esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Optimized Beam Training for Adaptive mmWave Communication
#### 4.4.2.1 Lstm  
Utilizing a \"long short-term memory (LSTM)\" network allows for tracking employer motion and making adjustments to the beam direction by analyzing signals obtained from prior beam training sessions, resulting in enhanced noise immunity. Previous beam training data are leveraged to better align beams. The structure has convolutional layers that provide input from prior beam patterns. To predict the optimal beam direction, LSTM looks at time series patterns. In the end, the robotic arm uses the beam direction with the greatest forecast success. This is useful because it reduces retraining costs and improves the robustness to noise in mobile situations.  
LSTM is chosen as the forecasting model because of its outstanding performance ability to learn sequences over time. In Figure 2A, the basic composition of LSTM is demonstrated. At each time slot t, the current input \(x_{t}\) is coupled through the cell state and output from the earlier time slot \(\{c_{t-1}\), \(h_{t-1}\}\) and provided to LSTM. This allows LSTM to acquire knowledge from past inputs. The suggested model based on LSTM is shown in Figure 2B. After the \(t-th\) wide beam training, the established indications are initially inputted to the preprocessing and convolution units to identify the initial features linked to \(y_{w,t}\). Then, the LSTM component adjusts the precise beam angle by analyzing the signals from the current and previous beam training sessions. In the end, the output module gives predicted probabilities \(\{\hat{p}_{1},t,\hat{p}_{2},t,\ldots,\hat{p}_{N_{T},t}\}\), and the optimal beam is chosen based on the highest probability, represented by index \(\hat{m}_{t}^{*}\). The model is trained using cross-entropy loss, where every practice sample's loss is equal to the mean of the UE's wide beam estimates.  
Figure 2: Structure of LSTM.
## 5 Experiment
### Discussion  
While the experimental results presented effectively demonstrate the prowess of the _DeepChaos+_ framework in reducing the bit error rate in WDM optical fiber communication  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **20\%** & **40\%** & **60\%** & **80\%** & **Each Data Point (Average)** \\ \hline BiLSTM & 3.2984 s & 2.4752 s & 1.5684 s & 0.6956 s & 0.0054 s \\ GRU-D & 5.6874 s & 3.315 s & 2.1064 s & 1.1896 s & 0.0092 s \\ DeepChaos+_tiny_ & 0.051 s & 0.03388 s & 0.02464 s & 0.01248 s & 0.00007 s \\ DeepChaos+_small_ & 10.102 s & 0.06776 s & 0.04928 s & 0.02496 s & 0.00016 s \\ DeepChaos+_medium_ & 0.153 s & 0.10164 s & 0.07392 s & 0.03744 s & 0.00025 s \\ DeepChaos+_large_ & 0.204 s & 0.13552 s & 0.09856 s & 0.04992 s & 0.00034 s \\ DeepChaos+ & 2.5764 s & 1.0248 s & 0.5596 s & 0.2804 s & 0.0021 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparing the inference time of DeepChaos+ with other state-of-the-art (SOTA) models across different training set and testing set ratios of 20%, 40%, 60%, and 80% for training.  
Figure 4: The figure on the **left** illustrates the performance of DeepChaos+ on the testing set of training datasets of 20%, 40%, 60%, and 80%. The figure on the **right** displays the BER (bit error rate) of DeepChaos compared to the other methods, particularly on the 60% and 80% datasets.  
systems, it is important to acknowledge the limitations associated with the use of simulated datasets. The simulated environment, although carefully designed to mimic real-world conditions, may not fully capture the inherent complexities of actual optical communication systems. To address this limitation, future research should focus on validating the DeepChaos+ framework using actual experimental data obtained from real-world optical communication systems. This approach will assess the robustness and generalizability of our model under more complicated conditions. Additionally, incorporating real-world data will provide deeper insights into the practical applications of DeepChaos+ for reliably improving the performance of optical fiber communication systems, ensuring that the proposed solutions can be effectively implemented in practical, large-scale deployments.
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 3 Methodology
### Model Building Process
For each group, we evaluated the importance of the candidate variables using the random forest method and selected the variables constituting the top 50% importance from each group to form new variable groups. Then, the new temperature group (including EDD variables and ATT variables), SPI group, GPPa group, and NDVIa group were combined in pairs to form 5 groups: temperature group variables + SPI variables; temperature group variables + GPPa variables; temperature group variables + NDVIa variables; SPI variables + GPPa variables; and SPI variables + NDVIa variables. For each group, we reevaluated the importance of the candidate variables by the random forest method and selected the variables constituting the top 50% importance from each group to obtain the final candidate independent variables. Then, we divided the final candidate independent variables into two groups: the GPP group and the NDVI group. The GPP group included temperature group variables, SPI variables, and GPPa variables selected for the second time; the NDVI group included temperature group variables, SPI variables, and NDVIa variables selected for the second time. Finally, we built two yield estimation models (referred to as Yield Models 3 and 4) based on the two group variables using the random forest method. Figure 2: Technical flowchart of the study.
## 4 Performance Evaluation and Comparative Analysis  
In this section, we first evaluate the performance of our proposed caching technique, PF-ClusterCache, and then we perform a comparative analysis with three different caching approaches. These are the well-known Leave Copy Everywhere LCE [27], which is used as a yardstick for comparison in many studies on caching in NDN; the Caching Fresh and Popular Content (CFPC) [23], which is, similar to PF-ClusterCache, an approach based on both popularity and freshness; and the PoolCache [29], which is, similar to PF-ClusterCache, a collaborative caching scheme based on pooling the cache storage of several nodes but with no explicit treatment of data popularity and no consideration of data freshness. The performance evaluation and the comparative analysis are conducted by simulation using the ndnSIM simulator [40,41].
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.2.2 Interference Management
\(h_{m_{t},B}\) and \(h_{m_{t}^{\prime},B}\) denote the channel coefficient between the eNB and D2D transmitter \(m_{t}\), and between the eNB and D2D transmitter \(m_{t}^{\prime}\), respectively, \(h_{n,m_{t}}\), \(h_{n^{\prime},m_{t}}\), and \(h_{m_{t}^{\prime},m_{t}}\), denote the channel coefficient between D2D receiver \(m_{r}\) and CU \(n\), between D2D receiver \(m_{r}\) and CU \(n^{\prime}\), and between D2D receiver \(m_{r}\) and D2D transmitter \(m_{t}^{\prime}\), respectively. From Equations (13)-(19), the throughput of the system can be calculated using [PERSON]'s equation as follows:  
\[T=log_{2}(1+SINR). \tag{20}\]  
Therefore, the sum throughput of the system can be expressed as:  
\[T=T_{n}+T_{m}. \tag{21}\]  
From (20), we can derive the overall system throughput as follows:  
\[T=\sum_{m=1}^{M}\sum_{n=1}^{N}\Bigl{[}log_{2}\Bigl{(}1+\gamma_{n}^{k}\Bigr{)} +log_{2}\Bigl{(}1+\gamma_{m}^{k}\Bigr{)}\Bigr{]}. \tag{22}\]  
From Equations (13) and (14), Equation (22) becomes:  
\[T=\Biggl{\{}\sum_{n=1}^{N}\sum_{m=1}^{M}\Biggl{[}log_{2}\Biggl{(}1+\frac{P_{n} ^{k}\cdot l_{n,2}^{k}\cdot|h_{n,B}|^{2}}{l_{n}^{m_{t}}+l_{n}^{m_{t}^{\prime}} +\sigma_{n}^{2}}\Biggr{)}+log_{2}\Biggl{(}1+\frac{P_{n}^{k}\cdot l_{n,2}^{k} \cdot|h_{n,B}|^{2}}{l_{n}^{m_{t}}+l_{n}^{m_{t}^{\prime}}+\sigma_{n}^{2}} \Biggr{)}\Biggr{]}\Biggr{\}}. \tag{23}\]  
However, due to the interference induced by DPs to CUs while sharing resources, the throughput of CUs is reduced. Therefore, we can calculate the spectral efficiency of CUs as follows [27]:  
\[E_{n}=log_{2}\Biggl{(}1+\frac{P_{n}^{k}\cdot l_{n,B}^{-\alpha}\cdot|h_{n,B}|^{ 2}}{\sigma_{n}^{2}}\[PERSON]{)}-log_{2}\[PERSON]{(}1+\frac{P_{n}^{k}\cdot l_{n,B}^{- \alpha}\cdot|h_{n,B}|^{2}}{l_{B}^{m_{t}}+l_{B}^{m_{t}^{\prime}}+\sigma_{n}^{2} }\Biggr{)} \tag{24}\]  
Thus, the throughput gain of CUs is expressed as follows:  
\[G_{n}=log_{2}\[PERSON]{(}1+\frac{P_{n}^{k}\cdot l_{n,B}^{-\alpha}\cdot|h_{n,B}|^{ 2}}{l_{n}^{m_{t}}+l_{n}^{m_{t}^{\prime}}+\sigma_{n}^{2}}\Biggr{)}+log_{2} \Biggl{(}1+\frac{\delta_{m_{n}}^{k}\cdot P_{n}^{k}\cdot l_{n,2}^{-\alpha}\cdot| h_{m_{t}^{\prime},m_{t}}|^{2}}{l_{n}^{m_{t}}+l_{n}^{m_{t}^{\prime}}+\sigma_{n}^{2}} \Biggr{)}-log_{2}\Biggl{(}1+\frac{P_{n}^{k}\cdot l_{n,2}^{-\alpha}\cdot|h_{n,B}| ^{2}}{\sigma_{n}^{2}}\Biggr{)} \tag{25}\]
## 4 Case Study
### Results and Analysis
#### 4.2.3 Discussion  
Based on the results of Section 4.2, the conclusions can be summarized by stating that the formulated reverse logistics facility location model and the proposed multi-objective algorithm are effective for solving the problem of reverse logistics location based on energy consumption.  
To illustrate the advantages of this study, the contributions and significance can be summarized as follows. From the theoretical viewpoint, a double objective function optimization model of reverse logistics facility location was constructed in which the capacity of recycling center, classification center, processing center and the transportation volume between different recycling facilities were taken as constraints, and the two objective functions of low-carbon and maximum revenue were used to optimize the location scheme of logistics facilities. From the practical viewpoint, the aim of the multi-objective optimization of reverse logistics location is to put the findings into production and use in daily life. In addition, this study proposes the solution framework of the multi-objective problem of reverse logistics location, which can be used in different cities/areas with different features/constraints.  
This study presents an accurate and systematic method/tool to solve the multi-objective optimization problem of reverse logistics location and can assist researchers in better comprehending the multi-objective optimization theoretically, as well as assisting designers in developing a better security system in urban planning.
## 6 Smart Parking  
In this section, a proposal for a solution for smart parking is given, followed by the previous analyses and the results of an experiment on the functionality of NB-IoT parking sensors in real-time, which supports the hypothesis of the effectiveness of this solution.  
This intelligent parking system uses NB-IoT wireless radio communication technology. In this scheme, the parking place can be reserved via a smartphone application that helps drivers to find and reserve spots, park their vehicle, and pay, as well as helping the parking managers to manage the whole parking area and the reservations. This would allow the allocation services to enable the detection of free parking spaces for drivers in advance. The parking spot data collected by the embedded sensors at the parking space travels via NB-IoT and is sent to the server. In this way, by using the application the drivers can easily find the nearest parking spaces in real-time, with the information provided by the server.  
Some of the current smart parking solutions are based on radio technologies such as RFID, ZigBee, Bluetooth, or a combination of these options. Such solutions based on short-range wireless communication technologies have the drawback of a short battery life for the sensors, high installation costs, and limited coverage. Other approaches use long-range LPWANs such as LoRa and NB-IoT [49]. Based on the previous analysis, this paper suggests NB-IoT as a long-range mobile technology to be utilized.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 3 Numerical Example
### Brief Description of the Numerical Example  
A simply supported bridge with a length of 30 m composed of C50 concrete is taken as a numerical example to demonstrate the effectiveness of the proposed method. The finite element model of this bridge was built with Midas Civil software (version number: 8.32), as shown in Figure 3. A total of 60 elements with lengths of 0.5 m and 61 nodes compose the model, the parameters of which are listed in Table 1.  
To simulate the quasi-static load test, a quasi-static moving load (100 kN or 50 kN) is implemented to obtain the displacement ILs of the modelled bridge. A total of 9 sensors are placed along the superstructure of this bridge, as shown in Figure 3. The damaged region is modelled by reducing the stiffnesses of one element with different degree of damage. For example, 5% extent of damage is occurred at the 3 m position of the bridge, which means the stiffnesses of element at this position is reduced 5%. All the detailed damage cases are listed in Table 2. All simulations are executed in the MATLAB software environment (version number: 9.2.0.556344).
# A Coarse-Grained Molecular Model for Simulating Self-Healing of Bitumen
## 4 Conclusions  
In this work, the CG model was established based on the data of the MD simulation of bitumen, the force field parameters were calculated, and the coarse-grained force field suitable for the CG molecular model of bitumen was developed and verified. At the same time, the model was used in the study of self-healing bitumen. The main research conclusions are as follows:  
According to the structure characteristics of the four-component bitumen model with twelve molecules, 16 mapping schemes of CG beads were divided; the integrity of the benzene ring functional groups was preserved as far as possible, while the thickened benzene ring structure was mapped by using two CG beads connected to each other. The Boltzmann inversion was used to calculate the non-bond potential energy, and the harmonic potential was used to calculate the bond potential energy to obtain the force field parameters. Compared with the all-atom model, the CG model has a longer simulation time and larger scale, which will be helpful in studying the self-healing behavior of healing agent-doped and aged asphalt at the mesoscopic scale.  
By studying CG models of microcracks in bitumen, it was found at the molecular level that angle and van der Waals forces are the driving forces for self-healing; the molecules \"unfold\" into a straighter shape as the van der Waals forces pull the two sides of the crack together, rather than stretching to fill the vacant. The variation in cracking rate showed that increasing the temperature near the glass transition temperature of bitumen accelerates the molecular diffusion movement more significantly. At the same time, the diffusivity of saturates, aromatics decreased greatly during the healing process, proving that the components mainly contributing to the self-healing behavior of bitumen are the saturates and aromatics.  
Figure 12: Healing time of microcrack model.  
**Author Contributions:** Conceptualization, L.H. and A.A.; methodology, Z.Z. and F.L.; software, L.Z.; validation, Z.Z. and F.L.; investigation, L.H., A.A., J.Z., J.V., K.J.K. and A.C.F.; resources, L.H. and W.V.d.B.; data curation, F.L.; writing--original draft preparation, F.L. and Z.Z.; writing--review and editing, A.A., A.C.F., R.B., J.Z., J.V., K.J.K. and L.H.; visualization, Z.Z. and F.L.; supervision, L.H. and A.A.; project administration, L.H. and W.V.d.B.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (52278440, 52111530134), China Education Association for International Exchange (2021090), General project of Chongqing Natural Science Foundation (cstc2020)cyjmsmx0431).  
Not applicable.  
Informed Consent Statement: Informed con-sent was obtained from all subjects involved in the study.  
Not applicable.  
The authors acknowledge and thank [PERSON] from Moscow Automobile and Road Construction State Technical University for discussion and cooperation.  
The authors declare no conflict of interest.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 7 Security Analysis
### Protocol Security Analysis
#### 7.1.1 The Analysis Based on Set 1  
* Secrecy: The \(FID\) and \(D1\) are never revealed to the attacker, hence secrecy is achieved. Also property covers the data confidentiality and privacy.
* Aliveness: The \(SPAAA\) obtains the aliveness of \(UE\) when the \(UE\) sends a cache authorization request to \(SPAAA\) with an access token, while \(SS\) does when the \(UE\) sends cache or share the message. The \(SS\) responds with acknowledgement strings, (\(Ack\_1\) and \(Ack\_2\)) for tokens (\(ChT\) and \(ShT\)), respectively.
* Weak Agreement: When \(SPAAA\) achieves weak agreement in form of non-injective agreement on access token with \(UE\) and \(SS\) achieves this when Acknowledgement strings is received by \(UE\).
* Non-injective Agreement: This is achieved when the \(UE\) obtains a non-injective agreement on access token with the \(SPAAA\). The \(SS\) achieves this on \(ChT\), \(ShT\) with  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Properties** & **Query** & \
\begin{tabular}{l} **Expected** \\ **Output** \\ \end{tabular} &
\begin{tabular}{l} **Proverif** \\ **Output** \\ \end{tabular} \\ \hline ChT & Secrecy & True & True \\ ShT & Secrecy & True & True \\ FID & Secrecy & True & True \\ Ack\_1 & Secrecy & True & True \\ Ack\_2 & Secrecy & True & True \\ UE-SS & Non-injective & True & True \\  & Agreement & & \\ SS-UE & Injective & True & True \\  & agreement & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proverif Query Checks.  
Figure 6: DCSS Protocol Safe Results.  
the \(UE\), when it is generated by \(SPAAA\) and sent to UE. The tokens \(ChT/ShT\) includes labels, therefore SS obtains the assurance on \(ChT/ShT\) from the \(SPAAA\).
* Injective Agreement: The tokens between the \(SPAAA\) and \(SS\) are fundamental to the protocol's security goal. The injective agreement on labels with the SP assures the \(UE\) that \(SS\) is known and trusted. The UE obtains the injective agreement on \(ChT\), \(ShT\) and \(AcT\) with the \(SPAAA\) and \(SS\) respectively to assure that the sessions with \(SS\) and were authorized the \(SPAAA\). Simultaneously, \(SS\) is assured over tokens used that its session with \(UE\) was authorized by \(SPAAA\).
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 4 High-Order QAM and APSK Modulation and Demodulation
### QAM Modulation  
QAM is a modulation method that combines amplitude and phase modulation, which has better bandwidth utilization compared to single modulation. At time \(t\), the \(i\)-th baseband signal \(S_{k,t}(t)\) in the \(k\)-th QAM modulation symbol can be represented as:  
\[S_{k,t}(t)=A_{i}f(t-iT)\sin(\omega t+\varphi_{i}) \tag{16}\]  
where \(i=1,2,\ldots,N^{\prime}\), \(N^{\prime}\) is the number of bits included in each QAM modulation symbol; \(k=1,2,\ldots,M^{\prime}\), \(M^{\prime}\) is the number of constellations in the QAM modulation constellation; \(\omega\) is the angular frequency; \(\varphi_{i}\) is the initial phase of the modulation carrier corresponding to the \(i\)-th baseband signal; \(A_{i}\) is the impulse amplitude of the \(i\)-th baseband signal; \(T\) is the  
Figure 3: Random impulse waveform of the improved noise model with m = 0.02.  
impulse width of the baseband signal, and \(f(t-iT)\) is the rectangular wave generated by the \(i\)-th baseband signal with an impulse width of \(T\).  
The RF signal \(S_{k}(t)\) corresponding to the \(k\)-th QAM modulation symbol is defined as follows:  
\[S_{k}(t)=\sum_{i=1}^{N^{\prime}}A_{i}f(t-iT)\text{sin}(\omega t+\varphi_{i}) \tag{17}\] \[=I_{k}(t)sin\omega t+Q_{k}(t)cos\omega t\]  
where \(I_{k}(t)=\sum_{i=1}^{N^{\prime}}A_{i}f(t-iT)cos\varphi_{i}\), \(Q_{k}(t)=\sum_{i=1}^{N^{\prime}}A_{i}f(t-iT)sin\varphi_{i}\), \(I_{k}(t)\) and \(Q_{k}(t)\) are mutually orthogonal at time \(t\), and regardless of the time \(t\) (17) can be expressed in the complex form:  
\[S_{k}(t)=I_{k}+jQ_{k} \tag{18}\]  
where \(I_{k}\) and \(Q_{k}\), respectively, represent the real and imaginary components of the \(k\)-th modulation symbol \(S_{k}\).  
When \(N^{\prime}=4\), it is 16 QAM modulation, which means that each QAM modulation symbol contains 4 information bits. Figure 4 shows the 16 QAM modulation constellation mapping, where each modulation symbol corresponds to a Gray code in the constellation mapping. The 4-bit Gray code sequence \(\{b_{1},b_{2},b_{3},b_{4}\}\) is divided into real and imaginary parts, where \(b_{1},b_{2}\) represent the real part of the 16 QAM symbol and \(b_{3},b_{4}\) represent the imaginary part. The number of real and imaginary bits is the same, which facilitates the subsequent decision calculation for soft demodulation.
# Behavioural Realism and Its Impact on Virtual Reality Social Interactions Involving Self-Disclosure
## 3 Results
### Results for the Enjoyment Measure  
The ANOVA for the enjoyment ratings showed a significant effect of avatar type (\(F\)(1,56) = 4.57, \(p\) = 0.04, \(\eta_{p}\)\({}^{2}\) = 0.08). The iClone UNREAL avatar (\(M\) = 3.64, \(SD\) = 0.80) had higher ratings for enjoyment than the Vive Sync avatar (\(M\) = 3.45, \(SD\) = 0.86). [PERSON]'s measure of effect size (\(d\) = 0.23) indicated a moderate effect (See Figures 7 and 8). The mean ratings of \"quite a bit\" indicate that participants generally had a positive perception of both avatars. There was no significant effect of disclosure source (\(F\)(1,56) = 0.97, \(p\) = 0.33, \(\eta_{p}\)\({}^{2}\) = 0.02). The source of disclosure, either from the participant (\(M\) = 3.52, \(SD\) = 0.79) or the interviewer (\(M\) = 3.56, \(SD\) = 0.75), did not significantly influence enjoyment ratings (Figure 7). There was a significant effect of disclosure type (\(F\)(1,56) = 35.55, \(p\) < 0.01, \(\eta_{p}\)\({}^{2}\) = 0.39). Positive disclosures (\(M\) = 3.67, \(SD\) = 0.72) resulted in higher ratings of enjoyment when compared to negative disclosures (\(M\) = 3.41, \(SD\) = 0.82). [PERSON]'s measure of effect size (\(d\) = 0.32) indicated a moderate effect (Figure 8). No significant interaction effect was observed between avatar type and disclosure source (\(F\)(1,56) = 0.05, \(p\) = 0.83, \(\eta_{p}\)\({}^{2}\) < 0.01), avatar type and disclosure type (\(F\)(1,56) = 0.80, \(p\) = 0.35, \(\eta_{p}\)\({}^{2}\) = 0.02), disclosure source and disclosure type (\(F\)(1,56) = 2.44, \(p\) = 0.12, \(\eta_{p}\)\({}^{2}\) = 0.04), or between all three (\(F\)(1,56) = 0.17, \(p\) = 0.69, \(\eta_{p}\)\({}^{2}\) = 0.00), suggesting that the effects of avatar type, disclosure source, and disclosure on perceived enjoyment ratings were independent of each other. These results demonstrate that the iClone UNREAL avatar and positive disclosures led to higher enjoyment ratings.  
Figure 6: Realism ratings for avatar types according to disclosure type. Error bars represent 95% confidence intervals.  
Figure 7: Enjoyment ratings for avatar types according to disclosure source. Error bars represent 95% confidence intervals.