esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method
## 4 Experimental Results
### Functional Data Analysis
Table 6 shows the RMSE values for six environmental variables. From Figure 5 and Table 6, we can get the following results. First, all environmental variables are included in the model, and have the most predictive power. Second, among the individual environmental variables, ground temperature, sunshine, and rainfall exhibited high predictive power. Third, wind speed, mean temperature, and humidity showed the lowest predictive power. To summarize, we discuss the following results. First, we first find that both the ground and mean temperature have a high positive correlation with the onions weights. It can also be seen that humidity has a small positive correlation with the onion weights. Finally, three of the environmental variables, such as wind speed, sunshine, and rainfall, have a small negative correlation with onion weight. Second, we can confirm that ground temperature, sunshine, and precipitation have a significant effect on onion growth and are very significant in the goodness-of-fit test. On the other hand, mean temperature, wind speed, and humidity did not significantly affect onion growth. In conclusion, to promote onion growth, the appropriate ground temperature and amount of sunshine are essential, rainfall and humidity must be low, and appropriate wind or mean temperature must be maintained. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Environmental** & **Wind** & **Ground** & **Mean** & **Humidity** & **Sunshine** & **Rain Fall** & **All** \\
**Factors** & **Speed** & **Temp** & **Temp** & & & **Variables** \\ \hline RMSE & 0.2948 & 0.2840 & 0.2940 & 0.3058 & 0.2770 & 0.2700 & 0.1300 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMSE for six environmental factors and all variables. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Max (R2)** & **Mean (R2)** & **F-Statistic** \\ \hline wind speed & 0.71 & 0.19 & 4.97 \\ ground temp & 0.46 & 0.2 & 5.3 \\ temperature & 0.46 & 0.13 & 4.87 \\ humidity & 0.27 & 0.11 & 4.53 \\ sunshine & 0.63 & 0.26 & 5.5 \\ rainfall & 0.49 & 0.33 & 5.75 \\ All variables & 0.94 & 0.83 & 29.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coefficients of determinations and F-statistics for six environmental factors. Figure 5: Plots for actual observations and predicted values. (**a**) Actual values for onion weights, (**b**) predicted values for weights by wind speed, (**c**) predicted values for weights by mean temperature, (**d**) predicted values for weights by ground temperature, (**e**) predicted values for weights by humidity, (**f**) predicted values for weights by sunshine, (**g**) predicted values for weights by rain, (**h**) predicted values for weights by all variables.
## 4 MRI Appearance of Bladder Cancer and VI-RADS  
Before discussing the MRI appearance of BC, an accurate description of the MRI appearance of the bladder wall is required. In detail, the bladder wall is composed of three strata: mucosa, muscularis propria, and serosa/adventitia [9]. The mucosa includes the surface urothel and underlying subepithelial connective tissue, also known as lamina propria, which is thinner at the trigone and thicker at the dome. The muscularis propria (detrusor muscle) is made up of smooth muscle bundles organized into an inner and an outer layer with different orientations. Finally, the serosa, a part of the visceral peritoneum, covers the bladder dome, while a layer of adventitia, composed of connective tissue, covers the remaining areas of the bladder. Unfortunately, MRI does not allow the visualization of all the described layers. The inner layer is not seen on T2w images as well as on DWI/ADC, while it appears as a hyper-intense line on DCE-MRI because of its early enhancement. However, in the case of an inflamed urothel and lamina propria, the inner layer appears as a thickened hyperintense line on T2w images and hypointense line on DWI, due to oedema. The detrusor muscle is seen in all MRI sequences and appears hypo-intense on T2w images, while it is characterized by intermediate signal intensity on DWI/ADC. After the contrast injection, it enhances slowly and progressively. Any intravesical lesion with a T2 signal intensity intermediate to urine and muscle, a high DWI signal associated with a low signal at ADC map, and which shows postcontrast early enhancement at DCE-MRI, should be considered as a suspicious lesion [6]. As mentioned above, in clinical practice, the differential diagnosis between MIBC and NMIBC is crucial. In this setting, mpMRI may be a very promising imaging tool in the accurate evaluation of BC and in its local staging thanks to morphological high-resolution T2w images and functional DWI images and DCE sequences. For that, [PERSON] et al. proposed VI-RADS, an MRI-based scoring system assessing tumour appearance in T2w, DWI and DCE images to standardize the image acquisition reporting of bladder mpMRI [6]. VI-RADS is composed of a 5-point scale, which reflects the overall risk of muscle invasiveness: each bladder lesion is assigned a final score from 1 to 5: VI-RADS 1 represents very low likelihood of muscle invasiveness and VI-RADS 5 a very high likelihood of detrusor muscle invasion. This scoring could be applicable both to untreated patients and those who have received diagnostic TURBT before re-TURBT. The VI-RADS algorithm for the overall score assessment considers T2w images as the primary sequence for BC staging for a VI-RADS score from 1 to 3, and DWI and DCE as the dominant sequences for VI-RADS 4 and 5. In case of discordance between T2w images and DCE-MRI scores, DWI should be considered as the dominant sequence to maximise accuracy [9].
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 5 Discussion
### Research Potential
#### 5.2.2 Experimental Research  
Many of the works describing vulnerabilities and protection topics mentioned in this paper contain experimental results collected from a small (single node and single gateway) to a middle-scale (5-10 nodes and a single gateway) LoRaWAN network. While such setups deliver acceptable data for proof-of-concept validation, they fail to assess the performance in real operating conditions, i.e., in LoRaWAN networks with hundreds of nodes and several gateways. To this end, further efforts can be made, for instance, towards evaluations for jamming resilience. In particular, a study including several reactive jammers targeting a multi-gateway network will reveal the true effects of physical-layer DoS attacks. Such large scale experiments might also be helpful to study the difference between packet collisions and jamming. In a similar vein, the effectiveness of, e.g., radio-frequency fingerprinting and secret-key agreement techniques should be verified in large scale networks with commodity LoRaWAN equipment. Here, akin to the jamming attack evaluations, a potential focus point might include robustness evaluations against packet collisions during continuous operation.
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 4 MSNUNet
### MSU-Subnet  
In current CNN designs, such as VGG [34], ResNet [35], and DenseNet [36], small kernels of sizes of \(1\times 1\) or \(3\times 3\) are commonly used for feature extraction. However, these small kernels have limited receptive fields, resulting in shallow output feature maps that only capture local features and fail to capture global features. To overcome this limitation, we propose a novel multi-scale feature extraction subnetwork, namely MSU-Subnet, to capture multi-scale features in high-resolution feature maps. Figure 2a showcases the structure of MSU-Subnet, which consists of three main components:  
**(i)**: **A U-shaped encoder-decoder structure:** The subnetwork takes intermediate feature maps (with a size of \(h\times w\) and a number of channels equal to \(c\)) as inputs and employs a U-shaped architecture with seven blocks to extract multi-scale features. By progressively downsampling the feature maps and encoding them into a high-resolution feature map (with a size of \(h\times w\) and a number of channels equal to \(c\)) through progressive upsampling, skip connections, and Convs, this structure effectively avoids the loss of fine details encountered with direct upsampling at larger scales. Additionally, by extracting features from deeper levels, the network can capture more diverse receptive fields and richer local and global features. The extracted multi-scale features can represent noise detail features of various granularity, enabling the network to capture a more accurate noise distribution and enhance the robustness of the model.
**(ii)**: **MSCAM:** Serving as the base module for both MSU-Subnet and the entire network, MSCAM aggregates multi-scale features within the network. Not only does MSCAM aggregate multi-scale features within the network; it also utilizes CA to extract the correlation between feature channels. This allows the network to selectively attend to relevant features, thereby enhancing the effectiveness of feature extraction.
**(iii)**: **Residual connection:** Experimental results indicate that the denoising quality of a network tends to decrease beyond a certain number of layers, potentially causing image degradation during network training. To mitigate this problem, residual connections are utilized to learn the residual mapping of the stacked layers, enabling the easier training of deeper networks.  
By allowing the network to extract features at multiple scales, MSU-Subnet enhances the capabilities of feature extraction. In addition, the U-structure of MSU-Subnet offers a low computational overhead, as most operations and manipulations are applied to the downsampled feature maps.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
###### Abstract  
Nowadays, mega-constellations of Low Earth Orbit (LEO) satellites have become increasingly important to provide high-performance Internet access with global coverage. This paper provides an updated comparison of four of the largest LEO mega-constellations: Telesat, SpaceX, OneWeb and Amazon. It describes the gateway design workflow from the patch antenna to phased array analysis. Patch antennas are developed for both transmission and reception after a thorough examination of the four systems. The results of electromagnetic simulation using Advanced Design Software (ADS) Momentum are shown, including their radiation pattern. Finally, a model of the gateway phased array using SystemXue is obtained using hexagonal, circular, and square arrays. According to the required effective isotropic radiated power (EIRP) and gain, the antenna sizes for the four constellations are estimated. As an example, for SpaceX constellation, a reception antenna with 8910 radiating elements using a hexagonal distribution with a gain of 46.9 dB and a sensitivity of \(-113.1\) dBm was obtained.  
gateway; Low Earth Orbit constellations; patch antenna; phased array; satellite communications +
Footnote †: journal: _Sensors_
## 5 Design of Experiments  
### Datasets  
The models in this article were evaluated using the ToN-IoT, Edge-IIoT, and UNSW2015 databases. The ToN-IoT dataset is made up of heterogeneous sources, including Win7, Win10, Network, and IoT. The dataset contains 2,233,921 records of normal and attack data. The training set had 22,339,021 network packet vectors, while the test set included 461,043 vectors and 43 features in CSV format, which can be utilized on any platform. The UNSW-NB15 dataset has over 100 GB of network packets split among 2,540,044 vectors and 42 features in four CSV files. The data include normal vectors and a variety of attack vectors (backdoor exploits, DoS, shellcode, worms, and reconnaissance are examples of fuzzers). The Edge-IIoT dataset contains 14 different types of attacks related to IoT and IIoT protocols, which are divided into five categories (i.e., DoS and DDoS attacks, information gathering, injection attacks, man-in-the-middle attacks, and malware attacks). The Edge-IIoT dataset contains 1,909,671 samples, 15 classes, and 61 different features. Sets of 1,527,736 and 381,935 data for training and testing are available.  
These three datasets were divided into 80% training and 20% testing sets, reducing the risk of overfitting and allowing for comprehensive model performance monitoring over the entire dataset. Because the data were balanced, we utilized the class weights. After conducting some experiments to tune the hyperparameters, the batch size was 64, which is suitable for memory that stabilizes the training process without oscillation and converges to general performance. The depth was reduced to 5 or 6 for some data and remained 10 for others; some dataset need a more complex model to converge to a satisfactory F1-score, while some converge with a module depth of 5 or less. However, we tried to keep the residual layer after every three modules. In terms of the number of epochs, 50 is an overestimate that may not be reached, because the model was stopped early if it began to overfit by tracing the validation accuracy. We used the Adam optimizer and a learning scheduler to converge quickly and to find the best learning rate.
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
## 1 Introduction
Different RA contents (10-50%), virgin aggregate temperatures (160-190 \({}^{\circ}\)C) and RA moisture contents (0-5%) were used to study their influence on temperature evolution and RAP binder transfer between the two materials. It was concluded that the DoA decreases with the increase of moisture content and RAP content, and a higher temperature of superheated virgin aggregate or a longer mixing time is therefore needed. Furthermore, the amount of binder transfer increases as the temperature of superheated virgin aggregate increases and reaches its maximum at 180 \({}^{\circ}\)C. The DEM simulations were found to be a good method for studying the process of superheated virgin aggregate blended with RAP materials and have shown the potential for evaluating DoA under different conditions. [PERSON] et al. [8] evaluated the environmental impacts of using RAP with different degrees of binder activation (0, 20, 50, 70, 80 and 100%) by means of a life cycle assessment (LCA). With the increase of DoA, great benefits can also be seen from other impact categories (especially marine ecotoxicity, fossil fuel depletion and human toxicity). This previous research clarified the definition of DoA and tried to establish an indicator to represent DoA, while less attention was paid to improving the activation of RA binder. In addition, the preheating temperature used in practical construction is still limited by standards and equipment. Due to the low preheating temperature, the activation of RA binder may be adversely affected, which will also have an impact on the performance of RAM. Therefore, it is necessary to study the feasibility of increasing preheating temperature and take measures to improve the activation of RA binder.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Hydrodynamics in Submerged HF Membranes
#### 5.2.2 Bubble Characteristics  
Several techniques have been used to characterize the hydrodynamic conditions in submerged HF systems. Particle-image velocimetry (PIV) [205] has shown that the bubble size increases with height along the membrane module due to the reduced hydrostatic pressure as the air bubbles move upward, and varies over a wide range of 0.2-50 mm, with a predominant size range of 3-5 mm. A strong sheltering effect attributed to the hydrodynamics was observed within the hollow fiber module that resulted in a 10-fold reduction in the axial velocity relative to the velocity outside the fiber bundle [110]. [PERSON] et al. [206] used a bi-optical probe to characterize the bubble velocity, distribution, and size throughout submerged full-scale HF modules. The shear stress was observed to be an important parameter in controlling particle back-transport from the membrane surfaces. [PERSON] et al. [207] studied the sparged bubble characteristics and the induced shear forces at the surface of submerged hollow fiber membranes using an electrochemical method. The shear stress was observed to be highly unpredictable over time and heterogeneously distributed within the module, ranging from 0.1 to over 10 Pa. Also, no correlation was observed between the shear stress and the bubble frequency or rise velocity. However, this does not corroborate with the general observation of better fouling control with increased aeration intensity (see Section 5.2.3) and also with the results of [PERSON] et al. [194], who observed an increase in the shear stress with increasing bubble frequency for all bubble types. These disparate observations highlight the challenge in achieving well-distributed two-phase flow in the submerged HF module. The importance of this was shown by [PERSON] et al. [208], who used X-ray computer tomography to detect the instantaneous displacement of fibers in an aerated HF bundle. The data were then incorporated into a computational fluid dynamics (CFD) simulation to allow optimization of the module design and aeration. More recently, [PERSON] et al. [204] used a high speed video camera to characterize the bubble characteristics and direct observation through the membrane (DOTM) to determine the corresponding critical flux of micron-sized polystyrene particles. They found that the local bubble momentum and bubble size had the most positive correlation with the local critical flux.
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### Research and Design
#### 2.7.3 Evaluation Index  
In order to evaluate the proposed model, six evaluation indicators are introduced for comparison with other models in this study. MAE and RMSE are used to indicate the degree of deviation between the predicted value and the true value, and \(R^{2}\) is used to express the correlation between the true value and the predicted value; \(P_{MAE}\), \(P_{RMSE}\) and \(P_{R^{2}}\) are used to measure the degree of improvement of the proposed model compared with other models. Their definitions and formulas are shown in Table 2, in which \(y_{i}\) is the observed value and \(\hat{y}_{i}\) is the predicted value.  
Figure 7: EX5 k value graph.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Equation** & **Definition** \\ \hline MAE & \(MAE=(\sum_{i=1}^{n}\lvert y_{i}-g_{i}\rvert)/n\) & Mean absolute error \\ \hline RMSE & \(RMSE=\sqrt{\left(\sum_{i=1}^{n}(y_{i}-g_{i})^{2}\right)/(n-1)}\) & Root mean square error \\ \hline \(R^{2}\) & \(R^{2}=1-\frac{MSE(g_{i})}{Var(y)}\) & Determination correlation coefficient \\ \hline \(P_{MAE}(\%)\) & \(P_{MAE}=\lvert(MAE_{1}-MAE_{2})/MAE_{1}\rvert\) & Boosting percentage of MAE \\ \hline \(P_{RMSE}\) & \(P_{RMSE}=\lvert(RMSE_{1}-RMSE_{2})/RMSE_{1}\rvert\) & Boosting percentage of RMSE \\ \hline \(P_{R^{2}}\) & \(P_{R^{2}}=\lvert(R^{2}_{1}-R^{2}_{2})/R^{2}_{1}\rvert\) & Boosting percentage of \(P_{R^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation index formula.  
Figure 8: _Cont._
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 7 Experimental Tests
### 67n.s1 One-Phase to Ground with Compensated Neutral
#### 7.3.2 Fault 67N.S1 between PR2 and PR3  
According to the same configuration which determines a duration of the LoRa packet equal to 209.85 ms, the system was assessed with the 67N.S1 fault located between PR2 and PR3. The fault is detected by the protections SR, PR1, and PR2, as seen in the scheme in Figure 12. The corresponding recorded signals are shown in Figure 16.  
As seen for the fault 50.S3, at the end of operations, the fault section is isolated, the line between SR and PR2 is powered by the upper primary substation PS1, and the line between PR3 and the TR is supplied by the lower primary substation, as indicated by the green arrows in Figure 12.  
The mean values of the time intervals of interests are reported in Table 16.  
As can be noticed, the fault is cleared within the maximum allowable time, considering that from the moment in which the fault is detected to the moment when the circuit breaker is commanded, a time of \(T_{PR2\_TRIP}\) = 0.32957 s elapses.  
The Close command is instead transmitted to the border TR within a time of \(T_{IB\_CLOSED}\) = 0.76248 s from fault detection.  
By comparing the two considered fault position, the values reported in Table 17 are observed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interval** & **Measurement Method** & **Max Value [s]** & **Measured Value [s]** \\ \hline \(T_{SR\_TRIP}\) & \(t^{f}_{SR\_CB}\)-\(t^{r}_{FAULT}\) & 0.33000 & 0.32839 \\ \(T_{PR1\_TRIP}\) & \(t^{f}_{PR1\_CB}\)-\(t^{r}_{FAULT}\) & - & 0.54391 \\ \(T_{PR2\_Rx}\) & \(t^{f}_{PR2\_Rx}\)-\(t^{r}_{FAULT}\) & - & 0.76427 \\ \(T_{PR3\_Rx}\) & \(t^{f}_{PR3\_Rx}\)-\(t^{r}_{FAULT}\) & - & 0.98106 \\ \(T_{IB\_CLOSED}\) & \(t^{r}_{TR\_IB}\)-\(t^{r}_{FAULT}\) & - & 1.19899 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Estimated time intervals during the fault 67N.S1 between SR and PR1.  
Figure 15: Digital signals acquired during the fault 67N.S1 between SR and PR1.  
As determined in the previous sections, the tripping time and, therefore, the clearing of this fault are the same and do not depend on the position of the fault. The position of the fault, however, influences the reclosing time and, thus, the time required for restoring the network downstream of the fault.