esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN
## 4 Performance Evaluation
### Average End-to-End Delay  
Average end-to-end delay is defined as the total average time a source platoon takes to deliver a message to the destination platoon. Figure 6 shows the comparison of average delay in terms of vehicular density. It can be clearly seen that all cases show the same pattern in which delay decreases with an increase in vehicular density. This is logical because, as vehicle density increases in case (_i_), communication becomes more efficient, resulting in reduced delay, since vehicles are consistently within the communication range, facilitating straightforward packet delivery from the source to the destination platoons. Conversely, for the other two proposed cases, a slight increase in delay is observed. This occurs because when vehicles cannot immediately find nearby counterparts due to low visibility within their communication range, they pause momentarily, allowing for other vehicles to come within range. If no suitable vehicles are found, they initiate communication with the LCs if they are within the proximity of the platoons. This brief pause introduces a minor delay, but it ensures that all packets are successfully transmitted, maintaining  
Figure 5: Performance comparison in terms of PDR as a function of vehicular density.  
efficient and reliable communication, albeit with a small delay trade-off. Similarly, the reference schemes exhibit high delays due to the lack of LCs that manage traffic, resulting in the continuous broadcast of warning messages.
## 3 Single Transmitter Station Calibration Method  
### Photoelectric Scanning Multi-Angle Resection Positioning  
The positioning of a single laser transmitter can be calibrated based on a combination of photoelectric scanning angle measurements and single image space resection using a pre-designed receiver array, as illustrated in Figure 4. Here, the laser transmitter can be regarded as a fixed position camera, and the receiver array includes a fixed number of photoelectric receivers mounted at known positions within a receiver array coordinate system _o-xyz_ with its origin (i.e., the probe point) placed in constant contact with a known measurement point within the transmitter station coordinate system _O-XYZ_. These receivers serve as control points in the array coordinate system with corresponding points in the transmitter station coordinate system. Then, the photoelectric scanning multi-angle resection positioning equation can be established according to the scanning angle information measured by each receiver on the receiver array. a sufficient number of control points on the receiver array provide for a sufficient number of spatial geometric constraints to solve the rotational and translational relationships between array coordinate system _o-xyz_ and transmitter station coordinate system _O-XYZ_ to achieve spatial positioning.  
The array coordinate frame _o-xyz_ can be regarded as a coordinate frame lying intermediate with the transmitter coordinate frame _O-XYZ_ defined in Figure 2, and the origin of the array frame and the coordinates of the control points in _o-xyz_ are known structural parameters. The rotation of the transmitter station head then yields the scanning angle information measured for each receiver on the receiver array in the _O-XYZ_ coordinate frame based on Equation (1). a similar scanning angle equation can also be obtained for each receiver mounted on the receiver array. Therefore, the photoelectric scanning multi-angle resection positioning model of the _O-XYZ_ frame and the _o-xyz_ frame can be established by combining the above measurement information. The model is then applied to obtain positioning information based on a least-squares optimization approach.  
Figure 4: Schematic illustrating the photoelectric scanning multi-angle resection positioning method using an external receiver array. The receiver array includes a fixed number of photoelectric receivers mounted within a known array coordinate system _o-xyz_ with its origin (i.e., the probe point) located at a known measurement point within the transmitter station coordinate system _O-XYZ_. These receivers serve as control points in the _o-xyz_ frame with corresponding points in the _O-XYZ_ frame obtained by laser scanning.  
Figure 3: Principle of single image space resection, where A, B, and C are known control points on the ground, and a, b, and c are corresponding points in the image.
## 3 Materials and Methods
### Analytical Equipment  
MEKC separations were performed on a P/ACE MDQ Capillary Electrophoresis System (Beckman Coulter, Fullerton, CA, USA) equipped with an automatic sample dispenser and a UV/VIS-DAD detector set to an analytical wavelength of 200 nm. The MDQ system also included a capillary thermostat to maintain a constant temperature throughout the separation process. Data acquisition was managed using 32 Karat 8.0 software (Beckman, Fullerton, CA, USA). Sample preparation was conducted using an SPE system (Agilent Vac Elut SPS 24 Manifold, Santa Clara, CA, USA), and sample evaporation was carried out with a CentriVap vacuum concentrator (Laboconco, Kansas City, MO, USA). Buffer and solution pH values were measured with a pH meter (Mettler Toledo, Warsaw, Poland).
# Analyzing the Economic Performance of a TLS-Based Structural Safety Diagnosis Process
## 2 Materials and Methods
### Research Methodology  
#### 2.4.1 Overall Framework  
For this study, an overall framework was established, as illustrated in Figure 1.  
The case data collection process is as follows. The data was collected by a company specializing in architectural structures located in Seoul, South Korea. This company is a structural engineering design office that performs various tasks such as structural design and engineering, construction quality control, renovation of aging facilities, safety diagnosis, and repair and reinforcement of fire-damaged buildings by introducing 3D scanning reverse modeling. To compare the labor-intensive traditional visual inspection method with the TLS-based inspection method, the two methods were applied to an actual structural safety diagnosis project. This collected data on the differences between the two methods. Details of the collected case data are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Classification** & **Project A** & **Project B** & **Project C** \\ \hline Use & Apartment complex & Conveyor belt system & Automobile manufacturing plant \\ \hline Structure & Reinforced concrete structure & Steel frame structure & Steel frame structure \\ \hline \multirow{2}{*}{Scale} & \(-\) 2 basement levels & \multirow{2}{*}{52,000 mm \(\times\) 26,000 mm \(\times\)} & \multirow{2}{*}{5,000,000 m\({}^{3}\)} \\  & \(-\) 24 floors above ground & \multirow{2}{*}{16,000 mm} & \multirow{2}{*}{5,000,000 m\({}^{3}\)} \\  & \(-\) 6 buildings & & \\ \hline Investigation & \multirow{2}{*}{Fire-damaged area} & Displacement of & Displacement and \\ target & & steel members & deformation of pipe racks \\ \hline \multirow{2}{*}{Notable points} & Fire incident occurred & \multirow{2}{*}{Completed in 1987} & \(-\) Large site \\  & during construction & & (aging structure) & – Investigation conducted \\ \hline \multirow{4}{*}{Main tasks} & \(-\) Investigate fire damage & \(-\) Prepare structural drawings & \(-\) Measure pipe rack \\  & \(-\) Associate repair and & \(-\) Analyze displacement and & displacement and deformation \\ \cline{1-1}  & reinforcement quantities & \(-\) Repair and reinforcement measures & – Estimate repair and \\ \hline Contract period & 2 months (44 days) & 4 months (88 days) & 8 months (176 days) \\ \hline \end{tabular}
\end{table}
Table 1: Project-specific overview and scope of work.
## 3 SCH Nonlinear Mathematical Model (NMM)
### Nonlinear Model Modification
#### 3.2.2 Forward Flight Modification  
There are two kinds of parameters that play an important role in high speed. One is the airframe drag, the other is the aerodynamic coefficient of the wing. These two aspects have a great impact on the following parameters: \(X_{u}\), \(L_{p}\), and \(L_{\theta_{a}}\). Other parameters do not change greatly from hover to 32 m/s, which means the hover modification is still valid. In high speed, \(X_{u}\) can be modified in a similar way in Equations (40) and (41) according to \(X_{u}\)(IDM).  
But the lateral derivatives are different. Primarily, suggest that the dynamic model of the helicopter components was accurate in high speed. Then the error of lateral derivatives was mainly caused by the error of prediction on the wing part. During lateral control and rolling, the actual inflow of the wing was poorly estimated due to the interference of airframe and rotor. Thus, modifying the actual inflow angle of the wing proportionally was the best and most efficient way to correct these lateral derivatives in NMM by using \(L_{p}\)(IDM) and \(L_{\theta_{a}}\)(IDM).  
For example, in a roll movement, the change of the effective inflow angle \(a\) at different lateral position \(y\) can be approximately expressed as \(\Delta a(y)=py/u\)[24]. If we introduce a proportion parameter \(\xi\) in the inflow angle, then \(\Delta a(y)=\xi py/u\). Consequently, \(L_{p}\) will be changed with respect to the value of \(\xi\). Similarly, in aileron maneuver, \(\Delta a\approx\xi\theta_{a}\). It is discovered that, by defining one \(\xi\), both \(L_{p}\) and \(L_{\theta_{a}}\) can approximately fit the IDM results.  
Finally, 10 mentioned modified parameters are gathered in Table 5. Both the previous values and the updated values are shown in the table. In Table 6, the eigenvalues of the linearized-modified NMM model at hover are compared with the IDM results. The eigenvalues in \([\zeta_{e},\omega_{e}]\) denotes the second-order system and \((\sigma_{e})\) represents for first-order system. \(\zeta_{e}\) is the damping ratio, \(\omega_{e}\) (rad/s) is the natural frequency and \(\sigma_{e}\) is the real part of the complex eigenvalue. From the table, we can see the eigenvalues of NMM at hover are close to the IDM's, which indicates a credible performance of the updated NMM. In the following sections, \"NMM\" indicates the results from the updated NMM.
## 3 Motion Prediction of the Surrounding Vehicles
### Longitudinal Motion of the Vehicles  
In the next step, the longitudinal motion of the vehicles is predicted. The computation of the density function is based on the NGSIM dataset. Firstly, a start point is selected randomly on the given road segment. The current longitudinal acceleration \((a_{c})\) value is saved and assumed that the vehicle moves with it along the whole segment. The given horizon is predefined and the length of it is set to \(T=8\,\)s. The whole horizon is divided into 10 equidistant segments \(t\in[t_{1},t_{2}\ldots t_{n}],n=10\). The acceleration values at the previously defined time step \(t_{i}\) are saved and compared to the acceleration value at the start point \((a_{c})\). The difference between them are recorded \(a_{d,i}=a_{c}-a(t_{i})\), \(i=1\ldots 10\). During the determination of the density function associated with the longitudinal motions, the whole dataset is used unlikely to the lateral case.  
Figure 7: Density and distribution function of the lateral accelerations.  
The dataset is divided into disjoint sets (\(\mathcal{A}\)) according to the initial acceleration values. \(a_{d,i}\in\mathcal{A}_{j}\), if \(a_{d,i}\in\{A_{l,j}A_{u,j}\}\) and \(a_{d,i}\
otin\mathcal{A}_{j+1}\) if \(k=1\ldots n\), \(A_{l,k}>A_{l,k+1}\) and \(A_{u,k}<A_{u,k+1}\). \(A_{l}\) and \(A_{u}\) denote the lower and upper bounds of the given set. To predict the possible longitudinal motion of the vehicles, Gaussian density functions are calculated to the previously computed sets. The Gaussian density function can be formulated as:  
\[f_{k,i}(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{1}{2}(\frac{x-\mu}{\sigma})^{2 }}\quad k=1\ldots n, \tag{11}\]  
where \(k\) denotes the \(k^{th}\) prediction time step, and \(i\) gives the \(i^{th}\) set according to the initial acceleration as it is described below. Using these, the possible reachability areas can be determined, and also the probability of collisions during the motion-planning process. In Figure 8 the density functions are presented.  
In Figure 8 the density functions are depicted where the bounds of accelerations are set to \(A_{l}=-0.25\,\mathrm{m}/\mathrm{s}^{2}\), \(\mathrm{A_{u}}=0.25\,\mathrm{m}/\mathrm{s}^{2}\). In the right side of the pictures the results are shown, in which the bounds are set to \(A_{l}=-1\,\mathrm{m}/\mathrm{s}^{2}\), \(\mathrm{A_{u}}=1\,\mathrm{m}/\mathrm{s}^{2}\). Note that at increased acceleration values, the uncertainty increases as well. The time step is set to \(t_{i}=0.8\,\mathrm{s}\). The possible longitudinal and lateral movements are presented based on the NGSIM data. In the followings, these functions are used to predict the possible motions of the vehicles, and also help to compute the optimal value of the lateral acceleration, which is essential during the trajectory planning.
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 5 Substring Position Searchable Symmetric Encryption  
### Algorithm Definitions  
**Definition 12**: _(Substring Position Searchable Symmetric Encryption (SSP-SSE))._ A tree-based SSP-SSE scheme over a set of documents \(D\) is a tuple of six polynomial-time algorithms (KeyGen, BuildTree, Encrypt, ConstructQuery, Search, Decrypt), as follows:  
1. \(K\leftarrow\textit{KeyGen}(1^{k})\): a probabilistic key generation algorithm to setup the SSP-SSE scheme. The algorithm takes a secret parameter \(k\) and outputs a set of secret keys \(K\).
2. \((\Lambda)\leftarrow\textit{BuildTree}(D)\): a deterministic algorithm to build a position heap tree \(\Lambda\). The algorithm takes a document collection \(D=(D_{1},\,\ldots,\,D_{l})\) and outputs a position heap tree \(\Lambda\).
3. \((l,C)\gets Encrypt(K,\Lambda,D)\): a probabilistic algorithm to encrypt a position heap tree \(\Lambda\) and document corpus \(D\). The algorithm inputs a set of secret keys \(K\), a position heap tree \(\Lambda\) and a documents corpus \(D\). The output of algorithm is a searchable index \(I\) and encrypted collection \(C=(C_{1},\,\ldots,\,C_{l})\).
4. \([(Q)\leftarrow\textit{ConstructQuery}(K,\chi)]\leftrightarrow[(L)\gets \textit{Search}(I,Q)]\): two deterministic algorithms that are executed interactively between the cloud user and the cloud provider. The ConstructQuery algorithm inputs a set of secret keys \(K\), a substring \(\chi\), and it outputs a search query \(Q\). The Search is an algorithm that inputs a searchable index \(I\) and a search query \(Q\). The algorithm finds the set of matching encrypted document identifiers \(L\in C\).
5. \((D_{i},\textit{pos}_{D_{i}})\gets\textit{Decrypt}(K,C_{i})\): a deterministic algorithm that takes a set of secret keys \(K\) and a ciphertext \(C_{i}\) as input and outputs an original document \(D_{i}\), \(\forall i\in[1;n]\) and a set of \(\chi\)'s positions \(\textit{pos}_{D_{i}}\) in \(D_{i}\).  
**Definition 13**: _(SSP-SSE correctness)._ We say that the tree-based SSP-SSE scheme is correct if \(\forall k\in\mathbb{N}\), \(\forall\)\(K\) produced by \(\textit{KeyGen}(1^{k})\), \(\forall\)\(D\), \(\forall\)\(\Lambda\) output by \(\textit{BuildTree}(D)\), \(\forall\)\(\chi\), \(\forall\)\(i\in[1;n]\):  
\[\begin{split}\textit{Search}(&\textit{Encrypt}(K, \Lambda,D),\textit{ConstructQuery}(K,\chi))=\\ &=C(\chi)\bigwedge\textit{Decrypt}(K,C_{i})=(D_{i},\textit{pos}_{ D_{i}})\end{split} \tag{4}\]  
The SSP-SSE correctness ensures the proper output if all SSP-SSE algorithms are executed honestly by the cloud provider.
## 4 Experiments
### LR-ALBERT Performance Test  
LR Distillation distills the teacher model into the student model based on RKD. At this time, the student model can have various reuse counts. We distill the ALBERT-Base and ALBERT-Large models into student models with various reuse counts. The GLUE benchmark table and computing performance (Memory Usage, Fine-tuning, Throughput per Second) of LR-ALBERT after distillation are presented. The performance presented in Figure 6 is normalized as a percentage by dividing the measurement result of LR-ALBERT by the measurement result of the existing teacher model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **MNLI** & **QNLI** & **QQP** & **RTE** & **SST** & **MRPC** & **CoLA** & **AVG** \\ \hline
**MAE** & 81.1 & 90.1 & 87.3 & 71.1 & 90.6 & 86.9 & 48.1 & 79.31 \\
**BASE** & 82.3 & 89.4 & 87.3 & 73.8 & 90.2 & 86.7 & 52.5 & 80.3 \\
**MAE** & 83.3 & 90.8 & 90.1 & 75.1 & 91.8 & 89 & 56.3 & 82.34 \\
**LARGE** & 85.3 & 91.1 & 89.5 & 78.5 & 92.7 & 90 & 59.3 & 83.77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MAE vs. MSE GLUE benchmark.  
Figure 5: Entropy trend during distillation using the MAE and MSE methods, according to the learning process (**Left**: Base-size model, **Right**: Large-size model).  
According to the results, the base and large models exhibited good performance when distilled into LR-ALBERT, which has approximately half the number of layer reuse instances. The GLUE benchmark performance of the LR-ALBERT model, with a half level of layer reuse, dropped by around 1-2%, but the memory usage decreased to about half, and the processing speed increased by roughly two times. We suggest setting the layer-reuse frequency of LR-ALBERT to half of its original number as a reasonable benchmark.
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 3 Analysis of the Delay Performance
### Queuing Model  
For our queuing system analysis, we consider a usual statistical model such as that in [37], which is widely used in the SNC methodology. Then, the cumulative arrival, achievable service, and departure processes of user \(k\) during time interval \([\tau,t)\) are defined as follows.  
\[\left\{\begin{array}{l}A_{k}(\tau,t)=\sum\limits_{i=\tau}^{t-1}a_{k}(i),\\ R_{k}(\tau,t)=\sum\limits_{i=\tau}^{t-1}r_{k}(i),\\ U_{k}(\tau,t)=\sum\limits_{i=\tau}^{t-1}u_{k}(i).\end{array}\right. \tag{20}\]The instantaneous traffic arrival \(a_{k}(i)\) models the number of bits that arrive at the queue within a discrete time slot \(i\). The increment of service process (service rate) \(r_{k}(i)\) is independent of time and equal to the maximum achievable data rate \(r_{k}\big{(}\hat{\gamma}_{k},\varepsilon_{k}^{d}\big{)}\). The departure process \(u_{k}(i)\) denotes the number of bits that arrive successfully at the destination and is subject to both the service process and the amount of data waiting in the queue. In addition, we assume that all queues are work-conserving first-come-first-served queues. Then, the queuing delay \(w_{k}(t)\) of user \(k\) at time slot \(t\) is defined as the number of time slots required for all data that arrive before time \(t\) to depart from the transmit buffer and is expressed as  
\[w_{k}(t)=\inf\{u\geq 0:A_{k}(0,t)\leq U_{k}(0,t+u)\}. \tag{21}\]
# A Broadband Active Microwave Monolithically Integrated Circuit Balun in Graphene Technology
## 3 Results  
We first discuss the graphene material used in this work. Figure 1 presents the atomic force microscopy (AFM) image of 60 \(\times\) 60 \(\upmu\)m\({}^{2}\) graphene surface on SiC. It shows the presence of SiC steps which are several tens of \(\upmu\)m wide. Two typical Raman spectra measured on the sample are illustrated on Figure 1b, after subtraction of the Raman signal coming from the SiC. The G and 2D peaks, a characteristic feature of graphene, are clearly visible (with G peak at 1596 cm\({}^{-1}\) and 1592 cm\({}^{-1}\)) [23]. The D peak intensity is either not detected or much smaller than our G peak, indicating that structural defects are absent or present in very small a mount [24]. The width of 2D peak and the low value of the ratio I\({}_{\text{2D}}\)/I\({}_{\text{G}}\)\(<\) 1 indicates that the graphene is mainly a bilayer.
# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## 5 Experimental Results  
Before applying the proposed method to the RDCs, we checked the prediction results of an SDC using Seasonal Auto Regressive Integrated Moving Average (SARIMA), a conventional time-series forecasting model. This model is an extended version of ARIMA, supporting the modeling of the seasonal components of the time-series data. We used NEDC as the SDC and predicted the velocity after training the model. The mean absolute percentage error (MAPE) is used as criteria for evaluating the performance.  
\[MAPE=\frac{1}{N}\sum_{t=1}^{N}\left|\frac{x_{t}-\hat{x}_{t}}{x_{t}}\right| \tag{13}\]  
where \(x_{t}\) and \(\hat{x}_{t}\) are ground truth and predicted value, respectively.  
As can be seen from Table 2, the SDC can be predicted accurately enough without using the DL model. However, the accuracy for the RDC is much lower than the SDC. Therefore, we will use the proposed DL method to improve the predictions for the RDC.  
To validate our proposed model using RDC, we collected real EV driving data through OBD II. Our dataset of EV speed was obtained from actual driving and divided into four road segments: location A to location C, location C to location D, location D to location E, and location E to location F. Each route has 11 driving cycles, and we took the seen driving cycles of each route as the training set and the rest as the test set. We first verified the performance of our speed predictions. by analyzing the performance of our model using the grid search method with different combinations of hyperparameters. The purpose was to find the best combination of hyperparameters and then verify the effect of each component on the results. Once we obtained the best model, we predicted the speed/acceleration along with the SOC values and compared them with some commonly used methods.
## 3 Typical Applications of ACE-FHBOC Multiplexing
### Decomposed Form of ACE-FHBOC  
Except for the direct form in (11), ACE-FHBOC can also be expressed in the decomposed form in which each signal component is modulated with a complex frequency-hopping subcarrier. By using the intermodulation construction (IMC) method [27], we can derive the subcarrier waveform for \(s_{\text{UI}}(t)\), \(s_{\text{LI}}(t)\), \(s_{\text{UQ}}(t)\), \(s_{\text{LQ}}(t)\), and their corresponding IM terms. The decomposed expression of a Type-I ACE-FHBOC signal can be formulated as  
\[\begin{array}{l}s_{\text{A1}}(t)=c_{\text{UI}}(t)[u_{s1}(t)+\text{i}\widetilde {u}_{s1}(t)]+\text{j}c_{\text{UQ}}(t)[u_{s2}(t)+\text{j}\widetilde{u}_{s2}(t )]\\ \ \ \ \ \ \ \ \ \ \ \ \ \ +c_{\text{LI}}(t)[u_{s1}(t)-\text{j}\  
\[u_{s2}(t)=\left\{\begin{array}{ll}0.5\text{sign}[\cos(\psi(t)-\vartheta+0.5\pi)]+ \sin(\theta)\text{sign}[\cos(\psi(t))]&0\leq t\leq T_{c}\\ +0.5\text{sign}[\cos(\psi(t)+\vartheta-0.5\pi)],&\text{others}\\ 0,&\text{others}\end{array}\right. \tag{24}\]  
\[u_{p1}(t)=\left\{\begin{array}{ll}-0.5\text{sign}[\cos(\psi(t)t-\vartheta)] +\cos(\theta)\text{sign}[\cos(\psi(t))]&0\leq t\leq T_{c}\\ -0.5\text{sign}[\cos(\psi(t)+\vartheta)],&\text{others}\end{array}\right. \tag{25}\]  
\[u_{p2}(t)=\left\{\begin{array}{ll}-0.5\text{sign}[\cos(\psi(t)-\vartheta+0.5 \pi)]+\sin(\theta)\text{sign}[\cos(\psi(t))]&0\leq t\leq T_{c}\\ -0.5\text{sign}[\cos(\psi(t)+\vartheta-0.5\pi)],&\text{others}\end{array}\right. \tag{26}\]  
where \(\psi(t)=2\pi(f_{0}+g(t)f_{d})t\), \(\vartheta=\text{atan}(\lambda)\), and \(\theta=\text{atan}(\lambda)\). The decomposed expressions of Type-II and Type-III ACE-FHBOC are derived in Appendix A.
## 4 Validation Through Sea Trials
### Processing and Analysis of Sea Trial Data with Added Ocean Noise
#### 4.4.1 Extraction of Ocean Noise  
On the day of the experiment, multiple sets of sea trial noise were extracted at different time intervals, and the different groups of noise were mixed in random proportions before being added to various portions of the sea trial data for Monte Carlo simulations.