esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Vegetation Phenology Metrics and Greenness Derived from MODIS Vegetation Indices  
The time series of NDVI and EVI from MODIS were used to estimate phenology metrics from the TIMESAT software package [28]. Major phenology metrics included in this study were the start of season (SOS), end of season (EOS), length of season (LOS), and peak value of NDVI/EVI. The sum and average NDVI/EVI were also calculated corresponding to the SOS and EOS in each year. As mentioned before, the IP did not have  
\begin{table}
\begin{tabular}{c l l} \hline
**Year** & **Native Pasture** & **Introduced Pasture** \\ \hline \multirow{3}{*}{2009} & (1). Stocking rate 0.39 head/ha for & (1). Burned on 15 April 2009; \\  & May–October & (2). May–1 st, Weed control and fertiliza-tion \\  & & (3). July–1 st, Hayed \\ \hline \multirow{3}{*}{2010} & (1). Stocking rate 0.37 head/ha for & (1). Burned on 18 March 2010; \\  & May–October & (2). July–1 st, Hayed \\ \hline \multirow{3}{*}{2011} & (1). Stocking rate 0.45 head/ha for & \\  & May–October & (1). July–1 st, Hayed \\ \hline \multirow{3}{*}{2012} & (2). Rested all year & (1). Stock rate 0.83 hd/ha for January–May and \\  & & 0.32 hd/ha for June–July \\ \hline \multirow{3}{*}{2013} & (1). Burned on 3/6/2013; \\  & (2). Stocking rate 0.78 head/ha for & (1). Stock rate of 0.64 hd/ha for January–March \\  & for December & (1). 9 April 2014 Burned; \\ \hline \multirow{3}{*}{2014} & (1). Stocking rate 0.78 head/ha for & (2). 7/23 60\% of East half of pasture cut for hay, \\  & April, May, July and November & (3). Stock rate of 0.40 hd/ha for \\  & September–December \\ \hline \multirow{3}{*}{2015} & (1). Stocking rate 0.78 head/ha for & (1). Stock rate of 0.40 hd/ha for \\  & February and 0.39 head/ha for & January–February and June–July; \\  & June–July & (2). Stock rate of 0.96 hd/ha for \\  & August–December \\ \hline \end{tabular}
\end{table}
Table 1: Major management practices in the native pasture (NP) and introduced pasture (IP) from 2009 to 2015.  
homogeneous land cover type until 2009, thus the phenology metrics and greenness data were only used from 2009 onwards for the IP, while they were used for the entire study period (2000-2015) for the NP.
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 6 Conclusions  
A passive bistatic ground-based synthetic aperture radar (PB-GB-SAR) system using the geostationary satellite digital TV signal instead of a dedicated transmitter has been designed and validated in this study for high-resolution SAR imaging and high-accuracy displacement estimation purposes. Link budget analysis has proved the applicability of PB-GB-SAR and provided the fundamentals for system parameter selection. The proposed LNB frequency/phase synchronization method can improve the coherence between the reference and surveillance channels. The back projection algorithm and the range migration algorithm can generate focused SAR images. The displacement of the target can be effectively estimated. Some discussions indicate the improvement directions of PB-GB-SAR. We think the developed PB-GB-SAR system has the potential to be used for monitoring landslide, building, and dams, but needs further study.  
[PERSON] conducted the experiments, analyzed the data, and wrote the manuscript; [PERSON] provided some suggestions for data processing and reviewed the manuscript; [PERSON] helped to conduct the experiments; [PERSON]-[PERSON] and [PERSON] conducted LNB synchronization, [PERSON]-[PERSON] also gave some suggestions and reviewed the manuscript; [PERSON] guided the research, gave some suggestions, and provided some materials for the experiments.  
The Besancon Observatory (France) supported this research activity through an SRO grant. This work was also supported by JSPS Grant-in-Aid for Scientific Research (A) 26249058.  
The authors declare no conflict of interest.
## 5 Discussion  
The present work investigates the criterion for selecting programs for those listening tests focusing on the low-frequency evaluation of loudspeakers. The hypothesis of this work is that some features of the program are related to its ability to reveal the perceptual differences between different devices under test. Therefore, by analyzing the relationship between the program features and the discrimination ability of the program, a predictive model could be built to estimate if the program is suitable for the task.  
Figure 9 summarizes the whole work methodology. The methodology can be divided into two parts. In the first part, a listening test was conducted to assess the discrimination ability of the programs in the low-frequency range. The examined programs were classified into two groups: C1 with programs better at revealing the spectral equalization in the low-frequency range and C2 with programs poor at detecting the equalizations. In the second part, some statistical analysis methods were applied to identify the significant audio features related to the discrimination ability of the program, and also to build a predictive model which could estimate if the program is suitable for the listening tests. Seven features were found to significantly differentiate between the two program groups. In order to minimize the effect of multicollinearity in these significant features on the result of discriminant analysis, a PCA was first performed, extracting two independent principal components that represent the energy-related features and the rhythmic features respectively. Based on the two components, a statistically significant discriminant function was derived, which calculates the discriminant score for each program. By comparing the discriminant score to the cutting score, the group membership of the program could be predicted.  
It should be noted that the whole methodology shown in Figure 9 could be used for selecting program material for other perceptual evaluation tasks with different purposes, such as evaluating the quality of high-frequency or across-all-frequency reproductions of the loudspeakers or other time-domain related parameters like clarity. The point is that for evaluation tasks with different purposes, the first part of the methodology should be carefully designed accordingly. The objective of the listening test in the first part is to evaluate the ability of the programs to reveal differences among the devices under test. In this work, six equalizations in the low-frequency range were applied to the programs. For other perceptual evaluation tasks, the experiment setup should be modified to fit the research objective.  
In this section, the effects of the two principal components and the seven features on characterizing the two program groups are discussed.
# Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots
## 3 Self-Collision Avoidance Strategy
### Real-Time Self-Collision Avoidance  
As illustrated above, the end-effector repulsive velocity \(v_{re,k,end}\) calculated by (14) can have different directions from the original end-effector velocity \(v_{end,k}\) (\(k=l\) or \(r\)), which is planned in advance for a dual-arm task. The end-effector repulsive velocity can drive the effector to an uncertain direction, and the desired task may not be finished. To solve this problem, the algorithm modifies only the magnitude of the original end-effector velocity and keeps the direction unchanged.  
Using the end repulsive velocity \(v_{re,k,end}\) calculated in (14), the end-effector repulsive velocity \(v_{re,k,end}\) (\(k=l\) or \(r\)) is then projected to the original velocity of the end-effector \(v_{end,k}\) (\(k=l\) or \(r\)), which is represented by the red lines in Figure 6.  
The angle \(\gamma_{k}\) between \(v_{end,k}\) and \(v_{re,k,end}\) (\(k=l\) or \(r\)) and can be calculated as  
\[\gamma_{k}=\arccos\left(\left(\frac{v_{re,k,end}}{\left\|v_{re,k,end}\right\| }\right)^{T}\frac{v_{end,k}}{\left\|v_{end,k}\right\|}\right)\ (k=l\text{or}\ r), \tag{15}\]  
The projected repulsive velocity \(v_{pro,k}\) (\(k=l\) or \(r\)) can be calculated as  
\[v_{pro,k}=\left\|v_{re,k,end}\right\|\cos\gamma_{k}\frac{v_{end,k}}{\left\|v_{ end,k}\right\|},\ (k=l\text{or}r), \tag{16}\]  
Summing the projected repulsive velocity \(v_{pro,k}\) and the original end-effector velocity \(v_{end,k}\), the new velocity of the end-effector \(v_{new,k}\) (\(k=l\) or \(r\)) is obtained as  
\[v_{new,k}=v_{end,k}+v_{pro,k},\ (k=l\text{or}\ r), \tag{17}\]  
Next, the new joint velocity can be calculated using (18), where \(\dot{\theta}_{new,k}=\left[\dot{\theta}_{k,1}..\dot{\theta}_{kj-1},\dot{\theta }_{kj}\right]\in R^{j}\) (\(k=l\) or \(r\)) are the new joint velocities:  
\[\dot{\theta}_{new,k}=\left.\int_{r\
u,k}^{\#}\left[\begin{array}{c}v_{new,k} \\ 0\end{array}\right],\ (k=l\text{or}\ r), \tag{18}\]
## 2 Channel Statistics and System Structure
### System Structure  
The structure of the coherent optical system model is described in Figure 1. In the transmitter, a Mach-Zehnder modulator (MZM) is used to modulate the laser. The in-phase and quadrature branches of the MZM are driven by two electrical pseudorandom binary sequences (PRBSs). The modulated optical signal is amplified by an Erbium-doped fiber amplifier (EDFA) and transmitted over the transmitter antenna.  
In a coherent receiver, the received optical signal, after passing through the free-space link, is amplified by the pre-EDFA, and a homodyne IQ receiver is used for signal detection. The state of the polarization (SOP) of the local oscillator (LO) and received signal are assumed to be the same by using a polarization controller (PC). The received signal beats with the local oscillator (LO) in a 2 \(\times\) 4 90\({}^{\circ}\) hybrid, and the output signals are detected by two balanced photodetectors (BDs). The resulting electrical in-phase and quadrature signals are then further processed by high-speed DSP.
## 4 Results
### Main Experimental Results
#### 4.4.4 Thinning Urgency  
The next research question addressed is whether it is possible to detect the need for thinnings and predict their urgency accurately. Compared to the model in Section 4.4.3, this question adds another layer of complexity to the network since it has to assess the thinnings urgency. To answer this question, the ground truth _masked TU_ was used. The idea of ground truth _TU_ is to predict thinnings and their urgency directly. Hence it differentiates between very urgent thinnings (_masked thinning 1_), less urgent thinnings (_masked thinning 2_) and no need for thinning _masked no thinning_. A detailed description of the ground truths is provided in Section 3.2.7. Only the masked version is considered here to focus on the commercial forest. Experiments with the full data can be seen in [62].  
Constraining the problem by just taking into account commercial forest increased the performance slightly in the case of ground truth _masked TN_ (Section 4.4.3). Accordingly, the same strategy was applied to the model _masked TU_. However, the results show no significant overall improvement in the mean \(F_{1}\) (Table 10) over model _TU_. Although the model _masked TU_ achieves a higher \(F_{1}\) score in class _thinning 1_ (51.48%) compared to model _TU_ (41.03%), all other class scores achieve worse performance. Since we are particularly interested in predicting the urgency of thinning, model _masked TU_ provides a slightly better alternative than model _TU_. Thus one can conclude that the restricted model _masked TU_ gives a small improvement in class _thinning 1_ compared to the model _TU_. Figure 7f shows examples of the semantic segmentation results from the thinning urgency experiment. It is clear that the distinction between the two thinning classes (colors red and yellow) is not always very accurate.  
Fortunately, perfect accuracy is not needed because the forestry management procedures can tolerate the slight inefficiency of postponing an urgent thinning until the next thinning review cycle.
# LTM-LoRaWAN: A Multi-Hop Communication System for LoRaWAN
## 3 Proposed Protocol Design
### Identification of Being out of a Gateway Coverage  
The first phase in the proposed system is concerned with a device identifying itself as out of the gateway coverage. Therefore, delivering its messages requires collaboration from other LoRaWAN devices to pass on its messages to the gateway. The standard LoRaWAN offers an Adaptive Date Rate (ADR) to allow devices to control their data rate and, hence, increase the network efficiency. ADR operates on two different sides: the network server side and the end devices side. The ADR feature at the LoRaWAN device side is optimized and modified by the proposed system to serve for identifying out-of-range devices. ADR employs a counter to maintain connectivity with the gateway. The counter is increased for each transmission and reset to zero value if the device receives an ADR command or a downlink message. In the case that the counter reaches a predefined limit, the device must regain connectivity by increasing its transmission power and spreading factor, which allows longer radio coverage with a lower data rate. This feature has been modified and utilized by the proposed system to identify being out of the radio range of the gateway [24].  
When the spreading factor has been increased to its maximum value, 12, with no ADR command from the network server being received nor a downlink message for a predefined limit, it indicates that the end device is out of the gateway coverage even though it is using the maximum available SF and transmission power. The limit is defined by standard LoRaWAN as the sum of the number of acknowledgment limits and the number of acknowledgment delays. Once an end device reaches this point, it is regarded by the proposed system as an out-of-range end device, and its ADR mechanism is disabled. The proposed system then forces the current device to switch to the proposed mechanism LTM-LoRaWAN to be able to transmit its traffic despite being out of the coverage of the gateway. Algorithm 1 shows the pseudocode for identifying out-of-coverage LoRaWAN devices.  
Figure 2: Network Topology.
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
###### Abstract  
The smart grid (SG) is an integration of a traditional power grid with advanced information and communication infrastructure for a large number of electrical applications. Despite all these advantages that the SG will bring, certain issues arise when designing a high-quality SG communication network. One of the critical challenges is that the existing routing strategies in smart power grids are incapable of guaranteeing differentiated QoS requirements considering the network dynamics. To address this limitation, we propose an SDN routing algorithm called the QoS-guaranteed and congestion-controlled OpenFlow routing strategy (QCORS) to satisfy the various communication demands by utilizing the flexibility of SDN. Gaining from its open and programmable idea in SDN, the proposed strategy is expected to divide the link into different congestion levels based on predicting the future congestion status from transmission links. Then packets are expected to be transmitted to routers through links under lower load conditions. The simulation results have demonstrated that the proposed method can reduce the average peer-to-peer delay of all the vocational flow and guarantee the reliability of the network.  
s +
Footnote †: journal: Applied sciences  
article
# Global Regulatory Challenges for Medical Devices: Impact on Innovation and Market Access
## 5 Comparison and Global Challenges of Medical Device Regulations
### Comparative Analysis of Medical Device Regulations across Major Markets  
When comparing the regulatory frameworks for medical devices across the EU, USA and Japan, several similarities and differences emerge. All three of these regions classify medical devices based on risk, with varying classifications dictating the level of regulatory scrutiny. Each region mandates compliance with specific regulatory controls: CE marking and NB oversight in the EU, FDA clearance or approval in the USA and PMDA approval in Japan. Post-market surveillance is a common requirement across all of these regions, focusing on monitoring device performance, safety reporting and regulatory updates [11; 12; 13].  
However, notable differences exist. The EU's regulatory framework emphasizes decentralized procedures overseen by NBs and integration them through EUDAMED for transparency and traceability [11; 51]. Additionally, the fact that the EU is a union of several countries introduces significant complexity into its regulatory system compared to the US and Japan. Although all the EU countries operate under the same regulations, variations can arise in their interpretation and application. The USA system, overseen by the FDA, incorporates a structured classification into three risk-based classes with pathways like 510(k) and PMA, coupled with stringent adherence to the Current GMP for consistent quality control [12; 57; 60]. In Japan, the system is characterized by rigorous evaluation processes by the PMDA, alongside adherence to the JMDN for device classification [72].  
There are deadlines set by the regulatory authorities for the review of medical devices, which tend to extend with the complexity and risk level of the device. However, these deadlines are not strictly fixed and can vary due to various factors, such as delays resulting from requests for additional information or the current workload of the regulatory authorities.  
Overall, while each region prioritizes device safety and efficacy through distinct regulatory approaches, they converge in their commitment to ensuring public health and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Definition** & **Pre-Market** & **Post-Market** \\ \hline I & \begin{tabular}{c} General medical \\ devices \\ \end{tabular} & Self-declaration process & Regulatory oversight \\ \hline II & \begin{tabular}{c} Controlled \\ medical devices \\ \end{tabular} & \begin{tabular}{c} Third party certification \\ Minister’s approval (review by \\ PMDA) \\ \end{tabular} & Risk management system \\ \hline III/IV & \
\begin{tabular}{c} High-risk medical \\ devices \\ \end{tabular} &
\begin{tabular}{c} Minister’s approval (review \\ by PMDA) \\ \end{tabular} & Periodic regulatory \\ \hline \hline \end{tabular}
\end{table}
Table 3: Medical devices’ classification and regulatory requirements in Japan.  
facilitating market access for medical innovations. Table 4 provides a comparison of medical devices legislation across the major markets.
## 1 Introduction
### Brief Description of SAR and Its Advantages Compared to Optical Sensors
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**SAR Mission**} & \multirow{2}{*}{**Launch**} & **Out of Service** & \multirow{2}{*}{**Band \(*\)**} & **Spatial Resolution (Azmith & **Repeat** \\  & & & & **and Ground Range) (m) \(**\) & **Cycle (Days)** \\ \hline ERS-1 & 1991 & 2000 & C & 30 & 35 \\ ERS-2 & 1995 & 2011 & C & 30 & 35 \\ ENVISAT/ASAR & 2002 & 2012 & C & 30 & 35 \\ Radarsat-1 & 1995 & 2013 & C & 8-100 & 24 \\ Radarsat-2 & 2007 & & C & 2–160 & 24 \\ Sentinel-1 & 2014 & & C & 5–40 & 12 (6 ***) \\ J-ERS-1 & 1992 & 1998 & L & 18 & 46 \\ ALOS/PALSAR & 2006 & 2011 & L & 10–100 & 44 \\ COSMO-SkyMed & 2007 & & X & 1–30 & 16 (4 ***) \\ TerraSAR-X & 2007 & & X & 1–40 & 11 \\ \hline \end{tabular}  
* * X-band: \(\lambda=3.1\) cm; C-band: \(\lambda=5.6\) cm; L-band: \(\lambda=23.6\) cm; ** Depending on the imaging mode; *** With Sentinel-1 B, the repeat cycle will be reduced to 6 days (see also Section 5); **** Using all satellites of the four-satellite-constellation, the repeat cycle of COSMO-SkyMed reduces to 4 days.  
\end{table} 
Table 2: Synthetic Aperture Radar (SAR) missions relevant for this review paper (modified after [23]). The advantage of SAR is the use of the phase difference at SAR interferometric analysis (the latter is explained in detail in Section 1.3). Therefore, using SAR and especially the interferometric phase, there is a higher possibility to detect also apparently intact buildings as damaged ones (Figure 1)--presumed high enough spatial resolution of the SAR sensor and visibility of the building within the SAR image. Due to the side-looking imaging geometry of the SAR system, layover and shadowing effects greatly influence the visibility of a certain object within the SAR image [27].
## 6 Finite Element Analysis of Prestressed Hollow Slab
### Establishment of the Finite Element Model for the Prestressed Hollow Slab Reinforced Components
#### 6.3.1 Theoretical Calculation Model of Material Compressive Strength  
In the past few years, scholars have conducted extensive research on the fatigue durability of pre-stressed flexural components under freeze-thaw environments, and found that there exists a correlation between freeze-thaw erosion and fatigue. Several macro damage models of materials and theoretical calculation models of material physical properties have been established. [PERSON] et al. [40] found that a change in the compressive strength of concrete could be measured by 300 freeze-thaw cycles of concrete, and a model for the decay of compressive strength of concrete under freeze-thaw erosion was derived and calculated.  
\[f_{c,c}(\widetilde{n})=C_{1}f_{c,c}\Big{(}\widetilde{0}\Big{)}e^{-\lambda_{1} \widetilde{n}} \tag{1}\]  
In the formula, \(f_{c,c}(\widetilde{n})\) indicates the residual compressive strength of the concrete as a function of the freeze-thaw cycles. \(C_{1}\) and \(\lambda_{1}\) are the material characteristic parameters and \(f_{c,c}\Big{(}\widetilde{0}\Big{)}\) indicates the initial compressive strength of the concrete.
## 4 Seismic Response of CSBs Under Uniform Excitations
### Seismic Responses of Towers and Priers
#### 4.2.3 Strains and Bending Moment  
The strain of 3# tower bottom section on both sides subjected to ScEL excitation are shown in Figure 18a. The strain responses on different sides (Sec T3-1E and Sec T3-1W) are changing nearly oppositely due to the bending effect. The maximum strain for Sec T3-1E is 24.1 \(\upmu\)e. Based on strain response on the section edge, we can calculate the section bending moment, shown in Figure 18b. It shows the maximum bending moments of 3# tower is approach to 32 N-mm.  
Based on the above process, the maximum bending moments on other critical sections are calculated and shown in Figure 19a,b. For the sections above the girder, such as Sec T3-3, Sec T3-4, Sec T3-5, the bending moment are very small when subjected to the ScEL motions. However, the moments increase obviously from 11 N-mm to 32.8 N-mm for the tower below the lower cross-beam. On the contrary, when subjected to the ScTCU, the moments of section below the lower cross-beam also are large. For example, the moment reaches to 85 N-mm for secT3-3 subjected to ScTCU. Figure 19b shows the bending moments of the 4# tower. The similar conclusion can be drawn in the figure.  
Figure 17: Maximum response of 3# tower for (**a**) displacement; (**b**) relative displacement.  
Figure 18: Seismic response of sectionT3-1 under ScEL (**a**) Strain response; (**b**) Bending moment.  
Comparing the seismic response subjected to different NF motions, the bending motions subjected to the PNF (ScTCU) are significant larger than that of NNF (ScEL), about 2.98-4.50 times. This means the bending moments of tower are sensitive to the velocity pulse.  
As we can see, the bottom sections is the most critical section for all piers. Table 5 shows the maximum bending moments of piers bottom sections. The largest responses happen on piers on the edge, such as 0# and 7#. For the 0# pier, bending moment subjected to ScEL and ScTCU are 10.23 N-mm and 13.84 N-mm respectively.