esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 6 Conclusions  
For the recognition of hand gestures drawn from PSL static letters, it is not necessary to use 10 sensors monitoring the flexion of all members of each of the 5 fingers to achieve classification performance exceeding 97%.  
For the problem of recognizing the tested static hand gestures, three sensors at the level of the metacarpal joints are most relevant: on the middle finger, on the thumb, and on the index finger.  
The classification process based only on these three values achieves a performance of 95% for the k-NN method for \(k=1\). It is also the most resilient to reductions in the dimensionality of the input data and to interference from hardware defects.  
Despite the exclusion of key features for some characters, the recognition rate for most characters is sustained at a level close to 90%.  
The above conclusions apply only to the studied case of the static gestures. The next step in the research will be to test the validity of reducing the number of sensors in the dynamic hand gestures of an actual PSL manual alphabet recognition task, also performed in sequence. Further studies will solicit the participation of PSL users when the feasibility of using fewer sensors in recognizing actual PSL letters is confirmed by preliminary results. With their participation, the plan is to continue building a database of glove signals when using PSL. If the above proposal is positively received by the sign language community, the possibility of reducing the number of sensors in data glove-type devices used as sign language translators can bring significant benefits to both designers and potential users. We believe that using fewer sensors would not only simplify the design and thus reduce the number of unreliable components and the price of the device but would also simplify and speed up the sign recognition process.  
J.P.: conceptualization, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, visualization. P.S.: methodology, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Ethics Committee of Technical University of Lodz (1/2021, date of approval: 28 October 2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this article are publicly available at: [[https://figshare.com/search?q=10.6084%2](https://figshare.com/search?q=10.6084%2) Fm9.figshare.20304132]([https://figshare.com/search?q=10.6084%2](https://figshare.com/search?q=10.6084%2) Fm9.figshare.20304132) (accessed on 13 July 2022).  
[PERSON] would like to thank Lodz University of Technology for granting a scholarship.  
The authors declare no conflict of interest.
# Monitoring Pediatric Head CT Scan Dose Levels:
## 1 Introduction
The CTDIvol is a measure of the radiation dose delivered to a patient during a CT scan. It takes into account both the radiation intensity and the scan range and is expressed in units of milligray (mGy) [7]. Milligray is the standard metric used in CT dose monitoring and is used to estimate the dose delivered to a patient during a scan. The CTDIvol value is displayed on the CT scanner and is recorded in the patient's medical records. The DLP is a measure of the total radiation dose delivered to the patient during the entire CT scan and is typically reported in units of mGy-cm. The DLP is calculated by multiplying the CTDIvol by the length of the scan, and it provides an estimate of the total radiation exposure to the patient [7]. DRLs for CT scans are typically established using a combination of CTDIvol and DLP and are based on the radiation doses delivered to a representative sample of patients undergoing the same imaging procedure. The establishment of DRLs helps to ensure that radiation doses are kept as low as reasonably achievable while still providing the necessary diagnostic information. The use of both CTDIvol and DLP in the establishment of DRLs is important because it provides a comprehensive assessment of the radiation dose delivered to the patient during CT scanning [12]. While the CTDIvol provides information on the radiation dose delivered per unit length of the scan, the DLP provides an estimate of the total radiation exposure to the patient, taking into account the length of the scan. In this context, this study is the first attempt, in the region, to summarize the results of a pediatric head CT survey in the Emirate of Abu Dhabi, United Arab Emirates (UAE). The work aims to provide local DRLs for the Emirate of Abu Dhabi, as well as contribute to national efforts to establish national DRLs for the country.
## 4 Results and Discussion  
### Geological Hazard Identification Results Based on the InSAR Deformation Rate  
According to the processing in Section 3.2, the average deformation rate and kernel density analysis results of the area from 2018 to 2023 were obtained, as shown in Figure 4. The overall annual deformation rate along the line is between \(-\)35.81 mm/y and 13.65 mm/y, as shown in Figure 4a. Negative values indicate deformation movement away from the satellite, and positive values indicate ground uplift and movement toward the satellite. Most of the deformation rates in the study area are concentrated in the range of \(-\)5 mm/y to 5 mm/y. The areas with high deformation rates are relatively concentrated, mainly including the deformation area of the low mountain and hill deformation area in Baishui County, the Weihe Plain deformation area, and the Qinling deformation area in Huazhou District. These areas are predominantly located in farmland and along the edge slopes of loess plateaus. The lithology involved in the deformation area are mainly composed of the Quaternary Lishi and Malan loess, which are significantly influenced by external factors related to collapsibility, such as rainfall, irrigation, and groundwater extraction.  
Hotspot analysis and kernel density analysis were conducted on the InSAR deformation results to obtain the spatial distribution of the deformation points, as shown in  
Figure 4: (**a**) Annual deformation rate diagram along the line. (**b**) Kernel density analysis diagram of deformation rate along the line.  
Figure 4b. Combining the deformation rate and kernel density analysis results, a total of 52 disasters were identified along the transmission line. Most of these geological hazards are located very close to the transmission line, posing a significant potential threat to the construction and stability of the transmission infrastructure. Different types of geological hazards exhibit distinct deformation characteristics. It is generally believed that a landslide shows a concentrated area of high deformation rates within the slope body, often displaying non-linear trends over time, characterized by gradual changes followed by abrupt accelerations [34; 35]. In areas affected by land subsidence, deformation rates typically manifest as widespread, uniformly slow subsidence. The rates generally exhibit linear or slightly non-linear trends over time and are often linked to issues such as excessive groundwater extraction and mining activities. For instance, typical subsidence regions like Xi'an frequently experience prolonged and continuous subsidence, leading to regional-scale deformation [34]. Based on the deformation characteristics of different geological hazards and combined with Google imagery, the geological hazards were preliminarily classified into two main types: landslides and land subsidence. As shown in Table 2, eight landslides and 44 land subsidence points were identified.
## 2 Theoretical Background
### Demand-Side Management
Accordingly, three group of DRs are identified, based on the party that initiates the demand reduction action, as depicted in Figure 3: price-based DRs, incentive or event-based DRs, and reduction bids. In price-based DRs, the market initiates the DR event by offering customers static or dynamic pricing schemes to influence the load profiles of end-users. In incentive or event-based DRs, the utility sends specific DR requests (typically active power limitations), and customers are offered fixed or time-varying payments in case of acceptance and execution of the given request. In this case, penalties are also considered in case of acceptance but eventual failure to accomplish the DR request. In reduction bids, customers initiate the DR request and send demand reduction bids to the utility by offering an available demand reduction capacity and the requested price. Figure 3: Classification of Demand Response (DR) schemes according to the party that initiates the demand reduction action. Adopted from [13; 14]. Alternately, DR schemes can be classified into two more groups, according to the objective of the DR request: Market DRs and Physical DRs. While Market DRs--which include real-time pricing schemes and incentive-based schemes--are driven by economic factors, Physical DRs (which include grid management DRs and emergency signals) are mainly related to reliability requirements. It is evident that the implementation of advanced DR schemes, particularly if involving the active participation of end-users and small DERs, is central for the management and control of modern UESs. However, as remarked by authoritative studies, such as (but not limited to) [1; 2], the formulation of DR schemes is still rooted in centralized mechanisms, where supervisory controllers are enabled to remotely control single consumers' electrical equipment by means of hierarchical structures [26]. We argue that, in this case, the hierarchical approach could be successfully replaced by exploiting the possibility that end-users would independently respond to specific DR requests by modifying their net power profiles through the use of a local VPP able to implement the combined control of their owned DERs (such as photovoltaic systems or wind turbines, controllable loads and storage systems).
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 2 Preliminaries and Process Description
### Description of the Aircraft Descent and Approach Process at Airports
#### 2.2.2 Step-Down Descent Approach (SDA)  
In air navigation, if the aircraft flies under Instrument Flight Rules (IFR), which represents a set of rules governing the navigation of aircraft using instruments, then the instrument approach procedures (IAP) must be followed. The IAP consists of four approach segments along the aircraft flight path, namely the initial, intermediate, and final approach and, as a backup plan to use if needed, a segment for a missed approach. Typically, the initial approach segment starts at the en route (i.e., cruise) altitude from an IAF and ends when the aircraft joins the intermediate approach segment, where the later ends at the final approach fix (FAF).  
SDA is the conventional arrival procedure that pilots and air traffic controllers have been accustomed to for many years. In SDA, an aircraft begins its initial descent at the TOD point and continues descending gradually in a series of steps along the descent path. This step-down descent occurs because the aircraft descends over a stair-like path from the current altitude to a new altitude, due to the controller instructions and/or airspace constraints. During the SDA, the aircraft gradually levels-off by transitioning from the initial to the intermediate to the final approach segments through predefined fixes that indicate the start and end of each approach segment. To fly from the fix that marks the end of the previous approach segment to the fix that marks the subsequent one, the aircraft must increase speed by employing thrust to maintain altitude [31]. Figure 2 illustrates the SDA profile and the approach segments of the IAP.  
SDA also requires communication between the pilot and controller to inform and authorize air movement, which means more workload on both the aircrew and controller during a critical phase of flight that requires situational awareness and additional concentration. Once an aircraft has reached the fix or waypoint that marks the end of the previous approach segment and marks the subsequent one at the new altitude assigned by the controller, the pilot needs to utilize engine thrust to maintain altitude and prepare for further instructions from the controller with respect to approach. Air traffic may be expedited during periods of high demand at airports when using SDA through radar vectoring; however, the utilization of engine power increases fuel burn, which, in turn, increases emissions and noise levels at lower altitudes [31].
# Estimating CPT Parameters at Unsampled Locations Based on Kriging Interpolation Method
## 5 Discussion  
The Kriging method treats soil parameters as regionalized variables and considers the mutual influence by assigning different weights according to the semivariogram. In this study, we use the Kriging method combined with CPT data to make a reasonable estimation of the soil parameters at unsampled locations. Since CPT data is continuous and sufficient, our method can estimate parameters of locations within a certain scope and overcome the data insufficiency problem caused by relatively large sample spacings of the previous studies. As shown in Figure 9, line F5 roughly reflects a similar trend as the other six sampled lines, with a fluctuation between depth 700-2000 mm for both \(q_{\rm c}\) and \(f_{\rm s}\), and the data tends to keep stable under 2.5 m. The bearing capacity analysis further proved the effectiveness of the Kriging method.However, there are also some limitations for this study: The Kriging method relies on semivariograms, which need to be figured out on parameters at sampled locations. However, the number of available CPT soundings is limited, leading to instability of the result. For each cross-section, we only used six known \(q_{\rm c}\) and \(f_{\rm s}\) to set up the experimental semivariogram, which could not be quite accurate. More CPT tests are required for a better estimation. Due to the shift of \(f_{\rm s}\), the maximum interpolation depth is 4775 mm, which cannot meet the requirement of calculating depth of the LCPC methods, so the calculation may have errors. What is more, the LCPC method poses an upper limit for \(f_{\rm s}\), which may cause underestimation of the total bearing capacity, and the influence distance above and below the pile tip used in \(q_{\rm b}\) calculation is different for different methods [20]. [PERSON] et al. [21] suggested that the influence distance given by the original method may be improper, and further study is needed to revise it depending on the soil type.
## 3 RS Applications in Ocean
### Sea Level (SL)
#### 3.4.4 Summary and Future Direction  
Despite the advantages of RS techniques over traditional SL mapping approaches, RS systems generally suffer from coarse spatial resolution over ocean environments. Data acquisition over an extended period is essential to obtain reliable results and to obtain comprehensive findings of SL changes. In this regard, improving the knowledge of SL either by developing more advanced RS systems with better spatial and temporal resolutions or by synergy of observations (i.e., different RS systems), consistent data acquisition, and developing advanced methodologies based on ML algorithms can assist in resolving some of the limitations of RS systems for SL studies [232; 233]. In particular, employing ML and DL algorithms for accurate SL estimation should be considered in future studies due to their high potential for providing accurate SL data [158]. Furthermore, integrating SL observations acquired by different sources is highly required in order to improve our understanding of the global-to-local SL dynamics and resolve the limitations of single-source observations. Finally, the continuation of using RS observations to measure SL by developing and constructing new sensors with improved capabilities is an essential prospect [229; 230; 231].
# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 2 Related Works
### Rolling Window Validation  
Other techniques focus on an iterative approach to training to improve predictions. Rolling window validation has emerged as a fundamental technique for evaluating the performance of deep learning models in time series forecasting from the challenge of time-dependent data. This approach involves sequentially training models on a fixed-size training set while testing them on subsequent time periods, effectively simulating real-world forecasting scenarios [38]. Employing rolling windows can ensure that model assessments are robust and reflect the dynamic nature of time series data, which may exhibit non-stationarity, seasonality, and trend shifts over time [39].  
Recent studies have highlighted the advantages of rolling window validation over traditional fixed-split methods, as it allows for continuous updates to model parameters and hyperparameters, facilitating the capture of evolving patterns within the data [40]. Additionally, this methodology enhances the generalizability of models by providing multiple evaluations across different temporal segments, ultimately leading to more reliable insights into model performance [41]. Notably, advancements in deep learning architectures, such as LSTMs and transformers, have underscored the importance of effective validation strategies, with rolling window validation being integral to optimizing model training and selection processes in various forecasting applications [42].  
All these methodologies are based on the principle of the impact of the size of the time series on predictive models, some focusing on iterative training that includes the most recent observations. The proposed model focuses on a methodology that begins by training with the most recent data [43], allowing the model's behavior to be observed by adding historical data, obtaining on the one hand information on the model's ability to generalize by incorporating more data, and on the other hand understanding whether the data history generates an impact on the prediction capacity.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 2 Performance Parameters
### Electro-Physical Parameters  
Constantly changing meteorological parameters can cause fluctuations in a solar plant's electricity output. Hence, it is imperative to monitor the solar plant's electro-physical parameters for better fault diagnostics. Generally, the main electrical components of solar plants are [8; 22]:  
* Solar panels which produce a direct current (DC).
* Inverters that transform DC into alternating current (AC) [24].
* Batteries for electrical energy storage.  
A solar array contains multiple integrated solar panels with each panel having 36-72 cells/panel. With an average lifespan of 20-25 years, typical historical averages of solar panel degradation have been reported at about 0.8% per year [12; 24]. However, the primary maintenance issue arises with inverters, which have an average lifespan of 5-10 years [12; 13]. Non-functioning and incorrect inverter sizes lead to reduced electricity generation, thus making it imperative to monitor electrical parameters at each level.
# Unraveling Convolution Neural Networks: A Topological Exploration of Kernel Evolution
## 3 Experiments
### Quantitative Analysis of Betti Curves  
Building upon the insights gathered from the studies on grayscale, color, and synthetic images, we conducted another experiment that aimed to quantitatively analyze the evolving topological features of the convolutional kernels through a new lens--the centroids of Betti curves. This phase focused on the datasets from Categories A and B. The objective was to calculate the centroids of the Betti-1 curves, which had been tracked throughout the training process for each dataset. To achieve this, we employed Simpson's Rule--a well-known method for its accuracy in numerical integration. This method helped us accurately determine the coordinates of the Betti curve centroids. The process included collecting the Betti curves at various stages of the training iterations. For each of these curves, we calculated the centroid coordinates, which provided us with a two-dimensional representation (x and y coordinates) of the curve's central point. These coordinates were then plotted against the number of training iterations, creating a dynamic trajectory that illustrated the evolving topological structure in the kernel space as the network progressed.  
The experimental results, as shown in Figures 7a,b, illustrate the changes in the centroid coordinates of the Betti-1 curves across iterations for various datasets. Figure 7a displays the trajectory of the x-coordinate of centroids under the Betti-1 curve throughout the course of the training iterations. It can be observed that the x-coordinates for color image datasets, such as CIFAR-10, CIFAR-100, and BUTTERFLY-100, show a more pronounced increase compared to those of grayscale image datasets, such as MNIST, Kuzushijij-MNIST, and Fashion-MNIST. Similarly, in Figure 7b presents the trajectory of the y-coordinate of centroids under the Betti-1 curve versus iterations. Here, too, the color image datasets demonstrate a more substantial decrease in the y-coordinates over iterations than the grayscale datasets.  
The results indicate that the topological changes in convolutional kernels, represented by centroid shifts for color images, are more significant in both the x and y directions compared to those for grayscale images. This could imply that the convolutional networks are learning more complex features or adapting more extensively when trained on color images as opposed to grayscale images.  
Figure 7: The Betti curves for the third-layer convolutional kernels of the two special datasets. (**a**) Centroid x-coordinate of area under Betti-1 curve vs. Iteration. (**b**) Centroid y-coordinate of area under Betti-1 curve vs. Iteration.
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 3 Proposed Quality Control Method  
Outlier/anomaly detection is an active research field spanning a wide range of applications from manufacturing quality control to astronomical detections. Machine learning techniques are widely used for outlier detection and automation of quality control processes [14]. Despite an emerging trend in the use of machine learning methods for Earth Observation applications, the calibration and validation of satellite measurements are most often handled manually by instrument specialists. Utilizing the capabilities of machine learning tools for calibration and validation activities can help to better understand the behavior of the data.  
Outliers can be defined as sample measurements that have a distinct deviation in their properties when compared to the major proportion of the data [15]. Visually, these measurements are regions of low density in the sample space, i.e., have a significantly low number of neighboring points within a threshold distance compared to rest of the sample space. Machine learning tools that are widely used for outlier analysis includes the supervised classification techniques such as Neural Networks (NN), K-Nearest Neighbors (K-NN), Decision trees, Support Vector Machines (SVM) etc. [16]. As noted in Figure 1, there are distinct regions away from the GMF line and the 1:1 line which should be detected and removed. For the CYGNSS quality control filter, supervised training of a Neural Network is used for outlier detection and removal. The details of the quality control filter design are explained in this section.