esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review on Image Captioning
## 4 Discussion
### Model Architecture and Computational Resources
#### 4.1.1 Encoder--CNN  
As there is usually only one encoder in the model, the performance is highly reliant on the CNN deployed. Even though we identified five convolutional networks in our research, there are two which stand out and were used the most. The first most popular choice for the feature extractor from images is VGGNet, preferred for the simplicity of the model and for its power. During this study it was found that VGG was used in 33 of 78 reviewed articles. However, the same number of articles which used ResNet as an encoder was also found. ResNet wins for being computationally the most efficient compared to all other convolutional networks. In ref. [88] a clear comparison of four networks--AlexNet, VGGNet, ResNet, and GoogleNet (also called Inception-X Net) was made--results are presented in the Table 11 below.  
It is clear from Table 11 that ResNet performs best--from both Top-1 and Top-5 accuracy. It also has much fewer parameters than VGG which saves computational resources. However, being easy to implement, VGG remains popular among researchers and has the second highest result, regarding the review from ref. [88]. The newest research mostly focuses on prioritizing simplicity and speed at a slight cost in performance. It is a matter for a researcher to decide if he or she needs more precision in the results, a more effectively performing model, or more simplicity.
# Automatic Selective Encryption of DICOM Images
## 4 Performance Analysis and Discussion
### Approach I
#### 4.1.1 Segmentation Accuracy  
As the goal of the presented work is to minimize the encryption time, segmentation accuracy is not crucial to this work. However, it is essential to secure data that is considered important by physicians using sophisticated encryption algorithms. Hence, it is important to provide acceptable accuracy in the segmentation stage in comparison with manual segmentation. Accordingly, the provided dataset is segmented based on the proposed approach, and then compared with the physicians' manual segmentation. A similarity metric in Equation (1) is used to evaluate the quality of the segmentation approach, where \(R_{i}\) is the segmented image of region \(i\in\{1,2,3\}\) and \(O_{m}\) is the original image:  
\[Acc=\frac{\sum R_{i}(pixels\ \in\ ground\ truth)\ AND\ O_{m}(pixels\ \in\ ground\ truth)}{\sum O_{m}(pixels\ \in\ ground\ truth)}\times 100\% \tag{1}\]  
The multi-region segmentation of the adopted dataset shows that the adopted approach accuracy is 100% based on the LSE, where 100% of the manually selected area is segmented as the most important region (R1). Figure 9 presents an example of segmented images based on the LSE at security level 1, which means that ROI is segmented into three different regions. However, it was noticed that none of the important pixels was in region 3. As shown in these images, R1 and R2 contain the most important pixels (not the background pixels).  
Figure 9: Mammography segmentation based on the multi-region approach at security level 1: (**a**) original image, (**b**) ground truth segmentation, (**c**) most important region, and (**d**) the second most important region.
## 2 Background of the System Modeling and FOPID Controller
The electronics of the tested quadcopter comprised a Microcontroller Unit (MCU) interfaced with various components, including the power supply, receiving unit, DC motor power boards, Inertial Measurement Unit (IMU), SONAR, and infrared (IR) modules. The integration of these devices with the MCU facilitated both guided and autonomous flight capabilities. In Table 1, the values of the parameters of the quadcopter dynamic model are given. The FOPID has gathered increasing attention and usage in several control engineering fields in recent years. Developing the FOPID controllers is more difficult than developing the traditional PID controllers because five parameters such as \(K_{p}\), \(K_{i}\), \(K_{d}\), \(\lambda\), and \(\mu\) are tuned in the structure of FOPID controllers. Several methods can be used to tune the parameters of the FOPID controller including optimization algorithms. The FOPID controller structure used in this study is as shown in \[\ddot{x}\ =\ (cos\psi sin\theta cos\varphi+sin\psi sin\varphi)\frac{u1}{m} \tag{1}\]3 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Parameter** & **Value** & **Unit** \\ \hline mass of the quadcopter & m & 1 & Kg \\ arm length & L & 0.24 & M \\ gravity & g & 9.81 & m\(\cdot\)s\({}^{-2}\) \\ body moment of inertia & \(I_{\mathrm{xx}}\) & \(8.1\times 10^{-3}\) & N\(\cdot\)m\(\cdot\)s\({}^{2}\) \\ around \(X\)-axis & \(I_{\mathrm{yy}}\) & \(8.1\times 10^{-3}\) & N\(\cdot\)m\(\cdot\)s\({}^{2}\) \\ body moment of inertia & \(I_{\mathrm{zz}}\) & \(14.2\times 10^{-3}\) & N\(\cdot\)m\(\cdot\)s\({}^{2}\) \\ around \(Z\)-axis & \(I_{\mathrm{zz}}\) & \(14.2\times 10^{-3}\) & N\(\cdot\)m\(\cdot\)s\({}^{2}\) \\ rotational moment of & & & \\ inertia around the motor & \(J_{\mathrm{r}}\) & \(1.08\times 10^{-6}\) & N\(\cdot\)m\(\cdot\)s\({}^{2}\) \\ axis & & & \\ thrust factor & b & \(54.2\times 10^{-6}\) & N\(\cdot\)s\({}^{2}\) \\ drag factor & d & \(1.1\times 10^{-6}\) & N\(\cdot\)m\(\cdot\)s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the quadcopter.
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 3 Processing Capability
### Level 1  
The FORCE Level 1 Archiving Suite (L1 AS) assists in acquiring and managing Level 1 data. L1 AS has two different routines for Landsat and Sentinel-2, respectively (Figure 3). The main difference is that Landsat data need to be downloaded manually, while Sentinel-2 images are automatically retrieved by FORCE.  
Figure 2: Overview of FORCE, general workflow. ARD—analysis ready data; hARD—highly analysis ready data; hARD+—highly analysis ready data plus; DEM—digital elevation model; CSO—clear sky observation; LSP—land surface phenology; CF—continuous field; CR—coarse resolution; MR—medium resolution; WVDB—water vapor database; ESA—European Space Agency; USGS—U.S. Geological Survey (USGS); NASA—National Aeronautics and Space Administration.  
Once Landsat data were downloaded from USGS, L1 AS ingests new images into local data holdings. L1 AS keeps track of data versioning and tiers, which means outdated/lower-ranked data is replaced with newer/improved data, thus preventing data redundancy. On successful ingestion, the image is appended to a file queue, which controls Level 2 processing. The file queue is a text file that holds the full path to the image, as well as a processing-state flag. This flag is either _QUIEUED_ or _DONE_, which means that it is enqueued for Level 2 processing or was already processed and will be ignored next time.  
The Sentinel-2 routine is similar to the one above, but ESA provides an application programming interface (API) for data query and automatic download. Based on a coordinate string list, cloud cover, and date range, a metadata report is pulled from the Copernicus API Hub. Each hit is compared with the local data holdings, and missing images are downloaded. A file queue is generated and updated accordingly.
# Leakage Detection Method of Coal Mine Power Supply System Based on 3 AC
## 2 Leakage Protection Method Based on 3 AC Detection Circuit  
### The Composition of the Detection Circuit  
In the three-phase power supply system where the neutral point of the transformer is not grounded and when the three-phase insulation resistance is basically equal, even if the resistance value is small, the current flowing into the earth remains symmetrical in magnitude and phase [16; 17; 18]. Additionally, zero-sequence voltage and current are not generated, and traditional methods fail [19].  
In order to solve this problem, this paper proposes a method named 3 AC, that is, three capacitors \(C_{1}\), \(C_{2}\), and \(C_{3}\) of different sizes are artificially added at the head of the power supply system. It must be fulfilled that the formula \(C_{1}=2C_{2}=2C_{3}\) is set to form an  
Figure 1: Solid-state automatic voltage regulation system based on series compensation principle.  
asymmetric capacitor filter, which is equivalent to manufacturing a constant zero-sequence component in the system. Even under the faulty condition of three-phase insulation resistance uniformly and symmetrically declining [20], this zero-sequence component introduced by the capacitor can still be used for detection, thus solving the problem that traditional methods cannot detect such faults [21]. The electrical principle is shown in Figure 2.  
The method of adding three-phase asymmetric capacitance (3 AC for short) is not only able to effectively detect single-phase leakage but to also identify leakage faults of two-phase and three-phase insulation drops. By targeting characteristics of large voltage fluctuation ranges of long-distance on-load automatic voltage regulation power supply systems, this proposed method exhibits adaptability under varying conditions.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 3 Results
### Validation of Time Series S2-L2A PWV  
To study the temporal stability of the proposed algorithm, MOD05 and S2-L2A products from 11 days in 2020 were selected; the comparison results are shown in Figure 4. S2-L2A had a lower PWV value than MOD05, which was the same as the result in Section 3.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PWV** & **R** & **RMSE (cm)** & **Bias (cm)** \\ \hline S2-L2A & 0.929 & 0.114 & \(-\)0.005 \\ MOD05 & 0.947 & 0.343 & 0.292 \\ ESA-L2A & 0.908 & 0.346 & \(-\)0.319 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of various PWV products.  
Figure 3: Scatterplots of PWV retrieved by S2-L2A versus PWV estimated from (a) MOD05 and (b) ESA-L2A.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PWV** & **Maximum** & **Minimum** & **Mean** & **Standard Deviation** \\ \hline S2-L2A & 3.600 & 0.282 & 1.649 & 0.391 \\ MOD05 & 3.549 & 0.484 & 1.931 & 0.450 \\ ESA-L2A & 3.005 & 0.161 & 1.231 & 0.314 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical results (unit: cm) of PWV retrieved from S2-L2A, MOD05, and ESA-L2A.  
As shown in Figure 4, there was a strong correlation between the two sets of PWV measurements. The R value ranged from 0.802 to 0.977, with a mean value of 0.862. The RMSE between S2-L2A and MOD05 PWV varied from 0.044 cm to 0.249 cm, with a mean value of 0.129 cm. The Bias value between S2-L2A and MOD05 PWV varied from \(-\)0.242 cm to 0.003 cm. Except for a positive Bias value on the last day, all other values were negative, indicating that S2-L2A underestimated PWV compared with MOD05. The results showed that all PWV data retrieved by S2-L2A had good agreement with MOD05, indicating that the algorithm provided an effective and accurate way to retrieve PWV.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Soil Resistivity Analysis
#### 3.2.1 Measurement Techniques and Soil Layering Impact  
Soil resistivity is measured using techniques such as the Wenner and Schlumberger methods [15]. In addition, the impact of soil layering is analyzed by considering variations in resistivity at different depths. The total ground resistance of a system depends on the resistivity of the individual layers, as shown by the following:  
\[R_{total}=\frac{1}{\sum_{i=1}^{n}\frac{1}{R_{i}}} \tag{9}\]  
where \(R_{i}\) is the resistance of the individual soil layer \(i\), \(n\) is the number of soil layers.
# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 3 Probability Prediction Combination Model
### Optimal Combination Method  
The optimal combination method is based on the functional relationship between the combination model and the single model. The objective function is constructed under certain constraints, and the weight coefficient of the single models in the combination method is solved by maximizing the objective function.  
The non-optimal combination method mainly follows the simple and convenient principle of obtaining the weight coefficient using methods such as the arithmetic average method, the root mean square error reciprocal method, the entropy method, and so on. The accuracy of the non-optimal combination method is generally lower than that of the optimal combination method. In this paper, the interpretation of the optimal linear combination method is given by taking the sum of squared prediction errors as the objective function to solve the weight coefficient.  
If \(x_{t}\) is the true value of the \(t\)-th sample, \(x_{it}\) is the predicted value of the \(t\)-th sample in the \(i\)-th single model, and \(e_{it}\) is the error value of the \(t\)-th sample of in the \(i\)-th prediction model, then \(e_{it}=x_{t}-x_{it}\). In order to ensure the validity and unbiasedness of the results, the weight coefficient needs to meet the following conditions:  
\[\sum\limits_{i=1}^{m}w_{i}=1 \tag{14}\]  
where \(w_{i}\) represents the weight coefficient of the \(i\)-th single model. The sum of the squared error of the combination model can be expressed as follows:  
\[J=\sum\limits_{t=1}^{n}e_{t}^{2}=\sum\limits_{t=1}^{n}\sum\limits_{i=1}^{m} \sum\limits_{j=1}^{m}w_{i}e_{it}w_{j}e_{it} \tag{15}\]  
Therefore, the linear combination model can use the sum of the squared error as the objective function. If \(W=(w_{1},\cdots,w_{m})^{T},E_{ij}=e_{i}^{T}e_{j}=\sum\limits_{i=1}^{N}e_{it}e_ {jt},E=\left(E_{ij}\right)_{n\times n}\), \(R\) is an \(n\)-dimensional column vector where each element is 1, and \(E\) is a square matrix of \(n\times n\), which represents the information matrix of the combined prediction error, then the solution of the weight coefficient can be expressed as an optimization problem.  
\[\begin{array}{c}\text{min}J=\sum\limits_{t=1}^{n}e_{t}^{2}=L^{T}EL\\ s.t.R^{T}L=1\\ L\geq 0\end{array} \tag{16}\]  
The solution is described in detail in [38]. For the solution of the weight coefficient in the optimal combination, this paper uses the IMGWO proposed in this paper to solve the problem. The position of wolves is the value of the weight coefficient.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 5 A Taxonomy for Gait, Balance, RoM Analysis  
A new taxonomy for wearable sensor technology that allows comparison of different systems that share similar characteristics and capabilities is presented in Figure 2. In this survey, the systems are categorized into two levels. The first level specifies the analysis to be performed, which can be either _gait analysis_, _balance assessment_, or _Range of Motion (RoM) analysis_. The second level specifies the categories of parameters extracted from the analysis, which can include _rhythm and phase_, _pace_, _variability_, _postural control_, or _asymmetry_. Rhythm and phase parameters are variables that reflect gait rhythm, timing, and duration; pace parameters give information about speed and/or length measurements; postural control is an integrative process used to maintain body's position relative to gravity and of its segments relative to each other; asymmetry parameters are those that look for differences between limbs; and variability is the fluctuation of parameters, which can offer a complementary way of quantifying and indicating mobility deficits [104].
## 2 Related Work
### Centralized Routing Protocols  
In order to maintain high Quality of Service (QoS) while reducing the wireless service cost, many content delivery applications for VANETs need an infrastructure, e.g., the Road Side Unit (RSU), to bear the central control. This kind of architecture is often named drive-thru networks [20; 21; 22]. Predictive Directional Greedy Routing (PDGR) [23] chooses the next-hop relay based on its present neighbors and the possible expected neighbors in the very near future. In PDGR, the packet carrier obtains the information of a possible future neighbor based on the two-hop neighbor information. [PERSON] et al. [24; 25; 26] designed an adaptive video downloading scheme for throughput enhancement over the drive-thru Internet. [PERSON] et al. [27] proposed a multi-objective optimization framework that can enhance the video playback quality with an adaptive retransmission limit adaptation scheme. Link State-aware Geographic Routing (LSGR) protocol [28] contrived a routing metric called Expected One-transmission Advance (EOA) to improve the greedy forwarding algorithm by explicitly incorporating the link state and packet's advance. With the consideration of the link stability from the EOA metric, the transmission efficiency can be improved by diminishing transmission failures. [PERSON] et al. [29] utilized the distance method to select forwarding nodes, and proposed the distribution-adaptive distance with channel quality protocol. In these schemes, it is the controlling node that handles the whole data transmission scheduling, which will leave a high load burden on the centralized side. Since the RSU deployment is still underway, the problem becomes more serious. This motivates us to focus on the decentralized scheme design.
## 6 Results and Discussion
### Parameter Tuning for Scenario2  
Similarly, within the context of _Scenario_2 we prepared a compilation of various sets of hyperparameter values to experiment with, focusing on the \(FNN\), \(RNN\)-\(LSTM\), and \(RNN\)-\(GRU\) models. The specific hyperparameter values are detailed in Table 5.  
The parameters outlined in Table 5a were first utilized to assess the functionality and reliability of our function. When the performance had been confirmed, the parameter set leading to the lowest validation MAE was chosen as the optimized or tuned parameter set, displayed in table (Table 5b).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**(a)**} \\ \hline
**Parameters** & \multicolumn{2}{c}{**Value**} \\ \hline Number of nodes & & [2, 4, 8, 16, 32, 64, 128] \\ Number of hidden layers & & [1 to 5] \\ Look back period & & [5 days to 10 days] \\ Dropout & & [0, 0.05, 0.1, 0.15] \\ Epochs & & [up to 1 million] \\ \hline \multicolumn{2}{c}{**(b)**} \\ \hline
**Parameters** & **FNN** & **LSTM** & **GRU** \\ \hline Time period & 48 & 48 & 48 \\ Delay & 20 & 20 & 20 \\ Pred\_batch\_size & 48 & 48 & 48 \\ Number of hidden layers & 2 & 1 & 2 \\ Dropout & 0 & 0 & 0 \\ Number of nodes & 32 & 32 & 32 \\ Epochs & 351 & 69 & 51 \\ Look back period & 10 days & 5 days & 5 days \\ Train\_fraction & 1 & 1 & 1 \\ Validation loss (MAE) & 170.41 & 195.31 & 200.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental hyperparameter testing setup for _Scenario_1 (**a**) hyperparameter variations and (**b**) tuned parameters for _Scenario_1.  
Figure 14: Breakdown of dataset into training, validation, and testing sets.
## 4 Simulation Experiment
### Discussion  
Acoustic contrast, reconstruction error, and array effort are important indexes to demonstrate the sound field control ability. These three indicators are difficult to achieve the best at the same time, and there is a tradeoff relationship between them. Generally, if one of the three indicators is better for a sound field control method, the other two indicators are worse. Overall, as shown in Section 4.2 of this article, the largest acoustic contrast is obtained by the ACC method due to its focus on maximizing acoustic contrast, but it does not perform well in terms of reconstruction error and array effort; the smallest reconstruction error is achieved by the PM method in the bright zone, as it strives to minimize the reconstruction error in the bright zone, but it presents poor results in acoustic contrast and loudspeaker array effort. The EDPM method in the bright zone is similar to the PM method in the bright zone, but it has some improvements in the solving method, which is superior to the PM method in the bright zone in acoustic contrast and array effort performance, but inferior to the PM method in the bright zone in reconstruction error performance, and fails to perform better than the PM method in the bright zone in all indexes. The ACC method, PM method in the bright zone, and EDPM in the bright zone all control or optimize only one of these indexes: acoustic contrast, reconstruction error, and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Average Array Effort (dB)** \\ \hline ACC & 0 \\ PM method in the bright zone & \(-1.4078\) \\ EDPM method in the bright zone & \(-4.6614\) \\ Ours & \(-9.4790\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average array effort comparison of four methods with respect to frequency variation.  
Figure 10: Array effort diagram of four methods with respect to frequency variation.  
loudspeaker array effort, and they do not take into account the other two indexes, so they perform better in one index and poorly in the other two indexes. However, our approach is designed to reduce reconstruction error in the bright zone and loudspeaker array effort as much as possible, and to exert influence on sound pressure and particle velocity in the dark zone range. Our approach attempts to control or optimize acoustic contrast, reconstruction error, and array effort at the same time. Therefore, it is superior to the PM method in the bright zone and the EDPM method in the bright zone in terms of acoustic contrast, better than the ACC method in terms of reconstruction error, and significantly better than the ACC and PM methods in the bright zone and the EDPM method in the bright zone in terms of array effort, which can ensure the most stable reconstruction system.
## 3 Overview of the Proposed Method
### Define the Usage Context of the Automation Function to Be Tested
#### 4.1.1 Example: Choice of the Automation Function  
The contributions of this paper will be illustrated on the \"Traffic Jam Chauffeur (TJC)\" function. The TJC is a level 3 automation function, designed to support autonomous driving on divided lanes (motorway or expressway), in congested conditions, where the speed is limited to a maximum speed Vmax (in the range of 60-70 km/h). The Ego vehicle (the vehicle equipped with the automation function, corresponding here to the AV) follows the vehicle in front of it, keeps to its lane, manages acceleration and braking within the authorized speed limit, and keeps a safe distance. The driver can perform other activities but must remain alert and be able to regain control of the vehicle within 10 s when prompted by the system. The system should allow the driver to activate the function and to intervene at any time, and should give them enough time to do so.