esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Behavior of Concrete Containing Wood Biomass Fly Ash
## 3 Results and Discussion
### Compressive Strength  
Compressive strength was determined on three specimens for each mix. Figure 5 shows the average compressive strength measured after 28 days and the standard deviation. Replacement of cement with WFA resulted in a decrease in the compressive strength. Compressive strength also decreased with the increase in cement replacement. WFA2 contributed more to the decrease in strength compared to WFA1. It is interesting to note that mixes with WFA2 had a higher density and lower porosity than mixes with WFA1 (Table 4). WFA1 had coarser particles than WFA2 and cement, which loosened the particle packing. Although WFA2 allowed for the production of mixes with denser particle packing, it decreased the compressive strength. This could be explained by the lower stiffness of WFA2 particles compared to WFA1 [59].  
A decrease in strength due to the replacement of cement with WFA has been reported by various researchers [20; 28; 49; 60; 61; 62] and has been attributed to the lower content of cementitious material or the incorporation of weak, unburned carbon particles into the cement matrix. WFA are less reactive than cement, especially in short periods. It was discussed that within the first 28 days, most of the influence of cement replacement by WFA can be explained through filler and filling effects [2].  
Figure 4: Results of the capillary absorption measurements.  
Figure 5: Compressive strength of concrete after 28 days and after 2 years.  
The compressive strength measured at 2 years of age corresponded very well to the 28-day strength. All mixes showed a slight increase in compressive strength compared to the 28-day strength. This indicates a very low reactivity of WFA used in this work. However, it should be noted that the curing conditions of the specimens were different. The standard concrete cubes were cured in air at a relative humidity \(>\)95%, while the specimens used for the drying shrinkage measurement were cured at a relative humidity of 65 \(\pm\) 5%.
## 2 Materials and Methods
### Characterization  
Crosslink density calculation was performed through an equilibrium swelling method. NBR membrane samples with dimensions of 10 mm length, 10 mm width and 2 mm height were prepared and immersed in the beaker filled with 50 mL toluene. The beaker was kept in dark for 7 days at 25 \({}^{\circ}\)C. Volume fraction and crosslink density of NBR films were calculated using Equations (1) and (2), respectively [37]. The density of rubber specimens was determined by using a precision balance with a density determining kit (model ES 320A). Five tests were conducted for each NBR film and average value was calculated.  
\[v_{r}\ =\ \frac{\frac{w_{2}}{\rho_{2}}}{\frac{w_{2}}{\rho_{2}}\ +\ \frac{(w_{1}-w_{2})}{\rho_{1}}} \tag{1}\]  
where \(v_{r}\) is the volume fraction of rubber; \(w_{1}\) is the weight of swollen sample; \(w_{2}\) is the weight of sample after drying at 60 \({}^{\circ}\)C to constant weight; \(\rho_{1}\) is the density of the solvent (0.87 g/mol) [28]; \(\rho_{2}\) is the density of the nitrile rubber specimen.  
\[V\ =\ \left(-\frac{1}{2v_{s}}\ \right)\frac{\left[\ln(1-v_{r})\ +\ v_{r}\ +xv_{r}{}^{2}\right]}{\left(v_{r}\frac{1}{3}-\ \frac{v_{r}}{2}\ \right)} \tag{2}\]  
where \(V\) is the crosslink density; \(v_{r}\) is the volume fraction of rubber; \(v_{s}\) is the molar volume of solvent (toluene = 106.2 cm\({}^{3}\)/mol) [16]; x is the rubber-solvent interaction parameter (x = 0.435) [38].  
The morphology of the membranes surface was analyzed using a field emission scanning electron microscope (FESEM), model JOEL JSM 6701F supplied by JOEL USA Inc., Peabody, MA, USA. The membranes were sputter coated with platinum particles before scanning to enhance electron charging during imaging process. The samples were then attached using carbon tape on the FESEM sample holder for imaging. Fourier Transform Infrared Spectroscopy (FTIR) analysis was carried out using Spectrum RX1 Perkin Elmer Analyzer from Perkin Elmer, USA, to determine the functional groups in the membrane.  
The analysis was carried out at wavelength between 400 and 4000 cm\({}^{-1}\) at room temperature using attenuated total reflectance (ATR) technique. X-ray diffraction (XRD) analysis was carried out on the membranes using Siemens XRD Diffractometer Model 5000 supplied by DKSH Technology Sdn Bhd., Malaysia, which operates using nickel-filtered copper K\(\alpha\) radiation with \(\lambda\) = 0.154 nm. A step scan with 2\({}^{\circ}\)/min scanning rate between the scanning angles of 10\({}^{\circ}\) and 60\({}^{\circ}\) was performed to detect the ordered structure of GO in the membrane.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 2 Materials and Methods
### Theoretical Foundations
#### 2.1.2 Methods for Placing iBeacons  
In articles published by the manufacturer of this technology for internal routing, it is easy to see that a large number of iBeacons are needed to cover relatively small environments. One of the challenges for researchers in this field is to design an improved architecture to use the minimum number of iBeacons for the maximum coverage of space and measurement accuracy.  
As mentioned earlier, one of the most important issues in research today is to examine the best location for 3 iBeacons in a specific communication range [20]. In the research conducted by Dr. [PERSON] et al. in 2018, a main reference for this research, this is shown to be an effective method for the proper placement of this technology [7]. Research in Ref. [7] shows that a comparison between the random placement of iBeacons and the placement of the equilateral triangle as a prototype reveals a significant improvement in the latter, with a large error in the random state.  
This simple experiment shows the need for optimal placement to achieve the highest accuracy of local location estimation. In addition, other factors contribute to localization error that must be considered. Localization error is a function of factors such as the following:  
M: Mapping error  
A: Localization algorithm error  
B: Type of nodes used by iBeacon  
P: Location of iBeacons  
\[\text{L}_{\text{e}}=\text{f}\left(\text{M},\text{A},\text{B},\text{P}\right) \tag{1}\]  
Spot Locating of Receivers  
Today, there are various ways to estimate the location of objects in the network based on receiving radio signals to finally perform indoor locating by improving the existing methods with iBeacon transmitters, displaying the position of people on smart phones based on received signals from iBeacon, and then guiding those people to their targets in the best path, and in the most time-efficient manner, by using different filters. This is the Internet of things.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 5 Implementation
### Output Response
#### 5.2.3. GPT-2 Model Response  
The GPT-2 model was evaluated on its ability to generate responses for Swahili questions under both Fine-Tune and Retrieval-Augmented Generation (RAG) conditions. Table 14 presents a Swahili question and its English translation, alongside the responses generated under both methods. While the RAG approach improved the quality of responses compared to Fine-Tune, the overall performance of GPT-2 was less comprehensive than that of the mT5 and mBart models. For instance, the Fine-Tune response for GPT-2 only briefly associates [PERSON] with electricity energy, while the RAG response provides slightly more context by mentioning [PERSON]'s work with electromagnetism. However, the depth and specificity of the answers still fall short of the detailed explanations generated by mT5 and mBart under the RAG framework. Figure 21 further illustrates this comparison, showing that while RAG enhances GPT-2's ability to retrieve external information, its outputs remain relatively less detailed and contextually enriched compared to the other models. These findings indicate that while RAG improves GPT-2's performance, its capabilities in handling complex Swahili queries are more limited, highlighting the superiority of mT5 and mBart for tasks requiring detailed and nuanced responses.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Language** & **Question** & **Fine-Tune Answer** & **RAG Answer** \\ \hline _Swahili_ & Historia ya nishati ya umeme & [PERSON] alihusishwa na & [PERSON] alifanya kazi na \\  & ilianzia wapi? & nishati ya umeme. & sumakuumeme, ambayo ilikuwa \\ \hline _English_ & _Where did the history of electricity energy begin?_ & _[PERSON] is associated with electricity energy._ & _[PERSON] worked with electromagnetism, which was crucial for electricity energy._ \\ \hline \hline \end{tabular}
\end{table}
Table 14: Swahili question and answer with English translation for Figure 21.  
Figure 20: Response created by mBart model in both cases for Fine-Tune and RAG, with RAG showing an improvement response compared to Fine-Tune.
# Exploring the Oncogenic Potential of Bisphenol F in Ovarian Cancer Development
###### Abstract  
Ovarian cancer (OC) is a gynecological cancer characterized by high morbidity and mortality associated with poor survival outcomes. Bisphenol F (BPF), a widely used analog of bisphenol A (BPA), has recently gained attention due to its potential endocrine-disrupting properties and ubiquitous environmental presence. However, the carcinogenic potential of BPF in OC has not been well explored. This study investigates the effects of BPF on ovarian carcinogenesis by assessing its pathological impact on cellular processes, including cell proliferation, wound healing, and cell invasion. OC cells, SKOV3 were treated with varying concentrations of BPF (0.01-250 uM). Cell viability was assessed using Alamar Blue assay, and migration ability was analyzed using wound-healing assay. Further, the total antioxidative capability (T-AOC) was measured. Statistical analysis was performed using student's-\(t\)-test/ANOVA, with a significance set at \(p<0.05\). BPF exhibited a dual role in cell viability, enhancing cell proliferation at low concentrations (1 uM: \(p=0.034\); 10 uM: \(p=0.012\)) while exerting cytotoxic effects at higher concentrations (250 uM: \(p=0.021\)). Further, a wound-healing assay demonstrated that a lower concentration, 1 uM BPF promoted cell migration (\(p=0.0345\)), indicating its involvement in OC. However, a non-significant difference was observed in the invasive potential and T-AOC of BPF-treated SKOV3 cells. Our findings provide key insights into the effects of BPF on cellular processes linked with ovarian carcinogenesis, emphasizing the need for future experiments to comprehend its mechanisms of action.  
bisphenol F; cell proliferation; migration; cytotoxicity; ovarian cancer +
Footnote †: journal: Applied sciences
## 3 Numerical Model
### Fracture Slip and Open Model  
In order to simulate the development and evolution of cracks in the surrounding rock of the chamber, the Voronoi elements are arranged in the surrounding rock of the chamber, as shown in Figure 8, which are filled with triangular finite elements. Joint elements are inserted between Voronoi elements to simulate the development and evolution of cracks. The mean side length of 0.15 m of Voronoi elements was selected.  
Figure 6: Strain softening model of coal and rock mass.  
Figure 7: Relationship between Geological Strength Index (GSI) and strain.  
The Mohr-Coulomb friction law is applied to the failure of the joint elements to simulate slip and open between contacts. As shown in Figure 9, the contact behavior of a contact is simulated by a spring-rider, and the force was divided into normal stress and shear stress. In the normal direction of the contact, the stress-displacement relation is assumed to be linear, and the response is governed by the normal stiffness \(K_{\mathrm{n}}\) ([PERSON] et al. 2016) [15]:  
\[\Delta\sigma=-K_{\mathrm{n}}\Delta u \tag{11}\]  
where \(\Delta\sigma\) and \(\Delta u_{\mathrm{n}}\) are the effective normal stress increment and normal displacement increment, respectively.  
When the normal stress exceeds the tensile strength of the rock, the normal stress \(\sigma_{\mathrm{n}}\)=0 and tensile failure occurs.  
In the shear direction of contact, the response is governed by the shear stiffness (\(K_{\mathrm{s}}\)) and limited by the max shear strength (\(\tau_{\mathrm{max}}\)). The relationship between stress and displacement can be divided into two stages.  
\[\mathrm{If}\left|\tau_{s}\right|\leq c+\sigma_{n}\tan\varphi=\tau_{\mathrm{ max}},\ \mathrm{then}\tau_{s}=K_{\mathrm{s}}\Delta u_{\mathrm{s}}^{\mathrm{s}}, \tag{12}\]  
where \(\tau\) is the shear force and \(\Delta u_{\mathrm{s}}^{\mathrm{s}}\) is the elastic shear displacement increment.  
\[\mathrm{If}\left|\tau_{s}\right|>c+\sigma_{n}\tan\varphi,\ \mathrm{then}\tau_{s}= \sigma_{n}\tan\varphi, \tag{13}\]  
where \(\varphi\) is internal friction angle.  
Figure 8: The surrounding rock of the chamber with Voronoi elements.  
Figure 9: Vornioi element constitutive model and parameter calibration.  
\(K_{n}\) and \(K_{s}\) are the key parameters in the above contact model, and [PERSON] [24] ([PERSON] 1972) has provided their expressions as  
\[K_{n}=\frac{EE_{cm}}{L(E-E_{cm})} \tag{14}\]  
\[K_{s}=\frac{GG_{cm}}{L(G-G_{cm})} \tag{15}\]  
where \(L\) is the average spacing of joints. \(G_{cm}\) is the shear modulus of the rock mass, which can be calculated as  
\[G_{cm}=\frac{E_{cm}}{2(1+\mu)} \tag{16}\]  
To further calibrate \(K_{n}\) and \(K_{s}\), the FDEM is used to simulate uniaxial compressive and shear tests to obtain the stress-strain curves. They are then compared with the experimental measurements to obtain more accurate \(K_{n}\) and \(K_{s}\) through multiple adjustments.
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 2 Wellbore Pressure Model
### Linearization of Bottom-Hole Pressure Dynamics
\begin{pmatrix}\frac{\beta_{c}}{V_{c}}&0&0&0\\ 0&\frac{\beta_{a}}{\beta_{a}}&\frac{\beta_{a}}{\beta_{a}}&0\\ 0&0&-\frac{2F_{b}}{M}(\overline{q_{b}}+\overline{q_{r}})&\frac{(\beta_{c}- \beta_{a})g}{M}\end{pmatrix} , \tag{13}\]  
\[J_{c}= \begin{pmatrix}\frac{M_{a}}{M}\ \vdots\ \frac{M_{c}}{M}\ \vdots\ -\frac{2}{M}[(F_{c}M_{a}-F_{a}M_{c})\overline{q_{b}}+F_{a}M_{c} \overline{q_{r}}]\end{pmatrix} , \tag{14}\]  
\[J_{d}= \begin{pmatrix}0\ \vdots\ 0\ \vdots\ \frac{2F_{a}M_{c}}{M}(\overline{q_{r} }+\overline{q_{b}})\ \vdots\ \frac{(M_{a}\rho_{c}-M_{c}\rho_{a})g}{M}\end{pmatrix} . \tag{15}\]  
Equation (16) represents the detailed mathematical derivation of the transfer function that links the control signal \(u\)(s) to the bottom-hole pressure \(P_{b}\)(s) during drilling. It was derived using the Laplace transform of the system's nonlinear equations and incorporates the system's physical parameters and their interactions. This equation is highly complex and includes multiple terms that account for second-order dynamics and cross-parameter interactions. \[\frac{P_{b}(s)}{u(s)}=\frac{\frac{\partial\hat{p}_{a}}{\partial\hat{p}_{c}}\frac{ \partial\hat{p}_{c}}{\partial u}s^{2}+\left(\frac{\partial\hat{p}_{b}}{\partial \hat{p}_{a}}\frac{\partial\hat{p}_{c}}{\partial\hat{p}_{c}^{2}}\frac{\partial \hat{p}_{c}}{\partial u}-\frac{\partial\hat{p}_{a}}{\partial\hat{p}_{a}}\frac{ \partial\hat{p}_{c}}{\partial u}\right)s+\frac{\partial\hat{p}_{b}}{\partial \hat{p}_{c}}\frac{\partial\hat{p}_{c}}{\partial\hat{p}_{c}}\frac{\partial\hat{p }_{c}}{\partial u}\frac{\partial\hat{p}_{c}}{\partial u}-\frac{\partial\hat{p }_{a}}{\partial\hat{p}_{c}}\frac{\partial\hat{p}_{c}}{\partial\hat{p}_{c}}\frac {\partial\hat{p}_{c}}{\partial u}\frac{\partial\hat{p}_{c}}{\partial u}}{ \frac{\partial\hat{p}_{c}}{\partial u}\frac{\partial\hat{p}_{c}}{\partial u} \frac{\partial\hat{p}_{c}}{\partial u}}\,, \tag{16}\]  
\[\frac{P_{b}(s)}{u(s)}=\frac{K}{s(\tau s+1)}, \tag{17}\]  
However, to design the IMC controller, the model has to be modeled according to a predefined structure [18]. Equation (17), on the other hand, is a simplified representation of the system, modeled as a first-order process with an integrative element. This form was chosen to facilitate the design of the Internal Model Control (IMC) controller. The parameters \(K\) (gain) and \(\tau\) (time constant) were obtained by fitting the response of the detailed model (Equation (16)) to a first-order process with integration. This approximation captured the dominant behavior of the system while reducing its complexity, allowing for the straightforward application of control design techniques.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 5 Evaluation and Discussion of the Results
### Simulation Results
#### 5.2.3 The Number of WBAN User Evaluations  
To ensure the QoS for each application when the number of users increases is a challenging task for MADM. Therefore, to perform this evaluation, we assumed that the available networks were WiFi and LTE. Additionally, we assumed that the number of patients increases at the same time and that each patient application has a 400 Kbps up/download speed.  
Figure 5 illustrates that the percentage of LTE use increases in a linear fashion with the number of WBAN users. However, the percentage increases of the three methods (RSS-VHD, MADM without and with CCN) are substantially different from one another. The percentage of LTE use in the RSS-VHD approach significantly increased in comparison with that of MADM without and with CCN. This finding is caused by increasing the number of patients per access point and leads to a decrease in network quality and resource degradation. Thus, although the LTE network is more expensive than WiFi, it is still selected in the case of increasing the number of connections per WiFi-AP when it exceeds the maximum number of allowed connections.  
Figure 4: Percentage of handover performance.  
Figure 5: LTE use percentage with the number of users.
# Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
## 1 Introduction  
Global warming has caused the intensity and frequency of heat waves, droughs, and typhoons to intensify worldwide [1]. As a result, many countries are increasingly interested in the generation of renewable power to replace the use of thermal power [2]. In addition, international participation, such as that stipulated by the Kyoto Protocol and the Paris Climate Change Accord, has increased the demand for renewable energy. As of the end of 2019, global photovoltaic (PV) installations reached 627 GW, representing an annual increase of 115 GW from 2018 [3].  
Building-integrated photovoltaic power generation, which produces electricity from PV modules installed on roofs, windows, and other exterior parts of buildings, has recently begun to attract attention [4; 5]. As a result, the solar generation market requires high-efficiency modules to generate power in a limited space. However, conventional PV modules are manufactured by connecting 6-inch solar cells with metal ribbons, and because this type of system requires space for installing insulation between solar cells, these modules experience spatial output loss [6; 7]. The shingled technology of PV modules involves dividing each solar cell into a 3-6 pattern, applying an electrically conductive adhesives (ECA) to the front side of the divided cell, and bonding it to the rear side of another divided cell [8; 9]. As a result, the divided cells overlap with each other, producing a shingled string of structures without a busbar. In addition, the space required for insulation between solar cells is reduced, potentially making them more efficient than conventional PV modules [10]. However, shingled PV modules are often affected by shading. This isbecause the divided cells used in shingled PV modules are connected in series and are smaller than the solar cells of conventional PV modules [11]. Therefore, it is necessary to analyze the output loss of a shingled PV module with high accuracy according to the shading type and design the module structure such that the shading loss is minimized.  
In this paper, the shading characteristics of shingled PV modules and conventional PV modules were compared and analyzed. In the case of a shingled PV module, the shading characteristics in the shingled string were additionally analyzed for analysis. In addition, \(\text{PSpice}^{\textregistered}\) simulation software (OrCAD 17.2, Cadence, San Jose, CA, USA) was used to model the shingled PV module as a divided cell, and enabled the shading characteristics of the shingled PV module to be simulated.
# Simulation of the Influence of Wing Angle Blades on the Performance of Counter-Rotating Axial Fan
## 4 Analysis of Optimization Results
### Noise Estimation  
The noise sources of the counter-rotating axial fans for mines are mainly aerodynamic noise and mechanical noise. Aerodynamic noise is generated by the rotation and eddy currents of the airflow inside the fan [26]. As demonstrated above, the wing blade fan can effectively reduce the strength of the blade shedding vortex, so we analyzed the section of the trailing edge 1 L of the two-stage impeller blade here. Figure 12 shows the sound power distribution in the trailing edge area of the first and second stage blades of the wing blade fan and the prototype fan. Compared to the prototype fan, the sound power distribution of the wing blade fan at the trailing edge of both the first and second stage blades has been reduced. The maximum sound power level of the wing blade fan at the trailing edge of the first-stage blade is 50.95 dB, which is 0.17% lower than that of the prototype fan. The maximum sound power level in the trailing edge area of the two-stage blade is 53.32 dB, which is reduced by 1.62% compared to the prototype fan.  
Figure 11: Full flow analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Entropy** & **Wind Tube** & **Primary** & **Secondary** & **Total** \\  & **Production** & **Impeller** & **Impeller** & **Impeller** & **Total** \\ \hline Prototype fan & \(\hat{S}_{PROD}\) & 0.1186 & 2.2784 & 4.1418 & 6.5388 \\  & \(\hat{S}_{PROD,D^{\prime}}\) & 1.1334 & 29.7861 & 41.4495 & 72.3690 \\  & \(\hat{S}\) & 1.2520 & 32.0645 & 45.5913 & 78.9078 \\ Wing angle & \(\hat{S}_{PRO,D}\) & 0.1150 & 2.2760 & 4.2264 & 6.6174