esa-sceva/satcom-chunk-collection huggingface dataset:
# Indole-3-Acetic Acid Action in Outdoor and Indoor Cultures of _Spirulina_ in Open Raceway Reactors
## 2 Materials and Methods
### Biomass Characterization  
After 30 days of cultivation, in the stationary growth phase, the cultures were harvested and centrifugated (Hitachi, Himac CR-GIII, Tokyo, Japan) at 2000\(\times\)\(g\) for 20 min to recover the biomass. Subsequently, a washing step was carried out to eliminate salts; following which, the biomass was frozen at -80 \({}^{\circ}\)C, subjected to lyophilization for 48 h, and then stored at -20 \({}^{\circ}\)C until characterization. The lyophilized biomass was utilized to analyze lipids, carbohydrates, proteins, phycocyanin, and moisture content.  
Total lipids were directly extracted from the biomass using the organic solvent chloroform/methanol (1:2) and quantified through colorimetry [31], with readings correlated to a tripalmitin standard curve. Before carbohydrate and protein analyses, the cell walls of the biomasses were disrupted via an ultrasonic probe ([PERSON], CPX 130, Merrillville, IN, USA). Extracts were prepared by combining 5 mg of lyophilized biomass with 10 mL of distilled water and subjecting them to 10 cycles of sonication (59 s on/off). Quantification of total carbohydrates was conducted using the phenol-sulfuric method [32], referencing a standard glucose curve. Protein content was determined utilizing the colorimetric technique described by [PERSON] et al. [33], employing bovine serum albumin as a standard and including a preliminary step of protein solubilization with NaOH. Phycocyanin was extracted from the microalgal biomass [34], and the total pigment content was measured following the protocol of [PERSON] and Bogorad [35]. Furthermore, the purity of the phycocyanin extract was ascertained by calculating the ratio between the absorbance reading of the extract at 620 nm (indicative of pigment concentration) and the absorbance at 280 nm (reflecting protein concentration in the solution) [36]. The moisture content was assessed following the methodology outlined in the Official Methods of Analysis [37].  
After these analyses, lipid, carbohydrate, protein, and phycocyanin productivities were calculated. This calculation involved multiplying the results of biomass productivity by the fractions of each biomolecule found in the biomass of _Spirulina_ sp. LEB 18.
## 2 Hybrid Framework Description
### Adaptive Fusion Module
#### 2.2.1 Human Motion Classifier  
When the average gyro heading of a step sequence is near the gyro heading of each step, the straight-line motion can be detected. Referring to [42], the binary linear motion classifier can be defined as  
\[SL=\left\{\begin{array}{ll}1&\max\Bigl{(}\left|\psi_{j}^{g}-\bar{y}^{g} \right|\Bigr{)}<\tau_{SL}\\ 0&\text{otherwise}\end{array}\right., \tag{2}\]  
Figure 3: Results of foot-state detection. From top to bottom, top subfigure: detection results satisfying the angular rate energy condition (the red line shows the detection results, where 2 is the still phase, 1 is the stance phase, and 0 is the swing phase); middle subfigure: detection results satisfying the angular rate variance condition (the green line shows the detection results, where the high level indicates the still and stance phases and the low level indicates movement); bottom subfigure: the final detection results satisfying both conditions (2 is the still phase, 1 is the stance phase, and 0 is the swing phase).  
where 1 denotes straight-line motion; 0 denotes nonlinear motion; \(\psi^{g}\) refers to the gyro heading computed by the INS; \(\bar{\psi}^{g}=\frac{1}{SN}\sum_{j=1}^{SN}\psi_{j}^{g}\) refers to the average heading of the step sequence; \(j\) refers to the step index for \(j=1:1:SN\); \(SN\) denotes the size of the step sequence; \(\gamma_{SL}\) is the threshold. In our experiment, \(SN\) is set to 4 and \(\gamma_{SL}\) is set to 6\({}^{\circ}\).
# Fresh-Frozen Allogenic Bone Graft Usage in Treatment of an Odntogenic Keratocyst in the Mandible
###### Abstract
On the other hand, CBCT (cone-beam computed tomography) findings might easily describe their borders and estimate their expansion towards the cortical bone, tooth radices, and close proximity to the IAN (inferior alveolar nerve). When OKC is expanded through the cortical borders a possible soft tissue involvement might be seen, which requires some additional surgical excision steps during surgery. When accidentally found on MR resolutions (magnetic resonance), they can manifest as a high signal (T1-keratin contents), heterogenous signal (T2), and DWI (keratin restrictions) [9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Another situation is present when jaw asymmetry, swelling, teeth displacement, and pain might occur. Teeth resorption, cortical expansion, trismus (inflammation-based or mechanical obstacle presence, limited mouth opening (LMO)), and purulent fistulas are rare, however paresthesia (IAN, inferior alveolar nerve, Vincent sign) and pathological fractures are less likely to occur. Rarely, OKC might become infected, and an abscess might present in their proximity. In very rare situations, OKC (<0.5-1%) tends to transform into the PIOSCC--primary intraosseous squamous cell carcinoma (PIOC) [14; 15; 16; 17; 18]. When a biopsy is scheduled, macroscopically, a very thin-walled cyst-like appearance, with either fluid formation or greyish debris matter similar to purulent fluid or cheese-like brown masses, might be found. Differential diagnosis should include dentigerous cysts, odontogenic cysts (pericoronal position), radicular cysts, ameloblastoma, odontogenic myxoma, adenomatioid odontogenic tumor, CGCG (central giant cell granuloma), impacted tooth/pericoronal impacted tooth lesions, SBC (Stafne bone cyst, bone defect), Gorlin-Goltz syndrome, and other syndromes like [5; 6; 7; 8; 9; 10; 15; 16; 17; 18]. There are various treatment modifications that are often related to surgeons' preferences, size and location of the lesion, and adjacency to other vital structures like IAN, mental foramen, and maxillary sinus. In the presented case report, the authors want to focus on the usage of allograft fresh-frozen materials used in bone reconstruction after OKC as preparation for the dental implant rehabilitation process.
# Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
## 1 Introduction
The ensemble model exploits the diversity of individual models to make a combined decision; therefore, it is expected that the ensemble model increases classification accuracy [25; 26]. The binary class skin cancer classification has been performed in [15; 27; 28; 29], but many researchers could not address multiclass classification with better results. The recent approaches developed in [11; 19; 30; 31; 32] for multiclass skin cancer classification also failed to achieve higher accuracy. In this research, improved performance heterogeneous ensemble models are developed for multiclass skin cancer classification using majority voting and weighted majority voting. The ensemble models are developed using diverse types of learners with various properties to capture the morphological, structural, and textural variations present in the skin cancer images for better classification. The proposed ensemble methods perform better than both the individual deep learning models and deep learning-based ensemble models proposed in the literature for multiclass skin cancer classification. The following contributions are made in this research work:  
* Five pre-trained models are developed, and their decision is combined using majority weighting and weighted majority voting for the classification of eight different classes of skin cancer. * The pre-trained models with different structural properties are trained to capture the morphological, structural, and textural variations present in the skin cancer images with the following idea: residual learning, extraction of more complex features, improvement in the declined accuracy caused by the vanishing gradient, feature invariance through the residual learning, and extraction of the fine detail present into the image. * The proposed ensemble methods perform better than the expert dermatologists and previously proposed deep learning-based ensemble models for multiclass skin cancer classification. * A comparative study is conducted for the performance analysis of five fine-tuned deep learning models and their ensemble models on the ISIC dataset to determine the model with better performance. * In our proposed method, no extensive pre-processing has been performed on the images, and no lesion segmentation has been carried out to make the work more generic and reliable. The rest of the paper is organized as follows: Section 2 presents related work. Section 3 describes the proposed method. Ensemble methods are discussed in Section 4, whereas Section 5 discusses different deep neural network models followed by individual models in Section 6. The quality measures used to measure the performance of the proposed study are presented in Section 7. Section 8 discusses the results, followed by the conclusion.
## 6 Experimental Results and Analysis
### Empirical Correlations  
Experimental correlations using a tube mounted with continuous V-rib twisted tapes (CVRTs) for \(Nu\), \(f\), and \(TPI\) were created with least squares regression analysis. This methodology utilized V-rib attack angles (\(\beta\) = 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\)), forward and backward arrangements utilizing air as a test fluid under turbulent conditions with \(Re\) ranging from 6000 to 20,000, as the independent variable. The correlations derived from the current investigation are presented below.  
Backward arrangement:  
\[Nu=0.046 Re^{0.801}Pr^{0.4}\beta^{-0.069} \tag{17}\]  
\[f=15.034 Re^{-0.503}\beta^{-0.096} \tag{18}\]  
\[TPI=1.847 Re^{-0.044}\beta^{-0.037} \tag{19}\]  
Forward arrangement:  
\[Nu=0.037 Re^{0.799}Pr^{0.4}\beta^{-0.004} \tag{20}\]  
\[f=10.85 Re^{-0.505}\beta^{-0.004} \tag{21}\]  
\[TPI=1.646 Re^{-0.045}\beta^{-0.003} \tag{22}\]  
Figure 15: Effect of tape arrangement on the thermal performance index.  
Figure 16a-c compare the experimental data (\(Nu_{exp}\),\(f_{exp}\), and \(TPl_{exp}\)) with the predicted results (\(Nu_{tpral}\),\(f_{pral}\), and \(TPl_{pral}\)). The current testing results and predicted values of V-rib attack angles (\(\beta\) = 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\)) in the forward arrangement vary by \(\pm\)6%, \(\pm\)5%, and \(\pm\)2% for \(Nu\), \(f\), and \(TPl\), respectively. In the backward arrangement, they vary by \(\pm\)4%, \(\pm\)2%, and \(\pm\)1%, respectively.  
Figure 16: Comparisons between the present work and predicted data. (**a**) \(Nu\). (**b**) \(f\). (**c**) \(TPl\).
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Recurrent Clustering Mechanism (Rcm)  
The reason many techniques are not very practical is because the nodes near the sink consume more power than the nodes far from the sink, _RCM_[41] authors argued. To balance the network, a clustering technique was introduced. All nodes use the _FIFO_ queuing mechanism. Each cluster has a cluster head that coordinates the activities within the cluster. Each node has a timer, once the timer is equal to zero, the sensor node checks if it has a real packet in its buffer to transmit. In the case there is no real packet, a fake packet is sent instead to the sink. The remaining energy of cluster heads is computed every time there is a packet to transmit. The higher cluster head in terms of the remaining energy is selected to forward the packet. In this technique, the sink location is known to the adversary. Since this technique uses clustering and selects the highest cluster node with the remaining energy, _RCM_ improves the power consumption by half compared to other techniques, [PERSON], [PERSON] et al. [41] argued. Moreover, _RCM_ reduces the overhead because of the clustering mechanism. However, [PERSON], [PERSON] et al. [41] did not mention how they control the delay and delivery ratio. Overhead is still a concern because every time the node does not have a real packet, it generates a fake packet instead, which might lead to traffic overhead.
## 4 Complexity Analysis
### BCH Encoder and Decoder  
With generator polynomial \(g(x)\), let \(C\) be an \([n,m]\) BCH codeword over \(GF(q)\). The number of message bits is denoted by \(m\), and the number of encoded bits is represented by \(n\). The Galois field (GF) has \(q\) elements, where \(q=2^{k}\) for BCH codes [31]. The integer \(k\) is such that \(n=2^{k}-1\) and \(k\geq 3\). The following is a description of the process used to generate a codeword \(C\)[62]:  
1. The message vector \((u_{0},u_{1},...,u_{m-1})\) and its corresponding polynomial, \(u(x)\), are defined.
2. The remainder \(r(x)\) is determined by computing \(x^{n-m}u(x)\ mod\ g(x)\)
3. This means that \(c(x)=x^{n-m}u(x)-r(x)\) is the transmitted codeword.  
One can obtain the residual \(r(x)\) by doing a long division of \(x^{n-m}u(x)\) by \(g(x)\)[63]. In this procedure, \(O(n\ deg(g))\) arithmetic operations over \(GF(q)\) are needed to compute the remainder, while \(O(k)\) operations are needed to compute the first term of the codeword. In an OFDM system, each subcarrier carries \(log_{2}M\) bits, where \(M\) is the alphabet size. Therefore, the total number of bits in one OFDM symbol will be \(Nlog_{2}M\), where \(N\) is the  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Proposed OFDM** & **Conventional OFDM** \\ \hline BCH Encoder & \(O(kL)+O(nL\ deg(g))\) & - \\ BCH Decoder & \(O(Lnt)\) & - \\ FFT and IFFT & \(O(N\text{log}\,N)\) & \(O(N\text{log}\,N)\) \\ Viterbi Decoder & \(O(pN)\) & - \\ Equalizer & - & \(O(N^{2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational Complexity.  
number of subcarriers. It is then easy to show that when using BCH encoding in OFDM, the number of codewords required for one OFDM symbol is given by:  
\[L=floor\bigg{[}\frac{Nlog_{2}M}{n}\bigg{]}+1 \tag{18}\]  
In the above expression, \(floor[x]\) will output the greatest integer less than or equal to \(x\). Hence, the arithmetic operations required to generate codewords for one OFDM symbol will be:  
\[(O(kL)+O(nL\ deg(g))) \tag{19}\]  
Moreover, since the code rate is \(m/n\), the data rate has to be increased by a factor of \(n/k\) to compensate for the additional \((n-m)\) redundant bits being sent with the message.  
The complexity of the BCH decoder is \(O(nt)\), where \(t\) is the maximum number of errors corrected by the code [64; 65]. For OFDM, it is clear that the complexity to decode one OFDM symbol will be \(O(Lnt)\).
# End-of-Life Scenarios for Mass Timber: Assumptions, Limitations and Potentials--A Literature Review
## 2 Materials and Methods  
A preliminary literature search was conducted to observe the growing trend of research in mass timber, as well as the end-of-life of mass timber in recent years. Subsequently, a detailed literature review was performed specifically on the end-of-life considerations of mass timber.  
The preliminary literature search aims to observe the tendency of research in mass timber along with its EoL studies in the research field. The search on Scopus was performed using the terms \"Mass Timber\", \"Engineered Wood\", \"CLT\", \"GLT\", or \"LVL\". This search yielded a total of 12,488 results. However, current knowledge on potential EoL and post-use practices is said to be very limited [23]. This gap is confirmed when searching for studies specifically focused on the EoL of mass timber. A search using terms \"Mass Timber\", \"Engineered Wood\", \"CLT\", \"GLT\" or \"LVL\" combined with \"End-of-life\", \"End of life\", or \"EoL\" returned only 169 results. The observed trend will be discussed later in Section 3.1.  
The 169 articles on EoL of mass timber were then screened in detail for the literature review. The literature review follows the PRISMA guideline [24] to ensure a transparent and systematic approach to examining the current literature on the EoL of mass timber with the full process shown in Figure 2.  
The first filtering round applied Scopus automatic search filters with inclusion criteria of peer-reviewed scientific articles, English language, and publications from the past five years, resulting in 94 articles. A second manual selection focused on building-related studies, excluding irrelevant papers on topics such as comparisons with steel-concrete structures, non-construction wood products (e.g., 'tree shelters'), chemical treatments, and fire safety performance. This process resulted in 31 papers deemed suitable for the literature review [Supplementary Materials]. Additionally, 25 papers cited in the primary literature were identified as secondary sources, bringing the total to 56 papers included in the review. Each paper was analysed for research objectives, gaps, findings, limitations, and relevance to this paper's topic. In addition, the literature was categorised by topics of discussions on the EoL of mass timber.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell Edge  
In this section, the HetNet environment with the small cells deployed at the edge of the macro cell was considered, as shown in Figure 4. In practice, this kind of deployment is most effective at decreasing the interference among macro cells, as the UE in the macro cell edge area associated to the macro cell can be scheduled by the small cells. This traffic offloading from the macro layer will result in reduced distance between UE and the serving eNodeB, resulting in a decrease of the transmit power and interference of the UE, hence improving the received SIR. Furthermore, the interfering ring is separated from the central small cell eNodeB \(x\) by a distance of 4\(R\), which results in a decreased interference level offered to the central small cell.
## 3 Methodology
### Data Preparation  
To analyze the study region, the entire process of calculating and drawing the highway rockfall risk map was done in ArcGIS 10.7 (Esri, Redlands, CA, USA). Highway rockfall susceptibility index data was obtained using the digital elevation model (DEM) with a spatial resolution of 12.5 m and landsat8 OLI remote sensing image data. This is the most accurate data available to researchers at this stage, and it is freely available. The highway rainfall hazard index data was from the Guizhou Provincial Meteorological Bureau. The highway rockfall vulnerability was provided by the Guizhou Provincial Bureau of Statistics. It should be noted that the highways in Guizhou Province are a line element in the GIS. Some scholars have selected a buffer zone of 500 m along the highway as the study area to study the slope hazards along the highway [40]. For this paper, the buffer zone of 500 m along the highway in Guizhou Province was also selected as the research area. All the input data have a uniform reference coordinate format, \"WGS_1984_UTM_Zone_48N\", and the causative factors are transformed into raster data with a spatial resolution of 12.5 m for further model calculation.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 3 Methods
### Prediction Model Structure  
We chose to apply the multiple input-multiple output approach. The MLT model inputs were the results of IAQ monitoring in the kitchen, and the model predicted the air parameters in the living room. A diagram of the model structure is shown in Figure 2.  
The following parameters were involved in model building: T, RH, CO\({}_{2}\) concentration, and the response of SGP30 and SGPC3 sensors. The MLT model was presented with the measurement data collected in the kitchen 1 min ahead of the prediction, and the model predicted the IAQ parameters in the living room 2 s later. The model had 5 \(\times\) 29 inputs (5 indoor air parameters \(\times\) 29 values of each parameter in the kitchen) and five outputs (5 indoor air parameters in the living room).
## Materials and Methods
### Instrumentation and Data Management
\begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**All Data**} & \multicolumn{2}{c}{**Drought Comparison**} \\ \hline  & **Strategic** & **Non-Strategic** & **Strategic** & **Non-Strategic** \\ \hline Number of water systems & & & & \\  Garissa & 15 & 3 & 2 & 2 \\  Isiolo & 4 & 12 & 0 & 3 \\  Marsabit & 20 & 11 & 3 & 4 \\  Turkana & 16 & 19 & 0 & 7 \\  Wajir & 19 & 1 & 3 & 1 \\ \hline Median number of daily observations & & & & \\  Garissa (IQR) & 240 (49) & 620 (150) & 620 (1) & 620 (1) \\  Isiolo (IQR) & 480 (3) & 310 (220) & 0 (0) & 630 (130) \\  Marsabit (IQR) & 350 (230) & 360 (270) & 640 (2) & 640 (13) \\  Turkana (IQR) & 200 (110) & 430 (270) & 0 (0) & 640 (160) \\  Wajir (IQR) & 360 (150) & 600 (0) & 610 (41) & 600 (0) \\ \hline Median households served (IQR) & 480 (680) & 1700 (5700) & 420 (890) & 2500 (6800) \\ \hline Median livestock served (IQR) & 2300 (6600) & 0 (1100) & 3400 (13,000) & 0 (1200) \\ \hline Power source & & & & \\  Generator & 27 & 4 & 7 & 2 \\  Solar & 11 & 9 & 0 & 5 \\  Utility & 1 & 5 & 0 & 2 \\  Hybrid & 33 & 13 & 1 & 3 \\ \hline Used for filling water trucks & 13 & 11 & 1 & 5 \\ \hline Urban location & 3 & 11 & 0 & 4 \\ \hline Seasonally used pump site & 43 & 14 & 7 & 5 \\ \hline Alternative source: other pipe stand & 29 & 23 & 3 & 7 \\ \hline Alternative source: surface water & 36 & 7 & 4 & 3 \\ \hline \end{tabular}
\end{table} 
Table 1: Sample characteristics for all data and for the drought comparison sub-study. Interquartile range (IQR) represents the difference between first and third quartiles for all strategic and non-strategic boreholes. Observations were collected between the September 2016 and January 2019. A summary of pump site statuses, last report date, and the approximate volume extracted each week were made available through the Sweet Sense dashboard. Similar information was also available through the WMaaSP dashboard. Sensor installation and removal records as well as repair reports were logged using the mWater mobile and online applications [29]. In addition, asset inventory surveys were completed by Sweet Sense field officers and county water officers for each site to record information about water use, water quality, borehole characteristics, power source and characteristics, and water point management. Finally, precipitation was estimated using monthly Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) v2.0 according to the methodology described in [PERSON] et al. [28]. Gridded raster precipitation files with a resolution of 5 km were used to estimate rainfall in the proximity of each water system. However, these precipitation estimates were not used in this analysis as we found that seasonality (rainy vs. dry season) was a better predictor of use than daily precipitation values.