esa-sceva/satcom-chunk-collection huggingface dataset:
_Remote Sens._**2012**, \(4\), 303-326; doi:10.3390/rs4010303  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
Division of Science & Environmental Policy, California State University, Monterey Bay, Seaside, CA 93955, USA; E-Mails: [EMAIL_ADDRESS] (W.W.); [EMAIL_ADDRESS] (C.M.)  
NASA Ames Research Center, Moffett Field, CA 94040, USA;  
E-Mails: [EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (R.R.N.)  
Department of Watershed Sciences, Utah State University, Logan, UT 84322, USA;  
E-Mail: [EMAIL_ADDRESS]  
Bay Area Environmental Research Institute, Sonoma, CA 95476, USA;  
National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8569, Japan; E-Mail: [EMAIL_ADDRESS],  
Graduate School of Agriculture, Hokkaido University, Sapporo, Hokkaido 060-8589, Japan;  
E-Mail: [EMAIL_ADDRESS]  
Department of Geography and Environment, Boston University, Boston, MA 02215, USA;  
E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-650-604-6446; Fax: +1-650-604-6569.  
_Received: 15 December 2011; in revised form: 12 January 2012 / Accepted: 12 January 2012 / Published: 23 January 2012_  
**Abstract:** Algorithms that use remotely-sensed vegetation indices to estimate gross primary production (GPP), a key component of the global carbon cycle, have gained a lot of popularity in the past decade. Yet despite the amount of research on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of different vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) in capturing the seasonal and the annual variability of GPP estimates from an optimal network of 21 FLUXNET forest towers sites. The tested indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiationabsorbed by plant canopies (FPAR). Our results indicated that single vegetation indices captured 50-80% of the variability of tower-estimated GPP, but no one index performed universally well in all situations. In particular, EVI outperformed the other MODIS products in tracking seasonal variations in tower-estimated GPP, but annual mean MODIS LAI was the best estimator of the spatial distribution of annual flux-tower GPP (GPP = 615 \(\times\) LAI - 376, where GPP is in g C/m\({}^{2}\)/year). This simple algorithm rehabilitated earlier approaches linking ground measurements of LAI to flux-tower estimates of GPP and produced annual GPP estimates comparable to the MODIS 17 GPP product. As such, remote sensing-based estimates of GPP continue to offer a useful alternative to estimates from biophysical models, and the choice of the most appropriate approach depends on whether the estimates are required at annual or sub-annual temporal resolution.  
Gpp; LAI; EVI; NDVI; MODIS +
Footnote †: journal: Remote Sens.
## 5 Discussion and Use Cases
### Use Case (Hypothesis)  
As we noticed, encrypted network traffic analysis can help in proving evidence of device forensics. For example, an investigator may correlate the evidence collected from device and network packets to infer or validate the results. Therefore, the proposed strategy may help to analyze the real-time/offline application behavior (from encrypted network packets) which can be useful for the investigator.  
Use Case: For example, if sensitive official digital media was leaked in the organization using Signal app, to trace the activity, firewall logs can help investigate the activities assuming that the logs collection/retention policy is implemented in a way that an investigator can obtain information about devices, source IP, destination IP, payload sizes, services, protocols, and timestamps. Investigators can use the study of the behavioral analysis of the application to interpret the logs. Therefore, the proposed strategy for analysis of encrypted traffic of apps (such as Signal) can support in deducing the activities and involved users. For example:  
1. If documents were leaked and received by users on 5 th April around some specific time window such as 21:20 PM to 23:00;
2. Traffic dumps for the duration of interest were taken. Figure 22 depicts that the Signal app communicated with its DNS for that specific duration;
3. Identify and analyze the traffic for particular activity as shown in Figure 23 that depicts media message patterns were observed in a particular timeline with a specific source IP;
4. Finally, the investigator identified the source IP address from DHCP list;
5. A temporal analysis of a particular incident can be seen in Table 8.  
As in our lab, we had a controlled environment to study and analyze the behavior of the application ports and IP ranges. However, assumptions are as follows:  
1. Controlled environment, admin has control over network and firewall;
2. Logs are being maintained according to the specific time duration;
3. After an incident, investigators can obtain the firewall logs to analyze the performed activities.
## 2 Methods
### Estimation of the Uncertainties  
To evaluate the uncertainties relative to the parameters' estimation, we use a widely applied method [3; 7; 8; 16; 19] based on asymptotic theory [20], sometimes called profile-likelihood confidence region estimation [21]. It states that, if we want to estimate the confidence region of the parameters (in our case, of the tapered Gutenberg-Richter), we have to \"cut\" the log-likelihood function at a fixed threshold, and then look at the contour plot of this cut. Different thresholds correspond to different confidence intervals. For example, to obtain a 95% confidence region, we have to look at the \(LL=LL_{max}-2.995\) threshold, where \(LL_{max}\) is the maximum of the log-likelihood [8]. Hereinafter, the confidence region that describes the uncertainties on the parameters' estimation will be represented by the contour plot of the selected threshold (see Figure 4 for an illustrative example).  
This procedure is adopted to evaluate the goodness of the agreement between input and estimated parameters for the thousands of synthetic catalogs discussed in the previous paragraph. In particular, we verify that the input parameters are enclosed in the 95% confidence region for the estimated parameters about 95% of the simulations, obtaining a very good agreement. The results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Simulated** \\ **Events** \\ \end{tabular} & \begin{tabular}{c} **Magnitude of** \\ **Completeness** \\ **Thresholds** \\ \end{tabular} & \begin{tabular}{c} **Percentage of** \\ **Events for** \\ **Each** \\ **Completeness** \\ \end{tabular} & \begin{tabular}{c} **Mean of the** \\ **Simulations** \\ \end{tabular} & \begin{tabular}{c} **Mean of the** \\ **Estimated** \(\beta\)** \\ **Simulations** \\ \end{tabular} & \
\begin{tabular}{c} **Mean of the** \\ **Estimated** \\ **C\({}_{M}\)** \\ **C\({}_{M}\)** \\ \end{tabular} &
\begin{tabular}{c} **Mean of the** \\ **Confidence Regions** \\ **Containing the Values** \\ **Used in the Simulations** \\ \end{tabular} \\ \hline
100 & 5.5; 5.0 & 50\%; 50\% & 0.67 & 0.659 & 6.5 & 6.467 & 94.0\% \\ \hline
100 & 5.5; 5.0 & 50\%; 50\% & 0.67 & 0.669 & 6.5 & 6.498 & 95.0\% \\ \hline
100 & 6.0; 5.0 & 25\%; 75\% & 0.80 & 0.785 & 7.5 & 7.232 & 93.1\% \\ \hline
1000 & 6.0; 5.0 & 25\%; 75\% & 0.80 & 0.798 & 7.5 & 7.459 & 95.2\% \\ \hline
100 & 6.5; 5.3 & 75\%; 25\% & 0.55 & 0.546 & 7.0 & 6.992 & 94.9\% \\ \hline
1000 & 6.5; 5.3 & 75\%; 25\% & 0.55 & 0.551 & 7.0 & 7.001 & 94.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input and estimated \(\beta\) and \(C_{M}\) relative confidence region for thousands of simulated synthetic catalogs.
# Vision-Based System for Black Rubber Roller Surface Inspection
## 2 Visual Inspection System
### Defect Detection Based on the Deep Learning Method
#### 2.4.1 Preprocessing  
A total of 1063 defective pictures of the rubber roller are used for detection. Images are taken from video obtained through the lighting system. Detected faults include no defects, torn areas, porosity, and damage. To improve the accuracy during training, we perform preprocessing of these images.  
First, gamma correction is used to increase the contrast of the input image. The gamma factor is adjusted to achieve the most suitable image quality, the higher the gamma factor, the higher the contrast of the image. After some experimentation, the gamma factor value is chosen to be 6.  
\[\text{img\_contrast}=((\text{image}/255.0)\ ^{\text{-}}\text{gamma})\ ^{\star}\ 255. \tag{3}\]  
Then, image quality is improved using the GaussianBlur filter. The resulting image will then be less noisy and smoother. Filter parameters are adjusted to obtain the best image quality and are written using OpenCV command blur = cv2.GaussianBlur(img\(\_\)contrast, (3, 3), 0).
## 3 Results
### Ground Reaction Forces and Joint Dynamics
#### 3.1.2 Peak Extension Force  
The assumption of sphericity for PEF was violated; therefore, the Greenhouse-Geisser correction was applied. A repeated-measures ANOVA revealed significant main effects of velocity (F(2, 78) = 5.34, \(p<0.01\), \(\eta^{2}_{\mathrm{P}}\) = 0.12, and 1 - \(\beta\) = 0.72), time (F(3, 117) = 6.78, \(p<0.001\), \(\eta^{2}_{\mathrm{P}}\) = 0.15, and 1 - \(\beta\) = 0.82), and their interaction (F(6, 234) = 4.56, \(p<0.001\), \(\eta^{2}_{\mathrm{P}}\) = 0.18, and 1 - \(\beta\) = 0.88).  
Post hoc analysis showed that the PEF significantly increased at 30 s and 4, 8, and 12 min compared to baseline under both high- and medium-speed conditions (\(p<0.05\)), with Cohen's d ranging from 0.50 to 0.75 (95% CI [0.06, 1.12]). In contrast, no significant changes were observed under the low-speed condition. Furthermore, at 4, 8, and 12 min, the high-speed condition elicited significantly greater PEF values than the medium-speed condition (\(p<0.001\)).
## 2 Materials and Methods
### Demonstration Operations  
The optical terminal, transmitter and receiver were located on the roof of the Department of Physics building at The University of Western Australia at an altitude of 32 m above sea level. The drone hovered at an altitude of 120 m (the maximum allowed by local regulations, without exemptions), with a 650 m slant distance from the terminal site, completing the 1.3 km slanted folded link. While the drone was hovering, the pilot would use the optical payload camera to coarsely align the CCR with the optical terminal. At the optical terminal side, the drone was pulled into the MV system's field of view by slewing the mount while viewing the MV camera feed. Upon entering the field-of-view of the machine vision system, the tracking system could be engaged, automatically moving the mount to locate the CCR within the field-of-view of the QPD. The TT system then dynamically corrected pointing errors. Operating concurrently, the two pointing and trackingsystems effectively maintained fibre coupling while the drone hovered in place. If the TT system was disengaged by disconnecting the PID controllers, the MV system was not able to couple the laser light back into the SMF on its own, due to the beam size, link distance and wind buffeting of the drone.
## 3 Control Strategy
### Modelling of STF  
Hong-Sock calculated the integral in the synchronous reference frame (SRF) given in Equation (15)  
\[V_{xy}(t)=e^{i\omega t}\int e^{-i\omega t}U_{xy}(t)dt \tag{15}\]  
where \(U_{xy}\) and \(V_{xy}\) are instantaneous signals in the SRF as former and later integration. The transfer function \(H(s)\) is provided in Equation (16) by applying the Laplace transform to Equation (15).  
\[H(s)=\frac{V_{xy}(s)}{U_{xy}(s)}=\frac{s+j\omega}{s^{2}+\omega^{2}} \tag{16}\]  
In order to get STF with a cut-off frequency from \(H(s)\), a constant parameter \(k\) is introduced. Thus, \(H(s)\) is given in Equation (17).  
\[H(s)=\frac{V_{xy}(s)}{U_{xy}(s)}=\frac{k(s+k)+j\omega_{n}}{\left(s+k\right)^{2 }+\omega_{n}{}^{2}} \tag{17}\]  
By swapping \(U_{xy}(s)\) by \(x_{a\beta}(s)\) and \(V_{xy}(s)\) by \(x_{\alpha\beta}(s)\), Equations (18) and (19) are obtained:  
\[x_{\alpha}(s)=\left(\frac{k}{s}[x_{\alpha}(s)-x_{\alpha}(s)]-\frac{\omega_{n }}{s}.x_{\beta}(s)\right) \tag{18}\]  
\[x_{\beta}(s)=\left(\frac{k}{s}[x_{\beta}(s)-x_{\beta}(s)]-\frac{\omega_{n}}{s }.x_{\alpha}(s)\right) \tag{19}\]  
where \(\omega_{n}\) is the expected frequency of output and \(k\) is the filter's gain. If the value \(k\) increases the accuracy of extracting, the desired component increases and vice-versa. Therefore, by using an STF, distortional signals of both voltage and current can be acquired without any change in their magnitude and phase angle. The control structure of an STF is shown in Figure 5.  
Figure 5: The control structure of STF.
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 4 The CPS-IoT Monitoring Model
### Interference-Aware Clustering with Node Migration Protocol (INMP)
#### 4.4.1 The Node Migration Concept  
Figure 5 illustrates the node migration paradigm for a network represented by a graph of 15 nodes including three sink nodes \(S_{1}\), \(S_{2}\), and \(S_{3}\). The figure depicts a scenario where the three initial clusters have 3, 8, and 3 nodes respectively, leading to an unbalanced CPS-IoT network configuration with cluster 2 overburdened while the others are lightly loaded. In such a scenario, much higher energy consumption will be experienced at the sink node \(S_{2}\) compared to \(S_{1}\) and \(S_{3}\). The figure also reveals how through node migration, clusters 1 and 3 each received two additional nodes from the overburdened cluster 2, thus resulting in a more balanced CPS-IoT network environment. This migration of nodes can be potentially beneficial in terms of energy consumption and network infrastructure safety. In the figure, the initial network graph is shown on the top, with the green dashes showing its partitioning into clusters before node migration, while the red dashes reveal the partitioning after migration. The collection trees related to the different clusters are represented in the lower part of the figure with the broken red lines revealing nodes that  
Figure 4: CPS monitoring model.  
have been discarded from the initial tree while the full red lines reveal nodes that have been added through node migration.  
Reorganising clusters for a load balancing purpose is an efficient strategy for self-organising systems. The works presented in [39; 40] reorganise clusters for load-balancing with a focus on (i) balancing the processing load among fog nodes in [39] by having compute nodes located in the corresponding fogs measuring the network proximity to each other and self-organising into a hierarchical or a flat structure accordingly and (ii) providing resilient distributed decision-making in large-scale situated systems by devising a process of interconnecting edge devices into teams and applying a decentralised coordination pattern for partitioned integration and coordination of these devices, which continuously adapt to context change as proposed in [40]. The work presented in this paper applies a clusters' reorganisation model but with a focus on the CPS-IoT reconfiguration using a balanced cluster membership based on node migration.
## 2 Background
### Feature Extraction
#### 2.4.1 N-Gram  
In machine learning, one of the feature extraction methods is N-Gram. It involves converting the input data into a sequence of separate n tokens, which are usually words. N is an integer number, which is usually one in the one-gram method, two in the bi-gram method, or three in the tri-gram method. In practice, pyspark libraries implement this method using N-Gram class instances [30].
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 4 Security Analysis
### Node Capture Attack  
A node capture attack is both a challenging and interesting attack with the goal of taking control over a smart meter's communication after physically gaining access. This attack could easily be carried out because smart meters are placed on customer premises and not within the utility provider'sphysical premises. [PERSON] _et al._[21] presented studies on the attacks and vulnerabilities of smart meters in a NAN and list node attack as the least attended, yet significantly dangerous, to the smart grid network. Most of the schemes discussed in [22; 23; 24; 25; 26] show a vulnerability to node capture attacks. A successful attack could reveal shared keys, thereby permitting an attacker to participate in encryption and decryption processes or, in a worst case scenario, inject false data into the smart grid network to comprise other nodes.  
Our proposed group authentication scheme is secured against node capture attacks. Even if the keys are captured by an attacker and used to send data, the data packet would get validated for forwarding, but the packet would be tagged as an invalid packet since the time stamp of the packet sent by the attacker would not match the timestamp value expected by the NOC. The NOC records the timestamps of all the packets it receives, node ID-wise, so it knows what to expect in the next incoming packet from a particular node. However, if by some means, the malicious node is able to retrieve the timestamp information from the captured node and set its local clock to that of the captured node, then the scheme will be effectively broken. This condition breaks the assumption number 12 in Section 3.1.
## 3 Results and Discussion
### Purslane Powder Characterization
#### 3.1.3 Color of Purslane Powders  
Table 1 shows the color parameters of the purslane powders obtained. The \(L^{*}\) values of the color parameter range from 53.65 (PPK) to 39.50 (PPP), while PPB has an \(L^{*}\) value of 61.68. \(L^{*}\) represents the brightness of the color, which can be characterized by values from 0 to 100, whereby a value of 100 representing white (light) and a value of 0 for black (dark). The results presented show that the PPB sample was significantly darker than the PPP sample, while the PPK sample did not differ significantly from the other samples. Positive values of the \(a^{*}\) color coordinate for all three samples indicate that the samples have a  
Figure 3: Comparison of particle size distributions, mean particle diameters, and standard deviations of freeze-dried purslane powder obtained after grinding with applied mills. PPK = purslane powder after grinding with a knife mill; PPB = purslane powder after grinding with a ball mill; PPP = purslane powder after grinding with a planetary ball mill.  
pronounced slightly red color component, with the PPP sample having a significantly higher intensity of red pigments than the other two samples, while positive values of the \(b^{*}\) color component indicate the presence of yellow color in the samples, and notably higher intensity of yellow pigments had PPK and PPB samples. Markedly higher color saturation also had PPB (Chroma = 26.77) and PPK (Chroma = 25.91 \(\pm\) 0.04), which indicates that the grinding process on a ball mill had the least influence on the color of the sample. The hue values of all samples, although similar in range from 85.80 to 87.18, significantly differed between all three samples, with the PPB sample having the highest value, followed by the PPK sample, and the PPP sample had the lowest value. Based on the hue values, we conclude that the values are in the first quadrant, which means that yellow and green tones dominate in all samples. The obtained results of the characterization of purslane powder after different grinding methods are difficult to compare with the findings of similar studies, as no similar study was found in the available literature.
## 5 Clinical Applications in Myocardial Imaging
Compared to control individuals, patients with takotsubo cardiomyopathy showed significant differences in the change om T2* and native T1 values at baseline (i.e., during the acute event). However, these differences were no longer evident after five months [44]. Notably, myocardial energetics as assessed by 31P-CMR spectroscopy demonstrated a markedly reduced energetic state, which persisted after five months [44]. In contrast with takotsubo cardiomyopathy, USPIO-enhanced CMR results have not shown evidence of active macrophage infiltration and USPIO accumulation in myocarditis despite evidence of myocardial oedema in native T1 mapping and extracellular volume (ECV) imaging (Figure 3) [1; 45]. Although the data available are limited to a total of fifteen patients from two studies, USPIO uptake has not been demonstrated to be substantially increased in the myocardium of patients with myocarditis, even in the areas with confirmed myocardial oedema, as shown in native T1 and ECV imaging [1; 45]. This highlights the fact that different pathologies trigger different pathways that may not necessarily be mediated by macrophages. In the case of myocarditis, immune cell activation may be dominated by other cell types, such as lymphocytes, for example, which could explain the absence of USPIO accumulation in the myocardium [1; 45]. Additionally, in the study by [PERSON] et al. there was no correlation shown between USPIO uptake and native T1 values, which, as the authors mention, emphasises that myocardial oedema and macrophage infiltration do not necessarily occur simultaneously and may not be dependent on each other [1]. Nevertheless, given the limited number of patients in the abovementioned studies, it is impossible to draw firm conclusions, and further research is warranted to investigate this important aspect in detail. Figure 3: An example of a patient with acute myocarditis showing sub-epicardial LGE inferiorly and inferolaterally on 4-chamber and 3-chamber views (left) but no evidence of ferumoxytol uptake within the regions displaying LGE 24 h following infusion (right). CMR = cardiac magnetic resonance; LGE = late gadolinium enhancement. Reproduced with permission from [PERSON] et al. [7].
## 4 The Proposed Distributed Image Codec  
The distributed image coding and decoding scheme proposed in this paper is a block-based one that makes use of the correlation between neighbouring blocks. In block-based CS, an image, \(X\), with dimension \(N\times N\) pixels is divided into non-overlapping blocks of \(B\times B\) pixels. Let \(x_{i}\) represent the \(i\)-th block; then, its \(M_{r}\) CS measurements are given by:  
\[y_{i}=\Phi_{B}x_{i} \tag{9}\]  
where \(\Phi_{B}\) is an \(M_{r}\times B^{2}\) measurement matrix. The measurement rate per block is given by \(M_{b}=M_{r}/B^{2}\). Since the image data of neighbouring blocks are correlated, we postulate that their CS measurements will also be correlated. This correlation is empirically demonstrated in Section 4.1 for a variety of images.  
In our coding scheme, image blocks are classified either as key blocks or non-key (WZ) blocks. Key blocks are encoded with a higher measurement rate than non-key blocks, similar to the idea of key frames and non-key frames in video coding. Two block classification strategies, one adaptive and the other non-adaptive, can be adopted. Details of the encoding scheme will be discussed in Section 4.2, while that of the decoding scheme can be found in Section 4.3. While each key block is reconstructed from its own measurements, the decoding of WZ blocks requires the help of side information, due to a reduced sampling rate. Here, we propose using the CS measurements of key blocks as the side information. This process is discussed in more detail in Section 4.3.2.