esa-sceva/satcom-chunk-collection huggingface dataset:
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 3 General Concept of Throughput Analysis with Inter-Network Interference
\tag{9}\]  
Because of \(q_{i}^{\prime}<1\) from its definition, it can be rewritten as  
\[q_{i}=\min\left(1,q_{i}^{\prime}\right)=\min\left(1,\frac{\sigma O_{i}V_{i}}{ PZ_{i}}\right), \tag{10}\]  
which is valid for both the saturation and non-saturation states. The min function prevents \(q_{i}\) from exceeding 1. By using \(\lambda_{i}\), we have also another expression of transmission airtime as  
\[X_{i}=\lambda_{i}R_{i}T. \tag{11}\]  
From (5), (8), and (11), we have  
\[X_{i}=\frac{q_{i}Z_{i}G_{i}T}{\sigma}. \tag{12}\]  
Finally, the general expression of the frame transmission probability at the idle state (denoted as \(\tau_{i}\)) can be obtained from (7), (10) and (11) as  
\[\tau_{i}=q_{i}G_{i}=\frac{\sigma X_{i}}{Z_{i}T}. \tag{13}\]  
The derivation and explanation of the Equations (5)-(13) have been also presented in [11; 12; 13; 14]. Our analysis aims to consider the channel states of each network. From (2), we use \(v(i)\) to denote the set of networks within the carrier-sensing of Network \(i\), and Network \(h\) indicates the element of a set \(v(i)\). Network \(i\) is in the idle state or the carrier-sensing state when its adjacent network (e.g., Network \(h\)) can decrement back-off timer. When Network \(h\) is in the idle state, the probability \(U_{i,h}\) that the state of Network \(i\) in the idle state is  
\[U_{i,h}=1-f(X_{j},\gamma_{i,j});j\
eq h;j,h\in v(i). \tag{14}\]When the back-off timers of both Network \(i\) and its adjacent one become zero in the idle state, the two networks will transmit frame simultaneously. Let \(\gamma_{i,h}\) be the simultaneous transmission probability in this case. Then, we have  
\[\gamma_{i,h}=U_{i,h}\cdot\tau_{i}, \tag{15}\]  
where \(\tau_{i}\) is the frame transmission probability at the idle state, \(h\) is the elements in the set \(v(i)\).
## 4 Conclusions  
In this study, the key aroma-active compounds for Yongchuan douchi were successfully characterized. The GC-O identified 49 aroma compounds. Among them, 22 compounds (FD > 4) were selected by AEDA and 20 key aroma-active compounds (OAV > 1) were obtained by OAV calculation. The results were verified by an applied recombination analysis and omission experiments, which showed that 2,3-butanedione (butter, cheese), dimethyl trisulfide (garlic-like), acetic acid (pungent sour), acetylpyrazine (popcorn-like), 3-methylvaleric acid (sweaty), 4-methylvaleric acid (sweaty), 2-mehoxyphenol (smoky), maltol (caramel), \(\gamma\)-nonanolactone (milky), eugenol (woody) and phenylacetic acid (flora) contributed significantly to the aroma profile of Yongchuan douchi. In addition, these key aroma components resulted in the Yongchuan douchi with sauce-like, sour, nutty, smoky, caramel and fruity notes. Moreover, two unknown compounds presenting the flavor of herbal (FD = 4) and burnt (FD = 2) have not been characterized, and the 2,3-dihydro-3,5-dihydroxy-6-methyl-4(H)-pyran-4-one (FD = 256) still need to be obtained and evaluated in future work. Moreover, the results were only representative of Yongchuan douchi from Waizumu Douchi Company. If the key aroma compounds were the common content in most Yongchuan type douchi still needs to be confirmed.  
The results in this study provide useful information for deep understanding of the volatile aroma compounds of Yongchuan douchi, which may be used as suggestions in technology development and quality control of douchi production.  
Writing--original draft preparation, S.W.; writing--review and editing, B.S., N.Z.; visualization, Y.C., B.L.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YED0400600.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Thermal and Solar Properties
The thermal conductivity of the single glass pane was supplied by the manufacturer (1.1 W/mK, corresponding to a U-value of 5.7 W/m\({}^{2}\)K). Based on the measured data, the thermal conductivity estimated for the monolithic aerogel pane sandwiched between the glasses (15 mm thick) is calculated in compliance with (2); it is 0.0204-0.0209 W/mK, at about 38-41 \({}^{\circ}\)C (average temperature of the two sides glazing surfaces during the tests). The relative uncertainties (type B), calculated according to JCGM 100:2008 [19], are related to the fluctuation of the measured quantities during the test. They vary in the 2.6-6.2 % range for all the tests; the uncertainty is high (6.2 %) for test 2 at 50 \({}^{\circ}\)C, because of the not very stationary flux values (fluctuations of about 6 W/m\({}^{2}\)) and it was not considered significant. As introduced in the previous paragraphs, the thermal conductivity is usually referred to 10 \({}^{\circ}\)C and 23 \({}^{\circ}\)C, while the U-values measured in this work are referred to an average temperature of the sample of about 37 \({}^{\circ}\)C. The experimental data were then reported at 10 \({}^{\circ}\)C, following the procedure suggested by the ISO 10456 [20]. By applying the Equations (3) and (4) to the \(\lambda\) value reported in Table 3 and by using a f\({}_{\rm T}\) mean value of 0.00403, estimated for granular aerogels in previous research works [5], the \(\lambda\) of the only monolithic aerogel pane at 10 \({}^{\circ}\)C is 0.0182-0.0185 W/mK for the tests at 45 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, respectively. \(\lambda\)-values increase with temperature and a U-value of the composed glazing system of 0.99-1.00 W/m\({}^{2}\)K is found for mean temperature conditions of 10 \({}^{\circ}\)C.  
Finally, the solar factor (Table 4) was estimated on the basis of the methodology shown in paragraph 2.3, starting from optical data reported in [18] and the results of thermal tests (Table 3).
## 2 MHT for Data Authentication
### The Inefficiency of the MHT  
#### 2.2.1 The Iterated Processing of the MHT  
When verifying data using an MHT-based authentication scheme, a user receiving the segments of the data must compute the same hash value several times. In this paper, such a  
Figure 1: The structure of the MHT to verify 16 segments of data.  
procedure, repeatedly calculating the same hash value, is described as _an iterated procedure_. Figure 2 shows the impact of iteration of the MHT. In Figure 2, '\(x\) (\(y\))' denotes the number of iterations to compute each node value. A receiver computes the same node value \(x\) times. Additionally, the same node value is transmitted \(y\) times as witnesses. For example, \(N_{2}\) is labeled with \(x\) (\(y\)) = 8(8). Here, \(x\) = 8 means that a receiver computes the same \(V_{2}\) 8 times when verifying 8 segments assigned to \(\{N_{16},\ \ldots,\ N_{23}\}\). Additionally, \(y\) = 8 means that the same \(V_{2}\) is transmitted 8 times as witnesses for 8 segments assigned to \(\{N_{24},\ \ldots,\ N_{31}\}\).  
As shown in Figure 2, the number of iterated processes is dependent on the size of the MHT. If the MHT has \(K=2^{k}\) leaf nodes, a user verifying all segments of the data must compute the same \(V_{2}\), \(2^{k-1}\) times. For \(K=2^{4}\), the user computes the same \(V_{2}\) only 8 times, but if \(K=2^{16}\), the user must calculate the \(V_{2}\) a total of 32,768 times. Because of that, the user must calculate hash values 1,048,576 times to verify the data consisting of \(2^{16}\) segments, even if the user needs only 136 hash values.
## 3 Results and Discussion
### Hardened-State Properties of Mortars
#### 3.2.4 Dynamic Modulus of Elasticity  
The results of the dynamic modulus of elasticity (E\({}_{\text{c,dyn}}\)) at 91 days and 365 days of curing are shown in Table 7. E\({}_{\text{c,dyn}}\) values at 91 days of curing are between 35.2-38.0 GPa. Binary mortars that incorporate between 5% and 10% of RC show a decrease in this property between 4.7% and 4.9% compared to conventional mortar due to the dilution effect of RC [15]. The incorporation between 10% and 20% of FA also leads to a slight decrease in this property, between 3.7% and 7.4%, respectively, compared to the conventional mortar, because the clinker dilution effect still prevailed over the FA pozzolanicity [43]. Regarding the influence of simultaneously incorporating RC and FA, the reduction of E\({}_{\text{c,dyn}}\) was between 3.1% and 11.3% with respect to the conventional mortar.  
As for 365 days, the E\({}_{\text{c,dyn}}\) values are higher than those shown at 91 days, as expected, due to a higher degree of hydration of the cementitious matrices, being between 37.5-38.3 GPa. Regarding the average variation of this property with curing age, ternary mortars (with RC and FA) experienced the greatest increase (-7.7%), and the binary mortars with RC (-4.8%) and FA (-5.7%) also showed increases higher than that observed for the conventional mortar (-0.9%). This fact is related to the increased reactivity of FA in the presence of RC, as was observed in the results of compressive strength, and by the new hydration reactions that occur at longer ages in the presence of RC [45] and FA [46].  
Finally, Figure 6 shows the linear relationship between the dynamic modulus of elasticity and the square root of the compressive strength of the investigated mortars. The R\({}^{2}\) coefficients are higher than 0.83 in both cases. These findings have also been confirmed for conventional mortars [44].  
Figure 5: Results of flexural strength between 7 and 365 days of curing (a,b) and flexural strength as function of the square root of the compressive strength (at the same age).
## 2 Related Works  
### The Chan-Vese (CV) Model  
The Chan-Vese (CV) model [25] was initially derived from the Mumford-Shah (MS) functional [26]. The MS functional aims to find an optimal contour \(C\) that divides the image domain into disjoint subregions and a piecewise smooth approximation image \(I:\Omega_{i}\subset R\) from the original image \(I_{0}:\Omega\subset R^{2}\), and the energy functional of MS can be expressed as follows:  
\[E^{\text{MS}}(I,C)=\int_{\Omega}(I_{0}-I)^{2}dx+\mu\int_{\Omega\setminus C}| \
abla I|^{2}dx+v|C| \tag{1}\]  
where \(\mu,v\geq 0\) are positive weighting constants, \(|C|\) is the length of the contour \(|C|\), and \(\
abla I\) is the derivative of the function \(I\). However, the non-convexity of the above energy functional makes it difficult to be minimized. Besides, the MS functional is restricted to piecewise constant equations, so the CV model has been proposed to simplify and modify the MS functional. The basic idea of the CV model is to find a particular partition to separate a given image into foreground and background. The energy functional of the CV model can be defined as follows:  
\[E^{\text{CV}}(C,c_{1},c_{2})=\lambda_{1}\int_{\text{in}(C)}(I_{0}-c_{1})^{2}dx +\lambda_{2}\int_{\text{out }(C)}(I_{0}-c_{2})^{2}dx+v\cdot\text{len}(C)+\mu\cdot\text{area}(\text{in}(C)) \tag{2}\]  
where \(\mu,v,\lambda_{1},\lambda_{2}\geq 0\) are positive parameters, in\((C)\) and out\((C)\) represent the region inside and outside of the contour \(C\), and \(c_{1}\) and \(c_{2}\) are two constants that approximate the image intensity in in\((C)\) and out\((C)\), respectively. The Euclidean length term \(\text{len}(C)\) is used to regularize the contour. The first 2 terms in Equation (2) are the global binary fitting energy. This energy can be represented by a level set formulation, and then the energy minimization problem can be converted by solving a level set evolution equation. The evolution equation can be expressed as follows:  
\[c_{1}=\frac{\int_{\Omega}I_{0}\cdot H(\phi)dx}{\int_{\Omega}H(\phi)dx},c_{2}= \frac{\int_{\Omega}I_{0}\cdot(1-H(\phi))dx}{\int_{\Omega}(1-H(\phi))dx} \tag{3}\]  
\[\frac{\partial\phi}{\partial t}=\delta(\phi)\bigg{[}v\text{div}\bigg{(}\frac{ \
abla\phi}{|\
abla\phi|}\bigg{)}-\mu-\lambda_{1}(I_{0}-c_{1})^{2}+\lambda_{2} (I_{0}-c_{2})^{2}\bigg{]} \tag{4}\]  
where \(H(\cdot)\) is the Heaviside function, and \(\delta(\cdot)\) is the Delta function, which is derivative of the Heaviside function. For Equation (4), \(v\) is a scaling parameter. If \(v\) is small enough, small targets are likely to be extracted while if \(v\) is large, large targets can be detected.  
Whereas if the image intensities are inhomogeneous, the global fitting will not be accurate. Therefore, the CV model is not suitable for inhomogeneous images, and the segmentation results are affected by the position of the initial level set [3]. However, the CV model has better robustness to noise.
## 7 Security Analysis and Cryptographic Guarantees
### Mutual Authentication Guarantees
#### 7.2.5 Key Compromise Impersonation (KCI) Resilience  
Even if one party's private key is exposed (e.g., \(\text{sk}_{\mathcal{A}}\)), the adversary cannot impersonate other entities to that party due to the dependency of \(K\) on both ephemeral values. The session key derivation requires knowledge of both \(\kappa_{\mathcal{A}}\) and \(\kappa_{B}\), making unilateral computation infeasible.  
The protocol ensures that both participants contribute equally to the session key via independent random scalars. This contributory property prevents key control by any single party and ensures protocol fairness. Compared to the original GN-AK protocol, which also derives the session key from independent pseudorandom scalars \(d_{T}\) and \(d_{R}\), our enhanced protocol maintains this contributory structure but employs a scalar multiplication approach that explicitly intertwines both parties' ephemeral inputs through a double-base chain representation and point-halving strategy. This design, alongside our formal analysis, ensures robust KCI resistance under the explicit dependency on both ephemeral secrets. Thus, while the original GN-AK is also a contributory key agreement protocol, our modifications focus on tightening the symmetric influence of both parties' fresh values and facilitating formal proofs in the CK model. In the enhanced protocol, the session key is derived as \(K=[\kappa_{A}][\kappa_{B}]\Phi\), and both \(\kappa_{A}\) and \(\kappa_{B}\) are session-specific random scalars, secret, and freshly generated in each session. This symmetric contribution ensures that compromising a long-term private key is insufficient to compute valid session keys or impersonate another entity to the compromised party, as knowledge of the peer's random value remains indispensable.
# Robust Full-Screen Wavelength Calibration Algorithm
## 2 Spectrometer Construction and Related Principles
### Construction of the Scanning, Double-Layer, Secondary Diffraction, Linear-Array CCD Spectrometer
This grating group 2 operation improves the spectral resolution of the spectrometer. Through the aspheric lens, the parallel light of different angles of incidence is imaged into the linear-array CCD. The characteristic peaks in the mercury lamp are 253.65 nm, 312.57 nm, 365.02 nm, 404.66 nm, 435.84 nm, 546.07 nm, 576.96 nm, and 570.07 nm. According to these wavelengths, the wavelengths of 2048 pixels can be calibrated using the wavelength calibration algorithm proposed in this paper. In addition to achieving better calibration accuracy at the provided fitting points, it can also calibrate the wavelengths at other points. In this paper, the spectral range of the spectrometer is 200-1000 nm, and the spectral range of the 2400-line grating is 200-650 nm.
# Respiration Rate Extraction of Moving Subject Using Velocity Change in FMCW Radar
## 3 Results
### Respiratory Measurements of Subjects Leaning against the Wall  
The respiration rate extraction accuracies of the conventional and proposed methods are compared for ten subjects who leaned against a wall, thereby minimizing the movement of the human body.  
As shown in Figure 8, the conventional method yields the same signal cycle as that of the ground truth for a stationary human body, and can accurately determine the RR. The proposed method, in which inhalation and exhalation appear in one cycle, corresponds to two cycles per one cycle of ground truth, and can accurately determine the RR. In this case, both methods use radar to extract the correct values, and the respiration is extracted based on the chest movement associated with respiration, without any other movement of the human body.  
\[Accuracy[\%]=\frac{1}{d}\sum_{t=0}^{d}\biggl{(}100-\frac{|RR(t)-G_{t}(t)|}{ G_{t}(t)}\cdot 100\biggr{)} \tag{11}\]  
In Equation (11), \(d\) is the total experimental time. The RR extraction accuracy is calculated by comparing the ground truth and RR extracted by the two RR extraction methods, using Equation (11). We experimented with the motion conditions shown in Figure 5a. Table 2 shows the respiratory accuracy calculation values for all subjects obtained through the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject** & **Conventional** & **Proposed** \\ \hline Subject \#1 & 99.03\% & 99.36\% \\ Subject \#2 & 90.26\% & 93.55\% \\ Subject \#3 & 94.93\% & 98.50\% \\ Subject \#4 & 76.02\% & 92.77\% \\ Subject \#5 & 85.22\% & 95.34\% \\ Subject \#6 & 76.29\% & 94.79\% \\ Subject \#7 & 77.19\% & 91.58\% \\ Subject \#8 & 78.51\% & 84.10\% \\ Subject \#9 & 80.81\% & 92.93\% \\ Subject \#10 & 82.26\% & 95.97\% \\ \hline Average & 84.05\% & 93.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of respiratory rate accuracy for the case shown in Figure 5a.
## 5 Performance Evaluation
### Scenario II: Radio Interference  
The second set of simulations considers the impact of interfering radios. There are two (active) control loops in the system. At time t = 6s, two interfering nodes start to transmit data packets to another two nodes, respectively, and these transmissions last 6 seconds.  
Figure 8: Sampling period under slight interference.  
Figure 7: System output under slight interference.  
Figure 7 shows the control performance when each interfering node sends a packet every 10 ms. It can be seen that, while the interference doesn't cause stability problems when the time-triggered sampling scheme is used, the proposed flexible time-triggered sampling scheme yields better control performance in both control loops (particularly) when the interference is present. Just as in Scenario I, this improvement mainly benefits from the dynamic adjustment of sampling periods in smart sensors, as shown in Figure 8. The deadline miss ratios in both control loops are also well controlled under flexible time-triggered sampling, see Figure 9. The lower deadline miss ratios achieved under flexible time-triggered sampling relative to time-triggered sampling explain the performance improvement shown in Figure 7.  
Figure 10: System output under severe interference.  
Figure 9: Deadline miss ratio under slight interference.  
Figure 10 shows the sharp difference in control performance with different sampling schemes when each interfering node sends a packet every 8 ms. Under time-triggered sampling, the severe interference incurs system instability in both control loops. This doesn't happen when the flexible time-triggered sampling scheme is employed. The control performance of both loops remains good throughout the simulation. Again, this is a result of the sampling period adaptation that maintains the deadline miss ratios at relatively low levels (around the desired level). The graphs for sampling periods and deadline miss ratios in this case are omitted for brevity.
## 3 Requirements: What Should Technologies Do to Support Humans? For each cognitive challenge identified in the previous section, we derived possible ways of addressing it. These _general requirements for operator support strategies_ do not yet specify any technology. Altogether, we extracted 108 support strategies. In Table 1, we present two examples for each psychological area (for a full list, see Appendix A, Tables A1-A4). For instance, considering that people are prone to conditional sampling, it should be made explicit when events are overrepresented in samples. This could be done by informing operators that a particular geometry distortion of chocolate has only been observed when a machine fault occurred, which might overestimate its impact on the production process. \begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} “Chocolate bars have mostly been checked for hollow \\ bottoms when faults have occurred, which may \\ overestimate the impact of this distortion” \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of deriving requirements for support strategies from the cognitive factors presented in Section 2. These general requirements or support strategies can be condensed into the following _abstract goals for operator support_: technologies should facilitate the understanding of system relations, help debias the use and interpretation of data, support the integration of different data, direct attention to non-considered or unavailable information, and foster evaluation and critical thinking. Based on these general support strategies and abstract goals, we specified a number of _functional requirements for technologies_. This was done inductively by clustering our 124 support strategies into requirement categories, while a support strategy could be assigned to one or more categories. This procedure led to the following set of functional requirements:  
1. Provide models of the system and connect them with data: * Make structural relations and rules explicit; * Highlight constraints of the situation and equipment; * Enable semantic zooming and switches between levels of abstraction; * Process data in a context-dependent manner. 2. Provide and integrate data from different sources: * Make data from different sources available; * Pre-process and debias data to obtain a valid picture; * Make procedures of sampling and integration transparent. 3. Process and integrate data across time: * Provide timely information; * Enable tracking of changes (past, current, and future data); * Link current situation with historical data. These functional requirements imply that technologies must be able to integrate data from different phases of the plant lifecycle, different levels of the automation pyramid, different parts of the plant, and different points in time. Data integration should be possible even for brownfield plants, because many plants are quite old. The next section provides