esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Plant Material  
For this study, 63 samples of _Cucurbita_ spp. were obtained from different sources. Nearly half of the samples (\(n=31\)), belonging to the cultivars Boloverde (\(n=6\)), Dorado (\(n=8\)), Abanico 75 (\(n=9\)), from sp. _Cucurbita moschata_ Duchesne, and Mandarino (\(n=8\)), from sp. _Cucurbita maxima_, were harvested at the experimental center of the Universidad Nacional de Colombia in Palmira (Valle del Cauca) [\(3^{\circ}32^{\prime}05\"\)N \(76^{\circ}17^{\prime}44\"\)O; 1001 m.a.s.l.]. The harvesting process of these samples took different times to be completed, according to the characteristic ripening periods of each cultivar. Namely, from planting to harvest, cv. Mandarino took 70-80 days, cv. Boloverde 120-150 days, and cv. Abanico 75 and cv. Dorado, 90-100 days. The samples were sent to the laboratory within 2-3 days after harvesting; they were disinfected in a 200 mg/L sodium hypochlorite solution, rinsed, and kept in a freezing room at \(-30\)\({}^{\circ}\)C until processing, in a time no longer than two days after receipt. The genetic, morphological and physiological traits of these cultivars have been described elsewhere [12; 13]. The other half of the samples (\(n=32\)) was acquired directly from local suppliers from different regions of Colombia, at the wholesale collection center Corporacion de Abastos de Bogota S.A. (Corabastos), the same day of arrival in Bogota. These samples were whole butternut squashes from sp. _C. moschata_, in their commercial ripening degree, harvested in the departments Huila (\(n=8\)), Tolima (\(n=8\)), Valle del Cauca (\(n=8\)), and Meta (\(n=8\)). There was no available information of the harvesting duration of the commercial samples; however, at the climatic and agricultural conditions of these regions, harvesting occurs usually nearly 90 days after floration, or between 90 to 150 days after planting [14]. The samples were disinfected in a 200 mg/L sodium hypochlorite solution, rinsed, and kept in a freezing room at \(-30\)\({}^{\circ}\)C until processing, in a time no longer than two days after purchase. Three different preparation protocols (A, B, and C) were followed, corresponding to increasing analyte (carotenoids) isolation degrees in samples. First, the shell and seeds were manually separated from the pulp, which was subsequently portioned and processed with a knife blender (SharkNinja, Canada). The pulp after sole homogenization is referred to as _fresh pulp_ and corresponds to the lowest analyte isolation degree (A). The remaining portion of the fresh pulp was freeze-dried (\(-50\)\({}^{\circ}\)C in the condenser, \(30\)\({}^{\circ}\)C in the heating chamber, 1 mbar, 48 h), and it is referred to as _freeze-dried pulp_, which corresponds to the intermediate analyte isolation degree (B). Finally, freeze-dried samples were subjected to the extraction protocol described in Section 2.4 with hexane:acetone (1:1), and are referred to as _extracts_, corresponding to the highest analyte isolation degree (C).
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 5 Implementation and Evaluation
### Robustness  
We have measured the experimental data with attack ratios of 0.25 and 2.5 in window 60 to analyze the effect of different rates on HMM-R detection performance. Table 5 shows the performance of algorithms. With the increase of L-DDoS attack traffic, the true positive rate of the four machine learning methods gradually increased; the false positive and the false negative gradually decreased. When the attack ratio is low, the HMM-R algorithm is superior to other algorithms in three performance indicators. There are some optimizations when the attack ratio is relatively high. It shows that with the increase of L-DDoS attack traffic, the difference between the entropy of normal and L-DDoS attack traffic gradually increases; that is, it deviates from the normal traffic behavior profile to a greater extent. It also can be seen that HMM-R is better than the other four algorithms at a lower attack ratio (equivalent to a lower rate).  
Figure 6: Comparison diagram of performance Indices of each order of HMM-R under different windows. (**a**) Comparison of RP of HMM-R; (**b**) comparison of FA of HMM-R; (**c**) comparison of DR of HMM-R.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Algorithm** & **RP** & **FA** & **DR** \\ \hline \multirow{2}{*}{KNN} & 0.9833 & 0.0147 & 0.0247 \\  & 0.9960 & 0.0025 & 0.0088 \\ \hline \multirow{2}{*}{SVM} & 0.9814 & 0.0147 & 0.0247 \\  & 0.9960 & 0.0025 & 0.0088 \\ \hline \multirow{2}{*}{SOM} & 0.9814 & 0.0147 & 0.0247 \\  & 0.9911 & 0.0041 & 0.0237 \\ \hline \multirow{2}{*}{BP} & 0.9792 & 0.0181 & 0.0454 \\  & 0.9903 & 0.0307 & 0.0153 \\ \hline \multirow{2}{*}{**HMM-R**} & **0.9847** & **0.0072** & **0.0197** \\  & **0.9950** & **0.0049** & **0.0052** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of algorithms with attack ratio of 0.25 and 2.5 in window 60.
# R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption
## 4 Experiment and Analysis
### Results and Analysis  
The test results on ModelNet40 are shown in Table 1. The best recognition accuracy of the R-PointNet network model is 92.5%, which is a 3.2% and 2.1% improvement compared to PointNet and PointNet++, respectively. In Table 1, we can see that our method outperforms almost all state-of-the-art methods on the Modelnet40 benchmark. The only exception is RS-CNN. This is because RS-CNN can learn more meaningful fine shape information such as geometric topological constraints between points. However, RS-CNN does not perform as well on corruption dataset as R-PointNet does.  
We compare our R-PointNet with five representative networks: PointNet, PointNet++, DGCNN, RSCNN, and PCT. These five models stand for different architecture designs of 3D object recognition, and have achieved good accuracy on the clean dataset. In our experiments, the error rate of these popular networks is not 3\(\times\) larger on ModelNet40-C than ModelNet40 as in the original paper. But compared to the results in Table 1, the error rate still increased by 7-9% under point cloud distortion. The experimental results on ModelNet40-C are shown in Table 2. Combining Tables 1 and 2, the results demonstrate that the point cloud network architecture is still very vulnerable to common distortions. It is worth highlighting that the R-PointNet reaches 89.9% recognition accuracy, which outperforms other networks on Modelnet40-C.  
Finally, we select eight representative corruption types in the ModelNet40-C and test the performance of different networks under different corruption types in detail. The results are in Table 3.  
Table 3 presents the detailed accuracy of the six models evaluated on ModelNet40-C separably. As mentioned before, there is a significant decrease in recognition accuracy on each corruption compared to ModelNet40. From the perspective of the model, all networks have different shortcomings in several corruptions, and the performance of our R-PointNet is the most stable across different corruptions, with above-average recognition accuracies, and outperforms all networks in dealing with the noise corruption of the type Uniform and Gaussian.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Input** & **Overall Accuracy (M40)** \\ \hline PointNet [8] & pnt & 89.3 \\ PointNet++ [9] & pnt, nor & 90.6 \\ RSCNN [28] & pnt & 93.6 \\ DGCNN [12] & pnt & 92.2 \\ R-PointNet & pnt & **92.5** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall accuracy on ModelNet40 (M40) datasets. “pnt” stands for coordinates of point and “nor” stands for normal vector.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Overall Accuracy (M40C)** \\ \hline PointNet & 82.2 \\ RSCNN & 84.3 \\ DGCNN & 84.6 \\ PCT & 85.0 \\ PointNet++ & 86.9 \\ R-PointNet & **89.9** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall accuracy on ModelNet40-C (M40C) datasets. Characterizing the robustness of networks.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 2 System Model
Let be \(s_{q}\in\mathbb{C}\), with \(q=0,1,\ldots\), the OFDM waveform samples which carry the PSCCH/PSSCH resources allocated as in Figure 2, and the synchronization signal blocks. The transmitted signal, \(\mathbf{s}_{tx,q}\in\mathbb{C}^{N_{tx}}\), is given by  
\[\mathbf{s}_{tx,q}=\mathbf{f}_{a}s_{q}, \tag{3}\]  
where \(\mathbf{f}_{a}\in\mathbb{C}^{N_{tx}}\) is the precoder. Since our focus is only on the PSCCH search algorithm, we consider the simple random precoder discussed in [32], and given by  
\[\mathbf{f}_{a}=[e^{j2\pi\omega_{v}}]_{1\leq v\leq N_{tx}}, \tag{4}\]  
where \(\omega_{v}\), \(v\in\{1,\ldots,N_{tx}\}\) are i.i.d. uniform random variables with support \(\omega_{v}\in[0,1]\). At the receiver side, a minimum mean-squared error (MMSE) equalizer is adopted and applied to received signal \(\mathbf{s}_{rx,q}\in\mathbb{C}^{N_{rx}}\), \(q=0,1,\ldots\), where the channel between the transmitter and receiver is estimated based on DMRS.
## 4 Results and Discussion
### Microbiological Parameters of Groundwater  
The assessment of the main physiological groups of microorganisms showed a diverse microbial community in the groundwater samples, including aerobic and anaerobic bacteria. In the contaminated samples (Samples 2 and 3), the number of denitrification and sulphate-reducing bacteria was higher than that in the background well. The most polluted sample (Sample 3) contained the maximum number of denitrifying and sulphate-reducing bacteria, except for iron reducers, whose activities depend on anaerobic conditions (negative Eh), which dominated in the background water (Sample 1). In shallow groundwater locations, such as in the NCCP area (up to 10 m), due to the lack of organic matter and the oxidizing environment, natural denitrification is poorly developed [27].  
Based on the 16 SrRNA gene analysis (Table 3) of the microbial community in the water samples, the greatest number of representatives of the _Sulfurimonas_ genus was found in the contaminated groundwater (Sample 3). _Sulfurimonas_ reduces nitrates and oxidizes sulphur in lithotrophic growth [28; 29]. Bacteria of the genus _Gallionella_ are iron oxidants that form biogenic mineral phases of ferrihydrite [30]. Bacteria of the genus _Pseudomonas_ were also detected in Sample 3; these bacteria are known for their ability in anaerobic conditions to reduce nitrate and transition metals, including uranyl [31]. Bacteria of the family _Xanthomonadaceae_ are known for a wide variety of aerobic metabolic processes, including the nitrogen cycle (denitrification, iron oxidation, etc.), and form biofilms [32]. Parcubacteria are referred to as ultramicrobacteria. The organisms have small (generally < 1 Mb) genomes and highly reduced metallic capabilities. They have been implicated in hydrogen and sulphur cycles in anoxic sediments [33]. Some papers have reported that they are frequent satellites of bacteria of the genus _Plantomyces_, which are responsible for the anammox process [34].  
The background groundwater (Sample 1) is dominated by typical bacteria associated with the iron cycle (_Gallionella_ and _Geobacter_) and sulphur cycle (the genera _Sulfurimonas_, _Desulfobacteraceae_, _Desulfosporinus_, and _Desulfovibrio_), as well as organothrophic bacteria of the family _Comanonadaceae_ and class _Bacteroidales_, whose representatives have a wide range of metabolic characteristics; they reduce nitrate, nitrite, iron and other elements in anaerobic conditions. All of these bacteria can form biofilms [35].
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 4 RSU Allocation
### Communication in Bidirectional Traffic Flow  
In Figure 3, we consider a road segment with the eastbound flow \(V^{e}\in\mathbb{V}\) and the westbound flow \(V^{w}\in\mathbb{V}\). These flows are independent from each other, in terms of vehicle density and traffic conditions. The road segment can be divided into \(l\) different regions. Suppose that we have \(k\) RSUs and \(l\gg k\) RSUs for content allocation. We suppose that vehicle density in region \(i\in\{1,...,l\}\) can be represented as \(d^{e}(i)\) for eastbound flow and \(d^{w}(i)\) for westbound flow.  
Generally, we consider three types of data communications: (1) Vehicle communications in the same direction represent the inter-vehicle communications with one hop or multiple hops along the traffic flow of same direction. The communication time is determined by the relative speed of vehicles, which can be assumed to be stable for each specific region. Here, we can denote \(\tau_{s}^{e}(i)\) and \(\tau_{s}^{w}(i)\) as the communication time for the eastbound flow and the westbound flow in region \(i\); (2) Vehicle communications in the opposite direction represent the inter-vehicle communications between vehicles driving in the opposite direction. We denote \(\tau_{o}(i)\) as the relative communication time for the opposite traffic flow in region \(i\). Usually, we have \(\tau_{o}\ll\tau_{s}\), as the relative speed of vehicles in the opposite direction is much less than that in the same direction; (3) Vehicle-to-RSU communications represent the data communications between the RSU and the vehicles passing by. Denote \(\tau_{r}^{e}(i)\) and \(\tau_{r}^{w}(i)\) as the vehicle-to-RSU communication time in region \(i\) for the eastbound traffic and the westbound traffic. Rather than the relative speed in the previous two types of communications, the vehicle-to-RSU  
Figure 2: RSU scheduling strategy design.  
communication is determined by the absolute vehicle speed, as the allocated RSUs are static. Obviously, we have the relationship between \(\tau_{r}\) and \(\tau_{o}\) as follows:  
\[\frac{1}{\tau_{r}^{e}(i)}+\frac{1}{\tau_{r}^{w}(i)}\propto\frac{1}{\tau_{o}(i)}.\]
# Pedestrian Counting Based on Piezoelectric Vibration Sensor
## 4 System Design
### Data Acquisition  
The previous works [27, 28] showed that if four piezoelectric sensors (marked as S1, S2, S3, S4) are deployed spatially as shown in Figures 1 and 6, the vibration event in this 3 m by 3 m area can be detected.  
Although the piezoelectric sensors are not able to measure the signals statically over a long period of time, this feature will not have a real impact on the performance of the proposed approach. Intuitively, the proposed approach makes use of the space-differential and time-differential relationship features but not each sensor's absolute signal amplitude value, to detect the number of people. Similarly, the poor signal quality from the piezoelectric sensor will not affect the approach's feasibility.  
Figure 6 shows the data acquisition devices and the experimental setup. Four EPZ-27 MS44W piezoelectric sensors are deployed in the detected area to sense the vibration signals on the floor. The bandwidth of the sensor ranges from 0 Hz to 4400 Hz. The signal is amplified, sampled, quantize, and recorded with an R&S-RTB2000 oscilloscope. Time  
Figure 5: The logic about making prediction for the system. If the system predicts that there is more than one person in the area due to a vibration event detected, it will further check that the signal/vibration event is caused by footsteps. If it is, the system outputs the results. Otherwise, the system outputs 0 person as a result.  
Figure 6: Data acquisition devices and experiment setup.  
synchronization of the signals is handled by the oscilloscope. The sampling rate is set to 10 kHz. The maximum value of the sampling point amplitude is 0.1 V. Thus, the waveforms higher than 0.1 V or lower than \(-0.1\) V will be cut off.
## 3 Materials and Methods
### Features
#### 3.3.6 Flow Duration  
**Feature feasibility:** The duration of flow is typically the TCP session length. It represents the amount of time, that a system or service requires to conduct it's network functions with it's destination system. As the number of services in industrial network nodes is limited, the duration of flows is deemed to have little variance.  
**Our data:** With multicast protocol, the flow duration could not be meaningfully used, and is not a plausible detection feature in the network under scrutiny.
## 3 Materials and Methods
### Feature Selection
#### 3.3.1 Principal Component Analysis (PCA)  
Assuming dataset \(x^{(1)}x^{(2)},\ldots x^{m}\) has inputs of \(n\) dimensions, these \(n-\) dimension data must be reduced to \(k-dimensional\) (\(k\ll n\)) using PCA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Instances** & **Attributes** & **Classes** & **Source** \\ \hline SCLC(GSE60052) & 86 & 28,089 & **2** (79 small-cell lung cancer and 7 normal) & [53] \\ NSCLC(GSE81089) & 218 & 28,089 & **2** (199 non-small-cell lung cancer and 19 normal) & [54] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the datasets used in this study.  
The first step in PCA is raw data standardization whereby the raw data should have unit variance and zero mean defined in Equation (5).  
\[x_{j}^{i}=\frac{x_{j}^{i}-\overline{x}_{j}}{\sigma_{j}}\forall j \tag{5}\]  
In the second step, a covariance matrix of the raw data is calculated as shown in Equation (6).  
\[\Sigma=\frac{1}{m}\sum_{i}^{m}(x_{i})(x_{i})^{\ T}\Sigma\in R^{n*n} \tag{6}\]  
The third stage is calculation of eigenvector and eigenvalue of the covariance using Equation (7).  
\[u^{\ T}\Sigma=\lambda,\mu \tag{7}\]  
\[U=[u_{1},\ u_{2},\ \dots u_{n}],\ u_{i},\ \in\ R^{n}\]  
In the fourth step, raw data are projected into a k-dimensional subspace, and this is followed by choosing the top k eigenvector of covariance matrix. The corresponding vector is calculated as shown in Equation (8).  
\[x^{new}=\left[\begin{array}{c}u_{1}^{\ T}\chi^{i}\\ u_{2}^{\ T}\chi^{i}\\ \dots\\ u_{k}^{\ T}\chi^{i}\end{array}\right]\in R^{k} \tag{8}\]  
The raw data with \(n\) dimensionality is reduced to a new \(k\) dimensional representation of data.
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
## 3 Results
### Availability and Capacity of the Backhaul Link  
Availability of the backhaul link established in ED depends on the EDH variations. We have calculated availability considering the average measured path loss and predicted additional path loss variations based on the occurrence of EDH. Figure 9 shows the predicted availability vs. capacity for a 20 MHz channel bandwidth. Availability predictions for 30 km, 40 km, 55 km, 65 km, and 80 km distances use the average measured path loss with additional slow fading margin. For a 30 km link in ED using only 4 m high antenna towers, a minimum 99.7% availability is possible. For 40 km beyond the horizon distance, 65 Mbps utilizing 20 MHz bandwidth is possible with more than 99.51% availability. For a 50 Mbps data rate link an availability higher than 95% is possible at 80 km distance and with path length of 60 km, more than 99% availability is predicted in ED at 5.8 GHz. Therefore, the link at 60 km is likely to be in outage for only 1% of the time throughout the year.  
With maximum transmit power of 1-Watt equivalent to +30 dBm and horn antennas providing 20 dBi gains one can establish a back haul link utilizing ED, which provides high availability of more than 99.5% up to 40 km at 65 Mbps and availability of 99% up to 60 km at 50 Mbps. For longer distances, up to 100 km, 99% availability is possible with least data rate of 8 Mbps. For a low data rate, less fade margin is required, so a higher availability is possible. Whereas, for a higher data rate, higher Quadrature Amplitude Modulation (QAM) requires large fade margin, reducing availability of the backhaul link. For a higher data rate, either higher transmit power or increase in antenna gain is required to achieve similar availability. Therefore, based on these measurements we propose a system and method to achieve high availability and capacity in ED for beyond-the-horizon backhaul link, providing offshore IoT connectivity, using only 4 m high antennas, with standard off-the-shelf 5.8 GHz Wi-Fi equipment.
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 2 Related Work
### Location Based Protocols  
Vector Based Forwarding (VBF) is a location based protocol [18]. It introduced the concept of virtual pipes, a set of nodes from the source to the sink that can possibly forward the packets (a virtual circuit). Multiple nodes within the virtual pipe forward the data packets to counter packet losses and node failures. However, in case of networks with low density of nodes, voids may exist, which decreases the delivery ratio. The power consumption is mainly due to three-way handshaking. To solve the problem of voids, Hop-by-Hop VBF (HH-VBF) [19] proposes local virtual pipes, from one node to the next, rather than from source to destination. However, the signaling overhead is higher than in VBF. The Adaptive Hop-by-Hop Vector-Based Forwarding (AHH-VBF) protocol [20] is based on the HH-VBF protocol. AHH-VBF may change the direction of the virtual pipeline and its radius to restrict the forwarding range of the packets. As a consequence, the reliability of the packet delivery increases in sparse networks and duplicate packets are fewer than in dense networks. Power control is also employed. LB-AGR [21] routes the packets on the basis of the difference in available power, node density and proximity. Compared to VBF the packet delivery ratio is poorer, but the average energy consumption is significantly better.  
Directional Flooding-based Routing (DFR) [12] assumes that every node knows the locations of the next-hop node and the destination node. Forwarding nodes are selected on the basis of the link quality and angle of arrival. In Focus Beam Routing (FBR) [22], each node has location information of the final destination. The forwarding area is selected based on transmission power. If there is no node present in the forwarding area, the power is increased. Sector-Based Routing with Destination Location Prediction (SBR-DLP) [23] assumes that every node knows the movement of the destination nodes. Pre-planned movement makes implementation of SBR-DLP quite limited.  
In the Location-Aware Routing Protocol (LARP) [24], the sink nodes find their location using GPS and broadcast their location coordinates. The other nodes calculate their location by using the reference of at least three sink nodes. When the location of a packet destination is not known, the sender queries the sink nodes. The sender finds the next hop by broadcasting the location of the destination node and a \"moving direction\" packet. If a receiving node finds that it is in the same direction, it replies, and the packet is forwarded. Hop-by-Hop Dynamic Addressing Based Routing (H2-DAB) [25] uses dynamic addresses for the nodes, such that they reflect their depth levels using shorter addresses for nodes closer to the surface.