esa-sceva/satcom-chunk-collection huggingface dataset:
# Photocatalytic Cementitious Material for Eco-Efficient Construction--A Systematic Literature Review
## 4 Discussion
### Effect of Incorporating TiO\({}_{2}\) into Cement-Based Material  
Understanding the effect of TiO\({}_{2}\) incorporation on the behavior of cementitious composites is of great importance especially for possible construction applications. In the fresh state of the mix, reduction in workability is the main effect induced by adding TiO\({}_{2}\) materials [85]. Due to their nanometric size, the mix undergoes extensive alteration in the rheological behavior. This change is very pronounced and must be considered to achieve a targeted workability during construction processes. However, several researchers have studied the influence produced by the presence of TiO\({}_{2}\) fine particles on the cement hydration and the concrete strength [47, 78, 84, 86, 87]. The addition of TiO\({}_{2}\) fine particles generally increases the strength of concrete, which is attributed to the filling effect. However, there is no general consensus on the effect of TiO\({}_{2}\) fine particles on hydration kinetics. Hence, the impact of TiO\({}_{2}\) on cement hydration kinetics is still a subject of debate.  
Early research on this effect found that the presence of fine particles enhances the degree of hydration of the major cement phase [87]. Further studies by [PERSON], [PERSON] [78] as well as [PERSON] and [PERSON] [86] confirm that there were specific interactions between the cement matrix and the photocatalysts due to pozzolanic activity of the semiconductors. The addition of TiO\({}_{2}\) particle, which has a high surface area, provides additional nucleation sites for hydration product formation. TiO\({}_{2}\) may react with calcium hydroxide (Ca(OH)\({}_{2}\)) to form a product similar to calcium silicate hydrates (C-S-H).  
In contrast to the research discussed above, [PERSON] and [PERSON] [84] observed that the incorporation of TiO\({}_{2}\) into cement matrices did not influence the amount of hydration product. Their results indicated that TiO\({}_{2}\) was inert and chemically stable in the cement hydration reaction. Their findings suggest that the TiO\({}_{2}\) particles act only as fillers and did not influence the amount of hydration product. Recent study by [PERSON], [PERSON] [47] found that the addition of TiO\({}_{2}\) into iron-rich dark cement gave rise to a heterogeneous matrix, as a result of the TiO\({}_{2}\)-ferrite interaction. However, it is suggested that the predominant mechanism of incorporating TiO\({}_{2}\) into iron-lean white cement is physical entrapment, without significant chemical interaction. In this latter case, most TiO\({}_{2}\) in a white-cement matrix remained unaltered and almost fully available for the photocatalytic processes.
## 4 Flying Base Station Channel Capacity and Positioning
### Flying Base Station Channel Capacity
#### 4.2.1 Full-Duplex Case  
The flying base station relays the communication between the stationary base station-ary and the user. If the flying base station has full-duplex capability, it can fully utilize the channel bandwidth for communication (both ways simultaneously and \(W^{\prime}=W\)).  
Theorem 4.2: _If the flying base station has full-duplex capability, then the flying base station channel capacity is as follows:_  
\[C_{FU}=W\cdot\text{log}_{2}(1+\eta\ 2^{\gamma}\ \text{SNR}_{SU}) \tag{6}\]  
Proof: Use Equation (5) with \(W^{\prime}=W\), \(P_{\text{F}}=\eta\cdot P_{\text{S}}\), and \(d_{FU}=0.5\cdot d_{SU}\) from Theorem 4.1. The algebraic steps and derivations are omitted in this paper.
# Impact of Pore Geometry and Water Saturation on Gas Effective Diffusion Coefficient in Soil
###### Abstract  
Gas flow in soil plays a crucial role in terrestrial ecosystems, and numerical simulation of their movement needs to know their effective diffusion coefficients. How pore structure influences the effective diffusion coefficient has been studied intensively for dry porous media, but much remains unknown for unsaturated soils. Here, we employed the X-ray tomography technique at the pore scale to directly obtain the soil structures, the geometry of their pores and the water distribution under different water saturation levels were calculated using a morphological model. The results show that pore structures including porosity, interface area of gas-solid-water and pore diameter are closely related to water saturation. The increase of mean pore diameter with gas saturation can be fitted into a power law. We also investigated the impact of pore geometry and water saturation on the effective diffusion coefficients, which is independent of the molecular mass of gas after normalization. As the normalized effective Knudsen diffusion coefficient increases with average pore diameter following a power law, with the scaling factor related to pore geometry and the exponent is a constant, we explained and proved that the Knudsen diffusion coefficient increases with gas saturation, also following a power law.  
Keywords: pore-scale modelling; effective diffusion coefficient; Knudsen diffusion; pore structure; water saturation; unsaturated soil +
Footnote †: journal: Applied Physics Letters B
# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 4 Results and Evaluation  
### Smart Homes Datasets  
The Center for Advanced Research in Adaptive Systems (CASAS) at Washington State University is a renowned team for activity recognition in smart homes. We used the public datasets they posted on their website to conduct the experiments [8]. These data were collected from smart homes with sensors arranged. Additionally, the sensors were divided into the following six types, which were \"temperature sensor (T)\", \"infrared motion sensor (M)\", \"wide area infrared motion sensor (MA)\", \"light sensor (LS)\", \"Light Switch Sensor (L)\" and \"Door Switch Sensor (D)\". The home was divided into seven areas, which were Kitchen, Dining, Parlor, Porch, Toilet, Bedroom and Porch_Toilet.  
Additionally, we selected ten common daily activities in the datasets for training, which were \"Bed_Toilet_Transition\", \"Cook\", \"Dress\", \"Eat\", \"Med\", \"Personal_Hygiene\", \"Relax\", \"Sleep\", \"Sleep_Out_Of_Bed\", \"Toilet\". We combined similar activities with only temporal distinctions occurring as a whole. For example, \"Eat\", \"Eat_Lunch\", \"Eat_Breakfast\" and \"Eat_Dinner\" were merged into \"Eat\" and \"Cook\", \"Cook_Lunch\", \"Cook_Breakfast\" and \"Cook_Dinner\" were merged into one daily activity \"Cook\". Because temporal features are not included in the training samples, we did not classify activities with only temporal distinctions.
## 4 Theory of the Launcher System Design
### Calculation of the Magnetic Force and Magnetic Circuit Reluctance  
The mathematical equation for magnetic circuit reluctance is difficult to deduce because of the fringing effect that might make the area of the air gap difficult to estimate. Accordingly, adding an iron yoke around the launcher coil might be essential to attract most of the flux lines to a known area medium. In addition, this makes the magnetic flux path through the iron yoke defined. As displayed in Figure 11, the launcher coil generates magnetomotive force, letting the magnetic lines pass through two paths. The first path is the surrounding air and the second is through the iron yoke. Equation (5) is used to calculate each path's reluctance. Equation (6) illustrates the resultant reluctance of the launcher coil. Then, the inductance of the launcher coil can be calculated through Equation (7). It is noticed that adding the iron yoke increases the inductance values compared to the ordinary launcher which leads to a higher derivative of the launcher coil inductance with respect to the projectile position (\(\frac{dL(x)}{dx}\)) because the coil inductance is inversely proportional to the magnetic reluctance as in Equation (7). The force, as shown in Equations (1) and (8), is in direct proportion to the derivative of the launcher coil inductance with respect to the projectile position. There is a viscous friction force due to the air resistance inside the launcher barrel with a coefficient of b as shown in Equation (9). The air resistance coefficient depends on many factors such as the face area of the projectile, however, this value is initialized and will differ when experimentally determined. The projectile's motion is rectilinear so Equation (10) is used to evaluate the velocity equation. Equation (8) is divided by the projectile mass to obtain the acceleration equation of the projectile, as displayed in Equation (10).  
\[\Re=\frac{l}{\mu_{r}\mu_{o}A} \tag{5}\]  
where:  
\(\mathcal{R}=\) reluctance of magnetic circuit around the coil.  
\(l=\) length of the magnetic path.  
\(A=\) effective cross-section area of the coil.  
\(\mu_{o}=\) permeability of air.  
\(\mu_{r}=\) relative permeability of the material.  
\[\Re_{total}(x)=\Re_{projectile}+\frac{1}{\frac{2\Re_{apapg}+\Re_{variable\; aiggr}(x)+\Re_{pole}}{\Re_{surrounding\;air}}} \tag{6}\]  
Figure 11: Equivalent Magnetics circuit of the launcher.
## 4 Discussion
### Research Limitations  
This study adopted a single-group pre-test and post-test design, which may have introduced internal validity concerns. Due to the absence of a control group, the results could have been influenced by participants' natural growth or other external factors, such as gender, age group, and type of sport. Additionally, this study focused on PT primarily based on its research objective--examining the effects of a specific training method on basketball players' lower limb joint mobility and explosive strength. Therefore, the study concentrated on a single variable to obtain clearer results. Furthermore, although PT has been widely implemented in athletic training, its specific effects on advanced layup success rates and sports injury reduction in basketball players remain underexplored and require further empirical validation. Therefore, this study addresses this gap by providing targeted evidence on these performance and safety outcomes. Lastly, as this study adopted a single-group pre-test and post-test design, it was not possible to simultaneously examine the comparative effects of multiple training methods. Therefore, a training approach with the most potential and relatively solid research foundation was selected as the intervention.
# Channel Modeling for In-Body Optical Wireless Communications +
## 5 Conclusions  
This paper introduces a novel mathematical framework that models the optical signal's attenuation as it travels through any generic biological tissue. Initially, we extract analytical expressions for the absorption coefficient of the major generic tissue constituents based on published experimental measurements, which enable the estimation of not only the absorption coefficient of each constituent at any given wavelength but also the absorption coefficient of any generic tissue. Moreover, the phenomenon of scattering is incorporated into the proposed framework as a complex stochastic process comprised of a Rayleigh and Mie scattering component in order to model the impact due to the existence of smaller and larger particles, respectively, in the generic tissue. Next, the phenomena of absorption and scattering are incorporated into a unified framework, which is validated by comparing the analytical results with experimental data from the open literature. Moreover, we illustrate the pathloss as a function of the transmission wavelength for different complex tissues and tissue thickness and provide insightful discussions, as well as design guidelines, for future in-body OWC applications. Finally, the importance of scattering is highlighted in the results of this contribution, and, therefore, future research should further investigate novel techniques to mitigate its impact.  
Conceptualization, S.E.T.; methodology, S.E.T.; software, S.E.T.; validation, S.E.T., A.-A.B., N.D.C. and G.K.K.; writing--original draft preparation, S.E.T.; writing--review and editing, A.-A.A.B., N.D.C. and G.K.K.; visualization, S.E.T.; supervision, A.-A.A.B.; project administration, N.D.C.; funding acquisition, N.D.C. All authors have read and agreed to the published version of the manuscript.  
This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme \"Human Resources Development, Education and Lifelong Learning 2014-2020\" in the context of the project \"IRIDA-Optical wireless communications for in-body and transdermal biomedical applications\" (MIS 5047929).  
Not applicable.  
The authors declare no conflict of interest.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 4 Simulation Setup and Performance Evaluation
### Performance Evaluation
#### 4.2.2 Discovering Threshold Value  
The overall service time is basically the total time required by CRU to transmit the entire data at the destination. The perfect situation for CRU to broadcast information with a smaller amount of holdup to attain the least amount of overall service time. In order to reduce rapidly the overall service time, the presented system logically switches between reactive and proactive handoff judgments. In this way, the threshold value is discovered for the interarrival of the PU. The overall service time of the reactive and proactive decisions w.r.t. the handoff is illustrated in Figure 6. The total service time of a proactive handoff is lower than the reactive handoff up to 0.05 and the comparison is performed on the basis of PU interarrival rate. In the proactive method, the handoff process is performed prior to trigger the handoff event. Over the lower PU arrival rate, the proactive handoff shows better performance than the counterpart. When the speed of PU flows over 0.05, the reactive handoff method shows better results because the handoff process is completed after the handoff event. Therefore, threshold value 0.05 is extracted by PUTPOSH when it switches between proactive or reactive handoff.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **of** & **Values** & **Units** \\ \hline Packet Length & Primary User & 10 & Bytes \\ Packet Length & Cognitive Radio User & 10 & Bytes \\ Processing Time & Channel & 0.5 & MSec \\ Arrival Rate & Primary User & 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08 & Arrival per slot \\ Arrival Rate & Cognitive Radio User & 0.01 & Arrival per slot \\ Service Rate & Primary User & 0.5 & Slots per arrival \\ Service Rate & Cognitive Radio User & 0.4 & Slots per arrival \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental settings: parameters with their values and units.
## 3 Results
### Image Analysis for Classification Challenge
#### 3.1.1 Identification of the Material Type  
As previously explained, the identification of graphite morphology in cast iron is a critical step in the metallographic analysis, as it determines the subsequent tasks to be performed. In this section, we present the performance of several ML models applied to classify graphite structures into nodular, lamellar, or vermicular types using microstructural images. The results demonstrate the effectiveness of the proposed image analysis approach, which combines CV and ML techniques.  
The achieved results demonstrate that the proposed approach effectively processes microstructural images to extract relevant features for accurate material classification. In our case, the simple dense ANN, composed solely of fully connected layers, showed strong performance. With a three-layer configuration, it achieved an accuracy close to 95% and a loss of 0.22. Increasing the depth to five dense layers, the performance was improved. This model achieved the highest accuracy in our experiment, approximately 97%, with a minimal loss of 0.08, demonstrating the benefit of adding depth to the architecture for improved feature learning.  
On the other hand, the VGG11 model, a CNN architecture with 11 layers and small 3 x 3 convolutions, also performed well, obtaining an accuracy of 96.12% and a low loss of 0.12. This validates the effectiveness of convolutional layers for extracting spatial patterns in microstructural textures. In contrast, the ResNet18 architecture, despite its popularity for DL tasks, performed the lowest among the tested models.  
A summary of the results is presented in Table 1, showing that simpler architectures with careful design outperform deeper models for this specific application.
# Constructing Condition Monitoring Model of Harmonic Drive
## 2 Related Work
### Wavelet Transform  
Wavelet Transform contains rapidly decaying or finite-length waveforms to express the original signal through scaling and translation, which can avoid inaccurate frequency-domain or time-domain analysis results caused by fixed-length window functions. According to Equation (2), the wavelet transform uses the wavelet function \(\psi(t)\) and the scaling function \(\phi(t)\) to generate each sub-wavelet function \(\psi_{a,b}(t)\), thereby fitting the original waveform, where the wavelet function is called the Mother Wavelet, and the scaling function is called the Father Wavelet.  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{a}}\psi\Big{(}\frac{t-b}{a}\Big{)} \tag{2}\]  
Wavelet transforms can be categorized into discrete or continuous wavelet transforms. The judgment is based on whether scaling parameter a and translation parameter b in Equation (2) are discrete values. Discrete wavelet transforms were used in this research.
## 4 Evaluation
### Datasets  
We evaluated the performance of our approach against baseline models using the following network intrusion detection datasets: CIC-IDS2017 and NSL-KDD. The CIC-IDS2017 dataset reflects recent attacks, and, to some extent, it satisfies the criteria for reliable intrusion detection datasets proposed by [39], which are anonymity, attack diversity,complete capture, complete interaction, complete network configuration, available protocol, complete traffic feature set, meta data, heterogeneity, and labeling.  
The dataset was developed at the Canadian Institute of Cybersecurity of the University of New Brunswick (UNB) in 2017. The dataset comprises raw PCAP files, as well as 80 statistical features generated from the PCAP files, which were captured on different days of a week (from Monday to Friday). The dataset considers several attacks and sub-attacks, as depicted in Table 1.  
The NSL-KDD dataset was also generated at the Canadian Institute of Cybersecurity. This dataset was purposely created to solve the problem of the original KDDcup'99 dataset, which has about 78% and 75% of the training and testing set duplicated, respectively [40]. The NSL-KDD rectifies this problem and still retains the original 41 features.  
Table 2 depicts a breakdown of some of the attacks that exist in the dataset with more than five samples.
## 3 Materials and Methods
### Classifier Training  
Once the word-embeddings were extracted, the next stage consisted of sentence classification. For this, we explored four methods: Deep Convolutional Neural Networks [13] with or without pre-trained word-embeddings at the input layer, FastText [23], Support Vector Machines (SVM), and k-Nearest Neighbors (kNN).  
Learning curves were built for all models with increasing dataset sizes (1K, 10K, 100K, 1M, and 4.07 M samples). Each dataset was sampled from the full dataset, applying random balanced sampling so that, for each resulting dataset, both label classes (\"Eligible\" and \"Not Eligible\") had the same number of samples. We split each dataset into 80% samples for the training set and 20% for the test set. A standard 5-fold cross-validation was then performed for each dataset size.  
Because the accuracy concerning sentence classification depends on the dataset evaluated and we were unable to find any previous reports that used the present corpus for text classification, there are no clearly defined benchmarks with which to perform a comparison.  
For example, in different domains, the reported accuracy for classifying the \"Hacker News\" posts into 20 different categories using a similar method was 95%, while in the case of \"Movie reviews\", the reported performance was 81.5% [24]. In the medical domain, a high-performance model is potentially useful in a CDSS. Using previously published computer-aid systems and related work [25; 26; 27] as a basis, we defined the minimum target as an accuracy of 90%, and a Cohen's Kappa with a minimum of [0.61-0.80] for substantial agreement, or [0.81-1] for an almost perfect agreement [28].
## 1 Introduction
For this reason, to \"track\" the appropriate temperature window for the non-catalytic-reduction process of NOx in the flue gases, more than one injection level is necessary. Often, to determine the flue gas temperature distribution in the boiler's furnace chamber, tools from the field of Computational Fluid Dynamics (CFD) are used [9; 10]. In the presented work, commercial software Ansys Fluent v.14 was utilized for this purpose. The main difficulty in this approach is determining the boundary conditions for the fluid domain, particularly in grate-fired boilers, where conditions arise from processes occurring on the grate. Mathematical modeling of the reagent-injection process beyond the range of the droplet stream in a direction perpendicular to the boiler wall, from which the injection occurs, allows for determining the width of this stream. This parameter, in turn, allows for conclusions regarding the required spacing of the injection lances, which, for a given lance design, will provide the most complete coverage of the furnace chamber cross-section with the reagent. All modeling work that leads to the installation of a given boiler is ultimately verified through field tests. The final stage of the fine gas denitrification system design process is, therefore, its tuning and calibration on the object for which it was designed. There are numerous literature reports on the operation of SNCR installations in pulverized coal and fluidized bed boilers. Designs for SNCR installations in grate-fired boilers are primarily based on modeling these systems. This study presents unique field study results across a wide range of boiler and SNCR installation operating parameters. A technological challenge in conducting the presented research was the installation of an SNCR system capable of achieving a broad range of injection parameters in a real facility. Commercially installed systems do not have the regulatory flexibility of the research installation. The system described in this study allowed for, among other things, individual control of the injection parameters for each lance (typically, they are grouped by levels). The goal of this work is the final verification of the correctness of the assumed design parameters of the system and the optimization of its operational parameters, including:  
* The molar ratio of the reagent to the incoming NOx;
* The concentration of the reagent solution. The full scope of the work mentioned above, concerning a selected grate boiler, is presented in this article. The field test results, which allow final conclusions to be drawn about the impact of selected operational parameters on the operation of the SNCR installation, are particularly valuable.