esa-sceva/satcom-chunk-collection huggingface dataset:
# Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers
## 2 Materials and Methods  
The performance of the UCGA is evaluated by means of a computer-based simulation experiment. The software used during the study is the GA solver, which is the part of MATLAB packet. The researcher's role is to input the parameters, constraints and, crucially, to properly specify the objective function.  
To understand the GA solver configuration in the considered optimization problem, we focus on the system model illustrated by the block diagram from Figure 1. The designed equalizer acquires the samples of Binary Phase-Shift Keying (BPSK) modulated signal, transmitted over a time-varying Rayleigh fading channel. The resulting channel output \(\mathbf{r}\) is processed by a transversal filter, i.e., adaptive linear equalizer, responsible for providing estimates of the transmitted symbols, \(\overset{\mathbf{\wedge}}{\mathbf{r}}\). In detail, the estimate of the \(n\)th symbol of a data frame is defined as  
\[\hat{r}(n)=\sum_{l=1}^{L}c_{l}(n)\cdot r(n+l-\left\lfloor\
icefrac{{L}}{{2}} \right\rfloor) \tag{1}\]  
where \(L\) is the equalizer length, and the complex-valued numbers \(c_{1}(n)\cdots c_{L}(n)\), i.e., the elements of vector \(\mathbf{c}(n)\) from Figure 1, represent the tap coefficients of the equalizer in respective signaling interval.  
The adaptation is carried out by observing the error, i.e., the difference between the desired sequence and the equalizer output. The error volume controls the direction of the coefficients adjustment from one iteration to the next in order to approach the optimum set of values. The optimization procedure that the adaptive algorithm utilizes to update the coefficient values is based on the Mean Square Error criterion [12]; thus, the error signal is defined as  
\[\varepsilon(n)=|\rho(n)-\hat{r}(n)|^{2} \tag{2}\]  
During a training phase, a training sequence \(\widetilde{\mathbf{s}}\) is transmitted. The sequence is known in advance at the receiver side (\(\rho(n)=\widetilde{\mathbf{s}}(n)\)), so that the signal received in the training phase can be taken as a reference. The optimization criterion for the adaptive equalizer is to minimize the error (2) according to the MSE criterion. In other words, in the \(n\)th signaling interval the equalizer looks for the best vector \(\mathbf{c}(n)\) of filter taps.  
Having completed the training phase, the adaptive equalizer starts operating on the basis of decision-directed rule. The signal outputted by the filter is regenerated by a decision unit, so that it becomes an ideal BPSK symbol, i.e.,  
\[\rho(n)=\hat{\mathbf{s}}(n)=\arg\min_{\mathbf{s}\in\{-1,+1\}}|s-\hat{r}(n)|^{ 2}.\]  
Thanks to the reference samples received from the decision unit, the adaptive equalizer can track the statistical variations of the channel without wasting the bandwidth for passing any midamble through the channel. However, it might fail if the decision is made in favour of a BPSK symbol opposite to the actually transmitted symbol.
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
## 3 Data Processing and Interpretation
### Ocean Current Measurements  
On the one hand, due to the limited amount of data present, there is no sufficient reason to explain the source of phase imbalance between dual channels clearly. It is impossible to use a purely theoretical model to remove the influence of the phase imbalance. We can only use the phase imbalances of the sea-land junction scene data to subtract the corresponding land scene's phase imbalances to extract the currents. On the other hand, the backscattered echo from the sea surface is mainly caused by Bragg scattering, when the radar incidence angle is between 20\({}^{\circ}\) and 60\({}^{\circ}\). Taking into account the above factors, we selected the following two scenes from the third set of GF-3 ATI data for phase imbalance calibration and current velocity measurement: the L1A-level product in the area near Qingdao, China, obtained by the GF-3 on 29 October 2019. The GF-3 images for current inversion are shown in Figure 13, and the product parameters are shown in Table 8. The wind field data are the ECMWF reanalysis data, with a time resolution of 1 h and a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Causes** & **Effects** & **Importance** \\ \hline Antenna electronic & & \\ miss-pointing & Squint imaging & Main factor \\ \hline Attitude error & Baseline error & Minor factor \\ \hline Antenna phase center & & \\ position error & & \\ \hline Phase mismatch of antenna pattern in range & & \\ \hline Target elevation & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Causes of the space-varying of the phase imbalance in GF-3 ATI mode.
## 3 Results
### Results
#### 3.2.2 Influence of Data Characteristics  
For the two datasets, the relative performance of our algorithm was different: for ml-Small, it showed a higher performance than the baseline algorithms; however, it did not show the same for ml-1m. Therefore, we analyzed the characteristics of the two datasets.  
Table 5 shows the distribution of the number of users who watched a common movie.  
The number of users for ml-1m was 10 times higher than that for ml-Small, although the total number of movies was 3666, which was relatively small compared to the 9066 for ml-Small (Table 2). Additionally, the average number of users who watched the same movie from the 9066 movies was 11.03, while the average number of users who watched the same movie from the 3666 movies was 272.07. In other words, the movies that users watched were much more concentrated in ml-1m. These results showed that the characteristics of the two datasets were completely different.  
We calculated the sparsity according to the following formula:  
\[sparsity=1-\frac{|interactions|}{|all\ possible\ interactions|}. \tag{14}\]  
The sparsity values of the ml-Small and ml-1m datasets were 98.4 and 95.5%, respectively. Accordingly, we made the following assumptions. Owing to the nature of our method, which only included the preference--and not the ID--of a user in the input vectors, a good performance could only be achieved when the preference of each user was firmly distinguished. We inferred that the relative performance was low for ml-1m because the watching histories of the users had been duplicated to the extent that the extracted user profiles did not show a significant difference.  
To prove this, we tried to adjust the ml-1m dataset. Figure 6 shows the number of users who watched each movie in the ml-1m dataset. The most watched movie was watched by 3428 of the 6040 users. We assumed that such popular movies were so popular that they would result in more indistinguishable user profiles. We created a dataset named ml-1m-3 sig-out by excluding the 78 most watched movies that were outside 3\(\sigma\) from the mean (movies watched by more than 1427 users); this operation removed 152,841 interactions from the dataset.  
Table 6 shows the comparison of performances for the new dataset when the hyperparameters are same. Our method showed a higher performance when extremely popular movies were excluded. This implied that the user profiling in this study would be more effective in situations wherein the item selection of each user was very different. Additionally, for practical applications, it would be helpful to exclude popular items in the dataset to extract distinctive user preferences.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **ml-Small** & **ml-1m** \\ \hline Mean & 11.03 & 272.07 \\ Standard deviation (\(\sigma\)) & 24.05 & 385.07 \\ Min & 1 & 1 \\
25\% & 1 & 34 \\
50\% & 3 & 127 \\
75\% & 9 & 353 \\ Max & 341 & 3428 \\ \hline \hline \end{tabular}
\end{table}
Table 5: User per Movie.
## 3 Results and Discussion
### Linear Ordering Analysis for Building the Quality Ranking of Table Grapes  
Linear ordering analysis was used to build quality rankings of table grapes for the first time. First, the complex phenomena in the analysis was chosen as quality of red grapes expressed as individual phenolic content, TPC, ACN, and/or DPPH values. The specific variables used in the method were chosen based on the cluster analysis performed in order to separate groups of normalized variables, resulting in similar ordering of the objects (grape varieties), and then, one variable from each cluster was chosen (with the smallest distance in the cluster). There were: taxifolin (T), R, M, Q, and TPC. The quality of table grapes was also expressed by color coordinates (the second ranking). Similarly, cluster  
Figure 1: Projections of the variables (**a**) and scores (**b**) onto the factor plane defined by principal components (PC1 and PC2). Note: SA—sinapic acid; FA—ferulic acid; hCynA—hydroxycimanic acid; Res—resveratrol; Q—quercetin; T—taxifolin; M—myricetin; K—kaemperlo; H—hesperidin; Er—eriodictyol; R—ruin; L*—lightness, a* and b*—color coordinates, C*—chroma, h—hue angle; TPC—total polyphenol content; ACN—total anthocyanin content; DPPH—antioxidant activity. analysis was applied, and a*, L*, and h were chosen. Since a different effect of the chosen variables was observed on the quality of table grapes (L* color coordinates were marked as destimulants-the higher the values, the lower place in ranking--other variables were marked as stimulants), the normalization of the data was applied according the equations (1 and 2). The aggregation procedure (AM) was based 'not on the pattern objects' (arithmetic means of normalized values). Table 4 presents results (AM values and ranks) of linear ordering rankings for the chosen variables. The higher the value of AM, the higher the quality of table grapes. The correlation between results of these two rankings (rank) was statistically significant, as the Tau Kendall coefficient equaled 0.51 with \(p=0.000\). The AM values could be easily calculated based on the equations (1-3), using the values of color coordinates (L*, a* and h), and the rank of the grape variety, in the quality hierarchy of other grape varieties, could be determined. Since, there is significant correlation between the two rankings (Table 4), simple color measurements could give us information on the phenolic content. The grape varieties with the AM values above 0.7 (calculated from L*, a*, and h color coordinates) will show high phenolic content, grapes with AM values from 0.4 to 0.7 will show moderate phenolic content, and grape varieties with AM below 0.4 will show low phenolic content.
## 3 Methodology
### In-Line Dimensional Inspection
#### 3.2.3 Mesh Registration  
It is imperative for our system that the scanned triangular mesh \(M\) is represented in the same coordinate system \(W\) of the CAD reference \(\mathcal{C}\). The mesh registration process computes the rigid transformation \(T_{f}\in SE(3)\) that maps the mesh coordinate system \(W_{M}\) to the global coordinate system \(W\) i.e., \(T_{f}(W_{M})=W\). The rigid transformation \(T_{f}\) is computed by minimizing the distance between the transformed mesh and the reference CAD model:  
\[\begin{split} T_{f}=\arg\min&\sum_{x_{i}\in M}d(T_ {f}(x_{i}),\mathcal{C})\\ s.t.& T_{f}\in SE(3)\end{split} \tag{2}\]  
where \(d(T_{f}(x_{i}),\mathcal{C})\) is the closest distance from the point \(T_{f}(x_{i})\) to the reference \(\mathcal{C}\). To solve the minimization problem in Equation (2), the Iterative Closest Point (ICP) algorithm is implemented [32]. The ICP algorithm, transforms the previously defined minimization problem into the following equivalent one:  
\[\begin{split} T_{icp}=\arg\min&\sum_{x_{i}\in M}d(( T_{icp}\circ T_{0})(x_{i}),\mathcal{C})\\ s.t.& T_{icp}\in SE(3)\end{split} \tag{3}\]  
where \(T_{f}=T_{icp}\circ T_{0}\). To avoid local minima, the ICP algorithm requires an initial solution \(T_{0}\) such that \(T_{0}(W_{0})\) is close to the optimal solution \(W\). \(T_{0}\) has been previously computed in the planning step (Equation (1)).  
In optical-based dimensional inspection, selection of reference geometries for mesh registration is crucial for adequate estimation of datums and measurements [9; 10]. Therefore, the registration of the scanned mesh \(M\) is performed using only the punch zone of the workpiece, which characterizes the revolution axis \(A\). Figure 7  plots the results of the registration process. The colormap shows the distance from the scanned mesh \(M\) to the reference CAD \(\mathcal{C}\), with green zones indicating closeness between the models (\(d(x_{i},\mathcal{C})\approx 0\) mm), and red and blue zones indicating remoteness (\(d(x_{i},\mathcal{C})>0.5\) mm).  
Figure 7: Mesh registration results. The colormap shows the signed distance from the scanned mesh \(M\) to the CAD model \(\mathcal{C}\). (**a**) Initial guess from np-align (Figure 2c); (**b**) Iterative Closest Point (ICP) mesh registration.  
To save computational expenses, the distance \(d(T_{f}(x_{i}),\mathcal{C})\) is computed by previously meshing the CAD reference. This is done because we have observed from our experiments that computing point-to-CAD distance is more time-consuming than point-to-mesh distance.
## Case Study
### Network Determination and Training  
#### 4.2.1 Training Strategies  
In this study, the design and training of neural networks are conducted using the Deep Network Designer and Deep Learning Toolbox 14.5 toolboxes within MATLAB. As for computer hardware specifications, the CPU comprises an Intel(R) Core (TM) i7-9750H processor with 6 cores and 12 threads, operating at 2.60 GHz. Additionally, the GPU employed is an NVIDIA GeForce GTX 1650.  
The number of parameters of the target neural network can be estimated according to Equation (10). In the case of a two-layer LSTM network with 128 hidden units and an input data dimension of 203, the total number of parameters to be estimated is 339,968, i.e., \(2\times 4\)\(\times\) (128 \(\times\) (128 + 203) + 128). The Adam optimizer is employed to update model parameters, leveraging its advantage of adaptive learning rates. It only requires the setting of an initial learning rate, thus alleviating the need for any manual adjustment of learning rates during the training process. However, it is not advisable to set the initial learning rate too high, as this may lead to convergence to local optima.  
In this study, the initial learning rate is set to 0.001. The learning rate strategy is configured as piecewise, with a learning rate drop period of 50 and a learning rate drop factor of 0.5. The data shuffling strategy is set to shuffle once per epoch. Additionally, based on existing research experiences, the maximum number of training epochs, denoted as MaxEpochs, is set to 200, and the amount of data used per iteration, denoted as MiniBatchSize, is set to 20. Assuming that there are 100 samples in the dataset, it can be divided into 5 iterations per training epoch, i.e., 100 divided by 20 equals 5. The total number of iterations is 1000. The iterative steps for updating model parameters can be summarized as follows:  
Step 1: Build the LSTM model and initialize its parameters. Select 5 samples as the first batch;  
Step 2: Gradually propagate neural network information forward and downward through layers to obtain the prediction;  
Step 3: Calculate the loss function value based on the prediction and the theoretical value. Perform gradient descent and update iterative model parameters;  
Step 4: Input the second batch of sample data. Continue model parameter iteration according to Step 2 and Step 3;  
Step 5: Cycle through inputting each batch of sample data until all batches in the dataset have been input and updated. At this point, one epoch or one round of model training is completed;Step 6: Iterate through Steps 1 to 5 until either the maximum number of training epochs is reached or the training error reaches its minimum value. Upon reaching this criterion, the model training concludes, and the model becomes ready for response prediction.
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 4 AIoT Applications in Aquaculture
### Fish Behaviour Detection
Reinforcement learning approaches, such as Deep Networks, have also been explored to simulate schooling behaviour, allowing for studying group interactions without predefined rule-based models. These simulations successfully reproduce ordered formations within fish populations and hold potential for real-world applications in optimising aquaculture stocking densities and welfare assessments [62]. Acoustic and echogram-based monitoring methods offer an alternative to vision-based systems, particularly in environments with poor visibility. EchoBERT, initially developed for disease detection, has also been applied for behaviour analysis, utilising echogram data to identify stress-induced behavioural changes [132]. Similarly, acoustic imaging has been used to monitor fish swarm activity, achieving high accuracy in detecting abnormal behaviours [129]. These methods provide a non-intrusive solution for continuous welfare monitoring, though further improvements in signal processing and environmental adaptability are needed to enhance their robustness across diverse aquaculture settings. By integrating AI-driven models, acoustic sensors, and IoT-enabled monitoring systems, fish behaviour detection is becoming increasingly precise and scalable. Future advancements will likely focus on improving model interpretability, reducing computational complexity for edge computing applications, and enhancing system adaptability to varying aquaculture environments. The continuous refinement of these technologies will contribute to more efficient and sustainable fish farming practices, ensuring early intervention and improved stock management.
## 2 Taxonomy of DDoS Attacks
### DDoS Attack Architecture
#### 2.2.2 Protocol-Based DDoS Attacks  
The protocol-based attack is often referred to as the \"network-layer attack\". Rather than solely relying on overwhelming traffic volume, as the volumetric base attack does, it takes advantage of the protocol stack's layer 3 and layer 4 weaknesses to render the target server inaccessible. Existing server resources as well as other resources, such as firewalls, are consumed by this kind of attack. This attack is therefore frequently referred to as a \"resource depletion attack\" [30]. The attack magnitude is expressed in packets per second (pps). Examples include synchronization (SYN) floods, ping of death, transmission control protocol (TCP) floods, TCP-SYN floods, and their variations.
## 2 Related Work
### Data Fusion  
Data fusion is used for vehicle localization both in the autonomous [12,13] and cooperative modes [6]. While GPS measurements are considered the primary source of location information, additional sources of information, such as on-board sensors and cameras as well as digital maps, were used to compensate for GPS errors [12]. The use of different flavors of Kalman filters for location estimation via the fusion of vehicle sensors and GPS measurements was investigated [13].  
Landmarks and map information were used as auxiliary information sources to be fused with a vehicle's own motion measurements. Vehicles can communicate with fixed landmarks that are assumed to be integrated into the road infrastructure in order to measure the Angle-of-Arrival and the Round-Trip Time-of-Flight [14]. Vehicles then couple this information with their own movement dynamics in order to produce an extended Kalman filter that estimates the vehicle's position. A Monte Carlo localization scheme was adopted for vehicle position estimation via the fusion of LIDAR and vehicle odometry measurements [15]. LIDAR (Light Detection and Ranging) measurements were used to provide an abstraction of the environment in order to account for the constrained vehicle movement. Map and lane abstractions were also used for vehicle localization, with vision sensors used to extract the lane markings [16].  
Fusion of different localization techniques has been proposed in order to improve localization accuracy and provide reliable quantification of the accuracy of estimates made by different sources [17]. The authors claim that having enough knowledge about several localization techniques can enable the system to choose the best suitable technique or ensemble techniques for the current time, situation, application and objective.
## 2 Methods
### Change Detection--Damage Assessment
It ranges from single buildings for the availability of very high spatial resolution SAR data (e.g., High Resolution SpotLight TerraSAR-X imagery, _ca._ 1.0 m spatial resolution) to city blocks if data from lower spatial resolution SAR sensors is available (e.g., ERS, ENVISAT ASAR, _ca._ 30 m spatial resolution; _cf._ Table 2). In the absence of geographical information data, pre-event optical EO data can be used to identify the important segments of the AoI (e.g., city blocks). [PERSON] [74] proposed three damage classes, which were calculated on the average coherence change index for each segment. The author reported good visual correlation with damage maps based on IKONOS data.
## 2 Related Studies
It solves the problem with RRH switching-operation activation with a greedy algorithm and subchannel assignment with a Lagrangian multiplier. In addition, there is a need for location-based services to provide high-quality services to users. In order to address the issues, user mobility prediction has been studied [13; 14; 15]. In [13], an improved Markov algorithm is proposed to predict the user's next location from the trajectory. It predicts the user's next location using the history of the user's trajectory consisting of time-interval-representative location pairs. In [14], forecasting of a mobility model with a neural network is reported and analyzed in comparison with real mobility data; the mobility model is employed in an urban mobile ad hoc network and a back-propagation neural network is used for time-series prediction. In [15], predictions based on Markov models for vehicle routes in urban settings are developed; they consider not only dynamic traffic conditions in the urban area that affect the route selection, but also mobile taxi contexts, and separates roads into several segments. As this characterizes correlations between routes of vehicles and traffic conditions, trace-driven mobility patterns are extracted. As the number of UEs increases continuously, more BSs have been deployed to satisfy the increasing data demands. The BS switching operations have been actively underway to address energy issues in densely deployed BS environments. However, previous works mainly focus on defining the problem to maximize the energy efficiency through optimization techniques. In the realistic network environment, traffic conditions continuously change according to user mobility. Thus, BS switching operation is needed considering user mobility to provide energy-efficient, high-quality services in time-varying traffic conditions. In addition, the previous works mainly have interest in the pedestrians' mobility. However, we consider the users' mobility in vehicles, which have the consistent patterns and higher speed than pedestrians. With consideration of vehicular mobility, we propose RRH switching operation in the H-CRAN architecture as the BS switching operation. To improve energy efficiency in H-CRAN with high mobility environments, we propose that an energy-efficient power algorithm with mobility prediction provides RRH switching operation based on the predicted mobility. The RRH switching operation controls the activation and deactivation of RRHs according to the traffic. If more RRHs are activated when there are reductions in UEs according to the traffic or mobility in the cell, RRHs are switched off to increase the energy efficiency. Conversely, if there are UEs that cannot receive service owing to an increasing traffic in the cell,RRHs are switched on. With user mobility characteristics in the process of RRH switching, the focus is on reducing the number of users serviced by the suboptimal RRHs. We also present low-complexity power allocations that can be applied even in high-mobility environments.