esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Varying Communication Channel High Altitude Platform Station Link Budget and Channel Modeling
## 1 Introduction
[35] addressed the problem of efficient power allocation in the uplink of two-tier closed-access femtocell. Networks and the operational effectiveness of the proposed approach is evaluated through modeling and simulation. [PERSON] et al. [36] considered a downlink resource allocation in an Orthogonal Frequency Division Multiple Access (OFDMA) based heterogonous cellular network and proposed Non-Exclusive Spectrum Allocation scheme (NESA). [PERSON] et al. [37] tackled the problem of joint users' uplink transmission power and data rate allocation in multi-service two-tier femtocell networks. [PERSON] et al. [38] investigated the optimal power allocation problem for a femtocell network where the utilized air interface is single carrier frequency division multiple access (SC-FDMA) and derived the optimal power allocation when we protect the data rate of macro users by imposing a temperature limit on the interference arising from femto users to the macro base station. [PERSON] et al. [39] proposed a novel utility-based game theoretic frame work towards simultaneously allocating users' uplink transmission power and rate in multi-service two-tier open-access femtocell networks. These literatures did not consider the Hamming, PSK and Golay code mode. This paper introduced Hamming, PSK and Golay code into the HAPS wireless communication channel. This paper mainly combines spatial communication links under different carrier-to-noise ratio, elevation angle, and coding. The HAPS channel model of its near space is established and simulated. The remainder of this paper is organized as follows: In Section 2 we propose the system model. HAPS link budget and channel modeling are researched in Section 3. We discuss our findings by simulation analysis in Section 4, and conclude in Section 5.
## 3 Proposed Approach
### Sine-Cosine Algorithm  
Sine-Cosine algorithm (SCA) is a population-based optimization algorithm that was introduced by [PERSON] in 2016 [79]. The main idea of SCA is that each solution will update its position with respect to the position of the best solution in the search space using Equations (11) and (12).  
\[X_{i}^{k+1}=X_{i}^{k}+r_{1}\times sin(r_{2})\times|r_{3}P_{i}^{k}-X_{i}^{k}| \tag{11}\]  
\[X_{i}^{k+1}=X_{i}^{k}+r_{1}\times cos(r_{2})\times|r_{3}P_{i}^{k}-X_{i}^{k}| \tag{12}\]  
where \(X_{i}^{k}\) represents the position of the current solution in the \(i\)th dimension at iteration \(k\). \(P_{i}^{k}\) represents the \(i\)th dimension of the best solution so far, \(r_{1}\), \(r_{2}\), and \(r_{3}\) are three random variables, and \(||\) indicates the absolute value. To simplify Equations (11) and (12), both equations have been combined for final position updating as shown in in Equation (13).  
\[X_{i}^{k+1}=\begin{cases}X_{i}^{k}+r_{1}\times sin(r_{2})\times|r_{3}P_{i}^{k} -X_{i}^{k}|&,r_{4}<0.5\\ X_{i}^{k}+r_{1}\times cos(r_{2})\times|r_{3}P_{i}^{k}-X_{i}^{k}|&,r_{4}\geq 0.5 \end{cases} \tag{13}\]  
where the parameter \(r_{1}\) determines the updating direction, that represents the space between \(X_{i}^{k}\) solution and \(P_{i}^{k}\) solution. The parameter \(r_{2}\) determines the updating distance between the current solution and the best solution so far. The parameter \(r_{3}\), however, balances emphasizing or de-emphasizing the influence of desalination in describing the distance by giving random weights for the best solution \(P_{i}^{k}\). Finally, the parameter \(r_{4}\) is used to switch between the sine and cosine components in Equation (13). Figure 8 demonstrates the switching mechanism between sine and cosine algorithms with the range in \([-2,2]\). The exploration process in SCA is guaranteed in this range, since each solution may update its location outside the feasible search space.  
Any metaheuristic algorithm should achieve a proper trade-off between exploration and exploitation processes. In SCA, this balance between exploration and exploitation through optimization is obtained by decreasing the range of sine and cosine, as shown in Equation (14).  
\[r_{1}=a-k\frac{a}{K} \tag{14}\]  
where variables \(k\) and \(K\) represent current, and maximum iterations, respectively. \(a\) is a constant. Figure 9 explores the way of decreasing the range of the sine and cosine after a set of iterations at \(a=3\). Algorithm 3 presents the pseudo-code of the SCA algorithm.  
Figure 8: Solution update process toward or moving away from the best solution.
## 2 Materials and Methods
### Sample Preparation and Analysis
#### 2.2.1 Sample Preparation and Stable Isotope Analysis  
Using dry combustion (550 \({}^{\circ}\)C) in excess oxygen for 3 h, 5 mg of each berry sample was converted to CO\({}_{2}\). The resulting CO\({}_{2}\) was isolated from the other combustion gases by a cryogenic separation and then measured by isotope ratio mass spectrometry (IRMS).  
The isotopic results are reported in \"so-called\" \(\delta\) notation (isotopic composition or isotopic fingerprint/signature) relative to international standards, in compliance with Equation (1) [22]:  
\[\delta^{i}X=\frac{R_{sample}}{R_{standard}}-1, \tag{1}\]  
where \(i\) is the mass number of the heavier isotope of the element \(X\) (e.g., \({}^{13}\)C, \({}^{2}\)H, \({}^{18}\)O), \(R_{sample}\) is the isotope number ratio of a sample (\({}^{13}\)C/\({}^{12}\)C, \({}^{2}\)H/\({}^{1}\)H, \({}^{18}\)O/\({}^{16}\)O), and \(R_{standard}\) is that of the international standard (V\({}_{\rm PDB}\)--Vienna Pee Dee Bellemite for \(\delta^{13}\)C, and V\({}_{\rm SMOW}\)--Vienna Standard Mean Ocean Water for \(\delta^{2}\)H and \(\delta^{18}\)O). The delta values are multiplied by 1000 and are expressed in units \"per mil\" (%).  
To obtain the \({}^{13}\)C isotopic fingerprint of CO\({}_{2}\) resulting from berries, an isotope ratio mass spectrometer (Delta V Advantage, Thermo Scientific, Waltham, MA, USA) in line with a dual inlet system was used. Every day, before the samples' analysis, one working standard was measured. Its calibration was performed versus NBS-22 oil (IAEA--International Atomic Energy Agency)-certified reference material (\(\delta^{13}\)CVPDB = \(-\)30.03%). All berry samples were measured in duplicate. The uncertainty was \(\pm\)0.3%.  
A liquid-water isotope analyzer (DLT-100, Los Gatos Research, San Jose, CA, USA) was used to record the isotopic fingerprint of \(\delta^{2}\)H and \(\delta^{18}\)O from the water previously extracted from berries. A set of five working standards was available (working standard 1, \(\delta^{18}\)O = \(-\)19.57 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)154.1 \(\pm\) 1%; working standard 2, \(\delta^{18}\)O = \(-\)15.55 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)117.0 \(\pm\) 1%; working standard 3, \(\delta^{18}\)O = \(-\)11.54 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)79.0 \(\pm\) 1%; working standard 4, \(\delta^{18}\)O = \(-\)7.14 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)43.6 \(\pm\) 1%; working standard 5, \(\delta^{18}\)O = \(-\)2.96 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)9.8 \(\pm\) 1%). The uncertainty was \(\pm\)0.2% for \(\delta^{18}\)O and \(\pm\)1% for \(\delta^{2}\)H.
###### Abstract  
To our knowledge, the potential use of CO\({}_{2}\) as a heat-transmitting fluid for cooling applications in power plants has not been explored very extensively. In this paper, we conduct a theoretical analysis to explore the use of CO\({}_{2}\) as the heat transmission fluid. We evaluate and compare the thermophysical properties of both dry air and CO\({}_{2}\) and perform a simple analysis on a steam-condensing device where steam flows through one of the flow paths and the cooling fluid (CO\({}_{2}\) or air) is expanded from a high-pressure container and flows through the other. Sample calculations are carried out for a saturated-vapor steam at 0.008 MPa and 41.5 \({}^{\circ}\)C with the mass flow rate of 0.01 kg/s. The pressure of the storage container ranges from 1 to 5 MPa, and its temperature is kept at 35 \({}^{\circ}\)C. The pressure of the cooling fluid (CO\({}_{2}\) or dry air) is set at 0.1 MPa. With air as the heat-removing fluid, the steam exits the condensing device as a vapor-liquid steam of 53% to 10% vapor for the container pressure of 1 to 5 MPa. With CO\({}_{2}\) as the heat-removing fluid, the steam exits the device still containing 44% and 7% vapor for the container pressure of 1 MPa and 2 MPa, respectively. For the container pressure of 3 MPa and higher, the steam exits the device as a single-phase saturated liquid. Thus, due to its excellent Joule-Thomson cooling effect and heat capacity, CO\({}_{2}\) is a better fluid for power plant cooling applications. The condensing surface area is also estimated, and the results show that when CO\({}_{2}\) is used, the condensing surface is 50% to 60% less than that when dry air is used. This leads to significant reductions in the condenser size and the capital costs. A rough estimate of the amount of CO\({}_{2}\) that can be stored and utilized is also carried out for a steam power plant which operates with steam with a temperature of 540 \({}^{\circ}\)C (813 K) and a pressure of 10 MPa at the turbine inlet and saturated-vapor steam at 0.008 MPa at the turbine outlet. The results indicate that if CO\({}_{2}\) is used as a cooling fluid, CO\({}_{2}\) emitted from a 1000 MW power plant during a period of 250 days could be stored and utilized.  
heat transfer fluid; Joule-Thomson effect; CO\({}_{2}\) storage; steam condensation; power plant cooling +
Footnote †: journal: Applied Sciences
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 5 Experiment Results and Discussions
### The Flexibility of Data-Driven Function  
Comparing with the event-driven optimization model of the DIAT architecture and the E-SODA of the CEB Atlas architecture, the data-driven feedback model component of our proposed architecture provides a greater degree of flexibility for users to customize the algorithms according to the need of users, as the data-driven function is developed as a standalone component. It can operate independently by only receiving data processed by the central control system component. The comparison of the proposed IoT architecture, DIAT and CEB Atlas architecture is shown in Figure 15. In the figure, we can observe that our proposed architecture have a loosely-coupled data processing function and data-driven models development environment. It is different from the data-driven models of the other two architectures, which have combined the data processing function and the optimization algorithm. One of the advantages of the component-based IoT architecture proposed is the flexibility of each component to be attached or detached, without affecting the operation of the remaining components. As a result, the data-driven functions are flexible to be updated, customized or altered easily. Moreover, the data-driven model selector embedded in the system serves as an optimization model evaluator and recommender that provide higher efficiency and performance yield for real-world application.  
Figure 14: Minimum requirement for system interoperation for proposed architecture, DIAT and CEB Atlas.  
Figure 15: Data-driven function flexibility comparison between proposed architecture, DIAT and CEB Atlas.
## 3 Theoretical Part
### Digitalization and 3D Modeling of Cultural Heritage
#### 3.3.3 The Role of the Metaverse Today  
The metaverse, as a concept that combines AR, VR, and other immersive technologies into a unique digital environment, is becoming increasingly significant in contemporary society. Its role today extends far beyond the realms of entertainment and video games, crossing into the spheres of education, work, commerce, and even the preservation of cultural heritage.  
One of the key roles of the metaverse is the transformation of how we communicate and collaborate. By creating immersive, three-dimensional worlds, the metaverse enables users to connect and collaborate in real time, regardless of their physical locations. This technology offers new opportunities for teamwork, particularly in the context of remote work, where users can share information and collaborate in a shared virtual space, creating a sense of presence and closeness that transcends traditional forms of online communication.  
In education, the metaverse offers revolutionary opportunities for creating interactive and engaging experiences. Instead of passive learning, students can participate in simulations, virtual field trips, and other forms of interactive content that allow for deeper understanding and retention of information. For example, history students can \"visit\" ancient civilizations and explore historical events firsthand, while medical students can practice surgical procedures in a safe, virtual environment. The metaverse, therefore, is not just a tool for learning but also a means to foster critical thinking, creativity, and innovation.  
In commerce, the metaverse enables the creation of entirely new shopping experiences. Users can \"visit\" virtual stores, try on products through AR and VR technologies, and make informed purchasing decisions. Brands use the metaverse to create personalizedexperiences for their customers, allowing them to connect with products and services in new and exciting ways.  
In today's world, the role of the metaverse is becoming increasingly important. As a comprehensive digital platform, the metaverse not only transforms the ways we work, learn, and communicate but also opens new horizons for preserving and promoting cultural heritage. Its further development promises to enrich human experiences, creating new opportunities for interaction, education, and the preservation of our shared values [35].
# Smart Hospitals and IoT Sensors: Why Is QoS Essential Here?
## 5 Discussion
### Lesson 4: Critically Analyze the Results to Find Solutions  
One of the main problems when running a distributed system is a bottleneck. This problem occurs when a system provides services for many clients through the same node or module. As the number of clients increases, the node workload also increases. However, due to hardware limitations, this node can experience a performance loss due to overload periods. The node cannot handle the amount of work, which affects its performance directly. We removed this problem from the sensors by dividing the amount of work among several Data Gateway instances. Each Gateway serves only one client connection from the Core. Additionally, the QoS Service Elasticity strategy brings benefits directly to the sensors, improving data collection performance.  
Looking at the evaluation we performed, we still suffered from a bottleneck at the Core level. As we experimented with the middleware with only one core instance, we could visualize the delay increase according to client applications' growth. On the bright side, the services stacked at the sensors level helped to improve the results, even when the ascending delay occurred. Additionally, our middleware model also comprises resource elasticity, although we did not include it in the experiments. Including an additional QoS service can resolve directly the increasing effect: admission control. This strategy is common in network QoS solutions. Its main benefit is to prevent too many connections in the system. Instead of accepting all connections, client applications can only connect while the system supports them. Therefore, the system can deliver a stable performance for applications that are already connected.  
As a final point, the analysis of results must consider the medical point of view. Presenting results to the medical team is an important step. They can provide a different vision of the information the research team can extract from the results. Workshops and meetings with the medical partner are part of this process. In such moments, discussion raise possible customization to guide the next steps. It highlights the main aspects that the research team should address to achieve different outcomes.
## 4 Recent Developments in IRT Detection
### IRT Image Segmentation  
The basic method of image segmentation is thresholding, where an image is extracted based on a specified threshold value [58], the size of which can be determined in different ways, for example, by direct selection [64] or by finding the intersection of approximations of histogram values by normal distribution functions with peaks corresponding to the two highest histogram values [67]. [PERSON] et al. used thresholding to isolate the object from the background. They further conducted segmentation based on a multivariate co-occurrence matrix of attributes such as brightness level or brightness gradient [79]. For image binarization, i.e., for segmenting points into two categories, the Otsu method is often applied, which automatically finds the optimal threshold by adjusting the distribution of gray levels (brightness) into two classes with minimum within-class variance and maximum between-class variance [65, 88, 89]. [PERSON] et al. consider this method to be of reasonable quality to determine the background image [100]. A binary image can also be obtained in the case of setting a threshold in the form of a condition instead of a specific value [74]. [PERSON] et al. used triangular thresholding to binarize images, where for each histogram point between the peak and the end value, the distance to the line passing through the peak and the end point were calculated, and where the threshold was determined as the position at which the distance between the histogram points and the line is maximum [95]. K-means segmentation algorithm is used in IRTimage processing to reduce the number of colors by dividing them into K categories; thereby, the image is segmented into clusters representing the selected categories [78]. At the beginning of segmentation, points are randomly assigned to clusters; then the clusters are continuously updated, and points are reassigned between clusters so that the similarity of point values increases within the same cluster and decreases between different clusters [94]. The number of clusters can be quite low, for example, three [112], four [113], or six [80]. [PERSON] and [PERSON] used K-means segmentation to reduce the number of prototype symptom descriptors [63]. Another type of segmentation method is edge detection [92], with its typical examples including Sobel, Prewitt, or Canny edge detectors [66; 114]. [PERSON] et al. incorporated a phase matching-based edge detector into their algorithm for automatically matching infrared and visible images [84]. Edge detection can be followed up by detecting straight lines or circles with the Hough transformation, which is not prone to noise and missing image data [71; 90].
## 3 Experiments and Results  
### Complexity Analysis  
When calculating the complexity of the model, the influence of the activation function on the complexity of the model is neglected because the activation function has little influence on the complexity of the model. The formula for calculating the complexity is as follows:  
\[\left[\left(\sum_{l=1}^{D}K_{l}\times C_{l-1}C_{l}\right)S_{l}\right] \tag{23}\]  
where \(D\) is the network depth, \(K_{l}\) is the convolution core size of layer \(l\), \(C\) is the number of channels, \(S_{l}\) is the size of the input image. Formula (23) shows that the complexity of the network model is proportional to the size of the HR image, and the impact of the middle layer on the complexity of the network model is relatively greater than that of the first and last layer. In SRCNN, the size of the convolution core in the first layer is set to \(9\times 9\), but since our algorithm uses the original low-resolution image as input, the smaller convolution core can be applied to extract the same features. The size of the convolution core in the first layer is \(5\times 5\), and the size of the convolution core in the latter layer is \(3\times 3\). The following table compares the complexity of the 9-5-5 SRCNN algorithm with that of MPSR algorithm. According to Table 1, the complexity of the algorithm MPSR is 3.9 times lower than that of SRCNN.
# Analysing Mouse Skin Cell Behaviour under a Non-Thermal kHz Plasma Jet
## 3 Results
### Cell Viability  
Cell viability is represented as the percentage of viable cells normalised to the control group (Figure 6) and is highly dependent on chosen parameters. As the cells were treated via direct plasma treatment, it was expected that the closest distance of 10 mm--where the plasma was in contact with the cell culture medium--would have the biggest effect on cell viability. Results show that less than 60% of cells are still viable after just 10 s of plasma treatment, and there are almost no viable cells as treatment time increases further. Consequently, greater distances, e.g., 15 mm and 20 mm, leave more viable cells in well plates. However, after just 60 s of plasma treatment, the number of viable cells dropped below 20% at a distance of 20 mm, and none were viable for shorter distances.  
Figure 5: Concentration of NO\({}_{2}\)\({}^{-}\) (**left**) and H\({}_{2}\)O\({}_{2}\) (**right**) in PAM measured (**a,b**) 0 h, (**c,d**) 24 h and (**e,f**) 72 h after plasma treatment. Results are presented as mean values \(\pm\) SEM. \({}^{*}\)\(p\)\(<\) 0.03; \({}^{**}\)\(p\)\(<\) 0.002; \({}^{***}\)\(p\)\(<\) 0.0002; \({}^{***}\)\(p\)\(<\) 0.0001 (two-way ANOVA, [PERSON]’s post-tests; n \(>\) 3).
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
### Strategies to Improve Ionic Conductivity of SPEs
#### 3.2.3 Treatment of SPEs
The SPEs were irradiated with electron beam and gamma ray separately at two different doses (50 and 150 kGy), and it was found that, at the 150 kGy dosage, both types of radiation gave a higher conductivity (of the order of 10\({}^{-4}\) S cm\({}^{-1}\)) than that irradiated at the 50 kGy dose (around 10\({}^{-5}\) S cm\({}^{-1}\)) [81]. The authors have concluded that the electron-beam treatment is an effective approach as compared to gamma-ray radiation in making the SPE more conducting due to reduction in crystallinity. Recent work illustrated contradictory results, wherein no effect was observed on the conductivity upon neutron irradiation at varied dosages (15 to 47 kGy) on PVDF-LiBOB SPEs prepared via solution-casting route [82]. Ambient conductivity was the highest at 1.27 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\) for the non-irradiated SPE as compared to similar electrolyte after exposed to neutron radiation with lowest value at 4.30 \(\times\) 10\({}^{-7}\) S cm\({}^{-1}\) for SPE subjected to 47 kGy dose [82]. This was attributed to decrement in \(\mu\) and \(D\), as well as higher degree of crystallinity. The authors have deduced that an improvement in mechanical integrity in the electrolyte has occurred after being irradiated with a neutron [82]. [PERSON] et al. [83] have observed that there was a slight decrement in conductivity at ambient temperature for the solution-cast-prepared SPE based on PEO with ammonium perchlorate (NH\({}_{4}\)ClO\({}_{4}\)) salt after being irradiated with gamma rays, followed by minor conductivity increment at 50 \({}^{\circ}\)C. In the following sections, additional ingredients such as secondary polymer, plasticizers, fillers and other additives that have been integrated into various SPEs are discussed.