esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Procedure for a Control Strategy
### Design Non-Singular Fast-Terminal Sliding Variable
\tag{19}\]  
\(\square\)  
To arrive at a conclusion from Equation (19), the following Lemma is used. **Lemma 1**. : _[_49_]_ _For any real numbers \(z_{1}>0\), \(z_{2}>0\), and \(0<\varphi<1\), an extended Lyapunov function condition of finite-time stability can be given in the form of a fast-terminal sliding mode as \(\dot{L}(x)+z_{1}L(x)+z_{2}L^{\varphi}(x)\leq 0\), where the settling time can be estimated by_  
\[T\leq\frac{1}{z_{1}(1-\varphi)}\ln\frac{z_{1}L^{1-\varphi}(x(0))+z_{2}}{z_{2}}. \tag{20}\]  
From Equation (19), \(\
icefrac{{\theta_{i}}}{{\theta_{i}}}+1/2<1\) indicates that \(\dot{V}_{2}\leq 0\). Based on Lemma 1, the original point \(e_{i}=0\) is a globally balanced point in finite-time. In the next step, proof that the error state variable of the system (13) converges to zero in finite-time will be given. Equation (19) can be shown as  
\[\dot{V}_{2}=2V_{2}^{(\theta_{i}+1)/2}\Bigl{(}-\Gamma_{1i}V_{2}^{1-\theta_{i}} -\Gamma_{2i}V_{2}^{(1-\theta_{i})/2}-\Gamma_{3i}\Bigr{)}. \tag{21}\]  
Equation (21) can be expressed as  
\[ \begin{array}{ll}dV_{2}&=2V_{2}^{(\theta_{i}+1)/2}\Bigl{(}-\Gamma_{1i}V_{2} ^{1-\theta_{i}}-\Gamma_{2i}V_{2}^{(1-\theta_{i})/2}-\Gamma_{3i}\Bigr{)}dt\\ \Rightarrow dt&=-\frac{dV_{2}}{2V_{2}^{(\theta_{i}+1)/2}\Bigl{(}\Gamma_{1i}V_{ 2}^{1-\theta_{i}}+\Gamma_{2i}V_{2}^{(1-\theta_{i})/2}+\Gamma_{3i}\Bigr{)}}\\ &=-\frac{1}{1-\theta_{i}}\frac{dV_{2}^{(1-\theta_{i})/2}}{\Bigl{(}\Gamma_{1i} V_{2}^{1-\theta_{i}}+\Gamma_{2i}V_{2}^{(1-\theta_{i})/2}+\Gamma_{3i}\Bigr{)}}\end{array} . \tag{22}\]  
Setting \(V_{2}(T_{e_{i}})=0\) and taking the integral of Equation (22) during the time period where \(0\to T_{e_{i}}\) gives  
\[T_{e_{i}}=\frac{2}{(1-\theta_{i})}\frac{1}{\sqrt{4\Gamma_{1i}\Gamma_{3i}- \Gamma_{2i}^{2}}}\left(\tan^{-1}\frac{2\Gamma_{1i}V_{2}^{(1-\theta_{i})/2}(e_{ i}(0))}{\sqrt{4\Gamma_{1i}\Gamma_{3i}-\Gamma_{2i}^{2}}}-\tan^{-1}\frac{\Gamma_{2i}}{ \sqrt{4\Gamma_{1i}\Gamma_{3i}-\Gamma_{2i}^{2}}}\right). \tag{23}\]  
It can be seen that \(T_{e_{i}}\) is limited by \(T_{e_{i}}^{f}=\frac{2}{(1-\theta_{i})}\biggl{(}\frac{\pi}{2}-\tan^{-1}\frac{ \Gamma_{2i}}{\sqrt{4\Gamma_{1i}\Gamma_{3i}-\Gamma_{2i}^{2}}}\biggr{)}\frac{1}{ \sqrt{4\Gamma_{1i}\Gamma_{3i}-\Gamma_{2i}^{2}}}\). In fact, \(V_{2}(T_{e_{i}})=0\) means \(e_{i}(T_{e_{i}})=0\). In addition, it can be seen that the upper-bound of \(T_{e_{i}}^{f}\) is only dependent on the design constants, as \(\Gamma_{1i},\,\Gamma_{2i},\,\,\Gamma_{3i},\,\theta_{i}\) and the tracking positional error in Equation (13) approach zero in finite-time. Therefore, the proof of Theorem 2 is complete. The proposed control strategy forces the error state variables to reach sliding variables in finite time, as will be presented next.
## 9 Results and Discussion
LHIP is weak because it omits primordial security mechanisms as mutual authentication and key exchange. The authors of P-HIP propose the computation of unique pairwise keys when the device moves from session to session or changes from the network. P-HIP [36] provides the minimization of the communication cost by the use of minimal certificate size by the implementation of the ECQV. However, we think that optimization in minimizing the computational overhead on a constrained designed protocol such as HIP DEX is suitable than working on HIP BEX (the same case for CD-HIP [18]). P-HIP is suitable only for applications that are implemented over User Datagram Protocol (UDP) on the transport layer. UDP indeed is less consuming of energy in communication because it is quicker than Transport layer protocol (TCP) but TCP is more secure and guarantees the integrity of exchanged data that constitutes a critical requirement for sensitive data as those sent by a patient's medical sensor connected to a dedicated health monitoring system. In addition, P-HIP is suitable only for real-time applications in IoT with fix and regular data traffic. P-HIP is very weak against Distributed Denial-of-service (DDoS) attack and exactly the DrDOS (Distributed Reflection Denial of Service) that is based on the spoofing UDP parameter when the application uses UDP. To be used, E-HIP [22] must be added to all devices in the target network and is not compatible with other versions of the HIP protocol. E-HIP is limited for IoT solutions that define the responder as a powerful server and can not be defined for a T2T architecture where both the initiator and the responder are constrained nodes. Both compression and optimization deal with the improvement in minimizing the overall energetic costs of sensor nodes. Another benefit of adopting the compression of packets is that the number of fragments, to be sent, decreases comparing with uncompressed handshake packets and as consequence the probability of packets' lost decreases. We believe, it is natural that the cryptographic-key management must be lightweight and do not have a significant overhead since the number of communicating nodes in IoT is very high. We also demonstrated by real experimentation the efficiency of LC-DEX over multi-hops architectures, and we consider LC-DEX efficient WSN's authentication solution for IoT. It is worth mentioning that our solution software was programmed to support both IPv4, IPv6, and 6 LowPAN architecture. LC-DEX presents a novel security association establishment with a HIP DEX opportunistic mode. Opportunistic mode combined to the alleviation of the scalar multiplication of the key generation deal with an excellent energy consumption minimization that could be ameliorated in advantage.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 4 WUR in Cellular Standards
### Fifth-Generation NR LP-WUS and LP-SS  
The 3 GPP RAN1 group is currently drafting a 5G wake-up signal standard in a Rel. 19 work item, which consists of the design of two signals: a low-power wake-up signal (LP-WUS) and a low-power synchronization signal (LP-SS) for 5G NR.
# MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float
## 2 A Programming Language Based on Models
### Duration of the Dive  
The duration of a dive is needed in order to estimate the lifetime of the float and the amount of data transmitted each month. It depends on the duration of each stage of the dive. For the surface stage, we consider a constant duration of one hour, even if it can be shorter or longer for a real float, since it depends on the amount of data to transmit by satellite. The descent and ascent stage duration are computed from the depth defined in the mission configuration and an estimated speed of the float. The parking stage has a maximum duration if no ascent request occurs for some time. This maximum duration has to be defined by the developer in the mission configuration. It may be shortened if an application requests the ascent of the float.  
To estimate the mean duration of the parking stage, we use a Poisson law that gives the probability to have \(k\) ascent requests during the default parking stage duration (when considering a fixed probability for ascent requests):  
\[p(k)=\frac{\lambda^{k}}{k!}e^{-\lambda} \tag{1}\]  
The \(\lambda\) parameter represents the mean number of ascent requests during the default parking stage duration. It is computed from the mission configuration and the probabilities defined in an application (only probabilities leading to a function that trigger the ascent of the float are used). For example, if the maximum duration of the parking stage is 10 days, and the probability of the ascent is two per week, the \(\lambda\) parameter is equal to \(10*2/7=2.86\). The probability to have zero ascent request is:  
\[p(0)=e^{-\lambda} \tag{2}\]  
Additionally, the probability to have at least one ascent request is:  
\[p(k>0)=1-e^{-\lambda} \tag{3}\]  
The mean parking duration, which is also the mean duration before the first ascent request, corresponds to the mean interval of time between each ascent request (i.e., the invert of the probability defined in the MeLa language) multiplied by the probability to have at least one ascent request during the default parking duration.  
For example, the probability to have at least one ascent request is \(1-e^{-2.86}=0.94\), and the mean parking duration is \(7/2*0.94=3.3\) d. Figure 6 provides a curve of the mean parking duration as a function of the mean number of ascent requests for a default parking duration of 10 d.  
Satellite Transmission  
The amount of data transmitted by the float are computed from the recording functions called in the application. In the MeLa library, the recording functions are annotated with the amount of data they record for satellite transmission. For functions recording arrays, the amount of data is dependent of the array size passed as a parameter of the function. The probability annotations in the MeLa code are also used for the computation.
## 5 Results
### Performance Evaluation of DVB-T2 Using MER and EVM
#### 5.2.2 Native DVB-T2 with LDPC Coder Using EVM  
The CFO was evaluated using EVM when only Gaussian noise was applied and also with a TU6 channel.  
Figure 26 presents the EVM evolution as a function of the SNR in the presence of only Gaussian noise. At the SNR value of \(10\,\mathrm{dB}\), the EVM value was equal to \(-10\,\mathrm{dB}\). This behavior confirmed that the EVM was inversely proportional to the MER and SNR when the imperfection included in the signal was only Gaussian noise (cf. Figure 24).  
Figure 27 presents the EVM evolution as a function of the SNR in the presence of a TU6 channel. At the SNR values of \(10\,\mathrm{dB}\) and \(20\,\mathrm{dB}\), the EVM values were equal to \(0\,\mathrm{dB}\) and \(-10\,\mathrm{dB}\). This behavior showed the impact of the TU6 channel already highlighted with the MER performance. This result confirmed the proportionality of EVM to MER, which also occurred when a CFO was applied to this communication system. Furthermore, when the CFO was considered in the system, EVM values obtained were inversely proportional to the MER values obtained with the various CFOs.  
\begin{table}
\begin{tabular}{c|c c c|c c c|c c c|c c c} \hline \hline \multicolumn{13}{c|}{**OFDM vs. UFMC/NUCs: AWGN case only for 3 different BERs (\(10^{-3}\), \(10^{-4}\), \(10^{-5}\))**} \\ \hline \multirow{3}{*}{**CFO**} & \multicolumn{3}{c|}{**Native**} & \multicolumn{3}{c|}{**UFMC/NUC-**} & \multicolumn{3}{c|}{**Native DVB-T2**} & \multicolumn{3}{c}{**UFMC/NUC-**} \\  & \multicolumn{3}{c|}{**DVB-T2**} & \multicolumn{3}{c|}{**based DVB-T2**} & \multicolumn{3}{c|}{**CFO SNR**} & \multicolumn{3}{c}{**based DVB-T2**} \\  & \multicolumn{3}{c|}{**SNR [dB]**} & \multicolumn{3}{c|}{**SNR [dB]**} & \multicolumn{3}{c|}{**SNR [dB]**} & \multicolumn{3}{c|}{**penalties [dB]**} & \multicolumn{3}{c}{**CFO SNR**} \\ \hline \multirow{3}{*}{\(\epsilon\)} & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** \\  & \multicolumn{3}{c|}{\(10^{-3}\)} & \(10^{-4}\) & \(10^{-5}\) & \(10^{-3}\) & \(10^{-4}\) & \(10^{-5}\) & \(10^{-3}\) & \(10^{-4}\) & \(10^{-5}\) & \(10^{-3}\) & \(10^{-4}\) & \(10^{-5}\) \\ \hline
0.00 & 6.14 & 6.16 & 6.18 & 6.20
## 3 Results
### Variation Partitioning Tests (Partial CCA)  
The total inertia results of CCA were 3.525, where our final variables (Altitude, Temperature, Wind speed, Barometric Pressure, Slope angle, Slope ES, Slope NE, Slope SW, pH, EC, OM, CaCO\({}_{3}\), K, P, and Silt) together explained 74.5% of the variation (eigenvalues of 2.627). The first two canonical axes explained 37% of the variation. The CCA model was significant (pseudo-F value = 2.626; df = 15; \(p\) < 0.01; permutations = 999). For the 15 explanatory variables, we tested simple term effects. Simple term effects showed that altitude, slope angle, slope ES, and wind speed (in decreasing order of importance) were significant (\(p\) < 0.05; Table 3). All these studied gradients were further grouped into four major classes: climatic (temperature, wind speed, and barometric pressure), edaphic (pH, EC, OM, CaCO\({}_{3}\), K, P, and silt), geographic (altitude), and slope (slope angle, slope ES, slope NE, and slope SW). Moreover, variation partitioning tests (partial CCA) were performed (Table 4). Class (b) was the most explanatory variable (32.4%), followed by class (d) (21.1%), class (a) (19.5%), and class (k) (17.6%) (Figure 9).  
Figure 7: Beta diversity (\(\beta\)sim and \(\beta\)sne) among three plant communities of pure Deodar forest. (For the complete names of plant communities, see Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **df** & **Chi-Square** & **F** & **Pr(\(>\)F)** \\ \hline
**Altitude** & 1 & 0.39074 & 30.439 & **0.001** \\ Temperature & 1 & 0.16637 & 12.960 & 0.144 \\
**Wind speed** & 1 & 0.22077 & 17.198 & **0.019** \\ Barometric Pressure & 1 & 0.14212 & 11.071 & 0.342 \\
**Slope angle** & 1 & 0.29362 & 22.874 & **0.001** \\
**Slope (ES)** & 1 & 0.25023 & 19.493 & **0.012** \\ Slope (NE) & 1 & 0.14662 & 11.422 & 0.290 \\ Slope (SW) & 1 & 0.17210 & 13.407 & 0.161 \\ pH & 1 & 0.10719 & 0.8350 & 0.692 \\ EC & 1 & 0.13028 & 10.149 & 0.422 \\ OM & 1 & 0.10794 & 0.8409 & 0.693 \\ CaCO\({}_{3}\) & 1 & 0.13288 & 10.351 & 0.377 \\ K & 1 & 0.10015 & 0.7801 & 0.771 \\ P & 1 & 0.13604 & 10.598 & 0.343 \\ Silt & 1 & 0.12995 & 10.124 & 0.411 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contribution and ranking of the variables fit in our final model. Significant \(p\)-values are displayed in bold.  
Figure 8: Variation of diversity indexes among the three plant communities recognized in pure Deodar forest (GLM results and associated \(p\)-values are displayed at each plot). (**a–d**) Species richness (**a**), Shannon diversity (**b**), Simpson diversity (**c**), and Pielou’s evenness (**d**). (For the complete names of plant communities, see Figure 2).
## 4 Multi-Objective Optimization
### Sensitivity Analysis  
In multi-objective optimization, sensitivity analysis is usually performed on the parameters because of the large number of variables involved in the optimization. The efficiency of optimization search in multi-objective optimization is improved by removing the parameters that have low relevance to the optimization objectives. This sensitivity  
Figure 8: Multi-objective optimization flowchart.  
compares the relationship between the parameters and the objectives of the optimization in terms of the Pearson correlation coefficient. The Pearson correlation coefficient provides a more visual representation of the strength of the correlation between the parameters and the optimization objectives. The interval of Pearson correlation coefficient is \([-1,\,1]\). As the absolute value of the Pearson correlation coefficient gets closer to \(1\), it means that the correlation between the parameters and the optimization objectives is higher, otherwise the opposite is true. Let a set of random variables be \((P,\,Q)\); the Pearson correlation coefficient is calculated as shown in Equation (6) [31]:  
\[S= \frac{\sum_{i}\left(p_{i}-\overline{p}_{i}\right)\left(q_{i}-\overline{q}_{ i}\right)}{\sqrt{\sum_{i}\left(p_{i}-\overline{p}_{i}\right)^{2}\left(q_{i}- \overline{q}_{i}\right)^{2}}} \tag{6}\]  
where \(\overline{p}_{i}\) and \(\overline{q}_{i}\) are the mean of the random variable \(P\) and the mean of \(Q\), respectively.  
This correlation analysis is carried out on 124 sets of data extracted using a uniform Latin hypercubic sampling method set in automatic mode. The results of the sensitivity analysis are shown in Figure 9. Consistent with the previous section, the rib bypass magnetic bridges width \(b\) and height \(h\), the flux barrier angle C3, and the permanent magnet drop height \(H\) all have a high correlation with permanent magnet demagnetization. In the optimization process, due to the large number of parameters involved in the optimization and the possible coupling relationship between the parameters, the optimization effect of the key parameters is weakened. Therefore, removing the parameters that have very little relevance to the optimization objective not only reduces the impact on the key parameters, but also reduces the number of parameters and improves the efficiency of the optimization search. Therefore, this sensitivity analysis will exclude parameters for which the absolute value of the Pearson correlation coefficient is less than 0.1 for all optimization objectives. The rejected parameters will keep the original parameter values unchanged. From Figure 9, it can be seen that the absolute value of the correlation coefficients of the parameters \(q\), \(d\), C5, C6, and C7 with the three optimization objectives are all less than 0.1, so they will be eliminated from the subsequent multi-objective optimization.  
Figure 9: Sensitivity analysis results.
## 2 Theoretical Methods for TBR Prediction and Experimental Methods for TBR Measurement  
### Thermal Boundary Resistance  
When heat flows across an interface between two dissimilar materials, a temperature discontinuity arises at the interface owing to scattering of heat carriers at the interface. The TBR is defined as the ratio of the temperature difference at an interface to the heat flux flowing across the interface [25; 26]. The TBR, also referred to as the interfacial thermal resistance or Kapitza resistance, is the inverse of thermal boundary conductance (TBC). Thermal contact resistance, which occurs at the macroscopic asperity contacts of the surface  
Figure 1: Schematics of SHE in interconnects and transistor channels in semiconductor devices. The joule heat generated in both interconnects and transistor channels passes through several interfaces before dissipating through the heat sink connected to the substrate. The TBR of these interfaces dictates the overall thermal resistance of deeply scaled semiconductor devices.  
and air-filled gaps, differs from TBR [27]. In comparison, TBR exists even at atomically perfect interfaces and arises from a mismatch in the energy states of the heat carriers (phonons and electrons) on either side of the interface.
# Plasma-Sprayed Flexible Strain Sensor and Its Applications in Boxing Glove
## 4 Results and Discussion
### Performance Test of the Glove Prototype
#### 4.2.2 Response and Recovery Times  
The performance of the boxing glove was evaluated using the data measured by the testing platform. The data obtained by the multimeter was imported into the computer, and the Origin software was then used to process the data to obtain the relationship between the sensor resistance and time (Figure 15a). To improve the reliability of the data, the measured effective data (Figure 15b) were averaged, and the average response and recovery times of the flexible sensor were calculated to be 100 and 150 ms, respectively.  
Given the reaction limits of the human body, it is unlikely that a boxer could perform two strikes in approximately 100 ms. Therefore, considering the two performance indicators (i.e., response and recovery times), the flexible sensor prepared in this study can fully meet the real-time feedback of the instantaneous strike force during boxing matches.
## 3 Simulation Results and Optimizing Parameter Determination
The instantaneous temperature of the positive connecting piece is 611.53 \({}^{\circ}\)C, the negative connecting piece is 596.22 \({}^{\circ}\)C, the positive tab is 618.9 \({}^{\circ}\)C, and the negative tab is 598.92 \({}^{\circ}\)C. The maximum temperature (660 \({}^{\circ}\)C) is concentrated at the junction of the contact/non-contact area of the positive connecting piece, tab, and welded reinforcement structural part. The simulation results under a 10 kA continuous current show that the continuous over-current time of T2 group was 1.73 s, while the T1 group lasted 0.68 s and the C0 group lasted 0.44 s. This indicates that increasing the contact area between conductor structures has a more significant effect on temperature reduction than merely increasing the cross-sectional area. The most easily fused positions in all three groups were at the junction of the contact/non-contact area of the positive connecting piece and positive tab stack. Using 660 \({}^{\circ}\)C as the fusing threshold, the T2 groups exhibited a slower temperature rise rate and a more uniform spatial heat accumulation gradient, confirming that expanding the contact area effectively improves thermal resistance. Under high-amperage conditions, the control group showed significant high-temperature aggregation, whereas the T2 group, which included a thickened connecting piece and a welded reinforcement structural part, demonstrated better temperature suppression characteristics. This difference in thermal behavior confirms that expanding both the cross-sectional area and contact area enhances interface heat transfer efficiency, optimizing the three-dimensional heat transfer path within the guide components and significantly improving the current-carrying capacity and thermal stability of the structure.
## 3 Results and Discussion  
The measurements of stomatal conductance began when plants presented 5 pairs of leaves and were made on the adaxial and abaxial faces of leaves of pepper plants (_Capsicum annuum_ L.), located on the upper part of the plant and exposed to solar radiation. Three plants, randomly chosen, were evaluated per treatment. In each plant, a leaf from the top, fully expanded and exposed to solar radiation was selected (Table 2).  
As the availability of water in the soil decreases, the rate of transpiration decreases as a result of stomata closure. This is one of the important defense mechanisms that plants have against exaggerated water losses, and eventual death due to desiccation [20]. One of the main responses of plants to water deficit is the closure of stomata and, thus, the decrease in CO\({}_{2}\) in the leaf mesophyll, which causes the fall of photosynthesis. This allows physiological evaluations, such as gas exchange measures, to be used to assess the effects of water deficiency on plants [21].  
According to [PERSON] et al. [22], the stomata opening is conditioned by the plant water availability. When there is a water deficiency in the soil, caused by the increase in temperature, there may also be a decrease in photosynthesis. [PERSON] et al. [23] demonstrated that, inside greenhouses, a diffuse solar radiation can, on average, be 65% higher than outside the greenhouse. In our study, temperatures inside the greenhouse in August reached maximum daily temperatures varying from 26.4 \({}^{\circ}\)C to 32.4 \({}^{\circ}\)C, which theoretically can lead to stomatal closure, caused by water deficit, thus decreasing the photosynthesis which conditioned fruit development.  
During the entire pepper cycle and until pre-harvest, automated irrigation was applied. However, irrigation is essential for production in a protected environment, requiring a regular supply throughout its cycle [24]. According to [PERSON] et al. [25], for the best development of the pepper plant, the moisture content in the substrate must be kept close to 80% of the field capacity, during the period from fruiting to harvest. Therefore, both the lack of water in the soil and its excess can reduce crop productivity, even caused by salt excess in the substrate [26].  
It can be inferred that the use of the Reabilit(r) Algas biostimulant provided an increase in agronomic efficiency (AE) in stomatal conductance in the order of 27%, 20%, 12% and 4% in the T2, T5, T3 and T4 treatments, respectively, in comparison with the control. The SC values represented the stomata fully open and favouring respiration due to the absence of water deficit, during all the phenological phases of the pepper culture, the reason for the significant differences found in fruit production (Table 2). Such results may be related to the composition of the biostimulant, which presents biological compounds, such as CMAEs, amino acids, phytohormones and organic substances that assist in the development of the plant and stimulate cell elongation and hormonal balance in the tolerance to water deficit, when applying the lowest concentration of Reabilit(r) Algas.