esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The image intensity of high-resolution synthetic aperture radar (SAR) is closely related to the facet scattering distribution. In this paper, the effects of wind wave spectra, non-Gaussianity of the sea surface, and swell on the distribution of the facet normalized radar cross section (NRCS) simulated by the facet two-scale model (TSM) are analyzed by comparing the simulated results with the Sentinel-1 SAR data, the Advanced Scatterometer (ASCAT) data, and the geophysical model function (GMF) at the wind speed range of 3-16 m/s, the wind direction range of 0\({}^{\circ}\)-360\({}^{\circ}\), and the incidence angle range of 30\({}^{\circ}\)-50\({}^{\circ}\) under VV and HH polarizations. The results show that the Apel spectrum achieves a more consistent mean NRCS and NRCS distribution with the reference data at low incidence angles, while the composite spectra perform better at high incidence angles under VV polarization. Under HH polarization, the Apel spectrum always has a better performance. The upwind-downwind asymmetry of backscattering can be predicted well by the modified TSM, which is constructed by incorporating bispectrum correction into the conventional TSM. The distribution of the scattering simulated by the modified TSM deviates from the Gaussian distribution significantly, which is in good agreement with the Sentinel-1 data. Additionally, the introduction of swell widens the spread of the NRCS distribution, and the fluctuation range of the NRCS profile considering swell is larger than that without swell. All these changes caused by the introduction of swell make the distribution of the facet scattering more consistent with the Sentinel-1 data. Moreover, the scattering image patterns and scattering image spectrum of the Sentinel-1 data can be successfully simulated at various sea states with the consideration of swell.  
wind wave spectra; non-Gaussianity; swell; facet backscatter Article
Given the importance of rupture in encapsulation and delivery, our research emphasizes a novel methodology grounded in experimental validation. This approach addresses the complexities often overlooked by FEM and traditional methods, which often lack the sensitivity and precision required for nuanced analysis. Some authors, moreover, have investigated the coupled effects of encapsulation and mechanical properties for specific polymeric core-shell systems. Valuable examples include, for instance, the work from [PERSON] et al., who highlighted how the release of encapsulated perfume dye was demonstrated to be a function of deformation [15], and [PERSON] et al., who investigated [PERSON]'s modulus and the hardness of microcapsules within loading ranges that did not exceed the 'yield point' or 'rupture point' [16]. In this study, we advocate for the innovative use of nanoindentation for precise characterization [17]. This technique offers a high-resolution approach, measuring the force required for capsule rupture. It stands out as a method that can provide insights into the mechanical behavior of microcapsules under compression, a facet scarcely explored in the existing literature [18]. Thus, a novel nanoindentation-based methodology has been developed for high-throughput automated testing of fragrance-filled microcapsules. The technique is designed to produce vast amounts of statistically consistent data for a comprehensive multivariate analysis, allowing it to discern intricate relationships between the capsules' properties, microstructures, and performances. Through this investigation, we anticipate a deeper understanding of how the polymeric wall's molecular structure relates to the microcapsules' mechanical properties and On-Fabric delivery. The findings of this study will provide valuable insights into the design of microcapsules by leveraging molecular design to tune their delivery efficiency.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 4 A Closer Look at the Time-Based Functions
### Offline Modeling Functions
#### 4.3.1 Data Management  
Data management focuses on an effective and efficient way to ensure that AM data are captured, stored, and used appropriately. The Senvol Database [36] provides researchers and manufacturers with open access to data related to industrial AM machines and materials. Grants, a material information management technology provider, offers the product GRANTA MI:Additive Manufacturing, specifically customized for AM data capturing and use [37].  
At the same time, multiple database and data-management systems must be built to organize and manage the data generated from research and industry projects. The DMSAM (Data Management System for Additive Manufacturing) was developed by researchers at Penn State university to store and track all the data and information related to an AM part. NIST's Additive Manufacturing Materials Database (AMMD) is a data management system built with NoSQL (Not Only Structured Query Language) database technology and provides a Representational State Transfer (REST) interface for application integration. The database captures rich, research datasets generated by the NIST AM program based on an open Extensible Markup Language (XML) schema [38].  
In addition, as an open data management platform, the AMMD data management system is set to evolve through codevelopment of the AM schema and contributions of data from the AM community. To leverage the strength of all these database tools, a multi-tier AM collaborative data management system is needed with (1) distributed data storage facilitated by using common data terms and definitions, (2) collaborative linked data based on neutral data formats, (3) continuous knowledge management by extracting AM material process-structure-property relationships automatically from AM data, (4) lifecycle and value chain-based decision support, and (5) an adaptive data-generation system that helps the AM community to design experiments more efficiently. A collaborative, data management system is set to identify, generate, curate, and analyze AM data throughout the entire AM product lifecycle and can significantly reduce the cost and time associated with AM product deployment.
# Hydration, Setting and Crack-Resistance Properties of Activated HVFACs at Early Stages
## 3 Experimental Results and Observation
### Setting Time
#### 3.2.2 Effect of the Type and Dosage of Activators  
As shown in Figure 6, the type and dosage of AC both have a significant influence on the setting time and other properties of FACs, especially the ones at early stages. Compared to the control concrete, the concretes contain the chemical agents sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)), calcium chloride (CaCl\({}_{2}\)) and sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)), all having lower initial setting times, decreasing with degrees of 7.8%, 9.8% and 21.0%, respectively. Similarly, their final setting times decrease by 4%, 7% and 20%, respectively. This can be explained by Na\({}_{2}\)SO\({}_{4}\) being able to promote the hydration reaction with active Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\) in fly ash and being able to increase the hydration products of cement and lime to form hydrated calcium aluminate and hydrated calcium silicate, which is helpful to enhance the overall hydration reaction rate of the system and shorten the setting time of concretes. Subsequently, to investigate the more detailed effect of the activator of Na\({}_{2}\)SO\({}_{4}\), using an FAC with 20% of FA and activated by Na\({}_{2}\)SO\({}_{4}\), as shown in Figure 6, the effects of the dosage of Na\({}_{2}\)SO\({}_{4}\) on the initial and final setting times of FACs were provided. The result shows that the initial and final setting times of FACs both decrease with the dosages of AC, especially for the final setting time. When the dosage of AC is 2% of cement, the final setting time of FAC is only 47% of the one of the FAC without AC. Results from the above analyses can illustrate that Na\({}_{2}\)SO\({}_{4}\) can effectively promote the hydration action of HVFAC and reduce the setting time. In other words, the setting time of FAC can be adjusted through controlling the dosage of the activator Na\({}_{2}\)SO\({}_{4}\) in FACs.
## 3 Phase Sensitive Detection  
After clarifying the characteristics of yaw angle time history, this section details the design of statistical algorithms for calculating the number of hovers and S turns based on yaw angle data of the flight profile.  
PSD is a signal demodulation method that uses equilibrium position processing of the positive and negative phases of vibration signals. This method can be easily implemented with diode circuits and is commonly used in hardware devices such as dynamic strain gauges and wireless receiving devices. Since the angle time history vibrates near its median during hover or S turn maneuvers, PSD algorithms with median theory can be used to decompose the time history signal and process yaw angle time history data to count the number of hovers and S-turns.  
Let the yaw angle signal in a certain profile be as follows:  
\[\Psi_{0}=\{\psi_{0}(1),\psi_{0}(2),\cdots\psi_{0}(i),\cdots\psi_{0}(N_{0})\}, \tag{1}\]  
where \(\psi_{0}(i)\) is the \(i\)th sample point in signal \(\Psi_{0}\) and \(N_{0}\) is the length of \(\Psi_{0}\).  
The following subsections provide PSD algorithms for processing yaw angle signals, including median retrieval, removing median-crossing redundancy, and signal splitting.
## 4 Application of the Proposed Methodology  
### Information About the Used Database  
The proposed research methodology was used to identify and assess the causes of accidents involving construction scaffolding that occurred in Poland. The authors had access to post-accident protocols provided by the National Labor Inspectorate, and these protocols formed the database. Occupational accidents in the construction industry, which took place in the following voivodeships--Dolnoslaskie, Lodzkie, Malopolskie, Mazowieckie, and Wielkopolskie--were analyzed. The analysis covered the last eight years, i.e., the years between 2008 and 2015 (data for 2016 is not yet available). The direct source of information on the course of accidents was a set of about 700 post-accident protocols created by labor inspectors after the occurrence of an accident.  
\[ZW=\{w_{i}:i=1,\ \ldots,\mathrm{I}\} \tag{8}\]  
where \(ZW\)--an analysed set of accidents, \(w_{i}\)--an accident event (single accident) \(i\), which occurred in the years 2008-2015, \(\mathrm{I}=700\).  
A detailed analysis of the protocols revealed that during the assessed period of time there were 177 accidents at workplaces in which construction scaffolding was used  
\[ZWR=\{wr_{j}:j=1,\ \ldots,\mathrm{J}\} \tag{9}\]  
where \(ZWR\)--a set of accidents involving construction scaffolding, \(wr_{j}\)--an accident event (single accident) \(j\) involving construction scaffolding, \(\mathrm{J}=177\).  
Based on analysis of this set, 1132 causes of accidents were identified. Technical causes accounted for 24.6%, organizational for 48%, and human for 27.4% of all the identified causes.
## 3 Key Building Blocks
### M2M Communication Overlay
XMPP Publish subscribe valuable as base for one-to-many data delivery scenarios in M2M (XEP-0060), which can be used by XMPP entities to subscribe information of the presence of other entities, and receive notifications accordingly. Publish-subscribe support provides also base for Sensor-over-XMPP functionality, which defines basic metadata for M2M devices to describe their sensors and actuators [69]. Sensor-over-XMPP also defines sensor data formats and commands for actuating devices. In publish-subscribe system the interested parties can subscribe the data they are interested in, and subscribers receive the data only when changes occur. A very important extension to XMPP is support for including XML-data (XEP-0315) in data forms specified in XEP-0004. This is estimated to enable transfer of application specific M2M information via XMPP based M2M communication overlay in such a format that it can be applied by autonomic M2M manager. It can be applied in such a way that first, an XML information event is created within M2M asset device. Then it is transferred via XMPP overlay using the XML data forms solution, XMPP interworking proxy concept realization towards ETSI M2M SCL (GIP in Figure 7), and autonomic service capability solution to transfer the event into the autonomic M2M manager. Autonomic M2M manager analyses the content of the event against situation and automatically creates the required response action, based on available rules stored in the knowledge base. After it, the created action will be transferred over the network back to the M2M asset device. When applying the architectural principles, the communication over the XMPP, interworking proxy, and autonomic service capability are transparent to the applications specific information. This enables creation of novel application logic by making changes only into the application domain specific part of the system. Figure 10: M2M client connecting to XMPP Server.
###### Abstract  
This research presents the design and implementation of an Internet of Things (IoT)-based solution to measure the percentage of Liquefied Petroleum Gas (LPG) inside domestic stationary tanks. The IoT-based sensor, in addition to displaying the percentage of the LPG level in the tank to the user through a mobile application (app), has the advantage of simultaneously sharing the acquired data with an LPG filling plant via the Internet. The design process and calculations for the selection of the electronic components of the IoT-based sensor are presented. The methodology for obtaining and calibrating the measurement of the tank filling percentage from the magnetic level measurement system is explained in detail. The operation of the developed software, and the communication protocols used are also explained so that the data can be queried both in the user's app and on the gas company's web platform safely. The use of the Clark and Wright savings algorithm is proposed to sufficiently optimize the distribution routes that tank trucks should follow when serving different home refill requests from customers located in different places in a city. The experimental results confirm the functionality and viability of the hardware and software developed. In addition, by having the precise location of the tank, the generation of optimized gas refill routes for thirty customers using the heuristic algorithm and a visualization of them on Google Maps is demonstrated. This can lead to competitive advantages for home gas distribution companies.  
Internet of Things; hardware design; mobile app; optimization; wireless communication Article
## 3 Results and Discussion
### ANOVA Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **SS** & **Df** & **MS** & **F-Value** & **p-value** & \\ \hline Model & 71.64 & 9 & 7.96 & 21.89 & \(<\) 0.0001 & *** \\ Ds & 41.85 & 1 & 41.85 & 115.10 & \(<\) 0.0001 & *** \\ Fr & 2.49 & 1 & 2.49 & 6.84 & 0.0258 & * \\ Cc & 7.00 & 1 & 7.00 & 19.27 & 0.0014 & ** \\ Ds-Fr & 1.75 & 1 & 1.75 & 4.82 & 0.0529 & \\ Ds-Cc & 0.013 & 1 & 0.013 & 0.036 & 0.8539 & \\ Fr-Cc & 0.39 & 1 & 0.39 & 1.08 & 0.3236 & \\ Ds\({}^{2}\) & 15.18 & 1 & 15.18 & 41.74 & \(<\) 0.0001 & *** \\ Fr\({}^{2}\) & 1.45 & 1 & 1.45 & 3.98 & 0.0739 & \\ Cc\({}^{2}\) & 3.84 & 1 & 3.84 & 10.57 & 0.0087 & ** \\ Residual & 3.64 & 10 & 0.36 & & & \\ Lack of Fit & 2.64 & 5 & 0.53 & 2.64 & 0.1551 & \\ Pure Error & 1.00 & 5 & 0.20 & & & \\ Cor Total & 75.27 & 19 & & & & \\ \hline \hline \end{tabular}  
SS = sequential sum of squares; Df = degrees of freedom; MS = mean squares; F = Fischer’s variance ratio; p = probability value; * Significant (p \(<\) 0.05); ** Very significant (p \(<\) 0.01); *** Very significant (p \(<\) 0.001).  
\end{table} 
Table 3: Analysis of variance for loss rate.
## 5 Results and Discussion
### Computational Efficiency Analysis
#### 5.7.1 Effective Run Time and Memory Utilization  
To standardize computational effort, an _Effective Run Time_ (ERT) metric is introduced, calculated by multiplying CPU time by the number of parallel cores used. This allows direct comparison between 2D and 3D models despite different parallelization strategies.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Modulation Recognition  
With the increasing data traffic in smart cities, different modulation methods are employed in a communication system for efficient and effective data transmission by modulating the transmitted signal. In this context, modulation recognition aims to identify the modulation information of the signals under a noisy interference environment [44]. Modulation recognition aids in signal demodulation and decoding for applications such as interference identification, spectrum monitoring, cognitive radio, threat assessment, and signal recognition. The conventional decision-theory-based and statistical-pattern-recognition-based methods for modulation recognition become computationally expensive and time consuming for smart city applications [44]. DL can be used as an alternative to improve the accuracy and efficiency of modulation recognition as shown in Figure 5. [PERSON] et al. [44] investigated the applicability of a CNN and Long Short-Term Memory (LSTM) for modulation recognition as the former is good for the automatic feature extraction of spatial data and the latter performs well for sequential data. [PERSON] et al. [45] proposed the use of CNN and Recurrent Neural Networks (RNNs) for modulation recognition over additive white Gaussian noise and Rayleigh fading channels. The authors found that DL algorithms perform modulation recognition more accurately compared to ML algorithms such as the Support Vector Machine (SVM). To ensure the privacy and security of the transmitted data, [PERSON] et al. [46] proposed a CNN-based federated learning approach with differential privacy for modulation recognition.  
Figure 4: Deep Learning (DL)-based channel estimation.
## 5 Applications and Discussions
### Chaotic Time Series Prediction
#### 5.1.2 Simulation Tests  
As analyzed in Section 3, agents corresponding to the models of different working conditions. Therefore, the models of Mackey-Glass problem under different working conditions can be approached with the agent based fuzzy T-S models (sort by ABFT-SM). In this example, 600 data samples from \(t=101\) to \(t=700\) are used to illustrate the method. The output signal values of the six steps ahead are shown in Figure 1.  
Figure 1a-c are corresponding to the working condition 1 to 3 respectively. The outputs of ABFT-SM 1 to ABFT-SM 3 matched the fixed condition curves smoothly, which means that the agents can run effectively in fixed working conditions. The main initial values and indexes of the prediction systems of different conditions are listed in Table 2, where \(r_{a}\) is the cluster radius of the agents, both \(r_{a}\) and \(s_{i}\) are the initial values of the method, centers are the structure index and root mean square error (RMSE) is the performance index of the system.  
Figure 1d shows the tracking result of the ABFT-SMM, where the 600 data samples are consisted with three different conditions. The first 200 data samples come from condition 1, the second 200 data samples from condition 2, and the third 200 data samples from condition 3. The centers for the agents are calculated in different conditions, so here the number of the centers for the fuzzy T-S multi-model is 19. The RMSE of the ABFT-SMM in this time is 0.0056, it is very close to the accuracy of the single condition, and the computation complexity is proper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditions** & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\tau}\) & \(\mathbf{x(0)}\) \\ \hline
1 & 0.2 & 0.1 & 17 & 1.2 \\
2 & 0.2 & 0.09 & 19 & 1.0 \\
3 & 0.2 & 0.12 & 16 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Mackey-Glass example under three different working conditions.
## 3 Materials and Methods
### Solid-Phase Synthesis of P13#1  
The Rink amide resin (4-(2'/_A_'-Dimethoxy)-Fmoc-aminomethyl)-phenoxyacetamido-norleucyl-MBHA resin, copolymer styrene-1 % divinylbenzene (DVB), 100-200 mesh; 0.52 mmol g\({}^{-1}\), 0.300 g, 0.156 mmol) was washed twice with 4.5 mL of CH\({}_{2}\)Cl\({}_{2}\) and swelled in 4.5 mL of dry \(N\),\(N\)-dimethylformamide (DMF) for 20 min (twice). The resin was Fmoc-deprotected using a 20% \(v/v\) piperidine solution in dry DMF (two treatments, 4.5 mL each for 20 min). The resin was then washed five times with 4.5 mL of DMF. The first sub-monomer was attached to the resin via the addition of bromoacetic acid (0.434 g, 3.12 mmol) in dry DMF (2.60 mL) and \(N\),\(N^{\prime}\)-diisopropylcarbodiimide (DIC, 580 \(\upmu\)L, 3.74 mmol) on a shaker platform for 20 min at room temperature, followed by washing with DMF (3 \(\times\) 1 min). A solution of \(N\)-Boc-1,4-butanediamine (1.00 M in dry DMF, 0.600 \(\upmu\)L, 3.12 mmol) was added to the bromoacetylated resin. The mixture was left on a shaker platform for 20 min at room temperature; then, the resin was washed with DMF (3 \(\times\) 1 min), followed by CH\({}_{2}\)Cl\({}_{2}\) (3 \(\times\) 1 min) and then again with DMF (3 \(\times\) 1 min). Subsequent bromoacetylation reactions were accomplished by reacting the aminated oligomer with a  
Figure 10: Quantification of cytokine concentrations in the pouches of animals infected with the _S. aureus_ Newman strain and treated with PBS (CTL-), P13#1 or antibiotics. The cytokine concentration was measured at Luminex, and the data are expressed as pg/mL of wash. Each dot shows data from a single animal, the red line represents the median value and the data are from three independent experiments. The Mann–Whitney U-test was used to assess significance. Amp—ampicillin; Gen—gentamycin.  
solution of bromoacetic acid (0.434 g, 3.12 mmol) and DIC (580 \(\mu\)L, 3.74 mmol) in dry DMF (2.60 mL) for 20 min at room temperature. The filtrated resin was washed with DMF (3 \(\times\) 1 min) and treated again with the proper amine under the same conditions reported above. This cycle of reactions was iterated until the target oligomer was obtained. The cleavage was performed by treating the resin with a solution of trifluoroacetic acid in m-cresol (95% \(v/v\), 9.00 mL) on a shaker platform at room temperature for 2 h. The resin was then filtered away, and the filtrate was concentrated in vacuo to one-third of the initial volume. Then, the mixture was slowly added to 10.0 mL of stirred, cold diethyl ether. The white precipitate was centrifuged and washed several times with cold ether (10.0 mL). The linear oligomer (isolated as an amorphous white solid) was subjected to HRMS and HPLC (Figure S8 in Supplementary Materials).  
P13#1: white amorphous solid, 0.291 g, 68%; tr 10.2 min. HRMS (MALDI-FT-ICR) \(m/z\); [M + H]\({}^{+}\) Calcd for C\({}_{107}\)H\({}_{155}\)N\({}_{20}\)O\({}_{14}\)\({}^{+}\) 1945.2056; Found 1945.1884.