esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Changes in Volatile Abundance during Leaf Development  
Leaf volatiles changed during leaf development, and changes in individual volatiles are listed in both Table 3 and Table 4. The ratio of the content, by percentage, in mature leaf to young leaf was calculated, transformed into log\({}_{2}\) and is shown in Figure 3. The changes were not consistent among different cultivars. For example, linalool decreased in most cultivars \(>\)2-fold, with the exception of Zaoxiangyou, where it increased two-fold; Limonene was found to be \(>\)3-fold increased in mature leaves of Satsuma and \(>\)1-fold in Liubencheng, but decreased in Zaoxiangyou and Huyou (Figure 3); \(\beta\)-Elemene, on the other hand, decreased three-fold in Liubencheng, but increased by \(>\)3-fold in Zaoxiangyou. It was noted that the ratio of \(\alpha\)-citral and \(\beta\)-citral was highest in Satsuma, while it decreased in most cultivars and, especially, in Hongshigan (Figure 3). In addition, 2-hexanal increased two-fold in Huyou, yet only slightly increased in other cultivars, while citronellal increased by \(>\)3-fold in Yuhuanyou and decreased by \(>\)1-fold in Zaoxiangyou.  
Our study provides clear evidence that the development phase has an impact on leaf volatile production; some volatiles were increased during the developmental transition from young to mature leaves, while quantitative and qualitative cultivar-specific changes also occurred. It has also been reported previously that the overall emission rate of volatile organic compounds increased from young to older leaves in peppermint, although the qualitative composition of the volatiles changed only slightly [48].  
Figure 3: Ratio of volatiles in mature to young leaves from nine citrus cultivars. The full cultivar names corresponding to the abbreviations are as indicated in Table 1. Color code shown above the figure: red shows high, blue low. The blank cells indicate volatiles absent in both young and mature leaves.  
Linalool was found to be higher in young compared to mature leaves for eight out of nine cultivars, as has been reported recently for citrus leaf volatiles [19,30]. A biological role(s) in pollination attraction, protection of reproductive organs from oxidative damage and defense against herbivores and pathogens has been suggested for some volatiles [36,42,49,50].
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 4 Experimental Results and Discussion
### Robustness against Cropping Attacks  
In the process of transmission and storage, digital images may suffer some portions of data loss. To test the robustness against cropping attacks, four different cropping sizes, 50 \(\times\) 50, 100 \(\times\) 100, 170 \(\times\) 170, and 256 \(\times\) 256, are applied to the 512 \(\times\) 512 marked images. The cropped portions of sizes 50 \(\times\) 50 and 170 \(\times\) 170 are located at the upper left corner while the cropped portions of sizes 100 \(\times\) 100 and 256 \(\times\) 256 are located at the middle of the image. We take several logotype binary images as the secret images and consider two kinds of \(N\) values, which are 4 and 16. First, the original 90 \(\times\) 90 logotype secret images are rescaled to fit the size of applied images. For \(N\) = 4, the rescaled secret images are 256 \(\times\) 256 in size. For \(N\) = 16, the rescaled secret images are 128 \(\times\) 128 in size. Then, all the secret images are scrambled by Arnold transform with five iterations and embedded into the Lena image through CDMA multiplexing. Next, the cropping attacks are performed. Figures 11 and 12 display the cropped Lena images with number of users \(N\) equal to 4 and  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Secret Images** & \(N\) = 2 & \(N\) = 4 & \(N\) = 8 & \(N\) = 16 & \(N\) = 32  
replaced by a white block. After modulo operation and decreased by \(N\), 255 will be mapped to different values, \(-1\) for \(N=4\) and 8 for \(N=16\). For the first secret image, the pixels in the cropped region will be decoded as bit 0 for \(N=4\) and bit 1 for \(N=16\), respectively. Since there are lots of black pixels in the first secret image, this will lead to fewer error bits for \(N=4\) and more error bits for \(N=16\). As for the second secret image, the pixels in the cropped region will be decoded as bit 1 regardless of the \(N\) value since the decision threshold is set to zero. Due to the large amount of white pixels in the second secret image, this will lead to fewer error bits and lower BER. Besides, we can see that the extracted secret image is still recognizable even if cropping attacks cause some portions of data loss. This shows that the proposed scheme is robust to cropping attacks.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 2 Data Acquisition and Sub-Tasks for Pig Posture Detection  
### Data Acquisition for Pig Posture Detection  
#### 2.1.1 Non-Image Data from Animal-Attached Sensors  
Before the maturity of image processing technologies, researchers generally employed various animal-attached sensors to obtain physical parameters related to pigs, and then  
Figure 1: The correlation between external factors, psychological and physiological state of pigs, pig postures, and pig welfare and production. (The external factors include breeding conditions, environmental parameters, social interaction between pigs within the same enclosure, and invasive human activities [5; 6; 7]).  
indirectly evaluated the behavior or posture patterns of pigs through data-specific modeling analysis [21]. Sensors were initially used to monitor the behavior and activity of individual captive pigs in research. Considering the close relationship between drinking behavior and the health welfare of pigs, [PERSON] et al. [22] designed a high-frequency radio frequency identification (RFID) system that could automatically monitor and analyze the drinking behavior of individual pigs. [PERSON] et al. [23] used the acceleration data collected by a three-dimensional digital accelerometer to construct a multivariate dynamic linear model that can classify five types of activities and postures of sows in the farrowing house. By securing a tri-axial accelerometer to the hip of individual sows, three-dimensional motion data can be acquired and inputted to a Support Vector Machine (SVM) classifier for classification and quantification of the posture and posture transition of individuals [24]. Similarly, with a supervised machine learning approach, [PERSON] et al. [25] employed three-dimensional acceleration data from an accelerometer fitted on the neck of each pig to accomplish the classification of the five types of activities, including lateral and sternal lying.  
Compared with manual observation, animal-attached sensors can automate most operations originally performed by laborers with the assistance of machine learning technologies and can produce more convincible and objective judgments [26]. However, there are still several disadvantages preventing their real application. First, pig posture detection with animal-attached sensors is cannot be universally used due to the variation of data type between different varieties of devices. Then, as intrusive data-gathering devices, sensors always need to be fixed in pig bodies, which may have some negative impacts on pig health [8]. Moreover, the fixation and accuracy of sensors are likely to be disturbed by other pigs and the sensors themselves [23], and the sensors may fail to work [24]. Therefore, there is a certain difficulty in the popularization of sensors under commercial farm conditions.
# SEM Studies Assessing the Efficacy of Laser Treatment for Primary Teetis: A Systematic Review
## 3 Results
### General Study Outcomes  
The authors [PERSON] et al. [27], [PERSON] et al. [2], [PERSON] et al. [16], [PERSON] et al. [23] agreed that laser treatment of primary teeth gives similar or better results than conventional methods. In the primary dentition, according to [PERSON] et al. [22], the Er:YAG laser outperforms the conventional high-speed diamond bur in the removal of carious tooth \begin{table}
\begin{tabular}{c c} \hline \hline  & **Dentin Surface Irregularities** \\ \hline
**Er:YAG Laser** & **Er,Cr:YSGG Laser** \\ \hline [PERSON] et al. [2] & \\ [PERSON] et al. [13] & \\ [PERSON] et al. [16] & \\ [PERSON] et al. [17] & \\ [PERSON] et al. [20] & \\ [PERSON] et al. [21] & \\ [PERSON] et al. [23] & \\ [PERSON] et al. [24] & \\ [PERSON] et al. [26] & \\ [PERSON] et al. [27] & \\ \hline \hline \multicolumn{3}{c}{**Patency of Dentinal Tubules**} \\ \hline
**Open (Completely or Partially)** & **Closed** \\ \hline [PERSON] et al. [13] & \\ [PERSON] et al. [16] & \\ [PERSON] et al. [17] & \\ [PERSON] et al. [22] & [PERSON] et al. [2] \\ [PERSON] et al. [23] & \\ [PERSON] et al. [26] & \\ [PERSON] et al. [30] & \\ \hline \hline \multicolumn{3}{c}{**Presence of Smear Layer**} \\ \hline
**Present** & **Absent** \\ \hline  & [PERSON] et al. [13] \\  & [PERSON] et al. [22] \\ [PERSON] et al. [21] & [PERSON] et al. [23] \\ [PERSON] et al. [26] & \\ [PERSON] et al. [30] & \\ [PERSON] et al. [36] & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dentin surface characterization. structure. The use of Er:YAG lasers has also been successful in terms of microleakage. According to [PERSON] et al. [13], cavities prepared with the Er:YAG laser have irregular surfaces but show less microleakage after filling with composite resin. Similar results were obtained by [PERSON] et al. [31] and [PERSON] et al. [27]. [PERSON] et al. [32] stated that the demineralization of primary tooth enamel was inhibited for a period of one year without significant morphological changes, and studies by [PERSON] et al. [15] show that the use of a 0.75 W Er,Cr:YSGG laser without water cooling improves the enamel's resistance to demineralization. However, the use of a laser affects the long-term durability of enamel due to residual morphological damage on the surface. According to [PERSON] et al. [28], Er,Cr:YSGG laser treatment alone could serve as an alternative method of remineralization, possibly due to modification of the hydroxyapatite structure. [PERSON] et al. [33] investigated the combination of a Nd:YAG laser and CPP-ACP cream, which also proved successful. However, the use of the Er,Cr:YSGG laser, as stated by [PERSON] et al. [29], should be approached with more caution because of the occurrence of adverse morphological and spectral changes on the surface of laser teeth. As cavity preparation in deciduous teeth using laser power parameters of 10 Hz and either 200 or 300 mJ is a safe and efficient method for removing tooth structure, the use of 10 Hz and 400 mJ for cavity preparation causes dentin cracking as reported by [PERSON] et al. [26] (see Table 3).
## 2 Materials and Methods
### Model Training, Evaluation, and Optimization  
Using the derived feature set, a baseline implementation of the following classifiers was implemented using Python 3.9 [37] and scikit-learn 1.0.2 [38] with its default configurations: decision tree (DT), random forest (RF), support vector machine (SVC), k-nearest neighbor (kNN), naive Bayes (NB), perceptron, multi-layer perceptron (MLP) and logistic regression (LR). For the optimization phase, the four best-performing classifiers were selected to be optimized using hyper-parameter tuning and grid search (using GridSearchCV provided by scikit-learn). Therefore, the accuracy (number of correctly classified instances compared to the total number of instances) was used as the main metric for assessing the generalization performance of the models along with the \(F1\)-score (harmonic mean between precision and recall). To prevent data leaking from the training into the test dataset, the three-way holdout method [39] was used to establish three different datasets: one for training, one for optimizing the models, and one for assessing the final predictive performance of the models [40].
## 1 Introduction and Motivation  
The coupling of microwaves into a plasma-filled cavity through one or more injection waveguides is a fundamental aspect in Electron Cyclotron Resonance Ion Sources (ECRISs). The source performances in terms of charge states and extracted currents has been explained by taking into account the different patterns of the electromagnetic field that is excited into the cavity, for a specific frequency (or set of frequencies) [1, 2]. In order to enhance the source performances, several approaches have been used in the past years, such as the modification of the plasma cavity trying to better match the confining magnetic field profile [3], and the improvement of the microwave injection system by using modified or better cavity-matched waveguide configurations [4, 5, 6, 7].  
In this work, we compare the performances of different antenna systems radiating into cavities, possessing conventional and unconventional shapes, that can be modeled as a reverberation chamber. We discuss the possible antenna configurations and we present the numerical design of a novel plasma cavity in which its shape has been determined by the electron trajectories as they move under the influence of a _minimum-B_ magnetic field profile (i.e., a typical configuration used in ECRISs). The modal configuration introduced into the presented cavity is compared with that of a standard cylindrical cavity, for the same frequency interval. It will be shown that the new cavity's shape promotes the excitation of modes with an electric field maximum along its axis, where the plasma is typically generated. This could be advantageous in terms of plasma absorbed power, preventing the formation of the typical hollow plasma experimentally observed when the excited mode has an electric field minimum along the cavity's axis [8]. In fact, for a circular cylinder shaped cavity, TE and TM modes are expressed in terms of Bessel functions (and first derivative of Bessel functions), all equal to zero at the origin except the \(\mathrm{J_{0}}\left(\hat{\eta_{1}^{\prime}}\right)\)[9].  
As a second step, aiming at improving the ion source performances, we present a radically innovative microwave launching scheme that employs slotted waveguides which are side-coupled to the plasma cavity. It will be shown that this technique greatly improves the number of modes coupled inside the cavity with respect to the standard axially-coupled rectangular waveguide and at the same time makes possible a more homogeneous power transfer to the plasma thanks to the slots placed along the cavity outer wall. In addition, more space for other ancillaries is made available on the cavity's injection flange.  
Due to the non-conventional shape of the novel \"plasma-shaped\" cavity and relative twisted slotted waveguide injection system, standard manufacturing techniques based on subtractive processes could be difficult to employ. To overcome these difficulties, for the future realization we plan to use additive manufacturing techniques, and in particular 3D metal printing through high quality metal powder compound.
## 4 Fault-Tolerance Control Strategy
### Fault Detection  
The fault detection module detects the vehicle's steering system in real time during the vehicle track tracking process and detects the status information of the faulty motor during operation, mainly the target angle, actual angle, goal and practical moments' information of the motor. The main status information is the target angle, actual angle, goal and practical moments, etc. The steering system failure is determined by judging whether the ratio of  
Figure 12: Fault-tolerance control strategy.  
actual angle to target angle and the ratio of actual torque to target torque are greater than a fixed value.  
Introducing the fault diagnosis factor \(\lambda\) as Equation (19):  
\[\lambda_{i}=\frac{\delta_{\text{Actual}}}{\delta_{\text{Target}}},\;\lambda_{j}= \frac{T_{\text{Actual}}}{T_{\text{Target}}} \tag{19}\]  
In Equation (19), \(\lambda_{i}\) is the angle fault factor and \(\lambda_{j}\) is the torque fault factor.  
Considering the relationship between the vehicle's own load and the road adhesion factor, the fault factor is selected as 0.85. Therefore, by determining whether the fault factor thresholds for both the angle and torque are less than 0.85, if they are less than this value, the steering system is considered to have a fault. The fault detection strategy is described in Figure 13.
## 3 Two Motivating Examples
### Example: Scheduled Sprinkling  
A lawn sprinkler system for home use is needed. Three sprinklers, each with a head connected by a pipe to a common water supply, are installed. Water emits from the sprinkler heads according to a schedule set by the owner through a controller. When it is time to sprinkle water, each sprinkler head begins emitting water within 5 s.  
A plausible specification for capturing this requirement is by the Gherkin scenario of Figure 2. In this scenario, the three sprinklers have been set up (line 2), and the scheduled time has been set to 4:00:00 a.m. (line 3). When the scheduled time arrives (line 4), all three sprinklers should emit water within 5 s (lines 5, 6, and 7).  
It should be clear from the problem statement that the three sprinklers should act _concurrently_; a failure in any of the three should not prevent the other two from exhibiting the required behavior. For instance, assume that the pipes branching off to sprinklers A and C, respectively, are clogged, but the pipe to sprinkler B is normal. When the scenario is executed, sprinkler A and sprinkler C should fail to emit water within 5 s (line 5 and line 7), whereas sprinkler B should emit water normally (line 6).  
Under the current policy of the Gherkin language to interpret the steps sequentially and the policy of the existing supporting tools and to fail fast, the failure in the Then step for checking sprinkler A terminates the scenario, preventing sprinklers B and C from being checked. The result is that the developers will only know that the scenario has failed due to sprinkler A but would not know the status of sprinklers B and C. The developers will only know that sprinkler B would act normally and sprinkler C would fail by re-running the scenario after fixing the clog in sprinkler A. This is clearly inefficient.  
It can be noted that turning off the failing-fast feature does not solve the problem either so long as the steps are interpreted sequentially. After sprinkler A fails, five seconds have already expired, causing the normal-functioning sprinkler B to fail. More of the anomaly introduced by the sequential interpretation of steps by Gherkin and the failing-fast feature of tool support will be discussed in Section 5.  
On the other hand, the result of executing the scenario would reveal failures of sprinklers A and C and success of sprinkler B if the three assertions are executed _concurrently_. Further, the result remains so regardless of the failing-fast feature of the tool used.
## 1 Introduction
evaluated a crack of two-story reinforced concrete frame instrumented with embedded PZT-based smart aggregates [29]. Another embedded sensing technique is the self-sensing concrete. The self-sensing concrete is made by adding conductive fibrous materials into nonconductive normal cement to increase its electrical sensing capability as well as structural ductility [30; 31]. Thus, it can be used as a sensor for crack monitoring in structural hot spots. By inspecting cracks within the structural hot spots, the entire structure can be effectively monitored [32]. Fiber-reinforced cementitious composites (FRCCs) is one of the representative self-sensing concrete types. FRCCs can be typically made by inserting conductive materials such as steel fibers, glass fibers, synthetic fibers, graphite powders, carbon nanofibers and carbon black fibers into the cement matrices, enabling to have the self-sensing capability [33; 34; 35; 36; 37; 38; 39]. Thus, the electrical responses can be measured from FRCCs without additional sensor installation. [PERSON] et al. utilized the cementitious concrete with polymeric fibers to detect and localize cracks by employing an electrical impedance tomography method [40], and [PERSON] et al. demonstrated the applicability of crack detection and localization using nanocomposite cement-based beams [41; 42]. Also, [PERSON] et al. presented an electrical resistance tomography technique for localizing and detecting cracks in a concrete structure [43]. Since the electrical response is, however, sensitively affected by environmental variation [44], the only crack-induced feature should be carefully extracted from the measured electrical responses. In this study, an instantaneous multi-channel electrical impedance-based crack localization technique of using FRCCs is proposed. First, the instantaneous multi-channel electrical impedance acquisition system and crack localization algorithm are developed, and the FRCCs specimens are manufactured by inserting two different sizes of fibers into a mortar matrix. Then, experimental validation including temperature variation effect and three-point bending tests are performed. This paper is organized as follows. Section 2 describes the instantaneous multi-channel electrical impedance acquisition system integrated with FRCCs and explains the crack localization algorithm. Then, the temperature variation effect and crack localization are experimentally investigated in Section 3. Finally, this paper is concluded with a brief discussion in Section 4.
## 3 Methodology
### Performance Evaluation-Confusion Matrix Metrics  
Confusion matrix metrics are often used to quantitatively evaluate recognition performance. A confusion matrix generally involves the following parameters:  
* **a** is the number of healthy EEG records identified correctly,
* **b** is the number of healthy EEG records mistakenly detected as epilepsy,
* **c** is the number of epileptic EEG data mistakenly identified as healthy,
* **d** is the number of epileptic EEG data detected correctly.  
The following aggregate metrics can then be calculated based on the above parameters.  
* **Accuracy (AC)**, the proportion of total number of correct predictions, is \((a+d)/(a+b+c+d)\).
* **True positive rate (TP)**, the proportions of positive cases correctly identified, is \(d/(c+d)\).
* **False positive rate (FP)**, the proportion of negatives cases classified incorrectly, is \(a/(a+b)\).
* **True negative rate (TN)**, the proportion of negatives cases classified correctly, is \(a/(a+b)\).
* **False negative rate (FN)**, the proportion of positives cases classified incorrectly, is \(c/(c+d)\).
* **Precision (P)**, the proportion of the correctly predicted positive cases, is \(d/(b+d)\).
* **Sensitivity (SEN)** is \(\mathit{TP}/(\mathit{TP}+\mathit{FN})\times 100\%\), **specificity(SPE)** is \(\mathit{TN}/(\mathit{TN}+\mathit{FP})\times 100\%\).  
The criteria for performance evaluation are usually employed in biomedical studies, which include three parts: sensitivity (the proportion of the total number of labeled ictal EEGs that are correctly classified), specificity (the proportion of the total number of labeled inter-ictal EEGs that are correctly classified), and classification accuracy (the proportion of the total number of EEG signals that are correctly classified) [2; 6; 7; 8; 9; 10; 13; 14; 15; 16; 17; 18; 19; 25; 26; 27; 28; 29; 30; 31; 32; 34; 35; 38; 39; 40; 41; 49; 53; 59].