esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Park Falls, Wisconsin: EO-1 ALI NDVI vs. Landsat NDVI  
ALI analysis was comprised of two parts. First, it sought to determine the spectral difference between the calculated NDVI from the two sensors over the time series. In the comparison of NDVI from ALI and Landsat, the stratified images by cover type using the NLCD, only the low intensity development class (class 22) showed much variance, which had larger NDVI differences (between 0.05 and 0.10 \(\Delta\)NDVI) than the others (see Figure 5). This difference may be attributed to a smaller sample size compared to the other land cover classes. Second, it sought to identify changes in median \(\Delta\)NDVI over time that occurred, possibly showing the manifestation of increasing differences in SZA due to orbital drift of EO-1. However, this study shows that there was no systematic trend in \(\Delta\)NDVI occurring since the onset of precession. In Figure 5 for all classes, the median difference hovers above and below zero with no observable trend. It was hypothesized that if precession was affecting ALI data quality that the differences would increase in either a positive or negative fashion over time. Not including the low intensity development class (class 22), the highest deviation was 0.05 NDVI, found in several classes and years, but all before precession started in 2011.
## 4 Network Layer: The Main Building Block of Fixed Networks
### F1G PONs
#### 4.1.6 Fqc--2010-2018 (Ultra-Broadband Era)
The specification of the maximum loss budget between OLT and ONU/ONT is 35 dB. _QoS:_ From the MAC layer point of view and the QoS support perspective, an additional management layer is required for the dynamic assignment of wavelengths. According to Rec. ITU-T G.989.3 (2015)/Amd.1 (11/2016), resource allocation with QoS guarantee in TWDM-PON with dynamic assignment of the wavelengths and dual uplink line rate support are left to the implementor's discretion (proprietary dynamic wavelength bandwidth allocation--DWBA). For resource allocation with QoS guarantee within an individual TWDM-PON wavelength with a single transmission rate in the u  
Figure 8: The NG-PON2 architecture as in [33]; IF (interface), CG (channel group), CP (channel pair), CEx (instance of co-existence element), HE (head end), WM (wavelength multiplexer). parameters are: _Fixed bandwidth_, _Assured bandwidth_ and _Maximum bandwidth_. Based on these parameters, traffic flows can be aggregated. In the uplink, an aggregate traffic descriptor is constructed for each T-CONT based on the service specifications of the XGEM Port-ID flows multiplexed onto that T-CONT (four T-CONT types are considered, as in the case of BPON). The recipient of the uplink transmission is represented by an allocation ID (Alloc-ID). For each Alloc-ID, the ONU/ONT receives a transmission opportunity from the OLT, which is determined by a DBA algorithm. To ensure QoS guarantee, the DBA algorithm is based on status reports of the ONUs/ONTs or on traffic monitoring that is performed by the OLT [28].
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 4 Practical Evaluation of Transport-Layer Options
### Additional Considerations and Transport-Layer Approaches
While the first two options have been specifically developed as routing protocols for MANET environments, the latter has been designed to provide route-optimization mechanisms in multi-access links, and could represent a potential solution to support network routing in MANETs with a large number of nodes. In any case, at this point, it is unclear whether the AERO protocol is suited for the resource-constrained ad-hoc scenarios that we are considering in this paper, where the availability of flying nodes may be intermittent. As a final consideration, in our reference scenario, we assume that an infrastructure provider owns a fleet of SUAVs, each onboarding a general-purpose hardware and software platform (i.e., a compute node in cloud computing terminology). These compute nodes form an aerial cloud computing infrastructure that is under the control of a VIM, which is owned by the infrastructure provider. In addition, compute nodes are interconnected to build a multi-hop wireless ad-hoc network, which supports: (1) the automated provision of telecommunication services over the geographic area where SUAVs operate; and (2) the exchange of orchestration information between the MANO system and the compute nodes, which enables the management of the resources available at compute nodes (i.e., to support the deployment of that telecommunication services over the compute nodes). For simplicity, our reference scenario considers a single GCS, providing a facility with equipment that enables human operators to monitor and control the SUAVs themselves (e.g., transportation of SUAVs to a specific area, execution of pre-flight procedures, control and monitor of taking off/landing procedures, provision of waypoints to control the position of SUAVs, etc.). In this setup, we assume that the VIM runs at a server computer that is available in the GCS, and supports the deployment of virtual functions over the compute nodes transported by the SUAVs. In any case, it is also possible to divide a large geographical area into smaller regions, and use multiple GCSes (e.g., one per region) to support a larger-scale deployment of SUAVs. In this case, the use of a single VIM from one of the available GCSes is still feasible, as long as this VIM has network connectivity with all the compute nodes onboarded at the SUAV units (i.e., the aerial network built over the large area can support the communication of the VIM with every compute node onboarded by a SUAV). Alternatively, the infrastructure provider could use multiple VIMs, which would be deployed at different GCSes. In this case, the coordination of orchestration actions would be performed by the NFV orchestrator (NFVO), which should have network connectivity with each VIM (e.g., through a satellite data link). In any case (either using a single VIM or multiple VIMs), a VIM controls a set of compute nodes attached to SUAV units, a situation that is appropriately represented by our reference scenario.
# Numerical Analysis of a Moderate Fire inside a Segment of a Subway Station
## 2 Experimental Results from a Scaled Fire Test
### Mechanical and Thermal Loading  
In order to simulate service conditions, the structure was mechanically loaded in both horizontal directions according to the recommendations by the Chinese Standard for Metro Design [47] and Eurocode 2 [48]. The imposed loads accounted for the effective traffic load at the surface above the real structure (20 kN/m), the pedestrian load on both stories (4 kN/m), the earth pressure resulting from a 3.5 m-thick layer of covering soil (specific gravity \(=~{}19\) kN/m\({}^{3}\)), and the water pressure resulting from a 0.5 m-thick layer of groundwater (specific gravity \(=~{}9.8\) kN/m\({}^{3}\)). These loads were combined, using safety factors for dead load and live loads, amounting to 1.35 and 1.40, respectively. The resulting loading scenario was simulated by three sets of concentrated loads, referred to as P1, P2 and P3; see Figure 6 and Table 3. They were applied in nine steps. This took 70 min. Subsequently, the fire test was started.  
During the fire test, the temperature of the air inside the cross-section was increased according to a time-dependent temperature history (Figure 7). The latter was the result of a statistical analysis of documented fire accidents. Within 25 min, the temperature was increased to a target value of approximately 525 \({}^{\circ}\)C. It was kept constant thereafter. The prescribed fire load accounted for the ventilation of the real structure, with a speed of 2.5 m/s, automatic sprinkler devices and a heat release rate of 5 MW [40], in accordance with the Chinese Standard for Metro Design [47]. Two controlled heat sources of the furnace were used to produce the thermal loading. The heat was transferred to the tested structure by means of natural (unforced) convection.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **P1** & **P2** & **P3** \\ \hline Load (kN) & 192.0 & 151.2 & 120.0 \\ \hline \end{tabular}
\end{table}
Table 3: Applied mechanical loads.  
Figure 6: Support and loading conditions of the tested structure, adapted from [40].
## 2 Methodology
### INS and RTK Fusion
This adaptive fusion method ensures that the overall positioning solution remains stable and accurate, even in challenging environments, where GNSS alone would struggle to provide reliable results.
# Development and Biomechanical Evaluation of a Back Exoskeleton to Support Lifting Tasks
## 2 Exoskeleton Functionality and Design
### Exoskeleton Design  
The active back exoskeleton combines soft and rigid materials, with all components easily adaptable to different users. Soft textiles serve as human-system interfaces, ensuring high wearing comfort without restricting passive joint movement. The exoskeleton supports the wearer during lifting tasks by generating a backward pulling force on the upper body,  
Figure 1: (**a**) Segment weights and distances from the lumbosacral joint [20] and (**b**) illustration of the concept.  
which is applied using an electric actuator. Force transmission follows a path from the actuator, located at the back of the shoulder vest, to the back of the thigh via a steel cable. Two pulleys on each side guide the cable and maintain a consistent lever arm relative to the body. The total mass of the exoskeleton is currently 3.3 kg (without a special focus on lightweight design).  
The exoskeleton consists of four main components: (1) a supporting and force-transmitting structure, (2) an actuation system, (3) a sensory system, and (4) a control unit. The system is illustrated in Figure 2 with each component described in the following sections.
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Cryptography Authentication  
Cryptography can be utilized to secure data during transmission, storage, and computation. Cryptography is a technique that utilizes a key and algorithm to convert plain text (readable text) to ciphertext (unreadable text) [13]. There are two types of cryptography used: symmetric and asymmetric. The same key is utilized in symmetric key cryptography to encrypt and decrypt. It consists of five elements: plaintext, encryption algorithm, secret key, ciphertext, decryption algorithm. However, the asymmetric key cryptography uses different keys for encryption and decryption, referred as a public and private keys and known as public key infrastructure (PKI).
# Inertial Parameter Identification in Robotics: A Survey
## 8 Introducing the BIRDy Matlab Toolbox: A Benchmark for Identification of Robot Dynamics
### Implementation Details
#### 9.3.4. DIDIM Implementation  
As with the IDIM-IV, the simulation data are obtained by Euler (RK1) integration of the closed-loop DDM at a rate of 5 kHz. The following three stop criteria were implemented within the DIDIM method:  
* \(\left(\left\|\rho_{DIDIM}^{i+1}\right\|_{2}-\left\|\rho_{DIDIM}^{i}\right\|_{2} \right)/\left\|\rho_{DIDIM}^{i}\right\|_{2}<\text{tol}_{1}\) where \(\rho_{DIDIM}^{i}\) denotes the torque error at iteration i, defined as \(\rho_{DIDIM}^{i}=\boldsymbol{y}_{\tau}-\boldsymbol{W}_{s}^{(i-1)}\boldsymbol{ \hat{\beta}}_{DIDIM}^{i}\). Please note that unlike IDIM-IV, we here make use of the simulated joint positions to compute the observation matrix;
* \(\underset{j}{\max}\Big{|}\Big{(}\boldsymbol{\hat{\beta}}_{DIDIM}^{i+1}(j)- \boldsymbol{\hat{\beta}}_{DIDIM}^{i}(j)\Big{)}/\boldsymbol{\hat{\beta}}_{DIDIM }^{i}(j)\Big{|}<\text{tol}_{2}\), \(\forall\boldsymbol{\hat{\beta}}_{DIDIM}^{i}(j)\
eq 0\) with \(j\) the parameter index and \(i\) the iteration number.
* maximum number of iterations: \(i_{max}=10\).  
where the values of the function tolerance \(\text{tol}_{1}\) and step tolerance \(\text{tol}_{2}\) are set according to the guidelines given in ([PERSON] et al., 2018), namely \(\text{tol}_{1}=2.5\%\) and \(\text{tol}_{2}=2.5\%\).
## 2 Data and Methods
### Rotated EOF  
The 13-year, gridded, cloud-free SIF data allow us to investigate the temporal and spatial variability of SIF/GPP at the global scale by decomposing these multidimensional fields into empirical orthogonal functions (EOFs, [31; 32; 33; 34; 35; 36; 37]). We focused on 65\({}^{\circ}\) S to 65\({}^{\circ}\) N to concentrate the analysis of SIF/GPP variations in the non-polar regions. By organizing the SIF data in an M \(\times\) N matrix, where M and N represent the spatial (187,200 grid points) and temporal (146 months) elements, respectively, we can represent the SIF data matrix, \(s(x,t)\) by  
\[s(x,t)=\sum_{k=1}^{K}a_{k}(t)F_{k}(x) \tag{1}\]where \(x\) represents the spatial coordinates (longitude, latitude), and \(t\) represents the time coordinate. \(a_{k}\) is the temporal evolution functions (or the principal components), and \(F_{k}\) is the spatial eigenfunctions (or the EOF) for each mode, respectively.  
Prior to the EOF analysis, temporal mean and trend were removed from the SIF fields. Because SIF data contain strong seasonal cycles (Figure 1) that are not a focus of this study, we also removed the seasonal cycle by subtracting the long-term monthly mean SIF fields from each corresponding month in the 17-year time series. The resulting detrended, demeaned, and deseasoned data matrix constitutes the input for the EOF analysis. Solving EOF problem then entails the application of singular value decomposition (SVD) to break the covariance of SIF data into three matrices  
\[covariance(s)=U*D*V^{t} \tag{2}\]  
where \(U\) and \(V\) are orthonormal and \(D\) is diagonal. Then EOFs \(F_{k}(x)=V\), and the principal component \(a_{k}(t)=U*D\).  
Because of its orthogonality constraint, EOF analysis is known to have a tendency to produce unphysical modes, especially for data covering a large domain. Previous studies have shown that the drawbacks of EOF analysis can be alleviated by rotated EOF (REOF) analysis to better pick up localized patterns (e.g., [38]). We took this approach by performing an orthogonal EOF rotation known as varimax rotation ([39]). This procedure reduces the variances in the projection of the data, thereby putting the EOF basis closer to the actual data variability and increasing physical interpretability.
# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 3 Methodology
### Bounding Box Generation
In column (a), we define the labeled damages in our dataset; in column (b), we visualize the damages generated by all the models, which are grouped into clusters according to their IoU value with respect to the bounding boxes of other damages; and in column (c), we show the resulting clusters after performing the box fusion process and applying the voting system. Focusing on the first vehicle in Figure 3, we observe that five possible clusters can be generated. In column (b), the clusters with the highest probability of being chosen as valid predictions are \(c2\), \(c3\), and \(c4\). This is based on the number of matching predictions. However, in order to decrease the number of false positives, the minimum number of matching predictions in the cluster to be accepted as a valid prediction is taken into account. In the second vehicle, clusters \(c1\) and \(c2\) stand out for having the highest number of intercepted predictions, but cluster \(c3\), although very close to \(c2\), is not considered a candidate for merging due to the IoU value between it and \(c3\). This allows us to generate predictions that adapt to small damages and also to avoid generating predictions with large sizes. For the third vehicle, four clusters stand out, of which clusters \(c3\) and \(c5\) have more candidates or votes. Cluster \(c3\) has a higher probability of being considered as a prediction compared to the other clusters, since they have few predictions that intercept with each other. Finally, cluster \(c3\) is the only one that remains as a valid prediction, and the other clusters are eliminated, since most of them are predictions generated by one or two models. For the last vehicle, when the fusion algorithm defines the clusters, one of the prediction boxes seems to be assigned to cluster \(c1\). Nonetheless, this prediction box is associated with cluster \(c2\) because it shares a better IoU. In this way, we achieve variability in the generation of predictions, which allows us to cover multiple types of damage.
## 2 Results and Discussion
### Assessment of Cytotoxicityicity of Spilanthol in BV-2 microglial Cells  
In silico findings suggest that spilanthol attenuates neurodegeneration mainly by attenuating neuroinflammation inflammation via suppression of the TLR4 signaling pathway. Subsequently, we conducted in vitro studies to validate the in silico findings. The BV-2 cell line was employed for in vitro evaluation, as microglia predominantly contribute to the chronic inflammation linked with neurodegenerative diseases [5; 6]. Initially, the maximum non-cytotoxic concentration of spilanthol was determined, as non-cytotoxic concentrations had to be employed to evaluate the anti-neuroinflammatory efficacy. The cytotoxicity profile of spilanthol on BV-2 cells was assessed using the MTT assay, revealing that concentrations of spilanthol up to 100 uM did not exhibit a significant impact on the viability of BV-2 cells (Figure 5). Hence, 100 uM was determined as the maximum non-cytotoxic concentration. Spilanthol concentrations ranging from 25 to 100 uM were used in subsequent experiments to evaluate its anti-inflammatory properties while eliminating the confounding factors of cytotoxicity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**IKK\(\alpha\)**} & \multicolumn{2}{c}{**NF-kB1**} \\ \hline
\begin{tabular}{c} **Component** \\ **(avg \(\pm\) SD)** \\ \end{tabular} & **CID16048085** & **Spilanthol** & **CID5280961** & **Spilanthol** \\ \hline \(\Delta\)**EvdW** & \(-\)8.64 \(\pm\) 16.31 & \(-\)34.48 \(\pm\) 2.62 & \(-\)1.24 \(\pm\) 4.94 & \(-\)19.80 \(\pm\) 16.24 \\ \hline \(\Delta\)**Ec** & \(-\)1.37 \(\pm\) 3.20 & \(-\)2.87 \(\pm\) 2.67 & \(-\)0.44 \(\pm\) 1.75 & \(-\)1.20 \(\pm\) 1.43 \\ \hline \(\gamma\)**AMSA** & 2.82 \(\pm\) 5.50 & 7.63 \(\pm\) 2.15 & 0.37 \(\pm\) 1.49 & 3.54 \(\pm\) 3.03 \\ \hline \(\Delta\)**GR** & \(-\)1.57 \(\pm\) 2.96 & \(-\)6.15 \(\pm\) 0.37 & \(-\)0.20 \(\pm\) 0.78 & \(-\)3.27 \(\pm\) 2.68 \\ \hline \(\alpha\) & \multicolumn{4}{c}{\(-\)2.89} \\ \hline \(\alpha\) & \multicolumn{4}{c}{\(0.104758\)} \\ \hline \({}^{a}\)**AGbind** & \(-\)3.81 \(\pm\) 1.74 & \(-\)6.65 \(\pm\) 0.33 & \(-\)3.05 \(\pm\) 0.62 & \(-\)5.06 \(\pm\) 1.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Binding free energies (kcal/mol), including SD, of IKK\(\alpha\) and NF-kB1, complexed with reference ligand and spilanthol and evaluated using the SIE method (\(n\) = 100, avg, average; and SD, standard deviation).  
Figure 5: Cytotoxicity profile of spilanthol on BV-2 cells; cell viability was determined by MTT assay. Data show the mean \(\pm\) SD, \(n\) = 3 independent experiments. ***\(p\) < 0.001 compared with the negative control group; ANOVA followed by [PERSON]’s post hoc test.
## 3 Results and Discussion
### Thermal-Energy Performance of BIM with Pitched Roof  
Figures 11-13 reveal the direct, diffused and global solar radiation fluxes that the BIM were subjected to throughout the Typical Meteorological Year (TMY) at the location of Shah Alam, Malaysia, as previously adopted in [PERSON] et al. (2018). The highest fluxes recorded throughout the TMY for the direct, diffused, and global solar radiation were 965 W/m\({}^{2}\), 540 W/m\({}^{2}\), and 1120 W/m\({}^{2}\), respectively.  
Figure 9: Flexural strength (\(F_{f}\)) and thermal conductivity (\(k\)) for the composite insulation samples.  
Figure 11: Direct solar radiation flux throughout the Typical Meteorological Year (TMY) at the location of the BIM.  
Figure 10: Flexural strength (\(F_{\beta}\)) and tensile strength (\(F_{t}\)) for the composite insulation materials.  
Figure 14 reveals the annual cooling load and energy savings of the indoor environment of the BIM at varying roof configurations of \(C\), \(R\), \(R\)100, \(R\)200, and \(R\)300. The annual cooling load of \(C\) was 2.6671 MWh, and it was marginally reduced to 2.6488 MWh when the 20-mm silica-aerogel-incorporated composite cement paste was applied as render on the roof-top surface to form \(R\), hence generating an annual energy savings of 0.6909%. Furthermore, when a 100-mm thick silica-aerogel-incorporated composite insulation board  
Figure 12: Diffused solar radiation flux throughout the TMY at the location of the BIM.  
Figure 13: Global solar radiation flux throughout the TMY at the location of the BIM.  
was installed below the roof tiles of \(R\) to form \(R100\), a sharp drop in the annual cooling load to 2.3903 MWh, with an annual energy savings of 11.5801% relative to \(C\), was observed. Additional increments to the thickness of the insulation board to 200 mm, and then 300 mm, resulted in further reductions in the annual cooling load to 2.3491 MWh, and then 2.3319 MWh, with annual energy savings of, relative to \(C\), 13.5371%, and then 14.3745%, respectively. The highest gradients, based on their absolute values, in the decline in the annual cooling load, and the rise in the annual energy savings, were observed when the 100-mm thick insulation board was installed to \(R\). Therefore, the application of the 20-mm cement render, coupled with the installation of the 100-mm thick insulation board, is recommended as the optimum roof configuration.