esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay.  
Industrial Underwater Acoustic Sensor Networks (IUASNs); energy consumption; energy hole; network lifetime; End-2-End Delay (E2 ED); routing +
Footnote †: journal: Sensors
## 2 Methods
### Neuromagnetic Source Reconstruction  
To estimate cortical source activity from sensor-level MEG data, we applied a linearly constrained minimum variance (LCMV) beamformer [25; 26] implemented in MNE-Python. The reconstruction process involved several key steps.  
First, the MRI and MEG coordinate systems were co-registered for each participant using anatomical landmarks to generate a transformation matrix. The individual's cortical surface was reconstructed from T1-weighted MR images using the FreeSurfer _recon-all_ algorithm [27; 28; 29]. This process yielded a boundary element model (BEM) and defined the source space at an oct6 resolution.  
Second, a forward solution, modelling the contribution of each cortical source to the MEG sensor signals, was computed using MNE-Python's _make_forward_solution()_ function. This step integrated the co-registration transformation, the _oct6_ surface mesh (serving as the source model), the BEM, and preprocessed MEG metadata. To ensure consistent source orientation across participants, the forward solution was subsequently converted to have fixed orientations normal to the cortical surface using _convert_forward_solution()_ with the _surf_ori=True_ and _force_fixed=True_ parameters.  
Third, the LCMV beamformer was then applied to the preprocessed MEG data using the _make_lcmv()_ and _apply_lcmv_raw()_ functions to reconstruct neuromagnetic source activity across the _oct6_ cortical mesh. To obtain representative dynamics for functionally defined regions, we applied the Schaefer 400-region cortical parcellation atlas [30], which segments the cortex into regions of interest (ROIs), which are aligned with large-scale intrinsic connectivity networks identified from resting-state functional MRI [31] and assigns each of the 400 ROIs (200 per hemisphere) to one of 17 established functional networks. For each of these 400 ROIs, a single representative time course was extracted using MNE-Python's () _extract_label_time_course()_ function with the _mode='mean_flip'_ parameter. This method averages activity across vertices within an ROI after aligning their orientations by flippingthe sign of time series at vertices whose orientation deviates by more than 90\({}^{\circ}\) from the dominant orientation within that ROI.  
This pipeline resulted in estimating neuromagnetic dynamics for each participant at 400 functionally defined cortical locations, corresponding to the Schaefer atlas parcellation.
Radar remote sensing for soil moisture retrieval and surface water detection is common using both monostatic [7; 30] and bistatic geometry. The sensitivity of spaceborne GNSS-R (as a bistatic radar) to surface water and soil moisture has been widely studied [11; 16; 18; 23; 31; 32; 33]. Most of the studies have used observations from ground-based or space-based receivers, e.g., CYGNSS or Technology Demonstration Satellite-1 (TDS-1). Observational evidence demonstrates that GNSS-R is highly sensitive to inland surface waters, e.g., lakes and rivers [34]. Sistan and Baluchestan is one of the driest regions of Iran, with a slight increase in rainfall from east to west, and is a province at a high risk of flooding. The aim of this study was to indicate the capability of spaceborne GNSS-R for detecting and mapping of flood in the south part of Iran. The methodology for preparing and processing data is the same as those used described by the authors of [16; 18].
# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 5 Experimental Studies  
### Experimental Design  
To verify the effectiveness of the proposed FS algorithms, a series of experiments are carried out on 12 standard datasets, including two-category and multi-classification datasets. These were obtained from [[http://featureselection.asu.edu/datasets.php](http://featureselection.asu.edu/datasets.php)]([http://featureselection.asu.edu/datasets.php](http://featureselection.asu.edu/datasets.php)) (accessed on 18 January 2020) and [[http://archive.ics.uci.edu/mL/datasets.php](http://archive.ics.uci.edu/mL/datasets.php)]([http://archive.ics.uci.edu/mL/datasets.php](http://archive.ics.uci.edu/mL/datasets.php)) (accessed on 18 January 2020). They include microarray gene expression data, image detection data, email text data and so on. In addition, they are not only from different application fields, but also the number of features varies from 310 to 22,283, and the instances vary from 62 to 165, and this provides comprehensive experiments of the proposed and employed algorithms. Table 1 shows the details of the datasets.  
We verify the effectiveness of the BABCGWO and BABCWOA algorithms by comparing them with the ABC algorithm and their variants applied to high-dimensional datasets. The ABC algorithm without the scout bee phase is named the none-scout ABC algorithm (NSABC). The variants of the BABCGWO algorithm and BABCWOA algorithm with the added scout bee phase are named the BABCGWO with scout bees algorithm (BABCGWOWS) and the BABCWOA with scout bees algorithm (BABCWOAWS), respectively. To avoid contingency, all algorithms are run 10 times independently. The population size is set to 50; the number of iterations is 100. Each algorithm is implemented in MATLAB language.  
A suitable classifier is important when assessing the feature subsets. K-nearest neighbor (KNN) [39] is a common classification method that determines which category the classifier should be assigned to according to its K neighbors. In this research, the value of K is set to 5. In order to reduce the influence of over-fitting, the average classification error rate of 10-fold cross validation is taken as the fitness value. The fitness function is computed as follows:  
\[error=\frac{Number\ of\ misclassified\ samples}{Total\ number\ of\ samples} \tag{12}\]  
\[\text{fitness}=\frac{\sum_{i=1}^{10}error}{10} \tag{13}\]
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Access Control and/or Authorisation Delegation
#### 6.1.2 Integrity  
Since \(\mathcal{A}_{weak}\) does not have the ability to corrupt system components, the integrity of the data in all protocols remains unaffected.  
For \(\mathcal{A}_{middle}\), integrity is compromised in many protocols due to internal accomplices who can bypass access controls and potentially tamper with data. Although these protocols rely on encryption, digital signatures, and access control mechanisms, \(\mathcal{A}_{middle}\)'s accomplices may manipulate these mechanisms because they have privileged access to the system, making such protocols vulnerable to internal tampering. For example, while OAuth 2.0 and DTLS can guarantee integrity against external attacks from \(A_{weak}\), \(\mathcal{A}_{middle}\)'s accomplices within the system may alter or corrupt data without detection. However, the use of blockchain in protocols prevents even internal accomplices from undetected data alteration, since each modification must be validated by the network, preventing tampering without consensus.  
Similar conclusions can be drawn regarding the influence of \(\mathcal{A}_{strong}\) on protocol integrity as with \(\mathcal{A}_{middle}\). However, since \(\mathcal{A}_{strong}\) is more powerful and has broader access to the system, even fewer protocols remain protected. Nevertheless, blockchain-based protocols continue to offer strong resistance. To compromise the integrity of such systems, \(\mathcal{A}_{strong}\) would need to control more than 50% of the network, which is highly unlikely. Therefore, blockchain protocols maintain a high level of integrity assurance, even against strong adversaries.  
It is logical to assume that, for any adversary, the number of systems protected against confidentiality and integrity violations should be equal, as in the case of \(\mathcal{A}_{weak}\). However, for \(\mathcal{A}_{middle}\) and \(\mathcal{A}_{strong}\), there are more systems that appear to maintain integrity than confidentiality. The reason for this is that some delegation solutions [32; 34] include monitoring mechanisms capable of detecting suspicious activity or data manipulation and responding accordingly. For example, suppose an adversary may gain unauthorised access to data, thus violating confidentiality, without triggering any alarms, as long as the data remains unmodified. However, if the adversary attempts to alter the data, thereby violating integrity, the system's monitoring mechanisms are likely to detect and react to the intrusion.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
## 2 Network Layout
### Electronic Compensation  
The electronic compensation technique is used to filter the desired signals from the noisy data at user end. Similarly, it also overcomes the channel effects which are related to the RF processing steps involved at the user end. It is a cost effective approach as additional components are not required for its implementation at both transmitter and receiver end. Electronic compensation does not cause signal attenuation because this type of signal processing is performed at the extreme end of the transmission system, i.e., at the user end. It also provides the flexibility to add new sets of users to the multi-channel model, without affecting the overall downlink set up [21]. All the above-mentioned pros are achieved at the cost of few features, such as limited bandwidth and interference from surrounding devices, as compared to their optical counterparts. Different techniques are used for the electronic compensation process, such as feed forward decision feedback equalizer (FF-DFE) [22], nonlinear FF-DFE (NL-FF-DFE) [23], and maximum likelihood sequence estimator (MLSE) [24]. All these techniques have been studied previously to mitigate the channel effects from wireless transmission.  
Figure 1: Proposed system layout.  
The first two techniques can be used in cascade and are combined as EE to analyze their performance for mitigation of dispersion.  
The block diagram of the steps involved at the base station is shown in Figure 2, where EE block is installed at the end of transmission. Figure 2 shows a set of users at the central station and a composite multi-channel signal is formed by combining the output of each user. The multi-channel signal is transported over 300 km of optical fiber to the base station and suffers from severe dispersion effects which are effectively shown as \(\kappa(z)\) (channel response). At the base station, the signal is recovered by using a wide band photo detector. To compensate the unwanted signals, a combined equalizer is used at the end of the system. The signal analyzer is used to check the signal performance in terms of different performance metrics, such as BER, Q-factor, and eye-diagram. In this paper, we propose the use of EE to be used at the receiver end by employing the electronic equalizer block. At the receiver end, it abates inter symbol interference (ISI) and error free signal is demodulated to obtain the sent sequence. For the simulations, a commercial software package Optisystem was used. The components used in the proposed set up are not available for experimental analysis; hence, Optisystem simulations were used to model the components and obtain results.
## 5 Challenges and Future Research Direction  
The separation of the control plane from the data plane offered many network solutions; however, it poses many network challenges, among which include overhead, security threat, delay. Similarly, the limited switch memory also introduces several challenges such as security threat, higher update operation, which can affect the performance of SDN, especially in large-scale networks. Different efforts were made in recent years with different solutions for various use cases. Few research works have discussed the performance of SDN in dynamic and large scale-networks like Telco, where dense traffic flows are generated regularly. Such an environment is shown to have a massive number of flows containing a large number of packets at a short time interval. However, switch memory is constrained and cannot accommodate the required number of flow entries. Managing massive flows with a large number of packets in the limited switch flowtable remains as one of the challenging researches, which deserve more research efforts.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Case Study
#### 2.2.1 Priority Queue Data  
The data used for the case study was collected from 2003 to 2021 during a period of rapid urban growth (Figure 3). The priority queue of the Walnut Creek watershed was developed for the period 1 June 2018 to 26 August 2020 using 500 images from PlanetScope (Table 5). From these images 180 were used to create a before (1 June 2018-14 July 2019) image and 168 were used to create an after (15 July 2019-26 August 2020) image.  
The before and after satellite imagery was thematically classified from training samples generated from 12,363 OSM objects and barren land cover data captured as areas with BSI = <103, 105.5> and NDVI = <0.16, 0.22> (Figure 4).  
Figure 2: The study area is located in the (**A**) Walnut Creek Watershed (Neuse River Basin) of (**B**) Wake County, (**C**) North Carolina, United States of America. The most recent land cover provided by NLCD 2019 shows that the watershed is highly developed, but remains mostly forested in the south east.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Cloud Cover** & **GSD** & **Sun Azimuth** & **Sun Elevation** & **View Angle** \\ \hline count & 500 & 500 & 500 & 500 & 500 \\ mean & 0.01 & 3.78 & 131.96 & 44.37 & 0.59 \\ std & 0.01 & 0.17 & 18.12 & 15.79 & 0.54 \\ min & 0.00 & 3.50 & 89.90 & 15.90 & 0.00 \\
25\% & 0.01 & 3.60 & 113.65 & 27.70 & 0.10 \\
50\% & 0.01 & 3.80 & 132.05 & 45.50 & 0.40 \\
75\% & 0.02 & 3.90 & 149.53 & 60.73 & 1.00 \\ max & 0.05 & 4.70 & 160.80 & 70.40 & 1.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Raw PlanetScope Imagery Characteristics.  
The priority queue was manually evaluated by reviewing if the identified locations contained change. The review was conducted using GRASS GIS to toggle the before and after satellite imagery mosaics used during classification with the areas classified as high priority. Locations that were identified as errors were flagged and used to validate the accuracy of the priority queue at detecting change.
## 4 Discussion
Additionally, we noticed in Figure 4 that there is an existence of a weak Es layer when FSF appears at WK545. According to previous studies [19; 30; 56; 57], the midlatitude spread F may be associated with the coupling of Es layer and Perkins instability. It should be pointed out that Perkins instability is considered to be an important source of the generation of nighttime MSTIDs [57; 58]. Furthermore, the Es layer, presented as thin-layered structures at altitudes of 90-130 km, facilitates the performance of Perkins instability [57; 58; 59; 60]. Thus, we further investigated the possible mechanism. Figure 11 shows the two-dimensional maps of the detrended total electron content (TEC) over Japan at 13 UT (22 JST), 14 UT (23 JST), and 15 UT (24 JST) on 15 July 2003. We note that the periodic wave-like disturbances are clearly seen in the detrended TEC maps. Interestingly, the TEC wave fronts stretched from northwest to southeast, which is in good agreement with the normal propagation pattern under quiet magnetic conditions [31; 58]. As shown in Figure 5, satellite traces observed in ionograms can also be considered indicators of large-scale waves in the nighttime ionosphere [49]. When we relooked at the plasma density measured by CHAMP, it was found in Figure 10b that wave-like structures with small magnitude appeared at 40\({}^{\circ}\)N at approximately 1430 UT, indicating the possible existence of MSTIDs. Moreover, we also note the presence of wave-like perturbations in other satellite tracks over Japan on 15 and 17 July 2003. Thus, it is reasonable to infer that the FSF observed in our case may be the manifestation of the coupling of MSTIDs and the Es layer. However, it does not provide conclusive evidence that needs to be studied further in future works.
# Analysis of Available Components and Performance Estimation of Optical Multi-Band Systems
## 2 Review of Commercially Available Components
### Amplifiers
Other significant advantages of the Raman-type amplifiers  
Figure 1: Gain and noise figure of indicative available amplifiers assuming a total input power in each amplifier equal to 0 dBm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Amplifier Type** & **Operating Bands** & **Flat Gain Wavelength Range** & **Gain** & **Gain Flatness** & **Noise Figure** & **Max Output Power** \\ \hline  & mainly L & 1560–1610 nm & \textless{}35 dB & \(\pm\)2 dB & \textless{}6 dB & \textgreater{}2+22 dBm \\ EDFA & mainly C & 1525–1565 nm & \textless{}38 dB & \(\pm\)2.5 dB & \textless{}5 dB & \textgreater{}17 dBm \\  & L & 1573–1605 nm & \textless{}35 dB & \(\pm\)2 dB & \textless{}7 dB & \textgreater{}2+27 dBm \\ \hline  & S, C, L & 1520–1580 nm & \textless{}20 dB & \textless{}0.3 dB & 7 dB & 16 dBm \\ SOA & 1050–1080 nm & \textless{}1050–1080 nm & \textless{}40 dB & \textgreater{}0.3 dB & 8 dB & 18 dBm \\  & O & 1280–1300 nm & \textless{}25 dB & \(\pm\)1.5 dB & 7.5 dB & 12 dBm \\ \hline  & mainly C, L & 1525–1605 nm & \textless{}18 dB & \textless{}\textless{}1.5 dB & N/A & 31.8 dBm \\  & L & 1565–1605 nm & \textless{}15 dB & \textless{}\textless{}1.5 dB & N/A & 28.7 dBm \\ \hline  & & & & & \textgreater{}20 dBm/23 dBm \\ YDFA & 1055–1075 nm & 1055–1075 nm & \textless{}30 dB & N/A & \textless{}6 dB & on request \\  & 1025–1075 nm & 1025–1075 nm & \textless{}22 dB & \textless{}5 dB & \textless{}4 dB & \textless{}23 dBm \\ \hline  & O & 1280–1320 nm & \textless{}35 dB & \(\pm\)2 dB & \textless{}7 dB & \textgreater{}20 dBm \\ \hline  & & & & & & \\ \end{tabular}
\end{table} 
Table 3: Commercially available amplifiers and their characteristics. are the distributed amplification and their high output power (about 30 dBm), although such an output power may not be always desirable due to non-linear phenomena and due to safety reasons. Furthermore, due to the smaller gain of Raman amplifiers compared with DFAs, usually a hybrid Raman/EDFA amplification can be considered [17]. Overall, an important advantage of Raman amplifiers and SOAs compared with DFAs is that they offer a seamless gain over a large spectral extend, avoiding a waste of bandwidths for the frequency areas between the bands, e.g., 1510-1530 nm in Figure 1. The selection procedure for the best amplification scheme depends on the system demands. In particular, if a large system capacity is required, amplifiers that can be extended to more than a single band need to be considered. Next, if a large amplification gain is targeted, DFAs seem to be a good option, as they can provide an adequate gain over a large amplification range. Furthermore, if the impact of ASE noise is an important constraint, low-noise figure amplifiers need to be selected.
## 3 Transmission Point Selection in Multi-Band Multi-Connectivity Operation  
### Multi-Band Multi-Connectivity Operation in Mobile Networks  
Figure 1 shows examples of multi-band multi-connectivity operation in mobile networks, where PHY, MAC, RLC, PDCP, and RRC are components of the radio protocol stack defined in 3 GPP TS 38.300 [17]. In the figure, one band is selected and a single connectivity with the selected band is only available for the data transmission to a user, e.g., user 1 with FR1, in (a); two bands are selected and a multi-connectivity with the selected two bands is available for the data transmission to a user, e.g., user 4 with FR2 and FR3, in (b); and three bands are selected and a multi-connectivity with the selected three bands is available for the data transmission to a user, e.g., user 6 with FR1, FR2, and FR3, in (c). It is true that according to the terminal's capabilities and settings and the network environment supporting FR1, FR2, and FR3, the user can be served from one, two, and three transmission point(s) (or base station(s)) operating FR1, FR2, and FR3. In other words, a user can receive data via at least one connectivity as a result of the TPS algorithm.