esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
In this section, we present the empirical results from the implementation of the proposed SDN-enabled VANET framework. To evaluate the functional requirements, we examined the system's ability to locally classify vehicular sensor data using the computing and storage resources available in SDVs and to dynamically assign vehicular requests to the appropriate servers at the gNB. To evaluate the non-functional requirements, we analysed key performance indicators, such as network delay, packet loss, flow rates, and flow durations, collected via the SDN application plane. These metrics provide insight into the system's operational efficiency and reliability.  
We adopted a two-phase testing strategy. In the first phase, a Unit testing was conducted to evaluate the functional performance of individual components. This included testing of data integrity, traffic classification, communication flow, and resource allocation. This was followed by integration testing, where we examined interactions between modules and their coordination in processing and transmitting vehicular data.  
During preliminary testing, we observed a limitation in the simulation environment (Mininet), which restricted continuous data transmission to approximately 15 min. To accommodate this challenge, we structured each simulation session to include three separate transmission tests, amounting to 40 min per session. In total, ten simulation sessions were conducted, yielding 30 test runs. The aggregated results from these sessions were analysed to assess the overall performance of the framework across key metrics: traffic classification accuracy, transmission success, flow duration, flow rate, delay, and packet loss.
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## Appendix A Bit Error Rate Performance Analysis
### Derivation for the BER Relationship in Proposition 2  
In the investigated DF relay network, the transmitted information-bearing signal can be correctly decoded at the destination node if the relay performs correct detection, or if a relay makes an erroneous detection, which is rectified by further erroneous detection at subsequent relays [15]. Let \(\mathcal{P}_{\ell}^{E}\) denotes the BER after the \(\ell\)-th hop transmission starting from the source node. Then, the end-to-end average BER \(\mathcal{P}_{L}^{E}\) for the wireless relay system can be written as  
\[\mathcal{P}_{L}^{E} =\mathcal{P}_{L}(1-P_{L-1}^{E})+\mathcal{P}_{L-1}^{E}(1-\mathcal{P }_{L})\] \[=\mathcal{P}_{L}-\mathcal{P}_{L-1}^{E}(2\mathcal{P}_{L}-1), \tag{13}\]  
where \(\mathcal{P}_{L}\) is the average BER of the \(L\)-th hop transmission and can be calculated from (18).  
If the channel conditions are identical for all hops and transmission power of each hop fulfils the constraint in (4), the average BER of all individual hops are equal (denoted as \(P_{0}\)). Then, we can extend the expression (13) recursively and obtain the following relationship between the end-to-end average BER and the average BER of individual hops:\[\mathcal{P}_{L}^{\mathrm{E}} =\mathcal{P}_{0}-\mathcal{P}_{L-1}^{\mathrm{E}}(2\mathcal{P}_{0}-1)\] \[=\mathcal{P}_{0}-[\mathcal{P}_{0}-\mathcal{P}_{L-2}^{\mathrm{E}}( 2\mathcal{P}_{0}-1)](2\mathcal{P}_{0}-1)\] \[=\mathcal{P}_{0}-\mathcal{P}_{0}(2\mathcal{P}_{0}-1)+\mathcal{P}_ {L-2}^{\mathrm{E}}(2\mathcal{P}_{0}-1)^{2}\] \[=\cdots\cdots\] \[=\mathcal{P}_{0}+\mathcal{P}_{0}(1-2\mathcal{P}_{0})+\cdots+ \mathcal{P}_{1}^{\mathrm{E}}(1-2\mathcal{P}_{0})^{L-1}\] \[=\sum_{\ell=0}^{L-1}\mathcal{P}_{0}(1-2\mathcal{P}_{0})^{\ell}= \frac{1}{2}\Big{[}1-(1-2\mathcal{P}_{0})^{L}\Big{]}, \tag{100}\]  
where the last equality is obtained based on the properties of geometric progression ([46], p. 1).
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 3 Satellite Observing System Wettzell  
The Geodetic Observatory Wettzell is a GGOS core site and hosts instruments of all basic space geodetic techniques, including one DORIS beacon, three VLBI radio telescopes, two SLR laser telescopes, and several GNSS antennas [41]. Especially for this site, local tie vectors and reference point determination play an important role for the combination of space geodetic techniques to obtain a reliable and precise GGRF [42].  
The use of alternative measurement techniques and analysis procedures is performed to figure out whether it improves the derived components or the measurement procedure. The following subsections describe the measurement configuration for the novel measurement approach using close range photogrammetry, and the key aspects of the analysis.
# Strengthening Polymer Concrete with Carbon and Basalt Fibres
###### Abstract  
To date, composite materials, such as polymer concrete, have found wide application in various industries due to their unique properties combining high strength, resistance to aggressive media and durability. Improving the performance characteristics of polymer concrete is an important task aimed at expanding the areas of its application. One of the promising methods of increasing the strength of this material is the use of various fillers. In this paper, the effect of fillers, based on carbon and basalt fibres, on the mechanical properties of polymer concrete was investigated. The polymer concrete was made of the following components: rubble stone, sand, quartz flour and polyester resin. During the experimental work, the amount of carbon and basalt fibres in the polymer concrete mixture varied from 0 to 6%. Bending and compressive strength tests showed that the addition of carbon and basalt fibres increased these properties. The highest bending and compressive strengths were achieved when carbon fibre contents were up to 1.5%, while basalt fibres provided the highest strengths in the case of around 2%. These results confirmed that carbon fibres had a higher efficiency in strengthening polymer concrete compared to that of basalt fibres. This could be explained by the fact that carbon fibres had a higher tensile strength and modulus of elasticity, which allowed them to better redistribute loads within the composite material. The fibre length for carbon fibre, which gave the maximum increase in properties, was 10-15 mm. For basalt fibre, the maximum bending strength was reached at 20 mm and compressive strength at 10 mm. Increasing the content of carbon fibre above 2% and basalt fibre above 1.5% did not give further increase in mechanical properties. In conclusion, it could be stated that the use of carbon fibres as fillers offered significant advantages in strengthening polymer concrete, opening up opportunities for its use in more demanding conditions and in a wider range of industrial applications.  
Keywords:fibre concrete; concrete strengthening; basalt fibre; carbon fibre; polymer concrete +
Footnote †: journal: Applied sciences
# Recombinant Proteins-Based Strategies in Bone Tissue Engineering
## 4 Recombinant Proteins in Bone Tissue Engineering  
The advantage of using derivatives of natural biomolecules as scaffolds is that they could potentially be recognized by the cells surrounding the site of injury without generating any type of rejection, including their binding to specific cell surface receptors to generate immediate responses to start tissue formation by means of biochemical signals released from these cells [35].  
The development of recombinant proteins as either scaffolds or scaffold components also offers the advantage of generating chimera proteins, i.e., the original human protein plus the sequence of some other protein that is considered useful for the subsequent process of tissue regeneration [36]. The following fundamental steps must take place in order to produce recombinant proteins: (a) natural DNA sequence determination; (b) recombinant DNA design; (c) vector cloning; (d) host organism engineering and transformation; (e) culturing/protein production; and (f) protein purification. So, the advantages of using recombinant proteins, and not obtained directly from nature as scaffolds or part of them, lie in the fact that: (a) recombinant proteins can be obtained homogeneously purified, according to state-of-the-art molecular biology strategies, avoiding contamination problems; (b) chimera proteins can be developed, adding specific recognition sequences; (c) they can be obtained on low-cost industrial scales according to the technologies developed so far; and (d) on certain occasions, sequences can be inserted that provide, in addition to the natural ones, certain characteristics that allow the recombinant to gelificate under certain circumstances, or to acquire certain structures that allow them to be used for direct application in regenerative processes, without the need to be attached to other types of scaffolds.  
Given that some bone morphogenetic proteins (BMPs) are considered by many trautalologists to be the gold standard for application in bone regenerative medicine, we first briefly review them, as they are also present as part of other chimeric proteins mentioned later on [37].  
Other ligand proteins, such as vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), insulin growth factor (IGF), etc., are biomolecules with a recognised role in bone tissue repair, and have been cloned and applied in tissue engineering experiments [38; 39; 40]. There is not much information in the literature that chimeric proteins containing sequences of these ligands in their structure have been synthesized. This work focuses on highlighting the main recombinant proteins that can be used to construct scaffolds in their own right, i.e., not only to impregnate them, but also to make scaffolds from the beginning.
## 3 Proposed Method
### Calculation of Obstacle Scaling Factor (OSF)  
In order to define a so-called point of intrusion (PoI) within the RoI, that will define the point in the target image to be aligned with the bottom right corner of the source obstacle bounding box upon its inserting into the target image, a random point generator was applied which was able to generate a random PoI with image coordinates \(\left(x_{p},\ y_{p}\right)\). Multiple PoIs are generated for every source obstacle and for every target image for the  
Figure 4: (**a**) Detected rail tracks represented by red lines overlaying the original image. (**b**) Illustration of region of intrusion (RoI) definition (red trapezoid).  
purpose of synthetic obstacle intrusion. These PoIs are uniformly distributed across the entire RoI of the target image.  
In order to ensure true-to-scale insertion of the source obstacle into the target image, an OSF has to be calculated considering the pixel width of the rail tracks at the \(y_{p}\)-coordinate of PoI as follows:  
\[\text{OSF}=\frac{h_{p}}{h_{\text{source}}} \tag{8}\]  
where \(h_{\text{source}}\) is the obstacle height in source image (pixels) and \(h_{p}\) is the obstacle height in pixels in the final synthetic target image \(I_{\text{syn}}\). \(h_{p}\) is calculated based on the width of the detected rail tracks in the target image at the \(y\)-coordinate of the PoI as:  
\[h_{p}=s_{gp}\cdot\frac{S_{S}}{H} \tag{9}\]  
where \(s_{gp}\) is the distance between the corresponding points on the detected left and the right track (track gauge) in pixels at \(y_{p}\)-coordinate, and \(H\) is the averaged real-world height of the objects from a particular class.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 1 Introduction
The classification performance of the proposed method is compared with the existing methods on two datasets--the self-built ship dataset and the publicly available yet challenging VAIS dataset. The remainder of this paper is organized as follows. Section 2 provides a basic understanding of CNNs. Section 3 describes the proposed classification method in detail. Section 4 introduces the two experimental datasets employed in this study and discusses experimental results obtained and their subsequent analysis. Lastly, Section 5 lists major conclusions drawn from this study along with a brief discussion of future endeavors.
## 3 Materials and Methods
### Data and Method  
Several data (i.e., vehicular CO samples, meteorology and traffic flow data) were collected from the field during April 2017. Light Detection and Ranging (LiDAR) data were collected in March 2017, and the Worldview3 satellite image was captured in May 2017. Figure 2 shows the overall methodology, which consists of several steps. The hybrid model is designed to predict CO emissions at a specific time and location, for example, prediction maps based on different times of a day. The first step is data collection, which was achieved based on a gas analyzer and data loggers to simultaneously collect CO, temperature, humidity, and traffic information. The LULC map was extracted from the Worldview-3 image with spatial resolution of 0.3 m. The digital surface model (DSM) was derived from the Airborne  
Figure 1: Location map of the study area.  
LiDAR point clouds by using Environment for Visualizing Images (ENVI) software. The second step is the statistical modelling, which was applied by combining two models i.e., the correlation based feature selection (CFS) and Multilayer Perceptron (MLP) by using Weka software. The final step is the spatial modelling based on the regression equations derived from regression analysis and GIS techniques to generate microscale prediction maps for the traffic CO emissions during different times of the day.
## Appendix A Cellular Network Technologies
### Architecture
#### Cell Global Identifiers  
In order to uniquely identify a cell at a given instant, 2G networks make use of four parameters that differentiate each cell from all other cells in the world: the MCC, the MNC, the location area code (LAC), and the cell identifier (CI). The first two parameters identify the PLMN. The other two are defined as follows:  
* The LAC is a fixed-length code (16 bits/4 digits) that characterizes a location area (LA) in a GSM network;
* The CI is used to uniquely identify a cell in a given LAC (this means that the same value for CI can occur in different LACs). It consists of 16 bits and can thus assume 65,535 different values. In the early years of the GSM technology, due to the small number of cells in a network, the CI was unique for all the LACs.  
The concatenation of MCC, MNC, and LAC is usually called a local area identifier (LAI).  
In GPRS networks, besides the location area, there exists a routing area (RA), which is related to the data packet traffic across the network. Each RA is included in an LA; in general, an LA can include one or more RA. Each RA is identified by a routing area identifier (RAI), which is the concatenation of the LAI with a fixed-length code (8 bits/2 digits) called the routing area code (RAC).  
Finally, it is worth pointing out that there seems to be an interesting connection between the components of a GSM PLMN and the CIs. In some cases (see, for instance, [[http://www.erlang.com/forum/erlang/thread.htx?thread=686](http://www.erlang.com/forum/erlang/thread.htx?thread=686)]([http://www.erlang.com/forum/erlang/thread.htx?thread=686](http://www.erlang.com/forum/erlang/thread.htx?thread=686)) (accessed on 1 February 2022)), indeed, the identifiers of these components can be extracted from the CIs of the cells, as the CI of a cell, say, xXYTZ, consists of the BSC number xx, the BTS number YY relative to the specific BSC, and cell/sector Z relative to the specific BTS (0 in omnidirectional BTSs; from 1 to 3 in the case of sectoral ones).
## 4 Data and Methodology
### Methodologies
As a result, we do not consider the high power of the covariates. All reasonable covariates available are considered in this analysis. The first reason is that eliminating one potential variable is much riskier than including an extraneous one. CIA condition for PSM analysis requires sufficient covariates to estimate the propensity score. Omitting important variables would cause bias seriously [63; 64]. In contrast, including the nonsignificant variable in the propensity score model will not cause inconsistency in the p-score estimation. The second reason is that our sample is large enough to solve the support problem and variance-increasing problem resulted from the over-parameterized model [65]. Only 63 out of 9764 observations are off support when we include all variables and dummies in the \(p\)-score estimation. The standard deviations of \(p\)-score with and without insignificant variables are almost the same, approximately equal to 0.16. As a result, all available variables are included in the \(p\)-score estimation. Furthermore, based on the correct prediction rate and goodness of fit, we finally decide on the covariates with sub-variables of public financial support (PUBLOC, PUBGMT, PUBEU) and cooperation (CO1-CO7), and dummies for nations, sectors, and firm size. Several matching algorithms can be used to match the treated individuals with their counterpart from the comparison group. The nearest-neighboring (NN) matching means that \"the individual from the comparison group is chosen as a matching partner for a treated individual that is closest in terms of the propensity score\" [62]. It is 3 NN if three nearest-neighboring matching partners are assigned, and so on. With the concern that even the closest matching partner may be far away from the treated individual for \(p\)-score, caliper matching is proposed [62]. Combining these two guarantees, the matching partners are the closest, and they are within a certain distance for \(p\)-scores. [PERSON] [66] recommends (\(0.25*\text{std.dev.ofp\_score}\)) for the caliper, and we set it at (\(0.1*\text{std.dev.ofp\_score}\)) more strictly in this analysis to obtain a higher-quality match. In addition, we also test with kernel matching, where the weighted average of nearly all individuals in the comparison group is used to formulate the counterfactual outcome of treated individuals.
## 3 Results and Discussion
### Heat Stability and pH Stability of GE
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Stability**} & \multicolumn{2}{c}{**Solubility**} & \multicolumn{2}{c}{**Foaming Ability**} \\ \hline \multirow{2}{*}{**Treatment**} & \(\beta\)**-Glucan Retain** & \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Solubility (g/100 g)**} & \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Foam Capacity (\%)**} & \multirow{2}{*}{**Foam Stability (\%)**} \\  & **Ratio (\%)** & & & & & \\ \hline Heat (\({}^{\circ}\)C) & - & Temperature (\({}^{\circ}\)C) & & Temperature (\({}^{\circ}\)C) & - \\ \hline _Bolling_ & - & 20 & 0.28 \(\pm\) 0.04a & 20 & 45.65 \(\pm\) 1.24b & 55.55 \(\pm\) 1.23e \\
100 & 100 \(\pm\) 0.14a & 40 & 0.39 \(\pm\) 0.02b & 40 & 45.54 \(\pm\) 1.41b & 50.15 \(\pm\) 0.35d \\ Dry heat (\({}^{\circ}\)C) & - & 60 & 0.66 \(\pm\) 0.02c & 60 & 44.12 \(\pm\) 1.83b & 25.14 \(\pm\) 1.02c \\
140 & 100 \(\pm\) 0.21a & 80 & 0.79 \(\pm\) 0.02d & 80 & 41.67 \(\pm\) 2.42 ab & 13.98 \(\pm\) 1.32b \\
160 & 99.96 \(\pm\) 0.58a & 100 & 0.91 \(\pm\) 0.06e & 100 & 38.21 \(\pm\) 2.68a & 7.21 \(\pm\) 0.80a \\
180 & 99.36 \(\pm\) 1.01a & pH & - & pH & - & - \\
200 & 99.01 \(\pm\) 0.88a & 3 & 0.41 \(\pm\) 0.03a & 3 & 20.35 \(\pm\) 1.24a & 25.87 \(\pm\) 2.12a \\
220 & 98.21 \(\pm\) 1.83a & 5 & 0.66 \(\pm\) 0.03b & 5 & 31.08 \(\pm\) 1.92b & 32.26 \(\pm\) 1.82b \\ - & - & 7 & 0.91 \(\pm\) 0.05c & 7 & 45.76 \(\pm\) 1.23d & 55.55 \(\pm\) 1.23e \\ - & - & 9 & 0.87 \(\pm\) 0.02c & 9 & 39.55 \(\pm\) 2.06c & 51.28 \(\pm\) 1.37d \\ - & - & 11 & 0.86 \(\pm\) 0.05c & 11 & 32.67 \(\pm\) 1.34b & 46.87 \(\pm\) 1.95c \\ pH & - & NaCl concentration (\%, _v_/_v_) & NaCl concentration (\%, _v_/_v_) & NaCl concentration (\%, _v_/_v_) & 56.61 \(\pm\) 1.08a \\
3 & 70.45 \(\pm\) 3.78a & 0 & 0.91 \(\pm\) 0.01c & 0 & 45.85 \(\pm\) 1.34a & 55.61 \(\pm\) 1.08a \\
5 & 92.96 \(\pm\) 2.83b & 1 & 0.87 \(\pm\) 0.02b & 1 & 48.21 \(\pm\) 1.02b & 56.25 \(\pm\) 1.84a \\
7 & 100 \(\pm\) 0.85c & 2 & 0.84 \(\pm\) 0.02b & 2 & 54.67 \(\pm\) 2.25c & 64.81 \(\pm\) 1.23b \\
9 & 100 \(\pm\) 1.04c & 3 & 0.81 \(\pm\) 0.02 ab & 3 & 61.27 \(\pm\) 1.32d & 68.85 \(\pm\) 2.18c \\
11 & 99.27 \(\pm\) 0.57c & 4 & 0.78 \(\pm\) 0.01a & 4 & 65.73 \(\pm\) 1.86e & 69.23 \(\pm\) 1.56c \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stability, solubility, and foaming ability of \(\beta\)-glucan extract (GE) from highland barley. In summary, highland barley or bran which is rich in \(\beta\)-glucan can be smoothly processed with no loss of \(\beta\)-glucan under high temperature and weak acidic, neutral or alkaline condition in food industry.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 1 Introduction
Integrated reconfiguration framework: A unified dynamic reconfiguration framework is constructed by coupling the proposed clustering, feasibility evaluation, and optimization methods. The framework is validated on IEEE 33- and 69-bus systems, demonstrating superior performance in reducing power loss and improving voltage stability. The remainder of this paper is organized as follows: Section 2 presents the proposed load period segmentation method based on the improved fuzzy C-means clustering algorithm, including the incorporation of spatiotemporal similarity and adaptive time weighting. Section 3 describes the formulation of the multi-objective optimization model. Section 4 details the construction of the feasibility judgment model based on the basic ring matrix and discusses the enhancement strategies applied to the standard Equilibrium Optimizer (EO) and presents the design of the Improved Equilibrium Optimizer (IEO). Section 5 reports the results of simulation experiments conducted on IEEE 33- and 69-bus systems, compares the performance of the proposed method with other state-of-the-art algorithms, and validates its effectiveness and robustness. Finally, Section 6 summarizes the main conclusions and outlines future research directions.