esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Conclusions  
We have reported an approach to estimating the extent of Compound \(\times\) Individual matrix effects during volatile analyses. In this approach, the variances of the ratios of non-native standard pairs are determined, and the range of these values establishes the error expected from using a single surrogate standard during volatile analyses. This report specifically focused on the use of HS-SPME-GC-MS for the characterization of volatiles in plant populations (tomato and grape), although the approach should be equally appropriate for application to other analytical techniques or populations. We observed much greater Compound \(\times\) Individual matrix effects for compound pairs in the grape population, with RSD = 249% for the pair of hexanal and hexanol. We also observed that the best surrogate standard for a given compound could not be easily predicted from the chemical structures of the compound. Based on these results, in situations where surrogate standards are used in HS-SPME-GC-MS analyses, we recommend characterizing the extent of Compound \(\times\) Individual matrix effects to confirm that these effects are small in comparison to the desired accuracy.
# CacheHawkeye: Detecting Cache Side Channel Attacks Based on Memory Events
## 3 CacheHawkeye Design  
### Overview of CacheHawkeye  
We designed a system named _CacheHawkeye_ to detect cache side channel attacks. It detects cache side channel attacks through frequent memory access on specific addresses. The memory events monitored by the _perf_ tool automatically associate these sensitive memory addresses with data symbols. _CacheHawkeye_ detects attacks based on these data symbols. The structure of _CacheHawkeye_ is shown in Figure 2. _CacheHawkeye_ consists of three modules: monitor, cache attack library, and detector. In the cloud scenario, the hypervisor is responsible for the implementation of _CacheHawkeye_.  
The monitor controls the execution of unknown programs and reads the results of HPCs. We use the _perf_ tool to monitor unknown programs. Only privileged users can enable the monitor. The monitoring command is _perf men record -U-programName_. -U parameter is used to monitor events in user mode, which can avoid interference from memory activity in kernel mode. _programName_ is a binary file compiled by an unknown program. When the execution is finished, the monitor runs the _perf men report_ command to read memory events and record the results. To the best of our knowledge, this is the first time memory events have been used to detect cache side channel attacks.  
Figure 2: _CacheHawkeye_ architecture.  
Considering some programs that run permanently or have a long-running time, we assign a maximum running time in advance. If the execution time of the unknown program exceeds the maximum running time, the monitor stops the program. In this case, memory events can still be obtained.  
The cache attack library gathers existing known attacks. As a proof of concept, we chose the data segment and code segment from the shared library, as shown in Table 1. The data segment takes the AES of the _library_to library as an example. T-Tables of AES are vulnerable to cache side channel attacks, we store T-Tables's name into the cache attack library, as listed in the second row of Table 1. There is a cache side channel vulnerability of RSA in GnuPG. We store the name of function corresponding to sensitive memory addresses in the third row of Table 1. _CacheHawkeye_ stores these names in an array. Only the attack library needs to be improved when a new attack is revealed.  
The detector preprocesses memory event data from the monitor, analyzes it by the detection algorithm, and then makes a decision. The details of the detector will be described in Section 3.2.
## 3 Results and Discussion
### Soil Total Phosphorus, Available Phosphorus, and Selected Soil Chemical Properties
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Soil Properties**} & \multicolumn{1}{c}{**Value**} & \multicolumn{1}{c}{**Standard**} & \multicolumn{1}{c}{**Soil Properties**} & \multicolumn{1}{c}{**Value**} & \multicolumn{1}{c}{**Standard**} \\  & \multicolumn{1}{c}{**Obtained**} & \multicolumn{1}{c}{**Range**} & & \multicolumn{1}{c}{**Soil**} & \multicolumn{1}{c}{**Obtained**} & \multicolumn{1}{c}{**Range**} \\ \hline pH (water) & 4.32 & 4.6–4.9 & CEC (emolol(+)kg\({}^{-1}\)) & 5.33 & 3.86–8.46 \\ Bulk density (g-cm\({}^{-3}\)) & 1.01 & NA & Total Fe (emol(+)kg\({}^{-1}\)) & 7.51 & NA \\ Total P (mg\(\cdot\)kg\({}^{-1}\)) & 49.96 & NA & Tot. acidity (emol(+)kg\({}^{-1}\)) & 1.38 & NA \\ Available P (mg\(\cdot\)kg\({}^{-1}\)) & 2.48 & NA & Exch. Al (emol(+)kg\({}^{-1}\)) & 0.9 & NA \\ Organic matter (\%) & 5.60 & Nd & Exch. H (emol(+)kg\({}^{-1}\)) & 0.48 & NA \\ Total carbon (\%) & 3.25 & 0.57–2.51 & Exch. K (emol(+)kg\({}^{-1}\)) & 0.24 & 0.05–0.19 \\ Texture & SL & SL & Exch. Ca (emol(+)kg\({}^{-1}\)) & 0.76 & 0.01 \\ Sand (\%) & 74.84 & 72–76 & Exch. Mg (emol(+)kg\({}^{-1}\)) & 0.45 & 0.07–0.21 \\ Silt (\%) & 12.67 & 8–9 & Exch. Na (emol(+)kg\({}^{-1}\)) & 3.60 & 0.01 \\ Clay (\%) & 12.49 & 16–19 & & & \\ \hline \hline \end{tabular} Note: Standard range subjected to the soil development by [PERSON]. CEC is cation exchange capacity, NA is not available, SL is sandy loam. Tot. is total, whereas Exch. is exchangeable.  
\end{table} 
Table 4: Selected physico-chemical properties of the Bekenu Series. Figure 1: Effects of treatments on (**a**) soil total P and available P 90 days after incubation (DAI); (**b**) soil total P and available P at 49 day after seeding (DAS); (**c**) soil titratable acidity at 90 DAI, and (**d**) soil titratable acidity at 49 DAS. Means with a different letter indicate significant differences using Tukey’s test at \(p\leq 0.05\). Capital letters indicate titratable acidity, whereas small letters indicate H\({}^{+}\). The error bars indicate the \(\pm\) standard error of triplicate.
## 4 Experimental Validation
### Ground Simulation Facility
#### 4.1.3 Design of Ground Simulation Facility  
The block diagram of the ground simulation setup is shown in Figure 12. There are assorted LEDs arranged by 3 \(\times\) 3 array configuration on the wall of the dark box. The LEDs are driven by an external function generator to simulate TLE pulses in various amplitude and width. In particular, five 405 nm UV LEDs are employed to imitate randomly generated blue jets and the rest are used to simulate other interference sources.  
The TLE observation system is installed on the other side of the dark box at the distance of 0.54 m from the UV LEDs. The size of dark box is designed by taking into account the FOV of the detector and the camera lens so that the TLE observation system can have full coverage on the wall. The function generator can adjust the pulse duration and the peak voltage so that LEDs can emit light in a specific radiant power as being calculated in Section 4.1.2. A custom graphical user interface (GUI) software is designed to allow the user to control the TLE observation system and analyze the test results. In particular, the continuous data logging capability of the GUI allows the monitoring of the MaPMT data frames. This feature overcomes the capacity limit of the on-chip memory where only the 128 frames of the MaPMT data frame can be stored at once, and enables continuous data acquisition during the ground experiments for data analysis.  
Figure 12: Diagram of ground simulation setup. Blue jet is emulated by flashing UV LEDs in specific radiant power and duration.
# Simulative and Experimental Investigation of Vibration Transfer Path at Gearboxes
## 3 Simulation Model
### Structure of System Matrices  
By ignoring the gyroscopic effect, the equation of the motion of the entire system is as follows:  
\[M\cdot\left(\tilde{x}_{0}+\tilde{x}_{d}(t)\right)+K_{0}\cdot\left(\dot{x}_{0}+ \dot{x}_{d}(t)\right)+\left(C_{0}+C_{d}(t)\right)\cdot\left(x_{0}+x_{d}(t) \right)=f_{0}+f_{d}(t) \tag{4}\]  
where \(M\) is the mass matrix, \(K_{0}\) is the damping matrix, \(C_{0}\) is the mean stiffness matrix and is time-independent, \(C_{d}(t)\) is the time-varying part of the stiffness matrix, \(x_{0}\) is the static displacement, \(x_{d}\) is the dynamic displacement, and \(f_{0}\) and \(f_{d}\) represent the static and dynamic external force and torque, respectively. To calculate the damping matrix, the eigenmodes \(EV\) and eigenfrequencies \(\omega\) of the system are calculated through Equation (5). The normalized eigenmodes \(EV_{n}\) are calculated through Equation (6). Then, the modal damping matrix \(\widetilde{K}\) is calculated by the given damping factor \(v_{i}=0.02\) with Equation (7). At last, the modal damping matrix is transferred back to the orthogonal coordinate system with Equation (8). This coordinate system is identical to the coordinate system of the stiffness and mass matrices [32].  
\[\left[\omega^{2},\ EV\right]=eig\left(M^{-1}\cdot C_{0}\right) \tag{5}\]  
\[EV_{n}=-EV\cdot\left(\sqrt{EV^{T}\cdot M\cdot EV}\right)^{-1} \tag{6}\]  
\[\widetilde{K}=\begin{pmatrix}2\omega_{1}v_{1}&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&2\omega_{n}v_{n}\end{pmatrix} \tag{7}\]  
\[K_{0}=EV_{n}{}^{T}\cdot\widetilde{K}\cdot EV_{n} \tag{8}\]  
\(M\) is the mass matrix, \(C_{0}\) is the mean stiffness matrix and is time-independent, \(EV\) is the matrix of the eigenmodes, \(\omega\) is the eigenfrequencies of the system, \(EV_{n}\) is the normalized eigenmodes, \(v_{i}\) is the damping factor, \(\widetilde{K}\) is the modal damping matrix, and \(K_{0}\) is the damping matrix defined in the coordinate system of the stiffness and mass matrices [32].  
The static displacement of the nodes is time-independent and can be calculated through Equation (9).  
\[C_{0}\cdot x_{0}=f_{0} \tag{9}\]  
Figure 6: Reduced housing stiffness matrix with dimensions of \(24\times 24\).  
By using Equation (9) and ignoring \(C_{d}(t)\cdot x_{d}(t)\)[33], Equation (4) can be transformed into the following:  
\[M\cdot\ddot{x}_{d}(t)+K_{0}\cdot\dot{x}_{d}(t)+C_{0}\cdot x_{d}(t)=f_{d}(t)-C_{ d}(t)\cdot x_{0} \tag{10}\]  
Equation (10) has been used to simulate the dynamic behavior of the nodes in the test gearbox. Because of the high order of accuracy and lower computational cost, the Runge-Kutta method of the fourth order with the fixed time step \(1\times 10^{-4}\) s has been used to solve this differential equation numerically. For the simulation, MATLAB R2022a is used.
## 2 Background
One approach often found in the literature is the deletion of instances with missing data; however, this can lead to biased, misinformed analysis, or errors in the regression model [24]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Crop** & **Prediction Model** & **Dataset and Size** & **Metrics and Results** \\ \hline
[16] & soybean, maize & Recurrent neural network & 1500 cities in Brazil and USA & \(R^{2}=0.55\) for soybean in Brazil, \(R^{2}=0.75\) for soybean in US and \(R^{2}=0.71\) for maize in US \\ \hline
[17] & wheat & Random forest, multiple linear regression & 29 study sites in Australia & Forecast: 35 days before harvest \(r=0.85\), \(MAPE=17.6\%\); 60 days before harvest \(r=0.62\), \(MAPE=27.1\%\). \\ \hline
[18] & wheat & Decision trees, k-NN, SVM & 53 years data from Sanliurra city, 1965–2017 & Accuracy for classifying into early, mid and late sowing dates: k-NN = 0.921; decision tree = 0.862; SVM = 0.470 \\ \hline
[5] & maize, soybean & XGBT & Maize (\(n\) = 17,013) and soybean (\(n\) = 24,848), 28 US states, 2014–2018 (USDA) & \(MAE=4.7\), \(R^{2}=0.94\) for maize, and \(MAE=6.4\), \(R^{2}=0.92\) for soybean \\ \hline
[19] & corn & Linear regression, LASSO, random forest, XGBT & Corn Belt US States (\(n\) = 10,016), 1984–2018 (USDA and APSIM) & \(R^{2}\%=71.9\) for LASSO, \(R^{2}\%=72.5\) for Average, \(R^{2}\%=66.5\) for random forest \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work for yield prediction using the planting date and weather information. Computerised imputation (completion of missing values with potential or estimated ones) is an alternative to statistical or manual imputation methods when large amounts of data are involved, and can involve using mean values, regression (support vector machine, decision tree, random forest), ensemble based, k-NN, multiple imputation or similar values (hot-deck imputation) [13; 22; 23]. Furthermore, when data collection is not performed in a controlled environment, anomalies and outliers are often present. This poses a new challenge in building robust regression models, as each data point can greatly influence the result [25]. In this context, a study compared nine representative outlier detection (OD) models applied on eight regression datasets, to evaluate their performance [26]. The supervised regression models used to validate the unsupervised OD model performance are Ridge and Huber regression, and multi-layer perceptron regressor, with the evaluation metric being the \(MAE\). The results showed that the minimum covariance determinant and isolation forests algorithms perform best, while principal components analysis, histogram-based outlier detection and local outlier factor are to be considered second, especially as they are faster computationally. A similar comparison was performed by [PERSON] et al. [25], showing a proposed box plot adjustment using the D-k-NN method performing well in terms of \(MAE\) and computational efficiency, together with local outlier factor and minimum covariance determinant.
## 2 Numerical Models for Concrete Cracking in Statics and Dynamics
### Cracking Model in Dynamics  
The current literature [2; 3; 17; 18; 30; 31; 2; 3] appears to agree that three factors are linked to rate dependency, namely: micro-inertia, moisture effect, and structural inertia. In the present physical and mechanical approach, it is considered that the material inertia integrates the micro-inertia and moisture effect through the Stefan Effect [2; 3]. The influence of structural inertia should come automatically from a dynamic analysis [18], being a natural consequence of the inclusion of mass and damping matrices in the equation of motion. Therefore, it should not be considered a priori either through Dynamic Increase Factor (DIF) or by sophisticated constitutive models. This choice is supported by numerical simulations [18; 19; 20]. It is noteworthy to mention that, at a local (finite element) level, the structural inertia is not able to modify the strength of the material. It has been specified that the model leads to mesh-independent results (validated in the frame of previous studies) in statics, which is linked to the fact that the random distributions of \(f_{t,stat}\) and \(G_{ICstat}\) depend on the volume of the finite element. This dependency has been given by Equations (1)-(7), and has been elaborated and validated in statics. It is very unreasonable and difficult to experimentally determine these relations as a function of the strain rate. Hence, we propose the following assumption: the evolution with the strain rate of the random distributions of the model parameters is not an input but an output of the numerical simulations. By doing so, it is apparent that the standard deviation \(\frac{\sigma}{\mu}\) of both \(f_{t,dyn}\) and \(G_{IC,dyn}\) will be inferior to \(f_{t,stat}\) and \(G_{IC,stat}\). This point is physically relevant. Indeed, it is well-known that high-strength concrete is mechanically more homogeneous than normal concrete. In conclusion, the proposed assumption related to the evolution of the random distributions with strain rates are physically relevant but have to be validated.
## 4 Experimental Results and Analysis
### The Parameters Affecting Equalizer Performance
As shown in Figure 7, it can be observed that as the number of feedback taps increases, the BER decreases, but the computational complexity increases. When the tap numbers are (85, 16), there is a good balance between the equalizer's performance and computational complexity.
## Appendix B  
The update step of single trajectory PHD at time \(k\) can be expressed as Equations (13) and (14) in [23]. This theorem shows the target-to-measurement association in multi-trajectory. Now, we want to extend the concept to the extended trajectory space and hope the measuring density can only involve the single target currently instead of the whole trajectory at different times. According to Assumptions 4-6, the density of measurement in the extended trajectory space is referred to by [1, 23]  
\[\begin{split}&\ell_{k}(\{z_{1},\ldots,z_{n}\}|\{\mathfrak{T}_{1}, \ldots,\mathfrak{T}_{m}\})\\ =& e^{-\lambda_{c}}\left[\prod_{p=1}^{n}\lambda_{c} \dot{c}(z_{p})\right]\left[\prod_{p=1}^{m}\left(1-p_{D}(\mathfrak{T}_{p}) \right)\right]\\ &\times\sum\limits_{\sigma\in\Xi_{n,m}}\prod\limits_{p\sigma>0} \frac{p_{D}(\mathfrak{T}_{p})\;l\left(z_{p}|\mathfrak{T}_{p}\right)}{(1-p_{D }(\mathfrak{T}_{p}))\lambda_{c}(z_{\sigma p})},\end{split} \tag{13}\]  
where \(\sigma\;\in\;\Xi_{n,m}\) represents the whole likely associations between \(n\) measurements and \(m\) targets whether it can be detected or not, which follows the principle of one measurement for one target. Therefore, we can obtain the pseudolikelihood function of the PHD filter:  
\[L_{Z_{k}}(x^{i},R^{i})=\left(1-p_{D,k}(x^{i})\right)+p_{D,k}(x^{i})\times\sum \limits_{z\in Z_{k}}\frac{l_{k}(z|x^{i},R^{i})}{\lambda_{c}c+\iint\;p_{D,k}(x ^{i})l_{k}(z|x^{i},R^{i})D^{\tau}_{\omega_{k}}(x^{i},R^{i})dx^{i}dR^{i}} \tag{14}\]  
\(D^{\tau}_{\omega_{k}}\left(x^{i},R^{i}\right)\) is given by Equation (21). The final posteriori extended trajectory density can be derived from the pseudolikelihood function of VB-TPHD filter:  
\[\begin{split}& D_{\pi_{k}}(\widehat{\mathcal{R}})=D_{\omega_{k}} \Big{(}t,x^{1:j},R^{1:j}\Big{)}L_{Z_{k}}\left(x^{i},R^{i}\right)=D_{\omega_{k} }\Big{(}t,x^{1:i},R^{1:j}\Big{)}\left(1-p_{D,k}(x^{i})\right)\\ &\quad+p_{D,k}\big{(}x^{i}\big{)}\times\sum\limits_{z\in Z_{k}} \frac{l_{k}\Big{(}z|x^{i},R^{i}\Big{)}D_{\omega_{k}}\Big{(}t,x^{1:i},R^{1:j} \Big{)}}{\lambda_{c}c+\iint\;p_{D,k}\big{(}x^{i}\big{)}l_{k}\Big{(}z|x^{i},R^{ i}\Big{)}D^{\tau}_{\omega_{k}}\Big{(}x^{i},R^{i}\Big{)}dx^{i}dR^{i}}\end{split} \tag{15}\]
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 3 Results
### Respiration Rate Extraction
(**b**) RGB, NIR, and combined respiration rate extraction performance versus chest ROI brightness. where the morphology and/or length of a breathing cycle changed over time, we could not always find the first harmonic successfully with this method. Notably in this case, there can be multiple peaks in the spectrum related to the respiration rate, which increases the chance that the wrong peak is selected.
## 5 Conclusions  
In this paper, we have introduced a complete passive transmitter for QKD that can prepare decoy-state Bennett-Brassard 1984 signal states using coherent light. Our method employs sum-frequency generation together with linear optical components and classical photodetectors. In the asymptotic limit of an infinite long experiment, we have proven that such passive scheme can provide a secret key rate (per pulse) lower but comparable to the one delivered by an active decoy-state BB84 setup with infinite decoy settings. In practice, however, it is uncertain if the passive scheme will be able to beat active transmitters or passive solutions based on PDC sources in general; the answer will depend on various technologies [63, 64].  
The main focus of this paper has been polarization-based realizations of the BB84 protocol, which are particularly relevant for free-space QKD. However, we have also shown that similar ideas can as well be applied to other practical scenarios with different signal encodings, like, for instance, those QKD experiments based on phase encoding, which are more suitable for use in combination with optical fibers.
### Research Methodology  
Our work covers research performed in recent years in the area of NFV/SFC implementation frameworks. The methodology employed involved a critical review of the relevant contributions in report papers, journals, conference papers, and articles. We systematically queried the most relevant scholarly databases (IEEE, ACM Digital library, Web of Science, Scopus, etc.) to get all the required research papers that are relevant to our work. We started this process with 367 papers, of which 118 papers met our final selection criteria.  
We made use of some basic exclusion criteria (Figure 2) such as the exclusion of papers that were written in languages other than English, papers with no full text, research papers in duplicates, the year of publication, and the specific area(s) covered such as focusing on research papers in areas related to NFV, SFC, and SDN in service provider network environments.  
For the review of papers presented in our proposed taxonomy, we included research papers that present NFV implementation frameworks with some vNF chaining components, representative frameworks that address specific problems in the domain, e.g., vNF orchestration, traffic steering, SFC resilience, modular SFC deployment, etc. A total of 118 papers were finally selected, which include some notable related surveys, and experimental and theoretical works in NFV/SFC. The papers we have cited in our taxonomy cover fundamental equivalence classes of the state-of-the-art in NFV/SFC implementation frameworks.  
In addition to the more recent research papers presented in our work, we would like to note that some of the papers we considered present both the pioneering efforts for certain equivalence classes of NFV/SFC frameworks and still represent the state-of-the-art; thus, the most recent works (if any) are incremental to what we have presented. As a concrete example that justifies some of our choices (based on current literature), frameworks such as ClickOS [12], OpenNetVM [13], OpenBox [14], Slick [15], SNF [16],  
Figure 2: Paper analysis process.  
Metron [17], NetBricks [18], and GNF [19] constitute some of the notable pioneering efforts in this domain, which are still relevant in designing NFV/SFC frameworks for next generation networks.  
Each of these frameworks represent important aspects such as the use of lightweight packet processing elements, the use of packet acceleration frameworks, the decoupling of network infrastructure into control and data-planes, synthesizing packet processing elements for improved performance, resource allocation, and deploying containerized network functions at the network edge to support the edge computing paradigm. In terms of technological advancements and existing works in the literature, we have covered the core components that are still used in designing and implementing NFV/SFC frameworks to the best of our knowledge.