esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Performance Evaluation  
Table 2 summarizes the experimental results of our model and baseline models trained on the Amazon dataset.  
A detailed analysis of the results can be given as follows:  
(1) AP-MDSC versus traditional sentiment classification models: It is shown in Table 2 that our model obtains significant improvements compared to all single-domain sentiment classification methods, including LS-single, SVM-single, and LSTM-single. We speculate that the main reason is that those models only use single-domain data for training and cannot acquire general knowledge from other datasets. Compared to sentiment classification models that use all domain data indiscriminately, such as LS-all, SVM-all, and LSTM-all, our model achieves higher classification accuracy across all domains. This is because, although the models can gain general knowledge from a larger set of training data, they do not leverage domain information to filter out the sentiment features required for specific domains.  
\begin{table}
\begin{tabular}{c c c} \hline
**Domain Polarity** & **Negative** & **Positive** \\
**(Train/val/Test)** & **(Train/val/Test)** & **(Train/val/Test)** \\ \hline Book & 700/200/100 & 700/200/100 \\ \hline DVD & 700/200/100 & 700/200/100 \\ \hline Kitchen & 700/200/100 & 700/200/100 \\ \hline Electronics & 700/200/100 & 700/200/100 \\ \hline Amazon & N: 8000 (5600/1600/800) & L: 5939 & V: 2022 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical information on the Amazon sentiment analysis dataset. N: number of labeled samples; L: maximum sample length; V: number of words.  
(2) AP-MDSC versus other multi-task learning models: As shown in Table 2, our model outperforms other multi-task-based models, whether in terms of performance within a single domain or the average performance across the entire dataset, except for DAM. According to the analysis of DAM, these multi-task-based methods have various shortcomings, such as not fully considering the relationships between different domains or ignoring shared sentiment knowledge, while DAM can extract domain-related features from a shared feature pool at the feature level using domain attention, reducing the need to design a specific layer for each task. In contrast to DAM, although AP-MDSC does not achieve the best performance in all domains, it can achieve the best overall performance. This suggests that our proposed approach can improve multi-domain sentiment classification by simulating the ranking of the elements in the input text. Overall, the experimental results show that AP-MDSC is a powerful multi-domain sentiment classification algorithm.
## 3 Results and Discussion
### Validation on Different Datasets  
The improved HM-YOLO model was evaluated using four different strawberry datasets to assess its performance in recognizing strawberries at various stages of ripeness, and its detection performance was compared with the baseline model of YOLOv8n. As shown in Table 1, the HM-YOLO model achieved improvements of 1.6% and 1.2% in mAP\({}_{0.5}\) and mAP\({}_{0.50.95}\), respectively, on the SBDS dataset, which has the largest number of samples. Additionally, the precision and recall of the HM-YOLO model increased across all four datasets. Notably, the highest accuracy improvement of 2.5% was observed for the Straw_Cul dataset, while the most significant recall improvement of 1.5% was seen for the Straw_Det dataset. This phenomenon may be related to the greater impact of individual samples on overall performance in datasets with fewer samples. Overall, the HM-YOLO model significantly outperformed the baseline model in key performance metrics such as precision, recall, and mAP, demonstrating that the improved model is well suited to replace the baseline model for detecting strawberries at different ripeness stages in relatively complex environments.
## 3 Related Work and Literature Review
### Imperceptibility and the Human Visual System (HVS)  
As watermarking techniques developed, the focus shifted toward improved imperceptibility. The introduction of Human Visual System (HVS) models was a key step in this direction. HVS-based techniques embed watermarks in image regions where the human eye is less sensitive to changes, reducing visible distortions [14; 49]. [PERSON] et al. [50] leverage HVS orientation sensitivity by embedding watermarks in visually complex regions, where changes are less noticeable to the human eye. Similarly, [PERSON] et al. [12] adjust the embedding of the watermark based on the complexity of the image region to produce a stronger watermark within regions tolerating greater distortion. These approaches improve the visual quality of watermarked images, particularly in multimedia applications.  
Forensic procedure prioritises traceability and the preservation of the integrity of evidence throughout an investigation [51; 52]. Adaptive techniques such as Just-Noticeable Distortion (JND) are used to ensure that the watermarking remains intact despite processing. JND is the smallest visual change detectable by human perception [53; 54]. Digital watermarking uses JND to guide watermark placement, so watermarks remain hidden under normal conditions but are verifiable after processing [54]. Forensic watermarking may benefit from adaptive techniques that combine HVS and/or JND with robust hybrid transform-based methods, so watermarked images remain imperceptible and robust and preserve evidentiary value throughout forensic investigations.  
Although HVS-based models improve imperceptibility, they address only one aspect of watermark quality. Achieving security in the watermarking process requires additional protective measures.
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 5 Experimental Validation
### Thermal Vacuum Test
The sidereal edge optical microphotographs taken on the solar panel before and after the TV tests did not show cracks, plastic deformation, or dissociation in the attached stiffeners. These qualification-level tests and inspection results validated the structural safety of the VMLSA under launch and in-orbit thermal environments. The optimized design of the HRM, simplified electrical circuit, and solar panel deployment experiments carried out under different test conditions led to confidence in the performance of the deployment system. The highly damped deployable solar panel module proposed herein is effective for guaranteeing the structural safety of solar cells under launch environment without reducing the area of solar cell attachment and assuring reliable release action of the panel in a space environment.
## 2 Preliminaries and Models
### Pseudo-Random Keystream Generator--RC4  
As a popular PRF generator, with secret keys between communication nodes, RC4 can generate a keystream. This secret key is pre-computed during the system initialization. As any stream cipher, the generated keystream can be used for encryption by combining it with the plaintext using bit-wise  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(H\)SM/SM & HAN smart meter/ user/user node/sm \\ \(N\) & The number of users in a BAN neighborhood1 \\ \(k\) & The number of pairwise nodes for every user \\ \(K\) & Keystream based on stream cipher \\ \(M\) & RSA modular (large prime) \\ \(x_{(j,d)}^{i}\) & _user_; partitioned data into _user_; at timestamp \(d\) \\ \(x_{d}^{i}\) & _user_;’s data at timestamp \(d\) \\ \(r_{(j,d)}^{i}\) & _user_;’s pairwise key with _user_; at timestamp \(d\) \\ \(E_{(j,d)}^{i}\) & The encrypted form of \(x_{(j,d)}^{i}\) \\ \(\mathit{sd}_{i}\) & The secret key between CC and every node \\ \(\mathit{ind}_{i}[s](s=1,\cdots,k)\) & _user_;’s pairwise nodes set in serial timestamps \(T\) \\ \(LS(j,d)\) & _user_;’s locally spatial aggregation at timestamp \(d\) \\ \(L_{(j,d)}^{i}(j\in\mathit{ind}_{i}[s])\) & _user_;’s locally temporal aggregation for _user_; in \(T\) \\ \(AT(i,T)\) & _user_;’s temporal aggregation in \(T\) \\ \(AS_{d}\) & Spatial aggregation in a neighborhood at timestamp \(d\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations in the scheme.  
Exclusive-Or [19]. However our scheme is to replace the XOR (Exclusive-OR) operation typically found in stream ciphers with modular addition operation (+). To generate the keystream, RC4 needs two algorithms, i.e. Key-scheduling algorithm (KSA) and Pseudo-random generation algorithm (PRGA) [5; 14].  
KSA: KSA is to initialize a permutation with a variable length key between 40 and 2048 bits for PRGA.  
PRGA: once the permutation initialization of KSA has been completed, the stream of bits is generated using the PRGA.  
```
0: i = 0;  j = 0 // Two 8-bit index-pointers  S // The initial key keyed with a secret key Output: S // A permutation of all 256 possible bytes
1.for(i = 0; i \(\leftarrow\) 255; ++ i)
2.S[i] = \(i\);
3.end
4.k = 0;
5.for(i = 0; i \(\leftarrow\) 255; ++ i)
6.j = \(\{\)i \(\leftarrow\) 3[i] + key[i mod keylength]\(\}\) mod 256;
7.k = \(\{\)i\(\}\);
8.S[i] = S[i];
9.S[j] = k;
10.end
```  
**Algorithm 1**Key-scheduling algorithm (KSA)  
```
Input: i = 0;  j = 0 // Two 8-bit index-pointers Output: Z // Pseudo-random keystream
1.k = 0;
2.for(i = 0; i \(\leftarrow\) 255; ++ i)
3.i = (I + 1) mod 256;
4.j = \(\{\)j + S[i]\(\}\) mod 256;
5.k = S[i];
6.S[i] = S[i];
7.S[j] = k;
8.Z = S[S[i] + S[j]] mod 256];
9.end
```  
**Algorithm 2**Pseudo-random generation algorithm (PRGA)
## 2 Materials and Methods
### Evaluation Method  
In this study, Peak Signal-to-Noise Ratio (PSNR) was used to evaluate the performance of the wED algorithm. Within the calculation of PSNR, the input image is the image with noise, while the reference image is considered to be the desired signals only. The output, PSNR values were calculated as:  
\[\text{PSNR}=10\log_{10}(\frac{\text{peakval}^{2}}{\text{MSE}}). \tag{9}\]  
where peakval is the maximum value of the input image data, and MSE is the mean square error (MSE) between the input image and the reference image [42; 43]. The MSE is calculated as:  
\[\text{MSE}=\frac{\sum_{i=1}^{n}(\text{y}_{i}-\widetilde{\text{y}}_{i})^{2}}{n}, \tag{10}\]  
where y is the input data, \(\widetilde{\text{y}}\) is the reference data, and n is the total number of data points [44]. The PSNR values would be related to how close the input image is to the signal-only reference image. The PSNR values were used to evaluate the performance of algorithms.  
In in vivo studies, ground truth data are often unavailable. Therefore, in this study, the automatic registration [35] process from four successive acquisitions was applied to generate the reference data for quantitative evaluation. The four-time registration result would contain low noise and low motion artifacts, even though it had taken a long acquisition time. The automatic registration process utilized both rigid affine registration and nonrigid B-spline registration, using an open-source program, Elastix [45; 46]. The simplified registration model [35] is explained as follows. When registration is applied to the input image, I\({}_{1}\), and the reference image, I\({}_{0}\), the transformation matrix, T, can be defined as:  
\[\text{T}(\text{x})=\text{x}+\text{d}(\text{x}), \tag{11}\]  
where x is the special location of any pixel, and d(x) is the displacement caused by motion effects. Note that we only use one dimension, x, for simplicity, while \((\text{x},\text{y},\text{z})\) should be used in three-dimensional applications. In addition, the optimized transformation matrix, \(\widehat{\text{T}}\), under a cost function, C, is:  
\[\widehat{\text{T}}=\text{argmin}\;\text{C}(\text{T}_{\mu},\text{I}_{0},\text {I}_{1}), \tag{12}\]  
Figure 1: The diagram of the hand-held Swept Source OCT system used for pre-acquired datasets. “(\(\lx@math@degree\))”, “(\(\lx@math@degree\))”, and “(\(\lx@math@degree\))” close to “Circulator” are the Port 1, 2, and 3 of the circulator; CCD: charge-coupled device; PC: polarization controller.  
where \(\mu\) is the parameters vector, which contains parameters for both affine and B-spline transformation. After finding the best parameters vector with an adaptive stochastic gradient descent (ASGD) method, the registration process can be applied with weighting and averaging [35]. The registration process in this study was applied on MATLAB, whose results were used as the reference image when calculating PSNR.
## 5 Practical Application to the Real Machine
In this research we used the artificial neural network with back-propagation algorithm (BPNN) for predicting the tool-wear as shown in Figure 19 with the experimental values of input parameters described in Table 1.  
Figure 20 shows the neural network configuration and training parameters for predicting the tool wear. In the input layer, cutting force, feed rate, depth of cut, processing time, cutting speed and initial tool-wear are input neurons in which the initial tool-wear was assumed zero for the fresh tool. The force sensor was attached on the machine table for getting the value of cutting force. The values of cutting conditions such as feed rate, depth of cut and cutting speed, as well as the processing time were from the process planning for machining the product. Table 2 shows an example for predicting the tool-wear. To keep the quality of the machined part in consideration of the predicted tool-wear, if the amount of the tool-wear was out of the allowance limitation, the system required replacement of the cutting tool, otherwise the new cutting parameters were determined using another artificial neural network. Figure 21 shows the network configuration for generating the optimal feed rate. The depth of cut, the cutting speed and the amount of tool-wear were input values in the input layer. Table 3 shows the result of generation of the optimal feed rate. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Experiment No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline Cutting Speed \(v\) (m/min) & 370 & 95 & 370 & 95 & 370 & 595 & 370 & 595 & 495 & 495 & 495 \\ Feed Rate \(f\) (mm/min) & 2357 & 357 & 3790 & 3790 & 2357 & 2357 & 3790 & 3790 & 3153 & 3153 & 3153 \\ Cutting Depth \(a_{r}\) (mm) & 0.1 & 0.1 & 0.1 & 0.1 & 0.95 & 0.95 & 0.95 & 0.95 & 0.6 & 0.6 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental values of input parameters. Figure 19: Back propagation algorithm for ANN.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 6 Micromegas Readout of TPCs
### Solar Axion Searches: CAST and IAXO
The evolution of the background as a function of time is shown in Figure 18. To optimize the required background levels for the X-ray detectors of BabyIAXO and IAXO, a multi-approach strategy is considered where ground measurements, screening campaigns [85] of components of the detector, underground measurements, background models, and in-situ background measurements are combined with powerful rejection algorithms. The BabyIAXO detector design is largely based on the last CAST detectors, however, with a number of improvements. A substantially improved cosmic veto system should allow bringing the detector background to a level of \(\sim\)\(10^{-7}\) c/keV/cm\({}^{2}\)/s. A preliminary implementation of the active and passive shielding is shown in Figure 19. Additional improvements beyond this level are possible, following improvements in the shielding and veto extensions toward the pipe to the magnet, moving to a Xe-based operation and new electronics [86]. The final effect of these improvements in the background level remains to be quantified, but could potentially lead to the \(\sim\)\(10^{-8}\) c/keV/cm\({}^{2}\)/s levels. Micromegas detectors are a baseline technology for the X-ray detectors of BabyIAXO. However, additional technologies are under study. In the context of MPGDs, GridPix is a very interesting technology due to its excellent energy resolution and low energy threshold. Its background rejection in the region of interest has already been implemented in CAST [26; 27; 28; 29]. Further improvements are expected from the use of more radiopure construction materials and the use of the Timepix3 chip [81].
## 4 Methodology
### Classification Performance Evaluation  
In this work, 80% and 20% of the labeled pixels are randomly selected for training and testing, respectively, where the performance accuracy is calculated based on the testing dataset to avoid the problem of overfitting. In particular, True Positive (TP) denotes the correctly predicted positive values; False Positive (FP) is the value where actual class is negative and the predicted class is positive; False Negative (FN) means the scenario where the actual class is positive, but the predicted class is negative [29]. Various evaluation metrics can be defined based on these values such as accuracy, precision, and recall as in Equations (2) and (3). In addition, confusion matrix is also commonly used to visually assess the performance of various methods. In the confusion matrix, the rows denote the output class (predicted class) and the columns represent the target class (groundtruth class). A detailed explanation of the confusion matrix will be introduced where necessary in the following parts.  
The accuracy of the classification model for a particular class is defined by:  
\[Accuracy=\frac{\sum TP}{All}. \tag{2}\]  
In order to properly assess model performance for unbalanced datasets, Precision and Recall are also usually introduced [29; 32], which for a typical class are defined by  
\[Precision=\frac{TP}{TP+FP},\;Recall=\frac{TP}{TP+FN}\;\;. \tag{3}\]
# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 3 News Text Summarization Model Based on Framing Theory
### News Framing Recognition Module (NFRM) Based on Deep Learning  
#### 3.2.1 Constructing Frame Categories  
This study adopts the seven generic frames summarized by [PERSON] [47] as research categories: fact frame, responsibility frame, conflict frame, human interest frame, economic consequences frame, morality frame, and leadership frame.  
* Fact frame: Objectively describes the basic aspects of an event, including the cause, process, involved personnel, time, location, and other fundamental factual elements, aiming to answer the core question of \"what happened\".
* Responsibility frame: Attributes and assigns the cause or responsible party for the event's occurrence, or comments on and questions the actions of the responsible party, exploring the deep-rooted causes of the issue.
* Conflict frame: Emphasizes the conflicts of interest, divergent opinions, or contradictions among the involved parties in the event, highlighting the complexity and controversy of the event.
* Human interest frame: Evokes the audience's empathy and resonance by vividly depicting individuals' personal experiences and emotional journeys in the event, enhancing the infectiousness of the report.
* Economic consequences frame: Focuses on and evaluates the actual or potential impact of news events on the economy, including economic indicators, trade, gains, and losses.
* Morality frame: Examines news events from a moral and ethical perspective, discussing the moral concepts of fairness, justice, integrity, obligation, and rights behind the event, judging the moral responsibility and social norms of the parties involved, and prompting the public to reflect on and discuss moral values.
* Leadership frame: Focuses on reporting the role and performance of leaders and decision-makers in the event, assessing their decision-making abilities, crisis response strategies, and leadership roles for the public and team, and exploring how leaders' words and actions influence the event's process and outcome.  
The above seven frame types are derived from content analysis of a large number of news reports [44; 45; 50; 51] and have cross-linguistic and cross-cultural universality. Whether it is political news, economic news, social news, entertainment news, etc., they can all be effectively classified using these seven frames. Moreover, the news reporting characteristics they depict, such as causal attribution, emotional tendency, moral judgment, etc., can find corresponding semantic indicators in the text, facilitating the definition of clear  
Figure 1: Workflow of the news text summarization model based on framing theory.  
operationalized variables and enabling the possibility of automatic frame type recognition by computers.
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 4 Experimental and Analysis
### Physical Experiment  
To further validate the feasibility of the proposed solution, a physical platform as depicted in Figure 11 was constructed to conduct experimental tests on the demodulation circuit.
## 3 Results  
### Effect of Growth Promoters on Strain Q1  
The effect of CSN on the growth of strain Q1 was significantly different from that of the control, whereas the effects of other growth promoters on the growth of strain Q1 were not significantly different (Figure 1a). CSN was found to promote the growth of strain Q1. However, all four tested promoters did not significantly affect the growth of strain Q1 (Figure 1b).  
In the nitrification process, ammonia is oxidized to nitrite and nitrate by nitrifying bacteria under aerobic conditions. This process involves the following two steps: Equation (2), the conversion of ammonia to nitrite, and Equation (3), the conversion of nitrite to nitrate [26]. As shown in Figure 2, the ammonia, nitro, and nitroso nitrogen concentrations were used to screen the growth promoter CSN, which can significantly increase the denitrification efficiency of nitrifying bacteria. The initial NH\({}_{4}\)+-N concentration was 50 mg/L, as shown in Figure 2a, in which the ammonia nitrogen content was significantly lower after 24 h of GA and CSN treatment compared with the control and after 48 and 72 h of the four growth promoters treated in the medium. During 48 and 72 h, the ammonia nitrogen level was much lower in all four cultures that had been treated with growth promoters.  
\[\mathrm{2 NH_{4}}^{+}+\mathrm{3O_{2}\to 2H_{2}O+4H^{+}+2 NO_{2}}^{-} \tag{2}\]  
\[\mathrm{2 NO_{2}}^{-}+\mathrm{O_{2}\to 2 NO_{3}}^{-} \tag{3}\]Figure 1: (**a**) Effect of growth promoters on the growth of strain Q1; (**b**) effect of growth promoters on the growth of strain Q1 at different time points. Different letters indicate statistically significant differences (\(p<0.05\)).  
Figure 2: Effect of growth promoters on the denitrification capacity of strain Q1: (**a**) NH4\({}^{+}-\)N; (**b**) NO2\({}^{-}-\)N; (**c**) NO3\({}^{-}-\)N.  
Figure 2b,c indicates that ammonia nitrogen was first converted to nitrite and subsequently from nitrite to nitrate, which is compatible with the features of nitriifying bacteria. The clearance rate of ammonia nitrogen from strain Q1 rose by 13.08% at 72 h as a result of the CSN treatment. Figure 2b depicts the most striking conversion impact at 48 h, when strain Q1 treated with GA and CSN dramatically enhanced its conversion rate to nitric nitrogen, with GA performing better.  
The most pronounced response to the conversion to nitrate nitrogen at 72 h is shown in Figure 2c, and the rate of conversion of strain Q1 treated with GA, CSN, NAA, and DA-6 to nitrite was greatly increased in comparison to the control group, in which the effect of CSN was most noticeable. The impact of CSN was the clearest. Similar to Acinetobacter sp. T1, strain Q1 observed the creation of NO2-N and NO3-N during the elimination of NH4+-N [27]. Moreover, strain Q1 was more effective in removing nitrogen from the air than strain L7 of the Bacillus methylotrophicus (48%) [28].