esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 2 Feature Sets
### CAIA Feature Vector  
The name of \"CAIA\" is taken from the Centre for Advanced Internet Architectures at the Swinburne University of Technology ([[http://www.caia.swinburne.edu.au/](http://www.caia.swinburne.edu.au/)]([http://www.caia.swinburne.edu.au/](http://www.caia.swinburne.edu.au/))), which is the research group and institution hosting the authors that first used this vector to the best of our knowledge. In their paper, [PERSON] et al. [10] compared different machine learning techniques for performing traffic flow classification. Since then [10], the same features have often been used with minimal modifications in some relevant, frequently cited works related to flow-based traffic analysis with machine learning. For example, almost the same set is applied by [PERSON] et al. [11], who investigate why some features are so decisive for machine learning algorithms. The same feature vector was recently used by [PERSON] et al. [12]. Additionally, [PERSON] et al. [13]--yet not specifically referring to the previous works--used the same feature set, but in a unidirectional fashion. [PERSON] and [PERSON] [14] refer to the CAIA feature set as a ground benchmark for their experiments with encrypted traffic.  
The CAIA vector is originally presented in Reference [10] with 22 features that collect bidirectional information. We added 8 features related to TCP (Transmission Control Protocol) flags following the use in Reference [11]. Therefore, the final vector is formed by 30 features (Table 1).
# SHPB Experiment and MPM Simulation on Dynamic Properties of Unsaturated Clay Under Impact Loading
## 2 SHPB Test on Unsaturated Clay
### Results and Discussion
Therefore, the evolution of the lock-up was observed to be a function of strain rate, as illustrated in Figure 3.  
Moreover, Figure 3 shows that the dynamic response parameters, including the slope of the curve in the elastic stage, yield limit, peak stress, and maximum strain, increase with strain rate, indicating a significant strain rate effect for unsaturated clay under SHPB impact loading. The CS model, as shown in Equation (3), was generally used to characterize strain  
Figure 3: Dynamic stress–strain curves of unsaturated clay with strain rates from 204 s\({}^{-1}\) to 590 s\({}^{-1}\). rate dependency behavior of material [33; 34]. Because the determination of CS coefficients requires curve fitting, a rearranged form of Equation (3) is expressed as Equation (4). \[\frac{\sigma_{dy}}{\sigma_{st}}=1+\left(\frac{\dot{\epsilon}}{C}\right)^{1/P} \tag{3}\]  
\[\log\dot{\epsilon}=\log C+P\log\left[\left(\frac{\sigma_{dy}}{\sigma_{st}} \right)-1\right] \tag{4}\]  
where \(\sigma_{dy}\) and \(\sigma_{st}\) are the static and dynamic strength values, respectively; \(C\) and \(P\) are the intercept and slope of the fitting curve in a double logarithmic scale of the \(\left(\sigma_{dy}/\sigma_{st}-1\right)\) and the strain rate \((\dot{\epsilon})\) space, respectively. Figure 4a shows the calibrated CS coefficients of various geomaterials [25; 33; 35; 36; 37]. The results indicate that the CS model is appropriate for representing strain rate dependency behavior of various materials with a large range of strain rate. Figure 4b shows that when the strain rate of unsaturated clay in the SHPB test is in the range of 204-590 s\({}^{-1}\), the CS model agrees well with the measured data. In this case, the intercept \(C\) of 56 and slope \(P\) of 0.8 are determined in the CS formula of dynamic peak stress.
# Self\(-\)Mode\(-\)Locked \(2-\)um GaSb\(-\)Based Optically Pumped Semiconductor Disk Laser
###### Abstract  
We present a mode\(-\)locked GaSb\(-\)based optically pumped semiconductor disk laser operating at 2 um based on the self\(-\)mode\(-\)locked mechanism. Using the delay differential equation model, we discuss the influence of cavity length on the stability of self\(-\)mode\(-\)locking and design a \(Z-\)shaped long cavity for self\(-\)mode\(-\)locking. Employing an aperture and an F\(-\)P etalon in the cavity length of \(-\)365 mm, we obtain stable self\(-\)mode\(-\)locking at a center wavelength of 2034.5 nm, with a pulse duration of 255.48 ps and average output power of 173 mW at a repetition rate of 404 MHz.  
GaSb; semiconductor disk laser; mid\(-\)infrared; self\(-\)mode\(-\)locked
# A Review on Local Failure Probability Sensitivity Analysis
## 2 Local RSA with Respect to Deterministic Inputs
### Expression of the Local RSA with Respect to Design Parameters  
Computing the derivatives of \(P_{f}\) with respect to design parameters \(\delta\) is required for the gradient-based algorithms used to solve a RBDO problem [2], as previously mentioned. The expression of the dependence of \(P_{f}\) on the design parameters \(\delta\) is necessarily contained in the limit state function. Therefore, no matter the input space selected, the expression of the derivatives of \(P_{f}\) is a surface integral as in Equation (4) where one adapts the integral to \(f_{\mathbf{Z}}\) or \(f_{\mathbf{X}}\). In the rest of this article, we assume that the sensitivities with regard to design parameters are computed in the standard space of the inputs \(\mathbf{X}\). The surface integral is thus given by the following equation  
\[\frac{\partial P_{f}(\delta)}{\partial\delta_{\ell}}=-\int_{g(\delta,\mathbf{ x})=0}\frac{1}{\parallel\
abla_{\mathbf{x}}g(\delta,\mathbf{x})\parallel}\frac{ \partial g(\delta,\mathbf{x})}{\partial\delta_{\ell}}f_{\mathbf{X}}(\mathbf{ x})\mathrm{d}s(\mathbf{x}), \tag{5}\]  
with the same assumptions and notations as in Equation (4), for \(\ell\in[1,\ldots,n]\). This sensitivity depends then necessarily on the derivative of \(g\) with respect to \(\delta_{\ell}\) and its gradient \(\
abla_{\mathbf{x}}g\).
## 4 Discussion
### Nutrient Filtration by the Mangrove System  
Mangroves have repeatedly been proposed as natural filters for anthropogenic nutrients [21, 22, 23, 24, 91, 130, 131, 132, 133]. For DIN and PO\({}_{4}\)3-, tidal sampling and benthic fluxes indicated similar source and sink functions, highlighting the importance of sedimentary processes (for DIN and PO\({}_{4}\)3- dynamics) in the Naniu mangrove area. Studies have found that sediment biogeochemical processes dominate DIN and PO\({}_{4}\)3- removal (e.g., [24]). For these nutrients benthic fluxes may thus be used to estimate the filtration capacity of the mangrove sediment system (Table 3). The main source of nutrients to the estuary and adjacent coastal waters is the 9704 km\({}^{2}\) catchment of the Naniu River, with additional inputs from a 6500 ha aquaculture area [50]. At only 514 ha, the potentially filtering mangrove area is relatively minute. Expanding measured influx rates (Table 2, Figure 6) to the mangrove area, total mangrove NO\({}_{3}\)- uptake rate may range from \(-\)1374 to \(-\)9559 mol\(\cdot\)d\({}^{-1}\), while PO\({}_{4}\)3- uptake would be between \(-\)402 and \(-\)407 mol\(\cdot\)d\({}^{-1}\). Consequently, mangroves would remove only 0.1% to 0.5% of the 2,013,100 mol\(\cdot\)d\({}^{-1}\) NO\({}_{3}\)- inputs and 1.2% of the 34,200 mol\(\cdot\)d\({}^{-1}\) PO\({}_{4}\)3- inputs (Table 3). Benthic removal rates must be considered minimum ecosystem filtration service, as other system components, such as mangrove trees, further facilitate uptake of these nutrients.  
Low removal capacity has also been shown for an Australian mangrove receiving shrimp aquaculture effluents [65]. In a meta-analysis [PERSON] and [PERSON] [17] found that the nitrogen removal capacity of wetlands relates negatively to land-derived nitrogen input. The Naniu estuary has an area of 16,578 ha [134] and annually receives about 10,305 tN (\(7.7\times 10^{8}\) mol) of DIN from the river, 95% of which is delivered as NO\({}_{3}\)- [50]. Given this high input of 651.8 kgN\(\cdot\)ha\({}^{-1}\cdot\)y\({}^{-1}\) to the estuary, the low removal by mangrove wetlands of \(<1\%\) land-derived DIN is comparable to other impacted systems [17]. This low filtration service suggests that, due to the disadvantage ratio of mangrove wetland area to land-derived nutrient input, the mangroves do not significantly affect nutrient dynamics in the tidally dominated Naniu estuary through filtration. These results must be regarded as first estimates, and further studies with wider temporal and spatial resolution would aid the calculation of a full filtration service budget. It is clear, however, that given this low filtration capacity, current efforts at mangrove reforestation are unlikely to create an effective buffer for coastal eutrophication, but that nutrient input management will be required.
## 3 Results
### FTIR Difference Spectroscopy on the T127 Variants  
FTIR spectroscopy was applied to gather information on the structural changes of the T127 variants (Figure 8). For comparison, difference spectra of \(C\)rChR2-WT were chosen at time points that represented mainly the intermediates P\({}_{2}\)[380] (300 \(\upmu\)s) and P\({}_{3}\)[530] (6.7 ms), and were compared to the difference spectra of the T127A and T127S variants recorded at 8.4 ms after pulsed excitation. At this time, the P\({}_{2}\)[380] intermediate was predominant in the variant with minor contributions from P\({}_{3}\)[530] and P\({}_{4}\)[480] intermediates.  
The infrared difference spectra basically confirmed the observations made in the visible spectral range (Figure 6). The low accumulation of the P\({}_{3}\)[530] state in the T127A variant was observed by comparing the difference spectrum at 8.4 ms with the one at 6.7 ms of the WT. The band at 1737 cm\({}^{-1}\) assigned to the deprotonation of the proton donor to the Schiff base, D156 [16], was reduced in intensity, which indicated low accumulation of the deprotonated D156 species. This observation was expected as soon as the lifetime of the P\({}_{2}\)[380] state (with deprotonated Schiff base) was extended, as here in the T127 variant. The negative band at 1556 cm\({}^{-1}\) in the difference spectra of WT was due to the ethylenic stretching vibration of the retinal in ground-state ChR2 [32]. This band was hardly seen in T127A due  
Figure 7: Kinetics in the UV/Vis range recorded after pulsed laser excitation (450 nm). Traces at 380 nm (red lines), 470 nm (blue), and 530 nm (green) are shown for each variant. While the T127A variant (top panel) showed a prolonged lifetime of the P\({}_{2}\)[380] state and reduced P\({}_{3}\)[530] formation, the T127S variant (middle panel) had very similar kinetics to the WT (bottom panel).  
to spectral overlap by a positive contribution that is absent in the WT. Instead, a broad negative band at 1533 cm\({}^{-1}\) was registered, which was a marker for the contribution of the P\({}_{4}\)[480] photoreaction [35].
# Fast Format-Aware Fuzzing for Structured Input Applications
## 2 Background
### Current Fuzzing State-Of-the-Art
#### 2.2.2 Format-Aware Fuzzing  
Considering the inefficiency of the CGF test cases, various distinctive tools accept grammar guidance or expert-given format templates, which allow them to sense the target input format. Once the format information is sensed, the test cases that maintain the consistency of the _header_ and _trailer_ will be generated. We note such test cases as _format-well_. Representative work in this category includes: peach [7], LangFuzz [6], AFLSmart [8], QuickFuzz [9], FormatFuzzer [10], FaFuzzer [11].  
Targeting structured input applications, these solutions work well for constructing _format-well_ test cases for performing vulnerability tests on deep code. Nevertheless, the format awareness of such fuzzers relies on manually given format templates or grammar. This results not only in being labor intensive and manual errors, but also in an incapability to test targets in an unknown input format efficiently.  
Thus, several research works have investigated capturing the dependency between the test case and target, without offering a priori knowledge or expensive techniques including symbolic execution and taint analysis. Via systematic bit-/byte-flipping or random value padding, the fuzzer monitors and analyzes changes in the state of the target. It then identifies the dependencies in the partial _header_ and _trailer_ fields. It grants the fuzzer some level of format-aware capability. Such research includes Redqueen [12], GRIMOIRE [13], SLF [14], and WEIZZ [15].  
Redqueen [12] and GRIMOIRE [13] recognize dependencies based on whether the execution path has changed. Redqueen attempts to colorize the input with random bytes, identifies those fragments that caused the path coverage to change, and obtains the Input-to-State (I2S) dependencies for locating the _header_ fields. Input-to-State (I2S) is a strong correspondence between the input and the current program state [12]. Figure 3 illustrates an example of this link between the input fragment and the target variable. The fragment _signature_, highlighted by a yellow block, has a direct relationship with the variable _sig_, while the value of _sig_ is the direct copy of fragment _signature_. Based on Redqueen, GRIMOIRE recognizes the fragments that failed to trigger execution path changes as _trailer_ fields, generalizes those fragments, and applies input mutations such as recursive replacement and string replacement.  
SLF [14] and [PERSON] [15] focus on whether the operands of the _cmp_ instruction have changed. Both of them flip the test case byte by byte, run the target, and monitor the  
Figure 3: Example of an I2S dependency.  
Figure 2: Example of an inefficient.tif test case due to blind mutations.  
changed operand of the _cmp_ instruction to analyze indirect dependencies, for indexing the _header_ fields and fixing the \"magic byte\" and \"checksum\" roadblocks. We call this scheme byte-by-byte analysis.
## 3 Inverse and Forward Kinematics Problems  
In kinematics, joint movements are referred to as generalized or internal coordinates, and external coordinates are the ones that define the position and orientation of segments in reference to a global reference frame. Changing internal coordinates changes external coordinates, i.e., the position and orientation of the platform. The process of determining external coordinates for a given set of internal coordinates (joint movements) is referred to as forward kinematics. Determining internal coordinates (joint movements) for a given set of external coordinates (position and orientation) is referred to as inverse kinematics.
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 2 Materials and Methods
### Principle of the STS Triangulation System  
The principle of the STS triangulation positioning system is based on measuring the intensity of infrared light emitted by three transmitters/beacons placed at known positions \(B_{i}=(x_{i},y_{i})\). The switching of the beacons is synchronised--only one of them is active at any given time. The infrared light from the transmitter is recorded using 32 photodiodes uniformly placed around the perimeter of the circular receiver housing. The idea behind the positioning system is illustrated in Figure 2.  
Two microcontrollers with 16 analogue inputs each were used to measure IR light intensity, giving a total of 32 photodiodes for measurement. The intensity of the light \(I_{D_{n}}\) recorded by the n-th photodiode of the receiver (the black disk in Figure 2) depends on the angular orientation of the photodiode relative to the transmitting beacon \(B_{i}\), resulting in the measurement curves shown on the right-hand side of Figure 2. The post-processing of the recorded measurements makes it possible to determine the angles \(\varphi_{1}\), \(\varphi_{2}\) and \(\varphi_{3}\) which indicate the angular position of the beacons relative to the receiver. Finally, the \(\varphi_{i}\) angles allow the determination of the beacon--receiver--beacon angles (denoted as \(\gamma_{1}\), \(\gamma_{2}\) and \(\gamma_{3}\) in Figure 2) which are the input arguments to the algorithm that determines the position of the receiver in the \(xy\) plane.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Design
#### 4.5.3 Properties of the Carrier  
The proposed covert channel exploits the Packet Data Convergence Protocol (PDCP) in the 5G New Radio protocol stack. In particular, the five reserved bits of the PDCP header are used to store the hidden information. Since the reserved/unused pattern is not protocol-specific, the proposed hiding method can also be applied to other protocols of the 5G air interface. However, our analysis showed that PDCP is the most suitable protocol for a covert channel in 5G New Radio. In order for the covert sender to transfer the hidden information, data must be continuously transmitted over the 5G-air interface and processed by the PDCP layer. At best, the covert data are exchanged during the transmission of user data, such as a video stream. In contrast to deployment in the user plane, the proposed covert channel is less suitable for control-plane messages, since signaling messages are not sent as frequently. The operation of the proposed hiding method also does not depend on whether the encryption and integrity protection are configured for the overt communication link by the RRC layer.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 5 Blockage in Industrial Deployments
### The Proposed Approach  
Consider the LoS probability \(P_{LoS}\) as depicted in Figure 4. To estimate it, we need to first determine the probability that the LoS path does not intersect any machine higher than the LoS path at the point of intersection. Second, we need to count the number of intersections of the LoS path with the contact sides by determining the probability that the machine side at the 2D distance of \(x\) is lower than the LoS height at this distance. Since the LoS path can intersect each line perpendicular to the \(x\)- or \(y\)-axis only once, the number of intersections of the projection with the contact sides is equal to the number of points on the \(x\)- or \(y\)-axis corresponding to these lines.  
Figure 3: LoS blockage probability for selected objects.  
Figure 2: Methodological steps for LoS blockage probability assessment. (**a**) Setting up the scene. (**b**) Tracking objects in motion. (**c**) Visibility paths.  
By taking into account the features of the LoS probability, we note that in order to meet its requirements, all contact sides crossing the LoS trajectory must be lower than the height of the LoS at the point of their intersection. Therefore, to calculate the density of points that block the LoS path, we need to take into account (i) the probability that the machine will be higher than the LoS path and (ii) probability that a LoS path crossing the blocker is in fact blocking it, \(1-\kappa\). Note that the former condition results in a non-homogeneous PPP over the considered axes while the latter adds a constant filtering term, estimated at the previous stage. Further, we can find the probability that there are no sides blocking the LoS path by calculating the void probability of the resulting non-homogeneous PPP. In what follows, we start considering the case of D2D communications and then present the case of LoS blockage between BS and UE.
## 7 Conclusions  
We comprehensively evaluated KMC3 and CHTKC, the representative partition-based k-mer counting algorithm and non-partition-based k-mer counting algorithm, to determine the type of computing task suitable for them and the breakthrough point for further optimization. From the dimension of hardware setting, KMC3 is the best choice for large datasets in the case of small memory (less than 64 GB) and high IO bandwidth (using SSD or RAID0). From the dimension of dataset features, KMC3 is better at processing datasets with long k-mers (k > 24) and relatively low sequencing quality (Q30 < 90%), and CHTKC is good at counting short k-mers in relatively high sequencing quality datasets. In addition, CHTKC is more efficient for calculating the k-mer frequency of super-scale datasets on high-performance computing platforms.  
No matter which algorithm is involved, we cannot improve the performance anymore after the memory allocated for the algorithm reaches an upper bound. Moreover, the occupation of the buffer cache by memory allocation degrades the IO performance. Therefore, reducing the influence of the IO bottleneck is an inevitable step for optimizing the k-mer counting algorithm. We verified our judgment by filtering and compressing the consecutive first-occurring k-mers to reduce the number of disk access. However, the revision only targets large datasets or low sequencing quality datasets currently, and building a filtering method that is insensitive to the dataset is still a challenge. We also found almost all k-mer counting algorithms are platform-sensitive, and no algorithm is always highly efficient on arbitrary servers. In the future, we will continue to investigate strategies that can alleviate the influence of IO bandwidth. We will also develop self-configured k-mer counting tools to solve the platform-sensitive issues.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a15040107/s1](https://www.mdpi.com/article/10.3390/a15040107/s1)]([https://www.mdpi.com/article/10.3390/a15040107/s1](https://www.mdpi.com/article/10.3390/a15040107/s1)), Table S1: Comparison of KMC3 and CHTKC with various memory limit for H.1 and H.2 (k = 32); Table S2: CHTKC's correlation between the running time and the ratio of the number of distinct k-mers to the number of total k-mers. The total k-mers is set to 1 billion; Table S3: Comparison of KMC3 and KMC3-R with various memory limit (k = 32);  
Figure 11: Comparison of the dumper of KMC3, CHTKC, and Revised KMC3 with various sequencing data. (**a**) k = 32; (**b**) k = 64.