esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Correction to the Point Cloud Distortion Based on MEMS LiDAR System
## 2 Methods  
### Principle  
Figure 1 shows a schematic diagram of a MEMS LiDAR system. The pulsed laser beam generated by the laser is collimated by a collimator, and after passing through a beam splitter, a laser beam directly passes through a plane mirror deflection direction and hits the MEMS scanning mirror to illuminate the target. The MEMS mirror is a two-dimensional galvanometer that is driven by static electricity and can be deflected in the x and y directions at the same time. It can emit pulsed light into space to actively illuminate the object, thereby scanning the target. The other laser beam is directly transmitted to the photodetector (PD) in the processing module as a reference signal for pulsed laser emission. The backscattered light signal from the target surface is received by the non-imaging objective lens of the receiving system and transmitted to the photosensitive surface of the avalanche photodiode (APD). The detector converts the light signal into an electrical signal and then obtains the return signal of the laser after the subsequent processing module. Combining the reference signal recorded by the previous PD, the time of flight (TOF) of the pulsed laser can be calculated, and obtained the z-coordinate of the distance between the MEMS LiDAR system and the target. Then fused with the two-dimensional spatial coordinates (\(x,y\)) formed by the scanning trajectory of the MEMS mirror, the three-dimensional spatial coordinates (\(x\), \(y\), \(z\)) of the object can be obtained, and the three-dimensional coordinates (\(x\), \(y\), \(z\)) can be displayed the 3D point cloud images in real-time on the PC through the signal transmission.  
There are two main scanning methods for MEMS mirrors: point-to-point scanning and continuous scanning [17; 18; 19]. The frequency of point-to-point scanning is relatively low. In this way, the MEMS mirror must go through the process of starting, accelerating, and decelerating whenever it scans a point, so high-speed scanning cannot be performed; while the frequency of continuous scanning is relatively faster. In this paper, the continuous scanning method is selected, and the scanning trajectory of the MEMS mirror is simulated as shown in Figure 2, where x and y are normalized coordinates, and the range of value is \(-1\) to \(1\). Each point in the normalized coordinates will have a corresponding z coordinate, forming the three-dimensional coordinates of the target.
## 1 Introduction
While convolutional layers are well suited for extracting visual features from images and fully connected layers typically handle the final classification task, the \"black-box\" nature of these models complicates interpretation and validation of predictions, potentially undermining clinical trust and reliability [20]. Therefore, there is a pressing need for more interpretable approaches that clarify model reasoning and enhance prediction transparency. In our view, incorporating objective numeric feature analysis into CHD scoring systems is crucial for precise and reliable assessment of hip joint conformation. Recent research has focused on applying DL systems to precisely measure key objective metrics such as the Hip Congruency Index (HCI) [21; 22] and Femoral Neck Thickness Index (FNTi) [23; 24], both valuable for assessing hip joint health. A multidisciplinary team developed a DL-based approach using transfer learning to segment and delineate the femoral head and acetabulum from VDHE images, enabling accurate HCI calculations to quantify how well the femoral head fits within the acetabulum. Built on a U-Net architecture with EfficientNet modules, the model achieved high segmentation accuracy, with Dice scores (DS) of 0.98 for the femur and 0.93 for the acetabulum. This strong performance closely matched manual expert evaluations, demonstrating potential to reduce subjectivity and improve consistency in CHD assessments [22]. Building on this framework, researchers applied DL to measure the FNTi, an indicator of biomechanical stress and adaptation in dysplastic hips. The system combined a YOLOv4 network for joint detection with a ResNetv2 backbone for anatomical landmark prediction. Integrated with the segmentation framework used for HCI, this approach enabled precise extraction of femur border coordinates and femoral head circumferences to compute the FNTi, achieving an IoU score of 0.96 [24]. The resulting index showed a significant correlation with CHD severity according to FCI grading [23], and automated FNTi measurements demonstrated strong agreement with expert manual evaluations, with an ICC of 0.88, underscoring its reliability [23; 24]. Together, these automated systems for HCI and FNTi highlight the transformative potential of DL-driven approaches to enhance objectivity, reliability, and transparency in CHD screening, paving the way for more precise and reliable assessments within veterinary orthopedics. As another step in this direction, this study introduces a system for automated measurement of the FHC/DAE distance. Unlike previous AI models focused on direct CHD classification, this approach emphasizes transparency by providing a quantifiable feature to support multimetric CHD radiographic assessment. The primary objectives of this study were to develop the FHC/DAE system and evaluate its agreement and reliability with experienced examiners (P.F-G. and M.G.) to validate its clinical applicability. Our null hypothesis is that there is no agreement beyond chance in the FHC/DAE classifications made by the two examiners and between the examiners and the automated system.
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Stimuli  
Two German audio stories, uttered by two different male speakers, were used as the speech signals of the competing speakers. One story was from a German audio book website [26], and the other story was from a selection of audio books [27]. Before performing the experiment, participants reported no knowledge of the audio stories. Speech pauses from the audio stories that exceeded 0.5 s were shortened to 0.5 s. The audio stories were normalized to the same root-mean-square (RMS) value at a comfortable level which was individualized by each participant. The audio stories with no repetition were considered as the acoustic stimuli for the calibration phase, the open-loop AAD phase and the closed-loop AAD phase. The acoustic stimuli were presented at a sampling frequency of 44,100 Hz using MATLAB (MATLAB 2 in Figure 2), a Fireface UC audio interface system (provided by RME Audio, Germany) and two loudspeakers placed at the left side (with an azimuth of \(-45^{\circ}\)) and the right side (with an azimuth of \(45^{\circ}\)) and a distance of 1 m from the the participants. The visual stimuli consisting of an arrow for cueing were presented using a monitor in front of the participants. In addition, the EEG trigger markers synchronized with the acoustic and visual stimuli were generated using the Fireface UC audio interface system and a g.TRIGbox (provided by g.tec, Austria). The presentation of the acoustic and visual stimuli and the trigger marker generation were performed using the same computer employed for the cognitive-driven gain controller system (see Figure 2 and Table 1).
## 2 Characterization of Echo Envelope Fluctuation Features
### Characterization of Underwater Target Echo Envelope Fluctuation Intensity  
A high-frequency fluctuation of the echo envelope represents the fine features of an underwater complex target echo, but the energy of a low-frequency fluctuation of the echo envelope dominates the whole echo envelope. Normally, extracting the features from a high-frequency fluctuation of the echo envelope is not easy. The maximum fluctuation frequency could be used to characterize the high-frequency envelope fluctuation and its features extracted in the frequency domain [20], but the maximum envelope modulation frequency is obtained by using a threshold, and correctly choosing the threshold is difficult for actual sea data. In this work, the extraction of features from a high-frequency fluctuation of the echo envelope was carried out in the time domain.  
The intensity of the magnitude of the envelope signal fluctuates with time, and it can be characterized by a differential coefficient, through differentiation of the echo envelope. The low-frequency envelope fluctuation is suppressed, equivalent to high-pass filtering, and the high-frequency envelope fluctuation is enhanced.  
For convenient analysis in the time domain, Equation (1) is expressed as  
\[y_{1}(t,\theta)=\sum\limits_{i=1}^{N}a_{i}(\theta).\exp[j2\pi f(t-t_{i})] \tag{5}\]  
\[t_{i}=(i-1)\tau_{\theta} \tag{6}\]Deriving the formula, with time (\(t\)) as a variable, the echo envelope fluctuation intensity is  
\[y_{2}(t,\theta)=\left|\sum_{i=1}^{N}a_{i}(\theta)\cdot j2\pi f\cdot\exp[j2\pi f( t-t_{i})]\right| \tag{7}\]  
\[y_{2}(t,\theta)=2\pi f\left|\sum_{i=1}^{N}a_{i}(\theta)\cdot\exp[j2\pi f(t-t_{ i})]\right| \tag{8}\]  
\[y_{2}(t,\theta)=2\pi f|y_{1}(t,\theta)| \tag{9}\]  
From this expression, the echo envelope fluctuation intensity strongly relates to the carrier frequency of the incident pulse, and the echo envelope fluctuation intensity varies with the carrier frequency accordingly.  
The envelope fluctuation intensity is a time-changing value, and it cannot be used directly for discriminating between different echo signals. In this paper, the feature is characterized by two parameters, one being fluctuation intensity  
\[K(\theta)=\frac{1}{M}\sum_{n=1}^{M}|k_{n}| \tag{10}\]  
\[k_{n}=X(n)-X(n-1)\ n=1,2,3\ldots\ldots M \tag{11}\]  
where \(X\) is the discrete series of \(y_{1}(t,\theta)\), \(k_{n}\) is the difference between adjacent elements of \(X\), and \(M\) is the data length. \(K(\theta)\) is called the fluctuation intensity, which characterizes the whole fluctuation intensity of the echo envelope.  
Another parameter is the standard deviation (STD) of echo envelope magnitude fluctuation intensity,  
\[S(\theta)=\left[\frac{1}{M}\sum_{n=1}^{M}\left(k_{n}-\overline{E}\right)^{2} \right]^{\frac{1}{2}} \tag{12}\]  
where  
\[\overline{E}=\frac{1}{M}\sum_{n=1}^{M}k(n) \tag{13}\]  
where \(S(\theta)\) characterizes the inconsistency of the fluctuation intensity of the envelope that varies with time.
## Materials and Methods
### Chaotic Time Series Prediction with the ANN-ALO System  
EEG time series are in chaotic forms. Due to this, there is a need for a prediction solution strong enough to deal with chaotic time series. In order to achieve that, a hybrid approach rising over the ANN-ALO combination was considered in this study. The system briefly employs an ANN model which is trained by the ALO. Since it is a recent, effective optimization technique, ALO has been employed to train the ANN model rather than using traditional training approaches, e.g., the back-propagation algorithm.  
The approach designed here is a novel one with it is function being to employ ALO for the first time to train ANN on chaotic time series prediction. The novelty here deals with the prediction success according to alternative approaches and as a first-time alternative for the associated literature. The prediction approach is as follows: For training, particles of the ALO are associated with the weight and bias of the ANN (ALO was employed to optimize the weight and the bias). The ANN is trained according to the mean squared error (MSE) criteria by considering the differences between obtained output values and desired output values. In this context, the ANN model is, briefly, a multi-layer perceptron (MLP) that employs four inputs and one output. The ANN model tries to predict the value of \(x(time+3)\), by using the values \(x(time)\), \(x(time-3)\), \(x(time-6)\), and \(x(time-9)\). It is important to express here that there are many different combinations of lags used for the prediction approach. However, past studies that were performed by the author showed that these lags are the most appropriate ones for the different chaotic time series considered [101]. Additionally, some alternative lag options have been tested before in order to understand which one is appropriate to use (a report on this is provided in the next paragraphs). Of course, further investigations are always open to select alternative lags as long as the success of prediction approaches depends on choosing the optimum number of lags and time delays between each of them. In this study, the general prediction performance of the approach was evaluated according to the mean absolute error (MAE) criteria.  
Figure 3 shows a brief view of the ANN-ALO prediction system.  
While forming a hybrid system for a specific problem, it is important to adjust all parameters regarding the employed techniques to ensure that there is good enough synchronization among/between them. Thus, it is important to determine how both ANN and ALO should be adjusted to ensure desired interaction between them and to solve the problem of predicting chaotic time series which is one of the objectives of this research study.
## 3 Results and Discussion
### Principal Component Analysis  
In order to investigate the interconnection between varied applied treatments and plant physiology, morphology, and phytoremediation potential, a correlation within physiological, morphological, and phytoremediation parameters was estimated (Tables S5-S7), and biplots of PCA were plotted (Figure 5). The first principal component (PC1) and second principal component (PC2) accounted for 55.6 and 30.8% of the data variance, respectively, covering 86.4% of data variability. The control treatment located on the left side is separated from the biochar-amended treatments by PC1 (Figure 5a). At the same time, the control and 10% biochar-amended treatments are also differentiated from the remaining biochar-amended treatments by PC2.  
PC1 was mainly contributed by Zn and S accumulation in the roots, P accumulation in the aboveground biomass, plant root biomass, and stress indicator (Fv/Fm). It can be concluded that the incorporation of sludge-based biochar into contaminated DS positively affects plant biomass productivity, eliminating the negative impact of complex contamination. Furthermore, since Cr accumulation in roots, Cu, Mg, and K accumulation in the aboveground biomass, and plant stem diameter are the main contributors to PC2, lower application rates (2.5 and 5%) can be considered more suitable for application in _P. tomentosa_ phytoremediation of DS as it permitted them to avoid Cr accumulation in plant roots.  
Figure 4: Phytoremediation potential of _P. tomentosa_ in relation to HCH isomers (**a**) and PTEs (**b**). Different letters within the parameter indicate a statistically significant difference (\(p<0.05\)).
# UAV Formation Trajectory Planning Algorithms: A Review
## 6 Problems of UAV Formation Trajectory Planning Algorithm
### Physical Constraints of UAV Formation  
In the process of trajectory planning, the UAV in the formation is usually simplified into three degrees of freedom particles, ignoring its own constraints such as minimum turning radius, rolling angle and other restrictions on the running state. As a result, the current trajectory planning algorithm is difficult to adapt to the UAV with high maneuverability, there are errors between the release route and the planned route when the actual formation UAV performs the task, and the execution effect will also be affected.
## 1 Introduction  
The term \"cold chain\" is used in the context of food handling and distribution to describe the process of maintaining suitable conditions for products from the cooling or freezing phase through to the point of sale. This process involves various stages, including transportation, storage, and display.  
Detecting disruptions in the cold supply chain is important for several reasons. Firstly, it pertains to the safety of food and other products during transport. For goods that require specific temperature conditions, such as fresh food products, medicines, or medical materials, maintaining the correct temperature is crucial to preserve quality and prevent spoilage or product degradation. Detecting potential temperature disruptions can help avoid the loss of goods and health risks to consumers.  
Logistics efficiency is closely tied to maintaining the right conditions in the cold supply chain. Detecting disruptions allows for process optimization, increased transportation efficiency, and minimized losses. Precisely monitoring conditions also enables the optimizing resource utilization, such as isothermal containers or cooling inserts, resulting in cost savings.  
Detecting disruptions in the cold supply chain enables compliance with regulatory requirements and quality standards, which is crucial for businesses operating in industries subject to stringent regulations. Monitoring can provide documented evidence that transported goods were stored under the right conditions, which is essential for audits and inspections.  
Detecting disruptions in the cold supply chain has become an indispensable management tool, ensuring safety, efficiency, and regulatory compliance.  
Companies that process refrigerated and frozen foods make substantial investments in refrigeration equipment, temperature control devices, and monitoring equipment to ensure the preservation of their products. However, it is a well-documented fact that once these products leave the manufacturing facility, they are often exposed to temperature variations and abuse during transportation [1].  
Representatives from the Transport-Forwarding-Logistics (TFL) sector identify a significant challenge in their supply chain optimization efforts. This challenge is the inability to effectively analyze the risk of goods damage and the insufficient capability to monitor environmental conditions during distribution.
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
## 2 Materials and Methods
### Antenna Design--Computer Simulations
The distribution of the SAR parameter averaged over 1 g of tissue in the z-y plane cross-section, where the highest values occur, is presented in Figure 13.  
Figure 6:0 The distribution of the SAR parameter averaged over 1 g of tissue in the z-y plane cross-section, where the highest values occur for 3.5 GHz. Figure 6:1 The distribution of the SAR parameter averaged over 1 g of tissue in the z-y plane cross-section, where the highest values occur for 5.8 GHz. The simulated maximum specific absorption rate (SAR) values for both the 3.5 GHz and 5.8 GHz bands remained below the European Union's recommended limit of 2.0 W/kg, averaged over 10 g of tissue [22]. This compliance was demonstrated under the conservative assumption of continuous operation at the maximum permissible power for each respective wireless standard. It is anticipated that under typical operational conditions, where adaptive power control mechanisms are employed, the actual SAR values would be considerably lower.
## 4 Results and Discussion
### Swelling Ability  
Swelling ability study was conducted to understand the ability of HFs related to liquids absorption in the wound site [35]. In addition, swelling ratio of hydrogel films in RAS therapy is an important parameter for mucoadhesive phenomena. Swollen polymers increase surface contact and mechanical entanglement for hydrogen bonding interactions between the polymer and mucous networks [36; 37]. In this study, HFs were weighed and soaked in PBS pH 7.4 for two days. Figure 6 describes that the swelling ratio of Ch HF and ChAlg HF were 2.18 and 3.15, respectively. In addition, Alg HF could not be weighed due to totally dissolved in PBS and did not remain a film formation, indicating that the Alg rapidly dissolve in saliva fluid. The swelling ability of ChAlg polyelectrolyte complexes related to the hydrophilic character of Alg that increase the porosity and surface area of the network leads to improve the swelling ratio of the HF [38; 39]. In addition, heparin-based hydrogel containing epidermal growth factor immersed in PBS solution produced swelling values of 14.4 [40]. Ch is a cation which stabilizes the HFs in mucosa surface and strengthen the Alg structure when the HF is swollen and adherent in oral mucosa [41]. Interestingly, the swelling ratio of ChAlg/\(\alpha\)-M HF was 27.2 and significantly higher than that of ChAlg HF and Ch HF. \(\alpha\)-M in the hydrogel affects the hydrodynamic diameter profile of microgels. When \(\alpha\)-M is absorbed in the surface of microgel particle and not in filtrated into matrices of the gel, the gel will be fully swollen [42; 43]. In this study, \(\alpha\)-M in ChAlg/\(\alpha\)-M HF was in the surface of the hydrogel film by intermolecular interaction between \(\alpha\)-M at the 21 st and 22 nd hydrogen atom of Alg increasing the number of water that penetrated the network through hydrogen bond, resulting in the increasing of swelling ratio of ChAlg/\(\alpha\)-M HF.  
Figure 5: Degradability study of ChAlg/\(\alpha\)-M HF. (**A**) Trypsin; (**B**) TCA (trichloroacetic acid). Each value represents the mean \(\pm\) SEM of three experiments. * \(p<0.05\), compared to the Ch HF. + \(p<0.05\), compared to the Alg HF.
## 4 Discussion
### Limitations of the Review  
A major limitation of this review is the methodological variability among the included studies, which likely affected the consistency and comparability of the results. Different studies used diverse imaging techniques (e.g., CT scans vs. dynamic fluoroscopy), population characteristics (asymptomatic vs. symptomatic), and biomechanical parameters, making it difficult to draw uniform conclusions. These inconsistencies could lead to varying interpretations of how pelvic tilt and femoral torsion affect hip biomechanics.  
To address these limitations, future research should aim for standardized methodologies, including the use of dynamic imaging techniques to capture real-time biomechanics, and should focus on larger, more diverse populations, particularly those with clinical conditions. By adopting more consistent approaches, researchers can produce more comparable and clinically relevant results.  
Many studies in this review focused on healthy populations, which limits the applicability of the findings to clinical settings. Future research should prioritize including patients with specific conditions, such as osteoarthritis, femoroacetabular impingement, or hip dysplasia, to better understand how pelvic tilt and femoral torsion affect biomechanics in symptomatic individuals. By studying these populations, researchers can derive more clinically relevant insights, helping to tailor therapeutic interventions and improve outcomes for patients experiencing pain or dysfunction due to altered hip mechanics. This approach would enhance the clinical applicability of the research and inform more targeted treatment strategies. Some studies used small sample sizes, reducing the statistical power of the results. Most studies utilized radiographic or CT measurements, which may not accurately reflect dynamic movement during daily activities.
## 4 Applications
### Smart Industry  
With the development of the IoT, an increasing number of devices are being interconnected, including wearable devices, smart home appliances, industrial sensors, and autonomous vehicles [154]. However, these IoT devices are often constrained by limited computational power or battery life, making it challenging to meet the real-time computing requirements under the paradigm of ubiquitous connectivity. In this context, UAVs can serve as \"flying gateways\" to collect tasks from IoT devices and make appropriate decisions based on their offloading algorithms. UAVs can either process the collected IoT tasks using their own computational power or offload the tasks to nearby high-performance computing nodes. Once the computational tasks are completed, the results can be sent back to the IoT devices. With the support of efficient offloading algorithms, this strategy can minimize the average latency and fulfill the service requirements of various IoT devices.