esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Evaluation
### Data Sets  
The Amazon reviews data set [52] serves as a widely referenced data set for sentiment analysis across various domains. This data set encompasses reviews from four distinct Amazon product categories: (i) electronics, (ii) DVDs, (iii) kitchen appliances, and (iv) books. Each product category is treated as a separate domain, featuring 2000 labeled samples and an average of 3000 unlabeled samples. Twelve cross-domain tasks are formulated, connecting each pair of domains, such as kitchen \(\rightarrow\) books, \(\ldots\), books \(\rightarrow\) kitchen. A Multi-Layer Perceptron (MLP) is utilized as a feature extractor with the pre-processed version of the Amazon Review data [43] for fair comparison. Each review is expressed as a 5000-dimensional feature vector using the most frequently used features, with feature values equal to feature frequencies.  
The FDU-MTL data set [53] encompasses sixteen distinct domains, each containing reviews with binary sentiment labels. Within each domain, there are 1400 labeled samples and 2000 unlabeled samples, further divided into training (70%), validation (10%), and testing (20%). To provide an unbiased comparison to the competitors baselines methodologies that leverage this data set in UDA, three domains are chosen: baby, MR, and apparel. As a result, six transfer tasks are set up: baby \(\rightarrow\) apparel, baby \(\rightarrow\) MR, \(\ldots\), and apparel \(\rightarrow\) MR. As a feature extractor architecture, a CNN with one convolution layer is used.  
The Spam data set [54] is a text data collection that compromised of two subcategory from the UCI repository: the SMS-Spam data set [55] and the Enron Spam data set [56]. The Enron data set comprises 1440 labeled samples and 3500 unlabeled samples, consisting of emails exchanged among senior executives at Enron Corporation. In contrast, the SMS-Spam Collection data set contains short messages and encompasses 900 labeled samples and 1800 unlabeled samples.
# Dynamical Manipulation of Surface Plasmon Polaritons
## 3 Dynamical Focusing of SPP
The focal length of SPP is given by \(f=(4r_{m}^{2}-m^{2}\lambda_{sp}^{2})/4m\lambda_{sp}\), where \(m\) is the maximum order number and \(r_{m}\) is the radius of \(m\)th zone. The odd-order zones are illuminated by incident light and can give rise to SPP, while the even-orders are not illuminated. Thus, the focal length of SPP can be tuned by changing \(m\) and \(r_{m}\). Changing the effective index of SPP propagating along the metal surface can also modulate the focusing property of SPP lens [92]. Figure 4: (**a**) Controlled focusing of SPP with vortex incident beam [83]; (**b**) Adjusting the SPP focus by modulating the phase and amplitude of the incident light with a spatial light modulator (SLM) [36]; (**c**) A dynamically tunable and reconfigurable SPP lens immersed in the water [92]. Reproduced with permission from: [83], RSC, 2016, [36], Springer Nature, 2011, [92], Springer Nature, 2013. In Figure 4c, an arc-shaped grating SPP lens is immersed in the water. Besides the 808 nm incident light used to excite SPP, another blue-violet diode laser with a wavelength of 405 nm is utilized to heat up the gold film and generate vapor bubbles on the surface because of the laser-induced thermal effect. The size and the location of the bubbles can be modulated by moving the laser spot and adjusting laser power. The generation of the bubbles causes the local changes in the effective index for SPP during propagation. The effective index of SPP inside the surface bubble (1.02) is smaller than that outside the bubble (1.379). Thus, the truncated sphere shape bubble functions like the conventional concave lens in the free space. Without the bubble, the SPP are focused in the center. When a bubble is generated and its size is decreased gradually, the SPP focus moves from the boundary of surface bubble to the outside and the profile of the focus evolves from a spot to a collimated beam, as can be seen from the experimental results in Figure 4c.
## 3 Controller Design and Stability Analysis
### Stability Analysis
Notably, in order to make sure \(\Lambda_{i}>0\) always holds, all the action candidates \(\mathbbm{i}_{i,\mathbf{n}}=\left\{\mathfrak{u}_{i,1}^{(\mathbf{n})}...\mathfrak{u}_{i, p}^{(\mathbf{n})},...\mathfrak{u}_{i,p}^{(\mathbf{n})}\right\}\) should be positive._  
**Remark 11**. : _The proof of Theorem 1 requires the parameter \(\Lambda_{i}>0\) to be a constant. However, the parameter \(\Lambda_{i}\) is online tuned by a fuzzy Q learning mechanism, and therefore \(\Lambda_{i}\) is varying in the implementation of the proposed controller. To handle this issue, we design that the time interval between two consecutive fuzzy inferences of \(\Lambda_{i}\) in fuzzy Q learning is 20-times greater than the time interval between two consecutive control actions \(\tau\) (eg. 0.1s between two consecutive \(\hat{\mathfrak{u}}_{i}^{(\mathbf{n})}\) (equally, two consecutive \(\Lambda_{i}\)) while 0.001s between two consecutive \(\tau\)). Namely, \(\Lambda_{i}(t)=\Lambda_{i}(t^{*})\), \(\forall\ t\in[t^{*},t^{*}+20L]\); \(\tau(t)=\tau(t^{*})\), \(\forall\ t\in[t^{*},t^{*}+L]\). \(t^{*}\) is any time moment when the algorithm works. Thereby, not only can the time derivative of \(\Lambda_{i}\) be negligible in the proof of Theorem 1, but the computational load is also decreased._  
Figure 3: Flow chart of fuzzy Q learning to determine \(\Lambda_{i}\).
## 2 Forward and Backward Contribution Matrix
### Load Admittance Conversion  
Now, we will use current injection instead of power injection for the N-bus converged AC load flow. With the equivalent current injection \(\left[\overline{T}^{ori}\right]\), the solved AC network can be represented by:  
\[\left[\overline{T}^{ori}\right]_{N\times 1}=\left[\overline{Y}^{ori}\right]_{N \times N}\left[\overline{V}^{sol}\right]_{N\times 1} \tag{13}\]  
where \(\left[\overline{Y}^{ori}\right]\) is the original admittance matrix of the system and \(\left[\overline{V}^{sol}\right]\) is the solution of bus voltage vector. Note that \(\left[\overline{T}^{ori}\right]\) is the equivalent current injections of both the generator buses and load buses. The \"equivalent load-admittance\" for load bus \(i\), \(y_{ii}^{l}\), will be extracted from the original current injection by:  
\[\begin{array}{ll}\overline{I}_{i}^{ori}&=\left(\frac{\overline{S}_{i}}{ \overline{V}_{i}^{ori}}\right)^{*}=\frac{p_{i}-jq_{i}}{\left(\overline{V}_{i} ^{sol}\right)^{*}}=\frac{p_{i}-jq_{i}}{\overline{V}_{i}^{sol}\cdot\left( \overline{V}_{i}^{ori}\right)^{*}}\times\overline{V}_{i}^{sol}\\ &=y_{ii}^{l}\cdot\overline{V}_{i}^{sol}\end{array},\;i\in\mathit{Load} \tag{14}\]  
where \(\mathit{Load}\) is the set of load buses, and the \"equivalent load-admittance\" of bus \(i\) can be found as shown in Figure 2 from:  
\[y_{ii}^{l}=\frac{p_{i}-jq_{i}}{\left|\overline{V}_{i}^{sol}\right|^{2}}\]  
With the conversion of load into admittance \(y_{ii}^{l}\), an \(N\times N\) matrix can be created for the load currents as:  
\[\left[\overline{T}^{ori,l}\right]_{N\times 1}=\left[\overline{Y}^{l}\right]_{N \times N}\left[\overline{V}^{sol}\right]_{N\times 1} \tag{15}\]  
where \(\left[\overline{Y}^{l}\right]\) is a diagonal matrix with:  
\[y_{ij}^{l}=\begin{cases}y_{ii}^{l}&,\;\;\;j=i\;\mathit{and}\;i\in\mathit{Load} \\ 0&,\;\;\;j=i\;\mathit{and}\;i\
otin\mathit{Load}\end{cases}\]  
Thus, new matrix representation for \(\left[\overline{T}^{ori,l}\right]\) can be found.  
By separating the load current vector from the original current injection vector, \(\left[\overline{T}^{ori}\right]\), we have:  
\[\left[\overline{T}^{ori}\right]=\left[\overline{T}^{\mathrm{mod}}\right]+ \left[\overline{T}^{ori,l}\right]\]  
Figure 2: Load equivalent current modifications.
## 7 Evaluation
### Results  
From Table 7 and Figure 11, the judgments of semantic expressiveness indicated that MParser had better results since _Clear_ and _Perfectly clear_ had the largest percentage overall. Additionally, the _Perfectly clear_ percentage between short sentences (_N = 50_) and long sentences (_N = 25_), at 44% and 23%, respectively, indicated that performance with short sentences was more significantly clear in semantic expressiveness.  
Table 8 shows the experimental results using MParser and human expert labeling. The average \(\kappa\) values were 0.693 for inter-annotator agreement and 0.717 for intra-annotator agreement. As 0.6 < \(\kappa\) < 0.8 indicates substantial agreement, the empirical results showed good consistency between the predictions generated by our approach and those of experts. The analysis of the \(\kappa\) values between three experts found that the agreement \(\kappa\) values for experts 2 and 3 were relatively higher. Experts 1 and 2, 3 had a slight gap, but the \(\kappa\) values were still within the range 0.6 < \(\kappa\) < 0.8. Table 8 found that experts 2 and 3 had higher average \(\kappa\) values than expert 1 in intra-annotator agreement. In addition, we calculated average \(\kappa\) values for intra-annotator agreement between short sentences and long sentences, as shown in Table 9. The average \(\kappa\) value for long sentences was significantly lower than that for short sentences. This result is consistent with the trend for our intrinsic evaluation, which showed that the higher complexity of a sentence was more likely to cause disagreement in case grammar labeling. In summary, comparing expert and MParser outputs, inter-annotator and intra-annotator agreement presented substantial results, and there was no major disagreement between our MParser results and those of the experts.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & _Perfectly_ & _Clear_ & _Acceptable_ & _Unclear_ & _Very_ & _Total_ \\ \hline
**Group A** & 119 & 152 & 128 & 21 & 15 & 435 \\
**Group B** & 130 & 192 & 98 & 9 & 6 & 435 \\
**Group C** & 143 & 223 & 109 & 4 & 1 & 480 \\
**Group D** & 127 & 166 & 90 & 3 & 4 & 390 \\
**Group E** & 139 & 226 & 101 & 11 & 3 & 480 \\  & **30\%** & **43\%** & **24\%** & **2\%** & **1\%** & **2220** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The judgements of semantic expressiveness in intrinsic evaluation.  
Figure 11: The percentage of semantic expressiveness for short and long sentences in intrinsic evaluation.
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 4 Results and Discussion
### Validation  
The comparison result shows a high correlation between the inverted SM and the in situ values, as shown in Figure 5, with an R of 0.85 and an RMSE of 0.032 cm\({}^{3}\cdot\)cm\({}^{-3}\). Figure 5 reveals that the inversion value is generally lower than the measured one, especially when the soil is relatively humid. The slight underestimation can be explained from the following: In the MIMICS model, the vegetation canopy is considered to be uniform, but the actual situation is that winter wheat has a typical ridge-row planting structure with large gaps in the canopy. Therefore, the MIMICS model will underestimate the contribution of the soil  
Figure 4: Spatial distribution map of SM inversion value. (**a**) 16 April 2019; (**b**) 28 April 2019; (**c**) 10 May 2019.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Polarization** & \(\mathbf{a_{s}}\) & \(\mathbf{b_{s}}\) & \(\mathbf{c_{s}}\) & \(\mathbf{d_{s}}\) \\ \hline VV & \(-1.51\) & 2.01 & \(-0.17\) & 0.740 \\ VH & \(-0.116\) & 0.155 & \(-0.0142\) & 0.573 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The empirical coefficients for Equation (7).  
backscatter. Based on Equation (7), an underestimation of the soil backscattering coefficient will lead to an underestimation of the SM.  
Different combinations of single-temporal, dual-temporal, and three-temporal data were used as data sources for retrieving the soil moisture, and the inversion accuracy of the soil moisture is listed in Table 6.  
When the single-temporal remote sensing data of 28 April were used as the data source, the soil moisture and soil roughness were solved simultaneously from the only two equations, and the inversion accuracy of soil moisture was the lowest one. When the dual-temporal data act as the data source, we obtained four equations for solving three unknowns, and the inversion accuracy of soil moisture was significantly improved. In the case of the three-temporal data, the inversion accuracy of soil moisture was further improved. The reason is that the dual-polarization SAR data are not completely independent, which causes a certain uncertainty in solving the SM and Z parameters simultaneously based on the single-temporal SAR data.
## 1 Introduction
Our first pilot study [5] applied fuzzy stretching to assist image enhancement and then a contour tracking and region labelling method carried out the rest of the identification process; however, because it assumed that the shape of the cyst was oval, the accuracy of this study was not satisfactory. Later, we decided to apply the pixel clustering approach that decides the membership of a pixel to a clustered object based on fuzzy logic, so that the automatic cyst-detecting algorithm was not necessarily dependent on the shape's assumption. An approach with Possibilistic C-Means (PCM) appeared to be effective against speckle noise but tended to underestimate the cyst region, especially when candidate objects overlapped [13]. Fuzzy C-Means (FCM) replaced PCM in forming clusters [14] and generally showed better result. FCM is a popular unsupervised machine learning algorithm that assigns each datum a degree of fuzzy membership, with the distance measured to the nearest cluster centroid [15]. FCM allows each datum (pixel in the image in this case) to belong to two or more clusters with respect to the degree of membership in each cluster. Thus, FCM classifies the image into clusters with similar pixels in the feature space, iteratively minimizing the cost function defined by the distance between the pixel and the candidate cluster centers in the feature domain. With such flexibility, FCM has been successful in solving segmentation problems in many medical and engineering domains [16; 17; 18; 19; 20; 21]. However, it still suffers from object disconnection problems during learning, and our retrospective analysis of it [14] concluded that we need a better image-enhancement policy to overcome the difficulty of a cyst forming during the FCM process. Thus, we propose a better fuzzy stretching algorithm based on the trapezoid type of membership function under the FCM pixel clustering framework. In this experiment, we also investigate the validity of FCM quantization in forming a cyst by comparing it with ART2 learning [23], which was recently successful in extracting soft tissue tumor. Details of the image-enhancement algorithm are explained in Section 2, while the cyst extraction process by means of FCM is described in Section 3. Experimental result analysis is then discussed in Section 4, followed by a summary of this paper's main contribution in Section 5.
## 3 Case Study and Results Analysis
### Data Pre-Treatment  
Before the calculation, the data must be screened and normalized to put them in the range of 0 to 1 using the following formula:  
\[Z=\{z_{i}\}=\frac{y_{i}-y_{\min}}{y_{\max}-y_{\min}}\qquad i=1,2,3,...,n \tag{46}\]  
where \(y_{\max}\) and \(y_{\min}\) are the maximum and minimum value of sample data, respectively. The values of each data are in the range [0,1] for eliminating the dimension influence.  
Furthermore, this paper will use a quantum fireworks algorithm (QFA) to optimize the parameters \(C,\sigma\) of the wavelet support vector machine to find optimal parameters to improve the prediction accuracy. In the parameters optimization, we adopt the mean square error (MSE) as the fitness function to realize the process of QFA, and the formula of MSE is as follows:  
\[function(i)=\frac{\sqrt{\sum\limits_{i=1}^{n}\left(y_{i}-y_{i}^{\prime}\right)^ {2}}}{n} \tag{47}\]  
where \(y_{i}\) is the actual value; \(y_{i}^{\prime}\) is the prediction value; and \(n\) is the sample number.
## 3 Results
### Cross-Site Evaluation
#### 3.4.2 Evaluation at the University of Leipzig  
We evaluated the tool with three different audiences, each time with the same scenario. The first group consisted of six scientists from our institute, all medical informatics specialists with experience in clinical data and medical terminology. The aim here was to see whether they could be a target group as productive users of the tool, strategically as verifiers. The second group consisted of 24 Master's students of medical informatics as part of a course on information management in clinical research in Leipzig. They had minimal prior training in the field of medical documentation and medical terminologies. We wanted to determine how well \"untrained\" staff could carry out initial annotation and at the same time, show the students how medical terminology is used in practice using a realistic example. The third group consisted of 75 participants in a further training course: the TMF School [25] is an annual training program run by TMF e.V. and is aimed at experienced researchers and employees in research networks. These include not only medical researchers and medical IT specialists but also project managers, bioinformaticians, lawyers, etc. In addition to the training effect, it was particularly interesting to see whether there would be any differences in the annotations compared to those of the students.  
The data set to be annotated comprised 19 data elements. It was part of a genuine research project to recognize potentially inadequate medication in multimorbid patients. It is relatively simple in structure and includes demographic data, laboratory values, and medication administration. Users were provided with brief documentation on how to use the tool, and one value was annotated together as an example. The actual annotation work was scheduled to take 30 min.  
As a result, users found the tool helpful and illustrative. However, not everyone was able to use it without help. The estimated time window of 30 min proved to be far too short. In addition, it was not a formal systematic evaluation. Some participants obviously used the time to playfully familiarize themselves with the available functions, as the annotations were chosen randomly. Among the \"meaningful\" annotations, it is noticeable that many users obviously trust the string-based search and sorting, especially for more complex terminologies. For example, the SNOMED CT concept \"Age (qualifier value)\" is listed prominently in the first place for \"age\" and was therefore selected most frequently, although other concepts like \"Current chronological age (observable entity)\" appeared more suitable. This was not the case for LOINC annotations, where only one person chose \"43993-51 Age at delivery\" instead of \"30525-01 Age\".
# Real-Time Analysis of Facial Expressions for Mood Estimation
## 4 Performance Evaluation
### Material: Data Set for Evaluation  
To build the data set, 60 individuals (30 females) participated without being instructed to pose specific expressions or emotions. Data collection took place in natural environments, such as the participants' workplaces or homes, rather than in a laboratory setting. Consequently, the resulting video sequences exhibit diversity in background and visual distractors like non-frontal faces and the presence of facial artifacts (e.g., glasses or beards), see Figure 5.  
Figure 4: Example of the sequence processing, including the AU identification for each frame. The results of the different levels of mood estimation are: L1—Exhuberant, L2—Elated, L3—Positive.  
Figure 5: Frames of the different participants of the mood data set including examples of visual distractors.  
Participation was entirely voluntary, and participants were informed that the data set would be used exclusively for research purposes. Additionally, they were assured that all collected information would be kept private, anonymous, and confidential.  
To evoke different affective states, a set of stimuli video, lasting from one to four minutes each video, was prepared to elicit one of the eight mood categories. As a reference, the International Affective Picture System (IAPS) was used [36]. IAPS is a static image data set intended to elicit certain emotions. The IAPS data set contains 1182 images that were rated during a period of ten years by males, females and children using the Self-Assessment Manikin (SAM) questionnaire, a non-verbal pictorial assessment technique that directly measures the PAD dimensions associated with a person's affective reaction. Figure 6 shows icons used for each PAD dimension in the SAM questionnaire.  
The procedure to record each video followed the below steps:  
1. The experiment was explained to the participant, mainly the questionnaire to be completed in the last step. The participant signed the informed consent and demographic data were gathered.
2. A randomly selected video from the set prepared to elicit one of the eight mood categories was presented to the participant. A maximum of two videos was shown to the participants.
3. While the stimulus video was playing, the participant's face was recorded. The only recommendation given to the participant was to remain moderately centered in front of the camera.
4. At the end, participants completed the SAM questionnaire to obtain the mood values in the PAD space. Because PAD levels were difficult to understand for some participants, a post-processing stage was carried out, and sequences with strong discrepancy regarding the stimuli were discarded.  
Finally, we obtained a data set with 69 video sequences and we enriched the database metadata with the subjects' age and gender reported by themselves.