esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data +
## 4 Principles of Context Reasoning
### Overall Approach  
The reasoning process is fundamentally event-driven since it proceeds whenever a sensor event occurs. The sensor event is caused by resident activities or the movement of objects in the space, and then changes a state space. Assuming that the current context is \(\mathbf{C}_{\rho}\), if the reasoning procedure qualifies the changed state space as the next context \(\mathbf{C}_{\rho}\), the transition into \(\mathbf{C}_{\rho}\) is indeed performed, as shown in Algorithm 1. Otherwise, if the algorithm fails to find such \(\mathbf{C}_{\rho}\), the current context remains, and the algorithm waits for the next change in the state space. Algorithm 1 runs in two phases: (1) _MODELING_ and (2) _REASONING_.  
Figure 2: Context graph \(\mathbf{C}_{G}\) is created from \(\mathbf{\hat{S}}\). Note that the dashed edges are transactions, which are not illustrated in Figure 1; however, they might occur as the state space changes. For simplicity, we assume that it returns to \(\mathbf{C}_{1}\).  
```
1 INPUT: a training set of state spaces \(\hat{\mathcal{S}}\)
2 OUTPUT: a next context \(\mathcal{C}_{\rho}\)
3  Context Graph \(\mathcal{C}_{G}\leftarrow\) Build a context graph with \((\hat{\mathcal{S}})\) by Algorithm 2
4 State Space \(\mathcal{S}_{c}\leftarrow\) Initialize the current state space
5  Context \(\mathcal{C}_{\rho}\leftarrow\) Initialize the present context with \((\mathcal{S}_{c},\mathcal{C}_{G})\)
6  Context \(\mathcal{C}_{\
u}\leftarrow\)NULL //selected next context
7 while\(\mathcal{S}_{c}\) changes by any events
8 \(\mathcal{C}_{\
u}\leftarrow\) Reason context with \((\mathcal{S}_{c},\mathcal{C}_{\rho},\mathcal{C}_{G})\) by Algorithm 3
9 if\(\mathcal{C}_{\
u}\)\(\leftarrow\)NULL
10 \(\mathcal{C}_{\rho}\leftarrow\)\(\mathcal{C}_{\
u}\)
11 //output \(\mathcal{C}_{\rho}\) as the new present context
12 end if
13 end while
```  
**Algorithm 1**Context Reasoning Main
# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
## 2 Proposed Internet of Things (IoT) Glucose Monitoring System
### Histogram Calculation  
In general, image histograms provide global information about its intensity values, and are useful for enhancing images. Histograms are also a measure of data distribution that allows, among other things, analysis of its dispersion in different ways.  
In the context of the proposed work, the histogram is used as a descriptor of the acquired images that, at that time, represent the intensity values of the light that has been transmitted through the finger. Correspondingly, it is noticeable that by using the histogram as image descriptor, the analysis of light scattering in the finger is inherent.  
Major variations on glucose levels can be found in the time interval between fasting and a couple of hours after having a meal. In a previous work [42], a set of experiments with the red, green and blue histograms of fingertip images of users with \(8\) h fasting and two hours after they had meal were carried out. The blue histograms reported statistically significant variations according to Mann-Whitney statistical test [43]. To this regard, the use of histograms of the blue channel was considered in this paper.  
With the aim of performing an efficient data transmission, the histogram of the blue channel in the acquired images is computed directly on the RPi before broadcasting data to the IoT cloud computing stage. With this, instead of transmitting the whole image, i.e., more than three hundred thousand data, only \(256\) values are sent for each image, substantially reducing the data transmission latency.
## 1 Introduction  
Digital signature is a very important information security technology, which can realize data integrity, non-repudiation, identity authentication and other functions. It plays an important role in network security communication [1], e-commerce [2], e-government [3] and other systems [4; 5; 6]. To deal with specific application scenarios, some digital signature schemes with special properties have been proposed. Among them, designated verifier signature (DVS) [7] is a significant variant of digital signature. In DVS, the signer is allowed to designate a verifier to confirm the authenticity of a signature, but the designated verifier is unable to convince anyone that the signature was generated by the real signer. The reason is that the simulated signature produced by the designated verifier is computationally indistinguishable from the original signature created by the signer for the same message. This feature of DVS is called non-transferability, which is very useful in the fields of electronic voting, electronic tendering and software copyright [8; 9]. To avoid the signer's identity information being leaked, [PERSON] et al. [7] introduced the concept of strong designated verifier signature (SDVS). In SDVS, the validation of a signature must require the designated verifier's private key, and any third party cannot determine the real creator of the signature. That is to say, only the designated verifier knows the real identity of the signer. Thus, SDVS enhances the privacy of the signer's identity (PSI) and can be applied to some new fields [10]. For example, in cognitive computation [11], an intelligent robot authenticates the identity of its owner, but it must protect the owner's identity information.  
However, in a SDVS scheme, any third party does not know who generated the signature when the signer and the designated verifier dispute a signature. In this scenario, the undeniability property is very essential for SDVS. There are a few SDVS schemes with undeniability, and they were proved to be secure in the random oracle model [12; 13; 14]. Unfortunately, [PERSON] et al. [15] showed that the cryptographic scheme in the random oracle model may be insecure when the random oracle is instantiated by a concrete hash function. Therefore, it is of practical significance to study the SDVS scheme without random oracles.  
Most existing SDVS schemes in the standard model only possess existential unforgeability [16; 17; 18]. Namely, an adversary can easily obtain a new legal signature of the same message by modifying an existing message-signature pair. Strong unforgeability can prevent the above-mentioned modification and protect the integrity of a signature [19]. A SDVS scheme is said to be strongly unforgeable if it satisfies existential unforgeability and the adversary cannot produce a legal signature of a message that has previously been signed. Although strong unforgeability has already been considered in several SDVS schemes [20], none of them has the undeniable property in the standard model.
## 4 Cascade Experiment
### Result and Discussion  
As shown in Figures 9 and 10, the experimental analysis was carried out by sampling for 3000 s. Before the introduction of the security encryption scheme, the standard deviation of the time synchronization of the ring system composed of three devices was 2.6 \(\times\) 10\({}^{-11}\), the peak-to-peak value of jitter was 157 ps, and the TDEV was 15.4 \(\times\) 10\({}^{-12}\) at 1 s. With the introduction of the security encryption scheme, the measured standard deviation of time synchronization of the ring system was 3.1 \(\times\) 10\({}^{-11}\), the peak-to-peak value of jitter was 193 ps, and the TDEV was 2.1 \(\times\) 10\({}^{-11}\) at 1 s. After the security encryption scheme was introduced into the ring system composed of three cascaded devices, there was no significant difference in stability while ensuring the security of the system.  
As shown in Figure 11, the 10 MHz frequency stability transferred by a single device and multiple devices in cascade was the same. As the analog phase-locked loop was used in the development of the equipment, the stability of the cascade equipment was not affected by the change in the program and the improvement of the encryption algorithm.  
Figure 8: A ring time transfer network via a 150 km fiber optic link with three nodes.  
Figure 9: Comparison of jitter using three safe and non-safe equipment.  
Figure 12 shows the physical connection diagram of the three devices for a ring network cascade test. In this experiment, the physical test of one, two, and three cascades was completed before and after adding the security encryption function. The stability of TDEV was tested on a 150 km optical fiber link composed of three 50 km, as shown in Table 3. The TDEV increased by about \(1/\sqrt{\text{N}}\) when the N time transfer devices were cascaded. With the increase in cascaded devices and fiber link length, the change in the signal-to-noise ratio (SNR) would lead to the deterioration of the TDEV of the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stability (TDEV)** & **1 Equipment** & **2 Cascades** & **3 Cascades** \\ \hline No encryption & \(9.21\times 10^{-12}\) & \(14.1\times 10^{-12}\) & \(15.4\times 10^{-12}\) \\ \hline Encryption & \(10.7\times 10^{-12}\) & \(15.7\times 10^{-12}\) & \(20.8\times 10^{-12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of TDEV at 1 s averaging time with cascade equipment.  
Figure 10: Comparison of TDEV using three devices.  
The reason for the deterioration of the encrypted device compared with the unencrypted device may be the introduction of the SM2 security encryption algorithm, as the data lengths of ciphertext and plaintext were inconsistent. In addition, there were time delays in the process of data serial-to-parallel conversion in encryption and decryption functions and transfer. Many factors together caused the accuracy of a single security timing device to decline. However, the overall indicators did not significantly deteriorate.
## 3 Results and Discussion
### Parametric Study
#### 3.2.2 Multiple Interface Delamination  
The proposed formulation accurately models the deflection behavior of composite plates with delaminations occurring at a single interface. Additionally, it extends to scenarios with delaminations across multiple interfaces. To demonstrate its effectiveness, loading analyses were conducted on three laminate types, all subjected to simply supported (SSSS) boundary conditions. These analyses highlight the capacity of the formulation to simulate delaminations across multiple interfaces. In each case, the delamination is centrally located, with square delamination sizes set to 0.2a and 0.5a. Four different types of delamination namely single delamination (9\({}^{th}\) lamina), two ply delamination (6\({}^{th}\) and 12\({}^{th}\) lamina), three  
Figure 10: % Difference between linear and nonlinear maximum deflection for \((0/45/90)_{3A}\) lamina sequence and various boundary conditions, delamination location and size. (**a**) % Difference for SSSS and CFFF boundary conditions with delamination at center. (**b**) % Difference for CCCC and CSCS boundary conditions with delamination at center. (**c**) % Difference for SSSS and CFFF boundary conditions with delamination at edge. (**d**) % Difference for CCCC and CSCS boundary conditions with delamination at edge. (**e**) % Difference for SSSS and CFFF boundary conditions with delamination at corner. (**f**) % Difference for CCCC and CSCS boundary conditions with delamination at corner.  
ply delamination (\(4^{th}\), \(9^{th}\) and \(14^{th}\) lamina), and four ply delamination (\(3^{rd},7^{th},11^{th}\) and \(15^{th}\) lamina). All the plies are counted from the bottom of the laminate.  
As illustrated in Figure 11, the presence of multiple delaminations significantly influences the deflection behavior of composite plates. An increase in the number of delaminations leads to a corresponding rise in the maximum deflection. This suggests that delamination density directly impacts the structural stiffness, reducing it as more delamination interfaces are introduced.
## 4 Experimental Evaluation and Results
### Results and Discussion
The performance metrics such as accuracy, precision, classification error, f-measure, specificity, sensitivity, and the number of rounds consumed to reach the highest accuracy are considered for the performance efficiency comparison of the proposed framework with FedAvg, FedMA, and PSO with FedMA. Accuracy in the context of machine learning means  
Figure 4: Effect of Accuracy on Amount of Communication. Figure 5: Comparison of communication size consumed to reach 90% prediction accuracy. the percentage of all available instances that make the right predictions. Precision is defined as the percentage of correct predictions in the positive instance category. Classification errors are defined as the inaccuracies or percentages of errors available in the case. Three performance measurements are used to identify key features of heart disease. This helps to understand the behavior of different groups for a better selection of features. The results of these parameters are depicted in Tables 4 and 5. Our proposed framework achieves higher target accuracy in a lesser number of rounds as compared to vanilla FL, and PSO with FedMA for the heart disease dataset. As depicted in Table 4, our proposed method delivers a 22% reduction in the number of rounds as compared to FedSGD, FedAvg, and FedMA because the learning rate of our proposed model increases rapidly after every round which results in a 22% less number of rounds. As in Table 5, the proposed framework achieves better scores of prediction accuracy (92.89%), precision (94.2%), sensitivity (96.6), and specificity (81.8) as compared to existing FL algorithms on the heart disease dataset because the learning rate of our proposed model improves after every round of communication with less minibatch size. Hence our proposed framework is best suited for providing better heart disease prediction accuracy with privacy awareness as compared to existing FL algorithms. Moreover, the classification error of our proposed method is 11.8 which is less compared to FedAvg and FedMA due to the M-ABC optimization technique for feature selection and classification used in our proposed framework which results in less classification errors. The optimized features used for the M-ABC optimizer are shown in Table 6 with the details of achieved prediction accuracy. The M-
## 2 Affecting Factors
### Daily Changes  
#### 2.1.1 Foliar Level  
Leaves in different canopy positions with changing illumination and varying temperature have different PRI values. For example, shaded leaves had higher PRI values than sunlit leaves with higher irradiance that generated a larger xanthophyllic pigment pool and a higher potential diurnal pigment conversion [67; 82; 83]. In particular, illumination also led to a lower PRI toward midday [83] and a recovery in late afternoon on sunny days [76; 84; 85; 86; 87] but to a higher PRI toward midday on cloudy days [76]. PRI changed little over time with weak light [88] and did not vary with maximum photochemical efficiency (Fv/Fm) [87; 89], relative water content (RWC), CO\({}_{2}\) assimilation or stomatal conductance under low photosynthetic photon flux density (PPFD) (\(<\)700 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) [90]. In contrast, PRI values decreased under light saturation, due to the generation of a photoprotective reaction, which decreased the epoxidation state (EPS) [85]. PRI was also sensitive to increased NPQ during the initial growing stages in crops and to xanthophyllic pigments during the later growing stages [91]. PRI, however, was a poor diurnal indicator of the de-epoxidation state (DEPS) in different crown levels for spruce needles, probably due to the variation in the chlorophyll/carotenoid ratio [92].  
Drought [93; 93], temperature stress [87; 93], low nitrogen concentration [91; 94], iron deficiency [95], high altitude [96], high ultraviolet A/B [97; 98] and disease [99; 100; 101; 102] can potentially induce biochemical and physiological changes (e.g., chlorophyllic and carotenoid contents, xanthophyll cycle and NPQ) and inhibit the photochemistry of PSII, thus leading to a decrease in PRI, which was more apparent in young than in mature leaves as cadmium stress increased [103]. Variations in the angle and direction of illumination [104] and instrumental FWHM (full width at half-maximum) [69] can also change the optical properties, which then influence PRI values. [PERSON] and [PERSON] [67] reported that the sizes of both the facultative xanthophyllic and constitutive pigment pools affected PRI and that the latter but not the former caused PRI to vary with canopy position; they also suggested that lutein de-epoxidation might influence PRI variability in nature. Other studies have also reported that foliar chlorophyllic and carotenoid pigment pools, which characterize the plant physiological status, contribute greatly to the variability of PRI [91; 92; 105], so incorporating the effects of other photosynthetic pigments is necessary to use PRI as a more accurate proxy for biophysical parameters or plant status [88; 91].
## 5 Challenges and Future Work for SWE
### Assessing Data Quality, Provenance and Uncertainty  
Knowledge about the quality, provenance and uncertainty of sensor outputs is essential for making the right decisions based upon observations. At the moment, such information is often missing in observations and there is no unique way of how to incorporate it. As observations are usually inputs to environmental models, one aspect is to define a common method for integrating uncertainty into observations encoded as O&M. The Uncertainty Markup Language (UncertML) [129] is one approach which can be used as a basis for the integration with O&M. This would ensure that uncertainty information is communicated in a common way within Sensor Webs.
## 2 Proposed Analytic Model of E2 EAScheme  
Let \(N\) be the total number of LAP authentication events performed by the Pi. For each LAP event, the Pi and SDj execute \(m\)-SAPs, where the WAPs are a consequence of the SAP times. Suppose that the aggregate incoming/outgoing Pi authentication messages form a Poisson process with rate (\(\lambda\)), \(\left\{\text{N}(t):\text{t}\geq 0\right\}\), where \(t\) is the residence time that the Pi sends an authentication request to the GWN. Let \(\Psi\) (\(n\), \(m\), \(t\)) be the probability that there are \(n\)-LAPs for residence period \(t\); this means that the process does not reach the (\(n\)+1)-th LAP and the authentications were \(n\)-LAPs; that is, \(m(n-1)\)-SAPs and \(i\)-SAPs before time \(\boldsymbol{\tau}_{n,m+1}\), where \(0\leq i\leq m-1\). Thus, the total number of performed authentication events of the Pi at time \(t=(\boldsymbol{\tau}_{n,m+1}-\boldsymbol{\tau}_{1,1})\) is (\(m(n-1)+i\)). Therefore, according the probability function of the Poisson distribution [30], we have:  
\[\Psi(n,m,t)=\sum_{i=0}^{m-1}\frac{\left(\lambda t\right)^{(n-1)m+i}}{[(n-1)m+ i]!}e^{-\lambda t} \tag{1}\]  
let \(\Psi\) (\(n\), \(m\)) be the probability function that there are \(n\)-LAPs during the residence time and \(m\) is the performed SAPs for each LAP so that:  
\[\Psi(n,m)=\int\limits_{0}^{\infty}P\{N=n|T=t\}f(t)dt=\int\limits_{0}^{\infty }\Psi(n,m,t)f(t)dt \tag{2}\]  
where \(T\) is a non-negative random variable representing the residence time of the Pi. The expected number of authentication events through the residence time is given as:  
\[E(N)=\sum_{n=1}^{\infty}n\times\Psi(n,m) \tag{3}\]  
if \(C(m)\) is considered to be the total cost of transmitted messages in the E2 EA scheme through the residence time when the Pi requests authentication to monitor a specific SDj; then the total cost of all authentication phases is the expected number of authentication events multiplied by the cost of each event (i.e., the LAPs, SAPs, and WAPs phases), which can be expressed as:  
\[C(m)=E(N)\times[5\alpha+2(\alpha+\beta)m] \tag{4}\]  
where \(\alpha\) and \(\beta\) represent the overhead transmission cost of the authentication messages through the internet and WMSN connections. In the following subsections, the \(\Psi\) (\(n\), \(m\)), \(E(N)\), and \(C(m)\) are computed, wherein the residence time \(T\) has gamma, hypo-exponential, and exponential distributions, respectively.
## 3 Results and Discussion
### Application of Artificial Intelligence, Predictive Analytics, and Digital Twins
#### 3.4.3 Development and Implementation of Digital Twins  
Digital twins are increasingly employed to model and simulate the dynamic behavior of microgrid systems, providing virtual environments for testing control strategies and evaluating system performance under diverse scenarios [23; 27; 28]. These virtual replicas facilitate real-time monitoring and predictive maintenance, enhancing operational efficiency and reducing downtime [42; 48; 61].  
Research demonstrates that integrating AI-driven analytics with digital twins improves their predictive capabilities, enabling more accurate failure forecasting and optimized maintenance scheduling [30; 34; 62]. Digital twins also play a critical role in supporting scenario-based planning for system expansion and resilience assessments [32; 39].  
Despite these benefits, challenges remain in developing standardized frameworks that ensure interoperability and data consistency across different platforms and microgrid components [43; 63; 64]. Future research should prioritize the creation of open-source digital twin platforms and explore their integration with decentralized control architectures [65; 66; 67].
###### Abstract  
Defining the most profitable remote sensing platforms is a difficult decision-making process, as it requires agronomic and economic considerations. In this paper, the price and profitability of three levels of remote sensing platforms were evaluated to define a decision-making process. Prices of satellite, plane and UAV-acquired vegetation indices were collected in Italy during 2020 and compared to the economic benefits resulting from variable rate nitrogen application, according to a bibliographic meta-analysis performed on grains. The quality comparison of these three technologies was performed considering the error propagation in the NDVI formula. The errors of the single bands were used to assess the optical properties of the sensors. Results showed that medium-resolution satellite data with good optical properties could be profitably used for variable rate nitrogen applications starting from 2.5 hectares, in case of medium resolution with good optical properties. High-resolution satellites with lower optical quality were profitable starting from 13.2 hectares, while very high-resolution satellites with good optical properties could be profitably used starting from 76.8 hectares. Plane-acquired images, which have good optical properties, were profitable starting from 66.4 hectares. Additionally, a reference model for satellite image price is proposed.  
variable rate nitrogen; site-specific management; drones; profitability; sensors; SNR; Landsat 9; Pleiades-Neo; prediction models Article