esa-sceva/satcom-chunk-collection huggingface dataset:
# Aircraft Target Interpretation Based on SAR Images
## 4 Experimental Results and Analysis
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Index** & **FS** & **FSM** & **4 DDH** & **MRFCA** & **K-Mean++** & \(mAP_{\textbf{50}}\) & \(mAP_{\textbf{75}}\) & \(mAP_{\textbf{50-95}}\) & \
\begin{tabular}{c} **S-Target** \\ \(mAP_{\textbf{50-95}}\) \\ \end{tabular} &
\begin{tabular}{c} **L-Target** \\ \(mAP_{\textbf{50-95}}\) \\ \end{tabular} \\ \hline YOLOv5s & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 85.1 & 71.6 & 61.0 & 50.5 & 65.9 \\ \hline YOLOv5s+ & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 86.5 & 72.5 & 61.6 & 51.2 & 66.4 \\ \hline YOLOv5s+ & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 87.3 & 73.5 & 63.2 & 53.8 & 67.3 \\ \hline YOLOv5s+ & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 88.1 & 74.5 & 65.3 & 57.1 & 68.2 \\ \hline YOLOv5s+ & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 89.3 & 76.2 & 67.5 & 59.6 & 69.9 \\ \hline YOLOv5s+ & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 90.6 & 78.6 & 69.7 & 63.1 & 72.0 \\ \hline YOLOv5s+ & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & 88.1 & 74.2 & 63.8 & 54.3 & 67.7 \\ \hline YOLOv5s+ & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 91.4 & 79.3 & 70.3 & 63.6 & 72.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment results.
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 5 Experimental Application Results
### Evaluation of the Machine-to-Machine Access  
The IoT device for remote elevator control can be accessed from any machine connected to the same wireless network than the IoT device. This capability was tested by implementing an experiment based on the use of a mobile robot calling the elevator. The mobile robot used for this experiment is the APR-02 mobile robot [56] developed at the Robotics Laboratory of the University of Lleida. This mobile robot is a human-sized (1.76 m, 30 Kg) omnidirectional platform with the capability to navigate autonomously through tight spaces [57; 58; 59]. In the near future, the mobile robot is expected to use the IoT device for elevator control to navigate across different floors within the building.  
In this validation experiment, the mobile robot is located in front of the elevator (Figure 17), calling it through the IoT device. The calls to the elevator were implemented by using the transmission control protocol (TCP), that verifies the reception of the messages and resends them in case of error. In this experiment the access to the elevator was successful in 100% of the calls.
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
## 3 Methodology
### CountCOCO: Fine-Tuning for Numerically Aware Captioning  
To enable the model to generate captions that explicitly reflect object counts, we introduce CountCOCO, a numerically grounded captioning dataset designed to reinforce numerical reasoning during fine-tuning. This additional stage teaches the model to generate fluent descriptions while incorporating accurate count information.  
CountCOCO was constructed using the COCO 2014 [9] training set. For each image, captions were generated using Qwen2-VL [31], a large-scale VLM. The numbers were set between 2 and 10 to balance the diversity of the numbers while avoiding extreme values. The exact prompts used in this process are as follows: \"Find objects in the given image with a count between two and ten. Create a caption with one of those numbers that must include that number\".  
The initial caption generated by the VLM was post-processed and filtered using the following procedure:  
1. Extracted only the caption sentence for the image, removing all unnecessary text such as quotes or formatting artifacts (such as the phrase \"Caption:\").
2. Only captions that explicitly contain numeric values were retained, and captions that did not contain numeric information were discarded.
3. Because the dataset focused on numbers between 2 and 10, captions containing numbers outside of this range were removed.  
To assess the reliability of the generated numeric annotations, we conducted a sanity check on a randomly sampled subset of 500 image-caption pairs. Each pair was manually inspected to verify whether the number mentioned in the caption corresponded to the actual number of objects in the image. Although some noise was observed, most samples were consistent with the visual content, and severely inaccurate cases were rare. We consider this level of imperfection acceptable within the weak supervision paradigm widely adopted in vision-language tasks.  
Additionally, we analyzed the overall distribution of numeric values and found an overrepresentation of the number \"2\". To mitigate potential bias, we removed a portion of those samples to maintain a balanced distribution. These verification and filtering steps support the use of CountCOCO as a reliable dataset for fine-tuning numerically aware captioning models.  
**Structured caption format.** To further reinforce numeric grounding, we reorganize each caption into a structured format: Number \(|\) Caption. This format leverages the autoregressive nature of language models, encouraging the decoder to generate the count token first. By providing a clear and standardized input, this approach prevents the model from defaulting to vague quantifiers like \"many\" or \"several\" and encourages the model to generate captions that include counts. Fine-tuning on CountCOCO enables the model to align semantic descriptions with grounded count information, improving its numerical fluency without sacrificing language quality.
## 4 Development of Proposed Method
### VMD-Based Existing Methods  
In contrast to EMD, VMD may adaptively break down any signal into ensemble BLIMFs, where the center frequencies are computed online and all modes are extracted simultaneously [16]. In comparison with EMD, VMD successfully avoids mode mixing and is resilient to noise and sampling thanks to its solid mathematical foundation. From the perspective of denoising, a key characteristic of VMD is its capacity to partition the intended signal into a small number of starting BLIMFs while storing the majority of the noise in a small number of final BLIMFs. Therefore, a decent approximation of the signal may be achieved via partial reconstruction by rejecting the modes with noise. This section gives a brief overview of the existing VMD-based methods (see Figure 2) designed for signal denoising, and the interested reader is referred to the corresponding references for further details.  
As shown in Figure 2a, VMD-DFA provides an alternative to EMD-DFA [18]. While EMD-DFA demonstrates that the scaling exponent rises as the frequency of each mode in EMD falls, VMD-DFA demonstrates the opposite. This distinction can be attributed to the fact that the highest slope indicates either the maximum increase in frequency or the maximum decrease in long-range correlations, essentially signifying the largest rise in noise content. Therefore, the initial modes are utilized for performing denoising via the VMD-based method, which is in contrast to the latter modes being picked up to rebuild the denoised signal in the case of EMD-based denoising. As shown in Figure 2b, the VMD-DFA-DWT method [19] utilizes VMD to decompose the noisy signal into BLIMFs. Additionally, DFA defines significant modes to reconstruct the filtered signal. In order to preserve the desired information present in these modes, wavelet-based windowing is applied to suppress noise from a few BLIMFs rather than completely discarding it. Finally, the clean signal is synthesized by adding the windowed modes and the remaining thresholded BLIMFs.
# Aptamer-Based Biosensors for the Analytical Determination of Bisphenol A in Foodstuffs
## 4 Detection Techniques Applied in the Analysis of Real Food Samples
### Other Detection Techniques  
In 2018, [PERSON] et al. [56] developed a real-time quantitative polymerase chain reaction (RT-qPCR)-based biosensor. In this device, a biotin-modified aptamer DNA was fixed on the inner wall of a streptavidin-coated PCR tube via biotin-avidin interactions, and the DNA template was hybridized with the aptamer DNA. Upon the addition of BPA, the DNA template was released and affected the PCR process depending on the BPA concentration. [PERSON] et al. [49] developed a photo-electrochemical (PEC) sensor based on an Indium Tin Oxide (ITO) electrode coated with Au/ZnO pencils and functionalized with an aptamer. The PEC process converted photons in electricity by charge separation, and subsequent charge transfers were activated by light absorption. Due to the presence of Au in the ZnO nanopencil modified ITO electrode, the photo-stimulated current sensitively increased respect to that which was obtained using the ZnO/ITO electrode alone. After coating the BPA aptamer on the Au/ZnO nanopencil, the decrease in the intensity of the PEC signal generated on the aptamer/Au/ZnO/ITO electrode depended on the increase in BPA concentration.
## 5 Experiments
### Evaluation Metrics  
To analyze and compare our results with previous algorithms on the same scale, we selected five commonly used evaluation criteria for point cloud registration algorithms: the root mean square error (RMSE) and mean absolute error (MAE) of the rotation Euler angles and translation vectors. The time metric represents the duration required to obtain the output feature vectors using the respective network method on the test set. Let the true rotation matrix **R** be represented by the Euler angles \(\text{Eu}_{\text{gt}}\), and the predicted rotation matrix **R** by \(\text{Eu}_{\text{pre}}\). The rotation error is calculated as follows:  
\[\text{RMSE}(\textbf{R})=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\text{Eu}_{\text{pre }}-\text{Eu}_{\text{gt}})^{2}}, \tag{14}\]  
\[\text{MAE}(\textbf{R})=\frac{1}{n}\sum_{i=1}^{n}|\text{Eu}_{\text{pre}}-\text {Eu}_{\text{gt}}|. \tag{15}\]  
For the translation vector error, \(\textbf{t}_{\text{pre}}\) is the predicted translation vector, and \(\textbf{t}_{\text{gt}}\) is the true translation vector. The translation error is calculated as follows:  
\[\text{RMSE}(\textbf{t})=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\textbf{t}_{\text{ pre}}-\textbf{t}_{\text{gt}})^{2}}, \tag{16}\]  
\[\text{MAE}(\textbf{t})=\frac{1}{n}\sum_{i=1}^{n}|\textbf{t}_{\text{pre}}- \textbf{t}_{\text{gt}}|. \tag{17}\]
# An Improved Bi-LSTM-Based Missing Value Imputation Approach for Pregnancy Examination Data
## 2 Materials and Methods
### Traditional Machine Learning
#### 2.3.2 Outlier Detection  
Outliers are abnormal data that fall outside the cluster. Many fields in medical datasets are manually input, so input errors occur easily. For example, some decimals may be ignored, causing the value to increase 100 times, which obviously leads to abnormal data and results in difficulty in data analysis. This kind of abnormal data will affect the performance of our algorithm if we do not handle it. The concrete method to detect outliers is described as follows.  
Detection based on distance measurement [19]: First, we define the distance between samples and then set a threshold distance. Outliers are defined as data points that are far from the others. This method has the advantage of simplicity, which requires low computation and storage costs, but the threshold value is hard to set, and its computation complexity is too big, which is unacceptable for big datasets.  
Detection based on clustering algorithms [20]: A clustering model is built, and outliers are defined as data points that are far from all clusters or do not belong to any cluster significantly. This method has an advantage in that there are clustering algorithms that have been developed already, but the method is too sensitive to the selection of the number of clusters.  
Detection based on density [21]: The local density of outliers is significantly lower than for neighboring points. The score of an outlier of a sample is the inverse of the density of the area the sample is in. Different definitions of density correspond to different detection methods.  
Detection based on statistical methods: A statistical distribution method is built, and outliers are defined as data points with low possibilities. This method has the advantage of the solid foundation of statistics, but it is necessary to obtain the type of distribution of the dataset first; otherwise, it may cause a heavy-tailed distribution.  
As this dataset consists of examination indicators, which have relatively fixed ranges, we selected methods based on statistics. For each indicator, we used Matlab to generate a scatter diagram. As the number of outliers was too small, we detected them with a priori knowledge and manually selected them to fix or propose.
# Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots
## 3 Implementation
### System Architecture  
In addition to the coordinates of the obstacles, the algorithm needs as input the angles of the robot joints to estimate the actual relative position between the manipulator and the obstacles. Instead, the velocities of the joints to be sent to the robot controller are the output of the algorithm, and the cycle restarts by comparing the new configuration of the robot with the new position of the obstacles. Obviously, the actual operation of the algorithm depends on the speed of execution of the loop iteration; the faster the data are reprocessed and sent, the greater the responsiveness of the robot in moving away from the obstacle and, at the same time, reaching the set point. In the tests performed in this work, if no obstacles are perceived in the work area, the manipulator will follow a straight trajectory, moving the end-effector between the two established points, initial and final. This simple condition, without any perturbation, is used preliminarily to verify that the system architecture settings are properly adjusted so that the robot can move smoothly and continuously.  
Figure 2 shows the hardware and software architecture of the robotic system. The control algorithm is executed by a standard PC connected to the robot controller for data exchange; a closed-loop velocity control cycle is executed iteratively until the target point is reached within a predefined tolerance.  
Initially, the joint position of the robot is read using the Real-Time Data Exchange protocol (RTDE) [26], which allows synchronizing external applications with the UR controller over a standard TCP/IP connection, without affecting the performances of the UR controller. This communication method has been implemented through the Python programming language, allowing to receive data from the robot with a frequency of 500 Hz. Subsequently, this information is sent to a Matlab routine through the User Datagram Protocol (UDP), widely used in real-time applications. The Matlab routine executes the obstacle avoidance algorithm giving in the output the joint velocities reference signal to be sent to the robot controller. To do this, a TCP/IP communication is used between Matlab and a UR Script loaded on the UR5e controller that allows one to directly control the velocity of the joints of the robot by an external reference signal.
## 6 Performance Analysis  
In this analysis, we evaluate the performance of our scheme in comparison to the schemes proposed by [PERSON] et al. [47], [PERSON] et al. [6] and [PERSON] et al. [48]. The evaluation is conducted on a system equipped with an Intel Core CPU i7-6700 operating at 3.40 GHz and 8 GB RAM, running the Windows 7 operating system. Firstly, we adopt a bilinear pairing denoted \(E:G_{1}\times G_{1}\to G_{T}\), which ensures a security level equivalent to 80 bits, where \(P^{\prime}\) serves as the generator of \(G_{1}\). And \(G_{1}\) is defined as a super singular elliptic curve \(E^{\prime}:y^{2}=x^{3}+x\ mod\ p^{\prime}\), where \(p^{\prime}\) is a 512-bit prime number and \(q^{\prime}\) represents its order, which is a prime number of 160 bits. Let \(q\) denote the order of the group \(G\) on the super elliptic curve \(E:y^{2}=x^{3}+ax+b\ mod\ p,(a,b\in Z_{p}^{*})\); in this context, both \(q\) and \(p\) are 160-bit prime numbers. The symbols used throughout this section are as follows:  
* \(T_{bp}\): The duration of executing a bilinear pairing operation \(e(Q^{\prime},R^{\prime}),\ Q^{\prime},R^{\prime}\in G_{1}\).
* \(T_{bm}\): The duration of the scale multiplication operation \(x^{\prime}\cdot P^{\prime}\) of bilinear pairing, where \(x^{\prime}\in Z_{q^{\prime}},P^{\prime}\in G_{1}\).
* \(T_{ba}\): The duration of the point addition operation \(Q^{\prime}+R^{\prime}\) of bilinear pairing, where \(Q^{\prime},R^{\prime}\in G_{1}\).
* \(T_{mtp}\): The duration of a MapToPoint hash operation of bilinear pairing.
* \(T_{em}\): The duration of the scale multiplication operation \(x\cdot P\), where \(x\in Z_{q},P\in G\).
* \(T_{en}\): The duration of a point addition operation \(Q+R\), where \(Q,R\in G\).
* \(T_{h}\): The duration of an one-way hash function operation.  
The time taken for each operation is presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Operations** & **Running Time (ms)** \\ \hline \(T_{bp}\) & 5.086 \\ \(T_{bm}\) & 0.694 \\ \(T_{ba}\) & 0.0018 \\ \(T_{mtp}\) & 0.0992 \\ \(T_{em}\) & 0.3218 \\ \(T_{ea}\) & 0.0024 \\ \(T_{h}\) & 0.001 \\ \hline \end{tabular}
\end{table}
Table 2: Running time of operations.
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 2 Materials and Methods
### Continuous Flight Auger Piles (CFA)
#### 2.2.1 Static Load Tests of Piles  
One pile of each pile group under the bridge pillar was tested using the static load test. Overall, 9 CFA piles were tested and they remained as a part of the deep foundation after testing or as system piles.  
Loading equipment for pile load tests consisted of a steel beam (loading bridge) which was anchored to the 4 already manufactured piles acting as a counterweight during loading. Hydraulic presses were placed on the pile cap under the steel beam (Figure 2). Loading force was measured with the dynamometers and with the pressure sensors at hydraulic presses at the same time. Actual and overall settlement at every loading step were measured with the inductive displacement sensors attached on the reference measuring beams and equally positioned around the pile head. Pile head settlement was also controlled with the precise levelling geodetic method.  
Maximum loading force \(V_{max}\) was limited to 1.5 multiple of the design load \(V_{d}\). Pile head settlement was observed every minute until attenuation at every loading step. This was defined as a maximum change in settlement of 0.05 mm during last 15 min. The number of loading steps and load increment were set in accordance with the maximum load \(V_{max}\) during the test. At least 6 loading steps were applied with load increment in interval from 11% to 25% of \(V_{max}\). After 2/3 of \(V_{max}\) was achieved in the first cycle, the pile was unloaded to zero load with continuous record of pile head displacement during at least 3 unloading steps. After unloading and settlement attenuation, the pile was loaded up to \(V_{max}\) during the second cycle. Unloading during this cycle was performed in at least 4 unloading steps, with exception of pile No. 9, where only 3 unloading steps were realized.
# An Ultra-Thin Composite Metasurface with Hybrid-Damping Modes for Broadband Sound Absorption
## 3 Compound HR Structure with Hybrid-Damping Modes
### Calculation of Sound Absorption Coefficient
#### 3.2.2 Finite Element Simulation  
The numerical simulation is developed by using the commercial FEM package COMSOL MultiphysicsTM 6.0, in which the Pressure Acoustics module is adopted. The schematic diagram of the finite element model is illustrated in Figure 3. The domain of Narrow Region Acoustics is employed to characterize the viscous and thermal dissipation in the small holes and cavities, while the air before the unit is defined as the Pressure Acoustics domain. Since different losses of air medium are introduced in a homogenized way, this method requires less computational resources and can significantly improve efficiency compared with the Acoustic-Thermoacoustic module. The small holes employ the circular hole model, while the cavity utilizes the wide tube model. Due to the much larger impedance than that of air, hard boundaries are applied for all the cell cavity walls. The Floquet conditions are chosen on both sides of the metasurface unit for the periodic arrangement. A normal incident plane wave with unit amplitude is applied on the structure surface. The maximum size of the meshes for the cavity and holes is lower than \(1/8\) of the shortest incident wavelength, while the minimum mesh in the holes is of \(d_{v}/2\), where \(d_{v}=\sqrt{2\mu/\omega\rho_{0}}\) is the viscous boundary-layer thickness. The absorption coefficient is obtained as \(\alpha=1-|r|^{2}\), where r is the complex reflection coefficient of the average sound pressure over the incident boundary.
# Development of a Model Material for Dynamic Geotechnical Model Tests
## 4 Experimental Results and Sensitivity Analysis
### Establishment of a Multiple Linear Regression Model  
In multiple linear regression analysis, the dependent variable \(y\) is affected by multiple external variables \(x_{i}\). If there is a linear relationship between \(y\) and \(x_{i}\), then a multiple regression model can be established based on the dependent variable \(y\) and all influencing factors \(x_{i}\). According to the basic theory of multiple linear regression model [34], the mathematical expression of the model is as follows:  
\[y=b_{0}+b_{1}x_{1}+b_{2}x_{2}+\cdots+b_{\text{m}}x_{\text{m}}+\mu \tag{12}\]  
where \(b_{0}\), \(b_{1}\),..., \(b_{\text{m}}\) are the regression coefficients and are the percentages of materials.  
By substituting discrete data into (12), the following equations can be obtained:  
\[\left\{\begin{array}{l}y_{1}=b_{0}+b_{1}x_{11}+b_{2}x_{21}+\cdots+b_{\text{ m}}x_{\text{m1}}\\ y_{2}=b_{0}+b_{1}x_{12}+b_{2}x_{22}+\cdots+b_{\text{m}}x_{\text{m2}}\\ \vdots\vdots\vdots\vdots\\ y_{\text{n}}=b_{0}+b_{1}x_{\text{1n}}+b_{2}x_{\text{2n}}+\cdots+b_{\text{m}}x_{ \text{mn}}\end{array}\right. \tag{13}\]  
where \(n\) is the number of physico-mechanical parameters and \(m\) is the number of classifications of similar materials.  
The above equations can be written in the following matrix form:  
\[\left[\begin{array}{l}y_{1}\\ y_{2}\\ \vdots\\ y_{\text{n}}\end{array}\right]=b_{0}+b_{1}\left[\begin{array}{l}x_{11}\\ x_{12}\\ \vdots\\ x_{1n}\end{array}\right]+b_{2}\left[\begin{array}{l}x_{21}\\ x_{22}\\ \vdots\\ x_{2n}\end{array}\right]+\cdots+b_{\text{m}}\left[\begin{array}{l}x_{\text{m1} }\\ x_{\text{m2}}\\ \vdots\\ x_{\text{mn}}\end{array}\right] \tag{14}\]  
Figure 3: Range analysis of the dynamic physico-mechanical parameters. (**a**) Dynamic shear modulus; (**b**) dynamic elasticity modulus.  
That is, the matrix equation can be written as follows:  
\[Y=BX \tag{15}\]  
where  
\[Y=\begin{bmatrix}y_{1}\\ y_{2}\\ \vdots\\ y_{n}\end{bmatrix},B=\begin{bmatrix}b_{0}\\ b_{1}\\ \vdots\\ b_{m}\end{bmatrix},X=\begin{bmatrix}1&x_{11}&\cdots&x_{m1}\\ 1&x_{12}&\cdots&x_{m2}\\ 1&\vdots&&\vdots\\ 1&x_{1n}&\cdots&x_{mn}\end{bmatrix}.\]  
By solving the matrix equation, the regression coefficients of the multiple regression model and the multiple linear regression equation can be obtained.