esa-sceva/satcom-chunk-collection huggingface dataset:
# Feedback Devices for Cardiopulmonary Resuscitation: A Narrative Review
## 2 Classification of Feedback Devices for Cardiopulmonary Resuscitation  
The feedback device for CPR has undergone significant development since its early use as a simple metronome. The metronome provides a fixed beat to assist the rescuer in maintaining a steady compression rate, thereby ensuring the quality of CPR and increasing the likelihood of successful resuscitation [5]. Feedback devices for CPR are generally divided into two categories based on the type of feedback information: auditory-visual feedback devices and physiological information data-monitoring feedback devices. Auditory-visual feedback devices provide a certain beat sound and display compression depth graphically or through color differences and can use sound, graphics, or color to correct the rescuer's compression rate and depth in real time to ensure the quality of CPR. Physiological information data-monitoring feedback devices can monitor one or more parameters, such as cardiac output (CO), coronary perfusion pressure (CPP), mean arterial relaxation pressure (MARP), ECG amplitude spectrum area (AMSA), ETCO\({}_{2}\), pulse oxygen saturation waveform, and regional cerebral oxygen saturation (r\(\otimes\)Co\({}_{2}\)), to adjust the compression strategy.  
Studies have shown that maintaining standard external chest compression can significantly improve the survival rate of patients [6]. However, rescuers often experience physical exhaustion, making it difficult to maintain quality CPR for an extended period. The quality of CPR tends to decline after several minutes of CPR, with studies indicating that only 18% of rescuers are capable of providing qualified chest compression continuously after five minutes of chest compression [7]. The quality of CPR is also related to the physical fitness of rescuers [8], as well as their proficiency in CPR techniques. Thus, feedback devices for CPR play a crucial role in maintaining or improving the quality of CPR.
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 3 Results and Discussion  
### Analysis of the Average Measured Voltage and Conductivity  
Using the MATLAB program, the average output voltage for each applied load and fixed resistance was calculated from the raw data. The resistance and conductivity of the FSR sensors were then derived from these voltages to examine their response to varying loads. The input voltage of the Arduino used in this test was 5 V, thereby capping the measurable output voltage at 5 V.  
Figure 6 presents the measured voltages for both the Flexiforce A201-1 and Interlink FSR-402 sensors under applied loads ranging from 20 g to 870 g. The results show a  
Figure 4: FSR sensor output measurement circuit diagram.  
Figure 5: Static load characteristic test with loads, Arduino Mega2560, and the FSR sensor.  
clear relationship between the applied load and the output voltage, with both sensors demonstrating a decrease in voltage as the load increases, consistent with their variable resistor nature.  
Figure 6: The measured voltage as a function of resistance and load. (**a**) Flexiforce A201-1 and (**b**) Interlink FSR-402.  
Figure 7 compares the conductivity of the two FSR sensors under varying loads. The graphs indicate that the conductivity values for both sensors are distributed uniformly across the range of applied loads for each fixed resistance. Notably, the Interlink FSR-402 sensor exhibits slightly higher conductivity than the Flexiforce A201-1 sensor, likely because of differences in their construction and materials.  
Considering the issue of electrical noise in the circuit during data logging, the number of sensor measurements taken for each load was set to 1500 (approximately 3 min of measurement). The average of these 1500 measurements was used as the output value for each load. Based on the average value for each load, the voltage, resistance, and conductivity for each load were calculated. Although there was concern about noise from the wiring connected to the sensor during the measurement process, an examination of the 1500 measurements showed no significant external noise.
# Steering Dynamic and Hybrid Steering Control of a Novel Micro-Autonomous Railway Inspection Car
## 1 Introduction
However, when traversing rail surface defects or sharp bends, the difference in contact radii between the two wheels on the same wheelset can lead to hunting motion (undesirable lateral oscillations), compromising inspection accuracy and operational safety [10]. In this study, we propose a novel set of vehicle motion equations that integrate the natural self-guidance properties of the wheelset with an adaptive fuzzy logic controller (FLC). This approach enables more-accurate modeling of lateral dynamics and reduces the reliance on frequent corrective control inputs. Compared to conventional equations, our method offers several advantages, including lower positioning error, improved real-time computational efficiency, and enhanced stability under varying track conditions. The proposed hybrid steering system actively adjusts the torque distribution between the drive wheels, leveraging the natural self-steering forces of the wheelset to maintain stability while avoiding the high energy consumption typically associated with conventional active steering methods. This study not only developed an enhanced mathematical model but also conducted detailed simulations using SIMPACK and SIMULINK to evaluate system performance. By integrating intelligent control strategies with wheel-rail dynamic modeling, this research aimed to overcome the limitations of existing autonomous railway inspection methods while introducing a novel hybrid approach that enhances precision and efficiency in railway maintenance technologies.
# Metal Surface Defect Detection Using Modified YOLO
###### Abstract  
Aiming at the problems of inefficient detection caused by traditional manual inspection and unclear features in metal surface defect detection, an improved metal surface defect detection technology based on the You Only Look Once (YOLO) model is presented. The shallow features of the \(11\)th layer in the Darknet-53 are combined with the deep features of the neural network to generate a new scale feature layer using the basis of the network structure of YOLOv3. Its goal is to extract more features of small defects. Furthermore, then, K-Means++ is used to reduce the sensitivity to the initial cluster center when analyzing the size information of the anchor box. The optimal anchor box is selected to make the positioning more accurate. The performance of the modified metal surface defect detection technology is compared with other detection methods on the Tianchi dataset. The results show that the average detection accuracy of the modified YOLO model is 75.1%, which is higher than that of YOLOv3. Furthermore, it also has a great detection speed advantage, compared with faster region-based convolutional neural network (Faster R-CNN) and other detection algorithms. The improved YOLO model can make the highly accurate location information of the small defect target and has strong real-time performance.  
surface defect detection; YOLO; K-Means++ +
Footnote †: journal: _Algorithms_
# Review of Task-Scheduling Methods for Heterogeneous Chips
## 5 Research Status of Experiment
### Dataset
#### 5.1.3 Standard Benchmark  
Standard benchmark datasets consist of a series of standard test procedures designed to evaluate the performance of heterogeneous chips. These benchmarks provide a unified way to compare performance across different systems, such as the DSP-oriented benchmarking methodology [41], the Princeton Application Repository for Shared-Memory Computers [42], Rodinia [43], and the HPC Challenge Benchmark [44]. These standard benchmarks provide a range of programs and tasks designed to assess performance in a heterogeneous parallel computing environment. They are often used to test scheduling algorithms for heterogeneous computing systems. These benchmarks are specifically tuned to evaluate the heterogeneous chips. These tests are designed to evaluate the performance of heterogeneous chips when working and, therefore, are suitable for testing and evaluating the performance of heterogeneous chips.  
Compared with real workload data or synthetic datasets, the standard benchmark is easy to implement, has higher repeatability and universality, and can ensure the fairness and reliability of algorithm evaluation. Moreover, another advantage of standard benchmarking is that there is unified comparison benchmarking, which thus enables a more consistent and comparative experimental comparison between different methods. However, standard benchmarks also have some limitations, which often fail to fully reflect the complexity and dynamic changes of task scheduling in the real world, especially in highly heterogeneous computing environments, where standard benchmark datasets may not cover all potential load patterns and hardware configurations. Therefore, standard benchmarks are suitable for preliminary evaluation and algorithm comparison, but they must be combined with real load or synthetic datasets to cope with different practical scenarios and requirements.
# Multipath Error Fusion Modeling Methods for Multi-GNSS
###### Abstract  
The multipath error is considered to be the most limiting factor for high precision positioning applications. The sidereal filtering (SF) method can be used to mitigate the multipath error in the observation domain, and it has been successfully applied in the multipath mitigation in global positioning systems (GPS) and regional BeiDou navigation satellite systems (BDS2). However, there are few reports on the SF method in other systems. The performance of the SF method relies on the explicit orbit repeat periods of satellites in diverse systems or even different types of constellations. It is therefore inconvenient to utilize the SF method for multi-GNSS multipath error mitigation. Alternatively, a space domain multipath error reduction method, which establishes the multi-point hemispherical grid model (MHGM) using the residuals of the double-differenced carrier phase observations in the ambiguity-fixed period, has been modified. It is an integrated model for multi-GNSS, without considering the diversity of different systems and constellations. To compare the performance of MHGM and SF from a multi-GNSS point of view, the determination method of orbit repeat periods via the broadcast ephemerides is summarized, and the SF method is extended to the global BeiDou navigation satellite system (BDS3) and Galileo navigation satellite system. Further test results show that the performance of MHGM and SF are comparable from the perspective of root mean squares (RMS) and the power spectrum analysis of double-differenced residuals, as well as the static positioning results. This implies that the space domain MHGM can obtain similar correction effects as the SF method in the observation domain, but the former is more flexible for modeling with various systems' data. In addition, the established MHGM using the data of multi orbit periods demonstrates a better performance compared with that of only one orbit period, and an average improvement of 13.1% in the RMS of the double-differenced residuals can be achieved.  
multipath; sidereal filtering; multi-point hemispherical grid model; multi-GNSS; fusion solution +
Footnote †: journal: selected topics
# HandFormer: A Dynamic Hand Gesture Recognition Method Based on Attention Mechanism
## 4 Results
### Results on Jester Dataset  
To validate the effectiveness of the proposed method, the method was compared with the current state-of-the-art methods on both the Jester dataset and the NVGesture dataset. The experimental results on the Jester dataset are shown in Table 2. The results show that the proposed method improved the accuracy from 96.60% of the current state-of-the-art method DRX3D [14] to 96.72%, with an increase of 0.12 percentage points, demonstrating the feasibility of the proposed method. In addition, the proposed method obtained significant improvements in terms of parameter and computational complexity, as well as accuracy, compared to the SlowFast method [28]. Specifically, the proposed method achieved a 9.1-percentage-point improvement in accuracy while reducing parameters by 38.9% and FLOPs by 56.2%. The main reason for this performance is that the proposed method uses simple convolution operations to implement spatial and temporal attention operations while using MetaFormer as the underlying architecture. This feature allows the proposed method to extract spatial and temporal features of gestures using convolutional attention with relatively low parameter and computational complexity while maintaining high accuracy to represent differences between gesture actions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **FLOPs (G)** & **Parameters (M)** & **Accuracy (\%)** & **F1-Score (\%)** & **Sensitivity (\%)** & **Only** \\ \hline R3D-34 [29] & 25.420 & 63.791 & 85.48 & - & - & - \\ R(2 + 1D)-34 [30] & 25.813 & 63.748 & 84.75 & 91.20 & 86.30 & 89.21 \\ STNN [31] & - & - & 95.73 & 96.33 & 87.20 & 91.36 \\ STAM [32] & 137.464 & 104.807 & 88.94 & 90.02 & 90.63 & 92.55 \\ PAN [33] & 9.128 & 23.866 & 90.74 & 94.56 & 88.96 & 91.04 \\ DRX3D [14] & - & - & 96.60 & 95.21 & 91.23 & 93.33 \\ SlowFast [19] & 13.176 & 35.17 & 87.62 & 90.04 & 94.44 & 96.22 \\ CvT-MAM [23] & 11.544 & 26.979 & 92.26 & 96.66 & 89.30 & 90.64 \\ \hline Ours & 5.766 & 21.485 & 96.72 & 97.2 & 94.38 & 96.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed method on the Jester dataset.
# A Review of the Water Desalination Technologies
## Nomenclature  
AGMD Air Gap Membrane Distillation  
\([c]\) &  
+++ ==WARNING: Truncated because of repetitions==
\begin{tabular}{l l} ED & Electrodialysis \\ ERD & Energy Recovering Device \\ \(\eta_{\text{esc}}\) & Energy efficiency \\ Fe3+ & Iron ion \\ FEDCO & Fluid Equipment Development Company \\ FHP & Freshwater High-Pressure \\ FO & Forward Osmosis \\ GWI & Global Water Intelligence \\ H+ & Hydrogen ion \\ HCl & Chloride acid \\ HCO3- & Hydrogen carbonate ion \\ HDH & Humilification-Dehumidification \\ HEMI & Hydraulic Energy Management Integration \\ HPP & High pressure pump \\ H2 SO4 & Sulfuric acid \\ HY & Hydration \\ \(\iota\) & van't Hoff index \\ DXR & Ion-exchange resins \\ J & Joule \\ K & Kelvin \\ K+ & Potassium ion \\ \(1\) & liter \\ m3 & Cubic meter \\ m2 & Square meter \\ MCDI & Membrane Capacitive Deionization \\ MD & Membrane Distillation \\ MED & Multi-Effect Distillation \\ mg & milligram \\ Mg2+ & Magnesium ion \\ Mn2+ & Manganese ion \\ mol & mole \\ MSF & Multi-Stages Flash \\ MVC & Mechanical Vapor Compression \\ \(\
u\) & Number of ions formed \\ Na+ & Sodium ion \\ NaCl & Sodium chloride \\ NaOH & Sodium hydroxide \\ NF & Nano Filtration \\ NH4 OH & Ammonium hydroxide \\ NO2- & Nitrate \\ OH- & Hydroxide ion \\ \(P_{\text{sem}}\) & Osmotic pressure \\ ppm & Part per million \\ \(Q_{f}\) & Freshwater flow \\ \(Q_{last}\) & Thermal energy required \\ \(Q_{g}\) & Saline feedwater flow \\ \(Q_{sep}\) & Thermal requirement for the real plant \\ \(R\) & Ideal gas constant \\ RO & Reverse osmosis \\ RPX & Rotary Pressure Exchanger \\ RR & Recovery ratio \\ SC & Solar Chimney \\ SCMD & Sweeping Gas Membrane Distillation \\ SIR & Societa Italian Resine \\ SRF & Secondary Refrigerant Freezing \\ SO42- & Sulphate ion \\ SSD & Solar Still Distillation \\ \(S_{gen}^{IDS}\) & Entropy generation dead state \\ \(\zeta_{RPS}^{IDS}\) & Entropy generation restricted dead state \\ \(\tau\) & Absolute temperature \\ \(\tau_{0}\) & Temperature of the thermal source \\ \(\tau_{0}\) & Environmental temperature \\ TBT & Top Brine Temperature \\ TVC & Thermal Vapor Compression \\ VF & Vacuum Freezing \\ VMD & Vacuum Membrane Distillation \\ \(W_{lent}^{min}\) & Least work of separation \\ \(W_{grp}\) & Work required by the real desalination plant \\ \end{tabular}
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 6 Conclusions  
In this paper, we first propose a data augmentation algorithm called Mixer that mixes the feature representations and corresponding loss of Whisper. Mixer enhances the prior information deficiency caused by signal degradation in far-field speech recognition. Secondly, we further introduce Mixer-C, which mixes the feature representations from different channels with the same labels, enhancing the multi-channel information of far-field data. Extensive experiments demonstrate that both methods can effectively improve the performance of far-field speech recognition, and we analyze the characteristics and applicable scenarios of the two methods. The proposed methods are not only applicable to the Whisper model but can also be extended to other large-scale pre-trained models. This provides researchers with a general framework for optimizing and improving existing speech recognition systems, enabling them to perform better in various complex environments. The proposed method has broad application prospects in fields such as smart homes and in-vehicle voice systems. In the future, we will study more effective multi-channel information enhancement techniques in front-end processing.  
[PERSON] conceived the experiment(s); [PERSON] and [PERSON] conducted the experiment(s); [PERSON] and [PERSON] wrote the paper; [PERSON] analyzed the results. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62171470), Natural Science Foundation of Henan Province (No. 232300421240), and Henan Zhongyuan Science and Technology Innovation Leading Talent Project (No. 234200510019).  
Not applicable.  
The datasets used and analyzed in the current study are available from [[https://s3.amazonaws.com/dipoleo/DiPCo.tgz](https://s3.amazonaws.com/dipoleo/DiPCo.tgz)]([https://s3.amazonaws.com/dipoleo/DiPCo.tgz](https://s3.amazonaws.com/dipoleo/DiPCo.tgz)).  
The authors declare no conflicts of interest.  
Figure 6: WER (%) of Mixer-C under varying encoder layers.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 5 Experimental Results
### Performance Tests
The results obtained can be used as a benchmark dimensioning the ME system at the edge, given the required computing performance, and the constraints in terms of energy consumption and space occupation.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 3 Matrix Applications in Signal Estimation Theory
### Matrix Theory in Sensor Array Signal Processing
From (33), we can see that the input and the output are just related by a steering matrix \(\mathbf{A}(\boldsymbol{\theta})\) and the problem formulation is simplified using a steering matrix representation. Moreover, the estimation problem may also be addressed efficiently by exploring the special structure of the steering matrix. For instance, under the ULA geometry, we aim at estimating \(\boldsymbol{\theta}=[\theta_{1},\cdots,\theta_{N}]^{T}\) subject to  
\[\left[ \begin{array}{c}x_{1}(t)\\ x_{2}(t)\\ \vdots\\ x_{L}(t)\end{array} \right]\!\!=\!\!\left[ \begin{array}{cccc}1&1&\cdots&1\\ e^{-j\alpha t\cos\theta_{1}}&e^{-j\alpha t\cos\theta_{2}}&\cdots&e^{-j\alpha t \cos\theta_{N}}\\ \vdots&\vdots&\ddots&\vdots\\ e^{-j\alpha(L-1)d\cos\theta_{1}}&e^{-j\alpha(L-1)d\cos\theta_{2}}&\cdots&e^{-j \alpha(L-1)d\cos\theta_{N}}\end{array} \right]\!\!\left[ \begin{array}{c}s_{1} (t)\\ s_{2}(t)\\ \vdots\\ s_{N}(t)\end{array} \right]+\left[ \begin{array}{c}\
u_{1}(t)\\ \
u_{2}(t)\\ \vdots\\ \
u_{L}(t)\end{array} \right]. \tag{34}\]  
It can be observed that the steering matrix \(\mathbf{A}(\boldsymbol{\theta})\) for the ULA geometry is a Vandermonde matrix. Thus, the special properties of Vandermonde matrices can be exploited to develop efficient algorithms to estimate the DOA. For instance, the estimation of signal parameters by rotation invariant techniques (ESPRIT) [27] utilizes the so-called shifting property of Vandermonde matrices, which can be easily understood by considering two submatrices \(\mathbf{A}_{1}\) and \(\mathbf{A}_{2}\) of \(\mathbf{A}(\boldsymbol{\theta})\), obtained by removing the first and last rows of the steering matrix \(\mathbf{A}(\boldsymbol{\theta})\), respectively. Due to the structure of Vandermonde matrix \(\mathbf{A}(\boldsymbol{\theta})\) in (34), the shifting property manifests through the fact that matrices \(\mathbf{A}_{1}\) and \(\mathbf{A}_{2}\) are related by means of shift matrix \(\Phi\) via \(\mathbf{A}_{2}=\mathbf{A}_{1}\boldsymbol{\Phi}\), where \(\Phi\) is a diagonal matrix with diagonal elements \([e^{-j\alpha t\cos\theta_{1}},e^{-j\alpha t\cos\theta_{2}},\cdots,e^{-j\alpha t \cos\theta_{N}}]\). Therefore, the DOA estimation problem is simplified to that of finding \(\boldsymbol{\Phi}\), which can be addressed by exploring the eigenvalue decomposition of the sensor array covariance matrix. The interested reader is referred to [14; 26; 28] for more details about ESPRIT and other matrix factorization based algorithms for sensor array estimation. Figure 6: Array geometries.
# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 4 Simulations and Experiments
### Experiments  
The experimental setup is shown in Figure 10. The hardware and software communication are shown as in Figure 10a. It consists of the Intel Realsense D435 depth camera, computer, and Raspberry Pi mouse robots. The camera is mounted to the ceiling at 2.3 m above the ground and has a field of view that faces downward, as shown in Figure 10b. The USB-C port on the camera is used to connect it to the computer. Each robot has an AR tag on top, which allows the camera to track the robot's position and orientation. When the camera recognizes the AR tag and records the robot's position, it transmits this information to the computer through the USB data cable. When the computer receives the information from the camera, the computer generates the control signal and sends it to the robots. The experiment runs on a single desktop computer equipped with an Intel(R) Core(TM) i7-1195G7 CPU and 32 GB RAM.  
In the experiments, we set up a one-way, single-lane intersection scenario, shown in Figure 11. There are four robots involved, with one robot positioned on each lane facing opposite directions. We synchronize the entry of the robots from both lanes into the center simultaneously. The objective of the experiment is for the robot marked with a yellow arrow to swap positions with the one marked with a blue arrow, and for the robot indicated by a green arrow to exchange places with the one pointed out by an orange arrow without any collision.  
The AIM experiment results are presented in Figure 11. It displays the top-view snapshots from the experiment, including the initial robot configuration shown in Figure 11a. Figure 11b,c demonstrate that each robot, under a cBF control, maintains a safe distance from others and navigates autonomously towards the goal position. In Figure 11d, all robots successfully cross the intersection without collisions occurring. This collision-free movement is further illustrated in Figure 11e, where the blue curve represents the minimum distance between any pair of robots, exceeding the safety threshold \(2r_{\text{safety}}=0.3\) m indicated by the red curve throughout the entire experiment duration, ensuring no collisions took place. Figure 11f depicts all robots' trajectories during the experiment period.  
Figure 10: Experimental setup for the autonomous vehicles of AIM: (**a**) hardware and software communication and (**b**) the experimental scenario involves four autonomous vehicles in the Autonomous Intersection Management system.