esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Construction
### Annotation Statistics
#### 3.4.2 Bounding Box Resolution  
Since the monocular camera has perspective characteristics, the resolution (total pixels in the bounding-box) of the target close to the camera is high, and the resolution of the target far away from the camera is low. The camera position of the MOTChallenge is generally low, so it will focus more on the targets closer to the camera, and the distant targets will be ignored appropriately. NPSPT sets the camera up high to keep an eye on all players both near and far at the same time. Figure 6a illustrates three points: (1) the proportion of small target bounding-box in NPSPT is significantly large, (2) the bounding-box sizes of targets in NPSPT are relatively small, (3) the resolution size distribution of MOTChallenge is also relatively uniform.  
In addition, the resolution of the images in NPSPT are generally larger than that of MOTChallenge. Therefore, as shown in Figure 6c, the target image ratios (bounding box size relative to size of the whole image) of the tracking target of NPSPT are significantly  
Figure 4: These two subfigures show aspect ratio of bounding boxes between two consecutive annotated frames. (**a**) Comparison between our dataset and MOTChallenge dataset, both datasets are open source adn recorded by single cameras. (**b**) Comparison between subdatasets of our datasets.  
Figure 5: This two subfigures show distance between center of objects between two consecutive annotated frames. (**a**) Comparison between our dataset and MOTChallenge dataset. (**b**) Comparison between subdatasets of our datasets.  
smaller than that of MOTChallenge. Such a small target image ratios of NPSPT place high demands on the feature extraction capability of the tracking algorithm.
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 5 Results and Analysis
### Results for different Carrier Frequencies Using GA  
In Table 7, before BS elimination, the final setup showcases improved coverage values of 0.9618 for 28 GHz and 1 for 3.6 GHz, along with increased capacities of 0.9305 for 28 GHz and 0.9166 for 3.6 GHz. The elimination of specific BS, despite reducing the count to 60 for 28 GHz and 131 for 3.6 GHz, maintains coverage at 0.9618 for 28 GHz and 1 for 3.6 GHz. Capacities, while slightly decreasing to 0.8784 for 28 GHz and 0.8819 for 3.6 GHz, remain robust. This pattern underscores the effectiveness of the Genetic Algorithm in refining 5G network deployment. The optimization process demonstrates the algorithm's capability to streamline the number of base stations, showcasing an efficient balance between reduced infrastructure and improved coverage and capacity.  
Figure 8 presents two significant aspects of the GA optimization in the 5G RAN planning scenario at 28 GHz: Figure 8a shows the base stations after elimination and Figure 8b the Voronoi Plot. Figure 8a displays the base stations after elimination; the impact of the GA optimization process is evident, showcasing a reduced number of base stations. The improvement of the GA optimization process is evident, showingcasing a reduced number of base stations.  
stations compared to the initial configuration. Despite the reduction to 60 base stations, the coverage for 28 GHz remains high at 0.9618.
## 3 Results and Discussion
### Validation of Photoautotrophic Medium Optimization  
Afterwards, _C. amblystomatis_ cultures grown with the company's photoautotrophic basal medium were compared to those grown with the resultant photoautotrophic optimized medium. The biomass volumetric productivities, specific growth rates, and biomass production are described in Table 7.  
Cultures grown with the optimized medium registered a higher specific growth rate (0.35 d\({}^{-1}\)) compared to those grown with the basal medium (0.23 d\({}^{-1}\)), offering an improvement of 51.2%. Moreover, 55.8% and 65.8% improvements were registered for global and maximum productivity, respectively. Cultures grown with the optimized medium also achieved a higher maximum biomass concentration (1.91 g\(\cdot\)L\({}^{-1}\)) compared to that obtained with the basal medium, which reached 1.36 g\(\cdot\)L\({}^{-1}\), enabling an improvement of 40.8%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Volumetric Productivity**} & \multicolumn{2}{c}{**Specific Growth Rate**} & \multicolumn{2}{c}{**Biomass Production**} \\  & **g\(\cdot\)L\({}^{-1}\)** & **d\({}^{-1}\)** & **g\(\cdot\)L\({}^{-1}\)** \\ \cline{2-5}  & **Global** & **Maximum** & & \\ \hline Basal medium & 0.12 \(\pm\) 0.00 \({}^{\text{b}}\) & 0.17 \(\pm\) 0.01\({}^{\text{b}}\) & 0.23 \(\pm\) 0.01 \({}^{\text{b}}\) & 1.36 \(\pm\) 0.02 \({}^{\text{b}}\) \\ \hline Optimized medium & 0.19 \(\pm\) 0.01\({}^{\text{a}}\) & 0.27 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.35 \(\pm\) 0.01 \({}^{\text{a}}\) & 1.91 \(\pm\) 0.06 \({}^{\text{a}}\) \\ \hline \% of increment & 55.8 & 65.8 & 51.2 & 40.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum and overall volumetric productivity (g\(\cdot\)L\({}^{-1}\).d\({}^{-1}\)), specific growth rate (d\({}^{-1}\)), and biomass production (g\(\cdot\)L\({}^{-1}\)) of the photoautotrophic cultures grown with the company’s basal medium and optimized medium in 1 L bubble columns. Different letters indicate significant differences between media. Values are given as mean \(\pm\) standard deviation (\(n\) = 3).  
Figure 2: Response surface plots showing the interaction effects of (**a**) other macronutrients (Macro) and (NH2)2 CO; (**b**) micronutrients (Micro) and (NH2)2 CO, and (**c**) Macro and Micro of the photoautotrophic cultures.
# Evaluation and Characterization of Alginate Extracted from Brown Seaweed Collected in the Red Sea
## 2 Materials and Methods
### Biochemical Analysis of Sodium Alginate
#### 2.8.2 Total Proteins Analysis  
The total proteins were tested following [PERSON] et al. [49]. The protein was calculated using bovine albumin serum as the standard and was expressed as %DW.  
2.8.3. Lipid's Analysis  
The total lipids were estimated gravimetrically using chloroform-methanol according to the AOAC methodologies [50].  
2.8.4. Ash Analysis  
The ash content was measured by a gravimetric method using AOAC [39] by burning 1 g of the product taken in a silica crucible and kept in a muffle furnace at 575 \({}^{\circ}\)C for 24 h. After burning, the content was cooled, weighed, and expressed in terms of percentage.  
2.9. Statistical Analysis  
The data attained in this study were analyzed using one-way ANOVA at a 5% significance level, and were expressed as average \(\pm\) SD. Further multiple comparisons by [PERSON]'s grouping test were conducted using Minitab(r) (Version 19) software.  
3.1. Sodium Alginate Yields  
The yield of sodium alginate of the five studied species is presented in Figure 3. The alginate content was significantly different between the species. The highest average was recorded in _T. triquetra_ (22.2 \(\pm\) 0.56% DW), while the lowest average was found in _H. cuneiformis_ (13.3 \(\pm\) 0.52% DW), demonstrating a fluctuation in the alginate yield between the species of 8.9%.  
3.2. Physical Properties of Sodium Alginate  
The results of the measured physical-chemical properties, such as the color, pH, moisture content, and viscosity of sodium alginate, are given in Table 2. The color of the extracted alginate for all of the species was pale-yellow. On the other hand, the alginate content of the selected seaweeds showed highly significant differences between species, as well as for the moisture content and viscosity, while there were no differences in the pH values. The highest level of moisture content was observed in _P. boergesenii_ (13.1 \(\pm\) 0.01% DW) when compared with the other species. The highest alginate viscosity was recorded in _H. cuneiformis_ (134.4 \(\pm\) 0.4 cP), while the lowest one was recorded in _P. boergesenii_ (57.6 \(\pm\) 0.2 cP).  
Figure 3: Extracted yield variations of sodium alginate (% DW) between the examined brown algae species. Different letters indicate a significant difference at the level of \(p\leq\) 0.05.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Filter Module
#### 4.3.1 Parameter Calculation Unit  
This unit calculates the filtering parameters such as \(\beta\) and \(t_{c}\) according to Tables 8-12 of [41]. Based on the quantization parameter values and the BS of the neighboring P block and Q block, referred to \(QP_{p}\) and \(QP_{q}\), respectively, these filtering parameters are calculated. The LUT used to implement the parameter calculation unit has the outputs \(t_{c}\) and \(\beta\). These output values are relative to the inputs BS, \(QP_{p}\) and \(QP_{q}\).
## 3 System Model
### Performance Evaluation Parameters  
Evaluation metrics used for the proposed work are given here:  
* **Average Throughput**: Throughput is the rate at which information was sent through the network.
* **Temperature rise**: Represents the increase in the temperature of each node during data transmission.
* **Energy Consumption**: Energy consumed during the simulation time.
* **Simulation time**: The time period during which a simulation ran over the simulator
* **Average Temperature rise**: Represents the average increase of temperature for the overall network.
* **Average Energy Consumption**: Average energy consumption indicates the average decrease or consumed during the overall network during simulation time.
# An Improved GreenOFDM Scheme for PAPR Reduction
###### Abstract  
In this paper, we propose an improvement to a recent Peak-to-Average Power Ratio (PAPR) reduction technique for Orthogonal Frequency Division Multiplexing (OFDM), the GreenOFDM. This technique, which is inspired by SeLected Mapping (SLM), generates several waveform candidates using a given number of Inverse Fast Fourier Transforms (IFFT), and selects the one with the lowest PAPR for the transmission of the OFDM symbol. For \(U\) IFFTs, GreenOFDM provides better PAPR reduction capabilities than SLM-OFDM as it increases the number of waveform candidates from \(U\) (for SLM-OFDM) to \(U^{2}/4\). In this work, we propose an extension of the GreenOFDM that further increases the number of waveform candidates by a factor of 4 (from \(U^{2}/4\) to \(U^{2}\)), or equivalently reduces by a factor of 2 the number of IFFTs for a same PAPR performance. Compared to SLM-OFDM, the improved GreenOFDM technique reduces the complexity by requiring only the square-root of the number of IFFTs for a same PAPR reduction performance. Furthermore, exciting methods for additional complexity reduction are also implemented and discussed.  
OFDM; PAPR; Selected Mapping; GreenOFDM 2020
# Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa-BiLSTM-Attention Model
## 2 Literature Review
[PERSON] and other scholars proposed a robust, optimised BERT pre-training method (RoBERTa) + a Convolutional Neural Network (CNN) sentiment classification algorithm integrated with a sentiment lexicon, and the experimental results showed that the proposed method achieved improvements in terms of precision, recall, and F1 score [24]. In addition, scholars have explored other BERT variants for natural language processing, such as faster and lighter DPAL-BERT [25], Med-BERT for disease prediction [26], and PAL-BERT, which improves question-and-answer modelling [27]. (5) Sentiment classification based on large language models. Large-scale language models, represented by the GPT models, also play a key role in sentiment classification [28]. These models significantly improve the accuracy of sentiment analysis by leveraging extensive training data and an advanced Transformer architecture with over 100 billion parameters dedicated to linguistic tasks. [PERSON] and [PERSON] explored the effectiveness of the GPT-3 model in addressing imbalances in sentiment data and showed that model accuracy improved by about 12.76% on average on balanced datasets [29]. [PERSON] and other scholars investigated the performance of GPT-3.5 and GPT-4 in Cantonese sentiment analysis in the context of web-based counselling and compared their performance with other mainstream methods, including lexicon-based and machine learning methods. It was found that ChatGPT exhibited superior accuracy among many text analysis techniques and emerged as a promising tool for Cantonese sentiment analysis [30]. In addition, scholars have explored the application of other big models to natural language processing, such as the Megatron-Turing NLG [31] and LaMDA [32] models. In summary, there has been much scholarly discussion about various sentiment classification methods, and the development of various methods with their own advantages and disadvantages is also relatively mature. A sentiment dictionary can provide fast and easy-to-understand annotations, but it cannot deal with contextual and domain-specific vocabulary; deep learning can deal with complex sentiment classification, but it relies on a large amount of annotated data. Based on this, this study supplemented the existing sentiment dictionary with a self-constructed, domain-specific, new word sentiment dictionary based on the SO-PMI algorithm and then used the RoBERTa-BiLSTM-Attention deep learning model for sentiment training, which could effectively make use of the strengths of the sentiment dictionary and deep learning to compensate for the shortcomings of the two and meet the subsequent needs of further analyses of the different stages of the evolution of public opinion sentiment.
## 6 Discussion
### Mechanistic Interpretation of the Findings  
The Squeeze-and-Excitation module acts as an _adaptive channel selector_, amplifying latent signals that co-vary with regional demand shocks while attenuating noise introduced by sparse measurements. Because the recalibration weights are learned end-to-end, the model automatically re-prioritizes features when exogenous conditions shift (e.g., pandemic-induced mobility restrictions), explaining the stability of error curves across matrix-density settings.  
Reinforcement learning, in turn, supplies a _dynamic objective controller_. Instead of hard-coding trade-offs between environmental, economic, and social targets, the Double-DQN layer continuously updates these weights in response to immediate reward feedback. This design choice clarifies why ablation of the RL component harms adaptability more than raw accuracy: the base forecaster still predicts level changes, but fails to re-balance conflicting objectives once the external context drifts.
## 3 Results and Discussion

Table 3: Effect of turmeric addition and storage time on colour on the cross section of duck burgers during storage. An important issue in the production of meat products enriched with additives is to maintain appropriate sensory characteristics so that the enriched products are accepted by the consumer [45]. Sensory quality may be reduced in refrigerated meat products. Lipid autoxidation results, among others, from the high sensitivity of oxidation products to decomposition and reaction with other meat components, and to the complex effect of catalysts and natural antioxidants in the meat product. As a result of the oxidation of meat lipids, many compounds are formed that are responsible for the formation of undesirable aroma, taste, colour, and texture. These include low-molecular volatile substances, short-chain aldehydes, and acids formed from them as a result of oxidation [5]. In the authors' own research, it was noted that the 18-day storage time reduced the sensory quality of duck burgers in all the studied groups; however, a relationship was found between lipid oxidative changes and the sensory characteristics of the meat products. On the last day of the evaluation, the duck burgers with the addition of turmeric paste were rated higher for such characteristics as the desirability of aroma, taste, and juiciness, while the burgers with the addition of powder for their desirable aroma and intensity of taste (Figure 2). The effect of turmeric powder on the colour of meat products was accepted by the panel of judges (Figure 2). In the literature on the subject, there are various reports regarding the acceptability of the colour of meat products with the addition of turmeric. The studies of [PERSON] et al. [17] showed the acceptability of the colour of beef meatballs at the level of 0.2% turmeric powder; with a higher level of powder addition, the products were less acceptable. Additionally, [PERSON] [15] reported a beneficial effect of 3% water-soluble yellow pigment on the colour of breast muscle fillets in chilled chickens. [PERSON] et al. [18] showed that the addition of microcrystalline turmeric reduced the colour acceptance of turmedella, but had no effect on other sensory attributes. [PERSON] et al. [40] noted that the addition of turmeric powder to chorizo sausages in the amount of 2.1% did not lower the colour acceptability scores, but had a negative effect on the taste due to their  
Figure 1: TBARS (mg MDA/kg) value in duck burgers during refrigerated storage. (\(p\)-value in subsequent measurements: group I \(\leq\) 0.001; group II \(\leq\) 0.001; group III \(\leq\) 0.001; group IV \(\leq\) 0.001). Explanations: Group I—control, group II—with turmeric powder, group III—with turmeric extract, group IV—with turmeric paste. acidification. In the studies of [PERSON] et al. [21], it was shown that marinating chicken breast muscles in 7.5% microencapsulated turmeric extract had a beneficial effect on the sensory characteristics of the refrigerated product, including taste attractiveness. Figure 2: _Cont._
## 4 Performance Evaluation
### Hug Detection Performance
#### 4.2.2 Results  
Figure 8 shows the results obtained for the hug detection. The system had a hug detection accuracy of 81.8% when the robot was hugged. Regarding false positives, when a participant pressed on the upper and lower areas, this was detected as a hug 23.3% of the trials, and when they pressed on the sides, 12.5% of the trials were considered a hug. Figure 9 shows the data from one session, and the data collected during each instruction. The instruction for hugging was displayed five times during each session, and in the figure, the interval for the hug instruction is represented by the peaks of the dotted line. After collecting the data, we chose the polygon area that gave us better results representing hugs, and, in this case, it was 1.4. The hug detection performance resulted in precision = 0.84, recall = 0.82, and F-measure = 0.83.  
Figure 7: **(A)** participant during the “press left/right” instruction; **(B)** participant during the “press upper/lower” instruction and **(C)** participant during the “Hug” instruction.
###### Abstract  
This paper adopts the fluid-structure coupling algorithm based on the acoustic fluid element, the fluid dynamic artificial boundary, and the consistent viscoelastic artificial boundary of solid media to establish a finite element model of the dynamic interaction of the reef-island-seawater system. Then, a numerical simulation of the seismic response of the reef-island site is carried out to study the seismic ground motion distribution patterns of the reef-seawater site and the reef-island-lagoon site. The innovation of this article is that the influence of reef-island topography and fluid-structure coupling is considered in the analysis when vertical ground motion is input. The results show that the slope angle of the bottom layer has a significant influence on the peak ground acceleration distribution and peak size on the island slope surface and the reef platform. For high-frequency input motion, a smaller reef platform width will induce a larger peak acceleration response on the reef platform. Seawater has a significant suppressive effect on vertical ground acceleration. The more high-frequency components of the input bedrock motion, the more obvious this suppression effect will be. The existence of the lagoon will amplify the maximum peak acceleration on the reef platform. According to the calculation results, lagoon terrain can amplify the maximum horizontal and vertical peak accelerations on the reef platform by about 19 and 6 times relative to the free-field results, respectively.  
ref island; seawater; lagoon; seismic ground motion; artificial boundary +
Footnote †: journal: Applied Sciences
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 4 The Variable-Step Iteration Algorithm for Efficient Determination of Optimal Rotation Angle
### The Selection of Initial Iteration Value  
As shown in Figure 4, assume the number of interferences is \(K\) and their steering vectors are \(\mathbf{a}_{\mathbf{i}_{1}}\), \(\mathbf{a}_{\mathbf{j}_{2}}\),..., and \(\mathbf{a}_{\mathbf{j}_{K}}\). According to matrix augmentation [18; 19], it can be deduced that if the element on the diagonal is fixed, a covariance matrix with the follow form (please pay attention to the elements in orange boxes) has a relatively small minimum eigenvalue.  
Figure 4: The selection of initial iteration value.  
\[\mathbf{R_{xx}}=\left[\begin{array}{cccc}p_{j_{k}}+p_{j_{k}}+p_{j_{k}}+p_{j_{k}}+p_ {n}&r_{21}^{\star}&r_{31}^{\star}&r_{31}^{\star}\\ r_{21}&p_{j_{k}}+p_{j_{k}}+p_{j_{k}}+p_{j_{k}}+p_{j_{k}}+p_{n}&\end{array} \right] \tag{26}\]  
where \(r\) with subscript represents an element of the covariance matrix \(\mathbf{R_{xx}}\).  
In order to approach \(\mathbf{R_{xx}}\) like this, let  
\[\left\{\begin{array}{cccc}\overline{\mathbf{w}}^{\mathrm{H}}\mathbf{a}_{ \mathrm{j}_{1}}(\Delta\varphi)\approx\overline{\mathbf{w}}^{\mathrm{H}} \mathbf{a}_{\mathrm{j}_{K}}(\Delta\varphi)\\ \overline{\mathbf{w}}^{\mathrm{H}}\mathbf{a}_{\mathrm{j}_{2}}(\Delta\varphi) \approx\overline{\mathbf{w}}^{\mathrm{H}}\mathbf{a}_{\mathrm{j}_{K}\cdot 1}(\Delta\varphi)\\ \vdots\end{array}\right. \tag{27}\]  
According to the symmetry of the array, if the interferences have the same intensity, one possible \(\Delta\varphi_{opt}\) is  
\[\Delta\varphi_{opt}\approx 0-\frac{\varphi_{j_{1}}+\varphi_{j_{2}}+\cdots+ \varphi_{j_{K}}}{K} \tag{28}\]  
Otherwise, suppose the maximum interference power is \(p_{j\mathrm{max}}\) and that \(M\) interference with power \(p_{jm}\geq p_{j\mathrm{max}}/100\) is selected. The initial iteration value becomes  
\[\Delta\varphi(0)=-\frac{\varphi_{j_{1}}+\varphi_{j_{2}}+\cdots+\varphi_{j_{M}} }{M} \tag{29}\]  
In particular, for the four-element array, due to the periodicity of the residual power caused by the array geometry symmetry, initial iteration values can be adjusted by taking the remainder of \(\frac{\pi}{6}\).  
As a summary, the flow chart of the proposed algorithm is shown in Figure 5.