esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology: Data Pre-Processing and Models
### Super-Model  
Figure 2 shows the construction of two classes of models that divide the long term power load prediction into two stages. First, the temporal prediction models are trained to predict the future weather conditions given the past weather window. We implemented a Stacked Long Short Term Memory (LSTM) model to train the temporal weather prediction, and each weather factor was predicted using an individual LSTM model. The second model type associates the predicted future weather conditions with the community power load. We designed and trained the multilayer perceptron artificial neural network (ANN) to associate the weather conditions with the power demand.  
The main reason for building a hybrid machine learning algorithm with the LSTM and ANN models is because of the size, continuity and diversity limitations of the available data-set used to train the models. The previous research suggests using a single LSTM architecture to train a power generation or power draw model [3; 4]; we experimented with a single LSTM model that used the weather data as its inputs and predicted the future power load outputs, but the resulting prediction accuracy was less than 10%. The available data set is too small to train a single model--less than one year's worth of data that had to be divided into the training and validation subsets. Also the data set is not continuous and several time periods were missing power load measurements and had to be omitted. Furthermore, the data had low diversity. We define the data diversity as the number of unique weather conditions and the associated power load measurements grouped into the similarity bins. Semantically, each bin represents a unique weather regime with associated power load. Ideally, the bins would have a uniform frequency distribution of the number of data instances in each bin. On the contrary, the bin frequency distribution was skewed and with some bins with high frequency count and several bins with a few (less than 5) instances. Each bin had to be further split into the training and validation subsets. The compound effect of data scarcity and the lack of diversity resulted in low power prediction accuracy model.
# A Molecularly Imprinted Polymer for Selective Extraction of Phenolic Acids from Human Urine
## 3 Results and Discussion
### Optimization of MISE Procedure
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**4-HPA**} & \multicolumn{2}{c}{**3-HPA**} & \multicolumn{2}{c}{**4-HRA**} & \multicolumn{2}{c}{**PCA**} & \multicolumn{2}{c}{**SyA**} & \multicolumn{2}{c}{**Ha**} \\ \cline{2-13}  & & **Value** & **S.D.** & **Value** & **S.D.** & **Value** & **S.D.** & **Value** & **S.D.** & **Value** & **S.D.** & **Value** & **S.D.** \\ \hline \multirow{3}{*}{\(\phi-\left[\frac{\text{Langmuir}}{\text{Data}\text{K}_{\text{F}}}\right]^{ \text{th}}\)} & A = 1/(\text{O}_{\text{max}}\) & 0.0076 & 0.003 & 0.009 & 0.008 & 0.0024 & 0.010 & 0.0255 & 0.005 & 0.017 & 0.004 & 0.009 & 0.005 \\  & B + 1/(\text{O}_{\text{max}}\text{Data}\text{K}_{\text{F}}\) & 0.173 & 0.275 & 1.646 & 0.1031 & 2.186 & 0.136 & 0.0255 & 0.008 & 0.910 & 0.023 & 17.002 & 0.351 \\  & 0.008 & 0.008 & 0.009 & 0.009 & 0.009 & 0.009 & 0.009 & 0.009 & 0.009 & 0.009 & 0.009 & 0.009 & 0.009 \\ \hline \multirow{3}{*}{\(\phi-\left[\frac{\text{F}}{\text{F}}\right]^{\text{Fumillich}}\)} & A = \(\text{kg}\,\text{K}_{\text{F}}\) & - - & - & - & - & 0.008 & 0.005 & - & - & - & 0.0052 & 0.006 & - & - & 0.0081 \\  & b = 1/(\text{O}_{\text{max}}\text{K}_{\text{F}}\) & b = 1/(\text{O}_{\text{max}}\) & 0.008 & 0.042 & 0.087 & 0.026 & 0.027 & 0.020 & 0.819 & 0.029 & 0.085 & 0.020 & 0.081 & 0.028 \\ \cline{1-1}  & R & 0.992 & - & 0.993 & - & 0.995 & - & 0.995 & - & 0.991 & - & 0.986 \\ \hline \hline \end{tabular}  
* \(\text{C}_{\text{e}}\) is the concentration of solution at equilibrium (mg/L), Q is the corresponding adsorption capacity (mg/g), Q\({}_{\text{max}}\) (mg/g) and K\({}_{\text{L}}\) (L/mg) are constants which are related to adsorption capacity and energy or net enthalpy of adsorption, respectively. \(\text{K}_{\text{F}}\) and n are Freundlich constants which correspond to adsorption capacity and adsorption intensity, respectively.  
\end{table} 
Table 1: Langmuir and Freundlich isotherm parameters obtained by non-linear fitting for the MIP polymer.
## 4 Experiments
### Experimental Setup
#### 4.1.4 Implementation Details  
All of the experiments were conducted on a server with Intel Xeon E5-2698 v4 CPU, 256 GB RAM, and four NVIDIA Tesla V100 GPUs. The framework was implemented in Python 3.8 using PyTorch 1.9.0. Key hyperparameters are detailed in Table 2.  
To ensure reproducibility and to account for stochastic variations, each experiment was repeated 5 times with different random seeds. We report both of the mean values and standard deviations for all metrics.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 9 Challenges in the Hybrid Deep Learning Model for Brain Stroke Detection and Prediction
### Hyperparameter Tuning Complexity  
Training a hybrid model entails adjusting a variety of hyperparameters, including learning rates, batch sizes, dropout rates, and network design. Fine-tuning these parameters necessitates substantial testing and domain knowledge, making the model creation process time consuming. Furthermore, deep learning models may require distinct hyperparameter setups depending on the dataset, making transferability problematic. Automated hyperparameter optimization techniques, such as Bayesian optimization or evolutionary algorithms, can aid, but they incur additional computing expenses.
## 4 Sustainability Analysis  
To evaluate the sustainability of the material, we conducted a comprehensive multi-factor evaluation of MS/CID/ZrO\({}_{2}\) using the Sustainability Footprint (OSF) method [53], which has been applied to analyze the sustainability of various water purification materials. Based on the three core principles of ecological sustainability, economic sustainability, and social sustainability, we selected eight specific factors to evaluate the sustainability of MS/CID/ZrO\({}_{2}\). These factors included environmental friendliness, degradability, waste utilization, sources, ease of preparation, cost, removal efficiency, and recyclability. We evaluated each factor based on three levels, scoring them according to the criteria of low  
Figure 7: Plate counting method was used to test the antibacterial effect of MS and MS/CID/ZrO\({}_{2}\) against Gram-negative _E. coli_.  
(L, i = 1), medium (M, i = 2), and high (H, i = 3). Unlike the other factors, due to the negative impact of \"cost\" on the sustainability of materials, the effects generated by \"cost\" are opposite to other factors, corresponding to low (L, i = 3), medium (M, i = 2), and high (H, i = 1), respectively. The overall sustainability of the material was calculated using the OSF Formula (2):  
\[OSF=100\%\times\sum_{\text{j}=1}^{8}\left(\frac{\text{i}}{3}\right)_{\text{j} }\times\frac{1}{8} \tag{2}\]  
in which j represents sustainability and i represents the corresponding ranking score.  
We chose four previously published materials, including biochar, zeolite, diatomaceous earth, and metal-organic frameworks, to compare with MS/CID/ZrO\({}_{2}\) to study their sustainability. According to Formula (2) above, they correspond to sustainability values of 62.5%, 66.6%, 70.8%, 70.8%, and 75%, respectively, as shown in Figure 8. It can be found that the MS/CID/ZrO\({}_{2}\) reveals relative advantages in terms of cost, source, and ease of preparation. The production of MS increases dramatically every year, providing a continuous supply of raw materials for the preparation of MS/CID/ZrO\({}_{2}\). Due to its simple preparation process, it offers distinct advantages as an adsorbent for water treatment applications.
## 4 Change Detection Mechanism  
In this section, the analysis in Section 3 is extended to cases where there are new nodes joining the network or existing nodes leaving from the network during the operation. A detailed analysis is first provided on whether other nodes in the network can detect such changes. Then the problem of how each node can respond to such changes and adjust the transmission rate is addressed.
## 4 Discussion
In particular, templating in two radiological planes may also be very helpful with regard to the preoperative detection of possible contraindications for a certain implant, e.g., a retro torsion or an excessive ante torsion (Figure 5) of the femoral neck when planning the implantation of a short stem device with primary metaphyseal anchorage along the femoral neck. This study has some limitations. Although the external calibration marker had been positioned in a standardized way for all patients, an imprecise positioning may influence the correct scaling of the X-rays and the results of THA templating [30]. A high body mass index (BMI) that was not recorded in this study may complicate the exact positioning of the calibration marker. However, the correlation between BMI and magnification has been shown to be weak so that a major bias can be ruled out [31]. Second, due to the retrospective design, the study was not able to analyze whether templating with two radiological planes would have led to a measurable effect on complication rates within this particular study population. Furthermore, the study had an asymmetrical distribution between the CTHA and STHA groups (50 vs. 100 cases). This rather unconventional study design was chosen due to the fact that the rate of correctly planned size of digital templating of conventional stems has already been well analyzed by other studies, whereas there are very little data about short stems that were put into the focus of this study. Figure 5: Digital templating of a STHA (lateral view) in a patient with excessive ante torsion of the femoral neck is an obvious contraindication for an implant with orientation parallel to the femoral neck.
## 3 Results
Except for a brief time when the stimulus first turns on (Figure 6), the electric field in the tissue follows a sinusoidal time course. Therefore, to a good approximation, we could calculate the electric field analytically for a sinusoidal current that extends over all time. Figure 6: (**a**) The voltage in the tissue, \(V_{e}\), at position \(x=-10\) mm and (**b**) the tissue electric field, \(E\), at position \(x=0\), both as functions of time \(t\). This is the same data as in Figure 5, but over a shorter range of times. Figure 7: The peak tissue electric field intensity, \(E\), as function of the tissue resistance per unit length, \(r_{e}\). We assume \(V_{e}<<V_{i}\) and \(r_{i}<<r_{e}\). In that case, Equation (3) implies that \(V_{i}=\psi\); the second expression in Equation (1) then becomes  
\[\frac{\partial\psi}{\partial t}=-D\frac{\partial^{2}V_{e}}{\partial x^{2}}. \tag{10}\]  
Since \(\psi\) is known from Equation (7), we can simply integrate Equation (10) to obtain an approximate analytical expression for the tissue voltage  
\[V_{e}\approx\frac{r_{i}}{D}I_{0}\omega\text{cos}(\omega t)\left[\frac{x^{3}}{ 6}-\frac{L^{2}x}{2}\right], \tag{11}\]  
and for the tissue electric field  
\[E\approx\frac{r_{i}}{D}I_{0}\omega\text{cos}(\omega t)\left[\frac{L^{2}}{2}- \frac{x^{2}}{2}\right]. \tag{12}\]  
At \(x=0\), the amplitude of the electric field is thus \(\frac{L^{2}r_{i}\omega}{2D}I_{0}\), which agrees fairly well with Figure 2:0 We have performed several numerical simulations and found them to be consistent with this approximate analytical analysis. For instance, our calculations indicate that the electric field intensity in the tissue is proportional to the frequency. Figure 7 indicates the electric field is proportional to the tissue resistance per unit length, which follows from the factor of the diffusion constant in the denominator of our analytical solution. In traditional transcranial magnetic stimulation, the current through the coil is not sinusoidal but instead is delivered as a pulse. We have performed a simulation using a current pulse through the wire with an amplitude of 1 mA, a rise time of 10 us, and a subsequent decay of about 1 ms (Figure 8). The electric field in the tissue rises abruptly to a peak of 0.7 us after the pulse begins, and then decays and changes sign, but with a low amplitude. The peak amplitude of the electric field is about 50 mV/m, which is somewhat larger than found during a simulation with a sinusoidal current because of the abrupt rise in the pulse. In other words, during a Fourier analysis, frequencies greater than 3 kHz contribute to the rapid rise of the current waveform.
## 1 Introduction
### Objectives  
The deployment of iBeacons is challenging work, which can be affected by numerous factors, e.g., the number of iBeacons, spacing between iBeacons, interference with electronic devices, algorithms used for positioning and environmental conditions. Despite the amount of research on the accuracy of indoor positioning, the literature has only focused on limited aspects of the deployment, such as placement or TX power, approaching issues from the point of view of an approximate function or general solution to a problem. While some educational institutions have used iBeacons in physical learning spaces for teaching and learning purposes [10], for example, attendance monitoring [11; 12], dissemination of electronic teaching material and information [13; 14], combining augmented reality contents [15; 16] and for smart facility management purposes [17; 18], there is no research paper that has conducted a comprehensive study to analyse the various environmental factors and deployment factors on iBeacon positioning accuracy for physical learning spaces. For the sustainable deployment of iBeacons in the future, therefore, this paper aims to: (a) evaluate the influences of different environmental factors to iBeacon signals with a series of robustness testing, and (b) evaluate the performance of different deployment settings of iBeacon, i.e., deployment position, deployment density, space interval of fingerprint method, collection time interval of fingerprint method, iBeacon signal transmission interval, venue and crowd condition, with a series of performance testing under the same control environment.
## 4 Experimental Preparation
### Evaluation Index  
To evaluate the reconstruction performance of the proposed method, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used as evaluation indices in this study, and the definition of these two indices is explained as below. PSNR focuses on the pixel-level differences of the image, while SSIM takes into account the structure and perceived similarity of the image. PSNR and SSIM are highly interpretable and intuitive and have the characteristics of wide application and standardization. The use of these indicators can make the experimental results easier to compare and verify with other studies. The unit of PSNR is expressed in decibels (dB), where a higher PSNR value indicates a better reconstruction performance of the model. The SSIM ranges between 0 and 1, with a value closer to 1 indicating higher similarity between the reconstructed image and the original one.  
The PSNR is the ratio between the maximum possible power of an image and the power of corrupting noise that affects the quality of its representation. Given a reference image \(I\) and a test image \(K\), both of size \(m\times n\), the PSNR between \(I\) and \(K\) is defined by:  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\parallel I(i,j)-K(i,j)\parallel ^{2} \tag{7}\]  
\[PSNR=10\cdot\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right)=20\cdot\log_{10} \left(\frac{MAX_{I}}{\sqrt{MSE}}\right) \tag{8}\]  
Mean square error (\(MSE\)) refers to the mean square error between the reconstructed image and the actual image.  
The \(SSIM\) is designed by modeling any image distortion as a combination of three factors, which are loss of correlation, luminance distortion, and contrast distortion. The \(SSIM\) is defined as:  
\[SSIM\big{(}H,H^{\prime}\big{)}=l\big{(}H,H^{\prime}\big{)}*c\big{(}H,H^{\prime }\big{)}*s\big{(}H,H^{\prime}\big{)}=\frac{\left(2\mu_{H}*\mu_{H^{\prime}}+C_{ 1}\right)\left(2\sigma_{H,H^{\prime}}+C_{2}\right)}{\left(\mu_{H}^{2}+\mu_{H ^{\prime}}^{2}+C_{1}\right)\left(\sigma_{H}^{2}+\sigma_{H^{\prime}}^{2}+C_{2 }\right)} \tag{9}\]  
where \(l\), \(c\), and \(s\) represent the luminance comparison function, contrast comparison function, and structure comparison function, respectively; \(H\) and \(H^{\prime}\) represent the original image and reconstructed image, respectively; \(C\) is constant; \(\mu\) and \(\sigma\) represent the mean and standard deviation of image pixel values, respectively. The detailed derivation of \(PSNR\) and \(SSIM\) definition can be found in Reference [62].
## 4 Results
### Classification into Multiple Venue Types
#### 4.2.2 Deep Learning  
It is a famous neural network-based method developed from the famous H2O framework that uses information within the data in a layered form to extract useful patterns and classes. Each neuron is trained by modification based on the available information and combinedly predicts the output acting as a classifier [53; 54]. It works in an adaptive manner by optimizing the neurons instinctively through learning without human interaction saving the effort and time required for the classification. This deep-learning algorithm is based on the feedforward artificial neural network architecture. The hidden layers provide the capacity to learn complex relationships between the input and output variables. It uses a supervised learning approach, which requires labeled training data to train the model provided by our previous research. During the training process, the model learns the relationships between the input variables and the target classes through the optimization of a loss function. The loss function is a measure of the error between the predicted outputs and the true outputs. It performs exceptionally for our classification-and-prediction problem, as shown in the Table 4 confusion matrices for test data.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**PredictedTrue** & **Travel** & **Residential** & **Professional** & **Educational** & \
\begin{tabular}{c} **Shopping** \\ **\& Services** \\ \end{tabular} & **Food** &
\begin{tabular}{c} **General** \\ **Location** \\ \end{tabular} & **Entertainment** & **Sports** & **Hotel** \\ \hline Travel & 4378 & 22 & 9 & 13 & 12 & 6 & 3 & 14 & 0 & 11 \\ Residential & 99 & 2764 & 133 & 137 & 51 & 19 & 14 & 93 & 32 & 66 \\ Professional & 5 & 17 & 2685 & 12 & 5 & 4 & 6 & 41 & 1 & 8 \\ Educational & 28 & 52 & 15 & 7288 & 60 & 15 & 5 & 56 & 47 & 32 \\ Shopping \& Services & 27 & 38 & 19 & 55 & 8883 & 37 & 6 & 173 & 11 & 15 \\ Food & 391 & 186 & 248 & 378 & 481 & 2100 & 106 & 865 & 134 & 153 \\ General & 77 & 139 & 44 & 62 & 104 & 29 & 2281 & 199 & 49 & 35 \\ Entertainment & 25 & 22 & 108 & 42 & 64 & 58 & 45 & 14288 & 98 & 30 \\ Sports & 8 & 6 & 11 & 30 & 3 & 13 & 8 & 151 & 3829 & 0 \\ Hotel & 52 & 13 & 13 & 21 & 21 & 3 & 4 & 30 & 13 & 2514 \\  & & & & & & & Low & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion Matrix for Generalized Linear Model.
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 4 Experiments
### Shape Classification Task on ScanObjectNN Dataset  
Dataset: Although ModelNet40 is a widely adopted benchmark for point cloud analysis, its synthetic nature and the fast-paced advancements in this field may not fully address  
Figure 8: Some representative classification results. P represents predicted label, and T represents the ground truth.  
Figure 7: Confusion matrix on Point-Sim result. The class containing the most test objects in the test dataset has 100 objects.  
the requirements of current research. Thus, we have also undertaken experiments utilizing the ScanObjectNN [30] benchmark.  
The ScanObjectNN dataset consists of 15,000 objects, with 2902 unique instances found in the real world. These objects belong to 15 different classes. However, analyzing this dataset using point cloud analysis methods can be challenging due to factors such as background interference, noise, and occlusion. Our loss function, optimizer, learning rate evolution scheduler, and data augmentation scheme maintained the same settings as the ModelNet40 classification task. We used the overall accuracy (OA) and the number of parameters for evaluation.  
Experimental Results: The classification results obtained from ScanObjectNN are shown in Table 2. We assessed the accuracy of all methods by reporting the performance on the official split of PB-T50-RS. The model achieved an OA of 66.3% with 0 M parameters and 86.6% with 0.8 M parameters, thereby demonstrating the versatility of our proposed method and the robustness of our model under background interference, noise, and occlusion.
# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 2 Related Works
### Attention Mechanism in Object Detection Task  
The principle of the conventional attention mechanism is based on the visual selectivity of humans. In daily life, human beings always focus on the more important parts of a picture using their own previously-obtained information. The attention mechanism module adaptively updates the weights and screens the information to focus the attention on the more noteworthy information, ignores the useless information, and further converges the algorithm vision. Currently the main attention mechanisms are channel domain attention mechanisms, spatial domain attention mechanisms and fusion attention mechanisms.  
The spatial attention mechanism obtains the attention weight mainly through the relative position of all objects in the image. STNs (Spatial Transformer Networks) [34] converts the object in reverse space to reduce the degree of image information loss and improve the accuracy in the detection task.  
The representative algorithm for channel attention module is the SEnet (Squeeze-and-Excitation Networks) [35]. The method of the global average pool is used to extract and classify the information of different channels, and generates the probability statistics of each channel, and then relies on the fully connected layer to generate the weights between different channels, and finally outputs the weighted channel weights.  
The mixed domain attention mechanism is a fusion algorithm which fuses the spatial attention mechanism and the channel attention mechanism. The representative algorithm is the CBAM [30] module. This module proves that input sequence of attention mechanisms has an impact on the experimental results, The authors input the feature map into the channel attention mechanism and then the spatial attention mechanism, and achieved better feature extraction results.  
In recent years, various advanced attention mechanisms have been widely used in object detection. For example, [PERSON] et al. [36] embedded the channel attention mechanism into YOLOv5 framework to achieve the high-precision detection of the leaf disease. [PERSON] et al. [29] embedded a mixed attention mechanism into the Two-Stream Convolution Network to strengthen detection ability of the algorithm for human action recognition tasks. The above studies prove that adding an appropriate attention mechanism in the object detection task is helpful in improving the performance of the algorithm.  
Based on valuable previous studies, a novel network model, we called KCFS-YOLOv5, is developed to improve the detection precision of multi-scale objects in aerial remote sensing images.