esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contribution of the Research  
The contribution of the research work presented in this article is manifold. In this research work, we introduce a unique approach for modulation recognition of five modulation schemes, i.e., BPSK, QPSK, QAM, 16-QAM, and 64-QAM. Instead of using the HOCs directly, a linear combination (LC) of HOCs to create super-cumulants using arbitrary coefficients is used as the feature set. For every two super-cumulants, the coefficients are heuristically computed so that the distance between two modulation schemes is optimized. After that, these optimized coefficients are fed back into the classifier structure, where the distance is calculated using the KNN. From the extensive simulations, significantly higher recognition accuracy was achieved for all of the modulation schemes at lower SNRs. Moreover, for fading channels, there was a considerable improvement in the percentage recognition accuracy at a lower number of samples.
## 3 Throughput Performance Limits of Multimode Underwater Acoustic Networks
### Modeling of Throughput Performance Limits in Multimode Networks
#### 3.1.3 Probabilistic Modeling of System State Transition  
The system state transition probability \(P_{a}(\hat{s},\hat{s}^{\prime})\) is a deterministic function. The next state \(s^{\prime}\) is determined by the current state \(s\) and action \(a\).
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
###### Abstract  
In wireless sensor networks (WSNs), efficient energy conservation is required to prolong the lifetime of the network. In this work, we have given emphasis on balanced energy consumption and energy holes avoidance. This paper proposes a multi-gateway-based approach to reduce the transmission distance between the sender and the sink node. The area to be monitored is divided into regions and gateway nodes are deployed at optimal positions. We have designed a transmission scheme, in which sensors in the sink region communicate directly to the sink, sensors in the gateway region communicate directly to the gateway, and sensors in the cluster region transmit their data directly to their respective cluster head which transmits data to the gateway in its region. If the distance between a cluster head and the sink is less than the distance between the cluster head and the gateway node, the cluster head transmits data to the sink instead of the gateway node. We have compared the proposed protocol with Low-Energy Adaptive Clustering Hierarchy (LEACH), Gateway Based Energy Aware Multi-Hop Routing (M-GEAR), and Gateway Based Stable Election Protocol (GSEP) protocols. The protocol performs better than other protocols in terms of throughput, stability period, lifetime, residual energy, and the packet transmitted to the sink.  
Keywords:wireless sensor network; energy holes; multiple gateway nodes; balanced energy consumption; routing protocol +
Footnote †: journal: Informatics
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 4 Discussion  
Cheiloscopy has long been considered a potentially useful forensic identification method; however, its practical applicability has been limited by a lack of standardization and high operator-dependent variability [16]. While analog techniques, such as lipstick impressions transferred to adhesive surfaces, are simple and cost-effective [5], our findings align with previous studies demonstrating their significant reproducibility and reliability issues [2; 7]. For instance, analog lip prints in our study were often incomplete or distorted, with a usability rate of only 60%, consistent with literature reporting high exclusion rates due to poor imprint quality [7; 17]. In contrast, 3D cheiloscopy provided 100% usable impressions with minimal inter-operator variability, underscoring its potential to overcome the limitations of traditional methods.
## 5 Simulation Results  
In order to evaluate the performance efficiency of the proposed MAC scheme, we implement it using the ns-2 simulator version 2.30 [33] and compare its performance with the original IEEE 802.11 MAC via extensive simulations. We adopt the \"Propagation/Shadowing\" module among the three ns-2 propagation models in the simulations. The channel and transceiver parameters are chosen to emulate a wireless network with a short transmission range of 26.9 m. For validating a scheduled transmission, \(P_{th}\) is set to \(0.5\) for the results reported in this section. We choose the shadowing standard deviation of 0.01 dB, for the case of near-deterministic channels, and 4 dB (the default value in ns-2) for the case of moderate shadowing channels. We perform our simulations using the chain, grid, and random topologies as in prior work [14; 21; 25]. The Ad Hoc On Demand Distance Vector (AODV) protocol is used for routing in the simulations [37].  
During each simulation, the source nodes start to generate Constant Bit Rate (CBR) traffic at 10 seconds and the simulation terminates after 10 minutes simulation time. The estimation of \(\beta\) and \(\sigma_{dB}^{2}\) described in Section 4.4 is incorporated in the simulations. Through our simulations, we found that the number of failed concurrent transmissions is generally very small for all the case examined, even though the detection mechanism for multiple exposed nodes was not adopted (see Section 4.3). This is due to the fact that the networks have moderate density and the chance of having multiple exposed nodes for a free transmission is relatively negligible. Therefore the detection mechanism is not used for the results presented in this section. For network-wide performance, _goodput_, i.e., the total number of successfully received bytes at the agent level, and end-to-end delay of all received frames are measured for the data sessions. Each simulation is repeated ten times with different random seeds and the average of the ten trials are presented with 95% confidence intervals plotted as error bars in the figures.
## 2 Materials and Methods
### Low Earth Orbit Satellite Measurements and Ionospheric Data  
Additionally, we analyze ionospheric measurements provided by a LEO satellite, the European Space Agency's Swarm mission. This mission is a constellation of three LEO satellites that were successfully launched on 22 November 2013, and are still operating. This constellation is designed to provide measurements of the Earth's magnetosphere and ionosphere, studying the impact of the solar wind on the dynamics of the upper atmosphere [37; 38]. The Swarm-Alpha (A), Bravo (B), and Charlie (C) are three identical satellites that share the same design and payloads.  
All three satellites were put into a circular near-polar orbit with a low eccentricity. Swarm-A/C pair have the same orbit configuration (inclination of 87.35\({}^{\circ}\), altitude of \(\sim\)450 km, east-west separation of about 1-1.5\({}^{\circ}\) in longitude), while Swarm-B has a different one (inclination of 87.75\({}^{\circ}\), altitude of \(\sim\)510 km). These satellites fly above the F-layer peak (the peak altitude of the ionospheric electron density). In addition, Swarm-A/C fly in tandem, while Swarm-B moves away from the couple Swarm-A/C by covering different local times [37; 38].  
The Swarm spacecraft were equipped with different payloads, including GPS receivers and Langmuir Probes (LP), among others. We considered the ionospheric VTEC values associated with the point where the link path between GPS and Swarm-A satellite pierces the spherical thin shell located 400 km above the Swarm-A orbit. We also used in-situ electron density (\(N_{e}\)) measurements by LP at \(\sim\)450 km [38; 39] for each of the five selected DoYs in June 2021 ([[https://Earth.esa.int/web/guest/swarm/data-access](https://Earth.esa.int/web/guest/swarm/data-access)]([https://Earth.esa.int/web/guest/swarm/data-access](https://Earth.esa.int/web/guest/swarm/data-access)), last accessed on 17 January 2022).  
We used the Swarm Level 2 (L2) TEC (TECxTMS_2F) data product, which contains time series of slant and vertical (absolute and relative) TEC for each GPS satellite in view (at most eight due to instrumentation design). The cadence of the ionospheric TEC data is 1 Hz since it was changed from 10 s (0.1 Hz) to 1 s (1 Hz) on 14 July 2014 [38; 40].  
We also used the Swarm LP data, which is part of the EFI package (EFIX_LP_1B plasma data). LP provides measurements of in situ \(N_{e}\) and electron temperature with a 2 Hz sampling rate [38; 39].
## Materials and Methods
### Protein Production and Purification  
_E. coli_ Rosetta (DE3) strain harboring pET-15b-His-UK was grown in 750 mL of Luria-Bertani medium [0.5% (_w/v_) yeast extract, 1% (_w/v_) peptone, 0.1% (_w/v_) glucose, 0.5% (_w/v_) NaCl] with 100 ug/mL ampicillin, and 20 ug/mL chloramphenicol at 37 \({}^{\circ}\)C until the optical density at 600 nm (OD\({}_{600}\)) reached 0.6. Isopropyl \(\beta\)-D-1-thiogalactopyranoside (IPTG) was added to a final concentration of 0.5 mM. After 4 h at 37 \({}^{\circ}\)C, the culture was cooled in an ice bath and cells were harvested by centrifugation at 4 \({}^{\circ}\)C. The pellet (9 g of wet weight of cells) was suspended in Lysis buffer (50 mM Na phosphate, pH 7.4, 300 mM NaCl, 0.5 mM MgCl\({}_{2}\), containing 1 mM phenylmethylsulfonyl fluoride (PMSF), Complete protease inhibitor cocktail (Roche Applied Sciences, Penzberg, Germany) and DNaseI (Qiagen, Hilden, Germany) in a 10:1 ratio of buffer (mL)/gr of cell wet weight. After cell breakage with a French press, the crude extract was centrifuged at 15,000\(\times\)\(g\) for 40 min at 4 \({}^{\circ}\)C in a Sorval RC5 centrifuge and the supernatant was applied to a Nickel-Sepharose column (HisPrep FF16/10, GE Healthcare Life Sciences, Pittsburgh, PA, USA, 20 mL), equilibrated with 50 mM Naphosphate buffer, pH 7.4, 300 mM NaCl, 30 mM imidazole connected to a FPLC system. The column was extensively washed with the equilibration buffer and His-tagged UK114 was eluted with a linear 30 mM-1 M imidazole gradient in 50 mM Na phosphate buffer, pH 7.4, 300 mM NaCl (60 mL; 3 column volumes), monitoring the absorbance at 280 nm. After SDS-PAGE analysis, the fractions containing the recombinant protein were pooled and dialyzed for 16 h at 4 \({}^{\circ}\)C against 10 mM Tris-HCl, pH 7.4, 300 mM NaCl. To remove the tag, CaCl\({}_{2}\), and thrombin (GE Healthcare) were added to the dialyzed fraction at a concentration of 2 mM and 5 U/mg of tagged UK114, respectively, and incubated for 2 h at 27 \({}^{\circ}\)C. The fully digested protein was concentrated to a final volume of 10 mL in Amicon Ultra centrifugal filter devices (cut off 10 kDa, 4 mL, Millipore) and applied to a Superdex 75 (26/60) gel filtration column (GE Healthcare) previously equilibrated with 0.9% NaCl. The fractions containing UK114 were pooled after SDS-PAGE analysis. The removal of the tag by thrombin leaves three extra amino acids (GSH) at the protein N-terminus as compared to the predicted UK114 protein sequence (UniProt P80601). The identity of the fusion protein with the expected amino acid sequence was assessed by MS analysis of the purified protein (see Section 4.6). This procedure yields about 200 mg homogeneous UK114 from 9 g of starting _E. coli_ wet paste.
## 4 Discussion  
Germline pathogenic variants in _TGFBR2_ have been reported to underlie [PERSON] syndrome, an autosomal dominant Marfan syndrome-like connective tissue disorder characterized by hypertelorism, cleft palate with bifid uvula, joint laxity, scoliosis and arachidaty, translucent and easily bruised skin, and severe aortic aneurysms [10]. In the vast majority of the above cases, the pathogenic variants are missense mutations, with most residing in the cytoplasmic kinase domain [10]. Somatic mutations in _TGFBR2_ are also commonly seen in hereditary non-polyposis colorectal cancer (HNPCC) with defects in mismatch repair (MMR). Tissues harboring defects in MMR exhibit microsatellite instability (MSI) that is characterized by slippage of short to mid-length mononucleotide repeats (or homopolymers) that cannot efficiently repair in the absence of MMR [11]. Because of a polyA tract in exon 3, _TGFBR2_ is one of the most frequent targets of MSI: \(\sim\)90% of HNPCC cases harbor frameshift mutations in TGFBR2 [10]. Notably, this is the same polyA tract affected by the frameshift variant, c.458 del; p.Lys153 SerfsTer35, in each of our patients (two with GPP and the other two with AOID). Because of the location of this frameshift variant within the _TGFBR2_ locus, it is expected that transcripts derived from the variant _TGFBR2_ allele in our patients would undergo nonsense-mediated decay, resulting in decreased TGFBR2 protein and reduced TGF8 signaling, and thus potentially a different mechanistic impact to those variants seen in [PERSON] syndrome. Even if some truncated protein was produced and reached the plasma membrane, it would likely be secreted as the truncation occurs before the transmembrane domain. Such truncated and secreted type II receptors can act in a dominant-negative manner by preventing the interaction of the remaining functional type II receptor with type I receptor [16]. A third possibility is that the mutant protein does not make it out of the ER, thus reducing the overall surface levels of active TGFBR complexes.  
Figure 7: Representative images of immunohistochemistry for IL1B, IL23, and TNFA in the normal skin tissue, patient 1 (GPP), and patient 3 (AOID). IL1B and IL23 showed mildly increased expression in dermis of patient 3 whereas no difference between patient 1 and normal skin tissue. No significant difference of TNFA expression between normal skin tissue and patients 1 and 3 (Magnification, 8\(\times\)).
# Offloading of Atomic Tasks in Satellite Networks:
## 6 Conclusions  
This paper studies the offloading of atomic tasks in space-based networks. Aiming at the pain points of high response latency of multi-task requests, poor task calculation timeliness, and poor resource utilization in the spatial information network, we propose a low time complexity algorithm based on graph-related theories and edge computing-related ideas to achieve optimal offloading of satellite atomic tasks and cache and make full use of satellite resources. We compared the time complexity of several baseline algorithms for task offloading and analyzed the performance between the algorithms and found that our method has certain advantages in terms of time complexity and task execution timeliness. The time complexity of the algorithm designed in this paper depends mainly on solving the maximum flow. We optimize the maximum flow algorithm for dynamic networks to effectively improve the overall algorithm calculation efficiency so that we improve satellites' overall computing power and timeliness and improve the high-latency status of task calculations. In future research, there are still some problems to be solved. The model constraints constructed in this article are not perfect, such as power and radiation constraints. In general, the calculation methods for the maximum number of tasks, task offloading and caching strategies, and the second offloading of atomic tasks proposed in this paper can provide some new ideas for improving the timeliness and resource utilization of space-based networks.  
Conceptualization, Y.L.; methodology, H.F. and Y.L.; software, H.F.; validation, Y.L. and H.F.; formal analysis, H.F. and Y.L.; investigation, Y.L.; resources, H.F. and Y.L.; data curation, H.F. and W.Z.; writing--original draft preparation, H.F.; writing--review and editing, H.F. and Y.L.; visualization, H.F. and W.Z.; supervision, Y.L.; project administration, Y.L. and W.Z.; funding acquisition, Y.L. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant U1911401 and the National Natural Science Foundation of China under Grant U1435215.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: Performance comparison between OCMF and various classic methods. (**a**) Performance comparison between OCMF, ISSR and FCFS approach. (**b**) Performance comparison between OCMF, PSO, GA and DE approach.
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 2 Related Work
They used the log-Gabor odd-symmetric wavelets to calculate the orientation of phase congruency and construct a descriptor using the orientation and amplitude of phase congruency. Compared with the HOG, the HOPC is more robust for matching remotely sensed multimodal images. [PERSON] et al. demonstrated that the performance of the template-matching algorithm based on the HOPC is superior to those based on the NCC, MTM, or MI for remotely sensed multimodal images [9]. Recently, a novel template-matching method based on the channel features of oriented gradients (CFOGs) was proposed. This novel feature is an extension of the pixel-wise HOG descriptor [35]. Compared with the HOPC, the CFOG is more robust and efficient in matching multimodal images [35]. However, both the HOPC and CFOG handle the gradient reversal in a problematic manner, as described in part 2 of Section 3. Furthermore, they are sensitive to noise distortion, as discussed in Section 4. In recent years, deep learning-based methods are proposed for matching multimodal images or aerial images. [PERSON] et al. [23] proposed a unified approach for feature and metric learning, dubbed Match-Net. They developed a deep convolutional network to extract features from images and a network of three fully connected layers to measure the similarity. Match-Net can achieve better performance compared with the state-of-the-art handcraft methods according to their testing results. [PERSON] et al. [24] proposed a trainable end-to-end matching network, which is not just for learning the feature and the similarity, but also estimating the transformation parameters with a regression network. This method is further developed for aerial image matching in [25; 26], and the testing results confirmed that deep learning-based matching methods can achieve significant improvement compared with the traditional matching methods.
## 3 Applications
### The Mw 6.1 Chile Shallow Crustal Earthquake  
A strong shallow crustal earthquake struck 23 km WNW of Hacienda La Calera, Chile (Figure 2) on 23 August 2014 at 22:32 UTC. According to the CEA, GFZ, ISC-EHB, and USGS earthquake catalogue, the event occurred at a depth range from 30 to 42 km (Table 1) with less agreement. The seismic station is shown by a black triangle in Figure 2. The vertical component seismograms were band-pass filtered in the frequency band 0.1-0.6 Hz, normalized with respect to the maximum amplitude, and sorted by the distances. We have collected a set of 12 records with clear PKIKP and pPKIKP phases at distances ranging from 169.8\({}^{\circ}\) to 170.6\({}^{\circ}\) (Figure 2). The theoretic and observed traveltime of PKIKP are marked with red plus sign and filled circle, respectively, in Figure 2, and the blue indicates pPKIKP.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Event ID** & **Latitude (\({}^{*}\))** & **Longitude (\({}^{*}\))** & **Depth (km)** & **Magnitude** & **Author** & **Misfit (\({}^{\text{s}^{2}}\) )** \\ \hline null & \(-32.710\) & \(-71.390\) & 30.0 & 6.5 & CEA & 21.0 \\ gfz2014 qngj & \(-32.641\) & \(-71.284\) & 32.7 & 6.4 & GFZ & 3.0 \\ usb0005 rc & \(-32.695\) & \(-71.442\) & 32.0 & 6.4 & USGS & 6.1 \\
610572067 & \(-32.688\) & \(-71.351\) & 42.0 & 6.2 & ISC-EHB & 46.0 \\ null & \(-32.688\) & \(-71.351\) & 34.3 & 6.2 & This method & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The result and comparison for the shallow event occurred on 08/23/2014, Mw 6.1, Chile.  
In the inversion procedure, we chose an initial depth between minus and plus 20 km with the depth of ISC-EHB at random. The depth estimation of this event by the pPKIKP-PKIKP time intervals was shown in Figure 2 and four sets of criteria by different bulletin are shown in Table 1. In this case, taking the focal depth in ISC-EHB as the initial value, the depth estimation by using pPKIKP-PKIKP time intervals is 34.3 km, and the residual misfit is about 0.1 s\({}^{2}\), which is minimal in comparison to the four catalogs. The result also indicated that this method can estimate the focal depth for shallow events by using teleseismic pPKIKP and PKIKP phases.
## 2 The Node Deployment Strategy Based on 2-Fold Coverage
### Coverage  
As a key factor of the WSN's QoS, coverage is defined as the proportion of target points that all sensor nodes can monitor to the number of all target points in the area under the 0-1 perception model in this paper, where the 0-1 perception model is a typical idealized model. The target within the perceived radius of the sensor node can be 100% perceived by the node, but the target outside the perceived radius cannot be perceived by the sensor node. Figure 1 shows a typical 0-1 perception model diagram.  
The calculation formula for the network coverage is as follows:  
\[\textit{Coverage}=\frac{\sum\limits_{s\in S_{\textit{area}}}T(s)\Delta s}{\sum \limits_{s\in S_{\textit{area}}}\Delta s}. \tag{1}\]  
In the above formula, \(s\) represents the monitoring target point, \(\Delta S\) represents the area occupied by the monitoring target point, \(S_{\textit{area}}\) represents the monitoring area, and \(T(s)\) represents the coverage condition of the monitoring target point in the area. If it can be monitored by the surrounding sensor nodes, then \(T(s)=1\); otherwise, \(T(s)=0\).
# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 3 Machine Learning Techniques in Cybersecurity
### Artificial Neural Networks (ANN)
A deep learning approach called DeepFlow was proposed in [143] to directly detect malware in Android applications. The architecture consisted of three components for feature extraction, feature coarse granularity and classification. Two modules were used to assess malware sources from the Google Play Store. Experiments showed that DeepFlow outperformed SVM, ML-based algorithms and multi-layer perceptron (MLP). A summary of neural network approaches is shown in Table 4. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Objective** & **Dataset** & **Accuracy** & **Reference** \\ \hline \multirow{3}{*}{ANN} & Abilities to learn, classify and process information; faster self-organization. & RealSecure network monitor & 96.5\% & [135] \\ \hline \multirow{3}{*}{DeepFlow} & Custom-developed to distinguish malware. It uses the static taint analysis tool FlowDroid. Identifies sensitive data flows in Android apps. & Features extracted from malicious apps from Google Play Store & \\ \cline{1-1} \cline{2-5}  & Discovers layers of features and uses feed-forward neural network to optimize discrimination. & Custom dataset & 96\% & [140] \\ \cline{1-1} \cline{2-5}  & & KDD’99 Cup & 93.49\% & [141] \\ \cline{1-1} \cline{2-5}  & Network feature sample & 97.60\% & [142] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Deep machine learning algorithms used in cybersecurity.