esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Threaded Control with C/S Architecture Incorporating AES Encryption and TLS Protocol
### Multi-Layered Security Incorporating AES Encryption and TLS Protocols  
The TCP/IP model is a conceptual model used to understand internet communication protocols, as shown in Figure 5. It consists of five layers: the application layer, the transport layer, the network layer, and the data link layer with the physical layer. These five layers work in tandem to ensure the reliable transmission of data through the network. This paper incorporated TLS and AES encryption algorithms, which are able to secure data at multiple levels.  
The flowchart of the multilevel security protection that incorporates the AES encryption and TLS protocols is shown in Figure 5. It mainly involves the direct data transmission between the sender and the receiver.  
In this process, the AES encryption algorithm is used at the application layer and is responsible for encrypting and decrypting data. The TLS protocol, on the other hand, is used at the transport layer and is responsible for ensuring the security and integrity of the data during the communication process. With the combination of AES and TLS, end-to-end secure data transmission can be realized.  
Figure 4: AES encryption and optimization diagram.
# Transparent, Pliable, Antimicrobial Hydrogels for Ocular Wound Dressings
## 2 Materials and Methods
### Antimicrobial Activity  
The minimum inhibition concentration (MIC) was determined using the Mueller-Hinton (MH) broth micro-dilution methodology. Briefly, cefazolin and doxycycline (1.024 mg/mL in PBS) was serially diluted in a 50 \(\upmu\)L volume of MH broth in a 96-well plate starting from a concentration of 512 \(\upmu\)g/mL up to 3.7 ng/mL. Following this, bacteria were introduced in a 50 \(\upmu\)L volume into each well. Overnight culture of the bacterial strains on blood agar was used to inoculate the MH broth to reach an absorbance of 0.08 and 0.12 for _S. aureus_ and _E. coli_, respectively. The inoculum was then diluted 1:100 before being introduced into each well. The negative controls contained only 100 \(\upmu\)L MH broth, while 100 \(\upmu\)L MH broth containing bacteria served as a positive control. Plates were incubated for 24 h at 36 \({}^{\circ}\)C, and the MIC was determined as the concentration of antimicrobial molecules in the well that did not allow any bacterial colony formation. MIC was determined by three independent experiments with duplicate runs each time. Colony forming units (CFU)/mL for each bacterial inoculum were calculated following spread plating of 10 \(\upmu\)L of 1:100 dilution of the bacterial inoculum (used for MIC studies) on blood agar plates. The end concentration of the bacterial inoculum was maintained in the range of \(5\times 10^{4}\) to \(7\times 10^{5}\) CFU/mL.  
Zone of inhibition (ZOI) was measured using the following protocol. Hydrogels loaded with cefazolin or doxycycline were punched into discs of 9 mm diameter. As mentioned in the MIC study, _S. aureus_ and _E. coli_ were cultured in MH broth to reach an absorbance of 0.09 and 0.14, respectively. They were then spread end-to-end on MH agar plates using a cotton swab in three different directions. Following this, the hydrogel disks containing the antimicrobial and 2% w/v polysaccharide were positioned on the agar plates and incubated for 24 h at 36 \({}^{\circ}\)C. A similar set up was used for the control disks. The ZOI was measured as the distance from the disc where no bacterial growth was observed.
# Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing
## 3 Reference Architectures in DIGITbrain
### Components of Reference Architectures
#### 3.1.3 Data Analytics Reference Architecture (DARA)  
A DARA is composed of one or more SACs and SAFs, consisting of distributed components subject to orchestration, forming a concrete, end-to-end data processing architecture, and utilizing best-practice solutions for a particular use case in a particular domain. We define _end-to-end_ as from acquiring the data (see Data Source(s) in Figure 1) to visualizing the data for the end user (using data exploratory, business intelligence, or other solutions); however, there may be external entities accessing the data within the DARA (see External Components in Figure 1).
## 1 Introduction
### DGNSS in Space  
When it comes to ranging in space, the use of GNSS measurements has provided the primary method for determining the relative position of cooperating Earth orbiters in the last decades for applications such as formation flights, rendezvous and docking [24; 25; 26; 27]. However, in such context, differential GNSS scenarios are more challenging given the larger dynamics of the end users and the larger distances involved in the process, leading to decorrelation of the atmospheric delays and satellite orbital errors between users [28; 29; 30; 31; 32]. Despite this obstacle, in 1997, ETS-7 was the first mission to use GNSS signals for relative navigation in space with meter-level accuracy [33]. In 2002, the Low-Earth Orbit (LEO) formation flight Gravity Recovery And Climate Experiment (GRACE) mission involving baselines up to 250 km achieved Precise Baseline Determination relying on GNSS measurements fused with Radio Frequency (RF) ranging. By processing carrier phase and pseudorange measurements after resolution of the double differences integer ambiguity, they achieved millimeter accuracy in post-processing using the Least Square Ambiguity Decorrelation Adjustment (LAMBDA)method [34]. In 2014, the CanX-4/5 performed DGNSS-based baseline determination on cubesat avionics for the first time, reaching submeter-level accuracy [35]. However, integer ambiguity resolution has never been performed on board until now [36; 37; 38]. Limited onboard computational resources, imperfect knowledge of the spacecraft attitudes, and multipath may prevent the integer ambiguity resolution reducing the accuracy achievable in real-time. Estimation biases have been documented for legacy baseline estimation techniques in [39] where authors assessed a code-based differential ranging technique in LEO between cubesats with submeter-level accuracy. In [40], a kinematic technique based on differential pseudorange and carrier phase measurements is suggested for real-time precise baseline determination without Integer Ambiguity Resolution and applied in LEO.
###### Abstract
The present study investigates the composite electromagnetic scattering characteristics of a three-layer system consisting of a rough-surface vehicle, a snow layer, and soil. A CAD-based approach is employed to model the rough surface of the vehicle. The Monte Carlo method is used to simulate the interfaces between air and snow, snow and soil, and the vehicle's surface roughness. A four-component model is adopted to describe the dielectric properties of soil, while a mixed-medium dielectric model is used for snow. The finite-difference time-domain (FDTD) method is then applied to analyze the electromagnetic scattering behavior of the entire system.
# Local Community Detection Based on Small Cliques
## 4 Experiments
### Experimental Setup  
For all of these local community detection algorithms, we provide novel or improved implementations in C++ as part of the open source network analysis tool suite NetworkKit [13]. The exact implementations used and the scripts used to generate graphs, evaluate the detected communities and generate the plots are available online [14]. We aim to contribute the implementations to the next release of NetworkKit.  
We also compare against the global community detection algorithm Infomap [35] which has been shown to perform excellently on the synthetic benchmark graphs we use [11]. This allows us to see how a state-of-the-art global community detection algorithm performs in comparison to the local community detection algorithm. For Infomap we use the implementation provided by the authors to optimize a two-level partition, i.e., a disjoint clustering of the whole graph. To get the community of a seed node, we simply use the cluster detected by Infomap that contains the seed node.  
All results are obtained on a computer with an Intel(r) CoreTM i7 2600K Processor, run at 3.40 GHz with 4 cores, activated hyper-threading, and 32 GB RAM.  
We generate 20 random realizations for each synthetic graph and select a set of 20 random seed nodes for each of them. In our experiments on real world networks we use 100 randomly chosen nodes per graph as seeds, as there we have only one realization per graph.
## 3 Results and Discussion
### Method Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feed**} & \multicolumn{2}{c}{**Added**} & \multirow{2}{*}{**Recovery Rate**} & \multicolumn{1}{c}{**Intra Batch**} & \multicolumn{1}{c}{**Inter Batch**} \\  & **Concentration** & **(\%)** & **Coefficient of** & **Coefficient of** \\  & & **(\%)** & **Variation (\%)** & **Variation (\%)** \\ \hline \multirow{2}{*}{Chicken compound} & 0.05 & 89.90 & 6.57 & 7.09 \\ \cline{2-5}  & 0.5 & 89.64 & 5.32 & 6.18 \\ \cline{2-5}  & 5.0 & 91.45 & 5.56 & 6.65 \\ \hline \multirow{2}{*}{Concentrated chicken feed} & 0.1 & 90.35 & 8.14 & 9.53 \\ \cline{2-5}  & 1.0 & 89.56 & 5.42 & 6.92 \\ \cline{2-5}  & 10.0 & 91.82 & 7.73 & 9.94 \\ \hline \multirow{2}{*}{Chicken premix feed} & 0.5 & 90.47 & 6.05 & 7.36 \\ \cline{2-5}  & 5.0 & 93.25 & 5.47 & 7.77 \\ \cline{2-5}  & 50.0 & 95.83 & 4.43 & 6.98 \\ \hline \multirow{2}{*}{Pig compound feed} & 0.05 & 85.28 & 5.96 & 9.04 \\ \cline{2-5}  & 0.5 & 81.14 & 5.61 & 7.82 \\ \cline{2-5}  & 5.0 & 83.07 & 3.82 & 7.67 \\ \hline \multirow{2}{*}{Concentrated pig feed} & 0.1 & 82.64 & 10.04 & 11.12 \\ \cline{2-5}  & 1.0 & 81.19 & 7.76 & 7.85 \\ \cline{2-5}  & 10.0 & 85.75 & 6.80 & 7.96 \\ \hline \multirow{2}{*}{Pig premix feed} & 0.5 & 95.55 & 11.17 & 10.99 \\ \cline{2-5}  & 5.0 & 93.42 & 8.62 & 9.92 \\ \cline{2-5}  & 50.0 & 94.74 & 6.87 & 7.08 \\ \hline \multirow{2}{*}{Vitamin supplements} & 0.05 & 87.05 & 6.65 & 7.85 \\ \cline{2-5}  & 0.5 & 89.67 & 7.48 & 9.74 \\ \cline{2-5}  & 5.0 & 86.83 & 6.70 & 9.97 \\ \hline \multirow{2}{*}{Cattle concentrate} & 0.1 & 83.48 & 8.23 & 9.43 \\ \cline{2-5}  & 1.0 & 80.92 & 10.96 & 11.16 \\ \cline{2-5}  & 10.0 & 80.78 & 10.51 & 11.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Recovery rate and coefficient of variation in irmetcin H2B1a added to feed. Figure 11: Chromatograms of six macrolide insecticides in concentrated pig feed. (_Left: chromatogram of 250 \(\mu\)g/kg feed addition sample;_ **right:** _blank matrix with standard solution added_). Figure 10: Chromatograms of six macrolide insecticides in pig premixed feed. (_Left: chromatogram of 250 \(\mu\)g/kg feed addition sample;_ **right:** _blank matrix with standard solution added_). Figure 9: Chromatograms of six macrolide insecticides in premixed chicken feed. (_Left: chromatogram of 250 \(\mu\)g/kg feed addition sample;_ **right:** _blank matrix with standard solution added_).
## 4 Experimentation and Result Evaluations  
### Simulation Setting and Parameters  
For our simulation, we modeled a heterogeneous network that is represented by 20 user devices and composed of five small base stations and one main base station, which represents multi-user and multi-MEC, and all small base stations are connected to the main base station. The power gain (\(g_{nj}\)) is set to 100 milliwatts (mw). Next, the noise power (\(n_{0}\)), which refers to the amount of random electrical energy that interferes with the transmitted signal during wireless communication in a RAN, is set to \(-100\) dBL. Additionally, according to [41], we consider that there is no inner-cell interference in wireless fronthaul. The system bandwidth (\(W_{nj}\)) is set to 10 Megahertz (MHz). The size of the input tasks (\(D_{i}\)) is randomly distributed within the range of 1.5 KB to 2 KB. The computing resources of the MEC server (\(C_{i}\)) are set to 1 GHz, 1.5 GHz, 2 GHz, 2.5 GHz, and 3 GHz, respectively. The available computing resources of the user devices (\(V_{i}^{loc}\)) are randomly assigned from 10 to 100 MHz [14]. The number of the required CPU cycles per bit is \(C_{i}\) = 20 cycles. The time constraint (\(T^{max}\)) of mission-critical delay-sensitive signaling (e.g., MC-PTT signaling, MC Video signaling) IoT applications is set to less than 60 ms based on 3 GPP TS Release V17.1.0 (2021-06). The set of transmission powers (\(p_{n}^{tr}\)) for each UE is set to 200 mw. This means that the UE is capable of transmitting signals at power levels within this range, which can be used for wireless communication with other devices or networks. We set the number of iterations (\(i\)) to 60, and our algorithm is able to obtain the optimal result after it reaches 40 iterations. The performance metric that is used is total latency in milliseconds (ms), total cost that includes both energy and latency.
## 4 Trajectory-Planning Method
### RBFNN Mission Decision-Making Model  
Using the approximation ability of RBFNN for any unknown nonlinear function, this paper uses RBFNN for the training of post-fault mission samples and the generation of  
Figure 3: Launch vehicle trajectory-planning method.  
online mission decision-making models. Euclidean radial basis function is adopted to construct RBFNNs activation functions [2].  
\[\phi_{jk}=\exp\biggl{(}-\frac{1}{2\sigma^{2}}\|x_{j}-c_{k}\|^{2}\biggr{)} \tag{8}\]  
where \(x_{j}\) is the \(j\) input, \(c_{k}\) is the center of the kernel function of the \(k\) neuron in the hidden layer, and \(\sigma\) is the width parameter of the function.  
The output of RBFNN is:  
\[y_{p}=\sum_{k=1}^{h}\omega_{kp}.\phi_{jk} \tag{9}\]  
where \(\omega_{kp}\) is the weight coefficient from the \(k\)-th neuron in the hidden layer to the \(p\)-th output of the output layer; \(h\) is the number of neurons in the hidden layer.  
The number of input nodes is six, and the input attribute vectors are fault moment, fault magnitude, whether the target orbit is circular, whether the target orbit is an SSO, entry mode, and payload composition form. The number of output nodes is three. The output label vectors are perigee, apogee, and payload mass. The OLS algorithm can obtain the number of neurons and weight coefficients of the hidden layer. The mission decision-making samples generated offline in the previous paper are divided into training and test sets according to the ratio of 80% versus 20%, and the mission decision-making model is obtained by neural network training using RBFNN. When used online, the mission decision-making results under the corresponding fault conditions can be obtained according to the RBFNN input attributes at the fault moment for online trajectory planning by the ATU-IGM.
# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 3 Results
### Reliability of Discrete Metrics  
At initial foot contact, all sagittal plane variables demonstrated either good or excellent reliability (ICC\({}_{3,1}\) range: 0.781-0.956; Table 3). In the frontal plane, both the knee and trunk variables demonstrated excellent reliability, while the ankle, hip, and pelvis variables demonstrated moderate reliability (ICC\({}_{3,1}\) range: 0.674-0.745). In the transverse plane, the knee, pelvis, and trunk variables demonstrated either good or excellent reliability (ICC\({}_{3,1}\) range: 0.791-0.952), while the ankle and knee demonstrated moderate and good reliability, respectively. Across all planes, SEM values were low, ranging from 0.43 to 1.31\({}^{\circ}\) and averaging 0.68\({}^{\circ}\). All kinematic peak angles during stance demonstrated either good or excellent reliability (ICC range: 0.760-0.958), except for transverse plane rotations of the ankle and hip which demonstrated moderate and poor reliability, respectively (Table 4). Across all planes, SEM values were low (<1.1\({}^{\circ}\)), averaging 0.67\({}^{\circ}\). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \multicolumn{6}{c}{**Peak Angles During Stance**} & \multicolumn{6}{c}{**Peak Angles During Stance**} & \multicolumn{6}{c}{**SEM**} & \multicolumn{1}{c}{**MDC**} \\ \hline  & \multicolumn{3}{c}{**Mean (SD) [Deg.]} & \multicolumn{3}{c}{**ICC\({}_{3,1}\)**} & \multicolumn{3}{c}{**95\% CI**} & \multicolumn{1}{c}{**SEM**} & \multicolumn{1}{c}{**MDC**} \\  & \multicolumn{1}{c}{**Day 1**} & \multicolumn{1}{c}{**Day 2**} & \multicolumn{1}{c}{**Day 3**} & \multicolumn{1}{c}{**Day 3**} & \multicolumn{1}{c}{**Lower**} & \multicolumn{1}{c}{**Upper**} & \multicolumn{1}{c}{**[Deg.]**} & \multicolumn{1}{c}{**[Deg.]**} \\ \hline
**Ankle** & & & & & & & & & & & \\ Dorsiflexion & 23.3 & (3.6) & 23.9 & (3.1) & 23.6 & (3.3) & 0.760 & 0.663 & 0.837 & 0.73 & 2.0 \\ Rearfoot Inversion & 0.5 & (2.9) & 0.4 & (2.6) & 0.2 & (2.5) & 0.825 & 0.750 & 0.884 & 0.58 & 1.6 \\ External Rotation & 7.1 & (2.7) & 7.5 & (3.2) & 7.3 & (2.7) & 0.619 & 0.488 & 0.732 & 0.63 & 1.7 \\ \hline \end{tabular}
\end{table} 
Table 4: Reliability of peak joint angles during stance across three distinct sessions and three distinct speeds in 21 runners.
## 3 Methods
### Experimental Setup and Evaluation
#### 3.4.2 Crop Classification  
An incremental classification approach, frequently applied to classification using multi-temporal images [17; 71], was employed for crop classification since early crop mapping was the primary purpose of crop classification. The five incremental classification cases were considered in this study, as shown in Table 7. By starting the classification using only the 10 July Sentinel-2 image (case 1), the new Sentinel-2 image was added as an input along with all of the previously acquired Sentinel-2 images. As the optimal date for early crop mapping was at the end of July (see Figure 2), the 30 July Sentinel-2 image was considered the last image to be used for the incremental classification (case 4). The effect of using the virtual Sentinel-2 image on early crop mapping (case 5) was evaluated through comparisons with cases 3 and 4. In particular, the quality of the virtual Sentinel-2 image generation can be indirectly assessed by comparing case 5 with case 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cases** & **Input Features** \\ \hline CGAN\({}_{\text{2S}}\) & Sentinel-1 SAR features and all Sentinel-2 spectral bands \\ CGAN\({}_{\text{VNIR}}\) & Sentinel-1 SAR features and Sentinel-2 VNIR bands \\ CGAN\({}_{\text{RESH}}\) & Sentinel-1 SAR features and Sentinel-2 red-edge/SWIR bands \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison cases for virtual Sentinel-2 image generation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Training Samples** & **Test Samples** \\ \hline Corn & 314 (29.7\%) & 352,473 \\ Soybean & 314 (29.7\%) & 397,161 \\ Wheat & 41 (39.9\%) & 274 \\ Double cropping & 314 (29.7\%) & 51,511 \\ Alfafa & 73 (7.0\%) & 3407 \\ \hline Total & 1056 & 1,047,520 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numbers of training and test samples for five crop types. The percentage value in parentheses of the training samples indicates the relative proportion for each class.  
The crop classification accuracy was quantitatively evaluated using the reference samples shown in Table 5. After preparing a confusion matrix, the overall accuracy (OA) was calculated and used as a quantitative accuracy measure for five incremental classification cases.
## 3 Results
### Oxygen Fluxes and Gross Primary Production  
Oxygen fluxes differed significantly between light and dark conditions, among sites, and throughout the season (Figure 3, Table 5). At RS, both light and dark oxygen fluxes, as well as the resulting gross primary production (GPP), decreased from the early phase to the late seasonal phase. In contrast, at VS and, to a lesser extent, at BS, both dark and light fluxes and GPP peaked in midsummer.  
Figure 2: Biogenic silica (BSi), nitrogen (N), and phosphorus (P) stocks in the primary producer biomass in the three sampling periods. Error bars represent standard error (n = 6). Legend: RS = rocky shores; VS = vegetated sediments; BS = bare sediments. At site BS, BSi, N, and P were estimated from sedimentary Chl-a.  
At VS, the daily net oxygen flux was always negative, with a minimum in early summer (\(-2156\pm 417\) mg O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)). On the contrary, the net daily oxygen flux was positive at RS in the early phase at its peak (\(834\pm 269\) mg O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)) and in midsummer (\(582\pm 42\) mg O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)), becoming negative only in the late phase (\(-185\pm 67\) mg O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)). At BS, a slightly positive trend occurred during the sampled period: daily oxygen fluxes oscillated from a negative value in early summer (\(-590\pm 144\) mg O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)) to a positive value in late summer (\(322\pm 96\) mg O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)).
# Possible Blockchain Solutions According to a Smart City Digitalization Strategy
## 2 Blockchain in Smart City Solutions
### Data Collection Using IoT Sensors as Oracles  
The blockchain ecosystem can be provided with data in different ways, and we must trust in data provided by the blockchain network. To ensure trust, we need oracles, which are trustable entities that collect information from the external world. The oracles are IoT devices connected to the blockchain on one side and take on several key functions [18]:* Monitoring the blockchain network to check for incoming user or smart contract requests for measured data.
* Performing some type of computation before sending data to the blockchain network.
* Verifying and sending measured or calculated data to the blockchain to be processed by the smart contracts or directly written in the blockchain network.  
According to [19], data provided to the blockchain by oracles are:
* Web content.
* Sensor data.  
Both data sources are significant for digitalization strategy. Web content data includes data about city companies, city employees, documents, certificates, agreements, citizens, etc. Other sources are data collected by various sensors across the entire city like air quality sensors, cameras, traffic monitoring, smart home sensors inside buildings, user wearable devices, etc. Oracles can be divided into different types depending on their role [20]:  
* Software oracles--collect web content for the blockchain network.
* Hardware oracles--physical devices which provide sensor data.
* Inbound oracles--collect data from the external world as inputs for smart contracts.
* Outbound oracles--send information from blockchain network to the outside world.
* Consensus-based oracles--data provided to blockchain network are consensus from multiple sensors.