esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Influence of Surface Residual Stress on Corrosion Resistance
### The Influence of Residual Stress on Corrosion Appearance
After 2 or 3 h of corrosion, the surface oxide film is incomplete, and the material at the original surface rough peaks of the specimen is preferentially corroded. The defects caused by pitting corrosion also decrease or disappear, which is shown in Figure 13c,d. Meanwhile, with the increase in surface residual compress stress, the larger residual compress stress can effectively inhibit the corrosion reaction of specimen surface material. In addition, the inhibition effect on corrosion produced by residual compress stress is stronger than the promotion effect on corrosion produced by surface topography, the corrosion rate of surface material decreases, and the surface material removal of the specimens is also relatively uniform, so the surface roughness decreases with the increase in the residual compressive stress on the surface, as finally shown in Figure 13e. However, the specimen with a surface residual stress of \(-120.2\) MPa is not processed with shot peening, there are no apparent defects on the surface topography, and the anode film is not missing or uneven due to not being treated by shot peening. The inhibition effect on corrosion produced by surface compress stress is relatively balanced with the contribution effect on corrosion produced by surface topography. Therefore, there is no significant change in surface roughness after corrosion. By comparing the changes in surface roughness, it can be seen that the specimen with a surface residual stress of \(-120.2\) MPa has better corrosion resistance. It should be noted that the selected area may cause errors in surface roughness measurement results, which may have an impact on experimental results.
## 3 Linear and Non-Linear Predictive Analytics Comparison Framework: L&NL-PAC
### Predictive Methods  
The literature on predictive regression methods is extensive, and to take advantage of the many developments in the field, a careful selection of the potentially effective methods was conducted in this work. The selected methods include linear and non-linear approaches, and cover a wide range of classes of regression methods (see Figure 3). These classes contain different a priori assumptions regarding the distribution of predictors, response variables, and their relationship. Thus, they provide a suitable pool of methods to infer, from the data, which class/method leads to a superior prediction performance. Overall, 16 regression methods were included in this study and compared using L&NL-PAC. This set contains the most popular methods that have found more success in spectroscopic applications, as well as other relevant methods from the general field of regression modeling that have different assumptions regarding the underlying data structure (namely regarding the presence of effect sparsity, collinearity, non-linearity, etc.). Although additional methods could always be considered, the current pool of methodologies provides a comprehensive modeling basis to support the use of hyperspectral imaging for predicting the enological properties of interest. A summary of the regression methods within each class is provided in the following subsections. However, the reader is directed to additional references for a detailed description of the linear- and tree-based ensemble methods adopted [35], as well as for the kernel methods [36; 37; 38].
## 2 Results and Discussion
### TADF Exciplex Emitters  
Emitters investigated in the literature are typically single molecules for which their individual emission is observed during electroluminescence. But, molecules can also dimerize or interact with a second type of molecules in the solid state so that the emission of the single molecule is not detected anymore. In this case, the new emission different from that expected for the isolated molecule is detected, originating from the exciplex emission. This emission is based on the electronic interactions that can exist between the electron donors and electron acceptors in close vicinity in the solid state and exhibiting adapted energy levels. The advantages of this strategy are numerous: (1) the emission color of exciplexes can be finely and easily tuned, the number of combination between electron donor/acceptor and host matrix/guest emitter is unlimited; (2) Typically, an electron donor is opposed to an electron acceptor to produce an exciplex emission. The two partners exhibiting complementary charge transport abilities, the device architecture is thus greatly simplified [98; 99]. Once again, the pioneering works in this field were carried out by [PERSON] in 2012 and the concept was demonstrated with the _m_-MTDATA:3 TPYMB TADF exciplex, where _m_-MTDATA stands for 4,4'/_4\"-tris_[phenyl(_m_-tolyl)amino]triphenylamine and 3 TPYMB for _tris_(2,4,6-trimethyl-3-(pyridin-3-yl)phenyl)]borane [100]. If the EQE of OLEDs that were fabricated with the _m_-MTDATA:3 TPYMB TADF exciplex (EQE = 5.4%) clearly exceeds the theoretical limit that can be calculated from the PLQY of the film (\(\phi\) = 26%), this proof-of-concept was obtained with OLEDs emitting at 550 nm. Since these pioneering works, numerous TADF exciplexes have been reported in the literature [101; 102; 103; 104; 105; 106], and only few examples could be emitted in the deep blue region. In this field, two interesting examples have been reported in 2017 by using the TADF exciplex **30** (BTCz-PCz) and **31** (BTDCb-PCz):TmPyPB in a 99:1 ratio (where TmPyPB stands for 1,3,5-tri[(3-pyridyl)-phen-3-yl]benzene  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Emitter** & \(\Delta\)**EST (eV)** & **Td (\%)** & **PE (Lm/W)** & **EQE (\%)** & **CIE (\(\chi\), y)** & **Reference** \\ \hline
**1** & 0.54 & 90.0 & _n/a_ & 2.9 & _n/a_ & [66] \\
**2** & 0.45 & 140.0 & _n/a_ & 5.6 & _n/a_ & [66] \\
**3** & 0.32 & 270.0 & _n/a_ & 9.9 & (0.15, 0.07) & [66  
(see Figure 9) [107]. This is even the unique example of OLEDs based on deep blue TADF exciplexes. By using **30** or **31** as the host for TmPyPB, an EL emission corresponding to the exciplex emission could be detected at 436 nm with the two hosts, providing OLEDs exhibiting a low turn-on voltage of 3.4 V. Best exciplex OLEDs were obtained with **30**, with a deep blue emission of CIE coordinates (0.16, 0.21). CE, PE, and EQE of 4.64 cd/A, 2.91 lm/W and 2.36% were, respectively, determined.
# On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers
## 4 In-Vehicle Road-Grip Estimation
### On-Board Estimation of Actual and Potential Friction
Starting from the actual friction coefficient quantity, obtained in particular working conditions of the vehicle and therefore of the tire, and from the model parameters already able to properly represent the tire dynamics in run-time, the potential friction is evaluated in the following steps, represented in Figure 4:  
* Once the actual friction coefficient (point 1) has been calculated (17), the equivalent grip for the reference tire-road (point 2) can be evaluated: \[\mu_{x_{i,refRoad}}=\frac{F_{x_{i,refRoad}}}{F_{z_{i}}},\] (18)
* Furthermore, starting from the tire model parameters calibrated on a reference road surface, the model is able to provide a valuable output in terms of the maximum longitudinal force, achievable for the same conditions of vertical load, wheel alignment and vehicle longitudinal speed, at the optimal value \(\lambda^{*}\) of the slip ratio (point 3 in Figure 4): \[\mu^{max}_{x_{i,refRoad}}=\frac{F^{max}_{x_{i,refRoad}}}{F_{z_{i}}},\] (19)
* The potential friction coefficient (point 4) is obtainable, using the proportionality criterion already adopted for the point 2, assuming the linearity of the tire behavior within the working conditions of the vehicle, as follows: \[\widehat{\eta}_{x_{i}}=\frac{\frac{F_{x_{i}}}{F_{z_{i,refRoad}}}}{\frac{F_{x_ {i,refRoad}}}{F_{z_{i}}}}\mu^{max}_{x_{i,refRoad}}.\] (20)  
In Figure 5, the overall architecture of the developed model is shown. In particular, starting from the sensor-acquired input channels (on the left), the kinematic and load estimator calculate the vehicle state up to the kinematics on the wheel hubs. Then the tire model evaluates the state at the tire-road interface, and, using the above information, the actual and potential friction estimator module. Figure 5: Architecture of the vehicle state estimation system.
Although OS can activate CMA that contributes to the efficient removal of oxidized proteins [28], this phenomenon is less important quantitatively than macro- and microautophagy, because only particularly soluble cytosolic proteins and not organelles can be degraded by CMA [29]. In living mammalian cells the main role belongs to macroautophagy, which is able to break down the damaged cell organelles and to provide the integral parts of these for new cell organelles synthesis. In the elderly, the effects of OS on the insulin receptor-signaling pathway have a critical role in macroautophagy's decrease [29]. In NIH3T3 cells (murine fibroblast line) the OS, induced by hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), inhibits the Adenosine 5'-monophosphate-activated protein kinase (AMPK) signaling pathway responsible for autophagocytosis [30]. In chondrocytes AMPK activation leads to activation of the tuberous sclerosis complex, inhibits mTOR activity, and stimulates autophagy that recycles cells nutrients and provides ATP synthesis substrate [31]. In some cells, the senescence phenotype induced by OS may be accompanied by an increase in autophagic structures, but the lysosomal dysfunction impairs autophagic flux and makes it inefficient [32]. On the other hand, excessive OS and excessive autophagy can induce chondrocytes death in articular cartilage [33; 34]. H\({}_{2}\)O\({}_{2}\) induces OS in chondrocytes and mediates their senescence via the p53-p21-pRb pathway, induction of caveolin 1 synthesis, and activation of p38 mitogen-activated protein kinase (MAPK) [35]. Because H\({}_{2}\)O\({}_{2}\) has a stable structure, it was used in many studies to induce OS in cartilage or in chondrocytes' cultures [36]. Therapeutic Ultrasound (US) is an excellent option to avoid adverse events induced by non steroidian anti-inflammatory drugs in OA patients. US is used as a treatment option in OA for more than 50 years for reducing pain and improving functional articular parameters [37]. It has been shown that US stimulates chondrocyte metabolic activity [38], increases chondrocyte proliferation [39] and viability [40], and reduces proinflammatory cytokine synthesis in cartilage [41]. On the other hand, there are few studies on the US effect on OS, with inconsistent results. To date there is no study showing the effects of US on the OS level, chondrocytes ultrastructure, and autophagocytosis process. The aim of this study was to assess the effects of US exposure on OS-induced changes in human chondrocytes (HCH). For that, we exposed HCH cultures to the sequenced action of US and H\({}_{2}\)O\({}_{2}\) and assessed the ultrastructural changes of chondrocytes, and the synthesis of MDA--a marker of oxidative damage, and TNF-\(\alpha\), a very important cytokine for OA development, whose synthesis is stimulated by H\({}_{2}\)O\({}_{2}\)[42].
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 3 Key Technologies
### High-Reliability Data Transmission Design  
The tsunami monitoring system mainly provides real-time data support for tsunami warning, and reliable data transmission methods are key to ensuring the real-time aspect and accuracy of tsunami warning. The real-time transmission path of tsunami monitoring system data is as follows: pressure sensor \(\rightarrow\) underwater data collector \(\rightarrow\) surface satellite communication equipment \(\rightarrow\) remote data center. This article presents a design which combines underwater induction coupled transmission and a redundant satellite communication device on the water surface to ensure the reliability of real-time data transmission.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 3 Energy-Efficient Communication
### Algorithm Design
```
\(i=0\); whiletruedo \(\theta_{i}=\text{read\_input\_sensor}()\); \(\delta_{i}=\theta_{i}-\theta_{i-1}\); \([\Phi,\Gamma]=\text{select\_coefficients}(\theta_{i})\); send\_message\((\theta_{i})\); \([ir_{i},\epsilon\theta_{i}]=\text{receive\_message}()\); \([h_{i+1}]=\text{calculate\_next\_sampling}(\theta_{i},\delta_{i},ir_{i}, \epsilon\theta_{i},\Phi,\Gamma)\); \(\text{sleep\_}(h_{i+1})\); \(i++\);  end while
```  
**Algorithm 1**Sensor node algorithm. ```
Functioncalculate_next_sampling\((\theta,\delta,ir,\epsilon\theta,\Phi,\Gamma)\): \(\sigma=0.0\); \(Q=[0.002\ 0;\ 0\ 1.5]\); \(x=[\theta;\ \delta]\); \(u=[ir;\ \epsilon\theta]\); \(x_{0}=x\); \(P=\text{select\_P\_matrix}(\Phi,Q)\); for\(i=1\)to\(max\_limit\)do \(x_{i+1}=\Phi\ x_{i}+\Gamma\ u\); if\((\ \sigma\ x_{0}^{T}\ P\ x_{0}>x_{i+1}^{T}\ P\ x_{i+1})\)then break;  end if \(t=t+1\);  end if return\(t\);
```  
**Algorithm 2**Next sampling estimation based on error measurements. ```
Functioncalculate_next_sampling\((\theta,\delta,ir,\epsilon\theta,\Phi,\Gamma)\): \(\sigma=0.60\); \(Q=[0.002\ 0;\ 0\ 1.5]\); \(x=[\theta;\ \delta]\); \(u=[ir;\ \epsilon\theta]\); \(x_{0}=x\); \(P=\text{select\_P\_matrix}(\Phi,Q)\); for\(i=1\)to\(max\_limit\)do \(x_{i+1}=\Phi\ x_{i}+\Gamma\ u\); if\((\ \sigma\ x_{0}^{T}\ P\ x_{0}>x_{i+1}^{T}\ P\ x_{i+1})\)then break;  end if \(t=t+1\);  end if return\(t\);
```  
**Algorithm 3**The next sampling estimation based on Lyapunov dynamics.
# The Helical Resonator: A Scheme for Radio Frequency Plasma Generation
## 7 Experimental Results
Figure 14: **Top**: plot of the modulus (**left**) and phase (**right**), normalized to \(\pi\), of the input impedance of resonator 3, both in standard configuration and with the HV probe attached to the open end. The dashed curves are the fits obtained as described in the text. **Bottom**: same as above, but with the addition of a conducting shield around the resonator. The voltage amplification factor was measured for both resonators 2 and 3 by tuning the frequency on the resonance, that is, on the condition of maximum amplification. The output voltage was measured, using the Tektronix P6015A high voltage probe, whereas the input voltage was measured with a standard x10 oscilloscope probe, which was tested and shown not to alter significantly the circuit behavior. The resulting voltage amplification factors, for both resonators 2 and 3, unshielded and shielded, are given in Table 2, where they are compared with the predicted values given by \(1/\alpha H\), with \(\alpha H\) resulting from the fits. It can be seen that a reasonable agreement is achieved, confirming that the modeling described above is adequate to predict the performance of a given resonator. It should be emphasized that, as expected from the fit results, the best performance is given by the shielded resonators. In particular, resonator 3 achieved a voltage amplification of 100, while resonator 2 achieved a value of 80. In this latter case, this resulted in an actual measured \(V_{out}\) of 4.3 kV--more than appropriate to ionize most gases in a wide pressure range. It is to be remarked that the actual output voltage will depend on the input one, which in itself will be determined by the power of the amplifier used as the power source, and by the input impedance.
## 4 Experiments
### Datasets  
**(1) MOT17 Dataset:** It is a multi-person tracking benchmark dataset divided into 14 sequences with highly crowded scenarios, different viewpoints, weather conditions, camera motions, and indoor/outdoor environments. The dataset contains a public training/test split, where the training sequences have ground-truth files and detection files provided by three object detection state-of-the-art methods, while the test sequences just have the detection files. Hence, due to the scope of the performed experiments, and also due to the submission's constraints to obtain results on the test sequences, only the training sequences were used in this study. Since the multi-object tracking methods evaluated in this work do not require a training process, the training sequences were used as evaluation.  
**(2) ISR Tracking Dataset:** It is composed of 10,000 RGB-D raw images acquired by an Intel RealSense D435 sensor onboard a mobile robot platform [43], representing the object conditions under which robotic platforms may navigate. Nine object classes were annotated for multi-object tracking tasks, achieving a total of 32,635 object bounding boxes and 329 object sequences. For evaluation, the ISR Tracking dataset was reorganized into two sub-datasets: ISR500 and ISR200. In the ISR500, the dataset was divided into sequences of 500 frames, which gives a total of 20 image sequences. On the other hand, the ISR200 contains 50 image sequences, which are the result of partitioning the dataset into sequences of 200 images. On both sub-datasets, the train/test image sequence split was performed by interleaving the sequences, i.e., the first sequence was used to train, the second sequence was used to test, the third sequence was used to train, and so on.
## 2 Materials and Methods
### Experimental Procedure  
Proprioception (joint position sense) was assessed as the child's ability to re-identify a passively placed target position of the ipsilateral hip (90\({}^{\circ}\) of flexion) and knee (60\({}^{\circ}\) of flexion) according to the passive-ipsilateral JPR method (Figure 1). For the knee JPR test, children were seated blindfolded on the table with their lower legs hanging relaxed and unsupported (90\({}^{\circ}\) of knee flexion) while not wearing shoes and socks. The upper leg was fully supported by the table (90\({}^{\circ}\) of hip flexion) and hands were crossed on the chest. For the hip JPR test, the resting position was the same, only the children sat on an inclined cushion on the table (without back support) to align the baseline hip joint angle at 70\({}^{\circ}\) of flexion. From the resting position, the examiner moved the child's limb 30\({}^{\circ}\) to knee extension, resulting in a final knee flexion angle of 60\({}^{\circ}\) or 20\({}^{\circ}\) to hip flexion, resulting in a final hip flexion angle of 90\({}^{\circ}\), using an inclinometer distally attached to the moving segment. After experiencing and memorizing this joint position for five seconds, the child's limb was passively returned to the start position. Afterward, the examiner moved the ipsilateral limb back into the same range and the child was asked to re-identify the target joint position as accurately as possible by pressing a button synchronized to motion capture software.  
Depending on the region from which the child was recruited, testing was conducted at the Multidisciplinary Motor Centre Antwerp (M2 OCEAN) (University of Antwerp) or the Gait Real-time Analysis Interactive Lab (GRAIL) (Hasselt University) by the same trained examiner using the same standardized protocol and instructions. This protocol was first tested on seven pilot participants before the actual data collection started.  
For the current study, we focused on the concurrent validity of DeepLabCut markerless tracking with respect to three-dimensional (3D) Vicon optoelectronic motion capture to assess proprioceptive function. This required synchronized measurements of both systems. In the current study, 90 knee JPR test trials of 15 typically developing children and 126 hip JPR test trials of 21 typically developing children were screened for eligibility. Datacollection was conducted between April 2022 and June 2023. Trials were eligible for the current study if the JPR tests were captured on video, if there was accurate tracking of the markers for the 3D movement analysis and if there was visibility of the shoulder, hip, knee and ankle joints of the child in the sagittal video recordings.
## 3 Results
### Comparison of Downscaled SIF and GPP  
Figure 6 illustrates the correlation between three observational scales of SIF (0.05\({}^{\circ}\) SIF, SIF\({}_{\text{ECW}}\), SIF\({}_{\text{ECW}}\)) and the GPP values at the two flux sites. Across both sites, the correlation order between the three observational scales of SIF and GPP was as follows: SIF\({}_{\text{ECW}}\) > SIF\({}_{\text{ECA}}\) > 0.05\({}^{\circ}\) SIF. The correlation between 0.05\({}^{\circ}\) SIF and GPP was relatively weak (R\({}^{2}\) ranging from 0.47 to 0.65), while the correlation between SIF\({}_{\text{ECW}}\) and SIF\({}_{\text{ECA}}\) with GPP was significantly enhanced (R\({}^{2}\) ranging from 0.78 to 0.85). Under the downscaling condition of SHB1, the correlation between TROPOSIF (SIF\({}_{\text{ECW}}\), SIF\({}_{\text{ECA}}\)) and GPP surpassed that of eSIF, whereas at the SHB2 site, the relationship between eSIF's SIF\({}_{\text{ECA}}\) and GPP was superior to that with TROPOSIF, while SIF\({}_{\text{ECW}}\) remained equivalent. These results indicate a substantial error between coarse-resolution SIF and GPP, while the relationship between the GPP matched to the flux footprints and the SIF was significantly improved. Different observational scales revealed significant differences in the linear fitting performance between SIF and GPP, emphasizing the crucial role of maintaining footprint consistency for accurately estimating their linear relationship.  
Figure 7 illustrates the seasonal variations in the SIF (0.05\({}^{\circ}\) SIF, SIF\({}_{\text{ECA}}\), SIF\({}_{\text{ECW}}\)) and GPP at the SHB1 and SHB2 sites. The seasonal variations in the 0.05\({}^{\circ}\) eSIF and TROPOSIF show considerable deviations from the GPP variation curves at both sites. Post-downscaling, eSIF (eSIF\({}_{\text{ECA}}\), eSIF\({}_{\text{ECW}}\)) and TROPOSIF (TROPOSIF\({}_{\text{ECA}}\), TROPOSIF\({}_{\text{ECW}}\)) exhibited better correspondence with the GPP variation curves. However, during periods of localized GPP decrease, the downscaled TROPOSIF failed to decrease accordingly, while  
Figure 6: Scatter plots of SIF and GPP across varying observation scales: (**a**–**c**) show the scatter plots of the 0.05\({}^{\circ}\) SIF, SIF\({}_{\text{ECW}}\), SIF\({}_{\text{ECA}}\), and GPP values at the SHB1 site; (**d**–**f**) show the scatterplots of the 0.05\({}^{\circ}\) SIF, SIF\({}_{\text{ECW}}\), SIF\({}_{\text{ECA}}\), and GPP values at the SHB2 site. The red dots and lines represent eSIF and the blue dots and lines represent TROPOSIF.  
eSIF followed the GPP variation, indicating a more consistent seasonal pattern in down-scaled eSIF. For the flux footprint-weighted and -averaged SIF, the weighted SIF showed significant improvements in localized details compared to the averaged SIF.