esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Experimental Results  
#### 4.3.1 CPU Utilization Results  
The y-axis in Figure 5 shows the number of CPU cores used, ranging from 0 to the maximum of 4 cores, and the x-axis shows time steps from 1 to 2000 s. In Figure 5, we present the simulation up to the 500 s mark, as there are no changes beyond this point, and for reasons of clarity and space. The results of CPU utilization show that different resource-management strategies have different allocation patterns.  
According to the data, there is a noticeable initial surge in CPU core use for all four algorithms, which is expected given the necessity to distribute enormous workloads effectively at the beginning of the simulation. After that, for each algorithm, the average core use settles down to about one core per server. But with time, clear distinctions in their behavior become apparent: the power-saving method shows an extra spike in CPU core consumption, which could indicate periodic activity spurts or a change in the workload allocation approach.  
On the other hand, the location-based approach exhibits multiple spikes, which makes sense given that it relocates workloads around dynamically in response to user movement. This pattern points to difficulties in efficiently allocating tasks in situations where user movement affects resource requirements in real-time. In spite of these oscillations, the location-based approach shows lower CPU core utilization maxima than other algorithms, suggesting a more uniform distribution of workloads among servers. This feature represents its approach to resource optimization, which minimizes latency and increases overall efficiency in edge computing environments by allocating work depending on users' proximity to the system.  
The worst-fit technique as explained in Section 4.3.6, illustrates that high average CPU utilization with minimal variance signifies efficient load balancing and the best use of computational resources. The location-based algorithm's dynamic workload adjustment demonstrates its flexibility and reactivity to shifting needs, resulting in effective core usage and improved performance.  
Overall, the location-based algorithm works better than the others, proving to be more efficient at modifying the resource distribution in response to varying workload needs and server capacities. As can be seen from its higher average usage in Section 4.3.6, the location-based method achieves the best CPU utilization overall, demonstrating its ability to optimize resource management in edge computing environments.
## 2 Materials and Methods
### PET Image Analysis  
PET image quality was inspected visually and quantitatively. Mean PET images across all patients corresponding each MRAC method were created using non-TOF and TOF reconstruction. For creation of mean PET images, all PET images of each subject were transformed from individual space to Montreal Neurological Institute (MNI) space and were spatially normalized using the normalization tools of SPM8.  
A quantitative analysis was performed using 35 volumes of interest (VOI) in the cortical regions from the automatic anatomical labelling atlas (AAL). The absolute difference to _CTAC_ reconstructed _PET_ was calculated for each VOI with each of the _MRAC_ method, using Equation (1):  
\[\Delta\%=\frac{PET_{MRAC}-PET_{CTAC}}{PET_{CTAC}}\times 100\%. \tag{1}\]  
Figure 1: Different attenuation maps used in the PET image reconstruction, with (**A**) CTAC, (**B**) 2\(-\)class MRAC with attenuation coefficient of 0.100 cm\({}^{-1}\) for all tissues, (**C**) 3\(-\)class MRAC, (**D**) 2\(-\)class MRAC with attenuation coefficient of 0.100 cm\({}^{-1}\) and ignoring air cavities.  
A statistical comparison was also performed by linear regression and Bland-Altman analysis between _CTAC_ reconstructed PET VOI values and _MRAC_ reconstructed PET VOI values in both TOF and non-TOF reconstructed PET. Linear regression coefficients are tested for statistical significance with F-test, and their confidence intervals were calculated as Wald intervals [23].  
The differences in average VOI biases between non-TOF and TOF reconstructions were tested for statistical significance. First, the normality of the distributions was assessed with Shapiro-Wilk's test at 95% significance level. One distribution, the 3-class model with TOF reconstruction, did not pass the Shapiro-Wilk test of normality. Thus, the [PERSON]'s signed rank test was used to investigate whether inclusion of TOF had a statistically significant difference on the average VOI bias distributions.  
Pairwise comparisons were done with [PERSON]'s signed rank test with 95% significance level and a \(p\)-value < 0.05 denoting statistical significance. The Benjamini-Hochberg method [24] with 5% false discovery rate (FDR) to account for multiple comparisons was used. In the Benjamini-Hochberg method, the comparisons are sorted in ascending order and then given a rank \(\{1,\ldots,n\}\) based on their position. Then, all comparisons are considered statistically significant that have a rank smaller or equal than the largest rank where \(p<\frac{r}{n}\times FDR\) where \(p\) is the p-value of a comparison and \(r\) is the rank of the comparison, and \(n\) is the total number of comparisons.  
To inspect the differences between each of the method locally, bias atlas images were created according to [25] from each method across all subjects. Mean bias and standard deviation of bias across the whole subject group with each of the MRAC method were then calculated and visualized.
## 3 Research Data  
### Data Description  
We have access to field data of electric buses provided by several public transport companies. In addition, we have already been logging a car fleet for almost two years. An overview can be found in Table 1.  
The data meet a number of criteria necessary to achieve the objectives of the FeBaL project:  
* All vehicles operate the same NMC battery type. The difference in energy content of the batteries emerges only from the different number of parallel strings. This warrants comparability of the data.
* The buses use different charging strategies. Whereas some are only charged in the depot at the end of the day, others are charged opportunely, i.e., they are also recharged several times during a shift at fast charging stations (see Figure 1). The charging strategies can typically be characterized as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **Type (Num.)** & **Battery** & **Charging Strategy** & **Monthly Full** & **Logging Start** \\ \hline
1 & Car (19) &
\begin{tabular}{c} NMC, \\ 36 kWh \\ NMC V1, \\ 240-330 kWh \\ 240-330 kWh \\ 240-330 kWh \\ \end{tabular} & Depot & 1.0-4.5 & 02.2021–06.2021 \\
3 & Bus (25) &
\begin{tabular}{c} NMC V2, \\ 240-330 kWh \\ 240-330 kWh \\ \end{tabular} & Opportunity, Depot & 15.0–50.0 & 11.2021–03.2022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of available fleet data.  
Looking at both depot and opportunity charges guarantees variation in the data, e.g., small and large Depths of Discharge (DODs), which will lead to different aging rates.
* The vehicles, especially the buses, generate enough monthly energy throughput to ensure a detectable battery aging soon, see Figure 2.
* We can derive the necessary input data from the logged battery signals with the required sample rate, accuracy and resolution.  
In a proof-of-concept study, we analyzed the subfleet 3, a bus fleet of opportunity charges, because these vehicles were logged for the longest period, see Table 1.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Protein
#### 4.2.1 Plant-Based Proteins  
Zein Proteins  
Zein is the major protein of maize. Owing to its amino acid residues with polar and non-polar side chains, zein exhibits an amphiphilic character. It is suitable for use as a biomaterial for encapsulations and delivery of water-insoluble probiotics. In addition, zein has high resistance against gastric juice. Hence, it can extend the release of probiotics in the small intestines. Despite its high surface hydrophobicity, zein is highly unstable and tends to aggregate in aqueous solutions. For instance, zein beads are commonly coated with a layer of emulsifiers such as sodium caseinate and Tween 20 or ionic polysaccharides such as alginate and pectin [89].  
[PERSON] et al. [96] used zein protein-coated alginate microbeads to encapsulate _B. bifidum_. The probiotics encapsulated in zein protein (1, 3, 5, 7, and 9% (\(w/v\)))-coated alginate microbeads were higher in viability compared to those encapsulated in alginate microbeads (\(10^{5}\) log CFU/g) and free cells (\(10^{3}\) log CFU/g) after being stored for 32 days at 4 \({}^{\circ}\)C. Zein protein coating also enhanced the resistance of encapsulated _B. bifidum_ against the harsh conditions in gastrointestinal transit. Zein protein (5% (\(w/v\))) coating was also observed to increase the viability of carboxymethyl cellulose-coated _L. plantarum_ 299v in apple slices under simulated gastrointestinal conditions [12].  
To date, the utilization of soy protein in the encapsulation of probiotics is rare. Protein isolated from soybean is a potential probiotic encapsulation biomaterial owing to its high nutritional value, less allergenic nature, and good emulsifying, absorbing, and film-forming properties [41,89]. Soy proteins also possess high resistance against gastric juice. Therefore, they are efficient in delivering and controlling the release of probiotics to the gut. Nevertheless, heat-induced gel formation of soy proteins can affect the viability of heat-sensitive probiotics. Heat treatment could also cause protein denaturation, resulting in loss of functionality [90].  
Soy protein isolates have been used with gum Arabic to prepare nano-emulsion to encapsulate _L. delbreuckii_ subsp. _Bulgaricus_[50]. The presence of soy protein isolates in the emulsion can increase the stability and enhance the survival rate of probiotics during storage (at 27 \({}^{\circ}\)C for 40 days). In another study, soy protein isolates (20% (\(w/v\))) were employed with sodium alginate (4% (\(w/v\))) to encapsulate _L. plantarum_ using the extrusion technique. The inclusion of soy protein isolates increased the thermal resistance of _L. plantarum_. The viability of _encapsulated L. plantarum_ (a slight decrease from 9.10 log to 8.11 log CFU/mL) in mango juice remained high after the pasteurization process [21].
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 6 Defenses
### Secure Firmware Updates  
Firmware tampering can be prevented by implementing secure firmware update mechanisms. This includes signing firmware updates with cryptographic keys and verifying the integrity of the firmware before installation [10]. Regularly updating firmware and ensuring the updates are received from trusted sources can mitigate the risk of compromised device functionality due to malicious firmware.
## 4 Proposed Routing Scheme
### Distributed Detour Routing Scheme  
The distributed detour routing scheme comprises rapid detour routing, selective flooding, and a link recovery process. From a network-wide perspective, the distributed detour routing scheme is executed after a link failure, as illustrated by the red dotted box in Figure 3. In the proposed scheme, we consider only inter-satellite routing. Upon packet creation, the minimum hop region is automatically established. Subsequently, the next hop is selected to ensure the shortest path, and the packet is transmitted through the ISL accordingly. Packet forwarding is repeated until the packet reaches its destination. The proposed scheme receives a response message from the next hop in each packet-forwarding process. If a response message is not received within a predefined time, then the ISL is considered to have experienced a link failure. We assumed that link failure occurs due to instability in the PAT process for connecting the ISL. When a link failure is detected as shown in Figure 3, the distributed detour routing scheme is utilized. The first step in the proposed scheme is link-state monitoring, which detects a link recovery through hello exchanges. Link-state monitoring continues until  
Figure 3: The proposed detour routing scheme. link recovery is detected. In the next step, we form a pre-detour path using both the rapid detour and selective flooding algorithms shown in Figure 3:  
* Rapid detour is an algorithm that reroutes the original packet, which failed to transmit to its destination. This detouring is accomplished using only the precalculated routing table and geographical information without incurring any communication overhead to transmit link information. Consequently, Algorithm 1 only utilizes the given prior information to identify a rapid detour direction from the routing table to detour the link failure. A detailed description of Algorithm 1 is discussed in Section 4.2. * Selective flooding is an algorithm that establishes a pre-detour path within the MHR of the original packet so that the satellite can detour the link failure. Unlike the conventional flooding mechanism, selective flooding has a specified destination. Selective flooding aims to find a detour point that can cover the entire MHR of the original packet with minimal communication overhead. Therefore, in Algorithm 2, determining the destination for selective flooding requires finding the optimal detour point. A detailed description of Algorithm 2 is discussed in Section 4.3. The pre-detour path created by the distributed detour routing scheme detours link failures without any hop loss, with minimal communication overhead. When link recovery is detected through link-state monitoring as shown in Figure 3, the recovered node sends a recovery message along the pre-detour path to revert the routing path. The link recovery procedure is described in Section 4.4.
## 2 Materials and Methods
### Machine Learning (ML) and Statistical Analysis  
The performances of five ML algorithms--linear regression, ElasticNet, support vector machine (SVM), random forest regression, and LightGBM--were compared [31; 32; 33]. Strictly speaking, SVM is a generalized linear classifier, and the regression method employed here was support vector regression (SVR), an extension of SVM for regression tasks. These algorithms were used to estimate the radial abduction, palmar abduction, and pronation angles of the thumb based on parameters derived from the joint coordinates estimated by the model, with the values obtained using the angle sensor considered the ground truth. Linear regression and ElasticNet were utilized as fundamental regression techniques. SVM is a method used for both classification and regression. It determines the optimal hyperplane or decision boundary for distinguishing between two classes of data. Random forest regression is an ensemble learning method that combines multiple decision trees to enhance prediction accuracy. The LightGBM framework provides excellent classification performance while improving processing speed, minimizing memory consumption, and mitigating the risk of overfitting [34]. The Python-based ML library Scikit-learn was used to train the models. The workflow for this experiment is illustrated in Figure 7. Based on these models, the radial abduction, palmar abduction, and pronation angles were estimated, and the estimation accuracy was evaluated. A total of 10,800 images of right-thumb abduction movements were obtained from the 18 subjects, along with data on radial abduction, palmar abduction, and pronation angles collected by an angular sensor that was synchronized with the images. MediaPipe was used to detect joint coordinates based on the images, and eight parameters were created. The obtained images were randomly split into 80% training data for hyperparameter optimization during ML model development and 20% test data for evaluating the performance of each model. Hyperparameter tuning was performed for each ML algorithm using the training data, and the performance of the models was evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and correlation coefficient.
## 4 Application of UWSN
### Monitoring
#### 4.1.4 Navigation  
The ocean is big and dark, and the navigation equipment used on the surface is not suitable for underwater. In such environments, there is a massive need for navigation assistance for ships, vessels, and swimmers. Hence, UWSN plays a vital role in exploring and guiding submarines, swimmers and explorers in finding paths. In [87], proposed an on-demand localization algorithm for underwater sensors.
## 3 Results
### Evaluation of Deposition Distribution for Twin Nozzles Condition  
For quantitatively evaluating the deposition distribution of twin nozzles, the REGRESS, LS-SVM, ELM, and RBFNN models were built using spray heights, nozzle spacing, and horizontal positions. Numbers of the calibration sets and prediction sets were 72 and 24, respectively with a scale of 3:1. The results of the quantitative models are shown in Table 5, and the scatter plot of measured values vs. predicted values of deposition distribution for twin nozzles condition are shown in Figure 8. Performances obtained by LS-SVM and RBFNN methods were both better than models based on REGRESS and ELM, achieving relatively higher accuracy of prediction set. The RMSEP value of RBFNN was smaller than LS-SVM, indicating RBFNN was the best method for deposition distribution quantitative modeling. REGRESS with a \(Rc^{2}\) value of 0.8923 and RMSEP of 0.2525 mL/cm\({}^{2}\) showed the feasibility of the deposition distribution prediction. The overfitting and terms of uncertainty problems also occurred when the order of the polynomial regression based on REGRESS was increasing. ELM models did not achieve expected performance with an \(Rc^{2}\) value of 0.7830 and RMSEP of 0.2919 mL/cm\({}^{2}\) compared with other methods. The results showed that the deposition distribution could be accurately evaluated by nozzle spacing and spatial position of the nozzle using machine learning methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameter [a]**} & \multicolumn{3}{c}{**Accuracy of Calibration Set**} & **Accuracy of Prediction Set** \\ \cline{3-5}  & & \(R_{C}\)\({}^{2}\) & **RMSEC (mL/cm\({}^{2}\))** & \(R_{p}\)\({}^{2}\) & **RMSEP (mL/cm\({}^{2}\))** \\ \hline REGRESS & 3 & 0.8923 & 0.1546 & 0.7319 & 0.2525 \\ LS-SVM & (2.664, 314.218) & 0.8926 & 0.1547 & 0.7646 & 0.2291 \\ ELM & 18 & 0.7830 & 0.2194 & 0.6131 & 0.2919 \\ RBFNN & 358 & 0.8806 & 0.1628 & 0.7994 & 0.2183 \\ \hline \hline \end{tabular} Parameters of different models: the highest degree of the polynomial for REGRESS, the bandwidth of kernel function (sig2), and the trade-off between minimum model complexity and minimum training error(gam) for LS-SVM, the number of hidden nodes for ELM, the spreading coefficient for RBFNN.  
\end{table}
Table 5: Results of deposition modeling for twin nozzles condition.
## 2 Related Works
### Fault Model
#### 2.2.1 Tree Fault Model  
The dangers of a downed conductor are obvious to all. The possibility of fire, property damage, and anything that comes into contact with the live conductor are the major concerns where it produces arc and causes fault [15]. Tree and crane contact that cause fault are categorized under high impedance fault (HIF) where, in the HIF model used, the parameters \(Vp\) and \(Vn\) model the contact surfaces. During the HIF, current of positive half cycle value will be higher than the negative half cycle where the waveform is known as unsymmetrical and it was experimentally proven by [PERSON] et al. Therefore, to model this phenomenon, \(Vn\) must be greater than \(Vp\) (\(Vn\) \(>\)\(Vp\)), and \(Vn\) \(-\)\(Vp\) = \(\Delta V\), where \(\Delta V\) is unsymmetrical voltage. Moreover, it was shown that less densely packed contact surface yields a higher arc voltage than contact surface with high density. Using this as a guide, tree encroachment and crane are modeled to obtain the specified current magnitudes. Furthermore, the values for \(Rp\) and \(Rn\) parameters are randomly varied between +10% of the specified steady-state values and represent the effective fault resistance for positive and negative half cycles, respectively [16; 17; 18; 19; 20; 21]. The HIF model based on Emanuel arc is shown in Figure 2. The equations involved in these algorithms are derived in Equations (1)-(4).  
Therefore, the fault resistance for tree cause can be defined as:  
\[R_{Ftr}=\frac{R_{P}R_{N}}{R_{p}+R_{N}} \tag{1}\]  
Parallel of total \(R_{FCrtr}\) in Emanuel arc can be defined as:  
\[R_{FCrtr}=\frac{1}{R_{Ftr}+\ldots R_{Fn}} \tag{2}\]  
where  
\[R_{Fn}=\frac{R_{Pn}R_{Nn}}{R_{pn}+R_{Nn}}\ (n=1-6) \tag{3}\]  
Based on Equation (6), the fault impedance for a tree is defined as:  
\[Z_{Ftr}=\frac{I_{F}R_{FCrtr}}{I_{GA}+3k_{0}I_{0}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable Parameters** & **Quantity** \\ \hline Fault distance (km) & 10 to 100 (step size 10 km) \\ Phase angle (\({}^{\circ}\)) & 0, 30, 45, 60, 90, 180, 270 \\ Load (MW) & 200, 300, 330 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variable parameters set up in the simulation model.
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### Security for UAVs over IoT
#### 2.4.1 Security Component
To provide robust communication and endpoint security, secure and trusted channel protocols were proposed in Reference [46]. Furthermore, group key management techniques were considered in Reference [38], aiming to enable multicast communication using CoAP. To further strengthen the IoT drones, a trusted computing framework and execution architecture, were proposed in Reference [47]. Secure connections are enabled with connection firewalls and Intrusion Protection Systems (IPS) that can deeply inspect network packets. In addition,  
Figure 2: IoT security layers with UAV extensions. firewalls, intrusion detection systems, and colocation proofs were suggested in Reference [48] that are necessary against routing attacks, such as selective forwarding, sinkhole, and wormhole attacks [44]. The primary security threats in the perception layer are physical damages, such as vandalism and weather challenges. Therefore, hardware mechanisms such as tamper-protection and -detection systems have to be employed, as was suggested in References [36; 49]. In the perception layer, security is to ensure that only authorized users can have access to sensitive data that are produced by physical objects, and that's why authentication and authorization policies need to be defined. Authentication issues are addressed by combining light public and symmetric key mechanisms. According to Reference [50], RFC 6698 TLSA and Numberg's scheme are used to ensure data authenticity. The problem of authorization is addressed by combining open protocols such as Oath, HTTPS, U2 IoT, and OpenID Connect [51]. In addition, lightweight cryptography and digital signature schemes are needed to ensure secure end-to-end communications. Light versions of existing algorithms such as the Advanced Encryption Standard (AES), the RSA, and Elliptic Curve Cryptography (ECC) can be customized to be applied on mobile things.