esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Testing
### Experimental Apparatus
This is justified by the low spatial resolution of the acquired images, which affects the targets image coordinates that support the deformation measurement. A higher spatial resolution can be achieved with an image sensor composed by smaller pixels or by using a different lens that is capable of producing a higher image magnification with an acceptable narrow field-of-view. From the collected data, the main compressive mechanical properties of the tested masonry specimens were calculated using Equations (1)-(3), namely, the compressive strength, the modulus of elasticity, and the Poisson ratio, which were obtained from both the contact and the optical dimensional  
Figure 12: Measurement points in the specimen’s (**a**) frontal surface; (**b**) rear surface. Figure 13: Stress versus strain curve for the masonry specimen, id M0M10, obtained by (**a**) contact dimensional measurements; (**b**) optical dimensional measurements. measurement chains. An example of these measurement estimates is presented in Table 5 for a set of hollow and solid masonry specimens. Regarding the comparison of the main results, which generally provide a good agreement between the Poisson ratio and some discrepancy of values relates with the measurement of the modulus of elasticity, the differences found could be explained by the nature of the methods applied. In fact, considering that the testing is developed under dynamic conditions, the mechanical contact between the LVDT sensors and the specimen influences the estimates of measurement and its uncertainty. A better understanding of the impact of this source of error should be further studied in order to consider other types of specimens together with a sensitive analysis of the quantities that contribute to the uncertainty budget. Other possible sources of measurement uncertainty, namely, the time delay on acquisition and resolution, were disregarded. With respect to the compressive strength, the measurement sample shows that the solid specimens are characterized by higher estimates, as expected for this type of masonry, in addition to a higher dispersion of values, when compared with the hollow specimens. Independently of the used measurement system, the modulus of elasticity related to the solid specimens is also higher when compared to the hollow specimens. In terms of the Poisson ratio, similar estimates and experimental dispersion of values are obtained, considering both types of test masonry specimens. Comparisons between estimates and dispersion of values related to different measurement systems are not recommended at this stage, since the instrumental measurement uncertainty related to each dimensional measurement system is only presented in Section 4, in addition to the fact that we are dealing with different points of measurement and a complex spatial mechanical behavior of the specimens.
###### Abstract  
Current restrictions on clinical visits as a consequence of the COVID-19 pandemic has increased the need for home-based exercise regimes to facilitate useful, long term patterns of behaviour in individuals with Parkinson's disease (PD). This study aimed to evaluate the effectiveness of a 10-week home-based exercise program designed to target improvements in axial rigidity and gait. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), motor scale and rigidity items, Functional axial rotation-physical (FAR-p), functional reach test (FRT), and time up and go (TUG) test were recorded. In addition, the 10-metre walk test, the fall efficacy scale international (FES-I) and the global rating of change score (GROC) were also recorded. Eighteen individuals were divided randomly into two groups: a home-based exercise group (\(n=10\)) and a traditional physiotherapy control group (\(n=8\)). Participants in the 10-week home-based exercise group showed significant improvements (\(p<0.05\)) in the MDS-UPDRS rigidity item, FAR-p, step length, gait velocity, FRT and FES-I when compared with the control group. This study supports the use of home-based exercises in individuals with PD. These preliminary results also support the hypothesis that targeting axial deficits may be an effective approach for improving gait and reducing falls.  
Parkinson's disease; exercise; home-based; axial rigidity; gait +
Footnote †: journal: Applied Sciences  
Article  
The Effects of a 10-Week Home-Based Exercise Programme in Individuals with Parkinson's Disease during the COVID-19 Pandemic: A Pilot Study
## 2 Materials and Methods
### Measures  
Testing was performed on an open-space artificial grass soccer field whilst wearing soccer shoes. After the standardized warm-up, which included running at moderate pace for 10 min, 5 strengthening exercises, 5 dynamic stretching exercises, and 3 submaximal sprints [16], 3 trials of 3 different types of sprints were performed in balanced and randomized order with 5 min rest between trials. Therefore, participants performed 12 sprints in total [17]. Sprint start was performed 0.5 m behind the first pair of TG. For CS, the circumference of the center circle of the soccer field, with a radius of 9.15 m, was used [4] (see Figure 1).  
Single-beam TG (Brower Timing Systems, Utah, USA) were positioned at 5 m distances to capture split times at short (t\({}_{0-10}\)), intermediate (t\({}_{0-20}\)), and long (t\({}_{0-30}\)), distances with an sampling frequency of 0.01 s [18]. The sensors were mounted at the hip height. Due to potential inconsistencies in sprint initiation with TG [19], corrected split times and the MSS during sprints were calculated in the R software (version 4.0.2, R Foundation for Statistical Computing, Vienna, Austria) using the package of Jovanovic and Vescovi [20]. Utilizing 5 m split times for calculating MSS from the single-beam TG has been previously established to be highly reliable and valid [18]. For the validity analyses, the trial with the highest MSS recorded using TG in the first session was chosen, and the same trial was considered for the GPEXE It.  
The distance-speed-time signals of 40 m sprints were recorded by 18.18 Hz GPS (GPEXE It System, Exelio SRL, Udine, Italy) and processed with the GPEXE web app (version 8.2.41, Exelio SRL, Udine, Italy). The devices were used as recommended by the manufacturer. The mean \(\pm\) standard deviation (SD) number of satellites during the measurements was 9.20 \(\pm\) 0.44. Given that each measurement constituted continuous data, a custom Python code (numpy, scipy, and pandas libraries) was employed to discern between sprint trials and determine the outcome variables. To synchronize TG and GPEXE It, the initiation of the sprint start was defined at the point where the speed signal was three times greater than the SD of the signal at standstill. Additionally, the actual start was determined with a 0.5 m delay due to the preliminary start position behind the first TG. Subsequently, the t\({}_{0-10}\), t\({}_{0-20}\), t\({}_{0-30}\), and MSS of LS and CS were calculated from distance-speed-time signals. For the intrasession reliability analyses of the GPEXE It, all trials were considered.
## 5 Discussion
Both the 1D and 2D inversion results generally suggest a shallower depth compared to the stratigraphic well data, indicating that the sediments may thin out and become shallower toward the northeastern part of the Pingtung Plain. A distinct synclinal shape is apparent in the inversion sections, indicative of the Pingtung Syncline, which results from the thrusting of the Chaochou Fault to the east of the study area, implying that the sediment may thin out to the southwest of the Plain. This hypothesis aligns with previous tectonic and geological studies conducted in the Pingtung Plain [37; 42; 43; 54]. The nearly vertical features observed below both profiles suggest the possibility of step-fault existence buried beneath the Quaternary sediments, disrupting the lateral continuity of the Neogene bedrock. These structures may have been formed due to the upthrusting associated with the Chaochou Fault, as suggested by previous study [40]. Given that few studies have addressed the deep structure of this area, and considering the reasons mentioned earlier, the existence of these structures remains speculative. As such, the results offer a preliminary insight, providing a foundation for further investigation in the northern Pingtung Plain to further validate the findings presented in this study. A proposed preliminary conceptual model is presented in Figure 10.  
Figure 10: Proposed preliminary conceptual model based on the inversion results for (**a**) NW\(-\)SE line and (**b**) NE\(-\)SW line, incorporating the assumed step-faults.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 3 Validation of RADIANCE Generated Results against Analytical Solutions
### Element in a Plane to a Sphere  
Figure 13 shows the view factor calculation between a differential planar element to a sphere. As highlighted by the single dark polygon on the surface of the sphere in Figure 13a, the spherical surface was approximated by several individual polygons. The view factor can be calculated in such a scenario by using the superposition rule [1]. Assuming a surface \(J\) to be composed of \(N\) sub-surfaces \(J1\), \(J2\), \(J3\),..., \(JN\), this rule can be mathematically expressed as:  
\[F_{i-J}=F_{i-J1}+F_{i-J2}+F_{i-J3}+\ldots+F_{i-JN} \tag{9}\]  
For the example considered in Figure 13, the spherical surface was approximated as a composite surface made up of \(N=595\) planar polygons. The view factor between the differential planar surface and the approximated sphere is then calculated using Equation (9).  
An analytical solution for the view factor between a differential element and a sphere was proposed by [PERSON] [35]. The analytical solution, adapted from [PERSON] [4], can be expressed as:  
\[F_{dA1-A2}=\frac{R^{2}}{\left(1+E^{2}+H^{2}\right)^{3/2}} \tag{10}\]  
where \(r=d/2\); \(R=r/l\); \(H=h/l\); \(E=e/l\). For the values (\(d=2\), \(e=2\), \(h=1\), \(l=3\)) considered in Figure 13, the analytical solution as per Equation (10), is 0.0573 when rounded off to four decimal places. This is equal to the solution derived through RADIANCE in Figure 13b.  
Figure 12: View factor between a differential planar element to rectangle in plane 90\({}^{\circ}\) to plane of element as shown in (**a**) with the corresponding calculation in (**b**).
# Effects of Bogies on the Wake Flow of a High-Speed Train
###### Abstract  
The wake region of high-speed trains is an area of complex turbulent flow characterized by the periodic generation and shedding of vortices, which causes discomfort to passengers and affects the stability and safety of the train. In this study, the unsteady characteristics of the wake flows of three 1:1 scale China Railway High-Speed 380A (CRH380A) high-speed train models with different degrees of simplification were numerically investigated using the improved delayed detached eddy simulation (IDDES) method. Analyses of the aerodynamic forces, train-induced slipstream, and turbulent kinetic energy (TKE) were conducted to determine the effects of the bogies on the wake flow of the high-speed train. It was found that the existence of bogies on the bottom of the train, especially the last bogie, not only enhanced the wake flow but also introduced large perturbances into the wake flow. Moreover, the generation and evolution of the vortices in the wake flows were determined by analyzing the instantaneous flow fields and coherent flow structures that were obtained by the dynamic mode decomposition (DMD) method. The results showed that a pair of large, counter-rotating streamwise vortices in the real model of the high-speed train was generated by the cowcatcher and their intensity was significantly enhanced by perturbances that were introduced by the bogies on the bottom of the train.  
high-speed train; unsteady wake; bogie; DMD; vortex dynamics +
Footnote †: journal: Applied Sciences
## 3 Artificial Neural Networks
### Application of Artificial Neural Network to Islanding Detection  
The step-by-step islanding detection procedure adopted in ANN is depicted in Figure 6. In general, the methodology of applying neural networks in the detection of islanding operations can be broken down into three phases, as shown in Figure 6. In the first phase, which represents the training of the selected neural network, the input data first go through transformation and reduction with the various mathematical transformations, which are described in more detail in Section 4.2. Transformed data are then divided into testing and training datasets. The most common ratio of training and testing is 66/33 [44]. The training dataset then goes to the neural network which is learned to detect islanding operation. The second phase serves as a testing of a trained neural network. The testing dataset is sent to the neural network and the decision (islanding operation or not) is checked in order to evaluate the neural network accuracy. In the third phase, after the neural network is tested, implementation of the developed method to a real-world system follows. Most of the reviewed papers failed to realize this phase and only performed the first two. Some examples of successfully implemented islanding detection methods in practice are reviewed in Section 6.3.
# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 3 Materials and Methods
### Object Detection Model  
Object detection from images has significantly increased in performance with the development of deep learning algorithms. YOLO (You Only Look Once) emerged as a popular choice due to its real-time capabilities. First introduced by [PERSON] and [PERSON] in 2015, YOLO is a single-stage detector that processes the entire image in a single pass, eliminating the need for a two-stage [23] process. The YOLO algorithm divides the input image into a grid of cells, where each cell is responsible for detecting objects and their corresponding bounding boxes. This approach is similar to most high-end object detection models, including EfficientDet, and allows YOLO to predict and localize various classes of objects in an image. The YOLO architecture has been improved and adapted to various engineering application fields for better detection of objects under [24; 25; 26] scenarios. YOLO is often used in the YOLOv3 version for satellite image applications, where the input image is split into a grid of \(13\times 13\) or \(26\times 26\) cells, depending on the model size [23]. YOLO has proven to be an effective object detection algorithm due to its real-time performance and ability to handle various classes of objects. The network architecture of the YOLO algorithm is shown in Figure 4.  
The YOLO method looks at bounding box coordinates calculated by each cell and class probabilities to find objects. It then uses non-maximum suppression to remove overlapping bounding boxes. Many potential anchor boxes are generated around the image, and a candidate box is predicted away from each anchor box. A loss function is determined using the ground truth example as a guide. The probability that a given offset box overlaps a true element is calculated and added to the estimated loss function if this probability is more significant than 0.5. The model slowly pulls towards localizing only the true elements by rewarding and penalizing predicted boxes.
# Event-Based Probabilistic Embedding for POI Recommendation
## 2 Topic And Region Embedding
### Embedding of Events
\[p(\vec{l_{v}}|\vec{l_{r}},\Sigma_{r})=\frac{1}{2\pi\sqrt{\Sigma_{r}}}exp(\frac {-(\vec{l_{v}}-\vec{\mu_{r}})^{T}\Sigma_{r}^{-1}(\vec{l_{v}}-\vec{\mu_{r}})}{2}) \tag{2}\]  
where \(\mu_{r}\) and \(\Sigma_{r}\) denote the mean vector and covariance matrix, respectively. The event-embedding result is characterized by \(E_{u,v,t_{q},l_{q}}\) as follows. \[ \begin{split} E_{u,v,t_{q},l_{q}}&=[E_{r}^{(1)}, E_{r}^{(2)},\ldots,E_{r}^{(R)},E_{z}^{(1)},E_{z}^{(2)},\ldots,E_{z}^{(Z)}]\\ &=ReLU([P_{r}^{(1)},P_{r}^{(2)},\ldots,P_{r}^{(R)},P_{z}^{(1)},P_ {z}^{(2)},\ldots,P_{z}^{(Z)}])\end{split}  \tag{3}\]  
where \(ReLU(x)=max(0,x)\). Given event \(e=[u,v,l_{q},t_{q}]\), \(P_{r}^{(i)}\) and \(P_{z}^{(i)}\) are defined as:  
\[ \begin{split} P_{r}^{(i)}&=\theta^{(i)}_{t_{q}|r} \cdot\theta^{(i)}_{v|r}\cdot\theta^{(i)}_{r|u}\ast p(\vec{l_{v}}|\vec{l_{r}}, \Sigma_{r})\ast p(\vec{l_{q}}|\vec{l_{r}},\Sigma_{r})\\ P_{z}^{(i)}&=\theta^{(i)}_{t_{q}|z}\cdot\theta^{(i)}_ {v|z}\ast\theta^{(i)}_{z|u}\ast\prod_{w_{i}\in W_{v}}\theta^{w_{i}}_{w|z}\end{split}  \tag{4}\]  
where \(\cdot\) represents the dot product of two vectors, \(\theta^{i}_{z|u}\) is the probability that user \(u\) generates topic \(i\), \(\theta^{i}_{r|u}\) is the probability that user \(u\) generates region \(i\) and \(\theta^{w_{i}}_{w|z}\) is the probability that topic \(z\) generates word \(w_{i}\). In addition, \(\theta^{(i)}_{t_{q}|r}\), \(\theta^{(i)}_{v|r}\), \(\theta^{(i)}_{t_{q}|z}\) and \(\theta^{(i)}_{v|z}\) are defined as follows. \[ \begin{split}\theta^{(i)}_{t|r}&=[\theta^{(t)}_{t |z=1},\theta^{(t)}_{t|z=2},\ldots,\theta^{(t)}_{t|z=Z}]\\ \theta^{(i)}_{v|r}&=[\theta^{(v)}_{v|z=1},\theta^{(v )}_{v|z=2},\ldots,\theta^{(v)}_{v|z=Z}]\\ \theta^{(i)}_{v|z}&=[\theta^{(t)}_{t|r=1,i},\theta^{( t)}_{t|r=2,i},\ldots,\theta^{(t)}_{t|r=R,j}]\\ \theta^{(i)}_{v|z}&=[\theta^{(v)}_{v|r=1,i},\theta^{ (v)}_{v|r=2,i},\ldots,\theta^{(v)}_{v|r=R,i}]\end{split}  \tag{5}\]  
where \(\theta^{i}_{t|r,z}\) is the probability that region \(r\) and topic \(z\) jointly generate time \(i\) and \(\theta^{i}_{v|r,z}\) is the probability that region \(r\) and topic \(z\) jointly generate POI \(i\).
## 6 Conclusions  
This research sought to investigate the utilization of building and demolition waste, particularly lime and granite powder, in the production of cement-lime mortar. The main goal was to determine the optimal ratio of lime and integrate granite powder in order to develop a mortar with mechanical and physical characteristics appropriate for structural applications in masonry and construction. The research evaluated the impact of lime on hardening time, consistency, workability, and the strength properties of cement-lime mortar. The following results were derived from experimental data:  
The results indicated that lime waste and granite powder are suitable components for producing cement-lime mortar, exhibiting satisfactory physical and mechanical properties.  
The research presents a viable solution for the environmental challenges associated with extensive building and demolition waste in Mosul, especially in the aftermath of considerable combat devastation.  
The addition of lime (up to 25%) in lime-modified mortar, when compared to standard cement mortar, led to a 15% decrease in compressive strength and a 33% decrease in workability.  
The use of lime resulted in a reduction of 16% in mortar consistency and a 30% decrease in setting time.  
A 25% lime component led to reduced compressive strength during various curing durations (3, 7, and 28 days), with lime content having the most significant impact on the decrease in compressive strength relative to water-cement ratio and curing time.  
The ideal blend, consisting of 25% lime and 30% granite powder as a substitute for fine aggregate, demonstrated enhanced physical and mechanical characteristics.  
Nonlinear models were created to forecast the consistency, setting time, and compressive and flexural strength of cement-lime modified mortar, revealing that curing time significantly influenced compressive strength more than the water-cement ratio or lime amount.  
Figure 11: Measured vs. predicted flexural strength of modified cement-lime mortar.  
**Author Contributions:** J.M.A.: writing--original draft preparation, conceptualization, data curation, methodology, software, supervision; M.A.-G.: writing--original draft preparation, visualization, writing--reviewing and editing; A.A.: writing--reviewing and editing, validation, and investigation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 PFAC Infrastructure & Instrumentation
### Gavdos Sea-Surface Cal/Val Facility  
The Gavdos Cal/Val facility has been operational since 2004. It provides absolute calibration values for the Sentinel-3A SRAL altimeter and, more specifically, for its ascending Pass No.14, and for its descending Pass No.335 (Figure 4a). Additionally, this site has been engaged in sea-surface calibration of the Jason missions (Jason-1, Jason-2 and Jason-3, along the descending Pass No.18 and the ascending Pass No.109). In the past, Gavdos has provided Cal/Val results for Jason-1, Jason-2, Jason-3, Envisat, and SARAL/AltiKa satellite altimeters [9]. At present, three tide gauges of different measuring technology (KVR3-radar, KVR4-open air acoustic, and KVR5-pressure) have been used to provide water level observations at the \"Karave\" harbor site in the Gavdos inland (Figure 4b).  
Since the initial setup in 2001, the instrumentation installed at this sea-surface Cal/Val facility in the Gavdos island has been modified throughout this long period of operation to (a) compensate malfunctions, failures, and damages caused by extreme weather events, (b) to follow up changes in satellite altimetry measuring techniques, and (c) improve the instrumentation's uncertainty budget (Figure 5).  
Figure 4: (**a**) The location of the Gavdos sea-surface Cal/Val facility enables the provision of calibration services for Sentinel-3A (green) andJason (red) satellite altimetry missions. Ascending and descending passes for both missions are calibrated at the Gavdos Cal/Val facility. (**b**) The current setup of the “Karave” harbor site in the Gavdos Cal/Val facility.  
Figure 3: The operational status of the main instrumentation (microwave transponder, Global Navigation Satellite System (GNSS) receivers and meteorological stations) at the CDN1 transponder Cal/Val site as of 2014.