esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Performance Metrics
#### 6.2.1 Response Time  
The response time of a task refers to the time intervals among tasks to arrive into the system until its completion. Response time \(R_{e}\) is expressed as  
\[R_{e}=T_{c}-T_{a}+T_{t} \tag{10}\]  
where \(T_{c}\) represents the time required to complete a task, \(T_{a}\) represents the arrival time of a task, and \(T_{t}\) represents the transfer time of a task.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 4 Discussion and Application
### Method of Estimating Nonuniformity Error  
The method of estimating the IA error for a nonuniform flow field is established on the basis of the analysis and discussion above. We summarize the steps as follows:  
1. Firstly, the nonuniform flow field is simplified as a combination of a few quasi-uniform flow field regions.
2. Secondly, in each region of the quasi-uniform flow field, the line damping parameters, normalization coefficient, and IA are calculated according to the physical and spectral parameters of the flow field.  
Figure 9: Four absorption line error distribution results. The IA error changes as \(\delta/L\) (which is defined as the proportion of high temperature zone) changes. (**a**) swirl flame and (**b**) inlet oblique shock.  
3. Thirdly, find the corresponding error in the error distribution according to the line-shape parameter. If the fitting function used in the measurement is that of the Voigt numerical approximation algorithm, then the error distribution is found in the Supplementary Materials when \(n=2\). When \(n>2\), the error distribution can be calculated through the analysis described in Section 2. 4. Finally, the effect of isolation on the errors needs to be considered. When the isolation of an absorption line is poor, it is necessary to calculate the IA error generated by the absorption line overlap under a uniform flow field. The two errors are added together to obtain the estimated IA error under the nonuniform flow field.  
It is noted that considering the quasi-uniform flow field division and the isolation of the absorption line, the influence of the uncertainty of other parameters (including pressure, temperature, concentration, and line parameters) can be ignored. The estimated error is larger than the actual error.
## 2 Problem Formulation
### Land Transportation Cost in the Port Hinterland  
The cargo owner or shipper at point x can select to arrive at a port by road or railway. In order to better reflect the level of hinterland collection and distribution and port accessibility, this paper uses the weighted comprehensive transportation cost to calculate the land transportation cost as follows:  
\[C_{A}^{land}(x)=\sum\limits_{m=1}^{M}w_{m}\bigg{(}\delta^{m}x+\epsilon\frac{ x}{\overline{V}^{m}}\bigg{)} \tag{2}\]  
where \(C_{A}^{land}(x)\) represents the transportation cost from the point \(x\) to port A. \(x\) represents point \(x\) in the interval [0,1], and the shortest distance from it to port A is \(x\); \(w_{m}\) represents the freight sharing rate of the \(m\) transport mode; \(\delta^{m}\) the unit distance transportation cost of the \(m\) transport mode; \(\overline{V}_{m}\) represents the average running speed of the m transport mode; and \(\epsilon\) represents the value of time.  
Similarly, the cargo at point \(x\) can be transported by road or rail to border port b and then be transported to port B by cross-border transportation. The calculation formula of land transportation cost for transportation to port B is as follows:  
\[C_{B}^{land}(x)=C_{xb}^{land}+C_{bB}^{land}=\sum\limits_{m=1}^{M}w_{m}\bigg{(} \delta^{m}(b-x)+\epsilon\frac{b-x}{\overline{V}^{m}}\bigg{)}+C_{bB}^{land} \tag{3}\]  
where \(C_{B}^{land}(x)\) represents the transportation cost from point \(x\) to port B. \(C_{xb}^{land}\) represents the transportation cost from point \(x\) to border port \(b\), and its calculation method is the same as that of \(C_{A}^{land}(x)\). \(C_{bB}^{land}\) represents the cross-border land transportation cost from border port \(b\) to port B.
## 6 Security Analysis and Discussion
### Regulatory and Certification Implications  
SMART DShot's design characteristics directly support certification requirements across multiple regulatory frameworks:  
DO-178C Software Considerations for Airborne Systems [34]: Our deterministic ML algorithms enable formal verification through (1) mathematical proof of convergence for KFTC (\(\mathcal{O}(\rho^{k})\) convergence rate, Section 4.2.1), (2) bounded adaptation mechanisms in RLSTC preventing runaway parameter drift, (3) exhaustive test coverage demonstrated through our 32,000 Monte Carlo iterations across 16 scenarios, providing the structural coverage analysis required for DAL-A certification, and (4) source code traceability from requirements through our modular FPGA architecture (Figure 6).  
ISO 26262 Functional Safety for Automotive Applications (adapted for UAV systems) [35]: Our multi-algorithm ensemble approach satisfies ASIL-D requirements through (1) hardware-software co-design eliminating systematic failures via FPGA deterministic execution, (2) comprehensive fault injection testing demonstrated in our attack scenario evaluation (Table 6), (3) safe state achievement through graceful degradation mechanisms maintaining \(>\)50% control authority, and (4) freedom from interference verified through independent channel operation (Section 4.4).  
NIST Cybersecurity Framework Implementation [36]: Our layered security architecture directly maps to framework functions: (1) Identify: Comprehensive threat taxonomy (Figure 3) and attack surface analysis, (2) Protect: Multi-algorithm ensemble providing defense-in-depth, (3) Detect: 88.3% attack detection rate with \(<\)10 ms response capability, (4) Respond: Automated algorithm switching and graceful degradation, (5) Recover: Fallback mechanisms ensuring operational continuity.  
Practical Certification Strategy: We recommend a phased approach: (1) laboratory verification using our simulation framework to demonstrate compliance with timing and safety requirements, (2) hardware-in-the-loop testing leveraging our PolarFire SoC implementation for real-time validation, (3) controlled flight testing with incremental capability deployment starting with non-safety-critical functions, (4) operational validation through partnership with certified UAV manufacturers, and (5) regulatory engagement using our comprehensive documentation package to support certification authority review.  
Supporting Documentation: Our evaluation methodology provides certification-ready artifacts including: formal algorithm specifications (Equations (1)-(4)), comprehensive test results with statistical validation (95% confidence intervals), hardware implementation verification (Table 4), and security assessment with quantified risk reduction (CVSS score improvements). This documentation framework directly supports the evidence requirements for safety-critical system certification.
## 1 Introduction
[23], they conclude that the corrosion mechanisms in molten salts are complex and depend on many variables, such as the species of anion and salt cation, temperature gradient, concentration of impurities, inert gas, pressure of the atmosphere, alloying elements, composition and microstructure, in a certain way each of them greatly influence the mechanism and rates of corrosion. These authors also mention that the prior formation of consistent oxide layers can help improve corrosion rates. They propose an acid/base model, similar to that used for an aqueous corrosion system, where the main difference between a molten salt system and an aqueous system is the ion species. In order to understand the main aspects of the model proposed by [23] the acid/base model for oxygenated salts is presented in a general way. Corrosion products dissolved in the thermal storage system need to be known and identified since they may affect the thermophysical properties of the molten salt. Moreover, these corrosion products can cause degradation of the storage system material, and therefore it is necessary to perform characterization of novel materials through electrochemical testing and thus understand the mechanisms of corrosion. In this study, corrosion behaviour was monitored by electrochemical impedance spectroscopy (EIS) and linear polarization resistance (LPR) tests on different materials, such as AISI 304 and AISI 430 stainless steels, nickel-based alloy HR-224, and a new VM12 steel with 12% chromium content. The corrosion resistance in solar salt (60 wt.% NaNO\({}_{3}\)-40 wt.% KNO\({}_{3}\)) as in the ternary mixture (30 wt.% LiNO\({}_{3}\)-13 wt.% NaNO\({}_{3}\)-57 wt.% KNO\({}_{3}\)) at 550 \({}^{\circ}\)C were compared at different times. Also, the same tests were performed on the new VM12 material with the ternary mixture with two different exposure times. EIS tests require appropriate models that allow adjusting the impedance spectrum in such a way that they can be compared with the different corrosion models existing in the literature on molten salts.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 3 Improved YOLOX Detection Algorithm
### Feature Pyramid Improvement  
The label-scale information of the nighttime vehicle/pedestrian dataset was scattered and visualized as shown in Figure 2. Among them, the width and height of the target size after normalization are mainly concentrated in the position less than 0.07, i.e., the width of the detected targets is mostly less than 89.6 (1280 \(\times\) 0.07) and the height is mostly less than 50.4 (720 \(\times\) 0.07). It is obvious that most of the detected targets are comparatively small. Therefore, it is necessary to ensure the detection accuracy of the algorithm for small, dense targets.  
In the whole feature extraction and fusion process of the YOLOX algorithm, the input image is down-sampled five times in the backbone network, and feature maps sized 320 \(\times\) 320, 160 \(\times\) 160, 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20 are generated in sequence, and the last three feature maps are input into PAFPN for feature fusion processing. Among them, large-scale feature maps pay more attention to detailed information and retain a large amount of small target information, while small-scale feature maps pay more attention to semantic information. Although small-scale feature maps are better for understanding complex objects, the loss of information on small targets may be more serious owing to the smaller  
Figure 2: Distribution of sample label scale.  
resolution. Therefore, the model was improved by adding a new branch with a feature scale of 160 \(\times\) 160 (where the depth of the Cross Pseudo Supervision (CPS) component is 1) into PAFPN, and the feature fusion process is performed on four different scales of feature maps to obtain more shallow image feature information while retaining the deep image semantic information. The specific improved PAFPN network structure is shown in the fusion detection branch module in the Neck part of Figure 1.
## 4 Practice: Instance-Based Lower Bounds
### Generation of Witness Triples
#### 4.2.3 Efficient Conflict Check  
Depending on the method for generating witness triple candidates, an efficient check for conflict is necessary. Our method A can already exclude many conflict tests, yet method B essentially has to check every candidate pair \((x,c,v)\) and \((x,c^{\prime},v^{\prime})\). The following consideration allows for a fast evaluation of the test which does not require explicit path representations:  
\[(x,c,v)\text{ and }(x,c^{\prime},v^{\prime})\text{ are in conflict} \tag{1}\] \[\Leftrightarrow \pi(c,v)\cap\pi(c^{\prime},v^{\prime})\
eq\emptyset\] (2) \[\Leftrightarrow \exists k:k\in\pi(x,v)\wedge k\in\pi(x,v^{\prime})\wedge c,c^{ \prime}\in\pi(x,k)\] (3) \[\Leftrightarrow c\in\pi(x,v^{\prime})\wedge c^{\prime}\in\pi(x,v)\] (4) \[\Leftrightarrow d(x,c)+d(c,v^{\prime})=d(x,v^{\prime})\wedge d(x,c^{\prime})+d(c^{ \prime},v)=d(x,v) \tag{5}\]  
From (1) to (2) is basically Definition 1 for the case \(x=x^{\prime}\). If additionally \(v=v^{\prime}\), this is also implicitly covered. Furthermore, keep in mind that shortest paths are unambiguous. (2) is true if and only if there exists a common prefix \(\pi(x,k)\) of \(\pi(x,v)\) and \(\pi(x,v^{\prime})\), where \(c\) and \(c^{\prime}\) are part of this prefix (3). In turn, this is equivalent that the \(c\)'s are part of the shortest paths of each other's triple (4), which is only the case if the distance equations of (5) are fulfilled. The check for triples with coinciding targets proceed analogously. In practice, the distance lookups can be performed very quickly using a distance oracle like hub labels.  
```
1:procedurefindWitnesses(\(G\))
2:\(\mathit{SPTs}\leftarrow\textsc{ComputeShortestPathTrees}(G)\)
3:\(W\leftarrow\emptyset\)
4:\(\ell\gets 1\)
5:while\(\ell\leq\mathit{Diameter}(G)\)do
6:\(\mathit{WC}\leftarrow\mathit{collectCandidatesOfLength}(\mathit{SPTs},\ell)\)
7:while\(\mathit{WC}\
eq\emptyset\)do
8:\((x,c,v)\gets\mathit{WC}[0]\)
9:\(W\gets W\cup(x,c,v)\)
10:\(\mathit{WC}\gets\mathit{WC}\backslash(x,c,v)\)
11:\(\textsc{pruneConlictingTriples}(\mathit{WC},(x,c,v))\)
12:endwhile
13:\(\ell\gets 2\ell+1\)
14:endwhile
15:return\(W\)
16:endprocedure
```  
**Algorithm 1** The algorithm to find a set of non-conflicting witness triples.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 4 Evaluation
### Relation of Metric for Energy Efficiency  
In this section, before verification of the efficiency of the proposed method, we present the importance of the relationship between metrics. In addition, we show membership function (MF) is used for the decision of placement. We can set the rule through MF and then make a decision for placement based on the rule.
## 3 Results and Discussion
### Combustion Process Analysis
#### 3.3.2 Knock Combustion Process Analysis  
In this simulation the engine operates with a full load, which means the amount of injection was a maximum of \(5.16155\times 10^{-5}\) kg. Figure 9 also shows the comparison of the in-cylinder pressure during knock combustion and normal combustion. It can be observed from the Figure 9 that the ascending rate of in-cylinder pressure during knock combustion was greater than that of normal combustion. The present simulation could capture the correct amplitude of pressure oscillations generated by a knock, due to the use of a compressible code and an accurate auto-ignition heat release rate model shown in Figure 11. It can be seen from Figure 11 that there were two obvious pressure peaks. This phenomenon was due to the fact that the thermodynamic state of the end-mixture reached a state of auto-ignition as illustrated in literature [32; 33]. Under the compression and heating of the flame front, the instantaneous heat release created a tremendous pressure wave, which led to a sharp rise in cylinder pressure. Auto-ignition mainly started in the half of the cylinder located under the exhaust valves, which was illustrated by their higher temperature compared to intake ones. Such a location was observed experimentally in the literature [34], and was explained by the larger residence time of fresh gases there (the flame tended to burn fresh gases more rapidly on the exhaust side due to the tumble motion) [10]. Figures 12 and 13 show the process of the flame propagation and temperature cloud during the knock combustion, respectively. At 14 \({}^{\circ}\)CA, an auto-ignition point  
Figure 10: Flame propagation of normal combustion (Intake port on the right).  
Figure 9: In-cylinder pressure of normal combustion and knock combustion.  
is formed near the exhaust valve, and the flame gradually spreads around. Due to the limitation of the computer hardware, the output frequency of the selected 3D results is a result output per 5 \({}^{\circ}\)CA. Combined with the related findings, it can be concluded that the propagating detonation wave derived from multiple hot-spots auto-ignition indicates vital effects on knock intensity of in-cylinder pressure at 24 \({}^{\circ}\)CA, which is consistent with the research findings [11; 12].
# Towards a Glass New World: The Role of Ion-Exchange in Modern Technology
## 2 Glass Strengthening: The Ion-Exchange Contribution
### Ion-Exchanged Noble Metal Nanoparticles in Glasses for Sers Applications  
The interaction between light and noble metal nanoparticles (NP) at the material interface is the key point for the employment of one of the best performing optical investigation methods: the surface-enhanced Raman scattering (SERS) technique. These metallic nanostructures work as \"hot spots\" for the electric field of an interacting lightwave by means of the localised surface plasmon resonance (LSPR) effect [150]. When a molecule is close to one of these \"hot spot\", its Raman signal increases by several order of magnitude under the action of the enhanced field. This confers to the sensors/biosensors based on this optical technique ultra-sensitive detection limits, combined with the structural information content typical of the Raman spectroscopy [151].  
For the development of high-performance SERS devices, the optimisation of the substrates is mandatory. Ideal SERS platforms should ensure high efficiency (in terms of signal enhancement), reproducibility in response, reusability, long-term stability for the metal nanoparticles, low cost, and simplicity of the fabrication method [97,152]. Furthermore, considering that silver NPs--which offer the best SERS activity among all noble metal nanostructures--are prone to oxidation and sulfidation phenomena, it is easy to understand how obtaining SERS substrates adhering to ideal requirements is still a challenging task.  
Among all the materials and manufacturing methods for SERS substrates [153], ion-exchange in glass may represent an effective route towards the realisation of performing and reliable SERS platforms thanks to the following features: (i) efficient SERS activity of these substrates (from \(10^{5}\) to \(10^{7}\) signal enhancement); (ii) intrinsically low material and processing costs; (iii) adequate sample reproducibility by opportunely tailoring the process and post-process parameters; (iv) long-term stability for the metal nanoparticles by their embedding within the glass matrix (typically, tens/hundreds nanometres under the glass surface) during a suitable post-process treatment; (v) actual sample reusability by partial exposure of noble metal nanostructures at the surface and their further refresh through suitable chemical and/or thermal steps [105,154].
## 3 Results and Discussion
### Alginate Films
The region of Raman spectrum for alginate films covering the C-C stretching range revealed a large band centered at 1098 cm\({}^{-1}\) characteristic of conformational disordered C-C stretching with _gauche_ defects with a peak at 1068 cm\({}^{-1}\) representing _trans_ conformational \begin{table}
\begin{tabular}{c c c c c c} \hline
**Alginate 1\%** & **Alginate 2\%** & **Alginate 3\%** & **Alginate 4\%** & **Alginate 5\%** & **Assignments** \\ \hline
1162 * & 1165 * & 1164 * & 1162 * & 1160 * & \(\
u_{\text{s}}\)(C-C)\({}_{\text{T}}\), \(\
u\)(C-O)\({}_{\text{G}}\) \\
1270 * & 1291 * & 1271 * & 1291 * & 1275 * & \(\
u_{\text{s}}\)(C-C)\({}_{\text{G}}\) \\
1307 & 1317 & 1307 & 1315 & 1310 & 8(O-H) in plane \\
1344 & 1341 & 1344 & 1340 & 1340 & 8(O-H) in plane \\
1361 * & 1361 * & 1364 * & 1360 * & 1360 * & 8(O-H) in plane \\
1414 & 1414 & 1413 & 1414 & 1412 & \(\
u_{\text{s}}\)(COO\({}^{-}\)), H- bond \\  & & & & 1500 & hydrogen bond \\  & & & & 1570 & hydrogen bond \\
1611 & 1612 & 1613 & 1612 & & \(\
u_{\text{ss}}\)(COO\({}^{-}\)) \\
2935 & 2935 & 2935 & 2935 & 2934 & \(\
u\)(CH-OH) \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: Area ratios of four vibrational modes: \(\tau=Area_{1414}/Area_{1340}\) and \(\mu=Area_{1098}/Area_{1068}\). C-C stretching. In liquid phase, all C-C stretching are in _trans_ conformational. With the liquid-solid transition, the characteristic bands associated with the _trans_ and _gauche_ C-C stretching appeared. Thus, _gauche/trans_ area ratio (\(\mu=Area_{1088}/Area_{1088}\)) provided a measure of alginate intermolecular mobility [31]. In Figure 2, we present the evolution of this ratio according to the mass proportion of alginate in the water. From this figure, we can note that \(\mu\) remains more or less equal to 1.5 up to 3% of alginate in the water before a gradual increase to 4% and 5% to reach a value close to 5.4. Thus, it would seem that the stiffness of the biofilms increases with the mass proportion of alginate.
## 3 Results and Discussion
### Hydraulic Transmission and HMT Input-Coupled Transmission Characterization  
In order to characterize both the hydraulic transmission and input-coupled HMT, different simulations were performed with different load levels comparing the global efficiencies. Figures 15 and 16 show the global efficiency \(\eta=\frac{P_{\text{out}}}{P_{\text{in}}}\) for the hydraulic transmission and input-coupled HMT, respectively, in the function of vehicle linear velocity with different load levels. The test consists of an acceleration ramp from zero to the maximum vehicle speed.  
The comparison between the hydraulic and input-coupled HMT global efficiency shows a better efficiency of the input-coupled HMT with respect to the hydraulic transmission. This result is related to the hydraulic losses, which are lower in the input-coupled HMT because of the presence of the mechanical branch for the power split transmission. In fact, as shown in Figure 16, the higher efficiency value is achieved at the full mechanical point (FMP), at which the transmission work is purely mechanical. However, in the hydraulic transmission, the power transmission is only related to the hydraulic circuit and hence the hydraulic losses are higher.  
Figure 14: Mission test. (**a**) Instrumentation used. It consists of a control unit, GPS and a tablet for monitoring. (**b**) Speed profile.  
Moreover, the results show, for both transmissions, that the power partialization leads to a decrease in the global efficiency with the increase in speed.  
Hence, if the application justifies the increased costs of the input-coupled HMT related to a more complex system in terms of planetary gear and control system, the input-coupled HMT turns out to be the most advantageous.