esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### CNN Evaluation  
Table 6 lists the test data and classification accuracies for each trial. The mean accuracy was 86.67%. The accuracy is plotted in Figure 12a, and the confusion matrix is shown in Figure 12b. As shown in Figure 12b, misjudgment cases between classes A (Good) and C (Bad) were not found.  
Table 7 lists the IDs of the misclassified images and the probability values of the softmax layer. Figure 13 shows the misclassified images corresponding to the image IDs listed in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Trial** & \multicolumn{2}{c}{**A (Good) IDs for Test**} & \multicolumn{2}{c}{**B (Neutral) IDs for Test**} & \multicolumn{2}{c}{**C (Bad) IDs for Test**} & \multicolumn{2}{c}{**Accuracy**} \\ \hline
1 & 3 & 10 & 16 & 1 & 13 & 15 & 7 & 11 & 21 & 1.0000 \\
2 & 5 & 8 & 10 & 8 & 18 & 19 & 14 &  
The CNN classified B017, B019, and B020 as C (Bad) because the cracks were deep and thick. Such discrimination is difficult even for humans and is attributed to fuzzy boundaries in the classification process [30]. In addition, the probability values of B (Neutral) and C (Bad) in B017, B020, C008, C012, and C019 were simultaneously close to 0.5, indicating that the misjudgment was made in confusion. The CNN was confused and acted like humans in these cases.  
It is noteworthy that for A001, where the CNN determined A (Good) to be B (Neutral), we assumed that the annual rings were misrecognized as cracks. This problem can be addressed in the future by adding training image data.  
Considering the loss transition during training shown in Figure 11, training was performed successfully. As shown in Table 6 and Figure 12, the CNN could assess the overall crack severity appropriately, even though some misjudged cases were found. Furthermore, considering the results in Table 7 and Figure 13, we conclude that the CNN acts like a human and, therefore, has the potential to evaluate timber cracks on behalf of a human.  
We also evaluated the Recall, Precision, and F-measure [31], commonly used to evaluate the classifier for three or more categories. The Recall, Precision, and F-measure are defined in Equations (1)-(3). Table 8 shows the result.  
\[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{1}\] \[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}}\] (2) \[\text{F}-\text{measure}= \frac{2\,\times\,\text{Precision}\,\times\,\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{3}\]  
Given the results, we can observe that there is no excessive bias in the data set.
## 7 Conclusions  
A numerical investigation was conducted with the aim of predicting the service life of a liquid rocket engine thrust chamber. The throat region of the chamber was examined, since the maximum heat fluxes coming from the combustion chamber were expected to occur in this area. A damage model accounting for creep, plastic instability, and multiaxial low-cycle fatigue was adopted to estimate the service life of the chamber.  
Results demonstrated that the influence of creep on the thrust chamber failure is negligible with the current geometry, whereas ratcheting is expected to stop after about 30-35 thermal-mechanical loading cycles. Low-cycle fatigue might therefore be the primary cause of failure and, adopting the Wang-Brown multi-axial fatigue criterion, the cooling channel in the throat region was calculated to withstand more than 4000 cycles. This result was consistent with those achieved with similar geometries and loading conditions.  
In general, the present work allows us to understand the main factors the designer should consider when sizing a thrust chamber. As an example, the cooling efficiency should be as high as possible in order to reduce temperatures, thermal gradients, and stresses in the throat area, where creep could become a major factor, limiting the service life of the chamber significantly. Furthermore, creep should always be modeled with high fidelity numerical models because it can deeply affect the stress-strain behavior of the component.  
As a future activity, submodeling approaches will be adopted in order to obtain more realistic boundary conditions in the cut sections of the local model. A damage model will be also implemented so as to predict the local damage initiation and its further evolution through the material.  
Conceptualization, M.F., R.C., V.G. and M.P.; Data curation, M.F., M.P., V.G. and R.C.; Investigation, M.F., V.G., R.C. and M.P.; Writing--original draft, M.F. and R.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research has been supported by the Italian Ministry of University and Research (MIUR), in the frame of the HYPROB Program.  
The authors declare no conflict of interest.  
Figure 19: Number of cycles to failure—Wang-Brown criterion.
## 5 Experimental Tests
### Thermal Analysis  
To evaluate the temperatures reached by the components, a thermal analysis test was performed. Since the most-critical elements of the bench are the power converter and the diode, the analysis focused on monitoring their temperatures during different levels of power flow. The tests were conducted with 100, 200 and 300 W flowing through the converter and the diode. The ambient temperature during the thermal analysis was equal to approximately 25 \({}^{\circ}\)C. For each level of power, the thermal regime was reached. A thermal camera was used to record the temperatures of the components during the test. The camera adopted wa the FLIR E4 manufactured by Teledyne FLIR (Teledyne FLIR LLC, Wilsonville, OR, USA). Figure 9 shows the images collected with the thermal camera. With 100 W of power flow, the maximum temperatures recorded were about 43.6 \({}^{\circ}\)C on the DC-DC converter and about 37-38 \({}^{\circ}\)C on the diode. With 200 W of power flow, the maximum temperatures reached 51 \({}^{\circ}\)C both on the converter and the diode. As expected, the maximum temperatures were recorded in the case of 300 W of power flow and corresponded to approximately 67.5 \({}^{\circ}\)C. This temperature was recorded on the diode, while a slightly lower temperature of about 60 \({}^{\circ}\)C was observed on the power converter. Moreover, with the increase in the load current, a rise in the wire temperature was noted. However, the temperatures reached during the test were considered acceptable.
## 5 Results and Discussion
### Performance Analysis of FedLO
Figure 9: Training and validation accuracy—rice leaf disease dataset. The training accuracy commenced at 24.54% in the first epoch and steadily increased to 64.49% by Epoch 30. Simultaneously, the validation accuracy started at 31.56%, showing variability throughout training and reaching 53.28% by the conclusion of Epoch 30. The loss function consistently reduced during training, with the training loss starting at 2.0912 and diminishing to 0.9842 by Epoch 30. Similarly, the validation loss decreased from 1.8839 to 1.2788, signifying effective learning and adaptation of the model to the DS4 dataset. Observations indicate successes and challenges, with occasional fluctuations in accuracy and instances of potential overfitting, as suggested by the validation loss increments in certain epochs. However, the overall trajectory indicates a positive model learning and generalization trend. The experimental findings presented in Table 1 shed light on the comparative performance of various FL algorithms across four datasets. Notably, the results highlight FedLO's superior test accuracy compared to other algorithms, with the peak accuracy values presented in the table. At 30 epochs, FedLO consistently outperformed FedAvg across all datasets, showcasing its efficacy in achieving higher accuracy. This is a crucial observation, indicating that FedLO may offer a more robust and accurate solution in FL scenarios. In the context of FedAvg, the impact of the C on test accuracy is particularly noteworthy. The results in this table reveal that increasing the C leads to higher accuracy for FedAvg, reaching its peak at C = 1.0. However, this improvement comes at the cost of increased data transmission between the server and clients. The trade-off between accuracy and data transfer is evident, emphasizing the need for a balanced approach in FL settings. Looking at the individual datasets, FedLO consistently outperforms other algorithms, achieving the highest accuracy across CIFAR-10, MNIST, rice leaf disease, and WaRP datasets. Particularly in MNIST and WaRP datasets, FedLO demonstrates exceptional accuracy, reaching 99.14% and 87.10%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**DatasetAlgorithm** & **FedAvg, C = 0.1** & **C = 0.2** & **C = 0.5** & **C = 1.0** & **FedPSO** & **FedSCA** & **FedGWO** & **FedLO** \\ \hline CIFAR-10 & 0.5740 & 0.5919 & 0.6001 & 0.6022 & 0.6936 & 0.7141 & 0.7207 & **0.7403** \\ MNIST & 0.9598 & 0.9648 & 0.9709 & 0.9729 & 0.9757 & 0.9778 & 0.9863 & **0.9914** \\ Rice leaf disease & 0.8043 & 0.8112 & 0.8377 & 0.8597 & 0.8746 & 0.8867 & 0.8908 & **0.9027** \\ WaRP & 0.7829 & 0.7948 & 0.8139 & 0.8229 & 0.8308 & 0.8354 & 0.8402 & **0.8710** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test accuracy. Figure 12: Training and validation loss—WaRP dataset.
## 3 Case Study
### Active Boring Bar Design
The employment of designed covers compensates the loss of area due to material removed in correspondence of the slots and allows to obtain almost the same inertial properties of original boring bar ensuring a similar static response. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **A [mm2]** & **J\({}_{x}\)[mm4]** & **Jy [mm4]** \\ \hline Original boring bar & 760.38 & 45,042.61 & 47,493.32 \\ Boring bar with slots & 373.08 & 16,074.55 & 17,581.10 \\ Boring bar with covers & 514.32 & 42,032.73 & 43,644.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between original boring bar, boring bar with slots and active boring bar with covers. Figure 16: Active boring bar FE model for protective covers design. Figure 15: Boring Bar Finite Element model for stiffness estimation. The comparison between the dynamic responses of the different models is shown in terms of tooltip Frequency Response Function (FRF) in Figure 17c. Due to material removal, the dynamic response of the boring bar significantly changes, both in terms of natural frequency and peak amplitude. The employment of designed covers allows to obtain a dynamic response almost identical to the original boring bar. In particular, the difference in terms of natural frequency is less than 1%.
## 4 DCB Voltage Factor and Ratio
### DCB Voltage Ratio  
The diagram in Figure 3 shows that the output voltage of a DTLI is essentially the difference between the output voltages generated by the master and slave inverters. Consequently, the ratio between these voltages plays a critical role in shaping the distribution of space vectors in a DTLI. This voltage ratio variation directly affects two key aspects: the area of the space vector diagram and the number of output voltage levels produced by the inverter.  
To better illustrate these effects, Figure 13 presents the space vector diagrams of DTLIs for different voltage ratios. As shown, the voltage ratios of \(1:0.33,1:0.5,\) and \(1:1\) allow the generation of three, four, and five distinct output levels, respectively. In other words, the selected voltage ratio determines the number of achievable output levels, which directly influences the DTLI's performance characteristics.  
Figure 13: Space vector diagram of DTLIs with different voltage ratios.
# Differential Transform Method as an Effective Tool for Investigating Fractional Dynamical Systems
## 2 Methods
### The Rossler System of Integer and Fractional Orders  
The Rossler system was developed in 1976 by [PERSON] [19], and it is the simplest three-dimensional chaotic system with nonlinearity only in the third equation. The simplicity of mathematical equations makes it a model object for studying chaos and many different nonlinear phenomena [20; 21]. A fractional version of the Rossler system is tested for control, synchronization and various dynamic properties [22; 23; 24], also using DTM to integrate fractional equations [25].  
In the next step, comparative tests were performed for the classical integer-order Rossler system described by the following set of equations:  
\[\begin{split}\dot{x}&=-y-z,\\ \dot{y}&=x+ay,\\ \dot{z}&=b+z(x-c),\end{split} \tag{5}\]  
with \(a=0.4\) or \(a=0.4079\) (for a chaotic system), \(b=2,c=4\) and the initial values at \(t=0\) equal to \(x(0)=0,\ y(0)=1,\ z(0)=9\). In the fractional system, we assume that each equation has its own individual fractional time-derivative (in the Caputo sense):  
\[\begin{split} D_{t_{0}}^{\alpha_{1}}x&=-y-z,\\ D_{t_{0}}^{\alpha_{2}}y&=x+ay,\\ D_{t_{0}}^{\alpha_{3}}z&=b+z(x-c),\end{split} \tag{6}\]  
where \(t_{0}\) is equal to the length of time from which the past states of the system are taken into account when calculating the current state. Theoretically, the interval \(t_{0}\) starts at the initial moment, which is usually assumed to be 0.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 5 Conclusions  
This work explores the integration of DTN functionalities into IPv6 using ICMPv6, proposing a novel approach to address the challenges of deep-space communications. By comparing IPv7 and IPv6, we identified critical gaps in IPv6, such as the lack of store-and-forward mechanisms and suitable signaling for disrupted environments. To bridge these gaps, we proposed a modular architecture leveraging the LwIP stack and custom ICMPv6 messages to enable DTN capabilities while maintaining IPv6 compliance. Our design enables the coexistence of DTN and non-DTN traffic, ensuring interoperability with existing terrestrial infrastructures and expanding the usability of DTN solutions to new applications, such as IoT devices in challenged environments. As highlighted in this paper, the impact of our solution is limited to the addition of an extension header, which according to published standards should not cause incompatibility with existing networks. Furthermore, the modular design facilitates future enhancements and allows for easier testing and deployment in constrained devices.  
While this architecture demonstrates the potential for extending IPv6 to support DTN requirements, its implementation and validation in real-world scenarios will be critical for assessing its performance and scalability. Key areas for further research include addressing security concerns associated with ICMPv6-based signaling and exploring the implications of persistent storage on resource-constrained devices.  
This work sets the foundation for a new generation of IP-compliant DTN solutions, bridging the gap between terrestrial and non-terrestrial networks and enabling seamless communication across diverse infrastructures. By leveraging the strengths of IPv6 and adapting it to meet the challenges of DTN environments, we pave the way for a more interoperable and flexible networking paradigm.  
Conceptualization, A.C. and O.F.; funding acquisition, A.C.; investigation, A.C. and U.P.; methodology, A.C. and U.P.; project administration, A.C.; supervision, A.C.; validation, A.C., O.F. and U.P.; writing, A.C., O.F. and U.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Spanish MCIU/AEI/10.13039/501100011033/FEDER/UE through project PID2023-146378 NB-I00, and by the Secretaria d'Universitats i Recerca del departament d'Empresa i Coneixement de la Generalitat de Catalunya with the grant number 2021 SGR 00330.  
Not applicable  
Data Availability Statement: Data are contained within the article.  
Thanks to [PERSON] from i2 CAT for the discussions held on this topic and for the recommendations received.  
The authors declare no conflicts of interest.
## 2 Gaussian Process Data Modelling and Maximum Likelihood-Based Data Fusion Method
### Data Modelling and Data Fusion Principle from the View of Dimensional Measurement Science
Figure 3: Influence of systematic error, random error and the true value for the fused result: (**a**) measurement with large systematic error but small random error, fused with measurement with small systematic error but large random error, \(Tv<M1<M2\); (**b**) measurement with large systematic error but small random error, fused with measurement with small systematic error and large random error, \(M1<Tv<M2\) (bias to \(M1\)); (**c**) measurement with small systematic error and small random error, fused with measurement with large systematic error and large random error, \(Tv<M1<M2\) (bias to \(M2\)); and (**d**) measurement with small systematic error and small random error, fused with measurement with large systematic error and large random error, \(M1<M2<Tv\). In practice, the sensor with small measurement uncertainty usually possesses a small systematic error. This is particularly true when comparing an optical sensor with contact sensor which are widely used in multi-sensor CMMs, e.g., the laser sensor has large uncertainty and large systematic error while the touch trigger probe has small uncertainty and small systematic error. The case shown in Figure 3d is another case in which the fused result has a smaller uncertainty: it has a smaller bias than Data 1 but larger than Data 2. This is the case that should be paid much attention in multi-sensor data fusion for dimensional measurement. In general, with a sophisticated designed multi-sensor CMM, the case shown in Figure 3c is the most desired case statistically, since the true value should be among the two mean values of the different measurements and bias to the sensor with a higher accuracy in a well-calibrated situation. In fact, this situation has the highest probability in the measurement process. In this study, the mean value and associated uncertainty for the measurement datasets are not directly given by the measurement instrument but they are modelled using the Gaussian process modelling method. Due to the existence of measurement uncertainty, the data points in the measurement result cannot represent the mean surface but are Gaussian distributed along the mean surface. The use of Gaussian process data modelling aims to model the raw data to obtain the mean surface and the associated uncertainty at every position of interest. The mean surface can be treated as the best estimated true value for the surface. With a sophisticated designed multi-sensor CMM and a well-established Gaussian process model, the mean surfaces from two different sensors should be consistent with each other with small systematic error. However, in some cases in which the accuracy requirement is stringent, the measurement error from the hardware and the modelling error from the Gaussian process method may influence the final result, especially when one of the sensors has a large systematic error.
## 4 Drug Resistance Mechanisms in TB
### Intrinsic Drug-Resistance of Mtb
#### 4.3.1 Cell Envelope  
The constituents of the mycobacterial cell envelope are: the cytoplasmic membrane, the periplasmic space (PS), a network of peptidoglycan (PG), the arabinolactan (AG), the long-chain mycolic acids (MA) and the capsule, made of a loose matrix of glucans and secreted proteins [44]. As to the first-line TB drugs, the bactericidal agent INH inhibits MA synthesis, while the bacteriostatic EMB inhibits AG synthesis and may sensitize Mtb to other drugs [44].  
It is assumed that the innermost hydrophilic layers of PG and AG hinder the penetration of hydrophobic molecules. Instead, in the external part of the envelope, the PG and AG layers are linked to the hydrophobic MA layer, formed by long-chain fatty acids that restrict the penetration of hydrophilic drugs [18; 20]. In principle, more lipophilic drugs, such as rifamycins, macrolides, and some FQs, diffuse by passive transport into and through the lipid-rich cell wall [45; 46]. In early studies, mutants defective in the biosynthesis of cell wall components were very useful to demonstrate the role of the cell wall in the intrinsic resistance to drugs. For instance, a mycolate defective _Mycobacterium smegmatis_ mutant showed increased susceptibility to RIF, chloramphenicol (CF), novobiocin and erythromycincomi [47; 48]. Also, insertions in genes involved in the mycolate biosynthesis of Mtb (_mymA_ operon) showed enhanced chemical penetration and sensitivity to RIF, INH, PZA and ciprofloxacin [49].  
Small hydrophilic drugs traverse the cell wall of bacteria via water-filled porins, without energy consumption. _M. tuberculosis_ encodes at least two porin-like proteins (OmpA, encoded by _Rv0899_ and _Rv1698_), but the role of porins in Mtb drug uptake and susceptibility needs to be further investigated [18; 20; 50; 51]. Penetration of hydrophilic \(\beta\)-lactam antibiotics through the mycobacterial cell was about 100 times lower than in the _Escherichia coli_ cell wall [20]. The \(\beta\)-lactamases, probably in conjunction with slow drug penetration, were shown to be major determinants of Mtb resistance to \(\beta\)-lactams [52; 53]. In Mtb, the PG is remodeled by nonclassical \(\mathrm{\textsc{l}}_{\mathrm{\textsc{d}}}\)-transeptiddases (LDT). The structural basis and the inactivation mechanism of LDT and the active role of carbapenems were investigated, providing a basis for their potential use in inhibiting Mtb [54]. Indeed, the carbapenems IPM-CLN and MPM (both to be used with CLV, available only in formulations combined with amoxicillin) have been listed as add-on drugs in the recent WHO treatment guidelines of MDR/XDR TB [8].  
Overall, it is thought that anti-TB drugs have the peculiarity of being more lipophilic than many other antimicrobial agents, likely due to improved penetration through the waxy mycobacterial cell wall [45; 46]. However, the issue is perhaps more complex, since some studies showed that lipophilicity is an important but not exclusive factor of compound permeability [50; 55].
## 4 MAC Protocol for Scheduling Concurrent Transmissions
### Identifying an Exposed Terminal  
When a node first overhears an RTS and then a DATA frame from the same transmitter, it can be identified as an exposed terminal with regard to the overheard transmission. In practice, exposed terminals can be identified before receiving the complete frame as follows.  
The IEEE 802.11 PHY frame structure when direct spread spectrum sequencing (DSSS) is used is shown in Figure 3. As soon as the physical layer convergence protocol (PLCP) header, which precedes the current MAC DATA frame, is completely received and verified (by checking HEC, a 16-bit CRC code), the exposed node will know the MPDU (MAC protocol data unit) length. The exposed node can determine if the MAC frame is DATA from the length information since DATA frames are always longer than control frames (i.e., RTS, CTS, and ACK). By comparing the Length value with that in the preceding RTS, the exposed node can infer if the RTS and the DATA frame are from the same sender. It can further validate its inference if the interval between the DATA frame and the RTS is (SIFS + CTS + SIFS) plus some propagation delay.