esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current CORNERSTONE Platforms
### Passive Devices
However, Y-splitters have a wider bandwidth and a smaller footprint, and DCs can be easily tuned to achieve arbitrary power splitting, for example for tapping off a fraction of the power (e.g., 5%) for a reference measurement or test point. Wavelength-division (de)multiplexers (WDM) are irreplaceable components to transport large volumes of data. Common WDMs in Si photonics are the angled-MMI (AMMI) [48], arrayed waveguide grating (AWG) [49] and planar concave grating (PCG) [50], but all these devices have large footprints. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Foundry**} \\ \cline{2-4}  & **AMF (220 nm SOD)** & **CEA-Leti (310 nm SOI)** & **Imee (220 nm SOD)** \\ \hline Rib waveguide & \textless{}1.5 dB/cm (waveguide & \textless{}4.5 dB/cm & \textless{}1.4 dB/cm \\ Strip waveguide & configuration not specified) & \textless{}5 dB/cm & \textless{}0.6 dB/cm \\ Grating coupler & \textless{}4 dB & \textless{}2.5 dB & \textless{}2.5 dB \\ Reference & [11] & [12] & [14] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison with other major foundries. \(\lambda\) = 1.55 \(\upmu\)m in all cases. Waveguide dimensions are not specified in the information published by the foundries. Figure 2: 3D schematic of a rib-strip transition. Generally, AMMIs are easier to design and have lower loss, yet on the other hand they usually have fewer channels. However, the channel count can be increased by interleaving two devices [51]. Furthermore, the AMMI can be operated bidirectionally (BAMMI) so that any drift in the device performance either from fabrication tolerances or from temperature fluctuations during operation are balanced on both the multiplexor (MUX) and de-MUX [52], as shown in Figure 3.  
Photonic components with optical resonance are also commonly used in Si photonics platforms. For example photonic crystal cavities (PhC) [37] with a quality factor (\(Q\)-factor) as high as 600,000 have been experimentally measured on the SOI platform [53], or Fabry-Perot (FP) cavities formed by Sagnac loops [54] or microring resonators [55] with \(Q\)-factors measuring several tens of thousands. Many of the above mentioned components are available in an open source, license free CORNERSTONE standard design library [25], along with parametric components which are accessible in the CORNERSTONE PDK using Luceda Photonics' IPKISS software [56], e.g., parametric ring resonators and racetrack resonators with both an all-pass-filter and add-drop-filter. In order to facilitate circuit designers, the CORNERSTONE team is in the process of adding component models to their PDK.
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
###### Abstract
In [26] and [27] the authors have studied the use of polymers as gas transducers. The survey published [PERSON] et al. [28] covered gas-sensing technologies in general, suggesting the use of wireless technologies to transfer information, although no wireless sensor has been reviewed. The purpose of this survey is to provide a deep review and analyze the state-of-the-art of the available gas-measurement systems, performing a comparison of their used technologies, identifying and analyzing further opportunities related to the integration of embedded sensors with communication systems, using the IoT to enable devices and applications to be developed. Then, the main contributions of this survey are the following:  
* An analysis of the gas-sensing technologies evolution;
* A deep literature review on the most promising technologies to sense environmental gases through wireless sensors;
* The analysis of the most promising wireless-based solutions for ambient gas monitoring;
* The identification of open research issues on gas-sensing technologies and wireless gas sensors;
* The lessons learned from this study on gas sensors are shared. The rest of this document is organized as follows. Section 2 presents the most important gases, in terms of pollution monitoring, health issues, and accident prevention highlighting their main characteristics. Section 3 brings the opportunities in terms of IoT verticals and economic sectors offered by IoT-enabled multi-gas sensors. The evolution of gas-sensing technologies is addressed in Section 4. Technologies to sense gases and wireless communications support are elaborated in Sections 5 and 6, respectively. The most promising solutions in terms of sensing methods and IoT-enabled solutions are discussed in Section 7 and open research topics are identified. Lessons learned are shared at Section 8 and, finally, Section 9 concludes the study.
## 2 Class-D PA for Polar-Mode Architectures
### Envelope Nonlinearities in Polar-Mode RF PAs
#### 2.1.3 Class-D Transformer-Coupled Voltage Switching  
In the transformer-coupled voltage switching (TCVS) PA [18; 19], which is shown in Figure 3c, the input envelope (\(v_{E}\)) is directly applied to the transformer. This fixes the limited bandwidth problems associated with the current-feeding inductor (\(v_{E}\) distortion), while maintaining the supply envelope baseband component rejection properties of the TCCS topology (3), as seen if the voltage (\(v_{RS}\)) applied to the resonator is defined as follows:  
\[v_{RS}(t)=\frac{n}{m}v_{E}(t)s_{C}(t) \tag{4}\]  
making the TCVS PA potentially the best choice for polar-mode operation from the point of view of envelope linearity.
## 3 Results and Discussion
### Physicochemical Characterization
A more pronounced shifting (995 to \(\approx\)1065 cm\({}^{-1}\)) in the band associated with T-O stretching vibrations (where T=Si or Al) could be observed because of the large amount of C-(A)-S-H phases in the AA-WBA-6M sample. Both in SAM extraction and HCl extraction spectra, three peaks (\(\approx\)790, \(\approx\)930, \(\approx\)1050 cm\({}^{-1}\)) and a shoulder (\(\approx\)1162 cm\({}^{-1}\)) were also observed, which are characteristic of silica gels [56; 57]. Figure 8: FTIR spectra before and after SAM extraction of (**a**) WBA and (**b**) AA-WBA-6M. Figure 7: WBA and AA-WBA FTIR spectra. Figure 9 shows the SEM images of AA-WBA binders in backscattering electron (BSE) mode. The effect of alkali-activator concentration in the compactness and microstructure of the material is demonstrated. Two differentiated areas in all samples can be observed; a light grayish compact area which was essentially attributed to the C-(A)-S-H gel [52]; and a dark grayish disaggregated area composed of unreacted WBA. In the AA-WBA-2M sample (Figure 9a), it can be observed that the unreacted WBA area is prominent compared with the compact area. The same appearance on the AA-WBA-4M microstructure is shown in Figure 9b. However, there is a predominance of the C-(A)-S-H gel in the cement matrix in contrast with Figure 9a. The microstructure of the AA-WBA-6M (Figure 9c) shows a prominent lighter grayish homogeneous-colored matrix with two distinguished areas corresponding to C-(A)-S-H gel. The compact area is associated with the C-(A)-S-H gel formed by sodium silicate [52], while the rough compact area is ascribed to the C-(A)-S-H gel formed by NaOH [58]. The microstructure of AA-WBA-8M sample (Figure 9d) shows three distinguished areas: The same two light grayish areas of Figure 9c and a darker grayish area corresponding to the unreacted WBA. The formation of C-(A)-S-H gel to a greater or lesser extent, depending on NaOH in the alkali-activator solution used, was demonstrated. The SEM images reveals that the use of NaOH 6M led to a greater formation of the C-(A)-S-H gel in agreement with the selective extraction results mentioned above (Section 3.2).
###### Abstract  
Custom implants used for pelvic reconstruction in pelvic sarcoma surgery face a high complication rate due to mechanical failures of fixation screws. Consequently, patient-specific finite element (FE) models have been employed to analyze custom pelvic implant durability. However, muscle forces have often been omitted from FE studies of the post-operative pelvis with a custom implant, despite the lack of evidence that this omission has minimal impact on predicted bone, implant, and fixation screw stress distributions. This study investigated the influence of muscle forces on FE predictions of fixation screw pullout and fatigue failure in a custom pelvic implant. Specifically, FE analyses were conducted using a patient-specific FE model loaded with seven sets of personalized muscle and hip joint contact force loading conditions estimated using a personalized neuromusculoskeletal (NMS) model. Predictions of fixation screw pullout and fatigue failure--quantified by simulated screw axial forces and von Mises stresses, respectively--were compared between analyses with and without personalized muscle forces. The study found that muscle forces had a considerable influence on predicted screw pullout but not fatigue failure. However, it remains unclear whether including or excluding muscle forces would yield more conservative predictions of screw failures. Furthermore, while the effect of muscle forces on predicted screw failures was location-dependent for cortical screws, no clear location dependency was observed for cancellous screws. These findings support the combined use of patient-specific FE and NMS models, including loading from muscle forces, when predicting screw pullout but not fatigue failure in custom pelvic implants.  
custom pelvic implant; custom prosthesis design; finite element analysis; screw fixation durability; screw failure
###### Abstract  
The 5 September 2018 (UTC time) Mw6.6 earthquake of Tomakomai, Japan has triggered about 10,000 landslides with high density, causing widespread concern. We attempted to establish a detailed inventory of this slope failure and use proper methods to assess landslide susceptibility in the entire affected area. To this end we applied the logistic regression (LR) and the support vector machine (SVM) for this study. Based on high-resolution (3 m) optical satellite images (planet image) before and after the earthquake, we delineated 9295 individual landslides triggered by the earthquake, occupying an area of 30.96 km\({}^{2}\). Ten controlling factors were selected for susceptibility analysis, including elevation, slope angle, aspect, curvature, distances to faults, distances to the epicenter, Peak ground acceleration (PGA), distance to rivers, distances to roads and lithology. Using the LR and SVM, two landslide susceptibility maps were produced for the study area. The results show that in the LR model, the success rate is 84.7% between the landslide susceptibility map and the training dataset, and the prediction rate is 83.9% shown by comparing the test dataset and the landslide susceptibility map. In the SVM model, a success rate of 90.9% exists between the susceptibility map and the test samples, and a prediction rate of 87.1% from comparison of the test dataset and the landslides susceptibility map. In comparison, the performance of the SVM is slightly better than the LR model.  
Tomakomai earthquake; co-seismic landslides; landslide susceptibility mapping (LSM); LR model; SVM model +
Footnote †: journal: Remote Sensing, 2019, 11, 978; doi:10.3390/rs11080978
## 3 Results and Discussions  
### Thermogravimetric Analysis  
Figure 3 shows the Thermogravimetric Analysis-Differential Thermal Analysis (TG-DTA) curves of sewage sludge under nitrogen atmosphere, recorded at three different heating rates (i.e., 10, 20, 30 \({}^{\circ}\)C). The graph demonstrates the mass loss of sewage sludge over the temperature range from ambient temperature up to about 800 \({}^{\circ}\)C. The weight loss percentage increases with increase in the torrefaction temperature, which signifies the effect the torrefaction temperature has on the weight percentage change. Initial weight loss can be observed before 200 \({}^{\circ}\)C, which may be due to a loss of moisture of the sewage sludge. Furthermore, for the temperature range of approximately 200-500 \({}^{\circ}\)C,  
Figure 2: Schematic diagram of the fluidized bed used in this study.  
a higher weight loss can be observed, which may be associated to the thermal decomposition as the sewage sludge enters the pyrolysis zone. Additionally, it can be observed that the actual temperature range from start to finish of the sewage sludge decomposition is narrower at a lower heating rate with respect to a higher heating rate. It also results that higher heating rates are accompanied by higher reaction rates and elevated reaction temperatures, i.e., the Derivative Thermogravimetric Analysis (DTG) peak temperature shifts to a higher value with increasing heating rates. There are two main reasons associated with the effects of the heating rate on the TG and DTG curve shape and the characteristic temperatures. First, at higher heating rates, a temperature difference is generated between the sample and the thermocouple built-in sensor. This might result in lagging of the real sample temperature behind the thermocouple. Second, in the decomposition with volatile products, it takes time for those products to diffuse out of the sample and to be carried away by the flowing gas. A low heating rate is more likely to generate thermal equilibrium and give reproducible results for analysis. Similar results have been obtained by [PERSON] et al. [16].  
Thermogravimetric measurements suggest that the thermal decomposition of sewage sludge is a rather complex process that occurs in several stages, as it is clearly reflected by the presence of several DTG peaks (Figure 3). The complicated thermal behavior exhibited by sewage sludge is likely a consequence of its very complex chemical composition, which is characterized by the presence of the organic components, such as cellulosic, dead bacteria and microbes combined with a large fraction of inorganic material in different amounts. In addition, Fourier-transform Infrared Spectroscopy (FTIR) can be integrated with TGA for better qualitative assessment of thermal treatment and for better understanding of the evolved gases from the infrared spectral analysis providing an extra specificity to the measurement.
# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 3 Radiomics in Bone Strength Assessment
A repeatable ROI was necessary for accurate quantitative analysis of radiomics features [29, 34]. Five ROIs, including the femoral head, upper neck, lower neck, greater trochanter, and femoral shaft, were selected from the proximal femur of the subjects to extract texture features. It was demonstrated that the correlations between texture features from different regions and femoral strengths varied [35]. Comparing the texture features extracted from the upper femoral neck (the principal tensile region) of the proximal femur and the lower femoral neck (the principal compressive region) between the fracture and non-fracture groups revealed that the mean entropy of the principal tensile region in the fracture group was significantly lower than that in the non-fracture group (\(p\) = 0.029). In contrast, no significant differences (\(p\) > 0.190) were observed in any texture features from the principal compressive region between the two groups [30]. Hence, it was essential to choose an ROI that was both closely related to bone strength and exhibited high repeatability when assessing femur strength through radiomics.
## 5 Experiments and Results
### Impact of Parameters on Rule Extraction
By lowering \(cr\) and/or increasing \(d\), more critical stations are detected and more discordant patterns are mined. The two input parameters of the association rule mining algorithm also impact on the quantity and the quality of the rules extracted. Their effects can be measured by analysing the quantity of the mined rules and the resulting quality of rebalancing operations. The former effect is analysed in the following paragraph, whereas the latter is discussed in Section 5.5. As denoted in [9], an exponential decay of number of extracted rules can be observed as the value of _minSupport_ increases. As an example, we observed that by increasing the support threshold from 10% to 20% the number of rules drops from 120 to 40. Contrarily, variations in _minConfidence_ do not determine a great reduction of number of rules, but usage of rules with extremely high values of confidence is harmful to the system since they represent specific patterns that can be applied only to few critical situations. Considering the limits imposed by _minSupport_ and _minConfidence_ parameters in terms of quantity of quality of rules, we set the minimum confidence threshold to 50% and minimum support threshold to 10% as default parameters for our system.
## 4 Mechanical Properties of Paste Backfill Materials Made of Cement
### Materials and Preparation of Samples
#### 4.1.3 Testing Program  
The orthogonal experimental design method was used to create the testing program. The fly ash content, cement content, and paste density were the three significant contributors.  
Test specimens were created in the laboratory using a combination of cement percentages of 6%, 8%, and 10% for the cement content and 25%, 30%, 35%, and 40% for the fly ash content. See Table 3 for factors and levels of the orthogonal experiment.  
Thus, in accordance with Table 4, the testing program was created, and 16 samples, numbered from S1 to S16 (S1-S16), were tested. The masses of fly ash, cement, and waste rock added together equal the total solid mass. Also considered are the ratios of fly ash, cement, and waste rock to the total mass. The mass ratio of solids to the combined mass of solids and water is known as paste density. Equations (1)-(4) display the formulas for the waste rock content (\(X_{3}\)), fly ash content (\(X_{1}\)), cement content (\(X_{2}\)), and paste density (\(\rho_{P}\)).  
\[\rho_{P}=\frac{F+C+R}{F+C+R+W}\times 100, \tag{1}\]  
\[X_{1}\%=\frac{F}{F+C+R}\times 100, \tag{2}\]  
\[X_{2}\%=\frac{C}{F+C+R}\times 100, \tag{3}\]  
\[X_{3}\%=\frac{R}{F+C+R}\times 100=1-X_{1}-X_{2}. \tag{4}\]  
where the masses of fly ash (F), cement (C), waste rock (R), and water (W) are represented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Curing Time (d)** & **Paste Density (\(\rho_{P}\)%)** & **Fly Ash Content (\(X_{1}\)%)** & **Cement Content (\(X_{2}\)%)** & **Waste Rock Content (\(X_{3}\)%)** \\ \hline S1 & 1 & 72 & 25 & 6 & 69 \\ S2 & 1 & 74 & 25 & 8 & 67 \\ S3 & 1 & 76 & 25 & 10 & 65 \\ S4 & 1 & 78 & 25 & 12 & 63 \\ S5 & 3 & 72 & 30 & 8 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Testing program.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Name** & **Type Specification** \\ \hline
1 & Strength testing facility & WAW-1000D (Jinan Hensgrand Instrument Co., Ltd., Jinan, China) \\
2 & Digital curing chamber & SHBY-40B (Trading Company, Nanjing, China) \\
3 & Cement mixer & NJ160 (Shaoxing Kare Instrument Equipment CO., Ltd., Shaoxing, China) \\
4 & Mold & 70.7 mm \(\times\) 70.7 mm \(\times\) 70.7 mm \\
5 & Laser Particle Size Analyzer & WINNER3009 \\
6 & Laboratory Small Jaw Crusher & JXSC Machine (JXSC Mine Machinery, Ganzhou, China) \\
7 & Vibro-stand and soil sieve & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific parameters of each facility.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level** & **Factor** & **Paste Density (\(\rho_{P}\)%)** & **Fly Ash Content (\(X_{1}\)%)** & **Cement Content (\(X_{2}\)%)** & **Curing Time (\(d\))** \\ \hline
1 & 72 & 25 & 6 & 1 \\
2 & 74 & 30 & 8 & 3 \\
3 & 76 & 35 & 10 & 7 \\
4 & 78 & 40 & 12 & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Factors and levels of the orthogonal experiment.
# Implementation of Tools for Lessening the Influence of Artifacts in EEG Signal Analysis
## 5 Examples
XXREG file  
filename = 'SUJ_001_EXP_001_1.neg';  
XXPEprocessing EEG files  
cfg = [];  
cfg_dataset = filename;  
MEMore the audio  
cfg_channel = '('all', '-Audio');  
MEMorence channel  
cfg_refchannel = 'PG'E';  
MCronception by the reference point  
cfg_demean = 'yes';  
cfg_detrend = 'yes';  
cfg_brfiter = 'yes';  
cfg_brfitertype = 'fix';  
cfg_brfiter = 'top'ang';  
data_neg = 'ft'-processing(cfg);  
MSF_ft = 't_create_msf_fieldtrip(data_neg);  
MSF_second METHOD  
flag = 0;  
cleaned_data = fta_ica_partially_removed_components_method(data_neg, MSF_ft,fig);  
YVisualize the cleaned EEG data  
ft_database([],cleamed_data);  
**Method 3: Partial removal of ICs using artifact-free ICA unmixing matrix. ** In this case, a different unmixing matrix is found by using solely uncontaminated samples; to this end, an MSF is again required. An example of the results of the utilization of this method is shown in Figure 4. Note that this figure shows the same signal and MSF excerpts as in the previous cases, for comparison purposes; differences are clearly observable in the results with respect to those methods. Figure 3: Method 2: Partial removal of ICs. (**a**) HEOG and VEOG signals, together with artifact detection recorded in the MSF; (**b**) original EEG data (for the Fp1 channel, near the eyes, and Fz); and (**c**) data obtained after artifact removal with the proposed method (also for the Fp1 and Fz channels). An example of Matlab code to demonstrate the utilization of this method is provided in Box 3. **Box 3. ** Matlab example code for the application of the proposed function for the partial elimination of ICs using artifact-free unmixing matrix and MSF. ```
%XEEGfile filename= 'SUJ_001_EXP_001_1.neg'; %XYP=processingEEGfiles  cfg= 1;  cfg.dataset=filename; %Remove the audio  cfg.channel= '(all', '-Audio'); %Reference channel  cfg.refchannel= 'FCz'; %Correction by the reference point  cfg.demean= 'yes';  cfg.detrend= 'yes';  cfg.p1 filter= 'yes';  cfg.p1 rfreq= 250;  cfg.p1 rf1 type= 'if';  cfg.p1 rf1 dir= 'twopass';  cfg.p1 rf1 dir= 'yes';  cfg.p1 rf1 type= 'if';  cfg.p1 rf1 dir= 'twopass';  data.eqe= 'ft_processing(cfg); %NMSP creation  NSF_tf= 'ft_create_msf_fieldtrip(data_neg); %XTHIRM METHOD  fig= 0;  cleaned_data= 'ft_artefacts_free_ica_partially_removed _components_method(data_neg, MSF_ft,fig); %Visualize the cleaned EEG data  ft_databrowser([],cleamed_data);
```  
With these examples, this manuscript, and the associated data and code, responds to some of the issues highlighted in [13] by providing all the components necessary for the exact replication of the procedures, along with the experiments presented. Figure 4: Method 3: Partial removal of ICs using artifact-free unmixing matrix. (**a**) HEOG and VEOG signals, together with artifact detection results expressed in the MSF; (**b**) original EEG data (for channels Fp1, near the eyes, and Fz); and (**c**) data obtained after artifact removal with the proposed method.