esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Quantitative Comparisons
#### 4.2.6 Parameter Sensitivity Analysis  
In the proposed tracker, some parameters are hard to decide via theoretical derivation, thus their settings are mainly empirical. To investigate the effects of these parameters, we moderately traverse their possible settings and perform a parameter sensitivity analysis. Each parameter is examined individually on the OOTB dataset and its subset (scale variation), while the other parameter settings remain the same as the original setup in CFSA tracker. The tracking accuracy of OPE (precision and success rate) and speed in the form of relative fps normalized to the highest one are reported in Figure 7.  
The results of CFSA tracker with different number of proposals are shown in Figure 7a and we can observe that as the number of proposals increases, the performance of CFSA tracker increases first and then decreases. The main reason might be that more accurate proposals could help to estimate the position of the target, while more inaccurate proposals might add more background information that leads to tracking failures. However, with the increase in the number of proposals, tracking speed is getting slower and slower. Thus the number of proposals 5 should be the most promising. In Figure 7b, the number of scaling factors n for estimating the scale of the target object is investigated. We can observe that the performance of CFSA tracker can be improved by averaging several scaling factors of consecutive frames and the number of scaling factors has almost no effect on tracking speed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **CFSA** & **SAMF** & **DSST** & **KCF** & **CSK** & **SCM** & **MIL** & **ASLA** & **TLD** & **Struck** \\ \hline Speed (fps) & 18.5 & 8 & 24 & 149 & **269** & 0.4 & 28 & 7.5 & 22 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The running speed of CFSA and other state-of-the-art trackers on OOTB benchmark dataset. Bold and underline values indicate best and second running speed, respectively. fps: frames per second.  
Figure 7: The tracking accuracy and speed under different settings of parameters: (**a**) the number of proposals \(N\) (**b**) the number of scaling factors \(n\). In the legend, precision and success plots on OOTB dataset and its subset (scale variation) using OPE are shown, and relative fps is collected on the OOTB dataset. The values of highest and the second highest accuracies are shown in red and green respectively.
# Video Games in Rehabilitation Programs for People with Parkinson's Disease: A Systematic Review
## 2 Materials and Methods  
### Search Methodology  
This systematic review examined studies on the application of VR and video games as physical training tools (exergames) in rehabilitation programs for patients with PD. The review drew on data from several databases, including CINAHL, Brain, PubMed, Scopus, Web of Science, Cochrane Database of Systematic Reviews, and Google Scholar, and covered research articles published from 1 January 2018 to 31 June 2024. The search strategies incorporated specific keywords and combinations of Boolean operators: <<Parkinson Disease>>, <<videogames>>, <<virtual reality>>, and <<Rehabilitation>>, using the search equations: ((\"Parkinson Disease\"[Mesh] OR \"Parkinson's Disease\" OR \"Parkinson Disease\" OR \"Parkinson's\" OR Parkinson) AND (\"Virtual Reality\"[Mesh] OR \"Virtual Reality\" OR VR OR \"Augmented Reality\" OR AR OR \"Virtual Environment\") AND (\"Video Games\"[Mesh] OR \"Video Games\" OR \"Serious Games\" OR \"Digital Games\" OR \"Computer Games\" OR \"Interactive Games\" OR \"Exergames\") AND (\"Rehabilitation\"[Mesh] OR \"Rehabilitation\" OR \"Physical Therapy\" OR \"Physiotherapy\" OR \"Rehab\" OR \"Occupational Therapy\"). In the databases that allowed setting search parameters, we applied a filter to expand the search to include randomized controlled trials (RCTs), original clinical studies involving patients, and conference proceedings. The publication language was restricted to either Spanish or English. Figure 1 provides a detailed illustration of the different search strategies.  
Figure 1: Search strategies.
## 3 Methodology
### Task Component Design
#### 3.4.2. Trench Excavation  
To initialize data collection on excavation activities, an empty list named 'diggingInfo' is prepared. The algorithm (Figure 7) commences by iterating over each line in the provided lines, delineating the paths necessitating trench excavation. For every line, the area earmarked for excavation, designated as 'lineArea', is computed based on the line and its specified width. Subsequently, the algorithm generates a sequence of movement points for the excavator along the line, termed'stepPoints', which outline the sequential positions the excavator will traverse during the trench digging process.  
Figure 6: (a) Pseudocode for point excavation; (b) flowchart of trench excavation.  
Figure 7: (a) Pseudocode for trench excavation; (b) flowchart of trench excavation.  
As the excavator maneuvers through these points, it precisely navigates to each designated position. With precision, it calculates the ideal dumping area for excavated material based on the provided 'dumpingAreaHint', ensuring efficient soil disposal. Next, the algorithm meticulously analyzes the excavator's swing angles using 'excavator:SwingAngles0' to determine the optimal excavation range for every movement point. For each swing angle, the algorithm meticulously computes the area impacted by the excavator's bucket, referred to as 'bucketSquare'. This calculation precisely determines the specific segments of the trench requiring soil removal within the 'lineArea'. With keen precision, the excavator then executes digging at these identified intersections, considering the nuanced properties of the soil. Subsequently, the results of these strategic operations, along with comprehensive dumping area specifications, are seamlessly integrated into the 'diggingInfo' dataset.  
After processing all lines, movement points, and swing angles, the algorithm gracefully concludes its execution by delivering the invaluable 'diggingInfo' dataset. This repository encapsulates a wealth of intricate details regarding every trench excavation activity, meticulously documenting the precise locations and volumes of displaced soil. Such granular data serve as a cornerstone for subsequent project management and planning endeavors, empowering informed decision-making and strategic execution.
## 4 Practical Formula for Bearing Capacity and Reliability Analysis
### Reliability Analysis
#### 4.5.1 Reliability Analysis Method  
In CFST structures' reliability calculations, distributions such as normal, log-normal, and Type I extreme value are typically employed. These distributions are characterized by a coefficient of variation (\(\delta\)) and a mean (\(\mu\)). Their respective random number generation in MATLAB (R2018a) is as follows:  
1. Normal variable: \[X_{i}=normrnd(\mu,\delta,N,1)\] (11)
2. Log-normal variable: \[\mu_{1}=\log\biggl{(}\frac{\mu}{\sqrt{1+\delta^{2}}}\biggr{)};\delta_{1}= \sqrt{\log(1+\delta^{2})};X_{i}=\log\mathrm{rnd}(\mu_{1},\delta_{1},N,1)\] (12)
3. Type I extreme value distribution: \[\alpha=\frac{\sqrt{6}\mu\delta}{\pi}=0.78\mu\delta;m=-\mathrm{psi}(1)\alpha- \mu=0.58\alpha-\mu;X_{i}=-\mathrm{ev}rnd(\alpha,m,N,1)\] (13)  
According to the Chinese standard \"Unified Standard for Reliability Design of Building Structures\" (GB50068-2018) [50], the load effect \(S\) expression is as follows:  
\[S=\gamma_{G}S_{Gk}+\gamma_{Q}S_{Qk}=1.3S_{Gk}+1.5S_{Qk} \tag{14}\]  
Integrating Equations (10) and (14), the equation for the ultimate axial compression state \(Z\) of DS-CFCST can be obtained as  
\[Z=k_{p}\bigl{(}f_{c}A_{c}+k_{r}f_{y}A_{s}\bigr{)}-S_{G}-S_{Q} \tag{15}\]  
where \(k_{p}\) represents the uncertainty in the axial compression resistance model.  
MCS is a method based on principles of mathematical statistics and computer technology. The principle of using MCS to calculate the probability of failure is:  
Identification of Variables: Determining the types and distributions of random variables \(X_{i}\) impacting reliability.  
Figure 13: Comparisons between experimental data and the results derived from design codes and Equation (10). (**a**) ACI-318-11; (**b**) AISC 360-16; (**c**) BS EN 1994; (**d**) AIJ.  
Random Sampling in MATLAB: Executing extensive random samplings (minimum samples \(N\geq 100/p_{f}\)) to generate variable values.  
Function Definition: \(Z=g(x_{1},x_{2},\ldots,x_{n})\)  
Failure Probability Calculation: Substituting random values into the function and counting instances where \(Z<0\) (\(\eta_{f}\)). The failure probability is then calculated using  
\[p_{f}=n_{f}/N \tag{16}\]  
Reliability Index Determination:  
\[\beta_{MC}\ =-\Phi-1\ (p_{f}) \tag{17}\]  
Here, according to Equation (17), when the target reliability index is 3.2, the maximum failure probability \(p_{f}\) is \(6.87\times 10^{-4}\).  
Error Analysis:  
\[\beta_{e}=\left[2\left(1-p_{f}\right)/Np_{f}\right]^{\frac{1}{2}} \tag{18}\]
# Fabrication of Alumina-Doped Optical Fiber Preforms by an MCVD-Metal Chelate Doping Method
## 2 Materials and Methods
Figure 1: A schematic diagram for the modified chemical vapor deposition (MCVD)-chelate delivery system [14]. The Al(acac)\({}_{3}\) is placed in a sublimator consisting of a stainless steel plate, a heater, and a gas system (outlet and inlet). The stainless steel delivery tube is equipped with a heater to prevent any Al(acac)\({}_{3}\) vapor condensation. The standard MCVD delivery lines and the chelate delivery line are joined with a special rotary seal. A ceramic heater is placed just before the hot zone. an approximately 3.0-mm-thick disk (with known index difference) was cut from the preform, polished, and subjected to EDX analyses. The EDX analyses were performed using the point identification technique with \(10\times 10\) grid points mapped in each preform's core area and an acquisition time of 240 s per point. The fabricated preforms were pulled into fibers (125 \(\upmu\)m in diameter) using a standard fiber drawing tower and were also subjected to EDX point identification analyses in order to determine the amount and radial distribution of alumina in the core region. The efficiency of the fabrication process was determined by dividing the alumina concentration obtained from EDX analyses by that derived theoretically.
## 6 Result Analysis
### Simulation Analysis
#### 6.1.2 The Slalom Simulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Estimated Value** & \(\mathbf{AGHCKF}_{\text{RMSE}}\) & \(\mathbf{GHCKF}_{\text{RMSE}}\) & \(\mathbf{CKF}_{\text{RMSE}}\) \\ \hline The longitudinal velocity & 0.0446 & 0.0521 & 0.0354 \\ \hline The lateral velocity & 0.0042 & 0.0068 & 0.0613 \\ \hline The yaw rate & 0.0004 & 0.0022 & 0.0029 \\ \hline The left rear wheel adhesion coefficient (\(v_{x}\) = 40 km/h) & 0.3910 & 0.4287 & 0.4441 \\ \hline The right rear wheel adhesion coefficient (\(v_{x}\) = 40 km/h) & 0.3912 & 0.4319 & 0.4407 \\ \hline The left rear wheel adhesion coefficient (\(v_{x}\) = 80 km/h) & 0.2685 & 0.3441 & 0.3413 \\ \hline The right rear wheel adhesion coefficient (\(v_{x}\) = 80 km/h) & 0.2759 & 0.2768 & 0.2963 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The RMSE of the slalom simulation. driving, and the insufficient accuracy in calculating the tire force on the left rear wheel. This also indirectly indicates that the AGHCKF has a strong anti-interference ability.
# Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
###### Abstract  
Three-Dimensional Gaussian Splatting (3 DGS) has revolutionized novel-view synthesis, enabling real-time rendering of high-quality scenes. Two-Dimensional Gaussian Splatting (2 DGS) improves geometric accuracy by replacing 3D Gaussians with flat 2D Gaussians. However, the flat nature of 2D Gaussians reduces mesh quality on volumetric surfaces and results in over-smoothed reconstruction. To address this, we propose Dual-Dimensional Gaussian Splatting (DDGS), which integrates both 2D and 3D Gaussians. First, we generalize the homogeneous transformation matrix based on 2 DGS to initialize all Gaussians in 3D. Subsequently, during training, we selectively convert Gaussians into 2D representations based on their scale. This approach leverages the complementary strengths of 2D and 3D Gaussians, resulting in more accurate surface reconstruction across both flat and volumetric regions. Additionally, to mitigate over-smoothing, we introduce gradient-based regularization terms. Quantitative evaluations on the DTU and TnT datasets demonstrate that DDGS consistently outperforms prior methods, including 3 DGS, SuGaR, and 2 DGS, achieving the best Chamfer Distance and F1 score across a wide range of scenes.  
Keywords:surface reconstruction; view synthesis; Gaussian splatting +
Footnote †: journal: Applied sciences
###### Abstract  
The linearity of active mixers is usually determined by the input transistors, and many works have been proposed to improve it by modified input stages at the cost of a more complex structure or more power consumption. A new linearization method of active mixers is proposed in this paper; the input 1 dB compression point (IP1 dB) and output 1 dB compression point (OP1 dB) are greatly improved by exploiting the \"reverse uplift\" phenomenon. Compared with other linearization methods, the proposed one is simpler, more efficient, and sacrifices less conversion gain. Using this method, an ultra-high-linearity double-balanced down-conversion mixer with wide IF bandwidth is designed and fabricated in a 130 nm SiGe BiCMOS process. The proposed mixer includes a Gilbert-cell, a pair of phase-adjusting inductors, and a Marchand-balun-based output network. Under a 1.6 V supply voltage, the measurement results show that the mixer exhibits an excellent IP1 dB of +7.2\(\sim\)+10.1 dBm, an average OP1 dB of +5.4 dBm, which is the state-of-the-art linearity performance in mixers under a silicon-based process, whether active or passive. Moreover, a wide IF bandwidth of 8 GHz from 3 GHz to 11 GHz was achieved. The circuit consumes 19.8 mW and occupies 0.48 mm\({}^{2}\), including all pads. The use of the \"reverse uplift\" allows us to implement high-linearity circuits more efficiently, which is helpful for the design of 5G high-speed communication transceivers.  
5G; SiGe BiCMOS; linearization techniques; millimeter waves; mixers +
Footnote †: journal: _Sensors_
# Hardware Platform-Aware Binarized Neural Network Model Optimization
## 5 Experimental Results and Discussion
### Future Work  
Based on the final evaluation result, the presented solution has shown effectiveness in exploring the optimal BNN models for different hardware platforms and different critical hardware requirements. In particular, each hardware platform is provided a different optimal BNN model, which does not have the minimum number of MACs but would be the best one for the target requirements. However, with the MNIST, a small dataset, the proposed algorithm has not shown comprehensive benefits, and many potential issues that occur with more extensive datasets may not be solved. Because of the complexity in simulating the error affected by the IR drop and sneak path, the estimation charts for RRAM devices on more extensive datasets are not available. Therefore, in future work, Cifar-10 and ImageNet are going to be the next target datasets for the searching process. In particular, to reduce the searching time, which may be the biggest drawback of the proposed solution for an extensive dataset, the number of training times can be reduced for each model in Algorithm 3 (100 for MNIST dataset). In addition, in Algorithm 3, instead of adding two more channels for initialization and one channel to compensate for the  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Layers (\#)** & **LUTs** & **FFlops** & **Power** & **MAC OPs** \\ \hline
**md1** & 3 & 19,211 & 9104 & 1.126 & 5,989,368 \\
**md2** & 4 & 20,692 & 10,082 & 1.123 & 7,756,896 \\
**md4** & 6 & 21,410 & 10,936 & 1.256 & 8,916,432 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of actual hardware performance and MAC operations of various models.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Layers** & **Width Configuration** & **Accuracy on Target Device** \\  & & & **(\%)** \\ \hline FPGA & 4 & 23-21-22-21 & 98.37 \\ RRAM & 7 & 30-30-30-30-30-30-30 & 98.45 \\ GPU & 6 & 50-50-50-50-50-50 & 98.88 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Optimal BNN models for different hardwares.  
accuracy in the while-loop, 64 channels can be used for initialization and 32 channels for the while-loop. In terms of exploring the range of layers for the searching process, as introduced in Section 4.2, there can be more steps for the increasing number of layers and channels for bigger datasets.
## 2 Background
### Interoperability in IoUT
#### 2.5.2 Syntactic Interoperability  
Syntactic interoperability concerns the interoperability of data formats (e.g., csv, json, xml, rdf) when transmitting or receiving data or information or services between heterogeneous devices, sensors, and systems. The aim is to provide an approach able to encode and decode data sent from and received by everyone.
## 2 Overview of Near-Infrared Reflectance Spectroscopy (NIRS)  
NIRS is a fast, non-destructive analytical technique widely used to analyze organic constituents and other properties in various agricultural products with minimum or no sample preparation steps [37]. It employs a wavelength between 780 nm (12,500 cm\({}^{-1}\)) and 2500 nm (14,000 cm\({}^{-1}\)), providing more complex structural information about bond vibration behavior. Chemical bonds between light atoms in molecules such as C, N, O, and H with primary absorption in the infrared (IR) region have strong vibrational overtones and combination bands that absorb light in the near-infrared (NIR) region (780-2500 nm) of electro-magnetic radiation. The NIRS region in the electromagnetic radiation has a linear relationship between the absorbance and concentration (i.e., the Beer-Lambert-Bouguerre relationship), making it an important analytical tool [38]. The Beer-Lambert-Bouguerre relationship is a main rule in spectrophotometric analysis that gives a great opportunity to find the concentration of a substance by measuring the absorbance of its solution [38].  
Organic molecules absorb energy in the near-infrared region when they vibrate or are translated into an absorption spectrum within an NIR spectrometer. At 1300 nm, the NIR region is divided into short-wave NIR (SW-NIR) in the wavelength range of 700 to 1100 nm and standard NIR (780 to 2500 nm). The SW-NIR region is an absorption band of high overtones, whereas the traditional NIR region is an absorption band of the first or second overtone [39; 40]. The intensity of absorption decreases as the overtones increase. As a result, SW-NIR is frequently used in transmission analysis, where reflection is significantly reduced so that the amount of radiation attenuated by the sample is measured in transmittance modes, as opposed to standard NIR, which is used in diffuse reflection analysis, which is frequently used for the analysis of opaque solids and is associated with light scattering at the surface to obtain surface information of the samples [41]. Because of the interaction of electromagnetic radiation in the near-infrared region and biological tissues, NIRS has found widespread application in the quantitative evaluation of various crops.  
NIRS could be useful for qualitative measurement, but due to the overlapping and non-specific nature of NIR spectra, they become difficult to interpret. However, each peak has enormous hidden information of the molecular bonds absorbing in the respective wavelengths. NIR absorptions between 700 and 1050 nm are usually the second and third overtones of both C-H and O-H bonds, which are mainly for starch and water. Oil has a unique absorption band which appears as a duplet at two characteristic wavelengths of 1700 nm and 2300 nm, while water absorption is at 1925 nm, which is indicative of stretching and bending vibrations of O-H [41]. The combination band of NH at 2130 and 2190 nm is indicative of protein, whereas the first overtones region was the best for predicting starch (1452-1770 nm) [41].
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 4 Typical Hardware-Based LQEs: A Survey  
According to the analysis in Section 2, typical mapping models for hardware-based LQEs used in existing studies are summarized, as shown in Figure 3. These mapping models are classified according to the link metrics they used, including the \(RSSI\), \(SNR\), and LQI. For each kind of link metric, the frequently used methods for constructing the mapping model with \(PRR\) are also given. For example, the theoretical model, polynomial regression, and logistic regression are usually used to construct mapping models between \(RSSI\) and \(PRR\) in typical \(RSSI\) based LQEs. For each kind of modeling method, only one instance was chosen and analyzed in the following chapters.  
It should be noted that communication distance is usually used for analyzing and modeling radio links [9; 10], and not for online link quality estimation. However, there is already research that utilizes distance to assess the link quality in the design and deployment phase [11; 12]. For example, [PERSON] et al. presented a reliability model based on the mapping models between distance and \(PRR\), which was used to improve the link estimation and optimize the deployment parameters [12]. Meanwhile, the distance  
Figure 3: Typical mapping models between indirect metrics and packet reception rate (\(PRR\)).  
Figure 2: The experiment conducted using two nodes.  
between nodes may be acquired as deployment parameters or measured using appropriate ranging techniques. Therefore, it is meaningful to analyze the influence of environmental changes on these mapping models. With these considerations in mind, these mapping models are also classified and summarized here, although they are exactly not hardware-based LQEs.