esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Electromyography-Based Action Recognition and Manipulator Control
## 2 Method and Mathematical Background
### Feature Extraction
#### 2.3.1 Time-Domain Feature  
Although the sEMG signals belong to non-stationary, random, one-dimensional bioelectrical signals, they can be processed as stationary random signals in a short time. We chose MAV, Zero Crossing (ZC), Waveform Length (WL), and RMS for processing sEMG signals [25; 26; 27; 28]. Assume {\(x_{i}\)\(|=1\), \(2\),..., \(N\)} is a single-channel sEMG sequence. \(N\) is the number of data and \(x_{i}\) denotes the \(i\)th element. The equations were as follows.  
(i) MAV:  
\[MAV=\frac{1}{N}{\sum_{i=1}^{N}}|x_{i}| \tag{1}\]  
The MAV was proportional to the intensity of muscle movements.  
(ii) ZC:  
\[x_{i}=\sum_{i=1}^{N}\text{sgn}(-x_{i}\cdot x_{i+1}) \tag{2}\]  
where sgn(\(\cdot\)) is sign function.  
The number of times the signal changed from positive to negative amplitude and from negative to positive amplitude was called ZC. The human cerebral cortex generates the control signal first. Then the control signal is transmitted to the motor endplate, through the motor neuron via the axon, and its branches and the EMG signal is formed. Later the muscle fiber is stimulated to produce action, and the intensity of the action is closely related to the frequency of the conduction signal. Therefore, ZC reflects the frequency domain information, which is closely related to the intensity of the action.  
(iii) WL:  
\[WL=\frac{1}{N}{\sum_{i=1}^{N}}|x_{i+1}-x_{i}| \tag{3}\]  
The WL also contains some key characteristics, such as the duration, amplitude, and frequency of the signal.  
(iv) RMS:  
\[RMS=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}}{x_{i}}^{2}} \tag{4}\]  
The RMS is on behalf of the average power of the signal.
## 1 Introduction  
### Research Background  
Recommendation systems mainly recommend the content that users are most likely to be interested in according to their historical interaction behavior, personalized preference settings, and other information, which can help users make effective choices that are beneficial to themselves among many diverse and complicated options. In addition, in practical applications, it is often difficult to obtain relevant information such as the user's specific preference settings. For example, some users tend to use anonymous identities or browse related content directly on the website without accounts. Traditional recommendation methods are no longer applicable to the above application scenarios, so session-based recommendation technology was generated [1; 2; 3]. Session recommendation refers to the prediction of the next item that the user is most likely to buy or click based on the user's historical session data [4].  
For example, on the JD shopping website, the different products that users add to the shopping cart at one time constitute a session sequence, or the different news records that users browse on the internet for a continuous week are also a session sequence. In session recommendation, users are often anonymous, rather than a real, labeled user. The user's behavior in the session is not only affected by the current session, but also by the item conversion relationship from other sessions. There is a lot of information to be mined [5].  
This paper uses the natural advantage of graph neural network to capture and learn various complex association relationships from graph data, and studies how to use a graph neural network to model different levels of session item conversion relationships. A graph neural network session recommendation model (TSDA-GNN) that fuses sequence interaction timing and global distance awareness is proposed. We construct a global graph, session graph, and feature graph according to all session sequences, and use the powerful graph data learning and representation ability of a graph neural network to capture the conversion relationship between items from different levels. In the session graph, we use the interaction order of nodes as the weight information in the relationship matrix, and construct the connection matrix of the session graph, so as to model the conversion relationship between items more accurately.  
We construct a global graph to capture the global item conversion by aggregating all the pairwise item conversions in the session. We also introduce the average \"spacing\" between items in the global graph as a measure of the association between two items. Then, we fuse the feature information of the item and use a graph neural network to deeply mine the hidden user preference information from the item features.
# A Mathematical Model for Reduction of Trim Loss in Cutting Reels at a Make-to-Order Paper Mill
## 4 Solution Procedure
### Linear Programming Model  
At this stage of the solution procedure, a linear programming model is developed and implemented. The model includes constraints related to the satisfaction of ordered quantities. The over-produced reels are stocked for later usage or negotiated with client and those produced of width less than the minimum acceptable limit are either recycled or utilized in the form of less valuable products than the ordered items. To fill the order, the objective function of the model minimizes the trim loss area in cutting master reels of widths 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, and 60 inches, and stocked reels of widths 35, 30, 25, 22, and 18 inches.  
\[\textbf{Minimize}:Z=70\sum_{i=1}^{24}X_{70,c}+\ldots+60\sum_{c=255}^{232}X_{6,c}+35\sum_{c=253}^{288}X_{35,c}+30\sum_{c=259}^{243}X_{30,c}+25\sum_{c=244}^{2 46}X_{5,c}+22\sum_{c=244}^{289}X_{22,c}+18\sum_{c=250}^{250}X_{18,c} \tag{23}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Production** & **Widths (in)** & **Paper Type** & \begin{tabular}{c} **Number of** \\ **Reels** \\ \end{tabular} & \
\begin{tabular}{c} **External** \\ **Diameter (in)** \\ \end{tabular} &
\begin{tabular}{c} **Internal** \\ **Diameter (in)** \\ \end{tabular} \\ \hline UL 014 & 35 & GB * & 05 & 60 & 6 \\ UL 084 & 30 & GB & 10 & 60 & 6 \\ UL 101 & 25 & GB & 08 & 60 & 6 \\ UL 106 & 22 & GB & 01 & 60 & 6 \\ UL 107 & 18 & GB & 03 & 60 & 6 \\ \hline \end{tabular}  
* Grey Back.  
\end{table}
Table 3: Stocked over-produced and leftover reels.  
The obtained optimal solution comprises of four non-integer decision variables as presented in Table 4. The optimal objective function value is 5050.  
The solution is not implementable due to non-integer decision variables. An integer algorithm is used to solve the problem and the post-optimization procedure is described in the next subsection.
# Two-State Lasing in Microdisk Laser Diodes with Quantum Dot Active Region
## 4 Conclusions  
The features of two-state lasing in microlasers with quantum dot active region are investigated for different disk cavity diameters at room temperature. The two-state lasing emission occurs via the ground and the first excited-state optical transitions in the microlasers larger than the critical diameter, which is estimated as 14 \(\upmu\)m for room-temperature operation. The maximum optical loss at which GS lasing can be achieved was estimated at 33 cm\({}^{-1}\). The coefficient describing the dependence of optical loss on the disk diameter was evaluated as 421 cm\({}^{-1}\)\(\times\)\(\upmu\)m. The lowest threshold current densities of the microdisk lasers are in a good agreement with that of the stripe lasers, having nominally the same optical loss. Thus, the sidewall nonradiative recombination does not affect the microdisk laser characteristics. For possible application of two-state lasing in QD microdisk lasers (e.g., for a hybrid multilevel/spectral encoding), it is convenient to have a small difference between the threshold current densities for the first (GS) and the second (two-state) lasing. The experimental results obtained indicate that this difference decreases with decreasing microdisk size and completely disappears for microdisks of the critical diameter. Two-state lasing involving the first and the second excited-state optical transitions was observed for the first time in the smallest microdisk of 9 \(\upmu\)m in diameter.  
Conceptualization, I.M., A.Z. and M.M.; methodology, I.M. and Y.S.; investigation, I.M. and Y.S.; data curation, E.M., N.F., A.D. and K.I.; writing--original draft preparation, I.M., A.Z. and N.K.; writing--review and editing, M.M., E.M. and K.I.; project administration, I.M., A.Z. and N.K.; funding acquisition, I.M. and N.K. All authors have read and agreed to the published version of the manuscript.  
Study of microdisk lasers was supported by Russian Science Foundation grant No. 22-72-00028 ([[https://rscf.ru/project/22-72-00028/](https://rscf.ru/project/22-72-00028/)]([https://rscf.ru/project/22-72-00028/](https://rscf.ru/project/22-72-00028/)) (accessed on 18 February 2023)) Support of optical measurements was implemented in the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE University). Study of broad-area lasers was supported by the Ministry of Science and Higher Education of the Russian Federation under project FSRM-2023-0010.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the author's readiness to provide it on request.  
Figure 6: Threshold current density of GS (solid symbols) and ES1 (open symbols) lasing for stripe (squares) and microdisk (circles) lasers as function of total optical loss.  
**Acknowledgments:** The work was carried out on the equipment of the unique scientific setup *Complex optoelectronic stand*.
## 3 Results  
### Initial Siltration Dynamics and Environmental Conditions  
Based on the bathymetric surveys conducted on 1 April, 17 May, and 3 July 2024 (see Figure 4), the underwater terrain in the study area exhibits distinct spatial patterns. The riverbed is generally shallower along the channel margins, indicating higher elevation near the riverbanks. Additionally, the terrain shows a gradual elevation increase from downstream to upstream, consistent with a natural channel slope and fluvial sediment transport dynamics.  
By comparing the elevation changes between the survey dates, the siltation volumes for two specific periods--from 1 April to 17 May and from 17 May to 3 July--were calculated. Overall, the majority of 30 m \(\times\) 30 m grid cells exhibited siltation depths of less than 1 m. The spatial extent of bathymetric data was more concentrated in the 1 April survey, and a larger proportion of grids exceeded 1 m of siltation during the first period (April-May) compared to the second period (May-July). Spatially, siltation distribution revealed strong heterogeneity, with localized clusters of both high and low values observable in each time window, suggesting the influence of local hydraulic structures or morphological features.  
The environmental conditions also varied significantly between the two periods (see Figure 5). From April to May, the average water levels were higher, and the air temperatures were lower than during May to July. The mean water level was 49.59 m in the first period and 48.29 m in the second. Meanwhile, the average maximum air temperature increased from 26.13 \({}^{\circ}\)C to 29.66 \({}^{\circ}\)C. Furthermore, both the water level and air temperature fluctuations were greater in the first period, indicating more dynamic hydro-meteorological conditions early in the season. After early July, the water levels exhibited a gradual downward trend, with daily fluctuations progressively decreasing. The air temperature continued to show diurnal variability, but a clear seasonal decline was observed heading into the winter months. These seasonal changes in hydro-meteorological conditions are closely linked to the spatial and temporal dynamics of sediment deposition and resuspension observed in the study area.  
Figure 3: Workflow of monthly siltation prediction using iterative random forest modeling approach.
## 1 Introduction
### Basic SEM Operation
#### 1.2.2 Imaging Process in the SEM
The electrical properties of materials are studied by using the electron beam-induced current (EBIC) technique [61]. It is specifically used for semiconductor materials with localized charge carriers. An electron-hole pair in a semiconductor material is created with the help of the primary electron beam. With the application of an external voltage, the created charge carriers start moving in response to the acting electrical field, indicating a measurable current, which is used to investigate the electrical properties of materials. This technique is also valuable in the identification of defects, grain boundaries, and other microstructural features affecting electrical behavior. The electrical functionality of semiconductor devices is studied with the aid of voltage contrast imaging (VCI), which indicates the variations in electrical potential or voltage across the surface of the specimen [62].
# Advances in the Application of Microcapsules as Carriers of Functional Compounds for Food Products
## 3 Wall Materials
The authors concluded that the mixture of maltodextrin with arabic gum imparted a better protection to anthocyanin pigments and presented a higher encapsulation efficiency. In terms of particle morphology, that of maltodextrin/ arabic gum was smooth and had fewer agglomerates and concavities on their surface when compared to maltodextrin and maltodextrin/gelatin microcapsules. Inulin is another promising material for use in encapsulation. This polymer belongs to the group of fructans composed mostly of \(\beta\)(2-1) linked fructosyl-fructose units wherein each fructose chain is generally terminated with an \(\alpha\)(2-1) linked glucose moiety [80; 81; 82]. The chain lengths of these fructans range from 2 to 60 units, with an average DP of 12. DP influences important properties, such as solubility, thermal stability, sweetness power, and prebiotic activity [83; 84]. Standard chicory inulin has an average DP of about 10 to 12. Long-chain (or high performance) chicory inulin, from which the lower DP fraction has been physically removed, with an average DP of about 25, is also available [85]. Inulin has attracted attention from various researchers and industries due to reports of its many benefits, such as the nutrition of beneficial intestinal bacteria, the reduction of the risk of gastrointestinal diseases, the regulation of blood glucose, the promotion of mineral absorption, the improvement of the immune system, the prevention of osteoporosis, and the reduction of the risk of obesity and cholesterol level [86; 87; 88]. Inulin is not digested by the human digestive system, which does not have enzymes to separate the \(\beta\) (2-1) bonds in the fructose backbone. Therefore, most dietary inulin has the ability to stimulate the proliferation of specific bacteria in the lower colon (_bifidobacterium_ and _lactobacillus_), thus reducing the number of harmful species such as _Escherichia coli_ and _Clostridium_ spp. [89; 81]. The main applications of inulin are related to its technological properties, such as a substitute for sugars and fats in low-calorie foods, as a thickener, emulsifier, and gelling agent, and it may constitute a potential auxiliary agent for drying processes [80; 84]. In the field of bioactives encapsulation, the use of inulin as wall material enables its health benefits to be harnessed, and at the same time it allows the release of bioactive compounds in the colon due to its resistance to pH variations in the gastrointestinal tract [23; 90].
## 3 Results
### Effect of Printing Speed and Tubular Structure Diameter on Structural Irregularity and Stability
To investigate this, a set of tubular structures with different  
Figure 7: Effect of support travel speed and tubular structure diameter on the printed tubular structure quality; (**a–c**) cross-sectional and side views of printed tubular structures at different flowrates and travel speeds; (**d–f**) cross sectional view with boundary boxes of filaments printed at the 3 different parameter sets; (**g**) combined plot of the edge irregularity and edge ratio (defined as length/width of the boundary boxes in (**d–f**); (**h**) the cross-sectional and side views of tubular structures printed at different diameters (5 mm–8 mm); (**i**) tubular structure is deformed with d = 10 mm; (**j**) tubular structure is disconnected between layers with d = 12 mm; (**k**) irregularity plot of the printed structures in (**g**). diameters are printed and evaluated. The cross-sectional and side views of tubular structures printed at different diameters (5 mm-8 mm) are shown in Figure 7g,j is the irregularity plot of the printed structures in (g). Tubular structures with larger diameters are also printed, resulting in either deformed or disconnected structures. For example, the tubular structure in Figure 7i is deformed with d = 10 mm. In Figure 7j, the tubular structure with d = 12 mm is disconnected. The results show that when the diameter increases to 12 mm, the structures are not able to maintain integrity (see Figure 7j). The breakage happens during the support bath removal procedure when the overall structural stiffness is low. Therefore, this experiment indicates that, although printing within support bath enables arbitrary geometry fabrication, the structures should be carefully designed with structural stability after removal of the support bath.
## 6 Results
### Performance Analysis
For the Backdoor dataset (Figure 13), PbQGA maintains strong precision (87.1%) and accuracy (95.5%), but it falls short in recall (71.0%), the lowest among all methods. This indicates that while PbQGA is effective at making precise fraud predictions with fewer false positives, it struggles to capture all fraudulent cases, likely missing some important anomalies. In contrast, FvQGA achieves the highest recall (78.6%) and F1 score (85.2%), suggesting it is better at identifying fraudulent cases despite slightly lower precision (93.1%) compared to GbQGA (94.1%). GbQGA outperforms all methods in precision (94.1%) and accuracy (96.5%), making it the most precise model but slightly less balanced than FvQGA. IbQGA also shows strong performance, with an F1 score of 84.2% and recall of 75.8%, making it a good alternative. While PbQGA remains highly accurate, its lower recall suggests it may be too conservative, potentially missing fraudulent instances in high-risk scenarios. Figure 12: The average performance over 30 runs on [PERSON]’s disease dataset. Figure 13: The average performance over 30 runs on Backdoor dataset. For the Campaign dataset (Figure 14), FloQGA demonstrates superior performance across all evaluation metrics, achieving the highest F1 score (69.3%), precision (68.8%), recall (69.5%), and accuracy (87.3%). This suggests that FloQGA effectively balances precision and recall, leading to the most accurate fraud detection model among the tested algorithms. Compared to the other methods, GA (baseline) has the lowest F1 score (65.7%) and accuracy (83.4%), indicating weaker overall performance. The FvQGA and GbQGA models perform similarly, with F1 scores of 66.1% and 66.1%, respectively, and accuracy values of 84.1% and 85.7%, slightly behind FloQGA. IbQGA shows competitive performance with an F1 score of 69.1% and an accuracy of 87.1%, coming closest to FloQGA. However, FloQGA maintains a slight edge, making it the most reliable model for this dataset.
## 2 Materials and Methods
### Proposed UIM-VLC Channel Model
#### 2.4.1 Proposed LOS Link  
Using the same geometry as the reference channel model and the parameters as in Equation (31), and adding a new spectral transmittance function \(\zeta_{0}(\lambda)\) that accounts for the attenuation caused by scattering and absorption due to dust particles in the LOS link, \(\mathcal{A}_{0}(\lambda)\) can be defined as  
\[\mathcal{A}_{0}(\lambda)=\mathcal{C}\frac{(m+1)\,A_{R}}{2\pi d^{2}}\,\Omega_{ \mathrm{LOS}}\Psi_{\mathrm{LOS}}\,\mathcal{G}(\lambda)\zeta_{0}(\lambda)\,, \tag{76}\]  
where the optical filter gain, \(\mathcal{G}(\lambda)\), is the only spectral parameter apart from \(\zeta_{0}(\lambda)\). The optical concentrator gain, \(\mathcal{C}\), depends on an incident angle rather than wavelength [25]; the Lambertian mode number \(m\), PD surface area \(A_{R}\), the link distance \(d\), and the trigonometric expression \(\Omega_{\mathrm{LOS}}\) are all geometric parameters that do not depend on the wavelength. The shadowing weighting factor \(\Psi_{\mathrm{LOS}}\) does not depend on wavelength, as, by definition, shadowing objectsblock light throughout the visible spectrum. Combining Equation (76) into Equation (70), the proposed DC gain of the optical LOS link can be expressed as  
\[H_{\text{LOS}}^{\text{opt}}=\mathcal{C}\frac{(m+1)A_{R}}{2\pi d^{2}P_{T}}\, \Omega_{\text{LOS}}\Psi_{\text{LOS}}\int\mathcal{G}(\lambda)\zeta_{0}(\lambda) \Phi(\lambda)\,d\lambda\,. \tag{77}\]  
Equivalently, by combining Equation (76) with Equation (74), the proposed DC gain of the electro-optical LOS link can be expressed as  
\[H_{\text{LOS}}^{\text{el}}=\mathcal{C}\frac{(m+1)A_{R}}{2\pi d^{2}P_{T}}\, \Omega_{\text{LOS}}\Psi_{\text{LOS}}\int R(\lambda)\mathcal{G}(\lambda)\zeta_{ 0}(\lambda)\Phi(\lambda)\,d\lambda\,. \tag{78}\]
## 3 Results and Discussion
### Histopathological Findings  
The pancreas of the normal control and control _S. linfolium_ groups was observed, and the pancreas' histological structure and the Langerhans' islets (IL) implanted in the acinar cell were both normal and undamaged (AC). However, the diabetic rats' pancreatic sections showed significant Langerhans islet degeneration, and some necrotic areas were detected, with hydropic islet degeneration (IL) surrounded by fibrosis of pancreatic acini (AC), with some rats exhibiting severe islet atrophy. However, the diabetic rats given extracts of _S. linfolium_ phlorotannins (60 mg/kg) showed a noticeable increase in islet size, repaired necrotic and fibrotic alterations, and, most astonishingly, a considerably decreased percentage of degenerative cells in islets (Figure 6). The pancreatic islets of the diabetic group had severe degenerative alterations, and the certain cells had grown larger, with foamy or transparent cytoplasm [80]. Management of the metabolism of micronutrients depends on the pancreas. As a result, when diabetes affects the pancreas, the pancreas tissues may suffer intracellular damage [81]. According to research by [PERSON] and [PERSON] [82], the pancreas of diabetic rats showed severe damage to the islets of Langerhans and a small islet size. After 30 days of treatment, with diabetic rats receiving 150 and 300 mg/kg of hydroalcoholic extracts of the brown alga _Sargassum oligocystum_, there was significant regeneration of the beta cells and an increased pancreatic islet area, according to [PERSON] et al. [83]. The ability of the pancreas to regenerate after being destroyed by streptozotocin is thought to be a result of the presence of stable cells in the pancreas [84]. The pancreatic islets and acinar cells of the diabetic rats receiving methanol extract of _S. platensis_ exhibited a remarkable recovery [85].  
Figure 6: Photomicrograph of the pancreatic tissue stained with hematoxylin and eosin for the various treatment groups: (**G1**) control rats, (**G2**) rats that received _S. linfolium_ phlorotannins extract, (**G3**) diabetic rats administered streptozotocin, and (**G4**) diabetic rats treated with _S. linfolium_ phlorotannin extract.