esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Results and Analysis
#### 4.5.2 Ablation Study  
To assess the individual contributions of the IGWO optimization algorithm and the multi-head attention mechanism to the overall performance of the proposed IGWO-MALSTM model, we conducted a systematic ablation study. We designed four model variants using the same dataset (closing prices of rebar and HRC futures from the Shanghai Futures Exchange between December 2020 and 15 March 2023), identical training/testing splits (80% training, 20% testing), and consistent training procedures: LSTM, IGWO-LSTM, MALSTM and IGWO-MALSTM.  
As shown in Table 9, the base LSTM model yielded an MSE of 4.52 and an MAE of 1.587. Adding IGWO optimization improved both metrics to 3.14 and 1.383, highlighting the importance of effective hyperparameter tuning. In contrast, the MALSTM model, which incorporates multi-head attention but lacks parameter optimization, performed worse with an MSE of 5.37 and an MAE of 1.982, indicating that attention mechanisms alone cannot compensate for suboptimal configurations.  
The full IGWO-MALSTM model achieved the best results, with the lowest MSE of 2.63, MAE of 1.091, and MAPE of 2.79%, along with the highest R\({}^{2}\) score of 0.997. These findings demonstrate that IGWO and the multi-head attention mechanism work synergistically to enhance predictive performance.
## 3 Results and Discussion
### Shelf Life Estimation  
Changes in sensory and microbial parameters indicate product quality, potentially affecting the shelf life of fish products [65]. On day 0, the sensory characteristics score was above 8.24 for all parameters. During storage at \(-\)13 and \(-\)20 \({}^{\circ}\)C, the product began to lose its sensory characteristics; however, panelists still scored products with decreased sensory characteristics above 7. The product stored at \(-\)23 \({}^{\circ}\)C showed no significant (\(p<0.05\)) differences throughout the 90-day storage period. At \(-\)23 \({}^{\circ}\)C, test seafood HMR products maintained their sensory characteristics, obtaining a score above 8 from the panelists.  
The physicochemical properties of fish and fishery products correlate with their sensory properties and storage time [66]. Moreover, the microbial activity gives flavors and odors to products during the storage period [67], and the decreases in sensory properties could be caused by changes in microbial parameters (Table 10). The TBC of the HMR product at \(-\)18 and \(-\)13 \({}^{\circ}\)C significantly (\(p<0.05\)) increased during storage time; however, at \(-\)23 \({}^{\circ}\)C, there was no significant change (\(p<0.05\)). These results suggest that lower temperatures decrease microbial activity in the product, thereby increasing shelf life.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Temperature** & **Day** & **Color** & **Flavor** & **Odor** & **Texture** & **Overall Acceptance** \\ \hline \multirow{8}{*}{\(-\)13 \({}^{\circ}\)C} & 0 & 8.33 \(\pm\) 0.47 \({}^{\text{a}}\) & 8.38 \(\pm\) 0.49 \({}^{\text{a}}\) & 8.38 \(\pm\) 0.49 \({}^{\text{a}}\) & 8.24 \(\pm\) 0  
Moreover, the microbial quality of seafood can be classified into the following three TBC value-based groups: satisfactory (TBC \(<\) 5 log CFU/g), acceptable (\(\geq\)5 TBC \(<\) 6 log CFU/g), and unsatisfactory (TBC \(\geq\) 6 log CFU/g) [68]. Therefore, based on the TBC results of this study, the test seafood HMR product could be classified as satisfactory.  
The test seafood HMR product's expiry date and overall acceptance values were established using TBC and the simulator program. It identified the shelf life of test seafood HMR products was 29.62 months when the sensory evaluation, as the main criteria, had the highest statistical value. However, the final shelf life was set to 24 months by multiplying it with the safety factor (0.82), which considers temperature changes during production, purchase, storage, and consumption by the consumer; hence, the quality of HMR products could be maintained for 24 months. The shelf life of fishery products is influenced by temperature and limited by biochemical and microbiological changes. Low temperatures may reduce microbial activity, enzymatic autolysis, and oxidation in fishery products, thereby prolonging shelf life [69; 70; 71; 72]. Furthermore, although frozen storage shelf life has constraints, it may vary from a few weeks to years [73].
## 4 Simulation and Discussion
Figure 10 shows the convergence behavior of the proposed algorithm under different SNRs. Over a wide range of SNRs, the proposed algorithm exhibits good convergence, with convergence achieved within two to six iterations. However, as the SNR increases, the number of iterations required for convergence also increases, suggesting that higher SNR scenarios are more computationally demanding. This may result in longer processing times; thus, real-time applications may require adjustments to optimize for convergence  
Figure 8: The secrecy rate under the scenario of a moving UAV with processing delay and angular estimation errors. Figure 9: The secrecy rate under the scenario of a moving UAV with processing delay and angular estimation errors. time. As the SNR increases, the number of iterations required for convergence increases, due to the fixed choice of the initial iteration value. Therefore, in practical applications, the initial iteration value can be flexibly chosen based on the SNR to accelerate convergence. The proposed algorithm, while effective in improving the secrecy rate and enhancing the physical layer security, does have certain limitations. Firstly, the algorithm requires iterative optimization, as there is no closed-form analytical expression available for the problem. This iterative process increases the computational complexity, especially when the number of iterations or system parameters is large. Additionally, the algorithm necessitates the availability of partial channel state information (CSI) of both the legitimate user ([PERSON]) and the eavesdropper ([PERSON]), which may not always be feasible in practical systems due to limited feedback or imperfect channel estimation. Furthermore, the algorithm's performance might degrade under extreme conditions, such as a very low SNR, high mobility scenarios, or when the system operates with inaccurate angle of arrival (AoA) estimations. Future research will focus on addressing these limitations. One direction is to develop more efficient optimization techniques, potentially by leveraging machine learning approaches or adopting adaptive methods that reduce the dependency on iterative processes. Additionally, efforts will be made to enhance the algorithm's robustness against incomplete or imperfect CSI, including the development of robust estimation and feedback protocols. Another avenue for improvement is extending the algorithm to handle dynamic environments, such as fast-moving UAVs or networks with fluctuating channel conditions, to ensure reliable security performance across diverse real-world applications.
# Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent
## 2 Project Description
### Gaussian Module (GM)  
In this module, the entries are mapped into another representation space see Figure 1, to facilitate the classification of non-linearly separable patterns by the SVM. The mapping is done through the use of kernels. Allowing to transform the input space of \(N\) into a space of \(K\) dimensions. In this work, it was implemented the Gaussian, also called Radial-Basis Function (RBF), kernel which can be expressed as  
\[v_{j}(n)=e^{\frac{dist_{j}(n)}{2\sigma^{2}}}=e^{\frac{\sum_{i=1}^{K}(x_{i}(n) -x_{i}^{2}(n))^{2}}{2\sigma^{2}}} \tag{2}\]  
where \(c_{ji}(n)\) is the \(j\)-th center of the gaussian function associated with the \(i\)-th input, \(x_{i}(n)\) the \(i\)-th input, \(\sigma^{2}\) the variance of each kernel function, \(dist_{j}(n)\) the \(j\)-th distance from each input to the center, \(v_{j}(n)\) the \(j\)-th exit of GM in the \(n\)-th iteration and \(v_{0}(n)\) the bias.  
To maximize the throughput of the system and reduce computation time of Equation (2), most operations are executed in parallel. With the exception of summation, which is executed using a tree adder. To reduce resource consumption and decrease execution time in the FPGA, the exponential function is implemented through the use of Look-Up Tables (LUTs), where each address of the LUT stores a value of the exponential. This implementation choice implies in a delay of one sample in the algorithm execution. However, the impact on throughput is lower than if the exponential function was implemented using specific hardware circuitry.  
The \(j\)-th distance \(dist_{j}(n)\) shown in Equation (2), is computed in parallel as in Figure 2. It is important to note that all arithmetic operations are performed in parallel which increases performance regarding throughput.  
Figure 1: High-level system.  
Figure 2: Distance computation.  
Having the value of \(dist_{j}(n)\), the calculation of the Equation (2) is done using Read-Only Memory (ROM), composed of LUTs of depth 9. The circuit to convert the distance to memory addresses is illustrated in Figure 3. Being \(max\), the upper edge of the function domain and normalizer the circuit responsible for converting from distance metric to memory address. To discretize the gaussian a maximum value for the distance of 4 and a minimum of 0 was considered. Within that range the gaussian was evaluated at steps of 0.01 and the values were stored on the ROM memory, which resulted in 401 values being stored. The normalizer then divides the output of the mux with the discretization step.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 3 Biomedical Applications of Magnetic-Plasmonic Nanocomposites
### Drug Delivery and Targeting  
Multimodal magnetic nanoparticles have long been the subject of investigation for use as drug delivery vehicles, and many strategies for their synthesis, drug loading and release have been examined [67]. However, comparatively, few comprehensive studies on magnetic-plasmonic based drug delivery systems have been carried out, despite many potential theranostic applications which these systems would have. This could be due in part to a number of difficulties in the complexity of making these materials, and how biocompatible the resulting systems may be. For example, a balance must be drawn between the complexity of the nanomaterial and the time and cost of its synthesis with the increase in efficacy of the cancer treatment it can provide. Not only this, but the size and morphology of the magnetic plasmonic nanoparticles needs to be precisely controlled in order to optimise uptake in vivo--too small (<50 nm) and the nanoparticles are washed out of the body, too large (>300 nm) and they accumulate in the liver and spleen [18].  
In spite of these challenges, such multimodal nanoparticles comprising of anisotropically shaped Fe\({}_{3}\)O\({}_{4}\)@Au were prepared with a biocompatible mesoporous silica shell, which also allowed anticancer drug delivery [45]. The doxorubicin containing pores were capped with double stranded DNA, which decomposed under NIR light, causing the heating of the gold component of the nanoparticle. The magnetite, in this case, provided magnetic guidance, which was shown to increase uptake of the nanoparticles into HeLa cells (Figure 8).
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 4 ICN Migration Towards IoT Scenarios
### Event-Triggered Data Communication Support  
While Long-lived Interest can be applied to support both local and wide area operation, unsolicited data communication from authorized sources to report specific events (e.g., machine malfunctioning) may only be feasible in local areas, due to the multipath, broadcast nature that is the basis of the ICN/NDN design.  
Unsolicited data communications can be supported by leveraging ICN/NDN via a push-based Data packet model, such as pData, provided that data producers and consumers perform a negotiation first.  
An example for this situation can be a demand/response energy control use-case where smart meters would issue _pData_ packets to notify authorized entities about unusual peaks in energy consumption.
## 3 Formalization of Dima Optimization
### Aircraft Constraints
#### 3.2.4 Performance Constraints  
Performance constraints can include bandwidth constraints and reliability constraints.  
For bandwidth constraints, each virtual link requires a minimum bandwidth to guarantee a valid communication. If \(F_{s}\) communicates with \(F_{t}\), cables connecting the mapped devices should reserve enough bandwidth. Furthermore, the total reserved bandwidth of a cable should not exceed its capability. This yields the following constraint,  
\[\sum_{\begin{subarray}{c}F_{s}\in\mathcal{F}\\ \mathcal{V}(F)\
eq\mathcal{O}\end{subarray}}B_{F_{s}}\cdot V_{ij}^{s}\leq B_{ cable}\cdot\Vdash(D_{i},D_{j}),\quad\forall D_{i},D_{j}\in\mathcal{D}, \tag{7}\]  
where \(B_{F_{s}}\) denotes the bandwidth requirement of the virtual link sourcing from \(F_{s}\) and \(B_{cable}\) denotes the bandwidth of the cable.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 6 Conclusions  
DC-DC converters are critical for generating, transmitting, and storing power in satellites. This paper classifies the applications of DC-DC converters in satellites and provides a state-of-the-art review of isolated and non-isolated DC-DC converters for a range of satellite applications. The topologies are compared based on practical parameters like modularity, efficiency, reliability, redundancy, and power density. Electric propulsion is a promising future technology owing to its better fuel efficiency compared to chemical propulsion. Thus, various sources and DC-DC converters for electric propulsion are compared and contrasted. The research gaps pertaining to a variety of current and prospective satellite application domains, such as space-based solar power and wireless power transmission for satellite power distribution, are also highlighted in this paper. Conceptualization, R.R and A.M.M. ; methodology, R.R. ; formal analysis, R.R. ; investigation, R.R. ; resources, R.R. ; data curation, R.R. ; writing--original draft preparation, R.R. and A.M.M. ; writing--review and editing, R.R. and A.M.M. ; supervision, A.M.M. ; project administration, A.M.M. ; funding acquisition, A.M.M. All authors have read and agreed to the published version of the manuscript. This publication was supported by Qatar University through the Graduate Assistantship program. The statements made herein are the sole responsibility of the authors. No new data were created or analyzed in this study.
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 3 Multiple-Input Multiple-Output (MIMO) Radar Ambiguity Function
Making use of (3) and (16), the cross correlation function can be expressed as:  
\[r_{m,m^{\prime}}^{x}(\tau)=\sum\limits_{q=1}^{Q}\sum\limits_{q=1}^{Q}x^{rec}( \tau-q^{\prime}-q)\Delta t,\{c_{m,q}-c_{m^{\prime},q^{\prime}}\}\Delta f)e^{i \Omega_{m,q}}e^{j2\pi\Delta f/(c_{m,q}-c_{m^{\prime},q^{\prime}})\Delta t}e^{ j2\pi\Delta f/c_{m^{\prime},q^{\prime}}\tau} \tag{20}\]  
and the function \(\Omega(\tau,f,f^{\prime})\) can be expressed as:  
\[\Omega(\tau,f,f^{\prime})=\sum\limits_{m,m^{\prime}=1}^{M}\sum\limits_{q,q^{ \prime}=1}^{Q}x^{rec}(\tau-q^{\prime}-q)\Delta t,\{c_{m,q}-c_{m^{\prime},q^{ \prime}}\}\Delta f)e^{i\Omega_{m,q}}e^{j2\pi\Delta f/(c_{m,q}-c_{m^{\prime},q^ {\prime}})\Delta t}e^{j2\pi\Delta f/c_{m^{\prime},q^{\prime}}\tau}e^{j2\pi(fm- f^{\prime}m^{\prime})} \tag{21}\]  
where \(\chi^{rec}(\tau,v)\) describes the ambiguity function of \(u(t)\), which can be denoted by:  
\[\chi^{rec}(\tau,v)=\left\{ \begin{array}{ll}\frac{\Delta t-|\tau|}{\Delta t} \sin c(v(\Delta t-|\tau|))e^{j\pi v(\tau+\Delta t)},&\text{if}|\tau|<\Delta t \\ 0,&\text{otherwise}\end{array} \right. \tag{22}\]  
For \(M=1\), the function is the special case of the SIMO radar. For the general case of the MIMO radar \(M>1\), not only the auto-correlation functions but also the cross-correlation functions between the waveforms should be taken into account such that the function (22) is sharp around \(\{(\tau,f,f^{\prime})\big{|}\tau=0,f=f^{\prime}\}\).
## 2 Materials and Methods
### Sample Collection and Processing  
At the monitoring sites, there are three river sections: the outlet of the underground river (Q1), the sections without submerged plants (H1), and the sections with submerged plants (H2) (Figure 1d). Cloud coverage can have a significant impact on many environmental variables, including temperature and solar radiation [29]. Therefore, this study was conducted in favorable weather conditions, a gentle climate, consistent water flow, and pre-rainy season when water quality is stable. The study involved a three-day and two-night monitoring period from May 24 to 26, 2023, to observe the water's physical and chemical indicators and continuously measure CO\({}_{2}\) gas levels at the water-air interface. At each of the three sites (Q1, H1, and H2), a multiparameter meter (Ultrameter-II (6P), Myron L Company, Hilliard, OH, USA) was used to monitor the T (water temperature), EC (electrical conductivity), Ta (air temperature), and DO (dissolved oxygen) concentration of the river water at 4-h intervals. The specific EC, temperature, pH, and DO were 0.1 \({}^{\circ}\)C, 0.01 mg/L, and 1 \(\upmu\)s/cm, respectively. River water samples were collected every four hours from these three sites, after which the samples were placed in a 2 L polyethylene bottle. An inductively coupled plasma emission spectrometer (Intrepid II XSP, Thermo Fisher Scientific, Waltham, MA, USA) was used in the laboratory to measure the cations (K\({}^{+}\), Na\({}^{+}\), Ca\({}^{2+}\), and Mg\({}^{2+}\)) in the water samples after acidification with HNO\({}_{3}\)\({}^{-}\) to pH < 2. Additionally, ion chromatography (861 advanced compact IC Metrohm, Herisau, Switzerland) was employed to determine the anion content (F\({}^{-}\), Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\)). The sulfur content and total alkalinity were measured using the silicon molybdenum yellow colorimetric method [30]. The analysis was conducted at the Kunming General Survey of Natural Resources Center, China Geological Survey.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping
### SLAM-Based Autonomous Positioning and Navigation System  
The autonomous positioning of quad-rotor UAVs is a requisite for indoor obstacle avoidance, path planning, and grasping. Localization is the primary challenge; currently, quad-rotor UAV localization primarily falls into two categories [31; 32]: obtaining position information through the UAV's perception or relying on external devices, such as the  
Figure 4: Control system of the aerial manipulator.  
motion capture system (Vicon). However, the Vicon systems are expensive and require specialized environments, restricting the application range of aerial manipulators.  
Therefore, we integrated low-cost sensor data, such as LiDAR, IMU, with a laser altimeter module. SLAM [33; 34] was utilized to achieve high-precision pose estimation and environment mapping and realize indoor autonomous navigation for aerial manipulators.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 3 Materials and Methods
### Vegetation Cover Evaluation Based on Certainty Factor  
#### 3.4.1 Certainty Factor Analysis  
The Certainty Factor can be used to analyze the sensitivity of factors affecting the occurrence of an event, and is attributed to a binary statistical analysis method [58; 59]. In studies exploring ecological responses due to environmental factors, bivariate and multivariate statistical models such as the information value method have been progressively and widely used by researchers [60; 61]. Furthermore, CF has been used by many scholars for environmental risk management owing to its spatial predictive ability and its capability to capture complex nonlinear relationships, and thus improve the accuracy of prediction compared with correlation coefficients [62; 63]. When using CF to examine the connection between vegetation cover and topographic conditions, it is important to understand that vegetation cover is the dependent variable (i.e., event) and topographic factors are the independent variable. In summary, CF is effectively applicable for the study and is calculated as follows:  
\[\text{CF}=\begin{cases}\frac{\text{PPA}-\text{PPs}}{\text{PPA}(1-\text{PPs})} if:\text{PPA}\geq\text{PPs}\\ \frac{\text{PPA}-\text{PPs}}{\text{PPs}(1-\text{PPs})}if:\text{PPA}<\text{PPs} \end{cases} \tag{3}\]  
where PPA is the conditional probability of the event occurring in Grade A, and PPs is the prior probability of the event occurring in the entire study area. Taking this study as an example, LSW is categorized into multiple categories according to its numerical size, such as Grade A, B, and C. PPA is the ratio of the area of vegetation to the total area in Grade A, while PPs is the ratio of the overall vegetation area to the total area of the study region. Similarly, we can derive corresponding CF values for both Grade B and Grade C.  
According to the formula, the range of CF coefficients is [-1; 1], which can be divided into three categories by the size of the CF values: (1) When the CF value is very close to 0, such cases make it difficult to identify whether vegetation cover will occur; (2) When the CF is positive, it means that the certainty of vegetation cover occurrence is high. As CF gets closer to 1, it indicates that this grid cell is more likely to be a susceptible area for vegetation cover; (3) When the CF value takes a negative value, it means that the certainty of vegetation cover occurrence is low. The closer the CF is to \(-1\), the less vegetation cover is likely to occur in this cell.