esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synthesis of Amine-Functionalized Silica/Organosilica Membranes
### Sol-Gel Route  
The sol-gel technique is commonly used in the preparation of silica-based membranes, and it allows the hydrolysis and condensation processes to be performed under mild conditions (low-to-moderate temperature). Therefore, amine-functionalized silica-based membranes fabricated via sol-gel processing retain as much of amine groups as possible. To achieve uniform architectures with intercrystallite voids ranging from nano or sub-nano levels, the so-called \"polymer\" route is always considered. As mentioned above, in our earlier work, by adopting this method, as well as a moderate calcination temperature, we developed a series of amine-containing organosilica membranes for the utmost retention of amine groups. Following a similar route, much effort has been devoted to the fabrication of amine-functionalized silica-based membranes with microporous or nonporous microstructures [6; 7; 30]. The synthesis conditions and membrane performance are briefly summarized in Table 1. Note that these reported membranes have demonstrated scattered results concerning CO\({}_{2}\) separation performance in terms of permeance and selectivity. To some extent, the possible defects usually presented in silica-based membranes combined with a relatively wider pore size distribution may result in poor CO\({}_{2}\) separation performance. On the other hand, unhindered amines, such as APTES, were generally employed to serve as active sites for the reversible reactions with CO\({}_{2}\), which might show confined CO\({}_{2}\) mobility within these membranes due to the formation of stable carbamates. In cases, such as refs. [6; 30], membranes with precisely controlled pore sizes have demonstrated relatively high CO\({}_{2}\) separation performance. However, in those studies, the effect that amine type exerted on the membrane performance was not involved. In our previous studies, membranes with tertiary or sterically hindered amines showed a greater potential for CO\({}_{2}\) separation compared with membranes that contain unhindered amines fabricated under the same conditions. These studies are systematically compared here.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 5 Conclusions  
In this paper, we propose a high-precision ranging scheme based on 5G NR Co-Band signals. This scheme does not take up additional 5G communication resources and can realize fast capture and high-precision tracking of signals in multipath environments. Firstly, the receiving and transmitting models of the Co-Band PRS signal are introduced, and the general framework of the timing recovery scheme for this signal is provided. Then, we have designed a weighted window capture method to enhance the coarse synchronization performance of Co-Band signals in low-SNR conditions. We improve the accuracy of path time delay estimation and multipath tracking by pre-estimating the communication signals using the low-power characteristics of Co-Band signals. In addition, we propose the iterative DLL method, and simulation results show that the code phase RMSE is \(1.81\times 10^{-2}\) samples. Finally, the USRP is utilized to construct an indoor multipath environment, and the final ranging accuracy reaches 0.16 m@90%.  
However, due to the experimental conditions, only the ranging performance in an indoor environment is measured in this paper. The focus of future research will be on the design of higher-order phase-locked loop-aided DLL for highly dynamic environments and how to overcome the near-far effect under multi-base station conditions.  
[PERSON] and [PERSON] are both the principal investigators; [PERSON], [PERSON] and [PERSON] assisted [PERSON] in conceiving and conducting the experiments and analysis; [PERSON] analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Key Research and Development Program of China (No. 2022 YFB3904603).  
Figure 13: Test results in real environment.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 System Model and Problem Formation
### Assumptions and Calculations  
#### 3.3.1 Traffic Characteristics  
We consider only unicast traffic that contains a single source and single destination. QoS provisioned paths are computed for each single flow using APAC [52]. Thus, each end-to-end path is attached with each unicast flow. The end-to-end path for each flow remains constant.
# Robust Fuzzy Adaptive Sliding Mode Stabilization for Fractional-Order Chaos
## 3 Introduction to Fuzzy Systems  
A fuzzy control system (FLS) involves the control of a process using fuzzy linguistic descriptions. It has the advantage of easy development, implementation and maintenance of control systems [32]. It consists of four parts: the knowledge base, the fuzzifier, the fuzzy inference engine working on fuzzy rules, and the defuzzifier. The knowledge base for FLS is comprised of a collection of fuzzy If-then rules of the following form:  
\(R^{j}\)_: If \(x_{1}\) is \(F_{1}^{j}\) and \(x_{2}\) is \(F_{2}^{j}\) and... and \(x_{n}\) is \(F_{n}^{j}\), then \(y\) is \(\varphi^{j}\), \(j=1,2,...N\)_  
where \(x=(x_{1},...,x_{n})^{T}\) and \(y\) are the FLS input and output, respectively. Fuzzy sets \(F_{i}^{j}\) and \(\varphi^{j}\) are associated with the fuzzy functions \(\mu_{F_{i}^{j}}(x_{i})\) and \(\mu_{\varphi}(y)\), respectively. \(N\) is the number of rules. Through singleton function, center average defuzzification, and product inference [33], the fuzzy logic system can be expressed as:  
\[y(x)=\theta^{T}\xi(x)=\xi(x)^{T}\theta \tag{14}\]  
where \(\theta=[y_{1},y_{2},...,y_{N}]^{T}=[\theta_{1},\theta_{2},...,\theta_{N}]^{T}\) is a vector of the adjustable factors of the consequence part of the fuzzy rule and \(\xi(x)=[\xi_{1}(x),\xi_{2}(x),...,\xi_{N}(x)]^{T}\) is a regressive vector with the regressor (fuzzy basis functions) \(\xi_{i}(x)\) defined as (see [34]),  
\[\xi_{i}(x)=\frac{\prod_{i=1}^{N}\mu_{F_{i}^{j}}(x_{i})}{\sum_{j=1}^{N}\left( \prod_{i=1}^{N}\mu_{F_{i}^{j}}(x_{i})\right)} \tag{15}\]
## 3 Case study: Social Housing Stock Managed by EVha
### Block Consideration  
Attending to a general overview on the number of flats, and due to the historical matters commented, the study covers the total number of promotions historically managed by EVha even those that were amortized. Apart from the more than 14,000 dwellings in EVha management, a total number of 35,571 dwellings have been studied for the analysis. Attending to provinces, around 60% lay in Valencia, 30% in Alicante, and only 10% in Castellon.  
The first global approach of the buildings concerns their physical definition in blocks. The different buildings have been considered taking into account the physical location in the city, the height of the building and the orientation. For instance, when a building was composed of different heights (number of floors), for the purpose of this study, they have been considered as different building blocks. In other cases, the orientation of the flat may change such as in an L-shaped building or a flat at the endpoint of the building. In many cases, the conception of \"block\" has been closely related to that of \"staircase\", due to those possible changes of height, ground-floor use, orientation, or other variations in the linear, entire blocks.
## 5 Results and Discussions  
Performance of the proposed controller in the dynamic operating condition is surveyed in this section. Also, effects of the different virtual mechanical factors including the virtual inertia and the mechanical power error are assessed. The values of simulation parameters are specified in Table 1. The general structure of the proposed controller is given in Figure 8.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 1 Introduction
As a result, we obtain a modified audio signal whose appearance resembles a noise signal, i.e., it does not represent correlation with the original samples, providing a high level of security and confidentiality to third parties who wish to know the content of the said signal.
## 2 Materials and Methods
### Fuzzy Self-Tuning Particle Swarm Optimization  
Particle swarm optimization (PSO) is a widespread meta-heuristics for global optimization [15]. PSO is based on a population (i.e., the swarm) of candidate solutions (i.e., the particles) that move inside a bounded \(D\)-dimensional search space. The particles cooperate with the aim of identifying the global best solution to the problem under investigation, and the strategy exploited during the optimization balances the global exploration and the local exploitation capabilities of the particles. In particular, the convergence process is driven by two specific settings called cognitive attractor \(C_{\mathsf{cog}}\in\mathbb{R}^{+}\) and the social attractor \(C_{\mathsf{soc}}\in\mathbb{R}^{+}\) of the particles. The performance of PSO is also influenced by the so-called inertia factor \(\omega\in\mathbb{R}^{+}\), which is used to prevent chaotic movement of the particles, and by the maximum and minimum velocity (\(\vec{v}_{\mathsf{max}},\vec{v}_{\mathsf{min}}\in\mathbb{R}^{D}\)) allowed along each dimension of the search space, which can drastically affect the quality of the optimal solutions found. It is worth noting that the settings of this meta-heuristic must be accurately adjusted considering the characteristics of the optimization problem, a fine-tuning process that usually requires many trials, since these values cannot be determined analytically.  
To overcome this limitation, fuzzy self-tuning PSO [14], which is an improved version of PSO that does not require any user settings, has been recently introduced. FST-PSO dynamically adjusts the settings of each particle independently from the rest of the swarm thanks to a fuzzy rule-based system (FRBS). The FRBS evaluates the performances of the particle and its distance from the position of the current global best particle to (possibly) modify its settings during the optimization process.  
It has been shown in the literature that FST-PSO can outperform the classic PSO (and many competitor algorithms) in several benchmark [14] and real-world problems [21; 22; 23; 24].
# Comparison of Tensile and Fatigue Properties of Copper Thin Film Depending on Process Method
###### Abstract  
In this study, tensile and fatigue tests were performed and analyzed to evaluate an influence of fabrication method on mechanical characteristics of copper thin film which widely used in flexible printed circuit board (FPCB). In general, manufacturing methods are known to affect the mechanical properties of materials, especially for thin films. The copper thin film is manufactured by a rolling process or an electrodeposition process. Therefore, specimen for tensile and fatigue tests were fabricated using by etching process with the rolled and electrodeposited thin films. First, the tensile tests were performed to obtain the elastic modulus, 0.2% offset yield stress, and tensile strength of the rolled copper and the electrodeposited copper thin film. Second, the copper thin films in FPCB is most often subjected to mechanical or/and thermal cyclic loading. The fatigue tests were performed to compare the fatigue characteristics and to evaluate an influence of fabrication method. Tensile test results showed that the elastic modulus was similar for each process, but the 0.2% yield strength and ultimate tensile strength were greater in the rolling process. In addition, the fatigue test results show that the copper thin films by the rolling process are better than by the electrodeposition process in fatigue life in all region.  
thin film; tensile test; fatigue test; mechanical properties; fatigue properties +
Footnote †: journal: Applied Sciences
# Crawling Magnetic Robot to Perform a Biopsy in Tubular Environments by Controlling a Magnetic Field
## 6 Concluding Remarks  
We developed a CMR that can navigate through a tubular environment, control uncovering and covering motions of the biopsy needle, and extract sample tissue by utilizing an external magnetic field. The proposed CMR has the advantage of being able to use the needle selectively and safely via an external magnetic field. We proposed a design method and prototyped the proposed CMR using 3D printing technology. The mechanism of covering and uncovering motions utilized attractive and repulsive forces of magnets under external magnetic field. CMR is designed to navigate under 8 mT of external magnetic field. The puncturing force of the CMR was 0.46 N in 14 mT of external magnetic field, which satisfies the minimum force needed for biopsy. We verified experimentally the uncovering and covering motions of the cover and the ability of the CMR to extract tissue with the biopsy needle in a Y-shaped water-filled tubular environment.  
There are several future research areas of the proposed CMR to be applied to the human body. First, the CMR needs to be downsized in order to be able to navigate even through the blood-vessel environments. The size of the proposed CMR allows it to be applied to the common iliac artery or small intestine. Second, the structure of the proposed CMR needs to be improved considering the friction, flexibility, and shape of the tubular system of the human body and the pulsating blood flow. Third, a tracking system of the CMR is needed to identify the position and motion of the CMR inside the human body. Current X-ray imaging cannot detect the structures of the CMR except for the magnets. As a starting mechanism, we expect that the proposed CMR can contribute to extending magnetic robot applications in various medical fields.  
Project administration and Funding acquisition, G.J.; Investigation, E.J. and J.N.; Methodology, E.J. and J.N.; Validation, E.J. and W.L.; Visualization, E.J., W.L. and J.K.; Writing--original draft, E.J.; Writing--review & editing, G.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI9C1055).  
Not applicable.  
Not new data were created or analyzed in this study.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 10: (**a**) Experiment testing the crawling and biopsy motions. (**b**) Covered needle. (**c**) Uncovered needle. (**d**) Collected sample tissue.
## 2 Materials and Methods  
### Buffer Zones and Their Dimensioning According to [PERSON]  
Strategic reservoirs in DDMRP serve three basic purposes [1]:  
* Shock absorption: Dampening both supply and demand variability greatly reduces or eliminates the transmission of variability, which creates jitteriness and a bullwhip effect in the chain.
* Reduced running time: Separating the continuous time of the supplier from the consumable side of the tank immediately reduces lead times.
* Delivery order generation: All relevant demand information, delivery information, and inventory information are combined in the warehouse to create a \"net flow equation\" that is used in the planning and calculation of delivery orders. Thus, stacks are the heart of the scheduling system in DDMRP.  
The standard tray in DDMRP consists of three zones [38] (Figure 1):  
* Green zone: The green zone is the heart of the order generation process. It specifies the average frequency of orders and the typical order size.
* Yellow zone: The yellow zone covers the average need for an item during the rolling delivery time. The yellow zone is always calculated as the product of average daily usage and decoupled lead time.
* Red zone: The red zone is a security component embedded in the tray. It consists of two parts (a red base, and a red safety). The higher the variability associated with an item, the larger the red zone will be.  
The size of individual zones according to the Ptak and Smith methodology is determined depending on the combination of the stack profile and the individual characteristics of the item [7].  
Container profiles enable practical and efficient global management of large quantities of strategically disconnected parts. They represent a grouping of parts or similar characteristics. Tray profiles are families or groups of parts for which it makes sense to design a set of rules, guidelines, and procedures that can be applied in the same way to all members of that profile. For tray profiles, according to [1], the similarity of items is formed on the basis of three specific factors:  
* Item type (P--purchased items, M--manufactured items, D--distributed items).  
Figure 1: Buffer zones and purposes [1].  
* Lead time (S--short, M--medium, L--long). For manufactured parts, decoupled lead time (DLT) should be taken into account when evaluating the length of the running time.
* Variability (H--high, M--medium, L--low). When determining the profile of a storage tank, two types of variability are determined: demand variability (which assesses the risk of sharp fluctuations in the need for a given item) and supply variability (which assesses the risk and severity of failures in the sources of supply of a given item).  
According to the assigned stack profile, the stack is assigned a lead time factor and a variability factor. The values of the factors are determined on the basis of expert judgement from a range of recommended values (1).
## 2 Materials and Methods  
The proposed NSSRD framework, designed for the effective detection of low-altitude small drones, is composed of three main modules: grid partitioning, ROI image extraction, and enhanced object detection (Figure 1).  
First, the NSSRD framework divides the input image into N equal segments to efficiently detect small objects. Subsequently, through the ROI selection stage, only one segment, which is most likely to contain the object, is selectively extracted from the N segments, thereby minimizing unnecessary computations. This approach increases the processing speed and computational efficiency. The extracted segment undergoes a bilateral filtering process before being transformed into a high-resolution image to mitigate potential issues arising during resolution enhancement. This step suppresses noise that may be restored around the object during high-resolution transformation, thereby minimizing its negative impact on detection performance. The noise-supp  
Figure 1: Block diagram of the proposed NSSRD.  
formed into a high-resolution version using SRCNN, thereby maximizing the detail of the drone shape and improving detection accuracy. Finally, the transformed high-resolution image is passed through an object detector, ensuring successful object detection. The processing steps of the proposed NSSRD model are detailed in Algorithm 1.  
```
Input: input image I, image width W, image height H, maximum integer by which the image can be partitioned D, binarization threshold T, number of ROI images S Require: pretrained detector YOLO, weights (w\({}_{1}\), w\({}_{2}\), w\({}_{3}\)), biases (b\({}_{1}\), b\({}_{2}\), b\({}_{3}\)) Output: detections Bbox
1. Split input image I into D parts
2. for i = 1 : D //Loop for ROI image extraction
3. for j = 1 : D
4. \(\mathrm{I}_{(i)}^{\mathrm{gy}}\leftarrow\) Converting each grid image to grayscale
5. \(\mathrm{I}_{(i)}^{\mathrm{lap}}\leftarrow\) Laplacian filtering each grayscaled image
6. If \(\mathrm{I}_{(i,j)}^{\mathrm{lap}}(\mathrm{x},\mathrm{y})\)\(>\)= T, \(\mathrm{I}_{(i,j)}^{\mathrm{bin}}(\mathrm{x},\mathrm{y})\)\(\gets\) 1, otherwise \(\mathrm{I}_{(i,j)}^{\mathrm{bin}}(\mathrm{x},\mathrm{y})\)\(\gets\) 0 //Binarization
7. end
8. end if
9. if max\(\left(\mathrm{I}_{(i,j)}^{\mathrm{bin}}\right)==\) 1, \(\mathrm{I}^{\mathrm{coi}}\leftarrow\mathrm{I}_{(i,j)}^{\mathrm{bin}}\) //Select ROI
10. \(\mathrm{I}^{\mathrm{bil}}\leftarrow\) Bilateral filtering of ROI
11. for i = 1 : S //Loop for resolution enhancement
12. \(\mathrm{I}_{i}^{\mathrm{sr1}}\leftarrow\) max\(\left(0,w_{1}*\mathrm{I}_{i}^{\mathrm{bil}}+\mathrm{b}_{1}\right)\)
13. \(\mathrm{I}_{i}^{\mathrm{sr2}}\leftarrow\) max\(\left(0,w_{2}*\mathrm{I}_{i}^{\mathrm{sr1}}+\mathrm{b}_{2}\right)\)
14. \(\mathrm{I}_{i}^{\mathrm{sr}}\leftarrow\) w\({}_{3}*\mathrm{I}_{i}^{\mathrm{sr2}}+\mathrm{b}_{3}\)
15. end
16. Bbox= YOLO (I\({}^{\mathrm{sr}}\)) //Object detection
```  
**Algorithm 1**Noise Suppression Super-Resolution Detection (NSSRD)
# Infrared Fault Classification Based on the Siamese Network
## 3 Methodology
### Loss Function  
The loss function is one of the two essential elements required to compile a neural network model. This paper first classifies the required data, with two images of the same category assigned a label of 1 and two images of different categories assigned a label of 0. After these two images enter the common weight CNN feature \(h(x1)\) and \(h(x2)\), the similarity measure \(|h(x1)-h(x2)|\) is sent to the loss function for training and the optimization of network parameters. Comparative disadvantage can effectively handle pair data in the Siamese network. The comparison loss function is shown in Equation (5).  
\[TD^{2}+(1-Y)\max{(m-D)^{2}}, \tag{5}\]  
where \(D\) represents the Euclidean distance between two samples, while \(Y\) serves as the label of the sample pair. If two samples belong to the same class, \(Y\) is set to 1, indicating that both samples belong to the same category. \(N\) represents the number of sample pairs, and \(m\) is a threshold, which means only considering sample pairs with Euclidean distances between 0 and \(m\), and when the distance exceeds \(m\), their losses are considered to be zero.  
Our objective is to minimize the similarity loss of paired samples. When two samples belong to the same class, i.e., \(Y=1\), the loss can be calculated by:  
\[L(D,(Y,X_{1},X_{2}))=\frac{1}{2N}\sum_{2n}^{1}D^{2} \tag{6}\]  
When two samples fall into different classes, i.e., \(Y=0\), the loss can be expressed as:  
\[L(D,(Y,X_{1},X_{2}))=\frac{1}{2N}\sum_{2N}^{1}\max{(m-D)^{2}} \tag{7}\]  
This study aims to minimize the Euclidean distance within the feature space between similar classes while maximizing the distance between different classes. This comparative loss function effectively communicates the semantic relationship between samples. The BCELoss is employed in this paper to effectively classify solar panel infrared images.