esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Carbon red mud foams (CRMFoams) were successfully synthesized, with two different phenolic resin to red mud ratios (5 or 2.5), from commercially available polyurethane foams. The morphological and structural study of the foams revealed a highly porous structure (bulk density \(<\) 0.55 g/cm\({}^{3}\), apparent porosity P \(>\) 65%), which exhibited elliptical and spherical pores, sized from 50 up to 500 \(\upmu\)m, while their pore cell walls composed of partially graphitized carbon and various oxide and iron phases. The foams showed remarkable encapsulation efficiency as shape stabilizers for the n-octadecane (OD) and RT18 HC. Due to the enhanced porosity and the multiple reflections within the formed micropores, all of the as-prepared CRMFoams showed excellent EMI shielding performance in frequency of 3.5-9.0 GHz, making them promising candidates for use in thermal management and EMI shielding for electronic devices. Finally, the usage of alternate sized red mud and/with additives as well at different loading ratios could further enhance specific properties of the reported materials as for example the formed micropores whose geometry and connectivity determine the EMI and mechanical properties altogether.  
Figure 11: Reflection (**a**) and absorption (**b**) spectra from 3.5 to 9.0 GHz (C-band) for different CRMFoams. The electromagnetic interference shielding effect (SE) due to reflection (SER; **c**), and absorption (SEA; **d**) can also be seen in the frequency range 3.5–9.0 GHz.  
**Author Contributions:** Conceptualization, M.A.K. and G.K.; Methodology, M.A.K., G.K. and C.E.S.; Formal Analysis, M.A.K., G.K. and C.G.; Investigation, C.G., G.A., A.K., M.B. and Z.V.; Resources, M.A.K.; Writing--Original Draft Preparation, M.A.K. and G.K.; Writing--Review and Editing, M.A.K. and G.K.; Visualization, M.A.K., G.K., C.G. and C.E.S.; Supervision, M.A.K. and G.K.; Project Administration, M.A.K.; Funding Acquisition, M.A.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH--CREATE--INNOVATE (acronym: SEMI-WEB; project code: T2 EDK-02073).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors greatly acknowledge [PERSON] for the XRD measurements, [PERSON] and [PERSON] for the SEM measurements as well as the use of the XRD and SEM Laboratory Network Units of the University of Ioannina. Helpful and stimulating discussion with [PERSON] and [PERSON] are gratefully acknowledged. We would also like to acknowledge [PERSON] for his contribution in the experimental part.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
## 4 Experimental Results
Figure 7 represents the error vector magnitude of the 5/6G waves received at the respective RAUs. To see the effect of the different components of the link on the EVM, we show a back-to-back (B2B) scenario in which there was no fibre length and the modulator was directly connected to the receiver via a patch cord fibre. The other comparison is between 5 km and 10 km in length, which clearly shows to what extent the fibre impacts the EVM degradation. It can be observed that, in general, the received 6G waveform has larger degradation, which is visible due to its higher EVM %. At a high RF input power range, the EVM goes below the threshold of 3.5% set by the 3 GPP [38], while the 5G waveform undergoes less degradation. It can be seen that the dynamic range of allowed RF input power for a 6G signal is 10 dB, while the dynamic range for a 5G waveform signal is 18 dB. The higher degradation of the 6G signal is due to the losses that occur over the air interface. Similarly, the received optical power vs. EVM for the two waveforms is also shown in Figure 8. The results clarify that both of the waveforms can co-exist simultaneously, and the trial confirms that the dynamic range of operating the two waveforms is acceptable. Figure 8: EVM vs. optical power for 5G and 6G signals at respective RAUs. Figure 7: EVM vs. RF input power for 5G (B2B, 5 km and 10 km SMF length) and 6G signals at respective RAUs.
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 2 Topology Discovery in SDN Networks
### Performance of the Link Discovery (OFDP)
#### 2.6.2 OFDP Performance Metrics

Table 2: Number of Packet_Out messages in different topologies [39]. In [38; 39; 75], the authors use this metric to measure the extent to which OFDP is using the SDN-Controller to obtain the topology. The SDN-Controller uses its CPU to create, send LLDP Packet_Out messages, and process LLDP Packet_In messages. The average CPU utilization increases when the number of packets sent and received by the SDN-Controller increases. Accumulative CPU Utilization of SDN-Switches  
The authors in [42] used this metric to measure the extent to which OFDP is using the CPU for SDN-Switches. SDN-Switch is an essential part of discovering the topology. The SDN-Switch receives LLDP Packet_Out messages from the SDN-Controller and sends them to its active ports. As a result, the number of packets sent or received by the SDN-Switches will also increase the CPU utilization ratio. Bandwidth Consumed by OFDP  
As described in the OFDP methodology, there are two types of connections: between the SDN-Switches themselves and between SDN-Switches and the SDN-Controller. This metric is determined by the size of the exchanged OFDP packet to maintain the topology. In [39; 42], the authors used this metric to evaluate OFDP. Thus, this bandwidth can be especially important for measuring the performance of large networks and in-band control channels. Some researchers [38; 42; 76] have used this metric to evaluate OFDP and the performance of their topology discovery. Learning time is the time the SDN-Controller needs to learn about topology changes. The discovery process will be repeated every discovery interval. The discovery interval is the time interval between two discovery rounds. The problem is that when a topology change occurs, the SDN-Controller will wait for the next discovery round to learn about new topology changes. This means that learning time is at least equal to the discovery interval.
## 3 Results
### Region of Djelfa  
In Figure 7, the following variations can be distinguished. For \(TES\) = 0 h to 4 h, the electrical production per square meter of the heliostats increase respectively with the values \(SM\) = 1.2, \(SM\) = 1.6, and \(SM\) = 2, and then it decreases; for \(TES\) = 6 h to 12 h, the electrical production per square meter of the heliostats increase proportionally with to a converging value; for \(SM\) = 2.0, 2.2, and 2.4, the electricity production per m\({}^{2}\) of heliostat's increases proportionally with the \(TES\). For the other \(SM\), the electrical production increases until \(TES\) = 2 h, then it decreases. Except for \(SM\) = 1.8, the electrical production is interesting as \(TES\) exceeds 4 h. The configuration of the system becomes unprofitable (a large area, large dimensioning of the tower). On the other hand, the starting power of the plant is much better for \(SM\) = 1.5 and remains almost stable from \(TES\) = 4 h.  
Figure 6: (**a**) The effect of Thermal Energy Storage (\(TES\)) on electrical production/m\({}^{2}\) (surface heliostats) under different values of solar multiple. (**b**) The solar multiple effect on capacity factor (\(CF\)) under different \(TES\) values.  
The capacity factor (CF) evolves proportionally with the solar multiple (_SM_), the curves increase proportionally and tend towards close values except for \(TES\) = 0 h, 2 h and 4 h as shown in Figure 8.  
From above, it can conclude that the optimal point of operation of the system corresponds to the following coordinates \(SM\) = 1.5, \(TES\) = 4 h, and the electrical production is 18.45 GWh/year. Table 3 shows the model validation parameters simulated in Djelfa and Batna.  
Figure 8: (a) Effect of Thermal Energy Storage (\(TES\)) on electrical production/m\({}^{2}\) (surface heliostats) under different values of solar multiple (Djelfa). (b) The solar multiple effect on capacity factor (\(CF\)) under different \(TES\) values (Djelfa).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Type of Parameter** & **Simulated Case,** & **Simulated Case,** & **Simulated Case, Study** \\  & **Scenario 1 [14]** & **Scenario 2 [14]** & **Simulated Case, Study** \\ \hline Annual DNI (\(k\)Wh/m\({}^{2}\)) & 1907.30 & 1907.30 & 2416.30 \\ Hybridization (\%) & 0.00 & 15.00 & 0.00 \\ Net energy production (\(\mathrm{GWh/year}\)) & 18.15 & 44.40 & 18.45 \\ Net energy production difference (\%) & & & 1.60 (scenario 1.00 and study) \\ Annual capacity factor (\%) & 10.60 & 26.00 & 10.50 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the model validation.  
Figure 7: (a) Solar multiple effect (_SM_) on electrical production by surface heliostats under different values of \(TES\). (b) Solar multiple effect on the surface of the heliostats mirrors (m\({}^{2}\)) and the length of the tower (m).
## 3 Preliminaries
### Problem Formulation  
Assume the user-interested target in the area \(A\) to be monitored is called an event \(e\), and the event set \(E=\left\{{{{e_{1}}},{{e_{2}}},\cdots,{{e_{p}}}}\right\}\). At the same time, assume that in order to achieve full coverage of the event and full connectivity of the network, \(n\) sensor nodes need to be deployed. The sensor node set (referred to as the node set \(S=\left\{{{{s_{1}}},{{s_{2}}},\cdots,{{s_{n}}}}\right\}\)) and any node \({{s_{j}}}\) have the same sensing, communication, and mobility capabilities. Then, the MMCC problem studied in this paper can be described by the following mathematical model:  
\[Minimize\quad\mathop{\sum}\limits_{i\in n}{{{L_{i}}}} \tag{4}\]  
\[Subjectto\quad\varphi\left({{{e_{j}}}}\right)\geq 1,\forall{{e_{j}}}\in E \tag{5}\]  
\[BS\left({{{s_{i}}}}\right)\ \ =1,\forall{{s_{i}}}\in S \tag{6}\]  
where \({{L_{i}}}\) (\(i\in n\)) is the moving distance of the \(i^{\text{th}}\) node. The objective function (4) describes the length of the minimum moving distance, which belongs to the global optimization goal. The constraint Equation
## 5 Methods
### Safety Performance Functions  
Safety Performance Functions (SPFs) represent the shape of the model and allow the predicted number of accidents under 'base' conditions to be determined. The 'base' conditions are those for which all CMFs assume a value of 1, as will be discussed in Section 5.4. For roadway segments, the number of predicted crashes in the 'base' conditions calculated by the SPF depends on the traffic on the segment (\(AADT\)) and its length (\(L\)), as expressed in Equation (4). For intersections, the number of predicted crashes depends on the traffic on the main (\(AADT_{maj}\)) and secondary (\(AADT_{min}\)) roads, as reported in Equation (5). In the case of reduced traffic on the secondary road, it is possible to consider the total traffic (\(AADT_{tot}\)) as the sum of the traffic on the two roads, as indicated in Equation (6).  
\[N_{SPF,rs}=f(AADT,L) \tag{4}\]  
\[N_{SPF,i}=f\big{(}AADT_{maj},\ AADT_{min}\big{)} \tag{5}\]  
\[N_{SPF,i}=f(AADT_{tot}) \tag{6}\]  
For rural two-way, two-lane roads, the SPFs shown in Table 3[9; 10], divided by severity level, were taken into account. The distinguishing of severity levels avoids the inclusion of Property Damage Only (PDO) crashes, which are not registered in Italy. In fact, KAB and KABC severity levels (fatal injury--K, incapacitating injury--A, non-incapacitating injury--B, possible injury--C) were considered. The adoption of alternative SPFs resulted in a different value of the predicted number of crashes per year for 'base' conditions on average over the 9-year period considered for the study. In fact, the values of the predictedaccidents in 'base' conditions shown in Table 3 were obtained by applying the SPFs to the actual road conditions with reference to the traffic values of the 9 years considered.  
Given the need to know the traffic on secondary roads at intersections, it is assumed to be equal to a percentage of the traffic on the main road. The value of the percentage is assumed in relation to the importance of the secondary road, as shown in Table 4.
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## 5 Methods and Results
### Mutation Procedure  
The mutation is applied with a certain probability. The mutation operator allows modifying an individual's genes to find other parts that are very different. At this point, some parents are chosen from the population to apply the mutation operation. We use the inversion operator, which is simple in that it generates a point along the length of two chromosome genes and then inverts those two points (two stations). An example of inversion is shown in Figure 11.  
After the mutation, the sum of the quantities collected for the type \(w\) at the stations visited may be greater than the corresponding compartment capacity.  
To keep the feasibility of the solution, we propose the following modification algorithm:  
```
(\(p\) is the change point) For\(w=1\) to \(\hat{m}\) If\((\ q_{w1}+\ldots+q_{w,p}+\ldots+q_{w,i+m}>\mathbb{C}q_{w})\) \(q_{w,p}\gets 0\) Else Collected the waste type \(w\) in the station \(p\) in the corresponding compartment of vehicle \(v\) \(w\gets w+1\) get back to 3 End If End For End
```  
**Algorithm 3**Modification of the mutation operator.  
Before studying the example based on the real data of valorsul using our hybrid genetic algorithm (HGA), we treat the example presented in Section 5.1 to show the efficiency of the approach used HGA. Table 4 shows the results found by applying HGA method and the results that we found using the CPLEX software (exact method). To illustrate our results, we calculate the quantity collected for each type in each arc (TCW1 & TCW2), the number of visited bins for each type at each station (TVB1 & TVB2), and the bins overflow level in each arc, and the transport cost (TC). We obtained the results shown in Table 4.  
In Table 4, we can see that the results obtained are the same for both methods (CPLEX and HGA). This happens because if the vehicle collects a waste type in a station, it collects all the quantity existing in the bin.
## 4 Experiment and Results
### Results
#### 4.4.2 Impact of Food Image Quality on Visual Features from CNN  
The experimental configurations are as follows. In case of contrast, the blending factor is varied from 0 to 1 in steps of 0.1. For Gaussian noise, the standard deviation is varied from 10 to 100. For Gaussian blur, it uses the Gaussian kernel and is varied from 1 to 15. For JPEG compression, the quality varies from 2 to 20, as above 20 JPEG compression does not impact the robustness of food features. The parameter of 100 represents an uncompressed JPEG image. For the defocus blur, the kernel size of the disk kernel is varied from 11 to 17. Figure 9 shows various distortions on randomly selected food images. Figure 10 shows the results of InceptionResnetNetV2 under various quality distortions. We observed that Gaussian noise and defocus blur have significantly reduced the robustness of features compared to other types of quality distortions. As JPEG compression does not affect performance above 20, it is useful for compressing images in real-time food recognition systems. We noted the worst effect of quality distortions for the VireoFood-172 dataset due to the low resolution of original images in the dataset. It reduces model performance by 50.28% for Gaussian blur at the kernel value of 3, by 18.67% for Gaussian noise at S.D of 20, and by 56.35% for defocus blur at disk kernel value of 11.  
Figure 10: InceptionResNetV2 under various quality distortions in the image: JPEG compression, Gaussian blur, Gaussian noise, and defocus blur.  
Figure 9: Defocus blur, Gaussian noise and Gaussian blur on randomly selected images.
## 4 Outbound-Type Bundle Protocol
### Section between Relay Nodes  
The message sending sequence between relay nodes is shown in Figure 13. This is described in detail below.  
* After relay node \(n\) has received a \"hello message\" from relay node \(n+1\), it sends the list of node identifiers of the covered areas it owns (covered area list).
* Relay node \(n+1\) receives it and checks for information about any nodes that it does not have in the list. If it does not have information about certain nodes, it requests relay node \(n\) for that information (covered area request).
* Relay node \(n\) sends the requested information (send requested covered area).  
If there are still bundles that relay node \(n\) needs to send to relay node \(n+1\) after the above processing, the following procedure occurs:  
* If relay node \(n+1\) is linked to the covered area of the traveling node that can carry bundles to the destination area, relay node \(n\) sends the bundle to relay node \(n+1\) (send bundles).  
Figure 12: Structures of the messages exchanged between the sending node and a relay node.  
Figure 13: Procedure for relaying a bundle between relay nodes. Note: the bold arrow shows that a bundle copy is sent.  
* Relay node \(n+1\) returns an ACK to notify that it has received the bundle.
* After relay node \(n\) has received the ACK, it retains the bundle if its own covered area overlaps with the destination area, but discards the bundle if it does not.  
When relay nodes exchange bundles, they simultaneously exchange information about covered areas. Each covered area is associated with a node identifier. A covered area list includes a set of node identifiers of the covered areas whose information the relay node in question has. When a relay node receives a covered area list, it checks for the node identifiers of the covered areas whose information it does not have. If there are such node identifiers, it includes them in its covered area request and sends the request. The relay node that has received the request returns the requested information. In this way, all relay nodes concerned come to share information about covered areas.  
The structures of the messages exchanged above are shown in Figure 14. Owner node ID in the covered area header is the node that has created that covered area. Relay node ID is the node that sent the information about the covered area one hop back. \"Hop count\" is the number of hops needed to reach the node that owns the covered area in question. The latter two parameters are updated each time a bundle is relayed.
## 3 Results and Discussion  
### Chemical Composition of BCP  
The chemical composition of dried and crushed BCP is presented in Table 1. TDF followed by lipids and protein are the main components of BCP. The SDF/IDF ratio influences DF's physiological and technological properties and is important from a nutritional perspective. The SDF/IDF ratio in BCP is 1:5, which is lower than the recommended range of1:2- [8; 26]. Therefore, it is appropriate to explore additional technological modifications to enhance the SDF content. The difference in SDF and IDF hydration properties significantly influences the technological properties of pomace and the capabilities of food products [27]. The ratio between SDF and IDF also significantly affects physiological functions including gut motility and microbiota diversity, thereby influencing both health outcomes and functional properties in food applications [28].  
[PERSON] et al. [29] reported a different chemical composition of BCP from Germany with higher content of lipids (20.21 g/100 g d.w.), protein (15.71 g/100 g d.w.), and IDF (51.16 g/100 g d.w.) and lower content of SDF (3.97 g/100 g d.w.) and ash (2.66 g/100 g d.w.). In comparison, [PERSON] et al. [8] evaluated the chemical composition of BCP from the UK and Poland, reporting higher contents of IDF (61.3 and 61.9 g/100 g d.w., respectively) and SDF (25.1 and 30.0 g/100 g d.w., respectively). The chemical composition of pomace may vary due to the characteristics of the berry variety, climatic conditions, soil, and other environmental factors during cultivation, as well as the processing technology used. The amount and composition of dietary fiber in pomace may vary depending on the type of juice production technology used. In industrial juice production, enzymatic treatment with pectinases [30] is most often used before pressing, which, by hydrolyzing pectin, reduces the viscosity of the juice and increases the efficiency of the pressing process and the yield of juice. The insufficient enzymatic treatment has an impact on the pomace's dietary fiber composition, and pomaces may contain more pectin and a higher content of SDF, as well as a higher content of mono- and oligosaccharidescharides [29]. The high levels of SDF and mono- and oligosaccharidescharides in the studied BCP indicate that the black current berry was untreated or insufficiently treated with enzymes before juice pressing.
## 3 SPO-Based Collision Detection and Obstacle Avoidance Using APF
### Estimation of External Torque Using SPO  
#### 3.1.1 SPO  
The SPO is a nonlinear observer that only needs partial information (position) and yields all other states such as position, velocity, and perturbation \(\hat{\Psi}\). The perturbation is used to estimate all nonlinearities and disturbances in the system's dynamics. The general form of SPO can be expressed as [21; 26]:  
\[\dot{\hat{x}}_{1}=\hat{x}_{2}-k_{1}\cdot sat(\widetilde{x}_{1}\;)-\alpha_{1} \cdot\widetilde{x}_{1}, \tag{3}\]  
\[\dot{\hat{x}}_{2}=-k_{2}\cdot sat(\widetilde{x}_{1})-\alpha_{2}\cdot \widetilde{x}_{1}+\alpha_{3}\cdot\overline{u}+\hat{\Psi}, \tag{4}\]  
\[\dot{\hat{x}}_{3}=\alpha_{3j}^{2}(-\hat{x}_{3}+\alpha_{3}\cdot\hat{x}_{2}+ \overline{u}), \tag{5}\]  
\[\Psi=\alpha_{3}\;(-\hat{x}_{3}+\alpha_{3}\cdot\hat{x}_{2}), \tag{6}\]  
\[\overline{u}=\frac{1}{\alpha_{3}}*\left[-\left(\frac{C}{M}\right)*\hat{x}_{2}+ \left(\frac{1}{M}\right)*u\right] \tag{7}\]  
where \(\hat{\Psi}\) is the estimated perturbation at each joint; \(\hat{x}_{1}\) and \(\hat{x}_{2}\) are the estimated states (positions and velocity, respectively); hat (\(\wedge\)) and tilde (\(\sim\)) symbols represent the estimation and error states, respectively. The saturation function is denoted as. \(\hat{x}_{3}\) is the new state variable for estimating the perturbation. \(k_{1}\), \(k_{2}\), \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\) are constant values and can be obtained from \(k_{1}=3*e_{o}*\lambda\), \(k_{2}=k_{1}*\lambda_{d}\), \(\alpha_{3}=\sqrt{\frac{\lambda_{d}}{3}}\). \(\overline{u}\) is the compensated control input and \(u\) is the control input from the controller. \(\lambda_{d}\) is a positive constant used to tune the parameter for better performance and for stable pole placement [25] and \(\overline{u}\) is the compensated control input from the SPO.