esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions, Challenges, and Future Research
### Conclusions  
In this research, we introduced the Modified Rat Swarm Optimizer (MRSO) as an improvement over the original Rat Swarm Optimizer (RSO), specifically designed to address its limitations in handling convergence and exploration. The MRSO incorporates key modifications aimed at achieving a better balance between exploration and exploitation, which are critical for solving complex optimization problems effectively.  
We thoroughly tested the MRSO against eight recent and commonly used metaheuristic algorithms, including the SCA, the MRA, the LCA, the CSA, the TSA, the DOA, the EHO, and the WSO, on both classical benchmark functions and CEC 2019 benchmark functions. The results consistently demonstrated the MRSO's superiority in avoiding local optima and yielding better results than the standard RSO across a wide range of functions. The MRSO's competitive performance, particularly in handling multi-modal and high-dimensional problems, highlights its robustness and effectiveness as an optimization tool.  
Additionally, we applied the MRSO to seven real-world engineering design problems--Pressure Vessel Design, String Design, Three Bar Truss, Gear Train Design, Cantilever Beam, Welded Beam, and Tire Design. For this, we compared the MRSO's performance with four of the algorithms (the SCA, the LCA, the TSA, and the DOA). The results indicated that the MRSO not only outperformed the RSO but also consistently outperformed the other four metaheuristic algorithms in most cases. These findings emphasize the MRSO's reliability and efficiency in solving real-world engineering applications, where striking the right balance between exploration and exploitation is crucial to finding optimal solutions.
_applied_  
_sciences_  
_Article_  
**Analysis of Vertical Wind Shear Effects on Offshore Wind Energy Prediction Accuracy Applying Rotor Equivalent Wind Speed and the Relationship with Atmospheric Stability**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\)**, [PERSON] \({}^{2}\)\({}^{\copyright}\)**, [PERSON] \({}^{3}\), [PERSON] \({}^{4}\), [PERSON] \({}^{5}\), [PERSON] \({}^{6}\),  
[PERSON][PERSON] \({}^{7}\)\({}^{\copyright}\)**, [PERSON] \({}^{3}\)\({}^{\copyright}\)** and [PERSON] \({}^{8,*}\)  
**Abstract:** If the wind speed that passed through a wind turbine rotor disk area is constant, the hub height wind speed (HHWS) could be representative of the wind speed over the rotor disk area. However, this assumption cannot be applied to the large wind turbine, because of the wind shear effect by atmospheric stability. This is because the hub height wind speed cannot represent the vertical wind shear effect from the aerodynamics characteristic on the wind turbine. Using SCADA and offshore LiDAR observation data of the Anholt offshore wind farm, it is investigated whether the rotor equivalent wind speed (REWS) introduced in IEC61400-12-1 can contribute to the improvement of power output forecasting accuracy. The weighted value by separated sector area and vertical wind shear effect by difference between heights can explain the role of energy flux and atmospheric stability on the exact wind energy calculation. The commercial CFD model WindSim is used to calculate power production according to the HHWS and the REWS, and to compare them with the actual AEP of the local wind farm. The classification of atmospheric stability is carried out by Richardson number, which well represents the thermal and physical properties of the atmosphere below the atmospheric boundary layer, along with the wind shear coefficient and turbulence intensity. When atmospheric stability was classified by each stability index, the REWS-based predicted power output was sometimes more accurate than HHWS, but sometimes inferior. However, in most cases, using the REWS, it was possible to calculate an estimate closer to the actual power output. Through the results of this study, it is possible to provide a rationale for which method, REWS or HHWS, can more accurately calculate the expected power output and effectively derive the economic feasibility of the project by identifying the characteristics of local atmospheric stability before the wind farm project.  
**Keywords:** rotor equivalent wind speed; hub height wind speed; wind shear; atmospheric stability; offshore wind energy; Richardson number
# Spaceborne GNSS-R Observation of Global Lake Level: First Results from the TechDemoSat-1 Mission
## Data and Methods
### Data Preprocessing  
To derive reliable lake water level estimates, for each lake, a 1 km buffer is firstly made to reduce the influence from water-land interactions, and a landmark is then applied to extract the specular points that fell into the lake. We only use the samples with the antenna gain pointed at the specular point greater than 5 dBi. This gain threshold is the same as that used for estimation of sea surface height in [3] and soil moisture in [6]. The data are then filtered for (1) the SNR of the peak DDM pixel greater than 5 dB (since there have been uncertainties reported in the measured antenna gain pattern that increases with increasing distance from the peak of the main lobe [6]), and (2) the elevation angle of the specular points higher than 30\({}^{\circ}\) (to prevent multipath effects). The TDS-1 L1b data in September 2016 are filtered out due to the poor quality of the delay waveforms in this period. Data of descending tracks are also filtered out to remove orbital errors and minimize the effects of fluctuating tropospheric and ionospheric propagation delays, since descending tracks of TDS-1 are mainly during daytime hours when there are larger errors of tropospheric and ionospheric delays than during the nighttime [21].  
The CryoSat-2 Level 2 GDR data have three height values at each observed sample since the altimeter includes three measurement modes. First, we filter the data to remove random error, i.e., we only use data samples whose height differences among the three types of measurement are lower than 0.3 m. Second, for one single data sample, we use the average height of the three types of measurement. In order to make the comparison among TDS-1/Cryosat-2/Hydroweb on a consistent basis, the vertical datum of CryoSat-2 is converted to GGM02C according to the spherical harmonic coefficients.
## 4 Evaluation
### Performance Evaluation Metrics  
In the process of experiment, the related metrics are obtained by OMNeT++ including packet loss rate, average delay, and total number of packets forwarded. The notations involved in the following calculation formulas are explained in Table 1.
# Enhanced Label Noise Filtering with Multiple Voting
## 4 Novel Approach to Determine the Optimal Decision Point
```
Algorithm: Searching optimal decision point for MVFilter Input: E (training set), E\({}_{nf}\) (noise-free dataset) Parameter: numIter (number of iterations to search ODP), noiseRatio (the noise ratio in E), MVFilter (the multiple-voting based filter algorithm), t (number of single-voting filters in MVFilter), C (cost matrix) Output: ODP (optimal decision point) (1)\(costMatrix\leftarrow\emptyset\) (2) for i = 1,..., numIter do (3) randIndex\(\leftarrow\)RandomPermutation\(\left|E_{nf}\right|\) (4) noiseIndex\(\leftarrow\)randIndex(1:\(\left|E_{nf}\right|\times noiseRatio\)) (5) \(E^{\prime}\gets generateNoise(E_{nf},noiseIndex)\) (6) \(costVector\leftarrow\emptyset\) for m = 1,..., t do (8) noiseIndexDetected\(\leftarrow\)MVFilter(E', m) (9) index\(\leftarrow\)InterSection(noiseIndex, noiseIndexDetected) (10) indexE1\(\leftarrow\)noiseIndexDetected\(\setminus\)index (11) indexE2\(\leftarrow\)noiseIndex\(\setminus\)index (12) \(cost\leftarrow\left|indexE1\right|\times C_{01}+\left|indexE2\right|\times C_{10}\) (13) costVector(m) \(\leftarrow\)cost (14) end for (15) costMatrix = [costrMatrix; costVector]; (16) end for (17)\(ODP\leftarrow\underset{m=1:t}{\arg\min}(\text{costMatrix}(1:end,m))\)
```  
**Algorithm 3** Optimal decision point estimation for MVFilter. In Algorithm 3, it is assumed that another noise-free dataset E\({}_{nf}\) exists, which has the same distribution as E. Usually in a training dataset, some labels are certainly correct. These partial noise-free data are also used as a validation dataset in many applications. However, for a few applications, if E\({}_{nf}\) is unavailable, then this algorithm cannot be used directly. To solve this problem, we can directly use an MVFilter to mine the noise-free data from E. In this case, the loose noise detection policy is preferred by MVFilter. To generate E\({}_{nf}\), the main concern is to make less E2 errors. Therefore, a small decision point value (for example, one) should be used by MVFilter. By this method, E\({}_{nf}\) can be collected from E. Then, Algorithm 3 can be used. The parameter noiseRatio in Algorithm 3 should also be noted. This parameter represents the noise ratio in E (mislabeled percentage of E). It is used to decide the number of erroneous labels to generate in E'. Here, we assume this is prior knowledge. For many applications, through years of experience, the rough noise ratio in a noisy training set is usually known. If this value is totally unknown, we also provide a solution. This parameter can be estimated from E by using an MVFilter. To estimate this parameter more accurately, MVFilter should select a decision point that considers the E1 and E2 error simultaneously. The value t/2 is a reasonable decision point since this decision point usually has a good trade-off between E1 and E2 errors.
# Analyzing Demand with Respect to Offer of Mobility
## 4 Model and Analyzer
### Service Offer with Respect Mobility Demand Computation  
DORAM aims to analyze the transportation networks by investigating to what degree the offer satisfies the mobility demand. To do that, two main KPI are used: (1) pick-ups count and (2) drop-offs count at each stop \(s\) because they describe the service suitability (under-provision or over-provision) at s. For example, when 50 vehicle trips pass through a stop, 2000 people in a specific time slot should be picked up (or dropped off) indicates that the service is under-provisioned, while 20 pick-ups (or drop-offs) indicates that the service is over-provisioned, at the stop. The perfect rate depends on the restrictions and bus size. To analyze the trend of the daily pick-ups and drop-offs at stop \(s\), it is required to evaluate the motivation of the commuters to move. The commuters _stage-wise_ (see Figure 4) consider two types of trips to reach their destination: _(1) single-stage trip,_ taking only one vehicle x trip and _(2) multi-stage trips_, taking multiple vehicles. A stop then can be a _(i) beginning stop_ (\(s_{bgg}\)), _(ii) final stop_ (\(s_{fin}\)), or _(iii) transfer stop_ (\(s_{tra}\)).
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 3 Physical Layer Aided Authentication and Key Agreement (PL-AKA)
### Proposed PL-AKA Protocol
#### 3.2.1 Integration Strategy  
In the conventional PHY-CRAM [8], a certain threshold is applied to the binary hypothesis testing for authentication decision. Then, the BS may make a wrong authentication decision with the test statistics of \(\zeta\), due to noise and interference. Therefore, a core of the integration strategy is how to define a statistical range that is prone to the preemptive authentication failure. To this end, the preemptive authentication result in the proposed protocol is divided into three cases: \"Black\", \"Gray\" and \"White\", instead of a binary decision for conventional PLA as to whether the received signal is legitimate or not. In \"Black\" and \"White\", the BS can be sure about whether the received signal is from an intruder or from a legitimate IoT device, respectively, with high probability. On the other hand, in \"Gray\", the BS is not sure about whether or not the signal is a legitimate one. Therefore, in the proposed protocol, we stipulate that \"Gray\" is an ambiguous result about which it is hard to make a firm decision in the physical layer, and put off the final decision to the upper layer cryptography-based authentication. Note that the result in the PHY-CRAM method is determined in accordance with test statistic \(\zeta\). Then, to determine a preemptive result in the PHY-CRAM method, two thresholds denoted by \(\alpha_{0}\) and \(\alpha_{1}\) are used in the physical layer verification, while a conventional PLA method uses a threshold to make a decision between \"Black\" and \"White\". Thus, if the preemptive result is \"White\" or \"Black\", the authentication is complete (i.e., cryptography-based authentication is not performed). On the other hand, if the result is \"Gray\", cryptography-based authentication is performed to make a final authentication decision at the MME.
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 6 Conclusions  
This paper introduces a novel LLM-SC framework for textual data within wireless communication systems. We propose that leveraging the tokenizer of an LLM serves as an effective joint source-channel coder. An optimal LLM-enabled decoding and demodulation method is derived to resiliently demodulate text by integrating the LLM's contextual understanding. It is deduced that the pre-training of LLMs fundamentally constructs the encoding and decoding mechanisms for semantic communication, achievable without altering the original training process. Existing pre-trained models can be utilized for semantic encoding and decoding.  
Extensive simulations demonstrate that LLM-SC outperforms traditional communication systems in terms of bit error rate (BER) at the technical communication level and outcompetes current machine learning-based methods in semantic-level metrics. Compared to technical communication algorithms, LLM-SC excels in extracting contextual relationships from text and leveraging the receiver LLM's contextual understanding for error correction, thereby achieving lower BER. In comparison to other semantic communication systems, LLM-SC tends to demodulate meaningful sentences, thus outperforming in BLEU and sentence similarity metrics. Furthermore, the LLM acts as a semantic knowledge base in these systems, providing both sender and receiver with a probabilistic distribution of transmitted sequences.  
In conclusion, this pioneering study demonstrates initial feasibility and motivates further research in co-designing LLMs to advance intelligent communication systems. Beyond maximizing bit transmission, integrating higher-level semantics is crucial for unlocking future capabilities. This work makes a significant step toward LLM-empowered wireless systems focused on meaningful transmission.  
Conceptualization, Z.W. and R.L.; methodology, Z.W.; software, L.Z.; validation, S.W.; formal analysis, F.L.; investigation, K.L.; resources, L.Z.; data curation, R.L.; writing--original draft preparation, Z.W.; writing--review and editing, K.L. and H.M.; visualization, R.L.; supervision, F.L.; project administration, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Natural Science Foundation of China under Grant No. U2441226.  
Ethical review and approval were waived for this study because it is an in vitro study, and there is no human and animal participation.  
Not applicable.  
The source code and associated scripts (V1.0) for this study are openly available on GitHub at [[https://github.com/gujianhunwang/LLM_com](https://github.com/gujianhunwang/LLM_com)]([https://github.com/gujianhunwang/LLM_com](https://github.com/gujianhunwang/LLM_com)) accessed on 15 May 2025.  
The authors declare no conflicts of interest.
# Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm
## 3 Methodology
### Gaussian Process Regression (GPR)  
Gaussian process regression is a machine learning method based on Bayesian theory [22]. GPR is feasible for small size datasets, nonlinear, complex, and high dimensional regression problems [23; 24]. Unlike linear regression, GPR is a collection of random variables that have a joint Gaussian distribution with a mean and covariance function.  
For a training dataset with \(n\) training data points with inputs and target variables \(\{x_{i},y_{i}\}\) and \(i=1,2,\ldots,n\) respectively, the model is defined as:  
\[y_{i}=f(x_{i})+\epsilon_{i} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Cutting Speed** & **Nose Radius** & **Feed Rate** & **Depth of Cut** & **Surface** & **Average Cutting** \\  & **(m/min)** & **(mm)** & **(mm/rev)** & **(mm)** & **Roughness (\(\mu\)m)** & **Force (N)** \\ \hline  
5 & 2.52 & 18.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._where \(f(x_{i})\) is the learning function and \(\epsilon_{i}\) is Gaussian noise with zero mean and variance \(\sigma_{n}^{2}\). The target variables \(y_{i}\) are described with Gaussian distribution as:  
\[y\sim\mathcal{N}\Big{(}0,K(X,X)+\sigma_{n}^{2}I\Big{)} \tag{4}\]  
with \(K(X,X)\) denoting the covariance matrix. In this research, a Gaussian kernel is used as the covariance function and is written as:  
\[k\big{(}x_{p},x_{q}\big{)}=\sigma_{s}^{2}\mathrm{e}^{(-0.5(x_{p}-x_{q})^{T}W( x_{p}-x_{q}))} \tag{5}\]  
where the signal variance corresponds to \(\sigma_{s}^{2}\) and \(W\) is the width of the Gaussian kernel. For a point \(x^{*}\), the joint Gaussian distribution of the observed target values and the predicted values is given by:  
\[\left[\begin{array}{c}y\\ f(x^{*})\end{array}\right]\sim\mathcal{N}\bigg{(}0,\ \left[\begin{array}{cc}K(X,X)+ \sigma_{n}^{2}I&k(X,x^{*})\\ k(x^{*},X)&k(x^{*}x^{*})\end{array}\right]\bigg{)} \tag{6}\]  
and yields to the predicted mean value of the learning function \(f(x^{*})\) with the variance \(V(x^{*})\) are given as:  
\[f(x^{*})={k^{*}}^{T}\Big{(}K+\sigma_{n}^{2}I\Big{)}^{-1}y={k^{*}}^{T}\alpha \tag{7}\]  
\[V(x^{*})=k(x^{*},x^{*})-{k^{*}}^{T}\Big{(}K+\sigma_{n}^{2}I\Big{)}^{-1}k^{*} \tag{8}\]  
where \(\alpha\), \(k^{*}=k(X,x^{*})\) and \(K=K(X,X)\) present the prediction vector. The remaining parameters \(\theta=\left[\sigma_{n}^{2},\sigma_{f}^{2},W\right]\) are the hyperparameters of the Gaussian process that can be optimized using standard optimization algorithms.
## 7 Computational Results
### Inverse Problem with a Noise  
Measurements are usually made with devices that allow errors. For this reason, we now consider the same problem, but with randomly perturbed integral measurements \(E(t)\) with a noise:  
\[\overline{E}(t^{j})=E(t^{j})+\epsilon\sigma(j);\ \ \ j=0,1,\ldots,M,\]  
where \(E(t^{j})\) is estimated using (53), \(\epsilon\) is the amplitude of the noise in the measurements, and \(\sigma=\{\sigma(0),\ldots,\sigma(M)\}\) is a random vector uniformly distributed in the interval \((-1;1)\). In Table 2, for the case \(\
u=0.1\), \(\
u_{m}=0.2\), \(c=1\), \(X=\pi/2\), and \(T=1\), the errors in maximum norm of the flow velocity \(u(x,t)\), the magnetic field \(B(x,t)\), and the unknown function \(f\) with a measurement noise are presented for different values of the parameter \(\epsilon\). The results show that the errors of the flow velocity \(u(x,t)\), the magnetic field \(B(x,t)\), and the unknown function \(f(t)\) depend weakly on the amplitudes of the deviations in the measurements as opposed to the results obtained in [28].  
The results of the numerical computations (made by Matlab random generator) with additional perturbation with a noise \(\epsilon\) are presented in Figure 3 with mesh parameters \(M=32\) and \(N=40\): (a) the error of \(U_{ex}-U_{num}\) and \(\epsilon=0.1\); (b) the error \(f_{ex}-f_{num}\): \(\epsilon=0.001\) (solid blue line, circles); \(\epsilon=0.01\) (dash-dot magenta line, stars); \(\epsilon=0.1\) (dash red line, diamonds). It is clear to see that the noise in the measurements leads to the oscillations in the errors of the computed solutions.
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
## 2 Materials and Methods
### Simulation Protocols of Thermal Damage in the Tissue  
Before applying simulation protocols, we thoroughly tested our FEM modeling approach by comparing the results of simulations with the publication of [PERSON] et al. [14], as outlined in Appendix C.  
In the first simulation protocol (Protocol #1), we applied the following:  
* The simulated laser beam, with a wavelength of 1064 nm and a disc-shaped radius of 4 mm, was directed perpendicularly to the short-axis center of FEM Model #1, FEM Model #2, and FEM Model #3.
* The parametric study was undertaken by varying the fluence and pulse duration: we closely followed the clinical study [37] and accordingly used combinations of fluence and pulse duration of (i) 50 J/cm\({}^{2}\) and 25 ms, (ii) 60 J/cm\({}^{2}\) and 50 ms, and (iii) 80 J/cm\({}^{2}\) and 50 ms.
* We explicitly considered the (i) mode without forced cooling, with a value of the free convection coefficient h of 10 W/m\({}^{2}\)K [14; 38] and ambient temperature T = 22 \({}^{\circ}\)C for all three FEM models, and the (ii) mode with forced cooling by means of a cryogenic spray with a convection coefficient h of 5000 W/m\({}^{2}\)K [39] and spray temperature T = \(-\)50 \({}^{\circ}\)C [39] for FEM Model #1 and FEM Model #2 only. The forced cooling by cryogenic spray consisted of 10 ms of preliminary cooling, with a 5-ms pause immediately after the pulse and 15 ms of cooling thereafter, as outlined in the clinical study [37].
* The main outcomes of the simulations were temperature and thermal damage distributions after the completion of the irradiation.  
In the second simulation protocol (Protocol #2), we applied a wider laser beam with a diameter of 6 mm and a combination of fluence of 80 J/cm\({}^{2}\) and a pulse duration of 50 ms; in this protocol, we considered only the mode without forced cooling. We used the more realistic models, FEM Model #2 and FEM Model #3. The main outcome was a time-dependent curve of cumulative hair-follicle damage for each of the two FEM models.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Tissue** & \(c_{p}\) **[J/kgK]** & **\(k\) **[W/mK]** & \(\rho\) **[kg/m\({}^{3}\)]** \\ \hline Epidermis & 3200 [10] & 0.34 [10] & 1120 [10] \\ Dermis & 3500 [10] & 0.41 [10] & 1090 [10] \\ Subcutis & 2870 [6] & 0.3 [6] & 860 [6] \\ Hair & 2512 [11] & 0.63 [11] & 1300 [12] \\ \hline \end{tabular}
\end{table}
Table 2: Thermal properties of the tissues used in simulations, along with references: \(c_{p}\) is specific heat capacity; \(k\) is thermal conductivity; and \(\rho\) is density.