esa-sceva/satcom-chunk-collection huggingface dataset:
# Ni-Al Alloys as Alternative EUV Mask Absorber
## 4 Verification of Optical Constants
Figure 12b reveals a shift of Al L2 absorption edge from theoretical position at 17 nm to 16.6 nm wavelength. Close to the absorption edge, the refractive coefficient \(n\) of Al varies strongly with the wavelength, therefore the oxidation-induced absorption edge shift significantly impacts the value of \(n\) for the Ni\({}_{x}\)Al\({}_{y}\) alloys at 13.5 nm wavelength. The positive \(n\)-over-wavelength gradient moves the value of \(n\) closer to unity after shifting the absorption edge. The fitted optical constants of the Al\({}_{2}\)O\({}_{3}\) native oxide are similar across the Ni\({}_{x}\)Al\({}_{y}\) samples, suggesting there is almost no variation in native oxide composition and density. From Figure 12c, we can observe the Al L2 absorption peak has shifted further towards 15.5 nm, as there are more Al-O bonds in the surface oxide compared to bulk. Figure 11 shows a much smaller \(\kappa\) for the native surface oxide as measured by EUVR, compared to literature values for crystalline Al\({}_{2}\)O\({}_{3}\). The \(\kappa\) reduction is caused by a partial oxidation of the surface layer, resulting in a lower Al\({}_{2}\)O\({}_{3}\) density as compared to crystalline Al\({}_{2}\)O\({}_{3}\). The partial oxidation is corroborated by XPS, and TEM; the reduced density by XRR measurements (cf. Figures 8-10). A lower density would also result in larger \(n\), yet its value is comparable between EUVR measurement and literature. This can be explained by a combination of the Al L2 absorption edge shift, a slightly negative \(n\)-over-wavelength gradient of Al\({}_{2}\)O\({}_{3}\), and atypical orbital-atom interactions. Up to this point, we have presented experimental characterization results, leading up to the determination of realistic Ni\({}_{x}\)Al\({}_{y}\) optical constants with EUVR. In the next section, the measured optical constants will be incorporated in an absorber mask model to simulate and predict the impact on imaging of the Ni\({}_{x}\)Al\({}_{y}\) alloys.
# Delving Deep into Reverse Engineering of UEFI Firmwares via Human Interface Infrastructure
## 2 Background
### Static Code Analysis  
Static Code Analysis refers to a code analysis technique where the program code is scanned using lexical analysis, syntax analysis, control flow analysis, data flow analysis, and other technologies without executing the code. This is performed to verify whether the code meets specifications, security, reliability, maintainability, and other metrics.  
Programmers often make minor mistakes, such as missing a semicolon here or adding an extra parenthesis there. Most of the time, these oversights do not result in serious consequences: the compiler flags these errors, programmers correct the code, and the development process continues. However, this rapid feedback and response loop typically does not apply to most security vulnerabilities, which might lurk undetected for extended periods. As mentioned earlier, the longer software defects go unnoticed, the higher the cost to remedy them. The objective of static analysis is to inspect a program's text without attempting to execute it. Theoretically, static analysis tools can examine either the program's source code or its compiled form, with both offering similar advantages, although decoding the latter might be somewhat challenging [13].  
Manual review or auditing is a form of static analysis and is highly time-consuming. For it to be effectively executed, human code auditors first need to know which types of mistakes they should be looking for, before rigorously examining the code. Reviewing the application code can be performed at any stage of software development, but it is most effective when undergone early, as detecting and rectifying security vulnerabilities and quality deficiencies might be high in cost and risk later in the software development process. When these errors slip into the market and are discovered by customers, they can impact profits and tarnish a company's reputation. Reviews encompass not just the code, but all documentation, requirements, and designs generated by developers, as every step  
Figure 2: UEFI User Interface Components.  
in software development can harbor mistakes. Essentially, human-performed static code analysis can be categorized into two main types: self-review and third-party review, both of which are closely related to individual software processes and team software processes [14].  
Static analysis tools have advantages over manual reviews because they operate faster, meaning they can assess programs more frequently. They encapsulate some of the knowledge required to perform such code analysis in a way that the tool operator does not need the same expertise level as a human auditor. Just as programmers can rely on compilers to consistently handle the nuances of language syntax, operators of good static analysis tools can effectively use the tool without delving into the finer details of more elusive errors.
## 2 Materials and Methods
### Bonding of the Brackets  
In groups A and C, where the 3 step etch and rinse adhesive was used, the buccal surface of the teeth was etched with 37% phosphoric acid etching gel (Acido gel, Maquira, Portugal) for 30 s, rinsed, and dried with oil-free compressed air. Then, XT primer was applied and light cured for 5 s. After that, brackets were bonded to the center of the prepared buccal surfaces using Transbond XT adhesive. Excess material was then removed from around the brackets with a sharp explorer. Light curing using an LED light-curing unit (Slimax-C Plus LED Curing Light System, Beyes Dental Canada Inc., Toronto, ON,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Brand Name** & **Composition** & **Manufacturer** \\ \hline \multirow{2}{*}{Ceramic brackets} & Clarity(tm) Advanced & \multirow{2}{*}{Polycrystalline alumina} & 3M Unitek, Monrovia, \\  & ceramic brackets & & CA, USA \\ \hline \multirow{2}{*}{Metal brackets} & Victory Series(tm) Low & \multirow{2}{*}{Stainless Steel} & 3M Unitek, Monrovia, \\  & Profile & & CA, USA \\ \hline \multirow{2}{*}{Acid etching gel} & Acido Gel 37\% & \multirow{2}{*}{37\% phosphoric acid} & Maquira, Setubal, \\  & & & Portugal \\ \hline \multirow{6}{*}{Adhesive system} & Transbond XT & \multirow{2}{*}{\begin{tabular}{c} * TEGDMA, Bisphenol A diglycidyl ether \\ dimethacrylate, Hydroquinone, Camphorquinone, \\ Triphenylantimony, \\ 4-(Dimethylamino)-Benzene ethanol \\ \end{tabular} } & 3M Unitek, Monrovia, \\  & & & CA, USA \\ \cline{1-1}  & Transbond XT & \multirow{2}{*}{\begin{tabular}{c} Bisphenol A diglycidyl ether \\ dimethacrylate, Bisphenol A \\ Bis(2-hydroxyethyl ether) dimethacrylate, Silane \\ treated quartz, Silane treated silica \\ \end{tabular} } & 3M Unitek, Monrovia, \\  & & & CA, USA \\ \hline \multirow{6}{*}{Self-adhesive resin adhesive} & \multirow{2}{*}{U-Cem(tm)} & \multirow{2}{*}{\
\begin{tabular}{c} _Base_: methacrylate monomers containing acid groups, \\ methacrylate monomers, silanated fillers, initiator \\ components, stabilizer \\ _Catalyst_: methacrylate monomer, alkaline fillers, \\ silanated fillers, initiator components \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Vericom, Chuncheon-si, \\ Korea \\ \end{tabular} } \\  & & & Korea \\ \hline \hline \end{tabular}  
* TEGDMA (Trietihylene glycol dimethacrylate).  
\end{table}
Table 1: Bracket types and adhesive systems used in the study.  
Canada) was performed for 5 s at each surface (mesial, distal, incisal and cervical surfaces) of the metal brackets (in group A) and for 5 s through the ceramic brackets (in group C).  
In groups B and D, where the 2 step self-etch adhesive was used, the brackets were bonded using U-Cem adhesive and light cured for 20 s for both metal and ceramic brackets. For all groups, the intensity of the light cure was regularly checked with the built-in radiometer at >1200 mW/cm\({}^{2}\). All materials were applied according to the manufacturers' instructions.
## 2 Proposed Method
### RUL Prediction
#### 2.3.1 ELM-Based Transfer Learning  
Due to its fast training speed and high prediction accuracy, the ELM network has been widely used in the field of remaining life prediction of lithium batteries. However, due to the randomness of the initial values of ELM network parameters, the predictive performance of ELM networks trained with the same degradation samples of lithium batteries is often different. Therefore, we propose a transfer learning model based on an ELM network to overcome the shortcomings of traditional ELM networks and further improve the prediction accuracy of the proposed model.  
As shown in Figure 1, after we obtained the capacity degradation data of historical batteries, the ELM model was repeatedly trained, and m-trained ELM neural networks were obtained. We define these trained ELM neural networks as the ELM swarm (ELMS). Assuming that the number of input nodes of each ELM in the ELMS is \(n\), the first \(n\) field capacity degradation data of the target battery are selected and input into each trained ELM, and the multi-step prediction method shown in the figure is adopted to obtain the capacity prediction sequence of each trained ELM network. Calculate the mean absolute error (MAE) between the capacity prediction sequence for each ELM network and the actual capacity degradation data for the target cell:  
\[MAE_{i}=\frac{\sum_{j=1}^{k}\left|C_{i,j}^{predict}-C_{j}\right|}{k} \tag{12}\]  
where \(MAE_{i}(i=1,2,\ldots,m)\) represents the prediction result of the \(i^{th}\) ELM network; \(C_{i,j}^{predict}\) represents the \(i^{th}\) capacity prediction value obtained by the \(i^{th}\) trained ELM network; \(C_{j}\) represents the \(j^{th}\) actual capacity degradation data of the target cell; and k represents the amount of field capacity degradation data for the target battery. The smaller the MAE, the better the ELM can fit the existing capacity data of the target battery.  
Based on the MAE of all trained ELM networks, the transfer weight, \(weight_{i}(i=1,2,\ldots m)\), corresponding to the \(i^{th}\) ELM network is obtained using the following formula:  
\[weight_{i}=\frac{MAE^{max}-MAE_{i}}{MAE^{max}-MAE^{min}} \tag{13}\]  
where \(MAE^{max}\) represents the maximum value of MAE and \(MAE^{min}\) represents the minimum value of MAE.  
Then, the RUL predicted value of the target battery by the \(i^{th}\) trained ELM network \(RUL_{i}(i=1,2,\ldots m)\) is obtained by a multi-step prediction method. Based on the transfer weights obtained above, the final RUL predicted value \(RUL_{final}\) is calculated:  
\[RUL_{final}=\sum_{i=1}^{m}weight_{i}\times RUL_{i} \tag{14}\]
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
## Proposed Topology  
### Structure  
The circuit of CQBC and TCQBC are shown in Figure 1a,b. It can be seen that both these converters use two inductors, but their gain is less than the proposed topology. The switch is also directly connected across the load, which makes the voltage stress across the switch equal to output voltage V\({}_{\text{O}}\). The proposed converter structure is presented in Figure 1c with a voltage multiplier cell, which increases the output voltage gain. Two inductors (L\({}_{1}\) and L\({}_{2}\)), four diodes (D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\)), four capacitors (C\({}_{1}\), C\({}_{2}\), C\({}_{3}\), and C\({}_{4}\)), and two switches (S\({}_{1}\) and S\({}_{2}\)) are used to realize the proposed converter. This VMC can also be placed before diode D\({}_{1}\), but the voltage gain would not be that high, as in the converter case presented in Figure 1c. One more configuration is also possible in which the VMC is placed at two points before diode D\({}_{1}\) and before diode D\({}_{4}\). This increases the voltage gain further but makes the circuit more complex and less efficient.  
The VMC integration increases the output voltage and reduces the voltage stress on the diodes and switches with respect to the output voltage. This VMC can also be placed before diode D\({}_{1}\), but the voltage gain would not be that high, as in the converter case presented in Figure 1c. One more configuration is also possible in which the VMC is placed at two points before diode D\({}_{1}\) and before diode D\({}_{4}\). This increases the voltage gain further but makes the circuit more complex and less efficient.  
Figure 1: \(Cont\).
## 4 Discussion
### Uncertainty of Evaluation of the TCF and the Related CV and CA  
The TCF caused by 10 main types of agricultural inputs for agricultural production and the related CV and CA in Qinghai province on the Tibetan Plateau over 1995-2016 were evaluated. However, the authors should list some sources of uncertainty for evaluation of the TCF. Due to lack of relevant data, the construction, maintenance, and enhancement of agriculture infrastructure were not regarded as emission sources, which affected the evaluation of annual absolute TCF in the study area. Furthermore, the regional difference of emission coefficients for 10 main types of agrochemicals and agricultural energy also affected the TCF evaluation. Uncertainty of evaluation of CA could be unavoidable, because the 10 main types of agrochemicals and agricultural energy were mainly used in cropland and orchard. Thus, the TCF per unit of agricultural land area could not reflect the detailed CA in cropland, orchard, forest, and grassland. Uncertainty of evaluation of CV could also be unavoidable, because some new agricultural varieties were sometimes used, which affected the index for agricultural output value. However, the mentioned sources of uncertainty could not interfere with the general changing trend of CF caused by each main agricultural input in Qinghai province over 1995-2016. Referring to the study by [PERSON] et al. [52], more emission sources and detailed emission coefficients with regional characteristics should be taken into consideration to study the carbon footprint in future works. Located in the eastern agricultural area of Qinghai province, the Hehuang valley should be taken as a key study area to further study the carbon footprint of agricultural sector and its affecting factors. The valley has a land area accounting for about 26.3% of the total area of Qinghai province but a cropland area accounting for 60.8% of the province's total cropland area [53].
## 3 Results and Discussion
### Numerical Results
#### 3.3.2 Parametric Influences on Thermal Management
The result indicates that when the melting point is fixed at 37 \({}^{\circ}\)C (pure PCM), the melting curve is nearly isothermal showing a temperature control within the narrow range. When PCM melting range becomes wider (37-43 \({}^{\circ}\)C, 37-48 \({}^{\circ}\)C and 37-53 \({}^{\circ}\)C), the melting curve deviates further from isothermal and a large difference in melting start and completion is observed which results in less stringent temperature control. Additionally, with wider melting ranges, the temperature rises and time to complete melting increases which are detrimental for temperature control applications. Figure 13: The effects of increasing the thermal conductivity of PCM (Paraffin) on the temperature drop of HS and time for the material to melt completely. Figure 14b shows that as the melting start is shifted upward, the temperature rises and the phase change boundaries become less visible. It indicates that for the studied power input and PCM, the melting point ranges of 37-43 \({}^{\circ}\)C and 43-49 \({}^{\circ}\)C are optimal while the upper melting points achieve less stringent temperature control. For phase change temperature for various power inputs and heat sink designs, however, need more detailed simulation investigations and will be studied in future research. Conclusively, a pure PCM with sharp melting point and narrow melting range is recommended for strict temperature control applications.
# Dynamic Prediction Model for Progressive Surface Subsidence Based on MMF Time Function
## 2 Models and Methods
### The Existing Time Functions and Their Flaws
#### 2.2.2 Segmented Knothe Time Function  
Knothe time function does not correspond to the law of dynamic subsidence [12; 14]. In 2003, [PERSON] and [PERSON] proposed a segmented Knothe time function which assumes that the maximum subsidence velocity occurs at half the total time and that the subsidence at that moment is half the total subsidence [23], as shown in Equation (5).  
\[W(t)=\left\{\begin{array}{ll}0.5W_{0}[e^{-c(\tau-t)}-e^{-c\tau}],\;0<t\leq \tau\\ 0.5W_{0}[2-e^{-c(t-\tau)}-e^{-c\tau}],\;\tau<t<T\end{array}\right. \tag{5}\]  
where \(\tau\) is the moment of maximum subsidence velocity, \(T\) is the total subsidence time.  
The expression of subsidence velocity \(v(t)\) is shown in Equation (6).  
\[v(t)=\left\{\begin{array}{ll}0.5W_{0}[ce^{-c(\tau-t)}],0<t\leq\tau\\ 0.5W_{0}[ce^{-c(t-\tau)}],\tau<t<T\end{array}\right. \tag{6}\]  
\(W_{0}\) is 1500 mm, the influence coefficient c is 0.1, the total subsidence time \(T\) is 150 days and \(\tau\) is 35 days (\(\tau\approx T/4\)) [25]. The subsidence, velocity and acceleration are plotted in Figure 4a,b, respectively.  
Figure 3: (**a**) The subsidence of Knothe time function; (**b**) The velocity and acceleration of Knothe time function.  
According to Figure 4a,b, the subsidence has the shape of \"S\" and can improve the prediction accuracy. While the process of acceleration and deceleration of subsidence is symmetrical, it does not reflect the feature of rapidity during the active phase and the feature of long-term during the weakening phase.
# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
###### Abstract  
With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, a variety of location privacy-preserving schemes have been recently proposed. Among these schemes, the dummy-based location privacy-preserving (DLP) scheme is a widely used approach to achieve location privacy for mobile users. However, the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices. Moreover, the DLP scheme is inadequate to resist against an adversary with side information. Thus, how to effectively select a dummy location is still a challenge. In this paper, we propose a novel lightweight dummy-based location privacy-preserving scheme, named the enhanced dummy-based location privacy-preserving(Enhanced-DLP) to address this challenge by considering both computational costs and side information. Specifically, the Enhanced-DLP adopts an improved greedy scheme to efficiently select dummy locations to form a k-anonymous set. A thorough security analysis demonstrated that our proposed Enhanced-DLP can protect user privacy against attacks. We performed a series of experiments to verify the effectiveness of our Enhanced-DLP. Compared with the existing scheme, the Enhanced-DLP can obtain lower computational costs for the selection of a dummy location and it can resist side information attacks. The experimental results illustrate that the Enhanced-DLP scheme can effectively be applied to protect the user's location privacy in IoT applications and services.  
Internet of Things; location-based service; dummy location selection; location privacy +
Footnote †: journal: Information Information  
1
## 3 Results and Discussion
### Identification of Volatile Compounds in the Formulations
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Compound**} & \multicolumn{3}{c}{**Area of the Peak (\%)**} & \multicolumn{3}{c}{**Odor**} & \multicolumn{3}{c}{**Odor Threshold**} & \multicolumn{3}{c}{**Odor Threshold**} \\ \cline{3-10}  & **LLE** & **LELE-ME** & **Unl-ME** & **Cal RI** & **LiRI R1** & **Type/Description** & **(ppb)2** & **in Tomato Freits (ppb)3** & **Reference** \\ \hline \multirow{6}{*}{**C-Means**} & 6-Methyl-5-Burepent-2-one & 34.19 & 5.6 & - & 990 & 986 & floral, green & 50 & 50 & [35] \\  & D-Limmene & 2.9 & 1.64 & 0.92 & 1028 & 1028 & Lemon, Orange & 10 & 10 & [36] \\  & 2-Isobutylthiazole & 3.14 & 0.66 & — & 1033 & 1020 & Tomato leafy, green, musty & 2-3.5 & 3.5 & [19, 36] \\  & 2-5-Dimethyldace & 2.11 & 1.78 & — & 1088 & 1086 & — & — & — \\  & Dodecane & 0.2 & 17.06 & 25.76 & 1200 & 1200 & Alkane & — & — & [36] \\  & Clitronally formate & 1.12 & 0.24 & — & 1276 & 1275 & Finite, floral, rose-light odor & — & — & — \\  & Not identified & 2.14 & 2.08 & — & — & — & — & — & — \\  & Not identified & 0.15 & 0.09 & — & — & — & — & — & — \\  & Not identified & 0.12 & 0.27 & — & — & — & — & — & — \\  & Not identified & 1.88 & 2.17 & — & — & — & — & — & — \\  & Not identified & 0.37 & 0.47 & — & — & — & — & — & — \\  & Not identified & 0.25 & 0.26 & — & — & — & — & — & — \\  & Not identified & 0.37 & 0.31 & — & — & — & — & — \\  & Not identified & 0.26 & 0.36 & — & — & — & — & — \\  & 1-Dimethyl-4-(1-) & 0.17 & 0.13 & — & 1478 & 1485 & — & — & — \\  & Not identified & 0.33 & 0.3 & — & — & — & — & — \\  & Not identified & 0.04 & 0.5 & — & — & — & — & \\  & 1-Diminence & 1.27 & 0.21 & 0.06 & 1202 & 1196 & Lemon, Orange & 10 & 10 & [36] \\  & Not identified & 2.39 & 0.3 & — & — & — & — & \\  & 2-Isobutylthiazole & 4.9 & 0.20 & — & 1410 & 1406 & Tomato leafy, green, musty & 2-3.5 & 3.5 & [19, 36] \\  & Not identified & 1.2 & 0.13 & — & 1740 & 1741 & Citrus, Lemon & 32 & — [35] \\  & **-C-Trial** & 2.02 & 0.06 & — & 1740 & 1741 & Citrus, Lemon & 32 & — [35] \\ \hline \hline \end{tabular}  
* Areas of the peak expressed as relative percentage of the total area of all integrated peaks in the chromatography: Cal RI– Calculated Retention Index: 1A RI–Retention Index from literature 1  
\end{table} 
Table 1: Characterization parameters of volatile compounds in LLE, LLE-ME and Unl-ME identified through CC-MS analysis.
# Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
## 1 Introduction
In earlier works [7; 8], we presented a model for optimizing the movement of AVs at the intersection, developed a software simulator, and conducted an empirical study to verify the effectiveness of the model. In the present work, as a method to improve the safety of the system, we introduce a model, based on reputation and trust, to detect AVs transmitting incorrect data and their subsequent exclusion from the group interaction. We assume that this approach, combined with conventional security methods, can increase safety by detecting and blocking AVs transmitting incorrect data to other traffic participants. We also validated our model using our custom software simulator and implemented it on a real physical stand with models of AVs at the intersection. The paper is organized as follows. Section 2 describes what vehicular wireless networks are, their structure, and on which protocols they are based on, as well as their main security challenges. This section also provides an overview of solutions and approaches that use the mechanisms based on reputation and trust to solve the problem of incorrect data and to increase system security and safety. A formal description of our approach is given in Section 3. The description of the simulation setup, conditions, and assumptions, both for software and physical experiments, as well as the results obtained, are given in Section 4. Section 5 discusses the results obtained. Conclusions on the research conducted and plans for further development of the approach are given in Section 6.