esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Photoresponse
The photoresponse saturation emerges at V\({}_{\rm{G}}\) of 20 V. A high responsivity of 1.28 \(\times\) 10\({}^{5}\) A\(\cdot\)W\({}^{-1}\) is shown at an illumination power of 1 nW, V\({}_{\rm{G}}\) of 20 V and V\({}_{\rm{DS}}\) of 5 V. As shown in Figure 5b, a high specific detectivity of \(\sim\)7.17 \(\times\) 10\({}^{13}\) Jones is observed at an illumination power of 1 nW, V\({}_{\rm{G}}\) of \(-\)5.5 V and V\({}_{\rm{DS}}\) of 5 V, which are higher than the values reported in previous studies [1; 2; 5; 6; 8; 28]. As shown in Figure 5c,d, the photocurrent (\(I_{PH}\) = \(I_{light}\) - \(I_{dark}\)) and the logarithm of photoresponsivity of the device have a sublinear dependence on the logarithm of incident laser power from 1 nW to 3.28 uW, for V\({}_{\rm{G}}\) of 20 V, V\({}_{\rm{DS}}\) of 5 V. The photocurrent increases with laser power intensity, while photoresponsivity decreases with laser power intensity, which is similar to other 2D-based photodetectors [1; 6; 28; 35]. In our work, the responsivity and specific detectivity of the device are much higher than those of conventional out-of-plane WSe\({}_{2}\)/MoS\({}_{2}\) phototransistors [6], out-of-plane MoS\({}_{2}\) p-n homojunctions [2], monolayer MoS\({}_{2}\) phototransistors [28], multilayer MoS\({}_{2}\) phototransistors [8] and laser-induced p-type doped WSe\({}_{2}\) phototransistors [5] (Table 1). The responsivity and specific detectivity are approximately two orders higher than the out-of-plane WSe\({}_{2}\)/MoS\({}_{2}\) phototransistor [6], demonstrating laser treatment attributes to enhancing photoproperties. The out-of-plane MoS\({}_{2}\) p-n homojunction [2] has shown high photo responsivity and specific detectivity because they utilize a global illumination laser with a spot size of 2 mm, while we use a Raman laser spot (size of 1.04 um) of Gaussian beam (NA = 0.5). Though they have approximately ten orders lower incident power intensity of 73 pW\(\cdot\)cm\({}^{-2}\) compared to ours of 1.13 \(\times\) 10\({}^{11}\) pW\(\cdot\)cm\({}^{-2}\), we have higher responsivity, so we have better performance.
## 3 Related Work
This article extends our previous approach, solving a different BSP variant to determine optimal values for offset and headways, maintaining the number of trips of the real timetable, thus not impacting the provided quality of service. Regarding the related literature, the approach proposed in this article contributes by considering the extended transfer zones model accounting for transfers between different (geographically separated) bus stops of different lines; by considering the explicit demand of transfer trips for each pair of bus stops that defines a transfer zone to better focus on the quality of service offered to passengers; and by simultaneously adjusting offsets and headways for each line.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 4 Biomedical Applications
### Magnetic Resonance Imaging (MRI)  
Magnetic resonance imaging, or MRI, is a noninvasive medical imaging test that produces detailed images of almost every internal structure in the human body, including organs, bones, muscles, and blood vessels. In this regard MNPs were investigated as negative contrast agents suitable for tumor detection by means of MRI.  
For example, iron oxide nanoparticles conjugated with various antibodies were used to image glioma [161; 162], glioblastoma [163], breast [164], pancreatic [165], colorectal [166], prostate [167], and other cancers [168].  
Multimodal cancer imaging involving MRI, computed tomography, positron emission tomography, single photon emission tomography, and near infrared fluorescence can be assessed by using MNPs [169].  
Fluorescent-labelled MNPs offer versatile and powerful tools for visualizing biological structures and processes at molecular and cellular levels. Bioimaging using MNPs has shown promise in different preclinical studies for diagnosing diseases, monitoring disease progression, and evaluating treatment efficacy [170].  
It is worth mentioning that SPIONs have already been used for human sentinel lymph node MRI to evaluate intermediate and high-risk prostate cancer [171; 172]. An innovative method based on bimodal imaging using a new magnetic fluorescent hybrid tracer presented promising results in SLN detection as shown in the ongoing trial DRKS00032808 [173].  
In addition, functionalizing MNPs with various radioisotopes has garnered interest for biomedical imaging, drug delivery, and targeted therapy. MNPs functionalized with radioisotopes serve as dual-modal imaging probes, combining the magnetic properties of MNPs with the radioactivity of the attached isotopes. This enables sensitive and quantitative imaging of biological processes, such as tumor targeting, inflammation detection, and organ-specific imaging [174]. Moreover, the addition of a targeting ligand (e.g., antibody or peptide) represents a promising strategy for targeted therapy and theranostic applications, especially in cancer [175].  
Even if most of contrast agents approved by Food and Drug Administration are based on gadolinium, some MNPs are undergoing preclinical and clinical evaluation for safety and efficacy, with the goal of obtaining regulatory approval for clinical use. Plenty of commercial MNPs, used as agents involved in MRI applications, are available in the worldwide virtual market. However, a large portion of this category is still in the patenting stage. If this process will be successful, the range of MNPs employed in this field will increase considerably, since the use of MNPs has the potential to eliminate many of the current limitations (signal intensity, background signal, biocompatibility and toxicity, spatial resolution, biodistribution and clearance, magnetic field strength dependence, challenges in quantification) [176; 177].  
Table 1 contains other valuable examples of MNPs used as MRI agents and not only for cancer detection.
## 4 The Calculation of Reliability Based on Fuzzy Mathematics
### Fuzzy Seismic Spectra
#### 4.3.1 Fuzziness of Seismic Intensity  
Owing to historical reasons, earthquake academics and engineers still use discrete settings of seismic intensity, such as the 12 seismic intensity grades shown in Equation (24).  
\[U=\{I_{1},I_{2},...,I_{12}\}=\{1,2,...,12\}, \tag{24}\]  
where \(I_{i}\) and the number represent the grade of seismic intensity.  
Due to the variation of damage caused by earthquakes, seismic intensity, as a comprehensive measure of earthquake intensity, should be continuous and not divided into different grades. Thus, if degree 12 is still the highest intensity, the seismic intensity should be a continuous value on the real axis, and its value range should be a closed interval (Equation (25)), not discrete points.  
\[Z=\{I|I\in[0,12]\}=[0,12] \tag{25}\]  
As discussed above, the fuzziness of seismic intensity is due to the use of a discrete set of seismic intensity indicators to represent the variation in damage caused by earthquakes. Seismic intensity can become a deterministic variable without fuzziness when it is regarded as an earthquake intensity index that changes continuously. In this way, seismic intensity is actually a representation of ground motion parameters. In the seismic response spectrum shown in the Chinese seismic design specifications [21], seismic intensity represents the maximum of horizontal seismic coefficient \(\alpha_{max}\).  
When the discrete seismic intensity is transformed into a continuous variable, the relationship between \(\alpha_{max}\) and seismic intensity is shown as Equation (26):  
\[\alpha_{\max}=0.04\times 2^{(I-6)}. \tag{26}\]
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 4 Results
### Analysis  
Accordingly, we compare the five selected ML algorithms based on their classification performances described in Table 3.  
The results suggest that the LDA algorithm performed best for performance evaluation. It achieved the highest capability for anomaly detection concerning its F1-score. It is also important to acknowledge that this algorithm is the simplest to implement without advanced libraries. Although it showed the overall highest F1-score, there is a significant disparity between the precision and the sensitivity. A higher precision score means the model has low false-positive rates, whereas a higher sensitivity score means the model has a low false-negative rates. Table 3 suggests that the precision score of LDA is proportionally better than its sensitivity score, which means that the model might introduce more false negatives compared to false positives. The OC-SVM reached second in anomaly detection. It was slightly behind the LDA in the overall score but offered a better balance of the precision and sensitivity scores.  
Furthermore, this study investigated both linear models in a Raspberry Pi. It was essential to confirm the model's technical viability in this study for future implementation. Table 4 shows the experimental result on the Raspberry Pi.  
The LDA performed faster than OC-SVM in overall execution time, caused by it being a relatively more straightforward algorithm. This would influence the total power consumption in general. Both models result in a nominal size for the Raspberry Pi in terms of memory footprint. The OC-SVM offers a smaller size because it learns only from the original data (unsupervised) instead of finding correlations from the actual labels (supervised).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Execution Time [s] 1 ** & **Model Size [kB]** & **Power Consumption [mWh]** \\ \hline LDA & **3.00 \(\pm\) 0.17** & 40.17 & **4.288** \\ OC-SVM & 9.45 \(\pm\) 0.39 & **15.25** & 9.342 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental results on Raspberry Pi.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Precision 1 ** & **Sensitivity 2 ** & **F1-Score 3 ** \\ \hline LOF & 0.26 \(\pm\) 0.02 & 0.26 \(\pm\) 0.03 & 0.26 \(\pm\) 0.02 \\ CB-LOF & 0.64 \(\pm\) 0.02 & 0.65 \(\pm\) 0.02 & 0.64 \(\pm\) 0.01 \\ kNN & 0.34 \(\pm\) 0.01 & 0.34 \(\pm\) 0.01 & 0.34 \(\pm\) 0.01 \\ LDA & **0.97 \(\pm\) 0.01** & 0.75 \(\pm\) 0.01 & **0.85 \(\pm\) 0.01** \\ OC-SVM & 0.83 \(\pm\) 0.01 & **0.83 \(\pm\) 0.02** & 0.83 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: F-score for each algorithm regarding the anomaly class.
# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 3 Pose Regression Methods  
### System Overview  
Figure 1 illustrates an overview of the proposed method. Our method comprises two parts, A and B. Part A is dedicated to the training step of pose regression, and part B focuses on the avatar animation step. Part B renders avatars in virtual reality following users' motions in the physical world.  
As depicted in Figure 1, part A illustrates the comprehensive view of the training procedure. To prepare the dataset, we collect the positions and orientations of the upper body joints of a user from a diverse range of gestures and movements in every frame. Subsequently, we perform preliminary noise reduction and normalization processing on the dataset. We consider the positions and orientations of the head and two hands as inputs to our pose regression model. Our model is based on a classical regression model, comprising four convolutional layers and two fully connected (FC) layers. The resulting hyper-parameters of our pose regression model are utilized in B to estimate the upper body pose based on the VR user's controllers and head-mounted display (HMD) data.  
Figure 1: Our proposed pose estimation method for an avatar. Part A is network architecture for pose regression and Part B is the avatar animation step.
## 4 Community Detection on Induced Metric Space
### Computational Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Name** & **Spectral** & **SDP** & **GT** & **Metric** \\ \hline Facebook & 0.0097(5) & 0.1074(3) & 0.1044(7) & 0.1082 \\ Gplus & 0.0119(5) & 0.1593(3) & 0.1544(7) & 0.1602 \\ Twitter & 0.0035(5) & 0.0480(3) & 0.0465(7) & 0.0483 \\ Epinions1 & 0.0087(5) & 0.1247(6) & 0.1208(7) & 0.1254 \\ LiveJournal1 & 0.0039(5) & 0.0703(6) & 0.0680(7) & 0.0706 \\ Pokec & 0.0009(4) & 0.0174(3) & 0.0168(7) & 0.0175 \\ Slashdot0811 & 0.0005(5) & 0.0097(6) & 0.0094(7) & 0.0098 \\ Slashdot0922 & 0.0007(4) & 0.0138(3) & 0.0133(5) & 0.0138 \\ Friendster & 0.0012(5) & 0.0273(1) & 0.0263(7) & 0.0273 \\ Orkut & 0.0016(5) & 0.0411(3) & 0.0397(7) & 0.0412 \\ Youtube & 0.0031(5) & 0.0869(3) & 0.0838(7) & 0.0871 \\ DBLP & 0.0007(4) & 0.0210(3) & 0.0203(7) & 0.0211 \\ Arxiv-AstroPh & 0.0024(5) & 0.0929(6) & 0.0895(7) & 0.0931 \\ web-Stanford & 0.0007(5) & 0.0320(1) & 0.0308(7) & 0.0320 \\ Amazon0601 & 0.0018(5) & 0.0899(6) & 0.0865(7) & 0.0900 \\ P2P-Gnutella31 & 0.0009(5) & 0.0522(6) & 0.0503(7) & 0.0523 \\ RoadNet-CA & 0.0024(5) & 0.1502(3) & 0.1445(7) & 0.1504 \\ Wiki-Vote & 0.0026(5) & 0.1853(6) & 0.1783(7) & 0.1855 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of our approaches with other best methods in terms of conductance; the numbers inside the brackets denote the algorithm of the group.
## 2 Design  
The cesium oven is a very crucial component in time-keeping cesium beam frequency standards, and its design has a direct impact on the performance of this frequency standard. The main components of the cesium oven include the cesium reservoir, the jet tube, the sealing valve, the resistance heater, and the heating control system. The basic structure of the cesium oven is shown in Figure 1. Its volume is approximately 18.4 cm\({}^{3}\). The upper \"flat\" cylindrical material is solid oxygen-free copper, with temperature control sensors and heating tubes placed in the reserved holes within it; the lower \"thin\" cylinder is a hollow stainless steel, with the cesium bulb placed inside, and the thin wire at the bottom is the ionization electrode.  
To ensure the stability of the number of cesium atoms, it is necessary to set the upper temperature limit to approximately 140 \({}^{\circ}\)C and the lower temperature limit to around 20 \({}^{\circ}\)C, which is room temperature. At the same time, considering the nominal resistance value of the bridge circuit used for temperature measurement, the temperature control range is set from 20.77 \({}^{\circ}\)C to 141.16 \({}^{\circ}\)C, the temperature control accuracy is set to \(\pm\)0.005 \({}^{\circ}\)C, which means the temperature change does not exceed 10 mK, and the temperature resolution must be set to above 0.5 mK.  
Due to the complex shape and large volume of the controlled object, digital temperature control is adopted to avoid the drawbacks of analog temperature control, which is prone to oscillation during long integration times. The system structure diagram is shown in Figure 1.  
The output voltage of the bridge reflects the real-time changes in temperature. This voltage, after being filtered and amplified by the signal conditioning circuit, is converted into a digital quantity by an analog-to-digital converter. The controller then performs PID calculations based on these data, and the result is converted into a voltage signal. This signal, after being amplified by a power amplifier, controls the cesium oven temperature in real-time through the heating element.  
Due to the large temperature control range and high temperature resolution, a 24-bit ADC (analog-to-digital converter) is used. The ADC chip selected is ADS1232, which has a PGA (programmable gain amplifier) that can amplify the bridge signal internally, reducing the complexity of the signal conditioning circuit. The controller chosen is from the STM32L series. The DAC (digital-to-analog converter) chip selected is DAC8531, which has a 16-bit conversion precision.  
Additionally, since the target temperature range is above room temperature and only heating is required, the entire system can be powered by a single power supply. At the same time, because the resistance of the heating element is relatively high, to ensure the control current, the system uses a +24 V power supply and converts it to the +12 V and +3.3 V required by the chips.
## 3 Results  
To examine the effects of helices, a 0.036-inch quad-helix and W-arch were activated in parallel by 2 \(\times\) 2 mm (Figure 4). The forces required for activation were 2.4 N (quad-helix) and 4.0 N (W-arch). This means that the stiffness of the W-arch decreased by 60% due to the helix. A similar decrease in stiffness was obtained by reducing the wire diameter of the W-arch from 0.036 to 0.032 inches. When the 0.032-inch W-arch was activated, the force required was almost the same as that of the quad-helix (Figure 4). The maximum equivalent stress on the 0.032-inch W-arch (614 MPa) became higher than that on the quad-helix (437 MPa).  
Figure 4: Quad-helix made of 0.036-inch wire has same stiffness as W-arch made of 0.032-inch wire.  
Figure 3: Procedure for simulating orthodontic tooth movement.  
By iterating the calculation up to \(N=500\), the arch was expanded. In each iterative calculation, 5 to 20 iterations of the Newton-Raphson method were required for convergence. As a result, including these iterations, the total number of iterations was approximately 2500 up to \(N=500\).  
The movement patterns of the teeth were the same in all cases: the molar moved bodily, while the canine and premolars tipped (Figure 5A). In response to these movement patterns, compressive strain (\(-\)) occurred on the buccal side of the molar root and near the cervical region, while tensile strain (\(+\)) occurred near the apex on the buccal side of the canine and premolars. Additionally, the molar slightly tipped distally, which occurred because the mesial end of the arm slid along the lingual surface of the canine (Figure 5B).  
In the case of parallel expansion, the buccal tipping angle of the molar crown increased and then decreased to zero. This change suggested the molars initially tipped, and then became upright and moved bodily (Figure 6). Changes in buccal movement and the tipping angle of the molar were nearly identical between the quad-helix and W-arch.  
Figure 5: Movement pattern of maxillary teeth produced with W-arch in parallel expansion. (Incisors were not included in FEM model, but are illustrated to indicate their positions in sagittal view).  
Figure 6: Change in buccal movement and tipping angle of molar with \(N\) in parallel expansion.  
Using both the W-arch and the quad-helix, the dental arch was expanded with parallel and fan-shaped expansions (Figure 7). The shapes of the expanders returned to their initial shapes, and the forces acting on the teeth became very small. In the fan-shaped expansion, no force acted on the canine at \(N=500\), because the arm of the expander was not in contact with the crown.  
At \(N=500\), the tipping angles of the teeth and the deformed shape of the expander were almost identical between the quad-helix and W-arch (Figure 8 and Table 1).  
Figure 8: Comparison of expanding shapes of arms at \(N=500\) between W-arch and quad-helix.  
Figure 7: Expanding patterns of maxillary arch produced with W-arch and quad-helix in parallel and fan-shaped expansions.
## 3 Results and Discussion
### Fatigue Testing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Alternating**} & \multicolumn{4}{c}{**Fatigue Life (No. of Cycles)**} \\ \cline{3-8}
**Sr. No.** & **Load (N)** & \(\sigma\)=\(\frac{509.55\,\text{Q}}{D^{3}}\) (N mm\({}^{-2}\)) &
\begin{tabular}{c} **Stress** \\ **Sas\(\sigma\)\(\times\)K\({}_{\text{r}}\)** \\ **=1.28 \(\times\)\(\sigma\)** \\ \end{tabular} & **Trial 1** & **Trial 2** & **Trial 3** & **Average Cycles** \\ \hline
1 & 470 & 239 & 306 & 27,989 & 19,845 & 27,562 & 25,132 \\
2 & 390 & 199 & 254 & 163,382 & 111,389 & 141,479 & 138,750 \\
3 & 335 & 171 & 218 & 112,836 & 426,289 & 492,191 & 343,772 \\
4 & 295 & 150 & 192 & 791,337 & 667,862 & 819,439 & 759,546 \\
5 & 245 & 125 & 160 & 8,963,248 & X & X & 9,654,416 \\
6 & 195 & 99 & 127 & X & X & X & x \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results of base sample AA 2024. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Alternating**} & \multicolumn{4}{c}{**Fatigue Life (No. of Cycles)**} \\ \cline{3-8}
**Sr. No.** & **Load (N)** & \(\sigma\)=\(\frac{509.55\,\text{Q}}{D^{3}}\) (N mm\({}^{-2}\)) &
\begin{tabular}{c} **Stress** \\ **Sas\(\sigma\)\(\times\)K\({}_{\text{r}}\)** \\ **=1.28 \(\times\)\(\sigma\)** \\ \end{tabular} & **Trial 1** & **Trial 2** & **Trial 3** & **Average Cycles** \\ \hline
1 & 470 & 239 & 524 & 174,548 & 203,765 & 193,658 & 190,657 \\
2 & 390 & 199 & 435 & 678,799 & 648,976 & 645,685 & 657,820 \\
3 & 335 & 171 & 374 & 1,310,876 & 1,376,758 & 1,322,658 & 1,336,764 \\
4 & 295 & 150 & 329 & 1,409,597 & 1,419,027 & 1,395,121 & 1,407,915 \\
5 & 245 & 125 & 273 & 1,782,464 & 1,493,565 & 1,497,056 & 1,457,695 \\
6 & 195 & 99 & 217 & 1,946,526 & 1,516,786 & 1,495,766 & 1,653,026 \\
7 & 170 & 87 & 190 & 2,197,645 & 1,864,655 & 2,197,689 & 2,086,663 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results of welded sample with filler AA 4043. Figure 8: Comparison of S-N curves of weld fillers AA 4043 and AA 4047. Figure 7: S-N curves of Welded sample with filler AA 4047. Figure 9 shows that at log (no. of cycles) 5.0 to 6.5, i.e., the area which carries both the welded samples results and the base sample results, correlate with each other, with the welded samples showing better fatigue strength than the base samples. However, the superiority of the welded samples is only scattered in the middle of the horizontal axis, while at the lowest and the highest limits of the number of cycles, base samples have considerable fatigue life, though still less than those of the welded samples, as the welded samples were prepared with homogeneous environment conditions. Porosity and inclusion of slag and holes were avoided by evaluating the weld quality. Hence, welded samples were less affected by the stress concentration factor than those of base metals.
# New Developments and Progress in Absorption Chillers for Solar Cooling Applications
## 3 Newly Developed Commercial Absorption Chillers  
This section discusses the recently developed commercial water/LiBr absorption chillers with unique features for effectively using solar energy for small- to large-scale space cooling applications. These features include heat rejection without cooling towers (i.e., directly and indirectly air-cooled), high efficiency, compactness, use of dual heat sources with effective backup fuel, and a large temperature glide in the driving heat source.