esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
## 5 Results and Discussion
### Correct and Wrong Activity Magnitude Response in the Frequency Domain  
Extensive experiments were performed to design the testbed with settings for the values of different parameters for optimum and reliable results to ensure the testbed will work properly for real-world problems. While considering the initial testing of the system, we considered binary but multi motion activity to test the system. The results achieved for correct and wrong posture are shown in Figures 7 and 8 for all the 64 subcarriers. The magnitude response in the lab with other activities was going on, but the prominent amplitude changes were observed of the subject performing the activity. Although, the small-amplitude variation of the other students working in the lab are also observed because the system is very sensitive to human body motions. It is also observed that it gave strong amplitude changes in the LOS subject as compared to NLOS. NLOS motion is also present in the WCSI, but the response of LOS is more prominent. The randomly selected 10 th subcarrier of both activities is shown to analyze the trend in Figure 9. Thus, this trend of WCSI can be useful in the classification of multi activities with multi motions. The achieved results show that both activities can easily be recognized by the naked eye by comparing both of the activities. Amplitude response in the frequency domain of the WCSI is prominent enough to apply supervised machine learning for the classification and justification of system monitoring capabilities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sr.No** & **Subject** & **Body structure** & **Height (cm)** & **Weight (Kg)** \\ \hline
1 & Male & Ectomorph & 168 & 55 \\
2 & Male & Endomorph & 180 & 95 \\
3 & Female & Mesomorph & 168 & 60 \\
4 & Male & Mesomorph & 174 & 76 \\
5 & Male & Ectomorph & 176 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Subject participation in experiments.  
Figure 7: Result of correct activity using 64-subcarriers.
## 2 Chromatographic Techniques for Carotenoids Analysis  
The analysis of carotenoids in food matrices relies on various chromatographic techniques, each offering unique separation, identification, and quantification advantages. Figure 2 presents the number of methods published for each of these techniques since 2015.  
These methods address carotenoids' structural complexity and diversity by including a separation step based on the interaction between the stationary, analyte, and mobile phases, which depends on specific properties such as polarity in liquid chromatography.  
Figure 2: Trends in carotenoid determination methods: number of published studies (2015–Present). HPLC, high-performance liquid chromatography; UHPLC, ultra-HPLC; GC, gas chromatography; TLC, thin-layer chromatography; SFC, supercritical fluid chromatography. The keyword “carotenoid” and each technique name and acronym were used for the Web of Science (WOS) database search.  
Analyte determination is then performed using different detectors selected according to the goals of the analysis, facilitating accurate and reliable analysis in different applications. HPLC is the most widely used method for carotenoid determination due to its reliability, sensitivity, and ability to separate a wide range of compounds with high resolution. Since 2021, there has been a slight increase in the use of UHPLC, likely driven by its advantages in speed, resolution, and solvent efficiency, though its adoption remains limited due to higher costs and specialized column requirements [7]. Other techniques, such as SFC and GC, are rarely used, as SFC is still emerging as an alternative for nonpolar carotenoids [9]. Despite its lower frequency of use, TLC has remained stable over the years and continues to serve as a valuable complementary technique, particularly for preliminary screening and qualitative analysis. In some studies, TLC has been used alongside HPLC, highlighting its role as a supporting method for quick assessments before further chromatographic quantification [11]. These trends suggest that while HPLC remains the dominant technique, combining different analytical methods can improve accuracy and robustness in carotenoid research. A detailed discussion of the specific features of these techniques for carotenoid analysis, along with their advantages and limitations, will be presented in the following sections.
# Titanium Dioxide in Chromogenic Devices: Synthesis, Toxicological Issues, and Fabrication Methods
## 2 Synthetic Routes and Fabrication Techniques for TiO\({}_{2}\)
### The \"Green\" Route  
The methods described above represent the most common methods used to synthesize high quality TiO\({}_{2}\) nanomaterials, but they unfortunately have several drawbacks. They often require the use of toxic chemicals and long purification processes after synthesis, along with high-energy consumption. In this framework, the so called \"green chemistry\" may represent an eco-friendly alternative to traditional methods due to the use of natural, non-toxic agents (usually plants, microorganisms, or fungi) [33]. In particular, in the plant extracts, fito-constituents such as flavonoids, alkaloids, terpenoids, and polyphenols act as reducing and capping agents of metallic/metal oxide solutions. [PERSON] et al. [97] described the biosynthesis of anatase TiO\({}_{2}\) NPs with a size ranging from 20 nm to 90 nm by using the aqueous leaf extract of _Trigonella foenum-gracum_, an aromatic leguminous plant widespread in many Middle Eastern, European, and Asian countries. In addition, the authors observed strong antimicrobial activity of TiO\({}_{2}\) on _Staphylococcus aureus_, _Enterococcus faecalis_, _Klebsiella pneumoniae_, _Streptococcus faecalis_, _Pseudomonas aeruginosa_, _Proteus vulgaris_, _Bacillus subtilis_, _Yersinia enterocolitica_, and the fungus, _Candida albicans_. Additionally, _Azadirachta indica_ leaf extract was used by [PERSON] et al. [98] to produce spherical TiO\({}_{2}\), starting from an aqueous solution of Ti precursor. In the literature, other plants, such as _Nyctanthes arbortrististis_[99], _[PERSON] squamosa L_. [100], and _Echinacea purpurea_[101], were used to obtain TiO\({}_{2}\) NPs. In the future, green TiO\({}_{2}\)NPs may be used to fabricate chromogenic materials for smart windows or other suitable applications. In Table 1, the advantages and disadvantages of each route were reported, together with the type of nanoTiO\({}_{2}\) obtained. Figure 4: Schematic representation of a typical spray pyrolysis process.
# Narrowband Internet of Things (NB-IoT):
## 5 Open Research Questions and Discussion
### Mobility Management  
As seen in Section 2, most of the simulation works have ignored the mobility impact of NB-IoT channel modeling. However, for use cases that involve movement, Doppler shift has to be taken into consideration during channel estimation, which might slightly increase the device complexity to support handover and other mobility features such as the support for inter-RAT mobility during idle mode [93; 94]. The increase in NB-IoT UEs mobility makes the channel suffer from fast varying channel conditions, due to which adaptive transmission schemes that might involve channel estimation, error correction, etc. must be implemented.  
Therefore, applying intelligent/adaptive algorithms that are low power and optimal for repetition number, yet mobility-aware, is of great importance. The algorithms could involve low-power frequent CSI reporting, early data transmission by using both user and control plane in either Msg 3 or Msg 4.
# _electronics_
## 4 Conclusions  
In summary, we have investigated a hybrid hollow-core topological waveguide for terahertz on-chip communications. The topological bandgap can be widened by improving the deformation of expanded and compressed clusters. Within the bandgap of 16.1% relative bandwidth, two pseudospin modes can be guided along either forward or backward directions. The optimized design shows that the low-loss (below 0.1 dB/mm) bandwidths for forward and backward topological modes are 0.278-0.289 THz and 0.242-0.267 THz, respectively, which occupy 81.8% of the bandgap. We further exploit the proposed waveguide for terahertz on-chip communications. From the link budget analyses, the channel performance is GVD-dependent. At zero GVD frequencies, both forward and backward topological modes can be applied for short-range (50-100 mm) 10-Gbps transmission under the FEC threshold (BER below \(10^{-3}\)). Our work demonstrates the possibilities of high-speed on-chip full-duplex interconnection using a single photonic chip, which could be applied to next-generation information systems.  
Conceptualization, H.L.; methodology, H.L.; software, Y.Z. and Y.L.; validation, H.L.; investigation, Y.Z. and Y.L.; resources, H.L. and S.A.; data curation, H.L.; writing--original draft preparation, H.L., Y.Z. and Y.L.; writing--review and editing, S.A.; visualization, Y.Z. and Y.L.; supervision, H.L.; project administration, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities, grant number 2022 JBZY004 and the National Natural Science Foundation of China, grant number 62075007.  
The data presented in this study may be available from the corresponding author upon reasonable request.  
Figure 6: Communication performances (**a**) _Pr_, (**b**) _SINR_, and (**c**) BER as a function of wavelength length, where black solid, red dashed, blue dotted, and green dash–dotted curves represent carrier frequencies corresponding to forward mode with the minimum loss (frequency at 0.281 THz), forward mode with zero GVD (frequency at 0.2827 THz), backward mode with the minimum loss (frequency at 0.256 THz), and backward mode with zero GVD (frequency at 0.258 THz), respectively. The pink horizontal line indicates the FEC limit (BER \(<10^{-3}\)).
## 4 Conclusions  
Without the use of sintering additives, La-Co co-substituted SrM single phases having the compositions of Sr\({}_{1-x}\)La\({}_{x}\)Fe\({}_{12-x}\)Co\({}_{x}\)O\({}_{19}\) (0.0 \(\leq x\leq\) 0.3) are obtainable from polycrystalline bulk samples prepared by solid-state reaction in air by employing proper processing conditions. Therefore, we could accurately identify the effects of La-Co co-substitution on their structural and intrinsic magnetic properties up to the composition of \(x\) = 0.3. The decrease in \(c/a\) ratios and \(V_{cell}\) values with increasing \(x\) are surely due to the decreasing size effect of La\({}^{3+}\) substitution at the Sr\({}^{2+}\) site, exceeding the increasing size effect of Co\({}^{2+}\) and Fe\({}^{2+}\) occupation at the Fe\({}^{3+}\) site up to \(x\) = 0.25. However, the upturn in the lattice constant \(c\) from \(x\) = 0.25 to 0.3 is probably due to a rapid increase in Co\({}^{2+}\) occupancy of the \(2a\) site compared to that of the \(4f_{2}\) site, which requires further study. With increasing \(x\) from 0.0 to 0.3, \(M_{5}\) decreases from 75.90 to 72.07 emu/g because decreasing effect by Co\({}^{2+}\) and Fe\({}^{2+}\) occupation at the \(2a\) site (up-spin) becomes larger than increasing effect by Co\({}^{2+}\) occupation at the \(4f_{2}\) site (down-spin); \(H_{a}\) increases from 20.1 to 24.7 kOe, probably due to a lowering of the lattice symmetry, and causes a continuous increase in \(H_{cl}\) from 2.68 to 3.99 kOe. The significant difference in the structural and intrinsic magnetic properties between our bulk samples sintered at 1300 \({}^{\circ}\)C and sol-gel processed powder samples fired at 900 \({}^{\circ}\)C without additives may be due to a large difference in the firing temperatures, of which identification also requires further study.  
Conceptualization, K.L.; funding acquisition, S.-I.Y.; investigation, K.L. and S.-I.Y.; methodology K.L.; project administration, S.-I.Y.; software, K.L.; supervision, Y.-M.K. and S.-I.Y.; writing--original draft, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National R&D Program through the National Research Foundation of Korea (NRF) and was funded by the Ministry of Science and ICT (2020M3H4A2084420).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors are also grateful to Union Materials Corp. in Pohang, Republic of Korea for the supporting of raw materials for magnet fabrication.  
The authors declare no conflicts of interest.
# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 4 Experiment and Result Analysis
### Performance Comparison of Algorithms
The comparison results are given in Table 8. Obviously, the MFIC algorithm is improved significantly compared with the SC-based method from previous work and is superior to the Bayesian HSMM model and SIQCP solver, with the disaggregation accuracy increased by 11.1% and 7.1%. In addition, we compare with the latest NILM method based on random forest [6] by processing the low-frequency data set of REDD. The comparison results are shown in the Table 9. The random forest method is to use electrical features as training data and adopt random forest method to disaggregation. From the comparison, we can see that the proposed MFIC algorithm performs better in disaggregation. It can disaggregate various loads more accurately. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Appliance** & \(\mathbf{F_{S}}\) & \(\mathbf{F_{H}}\) & \(\mathbf{F_{DT}}\) & \(\mathbf{F_{U}}\) \\ \hline Air conditioner & & 0.12 & 0.89 & 99.9 \\ Washer dryer & 75.36 & 0 & 0.88 & 94.2 \\ Dishwasher & 35.97 & 0.04 & 0.32 & 99.2 \\ Oven & 79.13 & & & 85.6 \\ Refrigerator & 94.35 & 0.90 & 0.97 & 97.8 \\ Microwave & 25.91 & 0.47 & 0.97 & 97.9 \\ Stove & 44.4 & 0.21 & 0.33 & 98.9 \\ \hline
**House** & \(\mathbf{F_{S}}\) & \(\mathbf{F_{H}}\) & \(\mathbf{F_{DT}}\) & \(\mathbf{F_{U}}\) \\ \hline House 1 & 77.52 & 77.06 & 78.09 & 92.39 \\ House 2 & 82.17 & 82.38 & 81.41 & 98.58 \\ House 6 & 95.58 & 72.76 & 75.94 & 94.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of four low-rate NILM algorithms using the REDD database. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \multicolumn{4}{c}{**Disaggregation Accuracy (DA)**} \\ \cline{2-5}  & **House 1** & **House 2** & **House 6** & **Mean** \\ \hline MFIC & 90.3\% & 92.0\% & 95.5\% & 92.6\% \\ Bayesian HSMM & 82.1\% & 84.8\% & 77.7\% & 81.5\% \\ SIQCP & 78.4\% & 86.4\% & 91.6\% & 85.5\% \\ SC & 57.2\% & 65.4\% & 58.1 & 60.2\% \\ discSC & 58.1\% & 68.3\% & 53.9\% & 60.1\% \\ DSC(Greedy) & 60.8\% & 71.1\% & 61.7\% & 64.5\% \\ DSC(Exact) & 64.3\% & 74.9\% & 64.2\% & 67.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Disaggregation accuracy comparison with other methods.
## 3 Key Technologies Achieved by the System
### Sensor Data Compensation and Correction
#### 3.1.2 Temperature and Humidity Compensation and Correction  
Digital sensor DHT22 (Aosong (Guangzhou) Electronic CO., LTD, Guangzhou China) is used for temperature and humidity acquisition and values are output in a unibus data format. As the sensors exhibit non-linearity and hysteresis and errors exist between the acquired values and the true values, a standard tester of high-precision is used for compensation and correction. Taking temperature as an example and referring to the preference temperature range of henhouses, incubators with a constant temperature precision of 0.5 \({}^{\circ}\)C is used to design the test. The constant temperature range is 8-37 \({}^{\circ}\)C with a step length of 0.5 \({}^{\circ}\)C. To simultaneously compensate the errors caused by non-linearity and hysteresis, the sensors are tested for both forward and reverse paths. According to henhouse temperature measuring requirement in actual production, a linear fitting function with mean square error of 0.058 between sensor acquisition value and standard value is used as the forward compensation function using the Matlab (Mathwork, Natick, MA, US) fitting tool. Similarly, the reverse path is compensated using a linear fitting function. Two consecutive temperature values are read by the system. The first reading is a temperature value acquired in last cycle while the second reading is a real-time acquisition value. If the real-time acquisition value is larger than last acquisition value, the real-time acquisition value is included in the forward compensation function, otherwise included in the reverse compensation function. Final temperature value can then be computed, and temperature is compensated and corrected. As a result, temperature acquisition accuracy is raised. Figure 2 shows the verification test results of the compensation algorithm. The absolute error of temperature measuring decreased, with the maximum error being 0.4 \({}^{\circ}\)C, which met the temperature measuring requirement of henhouses.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 5 In-Laboratory Tests and Calibrations
### GoF Test: Calibration of the Nominal Distributions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**PRN under Test**} & \multirow{2}{*}{
\begin{tabular}{c} **C/N\({}_{0}\)** \\ **(dBHz)** \\ \end{tabular} } & \multicolumn{6}{c}{**PRN of the Reference Distribution Function**} \\ \cline{3-10}  & **PRN 1** & **PRN 5** & **PRN 6** & **PRN 10** & **PRN 16** & **PRN 17** & **PRN 21** & **PRN22** \\  & **(-110 dBm)** & **(-113 dBm)** & **(-116 dBm)** & **(-119 dBm)** & **(-122 dBm)** & **(-125 dBm)** & **(-128 dBm)** & **(-131 dBm)** \\ \hline
**PRN 1** & 59 & 0 & 0 & 0.0025 & 0.0045 & 0.0134 & 0.2165 & 0.6914 & 0.7371 \\
**PRN 5** & 55 & 0 & 0 & 0.0025 & 0 & 0 & 0.0135 & 0.5073 & 0.7371 \\
**PRN 6** & 52 & 0 & 0 & 0 & 0 & 0 & 0.2783 & 0.2731 \\
**PRN 10** & 48 & 0 & 0 & 0.0025 & 0 & 0 & 0.0449 & 0.7236 \\
**PRN 16** & 45 & 0.01279 & 0.0045 & 0.0270 & 0 & 0 & 0.0249 & 0.7011 \\
**PRN 17** & 42 & 0.5926 & 0.1389 & 0.0900 & 0.0492 & 0.0089 & 0 & 0 & 0.3056 \\
**PRN 21** & 39 & 0.7354 & 0.6764 & 0.3645 & 0.2950 & 0.0984 & 0.0045 & 0 & 0.0135 \\
**PRN 22** & 36 & 0.7354 & 0.7346 & 0.7368 & 0.7330 & 0.7159 & 0.4899 & 0.0045 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimated false alarm rate of each signal vs. each reference distribution. Figure 6: C/N\({}_{0}\) measurement of the GPS signals used for the calibration of the GoF method.
## 2 Materials and Methods
### Data Analysis  
The meta-analysis was conducted in Metawin 2.1 (Miami, FL, USA) and GraphPad 8.0 software (San Diego, CA, USA). Using the response ratio (\(R\)) [26] as a measure of effect size, each group of data was calculated using the following formula:  
\[R =\frac{X_{t}}{X_{c}} \tag{1}\]  
\[\ln R =\ln(\frac{X_{t}}{X_{c}}) \tag{2}\]where \(t\) deals with the average of the set of variables, \(c\) is the average of the control variables, \(\ln R\) is the logarithmic response, and the variance calculation formula is calculated as follows:  
\[v\ _{\ln R}=\frac{SD_{t}^{2}}{n_{t}\ X_{t}}+\frac{SD_{c}^{2}}{n_{c}\ X_{c}} \tag{3}\]  
where \(SD\) is the standard error and \(n\) is the number of repetitions. To facilitate interpretation, the formula percentage change = (\(R-1\)) \(\times\) 100% was used.  
Higgins I\({}^{2}\) statistic and Q test [27] were used to assess the degree of heterogeneity and indicate differences between studies. Fixed-effect models were used for studies with low heterogeneity (I\({}^{2}\)\(\leq\) 50% and \(p\)\(>\) 0.05), and random-effect models were used for studies with opposite heterogeneity (I\({}^{2}\)\(>\) 50% or \(p\)\(<\) 0.05). A forest map is produced from the pooled results. In the forest plot, the mean ratio response and 95% confidence interval (CI) are shown as dots and bars, respectively. We used Egger tests [26] and [PERSON]'s fail-safe number, N, to assess publication bias. [PERSON]'s fail-safe numbers of tomato yield, soluble solids, soluble sugars, sugar-acid ratio, lycopene, vitamin C, and nitrate were 48,086,680, 21,344, 190,874, 15,074, 619,422, 223,898, and 1153, respectively (Table 1), which were significantly higher than N\({}_{\rm f}\) (5 \(\times\) 350 + 10, 5 \(\times\) 95 + 10, 5 \(\times\) 67 + 10, 5 \(\times\) 51 + 10, 5 \(\times\) 47 + 10, 5 \(\times\) 119 + 10, and 5 \(\times\) 40 + 10, respectively), indicating that the result is reliable.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Yield and Quality** & **n** & **Egger Tests** & **N\({}_{\rm f}\)** \\ \hline Yield & 350 & 0.3169 & 48,086,680 \\ TSS & 95 & 0.1412 & 21,344 \\ SS & 67 & 0.2267 & 190,874 \\ SAR & 51 & 0.4486 & 15,074 \\ Lycopene & 47 & 0.2413 & 619,422 \\ VC & 119 & 0.2101 & 223,898 \\ Nitrate & 40 & 0.2392 & 1153 \\ \hline \end{tabular}  
* These showed that, with N\({}_{\rm f}\)\(>\) 5 \(\times\) n + 10, there is no potential bias in the total effect sizes for yield, TSS, SS, SAR, lycopene, VC, and nitrate (\(p\)\(>\) 0.05).  
\end{table}
Table 1: Exploring potential publication bias and robustness by Egger tests and [PERSON]’s fail-safe numbers (N\({}_{\rm f}\)).  
Figure 1: Geographic distribution of the experimental sites in this meta-analysis.
## 3 Mechanical Analysis
### NVH Analysis  
To model the electromagnetic-mechanical analysis using the results of the electromagnetic analysis, it is necessary to set the boundary condition of the model and couple the electromagnetic analysis results. Based on the electromagnetic analysis model and results, a mechanical analysis was performed. In this study, the modal and forced vibration analyses were performed using ANSYS EM and WORKBENCH. The forced vibration analysis was performed by mapping the electromagnetic force with the air gap, which converted the harmonic orders generated by electromagnetic analysis using ANSYS EM and WORKBENCH, according to the 3D rotor model. Figure 6 shows the NVH analysis process of the PMSM in this study.  
Figure 7a shows the displacement analysis results based on the center of the shaft axis using forced vibration analysis, indicating that the displacement of the 9-slot model is greater than that of the 12-slot model. Figure 7b,c show the results of the amplitude ratio and displacement analysis at rated load and no load. The red part has the highest resonance value. The analysis results show that the 9-slot model, with a large electric magnetic force, has greater deformation. The comparison of the electromechanical analysis results implies that the vibration of machines is electromagnetically influenced by MPF and UMF rather than torque pulsations.  
Figure 5: The modal analysis result of permanent magnet machines with two different pole/slot combinations: (**a**) 9-slot and (**b**) 12-slot.  
Figure 8 shows the NVH characteristic analysis results of PM machines with two different pole/slot combinations by applying an electromagnetic vibration source. As shown in Figures 7 and 8, the NVH characteristics of the 9-slot model are more significant than that of the 12-slot model. Moreover, it can be seen through electromagnetic-mechanical analysis that vibration and noise occur at a higher level than the 12-slot model in the case of the 9-slot model in the overall harmonic order. Additionally, the 8 th harmonic vibration component generated by the UMF is larger for the 9-slot model than the 12-slot model. The vibration amplitudes of the 9- and 12-slot models are 144 and 60 dB, respectively. Table 1 lists the comparative analysis results of PM machines with two different pole/slot combinations and an electromagnetic vibration source.  
Figure 6: The noise, vibration, and harshness (NVH) analysis process of the permanent magnet synchronous motor (PMSM).  
Figure 7: The analysis model of permanent magnet machines with two different pole/slot combinations: (**a**) electromagnetic analysis model and (**b**,**c**) prototype model.