esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 3 A Matrix Attention Model Based on the Time and Frequency Domains
### The Overall Model Architecture
#### 3.2.1 The Frequency Domain Block  
The function of the frequency domain block is to extract the seasonal information for prediction from the perspective of the frequency domain, and the frequency domain block consists of a frequency domain encoder and a frequency domain decoder. The purpose of the frequency domain encoder is to take the time domain sequence information through linear layer mapping and the discrete Fourier transform and then save it in the frequency domain sequence. The role of linear mapping is to save a time series \(X_{c}^{s}\) of length c in a time series \(X_{n}^{s}\) of length n. The process of linear mapping is described using Equation (3).  
\[X_{n}^{s}=linear(X_{c}^{s}),0<n\leq c, \tag{3}\]  
An alternative representation of \(X_{n}^{s}\) is \(X_{n}^{s}=(x_{1},x_{2},\cdots,x_{n})^{s}\).  
The discrete Fourier transform maps the sequence from the time domain to the frequency domain. The frequency domain sequence information parses the time series from another perspective, thus extracting features of the data that cannot be extracted in the time domain sequence. The process is shown in Equation (4).  
\[W_{n}^{s}=DFT[X_{n}^{s}]=\sum_{t=1}^{n}x_{t}H_{n}^{tk}, \tag{4}\]where \(H_{n}=e^{-i\frac{2\pi}{n}}\), DFT\([\cdot]\) is the discrete Fourier transform, \(W_{n}^{s}\) is the frequency domain sequence of the seasonal terms, \(X_{n}^{s}\) is the time domain sequence of the seasonal terms, \(n\) is the length of the sequence, \(k\) is the harmonic number, and \(t\) represents the time.  
\(W_{n}^{s}\) in the discrete Fourier transform is usually expressed as a complex number: \(W_{n}^{s}=R_{n}^{s}+j_{n}^{s}\). We divide the seasonal term frequency domain sequence \(W_{n}^{s}\) into two real sequences \(R_{n}^{s}\) and \(I_{n}^{s}\), which, after going through the matrix attention layer and the activation function, are again turned into a new frequency domain sequence \(W_{n}^{s}\). This process can be expressed using Equation (5):  
\[R_{n}^{\prime s}I_{n}^{\prime s}=\sigma(MAATT(R_{n}^{s})),\sigma(MAATT(R_{n}^{ s})),W_{n}^{\prime s}=Complex\left(R_{n}^{\prime s},I_{n}^{\prime s}\right), \tag{5}\]  
\(\sigma\) is the activation function, MAATT is the matrix attention mechanism, and \(Complex(\cdot)\) represents the construction of new complex numbers.  
The purpose of the frequency domain decoder is to transform the newly composed frequency domain sequence back into the time domain using discrete Fourier inversion to obtain a new time domain sequence \(X_{n}^{s}\), which is then mapped through a layer of linear mapping to obtain the frequency domain block output. The computational procedure is shown in Equation (6).  
\[X_{n}^{\prime s}=IDFT\left[W_{n}^{\prime s}\right]=\frac{1}{n}{\sum_{k=1}^{n}w _{rk}H_{n}^{-tk}}X_{o}^{s}=linear\left(X_{n}^{\prime s}\right), \tag{6}\]  
\(IDFT[\cdot]\) is the inverse discrete Fourier transform. \(Y_{o}^{s}\) is the frequency domain block output.
## 3 Challenges in Terahertz-Based FSO Communication
### Atmospheric Absorption  
A significant constraint of THz-FSO communication is the amount of atmospheric absorption; this emanates from the interaction of the THz waves with water molecules in the atmosphere [16; 54]. This results in exponential power losses, especially in areas of high humidity, which obstruct the signal, transmission range, and quality of the system [55; 56]. The attenuation of THz waves due to atmospheric absorption can be mathematically modeled using the generalized form of the Beer-Lambert law, which accounts for varying frequencies and environmental parameters:  
\[\mathrm{A(f,d)}=\mathrm{P_{0}}\cdot\mathrm{e}^{-\alpha(\mathrm{f})\cdot\mathrm{ d}} \tag{7}\]  
where \(\mathrm{A(f,d)}\) is the remaining power at frequency f after the signal has travelled d, \(\mathrm{P_{0}}\) is the initial transmitted power, \(\alpha(\mathrm{f})\) is the frequency-dependent attenuation coefficient (m\({}^{-1}\)), and d is the transmission distance (m).  
The frequency-dependent attenuation coefficient \(\alpha(\mathrm{f})\) is determined by the following:  
\[\alpha(\mathrm{f})=\sum_{\mathrm{i}}\frac{\mathrm{S_{i}}\cdot\gamma_{\mathrm{ i}}}{\left(\mathrm{f}-\mathrm{f_{i}}\right)^{2}+\gamma_{\mathrm{i}}^{2}} \tag{8}\]  
where \(\mathrm{S_{i}}\): the line strength of the i-th absorption line; \(\mathrm{f_{i}}\): the center frequency of the i-th absorption line; \(\gamma_{\mathrm{i}}\): the line width of the i-th absorption line (pressure and temperature dependent); f: the operating frequency (THz)  
This model reflects the dependence of the attenuation on the specific absorption lines of atmospheric water vapor, highlighting the sensitivity of THz communication to environmental conditions.
## 6 Chain Signature
### Chain Authentication Process  
Algorithm 4 shows the process of chain authentication of the message by the receiver. For messages without a signature, the receiver needs first to determine whether the sequence number of the messages is legal and then authenticate the node value of the message hash chain of the messages. For messages with a signature, the receiver needs to authenticate the signature and verify the sequence number of the messages and the node value of the message hash chain. The parameters used in Algorithm 4 are described below:  
* \(\mathit{chain\_seq}\): Sequence number counter.
* \(dk_{src}\): Sender's public key.
* \(HC\_ver\_node\): Message hash chain node value used for verification.  
```
0: Messages.
0: Verification status.
1: Obtain header information, message sequence number, payload, and node value of the chain from the message: \(hdr\), \(seq\), \(payload\), \(HC\_pkt\_node\gets DePkt(p)\).
2:if\(chain\_seq+1\
eq seq\)then
3:return -1. # A value of \"-1\" indicates that the sequence number is not sequential.
4:endif
5:if\(cur\_interval==\)\(Sig\_interval\)then
6:\(s\gets Get\_Sig(p)\).
7:if\(Ver(dk_{src},s)\
eq TRUE\)then
8:return -2. # A value of \"-2\" indicates an error in signature verification.
9:endif
10:else
11:\(HC\_ver\_node\gets HC\_Iteration(hdr,payload,HC\_node)\).
12:if\(HC\_ver\_node==\)\(HC\_pkt\_node\)then
13: The sender inserts \(HC\_ver\_node\) at the end of the message hash chain and updates message hash chain tail node \(HC\_node=HC\_ver\_node\).
14:return 0.# A value of \"0\" indicates that the authentication of the message is successful.
15:else
16:return -3.# A value of \"-3\" indicates an error in message hash chain verification.
17:endif
18:endif
```  
**Algorithm 4** The Receiver Verifying The Received Messages
## 2 Proposed Device Structures  
To investigate the influence of SLARCs and MLARCs on Si-SC performance, various device configurations are constructed utilizing the selected materials (SiO\({}_{2}\), Si\({}_{3}\)N\({}_{4}\), NQDs, and LiF). The SLARC-based Si-SC comprises four distinct configurations featuring continuous thin films, as illustrated in Figure 1a. For MLARC-based Si-SCs, two primary configurations are explored: continuous and micro-/nano-patterned structures, as depicted in Figure 1b. Within the MLARC category, four specific designs are implemented: a continuous MLARC with a SiO\({}_{2}\)/NQDs/LiF stack (b1), a continuous MLARC with a Si\({}_{3}\)N\({}_{4}\)/NQDs/LiF stack (b2), a patterned MLARC with a SiO\({}_{2}\)/NQDs/LiF configuration (b3), and a patterned MLARC with a Si\({}_{3}\)N\({}_{4}\)/NQDs/LiF configuration (b4).  
Figure 1: Schematic representation of silicon solar cell (Si-SC) structures. (**a**) Si-SC with single-layer anti-reflection coating (SLARC). (**b**) Si-SC with multi-layer anti-reflection coating (MLARC), including continuous and micro-/nano-patterned configurations. The SLARC and MLARC consist of SiO\({}_{2}\), Si\({}_{3}\)N\({}_{4}\), NQDs, and LiF layers.
# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 3 Prediction Model of TBM Penetration Rate Based on Machine Learning  
### Prediction Model of TBM Penetration Rate Based on Multiple Regression (MR)  
#### 3.1.1 Correlation Analysis of Penetration Index (FPI) and Rock Mass Parameters  
Firstly, the correlation between the penetration index (FPI) and rock mass in the TBM driving efficiency database is explored, and the rock mass parameters which have a great influence on the FPI are obtained. The correlation between the penetration index (FPI) and rock mass parameters is shown in Figure 2. It can be seen from Figure 2 that there is a good correlation between the penetration index (FPI) and the uniaxial compressive strength (UCS). The correlation coefficient R\({}^{2}\) is 0.74; the correlation coefficient between the number of rock volume joints \(J_{\rm v}\) and the penetration index (FPI) also reaches 0.63; the correlation coefficients between the penetration index (FPI) and the angle between the joint surface and tunnel axis \(\alpha\), the chiseling specific work a and the rock wear resistance index \(A_{\rm b}\) are 0.36, 0.38 and 0.13, respectively. According to the above analysis, the UCS of rock mass and the number of rock volume joints \(J_{\rm v}\) of rock mass, which represent the integrity of rock mass, will significantly affect the penetration rate of the TBM. Similarly, these two indexes show a good correlation with the penetration index (FPI), which is used to measure the boreability of the rock mass.  
Figure 2: _Cont._  
Figure 2: Correlation between field penetration index and rock mass parameters. **(a)** FPI-Uniaxial compressive strength UCS; **(b)** FPI-Number of rock volume joints \(\
u_{\
u}\); **(c)** FPI-Angle between joint surface and tunnel axis \(\alpha\); **(d)** FPI-Specific work of chiseling \(a\); **(e)** FPI-Rock abrasion resistance index \(A_{\rm b}\).
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 3 PV Model Architecture
### Data Pre-Processing  
Because the dimension (i.e., the units) of input data in (17) is totally different, the input data need to be normalized. The method is shown in (18):  
\[\overline{x}_{i}=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}} \tag{18}\]  
where \(x_{i}\) is the input data or output data, while \(x_{\max}\) and \(x_{\min}\) are the maximum and minimum of the value. As the measurement results cannot be exactly accurate, the measured power production and solar radiation data influenced by measurement errors could sometimes be less than zero. This is impossible in real practice. In this case, we set the data, of which the measured and predicted values are less than zero, as zero.
## 4 Case Study
### Result Analysis  
Based on the data from the case study, this section analyzes the application effectiveness of the integrated 3S framework across three core monitoring scenarios. The results demonstrate the precise identification of disturbance boundaries, efficient extraction of soil and water conservation measures, and accurate estimation of spoil volume changes, highlighting the reliability and precision of the outcomes.
## 4 Methodology
### The Proposed Feature Importance Fusion Framework  
#### 4.1.1 Ensemble Feature Importance  
Figure 2 shows our MME Framework. On Stage 1, data undergo pre-processing, such as transformation, noise reduction, feature extraction and feature selection. This stage is required for our framework, as we need to ensure that the data have no inconsistencies and that the features used to train the machine learning models are orthogonal by removing noise and features that are collinear, which could negatively impact the model's performance. The preference for features with low correlation guarantees that the feature importance calculation does not attribute random values of importance because a set of features contains similar information. No pre-processing is performed for this paper since the data are artificially created and undesirable features such as noise or non-informative features are kept to study the properties of the feature importance ensemble. On Stage 2, ML models are applied to the pre-processed data; subsequently, model-agnostics feature importance methods are applied to the ML models (Stage 3). The feature importance results are fused into a final feature importance in Stage 4.  
Figure 2: The four stages of the proposed feature importance fusion MME Framework. The first stage pre-processes the data, and the second step trains the data on multiple ML models. The third step calculates feature importance from the each trained ML model using multiple feature importance methods. Finally, the fourth step fuses all feature importance generated from the third step using an ensemble strategy to generate the final feature importance values.  
In Stage 3 and Stage 4, when multiple methods and ML models are employed in ensemble feature importance, we obtain several vectors, \(Q\), of feature importance values, \(A\), as follows: \(Q=[A_{1},A_{2},...,Ai]\), where \(i\) is the number of features and there is one \(Q\) for each ML model and feature importance technique combination. We need to establish a final decision from those vectors. Therefore, first we reduce the noise and anomalies in the decision. The feature importance vectors can be denoted as follows: \(\overline{\mathcal{V}}=[Q_{1},Q_{2},...,Q_{N}]\), where \(N\) is the sum of each model multiplied by the number of feature importance methods used. To reduce the noise and variance, we can take the average of all feature importance vectors, \(\overline{\mathcal{V}}\), which produces the variance, \(\sigma^{2}/N\). As \(N\) increases, the variance decreases. The variance and correctness of final feature importance vector can be further improved if the anomalies are removed prior to taking the average.
# Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge
###### Abstract  
An image recognition technique is proposed for determining optimal neck levels for standard metal gauges, in the process of validating pipe provers. A camera-level follow-up control system was designed to achieve automated tracking of fluid levels by a camera, thereby preventing errors from inclined viewing angles. An orange background plate was placed behind the tube to reduce background interference, and highlight scale numbers/lines and concave meniscus. A segmentation algorithm, based on edge detection and K-means clustering, was used to segment indicator tubes and scales in the acquired images. The concave meniscus reconstruction algorithm and curve-fitting algorithm were proposed to better identify the lowest point of the meniscus. A characteristic edge detection model was used to identify centimeter-scale lines corresponding to the meniscus. A binary tree multiclass support vector machine (MCSVM) classifier was then used to identify scale numbers corresponding to scale lines and determine the optimal neck level for standard metal gauges. Experimental results showed that measurement errors were within \(\pm\)0.1 mm compared to a ground truth acquired manually using Vernier calipers. The recognition time, including follow-up control, was less than 10 s, which is much lower than the switching time required between measuring individual tanks. This automated measurement approach for gauge neck levels can effectively reduce measurement times, decrease manmade errors in liquid level readings, and improve the efficiency of pipe prover validation.  
image recognition; neck level; concave meniscus reconstruction; camera-level follow-up
## 3 Architecture
### IPCRSA Overview
#### 3.1.4 User and User Client  
In IPCRSA, Users are the requesters of computing resources and may be individuals or organizations. Within IPCRSA, each User is assigned a unique identifier, USER_ID, which serves not only as an identity marker but also as its ID within the ICN environment, as depicted in Table 2 above. The mapping relationship between USER_ID and the User's IP address is also maintained by the aforementioned hierarchical name resolution system.  
The Client is a software or hardware tool that enables Users to access IPCRSA to initiate resource requests or use resources. Its primary functions are as follows: (1) Identifying the User and generating a USER_ID. (2) Receiving input for resource requests from the User, creating corresponding computing resource request packets, and dispatching them to the SN. (3) Interacting with the hierarchical name resolution system to complete tasks such as registering the mapping relationship between USER_ID and User's IP, and querying for the IP of SN associated with RSS_ID_L\({}_{x}\). (4) Receiving scheduling success or failure response packets and relaying the scheduling outcome back to the User. If the scheduling is successful, the User's service deployment commands can be forwarded from the Client to GN for execution.
## 4 Wireless Communication Protocol
### Wireless Medium Access Control Layer
#### 4.2.1 Smart Channel Access  
In addition to basic requirements of providing channel access, we envision the MAC for wireless multi-chip systems can benefit from some smart features. The smart channel access needs to enable the WIs to gain channel access and acquire transmission opportunities or bandwidth based on their traffic demands. This will reduce the bandwidth underutilization by the WIs while transmitting data over the wireless channel which in turn, will improve the performance of chip-to-chip data transfer. Such capability is required because the demand of the WIs to access the wireless channel varies both temporally and spatially [79]. Traffic patterns are observed to be varying in both temporal and spatial sense in conventional intra-chip NoCs as well [45,80]. This is because traffic interaction between cores in a many-core chip is often non-uniform and time-varying in nature. The variation is expected to be even more in a multi-chip system with heterogeneous chips involving multicore CPUs, memory modules and GPU elements. A uniform channel access will result in waste of transmission opportunities, which will limit the performance gain achievable utilizing the novel wireless technology. Transmission opportunities can be in the form of token possession period, number of frequency/wavelength channels or code channels depending on the MAC mechanism. In [81, 82, 83] heterogeneous intra-chip NoC architectures are proposed. In [79] the authors proposed a simple history-based prediction mechanism to estimate the traffic demand of the WIs in the context of a single chip wireless NoC. Then, by allowing the WIs to transmit based on the predicted traffic demand, authors have shown 23% improvement in energy efficiency for application-based traffic patterns over the wired counterpart. Hence, we believe exploration of smart access mechanisms enabled MAC is going to be a driving force to meet the stringent bandwidth and energy requirements of the future multi-chip systems.
## 2 Materials and Methods
### Numerical Approach
#### 2.2.3 Establishment of the DDES Model  
The DDES model [27] reconstructs the length scale (\(l_{DDES}\)) of the DDES model by introducing a delay function, which remarkably reduces the problem of modeled stress  
Figure 1: The established NEU model and coordinate system.  
depletion (MSD) and its direct consequence, namely grid-induced separation (GIS). The DDES method was implemented in the hybrid RANS-LES-based approach of this study.  
Similar to the DES method, replacing the length scale (\(l_{RANS}\)) in the turbulence model with the length scale of DDES (\(l_{DDES}\)) yields a DDES method based on the original turbulence model. The length scale (\(l_{DDES}\)) can be expressed in the following form:  
\[l_{DDES}=l_{RANS}-f_{d}\text{max}\{0,l_{RANS}-l_{LES}\} \tag{4}\]  
The delay function (\(f_{d}\)) can be defined as follows:  
\[f_{d}=1-\tanh[(c_{d}r_{d})^{3}], \tag{5}\]  
where \(c_{d}=8\), and the parameter \(r_{d}\) can be expressed by the following:  
\[r_{d}=\frac{\
u+v_{t}}{\sqrt{u_{i,j}u_{i,j}}\kappa^{2}d^{2}}, \tag{6}\]  
where \(v_{t}\) represents the turbulent viscosity coefficient, \(u_{i,j}\) denotes the velocity gradient, \(\kappa=0.41\), and \(d\) signifies the distance from the wall.  
The RANS length scale of the SST turbulence model (\(l_{RANS}\)) is defined as follows:  
\[l_{RANS}=\frac{k^{1/2}}{\beta^{*}\omega} \tag{7}\]  
The length scale of the LES-based approach can be solely determined by the grid spacing:  
\[l_{LES}=C_{DDES}\Delta, \tag{8}\]  
where \(\Delta\) represents the local grid spacing. Here, \(\Delta\) is taken as the maximum length of the line connecting the centers of adjacent mesh cells, i.e., \(C_{DDES}=(1-F_{1})C_{DDES}^{outer}+F_{1}C_{DDES}^{inner}\), where \(C_{DDES}^{outer}=0.61\), \(C_{DDES}^{inner}=0.78\), and \(F_{1}\) represents an internal function of the SST-based model.  
According to the DDES model, it can be seen that as \(f_{d}\) approaches zero, the RANS calculation can be adopted, while when\(f_{d}\) approaches 1, it is transformed into the traditional DES method. This approach is capable of protecting the RANS calculation in the attached flow boundary layer without affecting the DES calculation in other regions.
## 3 Results and Discussion
### Dsc  
Figure 9 shows the DSC curves of the SAR paste with each mixture proportion at 28 days. The curve of the M2 paste shows strong endothermic peaks at 45.77 \({}^{\circ}\)C and 412.00 \({}^{\circ}\)C, and endothermic peaks at 78.29 \({}^{\circ}\)C and 120.34 \({}^{\circ}\)C; the M4 paste shows endothermic peaks at 48.91 \({}^{\circ}\)C, 82.11 \({}^{\circ}\)C, and 421.77 \({}^{\circ}\)C, and a minor peak at 123.66 \({}^{\circ}\)C; the M6 paste shows endothermic peaks at 44.14 \({}^{\circ}\)C and 74.18 \({}^{\circ}\)C, and minor peaks at 119.2 \({}^{\circ}\)C and 416.0 \({}^{\circ}\)C; and the M8 paste shows endothermic peaks at 42.10 \({}^{\circ}\)C and 71.83 \({}^{\circ}\)C, and minor peaks at 114.91 \({}^{\circ}\)C and 417.16 \({}^{\circ}\)C.  
The thermogram of the cement paste shows two strong endothermic peaks at 117.9 \({}^{\circ}\)C and 446.9 \({}^{\circ}\)C [13]. The endothermic peak at 117.9 \({}^{\circ}\)C mainly corresponds to the dehydration of C-S-H gel and ettringite, and the endothermic peak at 446.9 \({}^{\circ}\)C can be attributed to the decomposition of portlandite. Therefore, the endothermic peaks below 100 \({}^{\circ}\)C mainly correspond to the dehydration of residual gypsum. The endothermic peaks from 119.2 \({}^{\circ}\)C to 123.66 \({}^{\circ}\)C are mainly related to the dehydration of C-S-H gel and ettringite, and those between 412.00 \({}^{\circ}\)C and 421.77 \({}^{\circ}\)C are mainly related to the decomposition of portlandite [32].  
The DSC analysis reveals that the endothermic peaks of the SAR paste between 412.00 \({}^{\circ}\)C and 421.77 \({}^{\circ}\)C became weaker as the seabed silt mass in the mixture increased, indicating that the quantity of portlandite in the paste was decreasing. This is in good agreement with the XRD and SEM results.  
Figure 7: SEM images of SAR specimens at 28 days.  
Figure 8: SEM images of the M4 specimen at 28 days. (**a**): 28 days; (**b**): EDS of the spot under the red block in (**a**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **wt\%** & **Atomic Percent** \\ \hline C & 16.96 & 26.70 \\ O & 43.26 & 51.14 \\ Mg & 0.90 & 0.70 \\ Al & 5.19 & 3.64 \\ Si & 5.24 & 3.53 \\ S & 7.34 & 4.33 \\ Ca & 21.11 & 9.96 \\ Total quantity & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Crystal elements in SAR specimen M4.  
Figure 9: _Cont._