esa-sceva/satcom-chunk-collection huggingface dataset:
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
## Related Work
### SDN and OpenFlow  
OpenFlow specification is generally introduced as the first standard protocol that is defined between control planes and forwarding planes of SDN architecture. The Open Networking Foundation (ONF) defines the SDN and OpenFlow specification [12; 13]. OpenFlow enables a network to be programmable by decoupling the control plane and forwarding plane. OpenFlow is designed to directly manage and transfer traffic, so it enables an SDN controller to directly interact with the forwarding plane of network devices. By using OpenFlow, controllers can provide remote administration for switches by controlling flow tables. By controlling the flow table, OpenFlow can enable a network to respond to real-time changes. OpenFlow specification introduces the packet format and actions based on the rules [19].  
Recently, a study was introduced to provide effective flow table management in SDN networks [20]. SDN technology is used in many areas for the flexibility and scalability of networks. Therefore, the size of the flow table to be handled by the OpenFlow switch and controller can be gradually increased. In [20], the authors proposed a hash-indexed wildcard scheme. The proposed scheme only keeps the index value of the matched flow of the wildcard-based flow table. It does not store entire flow information in a hash-based flow table; instead, it stores the location of the flow entry that exists in the wildcard-based table. However, the authors only focused on flow table management in SDN-based cloud networks. They did not consider IoT network environments.
# Frame-Based Phone Classification Using EMG Signals +
## 3 Results
### Speaker-Independent, Session-Independent Classification  
To evaluate session-independent classification, we employed the models trained with three sessions from the session-independent experiments and tested them with the remaining session from each of the other speakers. The results, presented in Table 5, indicate that classification accuracy varies greatly despite all models being trained on similar amounts of data.  
As explained above (Section 3.1), Session 004-102 contains ill-defined signals, probably due to detaching of the electrodes. This explains the bad results when this session is used to test any model. The same can be said for Sessions 003-102, 005-101, and 006-102. A comparison of speaker-independent experiments to speaker-dependent, session-independent experiments reveals a substantial decrease in the accuracy compared to the former.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Speaker** & **Training Session(s)** & **Testing Session** & **Validation Accuracy** & **Testing Accuracy** \\ \hline \multirow{8}{*}{001} & 102 & & 49.12 \(\pm\) 0.86 & 23.40 \\  & 102,103 & 101 & 45.08 \(\pm\) 0.89 & 27.89 \\  & 102,103,104 & & 42.50 \(\pm\) 0.74 & 30.41 \\ \cline{2-5}  & 101 & & 50.48 \(\pm\) 1.01 & 19.57 \\  & 101,103 & 102 & 46.85 \(\pm\) 1.07 & 22.11 \\  & 101,103,104 & & 43.81 \(\pm\) 0.70 & 24.54 \\ \cline{2-5}  & 101 & & 50.48 \(\pm\) 1.01 & 14.19 \\  & 101,102 & 103 & 48.16 \(\pm\) 1.14 & 18.09 \\  & 101,102,104 & & 45.00 \(\pm\) 0.34 & 18.25 \\ \cline{2-5}  & 101 & & 50.48 \(\pm\) 1.01 & 15.86 \\  & 101,102 & 104 & 48.16 \(\pm\) 1.14 & 22.38 \\  & 101,102,103 & & 44.49 \(\pm\) 0.37 & 24.93 \\ \hline \multirow{8}{*}{002} & 102 & & 42.80 \(\pm\) 0.96 & 10.00 \\  & 102,103 & 101 & 39.69 \(\pm\) 0.53 & 18.32 \\  & 102,103,104 & & 37.90 \(\pm\) 0.61 & 21.93 \\ \cline{2-5}  & 101 & & 43.71 \(\pm\) 0.48 & 20.90 \\  & 101,103 & 102 & 41.23 \(\pm\) 1.09 & 23.81 \\  & 101,103,104 & & 37.79 \(\pm\) 0.80 & 24.19 \\ \cline{2-5}  & 101 & & 43.71 \(\pm\) 0.48 & 17.79 \\  & 101,102 & 103 & 42.46 \(\pm\) 1.02 & 18.03 \\  & 101,102,104 & & 39.63 \(\pm\) 0.53 & 16.73 \\ \cline{2-5}  & 101 & & 43.71 \(\pm\) 0.48 & 19.01 \\  & 101,102 & 104 & 42.46 \(\pm\) 1.02 & 20.84 \\  & 101,102,103 & & 39.42 \(\pm\) 0.51 & 22.92 \\ \hline \multirow{2}{*}{003} & 102 & 101 & 42.41 \(\pm\) 1.07 & 20.66 \\  & 101 & 102 & 46.73 \(\pm\) 1.12 & 15.05 \\ \hline \multirow{2}{*}{004} & 102 & 101 & 41.29 \(\pm\) 1.37 & 10.95 \\  & 101 & 102 & 43.22 \(\pm\) 1.50 & 8.63 \\ \hline \multirow{2}{*}{005} & 102 & 101 & 51.45 \(\pm\) 0.54 & 11.83 \\  & 101 & 102 & 43.61 \(\pm\) 1.56 & 23.61 \\ \hline \multirow{2}{*}{006} & 102 & 101 & 28.39 \(\pm\) 1.31 & 16.02 \\  & 101 & 102 & 35.92 \(\pm\) 1.17 & 8.30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Speaker-dependent, session-independent classification results.
###### Abstract  
The methodologies and results presented in this study can serve as a blueprint for rural municipalities, cooperative energy planners, or agricultural communities seeking to enhance microgrid reliability and reduce costs. By leveraging locally available biomass (e.g., livestock waste) and optimal solar photovoltaic capacity, stakeholders can tailor hybrid energy systems to local resource conditions, lower greenhouse gas emissions, and foster community-level economic development. Additionally, regulatory bodies and policymakers can use these insights to formulate supportive policies--such as feed-in tariffs or biogas incentives--that accelerate the adoption of clean, resilient hybrid solutions in other semi-remote or grid-constrained settings.  
As global energy demand increases, ensuring a reliable electricity supply in rural or semi-remote areas remains a significant challenge. Hybrid energy systems, which integrate renewables, generators, storage, and grid connections, offer a promising solution for addressing energy reliability issues. In this context, the rural community of Aras de los Olmos, Spain, serves as the focal point because of its frequent power outages despite being connected to the main grid. This study investigates innovative solutions tailored to the community's unique needs. It highlights critical challenges in achieving reliable energy access and bridges the gap between existing limitations and sustainable, future-oriented energy systems. This is achieved by analyzing the current energy setup and evaluating potential alternatives. Two scenarios were evaluated: one optimizing the existing configuration for economic efficiency while retaining the grid as the primary energy source, and another introducing a biomass generator to enhance reliability by partially replacing the grid. Detailed technical, financial, and environmental assessments were performed using HOMER. These assessments identified an optimal configuration. This optimal configuration improves reliability, enhances stability, reduces disruptions, and meets growing energy demands cost-effectively. As will be indicated, the first scenario can reduce total costs to approximately USD 90,000 compared to the existing setup, whereas the second scenario can lower grid dependence by approximately 70%. In addition, introducing renewable energy sources, such as solar and biomass, significantly reduces greenhouse gas emissions and reliance on fossil fuels. Additionally, these solutions create local job opportunities, promote community engagement, support energy independence, and align with broader sustainability goals.  
hybrid energy systems; renewable energy integration; biomass generator; grid-connected reliability; HOMER analysis +
Footnote †: journal: Applied Sciences  
_Article_
## 4 Conclusions  
It is apparent that the selection of the optimal MAP parameters (i.e., gas/product volume ratio and initial headspace gas composition) is a complex issue and is a very important step in the design of MAP packages. Selection depends on the effect on microbial growth, desired food quality, and shelf life, as well as the appearance of a package. CO\({}_{2}\) emitters, which produce CO\({}_{2}\) in contact with water that is obtained from liquid leaking from the fish flesh, may enable reduced gas/product volume ratios in MAP fish and further increase shelf life. Increased shelf life can contribute to food waste reduction. In this study, the use of CO\({}_{2}\) emitters in MAP-packaged gutted sea bass (MAP: 50% CO\({}_{2}\)-40% N\({}_{2}\)-10% O\({}_{2}\)) + CO\({}_{2}\) emitter, gas/product volume ratio 3:1) was recognized as effective, as it significantly decreased the microbial growth (TVC, _Pseudomonas_ spp., _Enterobacteriaceae_ spp.) and increased the shelf life of the fish product. The shelf life of MAP and ACT-MAP gutted sea bass based on microbial growth (limit of acceptability = 7.0 log CFU g\({}^{-1}\) for TVC) was 18, 10, 6 and 22, 17, and 10 days at 0 \({}^{\circ}\)C, 5 \({}^{\circ}\)C, and 10 \({}^{\circ}\)C, respectively.  
Conceptualization, T.T. and P.T.; methodology, T.T., A.N., E.G., M.T., I.S., and E.D.; formal analysis, T.T. and E.D.; investigation, T.T., A.N., E.G., M.T., and I.S.; writing--original draft preparation, T.T. and E.D.; writing--review and editing, A.N., E.G., M.T., I.S., and P.S.; visualization, A.N., E.G., M.T., and I.S.; supervision, P.T. and T.T.; project administration, E.D.; funding acquisition, P.T.  
This research was funded by the Greek Operational Programme for Fisheries, Priority Axis \"Innovation in Aquaculture\", Project title: \"Application of smart and intelligent packaging for fish and development of a novel quality management and assurance tool for improved quality and extended shelf life\" (2019-2022) website: smartfish.chemeng.ntua.gr.  
The authors declare no conflict of interest.
# Strain Relaxation Effect on the Peak Wavelength of Blue InGaN/GaN Multi-Quantum Well Micro-LEDs
## 3 Results and Discussion
### Single InGaN/GaN Micro-Pillar Raman Measurements and Analysis
The strain analysis parameters are summarized in Table 1.  
Considering a strained InGaN-layer wurtzite crystal pseudomorphically grown along the \(c\)-axis on a thick GaN layer, the strain tensor \(\varepsilon\) in the InGaN well region has the following elements [17]:  
\[\varepsilon_{xx}=\varepsilon_{yy}=\frac{a_{GaN}-a_{InGaN}}{a_{InGaN}} \tag{3}\]  
\[\varepsilon_{zz}=-2\frac{C_{13}}{C_{33}}\varepsilon_{xx} \tag{4}\]  
\[\varepsilon_{xy}=\varepsilon_{xz}=\varepsilon_{yz}=0 \tag{5}\]  
where \(a_{InGaN}\) and \(a_{GaN}\) are the stress-free lattice parameters of the well and barrier layers, respectively. In this paper, the indium composition in the well layer is 0.25. and the unstressed lattice parameters and elastic constants of In\({}_{0.25}\)Ga\({}_{0.75}\)N can be calculated by linear interpolation of the data in [18]. The strain-induced band gap changes were considered, according to the following equations:  
\[E_{g}^{strain}=E_{g}+(a_{cz}-D_{1}-D_{3})\varepsilon_{zz}+(a_{ct}-D_{2}-D_{4}) \big{(}\varepsilon_{xx}+\varepsilon_{yy}\big{)} \tag{6}\]  
where \(a_{cz}\) and \(a_{ct}\) are the conduction-band deformation potentials along the \(c\)-axis and perpendicular to the \(c\)-axis, respectively. \(D_{1}\), \(D_{2}\), \(D_{3}\), and \(D_{4}\) are the shear deformation potentials. With Equations (4) and (6), the micro-pillar band gap and wavelength shift (\(\Delta E_{g}^{strain}\), \(\Delta\lambda\)) between the center and edge due to strain relief can be expressed in Equations (7) and (8):  
\[ \begin{array}{ll}\Delta E_{g}^{strain}&=E_{g}^{strain}(Edge)-E_{g}^{ strain}(Center)\\ &=2\Big{(}a_{ct}-D_{2}-D_{4}-\frac{C_{13}}{C_{33}}(a_{cz}-D_{1}-D_{3})\Big{)} \Delta\varepsilon_{xx}\\ &\Delta\lambda=\frac{h\times c}{E_{g}^{strain}(Edge)}-\frac{h\times c}{E_{g}^ {strain}(Center)}\end{array}  \tag{8}\]  
where \(c\) is the speed of light in a vacuum and \(h\) is [PERSON]'s constant. According to Equations (3)-(8) and our Raman analysis, \(\Delta\sigma_{xx}\) and \(\Delta\varepsilon_{xx}\), the peak wavelength shift of GaN/InGaN pillar between the center and edge reached 2.45 nm. The Raman shift data show that the strain relaxation at the edge of the pillar is notable, which also provides the reason as to why the small-size InGaN/GaN pillar has a large variation in PL (Figure 3d).
# Mode-Selective Photonic Lanterns for Orbital Angular Momentum Mode Division Multiplexing
## 4 Conclusions  
In conclusion, we analyze the periodic evolution of the PL output modes with respect to taper lengths, affected by possible mode phase differences varying along the taper. Then, we design a three-mode OAM-MSPL for MDM with mode crosstalk below \(-24\) dB and IL below \(-0.03\) dB. As the simulations performed revealed, the designed OAM-MSPL has a taper length error tolerance longer than \(1\) mm with mode crosstalk below \(-21\) dB and IL below \(-0.06\) dB, and works stably in the whole C-band and L-band with low loss and crosstalk.  
This paper was built on original ideas by [PERSON] ([PERSON]) and [PERSON] ([PERSON]), and both of them performed the calculations. All authors contributed to the writing and editing of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (61875019, 61675034, 61875020, 61571067), The Fund of State Key Laboratory of IPOC (BUPT), and The Fundamental Research Funds for the Central Universities.  
The authors declare no conflict of interest.
# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 4 Results and Discussion
### Numerical Evaluation  
In this section, two kinds of optimization problems are considered to evaluate the effectiveness of the proposed optimization approach. To elaborate further, they are first utilized to address some of the most well-known benchmark functions frequently used in different studies. Following that, EJAYA is used to solve feature selection, a discrete problem. Table 1 illustrates the functions utilized to evaluate the efficiency of the proposed approach [13]. For a better overview of these functions, they are also shown in Figure 1. Some algorithms, including the standard JAYA, CSA, particle swarm optimization (PSO) [67], DA [68], GOA [69], MFO [70], and the sine-cosine algorithm (SCA) [71], are considered for comparison with our method. The mentioned algorithms are executed under the following similar conditions: MI = 1000, DV = 30, and PS = 30. To provide a measure that accounts for all of the results from the 30 runs, giving us a comprehensive view of the algorithm's overall performance, we chose to use the average value. The average is particularly useful in our study to evaluate the general tendency and compare different algorithms under consistent conditions. All of the algorithms are then run 30 times. The tuning parameters for each algorithm are defined [35; 72]. To simulate the algorithm, it was implemented using MATLAB on a personal laptop equipped with an Intel i5 processor and 8 GB of RAM. * SCA tuning: \(r_{1}\) linearly reduces from 2 to 0. * DA tuning: The inertia weight (\(w\)) is set to decrease linearly from 0.9 to 0.5 over the maximum number of iterations. The enemy distraction weight (\(e\)) decreases linearly from 0.1 to 0.1 divided by (\(MI\)\(\times\) 0.5), where \(MI\) represents a specific factor. The separation weight (\(s\)) is determined by multiplying 2 by a random value and e. Similarly, the alignment weight (\(a\)), cohesion weight (\(c\)), and food attraction weight (\(f\)) are calculated by multiplying 2 by random values. * GOA tuning: The parameter \(c\) is adjusted using the formula \(c=c_{\text{Max}}-1\times\left((c_{\text{Max}}-c_{\text{Min}})/MI\right)\), where \(c_{\text{Max}}\) is set to 1 and \(c_{\text{Min}}\) is set to 0.00004. * MFO tuning: Linearly reduces from \(-1\) to \(-2\). * PSO tuning: Inertia factor (\(w\)) = 0.2, while the \(c_{1}\) and \(c_{2}\) = 2. * CSA tuning: The awareness probability (\(AP\)) is set to 0.1 and the flight length (\(f\)) is set to 1.5. * JAYA tuning: It is updated via the best and worst members. * EJAYA tuning: \(AW\) = 0.3 and \(a\) = 3. It is notable that we run the EJAYA for different values of the mentioned parameters and then compare the objective functions. From the comparisons, the algorithm found the better solution (minimum objective function) under these parameters.
## 4 Aeroelastic Coupling  
The data exchange between the aerodynamics module and structure module was accomplished by using local UNIX sockets. A loose coupling scheme was adopted. At each time step, the structure module predicts the kinematics by extrapolation and sends the predicted kinematics to the aerodynamics module; the aerodynamics module deforms the configuration based on the predicted kinematics, computes the aerodynamic forces, and sends them back to the structure module; the structure module receives the forces and iterates until convergence; and, after this, it advances to the next time step. The aeroelastic interface between the structural nodes of the beams and the aerodynamics surfaces was computed by an energy-conserving moving least-squared algorithm [32].
## 5 Challenges and Limitations in Carotenoids Analysis
### Challenges in Detection Systems  
Carotenoid detection relies on various chromatographic and spectroscopic techniques, but each approach presents specific limitations:  
* Low resolution of TLC: While TLC remains a cost-effective screening method, its low resolution restricts its ability to separate and quantify individual carotenoids, especially those with similar structures [23; 33; 34; 35; 36; 37; 38; 39; 40; 41]. This limitation makes TLC unsuitable for complex food matrices where multiple carotenoids coexist. To enhance separation efficiency, high-performance thin-layer chromatography (HPTLC) with densitometric detection has been proposed, allowing for semi-quantitative analysis of carotenoids [23; 33; 34; 35; 36; 37; 38; 39; 40; 41].
* High cost and environmental concerns of HPLC, UHPLC, and SFC: While HPLC and UHPLC remain the gold standards for carotenoid analysis, their high solvent consumption and operational costs limit accessibility particularly in low-resource settings [123]. Supercritical fluid chromatography (SFC) has been introduced as a solvent-reducing alternative, particularly for nonpolar carotenoids. However, wider adoption requires greater standardization of SFC methodologies to improve reproducibility and regulatory acceptance [124].
* Detection sensitivity and matrix interferences: DAD and UV-Vis spectroscopy are commonly used for carotenoid detection, but they face interference challenges in complex food matrices [125]. The integration of tandem MS (HPLC-MS/MS, UPLC-MS/MS) has significantly improved specificity and sensitivity, particularly for trace carotenoids in low-concentration samples [125; 126]. These techniques are now widely used for food authentication and quality control.
# The Influence of Tomato and Pepper Processing Waste on Bread Quality
## 2 Materials and Methods  
### Materials  
The wheat flour (type 750) used as the primary raw material in this study was obtained from a local supermarket in Lublin, Poland. The flour had an ash content of 0.72% d.b., a wet gluten content of 27.2% \(\pm\) 1.1, a gluten index of 98.3 \(\pm\) 0.5, a falling number of 301 s \(\pm\) 8, and an equivalent diameter of 0.12 mm. By-products from red bell pepper processing, namely pepper placenta (PB) without seeds, and by-products from tomato processing, specifically tomato waste (TP) containing seeds and skin, were also used. These by-products were sourced from Krokus, Pajakow, Poland.  
Before usage, TP and PB were prepared according to the procedure described previously by [PERSON] and [PERSON] [9]. The vegetable by-products were dried for 17 h in static dryers (EAC 30-Lab, ItalPast, Fidenza, Italy) using a low temperature profile (30-40 \({}^{\circ}\)C; 72-25% RH--relative humidity). Subsequently, the by-products were ground using a laboratory knife mill (Grindomix GM 200, Retsch, GmbH, Haan, Germany) to obtain flour with a particle size below 0.250 mm (>95%). The resulting flour was stored in dark sealed containers prior to bread making and chemical analysis. The equivalent diameter was determined based on the conducted sieve analysis of the used raw materials. Each time, a 100 g sample of the material was taken and sieved through a set of sieves (315, 250, 200, 180, 160, 125, 80, and 60 \(\upmu\)m) for 10 min using a sieve shaker (CISA BA 200N, Barcelona, Spain). The equivalent diameter for TP was 0.240 mm, and for PB, it was 0.180 mm.
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 5 Performance Evaluation
### Simulation Setup  
The first experiment aims to investigate the impact of blocking rate which is the ratio of the obstacle area to the entire area. We increase the rate from 0% to 30%. The second experiment aims to investigate the impact of the resolution, which is _CR_/_d_. We assume that the entire area size is \(128\) m \(\times\) 128 m, and \(d\) is fixed at 16 m. The number of MAN is one. The total number of unknown nodes is 500, and the communication range _CR_ of the first experiment is \(\sqrt{5}\times d\). The wireless channel model is the log-normal model [8; 16; 29], as shown in Equations (6) and (7), where, _Prec_ denotes the received power, while _Ptrans_ and _Ploss_ denote the transmission power of the MAN and the power attenuation during transmission, respectively. _X_\({}_{\sigma}\) is the Gaussian random variable where the standard deviation and mean are set to 2 and 0 according to the environment, which has different set parameters [16]. All the experiments are implemented in C++ programming language and all the results are the average of 100 simulations. Table 4 shows the experimental parameters that reference the Z-curve [8], which is the compared target in this paper:  
\[P_{rec}=P_{trans}-P_{loss} \tag{6}\]  
\[P_{loss}(d)=P_{loss}(d_{0})+10\gamma\log\biggl{(}\frac{d}{d_{0}}\biggr{)}+X_{\sigma} \tag{7}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Field Size & \(32\) m \(\times\) 32 m, 128 m \(\times\) 128 m, 1000 m \(\times\) 1000 m \\ Number of Unknown Nodes (\(N_{S}\)) & 500 \\ Number of MAN & 1 \\ Communication range (_CR_) & \(\sqrt{5}\,d,d,1.25d,\,1.5d,\,1.75d,\,2d,2.25d\) \\ Transmission Power (_Ptrans_) & 250 dBM \\ Path Loss (\(\gamma\)) & 3.3 \\ Power Loss \(P_{L}(d_{0})\) & 55 dB \\ \(d_{0}\) & 1 \\ Simulation Run & 100 \\ \hline \end{tabular}
\end{table}
Table 4: Experimental parameters.  
Figure 11: Comparison of APT, TPT, and WCL techniques in terms of localization error and coverage.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 4 EMF Exposure Level and 5G Technologies  
In this section, we will explore state-of-the-art research on EMF exposure in relation to the impacts of 5G technologies. Several studies have investigated the potential effects of electromagnetic radiation emitted by 5G networks. Additionally, there is a growing interest in developing EMF-aware resource allocation techniques for ultra-dense 5G networks and RIS-assisted 5G networks. These strategies strive to enhance network performance while mitigating the potential health hazards linked to EMF exposure. In summary, this section provides an overview of the latest research on EMF exposure in 5G networks, including both potential risks and emerging solutions.