esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 City Tagging  
Sensor network deployments in cities are assumed to increase dramatically within the coming decade, according to SENSEable City Lab [11]. This forecast, based mainly on the drastic price reduction of the sensor network components, fosters hope of creating a global centralized pervasive sensing network, which might be able to monitor urban environment in real time. Such a network might enable understanding and a re-design of the urban environment [40].  
The growing interest in environmental and ecological issues within cities requires real time monitoring capable of visualizing the impact of municipal decisions on the community. SENSEable City Lab goes even further with their concept of WikiCity, which interprets the city as a \"control system\", where the physical environment remains connected with digital space [41]. Ubiquitous sensor networks creating a digital representation of the real world can establish this connection between the physical and the digital. Moreover, these sensors introduce the possibility of a feedback loop, as they can not only view and analyze the city as a functional entity, but can also influence urban processes in real time by coupling them with decision support mechanisms, location-based services (LBS) or other spatial-temporal technologies.  
Therefore, there is a huge potential in creating pervasive urban sensing networks, based not on the top-down approach of the global network of urban sensors, but on a volunteered network of personal devices. Currently, mobile smart phones are already used as ubiquitous personal information devices to co-ordinate and adjust our plans on-the-fly and at a distance by receiving up-to-date information on our environment. Moreover, people are producing geo-localized information by using various social services, such as Facebook or Twitter. Such information could be used not only to monitor the city environment in real time, but also to create a feedback loop between the city management and citizens. Urban Informatics Lab developed an iPhone application FixVegas, which allows the sending of fix-o-gram requests directly to Brisbane City Council [42]. Thanks to this application, citizens can take a photo of the problem and submit it directly to the Council along with the coordinates of the reported issue. Residents can save time and get more involved in their city development without having to phone the Council or write to them.  
The City Tagging project is a research of exploratory character, which focuses on possibilities of using a similar approach for collecting sensory data about city space. The research was made with the use of commonly accessible tools, allowing quantitative measurement of urban environment stimuli, correlated with human response [43, 44].
## 4 Proposed System
### Segmentation
#### 4.1.1 Binary Mask from YCbCr and HSV  
Firstly, images are converted from RGB to YCbCr and HSV [52; 53]. YCbCr consists of three components: luminance, blue, and red difference. Also, three-component in the HSV: hue, saturation, and value. The mask frame of the HSV is produced with the upper and lower range of the arguments. Each skin pixel value of the HSV and YCbCr are measured in a standard range for comparing with the pixel quality. Finally, the output of HSV and YCbCr are combined into the binary masks with the Equation (1).  
\[BM(m,n)=HSV_{out}(m,n)+YCbCr_{out}(m,n) \tag{1}\]  
Here, \(BM\) defined the binary mask, \(m\) and \(n\) defined the index of of the image data. The mask is the output of a combination of two parts. Weighted skin produced a white area pixel in the front and a black pixel considered as the background of the mask.
## 3 Experimental Results and Analysis
### Experimental Results and Analysis
#### 3.3.2 Effect of the Leading Automated Vehicle on the Smoothing Velocity  
We considered the effect of the leading automated vehicle on the smoothing velocity. Figure 8 shows the spatio-temporal dynamics through the AV penetration rate at the non-signalized intersection. The points are color-coded based on the velocity. The points closer to the top denote smooth traffic. In contrast, the points closer to the bottom denote congested traffic. For the lower AV penetration rates, perturbations occurred due to stop-and-go waves of human-driven vehicle behavior, reducing the velocity in the non-signalized intersection area (ranging from 200 m to 220 m). As can be seen in Figure 8, almost all of the points are close to the bottom in the non-signalized intersection area with the lower AV penetration rates. This is because human-driven vehicles simultaneously approach the non-signalized intersection area and slow down according to the right-of-way rule. At the higher AV penetration rates, the points are close to the top, with the AVs slowing down in a shorter time, thereby producing fewer and shorter stop-and-go waves in the non-signalized intersection area. Full-autonomy traffic achieved the highest smoothing velocity of all AV penetration rates. Thus, the traffic congestion was partially cleared, and traffic flow became smoother as the AV penetration rate increased.  
Figure 7: The average reward curve over the 200 iterations based on the AV penetration rate.  
Figure 8: The spatio-temporal dynamics based on the AV penetration rate.
## 4 Discussion
We believe that the pump flow parameter is the most significant parameter to achieve a physiological controller. For example, an afterload or aortic pressure are essential parameters to be estimated or measured before the ventricles can push blood out of their respective semilunar valves. However, this parameter is not widely used to drive IRBPs [28]. Our results also showed that the controller was able to prevent suction by decreasing the left atrial pressure. As a result, the controller maintained a pump flow with a minimum value of 2 L/min in relaxation and a minimum value of 2.8 L/min in exercise conditions. A suction detection variable has also been used to control rotary blood pumps. This variable usually leads to a ventricular collapse when an IRBP works at high speed. A recent study was conducted by [PERSON] et al. [29] to detect ventricular suction for the rotary mechanism of an IRBP. In this study, intrinsic pump parameters were used in addition to a model that recognized suction without the use of sensors. The proposed method was evaluated using two pump states: ventricular suction and nonventricular suction. In 2019, [PERSON] et al. [30] implemented a feedback control strategy using a suction prevention unit integrated with pulsatility. The performance of the method and the suction prevention were evaluated to demonstrate the efficiency and feasibility of the designed strategy. This study had several limitations. Unlike the previous study [31], this work did not employ the mechanism of a Frank-Starling-like controller. This mechanism guarantees that the preload parameter (left ventricle end-diastolic volume) is utilized. This parameter has been proven to be a significant parameter for obtaining and implementing a physiological controller for patients with HF [32]. In addition, this study did not include a simulation of the baroreflex, which may have impacted the results and CVS hemodynamics. In the future, this study could be extended to evaluate and validate the proposed control system in mock circulation loops. In this validation, postural changes and Valsalva maneuvers could be simulated at realistic rates of change in physical mock loop resistance and compliance.
## 3 Results of Complete Soft Galloping (SG) Responses and Complete Hard Galloping (HG) Responses
### Soft Galloping (SG)
#### 3.1.1 Response Path 1: Increasing Velocity and No Manual External Excitation  
As the flow velocity increases, the triangular prism enters into the vortex-induced vibration (VIV) initial branch, followed by the VIV-galloping transition branch and finally the galloping branch, accompanied with a monotonically increasing amplitude. In the response path, the galloping can be self-excited by the VIV. For \(4.75<U_{r}<7.25\), \(A^{*}\) and \(f^{*}\) are both small but increase rapidly, indicating that the prism undergoes the VIV initial branch. The oscillation is induced by the vortex shedding, but the oscillation and vortex shedding are not synchronized well. When \(U_{r}\) increases to the range \(7.25<U_{r}<11\), \(A^{*}\) continuously increases from 0.66 to \(1.76,f^{*}\) increases to the maximum value 0.848 and then starts reducing, indicating that the prism undergoes VIV upper branch changes and initiates changes from VIV to galloping (VIV-galloping transition branch). The oscillation is induced by the combined forces of vortex shedding and unstable lift. When \(U_{r}>11\), \(A^{*}\) remains the monotonically increasing trend without any sign of suppression. Meanwhile, \(f^{*}\) levels at approximately 0.76, albeit with a slightly increasing trend, indicating that the prism undergoes the galloping branch. The oscillation is mainly induced by the lift force instability. This SG response path with the above characteristics were also found by [PERSON] [12], [PERSON] [13; 28] and [PERSON] [14].
## 5 User Study  
This section outlines the methodology used to evaluate the impact of the RJAS, integrated into the social robot Mini, on user engagement during a game-based interaction. We  
Figure 3: Workflow of the gaming experience. The green boxes represent the common game stages in the scenario. Distinctive icons represent the different stimuli (audio and visual). This figure is similar to the experimental setup previously mentioned in a related study [21].  
conducted a between-subjects user study with two experimental conditions defined by the state of the RJAS:  
1. _RJAS Active_: Volunteers play the \"Odds and Evens\" game with the robot while the RJAS is active. During the interaction, a series of visual and auditory distractors are introduced in order to shift the participant's attention away from the task. When the user seems distracted, the robot first turns towards the direction of the user's gaze, then turns back to the user and verbally acknowledges the distraction.
2. _RJAS Inactive_: The same game is played, but the robot does not respond to user distractions. Its behavior remains static in terms of attention, offering no gaze reorientation or verbal cues in response to shifts in user focus.  
The core hypothesis is that the participants in the _RJAS Active_ condition will show higher engagement levels than those in the _RJAS Inactive_ condition. To assess this, we rely on self-reported measures via post-interaction questionnaires and objective data obtained through the UBT, a telemetry system that captures attentional behavior during the task.
## 3 Results and Discussion
### OC and EC Fractions
#### 3.3.3 Effect of Wet Scavenging  
When the emission of pollution sources was stable, meteorological conditions were the important factors that affected the concentration of PM and its components, such as the atmospheric self-cleaning processes by rainfall or snowfall [50]. The scavenging activity of snowfall is a crucial indicator of air quality improvement in winter. In 2-4 January 2018, a moderate snowfall was observed in Xinxiang lasting for three days. It is clearly showed that both OC and EC had low concentrations in PE3 (Table 2). In particular, the concentration of OC significantly decreased on 2-4 January (Figure 3). In order to visually show the wet scavenging effect of snowfall on PE3, three days before snowfall and other days in PE3 was used as the pre-snowfall and snowfall period to calculate the clearance rate. The clearance rates of OC and EC were 65% and 59% for PM\({}_{1}\), 67% and 49% for PM\({}_{2.5}\), and 44% and 32% for PM\({}_{10}\), respectively. The carbonaceous components in PM\({}_{1}\) had the highest clearance rate, but as for the PM mass concentration, PM\({}_{10}\) had the highest clearance rate (59% for PM\({}_{1}\), 64% for PM\({}_{2.5}\) and 73% for PM\({}_{10}\)), which indicated that wet scavenging could effectively remove coarse particles and that OC was not predominant components in PM\({}_{10}\). In addition, we estimated the clearance rates of POC and SOC to further elucidate wet scavenging effect on OC. The clearance rates of POC and SOC were 59% and 90% for PM\({}_{1}\), 49% and 85% for PM\({}_{2.5}\), and 32% and 64% for PM\({}_{10}\), respectively. The clearance trends of POC and SOC for the three sizes were similar to that of OC. Notably, more than 85% of SOC was removed in PM\({}_{1}\) and PM\({}_{2.5}\) through wet cleansing while POC was not. This most likely related to the fact that SOC was composed of more hydrophilic components [50; 51; 52; 53; 54]. [PERSON] [51] found that precipitation was the most effective form of atmospheric cleansing of water soluble organic carbon (WSOC) contained in PM\({}_{1}\) and PM\({}_{2.5}\) aerosols, and snow and rain cleaned WSOC more effectively when precipitation lasted longer than 24 h. Accordingly, it has been reported that 10-80% of the OC in aerosols is composed of WSOC [52; 53; 54]. On mid-latitude areas, wet deposition can remove as much as 80% of the carbonaceous aerosols [55]. Compared with OC, the influence of snowfall events on the EC concentration was relatively lower, which may relate to that EC has a longer atmospheric residence time than OC as its smaller size [56; 57].
# Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths
## 4 Experimental Validation
### Device under Test and Experimental Setup
As it appears through this figure, both linearizers compensate for the nonlinearity of the output power versus input power characteristic. However, in the case of the APD, the residual dispersion observed is significantly higher than that observed in the case of the proposed predistorter. This is mainly due to the fact that the APD does not compensate for the memory effects of the DUT, and hence, significant dispersion in the output power characteristic remains. Figure 11: Measured output power versus input power characteristics of the DUT alone and the cascade made of the linearizer and DUT. (**a**) APD linearizer; (**b**) proposed linearizer. Figure 12: Measured output power versus input power characteristics of the cascade made of the linearizer and DUT: (**a**) APD linearizer; (**b**) proposed linearizer.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 3 Methodology
### Extraction of Spatial Factors at American Flux Sites  
In accordance with the first law of geography, spatial autocorrelation may influence the distribution of GPP, where similarities among different locations can lead to data correlations. When modeling based on site observation data, it is essential to employ a simple yet effective approach to account for the spatial effects of variables, thereby obtaining more accurate and stable results. To mitigate spatial autocorrelation in the spatial distribution of GPP, we adopted a method for extracting spatial factors. The ESF method derives these spatial factors by computing and filtering the spatial eigenvectors of the spatial weights matrix. Incorporating spatial eigenvectors effectively addresses the influence of spatial autocorrelation.  
The extraction of spatial factors comprises the subsequent four stages:  
1. Building the spatial weights matrix: The initial phase entails constructing the spatial weights matrix  
Extraction of eigenvalues and eigenvectors: We perform eigen decomposition on the centralized matrix, yielding eigenvalues and eigenvectors, while ensuring they satisfy the conditions of Equation (5): \[\text{Det}(\text{C}-\lambda\text{I})=0\] (5) where Det represents the matrix determinant operator, \(\lambda\) is the eigenvalue, and I is the identity matrix. Each eigenvector obtained through this process represents a specific spatial pattern. The relationship between the spatial weights matrix and its corresponding eigenvalues and eigenvectors is illustrated in Figure 3.  
4 Eigenvector screening: An initial screening of eigenvectors is carried out using a threshold of 0.25. \(\text{EV}_{\text{n}}\) must meet the following criteria with respect to their corresponding eigenvalues \(\lambda_{\text{n}}\): 1. \(\lambda_{\text{n}}>0\); 2. The ratio of the eigenvalue to the maximum eigenvalue in the set should exceed 0.25. The screened eigenvectors can reflect varying degrees of spatial clustering, with larger eigenvalues indicating stronger clustering, which are subsequently integrated with the model as independent variables.
# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 4 Discussion
### Challenges and Future Directions
#### 4.2.2 Inconsistent Preprocessing Pipelines  
Preprocessing is another challenge in pediatric neuroimaging studies. It is necessary to remove the non-brain tissue and noise in many tasks, especially for neuroimaging data of children with significant motion artifacts. However, replication and validation of results are often thus challenged by the variations in data inclusion criteria and preprocessing pipelines. The common preprocessing steps for sMRI include brain extraction, normalization to standard templates, brain tissue segmentation, and brain surface reconstruction [93].  
The fMRI preprocessing steps include brain extraction, motion correction, slice time correction, distortion correction, alignment to structural images, and confounds regression [52; 90]. The DTI preprocessing steps include distortion correction, Eddy current correction, brain extraction, alignment to structural images, and tensor fitting [60]. The mentioned preprocessing steps may involve multiple preprocessing softwares and adjustments may be applied to different pipelines in different studies. We listed the specified softwares and pipelines in our results. Common preprocessing softwares include SPM [164], AFNI [165], ANTs [166], FSL [167], Dpabi [168], and FreeSurfer [169]. Some studies use in-house preprocessing pipelines or did not specify the preprocessing steps. Preprocessing in single research projects may be time- and effort-consuming while variations of preprocessing pipelines restrict the replication of research results.  
Standardization in data preparation and preprocessing is an urgent need for conducting large-scale neuroimaging studies. Fortunately, efforts towards standardization have been contributed by different organizations. Many data-sharing platforms employ the Brain Imaging Data Structure (BIDS) format to adopt a standardized way of organizing neuroimaging and behavioral data [170]. Furthermore, the ABIDE dataset and ADHD200 consortium release both raw and preprocessed data with shared preprocessing pipelines [31; 34]. Standardization of preprocessing pipelines will greatly improve the efficacy of neuroimaging studies in the future.
# A Novel FFT_YOLOX Model for Underwater Precious Marine Product Detection
###### Abstract  
In recent years, the culture and fishing of precious marine product are heavily dependent on manual work, which is labor-intensive, high-cost and time-consuming. To address this issue, an underwater robot can be used to monitor the size of the marine products and fish the mature ones automatically. Automatic detection of marine products from underwater images is one of the most important steps in developing an underwater robot perceiving method. In the traditional detection model, the CNN based backbone suffers from the limited receptive field and hinders the modeling of long-range dependencies, due to the small kernel size. In this paper, a novel detection model FFT_YOLOX based on a modified YOLOX is proposed. Firstly, a unique FFT_Filter is presented, which is a computational efficient and conceptually simple architecture to capture global information of images. Then, a novel FFT_YOLOX model is introduced with fewer model parameters and FLOPs by replacing the standard 3 \(\times\) 3 kernel in the original backbone of the YOLOX model with a FFT_Filter, for an underwater object detection vision task. Extensive experimental results demonstrate the effectiveness and generalization of the visual representation of our proposed FFT_YOLOX model.  
deep learning; FFT; precious marine product detection; underwater; YOLOX 2022
# A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis
###### Abstract  
This review investigates the measurement methods employed to assess the geometry and electrical properties of through-silicon vias (TSVs) and examines the reliability issues associated with tsv in 3D integrated circuits (ICs). Presently, measurements of tsv primarily focus on their geometry, filling defects, and the integrity of the insulating dielectric liner. Non-destructive measurement techniques for tsu contours and copper fillings have emerged as a significant area of research. This review discusses the non-destructive measurement of contours using high-frequency signal analysis methods, which aid in determining the stress distribution and reliability risks of tsv. Additionally, a non-destructive thermal detection method is presented for identifying copper fillings in tsv. This method exploits the distinct external characteristics exhibited by intact and defective tsv under thermoelectric coupling excitation. The reliability risks associated with tsv in service primarily arise from copper contamination, thermal fields in 3D-ICs, stress fields, noise coupling between tsv, and the interactions among multiple physical fields. These reliability risks impose stringent requirements on the design of 3D-ICs featuring tsv. It is necessary to electrically characterize the influence of copper contamination resulting from the tsu filling process on the reliability of 3D-ICs over time. Furthermore, the assessment of stress distribution in tsv necessitates a combination of micro-Raman spectroscopy and finite element simulations. To mitigate cross-coupling effects between tsv, the insertion of a shield between them is proposed. For efficient optimization of shield placement at the chip level, the geometric model of tsu cross-coupling requires continuous refinement for finite element calculations. Numerical simulations based on finite element methods, artificial intelligence, and machine learning have been applied in this field. Nonetheless, comprehensive design tools and methods in this domain are still lacking. Moreover, the increasing integration of 3D-ICs poses challenges to the manufacturing process of tsv.  
TSV; reliability; integrated circuits; electronic packaging 22 May 20232034 July 202315 July 202318 July 202318 July 202320315 July 202318 July 202320315 July 2023138301. [[https://doi.org/10.3390/app13148301](https://doi.org/10.3390/app13148301)]([https://doi.org/10.3390/app13148301](https://doi.org/10.3390/app13148301))
## 4 Real-Word Examination and Evaluation
### Reliability Test  
The vehicles equipped with the RPAM system underwent extended and repeated real-world testing under the supervision of safety officers dispatched by Public Road Security. An auxiliary brake was installed on the passenger side of the vehicle for added safety. The test involved four male drivers of varying ages, all of whom possessed valid driver's licenses; however, their driving experience differed, with driving periods ranging from 3 months to 3 years.  
Each driver took turns operating the vehicle along the same route, with each driving session lasting for a total of 2 h. From the perspective of the working duration, the test results revealed the overall stability of the system. The domain controller's computing capacity was able to effectively handle continuous operations, encompassing tasks such as positioning, laser radar detection, and the concurrent functioning of the examination system's software and hardware. This setup ensured stable performance while maintaining sufficient charge in the vehicle's battery.