esa-sceva/satcom-chunk-collection huggingface dataset:
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 5 Conclusions  
This paper outlined the implementation of an E2E BICM-ID code for a TWR-FSO PNC system operating over the distribution of light intensity fading due to atmospheric turbulence. We examined the bit error performance for the system under moderate to strong scenarios. Turbulence-induced channel fading was considered to describe large-scale and small-scale scintillations by log-normal and gamma-gamma distributions, respectively. Then, we investigated the decoding process of the system by using a graphical description; this involved implementing the technique of ExIT charts. ExIT charts allow the two concatenated codes in isolation of each other to aid the construction of good BICM iteratively-decoded code. Thus, the convergence occurs depending on the SNR in terms of \(E_{b}/N_{0}\) with fewer iterations. Therefore, the start of the waterfall region can be known as the demapper affected by the corresponding value of SNR for the systems over log-normal and gamma-gamma distributions. The introduction of the results-based iterative decoding method had improved the performance of the TWR-FSO PNC system. The E2E performance of a BICM-ID code was evaluated on a TWR-FSO with PNC employed at the relay. This included the impact of a log-normal and a gamma-gamma distributions channel and then compared through simulation with a non-iterative convolutional code TWR-FSO PNC and uncoded TWR-FSO PNC systems. The simulation results along with the Monte Carlo simulation were matched to the exact analytical results of the ExIT charts.  
Figure 17: ExIT chart for the demapper E2E BICM-ID code with the PNC system employing the TWR-FSO weak link.  
Figure 18: ExIT chart for the demapper E2E BICM-ID code with the PNC system employing the TWR-FSO strong link.  
**Acknowledgments:** First and second authors would like to gratefully acknowledge the financial support from Iraqi Ministry of Higher Education and Scientific Research for carrying out this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 8 Conclusions and Future Work
Additionally, the proposed system design achieves a higher maintainability score MI = 61.28% compared to other dual-axis solar tracking prototypes due to the PME features of the ASPCB layout. Finally, by considering the time reduction property of each formulated metric, we could demonstrate that solar tracking systems with improved design modularity, accessibility, and predictive maintenance features can decrease the baseline MTTR from 6.25 h to 3.15 h, resulting in a 50.4% repair time reduction. Generally, solar tracking systems are overshadowed by fixed PV panel setups on the market due to the increased investment in additional components, and higher maintenance costs during the product's lifespan. By effectively implementing a maintenance-oriented metric system, solar tracking devices can become more competitive with fixed solar panel installations [84]. In future work, we propose to perform a more in-depth analysis of PV systems based on tracking devices and maximum power point tracking (MPPT) [85], evaluating their dependability from the cost, energy production, and maintainability points of view. A suitable metric system for standalone solar panels and CSP systems could be formulated to adopt a more balanced comparison strategy. Conclusively, by employing a maintenance-oriented metric system, future modern solar tracking systems can achieve higher reliability, cost savings, improved planning, extended lifespan, safety, and reduced environmental impact. The provided mathematical model demonstrates how these metrics can quantitatively reduce MTTR and overall maintenance efforts. Furthermore, the RBILBO-based fault diagnosis system proves beneficial for improving the PME, which enables predictive maintenance strategies, allowing for potential issues to be identified and addressed before they lead to system failures. Conceptualization, R.R. and F.M.P. ; methodology, R.R. ; software, R.S. ; validation, R.R., F.M.P. and R.S. ; formal analysis, M.V. ; investigation, R.R. ; resources, R.S. ; data curation, F.M.P. ; writing--original draft preparation, R.R. and F.M.P. ; writing--review and editing, F.M.P. ; visualization, F.O. ; supervision, M.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the corresponding author due to privacy reasons imposed by the co-authors who contributed to the software and hardware implementations. The authors declare no conflicts of interest.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 5 Current Problems and Discussion
### The Zero-Level Offset Correction  
Fluorescence of the non-vegetation regions is zero. In fact, because of rotational-Raman scattering and the disadvantages of the various retrieval methods, non-vegetation regions, such as the Sahara Desert, exhibit non-zero fluorescence values [33]. These fluorescence values are thought of as fluorescence bias; all retrieval methods should remove the fluorescence bias to obtain the reliable values. [PERSON] et al. found that non-linearity problems exist in the TANSO-FTS band 1 and proposed an empirical method to correct the resulting fluorescence by expressing fluorescence offset as a function of the average at-sensor radiance over Antarctica [36]. Based on this idea [36], [PERSON] et al. [34], [PERSON] et al. [44], and [PERSON] et al. [38; 39] increased the vegetation-free objects as reference spectra and employed a strict criterion for selecting reference spectra, such as the range of the fluorescence values and the average radiance, as well as the sun zenith radiance. The threshold values of these parameters have no common standard and were determined by the researchers according to the feature of the sensor. These fluorescence offset correction strategies select the vegetation-free regions as the target and build the relationship between the fluorescence offset and the average radiance, and the vegetation areas with the same average radiance as vegetation-free regions are thought to have the same fluorescence offset. [PERSON] et al. [97] suggested that the previous fluorescence offset correction methods do not consider dark current, stray light, and nonlinear responses, and developed an empirical correction scheme to mitigate zero-level offsets. The reasons for the formation of zero-level offsets are complicated and remain to be studied in the future. New zero-level offset correction methods have not yet been developed.  
Lack of Surface Data for Validating Sun-Induced Chlorophyll Fluorescence Derived from the Satellite Data  
The SIF derived from satellite data validation is a problem in current research. The most commonly used methods are cross-validated through other SIF products, such as OCO-2 SIF, GOSAT SIF, GOME-2 SIF and SCIAMACHY SIF, or NDVI data [9; 13; 33; 34; 35; 37; 38; 39; 40; 42; 97; 102; 124]. The lack ofsurface measurement data results in less support for the fluorescence product, which may limit its future. The SIF is considered a useful probe for detecting the condition of vegetation. It has a close correlation with photosynthesis, thus it is used to derive GPP. Uncertainties in the fluorescence observation due to variation in the sun-satellite view observation geometry can affect GPP estimation [133; 134]. The number of fluorescence measurements made near the ground is small, and the measurement range is limited. Thus, it is difficult to validate SIF satellite data using surface data. To validate SIF satellite data, it is essential to expand the ground fluorescence observation network.
## 3 The Monte Scarpino Landfill and the SRS Test-Bed
### Estimation of \(h_{0}\)  
As mentioned above, to estimate the altitude \(h_{0}\) of the zero-degree isotherm, SRS performs an extrapolation by assuming a linear vertical profile of the environmental temperature based on the measurements collected by two weather stations located in the neighborhood at two significantly different altitudes. In the specific test case, both of them are operated by ARPAL, and are located respectively at Monte Gazzo (distance from the top of the landfill 3.55 km, direction S (186\({}^{\circ}\)), 419 m AMSL) and at Monte Penello (distance 4.29 km, direction E (280\({}^{\circ}\)), 980 m AMSL).
## 3 System Overview
### Current Measurement Topologies
Once hardware connections were created, the switch connection was closed, and the capacitor was allowed to charge to power source voltage. Once we were sure that DUT had entered sleep mode, the switch was released and the capacitor was discharged, while DUT was powered by the capacitor. The discharge time had to be long enough to measure the voltage drop. Caution had to be taken so that the capacitor voltage did not fall below the normal DUT operating voltage level. The DUT's current can be computed using Equation (8) where \(I,\ C,\ \Delta V,\ t\) denote DUT's current, capacitance, voltage drop, and time duration, respectively. The constant current discharge of a capacitor can be estimated using  
\[I=C\frac{\Delta V}{t}. \tag{8}\]  
\[I=10\ \mu\text{F}\ \frac{176\ \text{mV}}{140\ \text{ms}}=13.29\ \mu\text{A}. \tag{9}\]  
Figure 11b shows oscilloscope readings that led to the result of Equation (9). When it was necessary to take into account the leakage current of the capacitor, then we repeated the experiment without DUT and measured the voltage drop within the same time. The result value can be added to Equation (8) as compensation for leakage current. In this research both an oscilloscope with a shunt resistor and a sensitive current digital multimeter was used to obtain the power profile. The Tektronix DMM916 multimeter was used to verify the deep sleep current measurement from the Equation (9). The multimeter reading was 19.5 \(\mu\)A for the deep sleep mode of the device. In our work we considered 20 \(\mu\)A as the deep sleep current for the rest of the calculation.
## 5 Conclusions  
We presented an experimental study of the thermoelectric energy harvesting potential at different latitudes using solar flat panels systems with buried heat sink. A data-logger that measured the temperature soil profile at different depths was built and installed at two different latitudes: one in Campinas, SP - Brazil (latitude 22\({}^{\circ}\) 54'20'' S), and another in Mataro, Catalonia, Spain (latitude 41\({}^{\circ}\) 32'17'' N). Since the output power in a TEG is proportional to the square of temperature difference between its hot and cold side, we calculated the integrals of \(\Delta T^{2}\) with respect to time for the several depths where a temperature sensor was installed into the soil. The obtained results show that at each latitude there is an optimal depth where the TEG must be installed to optimize the energy harvesting. The calculated results showed that a measurement of the soil profile temperature is mandatory at each latitude where a STEG with buried heat sink will be installed. It was also possible to show that in regions where the available solar radiation is low, the use of a buried heat sink can improve drastically the performance of the system, almost doubling it when we compared to the result obtained with an open air STEG. An experiment with a prototype STEG with buried heat sink was built and installed in Campinas, where the daily average energy stored in the supercapacitor during the 32 days in period (5 July to 5 August 2015) was 24.38 J per day.  
Conceptualization, P.C.-D., A.C. and J.A.S.D.; methodology, P.C.-D., A.C. and J.A.S.D.; circuit design, PCB layout and circuit tests, P.C.-D.; firmware and software, P.C.-D.; writing--review and editing, A.C. and J.A.S.D.; funding acquisition, P.C.-D.  
This research was funded by FAPESP grant number 2014/01470-3, CAPES and the European Regional Development Funds.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 4 Experimental Results  
In evaluating our proposed approach within the domain of art paintings, especially those distinguished by complicated structures and textures, we conducted experiments utilizing a dataset sourced from [[https://useum.org/](https://useum.org/)]([https://useum.org/](https://useum.org/)) (accessed on 31 October 2023). This  
Figure 2: High-resolution art painting completion with a size of around 1600 \(\times\) 2136 pixels. The order of image names, from top to bottom, follows a structured sequence: Girl, Man, Scenery, and Woman. (**a**) Input, (**b**) [PERSON] et al. [1], (**c**) Laplacian [32], (**d**) EdgeConnect [27], (**e**) Ours, and (**f**) Ground Truth.  
dataset is notable for its incorporation of a diverse array of genre paintings, prominently featuring impressionist landscapes and portraits. The integration of such a varied selection enables a nuanced understanding of how effectively our proposed approach navigates the intricacies inherent in diverse artistic styles and subjects. Our methodology is specifically applied to address randomly irregular missing regions, simulating common damages found in paintings.  
Two scenarios were considered for experimentation: high-resolution paintings with dimensions around 1600 \(\times\) 2136 pixels and low-resolution counterparts with sizes of approximately 400 \(\times\) 400 pixels. To establish a comprehensive evaluation, we benchmarked our approach against two comparison methods: EdgeConnect, representing a deep learning approach [27], and a traditional method denoted as [PERSON] et al. [1] and Laplacian [32]. Through this comparative framework, our aim is to measure the effectiveness and performance of our proposed method in restoring art paintings with varying complexities and resolutions.  
For the experimental setting, specific parameters were applied: the width size of the hole represents 10-20% of the original painting, with irregular shapes simulating frequent damage observed in art paintings within museum contexts due to wear or tear. The number of layers in the pyramid is set at 5, and the patch-based propagation uses a patch size of 15 \(\times\) 15 pixels.
## 5 Experiments
### Metric  
To verify performance improvements, we employed the mAP (mean Average Precision) score as a metric. The mAP score is a commonly used evaluation metric in joint detection tasks, assessing the accuracy of the estimated joint coordinates. The AP score utilizes OKS (Object Keypoint Similarity), a normalized distance measurement criterion between the predicted and actual key points. In OKS measurements, threshold values exist; values closer to 1 entail a stricter evaluation, whereas values closer to 0 offer a more lenient assessment. We adopt a standardized approach, averaging the results tested at threshold values of 0.5, 0.05, and 0.95. Additionally, for evaluating 3D coordinates, we also measure the MPJPE score. With the mAP accuracy measurement in 3D coordinates,  
Figure 8: Joint index for swing analysis. The numbers in the figure represent the index of each joint, with grey representing the area near the face, purple the arms, green the legs, and black the root joint.  
there is a tendency for the accuracy to significantly drop as the number of prediction axes increases. To counter this, our performance measurement calculates the accuracy using the error between the estimated pose's root joint distance to the target joint and the ground truth pose's root joint distance to the target joint.
# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 1 Introduction  
Conventional corporate structures, within a society where humans and robots need to collaborate, create obstacles that squander energy, devalue information, and limit knowledge transfer. At this point, the IIoT is transforming how businesses and, by extension, the manufacturing industry, operate [1]. The IIoT is relevant to ubiquitous connectivity, enabling objects, machines, and devices to exchange data autonomously across networks, eliminating the need for human intervention. This connectivity has brought about remarkable progress in industrial organizations, seamlessly merging the physical and digital realms and driving transformative changes in the economy and modern business practices. With the aid of sensors, businesses can effectively monitor performance and address areas that require improvement, thereby enhancing both the manufacturing process and the overall customer experience and ultimately adding value at every production stage [2]. In this context, the IIoT in industrial production is one of the most forward-thinking industrial technologies because it combines two digitization strategies, Artificial Intelligence (AI) and Industry 4.0 [3]. The combination of data analysis and high-level automation technologies significantly improves the industrial environment [4]. However, implementing IIoT systems is fraught with difficulties, notably in terms of security and privacy. The complex and heterogeneous nature of IIoT systems makes them vulnerable to sophisticated security attacks at various levels of networking and communication architecture. Ensuring availability, confidentiality, and integrity becomes a daunting task due to the chaotic nature of these systems. Consequently, there is a risk of mistrust in network operations, concerns about privacy breaches, and the potential loss of critical, personal, and sensitive data of the network's end-users. The comparison outlining the advantages and disadvantages of IIoT devices is presented in Table 1.  
One of the biggest challenges is the industrial privacy requirements in an IIoT ecosystem. Industrial privacy refers to the protection of sensitive information and personal data in the context of industrial environments, such as manufacturing plants, supply chains, and industrial processes. It encompasses the safeguarding of confidential business information, trade secrets, employee data, and customer information from unauthorized access, misuse, or disclosure. Industrial privacy is crucial for maintaining the confidentiality, integrity, and availability of sensitive data within the industrial sector. It involves implementing security measures, policies, and practices that address the unique challenges and risks associated with industrial settings, including the interconnectedness of industrial systems, the collection and analysis of machine-generated data, and the integration of new IIoT technologies and applications. Industrial privacy aims to ensure that sensitive information remains protected, both within the organization and in its interactions with external stakeholders,
## 4 Security Analysis of the Proposed Protocol
### Formal Security Proof
#### 4.1.2 Security Proof
Thus, we have the following:  
\[|\Pr[Succ_{3}]-\Pr[Succ_{2}]|\leq\ \frac{q_{R}^{2}}{2^{l_{1}+1}}+\frac{q_{P}^{ 2}}{2^{l_{2}+1}}+\frac{(q_{s}+q_{e})^{2}}{2p} \tag{3}\]  
\(\mathbf{Game_{4}}\): In this game, \(\mathcal{A}\) desires to guess \(B_{2},B_{5},B_{8}\), and \(B_{12}\) without asking the hash query. We can obtain:  
\[|\Pr[Succ_{4}]-\Pr[Succ_{3}]|\leq\frac{q_{s}}{2^{l_{1}}} \tag{4}\]  
\(\mathbf{Game_{5}}\): In this game, \(\mathcal{A}\) tries to guess \(A_{2}\) without asking the hash query. Similarly, we can obtain the following:  
\[|\Pr[Succ_{5}]-\Pr[Succ_{4}]|\leq\frac{q_{s}}{2^{l_{1}}} \tag{5}\]  
\(\mathbf{Game_{6}}\): In this game, via the corrupt \(\left(\Pi_{u_{i}}^{u},\alpha\right)\) query, \(\mathcal{A}\) computes \(A_{2}\). There are two cases we need to consider:  
Case 1, i.e., corrupt \(\left(\Pi_{u_{i}}^{u},\alpha=0\right)\): with respect to \"fuzzy keywords + honeywords\", the probability that \(\mathcal{A}\) guesses a physician's password is no greater than \(C^{\prime}q_{send}^{s^{{}^{\prime}}}\)[22; 23; 24];  
Case 2, i.e., corrupt \(\left(\Pi_{u_{i}}^{u},\alpha=1\right)\): the probability that \(\mathcal{A}\) guesses the values of \(A_{2}\) is less than \(\frac{q_{e}}{2^{l_{1}}}\). Therefore, we obtain the following:  
\[|\Pr[Succ_{6}]-\Pr[Succ_{5}]|\leq C^{\prime}q_{send}^{s^{{}^{\prime}}}+\frac {q_{s}}{2^{l_{1}}} \tag{6}\]  
\(\mathbf{Game_{7}}\): In this game, \(\mathcal{A}\) initiates a corrupt \((\Pi_{MS_{j}}^{m})\) query to compromise body area sensor node \(MS_{j}\), and then \(\mathcal{A}\) further obtains secret values \(x_{j}\) and \(r_{s}^{{}^{\prime}}\). However, \(\mathcal{A}\) cannot obtain \(r_{s}\) from \(r_{s}^{{}^{\prime}}\) since there is no PPT solution for solving the difficulty of the large number factorization problem [18]. Therefore, we can yield the following:  
\[|\Pr[Succ_{7}]-\Pr[Succ_{6}]|\leq Adv_{\mathcal{A}}^{RSA}(n) \tag{7}\]  
\(\mathbf{Game_{5}}\): This game simulates the attack where \(\mathcal{A}\) tries to calculate the session key, which means that \(\mathcal{A}\) no longer queries the oracle's execute query, send query, and corrupt query. However, similarly to the analysis in \(\mathbf{Game_{7}}\), \(\mathcal{A}\) cannot compute \(r_{s}\) from \(r_{s}^{{}^{\prime}}\). In other words, \(\mathcal{A}\)'s advantage in this game is equal to the advantage in \(\mathbf{Game_{7}}\). Thus, we can have the following:  
\[\Pr[Succ_{8}]=\Pr[Succ_{7}] \tag{8}\]  
Ultimately, we can observe that \(\mathcal{A}\) has no un-negligible advantage greater than \(\frac{1}{2}\); thus, \(\Pr[Succ_{8}]=\frac{1}{2}\). From Equation (1) to Equation (8) and with respect to the triangular inequality, we yield the following:  
\[Adv_{\mathcal{A}}^{P,D}=2\Pr[Succ_{1}]-1=2\Pr[Succ_{8}]-1+2(\Pr[Succ_{1}]-\Pr[ Succ_{8}])\leq\]  
In sum, we can conclude that adversary \(\mathcal{A}\) does not have a significant and strong advantage, \(Adv_{\mathcal{A}}^{P,D}\), in breaking the semantic security of the session key in our protocol. \(\square\)