rightarrow\) 0.80 PIR\_Int\_3 + 0.20 Gas\_3 & 5.63 \(\times\) 10\({}^{8}\) & 1.24 \(\times\) 10\({}^{5}\) & 2.15 \(\times\) 10\({}^{5}\) \\
4 & PIR\_Int\_3 \(\rightarrow\) 0.92 PIR\_Int\_4 + 0.08 Gas\_4 & 1.00 \(\times\) 10\({}^{8}\) & 1.19 \(\times\) 10\({}^{5}\) & \(-\)1.60 \(\times\) 10\({}^{5}\) \\
5 & PIR\_Int\_4 \(\rightarrow\) 0.83 PIR\_Int\_5 + 0.17 Gas\_5 & 2.84 \(\times\) 10\({}^{7}\) & 1.24 \(\times\) 10\({}^{5}\) & 9.37 \(\times\) 10\({}^{3}\) \\
6 & PIR\_Int\_5 \(\rightarrow\) 0.71 PIR\_Int\_6 + 0.29 Gas\_6 & 6.85 \(\times\) 10\({}^{3}\) & 8.85 \(\times\) 10\({}^{4}\) & 1.06 \(\times\) 10\({}^{5}\) \\
7 & PIR\_Int\_6 \(\rightarrow\) 0.73 PIR\_Int\_7 + 0.27 Gas\_7 & 5.47 \(\times\) 10\({}^{1}\) & 6.89 \(\times\) 10\({}^{4}\) & 2.06 \(\times\) 10\({}^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The thermal decomposition reaction mechanism and parameters for the polyisocyanurate (PIR) foam. Positive \(\Delta H_{r}\) corresponds to an endothermic reaction. \begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & \(C_{p}\) (J kg\({}^{-1}\) K\({}^{-1}\)) \\ \hline Glass fiber & 1.24 \(\times\) T + 442 \\ PIR & 4.86 \(\times\) T \(-\) 357 \\ PIR\_Int\_1 & 1684 \\ PIR\_Int\_2 & 1684 \\ PIR\_Int\_3 & 1684 \\ PIR\_Int\_4 & 1684 \\ PIR\_Int\_5 & 1684 \\ PIR\_Int\_6 & 1684 \\ PIR\_Int\_7 & 0.41 \(\times\) T \(-\) 883 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Heat capacities of condensed-phase components of the PIR foam. Figure 11 of Appendix A. They indicate that the model captures both the TGA and DSC data at the lower and higher heating rate well.
# Enhanced Feature Selection via Hierarchical Concept Modeling
## 5 Results and Discussion  
### The Original Performance Results  
Practically, the datasets in Table 2 were run using 10-fold cross-validation, which is the most used method. Cross-validation metrics further validated the stability and reliability of our selected features, providing key metrics such as mean accuracy, F1-score, and standard deviation. In this work, we selected accuracy as the primary metric to gauge the stability of model performance across different data splits. Accuracy was chosen for its straightforward interpretation and its widespread use as an effective measure of a model's ability to correctly classify instances across classes. For this, each dataset was divided into  
Figure 2: The experimental design.  
complementary subsets, performing the analysis on one subset, called the training set, and validating the analysis on the other subset, called the testing set. The results from our experiments for classification accuracy are shown in Table 3. To improve the interpretability of the classification accuracy results, we provide a color-coded heatmap visualization for Table 3 shown in Figure 3. This heatmap illustrates classification performance across different classifiers--DT, SVM, and ANN--for each dataset. Darker shades indicate higher classification accuracy, making it easier to identify which classifiers performed best for each dataset at a glance.  
In this experiment, the input data had not been reduced; instead, all original attributes were used for training. In Table 3, the results show the standard classification accuracy for  
Figure 3: The classification accuracy comparison across DT, SVM, and ANN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**No. Attributes**} & \multicolumn{3}{c}{**The Classification Accuracy (\%)**} \\ \cline{3-5}  & & **Org-DT** & **Org-SVM** & **Org-ANN** \\ \hline Dermatology & 33 & 84.99 & 84.50 & 85.09 \\ \hline Glass & 10 & 100.0 & 95.26 & 99.05 \\ \hline Iris & 4 & 78.67 & 76.98 & 77.00 \\ \hline Lung-cancer & 56 & 80.83 & 80.56 & 81.00 \\ \hline Movement & 91 & 73.06 & 72.69 & 72.88 \\ \hline Pageblocks & 10 & 97.94 & 96.05 & 97.88 \\ \hline Segmention & 19 & 89.09 & 88.63 & 88.70 \\ \hline Soybean & 35 & 96.00 & 95.98 & 96.00 \\ \hline Tunadromd & 242 & 96.64 & 99.10 & 99.22 \\ \hline Wine & 13 & 83.17 & 80.82 & 82.98 \\ \hline Zoo & 17 & 96.09 & 95.53 & 95.72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of datasets and classifier performances.  
each dataset when using DT, SVM, and ANN. Mostly, DT was the superior one. The accuracy results are used as benchmarks for evaluation the feature selection in the next section.
## 4 Proposed Approach
### Design Methodology
#### 4.2.11 Minimizing User-Side Hardware Dependencies  
To enhance accessibility and usability, our solution is designed to minimize reliance on specialized user-side hardware. Users can manage their digital identities without requiring trusted devices or hardware tokens, ensuring broader adoption and usability across diverse environments. This approach reduces barriers to entry while maintaining strong security guarantees through software-based cryptographic protections and secure authentication protocols.
# Systematic Mapping Study on Remote Sensing in Agriculture
## 3 Results of the Systematic Mapping Study
### MQ4. Are RSA Studies Empirically Validated?  
This question is closely related with the previous one, as both give an overview of how research is done. For this reason, Figure 4 shows the relationship between research types and empirical validation. Except for the review papers (which do not require validation), all the selected works were empirically evaluated. Most of the papers were evaluated through experiments, particularly data-based experiments. One paper explicitly stated using meta-analysis approach in its evaluation. Moreover, only 2% of the selected papers conducted case studies. These results demonstrate the importance of creating complete, verified and public available remote image databases for RSA research.
## 4 Experiments and Evaluation
### Metrics  
In order to thoroughly evaluate the performance of our watermarking technique, we employed several key metrics. These metrics are crucial for assessing the robustness, imperceptibility, and overall effectiveness of the watermarking scheme. The metrics used in our evaluation include the following:  
**Peak Signal-to-Noise Ratio (PSNR):** The PSNR between the original image \(I\) and the watermarked image \(I^{\prime}\) is calculated as:  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{\text{MAX}^{2}}{\text{MSE}}\right)\]  
where MAX is the maximum possible pixel value (typically 255 for 8-bit images) and MSE is the mean-squared error between the original and watermarked images:  
\[\text{MSE}=\frac{1}{mn}\sum_{i=1}^{m}\sum_{j=1}^{n}(I(i,j)-I^{\prime}(i,j))^{2} \tag{8}\]  
**Structural Similarity Index (SSIM):** The SSIM index between the original image \(I\) and the watermarked image \(I^{\prime}\) is given by:  
\[\text{SSIM}(I,I^{\prime})=\frac{(2\mu_{I}\mu_{I^{\prime}}+c_{1})(2\sigma_{I,I^ {\prime}}+c_{2})}{(\mu_{I}^{2}+\mu_{I^{\prime}}^{2}+c_{1})(\sigma_{I}^{2}+ \sigma_{I^{\prime}}^{2}+c_{2})} \tag{9}\]  
where \(\mu_{I}\) and \(\mu_{I^{\prime}}\) are the mean intensities of the original and watermarked images, \(\sigma_{I}\) and \(\sigma_{I^{\prime}}\) are the standard deviations, and \(\sigma_{I,I^{\prime}}\) is the covariance. The constants \(c_{1}\) and \(c_{2}\) are small positive constants to avoid division by zero.  
**Normalized Cross-Correlation (NC):** The NC between the original watermark \(W\) and the extracted watermark \(W^{\prime}\) is calculated as:  
\[\text{NC}(W,W^{\prime})=\frac{\sum_{i=1}^{N}W(i)W^{\prime}(i)}{\sum_{i=1}^{N} W(i)} \tag{10}\]  
where \(N\) is the number of bits in the watermark and \(W(i)\) and \(W^{\prime}(i)\) are the \(i\)-th bits of the original and extracted watermarks, respectively.  
**Bit Error Rate (BER):** The BER between the original watermark \(W\) and the extracted watermark \(W^{\prime}\) is calculated as:  
\[\text{BER}=\frac{1}{N}\sum_{i=1}^{N}\delta(W(i),W^{\prime}(i)) \tag{11}\]  
where \(N\) is the number of bits in the watermark and \(\delta(x,y)\) is the Kronecker delta function, that returns 0 if \(x=y\) and 1 otherwise.  
Figure 2: Images used in our experiments sourced from the COVID-19 dataset. From the left to the right are the COVID-19-pneumonia-12, COVID-19-pneumonia-14-PA, COVID-19-pneumonia-35-1, NORMAL2-IM-0441-0001, NORMAL2-IM-0894-0001, and NORMAL2-IM-1275-0001 images.  
These formulas provide quantitative measures for assessing the quality and fidelity of the watermarking process, aiding in the evaluation and comparison of different watermarking techniques.
# Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
###### Abstract  
To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differential evolution algorithm (ADDSDE) is proposed. Firstly, this entails using the chaos mapping strategy to initialize the population to increase population diversity, and secondly, a new weighted mutation operator is designed to weigh and combinentation strategies of the standard differential evolution (DE). The scaling factor and crossover probability are adaptively adjusted to dynamically balance the global search ability and local exploration ability. Finally, a Gauss perturbation operator is introduced to generate a random disturbance variation, and to accelerate premature individuals to jump out of local optimization. The algorithm runs independently on five benchmark functions 20 times, and the results show that the ADDSDE algorithm has better global optimization search ability, faster convergence speed and higher accuracy and stability compared with other optimization algorithms, which provide assistance insolving high-dimensionaland complex problems in engineering and information science.  
Keywords:differential evolution algorithm; adaptive dynamic disturbance strategy; Gauss perturbation; benchmark functions +
Footnote †: journal: Applied Sciences
## 5 Simulation Analysis
### Simulation of Improved Artificial Potential Field  
In order to verify that the improved artificial potential field method can overcome the problems of local minimum and goals being unreachable, the traditional artificial potential field method and the improved artificial potential field method are compared and tested. In the process of several simulations, they performed the same, and all could reach the target point. However, in order to highlight the advantages of the improved algorithm, we specifically find this special case and record the coordinate points to present it.  
The specific parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Symbol** & **Value** \\ \hline Attractive field factor & \(k_{att}\) & 15 \\ Repulsive field factor & \(k_{rep}\) & 5 \\ Obstacle influence distance & \(\rho_{0}\) & 1 m \\ Goal influence range & \(d_{0}\) & 3 m \\ Maximum number of iterations & \(n\) & 200 \\ Time step & \(\Delta t\) & 0.1 s \\ Angle influence factor & \(\alpha\) & 0.2 \\ Velocity influence factor & \(\beta\) & 0.1 \\ Distance influence factor & \(\gamma\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the improved potential field method.  
Figure 6: The flow-process diagram of the hybrid algorithm.  
The left side pictures represent the simulation results of traditional artificial potential field method, and the right side is the improved artificial potential field method. In the same environment, it can well show the excellent performance of the improved traditional artificial potential field method, which not only solves the local minimum problem, but also has shorter running path, and also proves the effectiveness of the improved algorithm.  
It can be seen from Table 4 that the robot using the traditional artificial potential field method halts and vibrates when halfway, since the value of the resultant force is equal to 0 in the case of the local minimum problem. The improved artificial potential field method solves this problem well, which again verifies the superiority of the improved artificial potential field method compared with the traditional artificial potential field method.
# A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters
## 3 PA Modeling Performance
### Comparison to a Simplified bi-CKV  
In the conference paper [15], a simplified case of the bi-CKV model (10) was proposed assuming \(q_{2}=q_{3}=q_{4}=\cdots\) and \(q_{1}^{\prime}=q_{2}^{\prime}=q_{3}^{\prime}=\cdots=q_{1}+q_{2}\). When grouping separately the terms with the envelope raised to an even power and the terms with the envelope raised to an odd power, the output is given by  
\[y(k)=\sum_{n=1}^{N}{}^{\prime}\sum_{\mathbf{q}_{2}=0}^{\mathbf{ Q}_{2}}h_{n}(q_{1},q_{2})x(k-q_{1})|x(k-q_{1}-q_{2})|^{n-1}+\\ +\sum_{n=2}^{N}{}^{\prime\prime}\sum_{\mathbf{q}_{2}=0}^{\mathbf{ Q}_{2}}h_{n}(q_{1},q_{2})x(k-q_{1})|x(k-q_{1}-q_{2})|^{n-1}. \tag{12}\]  
The prima (\({}^{\prime}\) ) indicates that only odd \(n\) indices are included in the first sum and the double prima (\({}^{\prime\prime}\) ) indicates that only even \(n\) indices are included in the second sum. Under this perspective, the GMP model can be considered a particular case of the bivariate CKV model. In Figure 6, the results of the model published in [15] are plotted with asterisks marks, exhibiting a superior performance compared to the univariate CKV model. The NMSE of the present bi-CKV model, indicated with a solid line in the same figure, displays an appreciable further improvement.  
The NMSE results for a class AB and a class J amplifiers are depicted in Figure 7. The evolution of the error versus the number of coefficients that the DOMP algorithm adds to the active regressors set under the restriction of the BIC rule demonstrates an optimum number between 15 and 20 regressors for the class AB PA model. The behavior of the class J PA is more complex--it exhibits not only gain compression but also a notable gain expansion for low power levels, and the corresponding optimum model contains a number between 20 and 25 regressors fixed by the BIC. The data show that conventional models are outperformed by the bivariate models in the two PA cases. It can be observed that even in the case that we continue adding more coefficients, the conventional models do not improve the NMSE. This fact indicates that, without bivariate terms, their stock of regressors lacks the necessary terms to reach a better precision.  
Figure 6: Comparison of NMSE for the bi-CKV model and the results published in [15] applied to a class AB power amplifier operated at 3.6 GHz with an average output power of 27.4 dBm.
###### Abstract  
As a typical pattern recognition method, communication signal modulation involves many complicated factors. Fractal theory can be used for signal modulation feature extraction and recognition because of its good ability to express complex information. In this paper, we conduct a systematic research study by using the fractal dimension as the feature of modulation signals. Box fractal dimension, Katz fractal dimension, Higuchi fractal dimension, Petrosian fractal dimension, and Sevcik fractal dimension are extracted from eight different modulation signals for signal pattern recognition. Meanwhile, the anti-noise function, box-diagram, and running time are used to evaluate the noise robustness, separability, and computational complexity of five different fractal features. Finally, Bback-Propagation (BP) neural network, grey relation analysis, random forest, and K-nearest neighbor are proposed to classify the different modulation signals based on these fractal features. The confusion matrices and recognition results are provided in the experimental section. They indicate that random forest had a better recognition performance, which could reach 96% in 10 dB.  
pattern recognition; fractal dimension; feature evaluation; random forest classifier +
Footnote †: journal: Applied Physics Letters
# Laser Inter-Satellite Link Visibility and Topology Optimization for Mega Constellation
## 3 Methodology and Analytical Approach
### Permanent and temporary LISLs  
Intersatellite links can be classified as permanent and temporary LISLs, also referred to as static and dynamic LISLs, depending on the duration of their connection. Permanent and temporary inter-satellite links are determined by the relative positions and motion relationships between satellites in the constellation, i.e., the geometric configuration of the constellation, the relevant performance parameters of the antennas, the phase difference, and the azimuth. These parameters also serve as the main parameters describing the satellite communication link. Permanent interstellar links are always connected and do not have to be dynamically reconstructed, while temporary interstellar links are often dynamically disconnected and dynamically reconstructed [19].  
Permanent LISLs established between satellites orbiting at the same speed within the same orbital plane are relatively simple to upkeep; between neighboring satellites orbiting at different speeds within the same orbital plane, there are some difficulties in establishing permanent LISLs due to the relative speeds, but over time, a large number of temporary LISLs are generated within different time slices. For instance, when observing the Starlink constellation from any region above the Earth, half of the constellation's satellites can be seen orbiting toward the southeast while the other half orbit toward the northeast. These satellites on mission generate a large number of intermittent LISLs between the crossed orbital planes. While this connection state cannot last for an extended period of time, such a link is essential for increasing the constellation redundancy and resistance to destruction, improving the routing options and thus reducing the path delay. As a result, determining the pattern of interstellar links between orbits at various stages of the cycle is an especially intriguing task for enhancing the flexibility of network communication.
## 3 Definitions  
Given a homogeneous WSN deployed in a building for fire hazard applications with \(N\) sensors and \(M\) sinks, each sensor can adjust its maximal transmission ranges to one of the \(k\) levels: \(r_{0}\), \(r_{1}\ldots\)\(r_{k\cdot l}=r_{max}\) by using different transmission power levels from \(p_{0},p_{l},\) till \(p_{k\cdot l}=p_{max}\). Initially, all sensors work in \(p_{0}\). From the application aspect, real-time and robustness are two main challenges. \(T_{max}\) is the maximum acceptable delay in reporting such a fire event to a sink node. It is required that each sensor \(i\) will report data packets to a sink node, such that:  
(1) A communication path from sensor to the sink can be found if such a path exists.  
(2) The end-to-end delay of the path is no more than \(T_{max}\).  
(3) The choice of route is adaptively changed in response to failed nodes (assumed to be caused by fire damage).  
(4) A suitable minimized power level (_min_\(\{p_{0},p_{l\ldots}\)\(p_{k\cdot l}\}\)) is selected to ensure transmission to satisfy (1), (2), (3) without unnecessary power dissipation.  
Each node in the network exists in one of four states (listed in the order of health degree from best to worst):  
\"_safe_\": initial state while no fire occurs.  
\"_lowsafe_\": one-hop away from an \"_infire_\" node.  
\"_Infire_\": when detects fire.  
\"_unsafe_\": detects that it cannot work correctly any longer due to a definite fire  
There is a STATE message recording current change of node state to notify its neighborhood nodes in a fire.  
STATE (INFIRE) message: If a sensor detects fire, it enters \"_infire_\" by broadcasting a message out to denote a new local fire source.  
STATE (LOWSAFE) message: The nodes in \"_safe_\" state that receive a STATE (INFIRE) message will become \"_lowsafe_\", and then broadcast a STATE (LOWSAFE) message to notify its neighbors. The nodes that hear the STATE (LOWSAFE) message will get to know the new state of its neighbors about fire and do nothing.  
STATE (UNSAFE) message: An \"_infire_\" node works until it cannot work correctly. Before it cannot work any longer, it enters into \"_unsafe_\" state and broadcast a message. Any nodes that detect its residual energy is too low to work will enter into \"_unsafe_\". And then broadcast a STATE (UNSAFE) message.  
Thus each sensor may change its state autonomously in response to the fire and messages it receives, as shown in Figure 1.