esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental
### Thermochemical Redox Cycles  
The functionality of the catalytic ACRM synthesized through co-precipitation was confirmed using a thermogravimetric analyzer (TGA, SETSYS Evo.,) sourced from SE-TARAM, France. The TGA setup (Figure 3), comprising a heating furnace with an enclosed  
Figure 2: Step-wise synthesis of ACRM using the co-precipitation method.  
alumina tube containing a graphite heating element, a temperature-regulating system, and a reaction chamber, was utilized for this purpose. A continuous flow of protective gas (Ar) was passed through the furnace to prevent damage to the graphite heating element from oxidizing gases. A chiller that provided ice-cold water to the furnace maintained the setup's temperature. A balance was positioned in the top section of the TGA setup, and the carrier gas (Ar) flowed from top to bottom to prevent oxidation or contamination caused by sample vapors. Two mass flow controllers were employed to measure and monitor the flow rates of the gases used in the TGA experiments- one for Ar and the other for the CO2/Ar mixture. A Pt-Rh thermocouple (type B) was placed near the alumina crucible to measure and regulate the temperature of the powder sample. Prior to conducting any TGA experiments, the air or O2 trapped inside the hollow of the furnace was removed. This evacuation procedure was performed using a two-step mechanism: first, by applying a vacuum, and then by purging the hollow with an inert carrier gas (Ar).  
To perform the TR and CDS experiments, a precise amount of approximately 100 mg of SCRM powder was loaded into a 100 \(\mu\)L platinum crucible and positioned accurately on a hanging balance. Several thermochemical cycles were conducted at temperatures ranging from 1000 to 1400 \({}^{\circ}\)C. The TR phase was carried out using inert Ar gas at 1400 \({}^{\circ}\)C for 60 min. Afterward, the CDS phase was performed by exposing the ACRM powder to a 50% CO2-Ar gaseous mixture at 1000 \({}^{\circ}\)C for 30 min. During the thermal reduction phase, the overall mass of the sample decreased due to the release of O2 from ACRM. On the other hand, during the CDS phase, the overall mass of the sample increased due to the re-oxidation of  
Figure 3: Thermogravimetric Analyzer (SETARAM Setsys, France).  
SCRM. The amounts of O\({}_{2}\) released and CO produced during multiple thermochemical cycles were calculated using mass loss and gain values per the given equations.  
\[n_{O_{2}}=\frac{\Delta m_{loss}}{\big{(}M_{O_{2}}\times m_{ACRM}\big{)}} \tag{1}\]  
\[n_{CO}=\frac{\Delta m_{gain}}{(M_{O}\times m_{ACRM})} \tag{2}\]
## 3 Materials and Methods
### GC-MS Instrumentation  
In the present study, the analytical method was developed such that the sample preparation as well as the chromatographic separation comply with green chemistry principles. Thus, in order to reduce its environmental impact, the sample preparation was miniaturized, and the solvent consumption was minimized by using SPME microextraction. For analysis of complex mixtures, GC offers a solventless and efficient separation, compared to other techniques, and the total run time is shortened [10; 32]. All GC-MS measurements were performed on an Agilent 7890A GC coupled to an Agilent 5975-C (inert XL EI/CI MSD) with a single quadrupole mass analyzer (Walnut Creek, CA, USA). The chromatographic separations were carried out using a capillary column HP-5 MS UI (Ultra Inert) (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) coated with a low-polar stationary phase (5% phenyl-methylpolysiloxane). The carrier gas consisted of helium with a purity of 99.999% supplied by Abdullah Hashim Gases. The mass spectrometer was operated in both scan and selected ion monitoring (SIM) modes. Data acquisition and analysis were performed using Chemstation software (version F.01.03.2357).
## 3 Discussion
### Impact of Repeated-Use Scenarios  
The current approach that is appropriate for single use food contact materials or article situations is not appropriate for repeated-use articles and needs to be re-evaluated. Compliance evaluations that are based on the third consecutive migration period might not be appropriate due to less frequent, i.e., intermittent use repeated-use food contact articles such as kitchen utensils or continuous repeated-use food processing equipment that is used more than three times.  
The experimental work published by [PERSON] et al. [8] has been used as example and as point of departure for investigations into the relevance of intermittent use scenarios is given in Figure 12 for Metilox (3-(3,5-Di-tert-butyl-4-hydroxyphenyl)propionic acid methylester, CAS 6386-38-5) and Irganox 1076 (3-(3,5-Di-tert-butyl-4-hydroxyphenyl)propionic acid octylester, CAS 2082-79-3). The orange line in Figure 12 reproduces experimental results for daily use (=intermittent use), the blue line represents no empty storage (=continuous use), and the green line represents a use once per week (=occaional use). Using this published work on intermittent use as an example, it can be shown that compared to repeated-use and occasional use, i.e., varying the time without food contact (empty time), significant differences in migration can be expected. The longer the empty time is, the closer the repeated-use migration comes to the single-use migration. It is anticipated that a systematic investigation into the impact of the empty migration time would be meaningful, with potential amendments to the legislation that is currently in place being one of its benefits. The cumulated migration results without \"no-contact\" times are given in Figure 13.  
From the repeated-use migration scenarios simulated in Figures 12 and 13, it can be concluded that continuous uses that occur one after the other, such as for a piece of food processing equipment, lead to a continuous decrease in the migrating amount. The decrease is faster if the migrant exhibits good solubility in the food (low partition coefficients) and lower in the case of a low-solubility migrant in the food. In case of intermittent use (e.g., 8 h contact of the material or article with the food followed by 16 h without food contact), the decrease in the migrating amount over time is lower compared to that of continuous use, and for materials or articles that are occasionally over time, then the migration amount stays close to the migration amount seen for a single-use application.
# Assessment of Laser Joining Quality by Visual Inspection, Computer Simulation, and Deep Learning
## 1 Introduction
These networks also employ encoder-decoder structures to preserve the prominent features of images [15]. The successful use of CNN-based DL algorithms for product quality monitoring has been reported by many researchers. This paper reports the results of a comparative analysis of the joining of polycarbonate (PC) and acrylonitrile butadiene styrene (ABS) polymeric materials with different characteristics using a diode laser. The weld quality was evaluated by (1) visual inspections, (2) computer simulations, and (3) DL techniques, and the results were then compared.
## 3 Energy-Efficient Optimal Power Allocation  
Since the demand for global coverage providing broadband services is increasing, supporting interactive multimedia traffic is expected as an essential component in satellite systems. In addition, the satellite traffic could be divided into two classes: non-real-time applications, such as email, remote login or ftp and real-time applications, such as voice and video.
## 2 The First and Second Parts of Making the Surgical Guide for TADs
### Part 2: The Procedure of Making the Surgical Guide for TADs
#### 2.2.1 The Surgical Guide Technique for TAD Placement  
The use of TADs in orthodontics has notably increased in recent years. TADs have allowed for the simplification of several treatment plans by producing skeletal anchorage. Since these are easy to place and remove and require no cooperation from the patient, there has been an increased use of these devices in orthodontic practices. However, TADs' stability heavily relies on bone thickness both on a vestibular and palatal level. The critical concern is that TADs are also difficult to place correctly, and there is a risk of dental damage, sinus or nasal perforation, chronic sinus inflammation, and even anchorage loss [62]. However, the stability of TADs depends on factors such as bone quality and quantity, as well as biological, technique-related, and clinical variables. It is important to note that TADs are challenging to place accurately; using surgical guides based on acrylic provides sufficient control of orthodontic mini-implant placement, and guide extension involving the teeth will improve the accuracy of TAD placement [63].  
By overlaying CBCT and digital models obtained from an intraoral scan, it becomes feasible to identify the optimal position for TADs. Once the ideal location has been determined, a surgical guide can be produced using a 3D printer. These guides ensure accurate and safe placement, reducing the risk of complications typically associated with the procedure.
# Research on Students' Action Behavior Recognition Method Based on Classroom Time-Series Images
## 4 Experimental Results and Analysis  
### Datasets  
Dataset 1: The AVA dataset [39] is a dataset oriented to the spatiotemporal action detection task. This dataset takes 1 frame per second as a keyframe to be labeled. There are 80 atomic action classes, including three major classes: posture actions, actions of human-human interaction, and actions of human-object interaction.  
Dataset 2: UCF101-24 is a dataset oriented to the spatiotemporal action detection task. This dataset is labeled frame-by-frame and contains a total of 24 classes of actions.  
Dataset 3: At present, there is no public student action behavior dataset; so, this paper constructs a dataset of students' action behaviors based on real classroom videos and annotated concerning the AVA dataset [39], which contains seven types of action behaviors, including listening, looking around, lying on the table, reading, writing, using mobile phone, and talking, as shown in Figure 8. The construction process of the dataset is as follows:  
Step 1: Collect real classroom videos from a university; screen and edit the videos; and select a total of 25 videos, each with a length of 5 min.  
Step 2: Firstly, video frames are extracted according to the frame rate of 30 frames per second; then, every 30 frames are extracted as keyframes, which are used to label the students' positions and action behaviors.  
Step 3: Use the VGG Image Annotation tool to label the position and action behavior categories of students in the keyframes, save the labeling information in CSV file format, and then process it into labeling files in AVA format.  
In addition, to model the interaction between students and the environment in the classroom video, it is necessary to correctly find the objects that students interact with. Therefore, this paper trains a target detection model based on YOLOv5 that is suitable for classroom scenes and then uses the model to detect objects such as cell phones and books in key frames, extracts coordinate information and category indexes of cell phones and books based on the detection results, and generates labeled files in the format of the COCO dataset to be used as auxiliary training data.
## 3 Results
### The Degradation Curve of the Rotation Stiffness  
The rotational stiffness degradation curves of four joints under positive and negative loading are shown in Figure 23. From the stiffness curve, it can be seen that the stiffness degradation of the joint decreases with the increase in loosening degree. The joint LJ-1 slips first and exhibits low stiffness during the initial positive loading. When the mortise interacts with the tenon, the stiffness of the joint increases. In the negative loading, the LJ-1 of the joint slips firstly, and the stiffness is low. The stiffness rises when the slip section is exceeded, and the tenon and mortise are in contact with each other. When loading to 0.049 rad, the stiffness of LJ-1 decreases rapidly and the tenon is destroyed.  
The loose joint LJ-2 encounters a minor amount of slip during positive loading, and the stiffness is low. The initial slip of the joint LJ-2 is longer than LJ-1 and the stiffness of the joint flattens as the rotation angle increases. The mortise and tenon are extruded and the stiffness of the joint increases. Then, the joint enters the strengthening section, the bending moment gradually increases and the stiffness gradually decreases.  
The stiffness of loose joints LJ-3 and LJ-4 is practically unchanged in the early stages of loading and the degradation phenomenon is weak. The slipping stage of the loose joint lasts longer during loading and the joint primarily relies on friction force to resist the external load. Therefore, the initial secant stiffness of the joint is low and the stiffness remains nearly unchanged as the rotation angle increases. The stiffness degradation curve shows that LPMT joints have clearly varying stiffness characteristics. Rotational stiffness during positive and negative loading is different and the rotational stiffness of the loose joints diminishes as the joint gap increases.
# Analysis of a Customized Clutch Joint Designed for the Safety Management of an Ultrasound Robot
## 3 Results and Discussion
### Clutch Performance
Although there are certain cases where the triggering force goes higher and reaches 32.06 N when the joint angles come to their maximum values, it should be noted that the joint angles rarely come to these extreme values kinematically in a real scenario. With the maximum force observed at 32.06 N, it should still be considered as safe for the patient as it is not excessive, has been observed in manual clinical scans, and would decrease to zero quickly when the ball moves out from the detent hole after the clutch is triggered. The second kinematic-based analysis of the clutch performance used more realistic joint parameters resulting from the inverse kinematics, which positions the US probe onto certain locations over an abdominal surface. The triggering force variation among those postures shown in Figures 4 and 5 are listed in Figure 8. The mean triggering force at the US probe end is found to be \(22.10\pm 1.76\)N (mean \(\pm\) standard deviation). The maximum triggering force is 25.68 N and the minimum triggering force is 19.94 N. Compared with the results from the exhaustive method, it can be seen that for the more realistic robot configurations, the mean and the minimum values of the triggering force are similar to the results from the exhaustive method, although the maximum value and the standard deviation are smaller than those found using the exhaustive method. This is because those configurations with joint angles coming to their upper limits do not usually occur realistically when the robot is scanning the abdominal area. Results from the second kinematic-based analysis further validate the successful working of the clutch mechanism concept as the identified minimum triggering value is adequate to ensure good US imaging, while the maximum triggering value is not excessive. The mean triggering value of the maximum allowable force at 22.10 N meets the expectation of the limiting force, and the standard deviation of 1.76 N indicates a relatively consistent triggering force between different postures of the robot.
# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 3 Design Pipeline
### Data Collection and Labeling
#### 3.1.1 Data Collection and Feature Selection
Specifically, \(\zeta\) is defined as the ratio of the interrogation radius to the product of the antenna effective aperture and return loss, as shown in Equation (2), which represents the observed terms after fixing the transmit power, interrogator antenna gain, and interrogation frequency, given the observed RSSI of the reflected signal:  
\[\zeta=G_{tag}^{-2}\cdot r^{4}\cdot R^{-1}=P_{Tx}\cdot G_{reader}^{2}\cdot P_{ Rx}^{-1}\cdot\left(\frac{\lambda}{4\pi}\right)^{4}, \tag{2}\]  
We remove a residual term \(\delta=-10 log_{10}\frac{f^{4}}{(f-(0.5*10^{6}MHz))^{4}}\approx-0.00941\) to compensate for a sawtooth artifact resulting from quantization of the observed RSSI as the interrogation frequency changes among 50 discrete channels per FCC regulations in the United States. In summary, we chose the following features for consideration during wireless respiratory state classification. * Feature 1: Reflected signal strength as measured at the interrogator, \(\zeta\) (\(P_{Rx-decsillated}\))
* Feature 2: The difference between the current observed RSSI from the minimum RSSI value observed in the recent time window (\(RSSI\_from\_min\))  
We normalized RFID signal strength (\(P_{Rx-decsillated}\)) data by frequency to utilize the signal for respiratory analysis. The resulting time-series data were filtered and signal-processed to determine the mean power spectral density, derived from the amplitude of the oscillatory behavior observed in the signal during short time windows. Figure 3 illustrates the two features (\(P_{Rx-decsillated}\) and \(RSSI\_from\_min\)) over a time window of 1.2 s.
## 3 Experiment Results and Discussion
### The Effect of Different Installation Angles on the Lift Generation and Rotational Speed Generation
Comparing all the CFF cases, the \(S_{t}\) number for  
Figure 18: The (**a**) rotational speeds; (**b**) lift forces; (**c**) the ratio between lift and power produced by the FWR in different installation angles. all the VFF cases of equivalent power output falls in the range of 0.3-0.36 with slightly higher \(P_{f}\) values. For example, the VFF case 4 (3 & 1 Hz) results in \(S_{t}\) = 0.31 and \(P_{f}\) = 0.11 while the CFF case of equivalent frequency (1.3 Hz) and power (1.09 W) results in \(S_{t}\) = 0.53 and significant smaller \(P_{f}\) = 0.02. It is clear that the VFF cases with an installation angle \(\theta_{0}\) = 30\({}^{\circ}\) result in higher \(P_{f}\) and optimal \(S_{t}\) number comparing with the other cases and installation angles.
## 3 Results and Discussion
### The Stratification Effects of Mode I Fracture Characteristics
#### 3.2.3 Fracture Toughness  
During the loading process, when the stress intensity factor around the crack tip increases to a particular critical value, the material can no longer withstand the stress state, resulting in rapid crack propagation. This critical value is referred to as the critical stress intensity factor, denoted as \(K_{\text{IC}}\). The \(K_{\text{IC}}\) value characterizes the material's resistance to crack growth, commonly known as its fracture toughness. The following formula is generally used to calculate the Mode I fracture toughness for CSTBD configuration specimens [41]:  
\[K_{\text{IC}}=Y\frac{P_{\text{max}}\sqrt{\pi a}}{\pi BR} \tag{3}\]  
\[Y=(1-0.5\alpha+1.56\alpha^{2}-3.18\alpha^{3}+10.1\alpha^{4}-20.78\alpha^{5}+ 20.13\alpha^{6}-7.51\alpha^{7})/\sqrt{1-\alpha} \tag{4}\]  
\[\alpha=a/R \tag{5}\]  
where \(a\) is the half-length of the crack in the disk, \(R\) is the radius of the disk, \(B\) is the thickness of the disk, \(P_{\text{max}}\) is the peak load, and \(Y\) is the dimensionless stress intensity factor.  
Figure 13 illustrates the relationship between fracture toughness and stratification angles across different numbers of stratification layers. The data reveal a pronounced effect of stratification on the rock's fracture toughness. Specifically, at a stratification angle of \(0^{\circ}\), where cracks are perpendicular to the stratification planes, the rock demonstrates the highest fracture toughness due to significant resistance to crack propagation along these planes, along with the predominant constraint imposed by the higher tensile strength of the rock matrix. Conversely, as the stratification angle increases, the fracture toughness decreases linearly. This reduction can be attributed to the reduced obstructive effect of the stratification planes on crack propagation, with cracks more readily advancing along these inherently weaker surfaces. Consequently, the lower tensile strength of the stratification planes compared to the rock matrix results in diminished overall fracture toughness when cracks propagate along these planes. Additionally, experimental observations indicate that, with an increasing number of stratification layers, the fracture toughness continues to decrease. The linear fit's slope becomes steeper with a greater number of stratification layers, highlighting a more pronounced reduction in fracture toughness with higher stratification angles. This suggests that a higher number of stratification layers exacerbates the influence of stratification angles on fracture toughness, as additional stratification planes create more potential paths for crack propagation, thereby further diminishing the rock's overall fracture toughness.
## 3 Results and Discussion
### Adsorption Isotherms and Kinetics  
The sorption process of two metal ions and two materials was determined with the use of Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherms. According to the obtained results, the highest correlation coefficient was fitted to the D-R isotherm model for both AP and PAP and Cr(VI) ions (R\({}^{2}\) = 0.99 and 0.91, respectively) and the Freundlich model for Cd(II) ions (R\({}^{2}\) = 0.99, and 0.91, respectively). The obtained results for the isotherm models are presented in Table 4. Additionally, it can be seen that the 1/n parameter is lower than 1 in each of the experiments, suggesting that the adsorption process is of a chemical nature. Figure 4: Point of zero charge of AP and PAP. To acquire knowledge about the characteristics of the mechanism of the removal process by the means of adsorption, the kinetics studies were carried out. To compare different kinetic models, the correlation coefficient was calculated for each chosen model. Three mainly used models were used: the pseudo-first-order, pseudo-second-order, and Weber-Morris model. According to the obtained results, the highest fit was with the PSO model (pseudo second rate). On Figure 5 and Table 5, the PSO scheme and parameters for all used concentrations for both Cr(VI) and Cd(III) and AP and PAP are presented. Studying the obtained results, one can see that the rate-limiting step is due to the surface adsorption-chemical binding of ions on the surface of chosen materials both before and after the pyrolysis process. This thesis is also supported by the changes in infrared spectra after ion removal. The lower values for the PSO constant can be connected with the high affinity of metal ions to the functional groups on the material surface [23]. Moreover, the calculated values of maximum sorption capacity qe are close to those obtained from the experiments. This fact proves the correctness of the pseudo second-rate order model for this phenomenon. [PERSON] et al. in their work used biochar derived from the non-customized matamba fruit shell for the wastewater treatment, where the PSO demonstrated the highest fit [24]. [PERSON] and colleagues presented a study of methylene blue removal with the use of rice husk, cow dung, and sludge biochar, also providing information that the pseudo-second-order had the highest correlation coefficient [25]. The adsorption mechanism can be connected with the surface complexation in which the pH value is slightly reduced after the removal process due to the displaced protons from the carbon surface. On the other hand, in this study, we did not see lowered pH of the solutions after interacting with sorbents. Thus, this phenomenon is probably not a complexation especially when studying the FTIR spectrum. Peaks corresponding to the C=C and C=O stretching vibration are lowered in both cases of Cr and Cd ions, which might be responsible for the delocalization of \(\pi\) electrons resulting in adsorption through C\(\pi\)-cation interactions. In Table 6, one can see the sorption capacities of other biosorbents used for the removal of toxic metal ions from wastewater.