esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Palliative Care Models in Idiopathic Pulmonary Fibrosis
## 1 Introduction  
Patients with idiopathic pulmonary fibrosis (IPF) and their caregivers experience a high symptom burden, inadequate preparation for end-of-life (EOL), and lack of access to community supports, resulting in patient suffering due to poor care and health-related quality of life (HRQol) [1; 2]. The aim of palliative care (PC) is to relieve suffering and improve HRQol for patients and their families affected by advanced illness. Therefore, clinical guidelines and expert consensus recommendations are to integrate PC early in the course of IPF given high needs, and in impaired HRQol up to 2 years before death [3; 4; 5]. Unfortunately, the palliative care needs of this patient population often remain unmet due to a lack of knowledge regarding symptom management and the optimal models of service delivery, and a culture of neglect among practitioners [5] despite calls for change [6; 7]. While the impact of PC on HRQol in IPF is unknown at this time, preliminary evidence suggests that early integration of PC into routine management may improve symptom management, HRQol, and end-of-life (EOL) care by meeting patient preferences and reducing unscheduled healthcare use and associated costs [8; 9]. The optimal model of service delivery and standards for PC interventions have yet to be determined for IPF patients [10]. Traditionally, patients with needs are identified and referred to PC experts; this care model is called specialist PC. However, given the increase in demand and shortage of PC experts, it is strongly recommended that all practitioners develop competency in PC to address such unmet needs of the patient populations [5; 11]. This model of PC delivered by professionals without dedicated training in PC is generalist or primary palliative care. The latter model is increasingly preferred given the growing population of chronic diseases with PC needs [12]. There are limited descriptions of primary PC models in ILD.  
We describe three clinic models of PC delivery for IPF patients two located in Canada and one in the UK. These three models represent a spectrum of PC delivery ranging from 100% primary to 100% specialist PC, including a hybrid model that involves PC multidisciplinary discussion where PC specialist input is sought when needed [13]. Specifically, we aimed to describe and compare the components of PC delivered within each model, the time to initiation of symptom therapies, engagement in advanced care planning (ACP) discussions, and the impact of PC delivery on EOL care.
###### Abstract  
Wave reflection and wave trapping can lead to long wave run-up resonance. After reviewing the theory of run-up resonance in the framework of the linear shallow water equations, we perform numerical simulations of periodic waves incident on a linearly sloping beach in the framework of the nonlinear shallow water equations. Three different types of boundary conditions are tested: fully reflective boundary, relaxation zone, and influx transparent boundary. The effect of the boundary condition on wave run-up is investigated. For the fully reflective boundary condition, it is found that resonant regimes do exist for certain values of the frequency of the incoming wave, which is consistent with theoretical results. The influx transparent boundary condition does not lead to run-up resonance. Finally, by decomposing the left- and right-going waves into a multi-reflection system, we find that the relaxation zone can lead to run-up resonance depending on the length of the relaxation zone.  
multi-reflection; resonance; long waves; run-up; boundary conditions +
Footnote †: journal: Applied Physics Letters
## 4 Experiments
### Comparison with state of the art  
In addition to the LSTM-like comparison in Table 3, another state of the art algorithm comparison is presented in Table 4, which shows the results from RGB data.  
It is obvious that our \"attention-again\" model outperforms these above methods significantly in three datasets. Although there are some other methods that combine the RGB data and flow data together to improve the performance in benchmark datasets, our model performs competitively against RACNN models in its category (models using RGB features only).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Pre-Train**} & \multicolumn{3}{c}{**Networks**} & \multirow{2}{*}{**UCF11**} & \multirow{2}{*}{**HMDB51**} & \multirow{2}{*}{**UCF101**} \\  & **ImageNet** & **GoogLeNet** & & & & & \\ \hline LRCN [27] & ✓ & - & ✓ & - & - & - & 82.9 \\ Soft-attention [25] & ✓ & ✓ & - & - & 84.86 & 41.31 & 77 \\ VideoLSTM [48] & ✓ & - & - & ✓ & - & 43.3 & 79.6 \\ JAN [50] & ✓ & - & - & ✓ & - & 50.2 & 81.6 \\ \(L^{2}STM\)[51] & ✓ & - & - & ✓ & - & + & 83.2 \\ \hline Ours & ✓ & - & - & ✓ & **90.1** & **52.6** & **85.8** \\ \hline \hline \end{tabular}
\end{table}
Table 3: State-of-the-art comparison with LSTM-like architectures. Classification accuracy is used as evaluation measure which represented as recognition accuracies (%). The **bold** text represents the best result.  
Figure 3: The comparison of the result of output of the last LSTM unit with softmax operation among the outputs of every LSTM unit: (**a**) comparison of loss; and (**b**) comparison of accuracy. The red line represents our method, and blue line denotes softmax.
# The Enhanced Adaptive Grasping of a Soft Robotic Gripper Using Rigid Supports
## 3 Design and Grasping Principles of the Proposed Gripper
### Rigid Supports  
Rigid supports are applied to the soft gripper in order to achieve wide-range bending more easily. The rigid metal support affects the inflation soft \"tail\" part (Figure 3(b1)), although not as much to as the \"head\" part (Figure 3(b1)). The center of the rigid supports (Figure 4a, Point O) is selected as the original point. Point A (the red dot in Figure 4a) indicates the position of the \"tail\" part of the finger. \(\theta\) is the angle between the \(x\)-axis and the rigid supports. The movements of point A are indicated along the x and y directions in Figure 4b.  
The curves in Figure 4b show that the value of x decreases as the angle increases, which can better help close the fingers to grab items more tightly, but the maximum dimension the gripper can grasp decreases. Therefore, 65 degrees is selected as the angle of the designed rigid supports, which can better help close the fingers and still grab items with a maximum dimension of 8 cm in diameter. The designed rigid supports with 65 degrees can make the gripper achieve wide-range bending. Force analyses are performed on soft fingers with/without rigid supports. Without the rigid supports, objects are lifted mainly by friction provided by the soft fingers; the governing equation is as follows:  
\[F_{Lifting\ force}=f_{friction}=\sum_{k=1}^{4}\mu N_{k} \tag{7}\]  
Figure 3: The bending of soft fingers and their fitting curve and relative curvature value. **(a1)**: Group 1 FEM result; **(a2)**: Group 1 original curve and fitting curve are highly coincident; **(a3)**: Group 1 relative curvature value; **(b1)**: Group 2 FEM result; **(b2)**: Group 2 original curve and fitting curve are highly coincident; **(b3)**: Group 2 relative curvature value; **(c1)**: Group 3 FEM result; **(c2)**: Group 3 original curve and fitting curve are highly coincident; **(c3)**: Group 3 relative curvature value.  
where \(\mu\) is the friction coefficient, which depends on the objects and the silicones, and \(N_{k}\) is the fingertip force, which is hard to enhance in high air pressure. When the air pressure becomes higher, the fingers expand like balloons but slightly increase the fingertip force, so the friction is hard to enhance in high air pressure. With the help of the rigid supports, objects are lifted mainly by the support force, which can be greatly enhanced in high air pressure due to the space limitation when the fingers expand like balloons. Rigid supports provide support force (Figure 4a, Force 2), which generates an inward torque to resist the outward torque generated by the grasped object (Figure 4a, Force 1). Therefore, soft fingers with rigid supports are able to grab items in smaller deformation.
## 2 Materials and Methods
### Land Cover Classification and Change
#### 2.4.2 Training Data  
Since fieldwork was not undertaken, all training and testing data in this study were collected via easily accessible high-resolution satellite imagery. Using the \"Generate Random Points\" tool in ArcMap, random points were generated throughout the PCNP shapefile. Using imagery from Google Earth and the ArcGIS satellite basemap, the random points were identified as belonging to one of the four training sample categories: forest, grass, wetland, or water. The 2017 update of the Belize Ecosystems map from [PERSON] and [PERSON] [38] and the Savanna Ecosystems Map of Belize from [PERSON] et al. [33] were used to aid in the spectral differentiation of classes. Additional classes for clouds and cloud shadows were used in the images from 1993 and 2011. A total of 198 points comprised the training dataset, with 133 of those being for the four land cover classes and 65 for clouds and shadows. Approximately 80% of the samples were used to train the classifier and 20% were saved for accuracy assessment [12]. The training and testing data were randomly split by the \"Subset Features\" tool in ArcMap.
## 4 Discussion
Downregulation of these pathways and some proliferation-related genes such as _ODC1_ and _RGCC_ might indicate reduced proliferative potential as well as possible oxidative damage to ribosomes to which the cells responded by downregulating ribosome biogenesis in order to prevent translation errors. Despite the downregulation of some inflammatory genes including _CXCL1_ and _IL18_ in all three treatments, the phagosomal pathway also appeared to be downregulated, which has been implicated in the pathology of bowel disease [131]. Gene ontology analysis showed enrichment of focal adhesion and cellular junctions in all three treatments, however, pathway analysis showed significant downregulation of cell adhesion molecules, adherens junction, and focal adhesion in H2O2-treated cells indicating damaging effect of H2O2 on cellular cytoskeleton and junctions, which might affect barrier function [139; 140]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gene** & **13-HPODE** & **LA** & **H2O2** \\ \hline _PLIN2_ & \(\uparrow\uparrow\)\({}^{3}\) & \(\uparrow\)\({}^{2}\) & N \({}^{1}\) \\ _HMGCS2_ & \(\uparrow\uparrow\)\({}^{3}\) & \(\uparrow\) & \(\uparrow\uparrow\) \\ _CPTIA_ & \(\uparrow\uparrow\) & \(\uparrow\) & n \\ _FABP1_ & \(\uparrow\uparrow\) & \(\uparrow\uparrow\) & \(\uparrow\) \\ _PCK1_ & \(\uparrow\uparrow\uparrow\) & \(\uparrow\uparrow\) & \(\uparrow\) \\ _CREB3L3_ & \(\uparrow\) & n & n \\ _DDIT4_ & \(\uparrow\uparrow\) & \(\uparrow\uparrow\uparrow\) & \(\uparrow\) \\ _AKR1C3_ & \(\uparrow\uparrow\) & \(\uparrow\) & \(\uparrow\) \\ _PEX6_ & \(\uparrow\uparrow\) & \(\uparrow\uparrow\) & \(\uparrow\) \\ _CAT_ & \(\uparrow\uparrow\) & \(\uparrow\) & \(\uparrow\uparrow\) \\ _CROT_ & \(\uparrow\uparrow\) & \(\uparrow\) & \(\uparrow\uparrow\) \\ _TTN_ & \(\uparrow\uparrow\) & \(\uparrow\) & \(\uparrow\uparrow\) \\ _RGCC_ & \(\downarrow\downarrow\)\({}^{3}\) & \(\downarrow\)\({}^{2}\) & \(\downarrow\) \\ _ODC1_ & \(\downarrow\downarrow\) & \(\downarrow\)\(\downarrow\)\(\downarrow\) & \(\downarrow\) \\ _COX20_ & \(\downarrow\) & \(\downarrow\) & \(\downarrow\)\(\downarrow\) \\ _CXCL1_ & \(\downarrow\downarrow\) & \(\downarrow\)\(\downarrow\)\(\downarrow\) & \(\downarrow\) \\ _IL18_ & \(\downarrow\) & \(\downarrow\) & \(\downarrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative gene expression. Comparison of expression of selected genes between cells treated with peroxididized linoleic acid (13-HPODE), non-peroxidized linoleic acid (LA), or hydrogen peroxide (H2O2) treatments (\(\uparrow\) upregulated; \(\downarrow\) downregulated).
## 4 Experimental Results and Discussion  
### Data Description  
In this study, a current signal obtained from a subway ZDJ9 turnout was utilized as the dataset. Because the turnout was an electronic actuator, its movement could be reflected by the current signal. Accordingly, Casco Corporation collected and provided the field data used in this study. The data were collected and tested on the Shanghai Metro Line 13.  
The current curve includes three-phase currents--A, B, and C--under a 380-V AC power supply. Compared with the B- and C-phase current curves, the A-phase current curve can provide more comprehensive information about the turnout action [33]. Therefore, in this study, the A-phase current curve was used to monitor the turnout status. The state transition of the turnout machine could be divided into two cases--that is, from the locked position to the reverse position, and from the reverse position back to the locked position. The waveforms and trends of the A-phase currents in these two cases were identical. Consequently, the normal operation of a turnout machine could be divided into the following four stages: unlocking, transition, locking, and release. The representative current waveforms of the four stages are shown in Figure 4. The sampling frequency of the ZDJ9 AC electric point machine for the rotating turnout was 25 Hz, and the time required to complete one state transition was 7-9 s. Because of the different fault phenomena resulting in different action times, the number of points collected was inconsistent. Therefore, unifying the number of points was necessary to ensure a consistent data input format. In this study, zero padding was adopted to uniformly pad each data group to 250 points.  
By analyzing the microcomputer monitoring turnout current field data and combining the experience of relevant experts in the field of turnout fault diagnosis, the current turnout curve could be divided into normal data and eight types of fault data, resulting in a total of nine types of curves, as shown in Table 1 and Figure 5. Due to the limited number of fault samples in certain categories in the dataset, the SMOTE method, implemented using MATLAB functions, was employed to expand the experimental sample set and balance the category data [34]. Information on the action current curve was concentrated in the curved part, which could then be transformed into a binary image by converting the curve-sequence data. Each image was of size \(32\times 32\). This processing method not only standardized the input format in the data preprocessing stage but also did not require complex signal-processing calculations or destroy the spatial structure of the current curve.  
Figure 4: A-phase current curve.
# Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method
## 5 Experiment and Discussion
### Comparative Experiment between GVMS and P26
#### 5.3.3 Comparison of Profile Deviation Results  
As shown in Figure 21, the total profile deviation \(F_{\alpha}\), the profile form deviation \(f_{fa}\), and the profile slope deviation \(f_{Ha}\) can be decomposed from the gear integrated error curve. Taking the gear integrated error curve of the right flank of the third gear tooth as an example, it can be calculated that \(F_{a}=1.9\)\(\upmu\)m, \(f_{fa}=1.2\)\(\upmu\)m, and \(f_{Ha}=2.2\)\(\upmu\)m. \(L_{a}\) is the length of the profile measurement, \(g_{a}\) is the length of the meshing line, \(C_{f}\) is the control point of the profile, \(F_{a}\) is the forming point of the tip, and \(N_{f}\) is the effective root point.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & **Tooth Flank** & **GVMS** & **P26** & **Error** \\ \hline \multirow{2}{*}{\(f_{\text{pmax}}(\upmu\text{m})\)} & left & 9.5 & 11.1 & \(-\)1.6 \\  & right & 7.0 & 7.4 & \(-\)0.4 \\ \hline \multirow{2}{*}{\(F_{p}(\upmu\text{m})\)} & left & 15.2 & 14.9 & 0.3 \\  & right & 15.1 & 12.9 & 2.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of pitch deviation (taking the absolute value of the maximum deviation).  
Figure 19: Pitch deviation (GVMS measurement results). (**a**) Single\(-\)pitch deviation (left). (**b**) Sector pitch deviation (left). (**c**) Single\(-\)pitch deviation (right). (**d**) Sector pitch deviation (right).  
Figure 20: Pitch deviation (P26 measurement result): (**a**) left flank; (**b**) right flank.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 4 Overview of Recent and Existing Commercial SATis5 Testbeds and Projects
### ESA ARTES Advanced Technology Project  
The \"European Space Agency Advanced Research in Telecommunications Systems\" (ESA ARTES) project has been addressing the SATis5 challenges. Preliminary results for specific use cases are presented in a whitepaper provided in [13]. There are seven use cases identified in [13] that can be enhanced with SATis5, namely:  
* SATis5 Use Case 1: This use case focuses on the improved reliability through alternative connectivity independent from terrestrial, e.g., a 5G core network integrated with a satellite backhaul network operating as a 5G network;
* SATis5 Use Case 2: Provides backhaul for ultra-low latency services deployed at the network edge, e.g., a large amount of video delivery using a satellite backhaul network;
* SATis5 Use Case 3: Provides mobile and nomadic network deployment, e.g., movable connectivity islands with satellite backhaul;
* SATis5 Use Case 4: Provides enhanced and mesh IoT and multimedia services, e.g., mMTC-NB-IoT;
* SATis5 Use Case 5: Provides global coverage use case, e.g., integrated multi-orbit satellite systems providing global service flexibility;
* SATis5 Use Case 6: Provides convergence with data processing and earth observation;
* SATis5 Use Case 7: Provides End-to-end secure services, e.g., governmental use cases.  
A SATis5 Practical Proof-of-Concept (PoC) testbed is developed to demonstrate the above seven SATis5 use cases and the PoC results obtained from the testbed are captured in [13]. The SATis5 PoC testbed includes:  
* Satellite Ground Systems: Two satellite hubs in Betzdorf and Munich;
* Satellite Ground Nodes: Many satellite-connected nodes are located at various 5G trial locations, including Berlin, Erlangen, Munich, Betzdorf, Killarney, and Nomadic with central nodes located in Berlin and in Betzdorf. Each remote node addresses a different use case;
* 5G Access Networks: LTE, NB-IoT-LTE, and non-3 GPP access such as 60 GHz WLAN, WiFi and LoRa. Fraunhofer FOKUS Open5 GCore is used to manage the connection of the devices;
* Satellites: The space segment is provided by a commercial satellite operator SES with its ASTRA 2F GEO satellite (28.20E) delivering seamless connectivity between the hub platforms in Betzdorf and Munich and the various 5G trial locations described above.  
Table 4 provides a summary of some key SATis5 PoC testbed results related to the above seven use cases.
## 4 Case Study and Economic Analysis
### Multi-Angle Economic Analysis
#### 4.3.1 Economy of Thermal Power Plants in Peak Shaving
The notice stipulates that for a peak-shaving level of 30%-60%, the service price thermal power plants could offer ranges from 0 to 44.64 $/(MWh) during normal operation conditions, while for peak-shaving level deeper than 60%, the upper limit of service price is increased to 59.52 $/(MWh). For the case in this paper, the service revenue at the peak-shaving level of 65% will reduce the marginal price by 3% if the compensation price is counted by 59.52 $/(MWh), but it still cannot cover the increased peak-shaving costs of UPI and UOI. It is clear that the operation economy of both UPI and UPI is reduced in deep peak shaving, even if the compensation for peak shaving is taken into account. Hence, thermal power plants will not involve in deep peak shaving on their own initiative in this case. In the future, only if the peak-shaving costs of retrofitted coal-fired units are further reduced, thermal power plants may participate in deep peak shaving actively.  
Then, we take UPI as an example to study how the marginal price of coal-fired units would change at different wind power penetration levels. The wind power penetration rate is 36% in case 1 and we add two cases, respectively, with 533 MW and 1067 MW wind capacity, which correspond to 25% and 45% wind penetration, respectively; the marginal price of units at 25%, 36%, and 45% wind power penetration is shown in Figure 10, where the dotted and solid lines respectively represent the marginal price with and without peak-shaving compensation (if compensated, the revenue price is 37.2 $/MWh). Figure 10 indicates that the marginal price will be reduced as the wind power penetration decreases. At 25% wind penetration, the marginal price of 65% peak-shaving level increases only by 6% compared to that of 45% peak-shaving level and if the compensation is considered, the marginal price of DPSW and DPSC period is basically the same as that of RPS period. At low wind penetration level, the peak-shaving demand is relatively small, and the peak-shaving costs of UPI is correspondingly reduced. Figure 10: Marginal price of UPI under different wind power penetration level.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 1 Introduction
* Objectivity: The presented results are based on well-defined methodologies and accurate experimental studies. Figure 1: Typical communication between a ground telemetry station and an on-orbit spacecraft [7]. * Currency: This survey presents an overview of the recent literature in the field of anomaly detection of spacecraft telemetry systems. * Coverage: Our work provides in-depth coverage of the various methods and schemes used to handle the spacecraft anomaly detection issue. The main contributions of this survey include the following:
* We review the existing literature and present a detailed taxonomy of the different approaches and strategies addressing the anomaly detection problem in spacecraft telemetry data. * We introduce the different types of anomalies in spacecraft telemetry data and describe benchmark datasets and evaluation metrics explored in this field. This allows us to perform a fair comparison among anomaly detection methods. * We compare and assess the performance of the different methodologies for the problem of anomaly detection in spacecraft telemetry data. The results provide researchers and practitioners an overall and comprehensive vision. This review is organized as follows. In Section 2, we introduce the anomaly detection process and the current types of anomalies in spacecraft telemetry data. Then, we review existing anomaly detection methods for spacecraft telemetry data in Section 3. Datasets and the performance metrics of the reviewed methods are provided in Section 4. Section 5 presents a series of finds and potential research axes. The final section concludes the survey.