esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sensing Theory  
Usually, a change in the external refractive index will affect the period of the fiber grating and the effective refractive index of the fiber core, which will further exert an impact on the transmission spectrum. In general, the sensitivity of the ambient refractive index can be obtained by calculating the intensity or phase change of the transmission spectrum. The cladding mode of the TFBG is directly related to the refractive index of the external environment; meanwhile, the core mode is not sensitive to changes in the ambient refractive index.  
In a single-mode fiber, the angle between the grid surface and the core axial direction enhances the coupling effect between the forward-transmitted core mode and the backward-transmitted cladding mode, and it reduces the inter-coupling process with the backward-transmitted core mode.  
The phase matching conditions are [25]  
\[\lambda_{B}=2n_{0}\frac{\Lambda}{cos\theta}=2n_{0}\Lambda_{g} \tag{1}\]  
\[\lambda_{i}=(n_{0}+n_{i})\frac{\Lambda}{cos\theta} \tag{2}\]  
where \(\Lambda\) is the grid period along the fiber core direction; \(\Lambda g\) is the grid spacing in the parallel direction; \(\theta\) is the inclination angle of the gate surface; \(n_{0}\) is the effective refractive index of the fiber core; and \(n_{\mathrm{i}}\) is the effective refractive index of the \(i\)-th cladding mode.  
In general, the response of the core refractive index to the external environment is extremely small, and it can be set as zero. According to the phase matching condition, it is concluded that a change in the ambient refractive index will shift the wavelength of the cladding mode. Therefore, the ambient refractive index can be detected by detecting the wavelength change of the cladding mode.
## 6 Noise Sources and Identification Performance
For example, any application that monitors only for specific isotopes will have effectively \"less noise\" and better RIID algorithm performance, particularly if the challenge problem modified ths isotope library size [issues (5) and (6)]. As another example, if the application only requires outlier detection in the context of recognizing non-NORM isotopes without distinguishing specific isotopes [17, 18] then the noise associated with characterizing the background is very different than if a single background measurement is compared to a \"background plus source\" measurement. Figure 5: Two measurements of \({}^{239}\)Pu illustrating calibration drifting. Any effect that distorts test spectra either randomly or systematically is a noise source (issue (7)). Performance degradation can occur due to noise introduced by detector issues such as dead time and pulse pileup. Detector dead time occurs when an incident \(\gamma\) temporarily locks the detector into a \"cannot detect at present\" mode. Detector live time is the actual clock time of the assay minus the dead time. Dead time effects are a function of count rate. Pulse pileup or peak summing occurs when two (or more) incident \(\gamma\)s arrive essentially simultaneously, resulting in a peak at an energy equal to the sum of the two incident \(\gamma\) energies. Such a peak is sometimes considered an artifact peak and regardless of whether data preprocessing attempts to remove such peaks or if the peak library is modified to anticipate them, peak summing is expected to degrade identification performance. Other characteristic peaks include a single escape peak, annihilation peak, and double-escape peak. These peaks are all the result of pair production in the crystal detector. The annihilation peak is at 510 keV and is the result of absorption of one of the two 510 keV \(\gamma\)s. When one of the two annihilation \(\gamma\)s escapes without detection, the single-escape peak is produced at the original \(\gamma\) energy minus 510 keV. Or, both the annihilation \(\gamma\)s can escape the detector without interaction and produce the double escape peak at the original \(\gamma\) energy minus 1,020 keV.
# Stochastic Analysis of a Large-Span Continuous
###### Abstract
Due to the straightforwardness of the MCS method, the reliable statistics of stochastic responses can be obtained using the large sampling size. Hence, the MCS method can be utilized to give reference solutions for checking the results obtained by other methods [22]. A large number of simulations can result in an excessive computational burden and renders the MCS method computationally time-demanding [23]. To improve the computational efficiency of the stochastic analysis, [PERSON] [24] proposed the pseudo excitation method (PEM), which is well known as the fast complete quadratic combination method. To consider the more complicate non-stationary case, the PEM has been extended to analyze multi-support structures under non-stationary seismic excitations, for which the wave passage effect and the coherency between input excitations are included [25; 26]. With the aid of the PEM, stochastic analysis under stationary and non-stationary excitations can be transformed into harmonic and deterministic transient analyses, which can be solved easily with direct integration [27]. In addition, meaningful work has been conducted to make the PEM more attractive for engineering practice [15; 28; 29]. Currently, PEM is one of the basic tools for the stochastic seismic analysis of structures and is applied extensively in the civil engineering industry [30; 31]. As for the modulated non-stationary seismic excitation, the pseudo static displacement of PEM is usually simplified based on the slowly time-varying characteristics of the modulating function. However, this slowly time-varying characteristic is not applicable for fully non-stationary seismic excitation. Hence, the expression of pseudo static displacement should be reconsidered for PEM under fully non-stationary seismic excitation. Following the above discussions, the non-stationary stochastic seismic analysis of a large-span CGHSR bridge is conducted here. The evolutionary power spectral density (EPSD), taking into consideration both the time-dependent intensity and frequency content, is utilized in order to characterize the fully non-stationary seismic excitation. The PEM is performed to efficiently analyze the non-stationary seismic response of the CGHSR bridge under tri-directional non-stationary multiple excitations. A framework is proposed to determine the pseudo static displacement using the proper orthogonal decomposition (POD) under fully non-stationary seismic excitation. The stochastic responses obtained by PEM are validated using the MCS method. In addition, according to the energy conservation principle, an equivalent modulated EPSD is derived, corresponding to the fully non-stationary one. Using the fully and equivalent modulated ground acceleration models, the stochastic seismic response of the bridge is analyzed and contrasted.
## 3 Shake Table Model Tests
### Results and Analysis
#### 3.4.2 Earth Pressure Analysis  
Figure 10 illustrates the peak distribution of lining earth pressure increments along the tunnel longitudinal direction under different anti-vibration measures implemented in the  
Figure 9: Peak distribution of (**a**) circumferential and (**b**) longitudinal strain increments along the longitudinal direction of the tunnel under the artificial wave.  
model tests. Generally, the earth pressure increments near the fault section were slightly higher than in other intact sections under all three input ground motions. The distribution became more uniform after the application of anti-seismic controls.  
In Figure 10a, it was evident that under El-Centro wave excitation, the maximum and minimum increments of lining earth pressure were 4.2 kPa and \(-\)3.7 kPa, respectively, without seismic measures. After grouting, installing seismic joints, and incorporating a shock absorption layer, the maximum increment of lining earth pressure decreased to 2.9 kPa, 3.7 kPa, and 3.8 kPa, respectively, with reduction ratios of \(-\)40%, \(-\)12%, and \(-\)9.5%. Simultaneously, the minimum increment of lining earth pressure was \(-\)2.8 kPa, \(-\)2.8 kPa, and \(-\)3.1 kPa, respectively, with reduction ratios of \(-\)24.3%, \(-\)24.3%, and \(-\)16.2%.  
In Figure 10b, under Taft wave excitation, following the grouting of surrounding rock, installation of anti-seismic joints, and implementation of shock absorption layers, the reduction ratio for the maximum increment of lining earth pressure was \(-\)28%, \(-\)9.3%, and \(-\)20.9%, respectively. Similarly, the reduction ratio for the minimum increment of lining earth pressure was \(-\)34%, \(-\)7.3%, and \(-\)7.3%, respectively. In Figure 10c, under artificial wave excitation, after the completion of grouting, installation of anti-seismic measures,  
Figure 10: Increment peak distribution of lining earth pressure along the longitudinal direction of the tunnel: (**a**) El-Centro wave; (**b**) Taft wave; (**c**) artificial wave.  
and integration of shock absorption layers, the reduction ratio for the maximum increment of lining earth pressure was \(-37\%\), \(-15.2\%\), and \(-26.1\%\), respectively. Additionally, the reduction ratio for the minimum increment of lining earth pressure was \(-41.4\%\), \(-7.3\%\), and \(-7.3\%\), respectively.  
Furthermore, the analysis of the peak position of earth pressure indicated that the peak position of earth pressure on the fault interface section changed after the installation of seismic joints and shock absorption layers.
# Electronically Controllable Phase Shifter with Progressive Impedance Transformation at K Band
## 2 Materials and Methods: Phase Shifter Working Principle and Design
### Baseline 3 dB/90\({}^{\circ}\) Coupler  
The basic element of the phase shifter is a conventional 3 dB/90\({}^{\circ}\) coupler. The coupler is designed for a working frequency of 18 GHz with a bandwidth higher than 2 GHz considering \(-\)20 dB in the \(|\)S\({}_{11}\)\(|\) level. The coupler symmetry imposes identical amplitude values levels at ports 3 and 4, with an accurate 90\({}^{\circ}\) phase difference between these ports. Figure 2 provides the model of the microstrip coupler, along with its performance results.  
Figure 1: Scheme of the reflection-based phase shifter.
## 5 Effective Area
The effect of the spacecraft is also evident by the difference in effective area between polar angles 0\({}^{\circ}\) and 180\({}^{\circ}\). At 59.4 keV, the effective area at polar angle 0\({}^{\circ}\) (\(+\)Z face) is similar to the \(-\)Y and \(+\)Y faces at \(\geq\) 5 cm\({}^{2}\). The spacecraft components beneath GMOD attenuatethe incoming photons as, at polar angle 180\({}^{\circ}\), the effective area is \(\sim\)0.05 cm\({}^{2}\). As expected, at higher energies (662 keV), the photons are capable of reaching the crystal through the spacecraft and there is a smaller difference in the effective area between the \(-Z\) and \(+Z\) of \(\sim\) 1 cm\({}^{2}\). Figure 12: GMOD FM effective area as a function of energy, where all the sources were positioned above the +Z face of the spacecraft at the CoC. Simulated photopeak effective area from [29] plotted in teal for comparison to physical measurements. Figure 13: Angular effective area of GMOD FM at 662 keV.
## 3 UAV Power Supply and Charging Mechanisms  
In recent years, there has been an increase in the application of UAVs in both commercial and military domains, due to their easy adaptability, the flexibility of deployment, and cost-effectiveness [13]. However, to fully exploit the capabilities of UAVs in different application domains, there is a need to consider the challenges and limitations of UAVs. One of the most important limitations in UAVs is power and energy consumption [19]. Commercial UAVs are usually powered with rechargeable batteries for operations, while large UAVs, such as military UAVs, use non-renewable resources, such as fuel and gas, to provide more energy to the UAV for longer flight time. In recent years, new and alternative methods for UAV power supply, including solar energy, wireless charging, laser beam charging systems, etc., have been developed and tested [43]. In the following paragraphs, we discuss different methods of power supply and charging mechanisms for UAVs while Figure 3 illustrates various UAV power supply and charging mechanisms.  
Figure 3: Various UAV power supply and charging mechanisms including wireless power transfer via a recharging UAV, a tethered UAV, a solar-powered UAV, and battery swapping at a charging station.
# Covert Channel Based on Quasi-Orthogonal Coding
## 4 Simulation Tests
### Simulation No. 4  
In previous simulations (simulation 1 to 3), the modulation of the covert information was 8 FSK. However, it should be examined what effect sorting has when the covert signal is the amplitude-phase modulation used in [1; 3; 7; 10]. Let's assume, as in simulations 1 and 2, that one 2 PSK covert symbol is transmitted using 16 16 QAM cover symbols. The FFT analysis (Figure 9a) of such a signal will show that each covert symbol is a constant value (on the frequency scale it has a non-zero value only for \(f=0\)). Orthogonalization will not provide any benefit (Figure 9f) because the block for which we perform orthogonalization is equal to the modulation value of the cover and, at the same time, the number of samples per covert symbol. This is because the average value of the random sorted and unsorted cover in such a case remains constant.  
Note that we obtained a relatively low BER for both the sorted and unsorted signal, even for a large estimation error. This is due to the high energy per bit (16 samples represent one bit of data) and, unfortunately, this comes at the expense of reduced resistance to steganalysis (as will be demonstrated in Section 5).
## 2 Materials and Methods  
### Description of the Studied Zone  
The hydrosystem Lagoon of Togoville-Lagoon of Aneho-Lake Togo is in the coastal zone of Togo between 6\({}^{\circ}\)14\({}^{\prime}\)38\({}^{\prime\prime}\) to 6\({}^{\circ}\)17\({}^{\prime}\)37\({}^{\prime\prime}\) of Northern latitudes and 1\({}^{\circ}\)23\({}^{\prime}\)33\({}^{\prime\prime}\) to 1\({}^{\circ}\)37\({}^{\prime}\)38\({}^{\prime\prime}\) of Eastern longitudes. The studied hydrosystem is continuously composed of the Lagoon of Togoville, Lake Togo, and the Lagoon of Aneho [38]. It flows into the ocean through its mouth at Aneho after receiving inputs from Haho, Zio, and Mono Rivers. The main tributaries of this lagoon system are Zio and Haho Rivers [39]. The phosphorites mining activities take place in this watershed with the rejection of various kinds of mining wastes [39; 40; 41; 42; 43; 44; 45]. This watershed contains the phosphoric quarries and processing plant and receives important and different kinds of mining waste through runoff, rivers, and atmospheric depositions [45; 46; 47; 48; 49; 50]. The studied area presents a subequatorial or Guinean climate with two rainy and two dry seasons alternatively (Figure 1). The main economic activities of the population of the coastal zone are fishery, agriculture, and livestock farming.  
The study area is a part of the Togoles sedimentary coastal basin. All of its sedimentary layers (about 600 m thick) are in fundamental discordance on a Pan-African metamorphic substratum aged \(600\pm 50\) Ma. These sedimentary deposits are successively those of Maastrichtian-Paleocene, Lower to Middle Eocene, Upper Oligocene, and Quaternary age. They are generally composed of sandy and marly facies with the presence of sandstones, sands, limestones, marls, conglomerates, laminated argillites with kaolinite, attapulgite, glaucoma, palygorskite, and other derived rocks. The Eocene is mainly characterized by the deposit of phosphorites [51; 52; 53; 54; 55; 56; 57; 58; 59]. This phosphoric deposit constitutes an ore that is being exploited in the coastal basin. Its chemical characterization showed that it is a carbonated fluorapatite having the general formula \(A=Ca_{10}(PO_{4})_{6-x}(CO_{3}F)_{x}(OH,F)_{2}\) where \(x\) is generally close to 1 [60; 61]. It contains a high concentration of different trace elements through the substitution phenomenon of Ca [35; 62].
###### Abstract
Specifically, the enhanced SoftLexicon approach is utilized to acquire word-level semantic information while avoiding potential out-of-vocabulary issues, resulting in more accurate identification of Chinese toponyms' word boundaries. The TCNN layer comprehensively considers both character- and word-level local semantic features as well as global semantic features from the input text, thus minimizing the loss of semantic information and effectively addressing the ambiguity of Chinese address entity elements. 2. This study introduces a dynamic FSM. In comparison to the traditional FSM, the dynamic FSM is capable of adjusting its state set based on the types of address elements. This adaptation avoids the limitations of depending heavily on the collected keywords of address elements and the threshold settings inherent to the FSM. Consequently, it can more effectively handle address information descriptions in network text that exhibit issues such as hierarchical element disorder and omission. The remainder of this paper is organized as follows. Section 2 presents the related work on address parsing and the application of FSM in address parsing. Section 3 presents the detailed deep learning architecture method using a dynamic FSM and the experimental data discussed in this study, and Section 4 presents and analyzes the experimental results. Finally, Section 5 summarizes the study and discusses future research directions.
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
### Communication Protocols  
String stability issues can be mitigated or even eliminated if the information delay between the leader and the followers is reduced. This can be accomplished by proactively informing the surrounding vehicles of actuation events as soon as the decision is made. Without loss of generality, we will assume that the primary actuation of interest is a braking event. SAE J2945/1 defines the communications for on-board vehicle-to-vehicle (V2V) systems [12]. Note that SAE J2945/1B is the standard governing Class 8 vehicles such as tractor trailers, but it defers to SAE J2945/1 for BSM communication scheduling It defines, among other things, the schedule for Basic Safety Message (BSM) transmission for each vehicle. A BSM contains information on the overall vehicle state as well as any high-importance events. However, even if SAE J2945/1 is used, there can be up to 0.1 s of delay before transmitting a BSM that says a hard braking event is occurring. To make matters more difficult, there is no guarantee that the BSM is received. If the wireless communication channel is unreliable, then the overall delay between event and message reception may be much higher. While SAE J2945/1 has mechanisms in place for estimating the probability of a remote vehicle's reception of a transmission, it does not allow BSM transmissions to occur any more frequently than 10 Hz.
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
## 4 Ergodic Capacity
By straightforward substitution of \(F_{1}\), \(F_{2}\) and \(F_{3}\) in (16), we can obtain the expression of ergodic capacity.
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 4 Experiments, Results, and Analysis  
This section discusses the experimental setup and various findings. We present our rigorous experiments in various parts. First, Table 2 shows the summary of individual classifiers, and Table 3 shows the hybrid classifier's implementation summary. All experiments presented in Table 2 were executed with 20 epochs and a batch size of 256. Several pieces of valuable information were gathered, including (a) trainable parameters, (b) training time, (c) model size, (d) accuracy, (e) epochs, and batch size. Two separate experiments were performed on NSL-KDD and UNSW_NB15 datasets; In (*a), models were trained and predicted on the given training and testing set. In (*b), we merged the training and testing set, shuffled the dataset, and recreated a new training and testing set based on the 70:30 split ratio. In (*c), models were trained on the given full dataset and created a training and testing set based on a 70:30 ratio. Second, Figure 6 shows the SHAP's local explanation of the KDD CUP 99 dataset. To conserve space, we only presented a single prediction from a single dataset. Figure 7 also shows the local explanation of one of the predictions using LIME. Lastly, Figure 8 shows the results of SHAP's global explanation by listing the top 20 most important contributing features.  
Table 2 shows that the LSTM model requires over 3 million trainable parameters, thus requiring a longer training time than MLP and CNN. Running the model on UNSW_NB15 (*c) requires over 8.5 h, making it an expensive algorithm for a resource-constrained IoT environment. Figure 4 compares the accuracy comparison of each of the classifiers. All three algorithms return similar accuracy, with LSTM performing slightly better on the UNSW_NB15 (*a) dataset.  
Table 3 shows the summary of hybrid classifiers. The _\"Autoencoder + TCN\"_ takes on average the least number of trainable parameters around 199K, whereas _\"Autoencoder + BLSTM\"_ takes the most over 9.2 million parameters. All other classifiers also require a large number of parameters, with _\"Autoencoder + LSTM\"_ taking over 3.3 million and _\"CNN + LSTM\"_ taking over 3.2 million trainable parameters. For an IoT environment, _\"Autoencoder  
Figure 4: Individual classifier models’ accuracy.  
Figure 3: TCN block architecture.  
+ _TCN_\" is convenient due to a smaller than 3 B, shorter training time, and better accuracy than other models.  
Figure 5 shows the accuracy comparison of each of the hybrid classifiers. \"_Autoencoder_ + _TCN_\" returns better accuracy compared to other algorithms. \"_Autoencoder_ + _BRNN_\" returns very low accuracy with only 23.30% on N-BotIoT and 31.90% on UNSW_NB15 (**a**\") datasets. Model training time is also an important concern in a live environment. As shown in Table 3, algorithms such as \"_CNN + LSTM_\" took over 9 h to converge on UNSW_NB15 (**c**) dataset. Models that converge faster can be trained multiple times during the day with new attack information to keep them up-to-date.