esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Mixed Loss Function
This helps to improve the performance of the model in complex image segmentation tasks, especially when the boundary between the target region and the background region is blurred or the size of the target region is different. When using the mixed loss function, the BCE loss and Dice loss are calculated separately, and then the two are added together to get the final loss value. Mixing BCE loss and Dice loss is an effective strategy for improving the performance of deep learning models in image segmentation tasks. This strategy can synthesize pixel-level and region-level information so that the model can better adapt to complex scenes and changing target shapes, to achieve more accurate segmentation results. The BCE loss prioritizes pixel-level binary classification accuracy for effectively detecting changed and unchanged regions, while the DICE loss focuses on accurate segmentation of changed areas to enhance detection performance in those regions. By combining these two loss functions, this study addresses both classification and segmentation optimization objectives, ultimately leading to improved model performance. This approach is illustrated in Formula (3), where the model considers both pixel-level classification accuracy and region-level overlap during training. This methodology helps enhance the model's efficacy in complex image segmentation tasks, especially in scenarios where distinguishing between the target area and background area is challenging or where the size of the target area varies. \[L_{total}=\lambda_{1}L_{BCE}+\lambda_{2}L_{Dice} \tag{3}\]  
Changed areas in remote sensing images typically consist of small areas with low proportions, resulting in a significant sample imbalance issue. While the BCE loss demonstrates some robustness to this imbalance, the DICE loss can better highlight small changing areas. By combining these two loss functions, the model's ability to address sample imbalance is further enhanced. Therefore, in this study, to leverage the strengths of both loss functions, the values of \(\lambda_{1}\) and \(\lambda_{2}\) are set to 1. In summary, the hybrid loss function, combining BCE loss and DICE loss, enhances the performance of remote sensing image change detection. This fusion of loss functions proves to be a successful method in enhancing deep learning model performance for image segmentation tasks. By integrating pixel-level and regional-level information, the model can effectively adapt to intricate scenes and changes in target shapes, resulting in more precise segmentation outcomes. This approach plays a vital role in the development of an efficient and dependable remote sensing change detection system.
## 5 Performance Evaluation
### Discussions  
The assembly efficiency of multiple paths is a great boon to unreliable sensor networks. Obviously, there may exist many feasible combinations. To save the energy cost, the set with minimum number of paths is chosen as the forwarding set (see Sections 4.2 and 4.3). We argue that sending a packet on more paths induces more energy cost, because more data packets have to be transmitted. Also, using more paths introduces more contentions which degrades energy efficiency. Even some paths in the set may have more hops, it is still more energy efficient to confine packets to a few paths. For the energy efficiency, as long as the delay and reliability constraints are satisfied we forward the data traffic over energy-rich nodes in order to implement an almost homogeneous energy dissipation rates for all sensor nodes in the network.  
The main limitation of this paper is related to a lack of sufficient understanding about the dynamics of several estimation tuning parameters. For example, \(\alpha\), \(\beta\) and \(w_{n}\) values were determined through  
Figure 6: Protocol operation energy overhead due to routing control packets for **(a)** varying traffic loads and **(b)** bit error rates.  
numerous simulation experiments. If we could build an analytical model for them, we would be able to dynamically select the optimal values to adapt to different situations. We left it for future work.
## 4 Results
### Signal Pre-Processing  
Due to the difference in the sensitive materials, different sensors will have different response sensitivities to the same circumstance. The influence of environmental factors such as temperature and humidity will cause a baseline fluctuation of the sensors [37]. In order to eliminate these effects, we performed the following processing on the original signal data:  
\[V_{t}=\frac{R_{0}-R_{t}}{R_{0}} \tag{10}\]  
where \(V_{t}\) is the real-time response sensitivity of the gas sensor, \(R_{0}\) represents the resistance value of the sensor in the ambient air, and \(R_{t}\) is the real-time resistance value in the detected gas. The response sensitivity curves of the E-nose calculated according to Equation (10) are shown in Figure 6.  
Figure 5: The E-nose original response signal of different categories of samples: (**a**) C-pure; (**b**) C-A15; (**c**) C-B15; (**d**) C-T15.  
Figure 6: Pre-processed E-nose response data on different categories of samples according to Equation (10): (**a**) C-pure; (**b**) C-A15; (**c**) C-B15; (**d**) C-T15.
## 2 Methodology
### Feature Selection Methods Based on Machine Learning
#### 2.1.2 Feature Selection with Random Forest
Therefore, we can also use the classification accuracy of the random forest classifier on the OOB data as the feature separability criterion, so as to calculate the importance of each feature:  
\[Score_{OOB}(i)=\sum\frac{ooberr2-ooberr1}{N} \tag{5}\]  
where \(ooberr1\) is the classification error of the OOB data, \(ooberr2\) is the classification error of the OOB data with adding noise on feature \(i\) and \(N\) indicates the number of trees in a random forest. We can understand that if a feature is randomly disturbed, the classification error of the OOB data will increase greatly, and it can be considered that this feature has a great influence on the classification result. The algorithm of feature section with random forest by the classification accuracy on the OOB data (RFFS-OOB) is briefly described as below. ```
0:
1. Generate random forest;
2. Calculate feature importance based the scoring function (5), and sort the scores;
3. The top ranked features are selected as the optimal feature subset. 0: Optimal feature subset. ```  
**Algorithm 4**Feature section with random forest by the classification accuracy on the OOB data (RFFS-OOB)  
In addition, for bootstrap sampling technology, about \(1/3\) of the samples will not be collected at the end, and they are called the out of bag (OOB) data [43]. The role of OOB data can be considered as equivalent to the test data. Therefore, we can also use the classification accuracy of the random forest classifier on the OOB data as the feature separability criterion, so as to calculate the importance of each feature:  
\[Score_{OOB}(i)=\sum\frac{ooberr2-ooberr1}{N} \tag{6}\]  
where \(ooberr1\) is the classification error of the OOB data, \(ooberr2\) is the classification error of the OOB data with adding noise on feature \(i\) and \(N\) indicates the number of trees in a random forest. We can understand that if a feature is randomly disturbed, the classification error of the OOB data will increase greatly, and it can be considered that this feature has a great influence on the classification result. The algorithm of feature section with random forest by the classification accuracy on the OOB data (RFFS-OOB) is briefly described as below. In order to improve the accuracy of feature selection results for the SBM and FNC data, we used SVMRFE, RFFS-GI and RFFS-OOB, and repeated them 20 times separately, counted the frequency of the selected features by each feature selection method and integrated the optimal feature subsets.
## 3 Results and Discussion
### Antioxidant Activities of Cu- and Zn-Amaranth Purees  
#### 3.3.1 Effect of Metal Concentration  
Figure 6 displays the significant effects of metal concentrations on the antioxidant activity of methanolic extracts of Cu- and Zn-amaranth purees. Based on the Cu-amaranth purees, the DPPH antioxidant activities reached their maximum levels when the puree was stabilised with 210 and 240 mg/L of CuSO\({}_{4}\). Furthermore, the FRAP values were highest when treated with CuSO\({}_{4}\) above 120 mg/L. The Zn-amaranth purees depicted no significant differences at the highest value of the DPPH antioxidant activity (300 and 1500 mg/L of ZnCl\({}_{2}\)). Nevertheless, the FRAP values increased when ZnCl\({}_{2}\) was above 1200 mg/L.  
Figure 4: Absorbance spectra of acetone extracts of fresh and metal-stabilised amaranth purees (Cu-amaranth puree: 120 mg/L of CuSO\({}_{4}\) at 80 \({}^{\circ}\)C for 15 min, Zn-amaranth puree: 1500 mg/L of ZnCl\({}_{2}\) at 90 \({}^{\circ}\)C for 15 min.  
Figure 5: Amaranth purees of (a) Cu-amaranth puree stabilised at pH 6 and 80 \({}^{\circ}\)C for 15 min with 210 mg/L of CuSO\({}_{4}\), (b) Zn-amaranth puree stabilised at pH 8 and 90 \({}^{\circ}\)C for 15 min with 1500 mg/L ZnCl\({}_{2}\), and (c) control amaranth puree (0 mg/L of Zn or Cu and heated at 80 \({}^{\circ}\)C for 15 min).  
Figure 6 shows lower antioxidant activity in amaranth puree with metal-free derivatives (at 0 mg/L of CuSO4 and ZnCl2) than in amaranth puree containing metallo-chlorophyll derivatives. Interestingly, the FRAP values of amaranth purees were significantly (\(p<0.05\)) higher than Zn-amaranth purees in the range of approximately 300 to 900 mg/L of ZnCl2. The higher FRAP values could be related to the other components in amaranth purees, which contributed to their higher antioxidant activity. According to [PERSON] et al. and [PERSON] et al., the antioxidant content was related to metal-treated samples [16; 56].  
In another study, [PERSON] et al. elucidated that metallo-chlorophyll derivatives, such as Cu-pyropheophytin and Zn-pheophytin produced higher antioxidant activities than metal-free derivatives (pheophytin and pyropheophytin) [57]. The findings in Figure 6 supported the higher antioxidant activity. Furthermore, the CuSO4 (210 mg/L) and ZnCl2 (1500 mg/L) stabilisation process on amaranth purees revealed the highest chlorophyll derivative contents. Thus, the relationship between antioxidant activity and chlorophyll derivative content of metal-treated amaranth purees was observed. Furthermore, metallo-chlorophyll derivatives, such as Cu- and Zn- pheophytin or pyropheophytin were reported to exhibit significantly higher antioxidant capacity than metal-free chlorophyll derivatives [57].  
Figure 6: Effect of metal (**a**) CuSO4 and (**b**) ZnCl2 concentrations on antioxidant activity of amaranth puree at pH 6 after heating at 80 \({}^{\circ}\)C for 15 min. Each value is expressed as mean \(\pm\) standard deviation (\(n=3\)) of triplicate analysis. The bars with different lowercase (a–e) and uppercase (A–D) letters indicate significant differences (\(p<0.05\)) in [PERSON]’s HSD test.
# Transmissive Polarizer Metasurfaces: From Microwave to Optical Regimes
## 6 Review of Recent Progress of MSs Based Polarization Conversion
### Multiband Transmissive MS-Based Polarization Converters
Table 2 enlists the key findings for the state-of-the-art transmission-based multiband LP-to-CP converters. Split circular ring-based structures were used by [89; 91; 92], where either multiple-sized rings were used for multiple operations [81; 91; 92] or the concept of a cascading band reject and bandpass filter to realize multiband operation was used [89]. Table 2 enlists the key summarized findings for the state-of-the-art multiband transmissive polarization converters. For the characterization and measurements of these multiple band LP-to-CP converters, usually, multiple antennas are used due to the unavailability of a single wideband antenna covering both/all operating bands. The distance between the antennas and the polarizer should satisfy the far-field condition, which is given by the following, to obtain an accurate measurement result: where \(d_{f}\geq 2\frac{D^{2}}{\lambda}\), \(D\) is the largest dimension of the antenna, and \(\lambda\) is the minimum free-space wavelength. Most of the polarizers are characterized in a far-field region [65; 81; 91]. However, these multiband polarizers can be placed in the near radiative field as well [89; 92]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & **Operating** & **Operating** & **No. of** & **Angular** & **Orthogonality** \\  & **Frequencies** & **Bandwidth** & **Layers** & **Stability** & **Orthogonality** \\ \hline
[65] & 7.6, 13 & 31.6, 13.8 & 4 & 25 & same \\ \hline
[87] & 18.95, 28.5 & 13.1, 10.5 & 2 & 20 & orth \\ \hline
[88; 93] & 19.95, 29.75 & 2.5, 1.7 & 3 & 30 & orth \\ \hline
[89] & 17.8, 36.5 & 25, 16.4 & 2 & - & orth \\ \hline
[91] & 20.6, 29.71 & 5.56, 3.97 & 2 & & orth \\ \hline
[92] & 9.5, 12.5 & 4.2,6 & 3 & 55 & orth \\ \hline
[94] & 0.73, 1.33 & 24\%, 30\% & 1 & - & \\ \hline
[81] & 8.45, 28.4, 38.8 & 27.22, 21.2, 17.5 & 2 & 25 & orth \\ \hline \hline \end{tabular}
\end{table} 
Table 2: State-of-the-art transmission-based multiband MS-based LP-to-CP converters. Figure 6: Multiband polarization converters; (**a**) 3D printed setup to hold the horn and polarizer (Reprinted, with permission, [88]; (**b**) quasi-optical measurement setup for dual-band converter (Reprinted, with permission, [93]; (**c**) sample sheet for dual-band LP-to-converter [84]; (**d**) free-space measurement system used in [81].
## 5 Analysis and Evaluation
### Hit Ratio  
Figure 7a shows the average hit ratio comparison versus different sizes of graphs. It illustrates that the average hit ratio of ENRS outperforms KNN-DNRS and Random-NRS at a minimum of 23.2% and 18.1%, respectively, and a maximum of 31.0% and 29.4%, respectively. We notice that in Figure 7a, all of the query hit ratios of ENRS, KNN-DNRS, and Random-NRS decline with the increase of node sizes because the user node distributions are more dispersed, thus, the names registered in the resolvers are more scattered correspondingly, leading to a lower hit ratio.  
Figure 6: The count of deployed Hierarchical Elastic Areas Managers (HMs) with different node sizes.  
Figure 7b shows the details of the hit ratios of HMs in each level. From Figure 7b we can learn that the HMs in the higher levels have a higher hit ratio and the hit ratio of Level-1 (L1) and Level-2 (L2) decrease when the node sizes increase. In ENRS, the higher the level of HEA, the wider the area it covers, thus more nodes are engaged in the same HM, leading to more name bindings aggregating in the same HM, so that the hit probability is improved. With the growth of nodes, more HEAs are produced, thus more HMs must be deployed to satisfy the different transmission latency conditions. Therefore, the EID stored in HMs are more dispersed and the hit ratios become lower.
# Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback
## 6 Discussion  
### Why Did APO-CF Outperform Other Automated Methods?  
Our experimental results demonstrate that APO-CF outperforms existing automated prompt optimization methods, including APO, APE, and OPRO. This superior performance can be attributed to two key factors: (1) its data-driven optimization strategy, and (2) its efficiency in achieving high performance with minimal LLM calls.  
Unlike other methods, APO-CF directly utilizes the confusion matrix from the training data to guide prompt modifications. This approach allows it to explicitly address common misclassification patterns, such as reducing false negatives, rather than relying solely on LLM-generated refinements. In contrast, APO iteratively updates prompts through multiple LLM calls, but without directly referencing training data patterns, which can lead to less targeted improvements. Similarly, OPRO recombines past prompt variations without considering the underlying data distribution, limiting its effectiveness.  
Moreover, APO-CF achieves these improvements with significantly fewer LLM calls than APO, demonstrating that a single well-informed, data-driven update can be more effective than multiple LLM-based refinements. This efficiency is particularly important in real-world applications, where API call costs and computational resources are constraints.  
Finally, other methods fail to capture the overall characteristics of training data, as they focus primarily on structural modifications rather than data-driven refinements. APO-CF's reliance on confusion matrix insights ensures that its prompt updates align more closely with actual model weaknesses, leading to a more reliable and scalable optimization approach.  
These results underscore the importance of data-driven prompt optimization over purely LLM-driven iterative updates, positioning APO-CF as a promising and efficient alternative.
## 3 Materials and Methods
### Segmentation Approach
#### 3.2.2 Loss Function  
The present work conducted multiple experiments employing sophisticated loss functions that address the unbalancing data problem, such as Generalized Dice [44], Tversky [45], and focused [46] loss functions, in addition to the conventional binary cross-entropy [47] loss function.  
Let us define \(x_{0i}\) and \(x_{1i}\) as the probability of the \(i^{th}\) pixel to be a fire and non-fire, respectively. Let us also take the ground truth annotated as \(y_{0i}/y_{1i}\).  
Figure 3: The segmentation module architecture: The encoder block is for feature extraction, deploying atrous convolutions with different rates (spatial pyramid pooling block). The decoder block helps reduce the obtained feature map.  
* **The Cross-entropy loss:** It is the default function with the deeplabv3+ model. It is formulated as follows [47]: \[T_{ce}=-\left(\sum_{i=1}^{N}y_{0i}\log x_{0i}+\sum_{i=1}^{N}y_{1i}\log x_{1i}\right)\] (1) where \(N\) corresponds to the number of classes in the dataset.
* **Generalized dice loss:** The mathematical formulation alleviates the class imbalance problem. The function is given as follows [44]: \[T_{dc}=1-2\frac{\sum_{i=1}^{N}x_{0i}y_{0i}}{\sum_{i=1}^{N}x_{0i}+\sum_{i=1}^{N }y_{0i}}\] (2) The function weights the contribution of each class to the final loss by the inverse size of the expected region. A negligible non-null constant is further introduced in the denominator to avoid invalid values during training.
* **Tversky loss:** The loss function is defined as follows [45]: \[T_{tr}(a,b)=\frac{\sum_{i=1}^{N}x_{0i}y_{0i}}{\sum_{i=1}^{N}x_{0i}y_{0i}+a\sum _{i=1}^{N}x_{0i}y_{1i}+b\sum_{i=1}^{N}x_{1i}y_{0i}}\] (3) The parameters \(a\) and \(b\) allow to adequately tune up the weights of false positives (FPs) and false negatives (FNs). The fact of deploying a value of \"\(b\)\" higher than \"\(a\)\" emphasizes FPs, diminishes the FNs, boosts the recall factor, and favorably improves the training process.
* **Focal loss:** It is a variant of the original cross-entropy loss that performs class weighting by down-weighting the contribution of every class with a corresponding modulating factor. The following equation gives the mathematical formulation: [46] \[T_{fc}(\alpha,\gamma)=-\left(\sum_{i=1}^{N}\alpha(1-y_{0i})^{\gamma}\log x_{0 i}+\sum_{i=1}^{N}\alpha(1-y_{1i})^{\gamma}\log x_{1i}\right)\] (4) The \(\alpha\) value scales the loss function linearly while \(Y\) is the focusing parameter of the function. Increasing the value of \(Y\) improves the sensitivity of the trained network. The optimal value of \(\alpha\) is 0.25, and \(Y\) is 2 [46].
## 4 Results and Analysis  
This section presents the results obtained from the proposed deep gated neural network (DGNN) and compares them with existing studies. Additionally, it includes the results from real-time monitoring in the cognitive workload monitoring system (CWMS). The Jupyter Lab tool was utilized for analysis due to its powerful interactive computing capabilities, and the proposed DGNN model was designed using the Python programming language.
# Development of Continuum Robot Arm and Gripper for Harvesting Cherry Tomatoes
## 4 Kinematic and Kinetic Formulations  
### A. Forward Kinematic Formulation  
Coordinate systems are set at every universal joint.  
The homogeneous coordinate transform matrices:  
\[\sum_{0}\rightarrow\sum_{1}\,\ \ H_{0,1}=\begin{pmatrix}R&u_{0,1}\\ 0&0&0&1\end{pmatrix},\ u_{0,1}=\begin{pmatrix}x_{0}\\ y_{0}\\ I_{0}\end{pmatrix} \tag{1}\]  
\[\sum_{i-1}\rightarrow\sum_{i,\ \ H_{i-1,\ i}}=\begin{pmatrix}R&u_{i-1,i}\\ 0&0&0&1\end{pmatrix},u_{i-1,i}=\begin{pmatrix}0\\ 0\\ L\end{pmatrix},\ (i=2,\ \cdots,\ n) \tag{2}\]  
\[R=\ R_{z}(\theta_{zi})R_{x}(\theta_{xi})R_{y}\big{(}\theta_{yi}\big{)} \tag{3}\]  
where \(x_{0}\) and \(y_{0}\) are an initial position of the base. \(R_{x}(\theta_{xi})\) and \(R_{y}\big{(}\theta_{yi}\big{)}\) are the rotation matrices of the \(i\)th universal joint that has two rotation angles \(\theta_{xi}\) and \(\theta_{yi}\), \(R_{z}(\theta_{z1})\) is the rotation matrix of the \(i\)th disk with a rotation angle \(\theta_{z1}\) along the axial axis and L is the length between neighboring universal joints (Figure 16).  
Multiplying the H-matrices successively, we obtain unit vectors and the position vector of the \(i\)th coordinate system;  
\[H_{0,i}=H_{0,1}H_{1,2}\cdots H_{i-1,\ i}=\begin{pmatrix}i_{i}&j_{i}&k_{i}&u_{ i}\\ 0&0&0&1\end{pmatrix} \tag{4}\]  
where \(u_{i}\) is the position of the \(i\)th universal joint \(\mathrm{U}_{i}\ (i=1,\ \cdots,\ n-1)\).  
The position vector \(p_{i}\) of the end-point \(\mathrm{P}_{n}\) and position of sliding plates \(\mathrm{P}_{i}\ (\mathrm{i=1,\ \cdots,\ n-1})\) of the manipulator are obtained by,  
\[\begin{pmatrix}p_{i}\\ 1\end{pmatrix}=H_{0,i}(0\ 0\ l_{i}\ 1)^{T},\ \ (i=1,\ \cdots,\ n) \tag{5}\]  
Figure 16: TakoBot kinematic structure.  
where \(l_{n}\) is a fixed length between the nth universal joint and the most distal plate.  
The position vectors of eight holes \(a_{i},\ c_{i},\ i,\ \hat{c}_{i}\) in the first section and for the second section \(b_{i},\ d_{i},\ \hat{b}_{i},\hat{d}_{i}\) at the base plate are determined as,  
\[\begin{split} a_{0}&=\begin{pmatrix}a_{x}\\ a_{y}\\ 0\end{pmatrix},\ \ \ b_{0}=\begin{pmatrix}b_{x}\\ b_{y}\\ 0\end{pmatrix},\ \ c_{0}=\begin{pmatrix}c_{x}\\ c_{y}\\ 0\end{pmatrix},\ \ d_{0}=\begin{pmatrix}d_{x}\\ d_{y}\\ 0\end{pmatrix},\\ \hat{a}_{0}&=\begin{pmatrix}\hat{a}_{x}\\ \hat{a}_{y}\\ 0\end{pmatrix},\ \ \ \hat{b}_{0}=\begin{pmatrix}\hat{b}_{x}\\ \hat{b}_{y}\\ 0\end{pmatrix},\ \ \hat{c}_{0}=\begin{pmatrix}\hat{c}_{x}\\ \hat{c}_{y}\\ 0\end{pmatrix},\ \ \hat{d}_{0}=\begin{pmatrix}\hat{d}_{x}\\ \hat{d}_{y}\\ 0\end{pmatrix},\end{split} \tag{6}\]  
where \(l_{i}\) is an axial length between the \(i\)th universal joint and the \(i\)th plate, which varies as the plate slides along rods, except \(l_{n}\).
## 5 Simulation Results
### Performance Comparison of GBD, MGBD and Proposed Scheme
In addition, the proposed scheme achieves the data rate which is accomplished with the smallest data streams at the lower transmit power compared to MGBD. When the number of data streams is lower than the \(\frac{2}{3}\) of the number of receive antennas, the better BER performance is achieved by GBD and MGBD obviously. However, the data rate which is accomplished with the reduced data streams is also lower than the \(\frac{2}{3}\) of the maximum data rate with the full data streams. In other words, the throughput performance is inefficient when the data streams are reduced too much. Thus, the proposed scheme is more efficient than GBD and MGBD in terms of throughput since the proposed scheme with slightly reduced data streams has better performance compared to GBD and MGBD.