esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Evaluation
### Other Models  
As seen in Figure 6, for both LOSADA2016 and LOSADA2018 databases, the VAD representation with Naive Bayes classification presents the worst performance in F1 score. On the other hand, for the DDVHSM database, it presents the best performance. For the CSSRS and Kaggle database, the tf-idf BOW using Ada Boost classification presents the worst performance but has a better performance in the LOSADA2016 and LOSADA2018 databases. For all databases except DDVHS, the best performance is given by the sentiment-based representation with PBC4 cip.  
Figure 7 shows that for AUC, VAD features using Naive Bayes classification present the worst performance for LOSADA2016 and LOSADA2018. It also shows that it has a higher performance in CSSRS, DDVHSM, and Kaggle databases, having the best performance in DDVHSM. The parser tree representation with Random Forest classification presents a similar performance in all databases in comparison with its counterparts.  
Figure 4: A comparison plot of the F1 score for the best proposed representation using PBC4 cip, and the available representations from Losada’s databases.  
Figure 5: A comparison plot of the AUC for the best proposed representation using PBC4 cip, and the available representations from Losada’s databases.  
As we have stated before, for all databases except DDVHSM, the best performance is given by sentiment-based representation with PBC4 cip.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 4 Results and Discussion
### Model Experimentation  
To ensure robust generalization, the HGE-ACN model was tested on a structured sign language recognition dataset with many samples, various signers, and real-world variations. Refinements for dataset details and ablation studies are as follows:  
The dataset includes 120 h of recorded sign language gestures from 50 different signers, covering 250,000 gesture sequences. The data were collected in indoor and outdoor settings under three lighting conditions and varying noise levels. The sensor configuration used a wearable glove-based system with six-axis IMU sensors and five-finger curvature sensors for precise motion and hand positioning. Preprocessing methods included min-max normalization, low-pass filtering, and temporal alignment techniques to improve signal quality before feature extraction. This dataset configuration ensures the model effectively generalizes across different user styles, environmental settings, and signing speeds.  
Ablation Study: Validating Module Contributions  
To validate the contribution of each HGE-ACN module, ablation studies were conducted by removing or modifying individual components and analyzing the impact on accuracy and inference time in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experiment** & **Description** & **Accuracy (\%)** & **Inference Time (ms)** \\ \hline Full HGE-ACN Model & Baseline model with all & 94.8\% & 45 ms \\ Without Graph Embedding & Only adaptive convolution is used & 88.3\% & 39 ms \\ (HGE Removed) & for feature extraction. & 85.7\% & 42 ms \\ Without Adaptive Convolution & Only graph embeddings are used & 81.5\% & 38 ms \\ (ACN Removed) & without adaptive convolution. & 86.1\% & 44 ms \\ Without Multi-Scale & Raw sensor inputs are used without & 81.5\% & 38 ms \\ Sensor Fusion & fusion techniques. & 86.1\% & 44 ms \\ Without Attention Mechanism & Temporal attention is disabled in the & 88.1\% & 38 ms \\ \hline \end{tabular}
\end{table}
Table 3: Ablation study of HGE-ACN components.  
Figure 9: Levenshtein Distance.
## 2 The TPUMSTSR Under RLV Model
### Optimal Power Flow Model for the Main Grid  
In the multi-stage optimization operation model of MCSs, Stage One involves decoupling the operation of the main grid and the CSs. The main grid is operated by the dispatch department, where certain independently connected thermal power units are directly controlled by the dispatch department. Meanwhile, MCSs connected to different nodes of the main grid are operated by independent operators.  
To achieve hierarchical operation between the main grid and CSs, in Stage One: Optimal Power Flow Model for the Main Grid, the dispatch department determines and sends active and reactive power scheduling requirements over a short time scale to each power source or CS based on the optimal power flow program. Each power source and CS must generate power according to the dispatch requirements at each time period and provide feasible output boundaries for the next time period to facilitate future calculations.  
Additionally, the dispatch department, IPPs, and CSs exchange information on DPR auxiliary services, including overall revenue and CA; specifically, IPPs and CSs provide their total PR revenue and total adjusted output to the dispatch department. The total PR revenue generated across the entire grid needs to be allocated among power sources that did not participate in PR, following predefined rules. The dispatch department aggregates the PR revenue data uploaded by IPPs and CSs and then redistributes the total auxiliary service revenue and total adjusted generation across the entire grid to IPPs and CSs for CA and economic optimization of their dispatch plans.  
As a result, internal power sources within each CS are coordinated by a central controller, which adjusts internal power output according to the overall dispatch requirements from the dispatch department. To enhance the flexibility of thermal power units and accommodate more renewable energy, internal power sources only need to share essential generation information with the central controller. This minimizes information exchange between the main grid and CSs while achieving the independent and privacy-preserving operation of CSs connected to different nodes of the main grid.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 2 Related Work
### Stacked Feature Fusion  
In this section, the polarimetric and spatial features are used for making feature fusion based on the traditional method for classification of PolSAR images. Figure 2 shows the processing flow of PolSAR image classification with a feature fusion scheme.  
In PolSAR image classification, the most commonly adopted approach is to exploit the polarimetric information. However, performance can be improved by concerning both polarimetric and spatial information in the classifier. Now, the problem is how to combine the features to decide the class that a pixel belongs to. Traditionally, the method is based on a \"stacked\" approach, in which feature the vector is built from the concatenation of polarimetric and spatial features [20; 30].  
Denote \(\mathcal{F}^{P}\) and \(\mathcal{F}^{S}\) as the polarimetric and spatial feature space, respectively. At a pixel \(X_{i}\), we have two feature vectors \(X_{i}^{P}\in\mathcal{F}^{P}\) and \(X_{i}^{S}\in\mathcal{F}^{S}\). Two feature vectors \(X^{P}\) and \(X^{S}\) are concatenated to form a new single vector,  
\[X^{C}=[X^{P},X^{S}]. \tag{2}\]  
\(X^{C}\) is used for the following SVM-based classification. The advantage of vector stacking based approach is simple to be implemented for different feature vectors. The \"stacked\" method needs only to concatenate to form a new vector. Unfortunately, from Equation (2), \(X^{C}\) does not include an explicit cross relation between \(X^{P}\) and \(X^{S}\). Therefore, the vector stacking method may lead to a bad performance because the weight and relationship between different features are not considered in this method. When a vector stacking scheme is used, the two feature vectors should be carefully normalized so that their contribution is not affected by the difference in scale.
## 3 Results and Discussion
### Linear Regression Measurement Corrections
\begin{table}
\begin{tabular}{c c c} \hline
**Coefficients** & **NO\({}_{\text{3}}\)– Electrode** & **NO\({}_{\text{2}}\)– Electrode** \\ \hline
**C1** & 392 & \(-\)480 \\
**C2** & 24.0 & \(-\)83.9 \\
**C3** & \(-\)0.79 & 1.46 \\
**C4** & \(-\)0.15 & 0.186 \\
**Standard Error (mV)** & 5.33 & 3.30 \\
**Standard Error [M]** & 0.00062 & 0.00050 \\ \hline \end{tabular}
\end{table} 
Table 6: Linear Regression Parameters and Terms for Nitrate and Nitrite ISE. \begin{table}
\begin{tabular}{c c c} \hline
**Coefficients** & **Ca\({}^{\text{2+}}\) Electrode** & **Cl\({}^{\text{-}}\) Electrode** \\ \hline
**C1** & 411 & \(-\)73 \\
**C2** & \(-\)557 & 923 \\
**C3** & 584 & \(-\)1064 \\
**C4** & 4.0 & \(-\)11.2 \\
**C5** & 1.92 & \(-\)3.25 \\
**C6** & \(-\)2.0 & 3.6 \\
**C7** & \(-\)0.0146 & 0.0333 \\
**Standard Error (mV)** & 1.65 & 3.71 \\
**Standard Error [M]** & 0.00038 & 0.00077 \\ \hline \end{tabular}
\end{table} 
Table 5: Linear Regression Parameters and Terms for Calcium and Chloride ISE. was desired. Temperature changes had a small impact on every electrode, but this was expected and was accounted for in the regression with the concentration temperature interaction term. This indicates that the regression model could properly handle the expected variations in concentrations and temperatures that may occur in various FGD process units. However, significant error occurs when ionic concentrations were above 0.01 M. For electropotential measurement systems, this change in activity compared to concentration will be the limiting factor when the assumption of constant activity is made (such as in this case). So long as ionic strength is below 0.1 M, and target ion concentration is significantly (factor of 10 or more) above the standard error measurements shown in Tables 5 and 6, the error can be assumed to be constant for all ISE's.
## 2 Theoretical Background
In the laminar flow state, the fluid moves smoothly and linearly along the fracture channel without prominent irregular movement. As for the head loss, the only head loss is the frictional head loss. Therefore, according to Equations (1) and (2), the frictional resistance coefficient of the fluid is obtained as follows [38]:  
\[f=\frac{96}{\text{Re}} \tag{5}\]  
where Re is the Reynolds number, representing the flow state of the fluid. The Reynolds number comprehensively reflects the changes in flow resistance, seepage path geometry, and fluid velocity, and its magnitude is the ratio of inertia force and viscous force in the process of fluid flow [39; 40]:  
\[\text{Re}=\frac{4\rho v^{2}e^{2}}{\mu ve}=\frac{2\rho ve}{\mu}=\frac{2\rho Q}{ \mu w} \tag{6}\]where \(\rho\big{[}ML^{-3}\big{]}\) is the fluid density; \(\
u\big{[}LT^{-1}\big{]}\) is the average velocity of the fluid; \(e[L]\) is the aperture of the fracture; \(w[L]\) is the width of the fracture; \(\mu\big{[}ML^{-1}T^{-1}\big{]}\) is the viscosity of the fluid; \(4\rho v^{2}e^{2}\) is the inertial force of the fluid; and \(\mu ve\) is the viscous force of the fluid. In rough fractures, the fluid moves along the fracture channel in a complex and irregular way. The fluid flow is affected by the roughness of the fracture, and the flow path is uneven. The effective length of the path in which the fluid actually flows through the fracture is \(L_{a}\), not \(L\). Therefore, by introducing the effective length of the path into the Darcy-Weisbach equation, the frictional resistance coefficient considering the fracture roughness can be obtained as follows:  
\[f=\frac{4\Delta Pw^{2}e^{3}}{L_{a}\rho Q^{2}} \tag{7}\]  
where \(f\) is the frictional resistance coefficient and \(L_{a}\) is the effective length of the rough fracture seepage path. In rough fractures, the seepage behavior of a fluid is not only related to the roughness of fractures but also to the tortuosity of fracture seepage paths. In the local scope of the seepage path, the seepage behavior of a fluid is mainly related to the tortuosity of the seepage path. The tortuous resistance coefficient reflects the influence of the tortuosity of the rough fracture on the flow rate and the regional head loss. According to Equation (3), the regional head loss is converted into the frictional head loss equivalent to the fracture length of \(L_{0}\). \(L_{a}-L\) is regarded as the equivalent length when calculating the tortuous resistance coefficient of the fluid. The tortuous resistance coefficient, considering the fracture tortuosity, can be obtained as follows:  
\[\lambda=\frac{4\Delta Pw^{2}e^{3}}{(L_{a}-L)\rho Q^{2}} \tag{8}\]  
where \(\lambda\) is the tortuous resistance coefficient and \(L_{a}-L[L]\) is the equivalent length of the rough fracture, \(L_{0}=L_{a}-L\).
## 3 Transmission Characteristics of Ultra-Bend-Resistant 4-Core SXC
### Macro-Bending Loss  
The macro-bending loss is one of the most significant characteristics of the multicore fiber cable used for short-reach OI in narrow space. The macro-bending loss (\(\alpha_{BL}\)) associated with the imaginary part of effective refractive index (\(n_{eff}\)) can be expressed as [22]:  
\[\alpha_{BL}=\frac{20}{\ln(10)}\frac{2\pi}{\lambda}imag(n_{eff}) \tag{1}\]  
where \(\lambda\) is the transmission wavelength. By using the equivalent index model [23], the effective refractive index of core \(k\) (\(n_{eff\_k}\)) in a bent 4-core fiber can be described as [23]:  
\[n_{eq}\approx n_{eff\_k}(1+\frac{D_{k}\cos\theta_{k}}{R_{b}}),\ \frac{D_{k}\cos\theta_{k}}{R_{b}}<<1 \tag{2}\]  
where the center \(O\) of 4-core fiber is the origin of the local polar coordinate shown in Figure 5. \(R_{b}\) and \(n_{eq}\) are the bending radius and equivalent refractive index of the bent core \(k\), respectively. \(\theta_{k}\) = 0 in the radial direction of the bend. As shown in Equation (2), the macro-bending loss of the fiber cores will have differences due to the different positions of the cores.  
In order to experimentally investigate the bending-resistant performance of the 4-core SXC at 1310 nm, 10 loops were imposed with a bending radius of 6.0, 8.2, 10.0 and 18.3 mm over the 4-core SXC and the measured results are shown in Table 1. It can be seen that the maximum value of macro-bending loss with a minimal bending radius of 6.0 mm is only 0.37 dB/10 turns and the average value is only 0.29 dB/10 turns. When the values of the bending radius (\(R_{b}\)) are 8.2 mm, 10.0 mm and 18.3 mm, the corresponding maximum values of measured macro-bending loss are less than 0.10 dB/10 turns, 0.04 dB/10 turns and 0.02 dB/10 turns, respectively. The excellent bending resistance of the 4-core SXC can be explained by inducing the deep F-doped trench-index profile and controlling the MFD of the 4-core fiber. Taking the case with bending radius of 18.3 mm as an example, by using the finite element analysis (FEA) method and solving Equations (1) and (2), the calculated values of the equivalent refractive index for the four cores are 1.4461, 1.4485, 1.4508 and 1.4485 at 1310 nm, respectively. And the corresponding values of macro-bending loss can be calculated as 6.4 \(\times\) 10\({}^{-4}\) dB/10 turns, 1.1 \(\times\) 10\({}^{-3}\) dB/10 turns, 9.7 \(\times\) 10\({}^{-4}\) dB/10 turns and 1.1 \(\times\) 10\({}^{-3}\) dB/10 turns, respectively. Actually, the torsional effect which can also induce the additional loss is inevitable in our measurement, which may mainly produce larger measured values than the theoretical values for the 4-core SXC. Perhaps, the combination of the refractive index trench and air holes around the fiber cores would be utilized to further suppress the macro-bending loss in the O-band [22].  
Figure 5: Simple description of the local polar coordinate in a bent 4-core fiber.
# Antitumor Effects of _Cannabis sativa_ Bioactive Compounds on Colorectal Carcinogenesis
## 3 Bioactive Compounds in the Prevention and Treatment of Colorectal Cancer
### Effects of Cannabis sativa Extracts on CRC
The CBGA-rich fraction included other compounds such as CBN (3.67%), CBCA (3.53%), terpen (0.72%), diterpenes (0.33%), and short free fatty acids (0.37%), which may have played a role in the outcome. [PERSON] et al. [59] conducted another study suggesting that a THCA-rich fraction and an EtOH extract of C. _sativa_ (at 190 \(\upmu\)g/mL) had a significant impact on reducing the production of IL-8, matrix metallopeptidase 9, and cyclooxygenase-2. The same effect was observed when the extract and fraction were combined. The authors also assessed the anti-inflammatory effects of pure CBD and found that it only exerted anti-inflammatory effects at low dosages (16 \(\upmu\)g/mL) and not in higher doses (252 \(\upmu\)g/mL) in HCT116 colon cancer cells. Since the THCA-rich fraction exhibited anti-inflammatory effects in a wide range of dosages, contrary to pure CBD, it can be concluded that THCA-enriched fractions have a higher anti-inflammatory potential in CRC cell lines. In this sense, combinations of fractions rich in different cannabinoids might have higher pharmacological potential than pure compounds [59]. Conversely, a study by [PERSON] et al. [87] proved that both CBD-rich _C. sativa_ extract (SFE-CO\({}_{2}\)) and pure CBD exerted the same antiproliferative effects on CRC DLD-1 and HCT116 cells at 5 \(\upmu\)M. This research also identified that the CBD-rich extract had a higher affinity for CB1 and CB2 receptors, compared to pure CBD. These conflicting findings regarding the effects of cannabinoid-rich extracts and pure compounds may arise from a large number of confounding variables, namely, synergistic interactions, the variability of substance concentrations, the plant's chemotype, or even the presence of additional bioactive compounds such as terpen, flavonoids, stilbenoids, and alkaloids. To progress in comprehending the pharmacological activity of _C. sativa_ extracts in CRC, it is essential that future studies decipher the impact of each factor.
## 3 Architecture of the Proposed Model  
The proposed model for target detection and tracking has been devised for scenarios where sink mobility is needed, as in surveillance applications that require a quick response when a target is detected, and real-time feedback of the target location for pursuit purposes. To this end, our proposal is defined as a comprehensive, multi-layer architecture.  
Concerning sensor elements, these are motes equipped with low-cost, highly available binary sensors, as Passive InfraRed (PIR) sensors. They are cheap sensors that detect the presence of heat from an object or body nearby. They are also capable of detecting the movement of people through the temperature change caused when moving. In fact, motion detectors usually use PIR sensors.  
The communication stack of sensor elements relies on the IEEE 802.15.4 standard, which defines the physical and MAC layers [35]. Since channel congestion is one of the main factors affecting performance in low-bandwidth environments, such as IEEE 802.15.4-based WSNs, the MRLG routing protocol is defined to be used in the network layer of the proposed architecture. MRLG is able to achieve a high performance by minimizing routing overhead when supporting high sink mobility levels. MRLG messages are encapsulated in the data field of IEEE 802.15.4 messages and travel towards the sink.  
With respect to the sink, it extends the sensor architecture by supporting an application layer on top of the MRLG routing protocol. The application layer is defined through the proposed intruder detection and tracking algorithm. This algorithm has been designed to perform data aggregation efficiently, in order to continuously track the intruder location, while the amount of information sent to the sink is minimal. Figure 1 depicts the architecture of the proposed model.  
Figure 1: Architecture of the proposed model.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### Nanofluid Stability  
Stability is a main problem of nanofluids. Van der Waals forces are the reason for the instability of nanofluids and are intensified by the increased number of nanoparticle collisions  
Figure 1: Different methods of preparing nanofluids.  
with each other due to Brownian movements, and the large surface area of nanoparticles themselves [55]. Nanofluids are susceptible to destabilization and leave deposits under the influence of gravitational force, electrostatic repulsive force, Van der Waals attraction, and buoyancy forces. The function of gravitational force and Van der Waals force is toward the destabilization of any colloidal suspension [56]. This means that these forces help the dispersed particles in adhering to each other and aggregates with bigger sizes will form as a result. These large particles will precipitate from the suspension under the influence of gravitational force and form deposits at the bottom of the container [57]. The settlement of the particles is due to their heavier weight that prevents Brownian motions maintaining the particles in a stable suspension. Based on theory, the main features that remarkably increase the conductivity of nanofluids and their stability are aggregation and clustering. However, this theory merely applies to specific groups of nanoparticles like single-wall nanotubes that have a high aspect ratio [58]. Therefore, destabilization of the nanofluid occurs as a result of the susceptibility of nanoparticles to form aggregates and clusters in the suspension. Accordingly, the two major stability aspects of nanofluids are nanoparticle aggregation and sedimentation.  
In the past few years, some of the many attempts for the preparation of stable nanofluids were partially successful. Different methods have been utilized for the stabilization of suspended nanoparticles (colloids) in nanofluids [59]. Thus, it is necessary to investigate the stability of nanofluids for suitable utilization in proper applications. The determination of effective factors on the stability of nanofluids is also of great importance [60]. Generally, several techniques are used for the evaluation of nanofluid stability; some of them are spectral absorbance and measurement of transmittance, settlement and centrifugation, \(3\omega\) method, measurement of zeta potential, TEM (transmission electron microscopy), and DLS (dynamic light scattering [38]. In general, two methods can improve the stability of nanofluids, which can be divided into two major categories: Physical methods and chemical methods as shown in Figure 2.