esa-sceva/satcom-chunk-collection huggingface dataset:
# SoK: Delegated Security in the Internet of Things
## 2 Application Domains of Security Delegation
### Authentication Delegation  
Authentication protocols authenticate subjects and distribute secret keys for future communication. A total of 21 papers addressing the delegation of authentication were identified during our literature search (see Table A1).  
In this application, the RC system wants to authenticate to a third party. It delegates this process to the delegate who performs authentication on behalf of the RC system and returns the result. Consequently, the RC system obtains credentials to access the third party directly (see Figure 2b).  
Example  
A delegation-based DTLS framework for Cloud-based IoT Services, called D2 TLS, has been proposed by [PERSON] et al. D2 TLS allows for establishing secure communication while ensuring secure storage of private keys [22]. Unlike DTLS, D2 TLS contains a Security Agent (SA), which does not have access to IoT devices' private keys in order to avoid key escrow problems. The presented solution contains three entities (see Figure 4): a client (RC system), the SA (elegate), and a cloud server (third party). First, the client sends a delegation request to the SA. Then, the SA performs a DTLS handshake with the server on behalf of the client in two steps. Finally, the SA sends the client credentials to access the resources of the server. To evaluate the proposed framework, the authors compare D2 TLS with DTLS with respect to delays, energy consumption, code size, memory requirements, and session overhead. In terms of delay and energy consumption, the D2 TLS shows significantly better results.
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 2 Methodology
### Subsystem Analysis Method  
#### 2.1.1 Gas Path Analysis Method  
The gas path system evaluates the performance of each engine component by utilizing a combination of mechanism-based and data-driven models. Sensor measurements, including temperature, pressure, and rotational speed, are used as inputs for feature extraction. In this study, the gas path analysis (GPA) method is employed to characterize the gas path behavior. The structure of the model-based GPA approach is illustrated in Figure 2. This simulation model estimates the thermodynamic parameters by incorporating both boundary conditions and component degradation states. Mathematically, the model is formulated as follows:  
\[y=f(x,D) \tag{1}\]  
where \(x\) denotes the boundary condition inputs. \(D\) represents the fault features of gas path. \(y\) is the simulated thermodynamic outputs. The function \(f(\cdot)\) is a nonlinear steady-state simulation model derived from energy and mass balance equations of the engine. It is solved iteratively under given inputs \(x\) and \(D\) to match measured outputs, enabling engine performance evaluations and degradation diagnoses.  
The GPA method evaluates five critical engine components--namely, the fan, high-pressure compressor (HPC), low-pressure compressor (LPC), high-pressure turbine (HPT), and low-pressure turbine (LPT). Based on these components, ten gas path fault features are extracted to quantify the degradation level induced by gas path faults. The gas path fault features \(D=\{DEF,\)\(DGF,\)\(DEHC,\)\(DGHC,\)\(DELC,\)\(DGLC,\)\(DEHT,\)\(DGHT,\)\(DELT,\)\(DGLT\}\) are summarized in Table 1. The boundary conditions \(x=\{pt_{1},Tt_{1},W_{t},n_{1},\)\(n_{2}\}\) include the air inlet total pressure \(pt_{1}\), air inlet total temperature \(Tt_{1}\), fuel flow rate \(W_{t}\), low-pressure rotor speed \(n_{1}\), and high-pressure rotor speed \(n_{2}\). These represent the engine's external state, provide sufficient constraints for the simulation model, and are standard measurable inputs in  
Figure 1: Multi-source information fusion diagnosis method.  
practice. The measurement parameters are \(y=\{pt_{13},Tt_{125},pt_{25},T_{125},pt_{3},T_{3},Pt_{45},Tt_{45},Tt_{5}\}\), where \(pt\) denotes total pressure and \(Tt\) is total temperature. The numeric subscripts indicate specific measurement locations along the engine: 13 and 125 correspond to the fan outlet and the fan outlet after mixing, 25 and 3 represent the inlet and outlet of the high-pressure compressor, and 45 and 5 refer to the inlet and outlet of the low-pressure turbine.  
Model-based diagnosis is typically regarded as the inverse process of simulation modeling. It involves comparing simulated thermodynamic parameters with actual measurements under given operating conditions. According to the comparison results, the health condition of the gas turbine is adjusted, and the calculation is iterated repeatedly until the deviation between the simulation results and the measured parameters meets the requirements.
## 3 Results  
Correlation coefficients calculated between the micromorphometric parameters measured on the microCT image tiles and the tiled 3D reconstruction suggested a strong linear relationship between the two with \(p\)-values of 0.777, 0.717, 0.705, 0.666, and 0.687 for BV/TV, BS/TV, Tb.Pf Tb.Th, and Tb.Sp, respectively, and an average \(p\)-value across all measured values of 0.605. This was further supported by consistently small (\(\sim\)1 \(\times\) 10\({}^{-8}\)) p-values, suggesting that the high Spearman correlation was not due to random chance.  
The Bland-Altman and mountain plots in Figure 5 were only constructed for the five previously mentioned parameters.  
The statistical tests for normally distributed differences yielded results that cannot reject this hypothesis. The t-tests for zero bias were significant for percent bone volume. A linear regression was performed on the means and differences, also suggesting zero slope for percent bone volume. Statistical tests and their results are presented in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Normality Test**} & \multicolumn{2}{c}{**One Sample \(t\)-Test**} & \multicolumn{2}{c}{**Linear Regression**} & \multicolumn{2}{c}{**Spearman Correlation**} \\ \hline  & **Statistic** & \(p\) & **Statistic** & \(p\) & **Slope** & **Intercept** & \(p\) & **Correlation** & \(p\) \\ \hline BV/TV & 2.160 & 0.340 & 1.313 & 0.196 & \(-0.033\pm 0.091\) & \(2.691\pm 3.556\) & 0.721 & 0.777 & \(1.389\times 10^{-10}\) \\ BS/TV & 22.399 & 1.000 & \(-11.977\) & \(9.971\times 10^{-16}\) & \(-0.839\pm 0.095\) & \(-0.006\pm 0.009\) & \(2.291\times 10^{-11}\) & 0.717 & \(1.441\times 10^{-8}\) \\ Tb.Pf & 31.800 & 1.000 & \(-3.824\) & \(3.933\times 10^{-4}\) & \(-0.800\pm 0.116\) & \(0.010\pm 0.003\) & \(1.436\times 10^{-8}\) & 0.705 & \(3.187\times 10^{-8}\) \\ Tb.Th & 5.364 & 0.068 & 13.423 & \(1.608\times 10^{-17}\) & \(0.441\pm 0.122\) & \(11.889\pm 9.635\) & \(7.118\times 10^{-4}\) & 0.666 & \(3.272\times 10^{-7}\) \\ Tb.Sp & 3.483 & 0.175 & 11.118 & \(1.257\times 10^{-14}\) & \(-0.247\pm 0.102\) & \(57.799\pm 10.844\) & 0.019 & 0.687 & \(9.561\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical tests were performed to compare five micromorphometric parameters: BV/TV, BS/TV, Tb.Pf, Tb.Th, and Tb.Sp., measured using microCT and 3D histological reconstruction. The normality test assesses the null hypothesis that the differences follow a normal distribution; the obtained results are not significant. The one-sample \(t\)-test evaluates whether the bias is zero. For BV/TV, the test is inconclusive, but for the rest of the parameters, this hypothesis must be discarded. Linear regression was used to test for a zero slope between the differences and means, which only holds for BV/TV. Spearman’s rank correlation coefficient quantifies whether there is a strong linear relationship between the values. This is true for all micromophometric parameters, with small accompanying \(p\)-values indicating statistical significance.
## 2 SnAgCu-Based Solders
### Wettability  
Due to the high melting point, Ni nanoparticles addition can form a frame construction in the solder joint, thereby promoting the spreading of the molten SnAgCu solder [35; 36]. During the wetting process, Ag nanoparticles in the composite flux gathered at the interface between the molten SnAgCu solder and the Cu substrate, reducing the surface energy of the system and greatly increasing the wetting force [37].  
Absorption of Fe\({}_{2}\)O\({}_{3}\) nanoparticles on the surface of the Cu substrate decreased the surface tension of molten SnAgCu-nano Fe\({}_{2}\)O\({}_{3}\) composite solder and thus the wettability of the solder was improved [38]. Figure 4 shows the effect of \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) nanoparticles on the spreading area of Sn-0.3 Ag-0.7 Cu solder; the maximum spreading area of the composite solder is 79 mm\({}^{2}\) with 0.12 wt.% Al\({}_{2}\)O\({}_{3}\) nanoparticles addition [39]. The wettability of the composite solder increases because \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) nanoparticles are easily adsorbed at the surface of Cu substrate and decrease the surface tension of the solder. According to the Young equation, the contact angle increases when the surface tension is reduced, resulting in a bigger spreading area; the schematic diagrams of the wetting and spreading process is shown in Figure 5.  
Appropriate content of GNSs can significantly increase the spreading area of SnAgCu solder; however, when the content exceeds 0.1 wt.%, GNSs tend to agglomerate and float onto the top of the molten solder and, hence, the wettability of the composite solder decreases [40].  
Figure 4: Spreading areas of SnAgCu–nano Al\({}_{2}\)O\({}_{3}\) composite solder [39].
## 3 Results
### Vertical and Humidity Correction on AOD  
To verify that the dry extinction coefficient of AOD after vertical and humidity corrections (\(\sigma_{\text{dry}}\)) is useful for estimating PM\({}_{2.5}\) concentration, the scatter plots of AOD&PM\({}_{2.5}\) from 09:00 to 16:00 (Beijing time) and the \(\sigma_{\text{dry}}\) and PM\({}_{2.5}\) are shown in Figures 4 and 5. According to Figure 4, the correlation coefficient between AOD and PM\({}_{2.5}\) increased by hours, and the correlation coefficient ranged from 0.11 to 0.44 (the lowest correlation r = 0.11 at 9:00 and the highest correlation r = 0.44 at 16:00). The correlation between the \(\sigma_{\text{dry}}\) and PM\({}_{2.5}\) was significantly improved (Figure 5), with the highest correlation r = 0.68 at 15:00. Comparison of Figures 4 and 5 shows that the correlation between the \(\sigma_{\text{dry}}\) and PM\({}_{2.5}\) after the vertical and humidity corrections is significantly improved, which is beneficial to the estimation of PM\({}_{2.5}\) concentration.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.2 Data Standardization  
Data standardization is a crucial preprocessing step in mood-based classification using song lyrics from Spotify. This is because the features extracted from the lyrics, such as word frequencies, sentiment scores, and topic model outputs, can have varying scales and units. For example, the frequency of a specific word might range from 0 to several hundred, while a sentiment score might vary from \(-1\) to 1. These differences in scale can affect the performance of machine-learning algorithms, as features with larger scales can disproportionately influence the model. Standardization addresses this issue by transforming the features into a standard scale, typically with a mean of 0 and a standard deviation of 1. This ensures that all features contribute equally to the model-training process and prevents features with larger scales from dominating the model.  
Standardization is crucial for algorithms sensitive to feature scaling, such as SVM and logistic regression, which rely on distance calculations and can be affected by differences in feature scales. For example, in SVM, the distance between data points is used to define the margin and find the optimal hyperplane, and in logistic regression, the weighted sum of features is used to calculate the probability of belonging to a particular class. In both cases, if the features have different scales, the features with larger values will significantly impact the results, even if they are not more important.  
Although our primary model, random forest, is less sensitive to feature scaling than other algorithms like SVM or logistic regression, we standardized the data for consistency and to facilitate comparison with different models. We used z-score standardization, transforming each feature by subtracting its mean and dividing by its standard deviation. This results in a distribution with a mean of 0 and a standard deviation of 1. The formula for z-score standardization is:  
\[z=(x-\mu)/\sigma\]  
where:  
\(z\) is the standardized value.  
\(x\) is the original value.  
\(\mu\) is the mean of the feature.  
\(\sigma\) is the standard deviation of the feature.
# A Systematic Analysis of Narrowband IoT Quality of Service
## 2 Narrowband IoT Fundamentals
### NB-IoT Physical Layer Radio Measurements  
The LTE specification defines a set of radio QoS parameters, which are used to estimate the channel between the eNodeB and the UE [16]. This section introduces the parameters that are most relevant to evaluate end-user QoS, as well as their relationship to each other.  
Figure 3: Resource allocation in NB-IoT uplink and downlink directions. Multi-tone allocations are highlighted in light gray with blue borders, and single tone allocations are dark gray with red borders.  
Figure 2: LTE resource grid. The NB-IoT Downlink (DL) is based on 15 kHz carrier spacing and OFDM, carrying reference symbols. The Uplink (UL) is based on 15 kHz or 3.75 kHz carrier spacing and SC-FDMA.  
* The Reference Signal Received Power (RSRP) is a linear average of the power in Watts of the resource elements carrying NRS in a given frequency bandwidth. Since NB-IoT downlink is based on a 15 kHz carrier spacing, the RSRP is the power of a single 15 kHz NRS.
* The Received Signal Strength Indicator (RSSI) is a linear average of the total power in Watts received in the measurement bandwidth from all sources, including external interference, noise, and others. In NB-IoT, the measurement bandwidth is exactly one PRB or 180 kHz. The RSSI depends on the cell load; it increases with the number of allocated subcarriers.
* The Reference Signal Received Quality (RSRQ) describes the ratio between RSRP and RSSI, where both measurements shall be made over the same set of resource blocks: \[RSRQ=\frac{RSSI[W]}{RSRP[W]}\] (1)
* The Signal-to-Interference and Noise Ratio (SINR) is the ratio between the received signal level and the interference power (\(P_{I}\)) from external sources, as well as the effective noise power (\(P_{N,eff}\)). If all 12 Resource Elements (REs) of the broadband channel are occupied with the same signal power as the NRS, the narrowband and broadband SINR are identical. \[SINR=\frac{RSRP[W]}{P_{I,15 kHz}+P_{N,eff,15 kHz}}=\frac{RSSI[W]}{P_{I,180 kHz}+ P_{N,eff,180 kHz}}\] (2)  
The SINR and RSRQ parameters are related to each other via the subcarrier activity factor \(x\), which defines the ratio of occupied RE in a Resource Block (RB): \(x=RE/RB\). In an unloaded NB-IoT cell, only the NRS are active, so \(RE=2\) and \(x=2/12\), while a fully loaded cell uses all subcarriers (\(x=1\)).  
The relation between the parameters SINR, RSRQ, and the number of occupied REs or the subcarrier activity factor was derived in [17]:  
\[SINR=\frac{12}{\frac{1}{\text{RSRQ}}-RE}=\frac{12}{\frac{1}{\text{RSRQ}}-12*x} \tag{3}\]  
This relationship is validated in Section 4.4 by analyzing the RSRQ and SINR measurements of the NB-IoT UE, which allows concluding on the subcarrier activity factor \(x\).
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 6 Application: Comparing DP Mechanisms
### Discussion  
At the beginning of this section, we asked whether it is safe to compare differential privacy mechanisms by 'comparing the epsilons'. We found that it is safe to compare epsilons within families of mechanisms (except in the unusual case of the over-truncated geometric mechanism). However, when comparing different mechanisms, it is _not_ safe to just compare the epsilons, since none of the refinements hold in general. Once a'safe' pair of epsilons has been calculated, then reducing epsilon in the refining mechanism is always safe. However, computing safe epsilons relies on the ability to construct a channel representation, which may not always be feasible.
###### Abstract
Fiber optic sensors can be classified into point, distributed, or long-gauge types. The FBG point sensor can measure small-sized areas, providing potentially highly precise measurement values. When utilizing a commercialized acquisition instrument, the FBG point sensor can achieve accuracy of less than 1 \(\mu\epsilon\) within a strain range of 2500-5000 \(\mu\epsilon\)[33]. However, it is important to note that the accuracy of point measurements can be influenced by the presence of discontinuities. Measurement points must be carefully selected to ensure accurate results [34]. Distributed sensing technologies offer extended measurement distances based on Rayleigh and Brillouin scattering, allowing for absolute measurements rather than reflecting merely relative changes compared to reference measurements [35]. In comparison to conventional Brillouin optical time domain analysis (BOTDA), which has a spatial resolution limited to approximately 0.5 m, improved BOTDA techniques can achieve spatial resolution of up to 20 mm [36]. However, the strain resolution typically remains low, often exceeding 20 \(\mu\epsilon\)[21; 37]. Long-gauge fiber optic sensors also enable measurement of the average strain between two separate points, unlike conventional sensors, making them suited to engineering structures with heterogeneous materials such as concrete [38]. Long-gauge FBG sensors allow for the precise measurement of average strain over a significant gauge length, spanning several centimeters or meters. This feature enables the high precision measurement of various parts of a structure or the entire structure itself, particularly for displacement measurement (with an error rate of less than 5%). Such sensors have been effectively employed in numerous structure monitoring applications [39; 40]. In this study, long-gauge FBG sensing was employed to identify the vertical displacement of a tunnel. The strain of the tunnel section was analyzed using a mechanical model for the establishment of the coupled strain separation model. The vertical displacement of the tunnel was then calculated by the proposed method. The remainder of this paper is organized as follows. Section 2 introduces the sensing principle of the long-gauge FBG sensor, and Section 3 describes the proposed method for measuring vertical displacement and its theoretical basis. Section 4 discusses the numerical modeling of the investigated tunnel. Section 5 provides a brief summary and concluding remarks.
## 3 High-Resolution Sequence Stratigraphic Division
### Identification of Short-Term Cycles via INPEFA  
INPEFA technology can obtain preferable trend lines and periodic characteristics (sedimentary cycle characteristics) hidden in well logging curves. The changes and turning points in trend lines can indicate the response characteristics of sedimentary changes and provide more intuitive indicators of cyclic interfaces [31].  
By conducting INPEFA on multiple curves, it was found that the well logging GR curve was the most sensitive to changes in lithologies in the formation. Therefore, the GR curve was selected as the dominant logging curve for INPEFA that could reflect the characteristics of sedimentary cycles preferably.  
Figure 6: Characteristics of different wavelet transform curves of GR.  
Using the well logging SP curve as the analysis attribute and the well logging GR curve as the result attribute, GR filtering curves for different depth windows were generated, setting multiple depth windows such as 1 m, 5 m, 10 m, 15 m, 20 m, 30 m, and 50 m in Direct software (Figure 7). Through comparative analysis, it was found that the GR filtering curve of the 10 m depth window could best reflect the characteristics of short-term cycles and could assist in identifying short-term sedimentary cycles.  
The trend lines generated in the INPEFA can be divided into two categories: positive trend lines (curve shifted to the right) and negative trend lines (curve shifted to the left). A negative trend line indicates an increase in sandstone and a decrease in mudstone, reflecting a decrease in the accommodation space. Conversely, a positive trend line indicates an increase in mudstone content and an increase in the accommodation space.  
Figure 7: The results of INPEFA with different depth windows.  
A negative trend line and an adjacent positive trend line form a periodic oscillation, which corresponds to a short-term cycle. The turning points between two periodic oscillations are commonly an interface between sandstone and mudstone phases, commonly corresponding to a fifth-order cycle boundary. According to the periodic oscillation, the entire Guantao Formation could be divided into \(11\) short-term cycles (\(11\) fifth-order cycles), named the SNG1-SNG11 cycles, from bottom to top (Figure 7).  
Therefore, the isochronous stratigraphic framework of the Guantao Formation in the Binxian area was systematically established with the advantages of identifying unconformity surfaces and continuous tracking through the seismic profile, combined with the high vertical resolution of well logging data with methods of the WTT and INPEFA. The target layer of the Guantao Formation was divided from bottom to top into one third-order cycle (LNG), four fourth-order cycles (MNG1 to MNG4), and eleven fifth-order cycles (SNG1 to SNG11) (Figure 8).
## 2 Materials and Methods
### Analysis with Different Configurations  
Two distinct motor types and two propeller configurations were analyzed, with a total of four combinations studied to optimize energy utilization. The analysis involved generating thrust-to-power plots for each combination to evaluate the efficiency of each motor-propeller pair.  
The data collected from this analysis were crucial not only for identifying the optimal configuration but also for understanding the energy management trade-offs inherent in each setup. For instance, one motor-propeller combination might offer more thrust at a higher power cost, making it suitable for short, high-intensity missions, while another might provide lower thrust but with greater energy efficiency, making it ideal for longer, low-power missions. Integrating insights from these analyses with energy management strategies, such as the potential use of hybrid storage systems, allowed us to optimize the UAV's operational capabilities and extend its mission endurance.