esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Quantization  
Quantization has emerged as a fundamental technique in deep learning, particularly for reducing model size and accelerating inference in resource-constrained environments [14]. This approach involves representing weights and activations with fewer bits, thereby compressing the model while striving to preserve its accuracy [15; 16; 17; 18]. By reducing the precision of the numerical representations, quantization decreases both the memory footprint and the computational demand, making it possible to deploy sophisticated models on devices with limited computational resources, such as mobile phones and embedded systems.  
The exploration of quantization techniques has led to the development of several innovative methods. Early approaches, such as binary and ternary weight networks [19; 20], have shown that substantial model compression can be achieved with minimal performance degradation. These methods rely on the observation that high-precision weights often contain significant redundancy, which can be reduced without severely impacting the model's ability to make accurate predictions.  
Furthermore, research has expanded into various forms of quantization, such as uniform and non-uniform quantization [21; 22]. Uniform quantization assigns equal spacing between quantization levels, making it straightforward to implement, while non-uniform quantization allows for more flexible and efficient use of bit-widths by adapting to the distribution of the model's parameters. Techniques like mixed-precision quantization have also been developed, where different parts of the network are quantized to different levels of precision depending on their sensitivity to quantization noise. This not only helps in maintaining accuracy but also optimizes the computational efficiency of the model.
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 2 Related Work
### VOEI Operation  
The \(CN\) must be aware of each UAV contextual information, such as, remaining energy \(RE_{i}\), location information \(L(X_{i},Y_{i})\), mobility trajectory \(traj_{i}\), speed \(s_{i}\), etc. In this sense, the \(CN\) could define a reliable and energy-efficiency routes, as well as detect the appropriate moment for UAV replacement, i.e., battery replacement/recharge or replace a given relay node \(RN_{i}\) in a route. In this sense, it triggers the UAV replacement process to keep the FANET connectivity level (dynamic paths) with a suitable quality level. Based on the collected information, the \(CN\) could detect an event at a given location. Afterwards, the \(CN\) establishes a route \(R_{SN,DN}\) between source \(SN\) and destination \(DN\) nodes via multiple relay nodes \(RN_{i}\) based on UAV contextual information and the event location.
# Research on Damage Localization of Steel Truss-Concrete Composite Beam Based on Digital Orthoimage
## 3 Static Test of a Steel Truss-Concrete Composite Beam
### Accuracy-Verified Data Acquisition
#### 3.4.2 Three-Dimensional Laser Point Cloud Data Acquisition  
Three-dimensional laser scanning technology is a non-contact, fast, and accurate measurement technique, and its application to the inspection of engineering structures has become a new trend of development [28; 29]. It is widely used in the field of accurate deformation monitoring for bridges, buildings, tunnels, pipe racks, and other projects [30]. [PERSON] [31] studied the influence of distance, incident angle, and target color on the accuracy of the scanner during measurement. Using the plane fitting method to analyze the accuracy, it was verified that the accuracy of a ground-based 3D laser is 1 to 2 mm, which meets the nominal accuracy; the angle measurement accuracy is about 15\({}^{\prime\prime}\); and the point position accuracy can reach the millimeter level. Moreover, this technology is based on the principle of laser ranging and can acquire a large amount of morphological data of the target object, so it is feasible to use 3D laser scanning data for the comparison and verification of images.  
The Leica ScanStation P50 3D laser scanner is used to verify the extracted structural holographic deformation data, the basic parameters of which are shown in Figure 21. The scanning resolution is 0.8 mm/10 m, the point accuracy is 30 mm/50 m, the target acquisition accuracy is 2 mm/50 m, and the noise accuracy is 0.4 mm/10 m. According to the analysis results of the scanning angle effect on the point cloud density obtained from the previous study [32], the following scanning measurement verification test scheme was used: the scanning pattern was three-station joint scanning, the scanning radius was set to 10 m, and the scanning angle was \([-30^{\circ},30^{\circ}]\). The layout of the site is shown in Figure 22.  
Figure 21: Basic parameters of Leica ScanStation P50 3D laser scanner.  
Figure 22: Layout of 3D laser scanning station.  
Under this scanning scheme, the theoretical value of high-density point cloud coverage on the side of the test beam is >91.5% with high accuracy, which makes the 3D laser scanning data valuable for verifying the morphological deviations extracted from the images. The verification process of 3D laser scanning on the holographic morphology of the structural images is detailed in Section 4.3. In order to match the 3D laser scanning accuracy verification test, 20 mm diameter coded marker points were arranged on the upper and lower chord node plates of the test beam, and the location and number of the marker points are shown in Figure 23. The results of the 3D laser scan are shown in Figure 24.
# A Prototype for Computing the Distance of Features of High-Pressure Die-Cast Aluminum Products
## 4 Results and Discussion
### Discussion  
The prototype supports operators' decision-making to ensure that workpiece production meets quality standards by verifying that the absolute error is in range. Otherwise, the piece is sent to and measured by the CMM, optimizing this resource. In this sense, the prototype works as an in situ verification support tool, for a preliminary sampling quality assessment of manufactured workpieces. Due to the nature of the piece (i.e., morphology and restrictions), a CMM RENISHAW AGILITY s12129 is used to obtain the measurements of each feature and compute the average of five measurements as a reference to validate the prototype. In this work, the metrics employed are mean absolute percentage error (MAPE) and accuracy [62]. For the CMM, the average values were a MAPE of 0.0180%, an accuracy of 99.9820%, and a standard deviation of 0.0003 mm. For the prototype using YOLOv3, MAPE was 0.1781%, accuracy was 99.8219, and the standard deviation was 0.2578 mm. Moreover, with the use of YOLOv11, MAPE was 0.1257%, accuracy was 99.8444%, and standard deviation was 0.1989 mm. In addition, the accuracy of the feature detected (drilled bore) in off-site tests had a mean of 88.9%, whereas the on-site tests reached 80.9% for YOLOv3 and 90.5% for YOLOv11, both promising values.  
This work used the YOLO architecture to detect a few workpiece features. The user iteratively identifies adequate bounding boxes to tailor the dataset to the use case to ensure the model's training on relevant features. On the other hand, the literature has reported that YOLOv3 obtained better average precision and mAP values than YOLOv4 in detecting internal defects in aluminum alloy welds [63]. Another example is the work developed by [PERSON] et al. [64], who used a custom-designed dataset tailored to specific fire and smoke detection. They executed several YOLO models and reported that YOLOv3 scored better than YOLOv10 for fire detection and YOLOv7 for smoke detection according to their mAP values. However, the authors acknowledge that replacing YOLO with newer architectures in the fast-evolving object detection field could offer further improvements in accuracy and speed. The results reported in the above subsections demonstrate that the mAP values of YOLOv11 are slightly better than those of YOLOv3.
## 3 Experiment
### Ship Temperature Regression Experiment
Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. Additionally, the R\({}^{2}\) and MAE values further demonstrate the model's robustness and accuracy in both training and testing phases. As shown in Figure 16, the longitudinal fitting curves of the ship are depicted. In (a), the two sets represent simulated curves of normal propagation, exhibiting high goodness of fit and minimal fluctuations. In (b), the first sample simulates the appearance of an abnormal heat source in the midsection of the ship. According to the model prediction, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{R^{2}}\) & \multicolumn{2}{c}{_MAE_} & \multicolumn{2}{c}{_RMSE_} \\ \hline
**Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline
0.753 & 0.812 & 5.365 & 5.782 & 3.472 & 3.852 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation result of ship brightness temperature prediction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **n\_Estimators** & **Max\_Depth** & **Subsample** & **Learning\_Rate** & **Gamma** \\ \hline Optimal value & 100 & 6 & 0.5 & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optimal values of key model parameters. there is a temperature difference of over 100 between the predicted temperature and the actual temperature between 49 and 56. This discrepancy indicates that the temperature in this area does not comply with the ship temperature model assuming a single heat source from the engine, thus suggesting an abnormal temperature range. In the second sample of (b), a simulated abnormal cold source appears in the midsection of the ship. The model predicts an abnormal temperature decrease in the range of 19-73, which deviates from the ship temperature model. Consequently, it is determined that an abnormal cold zone exists in the range of 19-73, affecting the transmission of temperature in the engine compartment.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 6 Outlook and Future Work  
This paper provides an overview of robust statistics and how it can be used to enhance the resilience of GNSS single point positioning (SPP) solutions in the presence of outliers. These large deviations from the nominal model might be caused in practice--in the GNSS context of interest here--by multipath propagation or hardware malfunctioning, for instance. SPP can be seen as a regression problem, for which this paper presents its robust alternatives leveraging the sound theory of robust statistics. At the same time, the article discusses the specific aspects of applying robust regression to GNSS SPP solvers, and supports the discussion with simulation results showing the improvements of such methods as well as their characterization. Additionally, the article considers the use of an experimental evaluation using real data, collected in a vehicular setup and including challenging propagation conditions such that the use of robust SPP methods is justified and shown in practice. Future research should provide a better (analytical) understanding of the loss-of-efficiency incurred by those methods, as well as the relaxation of the i.i.d. assumption among different satellites, and the use of robust techniques in recursive versions that yield to more sophisticated PVT solutions.  
**Author Contributions:** [PERSON], [PERSON]-[PERSON] and [PERSON] designed the concept for the paper and prepared the original draft; [PERSON] realized the measurement campaign; [PERSON] and [PERSON] provided data analysis and software development. All authors investigated on the obtained results and reviewed the paper.  
**Funding:** This work was partially supported by the National Science Foundation under Awards CNS-1815349 and ECCS-1845833, and by the DGA/AID under project 2019.65.0068.00.470.7501.  
**Acknowledgments:** The authors would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for their assistance in the arrangement of the measurement campaign.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Illustration of time span “A” where multiple bridges are present (**top left**) and positioning errors during such time (**top right**). Illustration of time span “B” for navigation under dense foliage (**bottom left**) and the associated positioning errors (bottom right).
## 4 Discussion
### Fever Detection and Application  
To measure body temperature, the most common and easy way is using a thermometer. However, it is hard to measure body temperature continuously with a thermometer. Another way to measure body temperature is to use a thermographic camera. The thermography camera is usually used for measuring the body temperature measurement at the same time. However, the price of a thermographic camera is not cheap. Therefore, in this study, we proposed a new method for detecting fever by using a very short-term PPG signal. Although our method cannot measure body temperature directly, detecting fever is more important. Especially in the ICU, it is necessary to monitor patients who have a fever. Fever is also a common sign of many infectious diseases and non-infectious diseases. Using a thermometer to measure body temperature is a cost of manpower and cannot be measured continuously. Using a thermographic camera is too expensive. However, PPG has been widely used in ICU for pulse oximetry and breathing measurement. Moreover, measuring PPG through the camera of a mobile is also available. In addition, iPRV can also provide not only ANS but also peripheral circulation information using short-term PPG [11, 14, 22, 28]. For homecare or other application, PPG is a cheap and non-invasive measurement, and can be applied to many situations. For example, PPG can be integrated with the watch or the camera of a cell phone can measure the PPG signal as well. Furthermore, the non-contact PPG is also developing [32, 33, 34, 35]. The PPG has the potential to be the health monitor in many situations.
# Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World
## 3 Ultra-Low Power MCU Architectures
### Data Processing  
The term Data Processing is generally used to indicate the collection and then the manipulation of data in order to extrapolate meaningful information. Sometimes, it can also indicate the transformation of data in an easy to handle format.  
When related to hardware systems, data processing involves the CPU, as it must fetch data from memory or sensors, process them (generally through an application specific algorithm) and finally store the results in memory.  
Figure 7: Dynamic voltage scaling: a closed loop decides the core voltage power supply.  
This data processing technique is important for IoT as it can significantly reduce the amount of data to store and transmit. IoT nodes can perform a pre-processing in loco, discarding useless information, releasing part of the burden from the central unit that is in charge of performing the complete elaboration.  
From a data processing point of view, energy saving can be obtained by increasing the parallelism, namely the amount of data per cycle that can be handled by the CPU or by optimizing the architecture with respect to the per-cycle power consumption. MCUs moved from initial 8-bit to 32-bit of today's most common devices (like ARM Cortex-M), but still engineers are focusing on reducing the power per instruction metric, since, in many applications, it is not required to handle multiple data concurrently.  
In MCUs, it is common to find optimizations for single cycle multiplication, like Multiply and Accumulate (MAC) dedicated instructions and arithmetic control support. From the instruction side, some improvements have been introduced as well, such as defining a set of instructions with reduced size (such as from 32- to 16-bit), saving energy in the storage and read process from memory.  
A very effective way to save power by processing data is through neuromorphic or quasi-digital approaches. As an example in [97], an Address Event Representation (AER) is adopted. This example relies on a neuromorphic event-based approach in which the only information to be transmitted is the source ID when a new event occurs. As a consequence, only when a change in the physical quantity is sensed, a certain amount of data is produced and sent. Indeed, AER [98; 99] allows representing information very efficiently, by exploiting the neuromorphic approach to reduce the switching activity and thus the power needed to process and send acquired data is significantly lowered. Stemming from this concept, [97] proposes an architecture based on the open MSP430 processor that, compared with its regular implementation, can save up to 50% of power.
# Parallel Load-Bearing and Damping System Design and Test for Satellite Vibration Suppression
## 3 Theoretical Principle  
In order to analyze the vibration characteristics of PAF, the satellite branch structure should be simplified. If the satellite is regarded as a rigid body and the PAF is regarded as a spring-damper system, the whole system can be simplified as a Single-Degree-of-Freedom (SDF) system, as demonstrated in Figure 3a. The traditional series-type method changes \(k\) or \(c\) to suppress vibration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Peak Frequency/Hz** & **Acceleration/(m/s2)** \\ \hline Axial(X) & 28.33 & 14.05 \\ Lateral(Y) & 7.5 & 3.1 \\ Lateral(Z) & 7.5 & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peak frequencies and acceleration transmissibility.  
Figure 2: Satellite branch structure.  
Then, the fundamental frequency would be \(\omega_{0}=\sqrt{\frac{k}{m}}\), and the damping ratio would be \(\xi=\frac{c}{2m\omega_{0}}\). When increasing the value of \(c\) to reduce vibration, \(\xi\) would increase as well. As Figure 3b shows, if \(\omega_{0}\) is greater than \(\omega/\sqrt{2}\), the transmissibility decreases as the damping ratio \(\xi\) increases. On the contrary, if \(\omega_{0}\) is less than \(\omega/\sqrt{2}\), the transmissibility increases as \(\xi\) increases.  
In practical engineering problems, the low-frequency vibration environment is usually under 100 Hz, while the lateral and axial fundamental frequencies of satellites are all under 50 Hz, which is less than \(100/\sqrt{2}\) Hz. Therefore, when \(\xi\) increases, the transmissibility increases, which is not expected.  
In that way, a parallel scheme was adopted for vibration suppression, which can effectively reduce the transmissibility as well as keep the fundamental frequency unchanged. In this scheme, the damping system is parallel to the PAF. The authors of [21] proposed a passive vibration isolation scheme based on the Stewart platform, which is shown in Figure 4. This new scheme is considered a parallel scheme, instead of a series-type scheme. In the present work, this scheme was adopted, and dampers were installed on the PAF for vibration suppression.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 5 Machine Learning for CML-Based Rainfall Measurement  
Through the uncertainty analysis, we can find that it is a very complicated process from RSL raw data acquisition to the generation of rainfall maps, and many factors and uncertainties may deteriorate the accuracy of rainfall measurement. In recent years, many works have applied machine learning algorithms to evaluate the relationship between the microwave link attenuation and rain rate, thus avoiding the tedious analysis of uncertainty sources and error quantization.
# High Resolution Brillouin Sensing of Micro-Scale Structures
## 2 Brillouin Sensing Principle
### Forward Brillouin Scattering  
Forward Brillouin scattering occurs when the optical pump and the probe co-propagate through the medium with the probe being frequency-shifted from the pump by the forward BFS. In this process, the propagation constants of the pump and probe waves are almost equal; therefore, according to the phase-matching condition, the propagation constant of the acoustic wave approaches zero. As a result, the acoustic waves in the forward Brillouin process do not propagate along the waveguide, but mostly propagate transversely. The BFS in the forward Brillouin process for a cylindrical medium such as optical fibre and for a certain acoustic mode (m) is expressed by [63]:  
\[\upsilon_{GB,m}=\frac{v_{t,m}y_{m}}{d\pi}, \tag{2}\]  
Figure 1: Backward Brillouin scattering under phase-matching and energy conservation conditions.  
where \(d\) is the fibre outer diameter, \(v_{t,m}\) is the \(m_{th}\) transverse mode acoustic velocity, and \(y_{m}\) the \(m_{th}\) zero of the Bessel function describing the boundary conditions for the free fibre surface [64]. This forward BFS as described in Equation (2) has been utilized to determine the strain coefficient in silica fibre [65] and the temperature coefficient in highly nonlinear fibre [66] and PCF [67], sound velocity in silica fibre [63], and the core diameter of the tapered fibre [37]. The intrinsic linewidth of the Brillouin gain spectrum is determined by the phonon lifetime and the structural irregularities of the waveguide. In the forward Brillouin interaction, an extra component contributes to the linewidth of the Brillouin gain spectrum of a given mode due to the transmission losses at the boundaries of the core [68]. These losses are dependent on the acoustic impedance of the surrounding medium and vary from one material to the other. Therefore, the linewidth of the Brillouin gain spectrum has been exploited for chemical sensing purposes in the forward Brillouin scattering process [68; 69; 70].  
The expressions represented in Equations (1) and (2) are valid under the assumption that the waveguide dimensions are much larger than the acoustic wavelength (\(w,h\gg\frac{2\pi v_{s}}{\Omega}\)) [34], where \(w\) and \(h\) stand for the waveguide width and thickness, respectively. However, as the waveguide dimensions approach the acoustic wavelength, the waveguide is no longer considered as isotropic [34], and the effect of boundary forces becomes important [71]. Therefore, a fully-vectorial calculation is required to obtain an accurate estimation of the BFS [32; 33; 34].
# A Single-Bit Incremental Second-Order Delta-Sigma Modulator with Coarse-Fine Input Buffer
## 2 Proposed Delta Sigma Modulator with Coarse-Fine Input Buffer  
The block diagram of the proposed circuit is shown in Figure 3. The circuit comprises two main parts: a coarse-fine, pre-charged buffer that acquires analog signals and a DSM that post-processes it. Both circuits require timing circuits to optimize their performance. The operational times of the coarse and fine buffers are partially shared. The input buffer internal chopper clock uses a frequency of 16 kHz. The unintended noise in the low-frequency band can be significantly reduced through chopper modulation. The sampling signal used was 125 kHz, higher than the chopper frequency, and the oversampling rate was set at 512. The circuit characteristics can be controlled by externally adjusting the resistor through a serial peripheral interface (SPI). Through SPI logic manipulation, modulator and buffer blocks can be enabled or disabled, and the performance of each block is tested.  
Figure 3: Block diagram of the proposed circuit.  
Figure 2: Working process of coarse-fine input buffer.