esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Analysis Regarding Devices Working with a Fixed Input Power to ARCM
#### 4.1.3 Anatomical Versus Simplified Body Models  
Models of the human body constitute a key element of numerical simulations concerning the direct biophysical effects of human EMR exposure. International regulations and standards are vague on this matter. For example, the IEC/IEEE 62704-1:2017 standard allows the use of both high-resolution or simplified [S] human body models, specifying the need to use models representative of the target population with respect to age class, body height, and mass, with appropriate dielectric parameters of tissues and the resolution of models. No reference body models have been published so far. Therefore, in practice, models of the body or its parts from these categories are used in research [7; 10; 11; 18; 20; 21].  
The minor relative difference in the parameters obtained in exposure scenarios with simplified [S] body models compared to the values in scenarios with anatomical [A] models  
Figure 6: The relative maximum local 10g-SAR values calculated in the WIT user models of the head, arm, forearm and chest, in the APD and CWED 2.45 GHz exposure scenarios, at 100 mW (20 dB) input power to the ARCM (all values as in Table 5; \({}^{\bullet}\)—reference case: the APD exposure scenario with anatomical [A] model of chest; Relative 10g-SAR = 10g-SAR in particular case/10g-SAR in reference case; whiskers—standard uncertainty of relative values determination \(\pm\) 35%).  
(both APD and CWED exposure scenarios) is smaller than the standard uncertainty of their determination (\(\pm\)(30-35)%), as shown in Table 5 and Figure 7: up to \(\pm\)12% in 10g-SAR; up to \(\pm\)18% in gain (except for exposure scenarios with the head, where it was up to 52%); and up to \(\pm\)9% in S11 parameter values. This indicates that when input power to the antenna is equal in the considered exposure scenarios, the use of simplified [S] models of the human body (numerical or physical) may be sufficient in order to analyze antenna performance and SAR in the body of a person using a wearable WIT device.  
However, we draw attention to the fact that the greatest observed variability in antenna gain values was obtained for the case of anatomical [A] models of a body part with a complex, irregular structure and its simplified counterpart (in head models, where the relative difference can reach up to 52%). This variability may lead to incorrect conclusions about optimizing a wearable WIT device's functionality and intended use, if these conclusions are based solely on simulation results using simplified [S] models. This is especially important when the ARCM is located at a distance of less than a few millimeters from the user's body.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 5 Antenna Integration into OLED
### OLED Effects on the Antenna Performances  
The comparison between the measured and simulated reflection coefficient of the antenna with and without the OLED is shown in Figure 15. From this figure, it can be observed that an acceptable agreement between the results was obtained and the bandwidth can well cover the desired WLAN frequency band. The transparent antenna return loss is affected by the OLED. The layers of the OLED also affect the antenna impedance, and it is less adapted at 50 \(\Omega\). The resonance frequency of the proposed antenna shifts to low frequencies with a narrow bandwidth.  
Figure 16 shows the comparison between the radiation patterns of the mesh antenna, with and without the OLED: Figure 16a in the XZ-plane, Figure 16b in the YZ-plane, and Figure 16c with the OLED in 3D view. These diagrams are oriented in the direction of the light emission from the OLED. The obtained directivity and gain of the antenna with the OLEDs are 6.67 dBi and 4.86 dBi, respectively, at 5.22 GHz. These values were improved after the integration of the OLED; this can be explained by the effect of the OLED layers, which are considered as the ground plane reflecting the antenna radiation.  
However, the materials used in the layers of the OLED light source are extremely critical for their efficiency and lifetime. The OLED panels contain ITO and aluminum electrodes and a metal foil layer, which serves as a reference ground for the antenna. The slight differences between the two results with and without the OLED can be interpreted by the effect of these layers on the antenna radiation. Since, the size of the layers is much larger than the size of the integrated antenna, these layers act as a large ground plane. This ground plane allows electromagnetic waves from the surface to be confined within the OLED panel, rather than radiating outwards. Therefore, these fields affect the electrical and radiation performance of the antenna.  
Figure 15: Comparison between measured and simulated return loss and with/without OLED.
## 3 Results
The difference in ZOI between B: 75 mg/mL C-CaOH\({}_{2}\) NPs concentration, and C: 100 mg/mL C-CaOH\({}_{2}\) NPs concentration was not significant, however, dose-dependent increase in zones observed. D: 150 mg/mL C-CaOH\({}_{2}\) NPs concentration showed the greatest ZOI. Compared to E: ciprofloxacin control, C-CaOH\({}_{2}\) NPs exhibited intermediate ZOI activity. The results for MIC and MBC are summarized in Table 2. For most of the aerobic pathogens, the minimal inhibitory effect was observed at the 1:16 dilution (3.12 mg/mL) except for _E. faecalis_ (0.78 mg/mL) and _K. pneumoniae_ (1.56 mg/mL) (Figure 3). All aerobes showed MBC values at 12.5 mg/mL except _P. aeruginosa_ (3.12 mg/mL) and _K. pneumoniae_ (6.25 mg/mL) (Figure 4). For _P. micra_, the MIC value that demonstrated minimal inhibition was higher than that of the aerobes at 15.62 mg/mL. Similarly, the MBC value that showed no growth was also high at 31.25 mg/mL. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Strains** & **MIC (mg/mL)** & **MBC (mg/mL)** \\ \hline _S. Aureus_ & 3.12 & 12.5 \\ \hline _S. Epidermidis_ & 3.12 & 12.5 \\ \hline _E. Faecalis_ & 0.78 & 12.5 \\ \hline _P. Aeruginosa_ & 3.12 & 3.12 \\ \hline _K. pneumoniae_ & 1.56 & 6.25 \\ \hline _E. Coli_ & 3.12 & 12.5 \\ \hline _P. Micra_ & 15.62 & 31.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Minimum inhibitory concentrations (MIC) and Minimum bactericidal concentrations (MBC) of C-CaOH\({}_{2}\) NPs against bacterial strains. Figure 2: Pair-wise comparison between C-CaOH\({}_{2}\) NPs concentrations for each bacterial strain. Only _S. Aureus, P. Aeruginosa, and E. Faecalis_ showed statistical significance between concentrations (marked with an asterisk). Figure 4: MBCs of C-CaOH\({}_{2}\) NPs on the tested pathogens. In a counter clock-wise direction: +ve C; positive control; 50–1.56 and 250–7.81: C-CaOH\({}_{2}\) NPs concentrations; and -ve C: negative control. Figure 3: MICs of C-CaOH\({}_{2}\) NPs on the tested aerobic and anaerobic bacteria. BS: Broth sterility; NS: C-CaOH\({}_{2}\) NPs sterility; PC: positive control Ciprofloxacinacin; wells from 50 to 0.3 and 250 to 1.9 are the two-fold serial dilutions of C-CaOH\({}_{2}\) NPs; and NC: negative control.
## 2 Related Work
### Feature Engineering  
In existing research on natural gas demand forecasting, feature engineering has been widely applied to various prediction tasks, such as energy demand forecasting and economic indicator prediction. Among these tasks, the selection of features is particularly crucial. By choosing the features that best reflect the actual situation, it is possible to more accurately capture patterns and trends in the data.  
[PERSON] et al. [25] introduced regional features and proposed an enhanced compound neural network (AComNN) to capture the time-zone-dependent context in cross-regional financial time series, achieving a 13.36% improvement in prediction accuracy compared to the baseline model. [PERSON] et al. [26] incorporated user consumption patterns and proposed a day-ahead probabilistic residential load forecasting method based on CNN-SE and micrometeorological data, significantly improving the feasibility and accuracy of predictions. The impact of weather features on predictions has also been demonstrated in the literature. For instance, [PERSON] et al. [27] improved solar power forecasting performance by reconstructing machine learning inputs through PV analytical modeling to explore key weather factors.  
However, in the field of natural gas demand forecasting, commonly selected features include per capita GDP, population growth rate, and natural gas prices [13; 28; 29]. The use of weather features, particularly regional and consumption usage features, has rarely studied.
## 1 Introduction
This in turn enhances the precision and efficiency of aerial surveying and mapping, making significant contributions to various scientific fields such as geospatial analysis, environmental monitoring, and resource management. Additionally, the development and application of this technology also contribute to advancements in remote sensing and aerial imaging techniques, furthering the capabilities and applications of aerial reconnaissance and mapping. Overall, image-motion compensation technology has made substantial scientific contributions by enabling the acquisition of high-quality aerial images for a wide range of scientific and operational purposes. The rest of this paper is arranged as follows. In Section 2, we analyze and calculate the forward image motion and the scanning motion of an aerial camera under some operating conditions, and the analysis results demonstrate the necessity for image-motion compensation. In Section 3, the image-motion compensation technique based on the multi-dimensional motion of the secondary mirror is studied, then the relationship between the deviation vector and the misalignment of the secondary mirror is studied, and the influence of the secondary mirror's motion on the distribution of the aberration is analyzed. In Section 4, a design case based on the image-motion compensation technique is presented. In Section 5, the actual experimental process is described and the experimental results are discussed. Section 6 presents the conclusions.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol  
In this section, first, we explain the network model of our proposed clustering protocol. We assume that there are \(N\) sensor nodes, which are uniformly dispersed in a square region of area 100 m \(\times\) 100 m. In the literature, different techniques are used to minimize the energy consumption and also some methods are applied to find out main sources of energy drainage in the network. We believe that in the clustering routing protocol the optimal number of CHs selections also takes part in increasing the stability of the network. The size of the cluster should be controlled because sometimes the over populated clusters also lead to depleting the available resources. In our defined model, initially, all of the nodes in the network individually check their suitability for CH selection. Then the BS decides and finalizes the nominated CHs on the basis of selection criteria. The BS is working as a central entity, which reserved the rights of elimination and recommendation. We also keep a balance between the cluster size by defining a criterion in which maximum eleven and minimum six \(C_{MN}\)s can jointly form a cluster. In case the maximum limit exceeds, the remainder of the nodes, joint with the other CH.  
We partitioned the working of our proposed method function into rounds. Every round is then further separated into four phases such as; (1) setup phase; (2) settling phase; (3) scheduling phase and (4) data transmission phase. Whereas, the random and the optimal number of CHs selections provide a way for load balancing and the uneven energy distribution problem as well as help in keeping the stability of the network. For the third and fourth phases included in the overall energy consumption of the network, more details concerning each of the phase are explained in the next subsections.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Advancing Personalized Medicine  
One of the most promising applications of AI in cancer genomics is its role in advancing personalized medicine. AI stands out as a powerful tool for merging genomic data (from tumor sequencing), clinical data (such as patient health history and treatment responses), and imaging data (such as CT scans or MRIs) to provide a holistic view of the patient's cancer [79]. The integration of these diverse data types is crucial because it offers a deeper overview of the disease, including not only genetic variants but also how those variants physically manifest (via imaging) and how they interact with a patient's overall health and previous treatments. By combining these datasets, AI models can help oncologists developing personalized therapy plans that are specifically tailored to the patient's genetic makeup, cancer type, stage, and other unique characteristics [80].  
For example, AI can suggest the most appropriate targeted therapies based on the patient's specific genomic alterations, such as the presence of EGFR variants in non-small cell lung cancer or BRCA1/2 variants in breast cancer, both of which can significantly affect treatment options [81; 82]. AI can also identify the probability of treatment success or failure based on the patient's genetic profile and historical data from similar cases. In addition, AI can help with optimizing treatment regimens by analyzing imaging data to track tumor progression and detect early signs of drug resistance. For instance, radiomics, an emerging field that applies machine learning to medical imaging, can extract quantitative features from imaging scans that reflect tumor characteristics [83].  
Another critical aspect of personalized medicine is the development of predictive models that estimate how a patient's tumor will respond to specific drugs or treatment regimens. These models can be used to identify patients who may benefit from targeted therapies, immunotherapy, or chemotherapy, and they can help avoid unnecessary complications by predicting adverse reactions to drugs [84].  
DL algorithms are particularly valuable for creating these predictive models. They can analyze historical patient data to identify patterns and trends that would be difficult for clinicians to detect manually [85] (Figure 3).  
Furthermore, predictive models can help with optimizing therapy over time by continuously learning from new data. As a patient progresses through treatment, new genomic or clinical data can be incorporated into the model, allowing it to adjust therapy plans in real time. For example, if a patient's tumor develops resistance to a particular drug, AI can predict this resistance early and suggest alternative treatments that might be more effective [86; 87]. Through AI-driven predictive modeling, personalized cancer therapy is becoming more precise and timely and tailored to each patient's needs.
# Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning
###### Abstract  
Three-dimensional ground penetrating radar (3D GPR) is a non-destructive examination technology for pavement distress detection, for which its horizontal plane images provide a unique perspective for the task. However, a 3D GPR collects thousands of horizontal plane images per kilometer of the investigated pavement. The existing detection methods using GPR images are time-consuming and risky for subjective judgment. To solve the problem, this study used deep learning methods and 3D GPR horizontal plane images to detect pavement structural distress, including cracks, repairs, voids, poor interlayer bonding, and mixture segregation. In this study, two deep learning methods, called CP-YOLOX and SViT, were used to achieve the aim. A dataset for anomalous waveform localization (3688 images) was first created by pre-processing 3D-GPR horizontal plane images. A CP-YOLOX model was then trained to localize anomalous waveforms. Five SViT models with different numbers of encoders were adopted to perform the classification of anomalous waveforms using the localization results from the CP-YOLOX model. The numerical experiment results showed that 3D GPR horizontal plane images have the potential to be an assistant for pavement structural distress detection. The CP-YOLOX model achieved 87.71% precision, 80.64% mAP, and 33.57 sheets/s detection speed in locating anomalous waveforms. The optimal SViT achieved 63.63%, 68.12%, and 75.57% classification accuracies for the 5-category, 4-category, and 3-category datasets, respectively. The proposed models outperformed other deep learning methods on distress detection using 3D GPR horizontal plane images. In the future, more radar images should be collected to improve the accuracy of SViT.  
3D GPR; horizontal radar images; deep learning; distress recognition and localization +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Co-Occurrence/Co-Exclusion Relationships between Genera and Microbial Metabolites  
The co-occurrence and co-exclusion relationships between microbial genera and metabolism can be visualized in Figure 6. Using data from an undirected network, 321 connections were found, each representing interactions between microbial species found in the fermentation process and chemical targets with variable weights (Figure 6). It was possible to observe how _Enterobacter_, _Kluyvera_, _Pantoea_, _Serratia_, _Hansenispora_, _Pseudomonas_, _Fusarium_, _Cladosporium_, and _Penicillium_ interact with various key substances, including glucose, fructose, acetic, lactic, and citric acids, ethanol, benzaldehyde, and 4-heptenal. _Kluyvera_ and _Serratia_ showed the highest number of connections (12 each) and a significantly positive average weight (0.636), indicating strong interactions with compounds such as benzaldehyde and 4-heptenal. Interestingly, _Fusarium_ and _Cladosporium_, both with predominantly negative connections, indicate possible inhibitory effects or chemical competition with other microorganisms during the fermentation. This may also be related to their primary actions on complex carbohydrates present in coffee pulp [26; 51], such as pectin and cellulose, which were not measured in this study. _Enterobacter_ and _Pseudomonas_ displayed a variety of interactions with substances such as decanal and glucose, but with low average weights, suggesting weaker or less influential interactions. Interestingly, 4-heptenal was a common target for a variety of sources, possibly serving as an important intermediary in the production of other key metabolites [2]. These findings demonstrate the adaptability of new microbial groups to subtropical climate conditions, possibly with modifications in metabolic pathways that affect the synthesis of compounds during coffee fermentation. These microbial adaptations to non-tropical conditions can be optimized to maintain or improve coffee quality. This includes the possibility of developing specific starter cultures that can be used to control or direct the fermentation process in favor of desired sensory characteristics [52; 53].
## 4 Uncertainty Analysis  
Measurement uncertainty analysis is also carried out based on the guide to the expression of uncertainty in measurement (GUM) [23] to verify the feasibility of the proposed method with the employment of an optical frequency comb laser. Since the mean of pitch values evaluated from multiple angles of diffraction of diffracted modes is evaluated in the proposed method, at first, the uncertainty of evaluating the pitch by a specific diffracted mode is evaluated. Then, the probability distribution of pitches from the angles of diffraction of entire modes was obtained to evaluate the measurement uncertainty of the proposed method. Equations (4) and (5) can be modified as follows by considering the refractive index of air \(n_{\text{air}}\):  
\[P_{\text{pos},i}=\frac{\lambda_{\text{pos},i}}{2n_{air}\cos(\frac{\alpha- \beta}{2})\sin(\theta_{\text{pos},i}+\frac{\beta}{2})} \tag{8}\]  
\[P_{\text{neg},i}=\frac{\lambda_{\text{neg},i}}{2n_{air}\cos(\frac{\alpha- \beta}{2})\sin(\theta_{\text{neg},i}-\frac{\beta}{2})} \tag{9}\]  
In the following, the contribution of each parameter in the equations is evaluated. 1. Angular misalignment \(\alpha\) of the measurement laser beam about the \(Z\)-axis with respect to the \(X\)-axis (\(u(\alpha)\))  
At first, the contribution of \(\alpha\) was evaluated. As described in the previous section of this paper, \(\alpha\) was evaluated by using the zeroth-order diffracted beam reflected from the retroreflector; the \(Y\)-directional displacement of the focused laser beam on the CCD image sensor was converted into the angle \(\alpha\) based on the principle of laser autocollimation [22]. Denoting the \(Y\)-positions of the focused laser beam on the image sensor when measuring the reflected beam from the retroreflector and the zeroth-order diffracted beam from the diffraction grating as \(Y_{\text{retro}}\) and \(Y_{0}\), respectively, Equation (6) can be rewritten as follows:  
\[\alpha=\arctan(\frac{Y_{retro}-Y_{0}}{F}) \tag{10}\]  
Then, the standard uncertainty \(u(\alpha)\), which is the contribution of \(\alpha\), can be evaluated by the following equation:  
\[u_{\alpha}=\sqrt{[c_{Y\text{retro}}\cdot u_{Y\text{retro}}]^{2}+[c_{Y0}\cdot u _{Y0}]^{2}+[c_{F}\cdot u_{F}]^{2}} \tag{11}\]  
Figure 12: Compensation of the influence of the measurement laser beam spectrum. (**a**) Grating pitch calculated by using the spectra of the first-order diffracted beam \(L_{1}(\lambda_{i})\); (**b**) Grating pitch calculated by using the normalized spectra \(I_{\text{norm}}(\lambda_{i})\). where \(u_{Y\text{retro}}\), \(u_{Y0}\) and \(u_{F}\) are the standard uncertainties of \(Y_{\text{retro}}\), \(Y_{0}\) and \(F\), respectively, while \(c_{Y\text{retro}}\), \(c_{Y0}\) and \(c_{F}\) are the sensitivity coefficients of \(Y_{\text{retro}}\), \(Y_{0}\) and \(F\), respectively. Table 1 summarizes the contributions of each parameter on \(u_{\alpha}\). The \(Y\)-position of the focused retroreflected beam and zeroth-order beam on the profiler was obtained as a mean of 200 repetitive trials with a standard deviation of 0.333 \(\upmu\)m and 0.299 \(\upmu\)m, respectively.