esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Micro-Doppler Signals  
### Backgrounds  
Moving targets generate Doppler frequency in the radar return signal due to velocity. Since most targets are not rigid bodies, different parts of a target may have additional vibrations and rotations. For example, when a person walks, his arms naturally swing. These movements generate additional Doppler shifts, called micro-Doppler effects. Much research regards these to be useful information for identifying the target.  
The returned radar signal by a moving rigid body is modeled as shown in Equations (1) and (2), where \(f_{0}\) is the carrier frequency, \(A\) is the intensity of the receiving signal, \(\Delta v\) is the moving speed of the target, \(c\) is the light speed, \(\Delta f=\frac{\Delta v}{c}f_{0}\) is the Doppler shift and \(\theta\) is the phase.  
\[x_{r}(t)=A\cdot\exp(j(2\pi(1+\frac{\Delta v}{c})f_{0}t+\theta)) \tag{1}\]  
\[x_{r}(t)=A\cdot\exp(j(2\pi(f_{0}+\Delta f)t+\theta)) \tag{2}\]  
The returned radar signal having the micro-doppler effect can be modeled as a form of a combination of \(M\) sine-wave signals as shown in Equation (3).  
\[x_{r}(t)=\sum_{i=1}^{M}A_{i}\cdot\exp(j(2\pi(f_{0}+\Delta f_{i})t+\theta_{i})) \tag{3}\]  
After the application of a matched filter and a low pass filter, we can get the base-band signal as shown in Equation (4).  
\[x_{b}(t)=\sum_{i=1}^{M}A_{i}\cdot\exp(j(2\pi\Delta f_{i}t+\theta_{i})) \tag{4}\]  
Equation (5) shows the discrete signal sampled by the sampling rate \(Ts\) and Equation (6) shows the signal form converted by DFT (Discrete Fourier Transform), where \(N\) is the number of samples.  
\[x_{s}[n]=x_{b}(t)\mid_{T_{s}} \tag{5}\]\[X[k]=\sum_{n=0}^{N-1}x_{s}[n]\exp(-\frac{j2\pi}{N}kn) \tag{6}\]  
The DFT signal \(X[k]\) is calculated from \(N\) samples of \(x_{s}\), which contains information on the variation of micro-Doppler signals over time. Therefore, even if a spectrogram image used by the previous studies is not applied, we can extract the feature information related to micro-Doppler effects from \(X[k]\).  
To reflect variability of \(X[k]\) over time, we devise a method of utilizing previously generated feature information. Equation (7) shows the feature generated by a base classifier. And, we reuse this feature information as the input data for a blender to combine base classifiers in the proposed stacking method. Equation (8) shows the feature data for a blender. The structure of \(Base\_Classifier(\cdot)\) is shown in Figure 2 and the optimal weights of \(Base\_Classifier(\cdot)\) are calculated after training.  
\[Feature(t)=Base\_Classifier(X[k]) \tag{7}\]  
\[Feature_{blender}(t)=\sum_{\tau=0}^{j}Feature(t-\tau) \tag{8}\]
# Utilizing Machine Learning for Detecting Harmful Situations by Audio and Text
## 5 Experimental Result
### Results on Audio-Based Inputs  
Our next set of experiments examined the ability of ML methods to correctly detect insulting and unsafe situations from audio files. We compared different audio representations as described in Section 4.2.1. The audio sources were derived from the recorded audio files, as described in section 4.2.1. In particular, the input of the RNN method consisted of MFCCs, whereas the other audio sources are analyzed by the DNN audio classifier described in Section 4.2.3. Recall from Section 4.2.1 that the following data were extracted for the audio-based prediction challenge: the Eight Emotions Vector, as classified by Wav2 Vec2 Feature Extractor; FSFM Vector constructed from a concatenation of the five audio features, Wav2 Vec-BASE Pretrained vectors with a length of 768, with or without fine-tuning on our data, and Wav2 Vec embedding vectors, with length of 1024, fine-tuned for emotions, with or without fine-tuning on our data. Our results are presented in Tables 9-11. In fact, the accuracy achieved with audio-only prediction was much lower than that achieved with text-only prediction. This means that audio signals provide only partial clues about situations with respect to text information. We can also observe that the accuracy of situation predicting via eight emotions vector represented results close to those predicted via randomly created vectors. However, emotion prediction using fine-tuned vectors improved the prediction accuracy when only audio data were available.
## 8 Conclusions  
In this paper, in situ tests were conducted to investigate the mechanical behavior of soil nail and prestressed anchor enhanced retaining structures. Three types of different retaining structures, including (i) soil nails; (ii) soil nails and prestressed anchors without unbonded parts; and (iii) soil nails and prestressed anchors with a 2.5 m unbonded length, were investigated. The effects of different factors, such as prestressing and unbonded length were discussed. The results obtained may assist practicing engineers in the design of nails and anchors for civil engineering applications. In particular, it was found that:  
1. The interface friction stress of the soil nail is affected by the density of surrounding soil. The friction stress is higher when soil is denser and harder, which is characterized by stress concentration; the friction stress is lower and uniform when soil is disturbed or looser, which is consistent with theoretical hypotheses;
2. The distribution pattern of soil nail strain along the full length in disturbed soil is bimodal, which indicates that there are two potential slip surfaces, and it is inferred that there are even more. This is one of the important characteristics of disturbed soil different from general clay;
3. The influence of prestressing on the internal force of soil nails is governed by tensioning methods. When the anchor is tensed synchronously, its ability to limit the deformation is obvious, and the upper soil nails are affected. When the anchor is tensed later, the lower soil nails are affected;
4. The prestressed anchor which is anchored along the full length has no unbounded part to effectively transfer the pretension stress to the soil behind. Compared with the ordinary tension anchor, the effective range of compressive stress is smaller, and the effect of active support is poor;* The influence of pretension stress on the internal force of soil nails differs along the full length. The internal force close to the end declines rapidly, decreases gradually along the full length, and reduces to the minimum on the nail tail.
## 5 Discussion
### Grid Point Constructed and Layered Interpolation  
To further understand the advantages of adding grid points and layered interpolation, the LRBF spatial interpolation method was compared with the RBF with the grid points (RBFGP) method, which only adds grid points without layered interpolation. The interval distances of the grid points are 7 m, 12 m, and 17 m (Figure 17), respectively. The root mean square error statistics of each method with different interval densities are shown (Table 2).  
In summary (Table 2), the accuracy of LRBF is higher than that of RBFGP for the same grid interval distance after a layered approach that improves the interpolation accuracy of RBF. Compared with generating all the grid points at once, the layered interpolation that generates the grid points allows RBF to ensure the approximation of the basis function in the close range in the local range.
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 1 Introduction
In the former stage, a denoising subnetwork (DN-SBN) is constructed to remove the noise in the received codewords. Here, we utilize 1D convolution layers with residual connection to construct the base subnetwork. Then, we add an attention module after each convolution block to separate the valid information from the noise. In the decoding stage, a decoding subnetwork (DC-SBN) with a fully connectivity and attention mechanism is constructed. The designed attention module can refine the input information and preserve only the effective information for decoding. By cascading these attention modules, the performance of the decoder can be gradually improved. Experimental results show that our proposed DIR-Net outperforms existing decoding methods and obtains state-of-the-art performance. The main contributions of this paper are summarized as follows:  
1. We design a novel information optimization module for polar decoding based on the attention mechanism. This module can avoid the interference of invalid information during the decoding. 2. With the designed information optimization module as the core, we construct an advanced deep learning decoding network. To our knowledge, this is the first time that the attention modules are used completely to construct a decoding network for polar codes. The network refines information step-by-step by cascading multiple attention modules and exhibits excellent decoding performance. 3. We evaluated the effectiveness of the proposed decoding network in the Gaussian channel and the Rayleigh fading channel, respectively. The experimental results demonstrate that our proposed decoding network can effectively suppress the interfering information and achieve high BER performance in both channels. The remainder of the paper proceeds as follows: the decoding method of polar codes is first introduced in the next section. Then, the details of the proposed method are presented. In Section 4, the experimental results are shown to demonstrate the effectiveness of our method. We summarize our study in the last section.
## 3 Results  
_Anguilla anguilla_ exposed (in vivo) to 4 mg/kg BNF had a liver microsomal EROD activity of 9.65 pmol/min/mg of protein. The addition of 5 uL of DMSO did not affect liver microsomal EROD activity (\(p\) > 0.05, student \(t\) test). On the other hand, the addition of 0.1, 0.3, 0.9, and 2.7 \(\upmu\)M of B[a]P significantly inhibited EROD activity by 53%, 15%, 9%, and 6%, respectively (Figures 1 and 2).  
Exposure of hepatic microsomes to cortisol (DMSO+C), presented a significant increase of EROD activity when compared to its respective control (DMSO Control) (Figure 1).  
Figure 1: In vitro effects of benzo[a]pyrene (B[a]P) (0.1, 0.3, 0.9, and 2.7 \(\upmu\)M) without and with liver microsomal pre-exposure to 5.997 ng/mL of cortisol. Bars represent mean \(\pm\) SE (n = 4). Asterisks (*) indicate differences between presence and absence of cortisol (* \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001); the letters “a” and “b” indicate differences with control without and with cortisol, respectively.  
Figure 2: In vitro effects of benzo[a]pyrene (B[a]P) (0.1, 0.3, 0.9, and 2.7 \(\upmu\)M) without and with liver microsomal pre-exposure to 5.997 ng/mL of E\({}_{2}\). Bars represent mean \(\pm\) SE (n = 4). Asterisks (*) indicate differences between presence and absence of cortisol (* \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001); the letters “a” and “b” indicate differences with control without and with E\({}_{2}\), respectively.  
Furthermore, the presence of cortisol provided a protective effect over EROD activity inhibition by B[a]P. All tested B[a]P concentrations showed a significant increase in EROD activity in the presence of cortisol, when compared to B[a]P individual exposure, with the highest increase observed for 0.1 uM B[a]P. However, increments did not correspond to a complete recovery to control activity levels. The efficiency of recovery decreased with increasing B[a]P concentrations (Figure 1).  
The assessment of the effects of E\({}_{2}\), alone or in combination with B[a]P, shows that in vitro exposure of hepatic microsomes to 5.997 ng/mL of E\({}_{2}\) (DMSO+E\({}_{2}\)) leads to a non significant increase of EROD activity, when compared to DMSO control (absence of E\({}_{2}\)) (Figure 2). Similar to cortisol, E\({}_{2}\) was also able to partially revert liver microsomal EROD activity inhibited by B[a]P. Indeed, all tested B[a]P concentrations also showed a mild but significant increment in EROD activity in the presence of E\({}_{2}\); however, like cortisol, recoveries did not reach control activity levels (Figure 2). On the other hand, recovery efficiency did not decrease with B[a]P concentration increase. Here, the highest recovery was observed at 0.9 uM B[a]P. All differences with control were \(p<0.001\).
# Inorganic Salt Hydrate for Thermal Energy Storage
## 2 Properties of Inorganic Salt Hydrated PCMs  
Salt hydrates can be generally described as formula of AB\(\cdot\)nH\({}_{2}\)O, where n represents number of water molecules and represents salt composition. During phase transformation dehydration of the salt happens, the process can be expressed as (1) and (2). \[\text{AB}\cdot\text{nH}_{2}\text{O}\rightarrow\text{AB}+\text{nH}_{2}\text{O} \text{-Q} \tag{1}\]  
\[\text{AB}\cdot\text{nH}_{2}\text{O}\rightarrow\text{AB}\cdot\text{mH}_{2}\text{O }+\text{(n}-\text{m)}\;\text{H}_{2}\text{O}\text{-Q} \tag{2}\]  
According to the phase transition performance, the salt hydrates can be divided into three categories: salt hydrates with congruent melting behavior, incongruent behavior and semi-congruent behavior. There is a good deal of salt hydrates that can be regarded as available candidates for PCMs. However, a large proportion of salt hydrates which process large transition latent heat and moderate phase change temperature cannot melt congruently. The most meritorious ones are salt hydrates with congruent melting behavior, low supercooling degree and competitive profitability. Thermophysical, kinetic and chemical properties of such salt hydrates, which can possibly meet the requirements, are presented in Table 1. The most representative salt hydrates are sodium sulfate decahydrate, calcium chloride hexahydrate, sodium phosphate, dodecahydrate and magnesium chloride hexahydrate [5; 11]. However, the phase transition temperature range of single salt hydrate cannot always meet the requirements of practical application. Therefore, two or more salt hydrates can be mixed to adjust the melting point. Salt hydrate mixtures can be classified as eutectics with congruent behavior and non-eutectics with incongruent melting points. The eutectics do not tend to segregate during melting and freezing process because they freeze to a friendly blend of crystals [12; 13; 14; 15]. Binary or multicomponent eutectics with a common melting point and large latent heat can reduce the melting point of single constituent salt hydrate and then they can be utilized in wider fields [16]. Besides, the eutectics can also make up for the deficiencies of single salt hydrates such as supercooling and phase separation. [PERSON] [17; 18] respectively added Na\({}_{2}\)HPO\({}_{4}\cdot\)10H\({}_{2}\)O to Na\({}_{2}\)SO\({}_{4}\cdot\)10H\({}_{2}\)O and Na\({}_{2}\)CO\({}_{3}\cdot\)10H\({}_{2}\)O to prepare two novel eutectics. It demonstrated that Na\({}_{2}\)HPO\({}_{4}\cdot\)12H\({}_{2}\)O can effectively inhibit phase separation of Na\({}_{2}\)SO\({}_{4}\cdot\)10H\({}_{2}\)O and Na\({}_{2}\)CO\({}_{3}\cdot\)10H\({}_{2}\)O. The melting point and supercooling degree of the prepared eutectics also decreased. Some of salt hydrate eutectics with potential use as PCMs are listed in Table 2.
## 3 Research Models
### DCC Model  
To capture the dynamic conditional EUA-WTI correlation, the existing studies mainly follow Engle [34] to use the DCC model as follows:  
\[h_{t}^{EUA, WTI}=\rho_{t}\times\left(h_{t}^{EUA}.h_{t}^{WTI}\right)^{\frac{1}{2}} \tag{8}\]  
\[\rho_{t}=q_{t}\ /\ \sqrt{1+q_{t}^{2}} \tag{9}\]  
where  
\[q_{t}=\tau+\pi\cdot q_{t-1}+\lambda\cdot e_{t-1}^{EUA}\cdot e_{t-1}^{ETI}/ \sqrt{h_{t-1}^{EUA}\cdot h_{t-1}^{ETI}} \tag{10}\]  
Equation (10) depicts the adopted DCC model consisting of three components: (1) the constant unconditional correlation (\(\tau\)), (2) the lagged conditional correlation as a linear function of \(q_{t-1}\), and (3) the cross-product term of the lagged standardized residuals. With the transformation function represented by Equation (9), we ensure that the conditional correlation \(\rho_{t}\) lies between \(-1\) and \(1\). In particular, \(\rho_{t}\) has the same sign as \(q_{t}\). The larger the magnitude of \(q_{t}\), the closer \(\rho_{t}\) approaches \(1\) or \(-1\). It is worth noting that the CCC (constant conditional correlation) model is a special case of the DCC model under the restriction of \(\pi=\lambda=0\) in Equation (10).
# Safety-Aware Optimal Attitude Pointing for Low-Thrust Satellites
## 2 Methods
### Trajectory Generation
The initial points are indicted by \(+\) and the final points by \(*\) (initial and final points from Equation (22). Figure 3: Frobenius norm of the difference between \(R(t)R(t)^{T}\) and the identity matrix for the trajectories shown in Figure 4a (dashed) and Figure 5a (solid). The following steps were then used to solve the optimal control problem of moving the vehicle from the initial to the final configuration:  
* Step 1: Determine the solution \(R(1,\lambda_{0},c_{i}^{0})\) of (1) that minimizes the cost (10) by integrating the equations using (15) in terms of a chosen value \(c_{i}^{0}=[1,1,1]\) the unknown boundary conditions \(\lambda_{0}\) on the (imaginary) time domain \([0,1]\). * Step 2: Construct the \"error matrix\" \(\widetilde{R}(1,\lambda_{0},c_{i}^{0})=R(1,\lambda_{0},c_{i}^{0})-R_{D}\) and the function: \[S(\lambda_{0})=\|\widetilde{R}(\lambda_{0},c_{i}^{0})\|\] for the fixed choice of \(c_{i}^{0}\). * Step 3: Find \(\lambda_{0}\) that minimizes \(S(\lambda_{0})\) for the particular choice of \(c_{i}^{0},T_{f}=1\), using a local minimizer. After Step 3 was completed, an initial trajectory was determined in terms of the particular choice of variables \(c_{i}^{0}\). We went on to apply RL to allow the vehicle to reactively choose a new optimal path that avoided the obstacles by adapting the \(c_{i}\) values away from the chosen initial values \(c_{i}^{0}\). The initial choice of one for each \(c_{i}\) value was made, which corresponded in the case that energy was being evaluated to equal inertia values for each axis.
# Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
## 5 Illustrative Examples and Numerical Evaluation
### Fan Filtering of Plane Wave Image  
We present a 2-D fan filter for ground roll attenuation in this section. A specific kind of Rayleigh wave with low frequency, low velocity, and high amplitude is called ground roll.  
Figure 4: 2-D FIR and IIR fan filters described in Section 5.3: (**a**) initial 2-D FIR fan filter of order (49,49); (**b**) reduced 2-D IIR fan filter of order (34,34); (**c**) group delay \(\tau_{1}\) of reduced 2-D IIR fan filter; (**d**) group delay \(\tau_{2}\) of reduced 2-D IIR fan filter; (**e**) impulse response of 2-D IIR fan filter; (**f**) magnitude contour of the reduced fan filter.  
It is the main type of coherent noise in land seismic surveys [56]. We generate an image via the sum of plane waves (PWs) with two angles, \(\theta_{1}=80^{\circ}\) and \(\theta_{2}=100^{\circ}\), measured counterclockwise from the horizontal. The wave has \(\omega_{1}=100/512\times 2\pi\) rad/pixels and \(\omega_{2}=53/512\times 2\pi\) rad/pixels over the size \(N=512\) pixels of the output image. 2-D FIR and IIR fan filters are designed to be applied to this PW image, where it is required to design a reduced-order 2-D IIR fan filter to satisfy the following design specifications:  
\[\mid H(\omega_{1},\omega_{2})\mid=\left\{\begin{array}{ll}1&\left|\omega_{1} \right|^{2}\right|\leq 0.57|\omega_{2}^{2}|\\ 0&\left|\omega_{1}\right|^{2}\right|>0.57|\omega_{2}^{2}|\end{array} \tag{20}\]  
Figure 5a shows the magnitude response of the initial 2-D FIR fan filter of order (24,24) designed using the window method. The reduced 2-D IIR fan filter of order (17,17) obtained by using the proposed method is shown in Figure 5b.  
For comparison purposes, the 2-D FIR fan filter of order (24,24) shown in Figure 5a is applied to this PW image first, where Figure 6 shows the original and filtered images and their 2-D spectra. The reduced-order 2-D IIR fan filter of order (17,17) shown in Figure 5b is then applied to the same image, and the result is shown in Figure 7. In terms of the computation complexity, the 2-D FIR fan filter of order (24,24) has a number of coefficients C equal to 625, whereas for the reduced-order (17,17) 2-D IIR fan filter, we have \(18\times 18\) for the numerator plus \(2\times 18\) for the denominator (i.e., the number of coefficients is \(C=360\)), which is reduced to less than 58%. For implementation, we can see that the obtained 2-D IIR fan filter requires less than half the number of coefficients required by the 2-D FIR fan filter. The results show that the reduced-order 2-D IIR fan filter designed using the proposed method has good filtering performance on the PW image as well as the advantage of a low implementation cost in terms of adders and multipliers over the 2-D FIR fan filter.  
Figure 5: Magnitude response of 2-D FIR and IIR fan filters described in Section 5.4.
## 3 LoRaWAN Networking
Nevertheless, the second window uses a static setting (i.e., DR0 (SF12/125 KHz)) at 869.525 MHz. * Class B: This class is an optional mode for ED that needs to receive additional information from the base station as acknowledgment (ACK) that using this mode is better. Despite no former transmission from the EN, the GWs open more reception windows by transmitting synchronization beacons. The number of receiving windows allowed by Class B is more than Class A, but with more energy consumption than Class A. After each UL transmission, the extra regular receiving windows are opened, in addition to the synchronized opening of the two standard receiving windows, called ping slots. The GW beacons are sent periodically every 128 s to guarantee synchronization. The usable beacon window is a time period between two beacons. It is grouped into 212 ping slots of 30 ms, each counting from 0 to 4095 [36]. * Class C: The nodes keep their reception windows open all of the time, only shutting them when the UL sends a packet to one of them. These ENs increase power consumption, since Class C forces the devices to receive constantly, which is incompatible with Class B. The devices that support Class C do not implement Class B. Selection of the mode operation or class depends on the application. Thus, Table 2 can guide the right class selection by comparing the classes adopted on some criteria  
Figure 8: Class time diagram of LoRaWAN devices. such as the access approach, collision, downlink latency, real-time support, and power consumption [37]. LoRaWAN LimitationsThe limitations on LoRaWAN as a MAC layer are a result of limitations in the physical layer (LoRa). Obeying the duty cycle restriction that is imposed by authorities on corresponding frequency spectrum regulations in some regions is another possible bottleneck of a LoRaWAN network. Such limitations may substantially restrict the downlink traffic, present a minimum delay between the sequential packets sent by a device, and limit the peak throughput. In LoRa networks, duty cycle restrictions exist on the maximum time of the end devices to stay on or allow transmission per hour. This restriction decides the available time for each channel during packet transmissions, which changes randomly for different end nodes. Thus, it limits the number of transmitted packets from the end devices. Although this duty cycle increases the network's robustness against interference, it is often not possible for a larger scale of deployment because of the 1% duty cycle limit on the downlink gateway transmissions, causing an additional delay.