esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Equilibrium Experiments
#### 2.5.1 Langmuir Model  
The Langmuir isotherm equation describes chemical adsorption. The adsorbed substance forms a monomolecular film on the surface of the solid phase. The Langmuir adsorption isotherm is the basic and most widespread adsorption equation that can be considered as the initial equation for a number of more detailed studies [57; 58]. The linear form of the Langmuir isotherm is given below (5):  
\[\frac{C_{e}}{q_{e}}=\frac{1}{Q_{0}b}+\frac{C_{e}}{Q_{0}} \tag{5}\]  
where \(C_{e}\)--equilibrium DY50 concentration (mg/L), \(Q_{0}\)--monolayer capacity (mg/g), \(b\)--Langmuir constant (L/mg), \(q_{e}\)--amount of DY50 adsorbed at equilibrium (mg/g).  
The values of the Langmuir parameters were obtained from the plots \(C_{e}/q_{e}\) vs \(C_{e}\).  
A characteristic feature of the Langmuir isotherm is the dimensionless separation factor \(R_{L}\) which can be calculated as follows (6):  
\[R_{L}=\frac{1}{1+bC_{0}} \tag{6}\]  
where \(C_{0}\)--initial DY50 concentration (mg/L), \(b\)--Langmuir constant (L/mg).  
In addition, the value of the dimensionless \(R_{L}\) coefficient determines the shape of the isotherm: unfavorable (\(R_{L}>1\)), linear (\(R_{L}=1\)), favorable (\(0<R_{L}<1\)) or irreversible (\(R_{L}=0\)) [57].
## 2 Transmission Line Ice Cover Monitoring System  
The design for the process of collecting images of iced conductors is illustrated below. It is divided into three parts: the image acquisition system, the signal transmission system, and the control system. The main process is as follows: Direct current is supplied by solar panels to power the cameras installed on the transmission lines. These cameras are controlled by a main control board for capturing images. The images are then transmitted to the control center system via a 4G or 5G network and optical fiber. Finally, they are sent to a computer for post-processing. The main principle is shown in Figure 1:
## 3 Results and Discussion
### Air Drying Results  
The majority of the environmental impacts for Air Drying are in the climate change category, as shown in Figure 3. The scores for each of the damage categories produced by individual unit processes in the Air-Drying treatment train (dewatering, air drying, and transportation) are displayed in Figure 4A. Clearly, the Air-Drying step itself, which includes placing biosolids in windows and turning them once a week, contributes the most to the climate change and resources impact categories. The biogenic emissions of carbon dioxide and methane account for roughly 90% of the impacts in the climate change category. In comparison, transportation contributes the most to the impacts in the human health and ecosystem quality categories.  
Figure 4: Contributions of individual unit operations to the overall environmental impacts of (**A**) Air Drying: (**B**) Lagoon Storage; (**C**) Composing; (**D**) Direct Heat Drying; and (**E**) TPAD.  
Figure 3: Overall environmental impacts for each Class A biosolids treatment process train using modified IMPACT 2002+ method. Different colors of stacked bars represent four damage categories.  
The environmental impacts of Air Drying in Figure 3 are further categorized in terms of their sources (utilities/fuel, materials, biogenic emissions, and infrastructure), as shown in Figure 5A. Biogenic emissions account for the majority of impacts for Air Drying, followed by utilities/fuel, which comprises the electricity required for dewatering and the diesel needed for moving and turning biosolids.  
Sensitivity Analysis--Biogenic Emissions  
As described in Methods, Section 2.2.1, a sensitivity analysis was conducted on the effects of Air Drying emissions on environmental impacts due to the lack of available experimental emissions data. Altering the percentage of initial carbon and nitrogen released as methane and nitrous oxide, respectively, as described above created final environmental impacts that ranged from 33.5 pt. (Scenario 1) to 29.1 pt. (Scenario 3). This range of values would not result in a change in the rankings of the processes in terms of their overall environmental impacts, but it highlights the significance of the contributions of biogenic emissions to the potential impacts of Air Drying and the need for further research in this area to better quantify these emissions. Presumably, altering the percentage of initial carbon  
Figure 5: Utilities/fuel, materials, biogenic, and infrastructure sources of environmental impacts of (**A**) Air Drying; (**B**) Lagoon Storage; (**C**) Composting; (**D**) Direct Heat Drying; and (**E**) TPAD. Note that the vertical axes in the panels are scaled differently but all have the same units of pt.  
and nitrogen released as methane and nitrous oxide would have similar effects on the overall environmental impacts of Lagoon Storage as well, because that process train ends with an Air-Drying stage.
## 1 Introduction
### Background
#### 1.1.2 Privacy  
Data privacy is concerned with the correct treatment of data, such as whether or not personal information is shared with third parties. With the rise of big data, data privacy has sparked a heated discussion since it is both necessary and dangerous. On the one hand, data availability provides the foundation for improved AI system performance. Without enough training data, AI models are potentially biased and unable to generalise to unseen datasets. On the other hand, careless release and handling of private data is extremely risky and can result in privacy breaches, trustworthiness, penalties, or civil suits [42]. The United States Health Insurance Portability and Accountability Act (HIPAA) [43], the Electronic Communications Privacy Act (ECPA) [44], the Children's Online Privacy Protection Act (COPPA) [45], and the EU's GDPR [46] are just some examples of data privacy regulations throughout the globe.
## 5 Discussion
### LULC Prediction by CLUE-S Model  
According to binary logistics regression analysis, the derived AUC values for each LULC type allocation under the CLUE-S model exhibit good fit (0.80) and excellent fit (0.99) between the predicted and real LULC transition, as mentioned by [PERSON] et al. [73]. These results imply that the significant driving factors on LULC change, including soil drainage, distance to stream, distance to water body, distance to village, slope, distance to road, annual rainfall, elevation, and population density at subdistrict level, are suitable to apply for each LULC type allocation under CLUE-S model.  
As a result of LULC prediction under three scenarios by the CLUE-S model, the predicted LULC data of three scenarios can deliver realistic results as an expectation. The deviation values between the required land area and the predicted area of each LULC type under three scenarios are very small and vary from \(-0.0023\) to \(0.0033\%\) or \(-0.23\) km\({}^{2}\) (underestimation) to \(0.33\) km\({}^{2}\) (overestimation). In principle, the deviation value depends on iteration driving factors of each LULC type, which indicate the maximum different allowance between the land requirement and land allocation of LULC type under the CLUE-S model [53; 54].  
Consequently, the CLUE-S model can be an effective tool to predict LULC data according to specific policies as the scenario. Essentially, the suitable multiple linear equations from the logistics regression analysis for each LULC type allocation, a land requirement of different scenarios assigned by policy transformation, and model parameters (elasticity and LULC conversion matrix) are very important for predicting LULC under the CLUE-S model.
# Evaluation of Present Accelerated Temperature Testing and Modeling of Batteries
## 2 The Arrhenius Model and \(Q_{10}\) Factor  
The Arrhenius model was developed as a phenomenological equation to model the temperature dependence of certain chemical reactions [26], as in Equation (1).  
\[K=Ae^{-E_{a}/RT} \tag{1}\]  
where \(K\) is the rate constant, \(A\) is the frequency factor, \(E_{a}\) is the activation energy representing the energy barrier for the chemical reaction, \(R\) can be the universal gas constant or Boltzmann's constant, and \(T\) is the absolute temperature (in kelvin). This equation has been extended to model temperature-induced processes and degradation rates of everything from materials to electronic, chemical, and electrochemical systems [27; 28; 29; 30].  
The Arrhenius model can be plotted on a logarithmic scale of the rate coefficient versus the reciprocal temperature, resulting in a linear equation.  
\[logK=logA-E_{a}/RT \tag{2}\]  
The decade rule is an off-shoot of the Arrhenius equation in which a \(Q_{10}\) factor is defined as the change in the reaction rate with a 10 \({}^{\circ}\)C change in the temperature. Thus, a \(Q_{10}\) value of 2.0 implies a two-fold increase in the reaction rate with a 10 \({}^{\circ}\)C increase in temperature. The \(Q_{10}\) factor can be calculated as  
\[Q_{10}=\frac{k_{2}}{k_{1}}=e^{E_{a}/R\cdot(\frac{1}{T_{1}}-\frac{1}{T_{1}+10})} \tag{3}\]  
where \(k_{2}\) is the reaction rate at \(T_{1}+10\)\({}^{\circ}\)C and \(k_{1}\) is the reaction rate at \(T_{1}\).  
When the \(Q_{10}\) factor is plotted with temperature at different activation energies in Figure 1, it can be observed that it is not a constant and it only doubles at one temperature-activation energy value; it in fact decreases with temperature and increases as a function of activation energy. This is important to know because numerous technical articles report that there is a doubling of degradation rate with a 10 \({}^{\circ}\)C increase [25; 31; 32], or try to determine the \(Q_{10}\) factor [10]. No more will be said about this decade rule, since it is simply rarely correct from the perspective of mathematics.  
Figure 1: \(Q_{10}\) factor as a function of temperature \(T_{1}\) and activation energy \(E_{a}\).
## 2 Related Works
### Practical Applications and Emerging Opportunities  
The theoretical advancements in QKA have naturally led to exploration of practical applications, particularly in security-critical environments where traditional cryptographic approaches face significant limitations. The Internet of Medical Things (IoMT) represents a particularly compelling application domain for QKA protocols due to its unique combination of stringent security requirements and operational constraints.  
IoMT environments present distinctive challenges that align well with QKA's inherent advantages. The distributed nature of medical networks, comprising various sensors, monitoring devices, and healthcare systems, necessitates collaborative security approaches rather than centralized key management solutions. Traditional quantum key distribution protocols, which rely on trusted centers for key generation and distribution, may introduce single points of failure that are unacceptable in critical healthcare applications where patient safety and data integrity are paramount.  
Furthermore, the resource-constrained nature of many IoMT devices creates additional requirements for cryptographic protocols. Medical sensors, wearable devices, and implantable systems often operate under strict power consumption limits and computational constraints. QKA protocols that minimize communication overhead and avoidcomplex quantum operations, such as those requiring quantum memory or sophisticated entanglement manipulation, are particularly well suited for these environments.  
The security sensitivity of medical data processing adds another dimension to QKA's applicability in IoMT contexts. Healthcare systems handle highly sensitive patient information that must be protected against both external threats and potential insider attacks. QKA's collaborative approach, where all participants contribute equally to key generation, provides natural protection against single-party dominance or manipulation, addressing the multi-faceted threat landscape characteristic of medical environments.  
This convergence of technological capability and practical need establishes a strong foundation for exploring advanced QKA implementations, particularly those based on novel approaches such as Greenberger-Horne-Zeilinger (GHZ) state applications (GSA-based QKA), which promise to address the specific challenges of modern distributed security-critical systems while maintaining the fundamental advantages that make QKA an attractive alternative to traditional quantum cryptographic approaches.
DWV is transmitted vertically and horizontally by the ectoparasite _V. destructor_[32; 39], causing deformed wings and shortened abdomens often observed on honey bees with a reduced life span [23; 39; 40], associated with bee colony weakening and mortality. Threegenetic variants of DWV were discovered and identified as types A, B, and C, but the DWV-A and -B are the most widespread variants [41; 42]. As with viruses, _V. destructor_ mite is also largely widespread and it weakens the bees, feeding on the fat body [43] both of adult bees and their brood. Many studies shown that it also acts as a vector for many viruses affecting honey bees [44], in particular DWV, leading to the speculation that viruses and mites had a synergistic negative effect on the honey bee colonies [45; 46]. However, the mechanisms that underlie _Varroa_-mediated vectoring and transmission of viruses to honey bees remain poorly understood and the correlation between virus detection and _Varroa_-infestation level is still not so clear [47]. The trypansomatids _Critihidia mellificae_ and _Lottaria passim_ are unicellular obligate parasites that infect _A. mellifera_ colonizing the digestive system [48; 49]. _C. mellificae_ was described for the first time in 1967 [50], while [PERSON] et al. [48] recently characterized _L. passim_. These pathogens are currently receiving much attention given that their presence has been correlated with colony losses especially when detected in association with _N. ceranae_[51; 52; 53; 54], even if their potential pathogenicity and prevalence have not been fully elucidated [55; 56]. However, nowadays _L. passim_ appears to be the predominant one in honey bees worldwide, in contrast to _C. mellificae_[57; 58; 59; 60]. The purpose of this study was to monitor the health status of nine apiaries situated in the Veneto region (northeast of Italy) during spring of 2020 and 2021, evaluating the presence and prevalence in honey bee colonies of 13 pathogens, known and less known or emergent (i.e., _L. passim_, _C. mellificae_, DWV variants). To date, there is a lack of information on the occurrence and spread in the Veneto region, and generally in Italy, so different honey bee colonies across different geographic areas were studied. In this way, we could stimulate and extend epidemiological studies aimed at investigating the potential relationships between colony health and pathogen occurrence and their dynamics on a larger scale, and possibly compare these results with data originating from colonies in the surrounding European countries [61].
## 4 Control System and Problem Statement
### Enhanced SMC-Based Controller Design
The framework overview of vehicle lateral and longitudinal integrated controller is shown in Figure 4. The lane change control method integrates the sliding mode control method and neural network, which has certain anti-disturbance ability. Lane-changing control is the core of the lateral controller. The lateral controller and longitudinal controller work together to complete the lane change driving process.
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 6 Privacy-Preserving Blockchain-Based Solutions
### Dynamic Navigation
The proposed scheme achieves payment control for map service platform (MPS) and also assures completion quality for vehicle users so that benefits of MPS can be maximised. The main entities of SPIR are the certification authority (CA), the pseudonym CA (PCA), the map service platform (MPS), the trace manager (TM) and vehicle users. In this suggested model, when MSP is interested in information relevant to road conditions, it publishes the type of needed data. Then, vehicle users bid to provide the required data that fulfils MPS's requirements. Afterwards, the MSP defines a winner according to its budget and vehicle user's quotation and this winner provides the exchange data with a reward. Blockchain technology is used to support the payment system so that it is safe and secure. In SPIR, the privacy of vehicle users is protected, as pseudonyms are used to provide anonymity and to protect the identity of users while communicating with MSP. CAs verify users' identities and sign pseudonyms submitted by them, while not knowing the content of signed pseudonyms due to adoption of the randomised RSA-based partially blind signature technique. In addition, PCA is not aware of the real identity of vehicle users but verifies the validity of the signature of their pseudonyms. Furthermore, TM which does not participate in the processes of signature publication and verification, can trace malicious users, find the real identity from their pseudonyms and inform CA which sustain a credit account for each user to record his real identity and credit value. The use of blockchainprevents malicious behaviours, as it assists in subtracting credits from malicious vehicles and informing CA via blockchain for recent changes. In their next attempt to register, CA after checking their account refuses their registration, and thus, the system is safe. Moreover, fake pseudonyms and certificates, not related to any vehicle user's identity, cannot tamper with the real users' identity if CA is attacked. Lastly, vehicle users can change their pseudonyms whenever they want, without making it easy for others to predict the next pseudonym change. Results of theoretical analysis and simulations in Matlab and PEPA have shown that SPIR guarantees the security and reliability of data in real-time map updates without leaking of private information and also that vehicles and MSPs will effectively receive fair rewards or budgets.
# Unrepeated 240-km 64-QAM Transmission Using Distributed Raman Amplification over SMF Fiber +
## 5 Conclusions  
The possibility of unrepeatered transmission of a 28-Gbaud Nyquist-shaped optical 64-QAM signal up to 240 km was demonstrated by combining experimental and numerical results. Theoretical investigation through numerical simulations shows considerable improvement in the valid achievable  
Figure 8: Constellation diagrams showing the simulation of best-case results assuming an ideal launch signal with (bottom) and without (top) applying DBP.  
Figure 9: Comparison of optimal experimental and simulation results.  
distance below the FEC limit if the hardware constraints in the transmitter are suppressed. Taking into account the measured loss of standard SMF-28 fiber in our experimental set-up, theoretically, the distance could be extended with implementation of low-loss fiber, using simple random DFB fiber laser-based Raman amplification with a single pump wavelength. This amplification method is compatible with direct detection [4], advanced formats with coherent modulation [5,15,16], and ROPA [19] using both low-loss and standard SMF fiber. This means that our proposed set-up could be readily used to upgrade existing installed standard single-mode fib-r links.  
Conceptualization, P.R., G.R., and J.D.A.-C.; methodology, P.R., G.R., X.P., O.O., and A.U.; software, X.P. and M.J.; formal analysis, J.D.A.-C.; investigation, P.R., G.R., X.P., O.O., A.U., and M.T.; resources, J.D.A.-C.S., and M.T.; writing--original draft preparation, P.R.; writing--review and editing, J.D.A.-C.; visualization, G.R. and X.P.; supervision, J.D.A.-C.; project administration, J.D.A.-C. and S.S.; funding acquisition, J.D.A.-C., S.P., G.J., R.S., S.S., and M.M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by MSCA IF grant SIMFREE (No. 748767), Swedish Research Council (VR) project PHASE (2016-04510), the People Program of the European Union FP7 under grant (608099) and project GRIFON (324391), Spanish MINECO grant TEC2015-71127-C2 and Spanish MICINN grant RTI2018-097957-B-C33, Comunidad de Madrid grant S2018/NMT-4326 SINFOTON2-CM, Swedish VINNOVA-funded project Center for Software-Defined Optical Networks (no. 2017-01559), and UK EPSRC program grant PHOS (EP/S003436/1).  
We thank [PERSON] and [PERSON] for providing FBGs.  
The authors declare no conflict of interest.
###### Abstract  
There is an increased exploration of the potential of wireless communication networks in the automation of daily human tasks via the Internet of Things. Such implementations are only possible with the proper design of networks. Path loss prediction is a key factor in the design of networks with parameters such as cell radius, antenna heights, and the number of cell sites that can be set. As path loss is affected by the environment, satellite images of network locations are used in developing path loss prediction models such that environmental effects are captured. We developed a path loss model based on the Extreme Gradient Boosting (XGBoost) algorithm, whose inputs are numeric (non-image) features that influence path loss and features extracted from images composed of four tiled satellite images of points along the transmitter to receiver path. The model can predict path loss for multiple frequencies, antenna heights, and environments such that it can be incorporated into Radio Planning Tools. Various feature extraction methods that included CNN and hand-crafted and their combinations were applied to the images in order to determine the best input features, which, when combined with non-image features, will result in the best XGBoost model. Although hand-crafted features have the advantage of not requiring a large volume of data as no training is involved in them, they failed in this application as their use led to a reduction in accuracy. However, the best model was obtained when image features extracted using CNN and GLCM were combined with the non-image features, resulting in an RMSE improvement of 9.4272% against a model with non-image features only without satellite images. The XGBoost model performed better than Random Forest (RF), Extreme Learning Trees (ET), Gradient Boosting, and K Nearest Neighbor (KNN) based on the combination of CNN, GLCM, and non-image features. Further analysis using the Shapley Additive Explanations (SHAP) revealed that features extracted from the satellite images using CNN had the highest contribution toward the XGBoost model's output. The variation in values of features with output path loss values was presented using SHAP summary plots. Interactions were also observed between some features based on their dependence plots from the computed SHAP values. This information, when further explored, could serve as the basis for the development of an explainable/glass box path loss model.  
Keywords:path loss; hand-crafted; satellite image; feature extraction; environment +
Footnote †: journal: Applied Sciences