esa-sceva/satcom-chunk-collection huggingface dataset:
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
###### Abstract  
With sensors becoming increasingly ubiquitous, there is tremendous potential for Internet of Things (IoT) services that can take advantage of the data collected by these sensors. Although there are a growing number of technologies focused on IoT services, there is relatively limited foundational work on them. This is partly because of the lack of precise understanding, specification, and analysis of such services, and, consequently, there is limited platform support for programming them. In this paper, we present a formal model for understanding and enabling reasoning about distributed IoT services. The paper first studies the key properties of the IoT services profoundly, and then develops an approach for fine-grained resource coordination and control for such services. The resource model identifies the core mechanisms underlying IoT services, informing design and implementation decisions about them if implemented over a middleware or a platform. We took a multi-agent systems approach to represent IoT services, broadly founded in the actors model of concurrency. Actor-based services can be built by composing simpler services. Furthermore, we created a proximity model to represent an appropriate notion of IoT proximity. This model represents the dynamically evolving relationship between the service's sensing and acting capabilities and the environments in which these capabilities are exercised. The paper also presents the design of a runtime environment to support the implementation of IoT services. Key mechanisms required by such services will be implemented in a distributed middleware.  
Internet of Things (IoT); actors; formal model; runtime environment; resource-bounded; IoT services +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Results
### Evaluation of GPP Estimated by the TL-CLUE Model
#### 4.2.1 Verification of GPP Estimation against Sites
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Vegetation**} & \multicolumn{3}{c}{**TL-CLUE**} & \multicolumn{3}{c}{**TL-LUE**} & \multicolumn{3}{c}{**Difference**} \\ \cline{2-10}  & **RMSE** & **Bias** & **Rbias (\%)** & **RMSE** & **Bias** & **Rbias** & **RMSE** & **Bias** & **Rbias** \\ \hline CRO & 3.02 & 0.87 & 25.11 & 3.26 & 1.34 & 38.88 & \(-\)0.24 & \(-\)0.47 & \(-\)13.77 \\ DBF & 2.75 & 0.80 & 18.57 & 2.95 & 1.11 & 25.66 & \(-\)0.20 & \(-\)0.31 & \(-\)7.09 \\ ENF & 2.58 & 1.12 & 30.96 & 3.03 & 1.47 & 40.43 & \(-\)0.45 & \(-\)0.34 & \(-\)9.47 \\ MF & 2.48 & 1.38 & 36.33 & 2.84 & 1.68 & 44.03 & \(-\)0.35 & \(-\)0.29 & \(-\)7.70 \\ GRA & 1.76 & 0.65 & 31.53 & 1.97 & 0.91 & 44.19 & \(-\)0.22 & \(-\)0.26 & \(-\)12.66 \\ OSH & 1.93 & 0.96 & 80.06 & 2.19 & 1.14 & 95.24 & \(-\)0.27 & \(-\)0.18 & \(-\)15.18 \\ WSA & 1.23 & 0.56 & 29.90 & 1.45 & 0.75 & 40.40 & \(-\)0.22 & \(-\)0.20 & \(-\)10.50 \\ SVA & 2.35 & 1.95 & 22.87 & 2.52 & 2.10 & 24.58 & \(-\)0.17 & \(-\)0.15 & \(-\)1.71 \\ All & 2.26 & 1.04 & 34.42 & 2.53 & 1.31 & 44.18 & \(-\)0.26 & \(-\)0.28 & \(-\)9.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of eight-day GPP estimated by the TL-CLUE and TL-LUE models for different vegetation types. The uncertainty of the eight-day GPP estimation quantified by RMSE (g C m\({}^{-2}\) d\({}^{-1}\)) and Bias (g C m\({}^{-2}\) d\({}^{-1}\)) in the TL-CLUE model is lower than that in the TL-LUE model. In terms of overall vegetation, the accuracy of the eight-day GPP estimation is increased by approximately 9.76% by considering the seasonal difference in the CI in the TL-CLUE model.
## 3 DARP Performance
### Statistical Analysis of DARP  
The Analysis of Variance (ANOVA) tool is an extended and widely used statistical method [22; 23; 24; 25]. The ANOVA analysis is a powerful statistical tool that allows to contrast the null hypothesis that the means of several populations are equal (in our analysis, the value of the dependent variable when the following relevant factors are modified: the Baselevel Threshold (TH_B), the Role Threshold (TH_R), the ACK Timer (T_ACK), the Reconnection Timer (T_REC), the Down Timer (T_DOWN) and the Link Timer (T_LINK)) against the alternative hypothesis that at least one of the populations differs from the others in terms of their expected value. This contrast is fundamental in the analysis of experimental results, in which it is interesting to compare the results of several factors with respect to the dependent variable.  
For the scenarios considered in this work, which have a certain fixed complexity, such the one commented in Section 3.1. It is necessary to carry out some adaptation in the parameters according to the different levels shown in Table 2.  
The necessity of carrying out a precise analysis, lead us to use ANOVA, where a large set of samples is considered, in order to verify and analyze the performance of DARP under different conditions. This large set of samples requires a high number of simulations, which by extension requires a very high computational time (exponentially scaling up to an unmanageable level). In order to control and reduce this large number, a fixed set of levels for every factor have been considered,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Value** \\ \hline
**Parameter** & \\ \hline Carrier Frequency (GHz) & 2.4 \\ Carrier Sense Sensitivity (dBm) & \(-85\) \\ Transmit Power (mW) & 1.0 \\ Sensitivity (dBm) & \(-85\) \\ Thermal Noise (dBm) & \(-110\) \\ SNR Threshold (dB) & 4 \\ Path Loss Alpha & 3 \\ T\({}_{\rm alive}\) (s) & 600 \\ Payload Size (Bytes) & 70 \\ Maximum Simulation Time (s) & 1200 \\ CPU Active Drain (mA) & 7.6 \\ CPU Sleep Drain (mA) & 0.237 \\ CPU Radio Idle State (mA) & 1.38 \\ CPU Radio Detection (mA) & 9.6 \\ CPU Radio Sleep State (mA) & 0.06 \\ Battery Capacity (mA) & 1500 \\ Battery Voltage (v) & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main configuration parameters for the battery module, PHY, MAC and APL layers.  
selecting significant enough values from previous analysis [11]. The results obtained from the statistical analysis provide a good insight in the performance of DARP in Smart Cities. The following section delves into the results of the statistical analysis, presenting the main results.
## 5 Results and Discussions
### Experimental Results
#### 5.1.2 Damage Characteristics Examined Using the Wave Velocity Ratio  
[PERSON] [22] indicated that when concrete is subjected to heat damage, the Poisson's ratio of concrete will decrease with an increase in maximum temperature. This is because the heat-damage resulting in concrete cracks increases to decrease the lateral deformation. According to the shear-pressure wave velocity ratio (V\({}_{\mathrm{s}}\)/V\({}_{\mathrm{p}}\)) relation to Poisson's ratio, as shown in Equation (2) [15], V\({}_{\mathrm{s}}\)/V\({}_{\mathrm{p}}\) increases with a decreasing Poisson's ratio. In other words, as the maximum temperature increases, the measured V\({}_{\mathrm{s}}\)/V\({}_{\mathrm{p}}\) of concrete should be increased. UP measurement was used in this study to measure the V\({}_{\mathrm{s}}\) and V\({}_{\mathrm{p}}\) of concrete after heat treatment. The normalized V\({}_{\mathrm{s}}\)/V\({}_{\mathrm{p}}\) was then calculated.  
\[\frac{\mathrm{V}_{\mathrm{s}}}{\mathrm{V}_{\mathrm{p}}}=\sqrt{\frac{(1-2 \
u)}{2(1-\
u)}}, \tag{2}\]  
where \(\
u\) is the Poisson's ratio.  
Figure 9 shows the V\({}_{\mathrm{s}}\)/V\({}_{\mathrm{p}}\) relation to maximum temperature and reduced ratio of stiffness, strength, and toughness. As expected, as V\({}_{\mathrm{s}}\)/V\({}_{\mathrm{p}}\) increased with an increase in maximum temperature,  
Figure 8: Maximum temperature effect on post-peak toughness.  
Figure 7: Rock failure behavior classification in uniaxial compression adapted from [PERSON] (1968).  
as shown in Figure 9a, the \(\rm V_{s}/V_{P}\) also increased with an increase in the reduced ratio of stiffness, strength, and toughness, as shown in Figure 9b\(-\)d. It is interesting to note that \(\rm V_{S}/V_{P}\) has a high positive correlation with the maximum temperature and the reduced ratio of stiffness, strength, and toughness. The coefficient of determination (R\({}^{2}\) value) between \(\rm V_{S}/V_{P}\) and maximum temperature, reduced ratio of stiffness, strength, and toughness reached 0.82, 0.94, 0.93, and 0.88, respectively. This suggests that \(\rm V_{S}/V_{P}\) could be applied to identify the degree of thermal-induced damage in concrete based on the experimental result in Figure 9.
## 3 Results and Discussion
### Optimization for Both the Coefficient of Friction and Wear Rate  
The desirability function for multiple objective optimizations was applied to analyze the correlations between all factors, and it was accomplished by using Minitab 16.2.0 software (Minitab Inc., State College, PA, USA). The input parameters are displayed in Table 9, and the target parameters were chosen to be the minimum value of the COF and wear rate.  
The best set of parameters obtained after the cross-analysis was with the aluminum nanoparticle concentration of 0.6667 wt %, SDS concentration of 2 wt %, and 10 wt % water content of glycerol, as shown in Table 10. With the desirability level of one, the predicted minimum values for the COF and wear rate are 0.0494 and 7.8155 \(\times\) 10\({}^{-10}\) (cm\({}^{3}\)/N\(\cdot\)m), respectively.  
Figure 5: Contour plot for coefficient of friction at: (**a**) SDS of 2 wt %; (**b**) water of 10 wt %.  
Figure 6: Contour plot for wear rate (10\({}^{-10}\) cm\({}^{3}\)/N\(\cdot\)m) at: (**a**) SDS of 2 wt %; (**b**) water of 10 wt %.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **Object** & **Lower Bound** & **Target** & **Upper Bound** & **Weight** \\ \hline A: Nanoparticles (wt \%) & Is in range & 0 & - & 1 & - \\ B: SDS (wt \%) & Is in range & 0 & - & 2 & - \\ C: Water (wt \%) & Is in range & 10 & - & 50 & - \\ Coefficient of friction & Minimum & 0.0514 & 0.0514 & 0.0795 & 1 \\ Wear rate (10\({}^{-10}\) cm\({}^{3}\)/N\(\cdot\)m) & Minimum & 8.2515 & 8.2515 & 14.0140 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Constraints and targets for multi-objective optimization.  
The percentage errors between the measured and predicted values for COF and wear rate are 3.13 and 2.46, respectively. The results are consistent with the analysis mentioned earlier. The predictive model has high confidence and is entirely relevant to the optimum levels for both COF and wear rate.
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 5 Results
### Sedimentary Rocks  
The lowest \(\varepsilon\) values are related to quartzarenites and sandstones, both showing a coarse-grained structure and the presence of quartz minerals. The sandstone average \(\varepsilon\) (0.935) matches with the literature's available data for the same rock type provided by FLIR [20]. The intermediate sedimentary average \(\varepsilon\) values (0.945-0.95) belong to dolostone, limestone and calcisiltite, united by the carbonate rock nature and the fine-grained structure with a variable micro-porosity. The greatest values refer to traverting and calcarenite samples, which are also carbonate rocks, although they are formed by different processes, but affected by macroscopic porosity. In fact, both rock types are characterized by a surface hosting millimetric to centimetric visible voids with different persistence (Figure 2).
## 1 Introduction
Accordingly, this paper aims to reduce the uncertainty caused by changes in crop rotation and cropping area by integrating high spatial resolution multi-temporal China Environmental Satellite (Huan Jing Satellite, HJ-1) charged-coupled device (CCD) images and time series MODIS NDVI products. A method is proposed to generate an adjusted NDVI using an uncropped arable land ratio (UALR). Crop condition assessments (both current snapshots and seasonal dynamics) are compared with and without the use of the proposed method.
## 3 Results
### Boundary Layer
#### 3.1.2 Relationship Between Surface Roughness and Boundary Layer  
Theoretically, an increased topography should induce similar effects to increase fluid velocity on boundary layer behaviour; however, the O-ring simulation setups (Figures 7-10) demonstrated negligible variations in flow characteristics across a range of surface topographies (5 \(\upmu\)m to 100 \(\upmu\)m). This lack of observable difference is attributed to the roughness height remaining significantly smaller than the laminar sublayer thickness in all simulated  
Figure 10: Boundary layer of Setup 6 (1 m/s, 100 \(\upmu\)m).  
cases, effectively rendering the O-ring surface hydraulically smooth. This interpretation is not unrealistic and is the case in [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]'s [3] application of O-ring planes and with [PERSON]'s [15] assertion that when the roughness element diameter is smaller than the laminar sublayer thickness, the surface behaves as a smooth boundary [15; 19; 20; 21]. However, as the surface roughness increases past this critical value, friction is increased on nearby flowlines, expediting the transition from laminar to turbulent flow within the boundary layer; subsequently, this can induce separation points in regions where flow is already unstable, thus increasing drag on the airfoil.
# Mobile User Connectivity in Relay-Assisted Visible Light Communications
## 4 Simulation Results
In the following, we show how the RN area changes based on the elevation of the MP for both AF and DF-based links for a range of elevation angles \(\theta_{MP}\), an azimuth angle of -20\({}^{\circ}\), and a BER of \(<10^{-3}\) as illustrated in Figure 8a. The maximum covered area is achieved for \(\theta_{MP}\) of 5\({}^{\circ}\), therefore RN can be placed up to 2 m from the Rx. Increasing \(\theta_{MP}\) to 25\({}^{\circ}\) results in the reduced distance between the RN and Rx by \(\sim\)10 cm. For \(\theta_{MP}\) of 65\({}^{\circ}\) the maximal RN position in the \(y\)-axis is only 1.6 m. Note that, for 25\({}^{\circ}<\theta_{MP}<\) to 45\({}^{\circ}\) the coverage area is changed by 0.67 m\({}^{2}\). Figure 6: Impulse response for VLC with RN for: (**a**) source to RN; and (**b**) RN to the Rx. The final result illustrates how the RN area can be extended either by increasing the transmit power \(P_{out}\) (i.e., more LEDs on the MP) at the RN or by changing the target BER. In Figure 8b,  
Figure 8: **(a)** Elevation angle dependence of RN for both AF and DF-based links for a range of irradiance angle \(\theta_{MP}\), an azimuth angle of \(-20^{\circ}\) and a BER of \(<10^{-3}\); and **(b)** RN transmitted power and the BER profiles for both DF and AF-based links. Figure 7: Azimuthal dependency of the RN with an elevation angle of \(5^{\circ}\) with RN oriented toward the: **(a)** right wall; and **(b)** left wall. Curves show the borders where the RN can be used and ensures a BER of \(<10^{-3}\) for the entire link. Insets in **(a)** illustrate impulse responses of the complete relay-assisted link. we compare the transmit power from the relay MP for both DF and AF modes for the optimum azimuth and elevation angles of -20\({}^{\circ}\) and 5\({}^{\circ}\), respectively. For example, for a BER of 10\({}^{-3}\) using a LED array of 1 \(\times\) 14 with the \(P_{out}\) of \(\sim\)2.8 W the coverage area for relay-assisted communications is increased by \(\sim\)1.43 m\({}^{2}\) compared to the LED array of 1 \(\times\) 10. Note that, in case of \(P_{out}\) of 2 W and lower BER target 10\({}^{-6}\) we can observe reduced coverage area as expected.
## 4 Experimental Results
### Experimental Results
#### 4.2.2 Community Division Results  
Figure 4 shows the number of subgraph partitions and the number of communities in each period obtained by topological partitioning and clustering. The number of communities is larger than the number of submap partitions. This is because some submap partitions are divided into different application communities due to different data transmission behaviors, such as malicious users and normal users in DDOs attacks. Although the communication relationship is the same, it is divided into different communities due to different communication behaviors.  
To illustrate the effectiveness of our method, we use the method proposed in [PERSON] et al. [11] to identify the same data. Since the [PERSON] method uses a single-mode projection for the bipartite graph in community identification, it can only identify communities from one side of the network at a time. We use the community detection algorithm in [PERSON] et al. [11] for the internal and external IPs in the enterprise network; the results are shown in Figure 4. The results show that for the same dataset the number of communities identified by our method is much larger than that obtained by [PERSON]'s method. Through further analysis of the results, we found that [PERSON]'s method does not distinguish between different data transmission characteristics (such as the DDOs attack situation) in the same communication relationship. Moreover, the communities identified by [PERSON]'s method do not have a  
Figure 4: Number of partitions and communities in different time periods.  
Figure 3: Modularity in different time periods.  
service node (leader node). Thus, it is difficult to visualize the characteristics of the communities. Our method takes into account the communication relationship and data transmission characteristics when identifying the communities and retains the leader nodes in the communities. These help to more accurately identify and research the communities. This is the advantage of our method compared with existing research methods.
## 1 Introduction  
Prosthetic rehabilitation for the treatment of tooth wear has become increasingly diffused over the last two decades [1], since new approaches have emerged to reconstruct worn teeth in a minimally invasive way [2; 3]. The new procedures include a wax and a mockup that are primarily used to design and previsualize both aesthetic and functional outcomes. The mockup is often limited to the treatment of anterior areas, mainly to test aesthetics and phonetics [1].  
However, in cases of extensive rehabilitation, it is also used on posterior teeth, to manage a reversible and effective procedure, and evaluate and test functional aspects [2; 4]. In some cases, mockup is an important step to achieve a new occlusal vertical dimension (OVD), and a guide for the preparation of anterior and posterior teeth. Thus, it represents a mandatory step in the entire procedure. Especially when the treatment requires OVD reconstruction. A recent systematic review pointed that there are many methods to detect the OVD, depending on clinical judgment and clinical preferences (in particular, facial aesthetics and mandible physiological rest position). It could be concluded from the [PERSON] et al. study that, in general, all the available techniques have merits and are helpful for routine clinical use [5].  
Recently, a computer-aided manufacturing method, which simplifies the preparation of both mockup and final prosthesis, was introduced. This digital technique provides the adequate design for mockup from the initial scan [6]. With this digital approach, data concerning fracture, sensitive teeth, pain, gingival recession, deboning, chipping, micro leakage, discoloration, secondary caries and root canal therapy were evaluated with satisfactory clinical results [6; 7].  
However, literature lacks data about the function of masticatory muscles in these subjects after rehabilitation. Thus, the aim of the present study is to analyze the effect of digitally designed and CAD/CAM milled mockups and traditional mockups on masticatory muscle activity in patients with teeth wear [8; 9], rehabilitated with an increase of OVD. The null hypothesis is that there is no difference between the effects of the two techniques on masticatory muscle activity.
## 5 Applications Across Diverse Sectors
### Recent Innovations
#### 5.1.4 Bioprinting and Tissue Engineering
The application of 3D printing in bone tissue engineering continues to evolve, with the development of biocompatible materials such as collagen-calcium phosphate scaffolds. Moreover, the use of thermoplastics and ceramics, due to their suitability for the load-bearing and calcified nature of bone, underscores the potential of 3D printing to advance bone tissue regeneration and repair [66]. Challenges in tissue engineering with scaffold-based methods include achieving uniform cell distribution, enhancing vascularization, and improving cell attachment to scaffold materials. Addressing these issues requires incorporating living cells, bioactive molecules, and biomaterials into three-dimensional scaffolds [67]. The significant expansion of 3D bioprinting is driven by its application in medical fields such as the cosmetic and pharmaceutical industries. This expansion is fueled by the critical need for organ transplants, especially given the limited availability of organs and the growing demand from an aging global population [46; 68].