esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Fit Modeling for Temperature Mitigation in
###### Abstract  
The purpose of the study is to develop a general method to predict local temperature changes from mitigating the urban heat island effect using local climate engineering. Specifically, the effects of a plume of calcite particles above cities have been found. Previous modeling work has been carried out with supercomputers, but those models have limited geographies and timelines. The main goal of this work is to produce a method that can be applied more generally and more quickly. This overcomes limited modeling data in arid regions. Arid cities show the most effective use of calcite plumes for local solar radiation management, but those areas have limited data. The new method is to use numerical fit techniques using actual weather data. The default heating and cooling rates are fit to historic rates, and then the radiative properties of the calcite are used to predict the change in the heat transfer rates. Air temperatures at a standard height of 2 m are predicted. The key findings are that the numerical fit gives comparable results to the full supercomputer model, but the numerical fit gives predictions of greater temperature change. This was explained as primarily due to how advection is handled differently by the methods. Adjustments in the methods are discussed so that the effect of advection is included. The conclusion is that numerical fit provides a method that can easily be applied to arid regions.  
urban heat mitigation; arid climates; solar radiation management; single column model; aerosol reflectivity Article
## 3 Cryptographic Preliminaries  
### Notation  
We denote a scalar value in lower-case letters, e.g., \(x\). We denote vectors in lower-case bold letters, e.g., \(x\), and we refer to the \(i\)th element of a vector as \(\mathbf{x}_{i}\), with indexing starting from one. For any two vectors \(\mathbf{u},\mathbf{v}\in\mathbb{R}^{n},\mathbf{u}+\mathbf{v}\) and \(\mathbf{u}\times\mathbf{v}\) signify component-wise addition and multiplication, respectively, while \(\mathbf{u}\cdot\mathbf{v}\) denotes the dot product between \(\mathbf{u}\) and \(\mathbf{v}\). We use \(\left<\mathbf{u}\right>_{r}\) to denote the cyclically leftward-shifted (rotation) vector of \(\mathbf{u}\) by \(r\) positions, with the understanding that a negative \(r\) corresponds to a rightward shift. We denote a matrix in capital bold letters, e.g., \(\mathbf{W}\), and we refer to the \(i\)th column of a vector as \(\mathbf{W}_{i}\). We let \(\mathbf{x}\cdot\mathbf{W}\) denote vector-matrix multiplication. Additionally, we use \(\mathcal{R}\) to denote the cyclotomic ring \(\mathbb{Z}[X]/\left<X^{N}+1\right>\), where \(N\) is a power of two, and \(\mathcal{R}_{q}=\mathcal{R}/q\mathcal{R}\).
## 4 Experiments
### Neural Topic Ensembel Model  
Throughout the previous experiments, both the baseline and proposed models performed the experimental tasks using only one model. In contrast, we propose an ensemble learning model that combines two pretrained models with the topic attention model (TAM) [29]. A pretrained BERT-based model obtains CLS tokens containing information from all encoder representations. Each received CLS token and final encoder representation m, obtained through the TAM, are then combined to obtain s, which is applied to the desired task. The resulting architecture, depicted in Figure 5, is referred to as the ALBERT neural topic ensemble model (ANEM) or ELECTRA neural topic ensemble model (ENEM).  
As in the previous experiment, performance was evaluated by averaging the results of five epochs repeated nine times, with the dropout rate set to 0.1. The experimental results are listed in Table 5. The best results comparing the baseline model, the proposed model, and the ensemble model are highlighted in bold.  
For the MRD and 20 Newsgroups datasets, both ANEM and ENEM appeared to perform worse than the baseline models, with a significant difference for ANEM on the MRD dataset. For the YELP dataset, although the baseline model outperformed ANEM, ENEM somewhat outperformed its baseline in terms of precision, recall, and F1 score.  
The underperformance of the ensemble model compared to both the baseline models and our previously proposed model may stem from its reliance on a single CLS token for integrating topic vectors. By using only one token to encapsulate all the information, the model may struggle to leverage the combined information effectively. Therefore, utilizing a topic vector for each token, which contains specific information, could facilitate more nuanced processing and lead to further performance improvements.  
Figure 5: Structure of ALBERTA (ELECTRA) neural topic ensemble model.  
These results indicate that the proposed models outperformed both the baseline and ensemble models, with the exception of ELECTRA on the 20 Newsgroups dataset.  
In conclusion, while the proposed neural topic models demonstrated improved performance in most cases, further investigation is required to understand the performance drop observed with the ENTM on the 20 Newsgroups dataset.
## 2 Methods
### Data Preprocessing and Imputation  
Before clustering analysis, the dataset was cleaned and prepared. Firstly, the features (i.e., the variables) were standardized by applying \(\widetilde{x}=\frac{x-\mu_{x}}{\sigma_{x}}\), where \(x\) is the original feature, \(\sigma_{x}\) represents its sample standard deviation, \(\mu_{x}\) its sample mean, and \(\widetilde{x}\) is the standardized feature. This ensures that all features have zero mean and unit variance, which is required by many clustering techniques, especially those that employ Euclidean distances (typically inside the Gaussian similarity function) to compute the similarity between the data points; otherwise, larger features would be considered as being more dissimilar only by virtue of the scale that they were measured in.  
Secondly, missing values were imputed by means of k-Nearest Neighbors imputation (with \(k=5\)), which simply replaces the missing value by the mean value of the \(k\) nearest points (in terms of Euclidean distance) for that feature. Table 1 contains some summary statistics for the dataset. It must be noted that these imputations were only applied for the clustering phase and, after obtaining the clusters, any further statistical tests were performed on the actual data, with no imputation applied. For both imputation and clustering steps, Python library scikit-learn (ver. 0.23.1) [33] was employed.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 5 Numerical and Simulation Results
### Contentions versus GWs Number  
In this section, we consider the total contention reduction ratio by optimal placing of 1-5 GWs placed by PGL algorithm with capture and IC and capture only (Figure 15, which presents a comparison to Ref. [8]). The contention reduction ratio is defined \((N-\bar{n}-1)/(N-1)\), where \(N\) is the total number of SNs and \(\bar{n}\) is the average contention. We note that \(N-1\) is the contention of a SN when no capture is possible; in this case, any interference level is enough to prevent a packet from being decoded. The contention reduction ratio can be interpreted as the average fraction of SNs whose colliding packet will not cause a given SN's packet to be lost. A perfect contention reduction ratio would be 1, which would mean that no SN's packet would be lost in a two-way collision with any other SN's packet. In this simulation, the number of SNs is increased up to 500 following a uniform random spatial distribution in a 100 m \(\times\) 100 m area with the 1 m \(\times\) 1 m pixel density. It is shown obviously in Figure 15a that a small number of GWs placed by optimal algorithm of PGL with capture and IC can decrease the contentions significantly. In other words, many deployed GWs is not necessary for some network layouts. For example, two GWs can reduce the contention level to above 90%, and 3-5 GWs provide little additional benefit on reducing the contention level. Therefore, the optimal number of GWs can be decided in the network infrastructure, based on this algorithm and other requirements.
# Temperature Control Unit--Modeling and Implementation of a Particle Filter on a Microcontroller
## 6 Discussion  
Due to the simplicity of the assumed models, some features have been ignored, such as the nonlinearities of the system, the presence of two input signals at the same time, or the impact of the external temperature. This allowed for an effective implementation of an algorithm that operates with a real object on a device with low computing power. The mentioned features were effectively compensated for by the process noise generated in the particle sampling step. The heating process appeared to be more predictable, and the selection of parameters was unproblematic. The cooling process is also more complex to model.  
The results were also obtained for various variances and temperatures (see Appendix A). They show that the assumed values of the process and measurement noise variance have a critical impact on the quality of the PF performance. If the value of \(\sigma_{v}^{2}\) is too high, the results affect the significant deviations of the mathematical model from the real object--in the particle sampling process, significant variance noise is added and later particles may not be able to match the actual measurements. For this research, better results were obtained in the case with lower variance values of the process noise (see Figure 14,e, for example), which proves that the model is well fitted, despite its simplification. It is also shown by the correct operation of the algorithm and the good adjustment of the estimate to the measurements in the case of different temperature values (operating points), as one can see in Figure 14,j.  
The variance of the measurement noise is responsible for the inaccuracies of the sensor. In each test case, the best effects were observed for the \(\sigma_{n}^{2}\) value set comparable to the resolution of the measuring device. An overly high value of the setting resulted in the limiting of the confidence in the measurement in favor of the mathematical model. Ultimately, a compromise must always be made between trusting the model and the measurements. This allows us to compensate for any inaccuracies by the particle filter.  
With correctly selected PF settings, a sufficiently large number of particles is needed for an effective state estimation. The number of particles must also be well suited to the computing power of the microcontroller to its capabilities.  
In the case of precise process control, the use of feedback from the estimated value may improve the algorithm's performance. Estimated values are commonly used in control, for example, in the active disturbance rejection control (ADRC) method [39]. Improving the sensory information may be necessary, for example, if the resolution of the measuring device is too low.
## 2 Materials and Methods  
### Materials  
The experimental data used in this paper are obtained from the University College Dublin Sleep Apnea Database (UCDDB). It contains 25 (4 females and 21 males; mean age = 49.96 years; range: 28-68) full overnight polysomnograms (PSGs) with simultaneous three-channel Holter ECG. The polysomnogram records were obtained by utilizing the Jaeger-Toennies system, and a 10-20 electrode placement system was used to describe the locations of scalp electrodes. Each record in the database consisted of 2 EEG channels (C3-A2 and C4-A1), 2 EOG channels, and 1 EMG channel, with the sampling frequency at 128 Hz. According to the R&K criteria, each EEG signal was divided into segments of 30 s (3000 data points), with each segment corresponding to a single sleep stage including AWA, REM, S1, S2, S3, S4, Artifact, and Indeterminate. In order to delete artifacts like body movements or arousals, EEG signals labelled _Artifact_ and _Indeterminate_ were filtered and removed.  
Subjects in this database were diagnosed with possible obstructive sleep apnea, central sleep apnea or primary snoring: (i) mean PSG recording = 6.9 h; range: 5.9-7.7; (ii) mean AHI = 24; range = 2-91 [36]. These subjects are grouped according to the severity of the sleep breathing-disorder which is evaluated by the apnoea-hypopnoea index (AHI) for statistical analyses: Group A (\(n\) = 11) AHI \(<\) 15 (mild); Group B (\(n\) = 8) AHI \(15\leq\) AHI \(<\) 30 (moderate); Group C (\(n\) = 6) AHI \(\geq\) 30 (severe). Detailed information of the grouped subjects is presented in Table 1, and Figure 1 gives examples of 30 s epoch C3-A2 and C4-A1 EEG for three Groups.  
As an information measure, entropy determines the uniformity of proportion distribution [37]. Given a time series, the greater the probability of generating new patterns means the greater its irregularity, and entropy has been widely used in evaluating the degree of randomness (or inversely, the degree of orderliness) of a time series [38]. Reports have proved that entropy can measure the irregularity of a signal, and the higher the irregularity of a signal the greater its corresponding entropy will be [39]. Specifically, fuzzy entropy (FuzzyEn) can be a measure of time series complexity in EEG signals--smaller values indicate greater sequence self-similarity, while larger values indicate more complex signal sequences. In this paper, fuzzy entropy was extracted from both C3-A2 and C4-A1 EEG channels to calculate the laterality index (LI) between left- and right-hemisphere neural activity. The regional differences in the stability of sleep across the hemispheres can be approximately determined by the LI distribution. Furthermore, the cross-zero switching frequency of LI was calculated to evaluate the interhemispheric brain switching. In this paper, we mainly explore the correlations of LI distribution and cross-zero switching rate with the severity of OSA and the corresponding sleep states. The block diagram of the method is illustrated in Figure 2, and the data is shown for a 52 year-old male (ucddb006) with an AHI of 31.
## 3 Results and Discussion
### Ductility  
The ductility of the material is indicated through the energy absorbed by a material until a complete failure takes place. In fact, material ductility is a crucial factor in the design of structures [11; 37; 38]. To quantify the ductility of a reinforced concrete structure, the ratio of curvature of the concrete from the crushing to the yielding of the reinforcement is usually reported as the ductility index [10; 18; 39; 40; 41; 42].  
As over-reinforced beams were used in this study, the use of the yielding of reinforcements to find the ductility index was more challenging. Alternatively, ductility indices were evaluated at 90% of the spalling load based on the behaviors of the over-RC beams at the spalling load, peak load, and post-peak response. This phenomenon is different from the seismic design methodology explained in Euro-code 8 [43], where a post-peak value of 85% of the maximum bending value was used to define the local sectional ductility index.  
The definition of ductility presented in this study was based on the load-deflection curve under compressive loading. Therefore, the displacement ductility ratios were defined as \(\mu_{1}=\Delta_{\text{peak}}/\Delta_{\text{spall}}\) and \(\mu_{2}=\Delta_{0.9,\text{spall}}/\Delta_{\text{spall}}\), while the center point deflection at 90% of the spalling load was defined as \(\mu\) 0.9, spall. Tables 6 and 7 illustrate the BCSP-15 beam, where the  
Essentially, increasing the thickness of steel plates led to higher ultimate deflection and ductility. A ductility of 10%, 19.1%, and 17% was achieved in the BCSP-6, BCS-10, and BCSP-15 beams respectively, which was higher than the ductility of the CB-beam according to the spalling load (\(\mu_{1}\)). However, the ductility of the BCSP-15 beam was based on the yield load. Meanwhile, the ductility index (\(\mu_{2}\)) for the BCSP-6 and BCSP-10 beams amounted to 2.7 and 2.9 respectively due to the increase in the thickness of the steel plate in the compression zone. As a result, the beam stiffness increased which restricted the crack propagation and improved the ductility. An improvement in the BCSP-15 ductility index (\(\mu_{2}\)) by 2.2 was observed. For this reason, the toughness of the compression zones of the BCSP-15 beam posed a minor influence on structural ductility. However, the capacity strength significantly increased due to the inverse relationship between flexural capacity and ductility, as shown in Tables 6 and 7.
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 3 Methodology
### Image Preprocessing
#### 3.3.1 Contrast Stretching  
Contrast Stretching was applied to the collected data to enhance the image quality. Image contrast is a measure of the spread of its histogram. Contrast stretching aims to expand the dynamic range of an image. It transforms the grayscale level in the range \(\{0,\)\(1,\)\(\ldots,\)\(L-1\}\) using a piecewise leaner function [17] to determine the limits over which image intensity values will be extended. These lower and upper limits will be called \(a\) and \(b\), respectively (for standard 8-bit grayscale pictures, these limits are usually 0 and 255). The histogram of the original image is examined to determine the value limits (lower = \(c\), upper = \(d\)) in the unmodified image. If the original range covers the full possible set of values, straightforward contrast stretching will achieve nothing, but even then, sometimes, most of the image data are contained within a restricted range; this restricted range can be stretched linearly, with original values that lie outside the range being set to the appropriate limit of the extended output range. Then, for each pixel, the original value \(r\) is mapped to the output value \(s\) using the appropriate function. The equation for the original value \(r\) is mapped to the output value \(s\) for each pixel as follows:  
\[S=(r-c)\left(\frac{b-a}{d-c}\right)+a \tag{10}\]
## 6 Imperceptibility Metrics  
The imperceptibility of the watermark is assessed using three key metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalised Cross-Correlation (NCC) [76; 77] as shown in Table 4. These metrics provide quantitative evaluations of how well the watermark preserves the visual and structural integrity of the original image.  
The SSIM is a number that lies in \([0,1]\), and higher values indicate the greater similarity between the images.  
The metrics are defined as follows [76]:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Avg. Original Features** & **Avg. Matched Features** & **Avg. Feature Match Ratio** \\ \hline BOSSBase256 & 381.4 & 261.0 & 0.684 \\ BOSSBase512 & 475.4 & 328.3 & 0.690 \\ COCO2017 & 495.9 & 341.2 & 0.688 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Feature extraction matching across public datasets.  
**PSNR:**  
\[PSNR=10\cdot\log_{10}\left(\frac{255^{2}}{MSE}\right)\]  
where \(MSE\) is the mean square error between the original and watermarked images.  
**SSIM:**  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2}+\mu_{ y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean values, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances, and \(\sigma_{xy}\) is the covariance of the images \(x\) and \(y\).  
All datasets exhibit PSNR values above 46 dB. BOSSBase512 achieves the highest average PSNR (49.37 dB), which highlights improved imperceptibility with larger image sizes. Consistently high SSIM values (\(>\)0.99) across all datasets demonstrate the preservation of structural information. The slight decrease in SSIM for COCO2017 (0.9935) can be attributed to the complexity of the colour images. Near-perfect NCC values (\(>\)0.99) indicate a strong correlation between the original and watermarked images, which further confirms the imperceptibility of the method.  
_Imperceptibility Assessment_  
We evaluate the imperceptibility of the watermark using subjective and objective measures. Subjective evaluation involves human observers rating the visual quality of original and watermarked images. However, this method can be time-consuming and subject to individual perceptual variations [78]. For an objective assessment, we analyse image statistics, specifically the histogram of pixel intensities [79]. Similar histograms between the original and watermarked images suggest minimal alteration of the overall pixel distribution. Figure 4 shows the histograms of the original and watermarked images. The near-identical distributions indicate that this proposed watermarking scheme introduces minimal changes to the image's pixel intensity profile.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Avg. PSNR (dB)** & **Avg. SSIM** & **Avg. NCC** \\ \hline BOSSBase256 & 48.91 & 0.9967 & 0.9998 \\ BOSSBase512 & 49.37 & 0.9956 & 0.9997 \\ COCO2017 & 46.13 & 0.9935 & 0.9993 \\ Disk JPEGs & 47.53 & 0.9960 & 0.9967 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PSNR, SSIM and NCC results across all four datasets.