esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation and Discussion
### Simulation Conditions  
Figure 6 shows the evaluated simulation model with one sender, one receiver and one cybererrorist. The sender and the receiver can use up to three RATs for communication. In the evaluation, it is assumed that the distance between the sender and the receiver is accurately measured using UWB. Assuming that the distance is always changing, distance information with the length of shortened information is generated by the uniform random number for each codeword using the function of MATLAB. The sender and the receiver use the same value of distance information. Assuming that the cybererrorist cannot get any distance information at all, a random value independent of distance information is used for the cybererrorist. Details of shortened RA code parameters are described in  
Section 4.3. The encoder and decoder of the RA code use the function of the Communications Toolbox of MATLAB. A total of 5 million codewords were simulated. Codewords of shortened RA code are defined as \(c(0),\ c(1),c(2),\cdots,\ c(n-m-1)\). Assuming communication using two RATs with almost the same capability, such as 2.4 GHz band and 5 GHz band of wireless LAN, \(c(0),\ c(2),\ c(4),\cdots,\ c(n-m-2)\) are assigned to the first RAT and \(c(1),\ c(3),c(5),\cdots,\ c(n-m-1)\) are assigned to the second RAT. When three RATs are used for communication, codewords are allocated sequentially from the beginning as in the case of two RATs. Since the codewords are distributed equally to each RAT in this manner, the wiretapping resistance and the attack resistance of each RAT are equal. The communication model will be described. If RAT communication is completely lost due to the attack, the received codeword is a random value. So, the codewords received via the attacked RAT are generated by the uniform random number of MATLAB for each decoding process. The unattacked RAT has no communication errors. So, the codewords received via the unattacked RAT for the decoder are the same codewords assigned by the RAT. The cyberterrorist shall be able to wiretap and attack simultaneously. In other words, when two RATs are used for communication, one RAT can be wiretapped while the other RAT can be attacked simultaneously. We confirm the information-theoretic effect by evaluating the mutual information between the sender and the receiver when some RATs are attacked and the mutual information between the sender and the cyberterrorist when some RATs are wiretapped. Then, we confirm the information-theoretic security capability against wiretapping and attacks by using the secrecy capacity.
## 2 Places, Instruments, and Data
### Data
#### 2.3.3 ECMWF Reanalysis Data  
The ECMWF supplies both global numerical weather predictions (NWPs) and reanalysis history datasets of the atmosphere, land surface, and oceans. The ECMWF reanalysis data are based on the integrated forecast model and provide spatially continuous wind products globally with an interpolated spatial resolution of up to 0.125\({}^{\circ}\) longitude/latitude and a temporal resolution of 3 h. Two reanalysis datasets are available for sea surface winds until 1 September 2019: ERA-Interim and ERA5.
## 3 Cluster Routing Protocol
### Route Selection  
A cluster-based protocol under channel fading is studied in this paper. As implied by the previous study background and network topology, the network topology in the PEAFC-CL protocol is similar to that in the LEACH protocol. In such a topology, a communication mode based on single-hop transmission is applied between clusters. Routing selection is not a complex process for nodes after the cluster set is created. The role of a node is first determined (CH or member node). A CH is able to directly communicate with the BS. A member node is required to determine whether the fading loss of the communication with the BS by taking their own CH as the routing node is less than the fading loss of direct communication with the BS through the node itself. If so, then the CH is selected as its own routing node. Otherwise, data transmissions can be directly achieved with the BS.
# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
## 2 Materials and Methods
### Description of Initial Datasets
During the clustering of transport areas of the city, 24 factors from the dataset of the city macromodel were considered as independent variables, among which we can distinguish the distance of the source area from the city center, the number of exits and entries from/to the area, the number of workers in the area, the average travel time of different modes of transport, the average travel speed, etc. As the desired value (dependent variable y), the share of trips by various types of transport from and to the area is determined, which is expressed as a percentage of the following values: the share of trips from the area by private transport (from_car_%); share of trips from the area by public transport (from_bus_%); the share of trips from the area by bicycle (from_bike_%); proportion of pedestrian trips from the area (from_ped_%); the share of trips to the area by private transport (to_car_%); share of trips to the area by cubicx (to_bike_%); share of pedestrian trips in the area (to_ped_%). The dataset contains 400 rows (399 observations) and 32 columns corresponding to the number of dependent and independent variables. \begin{table}
\begin{tabular}{c c c c} \hline
**Column Title** & **Value 1** & **Value 2** & **Value 3** \\ \hline L\_to (Euclidean distance from source area to target area) & 0.30 & 0.35 & 0.44 \\ L\_centr (Distance of the source area to the city center, km) & 0.24 & 0.24 & 0.24 \\ L\_bike\_lane (Length of bike lanes, km) & 0 & 0 & 0 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ t\({}_{0}\) (Travel time with free road network) & 241.32 & 227.49 & 350.04 \\ t\({}_{a}\) (Travel time with load of the road network) & 248.27 & 227.49 & 352.56 \\ S\_path (The length of the path along the road network) & 0.56 & 0.45 & 0.60 \\ k\({}_{l}\) Coefficient of uneven path & 1.88 & 1.28 & 1.36 \\ t\({}_{d}\) (Travel delay time, s) & 6.94 & 0.01 & 2.52 \\ k\({}_{x}\) (Congestion factor) & 1.02 & 1.00 & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 1: Output of the “Structure of mobility” dataset in an abbreviated and transposed form.
# Asymptotic Performance of GNSS Positioning Approaches under Cross-Correlation Effects
## 7 Conclusions  
This study presents a comprehensive analysis of the asymptotic performance of the 2 SP and DPE while considering cross-correlation errors. The results show that the pseudo-orthogonality of PRN codes leads to a degradation in the pdf of each synchronization parameter for the 2 SP. Consequently, the initial estimate of the synchronization parameter in the 2 SP is not the ML. Quantitative results are presented to evaluate the performance enhancements achieved by the DPE when treating cross-correlation errors as AWGN. Theoretical expressions for the estimated variances of both approaches are derived, providing a more strict result that shows that the 2 SP is not asymptotically efficient, and the DPE consistently outperforms the 2 SP in GNSSs. Simulation tests further validate the superior performance of the DPE, highlighting its ability to resist cross-correlation errors through the joint processing of satellite signals. This key feature positions the DPE as a robust solution that is capable of addressing challenges related to multi-access interference and near-far effects.  
Conceptualization, Y.D. and J.W.; Formal analysis, Z.T.; Investigation, Y.D. and Z.T.; Methodology, Y.D. and Z.T.; Software, Y.D.; Supervision, J.W. and Z.T.; Validation, Y.D., J.W. and Z.T.; Visualization, Y.D.; Writing--original draft, Y.D. and Z.T.; Writing--review and editing, Y.D. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Nos. 62271223 and 62171191).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no conflicts of interest regarding the publication of this manuscript.
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 4 Results Analysis  
Various simulation experiments are setup in order to discuss the estimated performance of the developed GGS-MAF algorithms by considering different inputs. In all the simulations, the additive noise \(r(n)=v(n)+i(n)\), where \(v(n)\) denotes a white Gaussian noise (WGN) with SNR of 30 dB, and \(i(n)\) represents the impulsive interference which is modeled by the Bernoulli-Gaussian (BG) distribution, and \(i(n)=b(n)g(n)\). Herein, \(g(n)\) is another WGN and \(b(n)\) denotes a Bernoulli process which has a probability of \(p(b(n)=1)=0.1\), and the signal-interference ratio (SIR) is 15 dB, and \(L=1024\). In these simulations, the regularization parameters are set to be \(\varepsilon_{\mathrm{NLMS}}=0.01\), \(\varepsilon_{\mathrm{GGS-MAF}}=\varepsilon_{\mathrm{PNLMS}}=\frac{1}{L} \varepsilon_{\mathrm{NLMS}}\)[26], the step sizes are set to be \(\mu_{\mathrm{LMS}}{=}\mu_{\mathrm{ZA-LMS}}{=}\mu_{\mathrm{RZA-LMS}}=3\times 10^{ -4}\), \(\mu_{\mathrm{MEC}}=2.5\times 10^{-4}\), \(\mu_{\mathrm{LMF}}=3\times 10^{-5}\), \(\mu_{\mathrm{NLMS}}=0.3\), \(\mu_{\mathrm{PNLMS}}=\mu_{\mathrm{PINLMS}}=\mu_{\mathrm{ZA-PNLMS}}=0.1\) and \(\mu_{\mathrm{GGS-MAF}}=0.065\). The performance of GGS-MAF algorithms, defined by using NM which has a definition of \(10\mathrm{log}_{10}(\left\|\mathbf{h}-\mathbf{\hat{h}}\right\|_{2}^{2}/\left\| \mathbf{h}\right\|_{2}^{2})\), is discussed over two group sparse systems. The first group sparse system has one group active channel coefficients distributed in [267,282], and the second one possesses two group active coefficients appeared in [267,282] and [778,793], which are given in Figure 1.  
Figure 1: Performance comparisons of the four generalized group-sparse mixture adaptive filtering (GGS-MAF) algorithms.
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 2 Materials and Methods
### Methods
#### 2.2.3 SE Attention Mechanism
This residual module enables the network to better fit an identity mapping, facilitating the network's ability to capture small target disease spots or edge details in images. This enhances the detection of small target disease spots, significantly improves model performance and generalization, and reduces model complexity and training costs, making it more suitable for potato leaf disease spot segmentation tasks. Additionally, this module directs the network's focus toward important features and facilitates better generalization to new data, thereby enhancing the model's performance in potato leaf disease spot segmentation tasks.
# Image Caption Generation via Unified Retrieval and Generation-Based Method
## 4 Experiments
### Comparison with the State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Cross-Entropy Loss**} & \multicolumn{4}{c}{**Self-Critical Loss**} \\ \cline{2-13}  & **B@1** & **B@4** & **M** & **R** & **C** & **S** & **B@1** & **B@4** & **M** & **R** & **C** & **S** \\ \hline SCST [8] & - & 30.0 & 25.9 & 53.4 & - & - & 99.4 & - & 34.2 & 26.7 & 114.0 & 55.7 \\ ADP-ATT [6] & 74.2 & 33.2 & 26.6 & - & 108.5 & - & - & - & - & - & - & - \\ LSTM-A [44] & 75.4 & 35.2 & 26.9 & 55.8 & 108.8 & 20.0 & 78.6 & 35.5 & 27.3 & 56.8 & 118.3 & 20.8 \\ VS-LSTM [42] & 76.3 & 34.3 & 26.9 & - & 110.2 & - & 78.9 & 36.3 & 27.3 & - & 120.8 & - \\ Up-Down [23] & 77.2 & 36.2 & 27.0 & 56.4 & 113.5 & 20.3 & 79.8 & 36.3 & 27.7 & 56.9 & 120.1 & 21.4 \\ RFNet [45] & 76.4 & 35.8 & 27.4 & 56.5 & 112.5 & 20.5 & 79.1 & 36.5 & 27.7 & 57.3 & 121.9 & 21.2 \\ UGRIC [11] & - & - & - & - & - & - & 81.3 & 38.2 & 28.4 & 58.6 & 123.5 & - \\ GCN-LSTM [43] & 76.5 & 36.8 & 27.9 & 57.0 & 116.3 & 20.9 & 80.5 & 38.2 & 28.5 & 58.3 & 127.6 & 22.0 \\ Ours & 76.7 & 36.5 & 28.0 & 56.8 & 115.1 & 21.2 & 80.0 & 38.3 & 28.7 & 58.5 & 128.0 & 22.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of our model and several state-of-the-art single models evaluated on MSCOCO “Karpathy” offline test split.
## 1 Introduction
On the other hand, there are almost no studies that optimize the hyperparameters of the YOLO algorithms, other than [11], where a modified whale optimization algorithm (MWOA) is proposed by combining the whale optimization algorithm (WOA) and GWO to enhance the vehicle detection performance of YOLOv5. The algorithm optimizes the 12 default hyperparameters of the YOLOv5 model. However, it uses a dataset with normal weather conditions as the dataset; therefore, it does not address AWCs. As a result, the optimization of the YOLO hyperparameters are an open research topic for AWCs. Therefore, in the literature reviews, no studies on the optimization of hyperparameters of YOLOv5, YOLOv7, and YOLOv9 using metaheuristic optimization algorithms for object detection in autonomous vehicles under AWCs have been found. Normally, the default hyperparameters are idealized for general usage. In addition, the parameter range is determined to be adjusted according to different applications. Although the default values of the hyperparameters can be successful for images without AWCs, there is a need to find the optimum values of the hyperparameters in AWCs. Since the number and range of hyperparameters are many and large, it is challenging to determine these parameters through trial and error. In particular, the optimization is crucial for object detection in autonomous vehicles under AWCs when compared to normal weather conditions. This is because, as can be seen from simulation studies, the parameter changes in AWCs greatly affect object detection performance. The effect of these metaheuristic algorithms on object detection performance on different datasets and different YOLO versions is investigated. This study proposes to improve the performance of object detection using YOLOv5, YOLOv7, and YOLOv9 algorithms in AWCs by using GWO, ARO, and CLEO metaheuristic optimization algorithms. In order to increase this performance, we aim to minimize the 1-mAP value as the objective function. The hyperparameters of the YOLO algorithms are employed to find optimum values for the datasets RTTS containing mainly normal weather conditions in addition to foggy conditions, as well as DAWN containing AWCs. YOLO object detection algorithms in AWCs have been significantly improved by GWO, ARO, and CLEO in this study.
## 5 Experimental Results
### Comparison between the Proposed System and Other Existing Systems  
A comparison between the proposed system and other existing systems is presented in Table 3. This comparison is based on the major advantages, validation, decision support, and critical challenges.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & \multicolumn{2}{c}{**Major Advantages**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Decision Support System**} & \multicolumn{2}{c}{**Critical Challenges**} \\ \hline \multirow{8}{*}{[26]} & \multirow{4}{*}{\(\bullet\)} & Collected environmental data & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{No Support} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Cost of system} \\  & & (air/soil temperature and & & & & & \\  & humidity) related to the & & & & & & \\  & growth of farm products over & & & & & & \\  & a period of three months. & & & & & & \\  & Web-based dashboard is also & & & & & & \\  & presented, to validate the & & & & & & \\  & LoRaFarM architecture. & & & & & & \\ \hline \multirow{8}{*}{[27]} & \multirow{4}{*}{\(\bullet\)} & Intelligent data collection and & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Experimental} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Statistical indicators} & \multirow{4}{*}{\(\bullet\)} & Communication covering \\  & & processing. & & & & & & Power consumption increases \\ \cline{1-1}  & & Data Management and & & & & & & with communication range \\ \cline{1-1}  & & Interpretation level & & & & & & \\ \hline \multirow{8}{*}{[28]} & \multirow{4}{*}{\(\bullet\)} & Monitoring temperature, & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Simulation} & \multirow{4}{*}{\(\bullet\)} & Advice on & \multirow{4}{*}{\(\bullet\)} & Communication covering \\  & & humidity, soil moisture level & & & & scheduling & & Power consumption increases \\ \cline{1-1}  & & A notification in the form of & & & & & irrigation & \\ \cline{1-1}  & & SMS will be sent to farmer’s & & & & & \\ \hline \multirow{8}{*}{[29]} & \multirow{4}{*}{\(\bullet\)} & Intelligent control with & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Experimental} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Activation of a water pump} & Solar cell system is generally \\ \cline{1-1}  & & Weather Station and Artificial & & & & & & \\ \cline{1-1}  & & Neural Network. & & & & & irregular and extensively \\ \cline{1-1}  & & The fuzzy control and ANN & & & & & & influenced by the weather \\ \cline{1-1}  & & allow the prediction of the & & & & & changes \\ \cline{1-1}  & & internal temperature of the & & & & & & changes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Qualitative comparison between the proposed system and other existing systems.  
Figure 18: PL and RSS of Hata propagation model results.
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
## 2 System Model and Protocol Descriptions
### Hybrid Cooperative Cognitive Radio Networks Based on Pure OMA
We consider perfect SIC in practical scenarios, the achieved rate for SU's packet at SR is expressed as:  
\[y_{PR}=\sqrt{P_{S}}h_{SP}x_{P}+n_{PR}, \tag{12}\]0
2. If ST fails to decode the PU's packet, PT will retransmit that the PU's packet failed previously while SU keeps silent. The achieved rate of the channel \(\text{PT}\rightarrow\text{PR}\) is the same as Equation (13).
## 2 Proposed Method
### Actuators Housing Strategy and Preload Estimation
proposed approach (schematized in Figure 7), each actuator is modeled as a single lumped stiffness (\(\mathrm{K_{P}}\)), while each slot is modeled as a three lumped stiffness in parallel, in order to consider both boring bar (\(\mathrm{K_{B}}\)) and contact stiffness (\(\mathrm{K_{H}}\)). \(\mathrm{K_{H}}\) can be computed based on Hertzian theory. In particular, given compression force, material property of the two bodies in contact and their geometry, it is possible to estimate contact circle radius and depth of penetration, and then its equivalent stiffness. \(\mathrm{K_{P}}\) can be experimentally measured, while being bar stiffness (\(\mathrm{K_{B}}\)) is evaluated by means of an FE approach estimating the slots elongation due to computed preload applied in correspondence with the estimated contact area. To guarantee the contact between actuator and boring bar, slot length must be equal to actuator length. Due to preload \(P\), actuator length decreases in \(x_{P}\), while the distance between the wall of the slot increase as a result of both the elastic yielding due to contact between them (\(x_{H}\)) and of the boring bar deformation (\(x_{B}\)). Then, assuming a slot of nominal length \(L_{0}\) and an actuator of length \(L_{p}\), the following equation must be respected:  
\[L_{o}+x_{B}+2x_{H}=L_{P}-x_{P}+i. \tag{1}\]  
In order to guarantee the contact between slot and actuator, interference should be able to compensate the relative displacement between them. \[i=x_{B}+2x_{H}+x_{P}. \tag{2}\]  
In accordance with the model proposed in Figure 7, interference (\(i\)) may be expressed as:  
\[i=\frac{2P}{K_{H}}+\frac{4P}{K_{B}}+\frac{P}{K_{P}}. \tag{3}\]
## 2 MPS Method
### Procedure of Numerical Simulation  
To calculate the sloshing-induced force in ship tanks, liquid sloshing is numerically simulated by using the MPS method. As a CFD method, the procedure of MPS method can be divided into the following three stages which are described as follows:  
1. Pre-processing stage. Based on the 3D design data of ship tanks, the computational domain is firstly discretized into fluid particles. Meanwhile, two kinds of solid particles are arranged to satisfy the solid boundary condition. After that, the particle information, including position, velocity, acceleration and pressure, etc., and calculation parameters, including initial particle distance, operator support distance and time step, etc., are initialized. The flow of preprocessing is illustrated in Figure 1.
2. Solving stage. First, the neighbor particle list is established by using the cell-linked-list (CLL) technique [59]. After that, the semi-implicit algorithm is used to obtain particle information. In this process, the key role is to solve the PPE, as shown in Equation (10). At each time step, PPE is firstly transformed into a large system of linear equations, whose coefficient matrix is a typical symmetric sparse matrix. Thus, the conjugate gradient (CG) method is employed to solve this linear system [19]. Then, the pressure of each particle is obtained and is used to calculate the pressure gradient, as shown in Equations (5) and (6). Finally, the pressure gradient is used to update particles' velocity, displacement, and other information. These pieces of information will be recorded occasionally since the computational step is too small.
3. Post-processing stage. In this stage, the output data are used for data analysis, e.g., visualization of the liquid sloshing, computation and analyzation of sloshing-induced force acting on bulkhead, etc. The flow chart of MPS numerical simulation is illustrated in Figure 2.  
Figure 1: Illustration of preprocessing.  
Figure 2: Flow chart of MPS numerical simulation.
# Assessing the Performance of the Multi-Beam Echo-Sounder Bathymetric Uncertainty Prediction Model
## 1 Introduction
[PERSON] [23] investigated the impact of varying parameters, such as the vessel speed, swath width, track configuration, and density of the measurements on the final bathymetric grid using a virtual survey simulator. Despite an impressive amount of research carried out toward theoretical and empirical modeling of the MBES bathymetric uncertainties, there has been little effort to validate the often used bathymetry uncertainty prediction model in varying conditions (either environmental or MBES settings). Comparison between the measured and modeled uncertainties provides one with insight into how realistic the modeling is and can also give directions for future improvements of the bathymetric uncertainty prediction model. This can be also beneficial for the approaches relying on the modeled uncertainty to give an estimate of the soundings' uncertainty in a grid, such as the CUBE [9; 10; 24]. These issues have motivated us to assess the performance of the bathymetric uncertainty prediction model by comparing the modeled and measured vertical uncertainties for current state-of-the-art MBES systems and seek for improvements. This paper is organized as the following. In Section 2, a method for obtaining the depth uncertainties from the measurements such that a fair comparison with the modeled uncertainties can be made is presented. The description of the datasets is given in Section 3. We present the results in Section 4 and discuss the relevant issues. Concluding remarks of this paper are given in Section 5.