esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 5 Results and Analysis
### Analysis  
The results presented in Figures 9 and 10 illustrate the regression plots for learning with 18 and 45 neurons, respectively. The correlation coefficient (R) is a measure of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Session**} & \multicolumn{5}{c}{**Accuracy Criterion**} \\ \cline{2-6}  & **MSE** & **RMSE** & **MAE** & **MaxPE** & **R** \\ \hline
1 & \(8.67\times 10^{-12}\) & \(2.94\times 10^{-6}\) & \(2.34\times 10^{-6}\) & \(9.31\times 10^{-6}\) & \(9.99\times 10^{-1}\) \\  
relationship between the network outputs and the targets. A correlation coefficient close to 1 indicates good learning, while a value close to 0 indicates weak learning.  
Regression plots provide a visual representation of the network outputs compared to the targets across learning, validation, or test datasets. A perfect fit occurs when the data points align along the 45-degree line, indicating that the network outputs perfectly match the targets. In our study, the learning process achieved a correlation coefficient of R = 0.89591 when using 18 hidden neurons and R = 0.99979 when using 45 hidden neurons. These values indicate a strong performance in terms of learning.  
Figures 11 and 12 demonstrate the impact of varying the number of hidden neurons on the estimated Bit Error Rate (BER) value. When using 45 hidden neurons, the correlation coefficient indicates good learning performance (R = 0.99979) compared to the network trained with 18 hidden neurons (R = 0.89591). However, the model's generalization capacity, which refers to its ability to perform well on unseen data, diminishes when using 45 neurons, as depicted in Figure 12. In contrast, the model trained with 18 neurons (Figure 11) exhibits greater precision, with errors ranging between \(-1\) and 1. Conversely, the model trained with 45 neurons loses precision, leading to higher error values. This phenomenon is attributed to overfitting, where an increase in hidden neurons improves the learning process but compromises the accuracy of the model.  
Thus, depending on the specific problem being studied, it is crucial to strike a balance between learning performance and model accuracy. In our case, the results indicate that the model based on 18 hidden neurons achieves a better compromise between the two. Consequently, for optimal prediction performance, it is recommended to calibrate the network with 18 hidden neurons.  
In conclusion, based on our analysis, using 18 hidden neurons yields superior prediction performance in our case study.  
Figure 11: Bit Error Rate (BER) estimation based on 300 samples utilizing a network architecture with 18 neurons.  
Figure 12: Bit Error Rate (BER) estimation based on 300 samples utilizing a network architecture with 45 neurons.  
Figure 10: Network regression plot illustrating learning performance with 45 neurons.
## 1 Introduction
Many studies have also found that an increase in the proportion of diffuse radiation on cloudy days enhances photosynthesis in plants, resulting in differences in the maximum light use efficiency (LUE\({}_{\text{max}}\)) under different weather conditions [28; 29]. In addition, under cloudy conditions, the application of some stress indicators based on remote sensing (such as the Land Surface Water Index (LSWI)) is limited owing to cloud contamination [30; 31], which will change the structure of the optimal LUE model. However, few studies have considered weather factors when simulating GPP using the LUE model. Cork oak (_Quercus variabilis_) is the main tree species for afforestation and the main constructive tree species for degraded habitats in northern China. This tree not only maintains a stable ecosystem structure but also performs remarkable water and soil conservation functions, and the proportion of cork oak in China's water and soil conservation projects can reach 80% [32]. Because of wide spread in China, the cork oak plantation also plays an important role in carbon sinks [29]. However, most of them are located in lithoid mountain regions with thin and barren soils. Seasonal drought is the most important factor affecting forest growth in this region; especially in spring and early autumn, plantationsoften suffer from moderate or severe water stress [24], which increases the uncertainty in estimating GPP based on the LUE models. Under the change in global warming, it is expected that rainfall in northern China will decrease, and the frequency, duration, and extent of drought will increase. Therefore, it is necessary to determine an optimal structure of the LUE model for cork oak plantations to accurately estimate changes in GPP. The specific research objectives were (1) to assess the importance of water and temperature stress scalars in LUE models in the cork oak plantation; (2) to evaluate the performance of seven LUE models with typical water stress scalars in simulating the GPP of cork oak plantations under different weather conditions; and (3) to evaluate the performance of seven LUE models modified by PWSI under different weather conditions.
## 4 The Development of 3 SMVI Software and Integration
### Software Utilization and Algorithms  
The data was implemented and published in cloud server-based MQTT on IoT applications. Hence, the captures of the images were data acquisition, facilitating data integration and communication during machining between inspection systems and VNC cloud servers through IoT-based technology. The main interface, as shown in Figure 5, requires two inputs from the user to carry out the measurement and inspection procedures. The 3 SMVI system takes two inputs for measuring and inspecting circular objects. The diameter of the object is the first input, which is used to calibrate the system. The second input, which is used for inspection, is the maximum allowable roundness error value. To perform measurement and inspection procedures, the system employs a variety of algorithms based on computer vision and image processing.  
Figure 4: The integration of 3 SMVI hardware- and software-based IoT.  
Figure 5: The 3 SMVI main interface system.
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 3 Genetically Modified-Bacteria for Colorectal Cancer (CRC) Treatment
### CRC Treatment via Metabolic Engineering of Engineered Bacteria  
Instead of delivering therapeutic proteins by constructing recombinant plasmids, another strategy is to reprogram bacteria via the design of metabolic pathways for synthesizing therapeutic molecules [22; 101]. For instance, an EcN strain was developed to generate an engineered strain, EcN-BUT, that can synthesize biobutyrate from glucose. In this strain, the heterologous gene clusters phaA, hbd, crt, and ter were integrated into the EcN genome to construct a metabolic pathway from glucose to butyryl-CoA. Next, overexpressed endogenous atoDA converts butyryl-CoA into biobutyrate. In addition, the endogenous aceEF was added to enhance the expression level of acetyl-CoA, and some competing reaction processes that may affect acetyl-CoA synthesis also were blocked. Finally, EcN-BUT synthesized biobutyrate reaching 20 mM. In vitro, the treatment of HT29 CRC cells with produced biobutyrate led to cell cycle arrest in the G1 phase and inducedapoptosis. In HT29 tumor-bearing mice, the administration of ECN-BUT showed tumor-specific colonization and a 70% reduction in tumor volume [102]. Another strategy targeted the pathway for the synthesis of ammonia to L-arginine. In this study, ECN was also used as a modified therapeutic vector. First, the arginine repressor gene (ArgR) encoding the negative regulator for l-arginine biosynthesis was deleted from the ECN genome. Next, N-acetyl glutamate synthase (ArgA) was replaced by ArgA\({}^{\text{br}}\), which works without being affected by high levels of L-arginine. In MC38-tumor-bearing mice, this metabolically engineered ECN strain was able to colonize tumors and continuously convert ammonia, which accumulates at tumor sites as a metabolic waste product, into arginine. An increased arginine concentration enhances anti-tumor T-cell responses. This strain combined with programmed death-ligand 1 (PD-L1)-blocking antibodies resulted in the complete eradication of MC38 tumors in 74% of mice [103]. This metabolic engineering strategy, based on bacterial genome modification, enables the synthesis of other therapeutic molecules in addition to proteins and removes limitations on the type of therapeutic substance delivered. Notably, the reconstruction of the microbial metabolic pathways needs to consider whether it affects microbial growth and whether the concentration of the target product reaches a functional level.
## 3 Channel Estimation in Different Frequency Bands
### Channel Estimation for RIS Systems in High Frequency Bands  
In this section, we focus on the channel estimation for RIS systems in high frequency bands. As one of the key technologies of 5G, the larger bandwidth of mmWave brings greater channel capacity and higher communication rates. Higher band THz communication is also considered one of the key technologies for future 6G development. However, the path propagation process generates severe path loss and the path is easily blocked by obstacles; the hardware complexity and huge power consumption associated with the high frequency bands are the main problems it currently faces. The RIS, with its low cost, low power consumption, and the advantage of reducing the path loss by reflecting the signal, can make up for the shortage in the high frequency bands. Accurate CSI can design the optimal phase dependence of RIS and thus improve the performance, while the accuracy of channel estimation determines the acquisition of CSI. A suitable method with high accuracy, low training overhead, and complexity is difficult to achieve at the same time slot when selecting a method for channel estimation.  
In the high frequency bands, multipath scattering is sparse and can be modeled by the angular domain. This modeling approach uses only a small number of parameters, which reduces the pilot overhead. RIS cannot transmit or receive signals due to its passive characteristic; therefore, the channel estimation is accomplished by designing from the hardware and transmission protocol aspects. In terms of hardware design, there are fully-passive RIS and semi-passive RIS. The difference is that fully-passive RIS is a passive reflective element that can only reflect signals, while semi-passive RIS can receive signals by installing a small number of active elements. Due to the difference in hardware, the existing channel estimation methods can be divided into cascaded channel estimation and separate channel estimation.  
In the mmWave band, due to its channel sparsity, only the angle, gain, and time delay need to be estimated for channel estimation, and the required pilot overhead is reduced greatly. In the RIS-assisted wireless communication system, cascaded channel estimation methods and separated channel estimation methods have their own advantages and disadvantages. The channel estimation methods in high frequency bands are described in Figure 3. Methods are based on fully passive RIS and semi-passive RIS in different scenarios, which are divided into five main aspects: single-user, multi-user, practical application, assistance, and wideband systems.
## 2 Mathematical Model
### Generalized Model Description
#### 2.2.1. Scheduling Constraints: Job On-Off Formulation
In mathematical form it is represented in the following condition: \(\forall t_{1}\in T\), if \(U_{j_{p},t_{1}}=1\) then \(\forall t_{2}\in T,t_{2}<t_{1},U_{j_{s},t_{2}}=0\), and the corresponding constraints are:  
\[U_{j_{s},t_{2}}+U_{j_{p},t_{1}}\leq 1,\ \forall\{j_{p},j_{s}\}\in P,\ \forall t_{1}\in T,\ \forall t_{2}\in T,\ t_{2}<t_{1}; \tag{6}\]  
Secondly, we state that if these jobs are implemented in one period, total duration of both jobs is less than period duration. Otherwise it means that in this period \(j_{p}\) and \(j_{s}\) cross each other. \[d_{j_{1}t}+d_{j_{2}t}\leq d,\ \forall t\in T,\ \forall(j_{1},j_{2})\in P. \tag{7}\]  
We note that we use the Constraints (7) to take into account a case when in pair predecessor-successor both jobs are implemented in one period, which is the last period for predecessor and the first period for successor.
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 3 Graph Databases
### Neo4j  
Nee04j is an open-source Java graph database released in 2007. It uses a persistent Java engine where it is possible to store graph structures instead of tables. It is also one of the most widely used graph databases in several domains, such as healthcare, government,  
Figure 2: Data visualization in Nebula Graph Studio.  
automated manufacturing, military, and others [19]. The query language is Cypher, which is inspired by SQL, and allows us to focus on the data we want from the graph [20].  
In DB-Engines' 2022 ranking, we can see that Neo4j is the first-ranked database that stands out for its features [20]:  
* Scalable database optimized for storing and querying large graphs distributed in a cluster on multiple machines;
* Flexible and suitable for handling data with unstructured formats;
* It has an easy-to-understand query language. Its main advantages are [20]:
* It has a high-performance distributed cluster architecture;
* Graph fragmentation minimizes query latency;
* It has a cloud service called AuraDB and is fully managed with automatic updates and backups. Neo4j also has some limitations, namely [20]:
* It does not directly accept RDF-formatted data;
* It consumes a large amount of memory. The Neo4j interface is interactive, intuitive, and easy to use, as shown in Figure 3.
# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 3 Materials and Methods
### Network Architecture
The \(m\) and \(n\) represent the row and column indexes, respectively. \[G[m,n]=(f\times h)[m,n]=\sum_{j}\sum_{k}h[j;k]f[m-j,n-k]. \tag{1}\]  
A ReLU activation function is used in several convolution layers to make learning in the model using training data faster and more effective by keeping positive values and mapping values to zero values. The ReLU activation function's equation is as follows:  
\[f(x)=max(0,x). \tag{2}\]  
Figure 1: Residual block or skip connection on ResNet. The pooling layer is the layer that follows the convolution layer and the ReLU activation function. This layer is made up of a filter with a fixed stride size that moves across the feature map area. To improve computing speed, the pooling layer reduces the number of parameters and spatial size of an image. At each filter change, Max Pooling selects the maximum value in the area to be chosen. In the subsequent stages, there are differences in the number of layers for each architecture. In the second stage, ResNet-50 and ResNet-101 have four layers, while ResNet-152 has eight layers, with respective kernel sizes of 128, 128, and 512. ResNet-50 has six layers in the third stage, ResNet-101 has 23 layers, and ResNet-152 has 36 layers, with kernel sizes of 256, 256, and 1024. Each architecture has three layers in the final stage, with kernel sizes of 512, 512, and 2048, respectively. Finally, the network includes an average pooling layer followed by a fully connected layer with 1000 neurons (ImageNet output classes). Average pooling reduces the image by taking the average value of the specified area at each filter change, as the name implies. The fully connected layer connects all activation nerves from the previous layer to the next. The feature map obtained from the convolution and pooling layers is fed into the full connection layer. The equation is output at the full connection layer. \[h(x)=f\left(b+\sum_{i}w_{i}x_{i}\right). \tag{3}\]  
A bottleneck design is used for deeper networks such as ResNet-50, ResNet-101, and ResNet-152. Three layers are stacked together for each residual function. These layers are made up of 11, 33, and 11 convolutions. The 11 convolutional layer is in charge of shrinking and then restoring the dimensions. With smaller input and output dimensions, the 33 layers act as a bottleneck. Skip connections are performed every three layers in the ResNet architecture. Convolutional operations in the residual blocks are performed with a stride size of 2 when transitioning from one stage to the next. As a result, the input size in terms of height and width is cut in half.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 3 Method  
A dual-component approach for edge-assisted visual SLAM to preserve privacy is proposed. ORB-SLAM3 is applied as the application environment for our approach.  
To protect the transmission data in ORB-SLAM3, the privacy-sensitive areas in the input image are realistically inpainted, and each point of the output point cloud is encrypted into a line. To provide more detail, firstly, the approach presents a method by combining object detection and image inpainting. By inpainting the privacy-sensitive areas in the image based on the surrounding scene, it is ensured that private information is invisible and the inpainted area is natural. Secondly, the output 3D point cloud is encrypted into a 3D line cloud, where each line has a random direction and normalized length. This increases the difficulty of restoring the original 3D point clouds, making it challenging to reconstruct the original scenes. Moreover, the privacy-preserved SLAM maintains accurate pose estimation.  
This section illustrates our approach by describing the framework and workflow. The theoretical basis, algorithm workflow, and models are also introduced in detail.
# Design of an Energy Harvester Based on a Rubber Bearing Floating Slab Track
## 4 Performance Comparison of Energy Harvesters
### Load Match
In addition, it can be seen from the shaded part under the curve that when the load resistance \(R_{L}\) was within a certain range (50-200 K\(\Omega\)), the output voltage of the energy harvester was greater than 1.4 V, being able to meet the input requirements of the energy harvesting circuit. And the output power was able to reach a high level, up to 0.2 mW. Figure 11: Influence of load resistance on the electrical output characteristics of the cymbal energy harvester. When \(R_{L}=100\) K\(\Omega\), the maximum output power was 0.23 mW, and the internal resistance of the spherical piezoelectric energy harvester was calculated to be 100 K\(\Omega\). Compared with the disk and cymbal energy harvester, the matching impedance of the spherical energy harvester was slightly larger than the other two, at 100  K\(\Omega\). At the better load resistance, the output power of the spherical energy harvester was two orders of magnitude higher than that of the disk one and 53% higher than that of the cymbal energy harvester, which was also able to reach the microwatt level. The output electrical properties of the three energy harvesting structures was as shown in Table 3.  
The same load of 0.3 MPa was applied to the disk, cymbal, and spherical energy harvesting structures, and the power output efficiencies were 2.03 \(\times\) 10\({}^{-3}\) mW, 0.15 mW, and 0.23 mW, respectively. In terms of the three types of energy harvesting structures, the disk energy harvester is simple in structure, easy to make, and long in service, but its energy harvesting efficiency is low. The energy harvesting efficiency of the cymbal type is higher than that of the disk type, but due to its complex structure, it can easily produce stress concentration, which is not conducive to long-term service. Compared with the disk energy harvesting structure, the output of the spherical energy harvesting structure is greatly improved. Compared with the cymbal energy harvesting structure, although the spherical energy harvesting structure also has a certain stress concentration phenomenon at the bottom of the spherical shell, the stress concentration phenomenon is weakened due to the good stress equalization of the spherical shell structure. Therefore, the spherical energy harvesting structure not only has good mechanical properties, but also has better electrical properties. Figure 12: Influence of load resistance on the electrical output characteristics of the spherical energy harvester. \begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **Disk Structure** & **Cymbal Structure** & **Spherical Structure** \\ \hline Output voltage/V & 0.62 & 6.5 & 6.7 \\ Electrical output power/mW & \(2.03\times 10^{-3}\) & 0.15 & 0.23 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of electrical properties.
# Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN
## 2 System Model and Assumption
The total computation of a node \(k\) in time frame \(T\) and its total energy consumption can be expressed by Equations (3) and (4) respectively. \[\Phi_{k}^{t}=\Phi_{k,loc}^{t}+\Phi_{k,eff}^{t}=\frac{Tf_{k}^{t}}{\delta_{k}}+B \tau_{k}^{t}\log_{2}(1+\frac{p_{k}^{t}h_{k}^{t}}{B\sigma^{2}}) \tag{3}\]  
\[E_{k,con}^{t}=E_{k,loc}^{t}+E_{k,aff}^{t}=\kappa_{mob,k}\big{(}f_{k}^{t}\big{)} ^{3}T+p_{k}^{t}\tau_{k}^{t} \tag{4}\]  
where \(\kappa_{mob,k}\) denotes the power consumption parameter of node \(k\). Assume that the initially available energy of the node \(k\) in the time slot \(t\) is \(E_{k}^{t-1}\), and the energy buffer queue of the sensor node can be represented by  
\[E_{k}^{t}=\left[E_{k}^{t-1}-E_{k,con}^{t}\right]^{+}+E_{k,har}^{t} \tag{5}\]  
Therefore, the sensor node data buffer queue can be represented as  
\[Q_{k}^{t}=\left[Q_{k}^{t-1}-\Phi_{k}^{t}\right]^{+}+D_{k}^{t} \tag{6}\]  
The HAP receives the offload data from all member nodes for computing and processing and back to the MEC server. The total computation and computation energy consumption of the MEC server during the time period \(T\) can be calculated by Equations (7) and (8). \[\Phi_{MEC}^{t}=\sum_{k=1}^{K}\Phi_{k,off}^{t} \tag{7}\]  
\[E_{MEC}^{t}=\kappa_{mob,S}\big{(}f_{MEC}^{t}\big{)}^{3}\delta_{S}\Phi_{MEC}^{t} \tag{8}\]  
Consequently, the total computation and total energy consumption of the network can be expressed by Equations (9) and (10), respectively. \[\Phi_{total}^{t}=\sum_{k=1}^{K}\Phi_{k}^{t}=\Phi_{k,loc}^{t}+\Phi_{k,eff}^{t} \tag{9}\]  
Figure 1:0
## 3 Results and Discussion
### Application of DC-FluTENG
Figure 7d provides the linear regression analysis that models the relationship between the output Voc and the input flow rate. We can observe a strong linear relationship with the linear regression curve given by \(V=K.Q+V0\) (V), where K is the sensitivity, Q is the flow rate, and V0 is the intercept. The results show a high sensitivity of about 48.4 mV/cc\(\cdot\)min and a linearity R\({}^{2}\) = 0.998, which indicate a great accuracy and potential application for measuring the water-flow rate. As a result, the value of the flow rate can be predicted based on the measured output voltage.