esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 4 Methodology  
The general idea of the methodology is presented in Figure 2. It is assumed that the historical data set from condition monitoring systems contains three regimes that follow the model discussed in [49; 50]. The degradation model consists of deterministic and random parts. Using a moving window, one may consider the original data in the window as independent and identically distributed (i.i.d.) observations, so simple statistical descriptors can be used to characterize their local properties. To increase resolution in the time domain, one may use overlapping. It may be helpful to precisely detect the regime change point.  
In many papers, it is shown that simple statistics are easy to use and may provide important information; however, they are usually correlated, so dimensionality reduction is recommended. By the appropriate transformation of extracted features, one may reduce the number of novel features and still preserve original information. It is proposed to use principal component analysis (PCA) here. In general, the PCA is used to reduce highly dimensional data to a data set of a lower size [51]. However, in some applications, the PCA is used to visualize reduced features in 2D or 3D space, so projection is into PC1-PC2 or PC1-PC3 space. We will follow this idea here as well to visualize the distribution of data in PC space and to understand the results of clustering. To find these three regimes, we use extracted features and several most popular algorithms for clustering implemented in the Matlab environment.  
In the case of really high correlation in input data space, it may happen that PC1 contains almost all information. Then, for 1D time series (PC1 corresponds to input data acquired in time), simple probability density function estimation may be enough to find threshold values that will be able to divide the input data into three regimes. The final step of the procedure is related to the validation of clustering. We present the results of clustering using different colors applied to time series. In Figure 3, more detailed explanation regarding moving window and statistical parameter extraction is presented.
## 3 Results and Discussions  
### Analysis of Water Quality and Flow Characteristics for Each Point  
When the average flow (by year) and the concentrations of six water quality factors in the upper, middle, and lower parts of the Gul-po stream were measured through the TMDL monitoring network (from 2016 to 2020) and then analyzed, it was found that the discharge in the upper and middle parts increased after 2018. This appears to be due to the influence of the ozone-treated reclaimed water supply project implemented in March 2019.  
Through this project, average flows of 30,000 and 15,000 m\({}^{3}\)/d are discharged to the Cheong-cheon stream, located in the upstream area of the Gul-po stream (upstream of point GulpoA1) and the Gye-san stream, located in the midstream area (upstream of point GulpoA2), respectively [26]. Accordingly, the flow in the upper Gul-po stream almost doubled from 2018 to 2019, and the water quality in the upper and middle parts has also improved. The T-N concentration increased rapidly, likely due to the influence of ozone treated reclaimed water with a higher T-N concentration than the upper part of the Gul-po stream (Figure 2).  
Figure 2a shows the T-N concentration in ozone-treated water; the concentration ranges from 8 to 16 mg. As shown in Figure 2b, the T-N concentration in the upper Gul-po stream increased to the T-N concentration range of ozone-treated water due to its discharge. Figure 2b has sections where the T-N concentration is lower than that of ozone-treated water (minimum concentration is 6 mg/L)--this appears to be due to the influence of the rainwater introduced into the stream during rainfall.  
The daily discharge of the Gul-po sewage treatment plant is 664,550 m\({}^{3}\)/day, and this represents most of the flow in the lower part of the Gul-po stream. Therefore, the lower part was affected by the water quality of the discharge from the Gul-po sewage treatment plant, while the influence of ozone-treated reclaimed water discharge (45,000 m\({}^{3}\)/day) on the upper part was found to be insignificant compared to discharge from sewage treatment plant. Table 2 shows the annual average water quality concentration and flow for each measurement point in the Gul-po stream.  
Compared to the upper and lower parts of the Gul-po stream, the concentrations of BOD, COD, and TOC were highest in the middle part. This is because water from the urban industrial complexes and road sections flows into the middle part and the water quality is lower compared to other points under the influence of surrounding pollution sources [27]. In addition, it appears that the Sam-jeong stream (a tributary of the Gul-po stream), which is located upstream of the middle part of the stream, has direct and indirect influence on the water quality in the middle part of the Gul-po stream due to the high population density compared to the administrative districts areas of Bucheon and Incheon in the upstream area, as well as untreated water discharged from livestock/agricultural farms and industrial complexes [28].
# Maximizing the Capacity of Edge Networks with Multicasting
## 7 Multicast Network Capacity
### Scalability Analysis  
From Theorems 5 and 6, it is clear to see that the system capacity is mainly relevant to the number of SBSs \(n\) if other parameters remain unchanged. Therefore, the system capacity tends to zero if \(n\) approaches to infinity. This result is consistent with the conclusion of [3]. However, the declining trend is slower than that of [3] because of the caching number of \(C_{i}\) in edge scenarios.
# Response of Seismically Damaged Steel Reduced Beam Section Joints under Fire
## 2 The Reference Archetype and the Considered Scenarios
### Investigated Loading Scenarios  
The examined joints were investigated as follows: first cyclic loading and fire were separately applied; afterwards, fire was applied following cyclic loading. With this regard, the cyclic loading was imposed by means of AISC341 [27] protocol up to two levels of seismic-like damage, namely 0.04 rad (which may correspond to significant damage limit state) and 0.06 rad (for near collapse limit state), and three alternative fire scenarios were simulated as shown in Figure 3. Thus, 36 scenarios were considered on four different beam-to-column joints, as summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Designed**} & \multicolumn{4}{c}{**Geometrical Features**} & \multicolumn{4}{c}{**Flexural Resistance**} \\
**RBS** & **a** & **b** & **c** & **R** & **Ze** & **M\({}_{\text{pl,Rd}}\)** & **M\({}_{\text{RBS,Rd}}\)** & **M\({}_{\text{RBS,Rd}}\)** \\  & **mm** & **mm** & **mm** & **mm** & **cm\({}^{3}\)** & **kNm** & **kNm** & **-** \\ \hline RBS-80 & 150 & 350 & 30 & 525 & 1729 & 779 & 614 & 0.79 \\ RBS-65 & 150 & 350 & 50 & 331 & 1419 & 779 & 504 & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features of the designed RBS.  
Figure 2: Moment-resisting (MR) and end-plate connection of both internal and external joints (**a**) and main geometrical features of the RBS connection (**b**).  
The joint assemblies were identify based on the following nomenclature:  
* The configuration of the assemblies: \"I\" for the internal joints and \"E\" for the external joints.
* The flexural resistance of the RBS: \"RBS-80\" and \"RBS-65\" for RBS having plastic moments equal to 80% and 65% of the beam plastic moment, respectively;
* The imposed seismic damage: \"ND\" (no damage) for the joint subjected only to the fire loads, and \"SD\" or \"NC\" (significant damage and near collapse limit state, respectively) for the joints subjected to a cyclic loading up to 0.04 rad and 0.06 rad, respectively.
* The fire scenarios are identified as S1, S2, and S3, as shown in Figure 3.  
For the sake of clarity, the identification code associated with an internal joint with resistance of RBS equal to 80% of the beam plastic moment, an imposed cyclic damage corresponding to 0.04 rad, and subjected to the second fire scenario is IJ-80%-SD-I.  
The three investigated fire scenarios (i.e., S1, S2 and S3) are depicted in Figure 3, where the bay and the storey where the fire action is modelled are shown (see Figure 3a), and the corresponding distributions of the temperature within the steel joints are pointed out (see Figure 3b).
# A Brief Review of Multipath TCP for Vehicular Networks
## 1 Introduction
Furthermore, there are already some works to combine multipath routing with MPTCP to improve the performance in vehicular networks [10; 11]. However, implementing MPTCP in vehicular IoT systems still face several key challenges. Firstly, utilizing different communication methods such as LTE or IEEE 802.11p would incur different transmission latencies. This could result in bufferploat at receivers, thus impacting the application level performance. Secondly, most vehicular applications require high quality-of-service (QoS) demands, so the MPTCP architecture need to be further improved to reach the goal. Third, due to the feature of constant movement of vehicles, it is hard to maintain a stable network topology in vehicular networks. Therefore, it is important to design an efficient routing protocol to offer stable communications. Due to the aforementioned challenges, a new MPTCP architecture that could improve communication efficiency with low overhead is of great interest. In this paper, we conduct a survey on the technical challenges and existing solutions regarding MPTCP, and the implementation of MPTCP in vehicular networks. We also give a brief introduction in regards to multipath routing in vehicular networks, which is the basis of achieving multipath data transmissions in highly mobile environments. The main contributions of this paper are as follows:  
* To the best of our knowledge, this is the first work that presents recent advances of MPTCP in vehicular networks and multipath routing in vehicular networks;
* We discuss both the technical issues of applying MPTCP in vehicular networks, and necessary improvements to support MPTCP applications in vehicular networks;
* We point out the future research directions related to applying MPTCP in vehicular networks. The remainder of this paper is organized as follows. We first introduce fundamentals of TCP and MPTCP in Section 2. We review the two important components of MPTCP, namely, the scheduler and congestion control (CC) in Section 3. In Section 4 we introduce recent studies that combine MPTCP with other techniques and some research efforts in applying MPTCP for IoT systems. We present the studies that employ MPTCP in vehicular networks in Section 5. The research efforts on multipath routing in vehicular networks are presented in Section 6. We discuss future research directions in Section 7, and conclude this paper in Section 8.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 3 Earth Observation
### Vegetation
(2018) propose an approach using data from the PS satellite constellation to create cost-effective management zone maps within agricultural fields in southern Brazil. The method estimates the aboveground biomass (AGB) of cover crops during the flowering stage, using the maximum vegetation indices (VIs) as a basis. The results show that several regression models effectively estimate cover-crop AGB using surface reflectance and VIs. The analysis confirmed a positive correlation between cover-crop AGB and cash-crop yield, substantiating satellite-derived AGB estimates' usefulness in creating management zones. [PERSON] et al. (2019) propose an approach to mapping high-resolution forage production in California's rangelands, crucial for the $3.4 billion annual cattle industry. The authors integrate Small Unmanned Aerial System (sUAS) and PS satellite imagery, and they achieved daily forage production maps at a remarkable 30 cm resolution. Their methodology demonstrated significant accuracy in predicting forage production by combining absorbed photosynthetically active radiation (APAR) estimates from NDVI with light-use efficiency (LUE) models. [PERSON] et al. (2020) propose a novel method to detect and map various rice disease species at fine spatial scales, addressing a critical need in agricultural management and insurance. The authors developed a set of normalized two-stage vegetation indices (VIs) to characterize disease progression and employed bi-temporal, high-spatial resolution imagery from PS datasets. The results show that the method can successfully classify healthy and unhealthy samples and even more successfully classify diseases such as rice dwarf, rice blast, and glume blight. The studies demonstrate the significant impact of remote sensing and nanosatellite technology on agricultural applications. They showcase advancements in high-resolution imagery, vegetation indices, and disease detection, collectively enhancing agricultural productivity and sustainability on a global scale. Table 3 summarizes the methods used and evaluation metrics for vegetation monitoring using EO data from Nanosatellites.
# SNR-Based GNSS-R for Coastal Sea-Level Altimetry
## 2 Geodetic GNSS-R Footprint  
The geometry of geodetic GNSS-R depends on the positions of the GNSS satellite and receiving antenna with respect to the reflecting surface. The extent and resolution of surface reflected GNSS multipath from a local horizontal plane covers an area around the specular reflection point. The first Fresnel zone (FFZ), as the sensing footprint of geodetic GNSS-R, is defined as a function of half-wavelength (\(\lambda/2\)) [17]. The FFZ is an elongated ellipse whose semi-minor and semi-major axes are defined as follows [18]:  
\[b=\sqrt{\frac{\lambda h}{\sin e}+\frac{\lambda^{2}}{4\sin^{2}e}a}=\frac{b}{\sin e} \tag{1}\]  
where \(h\) is the reflector height and \(e\) is the GNSS satellite elevation angle. The GNSS satellites repeat their ground track as a fraction of a sidereal day, i.e., the GPS orbital period is half a sidereal day. Therefore, the reflection points from rising and setting satellite arcs can be used to approximate the first Fresnel zone. Figure 2 shows the FFZs from the sea reflection for January 2021 at the POPR using the median elevation angles of each individual GNSS track. It should be noted that geodetic GNSS-R has a much larger and smaller spatial footprint than the tide gauge senor and radar altimeter, respectively.  
Figure 1: Tide gauge station at the Haiti Coast Guard Base in Port-au-Prince in the northwest (left panel) and northeast (right panel) directions.
## 4 Results and Discussion
### Semi-Empirical Models  
In Section 2, a method was presented for generating predictive solid models of the gyroid infill. These solid-model analyses were performed several times at each of several relative densities over a range of test materials with modulus values (\(E_{m}\)) from 1000 to 4000 MPa, and [PERSON]'s ratio values (\(v_{m}\)) ranging from 0 to 0.45. (Since the base material is isotropic, the shear modulus \(G_{m}\) is calculated from them using the standard isotropic relationship.) This range of properties was chosen to encompass the behavior of a variety of common 3D printing polymers, so as to allow for predictions with all of these materials. In order to best use the resulting predictions for design, a relatively simple equation is defined which fits the data. This is similar to how simple mathematical models have been used to predict the behavior of other cellular structures such as honeycombs or foams [45].  
Figure 11: Experimental results vs. finite element analysis (FEA) predictions for elastic modulus.
# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 3 Results and Discussion
### Spatial Region of Interest for Classification  
Figure 6 shows the procedure to create the mask of jujube specimen for pixel based defect detection. As the difference between the spectra for jujubes and background could be easily distinguished, a threshold of the reflectance at a single wavelength (760 nm) was used to segment jujube pixels and background pixels and to create a binary image. In a second step, a jujube mask was created by using morphological dilation and erosion to fill the holes inside the mask and remove isolated jujube pixels outside the mask. Due to the intensity distortion [14] caused by the curvature of the jujube, the acquired intensities of jujube pixels near the edge of the mask was too low and these pixels were highly possible to be misclassified as low reflectance classes such as black fungus and decay (Figure 6b). Although commonly used pre-processing technique such as normalization, dividing each spectrum by maximum value, could be used to minimize the intensity variation due to fruit curvature. However, the signal to noise ratio could not be improved by normalization. In this work, a new mask created by eroding approximately 5% of the original mask was used as the spatial region of interest. Defect classification was only performed on jujube pixels in new mask (Figure 6c).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{8}{c}{**Classified as**} \\ \cline{2-9}  & **Normal** & **Rusty Spot** & **Decay** & **White Fungus** & **Black Fungus** & **Crack** & **Glare** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{2}{*}{**Normal**} & 98.67 & 0.8 & 0.29 & 0 & 0 & 0.03 & 0.21 \\  & 98.51 & 0.53 & 0.35 & 0 & 0 & 0.05 & 0.56 \\ \multirow{2}{*}{**Rusty spots**} & 1.3 & 95.95 & 2.01 & 0.03 & 0.05 & 0.63 & 0.03 \\  & 1.44 & 93.83 & 3.78 & 0.05 & 0.08 & 0.71 & 0.11 \\ \multirow{2}{*}{**Decay**} & 0.05 & 4.45 & 94.02 & 0.02 & 1.29 & 0.17 & 0 \\  & 0.07 & 2.41 & 95.94 & 0.02 & 1.39 & 0.12 & 0.05 \\ \multirow{2}{*}{**White fungus**} & 0 & 0 & 0 & 98.68 & 0.48 & 0.84 & 0 \\  & 0 & 0 & 0.03 & 98.38 & 0.84 & 0.75 & 0 \\ \multirow{2}{*}{**Black fungus**} & 0 & 0.48 & 2.35 & 0.18 & 96.95 & 0.04 & 0 \\  & 0 & 0.29 & 2.17 & 0.33 & 97.21 & 0 & 0 \\ \multirow{2}{*}{**Crack**} & 0 & 1.15 & 0.19 & 1.27 & 0 & 97.39 & 0 \\  & 0 & 0.4 & 0.4 & 0.77 & 0.03 & 98.35 & 0.05 \\ \multirow{2}{*}{**Glare**} & 0.38 & 0 & 0 & 0 & 0 & 0 & 99.59 \\  & 0.24 & 0.05 & 0 & 0 & 0 & 0.05 & 99.66 \\ Model 2 & Model 4 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrices of the classification models with the use of linear scan dataset.  
Figure 6: (**a**) [PERSON], Green and Blue (RGB) images of jujube specimen, (**b**) predictive results shown in the original masks and (**c**) predictive results shown in the new masks created by eroding approximately 5% of the original mask.
# Security Risk Analysis of LoRaWAN and Future Directions
## 5 Security Threat and Risk Assessment of LoRaWAN v1.1
### LoRaWAN v1.1 Threat Catalog
#### 5.2.9. Rogue Gateway Attack  
From the beginning (including LoRAWAN v1.0), the gateways are always considered as legit and obeying relays. However, this assumption raises the question: \"What happens if adversaries hack/capture/replicate one or multiple gateways?\" As also mentioned in Section 4.1, \(GW\)s are vulnerable and therefore can be a target for persistent attackers as described:  
* **Beacon Synchronization DoS Attack:** Class B sessions might be vulnerable to Rogue-Gateway attack, which is in line with _Session Hijacking Attacks_. In LoRaWAN, Class B beacons are not secured by any means, indicating that an attacker can set up a _Rogue-GW_ to send fake beacons. This could result in class B end-devices to receive messages in windows out-of-sync with the _Rogue-GW_ and also increased collisions on packets being transmitted. Addressing this threat would require issuing a key that \(GW\)'s could use to authenticate beacon transmissions.
* **Impersonation Attack:** Gateways (\(GW\)s) can also be impersonated to set-up attacks against end-devices (\(EDs\)). \(ED\)s can be listened to and their network addresses can be determined. More importantly, a triangulation method (minimum 3 \(GW\)s are needed in this case to perform the intended capturing attack towards the \(ED\)) can be used to determine the physical location of the \(ED\)s.
## 3 Controller Design
### The Lower-Level Controller  
Based on the nonlinear current-velocity mapping of the hydraulic motors, a look-up method is adopted to directly obtain the controller current corresponding to the desired angular velocity of motors. However, the current-velocity mapping can only reflect the static characteristics of the hydraulic system, while in reality, the robot wheel speed is dynamically changing. In addition, the variation of the road conditions and the accuracy of the current-velocity mapping can also lead to the degradation of the control accuracy, so we added PID controllers as compensation for the look-up table method to reduce the velocity tracking error.  
According to the hydraulic motors mapping (7), we can define inverse mapping functions, which are given as  
\[\begin{array}{l}Mapping_{l}{}^{-1}\gets u_{l}=f_{l}{}^{-1}(w_{m,l},w_ {m,r})\\ Mapping_{r}{}^{-1}\gets u_{r}=f_{r}{}^{-1}(w_{m,l},w_{m,r})\end{array} \tag{24}\]  
Then, we can obtain the motor current input value according to \([w_{m,l}^{d},w_{m,r}^{d}]^{T}\),  
\[\begin{array}{l}u_{l}{}^{m}=f_{l}{}^{-1}(w_{m,l}^{d},w_{m,r}^{d})\\ u_{r}{}^{m}=f_{r}{}^{-1}(w_{m,l}^{d},w_{m,r}^{d})\end{array} \tag{25}\]  
where \([u_{l}{}^{m},u_{r}{}^{m}]\) are the control values according to inverse mapping functions.  
Based on the actual velocity \(w_{m,i}^{s}\) and the desired velocity \(w_{m,i}^{d}\), the PID controllers can be obtained with the following equation:  
\[u_{i}^{c}(t)=k_{i,p}e_{i}(t)+k_{i,j}\int_{0}^{t}e(t)dt+k_{i,d}\frac{de_{i}(t )}{dt} \tag{26}\]  
where \(i=left\), \(right\); \(u_{i}^{c}\) is the control values of PID controller; \(e_{i}=w_{m,i}^{d}-w_{m,i}^{s}\); and \(k_{i,p}\), \(k_{i,j}\) and \(k_{i,d}\) are the controller parameters.  
Combining (25) and (26), the controller output values of the lower-level controller are given as (27), and the overall framework of the lower-level controller is shown in Figure 5.  
\[\begin{array}{lcl}u_{i}&=u_{i}^{m}+u_{i}^{c}\\ &=f_{i}{}^{-1}(w_{m,l}^{d},w_{m,r}^{d})+k_{i,p}e_{i}(t)+k_{i,j}\int_{0}^{t}e( t)dt+k_{i,d}\frac{de_{i}(t)}{dt}\end{array} \tag{27}\]