esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Biosafety Evaluation of the Monascus Pigments
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Concentration** & & & & & & & \\
**(mg/kg bw Mice)** & **Body Weight** & **Liver** & **Kidney** & **Heart** & **Spleen** & **L/B\% H** & **M 1** \\ \hline Control & 26.04 \(\pm\) 0.52 & 1.42 \(\pm\) 0.12 & 0.16 \(\pm\) 0.14 & 0.14 \(\pm\) 0.10 & 0.12 \(\pm\) 0.15 & 5.46 & No \\
10 M & 27.26 \(\pm\) 0.58 & 1.50 \(\pm\) 0.25 & 0.17 \(\pm\) 0.11 & 0.15 \(\pm\) 0.09 & 0.12 \(\pm\) 0.08 & 5.51 & No \\
10 C & 26.11 \(\pm\) 0.41 & 1.46 \(\pm\) 0.02 & 0.16 \(\pm\) 0.04 & 0.14 \(\pm\) 0.02 & 0.12 \(\pm\) 0.18 & 5.60 & No \\
100 M & 26.50 \(\pm\) 0.23 & 1.48 \(\pm\) 0.05 & 0.16 \(\pm\) 0.21 & 0.14 \(\pm\) 0.10 & 0.12 \(\pm\) 0.15 & 5.60 & No \\
100 C & 27.80 \(\pm\) 0.44 & 1.56 \(\pm\) 0.05 & 0.17 \(\pm\) 0.01 & 0.15 \(\pm\) 0.07 & 0.13 \(\pm\) 0.08 & 5.60 & No \\
1000 M & 28.40 \(\pm\) 0.15 & 1.59 \(\pm\) 0.03 & 0.17 \(\pm\) 0.09 & 0.15 \(\pm\) 0.04 & 0.13 \(\pm\) 0.06 & 5.60 & No \\
1000 C & 27.28 \(\pm\) 0.54 & 1.55 \(\pm\) 0.01 & 0.18 \(\pm\) 0.04 & 0.15 \(\pm\) 0.06 & 0.13 \(\pm\) 0.08 & 5.70 & No \\
1600 M & 29.23 \(\pm\) 0.24 & 1.66 \(\pm\) 0.03 & 0.18 \(\pm\) 0.05 & 0.15 \(\pm\) 0.04 & 0.13 \(\pm\) 0.06 & 5.70 & No \\
1600 C & 28.11 \(\pm\) 0.29 & 1.71 \(\pm\) 0.04 & 0.19 \(\pm\) 0.07 & 0.16 \(\pm\) 0.09 & 0.13 \(\pm\) 0.30 & 6.10 & No \\
2900 M & 29.73 \(\pm\) 0.88 & 1.70 \(\pm\) 0.16 & 0.18 \(\pm\) 0.03 & 0.16 \(\pm\) 0.05 & 0.14 \(\pm\) 0.06 & 5.73 & No \\
2900 C & 28.61 \(\pm\) 0.98 & 1.83 \(\pm\) 0.06 & 0.207 \(\pm\) 0.04 & 0.16 \(\pm\) 0.06 & 0.14 \(\pm\) 0.02 & 6.40 & No \\
5000 M & 29.73 \(\pm\) 0.65 & 1.71 \(\pm\) 0.09 & 0.18 \(\pm\) 0.07 & 0.16 \(\pm\) 0.04 & 0.15 \(\pm\) 0.03 & 5.73 & No \\
5000 C & 28.61 \(\pm\) 0.98 & 1.95 \(\pm\) 0.05 & 0.20 \(\pm\) 0.02 & 0.17 \(\pm\) 0.03 & 0.14 \(\pm\) 0.02 & 6.80 & 30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effect of _Monascus_ pigment and carmoisine E122 at different concentrations on mean organ weight (g), hepatotoxicity, mortality, and other observations. Based on the results of the biosafety evaluation and findings reported by [PERSON] et al. (2019), the natural red pigment obtained from _M. ruber_ OMNRC45 can be considered a promising natural colorant. Conversely, the application of the synthetic dye carmoisine can induce several abnormal signs in mice, especially at high concentrations, and it must be replaced with a safe natural product.
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Kinematic Model of the Differential Drive Wheeled Mobile Robot  
The geometry and kinematic parameters of the DDWMR are shown in Figure 3[12].  
In Figure 3, the DDWMR has the important parameters as follows: \(v\) is the robot linear velocity (m/s); \(\theta\) is the robot orientation (rad); \(\omega_{r}\) is the angular velocity of the right wheel (rad/s); \(\omega_{l}\) is the angular velocity of the left wheel (rad/s); \(v_{t}\) is the linear velocity of the right wheel (m/s); \(v_{l}\) is the linear velocity of the left wheel (m/s); \(r\) is the radius of the right and the left wheels (m); \(b\) is the distance between the right and the left wheels (m); \(Q\) is the center of the axis between the right and the left wheels; \(G\) is the center of gravity of the DDWMR; \(a\) is the distance between \(Q\) and \(G\) (m).  
Assuming that [12; 29]:  
* The wheels are rolling without slipping,
* The center of gravity \(G\) coincides with the point \(Q\),
* The guidance axis is perpendicular to the robot plane.  
Based on Figure 3 and refer to [12; 29], it is easy to get the kinematic model of the DDWMR as follows:  
\[\left\{\begin{array}{c}\dot{x}=v\cos\theta\\ \dot{y}=v\sin\theta\\ \dot{\theta}=\omega\end{array}\right. \tag{1}\]
## 3 Wireless Communication and Power Transmission
### Antenna Design
In the research of conformal metasurfaces, there are also crucial aspects deserving attention. [PERSON] designed a conformal absorber metasurface to decrease the specific absorption rate (SAR) of implanted antennas. Reducing the SAR of implanted antennas is one of the significant design considerations in modern healthcare applications. The introduction of an absorber metasurface can restrict the backward radiation of the antenna, thereby regulating the SAR value. This technology reduces the maximum SAR value by 24% and significantly diminishes the average SAR distribution, thus alleviating the harm of electromagnetic radiation to human health [117]. Other antenna types: [PERSON], on the other hand, conducted an in-depth analysis of various types of antennas, such as planar antennas, wired antennas, conformal antennas, and spiral antennas, and elaborated in detail on the requirements and challenges faced by these different types of antennas [58]. Based on the specific requirements for implantable antennas in practical applications, he innovatively proposed a design scheme for a slotted patch antenna. In this scheme, both the substrate and the superstrate are made of Rogers RT6010 material. This patch antenna has the remarkable advantages of tiny size and easy integration. It has a wide radiation pattern and excellent frequency band characteristics, fully demonstrating the great application potential of patch antennas in the field of implantable electronic devices. All the implantable sensors of various types mentioned above must be extracted from the human body via minimally invasive surgery and other techniques once the preset monitoring period is concluded. This procedure will inevitably inflict damage on patients. Particularly when surgeries are carried out in certain critical areas (such as the brain, arterial blood vessels, etc. ), it will bring about unpredictable risks. In light of this, biodegradable electronic devices have already emerged as the development trend of implantable medical electronic devices. The latter part of this article will present the material selection and development status of biodegradable electronic devices.
## 3 Results
### Effect of Endophytic Colonization on Aplid Mortality  
The likelihood ratio tests to evaluate the effect of the experiment for both fungus sprayed leaves and unsprayed leaves were not significant (\(\chi\)2 1; \(p\) > 0.05). For leaves sprayed with the fungal suspension (i.e., leaves with conidia on the surface and endophytically colonized), there was, overall, a significant effect of treatment on aphid mortality (F 3,28 = 5.63; \(p\) = 0.0038; Table 2); there were significant differences between isolates and the control according to the [PERSON]'s test (EABb 01/33-Su: t\({}_{28}\) = 3.31; \(p\) = 0.006; EABb 04/01-Tip: t\({}_{28}\) = 3.81; \(p\) = 0.0017; EAMa 01/58-Su: t\({}_{28}\) = 3.60; \(p\) = 0.0029) (Table 2). For leaves that were only endophytically colonized (i.e., leaves not sprayed with the fungal suspension but on the same plant as fungus-sprayed leaves) the overall effect of treatment on mortality was also significant (F3,28=3.36; \(p\) = 0.0328; Table 3). The mortality caused by isolate EABb 01/33-Su was significantly higher than the mortality in the control ([PERSON]'s test t\({}_{28}\) = 3.01, \(p\) = 0.0135; Table 3). There were no significant differences between the mortalities caused by isolates EABb 04/01-Tip and EAMa 01/58-Su and the mortality in the control at \(\alpha\)=0.05, although these differences were statistically significant at \(\alpha\) = 0.1 ([PERSON]'s test t\({}_{28}\) = 2.22, \(p\) = 0.0796 for strain EABb 04/01-Tip; [PERSON]'s test t\({}_{28}\) = 2.36, \(p\) = 0.0585 for strain EAMa 01/58-Su; Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mortality (\%)**} & \multicolumn{2}{c}{**Kaplan-Meier Survival Analysis**} \\ \cline{2-5}
**Treatment** & \multirow{2}{*}{**Mean (\(\pm\) SE) 1 ** & \multicolumn{2}{c}{**Confidence Interval (95\%)**} \\ \cline{2-5}  & \multirow{2}{*}{**Mean (\(\pm\) SE) 2 ** & \multicolumn{2}{c}{**Lower Limit**} & \multicolumn{2}{c}{**Upper Limit**} \\ \hline Control & 0.80 \(\pm\) 0.06 & 6.56 \(\pm\) 0.25 a & 6.07 & 7.05 \\ EABb 01/33-Su & 48.15 \(\pm\) 0.18 1  & 6.15 \(\pm\) 0.28 b & 5.60 & 6.71 \\ EABb 04/01-Tip & 56.92 \(\pm\) 0.18 1  & 6.18 \(\pm\) 0.25 b & 5.70 & 6.66 \\ EAMa 01/58-Su & 53.68 \(\pm\) 0.18 1  & 5.77 \(\pm\) 0.28 b & 5.22 & 6.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Susceptibility of _A. gossypii_ adults to entomopathogenic fungal isolates after exposure to leaves with conidia on their surfaces and which were also endophytically-colonized by those fungi.
## 3 Multiple Reconfigurable Features in Antenna
### Frequency and Radiation-Pattern Reconfigurable Antennas
Depending on the states of the embedded diodes, the antenna can work at three different frequency bands. Another design of antenna with frequency and radiation pattern selectivity, shown in Figure 6b, was introduced in [66]; it is enclosed in a circular area, with six symmetrical main radiators and twelve parasitic elements. Three circular slots are etched in a circular ground patch placed on the midline of the microstrip feed line. By changing the diode states, the antenna can operate at the frequency bands of wireless broadband (Wibro), WLAN, Zigbee, and satellite-digital multimedia broadcasting (S-DMB) communications with the capability of steering the antenna radiation pattern to six different angles in each frequency band. In addition, since six radiation elements were used in the design, the antenna pattern can be steered to different angles in 45\({}^{\circ}\) steps to provide full coverage. Figure 6: (**a**) Configuration and (**b**) performance of the frequency and pattern reconfigurable antenna in [65]. Reproduced with permission from [65], Copyright IEEE, 2015. Reference [67] proposed a dipole driven element with calculated pixilation and a pair of parasitic elements to provide frequency and pattern switching. The antenna has modes at 900 MHz, 1800 MHz, or 2.1 GHz, with directional or omnidirectional radiation patterns. In [68], a novel planar antenna containing dual symmetrical radiators with rectangle parasitic elements and an inverted T-shape ground plane was proposed. By loading the parasitic elements and choosing different radiators for the feeding, the antenna can operate at three different frequencies with two different kinds of radiating pattern.
# A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm
## 2 Task Assignment Model
### Objective Function
#### 2.2.2 Task Revenue Function  
In a battle scenario, mastering the enemy's important intelligence is the key to victory. Assuming that, at the initial moment, the UAV only knows the approximate location of the target. When the UAV detects the target, the accurate location information of the target \(P_{j}=\big{(}x_{j},y_{j}\big{)},j\in[1,N_{t}]\) and the initial value of the target \(value(t_{j})\) can be obtained. This article uses the initial value of the target to express the importance of intelligence.  
In actual scenarios, the initial value of the task tends to decrease as the waiting time increases, so this paper considers that the effect of task execution will decrease with the increase of time. That is to say, the function value is inversely proportional to the time. To eliminate the dimensional influence between costs and benefits, we also normalize the benefits by dividing the actual revenue by the sum of the initial values of all the tasks. The specific operation is shown in Formula (7).  
\[f_{rew}=\frac{\sum_{i=1}^{N_{u}}\sum_{j=1}^{N_{t}}\big{(}x_{ij_{i}}\cdot value \big{(}t_{j})\cdot\varphi(t)\big{)}}{\sum_{j=1}^{N_{t}}value\big{(}t_{j}\big{)}} \tag{7}\]where \(x_{ijq}\) is a binary value, indicating whether UAV \(u_{i}\) is assigned to a task \(t_{j}\). Only when \(\mu_{q}\) is an attack task, the value of \(x_{ijq}\) is \(1\); \(value\big{(}t_{j}\big{)}\) is the importance of the target at the initial moment, and \(\varphi(t)\in\ [0,1]\) decreases with time, indicating that the value obtained by destroying the target varies with time, and the calculation formula of \(\varphi(t)\) is as follows.  
\[\varphi(t)=e^{-\varphi t} \tag{8}\]  
where \(\rho\) represents the rate of decline in revenue, the larger the \(\rho\), the faster the revenue from destroying the target decreases over time, and vice versa.  
However, in a real scenario, enemy information is dynamic and constantly updated. UAVs may be required to perform repeated or multiple intelligence searches at the same target point to ensure effectiveness and accuracy. But in a dynamic mission scenario, the optimal solution is to cover as many target points as possible in a short time. Therefore, UAV should try to select unsearched target points, rather than repeatedly search the same mission point. Therefore, we adjust the function of task revenue according to the actual situation. When UAVs conduct repeated searches of the same target, the information they find will largely overlap, so the value of the mission should be halved.  
The change of the function of task value is shown in Formula (9).  
\[value\big{(}t_{j}\big{)}=(\frac{1}{2})^{k-1}*value\big{(}t_{j}\big{)} \tag{9}\]  
where \(k\) is the number of UAV searches for intelligence on task point \(t_{j}\).
## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### DDM Results  
This section presents the results of the implemented ISSAEDO algorithm and other competitive approaches in addressing the DDM problem. Table 6 displays the values of  
Figure 4: Analysis of the actual data and simulation data obtained by the presented ISSAEDO for the SDM: (**a**) I-V curve and (**b**) P-V curve.  
Figure 5: Analysis of the SDM’s observed voltages and IAE as measured by the given ISSAEDO.  
the derived parameters as well as the accompanying RMSE results. The values of the seven unknown parameters are shown in Table 6: \(I_{ph}\), \(I_{ssl}\), \(R_{s}\), \(R_{sht}\), \(\gamma_{1}\), \(I_{ssl2}\), \(\gamma_{2}\). Based on the RMSE values displayed in Table 6, the presented ISSAEDO has the best RMSE value of (9.82484851E-04). MADE has the second best RMSE value of (9.8248485587E-04).  
The statistical outcomes (the minimum, maximum, mean, and standard deviation) retrieved from the presented ISSAEDO and competitive algorithms are shown in Table 7. In comparison to state-of-the-art approaches, the provided ISSAEDO algorithm achieves the best minimum RMSE values for the DDM. The MADE method is the only one that earns the best maximum RMSE and lowest STD values. The provided ISSAEDO algorithm is the only one that achieves the best mean RMSE values. This demonstrates that, when compared to previous algorithms, the given ISSAEDO is capable of achieving consistent and dependable solutions.  
In addition, the parameters obtained from the given ISSAEDO were employed to compute the measured voltages, simulated current, and IAE for the DDM. These values were subsequently graphed as characteristic curves. The comparison
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 4 Performance Evaluation of Improved ACA
### Performance Evaluation  
#### 4.2.1 Construction of Path-Planning Maps  
To validate our improved algorithm, path-planning simulations were performed on two regions (400 m \(\times\) 400 m) on high-resolution remote-sensing images of Beishan Town and Xingsha Street (28\({}^{\circ}\)24\({}^{\prime}\) N, 113\({}^{\circ}\)00\({}^{\prime}\) E; 28\({}^{\circ}\)15\({}^{\prime}\) N, 113\({}^{\circ}\)06\({}^{\prime}\) E) in Changsha, Hunan. Figures 9a and 10a show the satellite remote-sensing images of the two study cases. The images were converted to 20 \(\times\) 20 grids, and hence 20 \(\times\) 20 grid maps were generated. Figures 9b and 10b show the grid maps without consideration of trafficability; Figures 9c and 10c show the generalized barrier grid maps after weights were assigned to the grids based on the speed influence coefficient of surface features.
# Analysis of Seaweeds from South West England as a Biorefinery Feedstock
## 3 Results and Discussion
### Minerals
However, the presence of aluminium was observed in all but three seaweeds, and whilst this is not unusual [37], if used as a fertiliser, over time soils could become contaminated with elevated levels of aluminium which in acidified soils can lead to plant toxicity [38]. Transition metal molybdenum and heavy metals arsenic and lead were not observed in any of the samples which is testament to the water quality on the South West coast where the sampling took place. _Colpomenia peregrina_ had a consistently high mineral content across the board and was one of just two of the seaweeds assessed to contain copper at detectable levels. This was not unexpected since this seaweed grows best when attached by rhizoidal filaments to rock bed or molluscs such as oysters from which minerals are leached. Manganese was detected in most of the seaweeds and was particularly high in _Rhodomela confervoide_ at 0.142% of the dry biomass. Whilst this mineral is important in many industrial processes, such as metal alloy manufacture, it is also widely used in nutritional supplements. All of the seaweeds analysed contained detectable zinc and all but three of the seaweeds had iron at levels that could be used easily in nutritional supplements (the total daily recommended intake of iron is just 8.7-14.8 mg/day). It should be noted that the highest levels of this metal were observed in the physically smaller seaweed species such as _Lomentaria articulata_ (4 mg/g) and _Colpomenia peregrina_ (9.3 mg/g).
## 2 Framework
### Building Energy Forecasting  
Building energy consumption forecasting is one of the most important steps for retrofitting projects as it helps the decision maker forecast a building's energy consumption as well as costs incurred in the future by the building. However, it is a challenging task as it is affected by many factors, including ambient weather conditions, building structure and characteristics, lighting and HVAC systems, occupancy, and occupant behavior. While building energy modeling and simulation is by far the most-used method for building energy consumption forecasting, other methods, including artificial intelligence, are getting more and more popular. For example, to forecast the energy consumption without using building energy modeling (BEM) that enable designers to test and simulate only a limited number of design scenarios because of its extensive calculations and long times, the authors in [35] introduced artificial neural networks (ANNs) to forecast the energy consumption of a building using its descriptive data. The authors reported high accuracy during testing, with errors below 5% for over 99.5% of the testing dataset, which confirms that ANNs are a reliable substitute for BEM during ZEB design and energy consumption forecasting.  
In another paper, the research study [36] used artificial neural networks and support vector machines to forecast heating and cooling demands. Results showed that machine-learning-based methods offer accurate and fast predictions and can well substitute the time consuming multi zone dynamic simulation models that heavily depend on several parameters and user calibration.
## 5 Multi-Head Attention Mechanism Analysis
#### 5.3.3 Comparison and Analysis with Other Experiments  
In this paper, we proposed a speech emotion recognition model based on the improved TIM-Net network and multimodal fusion, which was experimentally validated on the IEMOCAP and MELD datasets. To evaluate our approach, we compared it with other models. The results show that our method performs well regarding WAR and UAR. The comparative results are presented in Tables 3 and 4, where \"CV\" stands for cross-validation, \"5-fold\" indicates five-fold cross-validation, and \"10-fold\" indicates ten-fold cross-validation. (Note: The UAR and WAR for MMBER in the table are results reproduced using only the pure IEMOCAP dataset after removing the enhanced speech and text data added by the authors of the MMER paper).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**CV Type**} & \multirow{2}{*}{**Modality**} & \multicolumn{3}{c}{**Metrics**} \\ \cline{3-6}  & & & **UAR (\%)** & & **WAR (\%)** \\ \hline MHA + DRN [52] & - & [S] & 67.40 & - \\ CNN + Bi-GRU [53] & - & [S] & 71.72 & 70.39 \\ MSCNN-SPU [54] & 10-fold & [S, T] & 78.20 & 77.40 \\ LightSR [55] & 10-fold & [S] & 70.76 & 70.23 \\ Article [56] & 5-fold & [S, T] & - & 76.31 \\ TIM-Net [35] & 10-fold & [S] & 72.50 & 71.65 \\ MMBER [57] & 5-fold & [S, T] & 78.69 & 80.18 \\ Mul-TAB (ours) & 5-fold & [S, T] & 81.92 & 83.85 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative results with other models on IEMOCAP dataset. In the modal column, \(S\) represents speech, \(T\) represents text, and \(V\) represents vision.  
Figure 17: The performance of TAB and multi-head attention modules on different datasets. The first five columns are the experimental results on IEMOCAP, and the last column is the experimental results on MELD.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**IEMOCAP**} & \multicolumn{2}{c}{**MELD**} \\ \cline{2-7}  & **1\_WA (\%)** & **2\_WA (\%)** & **3\_WA (\%)** & **4\_WA (\%)** & **5\_WA (\%)** & **UA (\%)** \\ \hline Mul-TAB & 78.9 & 83.9 & 75.1 & 79.7 & 76.2 & 63.9 \\ Without TAB & 78.2 & 82.9 & 75.1 & 79.5 & 77.9 & 60.2 \\ Without multi-head attention & 78.6 & 82.3 & 76.2 & 78.1 & 77.6 & 59.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiments. The table has been converted into a bar chart, as shown in Figure 17.
## 1 Introduction
In addition, the multipath interference is suppressed based on the reversibility of the linear canonical transformation and the suppression effect of Gaussian noise;
* The linear canonical transformation is used to process the signal after interference suppression, and the detection statistic is extracted to sense the aerial target;
* Through the analysis of the forward scatter signal and the characteristics of the linear canonical transformation algorithm, the linear canonical transformation and the matched filter algorithm are combined to estimate the target parameters. The remainder of this paper is organized as follows. In Section 2, the system model used in this paper is presented. The suppression of multipath interference is described in Section 3. Detection statistics and the detector design are proposed in Section 4. In Section 5, the performance of target detection is analyzed. Two methods of parameter estimation are proposed in Section 6. In Section 7, the performance of parameter estimation is analyzed. Section 8 shows the numerical results to verify the classification performance. Finally, Section 9 concludes the paper.