esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Model for Emotion-Driven Behavior Extraction from Text
## 5 Methodology
### Emotion-Behavior Pair Filter  
To extract emotion-behavior pairs, we adopt the prompt paradigm, which converts the task into a binary classification problem. The approach involves selecting an emotion span \(s_{e}\) from \(E\) and a behavior span \(s_{b}\) from \(B\), assuming a causal relationship between them. We generate a prompt by applying the selected spans to a template such as \"the person is \(s_{e}\), so this person \(s_{b}\)\". This template is denoted as \(\tau\).  
However, determining the causal relationship between emotion and behavior solely based on the spans can be challenging without considering contextual information and background knowledge. For example, it is difficult to judge the logical correctness of the statement \"he is happy, so he thanked the policeman\" based solely on the sentence itself. To address this, we incorporate the document \(D\) into the generated prompt, resulting in a more comprehensive understanding. This operation is referred to as \(Prompt\), and the resulting text is denoted as \(P\):  
\[P=Prompt(D,s_{e},s_{b},\tau) \tag{9}\]  
Figure 4 provides an example illustrating the steps involved in generating \(P\).  
Subsequently, we construct the input representation for \(P\) as \(P_{input}=\{[CLS],P,[SEP]\}\) and feed it into the encoder:  
\[Rep_{P}=BERT(P_{input}) \tag{10}\]  
where \(Rep_{P}=\{r_{[CLS]},r_{1},r_{2},\ldots,r_{p},r_{[SEP]}\}\) represents the token representations in \(P\), and \(p\) is the number of tokens in \(P\).  
Emotion-behavior pair filtering is considered a text classification task, and we utilize \(r_{[CLS]}\) as the input for the decoder. Finally, we feed \(r_{[CLS]}\) into a fully connected (FC) layer as the decoder, where the output is a binary classification indicating whether there is a causal relationship between the selected emotion span \(s_{e}\) and behavior span \(s_{b}\). The structure of the FC decoder is shown in Figure 3c. To obtain the final result of the emotion-behaviorpair filter, we apply the \(argmax\) function to determine the index of the output from the FC decoder:  
\[Y_{filter}=argmax(FC(r_{[CLS]})) \tag{11}\]  
If \(Y_{filter}\) is 1, this indicates that \(s_{b}\) is driven by \(s_{e}\), implying a causal relationship between them. Otherwise, no causal relationship is identified.  
Overall, the proposed methodology consists of three parts: an emotion extractor, a behavior extractor, and an emotion-behavior pair filter. Each part includes an encoder and a decoder. The encoders convert input tokens into their vector representations, while the decoders generate the predicted labels for each token or perform binary classification for the emotion-behavior pair filter.
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
## 4 Receding Horizon Control with Temporal Logic Planning
### Receding Horizon Control with Temporal Logic Planning
/*Main Body*/ repeat  Receive the signal strength \(Z_{k}\) at the position \(p_{k}\);  Update \(\Delta_{A1}\), \(\Delta_{A2}\ldots\) in \(\Delta_{A}\), \(\Delta_{B1}\), \(\Delta_{B2}\ldots\) in \(\Delta_{B}\), \(\Delta_{C1}\), \(\Delta_{C2}\ldots\) in \(\Delta_{C}\), and \(\Delta_{D1}\), \(\Delta_{D2}\ldots\) in \(\Delta_{D}\) according to the estimated positions of signal sources; if\(\Delta_{Aj}\), \(\Delta_{Bj}\), \(\Delta_{Cj}\), or \(\Delta_{Dj}\) (\(j=1,\ldots\)) are updated then  Select a node in \(\Delta_{Aj}\), \(\Delta_{Bj}\), \(\Delta_{Cj}\), or \(\Delta_{Dj}\); else  The given node is not changed in \(\Delta_{Aj}\), \(\Delta_{Bj}\), \(\Delta_{Cj}\), or \(\Delta_{Dj}\). endif if Case 1 is satisfied. then  Solve (12) to obtain \(u_{k}^{*}(0),\ldots,u_{k}^{*}(N-1)\). endif if Case 2 is satisfied. then  Solve (13) to obtain \(u_{k}^{*}(0),\ldots,u_{k}^{*}(N-1)\). endif if The given node in \(\Delta_{A1}\) is arrived then \(\Delta_{A1}\) is replaced by \(\Delta_{A2}\), \(\Delta_{A3}\ldots\) in order. endif if All the given nodes in \(\Delta_{A}\) are arrived then  we orderly replace \(\Delta_{A}\) with \(\Delta_{B}\), \(\Delta_{C}\), \(\Delta_{D}\), \(\Delta\). endif \(u_{k}^{*}(0)\) is used as control input at time \(k\); until\(p_{k}\in\Delta\)
```  
**Algorithm 1** Receding Horizon Control with Temporal Logic Planning (RHC-TLP).
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 5 Actuation Methods
### Shape Memory Alloy Actuators  
Shape memory alloys (SMAs) are metallic alloys that can be reversibly deformed. These materials can memorize their shape and are able to return to their pre-deformed shape by changing the temperature and the state of stress. Usually, these alloys undergo temperature-induced phase transformation between the less-ordered martensitic phase, and the ordered crystallographic austenitic phase. An SMA material is deformed at low temperature and then heat treated to memorize the shape; the plastic strain is recovered by increasing the temperature above the transition temperature. Frequently used shape memory materials are the alloys of nickel and titanium (Nitinol).  
Conventional endoscopes are controlled by a wire traction to achieve bending of the endoscopes inside the body cavities. Many researchers have proposed the use of SMA coil actuators to permit bending of the tip of the endoscope. [PERSON] et al. used a pair of SMA coil springs to achieve the bending of optical guide fibers. One of the coils is heated via Joule heating, causing it to recover its shape and rotate the actuation ring connected to its free end. The actuation ring is connected to one end of the pull wire while the other end of the wire is connected to the distal end of the endoscope. The actuation of the coil bends the tip of the endoscope in one direction. By halting the current in this coil and activating the other coil, the endoscope is returned to its original position [124]. [PERSON] et al. used three SMA coils distributed around the working channel of a catheter to achieve active bending of the endoscope in all directions [125].  
[PERSON] et al. developed an endoscope based on a CCD imager, and the omni-direction bending was achieved using three SMA coil actuators [126]. [PERSON] et al. fabricated an endoscope where the bending of the CMOS imager and light-emitting diodes (LEDs) was obtained using three SMA wires that permit a large bending angle at a lower expense. The fabricated endoscope is shown in Figure 19[70].
## 4 Discussion
### Texture and Color Analyses  
One of the main challenges faced today by the PB food industry is to achieve a pleasant visual appearance and texture attributes of the end-products.  
Considerable changes in the overall texture were observed after LAB fermentation, as suggested by SACCO and in accordance with [PERSON] et al. [58] and [PERSON] et al. [59]. LAB starters increased firmness by 37% to 42%. [PERSON] et al. [60] studied the technological performance of 64 EPS-producing LABs isolated from Lactobacillus, Streptococcus and Enterococcus genera and identified a strain from _Streptococcus thermophilus_ to be promising in improving the viscosity, flavor, consistency, color and appearance of fat-free yogurt drinks. In our study, the ST and STLL fermentations increased the consistency, cohesivity and viscosity of the beverages more than the STLP fermentation. [PERSON] et al. [58] attributed the difference in textural improvement after different LAB fermentations to the difference in the exopolysaccharideacharide (EPS) amounts produced. In particular, the network created by EPS and protein could be, in part, responsible for the texture improvement, especially for the viscosity. However, a reduction in viscosity after reaching its maximum was reported by [PERSON] et al. [61], and the decrease was attributed to a degradation of the heterocyclysaccharide produced, thus resulting in a partial loss of the network. The higher acidification produced by STLP could be behind the degradation of the EPS produced and, hence, the decrease in consistency and viscosity observed. The EPS production by the dairy LAB varies from strain to strain and is affected by several factors, among them the composition of the medium. Using black chickpeas as a medium and under the conditions of our study (pH of 4.5, T 30 \({}^{\circ}\)C), ST in monoculture and in a co-culture with _Lactococcus lactis_ showed to enhance cohesivity, consistency and viscosity more than in a co-culture with _Lactobacillus plantarum_.  
To understand better if the changes in texture observed were favorable or not, we have compared our results with the one obtained by [PERSON] et al. [62], who analyzed the textural parameters of soy, coconut, cashew and almond yogurt analogs and compared them with commercial dairy yogurt. Compared with the results of [PERSON] et al. [62], the firmness and cohesiveness of ST, STLL and STLP were higher. The consistency of the STLP beverage was similar to the dairy yogurt, while the consistency of the ST and STL beverages was in the range of those of the PB yogurt analogs. The viscosities of ST, STLL and STLP were in the range of those of the PB yogurt analogs.  
Fermentation increased the lightness of the three fermented beverages in contrast with findings by [PERSON] et al. [63]. The \(\Delta\)E values were between 3.5 and 4.05, meaning that the difference between samples is clearly detected [64].
## 2 Results and Discussions  
### Development of the HPLC-IV-ELSD Quantitative Method  
#### 2.1.1 Optimization of the HPLC Condition  
A reasonable elution program of acetonitrile and water was achieved to obtain a good separation profile of 10 saponins. Notably, the separation of G-Rg1 and -Re, which have been a challenge for RP-HPLC analysis of _Panax_ species, was solved. A UV detector at the wavelength of 203 nm is usually employed for analysis of PPT- and PPD-type saponins [20]. However, the detection of OCT-saponins, the main constituents of PV at 203 nm, is restricted due to the lack of chromophore in their structures. Therefore, to detect OCT-saponins, UV 196 nm should be used, but the detection at such a low wavelength might be prone to baseline instability and insufficient peak intensity.  
To overcome these limitations of UV, ELSD might serve as a better choice for the detection of OCT-type saponins in PV. We compared the performance of ELSD at a nebulizer gas pressure of 3.5 bar and 3.8 bar, and the pressure of 3.8 bar showed better reproducibility of increased peak area by the decrease of the relative standard deviation (% R.S.D.) (Table S1). The design of the detector selects droplets as a function of size. Thus, large droplets, which are more difficult to evaporate and cause an increased noise level, were discarded in the glass nebulization cell. Therefore, the higher nebulizing gas pressure could decrease the size of droplets and allow the eluent to enter the drift tube for detection,which led to an improvement of reproducibility. However, it is worth mentioning that too high a nebulizing gas pressure can permanently damage the drift tube.  
HPLC chromatograms detected by the UV detector and ELSD in the optimized condition are shown in Figure 2. From the chromatograms, it is notable that ELSD could well detect at least ten major saponins in PV, including the characteristic OCT-type saponins of PV, i.e., M-R1, M-R2, V-R2, V-R11, and p-RT4 even in low concentrations. In contrast, UV at 196 nm could only detect six peaks, including those of the major OCT-saponin M-R2, PPT-type saponins (N-R1, G-Rg1, and G-Re) and PPD-type saponins (G-Rb1, and G-Rd). Thus, the UV detector could only detect the major saponin M-R2 with very low peak intensity, but neglected the other OCT-saponins (M-R1, V-R2, V-R11, and p-RT4). This agrees with the advantage of ELSD as a universal detector, which could well detect most of the non-volatile compounds. Hence, the ELSD chromatogram could well reflect the relative correlations of saponin contents in PV extracts. M-R2 is the main constituent of PV extracts, followed by PPT-type G-Rg1, and then the PPD-type saponins G-Rb1 and G-Rd. The other saponins, including N-R1, G-Re, M-R1, V-R2, V-R11, and p-RT4, are minor compounds. In contrast, by not taking into account the quantitative results, the UV chromatogram of the PV extract could suggest erroneously that M-R2 is only a minor component in PV because of its very low-intensity peak.
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 2 Phoneme Clustering
### Baseline Phoneme Recognition with TIMIT Dataset  
TIMIT corpus is designed to provide speech data for studies of acoustic-phone and speech recognition systems [24; 25]. Phonetic transcripts for all sentences are provided in the TIMIT corpus distribution. A set of 61 phoneme labels from ARPAbet symbols are used in the transcripts. We subsequently collapsed the set of 61 labels into a smaller set of 39 labels in Table 2 during the scoring of performance of phoneme recognition. The baseline monophone classifier is developed by a recurrent neural network (RNN) [34; 35; 36]. From the input speech signals, mel-frequency cepstral coefficients (MFCCs) [37; 38] are extracted from the input speech, their first and second order derivatives are computed,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Vowels (19)**} & \multicolumn{4}{c}{**Consonants (31)**} & \multicolumn{4}{c}{**TIMIT Extension (11 + 1)**} \\ \hline \hline
**ARPabet** & **IPA** & **ARPabet** & **IPA** & **ARPabet** & **IPA** & **Symbol** & **Description** \\ \hline aa & \(\alpha\) & b & b & q &? & ax-h & Devoiced [a] \\ ae & ae & ch & f & r & i & eng & Syllabic [b] \\ ah & a & d & d & s & hv & Voiced [h] \\ ao & \(\beta\) & dh & \(\bar{\beta}\) & sh & f & bcl & [b] closure \\ aw & ao & dx & r & t & t & dcl & [d] closure \\ ax & o & el & l & th & \(\theta\) & gcl & [g] closure \\ axr & g & em & m & v & v & kcl & [k] closure \\ ay & au & en & n & w & w & pcl & [p] closure \\ eh & c & f & f & wh & aa & tcl & tcl & closure \\ er & g & g & y & j & pau & Pause \\ ey & e & hh & h & z & z & epi & Epenthetic \\  & & & & & & & silence \\ ih & i & jh & \(\$\) & zh & 5 & h\# & _Begin/end_ _marker_ \\ ix & i & k & k & & & & & & \\ iy & i & l & l & & & & & \\ ow & o & m & m & & & & \\ oy & a & n & n & & & & \\ un & o & ng & u & & & & \\ uw & u & nx & \(\bar{r}\) & & & & \\ ux & u & p & p & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: English phonetic symbols of the extended 2-letter ARPAbet used in the TIMIT dataset with mapping to IPA (International Phonetic Alphabet) symbols. The number of vowels is 19 and the number of consonants is 31. The begin/end marker is ignored in the phoneme recognition. There are 11 more special symbols used in the TIMIT dataset, so the total number of phonemes is 61.  
and the resultant 39 dimensional vector is obtained at every 10 milliseconds analyzed by 25-millisecond-overlapping windows. In this feature extraction process, all phonemes are pre-processed identically. The input to the baseline RNN is the 39-dimensional MFCC feature vectors and the number of output nodes is 39 one-hot vectors according to the 39 labels of the phonemes.
## 6 Scintillation Statistical Analysis  
The scintillation analysis is performed through a goodness of fit process taking into account the statistical distribution of the samples in the cases with and without scintillation effects.  
A scintillation-free reference curve \(S(x)\) is created best-fitting a Gaussian curve to the distribution of the samples \(w[n]\) in absence of scintillation. \(M(x)\) corresponds to the distribution of the samples \(y[n]\) for the time interval under test. If fit is accurate, \(S(x)\) is a good approximation, then there is no scintillation, and, vice versa, scintillation is declared present.  
The model curve for estimating \(S(x)\) is  
\[f(x,a,b,c)=a\exp\left(\frac{x-b}{c}\right)^{2} \tag{8}\]  
in which \(a\), \(b\), \(c\) are estimated on the basis of an iterative method that minimizes the sum \(S\) of squared residuals  
\[S=\sum_{i=1}^{N}r_{i}^{2}, \tag{9}\]  
where \(r_{i}=y_{i}-f(x_{i},a,b,c)\).  
Figure 8 reports an example, depicting the reference curve \(S(x)\) and the histogram of a set of samples that includes both scintalated and non-scintillated samples. As it can be noted, the Gaussian fitting is able to match only the non-scintillated part of the histogram, while in the presence of scintillation, the samples are distributed according to the typical Nakagami-m probability density function, thus deviating from \(M(x)\) obtained in case of no scintillation.  
Figure 7: Distribution of the samples after accumulation, \(w[n]\); (**a**) in presence of transition samples; (**b**) in absence of transition samples.  
In statistics, to evaluate the goodness of fit, the parameter  
\[R^{2}=\frac{\sum_{i=1}^{N}(f(x_{i},a,b,c)-\bar{y})^{2}}{\sum_{i=1}^{N}(y_{i}-\bar {y})^{2}} \tag{10}\]  
is used, where \(\bar{y}\) is the mean of \(y_{i}\), and \(a,b,c\) are the values of the best-fitting Gaussian distribution for the non-scintillated case. \(R^{2}\) can vary in the interval \([0;1]\). The greater \(R^{2}\), the better the fitting and vice versa. A measure of diversity can then be defined through a novel parameter denoted \(R_{4}\) and defined as  
\[R_{4}=1-R^{2}. \tag{11}\]
# Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
## 6 Conclusions  
To effectively detect and identify the anomaly data in satellite telemetry data, the novel detection and identification method based on the pseudo-period was proposed in this paper. Through the telemetry data temporal shape features, the salient points of the data were extracted to compress the raw data. As the characteristic quantity, the tilt angle of the adjacent data points was symbolized in the compressed data in order to obtain the corresponding string. Then, the pseudo-periods of the measured data were extracted using the standard pseudo-periodic sequence string. Taking the pseudo-period as the basic analytical unit, the phase-plane trajectory plots corresponding to each pseudo-period data were obtained. The phase-planes of each pseudo-period data were divided into certain regions, and the statistical values of the trajectory points in each region were taken as the features for achieving anomaly detection and identification in the measured data.  
Conceptualization and methodology, [PERSON] and [PERSON]; data curation and validation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON], and [PERSON]; All authors read and approved the submitted manuscript and agreed to be listed as authors. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grant 61502391, 61976179, and 51890884, by the National Key Research and Development Program, by the Key Industrial Innovation Chain Project in Industrial Domain of Key Research and Development Program of Shaanxi Province under Grant 2018 ZDCXLGY030203 and. by the Fundamental Research Funds for the Central Universities under Grant 3102019 HTXM005 and 3102017 HQZZ003  
The authors declare no conflict of interest.
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 5 Analysis of Examples
### Optimal Spinning Reserve Capacity with Different Wind Speed Correlation  
The installed capacity of the wind generator is 180 MW. The confidence level \(\beta\) is 0.9. The spinning reserve capacities of different wind speed correlations from 15:00 to 24:00 in one day are shown in Table 2.  
From Table 2, it can be seen that the up and down spinning reserve capacities rise with the increase of wind speed correlations. Wind speed with positive correlation will strengthen the synchronization (simultaneous increase and decrease) of different wind turbines' power output and increase the fluctuation of total wind power output. Therefore, compared with no correlation, the power system needs more up and down spinning reserve capacities. However, for the wind farms with negative wind speed correlation, their outputs can be complementary, so that the wind power output of the whole system becomes smooth. Therefore, the up and down spinning reserve capacity is smaller than in the case without considering wind speed correlation. Hence, the wind speed correlation has an important impact on the spinning reserve capacity of the power system with high wind power penetration, so it should not be ignored.
## 1 Introduction
Overall, the introduction of joint communication and sensing technologies will increase the resilience of sensing services. The sensor fusion and the addition of sensing redundancy/diversity will mitigate the errors occurring due to abrupt changes in environment and communication channels. However, every system will not function as a co-designed system. Depending on the needs and feasibility of the hardware, the nature of integration between communication and radar systems will vary. In some cases, tight collaboration at a software level will be sufficient, whereas a co-designed and shared RF hardware will be a necessary requirement in other cases [6]. However, it goes without saying that the feasibility of RF hardware that can address the needs of both functionalities will play an important role in the deployment of user devices. This paper focuses more on the RF hardware usage in the field of JCRS. JCRS is considered a subset of the field of Joint Communication and Sensing (JC&S), sometimes referred to as Integrated Sensing and Communications (ISAC). There can be different sensing methodologies other than radars. However, these alternative sensing methods (e.g., ultrasonic) are outside the scope of this paper. As the co-design opportunities are higher at mmWave frequencies, particular emphasis has been given to mmWave frequencies beyond 30 GHz and the bands close to this (e.g., 5G new radio (NR) band at 24-28 GHz). In Section 2, we dive deeper into the application scenarios and key enablers for RF hardware. In Section 3, we touch on the circuit level perspectives for JCRS front-ends, along with the antenna domain, beamforming (BF) enablement, and packaging challenges. In Section 4, a system-level viewpoint is presented, including the system validation approaches needed for JCRS enablement. Section 5 concludes this paper.
## 3 Results
### Optimization of Ionic Liquid Amount  
The volume of ionic liquid utilized in the extraction process is a critical parameter influencing the efficiency of Hg(II) ion preconcentration. To determine the optimal volume, a range of ionic liquid amounts were evaluated: 5 uL, 10 uL, 15 uL, 20 uL, 40 uL, and 75 uL. The performance of each volume was assessed based on its ability to effectively extract the Hg(II)-HMIMPF6 complex. Experimental results indicated that 15 uL of ionic liquid was the optimal volume for achieving maximum extraction efficiency (Figure 4). This volume was sufficient to form a stable Hg(II)-HMIMPF6 complex, facilitating complete extraction of Hg(II) ions. Volumes less than 15 uL, such as 5 uL and 10 uL, were inadequate for the full formation of the complex, resulting in incomplete extraction. Conversely, increasing the volume beyond 15 uL, including 20 uL, 40 uL, and 75 uL, did not yield any significant improvement in extraction efficiency. Thus, 15 uL was determined to be the optimal volume for the ionic liquid, providing the best balance between complex formation and practical efficiency in the extraction process.  
Figure 3: Effect of buffer solution amount (mL).  
Conditions: C\({}_{0}\): 2.5 \(\upmu\)g/L Hg(II); pH: 4.0, 2 mL; extraction time: 20 min; extraction temperature: 26 \({}^{\circ}\)C; extraction speed: 750 rpm; added dodecanol amount: 80 \(\upmu\)L; final volume: 150 \(\upmu\)L.