esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of the Extracted Chitosan
The peak at approximately \(19.5^{\circ}\) from (110) reflection is representative of the \(DD\) and it moves to higher angles while the \(DD\) increases. In fact, the peak at \(18.9^{\circ}\) of chitin (see Figure 3), moves to \(19.1^{\circ}\) for the extracted chitosan, to \(19.7^{\circ}\) for CS-80, to \(19.9^{\circ}\) for CS-90 and to \(20^{\circ}\) for CS-95. These results confirm that chitin was only partially deacetylated using this deacetylation process and that the extracted chitosan has a lower \(DD\) compared to the commercial ones. Subsequently, we calculated the crystallinity index (\(CrI\)) of the extracted chitosan, CS-80, CS-90 and CS-95. The results are shown in Table 5. The extracted chitosan presented a higher \(CrI\) (53.7%) compared to CS-80, CS-90 and CS-95 (42.8, 49.4 and 49.4%, respectively). Several studies report that there is an indirect correlation between the \(DD\) and \(CrI\) with the \(CrI\) decreasing with increased \(DD\)[18; 19], while other studied show opposite trends [20; 21]. According to [PERSON] et al. [22], the decrease in the \(CrI\) with increased \(DD\) is due to the fact that fewer acetyl groups are present, they are distributed more randomly and, consequently, the development of broad crystalline regions is not allowed. We found that the \(CrI\) increased with increased \(DD\) for the commercial chitosan (CS-80 has a lower \(CrI\) compared to CS-90 and CS-95). However, the extracted chitosan does not follow the same trend. On the contrary, the \(CrI\) of the extracted chitosan was more similar to the \(CrI\) found for chitin (CH-20, which was 61.6%), suggesting that the deacetylation was only partially efficient. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Extracted Chitosan** & **CS-80** & **CS-90** & **CS-95** \\ \hline \(DD\) \% & \(66.2\pm 1.6\) & \(89.9\pm 1.6\) & \(91.1\pm 1.1\) & \(93.7\pm 0.7\) \\ Expected \(DD\) \% & – & \(77.6\)–\(82.5\) & \(87.6\)–\(92.5\) & \(\geq\)92.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measured average degree of deacetylation (\(DD\)) of chitosan extracted form cada sloughs harvested in 2020 and of three commercial chitosan with known \(DD\) (CS-80, CS-90 and CS-95). For the latter, the \(DD\) provided by the supplier company is also indicated. Figure 6: XRD of: (a) extracted chitosan, (b) CS-80, (c) CS-90 and (d) CS-95.
# Elemental Profiles of Legumes and Seeds in View of Chemometric Approach
## 3 Results and Discussion
### Discrimination of Legumes and Oleic Seeds Samples Based on Their Type and Botanical Origins  
Factor analysis (FA) is a set of statistical methods and procedures that allow cross-examination of the relationships between large numbers of variables and detection of hidden trends. To visualize data structure and discriminate samples in view of their type and botanical provenance, FA was performed on raw data sets of legumes and oilseeds samples, using the elements (Ca, Mg, Na, K, P, Zn, Cu, Fe, Mn, Cr, and Cd) as columns and samples as rows. Nickel and Co were eliminated from the final data matrix based on the results of the Kruskal-Wallis test results. Lead was eliminated from the dataset as its concentration was below the LOQ.  
The results of the conducted FA are depicted in Figure 1a-c. Using [PERSON] criterion and the Cattell scree test plot there were obtained three factors (F1, F2, F3) that cumulatively explained 76.9% of the total variance, of which 47.6% was explained by F1, 16.5% by F2, and 12.8% by F3. The eigenvalues obtained for each factor were as follows: 5.23 (F1), 1.82 (F2), and 1.40 (F3). Factorial scatterplots for the analyzed samples are depicted in Figure 1a,b, while biplot of loadings drawn for F1-F2 in Figure 1c.  
There can be an observed clear diversification of samples in view of both their type (Figure 1a) and botanical origin (Figure 1b). The samples described by higher F1 values belong to pulses (Fabaceae family), while the lower ones to seeds of oil plants. Soybean samples can be found between these two groups (medium F1 values), which may be related to the dual role of this plant. Botanically it belongs to legumes (Fabaceae family), but it is also reckoned as a valuable source of oil. The oleic seeds (Asteraceae, Pedaliaceae, Cucurbitaceae, and Linaceae families) were differentiated by Na, except for poppy seeds described by the highest levels of Mn, Zn, Mg, Ca, P, Cu, Cd, and Fe (Figure 1c). Pulses' samples were characterized by the highest amounts of K. Such diversification of samples can supposedly be explained by specific usage of nutrients by plants' systems and elements cycling in ecosystems [34].  
Higher values of F2 correspond to samples of oleic plants seeds, except for poppy seeds (Papaveraceae family), which similarly to legumes were described by lower F2 values (Figure 1b). Elements responsible for the classification of legumes (Fabaceae family) and poppy seeds (Papaveraceae family) were Ca, K, Fe, Mn, and Cd, while oleic seeds (Asteraceae, Pedaliaceae, Cucurbitaceae, and Linaceae families) were described by Na, Mg, Zn, Cu, and P.
## 3 The Proposed Approach
### Service Characterization Model  
Two kinds of parameters are distinguished (Figure 2): (i) _Service Description Parameters_ that describe the service (Service ID, Version, Description); (ii) _Service Configuration Parameters_ that describe the service delivery (Observe, Periodic Notification, Simple Query).  
The service type identification is basically implemented by the _service description parameters_: _Version_, _Service ID_, and _Description_ (which is optional). The _Service ID_ is the most important one and it is mandatory. We conceived it as a globally-valid identifier for the type of service. In the CORE WG, several attempts to define a _resource identifier_[17] have been made but none in the direction of a _binary_ globally-valid identifier. Basically, discussions focused on semantic identifiers spelled the same way as URL or URN. Since the highest contribution to the packet sizes is given by the URI resource representation, we believe that a cooperative effort (of the research community together with vendors and manufacturers) to define a globally-recognized IANA numeration for objects' Service ID would greatly help in reducing the packet size. This would enhance both the network and node performance. Moreover, it is important to keep only machine-readable information for constrained nodes' exchanges. A human-readable information (like a resource representation), indeed, is not needed for the node functioning; it only adds a packet overhead and increases the energy and bandwidth consumption.  
For the Service ID, 5 bytes have been left for IANA numeration specification. However, this length may change according to the version of the IANA numeration in use, which is specified in the _Version_ field. The _Description_ parameter has been added for dealing with the case of those objects not yet included in the numeration (_i.e._, for storing either _Universal Product Code_ or _European Article Number_ codes). In Section 5 more space is dedicated to discussions on the service identification issue.  
As regards the _service configuration parameters_, these are basically three: observe _obs_; periodic notification _pn_, and simple query _sq_. All of them are flag parameters that are used to let another node subscribe a service with a certain delivery method. In particular, the _observe method_ can be used to receive a notification about changes in the state of a monitored resource. The _periodic notification_ method can be used to request periodic notifications; numeric parameters (x, y, z) to define the preferred periodicity (for space constraint reasons, details are avoided). For the _simple query_ case, setting the _sq_ flag to 0 or 1 means \"enable with authorization\" or \"enable\", respectively. The _simple query_ is the basic service; hence, restricting the access to it implies restricting the access to all other service delivery options. If the _sq_ flag is set to 0, then an authorization code will be required to access that particular service in any modality. The authentication issue is out of the scope of this paper, thus it is not addressed.  
Figure 2: Service Characterization.
## 2 Pretreatment Methods for Solids Reduction
### Chemical Pretreatment
#### 2.3.1 Acidic and Alkaline Addition
However, high concentrations of alkali can overly increase the pH leading to the point of inhibition of the microorganisms present in the sludges. [PERSON] et al. investigated chemical pretreatment at the pH levels of 2, 5, and 10 using a wide range of temperatures and contact times: 25, 40, 50, and 60 \({}^{\circ}\)C temperatures and 5, 10, 15, and 45 min of mixing time, respectively [40]. Pretreatment using NaOH at pH 10 for 15 min and a temperature of 40 \({}^{\circ}\)C resulted in an increase in sCOD by almost seven-times compared to untreated WAS with an sCOD of 2000 mg/L. This increase in solubility suggests that more organic material within the sludge is made available for the AD microorganisms. This observation is supported by the reported increase in methane composition within the produced biogas which was enhanced by 44% compared to the AD control. Temperatures between 25 and 45 \({}^{\circ}\)C at pH of 10 was determined to be the optimal temperature based on maximum biogas production from the alkaline pretreatment similar to previous studies done by [PERSON] and [PERSON] [40]. The operating time and temperature showed better solubility and biogas production as they were increased; however, in the presence of NaOH, the pH level played an important role. At the lower pH level of 2, the particles congregated and decreased dissolution which was the reason for the reported lower biogas production and lower sCOD/tCOD ratio, even when the temperature was increased. [PERSON] et al. studied the extent of organic matter degradation and biogas increase at different concentrations of NaOH on a mixture of sludge consisting mainly of primary sludge [43]. The optimal concentration of NaOH was 0.1 mol/L yielding a pH of 8. The organic degradation increased to 38% and biogas yield to 0.65 L/g VSS compared to the control which had 30% organic degradation and 0.64 L/g VSS biogas yield. In another study by the same research group, the impacts of NaOH and Ca(OH)\({}_{2}\) on the sludge disintegration of WAS at 0-40 \({}^{\circ}\)C was examined. A NaOH dose of 0.05 mol/L achieved 60% to 71% solubilization of organic matter during the first 30 min of the treatment [44]. At Ca(OH)\({}_{2}\) doses of 0.02 mol/L, 0.05 mol/L, 0.3 mol/L, and 0.5 mol/L, the sCOD increased from 275 mg/L to 1375 mg/L, 1365 mg/L, 984 mg/L, and 821 mg/L respectively. This proves that Ca(OH)\({}_{2}\) treatment decreases sludge dewaterability at doses over 0.02 mol/L (which was overall lower compared to NaOH). Free ammonia (FA) has been used as a pretreatment for secondary sludge digestion which obtained increased methane production according to a study by [PERSON] et al. [45]. They concluded that FA pretreatment at 250 to 680 mg N/L increased methane production by increasing the hydrolysis rate. The solubilization at 680 mg N/L for one day was 10 times more than untreated sludge (0.03 mg sCOD /mg VS). The FA can be obtained from the AD liquor without any additional processing unlike FNA extraction.
## 3 Design of Ternary Logic Circuits
### Our Optimized Design of Ternary Adder
The detailed comparison result can be seen in Table 1. More importantly, compared to the classical CMOS binary adder, our design shows a 7% reduction in the number of required transistors at the same data throughput. Another important parameter is the computation step, which has a direct impact on the computation speed. For the CMOS binary adder, the propagation delay of the logical gates in a 1-bit full binary adder is 10 (note that one AND gate or OR gate requires two times of gate delay). Therefore, the total computation steps for a 30-bit binary adder is \(10^{\circ}30=300\). While that of our ternary ripple-carry adder is only \(12^{\star}19=228\). In consideration of the data throughput, the normalized required computation steps in our design show a 30% reduction compared to the binary design. Figure 7: (**a**) Design of 1-trit full-adder. (**b**) Schematic diagram of the 19-trit ternary adder. A18A17... A1A0 and B18B17... B1B0 is ternary number of A and B, respectively. S18S17... S1S0 is the output summation result, C18C17... C1 is the Carry and the first input carry of C0 is 0.
## 3 The Communication Program for the Semi-Trailer ECPB In-Loop Testing System  
Due to the distance between the twin test vehicle and the ECPB hardware test bench, which is over 500 m and obstructed by buildings, traditional wired or wireless connection cannot meet the driving conditions required for the twin test vehicle. Therefore, an effective communication channel cannot be established. For this reason, an Internet distributed architecture is established to connect the twin test vehicle and the ECPB hardware test bench through data transmission and processing of cloud servers.
## 4 Experiments and Analysis
### Parameter Setting and Evaluation Metrics  
This paper evaluates the model using the official scoring script from SemEval 2010 Task 8, with the F1 score as the evaluation metric. We conducted the experiments using the PyTorch [18] deep learning framework. The hardware configuration is an NVIDIA RTX A5000 GPU with 24 GB of memory, an AMD EPYC 7371 CPU. A Warm-Up strategy [19] was employed, which means the model starts training with a minimal learning rate to ensure better convergence. As training progresses, the learning rate gradually increases until it reaches the initial learning rate setting. Subsequently, the learning rate slowly decreases. We set the Warm-Up step to 3200 and initiated the learning rate at \(1\times 10^{-5}\). A dropout layer was added to the model to prevent overfitting with a dropout rate of \(0.1\). We have provided details of the other main parameters of the model in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Relation** & **Train** & **Test** \\ \hline Cause–Effect & 1003 & 328 \\ Instrument–Agency & 504 & 156 \\ Product–Producer & 717 & 231 \\ Content–Container & 540 & 192 \\ Entity–Origin & 716 & 258 \\ Entity–Destination & 845 & 292 \\ Component–Whole & 941 & 312 \\ Member–Collection & 690 & 233 \\ Message–Topic & 634 & 261 \\ Other & 1410 & 454 \\ Totle & 8000 & 2717 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The pre-defined number of relationships in the SemEval 2010 Task 8 dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Number of GCN Layers & 2 \\ epoch & 12 \\ batch\_size & 8 \\ Top-K prediction set parameter K & 6 \\ hidden\_dim & 1024 \\ optimizer & AdamW \\ seq\_length & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter setting.
# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
## 7 Experimental Test Environment
### MPT Software Configuration
Second, the video was received via the single network path (first path) at IP address 10.1.1.2. VLC's recording feature was used to record the video received through either the MPT-GREtunnel or the first path. The recorded video files were evaluated using a custom Python 3.10 script that calculated the average SSIM, MSE, and PSNR values, which are publicly available on GitHub [28], created by the authors, as shown in Algorithm 1. ```
Input: - Original video \(O\), Streamed video \(S\). Output: - Average PSNR, SSIM, Average MSE. Step 1: - Create empty lists for storing PSNR, SSIM, MSE values. - Initialize frame_count to 0. Step 2: - Open and load \(O\), \(S\) for processing. Step 3: While (frames are available from both videos) do: - Read the current frame of both the \(O\) and \(S\). - If no more frames are available, exit the loop. - Convert frames F\({}_{1}\) of O, \(S\) to grayscale. - Calculate PSNR, SSIM, MSE between frames of \(O\), \(S\). - Append the calculated metrics to PSNR, SSIM, MSE lists. - Store the F\({}_{1}\) as the F\({}_{1-1}\) for the next iteration. - Increase frame_count by 1. Step 4: - Once all frames have been processed, close the video files. Step 5: - Calculate average PSNR, SSIM and MSE from their lists. Step 6: - Open a text file for each video. - Write (video name, and average PSNR, SSIM, MSE values). - Close the file. - Output a message indicating that the metrics saved to the file. End
```  
**Algorithm 1**Video Quality Evaluation
## 3 Results and Discussion
### Droplets Deposition Characteristics
#### 3.2.1 Droplet Drift Angle  
When the V-shaped wind speed was 20 m/s, the spray pressure was 0.5 MPa, and the crosswind speed was 2 m/s and 4 m/s, respectively, simulation experiments were carried out. The droplet's spatial distribution is shown in Figure 10.  
As shown in Figure 10, when the crosswind wind speed is 4 m/s, the droplets had obvious lateral drift. Additionally, the smaller the droplet diameter is, the further the lateral drifting distance is. The angle between the drifting profile and the droplet concentration calibration line was defined as \(\beta\). When the crosswind speed is 2 m/s, \(\beta\) is about 21\({}^{\circ}\). However, when the crosswind wind speed is 4 m/s, \(\beta\) is about 31\({}^{\circ}\), which indicates that the droplet drift is serious. Therefore, it is not suitable for spraying when the natural wind speed is higher.
## 5 Simulation Results
### Live Nodes Number  
The total lifespan of a WSN is indicated by the number of live nodes in the network. The proposed ESO-GJO protocol and the other three protocols are compared for the number of surviving nodes every round for the scenarios of 100, 200, and 300 nodes during the course of the network lifetime in Figure 5a-c. In the case of 100 sensors, the proposed ESO-GJO algorithm extends the network lifetime by about 8.14%, 5.24%, 119.87%, and 135.76%, respectively, compared to other routing protocols. In the case of 200 sensors, the proposed ESO-GJO algorithm extends the network lifetime by about 22.3%, 8.46%, 192.2%, and 180.6%, respectively, compared to other routing protocols. In the case of 300 sensors, the proposed ESO-GJO algorithm extends the network lifetime by about 16.9%, 4.97%, 181.5%, and 363.5%, respectively, compared to the other three protocols. As a result, the ESO-GJO protocol's suggested approach can successfully lower energy dissipation and increase the network's lifespan.
## 3 Integration of 5G NPN Deployment Options and Edge Computing Scenarios
### Standardone NPN with Private, On-Premises Edge
#### 3.1.3 Integrated Edge Data Centers  
A complex industry application can consist of several software components that may have different latency, reliability, etc., requirements, and the components are running in the on-premises edge and in the central cloud premises in a distributed way. The components with strict latency requirements are deployed at the on-premises edge, while components with relaxed latency and data privacy requirements could be deployed at the central (private or public) cloud premises, as shown in Figure 4.  
Figure 4: Integrated edge and central cloud premises by KubeEdge.  
Figure 3: Federated edge data centers in factory premise.  
In this case, cloud management should enable the handling of edge and central clouds in an integrated way, which could be treated, e.g., by KubeEdge [15].  
KubeEdge is an open-source cloud native computing foundation (CNCF) project [29] that extends Kubernetes to support edge computing sites and edge device management. It is based on a centralized control-plane approach--which means that the cloud manager entity is deployed in the central cloud--considering an edge infrastructure as part of the central cloud, as opposed to Kubernetes federation, where independent clusters are united. The new architectural elements of KubeEdge provide edge computing support. The CloudHub and EdgeHub components provide message-based communication between the master and the edge nodes over a single Transmission Control Protocol (TCP) connection. The device controller supports the control of edge devices as well as the reporting of their status. The edge controller is an extension of the Kubernetes controller, providing event channels and orchestrating state synchronization. KubeEdge also integrates a standardized interface to discover and query edge devices from the containers.
# Routes towards Novel Collagen-Like Biomaterials
## 3 Mussel Byssus-Silk, Collagen or Both?
### preCol-Based Biomaterials  
Extraction of preCols without partial hydrolysis is not possible from the byssus because of the high degree of crosslinking, while the extraction of soluble proteins from the mussel foot is not feasible due to low yields [24]. While efforts have been made to use reconstituted mussel byssus as functional biocompatible matrices that retain some of the properties of the natural threads, mainly the ability to modify their mechanical behavior upon metal-ion binding, the overall mechanical performance is not comparable to that of pristine byssus threads [98].  
The length of \(>\)1000 amino acids as well as the collagen-typical posttranslational processing challenges the biotechnological production in the same fashion that other full-length collagens do. Furthermore, it was shown that the expression of the isolated flanking domains [88] of most preCols is only possible in _E. coli_ when they are combined with stabilizing tags, such as SUMO (small ubiquitin-like modifier) [99].  
Despite these challenges, it was recently possible to express and purify non-hydroxylated preColD in the yeast _P. pastoris_, which showed the ability to form fibrils and which could be post-treated to selectively convert the unstructured flanking domains into \(\beta\)-sheets [87]. While it is unlikely that biotechnology produced, full-length mussel byssus collagens will directly be used as biomaterials for tissue engineering due to concerns regarding possible immunogenic properties, their block-like assembly could add a variety of functional domains to the toolkit of synthetic biology towards the design of tailored collagen-like materials for biomedical applications. Furthermore, it might be possible to rationally design block-copolymers which resemble the overall structure of preColD, but consist of domains that have independently been shown to be fully biocompatible.