esa-sceva/satcom-chunk-collection huggingface dataset:
# Cryogenic System for FTIR Analysis of Hydrocarbon Fuels at Low Temperature and Atmospheric Pressure
## 2 Materials and Methods
### Gasifier for Creating IR-Inactive Environment  
To maintain the experiment's integrity, a blowing system was connected to the diffuse reflection attachment. Introducing gaseous nitrogen into this attachment creates an inert atmosphere, which protects both the samples and the copper bar from frost buildup and ensures the experiment's consistency by providing an IR-inactive environment. The next step in our experimental procedure involves preparing the aforementioned gasifier--this includes verifying the gasifier's hermetic seal, filling the gasifier vessel with nitrogen, and connecting it to the spectrometer's cuvette compartment via a hose. This setup supplies the diffuse reflection attachment--through which IR radiation passes--with an inert, IR-inactive atmosphere, preventing water vapor from depositing on the samples during cooling. The gasifier's design is illustrated in Figure 4.  
The vessel (Figure 4, item 1) serves as the nitrogen storage container and functions as a Dewar vessel. The manometer (pressure gauge) (Figure 4, item 4) displays the internal overpressure, allowing us to estimate the remaining nitrogen supply time. The hose (Figure 4, item 2) connects the gasifier to the spectrometer, supplying the diffuse reflection attachment with an inert, IR-inactive nitrogen atmosphere. The capillary tube (Figure 4, item 5) facilitates nitrogen transfer, while the radiators (Figure 4, item 3) conduct heat and help generate gaseous nitrogen specifically. The lid (Figure 4, item 8) prevents nitrogen from escaping, the valve (Figure 4, item 6) controls nitrogen flow, and the check valve (Figure 4, item 7) protects against excessive pressure buildup.
## 3 Results
### Response Surface Analysis  
Design Expert software (22 version) was used to optimize the solution, and the optimal solution is obtained; when the flexible claw closure speed was 0.029 m/s, the electric cylinder extension speed was 0.085 m/s and the robotic arm movement speed was 0.15 m/s. At this time, the net picking rate was 91.7% and the damage rate was 2.1%. Using the value obtained at this time as the standard and as the variable value under the interaction in the response surface analysis, we can derive the response surfaces influenced by each interaction, as demonstrated in Figure 16a-f.  
From the response surface analysis depicted in the aforementioned figure, it is evident that the variation rule of the factors affecting the response surface is basically consistent with the results of the model and regression equation analysis of variance; A--flexible claw closing speed, B--electric cylinder extending speed, and the interaction term between AB and AC have a significant effect on the net picking rate and damage rate. The movement speed of the robotic arm had no significant effect on the net picking rate, but had a significant effect on the damage rate. Using the optimization function of Design Expert (22 version), an optimization analysis was carried out to constrain the levels of each test factor; the maximum net picking rate and the minimum damage rate of the dragon fruit picker were taken as the optimization indexes, and the optimal parameter combinations were obtained as follows: in the case of a flexible claw closure speed of 0.069 m/s\({}^{2}\), a push rod extension speed of 0.085 m/s\({}^{2}\), and a robotic arm movement speed of 0.100 m/s\({}^{2}\), the net picking rate of the dragon fruit picker was 91.7%, and the damage rate was 2.1%.  
Figure 16: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline Model & 9.06 & 9 & 1.01 & 24.25 & 0.0002 \\ A—Flexible claw closing speed & 4.96 & 1 & 4.96 & 119.55 & \textless{}0.0001 \\ B—Actuator extension speed & 2.21 & 1 & 2.21 & 53.13 & 0.0002 \\ C—Speed of movement of the robotic arm & 0.3613 & 1 & 0.3613 & 8.70 & 0.0214 \\ AB & 0.0225 & 1 & 0.0225 & 0.5422 & 0.0486 \\ AC & 0.0400 & 1 & 0.0400 & 0.9639 & 0.0359 \\ BC & 0.0625 & 1 & 0.0625 & 1.51 & 0.2594 \\ A\({}^{2}\) & 1.12 & 1 & 1.12 & 26.91 & 0.0013 \\ B\({}^{2}\) & 0.0825 & 1 & 0.0825 & 1.99 & 0.2013 \\ C\({}^{2}\) & 0.1146 & 1 & 0.1146 & 2.76 & 0.1405 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of variance regression equations for dragon fruit damage rate.
## 4 Results
### Predicted Deformations During Reservoir Imounding  
The reservoir level was raised to its maximum elevation level of 400 m between November 2006 and November 2007 in three phases: (1) initial impoundment to water level 250 m in December 2006; (2) water level kept constant at 250 m until May 2007; and (3) reservoir water level raised from 250 m to 400 m between June 2007 and November 2007.  
The observed response in points at elevation 235 m showed a change in the settlement rate by June 2007, when the phase 3 of impoundment began. This change was higher near the upstream face (points SV01-1-4 and SV01-1-5) than in the rest. The VEPP model captured this higher change in the settlement rate near the upstream side better than in the center and downstream sides. The predicted and observed settlement rates in November 2007 were in good agreement. The observed settlements at elevation 235 m were similar among the measurement points, being slightly higher in the center of the dam. The VEPP model captured the observed settlement pattern well, with similar settlements among points and higher in the center. In zone 3B, the prediction of settlements was very good in points SV01-1-4 and SV01-1-5, and it was slightly underestimated in points near the center, with a maximum difference of 0.15 m in point SV01-1-7. In zone 3C, the prediction with the VEPP model matched the observed settlements.  
The observed response in points at the elevation of 300 m shows a change in the settlement rate by June 2007, when the phase 3 of impoundment began. In zone 3B, this change was higher near the upstream face (point SV01-1-24) than in the rest. The VEPP model predicted the change in settlement rate in June 2007 well, but with higher settlement rates than the observed ones. The settlement rates predicted seemed to respond without significant lag time to the evolution of the reservoir level raise rates. By November 2007, the predicted settlements in zone 3B matched those observed in points SV01-1-4 and SV01-1-5, while there was an underestimation of 0.25 m in the settlement in point SV01-1-7. In zone 3C, there was a very good agreement between the observed and predicted settlements at the end of the reservoir impounding period.  
In summary, by November 2007, most settlements in the dam had been captured reasonably well in terms of magnitude and position, with a maximum difference of 0.25 m at point SV01-1-27 in zone 3B in the center of the dam.
## 4 Case Study
### Benefits Analysis of M\({}^{2}\)ES Apps System  
The M\({}^{2}\)ES Apps system has obvious architecture benefits compared to the traditional MES in dealing with the distributed manufacturing. This section will detail the performance, reliability, and scalability of the system on the basis of the proposed case.  
Performance of the M\({}^{2}\)ES Apps System mainly depends on System deployment time and Response time. A comparative experiment has been conducted for measuring the above metrics when deploying Apps between centralized and distributed under the same network, and the results are shown in Figure 12. It can be seen that the distributed architecture is superior in both Total System Deployment Time and Total Response Time. It can also be seen that the deployment time is shorter due to the multiple servers, and the response time is shorter on account of the shorter network routes between the server and the client.  
Reliability of the M\({}^{2}\)ES Apps system is mainly reflected in the fact that it will only affect limited Apps when a disturbance causes a node to out of service. Geographically separated and microservice-based architecture has enhanced the robustness of system greatly, and it has a greater tolerance for the crash or error reporting of a single App. Besides, the design of the independent  
Figure 11: User interface and data interaction of the In-stockroom App.  
Figure 12: Experimental results on comparison of different architectures.  
App is good for authority management, where it can be achieved by just assigning different Apps to different users.  
Scalability of the M\({}^{2}\)ES Apps system mainly focuses on the problem of frequent changes or adjustments in the current production. There are two types of changes normally. The first one is planned and controllable, such as the introduction of new products, the addition of new production lines or equipment, the changes in business processes and so on. Another change is unpredictable, such as the change of delivery time or quantity, the sudden interruption of production equipment, the quality problems of materials, and so on. The advantage of implementing an M\({}^{2}\)ES Apps system is that no matter changes are needed, developers only need to pay attention to the new business logic and design, and develop some supplementary M\({}^{2}\)ES Apps, without focusing on the compatibility issues. For the users, M\({}^{2}\)ES Apps system can also adapt to more usage scenarios by using different arrangements and combinations of Apps.
## 3 Results and Discussion
### Model Conformation Using Synthetic Data  
The model conformational analysis was performed using synthetic data, since data generation for NN evaluation was planned using EM field solver OpenEMS. The simulated scene was similar to the one shown in Figure 4: two identical antennas (transmitting \(A_{T}\) and receiving \(A_{R}\)) were placed at a distance of 40 mm and pointed towards a wall away from the dielectric under analysis; such a simulation setup represents the particular practical  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Property** & **Value** \\ \hline Size, mm & 60.5 \(\times\) 98.5 \\ Input voltage PoE or AUX, V & 12 to 48 \\ Antenna connector type & SMA \\ Pulse repetition frequency, MHz & 1.38 \\ Time window, ns & 18 to 50 \\ Sample count & 400 to 4096 \\ Equivalent sample rate, GSps & 8 to 130 \\ ADC sample size, bits & 8 to 12 \\ Communication interface & Ethernet \\ Pulse bandwidth, GHz & 2 to 4.5 \\ Pulse voltage on 50 Ohm load, V & 6 \\ Pulse rise time, ps & 55 \\ Pulse half width time, ps & 98 \\ Output impedance, Ohm & 50 \\ Dynamic range of the receiver, dB & 60 \\ Input noise level of the receiver, mV & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the upgraded radar.  
experiment setup established in the laboratory. The model conformation is described in depth in [39]. Conformance analysis can take a whole year to develop tools for analysis and perform necessary simulations and calculations; hence, we used a simplified analysis method, which showed weak conformance (around 40%) to the developed model. This can be explained by the nature of the model: it consists of two non-linear transcendental equations with very sensitive solutions, leading to high uncertainties in the solver, not even taking into account high error in the determination of the position in time of the reflected signals. Accordingly, the conformation tests proved that a mathematical model for material properties estimation is hard to develop, and that the material properties estimated according the developed model can be unreliable [39].
## 7 Brain Structure and Function
### Adverse Childhood Experiences and Brain Structure and Function  
The findings regarding the volume of the amygdala in the context of ACE exposure are conflicting; some studies suggest that exposure to certain ACEs (e.g., those experiencing parental depressive symptoms) results in larger amygdala structures, while others (e.g., severe forms of abuse) result in smaller amygdala structures [28]. In contrast, the findings regarding the functional role of the amygdala in those exposed to ACEs are more uniform, such that individuals exposed to ACEs often have a more active amygdala compared to unaffected individuals [79]. In relation to the hippocampus, [PERSON] et al. identified several studies that demonstrated a reduction in hippocampal volume among individuals exposed to ACEs compared to unexposed individuals, with evidence of sex-based differences such that males showed more reduction than females [28]. Given that the hippocampus plays a role in inhibiting the HPA axis, a decrease in hippocampal volume is expected to upregulate the HPA axis, increasing one's risk of developing certain mental health conditions (e.g., post-traumatic stress disorder) [28]. Other brain structures (e.g., different gyri such as the superior frontal gyrus and the middle temporal gyrus) have also been observed to change after exposure to ACEs though these structures are less studied [80]. Overall, research points to the role that ACEs play in shaping the brain. The hippocampus and amygdala are the most studied and could be targeted to help alleviate or prevent mental health conditions. For example, interventions targeting stress reduction (e.g., mindfulness-based interventions) have demonstrated reductions in gray matter density in the amygdala structure, reflecting the neuroplastic nature of the brain [81].
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 4 Experiment Results
### Implementation Details  
The PyTorch and GPyOpt machine learning frameworks were used to develop and validate our works. To apply our proposed algorithm, we trained ResNet50 [32] with CIFAR-100 [33] dataset. In addition, we also trained another network called MobileNetV2 [34] with EMNIST [35] dataset to evaluate the robustness of our proposed algorithm. To make new classes have similar features as the old classes, we divided the dataset to several sets, which were chosen randomly and mutually exclusive. As shown in Figure 2, we trained a base network with some randomly chosen classes, and then we updated the network with remaining classes by retraining a new branch network only. When starting to retrain the branch network, we used the cloned weights of the base network instead of randomly initialized weights to have a good starting point for learning a new task. We set the threshold of the combined classification accuracy to 2% or 3% less than the baseline accuracy, which was the combined classification without any layer sharing of the base network. The average computational time to train the branch network searching for a specific possible layer case took around 52.6 min on a machine with i7 3.4 GHz CPU and two GTX 1080 graphic cards. Furthermore, as the number of sharing layers increased,the number of layers in branch networks needed to be retrained decreased, and then the overall training time decreased.
# Robotics Applications in the Hospital Domain:
## 4 Robotic Systems in Hospitals Settings
### Transportation and Logistics Robots
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Robot System** & **Omnon Self-Navigating Autonomous Mobile**[46] & **TUG Intelligent Autonomous Mobile Robot**[22] & **Robotnik RB1—Versatile Autonomous Mobile Platform**[44] \\ \hline \multirow{4}{*}{**Description**} & Used in hospitals to optimize internal logistics and improve the efficiency of delivery of supplies, equipment, and other essential materials (cost \(\approx\) EUR 40 k) [46]. & Used in hospitals to optimize internal logistics, improve operational efficiency, and ensure a safe and hygienic environment for patients and staff (cost \(\approx\) EUR 13 k) [22]. & Used for delivering medications, transporting medical equipment, and removing waste in hospitals. This versatility makes it suitable for a wide range of tasks (cost \(\approx\) EUR 44 k) [44]. \\ \hline
**Stage** & Operational stage [46]. & Operational stage [22]. & Operational stage [44]. \\ \hline \multirow{4}{*}{**Features**} & Dynamically move materials in challenging environments: the robot can navigate through complex hospital layouts and adapt to changing environments without manual intervention. & Automatic docking and loading, automatic delivery, removal of trolleys. These features allow the TUG to operate autonomously, reducing the need for human intervention. & Versatile autonomous mobile platform that can be adapted to various log critical tasks in a hospital environment, such as delivering medicines, transporting equipment and materials, collecting waste and laundry, general logistical support, etc. \\ \hline \multirow{4}{*}{**Advantages**} & Does not require any modifications: it can be deployed in existing hospital infrastructure without needing significant changes. & Does not require any infrastructure for navigation: can navigate using its built-in sensors, making it easy to implement in various environments. & Modular design allows for easy adaptation to different environments and tasks: can be customized with different modules to handle various tasks, making it highly versatile. \\ \hline \multirow{4}{*}{**Disadvantages**} & Integration with other robotic systems can be complex, potentially limiting its use in more advanced automated multi-robot setups. & The robot system can only transport some specific carts, and is not compatible with all types of hospital trolleys. & May require specialized training for operation and maintenance; initial setup costs. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Related logistics and transport system robots.
## 6 Experimental Results
### Comparative Analysis  
In this supplementary section, the proposed MTS-SDWSN model is compared with two major existing models: Secure and Energy-aware Multi-hop Routing Protocol for WSN (SEMRP-WSN) [32] and Energy-Efficient Dynamic and Adaptive State Scheduling (EDASS) [33]. Those works are compared in terms of performance metrics such as energy consumption (J), packet delivery ratio (%), end-to-end delay (s), and attack mitigation/prevention rate (%).
# Unranking Small Combinations of a Large Set in Co-Lexicographic Order
## 1 Introduction
In addition, there are studies dedicated to the special case of developing new efficient algorithms for ranking and unranking \(m\)-combinations of an \(n\)-set where the obtained algorithms are independent of \(n\). Hence, such algorithms are effective when \(n\) is large and \(m\) is small. For example, [PERSON] et al. [29] presented \(O(m^{3m+3})\)-time ranking and unranking algorithms. These algorithms use several bijections from the set of combinations to other finite sets. Moreover, the order of the generated combinations is not one of the well-known variants for ordering the elements of combinatorial sets. [PERSON] and [PERSON] [30; 31] introduced another approach for unranking small combinations of large sets by using a gradient-based optimization algorithm. The order of the generated \(m\)-combinations of an \(n\)-set is the revolving door order starting with \((1,2,\ldots,m-1,m)\) and ending with \((1,2,\ldots,m-1,n)\). To search for each element \(c_{i}\) of the combination \(c=(c_{1},c_{2},\ldots,c_{m})\), it is necessary to solve a constrained minimization problem by a gradient-based optimization. This unranking algorithm has \(O(m^{2})\) time complexity when using a single processor, and \(O(m\log m)\) when using at most \(O(m/\log m)\) processors. However, there is no information about any algorithm for ranking combinations in this order. This paper focuses on unranking small \(m\)-combinations of a large \(n\)-set with using co-lexicographic order. The organization of this paper is as follows. Section 2 presents a brief description of a way for ranking and unranking combinations in co-lexicographic order and a detailed description of the proposed modification of the original unranking algorithm. In Section 3 we confirm the effectiveness of the proposed modification by performing several computational experiments and obtaining the computational complexity for the presented algorithm.
# The Long and Winding Road to Cardiac Regeneration
## 4 Biomaterials
### Natural Biomaterials
These polysaccharides provide structural support, promote cell adhesion, and modulate cellular behavior, making them valuable for cardiac scaffold development [179; 219]. The unique properties of these natural biomaterials allow for their tailored use in cardiac tissue engineering. They can be processed into different forms, including hydrogels, films, and 3D scaffolds, providing structural support and an optimal microenvironment for cell attachment, proliferation, and differentiation. Furthermore, these biomaterials can be combined with growth factors, drugs, or cells to enhance their therapeutic potential and promote tissue regeneration [220]. Natural biomaterials, including collagen, fibrin, gelatin, silk, chitosan, alginate, and hyaluronic acid, offer a wide array of options for cardiac tissue engineering. Their biocompatibility, biodegradability, and ability to mimic the ECM make them ideal candidates for the development of cardiac scaffolds [178]. Yet, amidst their inherent advantages of introducing a biocompatible and bioactive approach to tissue restoration, these natural materials also introduce specific considerations that necessitate deliberate attention [221]. The controlled biodegradation of these materials, although a desired trait, requires precise regulation. Excessive degradation velocity risks compromising the structural robustness of the regenerative framework, thereby affecting its enduring stability and efficacy. Natural biomaterials, harvested from biological sources, may exhibit variability across different batches or donors. Achieving uniform quality and consistent properties across diverse lots is challenging. The derivation of natural biomaterials from animal or human sources might entail the potential hazard of disease transmission or contamination. Rigorous processing, sterilization, and unwavering quality control stand as pivotal safeguards against these risks, upholding patient safety [222]. While natural biomaterials hold promise in creating a suitable microenvironment for cell attachment and signaling, they might be deficient in specific mechanical or biochemical attributes requisite for optimal cardiac tissue rejuvenation. Procuring ample quantities of these materials for clinical application is not without challenges. Their limited availability could potentially encumber the scalability of treatments, raising pertinent concerns. The temporal degradation and remodeling of natural biomaterials might precipitate alterations in their mechanical properties or bioactivity over time [223]. It is crucial to acknowledge that some patients might manifest allergic reactions or sensitivities to constituents present in natural biomaterials [224]. Thus, the identification and addressing of potential allergens are pivotal prerequisites to prevent any untoward reactions. The seamless amalgamation between natural biomaterials and native cardiac tissue represents an undertaking. Inadequ integration could result in the formation of scar tissue, compromised functionality, or incomplete regeneration [225].