esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of Coherent Optical Phonons in a Hexagonal \(\mathbf{YMnO_{3}}\) Thin Film
###### Abstract  
This paper reviews our recent study on a coherent optical phonon in a hexagonal \(\mathrm{YMnO_{3}}\) thin film together with related optical studies in hexagonal \(R\)MnO\({}_{3}\) (\(R=\mathrm{Y}\), Lu, Ho) compounds. Coherent phonons have been observed in \(\mathrm{RMnO_{3}}\) compounds by pump-probe spectroscopy with subpicosecond laser pulses, whereas the observation of coherent optical phonons was reported only in LuMnO\({}_{3}\). Recently, we succeeded in the observation of the coherent optical phonon in a \(\mathrm{YMnO_{3}}\) thin film. The generation process of the coherent optical phonon is assigned to a displacive mechanism, which is identical to that in LuMnO\({}_{3}\). The coherent optical phonon is observed in the temperature range from 10 K to room temperature, while the oscillation intensity strongly decreases as the temperature increases to the Neel temperature of \(\sim\)70 K from a lower temperature range. It is interesting that the temperature dependence is largely different from that in LuMnO\({}_{3}\). We describe that the result can be qualitatively explained by the property of an isostructural transition around the Neel temperature in \(R\)MnO\({}_{3}\) compounds. In addition, we briefly discuss ultrafast incoherent responses of excited electronic states from the viewpoint of the excitation photon energy of laser pulses.  
coherent phonon; multiferroic; ultrafast spectroscopy; \(\mathrm{RMnO_{3}}\)
# A Multi-Scale Video Longformer Network for Action Recognition
## 4 Experiments
### Dataset  
#### 4.1.1 Assembly Action Dataset  
Unlike typical human action datasets, the assembly action dataset primarily encompasses upper-body operations. These actions often involve the use of specific assembly tools and exhibit high repeatability with significant similarities among different actions. Since there is currently no publicly available standard dataset for assembly action recognition in the industrial domain, we established an assembly action dataset consisting of eight different assembly actions, including snipping, cutting, grinding, hammering, brushing, wrapping, turning screws, and turning wrenches, as shown in Figure 5. The dataset was captured using an Intel RealSense D435i camera, manufactured by Intel Corporation, Santa Clara, CA, USA, at a frame rate of 30 frames per second and a resolution of 640 \(\times\) 480. Each action was recorded in 100 video segments, with each segment lasting for 5 s, resulting in a total of 800 video segments. These segments were organized into corresponding folders based on action categories. The dataset was further split into a training set (90%) and a test set (10%), with 20% of the training set reserved for validation purposes.
## 3 Results
### Sensitivity to Ionizing Radiation
#### 3.5.2 Evaluation of Dose-modifying Factors  
Dose-modifying factors (DMFs) assess the additive effect of a tested drug when used in combination with ionizing radiation. They are the ratios of doses needed to obtain the same surviving fractions in cells irradiated with and without the tested ferrocenes [44]. Values below 0.8 were considered as showing an antagonistic effect, values between 0.8 and 1.2 as showing no effect, and values above 1.2 as showing a synergistic effect (Table 2).
## 3 Results
### Hop Travel Time  
Hop travel times were calculated from sensor data as the time interval of particle movement from the start of its motion till the end of its rebound from the bed surface. Various distribution models, such as Weibull, Beta, Gamma, normal, and exponential, were applied to obtain the best fit for different hop travel times by using goodness of fit metrics.  
Hop travel time (t) was fitted by an exponential function, as shown in the following:  
\[f_{t}(t)=\frac{1}{t}e^{-\frac{\overline{t}}{\overline{t}}} \tag{6}\]  
where \(\overline{t}\) is the mean hop travel time. Table 2 shows the hop travel time goodness of fit results for different distributions, for the minimum and maximum flow rates used in experimental runs. By using goodness of fit metrics (such as Chi square), the exponential was found to be the optimal fitting distribution for modeling the hop travel times, for both assessed bed surfaces.  
Figure 9 shows the empirical hop time intervals data and best fit probability density function (PDF) for the flow rate of 52.5 (L/s), particle density 1180 (kg/m\({}^{3}\)), surface B, where the \(x\)-axis is the hop travel time (in seconds), the y-axis is the event counts, and the red curve represents the exponential distribution best fitting the data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Distribution** & **Goodness of Fit** & **Goodness of Fit** & **Rank** \\
**for Travel Times** & **(for Q = 56 L/s)** & **(for Q = 42 L/s)** & **Rank** \\ \hline Weibull & 4.1 & 4.6 & 4 \\ \hline Beta & 3.2 & 2.8 & 3 \\ \hline Gamma & 6.4 & 7.1 & 5 \\ \hline Normal & 1.4 & 1.8 & 2 \\ \hline Exponential & 0.4 & 0.6 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Goodness of fit results and corresponding ranking for a range of distributions modelling hop travel times for the minimum and maximum flow rates used in the experiment.  
As shown in Figure 9, the exponential function was the best fitting for the measured hop travel times distributions, which is the same best fit PDF found by [29; 30; 63; 64].
## 4 Application of APD to Other Sites
### Halden Silt Site  
The Halden site is located in Southeastern Norway, approximately 120 km south of Oslo, and covers an area of about 6000 m\({}^{2}\) with predominantly flat topography. It has been extensively characterised through a combination of geological, geophysical, and geotechnical investigation techniques [53].  
The in situ tests selected as input for APD, along with the associated methods and further details about the Halden silt site, are provided in [6]. A comparison between the results generated by APD and the reference values interpreted for this site is presented in Figure 5.  
Performing DMT in silty soils can lead to partial drainage during membrane expansion, potentially affecting the DMT readings and introducing errors when deriving parameters using standard DMT correlations [54]. Correction methods, such as the approach proposed by [55], can be employed to account for these effects. However, for the DMT tests conducted at this site, correction for partial drainage was not possible due to the lack of time-dependent data.  
Figure 5a presents the measured unit weights alongside the values estimated by the three workflows. None of the selected methods succeeded in accurately predicting the measured unit weight, although the methods employed in the \(V_{s}\)-based workflow yielded the highest \(\gamma_{t}\) values. Alternative approaches for improving the prediction of \(\gamma_{t}\) include the use of machine learning techniques, such as the study presented in [56]. A crucial factor in the successful application of machine learning models is the quality and representativeness of the training databases. In the aforementioned study, an open-access CPT database was utilised for model training [57].  
Figure 5b shows that both the CPT- and \(V_{s}\)-based workflows yield good to reasonable estimates for \(E_{ood}\). In contrast, the DMT-based workflow underestimates the reference values in this case, likely due to partial drainage effects. One consequence of partial drainage is that the difference between the two DMT readings becomes too small, resulting in low values of \(I_{D}\) and, consequently, underestimated \(E_{ood}\) values [54].  
As illustrated in Figure 5c, the \(V_{s}\)-based workflow delivers the most accurate estimation of the small-strain shear modulus, \(G_{0}\). Overall, the CPT-based workflow estimates of \(G_{0}\) typically fall below the in situ reference values, and a similar underestimation trend is observed for most methods within the DMT-based workflow. With regards to the undrained shear strength, \(s_{u}\) (Figure 5d), the CPT-based workflow demonstrates a reasonable agreement with the values interpreted from anisotropically consolidated undrained triaxial (CAUC) tests. The \(s_{u}\) estimates from the \(V_{s}\)-based approach generally lie between the reference results from CAUC and direct simple shear (DSS) tests, while the values obtained from the DMT-based workflow tend to underestimate \(s_{u}\) compared to the reference data.  
Figure 5: _Cont._
## 3 Results and Discussion
### Mechanical Characteristics of Biopolymer Films  
The mechanical properties of biopolymer films must meet a specific strength requirement for their use as packaging. Table 5 presents the mechanical characteristics of biopolymer films produced from the ozone-treated film-forming solutions (FFSs). The ozone treatment of the FFSs significantly affected the tensile strength, toughness, and breaking strain of the films (\(p<0.05\)). The highest tensile stress that a film can withstand during testing without rupturing is referred to as its tensile strength. This characteristic affects whether edible coatings are suitable for use as packaging material [51]. Tensile strength is of great interest because maintaining the film's integrity is crucial to food quality and safety [56]. The tensile strength of films from ozone-treated FFSs ranged from 0.986 to 1.130 MPa, where ozone treatment increased this value. The tensile strength of the control sample was the lowest at 0.986 MPa, whereas the O-30 sample exhibited the highest tensile strength at 1.130 MPa. The observed values may be attributed to the highest stability and integrity of the films. The trend indicates that the use of ozone treatment has a considerable effect on improving the tensile strength of biopolymer films. One of the potential causes behind the improved tensile strength is the formation of cross-links between the polymer chains in the FFS. Ozone treatment can introduce oxygen-containing functional groups into the polymer matrix, facilitating cross-link formation. These cross-links are physical bridges between polymer chains, making the film more cohesive and structurally robust. [PERSON] et al. [57] reported that the increase of tensile strength related to the formation of intermolecular hydrogen bonds. [PERSON] et al. [53] revealed that the disulphide bond cross-linking of gelatine can significantly improve the mechanical strength of films. Elongation at break measures the ability of biopolymer films to stretch prior to breaking. A higher elongation at break value indicates that the film is more flexible and stretchable. The high elasticity of the films is advantageous as it facilitates their use as coatings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **Onset Tem.** \\ **T\({}_{0}\) (°C)** \\ \end{tabular} & \
\begin{tabular}{c} **Degradation Temp.** \\ **T\({}_{\text{max}}\) (°C)** \\ \end{tabular} &
\begin{tabular}{c} **Weight Loss** \\ **(\%)** \\ \end{tabular} \\ \hline Control & 152.05 & 238.50 & 93.56 \\ O-5 & 154.62 & 209.20 & 92.15 \\ O-10 & 156.78 & 214.50 & 90.35 \\ O-30 & 158.12 & 227.65 & 89.78 \\ \hline \hline \end{tabular} Data are the mean (\(n=3\)). Control: biopolymer film from non-ozonated FFS; O-5: biopolymer film from ozone-treated FFS for 5 min; O-10: biopolymer film from ozone-treated FFS for 10 min; O-30: biopolymer film from ozone-treated FFS for 30 min.  
\end{table} 
Table 4: Thermal characteristics of biopolymer films produced from the ozone-treated film-forming solutions (FFSs).
## 2 Materials and Methods  
In this study, a novel ESRO-SRP technique has been developed for secure communication among drones in the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the QOBL concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as RE, distance, and trust degree.
# Efficient Sleep-Wake Cycle Staging via Phase-Amplitude Coupling Pattern Classification
## 3 Results
### Top Performing Network of Protocol 4 Extrapolates Well across Animals and/or Days of Experiment
Analogously to Figure 4, the top row depicts each AUC value for each animal and each day, and the bottom row shows the average for all the animals across the five days of the dataset. Again, the classification of the REM stage was shown to be particularly optimal considering both the high AUC values and low variability. Figure 6: Extrapolation strategy 3. Training with all the days of the first animal resulted in high AUC values for the classification of the stages in the other animals, still with considerably small variability: (**A**) WK; (**B**) SWS; (**C**) REM, and (**D**) ALL. Considering that here our interest is to see the extrapolation across the dimension of the different animals, the AUC values were averaged for each animal across all days. Thus, in this figure, each row represents a sleep stage. The left column panels depict each AUC value for each animal and each day as a color-coded scheme (see the lateral bar for scale). The right column shows the average across all the days for each one of the ten animals in the dataset with the variability (s.e.m.) being depicted in the background shadow of the same color in a lighter hue. Once more, the classification of the REM stage was shown to be particularly optimal in terms of the AUC value. Figure 7: Extrapolation strategy 4. Training with a single animal and single day and testing on the rest of the dataset, the most demanding scenario, also resulted in high average AUC values across the days. (**A**) The color-coded panel on the left depicts the overall classification performances using all possible pairs of animals used for training and for testing. Each row is one animal used for training and each column is an animal used for testing. From this panel, the best performing animal (animal 7: the one that better predicted the others with a higher AUC on average; highest row-wise mean) and the worst performing animal (animal 10: the one that predicted the others with a lower AUC on average; lowest row-wise mean) were extracted. (**B–E**) The classification results using the best animal for each stage, including the individualized AUC (top row) and AUC averages for all the animals for each of the five days (bottom row). (**F–I**) Analogously to panels **B–E**, the classifications using the worst animal. Equal to the previous results, the REM stage displayed the highest performances and smaller variability. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **WK** & **SWS** & **REM** \\ \hline Strategy 1 & 0.878 & 0.950 & 0.890 \\ Strategy 2 & 0.856 & 0.951 & 0.935 \\ Strategy 3 & 0.789 & 0.951 & 0.924 \\ Strategy 4—best & 0.867 & 0.925 & 0.668 \\ Strategy 4—worst & 0.713 & 0.929 & 0.952 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sensitivity (recall) values for all extrapolation strategies.
# Facial Emotion Recognition Analysis Based on Age-Biased Data
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Dataset**}} & \multicolumn{1}{c}{\multirow{2}{*}{**MobileNet-V2**}} & \multicolumn{2}{c}{\
\begin{tabular}{c} **SE-ResNeXt50** \\ **(32 \(\times\) 4 d)** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **ResNeXt101** \\ **(64 \(\times\) 4 d)** \\ \end{tabular} } \\ \hline \multirow{7}{*}{Accuracy (\%)} & A/A & 70.03 & 77.26 & 72.32 \\  & A/K & 42.36 & 47.17 & 45.73 \\  & K/A & 45.31 & 48.33 & 46.67 \\  & K/K & 72.09 & 81.57 & 72.21 \\  & M/A & 56.30 & 58.96 & 56.24 \\  & M/K & 55.41 & 63.13 & 58.45 \\ \hline \multirow{7}{*}{Recall (\%)} & A/A & 72.20 & 75.25 & 70.21 \\  & A/K & 38.43 & 45.09 & 38.60 \\  & K/A & 41.78 & 47.07 & 45.14 \\ \cline{1-1}  & K/K & 69.81 & 78.61 & 62.40 \\ \cline{1-1}  & M/A & 56.37 & 62.80 & 59.60 \\ \cline{1-1}  & M/K & 51.47 & 55.36 & 53.31 \\ \hline \multirow{7}{*}{Precision (\%)} & A/A & 72.62 & 75.48 & 71.52 \\  & A/K & 38.50 & 46.96 & 40.61 \\ \cline{1-1}  & K/A & 41.78 & 47.63 & 45.04 \\ \cline{1-1}  & K/K & 69.81 & 79.97 & 63.85 \\ \cline{1-1}  & M/A & 56.37 & 64.81 & 60.63 \\ \cline{1-1}  & M/K & 51.47 & 58.30 & 53.75 \\ \hline \multirow{7}{*}{\(F1\) score (\%)} & A/A & 72.33 & 74.78 & 70.27 \\ \cline{1-1}  & A/K & 37.97 & 44.33 & 38.75 \\ \cline{1-1}  & K/A & 42.19 & 51.23 & 42.40 \\ \cline{1-1}  & K/K & 68.04 & 79.07 & 61.78 \\ \cline{1-1}  & M/A & 56.23 & 63.35 & 59.51 \\ \cline{1-1}  & M/K & 49.97 & 55.93 & 53.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Deviation between the three age-specific, opposite-age, and mixed-age architectures. Figure 5: MobileNet-V2’s accuracy according to different emotions: (**a**) angry, (**b**) fear, (**c**) happy, (**d**) neutral, (**e**) sad, (**f**) surprise. Figure 6: SE-ResNeXt50 (32 \(\times\) 4 d)’s accuracy according to different emotions: (**a**) angry, (**b**) fear, (**c**) happy, (**d**) neutral, (**e**) sad, (**f**) surprise.
## 4 Experiments
### Experimental Setup  
#### 4.1.1 Baselines  
We apply FedRecI2C to FRSs below to validate its effectiveness on various FRSs.  
* FedMF [12] adapts MF for FRS by updating user embeddings locally and aggregating item gradients globally.
* PFedRec [34] is a recently proposed personalized FR with fine-tuned item embeddings based on FedMF.
* FedRECON [35] is a personalized FL framework in the MF scenario, where user embeddings are retrained from scratch between each iteration.
* FedMAvg [23] combines the alternating minimization technique and model averaging to investigate the federated MF problem.
* FedNCF [36] is a federated variant of neural collaborative filtering (NCF) [37] that allows users to update local embeddings and transmit item embeddings and score functions to a server for global aggregation.
* FedGMF [36] is a federated version of generalized MF introduced by NCF.
* FedMLP [36] is a variant of FedNCF based on multilayer perceptrons.  
These baselines include two categories of recommender systems: MF and NCF. MF is widely adopted in recommendation systems because of its low number of parameters and fast training speed. It is particularly useful in cold-start phases and resource-constrained environments (e.g., for edge device deployment), where it serves as an initialization benchmark for complex models. Meanwhile, NCF has become a core module in mainstream deep learning-based recommendation frameworks (e.g., those of YouTube and Amazon). It shows irreplaceable advantages in processing multimodal data and enabling real-time personalized recommendations. Therefore, it is of great practical significance to explore the effectiveness of FedRecI2C for them and their variants in the context of FL.  
Note that PFedRec utilizes a Dual Personalization Mechanism (DPM) that fine-tunes global item embeddings before prediction, which can apply to the FRSs above. We highlight that the I2 CL and I2 CG outlined in this article will yield distinct outcomes contingent upon whether the FRS employs DPM before prediction. We will demonstrate this in experimental results.
## 4 Bibliometric Analysis
### Publication Trends  
This method involves knowing the trends in publication and finding measurable indicators of interest from the academic community [146]. Furthermore, it is interesting to determine whether cumulative production exhibits an exponential growth phase. Once this phase is reached, the number of publications tends to decrease, indicating a potential consolidation process within a technology or research topic under examination [149].  
Figure 4: Methodology of bibliometric analysis.  
Figure 3: Methodology of systematic literature review.  
Figure 5 shows the annual production of DDSMC. The yearly count of articles only exceeded the 10-mark threshold after 2008, and most of the articles emerged within the last five years. Furthermore, the exponential trend line exceeds the cumulative growth of the thematic interest production. As a result, a nonlinear regression process was performed to fit a sigmoidal model.  
S curves are used in the analysis of technology life cycles and innovation. Through these curves, it becomes feasible to estimate the inflection point, providing insight into the current state of technology and the time when production growth starts to decelerate. This information is valuable in determining whether it is an opportune moment to develop products such as patents, software registrations, etc. [150].  
The cumulative production was characterized using the three-parameter model given by  
\[f(t)=\frac{a}{1+e^{-\frac{(t-t_{0})}{b}}}, \tag{12}\]  
where \(f(t)\) is the cumulative production function; \(a\) represents the upper limit of scientific production, serving as the horizontal asymptote; \(t\) is the time measured in years; \(t_{0}\) is the inflection point; and \(b\) is a scale factor. The model parameters for this function were determined using RStudio as follows:  
\[f(t)=\frac{2621.22}{1+e^{-\frac{(t-2025.75)}{3.99}}}, \tag{13}\]  
The model's coefficient of determination was \(R^{2}=99.82\%\), and all statistical assumptions regarding the model were verified. Figure 6 shows the extended model until 2070. The model indicates an asymptote of 2621 articles, expected to be reached by 2048. Additionally, the inflection point is estimated to occur around 2026, so the deceleration of scientific production has not yet begun.  
Figure 5: Annual scientific production trend of 747 articles.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.6 Cloud Pre-Training  
In deep learning applications, where the number of data can be massive, training represents a costly task. Due to the limitations of processing, memory and other considerations, training on the edge is still challenging. Cloud pre-training is another efficient strategy that uses the collaboration between the edge and the cloud to improve data privacy and reduce network traffic.  
In applications such as that presented by [PERSON] [94] and [PERSON] and [PERSON] [84] the cloud will perform the computationally expensive training of the models and, once they are trained, the trained parameters are transmitted back to the edge nodes that will be able to execute the inference process.  
[PERSON], [PERSON], and [PERSON] [42] propose a slightly different approach, where the cloud receives the data and tries to predict an appropriate model for them. The selected one is then sent to the edge. The model manager stores it, allowing the inference module to use it to generate predictions quickly.  
To deal with the problem of predicting system disruption in Industry 4.0, [PERSON] et al. [95] propose a framework in which the cloud is responsible for the creation of the model that will be further handled by fog computing to predict a resource's location in real-time.  
[PERSON] et al. [40] present an adaptable and affordable design for a smart parking system. Data collected by cameras and other sensors are processed by advanced deep learning algorithms in edge devices. In this system, the vehicle tracking is performed by a trained Tiny Yolo model run on top of the Nvidia Jetson Tx2. The Tiny Yolo is very light and is, therefore, suitable for devices with limited resources. Moreover, CNN is used in applications of IoT. While comparing the experimental results with other solutions, the system presented good accuracy and a shorter time for inference.  
[PERSON] et al. [38] propose pre-training DNNs in the cloud server and sending the weights to the edge server, which operates a deep Q-learning process, presenting a framework with an efficient energy scheduling scheme with deep reinforcement learning.