esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 3 Methodology
### First Phase: Data Collection and Preprocessing
Lithology is closely linked to geomorphological and surface characteristics [34] and plays a crucial role in landslide susceptibility, as different rock types exhibit varying degrees of weathering, strength, and permeability. The lithology map (Figure 3e) was compiled from a 1:500,000-scale geological map, providing essential insights into the geological conditions influencing the slope stability. Additionally, other topographic factors were extracted from ASTER GDEM at a 30 \(\times\) 30 m resolution, ensuring consistency in data processing. In this study, all 16 landslide conditioning factors were standardized to the same spatial resolution (30 \(\times\) 30 m) to facilitate accurate susceptibility analysis and modeling. \begin{table}
\begin{tabular}{c c c} \hline
**Factor Category** & **Factor** & **Classifications/Ranges** \\ \hline Topographic & Slope (\({}^{\circ}\)) & 0.00–8.72, 8.72–19.19, 19.19–30.82, 30.82–43.32, 43.32–74.13 \\  & Aspect & North, Northeast, East, Southeast, South, Southwest, West, Northwest \\  & Elevation (m) & 422–798, 798–1281, 1281–1818, 1818–2399, 2399–3648 \\ Hydrological/Terrain Indices & STI & \textless{18.25, 18.25–66.91, 66.91–164.22, 164.22–352.78, \textgreater{}352.78} \\  & SPI & \textless{20.47, 20.47–88.80, 88.80–232.30, 232.30–573.93, \textgreater{}573.93} \\  & TWI & \textless{1.73, 1.73–26.6, 2.66–6.44, 6.44–10.22, \textgreater{}10.22} \\  & Plan Curvature & \textless{28.23 to $-0.05$, $-0.05$ to 0.05, 0.05 to 25.33} \\  & Profile Curvature & \textless{34.05 to $-0.05$, $-0.05$ to 0.05, 0.05 to 32.55} \\ Proximity Factors & Distance to Faults (m) & 0–500, 500–1000, 1000–1500, 1500–2000, \textgreater{}2000 \\  & Distance to Roads (m) & 0–300, 300–600, 600–900, 900–1200, \textgreater{}1200 \\  & & 463.58–484.42, 484.42–500.94, 500.94–518.63, 518.63–537.50, 537.50–563.85 \\  & Type & Farmland, Forest land, Grassland, Water bodies, Residential areas, Others \\ Soil Type & Soil Class & Eutric Regosol, Calcic Luivisol, Mollic Leptosol, Calcaric Fluivisol, Eutric Cambisol, Calaric Cambisol, Fimic Anthrosol, Cumulic Anthrosol \\ Geological & Lithology Group & Group 1: Gravel/sand/clayey silt, Group 2: Loess, Group 3: Granite/gnesis, Group 4: Marble/volcanic rocks, Group 5: Schist/quartzite \\ \hline \end{tabular}
\end{table} 
Table 1: Sixteen landslide conditioning factors. Figure 3: Thematic maps of the study area: (**a**) rainfall, (**b**) NDVI, (**c**) land use, (**d**) soil, and (**e**) lithology.
# Study on Fire Smoke Distribution Characteristics in Mountain Spiral Highway Tunnels
## 5 Conclusions  
To investigate the smoke diffusion characteristics in spiral highway tunnels and the impact on characteristics, a series of numerical simulations were conducted considering the radius of curvature, slope gradient, and altitude. The major conclusions are follows:  
Among the three key influencing factors, tunnel curvature exerts an independent effect on the dispersion pattern of fire smoke. Its ability to suppress the lateral spread of high-temperature smoke from the fire source remains unaffected by altitude or slope.  
The influence of altitude on slope primarily manifests as an enhancement of chimney effects in uphill areas and a suppression in downhill regions, which subsequently affects the length of high-temperature zones near the fire source.  
The impact of slope on altitude predominantly pertains to its effect on smoke layer thickness. An increase in altitude leads to a reduction in air buoyancy, resulting in a thicker smoke layer. However, an increase in slope mitigates this thickening effect, thereby lessening its impact on human visibility at the characteristic height.  
Conceptualization, Y.C. and Z.L.; methodology, Y.C.; software, Y.C.; validation, Y.C.; formal analysis, Y.C.; investigation, Z.L.; resources, Z.L.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Z.L.; visualization, Y.C.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41461016 and grant number 41761015.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 3 Three-Layer Cloud Dispatching Architecture
### Server Nodes Selection Layer  
After finishing the first two layers, the homogeneous tasks can be dispatched to homogeneous cloud clusters. However, the cloud workload can be unbalanced when a large number of tasks exits due to inappropriate task assignment. Based on the reasons above, this paper proposed a novel algorithm, the Advanced Cluster Sufferage Scheduling (ACSS), to solve the drawback of the MaxSufferage algorithm [12; 17; 18], especially in the heterogeneous environment. In ACSS, each task can be assigned to the appropriate server nodes by \(S_{j}\) which is calculated from the average \(ECT\) of the server node to reduce the influence of inappropriate assignment. The notation and details of the ACSS algorithm are shown in Table 3 and Algorithm 2.  
In general, there are three phases in the ACSS algorithm. At first, the \(EECT_{i}\) (the earliest expected completion time) and \(SEECT_{i}\) (the second earliest expect completion time) of the \(S_{j}\) are found in order to calculate the \(SV\) value in the \(SV_{j}\) calculation phase; the detailed process is shown in Lines (8) and (9) in the ACSS algorithm. Then, the MSV value will be set to the second earliest \(ECT\) value of task \(i\) in the second phase, which is called the \(MSV_{i}\) calculation phase, while task \(i\) has the maximum SV value among all \(SV\) values.  
In the final phase, the task dispatching phase, task \(i\) will be dispatched to \(S_{j}\) while \(MSV_{i}>ECT_{j}\) and \(EECT_{i}>AECT_{j}\) of \(S_{j}\). Conversely, when \(EECT_{i}<AECT_{j}\), task \(i\) can be dispatched to server node \(j\) where the \(ECT_{i}\) is approximate to \(AECT_{j}\) and the \(ECT_{i}\) needs to be larger than \(AECT_{j}\). As a result, the main concept is different from those of previous algorithms; the detail of the algorithm is shown in Line (11) and (12) of Algorithm 2.  
\begin{table}
\begin{tabular}{c l} \hline \hline \(TCT_{ij}\) & The task completion time of the task \(i\) in the server node \(S_{j}\) \\ \(ECT_{ij}\) & The expected completion time of task \(i\) in the server node \(S_{j}\) \\ \(r_{j}\) & The expected time server node \(S_{j}\) will become ready to execute for next task \\ \(AECT_{j}\) & The average expected completion time in the server node \(S_{j}\) \\ \(EECT_{i}\) & The earliest expected completion time of task \(i\) \\ \(SEECT_{i}\) & The second earliest expected completion time of task \(i\) \\ \(SV_{i}\) & The Suffrage Value of task \(i\) \\ \(MSV_{i}\) & The MaxSufferage Value of task \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The notations of ACSS algorithm in the server nodes selection layer.  
Based on the procedure above, the completion time and load balancing of the workload can be reduced efficiently in the heterogeneous cloud computing network. For the security issue, we can employ a Message Authentication Code (MAC) algorithm [18; 22] with a session key to confirm that the message truly comes from the sender and has not been modified. Subsequently, the example is provided to help understanding of the ACSS algorithm in the server node selection layer.
The chopping technique fundamentally cancels the offset and flicker noise caused by the upmodulating offset and the low frequency flicker noise by chopping the frequency and passing the signal through a low pass filter (LPF). However, upmodulated offset and flicker noise create ripples, which must be attenuated by a ripple reduction loop (RRL). The RRL integrates the ripple generated by the chopping technique at the output stage and eliminates ripples through the negative feedback of this signal to the input stage. However, this process results in a notch at the chopper frequency of the transfer function of the operational amplifier. As a result, operational amplifiers implementing the chopping technique have a low unit-gain bandwidth (UGBW). Such a low UGBW is poorly-suited to many applications because it cannot deal with fast input signals. To avoid this drawback, in this paper, a chopper-stabilized multipath operational amplifier including a high frequency path (HFP) and a low frequency path (LFP) is implemented in the IA. In general, flicker noise is dominant in the low frequency region, so the chopping technique and RRL are employed to LFP but not HFP. Therefore, the transfer function of the LFP is limited to UGBW due to notch formation at the chopper frequency, but the HFP is not, so the transfer function of the multipath operational amplifier connecting LFP and HFP in parallel does not form the notch. Consequently, the multipath operational amplifier architecture \begin{table}
\begin{tabular}{c c c c c} \hline  & **(a)** & **(b)** & **(c)** & **(d)** \\ \hline \(V_{O}\) & \(\frac{V_{B}}{4}\Big{(}\frac{\Delta R}{K_{0}+\Delta R/2}\Big{)}\) & \(\frac{V_{B}}{2}\Big{(}\frac{\Delta R}{K_{0}+\Delta R/2}\Big{)}\) & \(\frac{V_{B}}{2}\Big{(}\frac{\Delta R}{K_{0}}\Big{)}\) & \(V_{B}\Big{(}\frac{\Delta R}{K_{0}}\Big{)}\) \\ Linearity error & 0.5\%/\% & 0.5\%/\% & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of output voltages and linearity errors for voltage-driven Wheatstone bridges. Figure 1: Voltage-driven Wheatstone bridge configurations: **(a)** Single-element configuration; **(b)** Two-element configuration; **(c)** Second type of two-element configuration; **(d)** All-element configuration. resolves the shortcoming of having a low UGBW, and has the merit of being appropriate for a variety of applications. A small input signal is amplified by the three-operational amplifier IA, having high input impedance, excellent linearity, and adjustable gain; the amplified signal is then converted into a digital signal via a 12-bit successive approximation register (SAR) ADC. This paper is organized as follows. Section 2 explains the topology of the proposed Wheatstone bridge readout IC with a chopper-stabilized multipath operational amplifier. The experimental results are described in Section 3. A discussion and conclusions are presented in Sections 4 and 5, respectively.
## 2 Materials and Methods
### Adsorption Isotherms  
The batch experiment results were analyzed to reveal the adsorption mechanism using Langmuir, Freundlich, Temkin, and Dubinin-Radushevich (D-R) isotherms, and the results are consistent with the Freundlich isotherm. Analysis of the results against these models indicated the Freundlich isotherm provides the best fit for the results. The Freundlich isotherm is an empirical equation that describes heterogeneous adsorption. The Freundlich isotherm model is given by:  
\[q_{e}=k_{f}C_{e}\frac{1}{n} \tag{4}\]  
where \(n\) and \(k_{f}\) are the constants related to the sorption intensity and capacity of the adsorbent [43]. The linear form of the Freundlich model is:  
\[lnq_{e}=lnk_{f}+\frac{1}{n}lnC_{e} \tag{5}\]
## 2 Materials and Methods
### Kinematics of DTF and STF Decay Mechanisms
#### 2.1.2 Two-Step Decay: Sequential Ternary Fission  
In the first step, the compound nucleus of mass number \(A_{CN}\) breaks into two fragments: (\(A_{23}\) and \(A_{1}\)). The Total Kinetic Energy (TKE, the sum of the kinetic energy of the two fragments (\(A_{23}\) and \(A_{1}\))) is calculated considering no excitation of the fragments. The velocity plot for STF is shown in the bottom panel of Figure 1. At each decay step, the fragments are emitted on the opposite sides in the center of mass of the fissioning nucleus. The velocities of the fragments in the lab frame are obtained by summing up the cm velocities with the proper velocity of the source in the cm frame, namely \(\vec{v}_{cm}\) in the first step and \(\vec{v}_{23,cm}\) in the second one.  
We now analyze the kinematics of this two-step TF. Before the collision, the center-of-mass velocity is given by the following:  
\[v_{cm}=\frac{M_{p}v_{p}}{M_{p}+M_{t}}=\frac{\sqrt{2E_{p}M_{p}}}{M_{p}+M_{t}}.\]  
By using the momentum conservation law along the axis identified by the beam vector and the orthogonal one, we have the following:  
\[M_{1}v_{1}\cos(\theta_{1})+M_{23}v_{23}\cos(\theta_{23})=(M_{1}+M_{23})v_{cm}\]  
\[M_{1}v_{1}\sin(\theta_{1})+M_{23}v_{23}\sin(\theta_{23})=0\]  
From this, we obtain the following:  
\[\theta_{23} = \cot^{-1}\left[\cot(\theta_{1})-\frac{(M_{1}+M_{23})v_{cm}}{M_{1}v _{1}\sin(\theta_{1})}\right]\] \[v_{23} = -\frac{M_{1}v_{1}\sin(\theta_{1})}{M_{23}\sin(\theta_{23})}\]  
By recursively knowing the initial condition of the second fissioning nucleus (\(A_{23}\to A_{2}+A_{3}\)) and by using both the energy and momentum conservation laws, we obtain the following:\[M_{2}v_{2}\cos(\theta_{2})+M_{3}v_{3}\cos(\theta_{3}) =M_{23}v_{23}\cos(\theta_{23})\] \[M_{2}v_{2}\sin(\theta_{2})+M_{3}v_{3}\sin(\theta_{3}) =M_{23}v_{23}\sin(\theta_{23})\] \[\frac{1}{2}M_{2}v_{2}^{2}+\frac{1}{2}M_{3}v_{3}^{2}=\frac{1}{2}M_ {23}v_{23}^{2}+Q_{2}\]  
Thus, the angle and the velocity of the third fragment and the velocity of the second fragment are as follows:  
\[\cos\theta_{3}=\frac{1}{M_{3}V_{3}}[M_{23}v_{23}\cos(\theta_{23}) -M_{2}v_{2}\cos(\theta_{2})] \tag{5}\] \[v_{3}=\left[\frac{M_{23}v_{23}^{2}+2Q_{2}-M_{2}v_{2}^{2}}{M_{3}} \right]^{\frac{1}{2}}\] (6) \[v_{2}=\frac{-\beta\pm\sqrt{\beta^{2}-4\alpha\gamma}}{2\alpha} \tag{7}\]  
where  
\[\alpha =M_{2}(M_{2}+M_{3})\] \[\beta =-2M_{2}M_{23}v_{23}\cos(\theta_{2}-\theta_{23})\] \[\gamma =M_{23}^{2}v_{23}^{2}-M_{3}M_{23}v_{23}^{2}-2M_{3}Q_{2}.\]  
Then, by substituting the value of \(v_{2}\) in the expression of \(v_{3}\) and \(\theta_{3}\), the three equations for \(E_{2}\), \(E_{3}\), and \(\theta_{3}\) as functions of the three input parameters (\(E_{1}\), \(\theta_{1}\) and \(\theta_{2}\)) for each ternary fragmentation are obtained.
# Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks
## 5 Numerical Results
### Simulation 2: Number of BSs  
Next, with the number of antennas fixed, we compared the transmission speed and the utility of the three methods under different populations. \(N_{i}\) was fixed to 1200, while \(I\) was adjusted from 50 to 80.  
Figure 5: Performance of each area under different antennas: (**a**) data rate, (**b**) utility, (**c**) total utility.  
Figure 6a,b gives the transmission speed (Mbps) and average utility of each area. As \(I\) increased, there was no significant change in the utility and speed of the first and second areas of all three methods. This is because regardless of the routing schemes, a topology has to be built starting from the inner area; thus, the performance of inner BSs are not greatly affected by the increase of those in the outer areas. The average data rate and utility of the third area of PSR was roughly the same, while the average data rate and utility of SPT and CFC were significantly reduced. This is because SPT and CFC always favor inner areas. Thus, the speed of the outer areas is sacrificed when BSs of the first and second area increases. In contrast, as PSR focuses on the overall utility benefit, it can adjust antennas more efficiently and provides a milder performance drop of the outer BSs.  
Figure 6: Performance of each area under different number of base stations (BSs): (**a**) data rate, (**b**) utility, (**c**) total utility.  
Figure 6c shows that the total utility of all three methods was improved with the increase in the number of BSs. Although the average speed would be reduced with the increase of BSs, the average marginal utility of BSs increased as more BSs require more resources. However, the utility of SPT was the lowest because its links were too short. PSR again provided the highest total utility due to its flexibility of allocation. The results also showed that a higher total/average data rate did not always lead to better system utility because the resources also needed to be allocated fairly to prevent monopolization and starvation.
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 2 UWA-OTFS System Model
Then, the TF domain signal is converted into a DD domain signal by a symplectic finite Fourier transform (SFFT) as  
\[y[k,l]=\frac{1}{\sqrt{MN}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}Y[n,m]e^{- \beta\tau(\frac{n}{N}-\frac{m}{M})}. \tag{6}\]  
After parallel-to-serial conversion, \(y[k,l]\) is converted to \(y\) at a size of \(N\times M\). Finally, signal detection and demodulation will be performed to recover the transmitted signal as \(\hat{x}\). The multi-path effect and Doppler shift in UWA communication are more severe than that of radio communication. In UWA communication, assume that the maximum delay of the time-varying UWA channel is \(\tau_{\max}\) and the maximum Doppler shift is \(v_{\max}\). In OTFS modulation, from the view of the DD domain, the OTFS parameter design is related to the channel conditions. In the Doppler axis, \(1/T\) determines the maximum supportable Doppler shift as \(v_{\max}<1/T\). In the delay axis, \(1/\Delta f\) determines the maximum supportable multi-path delay as \(\tau_{\max}<1/\Delta f\). In time-varying UWA channels, the maximum multi-path delay \(\tau_{\max}\) is large, so the corresponding designed value of \(\Delta f\) should be small, and T is as large as \(T=1/\Delta f\). To support a certain data rate of \(NM\) subcarriers per frame, the OTFS system is designed with a total bandwidth \(B=M\Delta f\) and frame duration \(T_{f}=NT\). \(\Delta f\) is small, so the setting of \(M\) should be large for high data rate communication. \(T\) is large, and the frame duration \(T_{f}\) should not be too large for demodulation latency, so \(N\) cannot be too large. For effective OTFS communication in the UWA channel, a small value of \(N\) and large value of \(M\) should be selected to achieve efficient communication. Based on the above analysis of UWA OTFS, a large \(M\) and small \(\Delta f\) result in a high resolution for the frequency, which is sensitive to intercarrier interference. Additionally, when \(T\) is large and the value of \(N\) is small, the resolution of the corresponding Doppler shift decreases, which will affect the accuracy of signal detection. For the challenging UWA OTFS communication, this paper designs a DL-based signal detector for data recovery in the UWA channel with severe interferences.
## 3 Explicit Sensitivity Analysis of Objective Functions and Constraints
### Sensitivity of Stress Constraints with Respect to Design Variables  
In fact, if stress constraints are set on each element, the number of constraints is very large, which makes the calculation of stresses inefficient. At the same time, the maximum stress is not fixed in the optimization process, which makes it difficult to converge the optimization problem quickly. Therefore, the p-norm function is used to integrate all von Mises stresses into a global stress that approximates the equivalent maximum von Mises stress, expressed as:  
\[\sigma^{PN}=\bigg{(}\sum_{i=1}^{element}\bigg{(}\frac{\sigma_{e}^{VM}}{\sigma_{ S}}\bigg{)}^{p}\bigg{)}^{\frac{1}{p}} \tag{17}\]  
where \(p\) is the condensation parameter. When \(p\) tends to infinity, \(\sigma^{PN}\) is equal to \(max\big{(}\sigma_{e}^{VM}/\sigma_{s}\big{)}\) and the global stress constraint becomes.  
The sensitivity information of stress with respect to element density is obtained through the adjoint variable method. By introducing Lagrange multipliers, the Lagrangian function \(\theta\) for stress is constructed as:  
\[\boldsymbol{\theta}=\overline{\sigma}^{PN}-\mathbf{\lambda}_{3}^{\mathrm{T}} (\mathbf{K}\mathbf{U}-\mathbf{F}) \tag{18}\]  
The derivation of the sensitivity of the Lagrangian function with respect to the element density is as follows:  
\[\frac{\partial\boldsymbol{\theta}}{\partial x_{i}}=\frac{\partial\overline{ \sigma}^{PN}}{\partial x_{i}}-\mathbf{\lambda}_{3}^{\mathrm{T}}\bigg{(}\frac{ \partial\mathbf{K}}{\partial x_{i}}\mathbf{U}+\mathbf{K}\frac{\partial\mathbf{ U}}{\partial x_{i}}-\frac{\partial\mathbf{F}}{\partial x_{i}}\bigg{)} \tag{19}\]  
It is worth noting that the correction factor updates the sensitivity of global stress to element density. According to the chain rule, the derived information can be obtained as follows:  
\[\frac{\partial\overline{\sigma}^{PN}}{\partial x_{i}}=\sum_{i=1}^{Nelement}c \frac{\partial\sigma^{PN}}{\partial\sigma_{i}^{VM}}\bigg{(}\frac{\partial \sigma_{i}^{VM}}{\partial x_{i}}\bigg{)}^{\mathrm{T}}\frac{\partial\sigma_{i }}{\partial x_{i}} \tag{20}\]  
From the above equation, it can be seen that the sensitivity of global stress can be obtained by computing the derivative of the p-norm function with respect to von Mises stress, the derivative of von Mises stress with respect to stress components, and the derivative of stress components with respect to design variables.
## 4 Experiment Setup
### Experimental Parameters  
The proposed model was trained for 150 epochs and used Adam optimizer [33] with the learning rate \(3\times 10^{-4}\) and the parameter \(\beta_{1}\) is 0.9, \(\beta_{2}\) is 0.999. All the training and test utterances are sampled at 16 kHz. We trained model with batch size of 12. The channels per layer of the encoder (i.e., \(\text{C}_{i}\) in Figure 2) are 64, 128, 256, 512, 1024, respectively, and the channels per layer of the decoder (i.e., \(\text{H}_{i}\) in Figure 2) are 512, 256, 128, 64, 1. The preprocessing of speech before feeding it into model is similar to the method in [15]. Firstly, we set the length of the input single channel speech chunks as 1 s (i.e., 16,000 samples) and the overlap as 0.5 s (i.e., 50% of the input). Secondly, as previous works has proved that data augmentation of input speech was useful for the ability of model in denoising [9; 14; 15; 34], we apply random shift between 0 s and 1 s for input speech. The number of samples of random shift is 8000, so the length of input speech after shift is about 0.5 s (i.e., 8000 samples). Finally, the standard deviation of input speech is used to normalize it before feeding it into model and scaling back the output of model by the same standard deviation. In addition, interpolated sampling had been proved to help improve the model's denoising performance [14; 15], so, since interpolation filter is used to resampling the input and output of the model [15; 35] during training. The speech feature maps were divided into 64 groups(i.e., G is 64) along the channels in the Shuffle Attention. The measurements obtained by the random Gaussian matrix can reconstruct the better original signal with a higher probability, and it has universal applicability in the traditional CS. So, we chose a zero-mean random Gaussian matrix with independent and identically distributed entries as a measurement matrix \(\mathbf{A}\) when defining CS loss. The rows of \(\mathbf{A}\) are the measurement number, and the columns of \(\mathbf{A}\) are equal to the length of the input speech after preprocessing. The multi-resolution STFT loss is applied during training (\(M\) is set to 3), where Hanning window is applied, the FFT sizes are [512, 1024, 2048], the Window sizes are [240, 600, 1200], and the Frame shift are [50, 120, 240][29]. In the test phase, the test set will be enhanced directly without any preprocessing.