esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Accuracy and prediction time have been the most important issues during the development of methods for SCD prediction. Table 4 shows a comparison between the proposed work and other works reported in the literature that use the same database. As qualitative features, the steps used in the method are discussed, while the prediction time and accuracy are discussed as quantitative parameters. It is worth noting that for the method description, the steps have been divided into two bullets; in general, the first one represents the signal processing and feature extraction, whereas the second one describes the algorithm used for automatic pattern recognition. As can be observed in Table 4, most of the works use the raw ECG signal since its conversion to HRV adds an extra computational cost, e.g., in [15] the Pan-Tompkins algorithm is used to obtain the HRV signal. In the works that used ECG signals, all of them have higher values than 90% of accuracy; however, the prediction time changes greatly; for instance, the works [12; 15] report values shorter than 10 min, whereas the remaining works report values longer than 20 min, excepting the work [17] that does not report the prediction time. It should be noticed that the works that provide longer times consider signal processing techniques such as wavelet transform-based methods or EMD-based methods, indicating that the use of these techniques contributes to highlighting features or patterns associated with an SCD condition. Going back to the prediction times, it is evident that the longer the prediction time, the better the medical treatment that a patient can receive. In this regard, the proposed work and the work presented in [18] become the best solutions because 30 min of prediction time are provided. Although similar results are obtained, some advantages of the proposed method can be highlighted: (1) the accuracy is slightly higher, (2) during the design stage, several parameters in the wavelet transform (such as the mother wavelet and the decomposition level) have to be tested and selected to decompose the signal in order to obtain more suitable results, unlike the CEEMD that performs an empirical decomposition without the need of tunning parameters, (3) the proposed CNN is less complex, e.g., the proposal has one convolutional layer whereas the CNN presented in [18] has four convolutional layers, impacting seriously in the computational cost. In addition, in the proposal, different settings in the CCN structure are tested in order to select which ones provide more suitable results by establishing a balance between computational cost and accuracy. As a result, 97.5% of accuracy until 30 min before the event was obtained, where a computational time of 3.2 s is required. This computational time allows the real time since the analyzed time windows are of 1 min. Although promising results are obtained, they are preliminary due to the small database. In this regard, further research with larger databases that even include other rhythm abnormalities must be carried out.
# Calibration Method of Projectional Geometry for X-ray C-arm Fluoroscopy Using Sinogram Data
## 2 Materials Furthermore, Methods
### Calculation of SDD, Phantom, Source, and Detector Position  
The projectional radiography of the phantom is magnified based on the ratio between X-ray source-to-phantom distance and the source-to-detector distance (SDD). Equation (3) uses a triangular shape to express a proportional equation in which a point on the phantom is projected-expanded.  
\[S_{z}-D_{z}:S_{z}-P_{z}=P_{z}^{\prime}:P_{x} \tag{3}\]  
By defining \(S_{z}-P_{z}\) as the parameter \(z^{\prime}\), Equation (3) can be expressed as Equation (4).  
\[S_{z}:=z^{\prime}=P_{z}^{\prime}:P_{x} \tag{4}\]  
where, \(z^{\prime}=S_{z}-P_{z}\)  
When projected to the x-z plane, the system can be expressed as shown in Figure 5. Equation (5) expresses the coordinates of each point in Figure 5.  
Figure 4: A flowchart of the calibration method.  
\[\begin{split} P^{u,M}=&[P^{M}_{x},P^{M}_{z}]\\ P^{u,L}=&[P^{M}_{x}+W^{u}cos\theta_{y},P^{M}_{z}-W^{ u}sin\theta_{y}]\\ P^{u,R}=&[P^{M}_{x}+W^{u}cos\theta_{y},P^{M}_{z}+W^{ u}sin\theta_{y}]\\ P^{l,L}=&[P^{M}_{x}+Hsin\theta_{y}-W^{l}cos\theta_{y },P^{M}_{z}-Hcos\theta_{y}-W^{l}sin\theta_{y}]\\ P^{l,R}=&[P^{M}_{x}+Hsin\theta_{y}+W^{l}cos\theta_{y },P^{M}_{z}-Hcos\theta_{y}+W^{l}sin\theta_{y}]\\ \end{split} \tag{5}\]  
where,  
\(L\), \(R\), and \(M\) represent the left, right, and middle point in the 2D projection system, respectively. \(W\) and \(H\) represent the width and height of the phantom, respectively.  
Equations (4) and (5) can be substituted and summarized as Equation (6).  
\[\begin{split} sin\theta_{y}=&\frac{P^{u,L}_{x}+P^{u, R}_{x}-2P^{u,M}_{x}}{W^{u}\cdot(P^{u,L}_{x}-P^{u,R}_{x})}\cdot z^{\prime}\\ cos\theta_{y}=&\frac{P^{u,R}_{x}-P^{l,L}_{x}-\frac{ sin\theta_{y}}{x}\cdot W^{l}(P^{l,L}_{x}+P^{l,R}_{x})}{H(P^{l,L}_{x}-P^{l,R}_{x}) +2S_{z}\cdot W^{l}}\cdot z^{\prime}\\ \end{split} \tag{6}\]  
Since \(sin\theta_{y}^{2}+cos\theta_{y}^{2}=1\), the x- and z-coordinates of the phantom and SDD can be calculated by Equation (7).  
\[\begin{split} S_{z}=&\frac{P^{u,M}_{x}-P^{u,L}_{x} \cdot(W^{u}\cdot a+1)}{W^{u}\cdot b}\\ P^{u,M}_{z}=& S_{z}-z^{\prime}=S_{z}-\sqrt{(a^{2}+ b^{2})^{-1}}\\ P^{u,M}_{x}=&\frac{a}{S_{z}}z^{\prime}\\ \end{split} \tag{7}\]  
where, \(a=\frac{P^{u,L}_{x}+P^{u,R}_{y}-2P^{u,M}_{x}}{W^{u}\cdot(P^{u,L}_{x}-P^{u,R} _{x})}\), \(b=\frac{P^{l,R}_{x}-P^{l,L}_{x}-aW^{l}(P^{l,L}_{x}+P^{l,R}_{x})}{H(P^{l,L}_{x} -P^{l,R}_{x})+2S_{z}W^{l}}\)  
To calculate the 3D initial coordinates of the phantom and the source, the same process from Equations (3)-(7) is performed for the system projected to the y-z plane.  
Figure 5: System projected in two dimensions in the y-axis direction.
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 3 Interference Evaluation Model
### B2a Signal Reception Performance Degradation
Different integration times were selected to carry out the simulations to verify the usability of the model, and the variations of \(Pd\), \(\delta_{PLL}\), and \(P_{\epsilon}\) as functions of \(C/N_{0}\) under different integration times are shown in Figure 7.  
The results reveal a pronounced degradation in signal reception performance when the carrier-to-noise ratio drops below a certain threshold (30-35 dB-Hz). Specifically, the signal detection probability decreases significantly, while the carrier tracking error and demodulation bit error rate exhibit an increasing trend. However, the detection probability is more sensitive to changes. Notably, increasing the pre-detection integration time can partially mitigate the degradation in detection probability and demodulation error rate, particularly for the detection probability. This demonstrates that interference can  
Figure 7: The impact of \(C/N_{0}\) on the B2a signal detection probability \(Pd\) (**a**), carrier tracking error \(\delta_{PLL}\) (**b**), and the demodulation bit error rate \(Pe\) (**c**). significantly degrade receiver performance, but adjusting the integration time can influence the extent of this degradation.
# Assessment and Feasibility Study of Lemon Ripening Using X-ray Image of Information Visualization
###### Abstract  
Digital radiography (DR) is a mature technology and has been broadly used in medical diagnosis. Currently, it's also used for fruit quality inspection in the market. This purpose of the study is to conduct non-destructive experiments for visual comparisons of digital radiography images, further construct visualized grayscale image analysis technology, and analyze the changes in lemon quality and ripening using quantitative statistical methods. The materials used for the experiments were three lemons of different ripening. A general medical X-ray DR system for was used in this study for 2D digital radiography. The medical X-ray DR images were created based on the Digital Imaging and Communications in Medicine (DICOM) standard. Photometric interpretation of monochrome was applied to create multi-layered grayscale images. Then quantitative analyses and comparisons were performed with image matrix structures and grayscale pixel values in the tissues using visualization techniques and statistical methods. After layer segmentation on the radiological images, the correlations between the lemon structures and tissue changes were assessed by using the Kruskal-Wallis test. The results showed that the \(p\) values for lemon, fiber, and pulp were all under 0.05, while the peel layer did not exhibit significant change. The pulp layer is the best region for statistical analyses to determine the lemon ripening. In conclusion, this study can provide a solid reference for future quality classification in the agricultural market. The research findings can be referenced for developing computing techniques applied to agricultural inspection, expanding the scope of application of the medical DR technology.  
internal quality; ripening; nondestructive inspection; visualize grayscale images; X-ray imaging +
Footnote †: journal: Applied Sci. 2021, 11, 3261. [[https://doi.org/10.3390/app11073261](https://doi.org/10.3390/app11073261)]([https://doi.org/10.3390/app11073261](https://doi.org/10.3390/app11073261))
## 2 Materials and Data
### Gross Primary Productivity  
Gross primary productivity (GPP) is the amount of organic carbon fixed by photosynthesis per unit time by organisms, mainly green plants. MOD17A2H offers GPP products as 8-day, 500 m resolution composite products. This data product uses MODIS land cover data, leaf area index, photosynthetically active radiation fraction, and meteorological data from DAO and is fed into a light energy utilisation model. It is important for crop yield estimation, global carbon, and carbon trade [35; 36].
## 3 Results
### Adjustment Practices
#### 3.2.5 User Rating  
The spontaneous evaluation of the self-adjustments is shown in Figure 16. It presents the distribution of the awarded stars (1-5) in response to the question: \"Please rate your updated adjustments on a scale from 1 (far worse than before) to 5 (much better than before), where 3 is neutral\". The results indicate that approximately 40% of all self-adjustments resulted in a subjectively improved sound quality. For 33%, neither improvement nor deterioration was noted, while about 25% led to a poorer sound quality, indicating that the majority of self-adjustments (75%) did not negatively impact sound quality.  
Figure 16: Overview of the user rating of the self-adjustments.  
Figure 15: Number of iteration loops that the same program went through.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 5 Performance Evaluation
### Energy Efficiency  
The main factor that increases average power consumption in tree routing is frame retransmissions owing to packet loss caused by congestion and link breakage. In addition to energy efficiency, in tree routing, it is necessary to balance the energy consumption between sensor nodes to ensure the diversity of routing paths.  
The traditional tree routing selects a parent node based on the number of hops to the sink node; thus, the packet loss significantly occurs owing to congestion as the traffic bit rate increases. As a result, additional energy consumption increases owing to frame retransmissions. In addition, when the link quality is bad, the number of retransmissions also increases. To address this problem, the linear weighted sum-based scheme considers the link quality and buffer occupancy ratio together in the decision-making process, but it is difficult to respond to changes in network conditions because the weights between multiple metrics are not properly adjusted. As a result, as shown in Figure 7, the energy consumption per unit time is the highest, and the number of dead nodes is the highest.  
The MCMD-based scheme shows better performance than the linear weighted sum-based scheme because the decision maker knows the network environment in advance and determines appropriate weights accordingly. However, as previously analyzed, it is not possible to adjust the weights of metrics at runtime. By contrast, the proposed scheme recognizes the network environment through various cognitive metrics and finds the best policy to optimize the performance metrics through trial and error. As a result, the proposed scheme shows better performance than the MCDM-based parent selection scheme.  
Figure 7: Simulation results: power consumption ratio. (**a**) Power consumption ratio according to traffic bit rate; (**b**) Power consumption ratio according to bit error rate; (**c**) Number of dead nodes according to traffic bit rate.
# Guidelines towards Information-Driven Mobility Management
## Moving towards Content-Centric Mobility Management
### Guidelines  
ICN is a relevant architecture to be integrated into large-scale mobile environments. While current mobility management proposals aim at solving specific issues under specific scenarios, for instance, producer mobility, future solutions need to consider the following:  
* Producer and consumer mobility do not necessarily need to be treated independently, as has been done (by necessity) in prior approaches, which handle MN and CN mobility recurring to distinct mechanisms. In other words, the process of handling handovers should be the same for any node: any node becomes a MN. This can be supported by adding push-based communication support to ICN, via handover anticipation, for instance.
* Mobility anticipation mechanisms derived from context-awareness can be based on a MN's/CN's prior history and neighbourhood. Such concept is relevant to assist make-before-break handovers, thus eventually reducing the required signalling. In such cases, producers can perform data push towards a \"best\" neighbour based on a proactive caching strategy. Via this mechanism, packet loss can be reduced at the expense of a (potentially) small increase in overhead.
* The relevant aspect in an ICN context is \"when\" a move may occur, and not \"where to\" the node shall move. ICN provides global naming, so the location where nodes are should not be the key aspect, from a content-centric mobility management perspective.
* A proactive caching approach towards a \"best\" neighbour of a node can benefit from being associated with a specific Name Prefix, or specific metadata associated with the content to be transmitted. For instance, a timeout (TTL or TLV), or priority numbering.
* Once a move occurs, nodes should emit a notification. While this is the common procedure for consumers, producers can emit an Interest Update notification, as envisioned in the original ICN/CCNx design. This notification allows for a faster routing re-establishment.
* Naming in ICN is hierarchical and independent of location. Nevertheless, today it is common to consider a naming space associated with routing domains, e.g., \"/lusofona.pt/videos/\". While such choice does not impact mobility management, it may negatively impact route aggregation, when producers move. ICN applications would benefit from a set of guidelines for the development of the naming space.
## 3 Results  
Among the 23 patients, 5 (22%) were female and 18 (78%) were male. The age ranged from 14 to 76 years old (mean 37). A malignant primary bone tumour was diagnosed in 22 cases (96%), while 1 patient suffered a benign tumour (4%). The pelvic ring was affected in 16 cases (70%), whereas in 7 (30%) the appendicular skeleton and extremities were involved. In the group of pelvic ring, the sacro-iliac joint was involved in 3 patients (19%) and the hip in 13 (81%), requiring sacroiliac joint fusion in the first group and total hip replacement in the second group. Extra pelvic joint involvement was present in two ankles (one distal tibia and one astragalus) and in one shoulder (glenoid). Surgical margins were adequately \"wide\" in 21 cases (91%) and \"marginal\" in 2 (9%) on the soft tissue component. All patients with pelvic tumour (complex surgical anatomy) reported wide margins on the bone. (Table 1). At a mean follow up of two years (range zero to six), all patients were alive and implants were evaluable for oncological, mechanical, and infective complications. Six patients (26%) reported early implant infection (<1 months), four after pelvic, one after femur, and one after tibial reconstruction. All patients underwent early surgical debridement associated with antibiotics administration and only one case (sacro iliac joint prostheses) was removed at seven months due to the persistence of infection. Three patients (13%) reported implant mechanical complications at a mean follow up of nine months (range 0-19). One patient with astragalus custom made implant developed degenerative arthritis of the ankle and required a new custom-made prosthetic replacement of the distal tibia. One intercalary diaphyseal tibial reconstruction suffered of delayed implant integration and required additional surgery with structural allograft to improve healing process. In one case of sacro iliac joint reconstruction, a S1 root/screw conflict required screw removal. Three patients (13%) developed local recurrence at a mean follow up of 30 months (range 13-39). All recurrences occurred after pelvic reconstructions and three underwent hindquarter amputation with prostheses removal. Overall, three (13%) custom prostheses were removed due to complications (2 recurrence and 1 infection) at a mean time of 18 months (range 1-39) and with an implant specific survival of 74% at five years follow up (Figure 2).
# Mechanical Properties and Flexural Behavior of Sustainable Bamboo Fiber-Reinforced Mortar
## 3 Results and Discussion  
### Mechanical Properties  
#### 3.1.1 Density and Compressive Strength  
The control mix exhibited a density of 2132 kg/m\({}^{3}\), which was slightly higher than the mixes containing fibers. The addition of bamboo fibers had little effect on the density, revealing a reduction ranging between 0.7% and 2.5%, as shown in Table 3. In terms of performance, the control mixture achieved the highest 28-day compressive strength of 75.1 MPa, as shown in Figure 4. The inclusion of fibers led to a reduction in strength, which was directly correlated with the number of bamboo fibers added to the mix design as shown in Table 3. Accordingly, higher fiber contents resulted in lower compressive strengths. This behavior can be attributed to the difference in the compressive strength and E-modulus of the bamboo fibers and the cement matrix. A previous study of the authors on different grades of the bamboo culms revealed a compressive strength between 43.2 and 68.4 MPa and an E-modulus between 18.1 and 28.2 GPa [31]. The incorporated bamboo fibers reduce the overall strength of the matrix resulting in a higher loss of strength with increasing fiber content which is in compliance with the findings of other researchers [20; 54; 55]. In addition, a study of [PERSON] et al. on natural fibers found that air pockets were formed at some of the fibers resulting in a reduced compressive strength compared to the control mixture [56]. Furthermore, a lower aspect ratio, thicker and longer fibers (i.e., \"500 \(\upmu\)m\" batch), generally indicated a greater reduction in the compressive strength than thinner and shorter fibers (i.e., \"300 \(\upmu\)m\" batch), which was also found in [54]. In this respect, the reduction of the compressive strength for the 4 V% to 8 V% of fibers was within a range of 7.8-19.9% for the 300 \(\upmu\)m fibers and 9.1-27% for the 500 \(\upmu\)m fibers (see Figure 4 and Table 3). An average compressive strength of 60.2 MPa was recorded for the 8 V% mixture with 300 \(\upmu\)m fibers, while the corresponding figure was 54.8 MPa for the 8 V% mixture with 500 \(\upmu\)m fibers. Although a slight reduction in strength was observed, the overall findings reveal the comparable performance of the bamboo fiber reinforced mortar mixtures with those of commonly used mix designs for structural members in similar studies [20; 31; 54].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Mix** & **Density** & **SD 1** & **SD 2** & \
\begin{tabular}{c} **Compressive** \\ **Strength** \\ \end{tabular} & **SD 1** & **SD 2** &
\begin{tabular}{c} **Splitting Tensile** \\ **Strength** \\ \end{tabular} & **SD 1** & **SD 2** \\ \hline  & kg/m\({}^{3}\) & kg/m\({}^{3}\) & \% & MPa & kg/m\({}^{3}\) & \% & MPa & kg/m\({}^{3}\) & \% \\ \hline Control & 2132 & 11.2 & 0.5 & 75.1 & 1.1 & 1.5 & 7.2 & 0.50 & 6.9 \\ \hline
300-8P & 2
## 2 Simulation Methodology
### Representation of Human Segments by Ellipsoidal Bodies  
Representing the human body with ellipsoidal segments is an effective way to simplify the human model for carrying out robust analysis tasks. The greatest advantage of this method is that the parameters for comfort analysis can be calculated on the basis of the axes of the ellipsoid bodies, instead of using hypothetical data.  
A study on the deformations of human arm and leg [28] exhibited how the human portions could be represented by portions of ellipsoidal sections. Truncated ellipsoidal bodies were considered during the study in an academic environment [29] in order to validate the eigenvectors of different human parts, where the dimensions of human parts were taken from anthropometric data tables.  
The deviation of an original point to the displaced location of a particular ellipsoid section is shown in Figure 1 and can be derived from the following equation:  
\[D(u_{ij},v_{i})=d_{ij}=\|P_{ij}-e_{ij}\| \tag{1}\]  
\(i=1,\ldots\ldots M\),  
\(j=1,\ldots\ldots N_{i}\)  
\((u_{ij},v_{i})=\) the surface parameters of the point \(e_{ij}\) on surface \(C_{i}\).  
On the basis of the anthropometric data collected, head including neck, torso, arms, legs, and feet of a 50 th percentile male human body were modelled using ellipsoidal segments. From the derived ellipsoidal segments, ratios of lateral axes, average densities, and volumes were evaluated.
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## 2 First Experimental Verification of PTS Gas Sensors  
Because the PT-induced RI modulation is inherently small, researchers developed several methods to enable relatively non-complex retrieval of the signals. One of the methods uses interferometric signal retrieval. Although usually requiring several bulk optics-based components to set-up the interferometer, with careful design such PT sensors yield an acceptable SNR.