esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Development of the Proposed Agricultural Drone Battery Cell Balancing Algorithm
### Design and Development  
In this section, we describe the design and development of the proposed system. During the tests, we analyzed the characteristics of the battery of an agricultural drone using the proposed RF communications method and compared the results to those from a fully wired method. To emulate the spraying of pesticides at an actual site, we used a tank with 20 L of water and conducted the test by flying or hovering 1 m above the ground. Figure 11 shows a photograph of the test setup.  
The transmitter/receiver system shown in Figures 12 and 13 was developed to serve as a wireless base station and receive data related to battery usage via the RF sensor installed on the drone. The system receives the voltage output (0-3.3 V) from the current sensor and converts it into current using an analog-to-digital converter (ADC) before outputting the data. The conversion ratio of 6.6 mV/A translates to a 6.6 mV change per 1 A of current sensed per mV. An output of 0 V translates to 200 A, an output of 1.65 V represents 0 A, and an output of 3.3 V translates to 200 A.  
After the output of the current sensor is converted to a 10-bit value by the ADC, this is then converted to current using the 6.6 mV/A scale factor. The display shows the 10-bit ADC value and the sensor output voltage (mV) and current value. The display data transmission cycle is  
Figure 11: Testing prototype (**a**) and the transmission/reception module when mounted on an agricultural drone (**b**).  
Figure 12: Drone load measurement by system: Communications -RF 424 MHz, -Transmission output 10 dBm, Receive Sensitivity \(-124\) dBm, Current Sensor: -ACS759, \(-200\) A/Micom: -Arduino Mega 2560. (The part labeled (**a**) is the receiver, (**b**) is the receiver data display, and (**c**) is the transmitter.).  
programmed to send a message once per second. The following conversion factors are used: Voltage = (ADC Value/1023.0) * 5000 (5000 mV because the drive voltage of the Microm is 5 V); amps = (Voltage-ACS offset (1.65 V))/mV per amp (6.6 mV/A) [19].  
The proposed scheme implements an active balancing battery management system (BMS) control method. The system receives the cell voltage from the battery monitoring integrated circuit (IC) via a serial peripheral interface (SPI), calculates the minimum value and deviation of each cell, and turns on the balancing switch for each cell where there is a difference of 50 mV or more.  
When the cell deviation value falls below 50 mV, the cell balancing switch is turned off and the battery voltage and current flowing through the Field Effect Transistor (FET) are passed to the ADC so that the values can be used to control the charging and discharging processes. In addition, the charge and discharge FET gate is opened at 10 ms intervals to measure the direction of the voltage and current and to detect and control the charge and discharge state. The battery charge is determined based on the battery voltage and is used to decide when to cut off the charge and discharge.
###### Abstract  
Replacing mechanical utility meters with digital ones is crucial due to the numerous benefits they offer, including increased time resolution in measuring consumption, remote monitoring capabilities for operational efficiency, real-time data for informed decision-making, support for time-of-use billing, and integration with smart grids, leading to enhanced customer service, reduced energy waste, and progress towards environmental sustainability goals. However, the cost associated with replacing mechanical meters with their digital counterparts is a key factor contributing to the relatively slow roll-out of such devices. In this paper, we present a low-cost and power-efficient solution for retrofitting the existing metering infrastructure, based on state-of-the-art communication and artificial intelligence technologies. The edge device we developed contains a camera for capturing images of a dial meter, a 32-bit microcontroller capable of running the digit recognition algorithm, and an NB-IoT module with (E)CPRS fallback, which enables nearly ubiquitous connectivity even in difficult radio conditions. Our digit recognition methodology, based on the on-device training and inference, augmented with federated learning, achieves a high level of accuracy (97.01%) while minimizing the energy consumption and associated communication overhead (87 uWh per day on average).  
NB-IoT; machine learning; smart metering; lightweight digit recognition; federated learning +
Footnote †: journal: _Future Internet_  
Article
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 3 Methodology
### Model Performance Evaluation
#### 3.3.2 Comparison of Synthetic Data vs. Real Data  
In the second stage of the comparison, statistical tests are used to evaluate the realism of the synthetic data. Specifically, each set of synthetic data generated by the Activity-Based Model, according to three different input survey configurations, is compared to real survey data.  
Using the MAE, RMSE, and the K-S test, we assess which methodology produces the most realistic synthetic data. MAE provides an average measure of prediction error magnitude, while RMSE emphasizes larger deviations due to squaring the error terms. The K-S test measures the difference between cumulative distributions, with the D-statistic indicating the maximum deviation and the \(p\)-value indicating statistical significance. The respective mathematical formulas for MAE and RMSE can be found in Appendix A.
## 4 Results of Probabilistic Stability and Performance Assessment
### Inverter Power Level Impact on System Stability and Performance  
The inverter power level may affect a system's stability. Therefore, it seemed necessary to investigate this issue. Five different levels for inverter power in the probabilistic assessment were considered. Based on the prepared flow diagram, PDFs of the maximum damping factor and minimum damping ratio were calculated and are shown in Figure 8.  
Figure 7: Probability density functions (PDFs) and cumulative density functions (CDFs) of stability indices for different grid SCRs. (**a,c**) are PDFs and CDFs of the maximum damping factor; (**b,d**) are PDFs and CDFs of the minimum damping ratio. Colors correspond to different grid SCRs in Figure 6a.
# Using IoT for Cistern and Water Tank Level Monitoring
## 4 Experimental Results
### Database and Graphs  
While the water levels in the cistern and tank are measured and monitored on the Internet, water flow is sensed, water quality is measured, and water is delivered by the submersible pump from the cistern to the tank. Everything that happens is also recorded in a database.  
The output produced while monitoring water levels, water flow, water quality, and tank filling from the cistern is displayed on the dashboard and gauges, stored in a database, and plotted on graphs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Question** & **Excellent (\%)** & **Good (\%)** & **Moderate (\%)** & **Poor (\%)** & **Very Poor (\%)** & **N/A (\%)** \\ \hline
1 & 47 & 47 & 6 & 0 & 0 & 0 \\
2 & 47 & 33 & 20 & 0 & 0 & 0 \\
3 & 40 & 37 & 23 & 0 & 0 & 0 \\
4 & 53 & 27 & 17 & 0 & 0  
We used the InfluxDB database (InfluxDB is an open-source time series database developed by the company InfluxData. InfluxDB manages all types of time series data in a single, purpose-built database ([[https://www.influxdata.com/](https://www.influxdata.com/)]([https://www.influxdata.com/](https://www.influxdata.com/)), accessed on 17 September 2024)) to record data, which stores the data generated by sensors and storage and the time series data retrieved in fields such as Internet of Things sensor data, application metrics, operations monitoring, and so on. This database stores open-source time series. Here are some graphical output views of the database generated during testing.  
We have a database table that includes the four data variables recorded in the database generated during the run.  
Below, we include an example of this table, in which the four variables we recorded are stored in the database: water quality, flow rate, water level in the tank, and water level in the cistern (see example in Figure 20).  
In the same way, we recorded graphs of four variables: water quality, flow rate, tank water level, and cistern water level.  
We plotted the data from the database and include the plot of some of the data recorded in the database; see Figures 21 and 22.  
While the usage of data processing for using machine learning tools, with more extensive and more extended data collection, is outside the scope of this work, it is possible to perform data analysis to identify trends and patterns that reveal essential clues and increase water use efficiency at the household level, which can be scaled up to a neighborhood or city.  
Figure 21: Graphing the database. Here, values about water quality, flow rate, water level in the tank, and water level in the cistern are depicted.  
Figure 20: Database output. Values about water quality, flow rate, and water levels in the tank and cistern.
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Automotive Systems
#### 4.3.1 Autonomous Vehicle Fault Detection  
Autonomous vehicles (AVs) depend on a range of sensors including cameras, radar, lidar, and ultrasonic sensors to navigate and understand their environment. These sensors produce large quantities of real-time data that must be processed accurately to ensure the safe operation of the vehicle. In this context, fault detection is crucial for identifying sensor failures or perception issues that could result in accidents.  
One of the main challenges in AVs is the integration of multimodal sensors. For example, lidar offers 3D obstacle detection, while cameras provide visual information. Discrepancies between these data sources may indicate a fault in sensor calibration or perception logic [72].  
Machine learning plays a central role in identifying such faults. Algorithms like decision trees (DTs), support vector machines (SVMs), and deep neural networks (DNNs) are trained on historical sensor data to detect anomalies [73]. For example, machine learning models can identify miscalibrated cameras or malfunctioning lidar sensors that inaccurately represent object positions.  
Deep learning models, such as CNNs, excel at processing spatial information from camera feeds and can detect visual anomalies, including lane departure errors and object detection failures [41]. RNNs, especially LSTM networks, analyze temporal patterns to identify issues that develop gradually, such as system degradation or sensor drift [73].
## 6 Experimental Results and Discussion
### Performance Evaluation of MIMO RNCS
On the other hand, when compared with the MIMO A&F conventional strategy with or without pre-equalization scheme, the SISO D&F protocol achieved better capacity than the MIMO A&F conventional without pre-equalization, as can be seen in Figure 14; moreover, considering the pre-equalization schemes, the SISO D&F protocol reached approximately the same capacity as the A&F-ZF strategy. We also present the throughput of the 2 \(\times\) 2 MIMO D&F Co-operative System, which substantially improved the system throughput. More explicitly, the theoretical capacity of 64-QAM (13.95 Mbps is the total possible capacity per frame that our system would obtain when using the 64-QAM constellation and without any channel. This theoretical capacity has been calculated considering an ideal simulation (without channel and noise).) was reached at SNR =8 dB. Furthermore, the obtained throughput minimum was 11.17 Mbps, which could be achieved with the 2 \(\times\) 2 MIMO A&F-MMSE strategy at SNR =14 dB.
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 4 Experiment and Discussion
### Ablation Experiments
#### 4.2.4 Evaluation of Backbone Model  
Table 4 compares the performance of the updated anchor-free detector based on different backbones: ResNet101, Hourglass, and DLA34, and the effect of the deformable convolution layer was also experimentally compared. From the table, it can be seen that the performance of Hourglass or DLA34 is obviously better than that of ResNet101. The mAP of ResNet101 can reach 0.891, which decreases about six percentage points compared with either of the other two backbones. We added the DCN block over the ResNet101 and DLA34, since it adaptively adjusts according to the scale and shape of the objects. This module resulted in a promotion of 1.9% and 1% on mAP for the ResNet101 and DLA34, respectively. P-R curves of different backbone models are shown in Figure 7, our studies demonstrate that the DLA34 combined with DCN is considerably better compared with other backbone models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Backbone Model**} & \multicolumn{3}{c}{**AP**} & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**mRecall**} \\ \cline{2-2} \cline{5-5}  & **Car** & & & \\ \hline ResNet101 & 0.824 & 0.957 & 0.891 & 0.913 \\ ResNet101+DCN & 0.859 & 0.966 & 0.912 & 0.936 \\ Hourglass & 0.945 & 0.973 & 0.959 & 0.981 \\ DLA34 & 0.942 & 0.980 & 0.961 & 0.981 \\ DLA34+DCN & 0.956 & 0.987 & 0.971 & 0.987 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results of different backbone models used on the UCAS-AOD dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Term** & **DGA Module** & **CIoU** & **mAP** & **mRecall** \\ \hline ORIG & & & 0.942 & 0.968 \\ DGA & \(\surd\) & & 0.966 & 0.987 \\ UBR & & \(\surd\) & 0.964 & 0.985 \\ DGANet & \(\surd\) & \(\surd\) & 0.971 & 0.987 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of combining DGA and UBR.
## 5 Experiments
### Magnetic Field Test for Alignment and Offset Coils  
The measuring instrument used in the low-frequency magnetic field emission test is the ELT-400 from Narda, which is used to measure the omnidirectional magnetic field. When WCS works stably and the coupling coils are fully aligned, the magnetic field strength of the head, chest, cushion and low floor at 75 mm and 700 mm from the ground are measured. Figure 23 shows a testing for magnetic field strength at the antenna point, a 7.5 cm point from the ground and the seat point.  
These measurement results are compared with the simulation results in Table 10. It can be concluded that a position around the seat is less than 1 A/m, which is a safe level for the EMF exposure standard. Magnetic field strength at test points outside the vehicle are higher than the in-vehicle points, and the strength is the largest at a 7.5 cm point from the ground. It can be found that the experimental results are larger, while other sources of interference that exist outdoors are likely to be the influencing factor. A tiny error between the experimental results and the simulation results is allowed. Therefore,  
Figure 23: Testing at different points: (**a**) the antenna; (**b**) 7.5 cm point from the ground; (**c**) the seat.  
Figure 22: Experiment condition: (**a**) wireless charging test bench; (**b**) experiment vehicle.  
the experiment verifies the accuracy of the simulation model and the magnetic field simulation results in Maxwell.  
Magnetic field strength under different lateral offset conditions with respectively 60 mm, 120 mm, 180 mm, 240 mm, and 300 mm offsets are obtained by measurement. Three test points A2, A3 and A4 are selected to obtain the magnetic field strength under different offset conditions, as shown in Table 11. Each point takes three values, and finally takes the average value to be the magnetic field strength. From Table 11, it can be seen that the errors between experimental results and simulation results are 1.4 A/m, 2.1 A/m and 1 A/m, respectively. The prediction results of simulation are more accurate.
# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
## 2 Proposed Asymmetrical Bidirectional Rendering Approach
### Depth-Image Preprocessing  
In general, disocclusions arise because of depth discontinuity between the foreground and background objects. The higher depth discontinuity leads to a larger disocclusion. As the baseline increases, the disocclusion area would increase until the entire foreground object is projected onto the new background. Therefore, accurate depth value is important for the synthesized image quality. Affected by the device accuracy and the deficient stereo matching algorithm, the depth value of the foreground edge might be coarse. This means that the edge of foreground in the depth image is mismatched with that of the color image, causing ghosts to appear around the disocclusion, as shown in Figure 4b. The mixture of foreground and background pixels on the disocclusion boundary reduces the visual quality and interferes with the background texture propagation in hole filling. Therefore, a local depth-image preprocessing approach is proposed to correct the depth value of the foreground edge.  
Figure 4: Results of depth image preprocessing: (**a**) processed depth image; (**b**) warped image without depth image preprocessing; and (**c**) warped image with processed depth image.  
Figure 3: Flowchart of the proposed method.  
The preprocessing process includes two steps: foreground edge detection and depth calibration. For the right synthesized image, disocclusions appear on the right side of foregrounds and located between the foreground and background. Therefore, only one side of the foreground edge needs to be extracted. Based on the principle of disocclusion generation, the relevant right foreground edge can be detected according to depth discontinuity and marked as follows:  
\[E_{r}=\left\{\begin{array}{ll}1,&d(x,y)-d(x,y+1)>t\\ 0,&\text{otherwise}\end{array}\right., \tag{1}\]  
where \(d\) denotes the depth value; \(t\) represents the segmentation threshold and is set to 10 in the experiment. When the foreground edges that might produce ghosts are labeled, a one-dimensional template is used to process these pixels. For pixels in the template whose depth value is lower than the labeled pixel, its depth value is replaced by the labeled pixel's depth value. This means that the edge of the foreground is extended to the background by a certain length. The size of template is set to \(1\times 5\) as the ghosts are usually 1-2 pixels wide. The preprocessing result is shown in Figure 4c. It can be seen that most of the ghosts are removed and projected onto the corresponding foreground edges. In the case of large baseline, ghosts may appear on both sides of the disocclusion, so similar pre-processing is necessary for the left edge of foreground. Since this process only modifies the local depth values of the image, the blurring effect would not be produced in the synthesized image. However, large holes are not obviously reduced, and further processing is necessary.
## 3 Results and Discussion
### Comparison of Volatile Organic Compounds in Regular Raw Milk and DHA-Biofortified Raw Milk
Dimethyl sulfone, a major sulfur-containing compound, is recognized as a key odorant in various types of milk, and constitutes 25% of the volatile compounds in cow, goat, and sheep milk [28]. The aroma of dimethyl sulfone is described as resembling that of hot milk, leather, and cow sweat. However, its impact on milk flavor is a subject of discussion due to the lack of sufficient data regarding its content in milk, which varies considerably depending on dietary conditions. Sulfur compounds in milk primarily originate from the diet, although they can also be produced through the binding of cysteine and methionine within proteins, with \(\beta\)-lactoglobulin playing a crucial role in this process [18]. Experimental findings suggest that DHA- biofortified raw milk contains higher levels of dimethyl sulfone than regular raw milk, which is potentially attributed to the influence of supplementing dairy cow diets with DHA on the protein-binding mechanisms in these animals.
# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
## 3 AMR Framework
### Spatial Attention Mechanism  
The spatial attention mechanism allows the model to adaptively learn the attention weights of various regions by incorporating an attention module, whereas the temporal attention mechanism seeks to capture the significance of time series data. In this way, the model can focus more on important areas of the image and ignore unimportant areas. Using convolution operations and the sigmoid activation function to highlight certain regional traits, the spatial attention mechanism represents the significance of input features in the spatial dimension. The working mechanism is  
\[attention=\sigma(\text{conv}) \tag{15}\]  
where \(attention\) represents the attention weights obtained through an activation function, \(\sigma\) denotes the sigmoid activation function, and \(conv\) is the result of the convolution operation.  
To improve the quality of feature representation, we added two FC layers, each containing 128 neurons, along with scaled exponential linear unit (SeLu) activations to improve the network design. To combat overfitting, the dropout algorithm is judiciously employed. The output layer boasts Softmax activation with 10 neurons (11 under the RadioML2016.10a dataset), with each neuron corresponding to a distinct modulation scheme. Scaled exponential linear units (SeLus) are strategically incorporated to deepen the network's capacity. Dropout comes into play as a shield against overfitting, ensuring robust performance. The output layer determines the modulation mode of the modulated signal using Softmax.