esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation
### Routing State  
Our fifth set of experiments measured the routing state, which was defined as the maximum number of multicast routing entries stored per router. We used a topology with 50 routers and set the load threshold to five in this set of experiments. The experimental results are shown in Figure 11a-c. In the experiment shown in Figure 11a, the number of groups and number of receivers were set to 100 and 20. In the experiment shown in Figure 11b, the number of groups and the number of sources per group were set to 100 and 20. In the experiment shown in Figure 11c, the number of sources and the number of receivers were set to 20 and 20.  
We found that the amount of multicast routing states per router in our method was close to that in MCT-Sender and is smaller than other methods. Moreover, the number of multicast routing states in SBT was much greater than that in other multicast methods. This is because, in the core-based multicast methods, all sources in each group share a multicast entry, while in SBT each source requires a multicast entry. Meanwhile, in other core-based multicast methods, the multicast flow is transmitted according to the shortest path between the core and the receiver without considering the problem of traffic balance. This will cause many multicast flows to pass through the same router, resulting in more multicast entries in the router. In ILDM, since the multicast flows are directed to the low-load links, the number of flow states that each router needs to maintain is reduced. Although the routing state in MCT-Sender is close to that in ILDM, ILDM has advantages over MCT-Sender in terms of core load, the number of join requests, link load, and traffic concentration.  
Figure 11: The comparison of the maximum number of routing states. (**a**) The variation trends of the maximum number of routing states with a different number of sources; (**b**) The variation trends of the maximum number of routing states with a different number of receivers; (**c**) The variation trends of the maximum number of routing states with a different number of groups.  
Figure 10: The comparison of concentration ratio. (**a**) The variation trends of concentration ratio with a different number of sources; (**b**) the variation trends of concentration ratio with a different number of receivers; (**c**) The variation trends of concentration ratio with a different number of groups.
# Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes
## 4 Materials and Methods
### Temperature Sensor  
For the calibration of the sensors, the monitoring of the temperature of the water and the environment required the acquisition of a temperature sensor that was waterproof. The DS18B20 was the chosen model as it also met the required temperature range (\(-55\) to \(+125\)\({}^{\circ}\)C), worked with OneWire Protocol (a serial protocol that uses a single line of data plus Earth reference for communication. A master initiates and controls communication with one or more slave devices on the same bus), and can connect up to 126 devices on the same bus, thus, dispensing more wires from the circuit to the monitoring point.
# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
## 9 Information Fusion Infrastructure Overview
* Having built the squared Union U matrix, you carry out a post-product of matrix U and permutation matrix P, so you have columns change. The permutation matrix is chosen according to the private key of the cryptography of the algorithm. This matrix, having the same dimensions as matrix U, is composed from six 3 \(\times\) 3 matrixes of permutation, obtained from the same matrix as in (20): \[P_{0}= \begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix} ,\;P_{1}= \begin{bmatrix}1&0&0\\ 0&0&1\\ 0&1&0\end{bmatrix} ,\;P_{2}= \begin{bmatrix}0&1&0\\ 1&0&0\\ 0&0&1\end{bmatrix} \] (20) \[P_{3}= \begin{bmatrix}0&0&1\\ 1&0&0\\ 0&1&0\end{bmatrix} ,\;P_{4}= \begin{bmatrix}0&1&0\\ 0&0&1\\ 1&0&0\end{bmatrix} ,\;P_{5}= \begin{bmatrix}0&0&1\\ 0&1&0\\ 1&0&0\end{bmatrix} \]  
The i-th matrix, selected by k-th value of the key vector component (module 6), is placed on the main diagonal of the final permutation matrix so that we have always one element different from zero for each column and row, hence a block diagonal matrix. We must verify that the size of the Union U matrix is a multiple of three. On the other hand, you do a padding of the last permutation P matrix by including an equal matrix of 1 or 2 order as an extra diagonal block. * Finally, add the product of Union U matrix and permutation P matrix as in (21): \[F=UP\] (21)
* Obtain Matrix \(F\), divided and arranged in a line according to lines to form the output vector V, i.e., hybrid quantum code. When newly achieved, the hybrid quantum code is directly linked to the private key generated by RSA algorithm, to the quantum and the numerical code of native RSA. The data produced by the hybrid quantum code seems random to the external user's eye. In an identification process with this code, if you do not have the private key, then the user cannot be identified. It is reversible only if you have the key; the QNIF algorithm is fully reversible (given that you have the quantum code, naturally). To recover the original information, you only need to backtrack the QNIF algorithm steps. With the private key, you can transport readily the permutation matrix P, considering the transpose of single diagonal blocks as in (22), (23), and remembering that:  
\[P_{3}=P_{4}^{T} \tag{22}\]  
and  
\[P_{4}=P_{3}^{T} \tag{23}\]  
whereas for \(i=0,1,2,5\), being symmetric matrixes as in (24):  
\[P_{i}=P_{i}^{T} \tag{24}\]  
Union matrix \(U\) is given by (25):  
\[U=FP^{T} \tag{25}\]  
Hence, eliminating the possible padding, two vectors are obtained: that of the quantum component and that of the product of the two prime numbers.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Production of Edible Films and Coatings  
Generally, edible films and coatings are the primary forms of edible food packaging. The differences between edible films and coatings lie in their physical form and application process. Edible films are obtained as solid laminated sheets and then used as a food wrap that can be removed or consumed with food, whereas coatings are applied to foods in liquid form by coating, spraying, and immersion. After drying, an edible layer over the food is formed, and it can either be removed or consumed [3]. There are basically two processes for making edible films: wet and dry processes [6]. The main components used for the production of edible films and coatings include a film-forming material, a solvent, and additives (e.g., plasticizers, emulsifiers, antimicrobials). The specifications of these components are important; film-forming materials should be polymer-like polysaccharides, proteins, lipids, or composites, and they must have filmogenic properties. Solvents and plasticizers are selected on the basis of their compatibility with the selected polymer. Solvents can dissolve polymers and also modify their mechanical and barrier properties, which include alcohol, water, citric acid, acetic acid, lactic acid, and hydrochloric acid. Plasticizers can reduce brittleness, increase flexibility, decrease porosity, and improve the mechanical properties of films. However, the selection of each component will depend on the food product being used, and it does not alter the organoleptic properties of food. The effectiveness of edible films and coatings depends on the chemical composition of the polymer and the final properties of the films [12].
## 6 Results and Discussion
### Empirical Relation of HSC Using the GEP Model  
Gene expression programming is an individual supervised machine learning approach which predicts the mechanical compressive strength using tree-based expression. Moreover, GEP gives an empirical relation with input parameters as shown in Equation (9). This simplified equation is then used to predict the compressive strength of HSC. This equation comes from the expression tree which used a function set and terminal set with the mathematics operator as shown in Figure 3. It shows the relationship between input parameters and output strength. GEP utilizes linear as well as non-linear algorithms in the forecasting of mechanical properties.  
\[f_{c}(MPa)=A+B+C \tag{10}\]  
where  
\[A=\left(\frac{19.97*cement}{(water+superplasticizer)+15.31}\right) \tag{11}\]  
\[B=\left((-5.32+(-2.41))-\left(\frac{\left(-\frac{0.58}{\xi}Agg\right)+superplasticizer}{-0.50}* \left(\frac{F}{C}agg\right)\right)\right) \tag{12}\]  
\[C=\left(\left(-0.77*\frac{-4.77}{cement+32.4}*\left((water+superplasticizer)*8.64 \right)\right)+superplasticizer\right) \tag{13}\]  
Before running the GEP algorithm, the procedure starts with the selection of the number of chromosomes and basic operators that are provided by GEP software. The model uses hit and trial techniques where chromosomes of varying sizes and gene numbers are used with operational operators, thus ensuring the selection of the best model. The selected model has the best/fittest gene available within the population which gives adamant performance in making the model. The most feasible and desirable outcome used in the GEP model is \(f_{c}\), which is expressed in the form of an expression tree as shown in Figure 3. Expression tree uses a linkage function with a basic mathematical operator with some constants. It is worth mentioning here that the GEP algorithm uses the RMSE function for its prediction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**R2**} \\ \hline \multirow{5}{*}{**Fc**} & Validation & Testing & Validation & Testing & Validation & Testing \\ \cline{2-6}  & 1.22 & 1.42 & 0.475 & 0.495 & 0.967 & 0.041 \\ \cline{2-6}  & \multicolumn{2}{c}{**RRMSE**} & \multicolumn{2}{c}{**RSE**} & \multicolumn{2}{c}{**P(row)**} \\ \cline{2-6}  & Validation & Testing & Validation & Testing & Validation & Testing \\ \cline{2-6}  & 0.0186 & 0.021 & 0.072 & 0.053 & 0.024 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Random forest (RF) statistical analysis.
## 1 Introduction
In a recent systematic review, it was concluded that dental implant treatment is safe and predictable for diabetic patients; patients with well-controlled DM were able to achieve similar implant survival compared to healthy controls [29]. However, in poor-controlled DM, many studies reported impaired osseointegration [17; 21], higher risk of peri-implantitis [18] and higher level of implant failure [27]. Clinically, the osseointegration in well-controlled DM has not been investigated. A number of animal studies have evaluated bone healing around implants in patients with DM to understand its effects on osseointegration [30; 31; 32; 33]. The results from these studies were convincing that DM adversely affects bone healing around implants by decreasing intrafibrillar collagen mineralization [30], trabecular bone formation [31], bone removal torque [32], bone mineralization and bone-implant contact significantly [33]. Various kinds of animal models have been used as testing steps with dental implants before clinical applications in human. The most commonly-used animals for evaluation of bone implant interaction are the dog, sheep, goat, pig and rabbit [34]. Among many characteristics to be considered when choosing a specific animal for an experiment, bone macrostructure, microstructure, composition and remodeling are the most important factors for consideration. The pig is considered a good likeness to human; however, size and ease of handling may limit its use for research [34]. There are a number of studies evaluating bone-implant contact (BIC) in humans [35; 36; 37; 38; 39]. For ethical reasons, dental implants placed in humans jaw bone and later retrieved for histologic evaluation are mostly used as transitional implants to support provisional dentures during the healing period. Several studies conducted in animals show the adverse effect of DM such as peri-implantitis and disintegration on survival and healing of dental implants [30; 31; 32; 33]; however, the similarity of bone healing around biocompatible materials still varies between animals and humans. Therefore, the clinical study on humans is more appropriate for evaluating the effect of DM on bone healing and bone-implant contact. The purpose of this study was to compare bone-implant contact (BIC) and new bone formation at the bone implant connection in maxilla between healthy subjects and those with T2 DM. The null hypothesis is no significant difference in bone implant contact and new bone formation between healthy and T2 DM patients.
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 6 Simulation of Density Losses
### ADAS/DSRC Losses  
In order to show comparable results to the distance-based experiments, we will need to show the number of consecutive packets lost at each traveling speed. To do this, we use the linear regression formulas we developed thorough the simulation and apply them to the number of nodes in communication range found in Figure 14. These calculations will be shown up to 100 mph, and the values can be seen in Table 7 for each algorithm.  
Table 7 shows packet losses due to density collisions are much higher than those of distance-based, primarily due to nodes competing for transmission time slots. The table shows the number of consecutive losses per 1000 packets for each algorithm, with 100% losses being shown as per our linear equation, and the ADAS losses can be seen to be 124 at 70 mph. This level of loss is considerably high for successive packet collisions. For this reason, we will produce a scale showing 1-100% losses recorded as consecutive. Figure 15 shows the results from extrapolating the distance and packet losses based upon traveling speed. Table 8 shows the corresponding packet loss value to each percentage for clarification, and these have been rounded to the nearest whole packet.
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
## 1 Introduction
Then, the optimal weighting strategy for BeiDou-based LEO POD as well as the optimal precise orbit determination strategy is investigated.
# Internet of Things and Distributed Computing Systems in Business Models
###### Abstract  
The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.  
interact of things; distributed computer systems; business +
Footnote †: journal:  
1 The Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Universidade Europenia, 1200-649 Lisbon, Portugal 1
## 3 Results and Discussion
### GHG Emissions of BETs, FCETs, and ICETs-CNG  
The calculated predictions for GHG emissions (Figure 11) produce results comparable to the specific consumption (Figure 10). This may not seem surprising, but in addition to the specific consumption of the vehicles, the calculation of GHG emissions also includes the emission factors of the various energy generation paths. No distinction is made between fossil, renewable, and other generation paths for long-term (2050) prediction. Instead, the values used are based on the assumption of an expected generation mix that will consist primarily, but not exclusively, of renewable energies (Section 3.4 of [31]).  
As indicated in Figure 11, the use of BET and FCET drive technologies in day-to-day forwarding operations will save a high proportion of today's GHG emissions. The BET\({}_{2050}\) and FCET\({}_{2050}\) will emit only 18.7% and 27.5%, respectively, of the GHG emissions of the ICET-CNG\({}_{2024}\). On the one hand, these significant reductions are due to the lower specific consumption of the trucks (Figure 10). On the other hand, the improved energy generation structures in 2050 will have a reinforcing effect. In Germany, the emission  
Figure 11: Specific GHG emissions of a 20-ton truck. (**a**) Short tour. (**b**) Long tour.  
factor of electricity is expected to be reduced from 376 (2024) to 125 (2050) g/kWh, and the expected reduction for hydrogen is from 301 (2024) to 175 (2050) g/kWh (Table 8). The significantly greater improvement on the electricity side compared to hydrogen will increase the gap between BET\({}_{2050}\) and FCET\({}_{2050}\):  
\[gap(specific\ consumption)=1-\left(\frac{specific\ consumption(BET2050)}{specific \ consumption\ (FCET2050)}\right) \tag{1}\]  
\[gap(GHG\ emission)=1-\left(\frac{GHG\ emission(BET2050)}{GHG\ emission(FCET2050)}\right) \tag{2}\]  
Averaged for the two tours, the gap (_specific consumption_) results in 17.2% while the gap (_GHG emission_) is significantly larger at 40.9%--due to the reasons mentioned above (Figures 10 and 11).
## 3 Results
### PAM Induced Oxidative Damage in Melanoma (Protein and Lipid Level)  
PAM contains various kinds of long-lived RONS created by the cold plasma and amalgamation of cold plasma with the medium, which leads to oxidative stress within cells. This oxidative stress leads to changes in normal redox homeostasis within the cells. To determine the redox status in the induced melanoma, mice were treated with PAM and two biomarkers of oxidative stress (protein carbonylation and lipid peroxidation) were measured spectrophotometrically. The result indicates that the level of protein carbonyl in the control sample was around 13 nmol per mg of protein (Figure 3A).  
Figure 2: Anti-melanoma effect of PAM treatment in mice model. (**A**) Changes in the tumor volume of xenograft mice models. (**B**) Physiological observation of control and PAM treated nude mice bearing subcutaneous tumors on the right hind flank. (**C**) Photographs of solid melanoma excised from control and treatment mice on the 21 st day of the study are shown above the respective bar graphs. Bar graph shows the mean tumor weight of the excised control and PAM treated melanoma. Data are mean \(\pm\) SD of the mean, \(n=3\). \({}^{*}\)\(p<0.05\); \({}^{8}\)\(p<0.01\), \(\#\)\(p<0.001\).  
Treatment with PAM to the tumor significantly increased up to 2 times (27 nmol carbonyl per mg of protein) (Figure 3A), which indicates the induction of oxidative stress in melanoma by PAM treatment. RONS concentration enhanced by PAM, which induced oxidative damage, resulted in an increase in protein carbonylation.  
Lipid peroxidation is the oxidation of the lipid membrane by ROS, also enhanced by the application of PAM. MDA is a product of lipid peroxidation, which is formed by the autoxidation by free radical reaction generated by PAM. The induction in lipid peroxidation and MDA was detected by thiobarbituric acid reactive substances (TBARS) and expressed in nmol/gm of fresh weight of the melanoma tissue. In this study, it has been observed that PAM increases the MDA level. The analysis of the membrane-damaging potential of PAM revealed a significant (two times) increase in the formation of MDA as compared to the control samples (Figure 3B).
## 4 CSAMT Experiment in the Jinding Lead-Zinc Deposit
### CSAMT Data Quality Control  
For field data acquisition, the strength of the received signal and the duration of data collection are crucial to the quality of CSAMT data acquisition. Therefore, in this CSAMT field measurement, on the one hand, the signal-to-noise ratio was calculated through white noise measurement experiments to judge the strength of the received signal, and the transmission signal strength was adjusted accordingly. On the other hand, through data collection time experiments, the quality of measurement data under different collection times was analyzed to determine the optimal data collection time scheme.