esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Predictive Control Algorithms
(Nonetheless, the use of closed loop control in the hospital setting, even with IV insulin, there is marked variability among subjects in insulin sensitivity and insulin requirements based on confounding variables such as infection, physiological stress, fever, and comorbidities). The MPC glucoregulatory model determines the optimum insulin infusion rate that is expected to achieve the target glucose concentration. The outputs include insulin infusion rates that are expected, based on model predictions, to result in the target glucose concentration over a period of several hours. In is also possible to determine the frequency of glucose sampling in this method; if the glucose prediction crosses a border indicating unacceptable bounds, the MPC can suggest an additional (early) sample to be taken [24]. Glucose concentration, insulin dosage, and carbohydrate intake are some of the input variables that can be used in MPC. The insulin infusion rate and the time of the next glucose sample are the outputs in the Hovorka studies. [PERSON], [PERSON], and their colleagues have been instrumental in their adaptation of MPC to management of patients with type 1 diabetes by validating an MPC glucose control system utilizing an in silico model. Based upon data obtained from over 200 subjects [27], this population model was then modified to describe a patient population of persons with type 1 diabetes from which individual models of virtual patients could be extracted for running a computer simulated trial [26]. This in silico study effectively highlighted the fact that MPC systems improved glucose regulation over classical PID systems in limiting the oscillation of glucose levels. Moreover, through the recent efforts of the Juvenile Diabetes Research Foundation (JDRF), the University of Virginia and the Universities of Padova, Pavia and Montpellier, an in silico simulator for testing closed-loop algorithms is being used by the JDRF Artificial Pancreas Consortium members. A description of this in silico simulator (now based on a cohort of 300 patients with diabetes, including adults, adolescents and children) was just recently published in 2009 [27] and it has been accepted by the US Food and Drug Administration as a substitute to animal trials in the preclinical testing of closed-loop control strategies. For more information about the mathematics of MPC systems, including many non-medical applications, we recommend to the reader a review by [PERSON] et al [28].
# Explainable AI-Enhanced Human Activity Recognition for Human-Robot Collaboration in Agriculture
## 1 Introduction
Specifically, ML models, primarily based on supervised learning techniques, are trained on labeled datasets to recognize patterns and accurately classify different activities. In multi-sensor HAR systems, individual sensors typically consist of multiple physical channels to process the various directional components of motion signals. As a result, processing multi-channel signals and extracting relevant features are essential prerequisites. Nevertheless, the importance of physical channels often varies significantly, as not all channels contribute equally to the activity. A challenge arises from the independent processing of each channel, as the most relevant channels may be distributed across different sensors, complicating the process of sensor selection [28; 29]. Moreover, in practical applications, energy consumption cannot be minimized effectively, since all channels within a sensor remain active. Consequently, a key limitation of these models is their \"black-box\" nature, meaning that the decision-making process is often not transparent or interpretable [30; 31]. This lack of explainability can be problematic,especially in high-risk environments like agriculture, because it can hinder trust and safety, as well as learning and improvement. To tackle the aforementioned limitations, the present study leverages explainable artificial intelligence (XAI) for the first time in the relevant literature, to the best of the authors' knowledge. Specifically, we employed the SHapley Additive exPlanation (SHAP) method in combination with an eXtreme Gradient Boosting (XGBoost) classifier to analyze a dataset collected for HAR. The analyzed activities pertain to a collaborative human-robot setting, where an unmanned ground vehicle (UGV) assisted workers during the harvesting process by transporting full crates, as described in [32; 33; 34]. The calculation of SHAP values provides insights into sensor and channel importance, enabling the optimization of sensor selection and feature relevance. Overall, this approach contributes to the development of more efficient and energy-conscious HAR systems tailored for real-world agricultural applications. Concerning data collection, the data were gathered during outdoor experimental sessions in which twenty participants took part, each outfitted with five inertial measurement units (IMUs) placed at various positions of the body, alongside a UGV. Finally, to encourage further research, the dataset used in this study has been made publicly accessible in [35]. The structure of the remainder of this paper is as follows: Section 2 outlines the methodology for data collection, data pre-processing, and the development of the XGBoost-based HAR model, alongside the SHAP framework used to interpret the model. Section 3 presents the key findings from the SHAP-enhanced XGBoost model. Section 4 discusses the results in a broader context and explores potential directions for future research, while Section 5 concludes with the main insights from the study.
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Contrast-to-Noise Ratio (CNR)  
The contrast-to-noise ratio (CNR) is proposed as an objective means to provide a quantitative assessment of noise. Several CNR definitions may be found in the scientific literature [15]. Nevertheless, there are no standards specifically related to infrared thermography. There is, however, a standard for the evaluation of digital detector array systems for industrial radiology [24] from which a proper CNR definition can be adapted for the purpose of this study:  
\[CNR=\frac{\mu_{d}-\mu_{Sa}}{\sigma_{Sa}} \tag{2}\]  
where \(\mu_{d}\) is the average level of the signal in the defect region; \(\mu_{Sa}\) is the average level of the signal in the sound area (around the defect); and \(\sigma_{Sa}\) is the standard deviation of the sound area.  
The CNR was calculated frame by frame using Equation (2) in search of the maximum value, CNRmax, and thus the frame presenting the \"best\" contrast. This procedure is depicted in Figure 5.  
Figure 5a displays a frame (arbitrarily selected) in which an anomaly can be identified, i.e., detect 1. The same frame showing the defect and sound (around the defect) areas, manually selected to estimate the CNR at every frame, is presented in Figure 5b. A  
Figure 5: Determination of the frame with maximum CNR (defect 1 from sample S45 by PT): (**a**) Raw thermogram arbitrarily selected; (**b**) same thermogram showing defect and sound areas; and (**c**) CNR profile over time.  
transition zone is left between the defect and the sound area to reduce the effect of heat diffusion. Figure 5c shows the CNR variation frame by frame from which the CNR\({}_{\text{max}}\) can be determined, together with its corresponding frame number and time.
## 3 Results and Discussion
### Electrical Stability of the Fe\({}^{2+}\)-Containing S-Layer  
Film resistance of HEPES-cleaned SbpA films was subsequently tested upon sudden shifts in the environmental potential from -1 to +1 V (covering the Fe\({}^{2+}\)/Fe\({}^{3+}\) redox pair). By application of such pulses the resistance of the inner, structure-trapped, Fe (II) ions to externally-induced oxidation could be determined. This effect turned critical when test experiments under same conditions were performed during the protein adsorption step (Appendix A, Figure A4). The real-time response was monitored by EQCDD, as depicted in Figure 4.  
Starting from 0 V (t = 0), each of the potential pulses applied lasted a minimum of 5 min. Response of the Fe\({}^{2+}\) containing S-layer was compared to that from SbpA built from Ca\({}^{2+}\), which acted as negative control. Interestingly, S-layer remained inert during the initial frame (ca. 2 min) upon application of an oxidizing potential, then a rather abrupt linear drop of the frequency values took place down to -90 Hz, until the potential shifted to iron reducing conditions (-1 V). At this point, an immediate increase of \(\Delta\)f was triggered and frequency values reached a constant plateau of around \(-15\) Hz. Reversibility of the process could be demonstrated by a second round of pulses. Although the explanation to such a response is not yet optimally defined, one could speculate with the possibility of inducing local Fe (II) to Fe (III) transformations in areas where the recrystallization did not reach a final stage and iron would thus be more accessible. By this effect, the layer might partially lose the high packing degree when Fe\({}^{3+}\) occupies those positions. Therefore, water molecules might fill those vacancies, which would explain the increase in wet mass. As soon as reduction from Fe (III) (_ferric_) to a Fe (II) (_ferrous_) state is triggered, water is released and the starting situation is almost achieved. The permanent frequency change taking place might correspond to water that got trapped in the film structure along those cycles.
## 1 Introduction
Current laser-induced ultrasonic NDE systems use Q-switched lasers with nanosecond pulses and pulse energy levels of several millijoules (mJ), generating ultrasonic signal frequencies ranging from tenths of kilohertz (kHz) up to tens of megahertz (MHz) [29, 30]. Both through-transmission and pulse-echo ultrasonic spectroscopy methods are able to detect CFRP composites up to several centimeters [31, 32, 33]. Although remote ultrasonic energy generation and data collection are the ideal approaches, complex and relatively expensive instruments are required for ultrasonic interferometric detection [34, 35]. To the best of the authors' knowledge, currently, sophisticated laser-based ultrasound imaging systems for remote evaluation of CFRP composites are expensive and technologically immature. In this paper, we developed a picosecond pulsed laser-induced photoacoustic non-destructive evaluation (pNDE) system for the detection of multi-scale damage in CFRP composites using a picosecond pulsed laser and high-frequency ultrasound transducer. At the micro scale, the damage precursors of surface notches and matrix cracks were successfully detected and represented in 3D images. The size and position of the micro-scale defects in composites were evaluated with a high spatial resolution of 60 \(\upmu\)m. Scanning electron microscopy (SEM) images were obtained to validate all micro-scale surface notches on composites. At the macro scale, both delamination and transverse ply cracks were successfully detected and represented using the developed pNDE system. The size of delamination at different depths and the locations of transverse ply cracks were accurately measured.
# Effects of Pulsed Pressure Curing on Beef Quality
## 3 Results and Discussion
### Study of Physical and Chemical Parameters  
The pH of beef with different curing treatments is shown in Figure 2b. The pH values of SC, VC, PC, and PPC were 5.96, 5.93, 5.94, and 5.91, respectively. Although the difference between the pH values of the PPC curing therapy and the SC curing treatment in the control group was marginal, no statistical significance was observed (\(p>0.05\)). Proteolysis and phosphate diffusion from the curing solution into the meat were the major causes of the pH value dropping throughout the salting process [10].  
The moisture content of beef with different curing treatments is presented in Figure 2c. The moisture content values of SC, VC, PC, and PPC were 64.19%, 68.56%, 67.36%, and 72.16%, respectively. When compared to the control groups of SC curing treatment, the moisture content in the PPC curing treatment was considerably greater (\(p<0.05\)). This could be attributed to the pressure pulses driving force, which may have encouraged moisture penetration into the meat. This finding was in line with previous study that showed salting significantly increased the ability of pork mince and turkey breast to retain water [25]. Increased water retention capacity is associated with transverse muscle fiber stretch. This is because salt increases the electrostatic repulsion between muscle fiber filaments, causing the filament lattice to expand and trap water [26], implying that PPC curing procedure might substantially enhance meat pickling speed.  
The salt content of beef with different curing treatments is shown in Figure 2d. The salt content values of SC, VC, PC, and PPC were 0.57 g/100 g, 1.23 g/100 g, 1.18 g/100 g, and 1.86 g/100 g, respectively. The beef under PPC treatment had the greatest salt concentration, indicating that PPC was successful in promoting NaCl penetration. These findings are in line with the outcomes found by [PERSON] et al. [10], who investigated pork cured with help from pulse pressure.  
Figure 2: Effects of various curing techniques on the absorption of curing solution (**a**), pH (**b**), moisture content (**c**), salt content (**d**), cooking loss (**e**), centrifugal loss (**f**), and color (**g**) of beef. Different letters (a–d) in the same histogram indicate significant differences (\(p<0.05\)).  
According to the findings, the curing pork ion NaCl level was almost twice as high under pulse pressure as it was at normal pressure. According to [PERSON] et al. [27], the salt concentration of the lamb during pulse vacuum salting from 1.5 to 6 h was 1% greater than during atmospheric salting. The expanded muscle structure under vacuum during PPC treatment may have raised the salt content because it dissolves the free gas in the interior tissues and speeds up the diffusion of the therapeutic liquid into the muscle. When the vacuum is released and the atmosphere is once again in contact with the muscle, the healing liquid quickly penetrates via the tissue pores, speeding the migration of salt and raising the salt concentration [27].
# Precise Photon Correlation Measurement of a Chaotic Laser
## 7 Conclusions  
In conclusion, we precisely measured the second order photon correlation g\({}^{(2)}(\tau)\) of a chaotic semiconductor laser using self-convolution HBT interferometer. Based on the theoretical analysis, the ninth order self-convolution correction was sufficient to obtain experimentally the accurate g\({}^{(2)}(\tau)\) from the photon pair time interval distribution. The experimental results were in good agreement with the theory. The relative error caused by coherence time and mean photon intensity was analyzed, which was no more than 5%o within 50 ns delay time. As the order of convolution increased, the accuracy improved within a long delay time. In comparison with the traditional HBT measurement, this technique, which does not require high intensity and long optical or electric delay, is more useful for a weak light source, such as atomic fluorescence and single photon emission, whose quantum correlation is difficult to be detected. It is demonstrated that this technique provides a new way to measure high order quantum coherence precisely and will bridge the gap between nonlinear optics of chaotic lasers and quantum physics.  
[PERSON] and [PERSON] designed the whole work and wrote the manuscript [PERSON] carried out the theoretical calculations and analyzed the data; [PERSON] supervised the experiments; [PERSON] and [PERSON] contributed to the experiment and data processing; [PERSON] analyzed the data and edited the manuscript. All authors discussed the results at all stages. All authors have read and approved the final manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (Grants Nos. 61875147, 61671316, 61705160, 61731014), the Shanxi Scholarship Council of China (SXSCC) (Grant No. 2017-040), the Natural Science Foundation of Shanxi Province (Grants Nos. 201701D221116, 201801D221182), the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (STIP) (Grant No. 201802053), and the Program of State Key Laboratory of Quantum Optics and Quantum Optics Devices (Grant No. KF201905).  
The authors thank [PERSON] and [PERSON] for helpful discussions.  
The authors declare no conflicts of interest.
## 5 Discussion  
In this study, the FAPSO feature representation method has helped identify the type of wavelet family and the appropriate decomposition level for each GPCR protein class representation. There are 82 wavelet families to select from, including the Daubechies, Coiflet, Meyer, Biorthogonal and Reverse Biorthogonal families. Each wavelet family has characteristics such as orthogonality, the number of vanishing moments, and the type of symmetry found in the scaling and wavelet functions. These features can be adjusted to the characteristics of the studied protein sequence. The convolution result between PseAAC features and the wavelet family will produce low-frequency or approximation and high-frequency or detail coefficients. The approximation coefficient contains coarse or global information about the studied protein sequence. Meanwhile, the detail coefficient contains important information found in the studied protein. In this study, the Biorthogonal wavelet is the most selected wavelet family representing GPCR protein classes by FAPSO. The Biorthogonal wavelet contained symmetric and compact support, which appears suitable to represent this protein.  
The multiresolution analysis allows protein sequences to be studied using multiple decomposition levels and can be performed up to the log2 (N) level, where N is the length of the studied data. In this study, considering the size of the PseAAC feature used is 170, the maximum decomposition size allowed is seven levels. Different levels will produce many coefficients, which are global features and detail coefficients. FAPSO has identified that the root, family A, subfamily A Amine, and subfamily A Peptide nodes required decomposition level 1 to represent proteins in those classes. For family C and subfamily C CalcSense, decomposition levels 6 and 3 are required to represent proteins in that class. This is because protein C has a long and varied sequence size, in addition to a complex structure [54]. Therefore, the selection of the decomposition level also plays a vital role in determining the classification performance. This proves that each GPCR protein class requires a different wavelet family and decomposition level for feature representation.  
Nevertheless, it can be concluded that the FAPSO optimization algorithm alone cannot improve the GPCR hierarchical protein classification performance compared to the PseAAC algorithm. In this study, the number of iterations used in the FAPSO optimization algorithm is 100 iterations on each parent node of the GPCR protein hierarchy. The classification performance may increase if the number of iterations increases or if the FAPSO parameters are changed to appropriate values.  
The virtual class has been proposed to overcome the propagation error problem in top-down hierarchical classification. As a result, it can improve the classification results on the nodes in the hierarchy, especially when combined with the FAPSO feature representation algorithm.
## 4 Delay Compensation Controller Based on Model Prediction
### Test System Clock Synchronization  
Clock synchronization is important for correcting clock errors between different hosts in the system and is the basis for designing the delay compensation controller in this paper. To synchronize the system clock, a time node in the system must be selected as the reference time node for the entire test system. Before transmitting data at other time nodes, the reference clock information from the reference time node needs to be obtained as the current clock information of that node.  
During the test, the test cycle consists of three phases with four time nodes, as shown in Figure 5. Time node t1 represents the release time of twin test vehicle data; time node t2 represents the receipt time of data by the ECPB hardware test bench; time node t3 represents the release time of data to the cloud server by the ECPB hardware test system; time node t4 represents the receipt time of data by the twin test vehicle from the ECPB hardware test system. We define time node t1 as the base time node. Time nodes t2, t3, and t4 must obtain base clock information from time node t1 to ensure consistency in clock sampling during the test process and complete clock synchronization.  
Figure 4: Communication flow based on MQTT.
## 3 Results and Discussion  
### Mechanical Testing  
The maximum radial force of a stent depends on its design and the material used for its fabrication. Omega, U, and rhombus design Zn stents fabricated in this work were tested under compression. Resistance force generated by the stent was recorded in loading and unloading mode, as illustrated in Figure 8. A maximum value of 0.167 N/mm radial force (force over stent length) was calculated for the stent with rhombus design, which appeared in the middle compared to the other two designs. The other two stents showed either a higher radial force 0.25 N/mm (U-design) or a very high percentage strain and  
Figure 7: Segments of ribbons made of photo-chemically etched, uncoated Zn (shown) or AZ31 Mg were implanted subcutaneously in mice. The insert shows the original ribbon.  
low radial force of 0.07N/mm (Omega-design). Zn stents fall in a similar radial force range compared to AZ31 Mg stents (0.1-0.25 N/mm), fabricated by photo-chemical etching and with the same design patterns as with the Zn stents. Zn stents had lower values for this parameter compared to conventional stainless-steel stents, which had a radial force of 0.4N/mm, as calculated using the data in reference [43]. However, the radial force also depends on the stent design and nature of the metal or alloy. Stents with very high radial forces require higher pressure for balloon deployment at the selected vessel location, which may eventually cause injury and, later on, more pronounced intimal proliferation [43].  
Based on this mechanical testing, the rhombus design of the Zn stent showed intermediate values of radial force and percentage strain, which placed it at a \"sweet spot\" compared to the other two designs. For this reason, rhombus design stents were chosen for Parylene C coating and further analyses.
# Robust Structural Damage Detection Using Analysis of the CMSE Residual's Sensitivity to Damage
###### Abstract  
This paper presents a robust damage identification scheme in which damage is predicted by solving the cross-modal strain energy (CMSE) linear system of equations. This study aims to address the excessive equations issue faced in the assemblage of the CMSE system. A sensitivity index that, to some extent, measures how the actual damage level vector satisfies each CMSE equation, is derived by performing an analysis of the defined residual's sensitivity to damage. The index can be used to eliminate redundant equations and enhance the robustness of the CMSE system. Moreover, to circumvent a potentially ill-conditioned problem, a previously published iterative Tikhonov regularization method is adopted to solve the CMSE system. Some improvements to this method for determining the iterative regularization parameter and regularization operator are given. The numerical robustness of the proposed damage identification scheme against measurement noise is proved by analyzing a 2-D truss structure. The effects of location and extent of damage on the damage identification results are investigated. Furthermore, the feasibility of the proposed scheme for damage identification is experimentally validated on a beam structure.  
Keywords:noise robustness; sensitivity analysis; cross-modal strain energy; damage detection +
Footnote †: journal: Applied Sciences