esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### pH, Acidity, and Viable Bacterial Counts after Fermentation  
During the fermentation with _B. subtilis_ HA, the pH of the milk decreased from 7.08 to 6.56, but acidity increased from 0.11% to 0.26% (Figure 1). Generally, fermentation of soybean with _Bacillus_ spp. results in increased pH. However, in this study, fermentation of milk with _B. subtilis_ HA resulted in acid production and thus a lowered pH. It was reported that fermentation with _B. coagulans_, _B. macerans_, and _B. licheniformis_ resulted in high acid production, whereas fermentation with _B. subtilis_ HA resulted in low acid production [19]. However, we observed the opposite outcome, with _B. subtilis_ HA producing acid and decreasing the pH, which may be explained by the type of substrate (milk) used for the fermentation.  
The initial viable bacterial count of _B. subtilis_ HA was 6.80 log CFU/mL, which increased to 8.74 log CFU/mL after 1 day (Figure 2). It should be noted that _Bacillus_ strains can be potential probiotics because of their thermostable and acid-resistant properties [20], and so their increase is desirable.  
Figure 1: pH and acidity changes in the milk co-fermented by _B. subtilis_ and _L. plantarum._ (1) 1: 0% MSG, (2) 2: 1.5% MSG, (3) 3.3% MSG, (4) LAB: Lactic acid bacteria. Values are mean \(\pm\) standard deviation (\(n=3\)). Different letters indicate significant differences at \(p<0.05\) by [PERSON]’s multiple range test.  
Figure 2: Viable bacterial counts of milk co-fermented by _B. subtilis_ and _L. plantarum._ (1) 1: 0% MSG, (2) 2: 1.5% MSG, (3) 3: 3% MSG, (4) LAB: Lactic acid bacteria. Values are mean \(\pm\) standard deviation (\(n=3\)). Different letters indicate significant differences at \(p<0.05\) by [PERSON]’s multiple range test.  
For the co-fermentation, the incubation temperature was lowered from 42 \({}^{\circ}\)C to 30 \({}^{\circ}\)C, and MSG was added at 1.5% and 3% concentrations. The viable cell counts of _B. subtilis_ HA in the milk co-fermented for 5 days were greatly decreased, indicating 4.30 log CFU/mL (control), 4.09 log CFU/mL (1.5% MSG), and 4.44 log CFU/mL (3% MSG). On the other hand, the viable cell counts of _L. plantarum_ increased from 7.61 log CFU/mL to 9.06 log CFU/mL (control), 9.06 log CFU/mL (1.5% MSG), and 9.42 log CFU/mL (3% MSG).  
Therefore, _B. subtilis_ can support the growth of lactic acid bacteria, resulting in a suitable environment for producing higher amounts of GABA by lactic fermentation. The initial pH of the co-fermented milk was 6.59 (control), 6.60 (1.5% MSG), and 6.59 (3% MSG), which then decreased to 4.59, 4.91, and 5.09, respectively. The acidity of the milk co-fermented without MSG (control) increased to 1.45% by the end of fermentation. By contrast, the acidity of the co-fermented milk supplemented with MSG was about 0.75% after 3 days, and increased above 1% after 5 days. These results indicate that the decrease in acidity during the second fermentation was related to the MSG concentration added for the production of GABA.
## 3 Results
### Surface Temperature Uniformity  
The heat-transfer analysis model was verified by comparing it with heating experiment data, reflecting the thickness of each layer of the final selected multilayer mold. As shown in Figure 9, a similar temperature distribution was observed during heating. According to the analysis model, the maximum temperature reached was 176 \({}^{\circ}\)C, while the experimental results showed a similar maximum temperature of about 177 \({}^{\circ}\)C. The maximum error between the simulation model and the experimental results was 12%, with the average error staying within 10%, indicating comparable behavior. Similar temperature trends were observed during the heating period, as shown in Figure 9c.  
\[T_{u}=100\times\left(1-\frac{T_{t}-T_{p}}{T_{p}}\right) \tag{3}\]  
Figure 9: Temperature uniformity of multilayer structure mold: (**a**) RHCM experiment temperature contour, (**b**) RHCM simulation temperature contour, (**c**) CIM mold temperature contour, (**d**) temperature response of 6 points.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & \multicolumn{5}{c}{**Thickness Levels (mm)**} & \multicolumn{3}{c}{**Power Density (W/cm\({}^{2}\))**} \\ \hline CNT web film & 0.005 & 0.01 & 0.023 & 0.037 & & 57 \\ \hline Insulator & 0.5 & 1 & 2 & 4 & 10 & 57 \\ \hline Electric insulator & 0.25 & 0.5 & 1 & 2 & 4 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multilayer structure mold CNT web film, insulator, and electrical insulator optimizing parameters.  
In the experiment, the temperature uniformity \(T_{u}\) is calculated as Equation (3) based on the target temperature of about 170 \({}^{\circ}\)C. \(T_{t}\) is the target temperature, and \(T_{P}\) represents the measured temperature at the point. Temperature uniformity was 95.7%, 97.8%, 99.3%, 96.8%, 99%, and 99% at points 1-6, respectively, with an average uniformity of 98% at the highest temperature point. In the CIM process, temperature uniformities at the same point were 83.9%, 85%, 94.5%, 84.1%, 85.3%, and 95.8% at points 1-6, with an average temperature uniformity of 88.1%.
# Ising-Based Kernel Clustering +
## 4 Evaluation
### Comparisons with Kernel K-Means  
While the Ising-based kernel clustering optimizes the original objective function, kernel K-means clustering [29] as a representative of the state-of-the-art kernel clustering methods conducts quasi-optimization using the Gram elements. To reduce the computational cost, Kernel K-means calculates similarities among centroids \(\overline{\pi_{\alpha}}\), geometric center of clusters \(\alpha\), and the other data points \(x_{i}\) instead of similarities among all actual data points \(x_{i}\) and \(x_{j}\).  
Figures 13 and 14 are comparisons between the proposed Ising-based kernel clustering and the kernel K-means. Figures 13 and 14 show the ARIs and the execution times of each clustering method, respectively.  
Figure 13 shows that the ARIs of the proposed method are higher than that of the conventional method. This is because the proposed method searches for the global optimal solution. Since the kernel K-means performs quasi-optimization, the probability of obtaining solutions with high accuracy is low.  
Figure 13: The ARIs of the proposed Ising-based kernel clustering and kernel K-means.  
Next, Figure 14 shows that the execution times of the proposed method are longer than that of the kernel K-means. This is because the proposed Ising-based kernel clustering takes a long time to determine the coefficients of the kernel, Gram, and QUBO matrices. Thus, Ising-based kernel clustering is useful in the situation where clustering results with higher accuracy are required even if it takes a longer time.
## 2 Non-Parameterized Time-Frequency Analysis
### Wavelet Transform
#### 2.2.2 Application  
The number of research papers on wavelet transform-related topics far exceeds that of similar signal processing models. Moreover, a large number of optimized models based on DWT have emerged. One such method is the identification of homologous acoustic emission signals based on synchrosqueezing wavelet transform coherence (SWTC). This method aims to improve the accuracy of identifying homologous acoustic emission signals in structural health monitoring, thereby enhancing the precision of damage localization [17]. Additionally, an unsupervised tunnel damage identification method based on convolutional variational autoencoder (CVAE) and wavelet packet analysis has been proposed, with experimental results validating its efficiency and accuracy [18].  
The integration of wavelet analysis with deep learning models has led to a new breakthrough. SWT [19] represents an innovative approach for bearing fault diagnosis, which combines wavelet packet transform with convolutional neural networks and further optimizes network parameters through a simulated annealing algorithm. The Swin residual network (WSRN) based on multi-level discrete wavelet transform has been employed to reconstruct continuously missing data [20]. By replacing the traditional sampling layers with multi-level DWT, this approach significantly enhances the recovery of weak signals and the extraction of multi-scale features, thereby greatly improving the accuracy of data reconstruction.  
In the field of structural health monitoring, DWT has proven to be a powerful tool. However, its accuracy heavily relies upon the selected wavelet basis function and the number of vanishing moments, posing significant challenges in practical applications. To address this issue, one method [21] cleverly combines statistical properties with DWT, markedly improving the accuracy of steel beam crack identification.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Measuring the Success and Efficiency of ITSs in Europe
#### Recommendations for Improvement  
The following measures are suggested to improve the efficiency of ITSs [77]:  
* The mandatory collection and sharing of essential data to guarantee their quality and accessibility;
* Encouraging collaboration between stakeholders, such as local authorities, private enterprises and end-users;
* Strengthening support mechanisms, such as the INNOSUP program, which helps SMEs (Small and Medium-sized Enterprises) to develop innovations and helps to develop new technologies and transport solutions.  
By funding projects that promote emerging technologies in transport and mobility, the INNOSUP program, part of Horizon 2020, plays a key role in promoting innovation. The program accelerates the introduction of new ITS solutions to the European market by supporting SMEs [78].  
To further develop ITSs and achieve the goals of sustainability and enhanced urban mobility, it is essential to measure their efficiency and success in Europe. KPIs, together with strict evaluation frameworks and cooperation between stakeholders, guarantee coherent and efficient ITS deployment at the EU level. However, to solve the current problems, an integrated approach is needed that includes data standardization, interoperability and open data sharing.
## 3 Results
### Evaluation of the JIGSAWS Dataset  
In addition to our open surgical dataset, this paper also evaluates the performance of the R3D-MHA model on the publicly available JIGSAWS dataset in the field of robotic gesture recognition. The JIGSAWS dataset includes three basic surgical tasks: suturing (SU), knot tying (KT) and needle passing (NP), and provides two types of data: tool kinematics and RGB videos. The dataset also offers two cross-validation schemes: Leave-One-Supertrial-Out (LOSO) and Leave-One-User-Out (LOUO). In this study, we used 39 RGB video samples from the suturing task to evaluate the performance of the R3D-MHA model. This task was performed by eight participants, each conducting five experiments.  
In this study, to maintain consistency with previous online recognition validation methods, we used the Leave-One-User-Out (LOUO) validation scheme. Specifically, five full video data from one participant were selected to test the model's frame-wise accuracy for online recognition, while the remaining video data were processed according to the aforementioned method, with the step size for extracting overlapping segments adjusted to 1 due to the relatively short length of these videos. The specific experimental details are consistent with those described earlier. Table 7 shows the results of the LOUO three-fold cross-validation, with the best fold achieving 77.9% and the average of the three folds being 76.3%. We reported the frame-wise accuracy (FWA) for each complete video and the average results. Figure 8 shows the visualized recognition results for the best and worst videos.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Video00** & **Video01** & **Video02** & **Average (\%)** \\ \hline R3D & 61.3 & 75.7 & 82.1 & 73.0 \\ R3D-MHA & 62.6 & 75.7 & 81.8 & 73.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Online recognition results of three complete videos. The best results and methods by column-wise are shown in bold.  
Figure 7: Comparison of Best and Worst visualization results. Best (**a**), Worst (**b**).  
Table 8 shows the comparison of the accuracy of the proposed R3D-MHA method with some previous online surgical gesture recognition methods on the SU task of JIGSAWS. The results show that the R3D-MHA method outperforms methods based on manually extracted features and are 0.5% higher than those the C3 Dtrans method, which uses complete video streams for online rolling input. It is worth noting that although some models that perform better than R3D-MHA, such as 3D-CNN, MTL-VF and CNN+LC-SC-CRF, used additional training data, our model still achieved good results without using any pre-trained models or additional data. Furthermore, in our experiments, the validation method used was to test on complete video data, which also demonstrates the effectiveness of the R3D-MHA model in practical applications.
## 3 Results  
### Enrolment and Subject Characteristics  
Enrolment into the extension study targeted 8-year follow-up for the 17 subjects in the original clinical study. Ultimately, 15 of the original 17 subjects completed the single extension study visit, and their demographic characteristics are shown in Table 1. The study population was balanced by gender, and subjects received from 1 to 5 ChondroMimetic scaffolds, per knee, to treat trochlear donor sites for mosaicplasty in 13 subjects, and medial femoral condyle defects in 2 subjects. A total of 14 additional procedures since enrolment into the original interventional study were recorded by 12 individual subjects. The majority of these interventions involved either arthroscopy of knees or ankles or intra-articular injections. No interventions were specifically attributed to the ChondroMimetic treatment by the Investigator.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Characteristic**} & \multicolumn{1}{c}{**Chondromimetic (n = 15)**} \\ \hline Consented subjects from original study, n (\%) & 15 (88) \\ Follow-up, years & 7.9 \(\pm\) 0.3 \\ Age, years & 32.7 \(\pm\) 9.3 \\ Gender, n (\%) & \\ Male & 7 (46.7) \\ Female & 8 (53.3) \\ Body Mass Index, kg/m\({}^{2}\) & 25.3 \(\pm\) 4.2 \\ Smokers, n (\%) & 2 (13.3) \\ Index defects and implants & \\ Mosaicplasty donor site(s), n (\%) & 13 (86.7) \\ Medial femoral condyle defect, n (\%) & 2 (13.3) \\ Defect volume including missing bone, mm\({}^{3}\) & 1573 \(\pm\) 1233 \\ ChondroMimetic Implants/knee, median (range) & 2 (1–5) \\ Follow-up Pain, n (\%) * & \\ No knee pain & 11 (73.3) \\ Mild knee pain & 2 (13.3) \\ Moderate knee pain & 2 (13.3) \\ Follow-up Activity Level, n (\%) * & \\ Unlimited & 8 (53) \\ Slightly limited & 4 (27) \\ Moderately limited & 3 (20) \\ Additional interventions since treatment & \\ Index knee related procedures * & 12 \\ Non-knee related procedures & 2 \\ \hline \hline \end{tabular} \({}^{\ddagger}\) Plus-minus values are means \(\pm\) standard deviation unless otherwise indicated. \({}^{\ddagger}\) Determined using quantitative 3D magnetic resonance imaging (MRI) of 10 days postoperative scans. \({}^{\star}\) Hyaluronic acid injection (4); microfracture of new primary lesion (3); second-look arthroscopy (5). \({}^{\star}\) From physical examination and standardized interview during the 8-year study visit.  
\end{table}
Table 1: Follow-up Characteristics of Trial Subjects \({}^{\ddagger}\).  
There were no withdrawals from the extension study. The only missing data in the study was MRI analysis from 1 subject who did not yield usable scans due to movement.
## 5 Performance Analysis for Fixed-Gain Relaying
### Average BER  
Inserting (31) into (35), the average BER of fixed-gain relaying can be expressed as:  
\[\bar{P}_{e}^{F} =\frac{q^{p}}{2\Gamma(p)}\int_{0}^{\infty}\text{exp}(-q\gamma) \gamma^{p-1}d\gamma \tag{42}\] \[-A_{1}\frac{q^{p}}{2\Gamma(p)}\int_{0}^{\infty}\text{exp}(-q \gamma)\gamma^{p-1}\text{exp}(-\gamma/\hat{\gamma}_{RF})\] \[\times G_{2t,2t+1}^{2t+1,t}\bigg{[}\frac{C\gamma\phi_{m}^{t}}{ \hat{\gamma}_{RF}\hat{\gamma}_{FSO}}\big{|}\begin{matrix}B_{1}\\ B_{2}\end{matrix}\bigg{]}d\gamma\]  
Utilizing [33] (Equation (11)) and [34] (Equation (07.34.21.0013.01)), and the definition of \(\Gamma(x)\), the expression of the average BER is derived as:  
\[\bar{P}_{e}^{F} =\frac{1}{2}-A_{1}\frac{q^{p}}{2\Gamma(p)(q+1/\hat{\gamma}_{RF})^{ p}} \tag{43}\] \[\times\begin{matrix}G_{2t+1,2t+1}^{2t+1,t+1}\bigg{[}\frac{C\phi_{ m}^{t}}{\hat{\gamma}_{RF}\hat{\gamma}_{FSO}(q+1/\hat{\gamma}_{RF})}\big{|}B_{1},\Delta(1,1-p) \big{]}\\ \end{matrix}\]
# Analysis of All-Optical Generation of Graphene Surface Plasmons by a Frequency-Difference Process
## 3 Results and Discussion  
### Calculated Optical Spectra  
Figure 4 presents the spectral variation of the normalized differences in the reflectance, \(\Delta R=R-R_{0}\), transmittance, \(\Delta T=T-T_{0}\), and absorbance, \(\Delta A=A-A_{0}\) (the quantities with the subscript 0 correspond to the absence of the second beam), for the situation presented in Supplementary Material of Ref. [6] where \(\theta_{a}^{I}=20^{\circ}\) and \(\theta_{b}^{I}=40^{\circ}\). All these spectra show the SP resonance at \(\omega_{\text{SP}}=177\,\text{ps}^{-1}\). The reflectance of the probe beam in Figure 4 shows a dip at resonance, but it could be a peak if \(\theta_{a}^{I}\) were larger than the Brewster angle (situation corresponding to Figure 2 of Ref. [6]) because \(\Delta R_{a}\approx(r_{a}^{(0)}\Delta r_{a}^{*}+r_{a}^{(0)*}\Delta r_{a})\), \(r_{a}^{(0)}\) is nearly real and changes its sign at the Brewster angle, while its variation owing to the NL effect, \(\Delta r_{a}\), does not. The differential transmittance spectrum of the probe beam has a resonance peak for any \(\theta_{a}^{I}\) and the absorbance is diminished at resonance (Figure 4, right). At the same time, the absorbance of the pump beam is increased (and \(\Delta A_{b}>|\Delta A_{a}|\)) because of the SP-mediated photon downconversion (\(b\to a\)) and plasmon-related losses.  
The chosen value of the intensity of the pump, \(I_{b}=10^{16}\,\text{erg cm}^{-2}\text{s}^{-1}\), approximately correspond to the fluence of \(0.1\,\text{mJ}/\text{cm}^{2}\) and pulse duration of \(0.1\,\text{ps}\), mentioned in Ref. [6]. From Figure S1 of Supplementary Material of that article, we can find the depth of the resonant minimum of \(\Delta R/R_{0}\) of \(\approx 1.6\cdot 10^{-3}\), while our calculations predict a value of the order of \(10^{-9}\) at the SP resonance. Since \(\Delta R\) is quadratic in the second-order conductivity, our calculated result for it is lower than the value extracted from the experiment in Ref. [6] by roughly 3 orders of magnitude, as already mentioned in Section 2.1. It means that some important effect was not included in the model used in our calculations (it could be a thermal nonlinearity [21; 22]) or the perturbative approach is not valid because of the high intensity of the pump pulse.
###### Abstract  
This paper proposes, for the first time, the research concept of comparing energy and economy between transcritical cycle high-temperature heat pumps and subcritical cycle high-temperature heat pumps with new refrigerants. Experiments and simulations are conducted to compare the system performance and economy of two heat pumps, and the effects of different factors on the performance of two heat pumps are analyzed. The results show that R744/R1234 yf (90/10) and R515-1 are the preferred refrigerants for transcritical cycle heat pumps and subcritical cycle heat pumps, respectively. The COP of the R744/R1234 yf (90/10) transcritical heat pump is generally higher than that of the R515B-1 subcritical heat pump, and compared to the R515B-1 subcritical heat pump, the cost recovery period of the R744/R1234 yf (90/10) transcritical heat pump is about 9-15 years. Therefore, it is recommended that users who use heat pumps for a long time choose transcritical cycle heat pumps. Meanwhile, with the change of evaporation temperature, the system COP of the R515B-1 subcritical heat pump and R744/R1234 yf (90/10) transcritical heat pump increases by 61.11% and 65.91%, respectively. In addition, the optimal charge amount for the R515B-1 subcritical heat pump is 81.8% of that of the R744/R1234 yf (90/10) transcritical heat pump.  
high-temperature heat pump; R744 transcritical cycle; new refrigerant; COP; economic analysis; conserve energy and reduce emissions
# 3D Skeletal Joints-Based Hand Gesture Spotting and Classification
## 3 Proposed Algorithm
### Gesture Spotting
The Stereo Hand Pose Tracking Benchmark is a large dataset for 2D and 3D hand pose estimation with 21 joint points for 18,000 images. Due to the robustness and accuracy of its performance, the 3D position of the thumb and index fingertips detected by the network are used for finger speed calculation and other recognition features. In the case where the predicted joint becomes invisible with very low confidence, we estimate this joint position based on its last known position. * An LSTM network is a recurrent neural network of a special kind, in which current network output is influenced by previously memorized inputs. The network can learn the contextual information of a temporal sequence. In an LSTM network, the gates and memory cells at time t are given as follows: \[\left\{ \begin{array}{c}i_{t}=\sigma(W_{t}[x_{t},\,h_{t-1}]+\,b_{i}\\ f_{t}=\sigma\Big{(}W_{f}[x_{t},\,h_{t-1}]+\,b_{f}\\ o_{t}=\sigma(W_{o}[x_{t},h_{t-1}]+\,b_{o}\\ \widetilde{c}_{t}=\tanh(W_{c}[x_{t},h_{t-1}]+\,b_{c}),\\ c_{t}=\,f_{t}*c_{t-1}+\,i_{t}*\widetilde{c}_{t},\\ h_{t}=\tanh(c_{t})*o_{t}\end{array} \right.\] (5) where \(i,f,\) and \(o\) are the vectors of input, forget and output gate, respectively. \(\widetilde{c}_{t}\) and \(c_{t}\) are called the \"candidate\" hidden state and internal memory of the unit. \(h_{t}\) represents the output hidden state. \(\sigma(. )\) is a sigmoid function while W and b are connected weights matrix and bias vectors, respectively. * **Bi-LSTM network**: While the output of a single forward LSTM network depends only on previous input features, the Bi-LSTM network is known as an effective method for sequence labeling tasks, which is beneficial to both previous and future input features. Bi-LSTM can be considered as a stack of two LSTM layers, in which, a forward LSTM layer utilizes the previous input features while the backward LSTM layer captures the future input features. The benefit of the fact that the Bi-LSTM network considers both previous and future input features is its effectiveness to classify the frame in sequence frame, gesture frame, or transition frame. The prediction error can be reduced by using Bi-LSTM instead of LSTM. Figure 2: Gesture segmentation with Bi_LSTM and CTC loss. * **Connectionist temporal classification:** The Connectionist temporal classification CTC is known as the loss function which is highly effective in sequential label prediction problems. The proposed algorithm utilizes CTC to detect whether the sequence frames are in gesture frames or transition frames with input from a sequence of Soft-Max layer outputs.