esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Metal Analysis by ICP-MS and CVAAS  
In order to evaluate the concentrations of arsenic, cadmium, chromium, copper, manganese, nickel, lead, and zinc from surface waters, it used the inductively coupled plasma mass spectrometry (ICP-MS) technique, with the aid of a PerkinElmer Elm DRC-e II ICP-MS instrument (PerkinElmer LAS (UK) Ltd., Seer Green, UK).  
For mercury analysis, it employed the cold vapor atomic absorption spectrometry (CVAAS) procedure, a physical method based on the absorption of radiation at 253.7 nm by the mercury vapor. The Flow Injection Mercury System (FIMS) FIMS 400 Perkin Elmer is a stand-alone mercury analyzer that contains a light source and detector specific for mercury, where organic mercury compounds are oxidized, and the mercury is reduced to the elemental state and aerated from solution in a closed system [40].  
Both instruments were calibrated using the external calibration technique, in which the concentrations for the measured sample set were extrapolated using linear regressions made from raw counts per second data (ICP-MS) and absorbances (peak height) (FIMS) [41]. The experimental conditions, including instrumental selection, were set to validate and regulate the quality of data, accuracy, and stability of calibration.  
The calibration curves were drawn up using the ICP multi-element standard VI, Merck, Lot HC15474231 Certified Reference Material. The recovery values ranged from 95% (Cd) to 99% (Pb). For Hg, the calibration curve was drawn using the Mercury (Hg) Pure Plus Standard (10 \(\upmu\)g/mL Mercury in 5% HNO\({}_{3}\)) of Perkin Elmer Single Element Calibration Standard for AA and ICP-OES, the recovery degree being 96%. The linear regression coefficient (R\({}^{2}\)) had the following values: As (0.9985), Cd (0.9995), Cr (0.9962), Cu (0.9994), Mn (0.9982), Ni (0.9975), Pb (0.9987), Zn (0.9979), Hg (0.9969).
## 3 Results and Discussion
### Photocatalytic Studies
#### 3.7.2 ZnO NPs Structure and Reflux Time Effects  
As previously stated, the calculated average size for the prepared ZnO crystal is in the 50-55 nm range. The goal of preparing ZnO in the nanoscale range was motivated by the fact that the efficiency of photodegradation can be enhanced by advancements in ZnO structure. Because of its unique architectures and capabilities, ZnO nanostructure has sparked a lot of interest in photocatalytic research. Nanostructured ZnO has a nanoscale size and a larger surface area. The physicochemical qualities of a material with a high  
Figure 7: Effect of catalyst dosage on % COD reduction by ZnO NPs (\(n=3\)).  
surface-to-volume ratio are better. Nano ZnO has a stronger surface effect and quantum effect, which means that it has a larger specific surface area and so has a higher photodegradation efficiency. The solution was eventually kept under sunshine with constant stirring to optimize photodegradation efficiency for the optimum dose, and aliquots of samples were collected at intervals of 1, 2, 3, 4, 5, and 6 h. As shown in Figure 8, the deterioration of the paper mill effluent increased with reflux time and peaked at 5 h. This may be due to prevent more of the light penetrating power, when the ZnO crystal complete interaction is enhanced, which saturated the rate of photodegradation. Furthermore, this behavior is sometimes explained as the reach by degradation to some complicated organic compounds which are more difficult to be oxidized by photocatalysis especially in the case of industrial wastes.  
[PERSON] et al. [31] reported that the photocatalytic activity, the photodegradation of Congo red and methyl orange was maximum at 80 min of irradiation. [PERSON] et al. [32] found that the photodegradation efficiency increased as the photocatalytic process is exposed to more light for longer periods of time.
## Results and Discussion
### XRD Patterns  
XRD patterns of GO, nano-TiO\({}_{2}\) and nano-PDA@GO-TiO\({}_{2}\) composites are shown in Figure 4. The specific XRD of TiO\({}_{2}\) is characterized by five peaks that are positioned at the 20 values of 25.31\({}^{\circ}\), 38.01\({}^{\circ}\), 48.02\({}^{\circ}\), 54.45\({}^{\circ}\), and 62.59\({}^{\circ}\), which correspond to the(101),(112),(200),(105), and(204) lattice Plane of anatase phase of TiO\({}_{2}\) (JCPDS card no. 21-1272), respectively [35]. The GO shows a broad peak at 20 = 12.32\({}^{\circ}\), which corresponds to the characteristic diffraction peak of GO [36]. In addition, Aweak derivative peak appeared around 26\({}^{\circ}\), which should be attributed to the crystallization peak of residual graphite oxide [37]. However, after modification with dopamine, A new broad diffraction peak at 23.5\({}^{\circ}\) can be observed for the resultant nano-PDA@GO-TiO\({}_{2}\) composites, which was due to the stacking of the benzene rings of PDA molecules, demonstrates that the DA has successfully self-polymerized on the GO [38]. The reduction of GO by DA modified is also established by the appearance of broad peak observed in the region (22\({}^{\circ}\)-38\({}^{\circ}\)) and the disappearance of the peak at 12.32\({}^{\circ}\) in nano-PDA@GO-TiO\({}_{2}\) composites of XRD [34; 39]. At the same time, the spectrum of XRD for nano-PDA@GO-TiO\({}_{2}\) composites was similar to nano-TiO\({}_{2}\). This indicates that nano-TiO\({}_{2}\) is successfully loaded on the surface of GO to form nano-PDA@GO-TiO\({}_{2}\) composites.  
Figure 3: Fourier transform infrared spectrum of nano-PDA@GO-TiO\({}_{2}\) composites, graphene oxide (GO), and nano-TiO\({}_{2}\).
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 8 Privacy-Preserving Patient Monitoring
### Anomaly Detection with Privacy Focus  
As the narrative unfolds, the significance of anomaly detection in ensuring the prompt identification of potential health issues takes center stage. Leveraging differential privacy as a tool for anomaly detection introduces a layer of sophistication, emphasizing the delicate balance between controlled noise and accurate anomaly detection. The challenges with maintaining accuracy and utility while ensuring privacy are explored, connecting seamlessly with Differential Privacy-Aware ML [50].
## 3 Numerical Simulation  
### Numerical Simulation of Chaotic Signals  
To illustrate the noise reduction effect of the multiscale local projection algorithm, the Lorenz signal is used for simulation analysis. The equation is as follows:  
\[\left(\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{z}\end{array}\right)=\left(\begin{array}{c}\sigma(y-x)\\ -y+rx-xz\\ -bz+xy\end{array}\right), \tag{14}\]  
where \(\sigma=10\), \(r=28\), \(b=8/3\), and the Lorenz system exhibits chaotic characteristics. The \(x\) component of the Lorenz system  
To explain the advantages of multiscale local projection algorithm more intuitively, phase diagrams are adopted to show its effectiveness. Figure 10a is a phase diagram of the original signal, Figure 10b is a phase diagram after adding noise, Figure 10c is a phase diagram after noise reduction by the standard local projection algorithm, and Figure 10d is a phase diagram after noise reduction by multiscale local projection. It can be concluded that multiscale local projection can preserve the geometry of the attractor maximally in the case of filtering noise, and has a better denoising effect than standard local projection.  
In addition, to illustrate the denoising effect of the multiscale local projection algorithm, the following two evaluation indicators are used.  
1. Mean Squared Error (MSE) \[MSE=\frac{1}{N}\sum_{i=0}^{N-1}\left[\vec{x}(i)-x(i)\right]^{2}\] (15)
2. Cross Correlation (CC) \[CC=\frac{\langle x,\vec{x}\rangle}{\|x\|\cdot\|\vec{x}\|}\] (16)  
Figure 10: (**a**) Phase diagram of [PERSON] without noise. (**b**) Phase diagram of [PERSON] with noise. (**c**) Phase diagram of [PERSON] after noise reduction by the standard local projection algorithm. (**d**) Phase diagram of [PERSON] after noise reduction by multiscale local projection. (\(\tau=1\))  
Figure 9: Local enlargement of time domain plot after noise reduction.  
where \(x\) represents the original clean signal and \(\widetilde{x}\) represents the signal after noise reduction. A smaller MSE value means smaller deviation between the original signal and the denoised signal, and a bigger CC value means higher similarity between them. The evaluation results obtained by the above two algorithms are shown in Table 1. It can be concluded that the MSE value of the denoised signal obtained by multiscale local projection noise reduction is smaller, while the CC value is larger. It demonstrates that multiscale local projection has a better denoising effect than standard local projection.
## 5 Experimental Signal Analysis
### Planet Gear Fault--Experimental Signal Analysis  
The effect of planet gear wear on the gearbox is determined using the theoretical derivation. Figures 23-25 show the time domain waveform and the frequency domain spectra of the frequency domain for the health and wear signals for different input speeds and output loads. In all the cases, the frequency component of \(f_{m}\) is dominant, so the frequency for planetary gear is observed in the sidebands in the enlarged Fourier spectrum for the health signal.  
Compared to the health signal for 1200 rpm and loads of 32 N-m, 56 N-m and 80 N-m, the signal for the planet gear wear has different frequency domain spectra, as shown in Figure 23. The spectral peaks occur at the same frequencies of \(f_{m}\pm kf_{p}\) and \(f_{m}\pm kf_{p}\pm f_{H}\) (where \(k\in Z\)) as those for the health signal. The meshing frequency (\(f_{m}\)) is 216 Hz, the distributed damage frequency for the planet gear (\(f_{p}\)) is 4.6 Hz and the planet carrier (\(f_{H}\)) rotational frequency is 2 Hz.  
Figure 23 shows the enlarged Fourier spectrum for the wear signal. The sidebands at frequencies of \(f_{m}-2f_{p}-f_{H}\), \(f_{m}-2f_{p}\), \(f_{m}-2f_{p}+f_{H}\) and \(f_{m}-f_{p}\) have much larger amplitudes than those for the health signal, so the gearbox is faulty at the planet gear.  
For the vibration signal for a speed of less than 2100 rpm and loads of 32 N-m, 56 N-m and 80 N-m, the meshing frequency (\(f_{m}\)) is 378 Hz, the distributed damage frequency for planet gear (\(f_{p}\)) is 8 Hz and the rotating frequency for the planet carrier (\(f_{H}\)) is 3.5 Hz. The sidebands at frequencies of \(f_{m}+2f_{p}+f_{H}\), \(f_{m}+f_{p}+f_{H}\) and \(f_{m}+2f_{p}-f_{H}\) have larger amplitudes than those for the health signal, as shown in Figure 24.  
For a vibration signal for a speed of less than 3000 rpm and loads of 32 N-m, 56 N-m and 80 N-m, the meshing frequency (\(f_{m}\)) is 540 Hz, the distributed damage frequency for the planet gear (\(f_{p}\)) is 11.5 Hz and the rotating frequency for the planet carrier (\(f_{H}\)) is 5 Hz. The sidebands at frequencies of \(f_{m}+2f_{p}+f_{H}\) and \(f_{m}+f_{p}+f_{H}\) have much larger amplitudes than those for the health signal, as shown in Figure 25.  
Figure 23: Waveform for a broken planet gear, Fourier spectrum and enlarged Fourier spectrum for an input speed of 1200 rpm and a load of: **(a)** 32 N-m, **(b)** 56 N-m and **(c)** 80 N-m.  
Figure 24: Waveform for a broken planet gear, Fourier spectrum and enlarged Fourier spectrum at an input speed of 2100 rpm and a load of: **(a)** 32 N-m, **(b)** 56 N-m and **(c)** 80 N-m.
## 4 Experimental Results
### ReSTiNet Performance Comparison with Other Lightweight Methods  
ReSTiNet is compared with the other lightweight state-of the-art networks, such as MobileNet, SqueezeNet, and Tiny-YOLO in order to analyze the proposed model's further improvement. The \"Pascal VOC\" customized dataset is used to train the MobileNet and SqueezeNet models. The training operation is performed on the Tesla k80, which operates in similar experimental settings as ReSTiNet.  
The comparative findings of the four models are summarized in Table 5. As shown in Table 5, ReSTiNet outperforms Tiny-YOLO and MobileNet in terms of model size and achieves higher mAP compared with all three models. The model size of SqueezeNet is very impressive, while resulting in very low mAP. The proposed model is 10.7 MB, which is larger than SqueezeNet yet smaller than MobileNet and TinyYOLO. Compared with Tiny-YOLO, ReSTiNet reduces the model size of 82.31%, which is suitable for portable devices. ReSTiNet shows 51.09%, 35.38%, and 53.67% improvements in terms of mAP on Tiny-YOLO, MobileNet, and SqueezeNet, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tiny-YOLO** & **ReSTiNet** & **Dataset** \\ \hline Avg. test time & 74.486 (s) & 37.514 (s) & INRIA \\ \hline Model parameters & 11.043 (m) & 2.109 (m) & - \\ \hline FLOPs & 11.552 (bn) & 7.570 (bn) & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection time, parameter, and FLOP comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tiny-YOLO** & & **ReSTiNet** \\ \hline fire module & & ✓ & ✓ \\ residual learning & & & ✓ \\ dropout & & ✓ & ✓ \\ \hline MS COCO mAP(\%) & 19.0 & 24.37 & 27.31 \\ Pascal VOC mAP(\%) & 42.21 & 55.67 & 63.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments: Tiny-YOLO vs. ReSTiNet.
# Balloon: An Elastic Data Management Strategy for Interlaced Magnetic Recording
## 3 Motivation  
Compared with existing studies, our focus is on enhancing the write performance of IMR disks with high space utilization. To achieve this goal, the proposed strategy utilizes disk resources as much as possible to reduce the frequency of track rewrites and improve the efficiency of the cooperation between data placement and data shuffling.  
In real-world workloads, new write requests and update write requests alternate on disk, meaning that free tracks are not immediately exhausted. To better illustrate this phenomenon, we analyzed the block-level traces obtained from Microsoft Research (MSR) Cambridge [17; 18] and selected four traces, namely prn_0, src2_2, rsrch_0, and stg_0. These traces are write-intensive workloads, with the key difference being that the first two are dominated by spatial locality, while the latter two are dominated by temporal locality. In addition, since all existing IMR studies use a track-level data placement method (each logical track is mapped to a unique physical track, and incoming data are placed at the appropriate location on the physical track according to its offset within the logical track), we used the typical three-phase write management method as the baseline policy for data placement. Detailed information about the workload and experimental platform characterization can be found in Section 5.1. As shown in Figure 3, the temporal-locality-dominated workloads, rsrch_0 and stg_0, trigger top-track allocation when write progress reaches 6% and 9%, respectively. In contrast, the spatial-locality-dominated workloads prn_0 and src2_2 have slower track consumption rates, with top track allocation triggered only when write progress is 66% and 32%, respectively. Although rsrch_0 and stg_0 initiate top-track allocation early in the workload, the rate of free track allocation gradually slows as write progress increases. In summary, regardless of the rate of free track consumption at each stage of the workload, there are always free tracks available on the disk until the write progress reaches 100%.  
Inspired by the above observation, if free top tracks can be used to accommodate write interference data, then track rewrites can be efficiently suppressed. Therefore, the key to reducing rewrite overhead is to find a rational approach to using the free top track as a buffer track. However, this is not an easy task, and the challenge lies in the following aspects: (1) how to select the right candidate to act as a buffer track without accelerating the consumption of free tracks; (2) how to use buffer tracks to facilitate low-cost data shuffling; and (3) how to handle track rewrites when there are no reclaimable top tracks to act as buffer tracks.
## 2 Conventional Digital Image Correlation (DIC) Applications
### Bending and Buckling Analyses of Composite Conical Shells for Helicopter Tail-Boom Applications
#### 2.2.3 DIC Measurements  
The contour plot of the axial strain obtained by the camera pair, looking down at the top surface of the composite cone, just prior to and after buckling is shown in Figure 8a,b, respectively. As can be seen, prior to bucking, while the whole top surface of the cone was under compression, the strain concentration occurred near the small end of the composite cone with the maximum value of 7100 microstrains. This nonuniform strain distribution can be expected because the small end had less stiffness in comparison with the large end of the thermoplastic composite cone. Moreover, from Figure 8a, which shows the strain distribution prior to buckling, one can forecast the possible location of the failure based on the strain accumulation near the small end.  
Figure 8: Axial strain (\(\epsilon_{xx}\)) obtained by DIC before (**a**) and after (**b**) failure (top view) [15].  
Figure 7: Bending test setup with a composite cone installed for the test [15].  
The contour plot of the axial strain obtained by the camera pair, looking to the front side of the composite cone, just prior to the buckling is shown in Figure 9. The purple color shows negative strain (compression) at the top, and the red color represents positive strain (tension) at the bottom of the composite cone. Once more, an axial strain concentration can be seen near the small end (left side of Figure 9) where the failure occurred [15].
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 5 Discussion  
In this study, we introduce a new hand gesture command set and develop a vision-based HGR algorithm for the control of drones using single-handed gestures in FPV flights. Our approach aims to address the limitations of previous methods [8; 22; 23; 24], which either utilized a limited number of commands, restricting the drone flight dynamics, or required external devices and costly vision sensors. One of the key strengths of our method is its comprehensive command vocabulary, derived from categorizing drone movements into four elements, throttle, roll, pitch, and yaw, with each having three distinct states--increase, decrease, and neutral. This results in 81 possible commands, which we map to intuitive hand gestures based on user-friendly designs informed by a comprehensive user study [23]. Our lightweight HGR algorithm, leveraging an RGB camera, demonstrates accurate performance in classifying these gestures, making it suitable for deployment on edge devices. However, we observed a performance degradation in controlling the throttle for downward motions. This issue stemmed from the less intuitive design of the downward throttle gesture and noisy pose estimation caused by occlusions in the 2D RGB camera view. These findings highlight the need for further refinement in the design of certain gestures to enhance the intuitiveness and accuracy. Additionally, addressing hardware constraints, such as improving pose estimation under occluded conditions, is crucial for the practical deployment of our system. Future work will focus on exploring alternative gesture designs and more robust pose estimation techniques to mitigate these issues. We will also conduct practical manipulation tests in real-world scenarios to demonstrate the efficacy and robustness of our proposed HGR algorithm. These efforts will contribute to more dynamic and user-friendly drone control systems, advancing the field of human-drone interaction.
## 2 Materials and Methods
### Application of FR Model in SSURGO Database  
The wider application of our approach to estimate loblolly pine productivity requires readily available SI values. The SSURGO dataset has detailed data on all soils mapped in the United States including SI values for loblolly pine on the majority of soil series in the southeastern US. We tested the potential to predict loblolly pine productivity across broader regions in the southeastern US using SI derived for individual soil series for three counties in the southeastern US using SSURGO data. SSURGO has SI values of loblolly pine at base age 50 years which were converted to SI base age 25 using a base age invariant SI model [41]. To illustrate the utility of this approach, Kemper County, Mississippi was selected and loblolly pine productivity was estimated spatially in that county with the 3-PG model using the FR values derived from the SSURGO dataset.  
\begin{table}
\begin{tabular}{l l l r r} \hline \hline  & **Parameters** & **Meaning** & **Unit** & **Value** \\ \hline
32 & fN0 & Value of fN when FR is zero & & 0.50 \\
33 & Tmin & Minimum temperature for growth & \({}^{\circ}\)C & 4 \\
34 & Topt & Optimum temperature for growth & \({}^{\circ}\)C & 25 \\
35 & Tmax & Maximum temperature for growth & \({}^{\circ}\)C & 38 \\
36 & kF & Number of days production lost for each frost day & & 1 \\
37 & MaxAge & Maximum stand age used to compute relative age & & 40 \\
38 & nAge & Power of relative age in age modifier & & 3 \\
39 & rAge & relative age to make age modifier 0.5 & & 0.20 \\
40 & y & NPP to GPP ratio & & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Location** & **Lat. / Long.** & \begin{tabular}{l} **Planting Density** \\ **(Counts, State)** \\ \end{tabular} & **Ag** & **FR** & \begin{tabular}{l} **Avg max T** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} & **Precipitation** & **Soil Series** \\ \hline Lancaster, South Carolina & 34.55/-80.63 & 1097 & 12-16 & 0.34 & 36.13 & 1157.60 & \begin{tabular}{l} Applying \\ \end{tabular} \\ Covington, Alabama & 31.20/-86.25 & 1147 & 11-19 & 0.38 & 35.51 & 1547.86 & \begin{tabular}{l} Florida \\ \end{tabular} \\ Kemper, Mississippi & 32.40/-81.44 & 865 & 11-19 & 0.51 & 36.76 & 1429.97 & \begin{tabular}{l} Wilcox \\ \end{tabular} \\ Effingham, Georgia & 36.21/-76.94 & 1509 & 10-20 & 0.35 & 36.28 & 1208.37 & \begin{tabular}{l} Leefield \\ \end{tabular} \\ Bertie, North Carolina & 32.75/-88.45 & 1442 & 10-20 & 0.34 & 33.88 & 1280.63 & \
\begin{tabular}{l} Norfolk \\ \end{tabular} \\ Howard, Arkansas & 34.03/-94.02 & 1000 & 10-16 & 0.33 & 38.20 & 1375.83 &
\begin{tabular}{l} Sacul \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Location (County, State), latitude / longitude, planting density, range of age, FR, average maximum temperature, average annual precipitation, and soil series of the sites used for independent validation of FR derived from site index.