esa-sceva/satcom-chunk-collection huggingface dataset:
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 3 Methods  
In this section we describe the different models applied to the ICESat-2 data to assess the performance of ICESat-2 by comparing the significant wave heights from the models to the significant wave heights obtained from SIRAL (the radar altimeter carried by CryoSat-2).
# Lifting Activity Assessment Using Kinematic Features and Neural Networks
## 3 Results  
### Kinematic Feature Analysis  
Figure 2 shows the mean and standard deviation for each lifting condition of each extracted JSM (Figure 2). As reported in [34], the repeated measures ANOVA revealed a significant effect of the lifting condition on LEC. Furthermore, statistically significant effects were also detected for J\({}_{\text{trunk}}\) (df = 2, F = 7.12, \(p\) = 0.05), J\({}_{\text{elbow}}\) (df = 2, F = 9.39, \(p\) = 0.002) and J\({}_{\text{knee}}\) (df = 2, F = 15.62, \(p\) = 0.001). Post hoc analysis showed significant differences (all \(p\)\(<\) 0.001) between each pair of lifting conditions for LEC\({}_{\text{p}}\) and LEC\({}_{\text{M}}\) for all the CoMs considered and also for LEC\({}_{\text{k\_Upp+L}}\) (see [29]). Furthermore, as regard LEC\({}_{\text{k\_Tot}}\), significant differences were found between each pair of lifting conditions (LI = 1 vs. LI = 2: \(p\) = 0.001; LI = 1 vs. LI = 3: \(p\) = 0.005; LI = 2 vs. LI = 3: \(p\) = 0.021; see [34]). As regards J, the following significant differences were found (see Figure 2):  
* LI = 1 vs. LI = 2 and LI = vs. LI = 3 (\(p\) < 0.05) for J\({}_{\text{trunk}}\)
* LI = 1 vs. LI = 3 and LI = 2 vs. LI = 3 (\(p\) < 0.05) for J\({}_{\text{elbow}}\)
* LI = 1 vs. LI = 2 and LI = 2 vs. LI = 3 (\(p\) < 0.05); LI = 1 vs. LI = 3 (\(p\) < 0.001) for J\({}_{\text{knee}}\)
## 2 Materials and Methods
### Agricultural Plastic Surfaces (APS) Analysis  
The present study stems from the need to quantify the surfaces (expressed in hectares) of agricultural land covered by plastic. Indeed, this is the starting point in all studies dealing with spatial quantification of APW [22]. The data currently present in Italy do not allow a precise and accurate quantification of plastics used in agriculture. Officially, there is no precise quantification of how much plastic is used by different farms and, especially, where it is spatially located and chronologically present. In addition, the official censuses [23], as well as providing data on a large scale (at most at the level of the municipality), provide data every ten years and are therefore too large for constant monitoring of the situation in the territories. Knowing data at the district or municipal level is useful at the general planning level, but for more specific actions, such as the location of a strategic plastics collection center, it is necessary to increase the detail of the identification as much as possible.  
There are many techniques for quantifying agricultural plastic surfaces (APSs), but one of the most useful is spatial-data management provided by Geographic Information Systems (GIS). In particular, the use of satellite image classification exclusively or integrated with digital photointerpretation [24; 25; 26] is increasingly becoming the main component of rural land studies at the local level as well.  
In this work, it was decided to use this integrated methodology to quantify the actual area covered by plastics as also proposed in other studies [18]. The first phase, through the classification of satellite images by means of specific spectral indices, enabled realization of a first quick and expeditious detection of the plastics, which were subsequently modified and integrated on the basis of orthophotos.  
The year 2017 was chosen as the reference year because it is the year in which very-high-resolution orthophotos are freely available for the Basilicata Region [27]. All operations were performed with the open-source software QGIS.
# Fake Base Station Detection and Link Routing Defense +
## 8 Future Directions Discussion
### Generalizability Across Different Channels and Scenarios  
We plan to implement our scheme and evaluate its effectiveness and performances in different channel scenarios, including the cases when the surrounding base stations vary, the handover case, and the high-fading cases (e.g., urban environment with moving objects between the transmitter and the receiver). Section 7.3 varies the channel state by varying the transmission power and thus the received signal strength, but there can be even more channel scenarios which change the surrounding base stations beyond just the transmitter-receiver relationship.
# Homogenization of Ancient Masonry Buildings: A Case Study
## 6 LTW and SLTW Homogenized Models Identification
### Out-of-Plane (Shear) Behavior  
In order to evaluate the out-of-plane behavior, each couple of transverse wall is modeled as a Timoshenko beam having an axis-line along \(y\). Its bending and shear stiffness coefficients are piece-wise uniform along each story, and are evaluated as \(2E_{m}I_{i}\) and \(2G_{m}A_{ti}\) (for \(i=1,...,5\)), respectively, where principal inertia moment \(I_{i}\) and area \(A_{ti}\) include the thickness of the two external and internal longitudinal walls to the cross-section of each transverse wall. However, since the beams are included or attached to the pure shear plates of the homogenized model, they are forced to behave as pure shear beams as well. For this reason, a \"reduced\" equivalent shear stiffness \(GA_{i}^{*}\) (for \(i=1,...,5\)), also including the bending of the Timoshenko beams, is identified. This is made by a complementary energy equivalence between the Timoshenko beam (bending and shear stiffness coefficients \(2E_{m}I_{i}\), \(2G_{m}A_{ti}\), respectively) and a pure shear beam (shear stiffness \(GA_{i}^{*}\)), of length \(a_{yi}\), fixed at the base and subjected to a horizontal load at the top (details are given in Appendix A). The equivalent shear stiffness is found to be:  
\[GA_{i}^{*}=\dfrac{6E_{m}I_{i}G_{m}A_{ti}}{3E_{m}I_{i}+G_{m}A_{ti}a_{yi}^{2}} \tag{7}\]  
In defining the LTW homogenized model, the contribution of the distributed walls is finally included in the equivalent plates as \(S_{yi}=\dfrac{GA_{i}^{*}}{a_{x}}\) (reported in Table 3).  
Then, the SLTW homogenized model is defined, by stiffening the LTW model by localized beams, integral with the plate and having shear stiffness \(GA_{i}^{*}\) reported in Table 3 (the contribution of the partitions, incorporated in the plate, is subtracted).  
Note that the shear stiffness of the LTW model is much higher than that of the LW model.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 5 Experimental Results and Discussion  
An image mosaic was created using a program written in MATLAB. The computer has thirty-two Intel (R) Xeon (R) CPU (2.6 GHz) cores and 512 GB RAM. In this section, we will use real GF-3 ScanSAR image data to perform a series of contrast experiments to compare the traditional feature-based mosaic method with the method we proposed and analyze their results and performances in terms of various aspects.
## 5 Limits of Current Thermal IR Imaging for HRI Applications  
Studies reviewed in this section revealed thermal IR imaging ability to monitor physiological signs and affective states. Although they have sometimes shown incongruent results (e.g., nose tip temperature did not significantly change along the valence dimension of the emotions in [PERSON] et al. [63], whilst it did change in [PERSON] et al. [53]), these findings open exciting prospects for affective computing. One of the causes of inconsistency could be that a single discrete metric is maybe not always sufficient, since it could be susceptible to the complex physiological mechanisms [76]. In addition, there could be interaction effects between affective states. Nonetheless, all the affective states analyzed have the capability to induce ROI temperature variations. However, an important consideration is that all these studies were conducted using high-end thermal camera and performed in controlled laboratory settings. The thermal systems mostly used in literature are FLIR (Wilsonville, OR, USA) A655 sc with \(640\times 480\) spatial resolution, a 50 Hz sampling rate, and \(<\)0.03 \({}^{\circ}\)C  
Figure 4: Child facial temperature during the mishap condition. Adapted from [68].  
thermal sensitivity and a FLIR A325 sc with \(320\times 240\) spatial resolution, 60 Hz sampling rate, and \(<\)0.05 \({}^{\circ}\)C thermal sensitivity. In addition, all the observations reported were made in a climate-controlled room according to the International Academy of Thermology (IACT) guidelines [77]. In fact, IACT guidelines indicated that when performing a thermal IR imaging measurement, it is mandatory to control the temperature and humidity of the experimental room. They suggested a temperature range of 18-23 \({}^{\circ}\)C and a controlled humidity range between 40% and 70%. No direct ventilation on the subject and no direct sunlight (no windows or with curtains or blinds) is also recommended during the experimental measures. In conclusion, by analyzing the studies reported in this section, two main constraints were identified that are not suitable for HRI applications. Those are (1) the use of high-end and sized thermal imaging systems and (2) the circumstances in which the studies were conducted i.e., in restricted laboratory settings. On the other hand, HRI applications require daily life scenarios, eventually suitable for outdoor use, and technology embedded in commercial social robots, i.e., low-cost miniaturized sensors. In Sections 6 and 7, those limits are addressed in order to highlight the new improvements developed in recent studies. A special emphasis was placed on these two sections as they deal with crucial aspects for the use of thermal IR imaging in the HRI field. Finally, the last section focuses on the actual state of the art of thermal IR imaging-based affective computing applications.
## 4 Experiments
### Experimental Setup
#### 4.1.2 Performance Evaluation Measurement and Method  
Performance evaluation was compared by measuring the accuracy, precision, recall, and f-score. Accuracy represents the ratio of correctly recognized numbers among all N pieces of data. Precision is the ratio of the prediction and the actual value of the positive data among the positive prediction data and is defined in Equation (6). Recall is the ratio of prediction and actual positive data among actual positive data and is defined as Equation (7). The final f-score was calculated as the harmonic average of precision and recall, as shown in Equation (8). In addition, various model evaluations were conducted with 100 epochs and a batch size of 16. Adam was used as the optimizer, and the learning rate was set to 0.001.  
\[Precision=\frac{True\ Positive}{True\ Positive+False\ Positive} \tag{6}\]  
\[Recall=\frac{True\ Positive}{True\ Positive+False\ Negative} \tag{7}\]  
\[F-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{8}\]
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Technical Considerations
#### 3.3.2 Smart Contracts
Model inspection, which is an automated formal verification methodology that applies to systems that may be described using a finite-state model, deploys model checking software, such as NuSMV, to verify the results. Model inspection is most commonly used with Solidity language, although it may also be used to validate contracts written in other languages. Smart contracts are irreversible and aid in the establishment of trust between contractual parties. However, even in the event of flaws, vulnerabilities, or new business-logic specifications, a smart contract's code is typically not upgradeable. The modified code of the smart contract is usually deployed in a new case with a new contract address, which might cause difficulties [57]. One technique to upgrade a smart contract is to delegate calls from the proxy contract to the new logic contract. The data are held by the proxy contract, while the logic contract performs the new logic. For each change, the logic contract identity is restructured in the proxy contract [57].
# Queue Spillover Management in a Connected Vehicle Environment
## 3 Intersection Signal Control Modeling
### Signal Optimization  
The current queuing vehicles through the intersection of time \(t_{0}^{\prime}\) (at this point, \(L_{0}>L_{c}\)) compared with the current phase of the maximum green time; then get the current phase of the optimal control of green time as below:  
\[t_{r}=\left\{\begin{array}{cc}t_{0}^{\prime}&t_{0}^{\prime}<t_{\text{max}}\\ t_{\text{max}}&t_{0}^{\prime}\geq t_{\text{max}}\end{array}\right. \tag{17}\]  
where, \(t_{r}\) is the current optimal control green time.  
When the \(t_{r}\) equals to \(t_{0}^{\prime}\), the current phase green time requires an extension time of \(t_{0}^{\prime}-t_{g}\), because \(t_{r}\) does not reach the maximum green time, so the time of the other direct left-turn phase decreases respectively in \(t_{1}\) and \(t_{2}\), respectively, i.e., \(k_{1}\cdot t_{1},k_{2}\cdot t_{2}\). The proportional coefficients \(k_{1}\), \(k_{2}\) are taken according to actual vehicle queuing conditions.  
When the \(t_{r}\) equals to \(t_{\text{max}}\), the current phase green light time needs to be extended by \(t_{\text{max}}-t_{g}\), and the other straight-line and left-turn phase reduction times are \(t_{1}\) and \(t_{2}\), respectively.
## 5 Increasing Robustness with Location Verification and Computation  
The use of localization services in pervasive devices is increasing. Many technologies have been built upon an underlying assumption of available location information. These applications may have a need to securely verify the locations reported by pervasive devices, as such pervasive devices may be malicious or simply incorrect in reporting their location. Inaccuracies, whether malicious or accidental, are possible without regard to who or what is performing the localization. When a device is responsible for calculating its own location using trusted, externally provided information (as in _self-derived_ localization), the device may calculate and report a malicious or incorrect location. Likewise, when a device has its location calculated by trusted infrastructure, and this location is then reported back to the device (as in _network-derived_ localization), the device might maliciously alter information used by the network in calculating its location. For example, a malicious device can alter its signal characteristics.  
The goal of _location verification_ is to verify that location information reported by potentially malicious devices is accurate to some degree; service designers should view location verification as an additional, security-enhancing step in the process of localization. Location verification is a distinct problem from _secure localization_; the latter's goal is to allow devices to localize themselves in a hostile environment [61]. We do not consider the problem of secure localization in this paper.
## 4 Discussion on Regional Dust Events
### Dust Outbreaks over North America
High concentrations are also observed at this station on 19 and 20 March confirming the extension of the plume far from the emission areas in US. The comparison of CALIPSO data identified as \"dusty marine\" in Figure S1 in Supplementary Materials (NAM5 trajectory) clearly confirms the dust load within the boundary layer simulated by CHIMERE over the Pacific Ocean between 10\({}^{\circ}\) and 30\({}^{\circ}\) N, dust is mixed with sea salts (\"dusty marine\" category for CALIPSO products). The three events can also be observed thanks to the MODIS data despite the presence of clouds that limits the detection of the full emission areas. As depicted in Figure 12, MODIS data are compared with the AOD computed with CHIMERE at the same wavelength (555 nm), the pattern of over the emission areas in the southeast of Colorado on 11 March and two high concentrations area on 18 March over an area located in the intersection of Colorado, New Mexico and Kansas states and the other one over Texas. On 16 March, the pattern observed by MODIS is more difficult to reproduce, nevertheless the small structure over Texas also exists in the model outputs with a lower intensity, which is confirmed by the PM\({}_{2.5}\) observations in this region matching the predicted concentrations. Figure 10: Hourly time series of simulated and observed PM\({}_{2.5}\) concentrations at six stations of the US air quality network influenced by the three dust episodes: Seiling Municipal Airport (36.16\({}^{\circ}\) N-98.93\({}^{\circ}\) W), Big Bend National Park (29.30\({}^{\circ}\) N–103.18\({}^{\circ}\) W), Dallas Hinton (32.82\({}^{\circ}\) N–96.86\({}^{\circ}\) W), El Paso Utep (31.27\({}^{\circ}\) N–106.50\({}^{\circ}\) W), Odessa Gonzales (31.87\({}^{\circ}\) N–102.33\({}^{\circ}\) W) and Amarillo A and M (35.20\({}^{\circ}\) N–101.91\({}^{\circ}\) W). The orange shaded area represents the dust concentrations simulated by CHIMERE for the corresponding PM matrix. Figure 11: Modeled daily mean dust concentrations in the fine fraction of PM with CHIMERE versus observational data at selected IMPROVE stations (below 2000 m a.s.l.) from 10 to 21 March 2014. The circles represent the PM\({}_{2.5}\) observational data. Manned “dust” SYNOP observations are displayed at WMO stations (”star” symbol), a symbol is plotted if at least one observation is reported for a given day. Figure 12: MODIS AOD 555 nm (Merged DT/DB Aerosol Optical Depth Land and Ocean product) on the left column compared to CHIMERE AOD (second column) for the same period of the satellite passes (average values from 1800 to 2200 UTC on 11, 16 and 18 March from the top to bottom to fit with the satellite ascending and descending passes). Dust concentrations in the fine fraction of particles and PM\({}_{25}\) concentrations for the same periods are, respectively, displayed in Column 3 and 4. The circles represent the ground observational data for PM\({}_{25}\).
## 2 The Proposed Design Configuration  
Figure 2 displays the design of the proposed four-band shared-aperture antenna. The main circuit board is made of a single layer FRB5 substrate with a thickness of 0.508 mm and a dielectric constant of 2.5. The total footprint area is approximately 6 \(\times\) 10 mm\({}^{2}\). The proposed antenna is composed of four QMSIW antennas, shown in Figure 2a. Each QMSIW  
antenna is fed through a 50 Ohm coaxial connector, as shown in Figure 2b. Each QMSIW has its fundamental resonant mode frequency around 28 GHz, which is controlled by the open-end wall in each. In order to add two extra MMw frequency bands of operation, each QMSIW cavity is loaded by two L-shaped slots etched on its top surface (Slot 1 and Slot 2). These slots and other main components of the proposed antenna are labeled in Figure 2c. Slot 1 length controls the radiation around 38 GHz, while Slot 2 length contributes to the radiation around 0.13 THz. As can be clearly seen in Figure 2c, the four-QMSIW MIMO antennas are arranged orthogonally to achieve patterns and polarization diversities.  
Finally, the proposed shared-aperture antenna is the result of deploying the structure reuse concept in the 5.5 GHz Mw frequency band. One additional Port (Port 5 in Figure 2a) is used for function structure reuse. For the purpose of enhancing and tuning radiation at 5.5 GHz two modifications are made to the entire proposed structure: (1) A metal strip (referred to as a stub in Figure 2c) connects the top surfaces of QMSIW pairs. (2) A small gap is created in the upper part between the top two pair cavities. Accordingly, the Port 5 antenna at 5.5 GHz is simply a small open-loop antenna, where all SIW structures  
Figure 2: Configuration of the proposed QMSIW shared-aperture MIMO antenna (**a**) top view, (**b**) bottom view, (**c**) structure with all details and labels, and (**d**) structure with all of the dimensions. All dimensions are in mm.  
exhibit conventional strips. Figure 2d shows the detailed dimensions of the proposed antenna structure.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 3 Results and Discussion
### Parameter Analysis  
The input parameters for CSVM include p, \(s\), \(\lambda_{1}\), Pt, \(\lambda_{2}\) and k. The number of endmembers is denoted by p, which can be obtained by endmember number estimation algorithms. The grid step s, the weighting factor \(\lambda_{1}\) and the proportion for representative endmember  
Figure 4: (**a**) Pseudo-color image of Cuprite; (**b**) spectral signatures of endmembers in Cuprite.  
Figure 3: (**a**) Pseudo-color image of Jasper Ridge; (**b**) spectral signatures of endmembers in Jasper Ridge.  
extraction Pt will be set through the following spatial clustering analysis. The weighting factor \(\lambda_{2}\) and the number of clusters k will be determined in the following spectral clustering analysis.  
Indexes are first introduced to provide a quantitative assessment for the clustering performance, including Scatt, AvgPI and SvdPI corresponding to the following definitions.  
\[\text{Scatt}= \frac{\left\|\sigma(\mathbf{v}_{\text{i}})\right\|}{\left\|\sigma( \mathbf{X})\right\|}, \tag{11}\]  
where \(\sigma(\mathbf{X})\) is the variance of HSI and \(\sigma(\mathbf{v}_{\text{i}})\) is the variance of the ith partition.  
\[\text{AvgPI}=\frac{\sum_{\text{i=1}}^{\text{m}}\text{dist}(\mathbf{x}_{\text{ i}},\overline{\mathbf{x}})}{\text{m}}, \tag{12}\]  
where dist is the correlation coefficient between \(\mathbf{x}_{\text{i}}\) and the average vector \(\overline{\mathbf{x}}\) in the ith partition, and m is the number of pixels contained in this partition.  
\[\text{SvdPI}= \frac{\text{L}_{\text{1}}}{\sum_{\text{i=1}}^{\text{m}}\text{L}_{ \text{j}}}, \tag{13}\]  
where Lj is the jth singular value after singular value decomposition.  
The smaller the value of Scatt and the larger the value of AvgPI and SvdPI, the better the clustering results.