esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 3 Proposed Method
### Proposed CNN Structure  
The proposed CNN architecture in this study is illustrated in Figure 7. As shown in Figure 7, the CNN consists of an input layer and 6 convolutional (Conv) layers. The input layer has dimensions of \(1\times 1680\times 2\times N\), where 1680 represents the total number of resources in the resource grid considered in this study, 2 corresponds to the real and imaginary parts of the received signal, and \(N\) denotes the total number of data samples. The network includes batch normalization (BN), rectified linear unit (ReLU) activation functions, and max pooling (MaxPool) layers, which are sequentially arranged in the structure.  
The filter size for the convolutional layers is set to \(1\times 12\), and the number of filters gradually increases as the layers deepen, specifically to 16, 24, 32, 48, 64, and 96 filters. This incremental increase in the number of filters is designed to enable the CNN to extract progressively complex features, aligning with prior studies [17; 18; 19], which have shown that increasing the number of filters enhances data representation capabilities and learning accuracy in large-scale MIMO and mmWave systems. Furthermore, prior examples in OFDM systems demonstrate the effectiveness of this design in enabling the CNN to learn temporal and frequency-domain features effectively [20; 21; 22]. The increase in the number of filters in the CNN layer design is a critical criterion, ensuring efficient data processing in bandwidth- and noise-intensive channel environments. This approach maximizes the feature extraction capability, particularly in complex channel conditions [23; 24]. The filter size is optimized for effectively learning long time-series data, and previous studies have reported that such designs contribute to improving the SNR [25; 26].  
Batch normalization and ReLU activation functions are employed as key components to maintain training stability and prevent overfitting. Following each convolutional layer, a MaxPool layer with a size of \(1\times 2\) is used to reduce the data dimensions, thereby decreasing the computational load while retaining essential features [27; 28]. This multi-layer structure is designed to enable the CNN to efficiently learn spatial and temporal features.
###### Abstract  
Objective: The objective was to compare the shear bond strength (SBS) and the adhesive remnant index (ARI) amongst six orthodontic bracket groups. Materials and Methods: Three-dimensional printed polycrystalline alumina brackets (3 DBs), ceramic brackets (CBs), and metal brackets (MBs), divided into six groups, were bonded to bovine incisors using different bonding procedures. The SBSs were obtained using a universal testing machine, and the ARIs were assessed with a stereomicroscope. The statistical analyses included one-way analysis of variance (ANOVA) for SBS differences and [PERSON]'s exact test to show ARI differences amongst the groups (\(p<0.05\)). Results: No significant differences (\(p>0.05\)) were measured amongst the SBSs of the 3 DB groups (12.3 MPa, 12.6 MPa, 12.3 MPa, 11.0 MPa, respectively). The latter four groups generally had significantly lower SBS (\(p<0.001\)) than the conventional bracket groups, CB and MB (16.9 MPa and 19.3 MPa, respectively). Also, there was no significant difference in SBSs for the CB and MB groups (\(p>0.05\)). A high ARI for CBs and MBs (2) indicated that more than 50% of the adhesive remained on the enamel surface. The four 3 DB groups had no residual adhesive or less than 50% adhesive on the enamel surface after debonding (ARI scores 0 and 1). A significant difference in ARI levels existed across the types of brackets (\(p<0.05\)). Conclusion: Three-dimensional printed polycrystalline alumina brackets exhibited adequate SBS values for successful bonding. However, the values were lower compared to those for conventional ceramic and metal brackets. The majority of the adhesive remnant for the 3D-printed brackets was mainly located on the bracket base.  
3D-printed bracket; conventional bracket; shear bond strength; adhesive remnant index 10.390/app14020924
# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 5 Results
### Optimal Model Validation Analysis
#### 5.2.1 Data Validation in 2025  
In the 2025 data validation, the financial data of listed companies in 2025 were used. The data were preprocessed and standardized, and time series features were constructed using sliding window technology. From Table 13, the verification results show that the 1 CNN-1 BiLSTM-AT model has good generalization ability, with an overall accuracy of 98.4%. Among them, the prediction accuracy and recall rate of category 0 (non-ST) are close to 99%; the accuracy of category 1 (ST) is 90.5%, the recall rate is 95.0%, and the F1 score is 92.7%. The overall performance is stable, indicating the model has good application prospects and predictive ability in practical scenarios.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**ST**} & \multicolumn{3}{c}{**Non-ST**} & \multirow{2}{*}{**Training**} \\ \cline{2-2} \cline{4-7}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Time (s)** \\ \hline CNN & 0.990 & 0.814 & 0.893 & 0.972 & 0.999 & 0.985 & 162.52 \\ BiLSTM & 0.957 & 0.932 & 0.944 & 0.990 & 0.994 & 0.992 & 1279.94 \\ CNN-BiLSTM & 0.982 & 0.941 & 0.961 & 0.991 & 0.997 & 0.994 & 798.16 \\ CNN-AT & 0.982 & 0.949 & 0.966 & 0.992 & 0.997 & 0.995 & 163.34 \\ BiLSTM-AT & 0.958 & 0.958 & 0.958 & 0.994 & 0.994 & 0.994 & 1064.18 \\ CNN-GRU & 0.964 & 0.898 & 0.939 & 0.985 & 0.995 & 0.990 & 418.58 \\ Transformer & 0.982 & 0.957 & 0.970 & 0.994 & 0.997 & 0.995 & 151.87 \\ CNN-BiLSTM-AT & 0.991 & 0.966 & 0.979 & 0.995 & 0.999 & 0.997 & 430.97 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance evaluation of different models.  
Figure 10: Comparison of confusion matrix results of different models. Notes: the confusion matrix shows the number of true positives, true negatives, false positives, and false negatives for ST and non-ST classification.
## 4 Application to WWTP  
Wastewater treatment plants (WWTP) are large nonlinear systems characterized by the complexity of the biological and biochemical phenomena involved. The nonlinear dynamics of the system, the large range of time constants (from a few minutes to several days) observed in the different biological processes and the significant perturbations in the flow and load of the influent make the WWTPs a really challenging case study from the control point of view. The WWTPs have to be operated efficiently, minimizing the energy and recourses consumption while meeting the strict environmental regulations. Therefore, the advanced control strategies as the NLGPC proposed in this paper are a promising alternative for improving their performance and economics.
# Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications
## 3 Preliminaries
### LoRa Demodulation  
The received signal in the presence of interference and noise is given by:  
\[y_{k}(t)=x_{k}(t)+x_{k}(t)+w(t), \tag{3}\]  
where \(\bar{x}_{k}(t)\) is an interference signal (with a spreading factor the same as, or different than, \(x_{k}(t)\) and \(w(t)\) represents the complex white Gaussian noise with zero mean and variance \(\sigma_{n}^{2}\). The demodulation of a received LoRa signal is generally performed either through matched filtering or de-chirping. In the former, the received signal is cross-correlated with all possible combinations of the signature signals. A high correlation peak implies that the desired signature signal is presented within the received signal and the index of the peak denotes the starting of the desired signature signal, which is also useful to determine the range information between the transmitter and receiver. Although this approach has several advantages, including high range resolution and enhanced SNR [23], the major problem of this approach is its high computational complexity.  
The underlying principle of the latter case involves multiplying the received signal with a synchronized raw down-chirp having the same \(\mathcal{S}\) and \(B\). Assuming no noise and interference, the multiplied signal becomes:  
\[z_{k}(t)=x_{k}(t)\times Ae^{j2\pi\left(f_{k}t-\frac{Bt^{2}}{2T}\right)}, \tag{4}\]  
where the second term in the multiplication represents the raw down-chirp signal. The instantaneous frequency of the multiplied signal can be given as follows:  
\[f_{z_{k}}(t)=\begin{cases}f_{l}+f_{h}+\frac{kB}{2^{\mathcal{S}}}&\text{if $t \leq\tau$}\\ f_{l}+f_{h}+\frac{kB}{2^{\mathcal{S}}}-B&\text{if $t>\tau$}\end{cases} \tag{5}\]  
Equation (5) implies that the demodulated signal comprises two intervals, where each consists of a constant frequency. In both intervals, the frequency linearly depends on the offset, and the difference between the levels is \(B\). Now, suppose the signal is down-sampled at the rate of \(B\) Hz. In that case, the instantaneous frequency turns into a continuous frequency over the entire chirp and, after subtracting the term \(f_{l}+f_{h}\), is proportional to the shift \(k\). As a result, the resultant sampled signal becomes a pure sinusoid. When FFT is performed on that sinusoid, a flat response is obtained with the peak shifted by the \(k\) value [15]. The above process can be summarized by the following equation:  
\[\hat{k}=\operatorname{argmax}\mathcal{F}(z_{k}(t))-f_{l}-f_{h} \tag{6}\]  
where \(\hat{k}\) is an estimate of the transmitted symbol's index \(k\) and \(\mathcal{F}(z_{k}(t))\) gives the frequency domain representation after performing down-sampling on \(z_{k}(t)\) at the rate of \(B\) Hz. More details on the down-sampling and frequency domain representation can be found in [24].
## 5 Uniaxial and Biaxial Compression Tests
### Stress-Strain Curves  
Figure 13 shows the stress-strain curves under different confining pressures. These stress-strain curves can be roughly divided into four stages: elastic deformation, stable damage, rapid damage, and complete fracture. In the elastic deformation stage, axial loading did not cause damage, and the stress-strain curve was linear. In the stable damage stage, known as the plastic deformation stage, the slope of the stress-strain curve decreased gradually because the local stress exceeded the material strength, and more damage was initiated in the specimen. The stress increased slowly at this stage until the peak stress was attained. The stress at the starting point of this stage was approximately two-thirds of the peak stress. In the rapid damage stage, the stress-strain curve dropped sharply from the peak and then remained stable in the complete fracture stage. It can be seen that for confining pressures smaller than 20 MPa, the post-peak behavior in compression showed a typical brittle behavior; that is, the axial stress dropped immediately once it reached its peak. With increased confining pressure, plastic deformation became apparent, strain softening occurred, and the failure gradually changed from brittle to ductile.  
In general, the tendency of the simulated stress-strain curves matched well with the experimental curves [47], the data of which are scaled for comparison, as shown in Figure 13. The main deviation difference is that the elastic modulus obtained by numerical simulation does not increase with confining pressure as in the experiment. The main reason for this deviation is that the microcracking closure and specimen compaction were not considered in the numerical simulation.  
Figure 14 shows the peak and residual strengths under different confining pressures. The peak and residual strengths increased gradually with the increase in the confining pressure, showing a better regularity than the numerical simulation results with conventional damage models [44].  
The numerical simulation results were also well fitted by the generalized Hoek-Brown failure criterion [49], expressed as follows:  
\[\sigma_{1}=\sigma_{3}+\sigma_{\mathrm{c}}\sqrt{m\sigma_{3}/\sigma_{\mathrm{c} }+s}, \tag{20}\]  
where \(\sigma_{\mathrm{c}}\) is 74, the fitting parameter \(m\) is 0.9, and \(s\) is 1 or 0.  
Figure 13: Stress–strain curves under different confining pressures.
## Appendix B Optimization Model for Deteriorating Products  
Assume that \(Q\)is the amount of inventory replenishment the beginning of each cycle. Therefore, inventory level (\(I(t)\)) decreases due to the quantity demanded up to time \(T_{1}\), the length of the replenishment cycle. Therefore, the inventory at time \(t\in(0,T_{1})\) is governed by the differential equation  
\[\frac{dI}{dt}=-D-\theta I(t) \tag{1}\]  
with the initial condition \(I(T_{1})=0\). Solving the differential equation, we obtain  
\[I(t)=Q^{\theta t}-\frac{D(1-e^{-\theta t})}{\theta} \tag{2}\]  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Description** \\ \hline \(p\) & Retail price \\ \(h\) & Per unit holding cost \\ \(k\) & Fixed cost per order \\ \(k_{1}\) & Per unit purchase cost \\ \(\theta\) & Deterioration rate \\ \(I(t)\) & Instantaneous inventory level at time \(t\) \\ \(Q\) & Order quantity of product at each cycle \\ \(D\) & demand \\ \(T_{1}\) & Length of each replenishment cycle \\ \hline \hline \end{tabular}
\end{table}
Table 11: Summary of notations used in this study for inventory management.  
Therefore, the holding cost for the entire cycle is obtained as follows:  
\[\int_{0}^{T_{1}}I(t)dt=Q\frac{e^{\theta T_{1}}-1}{\theta}-\frac{D}{\theta}\bigg{(} T_{1}-\frac{1-e^{-\theta T_{1}}}{\theta}\bigg{)} \tag{11}\]  
From the boundary condition, \(I(T_{1})=0\), we obtain the replenishment quantity for each cycle as follows:  
\[Q=\frac{D(e^{\theta T_{1}}-1)}{\theta} \tag{12}\]  
The total profit for each cycle is obtained as follows:  
\[TP=<\text{sales revenue}>-<\text{holding cost}>-<\text{purchase cost}> \tag{13}\]  
\[-<\text{ordering cost}>-<\text{salvage cost}>\]  
Based on Equation (13), we make the following two cost functions: (i) profit function when the product is not deteriorating (\(\theta=0\)), (ii) profit function when the product is deteriorating (\(0<\theta<<1\)).
## 4 Discussion
### Yield and Water Consumption  
Mulching has been adopted in numerous parts of the world as an approach to increase crop productivity [38]. Maize yield in the JG treatment was 9.53% and 12.10% higher than that in the NF and WG treatments (\(p<\)0.05), and was significantly higher than that reported by [PERSON] et al. [39] and similar to that reported by many scholars. [PERSON] and colleagues [40] showed that plastic mulching dramatically increases crop yield. [PERSON] et al. [41] found that compared to conventional tillage without straw residue, integrating no tillage with two-year plastic and straw mulching improved grain yields by 13.8%, reduced soil evaporation by 9.0%, and reduced soil evaporation by 9.0%. However,in another study, straw mulding did not significantly affect yield under limiting soil water content [42]. In addition, ET\({}_{\text{c}}\) was approximately 500 mm in the JG, NF, and WG treatments during the entire growth season; no significant differences were detected among them, probably because of the thickness of maize straw in the JG treatment and organic fertilizer in the NF treatment, which requires further investigation. Our results of ET\({}_{\text{c}}\) were in contrast to those of previous studies. For example, [PERSON] et al. [43] reported that plastic film together with straw mulding decreased total evapotranspiration by an average of 4.60% (\(p<\)0.05) compared with no mulding; [PERSON] et al. [44] suggested that straw mulding resulted in 19.00% higher yield compared with no mulding, resulting in 36.20 mm higher transpiration and 44.20 mm lower soil evaporation; [PERSON] et al. [45] confirmed that crop water consumption was reduced by 32 mm under straw mulding compared with no mulding, with no significant differences in WUE. [PERSON] et al. [46] indicated that compared with no mulding, straw mulding increased maize yield by 10.6% and 12.5% under a drip irrigation system in 2016 and 2017, respectively, and achieved 6.1% lower water consumption. The contradiction was mostly responsible for the enhanced maize straw mulding amount in their study, which was significantly higher than that of our study. Moreover, this difference was closely related to differences in the region, maize variety, and irrigation approach.
## 4 Equipment and Methods
### Wireless Network Settings in the Laboratory Set-Up  
We followed the approach from [22] and conducted our measurements under conditions as close as possible to those of an anechoic Faraday cage. A network architecture was set up in the Laboratory for Modeling and Optimizing Information and Communication Networks and Services at the Department of Information and Communication Traffic (Figure 5; Department's official web site: [[https://www.fpz.unizg.hr/ikp/eng.php](https://www.fpz.unizg.hr/ikp/eng.php)]([https://www.fpz.unizg.hr/ikp/eng.php](https://www.fpz.unizg.hr/ikp/eng.php)), accessed on 26 March 2021). The architecture consisted of an iOS mobile phone with a pre-installed client for measuring throughput, IEEE 802.11b/g/n access point, workstation with spectrum analyzer, Line6 Relay G30 wireless audio transmitter, and server for measuring throughput. The path for down- and uploading for measuring throughput was iOS Throughput Client \(\rightarrow\) D-Link DIR-615 access point \(\rightarrow\) Internet \(\rightarrow\) Throughput Test Server.
# Classification of Malaria Using Object Detection Models
## 2 Method
### Parasite Classification Data Structure  
The dataset used was a public source dataset published first by authors [PERSON] et al. [24]. The dataset contained 172 images of microscope slide images. The name of each image indicated if the progression of the disease was one of the four stages: ring, trophozite, gametocyte and schizont. The dataset also had four folders, each corresponding to the kind of parasitic infection. They are Falciparum, Ovale, Vivax, and Malariae. Figure 4 represents a sample image from the dataset.  
The images had to be manually annotated since their labels were not explicitly provided for each image. The Labellmg tool is used to manually annotate the images and  
Figure 4: Sample image from the dataset.  
Figure 3: YOLOv5 architecture [16].  
create text files for each image containing the image's annotations. This tool was developed and launched with a primary focus on manual annotations. Once the manual annotation is complete, the annotations are exported in the required format. The tool was cloned from a GitHub repository [24] and then deployed on a laptop. It simply records the coordinates of each object as drawn by the user and saves it along with the label of the object in a text file of the desired format. The input to the LabelImg must be a jpeg file and hence all the images were converted to that format to suit the requirements. The graphical user interface of LabelImg is illustrated in Figure 5[25].  
Looking at the objects of interest used in dataset 1 for parasite classification, it was observed that the class Falciparum were greater in number. This caused a significant bias while performing training. Information about the class and the number of corresponding samples is given in Table 1.  
Generally, the performance of deep learning or object detection algorithm may depend on the data size and quality. The more data available for training, the better the algorithm learns and in turn performs. Currently, the dataset is rather small with a lot of underrepresented classes. Since the model is expected to perform better with more data, we perform augmentation to increase its size.
## 3 The Methodology
### Simulation Study
#### 4.1.5 Impact of Network Lifetime
The developed approach's performance in terms of compression and computing efficiency could be further improved by including event-driven tools and prospective optimization algorithms [37; 38; 39]. The integration of these tools could be investigated in the future.
# Theoretical and Numerical Simulation Research on Fire of Large-Span Spatial Structures
## 2 Numerical Simulation Model Verification and Fire Source Condition Setting
### Mesh Division of Finite Element Model  
The size of the internal spatial grid division should meet the following requirements [40]: Generally, the dimensionless expression \(D^{\star}/\chi\) is used to describe the rationality of the calculation results, where x is the grid division size and \(D^{\star}\) is the characteristic diameter of the fire source. The calculation method of \(D^{\star}\) is shown in Equation (1).  
Fire source characteristic diameter:  
\[D^{\star}=(\frac{Q}{\rho_{0}C_{0}T_{0}\sqrt{g}})^{0.4}=3.22\text{m} \tag{1}\]  
In the formula, \(D^{\star}\) is the characteristic diameter of the fire source (m); \(Q\) represents the heat release rate (kW), 20,000 kW; \(T_{0}\) is the environmental thermodynamic temperature (K), 286 K; the gas density (kg/m\({}^{3}\)) at \(\rho_{0}\) ambient temperature is usually 1.2 kg/m\({}^{3}\); the specific heat capacity of air at constant pressure \(C_{0}\) is generally 1.004 kJ/(kg\(\cdot\)K); \(g\) is the acceleration of gravity (m/s\({}^{2}\)); and the ratio of \(D^{\star}\) to grid elements should be between 4-16, so the grid size should be between 0.20-0.80. Considering the running time of the computer and the storage capacity of the computer, the grid size should be 0.7 m \(\times\) 0.7 m \(\times\) 0.7 m. The mesh division of the spherical dome structure model is shown in Figure 11.  
Figure 11: Mesh division of finite element model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **Natural Smoke Exhaust Area (m\({}^{2}\))** & **Natural Air Inlet Area (m\({}^{2}\))** & **Natural Air Inlet Area/Natural Smoke Exhaust Area** \\ \hline CASE0 & & 0 & 0 \\ CASE1 & & 20 & 40\% \\ CASE2 & 50 & 30 & 60\% \\ CAS3 & & 40 & 80\% \\ CASE4 & & 50 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Working condition settings.  
Figure 10: Slicing and measurement points layout: (**a**) Temperature slicing layout; (**b**) Layout diagram of measuring points in half space.
# Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
## 2 Materials and Methods  
### Low-Cost Receiver and Antenna  
The u-blox C099-F9P evaluation board was used for the ZED-F9P high precision GNSS module. This board allowed flexible connectivity options (including USB and Arduino UNO R3 header) and easy receiver configuration through the free u-center software, provided together with the receiver. The receiver tracked GPS L1 C/A and L2 C, GLONASS L1 OF and L2 OF, Galileo E1-B/C and E5b, BeiDou B1I and B2I signals. The manufacturer declared a positioning accuracy of 1 cm + 1 ppm in RTK mode, with a baseline limited to 20 km.  
The receiver was connected to a standard u-blox ANN-MB-00-00 patch antenna, having a circular ground plane (as recommended by the manufacturer). It was a Right HandCircular Polarized (RHCP) dual-band antenna (L1 and L2/E5b/B2I). The manufacturer provided information on the typical maximum phase center offset (PCO) and phase center variations (PCV) only for GPS L1 and L2 frequencies. PCO was lower than 5 mm in the horizontal plane, lower than 8.9 mm, and 7.6 mm in the vertical plane for L1 and L2, respectively. PCV was lower than 5 mm and lower than 10 mm over all azimuth and elevations, for L1 and L2, respectively. The declared PCO and PCV values did not fit with the antenna calibration model developed by [PERSON] et al. [26].