esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Scalability Analysis  
The scalability of the proposed load balancing model is a crucial aspect when considering larger or more diverse gaming environments. The simulation results demonstrate that the model effectively distributes the load across eight servers and dynamically activates fog nodes when servers approach their capacity limits. This approach ensures stability even during periods of high demand.  
To evaluate scalability, additional simulations were conducted under varied conditions, including increased numbers of clients, servers, and fog nodes. The results indicate that the system can adapt to larger user bases by proportionally increasing resources, maintaining low latency and balanced load distribution. For instance, in scenarios with double the number of users, adding two additional fog nodes effectively reduced latency spikes by 30% compared to configurations with static resource allocation.  
Moreover, the model supports dynamic adjustments based on user density and geographic distribution, which are essential for diverse MMO game environments. The use of fog nodes positioned closer to end-users significantly reduces latency, enhancing player experience, even in geographically dispersed settings.  
This analysis highlights the system's potential to scale seamlessly by leveraging a hybrid fog and edge architecture, ensuring consistent performance and a high quality of experience (QoE) as demand increases. Future work could include testing with real-world deployment scenarios to further validate scalability under live gaming conditions.
# Harnessing Agri-Food Waste as a Source of Biopolymers for Agriculture
## 2 Sources of Biopolymers
### Chitosan, an Important Adjuvant for Plant, Fruit, and Seed Protection  
Chitin is the most abundant amino polysaccharide present in the world; it is formed by N-acetylglucosamine (GlcNAcAc) residues [89], and it is present in nature in crystalline forms of \(\alpha\), \(\beta\), and \(\gamma\)-chitin. Traditionally, the main source of chitin is the crustacean waste deriving from the fishing industry [90], but it is not as sustainable as fungal chitin. Chitosan is a direct derivative of chitin obtained usually by alkaline deacetylation [91]; the result is a copolymer formed by \(\beta\) (1-4) linked D-glucosamine (GlcN) units. Chitosan (Figure 5) is characterized by a variable percentage of GlcNAc residues, as deacetylation is never complete. chitin, and consequently chitosan, can be extracted from fungal waste biomass as a secondary product; in addition, fungi can also successfully grow on agri-food waste.  
[PERSON] et al. [92] successfully extracted chitosan from _Aspergillus niger_ fungal waste following the production of citric acid, a common food additive. _Mucor circinelloides_ was able to grow on sugar bagasse hydrolysate and corn steep solids, and a total of 1.36 g/L of chitosan was extracted from the fungal biomass [93]. [PERSON] et al. [94] obtained chitosan from _Aspergillus terrerus_ grown using apple waste as the sole carbon source. Corn steep liquor and Cassava wastewater were used as a carbon and nitrogen source by _Mucor subtilissimus_ UCP 1262 and _Lichtheimia hyalospora_ UCP 1266; the chitosan extracted from the deriving fungal biomass exhibited a deacetylation degree >80%, comparable to commercial chitosan [95].  
In each of the papers mentioned, the fungal biomass first underwent an alkaline treatment, then chitosan was extracted from the alkali-insoluble fraction using a mild acid treatment.  
The antimicrobial properties of chitosan are useful in agriculture, as they can be employed to increase the shelf life of fresh fruit [93; 96]. Moreover, chitosan can also be used in seed protection, since it facilitates the attachment of the biological priming agent _Trichoderma asperellum_ to the seed [97], therefore significantly increasing seedling germination and inhibiting diseases [98]. Chitosan nanoparticles can also be used for the controlled release of herbicides, as they have been successfully used to encapsulate imazapic and imazapyr to reduce their toxicity and increase the efficacy of the treatment [99].
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 4 Simulation Results and Discussion
### Single Wind Turbine
#### 4.2.2 Direct Strike  
This section analyzes the performance of the comprehensive wind turbine model with the PMGS when 10 kA and 10/350 us lightning directly hits the turbine and its blade in two different states. The first state, a so-called in-service wind turbine, is completely opposite from the second type, and all of the connections exist. The second state is an out-of-service wind turbine, in which not only is there no connection between the synchronous generator and the set-up transformer, but the grid is also disconnected.  
In-service wind turbine: The rates of transient overvoltages during the direct strike are shown in Figure 13. According to the simulation, this raised the voltage to 1.2 kV and approximately 1.5 kV on the LV side of the set up transformer and the control circuit, respectively, while it was just around zero for the HV side. The simulation establishes that in the in-service situation, the wind turbine can operate normally, as the greatest transitions still remain below the upper limits.  
Figure 11: EMTP-RV Circuit of the comprehensive wind turbine model with PMGS.  
Figure 12: Impact of comprehensive wind turbine model with PMGS on transient overvoltages. (**a**) LV side of the main transformer; (**b**) HV side of the main transformer from node to ground; (**c**) control circuits.  
Out-of-service wind turbine: This subsection examines the impact of a direct lightning strike on the out-of-service wind turbine. The strike results in 1.2-kV peak voltages on the LV side of the main transformer, while the peak rises to 1.8 kV for the control equipment. The important fact is that the shape of the overvoltage on the HV side went up to 1.4 kV, compared to the negligible change when the lightning strikes the in-service turbine (see Figure 14).  
The main reason behind such an increased rate of voltage for the HV side and the SA is that the changes in the system configuration during the out-of-service state lead to alteration of the impedance characteristics. As a result, more lightning current passes through the earth and causes increased GPR, so the amount of voltage increases on the HV side through the absorbed SA energy. The consequence of such a phenomenon would be severe wind turbine failure. Hence, it seems to be crucial to separate the turbine generators from each other, the same as the transformers, to prevent burnout in the SA, transformers and cables when lightning directly hits the out-of-service tower.
# Fluorescence Correlation Spectroscopy Measurement Based on Fiber Optics for Biological Materials
## 2 Materials and Methods
### Background Intensity Correction  
In FCS, background intensity lowers the amplitude of the ACF, resulting in an overestimation of concentration. The background intensity of conventional FCS can be considered negligible in most cases because it is sufficiently lower than the fluorescence signal intensity. However, the background intensity of FB-FCS is higher than that of conventional FCS because of contamination from the excitation laser, which is reflected at the end surface and inside the fiber coupler. The photon count of this background intensity is approximately 150 kHz with a 1 \(\upmu\)W excitation laser power at the sample plane in our setup. The main source of such a high background was likely due to the autofluorescence of the optical fibers  
Figure 1: Experimental setup. LT, light trap. L, lens. M, mirror. OL, objective lens. EF, emission filter. PMT, photomultiplier tube.  
as reported by [PERSON] et al. [11]. In addition, strong reflection and scattering of excitation laser at the end surface of each fiber and back reflection at the junction of the optical fibers inside the fiber coupler might be also the source.  
To correct this background intensity effect on the obtained ACF, we applied the following background intensity correction [5; 12]:  
\[G_{\text{c}}(\tau)=\left(\frac{\overline{I}}{\overline{I}-\overline{I}_{\text{ bg}}}\right)^{2}G(\tau), \tag{1}\]  
where \(G\) and \(G_{\text{c}}\) are the measured ACF and the ACF with background intensity correction, respectively. \(\overline{I}\) and \(\overline{I}_{\text{bg}}\) are the average fluorescence intensity and the average background intensity, respectively. The average background intensity is usually measured on blank samples, such as pure water and pure solvent of the sample solution. Note that the factor \(\left[\overline{I}/\left(\overline{I}-\overline{I}_{\text{bg}}\right) \right]^{2}\) becomes very high. Thus, \(\overline{I}\) and \(\overline{I}_{\text{bg}}\) should be carefully determined especially in low concentration samples.
## 4 Results
### Analysis of KED-LN Performance  
Prediction accuracy plots and maps are given for KED-LN in Figure 7a-c. Clearly the largest prediction errors occur around the deep channel and can be a result of both over- and under-prediction (Figure 7b). There is a significant change in polarity from the western mouth (negative) to the eastern (positive), coupled with a decrease in the amount of anomalous points. A significant number (circa 0.4%) of over-predictions also occur to the far edges, mostly north and to a lesser extent south (Figure 7b). A GW correlation map of actual WD (_i.e._, the MBES WD data at the validation sites) _versus_ predicted WD (_i.e._, the KED-LN predictions of WD) is given (Figure 7c) and needs to be compared with the corresponding global correlation of 0.985 (from Table 2). Again, the poorest correlations are centrally located, but an area of poor correlation is also present off Howth (the NE corner).  
Conditional accuracy plots for KED-LN are given in Figure 7d,e, where it appears that in general KED-LN's performance does not strongly depend on depth or seabed type. Some notable residual dependencies can be observed though. For example, many of the largest (positive) residuals from KED-LN occur in shallow waters, near the channel (_i.e._, prediction is deeper than it should be); the other area, off Howth as mentioned above, shows a small cluster of large positive residuals with similar values,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **(MPE)** & **RMSPE** & **MAPE** & **Cor-Coef** \\  & **(Should be Zero)** & **(Tend to Zero)** & **(Tend to Zero)** & **(Should be One)** \\ \hline MLR & (\(-0.041\)) & 1.312 & 1.002 & 0.877 \\ KED-GN & (0.001) & 0.511 & 0.313 & 0.983 \\ GWR & (0.036) & 0.779 & 0.535 & 0.960 \\ GWRK & (0.038) & 0.761 & 0.522 & 0.962 \\ KED-LN & (0.027) & 0.470 & 0.283 & 0.985 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction accuracy results.  
near the shoreline coinciding largely with SEABED class 1. SEABED class 2 which largely coincides with the channel (see Figure 4c), also provides the most biased condition boxplot, followed by SEABED class 1, largely along the edges of the bay and associated to some rock outcrops and hardgrounds.  
Figure 7: Accuracy plots and maps for KED-LN at validation sites: (**a**) scatterplot of actual WD _versus_ predicted WD, (**b**) map of residuals (actual WD–predicted WD), (**c**) GW correlation map for actual WD and predicted WD, (**d**) scatterplot of actual WD _versus_ residuals (with useful thresholds), and (**e**) conditional boxplots for residuals with respect to seabed class (with useful thresholds). Observe that WD is always measured negatively.
## 5 Complexity Analysis  
In this section, the complexity of E-BIOSARP is studied and compared with those of the security schemes TinyHash, TinySec-AE, TinySec-Auth, EBSS, LBRS-Auth, and SRTLD. The complexity is studied based on the functions involved in each security mechanism by considering the number and processing time of the functions that are executed, which determine the time is required by a node to process a packet. The results of the comparison are presented in Table 2. In the table, we use the following notations: D, P, and H denote the size of a complete data packet, the size of the data payload of a packet, and the header size of a packet, respectively, such that D = P + H. Encrypt(D) and MAC(D) are functions that return the computing times required to apply the Encryption and MAC algorithms, respectively, to information of size D.  
In TinySec-AE, a data payload of size P is encrypted using the ciphertext stealing technique [42] to create encrypted data of the same size as the plaintext. TinySec-AE then applies a MAC algorithm to the encrypted data and the packet header, which leads to a computing time of [Encrypt(P') + MAC(D)]. For TinySec-Auth, this time is reduced to MAC(D) because only the data packet is authenticated with a MAC. The computing time for TinyHash is [Encrypt(D) + MAC(D)] because it uses HMAC for authentication and SHA1 for message digestion. LRBS-Auth and EBSS each create a MAC for the data payload.  
Table 2 shows the ranking of the security schemes in terms of the security functions used or complexity. The lowest complexity corresponds to the highest ranking; that is, number \"1\" represents the highest ranking. The encryption function has a shorter processing time than the MAC algorithm [43]; moreover, (S_ID + D_ID + Pkt_ID) < H < P. Therefore, SRTLD and E-BIOSARP  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Security Scheme** & **Security Functions** & **Ranking of the Scheme** \\ \hline TinyHash & Encrypt(D) + MAC(D) & 5 \\ TinySec-AE & Encrypt(P) + MAC(D) & 4 \\ TinySec-Auth & MAC(D) & 3 \\ LBRS-Auth and EBSS & MAC(P) & 2 \\ SRTLD and E-BIOSARP & Encrypt(S_ID + D_ID + Pkt_ID) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of security schemes.  
Figure 6: The output of actions taken by E-BIOSARP against adversaries.  
incur the lowest complexity because these approaches need only to encrypt three fields of the packet header, namely, _S_ID_, _D_ID_ and _Pkt_ID_.
## 3 Results
### Results on LBP  
The parameters to be considered on the LBP descriptor are the radius, the number of neighbors and the cell sizes. As mentioned before, images has been previously cropped into four different areas. According to the previous results in [2], the best recognition rate is obtained when each of these areas is not divided into cells. Therefore, as it is shown in Figure 7, the recognition rates for all the possible combinations with radius 1, 2, and 3, and neighbors 8, 10, 12, 16, 18, 20, and 24 have been calculated to select the optimum values.  
As shown in Figure 7, the parameters with the best recognition rate are radius 2 and 18 neighbors. This combination presents the following confusion matrix \(CM_{LBP}\):  
\[CM_{LBP}=\begin{pmatrix}27&3\\ 10&83\end{pmatrix} \tag{4}\]  
It implies that there are three false positives and 10 false negatives, thus having an error rate of 10.57% and, therefore, a successful recognition rate of 89.43%.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Machine Learning and Deep Learning-Based Techniques  
Numerous machine learning techniques, including neural networks [62; 63; 64] and SVM, employ linear hyperplanes rather than other algorithms to distinguish between classes. For a variety of applications, such as mineral and lithological mapping [65], random forest (RF) [66; 67] has been used extensively. If the classes are not linearly separable in the feature space, they are projected to a higher dimension to ensure linear separability [1; 68; 69; 70]. To learn the boundaries of different classes in the feature space, each of these methods relies on pre-classified training data [71; 35; 72].  
For lithological mapping, two popular machine learning algorithms--MLC and SVM--were utilized to determine which classifier would perform best for lithological classification. ASTER and Landsat 8 OLI multispectral data were evaluated for lithological classification in the Khyber range study area, resulting in a number of data combinations.  
ASTER's and OLI's band configurations in the VNIR and SWIR range are anticipated to yield additional lithological unit diagnostic spectral characteristics. The SWIR and TIR bands are typically thought to be the most crucial for differentiating lithologies [12]. For example, limestone displays spectral alterations in the TIR range (10.25-11.65 m) and SWIR range (2.10-2.30 m) [73; 74]. However, robust machine learning (ML) methods must be utilized to identify various rock types from limited data (without TIR bands). A streamlined sequence flow diagram of the aforementioned procedure is shown in Figure 5.
## 2 Materials and Methods
### Asbestos Detection Model Using a Convolutional Neural Network
#### 2.2.4 Classifier Using a Support Vector Machine  
Instead of FC layers, an SVM [29] can be employed as a classifier, which is an algorithm known for its relative robustness and high accuracy. A typical SVM-based classification (support vector classification; SVC) utilizes feature vectors to perform binary classification.  
The basic equations of the SVC while employing a hinge function as the loss function are depicted in Equations (1) and (2). The objective function is presented in Equation (3).  
\[y_{i}=\begin{cases}+1\text{ if }f(\mathbf{x}_{i})\geq 0\\ -1\text{ if }f(\mathbf{x}_{i})<0\end{cases} \tag{1}\]  
\[f(\mathbf{x}_{i})=\mathbf{w}^{\text{T}}\mathbf{\phi}(\mathbf{x}_{i})+b \tag{2}\]  
\[\min_{\mathbf{w},\mathbf{b}}\left[1/2\|\mathbf{w}\|+C\sum_{i=1}^{N}\max\{0,1-y_{i}f(\mathbf{x} _{i})\}\right] \tag{3}\]  
where \(\mathbf{x}_{i}\) and \(y_{i}\) are the feature vectors and classification labels (the actual category to be classified) of the \(i\)th data point, respectively. Data \(i\) are classified into Class 1 when \(y_{i}\) is 1 and Class 2 when \(y_{i}\) is \(-1\). \(\mathbf{w}\) is a vector of feature weights, \(\mathbf{\phi}\) is a function that maps vector \(\mathbf{x}_{i}\) to another vector, and \(b\) is the intercept. \(C\) is a regularization parameter, \(N\) is the number of data points, and \(\mathbf{w}^{\text{T}}\) is the transpose of \(\mathbf{w}\).  
SVM does not directly handle the feature vector \(\mathbf{x}_{i}\), but rather operates on the vector \(\mathbf{\phi}(\mathbf{x}_{i})\) that is obtained by applying the mapping function \(\mathbf{\phi}\). This pertains to the nonlinear relationship between the feature vector and classification outcomes. To solve the minimized problem expressed in Equation (3), the inner products of \(\mathbf{\phi}(\mathbf{x}_{i})\) or a kernel function \(K(\mathbf{x}_{i},\mathbf{x}_{j})\) must be calculated. Linear and radial basis function (RBF) kernels are often used, although several kernel functions exist. The definitions of the linear and RBF kernels are provided in Equations (4) and (5), respectively:  
\[K(\mathbf{x}_{i},\mathbf{x}_{j}):=\mathbf{x}_{i}^{\text{T}}\mathbf{x}_{j} \tag{4}\]  
\[K(\mathbf{x}_{i},\mathbf{x}_{j}):=\exp\left(-\gamma\big{\|}\mathbf{x}_{i}-\mathbf{x}_{j}\big{\|} ^{2}\right) \tag{5}\]  
where \(\gamma\) is the parameter of the RBF kernel function.  
An SVC that uses a linear kernel function can only be applied to linearly separable problems, similarly to a single FC layer. The RBF kernel function can be applied to problems that are not linearly separable. In this study, we developed models with linear and RBF functions and compared their classification performances.  
In the SVC, C in Equation (3) is a parameter. Similarly, \(\gamma\) in the RBF kernel is a parameter. Because the optimal values for the parameters vary depending on the specific case, we considered several values to obtain an appropriate set of parameters for this study.  
The scikit-learn [30] python library was used to implement a support vector machine in this study.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 3 Results
### Test Case B--IMP Mirror-Polished Copper  
The stitching result of each tested stitching algorithm is presented in Figure 10. A first-order form was removed to better observe small scale roughness in the stitching topographies. The CC stitching algorithm has failed, as a chain reaction occurred with local stitching. Other algorithms offer satisfactory results, but attenuated seams are slightly visible with the GEO and GOD algorithms, especially for the central elementary map. In addition, the indentation in one of the overlapping regions is deformed at the bottom edge. Best visual aspect, with no seams or deformed indentations, is obtained with the reflectance-based multimap stitching algorithm, the rising sun algorithm, proposed in this article (NR, EER and TSR).  
Figure 8: Three-dimensional view of the stitched topography in test case A. Stitching size: 2 \(\times\) 2.  
Quantitative criteria for test case B are grouped in Figure 11. The GOD algorithm was the fastest, while the EER algorithm was the slowest. Other algorithms are in the same order of amplitude for run time. The CC algorithm uses more memory, followed by NR, and then by the four others, which are comparable. The chain reaction failure of CC algorithm is highlighted by a high mean repositioning error and a lowest stitching quality. Best quality is obtained using reflectance-based multimap stitching algorithms (NR, EER and TSR). GOD provides in-between mean repositioning error and stitching quality.  
Figure 10: Three-dimensional view of the stitched topography in test case B. Stitching size: \(3\times 3\).  
Figure 9: Bar charts of the quantitative criteria for test case A: run time (**a**), memory usage (**b**), mean repositioning error (**c**) and stitching error estimator (**d**). NA = Non Applicable.