esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Such factors contribute to the model adaptability for use in real-world scenarios [30; 31]. In summary, this study not only fills the gap by evaluating several lightweight transfer learning models and datasets but also offers valuable insights for researchers and developers who aim to build a real-time hand gesture recognition module. By including subjective test results under different environmental conditions, this study further offers guidelines for creating an effective dataset for applications, like drone control, sign-language interpretation, computer vision-based HCI, and more. We summarize our contributions as follows:  
* This study presents an evaluation of five state-of-the-art lightweight deep learning models (including transformer-based) for hand gesture recognition. * This study presents subjective and objective performance evaluations to assess the datasets' quality and their impact on the models' performances. * In this study, we also exploit the Mediapipe hand landmark [32] (MHL) and OpenPose [33] models for the extraction of keypoints and assess the dataset quality by its success and failure rate in detecting landmarks under diverse lighting conditions and image quality. * This study also highlights the trade-offs between the models' complexities and accuracies in real-time application scenarios related to hand gesture recognition, offering practical guidelines for lightweight model selection in HGR systems. The rest of this paper is organized as follows. Section 3 describes the methodology, while the detailed results are reported in Section 4, which are discussed comprehensively in Section 5. Finally, we conclude our work in Section 6 with possible future research directions.
# Pose-Based Gait Analysis for Diagnosis of Parkinson's Disease
## 3 Experiments and Results  
### Experiment Setup  
Data augmentation was performed in two ways. First, we flipped the original motion time series obtained via pose estimation to simulate the backward movement of the subjects.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Feature** & **Related PD Symptom** & **Visual Perception** & **Computation** \\ \hline posture & hunched posture & nose-to-foot distance & mean \(D_{E}(nose,\,\text{toe})\) * \\ arm steadiness & arm tremors & wrist steadiness & mean \(\mathcal{S}(wrist)\) * \\ arm swing & reduced arm swing & nose-to-wrist distance & mean \(D_{E}(nose,\,wrist)\) * \\ arm swing symmetry & arm swing asymmetry & uneven arm swings & mean \(D_{E}(nose,\,\text{LWrist})\,/D_{E}(nose,RWrist)\) \\ step height & reduced step height & ankle-to-ground distance & \(max(ankle)\) * \\ step & reduced step length & left-to-right ankle distance & mean \(D_{E}(LAnkle,RANkle)\) \\ cadence & reduced cadence & lower step frequency & \(n_{steps}/F\) (see Figure 4) \\ speed & reduced gait speed & slower movement & \(step\times cadence\) \\ \hline \hline \end{tabular}  
* Mean values are taken across \(F\) frames.
* The side (left or right) closest to the camera is selected for each frame. All values are normalized with the total body height of the subject.  
\end{table}
Table 1: Description of features extracted from HPE.  
Figure 4: Estimating number of steps based on the peaks of step length. The peaks correspond to heel strikes, and counting them gives the number of steps \(n_{steps}\) taken by the subject in the video.  
The flipped motion time series was then appended to the original, thereby doubling the length of the time series. Then, each sequence was divided into subsequences with a uniform length of 180 frames, which is equivalent to 6 s each (assuming a frame rate of 30 frames per second) (Table 2). Classification was performed using a Random Forest classifier with 250 trees on the front view, side view, and all views. Each view was represented by a feature matrix \(X\in\mathbb{R}^{N\times f}\) and a label vector \(\mathbf{y}\in\{0,1\}^{N}\), where \(N\) is the number of samples and \(f\) is the number of features. Apart from accuracy, the precision, recall, and F1 score are also reported. Analysis of the features extracted in Table 1 is presented in Appendix A.
## 2 Methods
### Evaluation of the Predicted Variance and Kernel Projection of the Predictions of WDESen2  
In order to utilize the predicted variance of WDESen2 and the kernel projection, as described in Section 2.2.1, we perform selective prediction, as introduced for classification in [16] and applied to DL for regression problems in [17]. The general idea of selective prediction is to make the model abstain from giving a prediction if the uncertainty surpasses a specific threshold. We apply selective prediction to pixel-wise variances, i.e., uncertainties, from WDESen2 and pixel-wise values of the kernel projection. The first method, variance-based WDESen2 (VWDESen2), as visualized in Figure 5, abstains from predicting pixels that have the highest variance. In order to maintain comparability between different methods, we withdraw 5% of the pixels from the mean prediction of WDESen2. The second method is based on the underlying inverse problem, kernel-based WDESen2 (KWDESen2), where the 5% of the pixels from the mean prediction that have the highest values in the kernel projection of the prediction are withdrawn from the prediction.
# Experimental Evaluation of the Blackbody Radiation Shift in the Cesium Atomic Fountain Clock
## 2 Theoretical Research on the Blackbody Radiation (BBR) Shift
According to this formula, we can see that the uncertainty of BBR shift can be reduced by two methods. The first method is lowering the working environment temperature \(T\). Lowering the temperature can reduce the sensitivity coefficient of the uncertainty of BBR shift, thereby greatly reducing the uncertainty of BBR shift in a fountain clock. The cryogenic cesium fountain clock is based on this principle. The second method is reducing the uncertainty of the temperature, because the uncertainty of BBR shift is proportional to the uncertainty of the temperature. For every 0.1 \({}^{\circ}\)C decrease in the temperature uncertainty at \(T\) = 300 K, the uncertainty of BBR shift decreases by 2.0 \(\times\) 10\({}^{-17}\) approximately. For room-temperature fountain clocks, reducing the uncertainty of the system temperature is the most commonly used method to reduce the uncertainty of BBR shift. The cesium atomic fountain clock NTSC-F2 is described in [22]. To evaluate the BBR shift of the fountain clock system during stable operation, the temperature distribution for the atomic flight trajectory of the cesium fountain clock must be determined. The method adopted was to place temperature sensors near the atomic flight area to conduct real-time monitoring of the system temperature. To reduce the uncertainty of BBR shift of the cesium fountain clock, cesium atoms need to travel in an environment with a uniform temperature. The temperature uniformity of the atomic flight zone has two components: the uniformity of the spatial temperature distribution and the stability of the temperature over time. For this reason, it was necessary to conduct system design and temperature measurement research on the atomic flight zone of the cesium fountain clock in this study.
## 2 Materials and Methods
### HPLC-DAD Analyses  
Each lyophilized plant's tissue sample (lyophilized leaves and roots), weighing 50 mg, underwent extraction using 70% ethanol (2 mL, HPLC-grade purity, Merck, Darmstadt, Germany) in falcon tubes through sonification (Sonic 2, POLSONIC Palczynski ultrasonic bath, Warsaw, Poland) for two cycles, each lasting 30 min at room temperature. Post-extraction, the resultant extracts were centrifuged at 4000 rpm for 8 min (MPW-223E, MPW, Warsaw, Poland), and the supernatant was filtered using syringe filters (pore diameter 0.2 \(\upmu\)m Millex(r) GP, Filter Unit, Millipore, Bedford, MA, USA). Analysis of phenolic compounds was conducted via the HPLC-DAD method [22; 23; 24]. Employing a liquid chromatograph system (Merck--Hitachi, Japan) equipped with a Diode Array Detector (DAD) (L-245) operating within the ultraviolet (UV) light range (200-400 nm), an analytical column Purospher(r) RP--18e (4 \(\times\) 250 mm, 5 \(\upmu\)m; Merck, Darmstadt, Germany) was utilized with a flow rate of 1 mL/min at 25 \({}^{\circ}\)C, detection wavelength set at 254 nm, and an injection volume of 10 \(\upmu\)L. The mobile phase comprised ethanol:0.5% acetic acid in a 1:4 (\(v/v\)) ratio for solvent A, and ethanol for solvent B. Gradient elution proceeded as follows: 100% A for 0-20 min; 100-80%A for 20-35 min; 80-60% A for 35-55 min; 60-0% A for 55-70 min; 0% A for 70-75 min; 0-100% A for 75-80 min; and 100% A for 80-90 min, with a consistent flow rate of 1 mL/min. Triplicate injections were performed for all samples. Quantitative assessment of the active substances was expressed as mg/100 g DW (dry weight of the plant material). The results are shown as the average of three measurements along with the standard deviation.
## 4 Results and Discussion
### Results Using TF-IDF Features and the SMOTE Technique
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Accuracy** & **Class** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{LR} & \multirow{4}{*}{0.93} & 0 & 0.91 & 0.92 & 0.91 \\ \cline{3-5}  & & 1 & 0.98 & 0.92 & 0.95 \\ \cline{3-5}  & & \(-1\) & 0.90 & 0.94 & 0.92 \\ \cline{3-5}  & & Macro avg & 0.93 & 0.93 & 0.93 \\ \hline \multirow{4}{*}{RF} & \multirow{4}{*}{0.85} & 0 & 0.84 & 0.77 & 0.81 \\ \cline{3-5}  & & 1 & 0.97 & 0.84 & 0.90 \\ \cline{3-5}  & & \(-1\) & 0.77 & 0.94 & 0.85 \\ \cline{3-5}  & & Macro avg & 0.86 & 0.85 & 0.85 \\ \hline \multirow{4}{*}{DT} & \multirow{4}{*}{0.83} & 0 & 0.83 & 0.73 & 0.78 \\ \cline{3-5}  & & 1 & 0.92 & 0.82 & 0.87 \\ \cline{3-5}  & & \(-1\) & 0.76 & 0.94 & 0.84 \\ \cline{3-5}  & & Macro avg & 0.84 & 0.83 & 0.83 \\ \hline \multirow{4}{*}{KNN} & \multirow{4}{*}{0.52} & 0 & 0.29 & 0.88 & 0.44 \\ \cline{3-5}  & & 1 & 0.27 & 0.97 & 0.43 \\ \cline{3-5}  & & \(-1\) & 1.00 & 0.41 & 0.58 \\ \cline{3-5}  & & Macro avg & 0.52 & 0.76 & 0.48 \\ \hline \multirow{4}{*}{SVM} & \multirow{4}{*}{0.94} & 0 & 0.92 & 0.91 & 0.91 \\ \cline{3-5}  & & 1 & 0.98 & 0.93 & 0.96 \\ \cline{3-5}  & & \(-1\) & 0.88 & 0.94 & 0.91 \\ \cline{3-5}  & & Macro avg & 0.93 & 0.93 & 0.93 \\ \hline \multirow{4}{*}{AdaBoost} & \multirow{4}{*}{0.70} & 0 & 0.54 & 1.00 & 0.70 \\ \cline{3-5}  & & 1 & 0.95 & 0.74 & 0.83 \\ \cline{3-5}  & & \(-1\) & 0.98 & 0.36 & 0.52 \\ \cline{3-5}  & & Macro avg & 0.82 & 0.70 & 0.68 \\ \hline \multirow{4}{*}{GNB} & \multirow{4}{*}{0.78} & 0 & 0.92 & 0.65 & 0.76 \\ \cline{3-5}  & & 1 & 0.87 & 0.77 & 0.82 \\ \cline{3-5}  & & 1 & 0.87 & 0.77 & 0.82 \\ \cline{3-5}  & & \(-1\) & 0.65 & 0.93 & 0.77 \\ \cline{3-5}  & & Macro avg & 0.81 & 0.78 & 0.78 \\ \hline \multirow{4}{*}{ETC} & \multirow{4}{*}{0.80} & 0 & 0.78 & 0.71 & 0.74 \\ \cline{3-5}  & & 1 & 0.87 & 0.77 & 0.82 \\ \cline{3-5}  & & \(-1\) & 0.76 & 0.93 & 0.84 \\ \cline{3-5}  & & Macro avg & 0.81 & 0.80 & 0.80 \\ \hline \multirow{4}{*}{SGD} & \multirow{4}{*}{0.94} & 0 & 0.91 & 0.93 & 0.92 \\ \cline{3-5}  & & 1 & 0.98 & 0.94 & 0.96 \\ \cline{3-5}  & & \(-1\) & 0.92 & 0.94 & 0.93 \\ \cline{3-5}  & & Macro avg & 0.94 & 0.94 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results using TF-IDF features on the original dataset.
# Structural Strength Analysis of a Rotary Drum Mower in Transportation Position
## 2 Materials and Methods
### Experimental Set Up
#### 2.3.3 Strain Gauge-Based Strain Measurement  
In order to assess the RDM's deformation behaviour under actual operating conditions and to validate the FEA outputs, physical tests were performed on the machine during static and dynamic transportation. When planning the physical testing, the static linkage and dynamic transportation positions of the RDM in the total work cycle scenario were referred to, and experimental strain measurements were carried out. Equivalent stress calculations for the targeted element groups were performed in accordance with all the strain measurements that were recorded. A universal data acquisition module of HBM-QuantumX MX840A with eight channels, 24-bit resolution capability, and HBM K-RY81-6 series \(0^{\circ}/45^{\circ}/90^{\circ}\) three-elements, 120 ohm rectangular rosette strain gauges (SG) were utilised for the strain measurements [15; 16]. All physical testing focused on high measurement precision, and data were recorded at a sample rate of 50 Hz. The CATMAN data monitoring and processing software was utilised to convert the measured strain values into equivalent stress values and to record the outputs [17]. Measurements and data processing were conducted simultaneously in this software.  
In the physical tests, strain was measured using SG rosettes that were positioned at various places on the RDM structural members in the associated groups. The critical loading locations and optimisable components of the machine were taken into consideration when choosing the SG points, as well as their capacity to represent the high and low stress distributions that might occur on the elements under the machine's physical loading circumstances.  
Three SG rosettes were used for the component RDM165-A-004, whereas seven SG rosettes were utilised for components of RDM165-B-003 and RDM165-B-004 in consideration of the component dimensional size. In the bonding procedure of each SG, the bonding surfaces were carefully machined, finely polished, and cleaned with a chemical solvent (M-Bond 200 Catalyst), and the application stages were properly executed [18]. In SG bonding procedures, Vishay M-Bond 200 SG adhesive was utilised [19]. A special coating tape and cold silicone were used to isolate SG surfaces from the destructive effect of the surrounding environment. Figure 5 shows the setup in detail.  
Figure 5: Strain gauge set up in detail.
## 3 Preliminaries
### MCF-EON Architecture  
Figure 3 shows the node architecture [17] in MCF-EONs, where the transmission medium (i.e., MCF) achieves multiple SMF capacities by increasing the spatial dimension of a core. Therefore, this architecture can easily multiply the network capacity of the original EON architecture and carry more connection requests.  
Different cores on the same optical fiber link in Figure 3 are demultiplexed and multiplexed first, and then the spectrum exchange module and transceiver pool in the figure middle are used to allocate an appropriate amount of spectrum resources to each connection request. The transceiver pool provides an appropriate number of sub-transceivers according to the sub-carrier bandwidth allocated for each request. These functions can be implemented by the sub-modules in the EON architecture, such as a bandwidth-variable transponder and a bandwidth-variable spectrum selection switch.  
To allocate spectrum resources more flexibly, this architecture supports fiber switching, core switching, and spectrum switching, which means that each connection request can occupy different fibers, cores, and spectrum blocks on different links along its transmission path, respectively. However, the spectrum constraints, including continuity and consistency in this path, must be met by switching. These switching modules can make full use of network resources from a global perspective, thus reducing the network requests blocking ratio (RBR). As the focus of this paper, the basic idea and node architecture of core switching are introduced in the next subsection.  
Figure 3: Node architecture in MCF-EONs.  
Figure 2: Cross-section of a multi-core fiber.
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 2 Materials and Methods
### Wire-Grid
#### 2.2.2 Wire-Grid: Algorithm
Then, when the antenna in segment \(n\) is excited in the gap with voltage \(V_{\text{in}}\) (\(V_{\text{in}}=1\) V), the matrix of applied voltage (35) will look like \[\mathbf{V}= \begin{bmatrix}0\\ \dots\\ V_{in}\\ \dots\\ 0\end{bmatrix} ,\] (36) i.e., all elements of the matrix are zero, except for element \(n\), which is equal to the source voltage. Then, the SLAE solution gives the current distribution over the surface of the radiator. 14. Calculate the antenna input impedance as \[Z_{in}=\frac{V_{in}}{I_{in}}\] (37) where \(I_{in}\) is the current in the antenna gap. The inverse of impedance is complex conductance (admittance). 15. Consider the antenna as an array of \(N\) current elements and obtain its radiation pattern (RP). Here, the vector potential in the far zone is calculated as \[A=\frac{\mathrm{i}\mathrm{\SIUnitSymbolMicro e}^{-j\mathrm{\SIUnitSymbolMicro e }_{0}}}{4\pi r_{0}}\sum_{n=1}^{N}I(n)\Delta I_{n}\mathrm{e}^{j\mathrm{ \SIUnitSymbolMicro e}_{n}\cos\xi_{n}}\] (38) where \(r_{0}\) and \(r_{n}\) are the lengths of the radius vectors of the points of the far field and the source, respectively, and \(\xi_{n}\) are the angles between these vectors. The field components in the far field using the spherical coordinate system are defined as \[E_{\theta}=-j\mathrm{\SIUnitSymbolMicro e}A_{\theta},\] (39) \[E_{\varphi}=-j\mathrm{\SIUnitSymbolMicro e}A_{\varphi}.\] (40)
16. Calculate the antenna gain as \[G(\theta,\varphi)=\frac{|E_{\varphi}|^{2}+|E_{\theta}|^{2}}{30|I_{t}|^{2}R_{t} }r_{0}^{2}\] (41) where \(R_{t}\) is the real part of the input impedance. The block diagram of the Wire-Grid working algorithm is presented in Figure 3.  
Figure 2: Separate segments \(n\) and \(m\) of the wires and the distances between them.
## On the Extended Simple Equations Method (SEsM) for  
Obtaining Numerous Exact Solutions to Fractional Partial Differential Equations: A Generalized Algorithm and  
Several Applications  
**[PERSON]**  
Institute of Mechanics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 4, 1113 Sofia, Bulgaria;  
[EMAIL_ADDRESS] Tel.: +359-02-979-64-43  
**Abstract**  
In this article, we present the extended simple equations method (SEsM) for finding exact solutions to systems of fractional nonlinear partial differential equations (FNPDEs). The expansions made to the original SEsM algorithm are implemented in several directions: (1) In constructing analytical solutions: exact solutions to FNPDE systems are presented by simple or complex composite functions, including combinations of solutions to two or more different simple equations with distinct independent variables (corresponding to different wave velocities); (2) in selecting appropriate fractional derivatives and appropriate wave transformations: the choice of the type of fractional derivatives for each system of FNPDEs depends on the physical nature of the modeled real process. Based on this choice, the range of applicable wave transformations that are used to reduce FNPDEs to nonlinear ODEs has been expanded. It includes not only various forms of fractional traveling wave transformations but also standard traveling wave transformations. Based on these methodological enhancements, a generalized SEsM algorithm has been developed to derive exact solutions of systems of FNPDEs. This algorithm provides multiple options at each step, enabling the user to select the most appropriate variant depending on the expected wave dynamics in the modeled physical context. Two specific variants of the generalized SEsM algorithm have been applied to obtain exact solutions to two time-fractional shallow-water-like systems. For generating these exact solutions, it is assumed that each system variable in the studied models exhibits multi-wave behavior, which is expressed as a superposition of two waves propagating at different velocities. As a result, numerous novel multi-wave solutions are derived, involving combinations of hyperbolic-like, elliptic-like, and trigonometric-like functions. The obtained analytical solutions can provide valuable qualitative insights into complex wave dynamics in generalized spatio-temporal dynamical systems, with relevance to areas such as ocean current modeling, multiphase fluid dynamics and geophysical fluid modeling.  
**Keywords:** fractional nonlinear partial differential equations; extended Simple equations method (SEsM); generalized SEsM algorithm; time-fractional shallow water-like systems; analytical solutions  
**MSC:** 35A24; 35G50
# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 2 Methodology
The reward function parameters \(\lambda_{1}=0.5\) and \(\lambda_{2}=0.1\) were selected to balance the trade-off between discovering significant causal relationships and penalizing weaker, less relevant connections. Specifically, \(\lambda_{1}\) adjusts the weight for strong causal rewards, helping the model prioritize meaningful relationships, while \(\lambda_{2}\) introduces a mild penalty to reduce noise in the causal graph without being overly restrictive. These values were fine-tuned through empirical testing to optimize performance for supply chain scenarios. Our approach, which combines causal discovery with reinforcement learning, addresses limitations in traditional, theory-based structural models that often rely on fixed assumptions and expert-driven interpretations. Such models typically focus on predefined relationships and struggle with the high variability of real-world supply chains, where interactions between variables are dynamic and complex [9; 26]. By contrast, our data-driven framework adapts to observed data, uncovering causal relationships in real-time and quantifying their strengths. This flexibility allows our method to respond to shifting supply chain conditions, capturing both direct and indirect causal influences on delivery risk--something conventional structural models may overlook. Our experimental results in the next section further distinguish this approach by quantifying causal strengths, providing actionable insights beyond correlation. For example, while previous studies highlight shipment mode as influential, our model assigns a causal strength, indicating its true impact on late delivery risk. This enables supply chain managers to prioritize interventions based on data-driven causal influence rather than intuition alone [39]. By aligning with recent calls for more adaptable and quantifiable risk assessment tools, our framework not only confirms established risk factors but also provides a more granular, context-sensitive analysis that enhances decision-making in complex logistical environments. In the next section, we present the experimental results and analysis. We evaluate our model's performance using real-world supply chain data and compare it against existing methods to demonstrate its effectiveness in identifying the root causes of delivery risks.
# Investigation of Open Air Stability of CsPbBr3 Thin-Film Growth on Different Substrates
## Materials and Methods
### Substrates  
Four different substrates were selected among the more commonly used in optical device fabrication: ITO, soda-line glass, silica glass, and single-oriented polycrystalline gold. Glass slides coated with indium tin oxide (ITO) were purchased from Sigma Aldrich. Soda-line glass (20 mm \(\times\) 40 mm) slides for microscopy and silica glass slides (50 mm \(\times\) 50 mm) were purchased from UGQ Optics. Single-oriented polycrystalline gold substrates were obtained \"in house\" by depositing 100 nm of gold on soda-line glass slides (20 mm \(\times\) 40 mm) via magnetron sputtering. Before gold sputtering, the slides were coated with 10 nm thick tantalum film, which acts as an interlayer improving the adhesion of gold toward glass. After deposition, using a gas torch, the samples were heated to obtain a prevalent Au (111)-oriented surface as previously described [23; 24]. All substrates were cleaned with acetone and dried under pressurized air just before their use.  
Table 1 summarizes the types and characteristics of the chosen substrates.