esa-sceva/satcom-chunk-collection huggingface dataset:
# The Active Segmentation Platform for Microscopic Image Classification and Segmentation
## 2 Theory
### Image Moments  
In the continuous approximation, the moments of a planar function are defined by the integral  
\[M_{m,n}:=\iint\limits_{l}P_{m,n}(x,y)f(x,y)dxdy\]  
where \(P_{m,n}(x,y)\) is polynomial, parameterized by the integers \(m\) and \(n\). For example, the raw image moments are given by the homogeneous form \(P_{m,n}(x,y)=x^{m}y^{n}\). The moments, can be referred to the center of the image frame or to the center of mass of the image \((x_{c},y_{c})\), in which case, \(P_{m,n}(x,y)=(x-x_{c})^{m}(y-y_{c})^{n}\). The two main problems with such a choice is that the moments contain redundant information because the homogeneous polynomials are not orthogonal; also the computation loses numerical precision due to cancellation of large terms. Mathematically, a better choice of polynomials is a polynomial from an orthogonal family. Such polynomials enjoy an expansion property, that is  
\[f(x,y)=\sum_{m=0}^{\infty}\sum_{n=0}^{\infty}M_{m,n}P_{m,n}(x,y)\]  
Useful examples of such orthogonal families are the Legendre and Zernike polynomials.  
The Legendre polynomials form an orthogonal set on the interval \([-1,1]\) The Legendre polynomials enjoy a two term recurrence relation  
\[(n+1)L_{n+1}(x)=(2n+1)xL_{n}(x)-nL_{n-1}(x),\quad L_{0}(x)=1,\quad L_{1}(x)=x \tag{6}\]  
that was used for their computation in the present paper. The advantage of the recursion relation is that all Legendre moments up to a given order can be computed simultaneously.  
The Zernike polynomials are normalized on the unit disk in the complex plane. The radial Zernike polynomials can be defined for \(n-m\) even as:  
\[R_{n}^{m}(r):=\sum_{l=0}^{(n-m)/2}\frac{(-)^{l}(n-l)!}{l!((n+m)/2-l)!((n-m)/2- l)!}r^{n-2l} \tag{7}\]  
and 0 otherwise. The present paper implemented a recursive computation method given by the formula [31]  
\[R_{n}^{m}(r)=r\Big{(}R_{n-1}^{|m-1|}(r)+R_{n-1}^{m+1}(r)\Big{)}-R_{n-2}^{m}(r),\quad R_{0}^{0}=1 \tag{8}\]  
The orthogonal Zernike polynomials then are defined as  
\[V_{mn}(r,\theta):=R_{n}^{m}(r)e^{-im\theta}, \tag{9}\]  
where \(i\) is the imaginary unit. The normalization of the polynomials for grayscale images is not an issue because they have a fixed dynamic range, so an image can be always normalized to unit range prior to computation of an image moment.
## 3 Experimental Results and Specim Behavior  
### Columns without Strengthening  
For the purpose of comparisons of the results, five axially loaded, plain reinforced concrete specimens were also tested. All columns of this group showed very similar behavior. The diagrams in Figure 5 show the measured load versus strains and load versus displacement curves. Analyzing the diagram from Figure 5a, it can be noticed that column models showed almost linear behavior throughout the entire loading process up until failure. Registered behavior is typical for axially compressed columns made from high-strength concrete, and it shows that their stress-strain diagram is an almost straight line up to the ultimate strength. The longitudinal strains in concrete at failure were between 1.9% and 2.1%.  
Figure 4: Column specimens before the experiment. Plain RC column (**a**) and strengthened columns (**b–d**).  
Figure 3: A schematic view of the test setup: plain RC column (**a**) and strengthened column (**b**).  
For the plain RC specimens, first longitudinal cracks were detected at the ends of columns at loading around 90% of the ultimate force. At that load level, lateral strains in concrete were in axial tension limit value of 0.25%. Failure of the plain RC specimens was a result of the crack development and splitting of the concrete near the location of force application, as can be seen in Figure 6a,b. The cracks passed through the aggregate and cement in the zones of high stresses close to the steel bearing plates. Splitting cracks were not detected under the steel loading plate because of the frictional confinement. Under the steel bearing plate, an inverse pyramid was formed, and the column failed. Cracking on all specimens started at both ends of the column, but pronounced crack development and loss of cross-section due to concrete splitting and breaking in failure were registered only at one end, as seen in Figure 7. The ultimate load forces of columns from the control group registered during experimental testing are presented in Table 1, while the mean value of registered forces is given in Table 4.  
Figure 5: Measured axial load versus strains (**a**) and load versus deformation (**b**) relationships of plain reinforced concrete specimens (S0x series).  
Figure 6: Failure modes of plain RC columns. The bottom part of specimen S03 (**a**), and the top part of specimen S02 (**b**).
# Integrated Robotic and Network Simulation Method
## 4 A Case-Study Illustrating the Use of the IRoNS Method
### Choosing Solutions
#### 4.2.5 Topology Control  
When dealing with wireless communication with limited range and with nodes that are constantly moving, the physical communication topology is naturally variable. However, it is possible to  
Figure 2: Example of circumcenter rendezvous showing the smallest involving circle around robot 8 direct neighbors and its center as reference for that robot in that time instant.  
establish a logical communication topology, which goes beyond the physical topology, as it may ignore communication links or create virtual links through message routing protocols. This communication link management is called topology control.  
The communication topology has a significant impact on cooperative behavior [28], not only in network properties, but also on the dynamics of decentralized control algorithms. Each robot uses information from its \(n\) neighbors and, as we increase the number of neighbors, it has more information to deal with. We may then reach a point in which the algorithm receives too many messages, generating too much information that does not contribute to the task, but increases overhead and may negatively influence the network performance. On other side, limiting the number of neighbors, e.g., using a fixed topology, reduces traffic in the network, which can be helpful in low bandwidth protocols. However, decreasing it too much will negatively affect the cooperative task.  
In this example, we use topology control to provide a fixed logical topology during the entire simulation. This means that new communication links will not be created despite the robots approaching each other in the course of the rendezvous task. We have already shown that topology changes strongly impact the results when we compared fixed and dynamic topologies [19]. Thus, here we simply show that different numbers of fixed links already have significant impact on the task performance.
## 1 Introduction
Since most AFM studies on the nanoscale pore structure of organic rocks focus on visual descriptions or semi-quantitative analyses [16; 20], further analysis is needed to determine if there is a mathematical relationship between pore parameters, roughness, and fractal dimension in coal and its volatile matter content. China's coal and coalbed methane resources are undergoing varying degrees of exploration and evaluation within coal-bearing stratigraphic formations across different regions. In these exploration areas, the physicochemical properties of coal exhibit diversity. In this study, we collected coal samples of different ranks (i.e., with varying volatile matter content) from coalfields in Huainan, Anhui; Xingtai, Hebei; and Jincheng, Shanxi, among other locations. The purpose of this study is to observe the pore structure of coal samples with different volatile matter contents. The volatile matter of a coal sample refers to the gaseous substances released when the coal is heated under specified conditions in the absence of air and after correction for moisture. It is primarily composed of thermally unstable organics, including aliphatic hydrocarbons (such as methane and ethane), decomposition products of oxygen-containing functional groups (such as CO\({}_{2}\), H\({}_{2}\)O, and phenols), heteroatomic compounds (such as H\({}_{2}\)S and NH\({}_{3}\)), and tar vapors. Firstly, high-resolution AFM is used for three-dimensional morphological observation of coal's nanopores to obtain the spatial distribution of nanopores on the coal surface. Then, quantitative analysis of the coal surface is conducted using NanoScope Analysis and open-source Gwyddion software to obtain nanopore structure parameters and fractal dimensions. Nanopore structure parameters include pore count, facial porosity, average pore diameter, and shape factor. Combining the test results from SEM, we can more precisely characterize the complexity of pore surfaces on coal. Simultaneously, we study and analyze the correlation between coal sample volatiles and its pore parameters, roughness parameters, and fractal dimensions. By performing function fitting, we observe the relationship between these parameters and volatiles, leading to a core hypothesis: An increase in volatiles causes the nano-pore structure of coal to become coarser and more regular, reducing surface complexity. Through collaborative quantitative analysis using AFM (Atomic Force Microscope) and SEM, we verify the mathematical correlations between volatiles and pore parameters (number, pore size, shape factor), roughness (R\({}_{\rm a}\), R\({}_{\rm q}\), R\({}_{\rm r}\)), and fractal dimensions. This enables us to gain a clearer understanding of the relationship between coal sample volatiles and its physical properties and provides more accurate data support for further research on the occurrence, development, and utilization of coalbed methane.
## 9 Conclusions  
In dynamic environments, such as scientific laboratories integrated with IoT and CPSs, operational changes, sensor aging, machine degradation, software updates or the addition of new processes and equipment can lead to varied data distributions over time causing model degradation decay for anomaly (or fault) detection and classification. These changes necessitate the use of adaptive models capable of detecting anomalies even as data distributions evolve, ensuring a high model performance. Continuous anomaly classification monitoring in the rapidly changing conditions of scientific laboratories requires models that can dynamically learn and adapt to evolving data streams. In this context, we develop a continual learning algorithm for digital twin-enabled online anomaly classification in TWIN-ADAPT to adjust to concept drift while maintaining an accurate anomaly classification. Our experimental results with the TWIN-ADAPT model highlight the effectiveness and robustness of its adaptive and optimized classification approach in dynamic CPS settings. TWIN-ADAPT employs a dual-window strategy for drift adaptation, integrated with Particle Swarm Optimization, to continually adjust to evolving data streams. Our method is evaluated under simulated drift scenarios using different noise distributions to emulate anomalous drifts in the training and testing data distributions. The TWIN-ADAPT framework significantly outperforms its offline counterparts and demonstrates a competitive performance against benchmark drift adaptation algorithms for three industrial CPS components. This continual learning strategy of TWIN-ADAPT ensures a better accuracy and reliability of machine learning models used for digital twins to better suit the changing needs of industrial settings.  
Conceptualization, R.G. and K.N.; methodology, R.G. and K.N.; software, R.G.; validation, R.G., B.T. and Y.W.; resources, B.T., Y.W. and K.N.; data curation, R.G., B.T. and Y.W.; writing--original draft preparation, R.G. and K.N.; writing--review and editing, R.G., B.T. and K.N.; supervision, K.N.; project administration, K.N.; funding acquisition, K.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation, NSF OAC 21-26246, \"CC* Integration-Large\" MAINTLET: Advanced Sensory Network Cyber-Infrastructure for Smart Maintenance in Campus Scientific Laboratories.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
Any results and opinions are authors own and do not represent views of the National Science Foundation.  
The authors declare no conflicts of interest.
# Comparison of Distributed Tamper-Proof Storage Methods for Public Key Infrastructures
## 4 Alternative Implementations and Concept Analysis
### Public IPFS  
The previous concepts apply specifically for the private IPFS setup and the provided IPFS Software Development Kit (SDK) [27]. For the public IPFS variant, _Filebase_[30] offers a free to use API that exposes all IPFS functionalities. Though, the \(ID_{S}\)-\(PK_{S}\) mapping can be handled easier by leveraging the Filebase API. To query a given file, a custom GET request needs to be crafted that contains the following file name as a parameter. On request, the API will return the complete object stored under that file name. Alternatively, if the CID of the file is known, the file can also be queried using public IPFS gateways [31].  
An alternative way of accessing the public IPFS is the _Web3.Storage_ API. In the background, Web3.Storage uses the IPFS with a financial incentive concept called _Filecoin_. Using this incentive system, data can be guaranteed to be persisted for the long term, since each data hoster will be rewarded with Filecoin tokens. Whenever a user uploads a new file to the network, he will receive a unique CID for said file. This can then be used to query the data from the network. For users of the Web3.Storage API, this feature is free to use [32].  
Adding a new Sensor's public-key to the IPFS using the Web3.Storage API differs, again, in its way of mapping the \(ID_{S}\) to its corresponding \(PK_{S}\). Since the user receives a unique CID of the file and can use this CID to query it, a direct search for the key is not trivially possible. For this purpose, the API offers a method to query all files uploaded via a given Web3.Storage account. This feature can be exploited to receive a full list of all files. Since by definition the uploaded files will follow the naming scheme \(<ID_{S}>\)._json_, the returned list can be trivially searched for a given \(ID_{S}\). The list, therefore, also contains a direct \(ID_{S}\)-\(CID\) mapping, allowing the public key storing concept to work.  
To add the TPM's public key to the IPFS using the Web3.Storage API, the same methodology is used as in the previous approach using the private IPFS implementation. The TPM's public key is uploaded under a specific identifier which is programmed into the application server's logic.
## 2 Materials and Methods
### Determination of Titratable Acidity, Water Holding Capacity and Syneresis  
The titratable acidity in yogurt was expressed as lactic acid \(\%w/w\). Specifically, 25 g of yogurt sample was mixed with 25 mL of distilled water and titrated with 0.1 M NaOH (sodium hydroxide) solution, using phenolphthalein as indicator until the appearance of a pale pink color [16; 17; 18]. This process was applied on all yogurt treatments after one day of storage at 4 \({}^{\circ}\)C. Titratable acidity was expressed in \(\%\) (\(w/w\)) lactic acid and calculated using Equation (1), where V is the volume (mL) of NaOH used in the titration, A the molar mass, 0.009 for lactic acid, and D is the factor of dilution.  
\[\text{Titratable acidity }(\%\text{ lactic acid})=\frac{\text{V}*\text{ NaOH factor}*\text{A}*\text{D}}{\text{Volume of Sample}}*100 \tag{1}\]  
Syneresis and water holding capacity (WHC) of yogurt were also determined [19], after one day of storage at 4 \({}^{\circ}\)C. In brief, 10 g samples of fermented yogurt were transferred to centrifugal tubes and centrifuged at 5000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C using the high-speed refrigerated Hitachi Model CR22N (Koki Co., Ltd., Tokyo, Japan). Then the supernatants (whey) and pellets were weighed [20]. Syneresis and WHC were calculated with the following Equations (2) and (3) [21].  
\[\text{Syneresis }(\%)=\frac{\text{Wheight of whey }(\text{g})}{\text{Wheight of yogurt sample }(\text{g})}*100 \tag{2}\]  
\[\text{WHC }(\%)=\frac{\text{Wheight of precipitate }(\text{g})}{\text{Wheight of yogurt sample }(\text{g})}*100 \tag{3}\]  
All measurements were performed three times.
## 3 Proposed Method
### Second Stage: MYO and LVC Segmentation
#### 3.2.2 Mask Aggregation Process: The Extraction of a New ROI  
In this substage, the masks generated by the initial segmentation are used to delimit a new ROI. Thus, it is intended to eliminate more background regions and reduce the imbalance between classes of pixels.  
The mask aggregation process follows a few steps, as shown in Figure 5. For each volume, the MYO and LVC masks generated by EAIS-Net are combined, resulting in a new mask called supermask. This supermask comprises all regions of the volume where the structures of interest are found.  
However, the supermask inherits the inaccuracies produced by the coarse segmentation substage, such as wrongly predicted background regions (false positives). Then, a morphological opening is applied to reduce them using a circular structuring element of size \(5\times 5\). After that, some regions are disconnected from the supermask. Then, a region filter is applied to keep only the object with the largest area closer to the center of the image, which will always be the supermask. Next, a morphological dilation is applied to the supermask using the same parameters as the opening. Finally, the supermask resulting from this process extracts a new ROI from all the volume slices. Then this new ROI will be submitted to the next substage: the refined segmentation.
# Photodegradation Assessment of Calicotriol in the Presence of UV Absorbers by UHPLC/MSE
###### Abstract  
Calicotriol, a synthetic vitamin D\({}_{3}\) analogue widely used in psoriasis treatment, requires a detailed stability assessment due to its topical application and potential exposure to UV radiation. As a drug applied directly to the skin, calcipotriol is particularly susceptible to photodegradation, which may affect its therapeutic efficacy and safety profile. The present study focuses on the analysis of calcipotriol photostability. An advanced UHPLC/MSE method was employed for the precise determination of calcipotriol and its degradation products. Particular attention was given to the effects of commonly used organic UV filters--approved for use in cosmetic products in both Europe and the USA (benzophenone-3, dioxybenzene, meradimate, sulisobenzene, homosalate, and avobenzene)--on the stability of calcipotriol. Unexpected degradation of calcipotriol was observed in the presence of sulisobenze. Importantly, this effect was consistently detected in methanolic solution and in the pharmaceutical formulation containing calcipotriol and betamethasone, which is particularly significant from a practical perspective. This finding underscores the necessity of evaluating photostability under real-life conditions, as cosmetic ingredients, when co-applied with topical drugs on the skin, may substantially influence the stability profile of the pharmaceutical active ingredient. The research resulted in the first-time characterization of four degradation products of calcipotriol. The degradation process was found to primarily affect the \(E\)-4-cyclopropyl-4-hydroxy-1-methylbut-2-en-1-yl moiety, causing its isomerization to the \(Z\) isomer and the formation of diastereomers with either the R or S configuration. Computational analyses using the OSIRIS Property Explorer indicated that none of the five degradation products exhibit a toxicity effect, whereas molecular docking studies suggested possible binding of two of the five degradation products of calcipotriol with the VDR.  
calcipotriol calcipotriene; drugs photostability; UV absorbers; vitamin D analogs; psoriasis Article
## 4 Analytical Results  
Table 2 shows the simulation parameters deployed for the continuous wave laser (CW laser) transmitter, the G.652 SSMF optical channel and the OCDMA signature parameters. Furthermore, to meet the high-standard optical communication system requirement, we consider forward error correction (FEC limit) and Q-limit for the BER and Q-factor metrics, respectively.
# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 4 Proposed Methodology
### Input of Audio Signals  
In this stage, a windowing procedure is applied to the input audio signals in order to divide these continuous audio streams into smaller signal audio segments which are recorded by a microphone. This is accomplished by turning the potentially unlimited signal streams into a continuous series of finite blocks by sliding a window function over the audio stream data.
## 3 Simulations
### Robustness Analysis  
In this section, simulations are conducted to investigate the significance of introducing a detection method into the adaptive filtering process. The state detector was mainly used to enhance the robust performance of DMPFxNLMS, therefore, we only considered the performance in completely impulsive noise environments in this section. Considering the robust performance in the Sections 3.1 and 3.2, only the PFxNLMS, FxlogLMS, and DMPFxNLMS were chosen here for sake of clarity. These methods  
Figure 7: ANR performance (**a**) interference amplifying 15 dB; (**b**) interference amplifying 5 dB.  
have been demonstrated to perform robustly above. The signal with \(\alpha=1.1\) described by the S\(\alpha\)S distribution was introduced to simulate the completely impulsive noise environment.  
In Figure 8a, the PFxNLMS(Opt) and FxlogLMS had similar ANR performance, but the fluctuation ranges of ANR of them were bigger than the proposed DMPFxNLMS. The Figure 8b is used to show that DMPFxNLMS worked in its robust mode when sever noise happens. More exactly, most values fall in the interval \(\left(0,1\right)\), which proves that DMPFxNLMS worked in the robust state. The results demonstrate that DMPFxNLMS was capable of automatic parameter's setting, which really improves the robustness when burst noise happens.  
Figure 8c shows the Euclidean norm of the different methods, which is also used to explain the robust performance of adaptive filters. The FxlogLMS has smaller Euclidean norm values, but its fluctuation range is bigger than DMPFxNLMS. As for the PFxNLMS(Opt), both its Euclidean norm value and fluctuation range were bigger than DMPFxNLMS. It is also confirmed that the burst amplitude signals have little influence on the convergence rate of DMPFxNLMS, suggesting that it is more robust under these conditions.