esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 1 Introduction
In [14] the authors present a preliminary taxonomy of most UAV 3D path-planning algorithms, but no exhaustive description is provided. On the other hand, ref. [10] analyzes intelligent computational algorithms considering 2D and 3D scenarios. In contrast, our study concentrates on traditional optimization algorithms. The authors in [9] present a general description of 3D path planning algorithms based on the type of solution. Likewise, ref. [1] present basic concepts, algorithms, and parameters used in the path planning problem without performing a systematic study. In contrast, the study in this paper analyzes contributions in the last four years considering parameters used in the objective function, encompassing optimization objectives, environment characteristics, obstacles, number of UAVs, and optimization variables. In this regard, this paper presents a systematic review of the 3D path-planning techniques during the last four years, and it offers valuable insights and comprehensive analysis of existing path-planning solutions in UAVs operating in a 3D environment helping researchers, academics, and professionals involved in UAV technology, wireless communications, and path-planning algorithms. This paper first provides an overview of UAV-enabled communications systems, including the path planning algorithms. Then, a detailed description of the methodology used to perform the systematic mapping studio is presented. In the end, this paper presents the objective functions, the optimization variables, and the proposed solutions related to the optimization models, including optimal or exact methods and suboptimal or heuristic strategies. The rest of the paper is organized as follows. Section 2 provides a background for a better understanding of this study. Section 3 presents the research method, including the research questions and classification scheme. Section 4 provides the mapping study results, responding to the research questions formulated. Section 5 involves analyzing the identified trends and noting any observed gaps that require further investigation. Finally, Section 7 presents the conclusions.
# ETAS Space-Time Modeling of Chile Triggered Seismicity Using Covariates: Some Preliminary Results
## 5 Results
### Comparison of the AIC Values for the Two Areas  
The AIC values for the four models fitted to the data of the Northern catalog, with \(n=8049\) valid observations, are reported in Table 2. The first column reports the AIC values obtained when \(p\) is estimated together with other parameters, while the AIC values of the second columns are obtained from models in which \(p\) is fixed to 1.0001. The gain in the AIC values is evident in models with \(p\) estimated from the data.  
Similar AIC values for Southern area (\(n=27780\)) are reported in Table 3. Here, evidence is generally in favor of a fixed value of \(p\), especially for the model with three and four covariates, as already seen from the profile likelihood plot (Figure 8).  
The model with four covariates seems to perform better than the simpler models both for the North and South areas.  
No further covariates are introduced because no improvement in terms of AIC is observed.  
Figure 8: Profile likelihood for \(p\) in Northern area.  
Again, the big gain in terms of AIC is obtained at the second step, with the introduction of \(z\) (the depth) in the model for triggered seismicity. However, the main contribution is always given by the magnitude.
## Methods
### GPP Models: Definition, Inversion and Analysis
The first is the \"Flight\" dataset (and models), and only makes use of all the \(S_{\text{fp}}\) and EC data acquired the same day of the flight campaigns. \"Flight\" models predicted \(\mathit{GPP}_{\text{hh}}\) for the \(\pm\)15 days surrounding each flight campaign. Within this period we expect that changes in vegetation--not tracked by updated imagery--could lead to larger errors far from the day of the campaigns. For comparison, we fit 30 \"Daily\" modes using all the \(S_{\text{fp}}\) and EC data of the eleven overpasses corresponding to each single day of the period. On one side, this allows to evaluate model robustness against varying meteorological conditions within a narrow time window around the campaigns, while vegetation status can be assumed stable. On the other, further in time from the imagery acquisition, this could reveal model sensitivity to changes in vegetation properties which are not recorded by frequent flight campaigns. The impact of these changes would be assessed via model performance degradation and comparison between \"Daily\" and \"Flight\" models. For the comparison of the model performances, we computed the coefficient of determination \(R^{2}\), the Relative Root Mean Squared Error (\(RMSE\)) and Akaike's Information Criterion (AIC) [71]  
Partial correlation analysis was used to assess if differences between the variables involved in the models could explain the errors in predicted \(\mathit{GPP}_{\text{hh}}\). To do so, we computed the difference (\(D\)) between the value of each variable with the value shown the same half-hour during the day of each flight campaign (Equation (12)). This was done for all the days in the study period. \(D\) was calculated for the \(\mathit{GPP}_{\text{hh}}\), footprint-weighted \(\mathit{NDIVI}\) (\(\mathit{NDIVI}_{\text{fp}}\)), the different versions of \(\mathit{PRI}\) (\(\mathit{PRI}_{\text{fp}}\)), \(\mathit{PAR}\), time (\(t\)) and the ration ratio \(T_{\text{air}}\)/\(\mathit{VPD}\)--which should be related to \(\varepsilon_{\text{stress}}\) according to the MODIS algorithm. \[D_{i,bh}=x_{d=i,bh}-x_{d=0,hh} \tag{12}\]  
where \(x\) is any of the abovementioned variables and \(d\) is the number of days to the date when images were acquired and the subscript \"hh\" means a half hour period of the day an \(i\in(-15,15)\).
## 4 Numerical Calculation Results and Analysis
### Discussion of Seismic Time-History Response Results
#### 4.3.4 Base Shear and Base Moment of the Tank  
From the calculated results in Table 3, the base shear reaches the largest value under X + Z excitation of the El Centro ground motion. It has a significant increase compared with the result by the X excitation. Under the excitation of the other two seismic waves, the base shear also increases obviously with X + Z excitation. Among the three ground motions, the overturning moment is the largest under X excitation of the El Centro ground motion. The vertical seismic component does not increase the overturning moment of the tank except for the Qian'an ground motion. As the vertical excitation component increases the vertical response of the fluid in the tank, the response of the tank caused by the horizontal ground motion is weakened, and the overturning moment of the whole tank is reduced. In general, there is no danger of overturning due to the larger diameter of the tank.
# Toward a Trust Evaluation Mechanism in the Social Internet of Things
## 4 REK Trust Evaluation Model in the SIoT
### Reputation TI Evaluation Model
A modification of the PageRankTM model for the Reputation TI so-called Rep-Ranking is proposed as following:  
\[Rep_{Pos}(X)=\frac{(1-d)}{N}+\text{d}\times\left(\sum_{\forall i}Rep_{Pos} \times\frac{Exp(i,X)}{C_{Pos}(i)}\right);\;\forall i\;that\;Exp(i,N)>\theta \tag{11}\]  
\[Rep_{Neg}(X)=\frac{(1-d)}{N}+\text{d}\times\left(\sum_{\forall i}Rep_{Neg}(i) \times\frac{1-Exp(i,X)}{C_{Neg}(i)}\right);\;\forall i\;that\;Exp(i,N)<\theta \tag{12}\]  
\[Rep(X)=\text{Max}\big{(}min_{Rep},\;Rep_{Pos}(X)-Rep_{Neg}(X)\big{)} \tag{13}\]  
where:  
* N is total number of entities in the networks for calculating Reputation
* \(Rep_{Pos}(i)\) is called positive reputation of the entity \(i\) which considers only positive recommendations. * \(C_{Pos}(i)=\sum_{Exp(i,\;j)>\theta}Exp(i,\;j)\) is the total values of all positive recommendations that the entity \(i\) is currently sharing. Figure 9: Conceptual Reputation Model incorporating the Experience concept. * \(Rep_{Neg}(i)\) is called negative reputation of the entity \(i\) which considers only negative recommendations. * \(C_{Neg}(i)=\sum_{Exp(i,\;j)<\theta}(1-Exp(i,\;j))\) is the total values of all complements of the negative recommendations that the entity \(i\) is currently sharing. * \(Rep(i)\) is the reputation of the entity \(i\) that we are interested. * \(min_{Reputation}\) is a parameter indicating the minimal value of reputation (i.e., 0). This guarantee the reputation value will not go below the \(min_{Reputation}\). * \(Experience(i,X)\) is the Experience TI from the entity \(i\) toward the entity \(X\) described in Section 4.3. * \(d\) is the damping factor. Various studies on PageRank-related literature have tested different damping factors for ranking webpages on the Internet, and they have come up with an appropriate value around 0.85. The research on the damping factor for the Reputation TI model is left as our future work. Similar to PageRankTM, the Equations (8)-(10) form a normalized probability distribution of the reputations (positive reputation, negative reputation and overall reputation) after conducting a number of iterations throughout the network; as well as calculating and updating reputation values for all entities in the network after each iteration. Therefore, the reputation model can be implemented in a centralized system to calculate reputation values for all of entities in a social network. Details of the mechanism can be found in various related literature such as in [8; 56; 57; 58]. This approach could face a critical challenge when the size of a network dramatically increases (i.e., millions of entities). However, by using classification machine learning algorithms with an appropriate semi-distributed architecture, whole social network can be divided into smaller sub-populations, resulting in the feasibility of conducting the proposed reputation model [59; 60].
## 2 Results and Discussion
### The Impact of Different Potential Applications on Succinic Acid Production  
The exploration of various methodologies for the enhancement of fermentation reactions through the application of potential has yielded noteworthy findings. This study systematically evaluated the efficacy of different electrical strategies in augmenting the utility of MECs in producing valuable chemical compounds.  
In the context of this research, the introduction of NR as a mediator in the MEC systems was a pivotal variable. NR's role was to facilitate electron transfer, thus influencing the ORP within the system. By maintaining the ORP at a targeted level of \(-400\) mV, two distinct approaches were employed: one involved the application of a continuous low potential, while the other involved intermittent high-voltage electric pulses. These strategies were compared against a baseline non-electric MEC system, serving as a control within the experimental design.  
Both the continuous low-potential and the high-voltage pulse methods significantly outperformed the control in terms of the succinic acid production. Specifically, the continuous low-potential approach resulted in a 10.12% increase, achieving a concentration of 28.62 g/L, while the high-voltage pulse method saw a 13.08% increase, culminating in a concentration of 29.39 g/L of succinic acid after a 15 h fermentation period. These results were visually corroborated by Figure 2a, which graphically represents the enhanced production levels.  
Moreover, the impact of these electrical interventions on the metabolic directionality of the microbial consortia was profound. This was evidenced by the increased ratios of succinic to acetic acid, which were 33.34% and 8.14% higher in the continuous low-potential and high-voltage pulse systems, respectively, compared to the control. The peak ratios, occurring at the 10 h mark, were 2.44 for the continuous low-potential system and 1.98 for the high-voltage pulse system, indicating a more efficient biosynthetic conversion to succinic acid in the ORP-controlled environments.  
Interestingly, despite the differences in electrical strategies, there was no discernible variance in the cell growth between the two systems at the 15 h mark, as illustrated in Figure 2b. This suggests that the application of electrical potential, in this case, did not negatively impact the microbial proliferation.  
This study also delved into the energy efficiency of the two systems. It was noted that the total electric charge applied in the high-voltage pulse system was 562.88 Coulombs, which was substantially less--only 82.28%--of that used in the continuous low-potential system. This parameter was crucial in determining the overall sustainability and practicality of the electrical enhancement strategies.  
Given the superior succinic acid yields, favorable metabolic shifts, and lower energy input, the high-voltage electric pulse-based ORP-controlled MEC system (HPORP) emerged as the more advantageous approach. Consequently, it was selected for further experimentation, with the aim of refining the process and potentially scaling it for industrial applications.
## 3 Methodology
### MTSA-Related Accident Reduction Rate Theory  
The MTSA-related ARR is the ratio of the decrease in MTSA-related accidents at MTSA-assessed project sites to those at unassessed project sites.  
First, coastal passenger ship operation information near MTSA-unassessed project sites and the history of MTSA-related accidents were combined into a dataset. This dataset was then used to train a pre-implemented ML algorithm to derive the feature importance.  
Second, the trained model from the first step was applied to recent coastal passenger operation information near the MTSA-assessed project sites. If safety diagnostics were not performed, the probability of future MTSA-related accidents could be determined. This probability was then summed to obtain the number of accidents before applying MTSA (BNA).  
Third, the annual average number of MTSA-related accidents occurring on coastal passenger ships operating near the MTSA-assessed project sites was calculated. This number represented the number of accident cases after the application of MTSA (ANA).  
In the second and third steps, the BNA and ANA were calculated under the same environmental conditions. These values were substituted into Equation (1) to derive the MTSA-related ARR.  
\[ARR=\frac{\text{BNA}-\text{ANA}}{\text{BNA}}. \tag{1}\]
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
## 4 Proposed Framework for Climate Forecast Error Reduction
### Data Grid Adjustment  
The spatial grid of the Eta model output at 40-km resolution differs from the NCEP reanalysis grid. While the Eta provides a 61 \(\times\) 126 elements matrix in the region of interest, the NCEP reanalyses provide a matrix having 49 \(\times\) 101 elements for the same region. This is due to the difference in their spatial resolution; while the Eta model employs 0.4\({}^{\circ}\) resolution, NCEP employs 0.5\({}^{\circ}\). Consequently, grid adjustment between both datasets is necessary. Both datasets were scaled through interpolation to 0.1\({}^{\circ}\), leading to a 244 \(\times\) 504-point matrix.
# Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
## 2 Methodology
### Rationale for Case Selection  
This case study was selected because of its extensive monitoring sensors and the control over usage, operation, and maintenance of the building, which allowed tracer gas experiments to be conducted without complications. Indoor sensors were placed throughout the living room to capture thermodynamic behavior, while outdoor sensors installed on the southeast terrace provided data on external variables representing the study area's microclimate. Without this access, we would have had to rely on weather station data from the city, which do not accurately reflect local exterior temperature and wind speed (as it was seen in previous studies [50]), two crucial variables for air leakage calculations. By focusing on a single zone in this small-scale study, we aimed to maximize  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **From Outer to Inner Layer** & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Conductivity** \\ **(W/m-K)** \\ \end{tabular} & \
\begin{tabular}{c} **Density** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Specific Heat** \\ **(Jkg-K)** \\ \end{tabular} \\ \hline \multirow{4}{*}{**Roof**} & Clay tile & 20 & 1 & 2000 & 800 \\  & Timber batten (2x) & 30 & 0.15 & 475 & 1600 \\  & Waterproof membrane & 5 & 0.19 & 960 & 837 \\  & Reinforced concrete beam & 200 & 1.32 & 2000 & 1000 \\  & Timber board & 20 & 0.17 & 500 & 1600 \\  & XPS foam & 50 & 0.037 & 30 & 1400 \\  & Drywall & 15 & 0.25 & 825 & 1000 \\ \hline \multirow{4}{*}{**Facade**} & Perforated brick & 115 & 0.35 & 780 & 1000 \\  & Air cavity & 30 & - & - & - \\ \cline{1-1}  & EPS foam & 50 & 0.037 & 30 & 1000 \\ \cline{1-1}  & Hollow brick & 70 & 0.32 & 770 & 1000 \\ \cline{1-1}  & Gypsum plaster & 15 & 0.4 & 900 & 1000 \\ \hline \multirow{4}{*}{**Interior partition**} & Gypsum plaster & 20 & 0.4 & 900 & 1000 \\ \cline{1-1}  & Hollow brick & 75 & 0.32 & 770 & 1000 \\ \cline{1-1}  & Gypsum plaster & 20 & 0.4 & 900 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the living room envelope, including the roof, facade, and interior partition, as inserted in the building energy model.  
control over environmental variables. This approach facilitates a clearer understanding of the findings and establishes a strong foundation for future, more extensive investigations.
## 3 Results
### Haematological Toxicity  
Bone marrow irradiation in pelvic RT has been already addressed as a relevant factor promoting haematological toxicity in different subsites [132; 133]. Indeed, dose optimizationshould also be considered in PCa RT [117]. [PERSON] et al. (2015) conducted a study to identify clinical and dosimetric predictors of acute and late hematologic toxicity (HT) in chemo-naive patients undergoing whole-pelvis radiotherapy (WPRT) for PCa. By Analyzing data from 121 patients treated with various RT techniques (static-field IMRT, VMAT/Rapidarc, and tomotherapy), the researchers delineated pelvic bone marrow (BM) into specific regions and calculated their dose-volume histograms (DVHs). Significant differences in BM DVH were found among the treatment techniques, with tomotherapy associated with larger volumes receiving lower doses and smaller volumes receiving higher doses. Lower baseline absolute counts of white blood cells, neutrophils, and lymphocytes were identified as strong predictors of acute and late HT. It was found that higher BM volumes receiving 40 Gy (V40) were associated with increased risks of acute Grade 3 and late Grade 2 lymphopenia. The study developed two predictive models, which incorporated baseline absolute lymphocyte counts and specific BM dose constraints, showing promising areas under the curve (AUC) for predicting lymphopenia. These findings suggest that targeted dose constraints to pelvic BM can help mitigate hematologic toxicity, thereby potentially improving patient outcomes.  
Also, [PERSON] et al. (2024) performed a prospective data collection of PCa patients undergoing RT [129]. The results showed that a bone marrow volume > 1000 cc receiving \(\geq 15\) Gy correlates with significantly lower predicted hemoglobin levels. Plan optimization considering the further mentioned value did not affect target coverage in the selected series.
###### Abstract  
The aim of this study was to assess temperature changes with different continuous wave obturation systems when downpacking to 2 mm and 4 mm from the apical foramen in an open system not simulating the surrounding biological structures at body temperature. Sixty single-rooted teeth were divided into three groups: (A) Dia-Duo(r) (DiaDent Group International, Cheongju-si, Korea), (B) Elements Free(r) (Kerr Corporation, Orange, CA, USA) and (C) Calamus(r) (Dentsply Sirona, Ballaigues, Switzerland). The root canals were instrumented with Protaper Gold (Dentsply Sirona, Ballaigues, Switzerland) to size F2 (25.08). The root canals were filled by a continuous wave using an AH Plus(r) sealer (Dentsply Sirona). Temperatures during the obturation procedure were measured by a thermal imaging camera (Testo 875-1(r)) perpendicular to a vice where the teeth were held at \(-2\) mm and \(-4\) mm from the apical foramen. Comparisons were made by applying Student's \(t\)-test and ANOVA (\(p\) = 0.05). The continuous wave technique at \(-2\) mm with the Dia-Duo system(r) emitted average temperatures of 37.3 \({}^{\circ}\)C, Elements Free(r) emitted 39.85 \({}^{\circ}\)C and Calamus(r) emitted 40.16 \({}^{\circ}\)C. At \(-4\) mm, the Dia-Duo system(r) entered average temperatures of 34.81 \({}^{\circ}\)C, Elements Free(r) emitted 33.73 \({}^{\circ}\)C and Calamus(r) emitted 32.91 \({}^{\circ}\)C. There were significant differences between continuous waves at \(-2\) mm and at \(-4\) mm (\(p\) < 0.05). Dia-Duo(r) was the only system that did not present significant differences between the two lengths (\(p\) = 0.197). Regarding the heat emitted, the best system was Elements Free(r), since, at \(-2\) mm, it emitted the highest temperature without going above 47 \({}^{\circ}\)C. The Dia-Duo(r) system had lower temperatures. It could be concluded that not all systems transmit the same temperature to the apex and, therefore, to the periapical tissues. The surrounding conditions, such as temperature and humidity, have not been considered in this study.  
continuous wave technique; endodontic treatment; gutta-percha; heat packing; root canal obturation; temperature +
Footnote †: journal: Applied
## 3 Results and Discussion
### Reliability of the Nodes  
In general terms, the performance of the nodes was satisfactory. Obviously, the installation required periodic maintenance tasks, such as cleaning the solar panels, and eventual interventions due to occasional failures in the hardware of the nodes.  
The evolution of the battery voltage of the nodes is a useful indicator of the \"health\" of the node. As an example, Figure 14 shows the evolution of the battery voltage of one of the nodes (node 25) in typical conditions. As can be seen, the battery management algorithm starts the charge when the voltage falls below the lower threshold (3.95 V) and stops when  
Figure 12: Evolution of matric potential at 25 and 45 cm depth measured by node 04 with MPS-6 sensors (year 2020). The arrows indicate the irrigation events.  
Figure 13: Evolution of trunk diameter fluctuations (TDF) of the RDI30 treatment trees during deficit irrigation period (stage IV). The data was stored by node 23 equipped with linear variable displacement transducer sensors (year 2019).  
the upper threshold is reached (4.1 V). In this way, it works within the optimum voltage range for the battery and reduces the charge cycles, extending its useful life. During the charge there are different voltage peaks corresponding to the daylight hours (the solar panel supplies charge) and the hours of darkness during the night (there is no photovoltaic generation and the battery discharges due to the node consumption). The \"discrepancy\" that appears between days 82 and 87 (22 and 27 March 2020) corresponds to days when solar radiation was lower due to the existence of cloudiness (see Figure 15).