esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 6 Performance Evaluation
### Results
#### 6.4.1 PL-to-PM phase  
The first set of results is aimed to shed light on the performance of PL-to-PM communications and on possible settings for the relevant parameters. First, we evaluate the reception range of P2 MP V2V communications with different settings of the MCS. To this aim, we consider a scenario with one platoon.  
Figure 5 reports the probability of successful reception of the PL-to-PM CAM over the distance from the PL for different CQI settings. The metrics decreases with the distance, due to the weaker received signal. Moreover, lower probabilities are observed for higher CQI settings, which are less robust. In particular, at 200 m from the PL, which would resemble the position of a PM at the tail of the platoon (e.g., in case of a platoon with 20 vehicles and intra-platoon gap equal to 10 m), the reception probability is in the order of 0.9, for CQI values equal to or below 7. At a distance of 100 m, for CQI equals to 3, 5, 7, the metrics is very close to 1, thus ensuring very high reliability in the reception of the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Parameter** & **Value** \\ \hline Platoon and road settings & Number of lanes & 4 \\ \hline  & Road length & 2 Km \\  & Number of platoons (\(N\)) & varying \\  & Platoon size (\(L\)) & random \\  & Intra-platoon gap (\(d\)) & 10 m \\  & CAM size & 300 Bytes \\  & CAM update cycle (\(T\)) & 100 ms \\ \hline Network settings & Available bandwidth & 10 MHz (50 RBs) \\ \hline  & VUEs’ tx power & 10 dBm \\  & Resource allocation mode & Mode 3 \\  & Scheduling policy & Max C/1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main settings.  
CAM from the PL. A distance of 100 m from the PL could be the case of a platoon with 10 m-spaced vehicles, which is common platoon setting.  
Note that for CQI = 3, reliability is lower than that with CQI = 5-7. This can be ascribed to the fact that, in order to successfully receive a transmission, the receiver needs to correctly decode _all_ the RBs used for that transmission (i.e., the error probability is also proportional to the number of used RBs). Since a CAM sent with CQI = 3 occupies many more RBs than with CQI = 5-7 (as shown below), the probability of erroneous reception is larger.  
The choice of the employed MCS significantly affects the number of allocated RBs. For example, when considering a scenario with \(N=15\) platoons, the average number of allocated RBs per TTI is reported in Figure 6. As expected, it is larger for lower CQIs, which ensure a higher robustness at the expenses of a higher transmission overhead. A CQI value equal to 7 achieves a good trade-off in terms of allocated resources and ensured reliable coverage. Henceforth, we will refer to CQI = 7 for the PL-to-PM CAM transmission.  
Figure 5: Probability of successful reception of the PL-to-PM CAM, as a function of the distance from the PL (\(N=1\)).  
Figure 6: Average allocated RBs per TTI as a function of CQI used by the PLs (\(N=15\)).
Sensors_**2012**, _12_, 8544-8570; doi:10.3390/s120708544  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Configuring a Context-Aware Middleware for Wireless**  
**Sensor Networks**  
**[PERSON] *, [PERSON] *, [PERSON] and [PERSON]**  
Department of Computer Science, University of M \(\underline{a}\)aga, Campus de Teatinos, 29071, M \(\underline{a}\)aga, Spain; E-Mails: [EMAIL_ADDRESS] (L.F.); [EMAIL_ADDRESS] (E.P.)  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (N.G.); [EMAIL_ADDRESS] (J.C.); Tel.: +34-952-132-846; Fax: +34-952-131-397.  
_Received: 8 May 2012; in revised form: 11 June 2012 / Accepted: 12 June 2012 / Published: 25 June 2012_  
**Abstract:** In the Future Internet, applications based on Wireless Sensor Networks will have to support reconfiguration with minimum human intervention, depending on dynamic context changes in their environment. These situations create a need for building these applications as adaptive software and including techniques that allow the context acquisition and decisions about adaptation. However, contexts use to be made up of complex information acquired from heterogeneous devices and user characteristics, making them difficult to manage. So, instead of building context-aware applications from scratch, we propose to use FamiWare, a family of middleware for Ambient Intelligence specifically designed to be aware of contexts in sensor and smartphone devices. It provides both, several monitoring services to acquire contexts from devices and users, and a context-awareness service to analyze and detect context changes. However, the current version of FamiWare does not allow the automatic incorporation related to the management of new contexts into the FamiWare family. To overcome this shortcoming, in this work, we first present how to model the context using a metamodel to define the contexts that must to be taken into account in an instantiation of FamiWare for a certain Ambient Intelligence system. Then, to configure a new context-aware version of FamiWare and to generate code ready-to-install within heterogeneous devices, we define a mapping that automatically transforms metamodel elements defining contexts into elements of the FamiWare family, and we also use the FamiWare configuration process to customize the new context-aware variant. Finally, we evaluate the benefits of our process, and we analyze both that the new version of the middleware works as expected and that it manages the contexts in an efficient way.  
**Keywords:** context-aware; WSN; middleware; model-driven; configuration; AmI; AAL
## 3 Data Acquisition
### Geodetic Survey  
The reference frame is a geodetic network with 10 concrete pillars that surround the 15 check points. Pillars and target points (Figure 2) were set up in 2017. Each target point consists of one Leica 360 reflector (RFL) and one standard target sphere (\(\O\)145 mm) which are rigidly mounted and firmly attached to the rock.  
Since their installation had to be undertaken by abseiling, their verticality cannot be taken for granted. The determination of their attitude, which is crucial to transfer coordinates between the center for distances and the center for images, was obtained by photogrammetric methods. Figure 3 shows the photogrammetric data acquisition followed to obtain the vector and thus their attitude between centers.  
So far, two geodetic campaigns have been carried out by using a sub-millimetric EDM Kern ME5000 Mekometer (ME5000) [25; 26]. For each distance measurement, the meteorological parameters were measured and double-checked by using a traditional Thies Clima Assmann-Type psychrometer (\(\pm\)0.2 K) and a Thommen 3B4.01.1 aneroid barometer (\(\pm\)0.3 hPa) along with a network of 10 Testo 176P1 data-loggers. All the meteorological sensors were previously calibrated at the Universitat Politecnica de Valencia (UPV) calibration laboratory.  
Prior to the EDM adjustment, the following corrections were applied: refraction correction [27; 28; 29], EDM frequency drift correction and geometric correction [25]. Once these corrections were applied and their corresponding errors computed in order to contribute to the stochastic model, the resulting slope distances were 3D adjusted in the local coordinate system CP2017 (x,y,z) in two steps. In the first step, only distances between pillars were adjusted to provide a solution for the frame. In the second step, distances to check points are included in order to obtain their 3D coordinates. The applied method not only provides coordinates with an overall accuracy of 0.5-1 mm for pillars and 1-3 mm for check points, respectively, but also allows us to monitor the possible displacement of pillars (see Table 1). Finally, the EDM coordinates and their precisions were also converted into geodetic coordinates with ellipsoidal height (\(\varphi\),\(\lambda\),h) and TM30 with orthometric height (E,N,H) [2].  
As it can be seen in Table 2, the differences between the CRP and the EDM coordinates that were obtained in November 2019 are in the range of 1 to 3 cm, which are the expected values and demonstrates that the sub-millimetric EDM-based coordinates are consistent with the CRP-based 3D numeric model that is used as ground truth to validate the accuracy of the CRP survey.
## 6 Simulation Results
** To illustrate the ability of the proposed guidance law in following the varying desired straight-line paths, the desired straight-line path is set as \(0.1\,L\) in the beginning, and when the vessel sails \(100\,L\) (\(t=4446\,\)s), the desired straight-line path is changed to \(0.05\,L\). The water depth to draft ratio is set as \(2.03\). Other conditions are the same as those in Case 1. Simulation results are presented in Figures 10-12. The lateral position with different guidance laws are compared in Figure 10. It can be seen that the vessel can follow the varying desired straight-line paths under all the guidance laws, while the vessel under the proposed guidance law can reach the desired path with the smallest overshoot and the fastest speed. In Figure 11, the time histories of the rudder angle with different guidance laws are compared. It can be seen that the rudder operations with different guidance laws meet the rudder saturation and rate limits. The rudder angle is fluctuating at \(t=4446\,\)s, and the fluctuations in the proposed guidance law are more drastic than those in other guidance laws. It indicates that the good performance of the lateral position in the proposed guidance law is at the cost of the high demanding rudder operation. The comparisons of the desired heading angle and the true heading angle in the proposed guidance law are presented in Figure 12. It can be seen that the true heading angle can track the desired heading angle without steady-state error, which illustrates that the designed yaw tracking control system is robust enough to model uncertainties, unknown parameters, and external disturbances in following varying desired straight-line paths. In Table 4, the quantitative measure of the overshoot and settling time using different guidance laws are compared, and it can be seen that the overshoot is the least and the settling time is the shortest using the proposed guidance law when the vessel following the varying desired straight-line paths. Figure 9: The desired heading angle vs. the true heading angle in the proposed guidance law at \(y_{d}=0.05L\). Figure 11: Comparison of rudder angle with different guidance laws at varying desired paths. Figure 10: Comparison of lateral position with different guidance laws at varying desired paths.
# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
## 4 Results and Discussions
### Case 2: Askervein Hill Field Experiment (TU-03B)
On the windward slope, the TKE of the Mod RSM model is slightly larger than the experimental value, as the measurements obtained with the Gill anemometer may be about 20% lower than those of the Sonic anemometer [40]. It can also be seen on line AA and line B that the prediction results of the Mod RSM for the TKE are more appropriate. The numerical errors of all of the sites of Askervein hill are statistically presented in Figure 12. It can be seen that the MAE values of the wind speed and TKE for the Mod SST \(k\)-\(\omega\) and Mod RSM models are relatively small, at less than 0.12, while the MAE values of the standard model are much larger, and the RRSME values of the Mod RSM model are also smaller than those of the other models. A comprehensive comparison of the errors in the wind speed and TKE values shows that the Std SST \(k\)-\(\omega\) model gives poor results, while the Mod RSM model gives the best results. That is, the modified seven-equation turbulence model based on anisotropy assumptions has greater advantages in predicting the wind speed and turbulent kinetic energy in wind flows over complex terrain. Figure 10: TKE contour plots of the vertical slice along the \(x\)-direction through the RS point in Case 2. (**a**) TKE decay in the upstream region from the inlet boundary to the front of the hill; (**b**) TKE around the hill. Figure 11: Numerical results of the Askervein experiment. Normalized wind speed and TKE along lines A, AA, and B at 10 m from the ground, where \(U_{\text{ref}}=9.1\) m/s, \(k_{\text{ref}}=1.76\) m\({}^{2}\)/s\({}^{2}\), and \(z_{\text{ref}}=10\) m. ‘Exp’ stands for experimental data and ‘\(H/H_{\text{top}}\)’ stands for dimensionless hill height. (**a**) Wind speed-up on line A; (**b**) TKE increase ratio on line A; (**c**) Wind speed-up on line AA; (**d**) TKE increase ratio on line AA; (**e**) Wind speed-up on line B.
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 6 Conclusions and Future Work  
Early identification and management of dairy cow mastitis are critically important for maintaining the health of herds, ensuring the quality of dairy products, increasing production efficiency, safeguarding animal welfare, and reducing economic losses in dairy operations. This study presents an IGOOSE-XGBoost predictive model for subclinical mastitis in dairy cows, which integrates the XGBoost algorithm with an enhanced GOOSE optimization algorithm.  
Our work advances the field in several key aspects compared to previous studies. [PERSON] et al.'s [40] study, which utilized different machine learning models (Deep Learning, Naive Bayes, Generalized Linear Model, Logistic Regression, Decision Tree, Gradient-Boosted Tree, and Random Forest) and a large dataset of 364,249 milking instances, found that GBT was the most accurate, with 84.9% accuracy, in predicting subclinical mastitis. [PERSON] et al. [41] evaluated different methods for machine learning (Naive Bayes, Random Forest, and Extreme Gradient Boosting) for clinical mastitis prediction, reaching a 72% accuracy rate and accurately categorizing 85% of CM cows in their continuous model. In comparison, our model achieves higher accuracy (87%) while specifically addressing the more challenging task of subclinical mastitis detection. The experimental outcomes, scrutinized from both qualitative and quantitative standpoints, indicate that the IGOOSE-XGBoost model has higher accuracy in predicting subclinical mastitis (ACC = 0.87; REC = 0.88; PRE = 0.89; F1 score = 0.88), outperforming traditional methods. These results surpass those reported in the recent literature, in which typical accuracy rates range from 0.75 to 0.83. Furthermore, our regression model for SCS prediction achieves an R\({}^{2}\) value of 0.663, demonstrating stronger predictive power compared to conventional approaches. In future work, we intend to utilize different CEC test functions to evaluate the efficiency of the improved algorithm and apply it to cow disease prediction, thereby expanding the scope and applicability of our prediction framework to the veterinary and dairy industry.  
Methodology, R.G. and Y.D.; writing--original draft preparation, R.G.; writing--review and editing, R.G.; visualization, Y.D.; supervision, Y.D.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Basic research innovation group grant number [21]R7 RA858] and Gansu University Innovation Fund Project \"Research on Machine Learning Prediction Model of Subclinical Mastitis in Dairy Cows\" grant number [2022B-107].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion  
### Drying Characteristic of Cassava Leaves  
The initial moisture content of fresh cassava leaves was 69.50 \(\pm\) 0.79% w.b. After drying, the moisture content decreased to a constant value (MCeq) of 8.26 \(\pm\) 0.04, 5.53 \(\pm\) 0.33, 4.46 \(\pm\) 0.13, 3.50 \(\pm\) 0.11, and 3.01 \(\pm\) 0.02% w.b. at drying temperatures of 40, 50, 60, 70, and 80 \({}^{\circ}\)C, respectively (Figure 1A). The drying times required to reach MCeq were 20, 7, 6, 3, and 2.5 h for these respective temperatures. The Page model is consistent with previous studies indicating that higher drying temperatures result in shorter drying times due to increased drying rates [24; 25]. However, higher drying temperatures can also lead to the degradation of nutrients and bioactive compounds. Figure 1C illustrates the variation in drying rate with the moisture ratio during cassava leaf drying at various temperatures. Initially, the drying rate was notably high and gradually decreased as the moisture ratio declined. The drying process occurred during the falling rate period at all temperatures, with no constant rate period detected due to the insignificant amount of unbound moisture in the leaves [6]. Six widely used thin-layer drying kinetic models were applied to fit the drying curve of cassava leaves, and the statistical results are presented in Table 1. The Page model was identified as the most suitable for describing the drying behavior of cassava leaves at the temperatures between 40 and 80 \({}^{\circ}\)C, as it yielded the highest R\({}^{2}\) (0.9973-0.9999) and lowest MAPE (0.6074-3.6879%) and RMSE (0.0014-0.0042). This model is often used to describe the drying of various leaves, such as thymus, pandanus, olive, kaffir lime, mint, CTC tea, moringa, coriander, parsley, rosemary, nettle, and bay [6]. The drying curves fitted with the Page model are shown in Figure 1A. The moisture ratio continuously decreased during drying. The k value in the model equations, which represents the drying rate, increased with higher temperatures. Figure 1B shows the Arrhenius plot between ln k and the inverse temperature (1/T) according to Equation (7):  
\[\ln\ \mathrm{k}=\ln\ \mathrm{A}-\frac{\mathrm{E}}{\mathrm{RT}} \tag{7}\]  
where A is pre-exponential factor, E is activation energy (kJ\(\cdot\)mol\({}^{-1}\)), R is gas constant (8.314 J\(\cdot\)mol\({}^{-1}\)K\({}^{-1}\)), and T is the drying temperature (K). The result shows that the Arrhenius equation well described the relationship between k and the drying temperature. The activation energy was 57.6 kJ\(\cdot\)mol\({}^{-1}\), which is close to that for rue leaves (60.58 kJ\(\cdot\)mol\({}^{-1}\)) [26], but much higher than those for bushbuck leaves (26.05 kJ\(\cdot\)mol\({}^{-1}\)) [27] and broccoli leaves (27.06 kJ\(\cdot\)mol\({}^{-1}\)) [28]. Figure 1: _Cont._  
Figure 1: Changes in the moisture ratio of cassava leaves at different drying temperatures (**A**); lines are drawn using the Page model. Arrhenius plot of the logarithm of the drying rate obtained from the Page model (ln k) and the inverse temperature (1/T) (**B**). The drying rate as a function of moisture ratio (**C**).
# G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud
## 5 Discussion
### Ablation Study  
To evaluate the effectiveness of the G&G attack and demonstrate the crucial importance of each component, we conducted ablation studies by separately removing the autoencoder and the local geometric attack, and then analyzing the results both quantitatively and qualitatively. As shown in Table 5, removing any part causes a decrease in the ASR. The primary role of the autoencoder is to learn the distribution of point clouds. Geometry-aware methods consider two types of constraints. Removing either of these components resulted in the generation of numerous irregular surface fragments and noticeable outliers. The reason is that the AE has moved the point cloud to the vicinity of the decision boundary during the destruction-reconstruction process, and attacks that limit certain critical points increase the unrecognizability and uncertainty of the geometric structure while maintaining a smooth and dense surface as much as possible. The local geometry  
Figure 5: Visualizing the overall transferability of L3A-Attack (**left**), AdvPC (**middle**), and G&G attack (**right**). Elements in the same row correspond to attacks using the same surrogate network, while elements in the same column correspond to the networks to which the attacks are transferred. Since each attack is optimized on the surrogate network it is transferred to, values on the diagonal tend to be larger. For clarity, brighter elements indicate better transferability. We observe that G&G attack exhibits higher transferability compared to the others. The transferability score under each matrix is the average of all values within it, providing a summary of the overall transferability of these attacks. aware method reduces the generation of large numbers of outliers in adversarial attacks. Local geometric attacks are employed to suppress outliers, making adversarial attacks softer and more globally evenly distributed. Through experiments, we found that the ASR of overall attacks is higher--about twice that of local attacks--although the imperceptibility is poorer. This confirms our speculation. To confirm the superiority of the joint autoencoder, we compared the simple autoencoder in AdvPC [23] with the joint autoencoder used in the G&G attack. As shown in Figure 6, the joint autoencoder exhibits better convergence. Experiments have confirmed that, under similar imperceptibility metrics, the ASR of the joint autoencoder is higher, as shown in Figure 7. As the number of epochs increases, the network becomes more complex, and the query time also increases. The ASR increases significantly from 1 to 400 epochs and then stabilizes. Figure 6: In comparison to AdvPC, the G&G attack curve shows a more rapid increase, leveling off after 100 iterations. Furthermore, our method achieves better performance in terms of ASR.
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 3 Analysis of Meteorological Characteristics
### Meteorological Characteristics
This indicates a generally dry climate, where humid conditions are relatively infrequent. At Site02, the humidity distribution shifts upward, to the 40-80% range. Although slightly more humid than Site01, the region still remains relatively dry. In contrast, the distributions at Site03 and Site04 are skewed toward higher humidity levels, primarily between 65% and 95%. The steep curves reflect strong concentration and low variability. This suggests a generally more humid and stable climate. Site03, in particular, shows the most concentrated humidity distribution. This may be influenced by geographic factors, such as proximity to water bodies and local topography, resulting in a significantly wetter environment. Finally, regional differences in wind speed distributions are illustrated in Figure 2d. At Site02, Site03, and Site04, the wind speeds are mainly concentrated around 2 m/s. The steep distribution curves suggest limited variability and a relatively stable wind environment, with a low likelihood of extreme wind events. In contrast, Site01 exhibits a broader wind speed range, primarily between 2 m/s and 4 m/s. The maximum wind speed at this site exceeds 10 m/s, which is significantly higher than in the other regions. This indicates greater variability and an increased potential for extreme wind conditions. In summary, substantial differences were observed in the distribution characteristics of key meteorological variables, including air temperature, solar radiation, relative humidity, and wind speed, across the studied regions. These variations reflect the diversity in geographic settings, climate types, and natural conditions. Such regional disparities reveal the spatial heterogeneity of meteorological patterns. These differences also pose challenges for developing and generalizing temperature prediction models. Therefore, regional variability must be carefully considered to enhance models' adaptability and improve their prediction accuracy.