esa-sceva/satcom-chunk-collection huggingface dataset:
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 3 SCHC over LoRaWAN
### SCHC Fragmentation over LoRaWAN  
The SCHC specification in RFC8724 [1] presents the modes of operation for the fragmentation of a SCHC Packet: No-ACK, ACK-Always, and ACK-on-Error. These modes define the general operation used in all Low-Power Wide Area Network (LPWAN) technologies defined in RFC8376 [9]. However, for efficient performance, some parameters and modes of operation need to be set appropriately for each LPWAN technology or profile. This section describes the parameters and modes of operation when SCHC is used over LoRaWAN networks based on specification RFC9011 [2]. The LoRaWAN protocol is specified by the LoRa Alliance in [10].  
LoRaWAN uses only two of the three modes defined in [1]: ACK-on-Error and ACK-Always. The ACK-on-Error mode is used for uplink traffic and ACK-Always for downlink traffic. This work focuses on the fragmentation of the transmission of an IPv6 packet from the end device to the SCHC gateway. The ACK-on-Error fragmentation mode supports a variable MTU and the out-of-order delivery of fragments. Furthermore, SCHC fragmentation for LoRaWAN uses the concept of windows and tiles defined in Section 3.1 and shown graphically in Figure 3. All tiles must be of the same size, except for the last one. The size of the last tile must be smaller than or equal to the regular tile size.  
Remember that an IPv6 packet is divided into several tiles, and that several tiles can be grouped in several windows, as shown in Figure 3. The number of tiles transported in a Regular SCHC Fragment depends on the MTU that exists at that moment. There is the possibility that a group of tiles belongs to the window \(i\) and another group of tiles to the window \(i+1\), where all of them are transported in the same Regular SCHC Fragment. The last tile can be carried in a Regular SCHC Fragment or an All-1 SCHC Fragment.
## 2 Materials and Methods  
### Description and Preparation of the Active Ingredient  
The _Larix europea_ wood extract and _Camellia sinensis_ leaf extract containing the active ingredient were stabilised using a proprietary enzyme-based process able to graft \(\alpha\)-D-glucose residues to the pyrocatenechol and 3,4,5-trihydroxylated rings of taxifolin (dihydroquercetin) and epigallocatechin gallate (EGCG), respectively. The \(\alpha\)-D-glucosamine process was previously described by [PERSON] et al. in EP2027279B1 (Phenolic compounds with cosmetics and therapeutic applications). Briefly, the _Leuconstoc mesenteroides_ ATCC 10830a dextransucrase (EC 2.4.1.5) is produced as described by [PERSON] et al. [16]. The reaction media contain sucrose at 300 g L\({}^{-1}\), with the extract previously dissolved in DMSO at a final concentration 12 g L\({}^{-1}\) (final DMSO concentration 15%). The reaction takes place at pH 5.20 (sodium acetate buffer) at 30 \({}^{\circ}\)C. After 24 to 30 h of incubation under mild agitation, the enzyme is inactivated by decreasing the pH to 2.0 with concentrated sulfuric acid. The polyphenol-related substances are purified using an adsorbent resin (Diaion HP 20 from Mitsubishi Chemical Company, Tokyo, Japan). After adsorbing the polyphenol-related molecules onto the resin, the resin is extensively washed with water to remove all the water-soluble non-polyphenolic substances. Then, the extract is eluted with an alcohol, methanol. After complete alcohol removal by vacuum evaporation, the glucosylated substances contained in the extract are separated from the non-glucosylated ones by liquid-liquid extraction with ethyl acetate. Finally, the ethyl acetate present in the glucosylated extract-rich aqueous phase is removed by vacuum evaporation at 40 \({}^{\circ}\)C. The extract concentration is then adjusted by removing the excess water and filtered at 0.2 \(\upmu\)m. The sterile aqueous solution containing the stabilised extract is used as such for the preparation of the active ingredient. Taxifolin and its glucosylated derivatives are adjusted to a final concentration of 0.17% (\(w/w\)) and EGCG and EGCG glucosides at 0.03% (\(w/w\)).
## 2 Wind Turbine System
### Understanding the Wind Power Curve for Primary Frequency Control  
To understand the relationship between pitch angle and rotor speed with the response of the torque and stator current of the machine, the model of the wind turbine and PMSG should be presented first.  
The wind turbine model is a function of two main variables, the pitch angle (\(\beta\)) and the rotor speed (\(\omega_{r}\)), as given by [25]:  
\[P_{W}=\frac{1}{2}C_{P}(\lambda,\beta)A_{W}\rho_{air}V_{W}^{3} \tag{1}\]  
where \(A_{W}\) is the swept area of the WT and the power coefficient \(C_{P}(\lambda,\beta)\) is expressed as  
\[C_{P}(\lambda,\beta)=c_{1}\left(\frac{c_{2}}{\gamma}-c_{3}\beta-c_{4}\right)e^ {\frac{-c_{5}}{\gamma}}+C_{6}\lambda \tag{2}\]  
The coefficients \(c_{1}\) to \(c_{6}\) were taken from [25] (\(c_{1}=05176,\ c_{2}=116,\ c_{3}\ =\ 0.4,\ c_{4}\ =\ 5,c_{5}=21,c_{6}\ =\ 0.0068\)), and the tip speed ratio is defined as  
\[\lambda=\frac{R_{W}\omega_{r}}{V_{W}} \tag{3}\]  
Here, \(R_{w}\) represents the radius of the wind turbine, and \(V_{W}\) represents the wind speed.  
From the curve characteristics of the mechanical power Equation (1) and the power coefficient of Equation (2), the maximum power is achieved at the zero pitch angle and optimum tip speed ratio for underrated wind speed conditions. Figure 1 shows the characteristics of the wind turbine power for different pitch angles and different rotor speeds for a specific wind turbine. As the pitch angle increases, the power produced by the WT decreases. This helps identify the operating point of the WT system for de-loading purposes. In addition, the rotor speed can be used to reach a specific production of power. To implement de-loading to a WT system, the power is curtailed by a certain amount, (\(\Delta P\)), using either the pitch angle or rotor speed controller. In Figure 1, the power is curtailed to (\(P_{curt.}\)), providing a reserve power of \(\Delta P\).  
The power curve can be represented in terms of rotor speed for different pitch angles for several wind speeds. This will allow us to select an optimal operating point of rotor speed and pitch angle for the purpose of PFC. Figure 1 represents a three-dimensional plot of the power curve. During regular operation of WT, the maximum power is tracked. However, for PFC, the sub-optimal operating point is selected to reserve the required power in the WT system. The output power is plotted using a flat surface, where the intersections of the two surfaces demonstrate all possible sets of pitch angles and rotor speed values that can be used.
## 3 Fluorophore Coupled to a Resonant Cavity in the Strong-Coupling Regime
### Weak and Strong-Coupling Regime in the Resonant Case \(\omega_{0}=\omega_{c}\)  
In the _resonant case_, \(\omega_{0}=\omega_{c}\), the eigen-frequencies in Equation (10) become:  
\[\omega_{\pm}=\omega_{c}-\text{i}\,\frac{\gamma_{c}}{4}\pm\frac{\Omega}{2} \tag{14}\]  
where \(\Omega\) now reads:  
\[\Omega=\sqrt{4g^{2}-\left(\frac{\gamma_{c}}{2}\right)^{2}} \tag{15}\]  
One can clearly see that two different regimes in the time-domain are possible depending on the values of \(g\) and \(\gamma_{c}\):1. For a small coupling constant, \(g<\frac{1}{4}\gamma_{c}\), \(\Omega\) becomes purely imaginary, and the eigen-frequencies \(\omega_{\pm}\) (Equation (14)) are now complex, leading to a monotonic decay of the amplitude \(\alpha(t)\) in time (Equation (12)), known as the **weak-coupling regime.**
2. For a large coupling constant, \(g>\frac{1}{4}\gamma_{c}\), \(\Omega\) is real, and the eigen-frequencies \(\omega_{\pm}\) (Equation (14)) show a different real part, leading to _oscillations_ of the amplitude \(\alpha(t)\) in the time-domain, known as the **strong-coupling regime**.  
The general solution will not be considered in detail here but we shall focus on the (very) strong-coupling regime, i.e., when \(g\gg\gamma_{c}\). A very good approximation of the solution may be derived in this case by means of power series expansion:  
\[\Omega\simeq 2g-\frac{\gamma_{c}^{2}}{16g} \tag{16}\]  
\[\omega_{\pm}\simeq-\mathrm{i}\frac{\gamma_{c}}{4}\pm g \tag{17}\]  
\[A_{\pm}\simeq\frac{1}{2}\pm\mathrm{i}\frac{\gamma_{c}}{8g}\Big{(}1+\frac{ \gamma_{c}^{2}}{32g^{2}}\Big{)} \tag{18}\]  
Then, \(\alpha(t)\) becomes  
\[\alpha(t)\simeq\mathrm{e}^{-\frac{\gamma_{c}}{2}\frac{t}{2}}\mathrm{cos}(gt) \tag{19}\]  
which gives a survival probability \(P_{\mathrm{surv}}(t)=|\alpha(t)|^{2}\)  
\[\boxed{P_{\mathrm{surv}}(t)=\mathrm{e}^{-\frac{\gamma_{c}}{2}t}\mathrm{cos}^{2 }(gt)=\frac{1}{2}\mathrm{e}^{-\frac{\gamma_{c}}{2}t}[1+\mathrm{cos}(2 gt)]} \tag{20}\]  
One can see in this equation that \(P_{\mathrm{surv}}(t)=|\alpha(t)|^{2}\) oscillates at the frequency \(\Omega=2g\). These oscillations, also called \"vacuum Rabi oscillations\", are characteristic of the strong-coupling [17].
# Robustness of Deep Learning Models for Vision Tasks
## 3 Adversarial Attacks and Defenses
### Adversarial Defenses
#### 3.2.5. Adversary Detector Networks  
[PERSON] et al. [148] presented a method for detecting adversarial examples by enhancing the classification network with a small subnetwork that serves as a detector in Figure 25. Detectors, which are sometimes referred to as adversarial detection networks, train their inputs to classify network inputs as benign examples or specific adversarial examples. For this, they first train the classification networks on a benign (non-adversarial) dataset as usual and then adversarial examples are generated for each data point of the training set using one of the attack methods in Section 3.1.  
Figure 24. Overview of the Defense-GAN [146]. If the GAN is properly trained and has a strong representation of the data, the original clean image and its reconstruction should be similar.  
Figure 25. Overview of the adversary detector network.  
To train the classification network, benign datasets were first used, and then adversarial examples were generated for each data point of the training datasets via FGSM, BIM, and DeepFool attacks. Thus, a binary classification dataset that has double the size of the original dataset consisting of the original data (label 0) and its adversarial examples (label 1) was obtained. The training procedure of the detector minimizes the cross-entropy of the probability of the input adversary and freezes the weights of the classification network.  
This approach was successful in mitigating FGSM, BIM, and DeepFool attacks on the CIFAR-10 and 10-class ImageNet datasets. [PERSON] et al. [148] asserted that the locations of the detector network in different layers lead to different results depending on the type of attack and that the proposed method presents a greater challenge for attackers, as they must generate adversarial examples that can fool both the classifier and the detector. [PERSON] et al. [133] put forth a defense strategy similar to [PERSON] et al. [148] utilizing a binary classifier network trained to distinguish between adversarial and benign examples. Their method deals with the classifier and detector separately.  
The experimental results indicated that the detector in [PERSON] et al. [133] is sensitive to perturbation values in FGSM and BIM attacks and that a detector trained for FGSM attacks cannot detect other attacks. [PERSON] et al. [133] and [PERSON] et al. [134] proposed similar methods that changed the training procedure, but their detection results were limited and vulnerable to the detection of a specific attack.  
In conclusion, adversary detector networks are controversial methods for detecting adversarial examples. For example, powerful attacks, such as CW attacks, have a relatively high false-positive rate and respond sensitively to perturbation parameters to generate adversarial examples.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 4 Experimental Results
### Baseline  
MGARD is the leading error-bounded lossy compression technique that considers both error bounds on PD and QoI preservation. We compare our framework to the two versions of MGARD, as described in Section 3.1.4. MGARD uniform only focuses on achieving the best compression within PD error bounds and does not consider QoI preservation. MGARD non-uniform tries to reduce the errors of QoI with guaranteed PD error bounds. We set the \(s\) parameter of MGARD non-uniform to be \(-1\), as it is the best setting for the XGC dataset for QoI preservation. MGARD uniform is much better than MGARD non-uniform in terms of PD errors, whereas MGARD non-uniform performs better than MGARD uniform in terms of maintaining the QoI. Therefore, we use MGARD uniform for PD error comparison and MGARD non-uniform for QoI error comparison. The abbreviations and acronyms used are described in Table 1.
## 4 Failure Mechanism Verification
### Results and Discussion  
Through the test, the property parameters of solder joints at low temperature, the fracture surface samples, and the temperature range of ductile-brittle transition of solder joints could be obtained. Fracture strength and the displacement from the maximum tensile stress to the final fracture of Pb-free solder joints at different temperatures were obtained. Figure 11 shows the tensile strength and displacement curves of SAC305. According to the graphs and focus on the curve after the maximum tensile strength, it could be seen that the fracture mode of the solder joints was a ductile fracture at 25 \({}^{\circ}\)C. The slope of the curve after fracture changed slowly. With the decrease of temperature, slopes of the curve after the maximum tensile strength increased suddenly, meaning the mechanism changed from ductile fracture to brittle fracture. The transition temperature range of SAC305 solder joints was -70--80 \({}^{\circ}\)C.  
Figure 11: Tensile strength-displacement curve of Sn3.0 Ag0.5 Cu solder joints at different temperatures (a) 25 \({}^{\circ}\)C; (b) -50 \({}^{\circ}\)C; (c) -70 \({}^{\circ}\)C; (d) -80 \({}^{\circ}\)C; (e) -100 \({}^{\circ}\)C.  
Table 2 shows the data of displacement from maximum tensile stress to complete fracture. This parameter is the characterization of the material toughness. The larger the value, the better the toughness. With the decrease in temperature, the values of displacement decreased as well. That is to say, the characteristic of ductile fracture became weaker, and the toughness of SAC305 was worse. The relationship between the toughness and the displacement from maximum tensile stress to complete fracture could be explained as: when the toughness decreased, the displacement decreased as well. The shock resistance of SAC305 gradually decreased. With the brittle fracture stage, when the external stress reached the fracture limit, small displacement could cause the fracture of the solder joint.  
Figures 12-16 are the SEM graphs of the fracture surface at different temperatures. At 25 \({}^{\circ}\)C, -50 \({}^{\circ}\)C, and -70 \({}^{\circ}\)C, dimples were shown in the fracture surface morphology. It was obvious that the fracture mechanism was a ductile fracture. At -80 \({}^{\circ}\)C and -100 \({}^{\circ}\)C, the figures showed the mechanical changes to brittle fracture. Different surface morphology under lower temperatures showed different material fracture characteristics from that under higher temperatures. From Figures 15 and 16, intergranular fracture surface and river pattern could be perceived. They are the characteristics of brittle fracture. The results could be a supplementary instruction of the transition of material characteristics and its temperature range.  
Figure 12: SEM graph of fracture surface at 25 \({}^{\circ}\)C (ductile).  
Figure 13: SEM graph of fracture surface at \(-50\)\({}^{\circ}\)C (ductile).  
Figure 15: SEM graph of fracture surface at \(-80\)\({}^{\circ}\)C (brittle).
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 3 Preliminaries
### Motivation Scenario  
In this study, we adopted the typical network model shown in Figure 1. Figure 1 shows how UAVs in FANET are structured into different clusters. The CHs manage the clusters by authorizing various nodes to communicate with each other within the cluster and forward data between them. Furthermore, our approach includes ferry UAV nodes that gather and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Approach** & **Objective** & **Strengths** & **Weaknesses** & **Scalability and Adaptability** \\ \hline \multirow{4}{*}{EARVRT [26]} & Energy-aware routing & \multirow{4}{*}{Reducing unnecessary routing tables} & Reduced routing overhead, high & \multirow{4}{*}{Imited network (PDB), and reduced latency} & Moderate scalability and performs well in \\  & & & & Enhanced adaptability, & \\  & & & Enhanced adaptability, & \\  & & & & & \\  & & & Enhanced adaptability, & \\  & & & managed routing & & mobility and limited by \\  & & & local filtering & & overhead, and improved & overhead in \\  & & & route stability & & high-density FANETs \\ \multirow{4}{*}{K-MORP [28]} & K-means based mobility-aware routing & Efficient routing through clustering & Reduced routing overhead, enhances scalability, and resource management & Complexity in cluster formation and stability issues at high speeds & High scalability and effective in \\  & Mobility-based weighted clustering scheme & Enhancing FANET performance through optimized routing & Improves load balancing and communication efficiency & High computational complexity for real-time & Good scalability but \\  & & & & efficiency & data processing & dynamical environments \\ \multirow{4}{*}{DCFH [30]} & Dynamic clustering for fault handling & Improving fault tolerance in dynamic FANETs & Efficient in managing faults and improving data reliability & \multirow{4}{*}{Limited scalability due to complexity} & Moderate scalability and suitable for low to \\  & & & & data reliability & to complexity & moderate \\  & & & & mobility scenarios & \\  & & & & & \\  & & & Low clustering time, & & Adaptable to dynamic \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & high-speed UAV \\  & & & low delay & & \\  & & & & & \\  & & & & &  
distribute global network data, such as the location and load of other UAVs. This allows decisions to be made adaptively, based on a network view. A predefined mobility path is followed by ferry UAVs within the network. Data gathered from ferry nodes contain a variety of network metrics, including UAV position and load information. Utilizing this information, routing decisions can be made more effectively.
## 5 Simulation Study  
### Modeling the Network  
We used the OPNET Modeler simulation package because of its availability and credibility. Figure 12 shows the simulation network topology, with a network size of 50 nodes. Table 6 lists the parameters used in the simulation (default settings). We have conducted various simulation experiments and measured the key performance metrics, including average packet delay, throughput, traffic sent, traffic received, download file size, and download response time. The network configuration used in the simulation is shown in Figure 13a. The simulation aimed to assess the impact on ad hoc network performance of varying node densities, from N = 2 to 50 nodes. We considered both FTP and P2P traffic for the study of the system's performance.
## 3 Results and Discussion
### Morphology Study
Based on the analysis of the obtained results presented in Figure 10 and Table 6, it is evident that oxygen and silicon are the elements with the highest concentrations in the oxide coatings. The presence of silicon and sodium can be attributed to their roles as key components of the electrolytes used during the anodization process. This elemental composition suggests that the incorporation of silicon, in particular, may contribute to enhancing the properties of the oxide layer, potentially improving its protective and bioactive characteristics. \begin{table}
\begin{tabular}{c c c c} \hline
**Element** & **Atomic \%** & **Atomic \% Error** & **Weight \%** & **Weight \% Error** \\ \hline O & 46.1 & 0.4 & 36.6 & 0.3 \\ Na & 9.4 & 0.2 & 10.8 & 0.2 \\ Si & 26.5 & 0.3 & 37.1 & 0.4 \\ C & 10.1 & 0.1 & 6.1 & 0.1 \\ Mg & 7.8 & 0.1 & 9.5 & 0.1 \\ \hline \end{tabular}
\end{table} 
Table 6: The obtained results of the EDS analysis of the surface of the Mg_10 sample. \begin{table}
\begin{tabular}{c c c c} \hline
**Element** & **Atomic \%** & **Atomic \% Error** & **Weight \%** & **Weight \% Error** \\ \hline N & 0.0 & — & 0.0 & — \\ O & 46.6 & 0.3 & 36.3 & 0.3 \\ Na & 8.2 & 0.2 & 9.1 & 0.2 \\ Si & 28.1 & 0.3 & 38.5 & 0.3 \\ C & 7.0 & 0.1 & 4.1 & 0.1 \\ Mg & 10.1 & 0.2 & 12.0 & 0.2 \\ \hline \end{tabular}
\end{table} 
Table 7: The obtained results of the EDS analysis of the surface of the Mg_17 sample. Figure 11: The distribution of selected elements present in the composition of the Mg_17 sample. The analysis of the obtained results, presented in the Figure 11 and Table 7, indicates that oxygen and silicon have the highest concentrations among the elements. The significant presence of silicon and sodium can be attributed to their roles as components of the electrolyte used in the anodic oxidation process.