esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Intelligent Solution for the Optimization of Hybrid Energy Systems
## 2 Materials and Methods  
### Proposed Approach for Ontology Construction  
In this work, the problem revolves around the absence of a generic optimization solution for hybrid energy systems. The main objective is to set up a reliable and easy solution to optimize hybrid energy systems suitable for most sites (generic). In the same context, and to solve any research problem, two subproblems must be initially separated and finally integrated, which are: the conceptual subproblem and the operational subproblem. For this research, the following hypothesis is employed:  
* \"The development of an optimization generic solution for hybrid energy systems improves the choice of better technique and simplifies the task for setup of this type of systems\". According to this hypothesis, two variables can be noted, which are \"the developed solution\" and \"the hybrid energy systems\".
* \"The more energy sources there are at a site, the more efficient the hybrid energy system\". It is characterized by the variables: \"more energy sources\" and \"more efficient the hybrid energy system\".
* \"The importance of the solution increases with the increase in the number of cases treated\". Two variables can be extracted from this hypothesis: \"importance of the solution increases\" and \"increase in the number of cases treated\".  
In addition, the following main questions can be asked according to this hypothesis: what is the impact of the developed solution on the optimization of hybrid energy systems? It is also possible to extract from this question some subquestions, such as:  
* During bibliographic research, to what extent (year) can it be conducted?
* What are the advantages and disadvantages of these previous solutions?
* What is the best way forward to develop a solution?
* What are the techniques that can be used to test the solution?
* How to ensure the degree of relevance of the solution under development?* Is there a relationship between the characteristics of the site and the hybrid energy system that will be installed?
* What are the reasons for using a hybrid energy system instead of a nonhybrid energy system?
## 4 Results and Analysis
### Angle Spread
Therefore, in the industrial scenario, both ultra-large metal equipment and the height of the receivers can have a significant impact on the AS. For the visible light channel, the AS almost remained constant with an increasing transceiver distance and was mainly distributed between 1.6 and 1.8 \(\log_{10}(^{\circ})\). It can be seen from Figure 9 that there were MPCs with high power distributed randomly in the range of 0-360\({}^{\circ}\) at each receiver for the visible light band, which kept the AS in a stable state. Figure 8: PAS of the electronic wave channel: (**a**) d = 1 m, (**b**) d = 2 m, (**c**) d = 5 m, (**d**) d = 10 m, (**e**) d = 27 m.  
Figure 9: PAS of the VLC channel: (**a**) d = 5 m, (**b**) d = 10 m, (**c**) d = 21 m.  
wave frequencies, the standard deviation values varied between 0.12 and 0.14, with little difference and an average of 0.1234. Comparing the AS model of the industrial scenario with that of indoor hotspot scenarios in 3 GPP TR 38.901, it was found that for the electronic wave band, the \(\mu\) of the industrial scenario was much lower than that of the indoor office scenario. This was due to the blockage effect of the metal instruments caused by the lower height of the receivers. For the visible light band, there is no proposed model of the AS in the existing study. In [36], the median of the measured AS in the office was 1.40 (\(\log_{10}(^{\circ})\)), which is lower than the \(\mu\) in the industrial scenario. This is due to the fact that the abundant metal instruments generated rich reflection signals which improved the AS.
## 3 Results and Discussion  
### Characterization of Modified Binders  
Results of the laboratory modified asphalt binders' characterization can be observed in Table 5.  
In comparing the results in Table 5 with those provided for the empirical characterization of CAP 50/70 (Table 3) and SBS 60/85 (Table 4), it can be observed that, as expected and also demonstrated by other authors in similar studies [13; 14; 15; 16; 17; 18; 19; 20; 21], the binders modification caused a penetration decrease and a softening point increase. Notable among these results are the relatively low penetration obtained for the binder 3% NC + 2% SBS and the relatively high softening point for the binder SBS 60/85. This reflects gains in the stiffness in the first case in contrast with gains related to the sensitivity to high temperatures in the second case. These characteristics indicate that the asphalt mixtures formulated will be very resistant to permanent deformation. Thus, according to the results obtained in the empirical characterization of the binders, better performance is expected in relation to the permanent deformation for the mixtures SBS 60/85 and 3% NC + 2% SBS. However, it is important to highlight that the behavior prediction of asphalt mixtures based on the modified binders' empiric characterization shows high limitations.  
In relation to the elastic recovery property, the obtainment of a higher value is observed for the binder SBS 60/85. This result is due to the relatively high content of the elastomeric polymer added to the material (approximately 4%). On the other hand, the low elastic recovery of the binder 3% NC is related to the fact that, in the modification, the nanoclay does not function as an elastomeric product.  
With regard to storage stability, the tests carried out with binders modified by nanoclay (3% NC and 3% NC + 2% SBS) revealed that they do not show significant phase separation. By way of  
Figure 3: Load configuration considered for the numerical simulations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Propriety** & **Unit** & **Standard** & \multicolumn{2}{c}{**Asphalt Binder**} \\  & & & **3\% NC** & **3\% NC + 2\% SBS** \\ \hline Penetration & 0.1 mm & ASTM D 5 [35] & 55 & 36 \\ Softening point & \({}^{\circ}\)C & ASTM D 36 [36] & 50.2 & 56.9 \\ Elastic recovery & \% & ABNT NBR 15086 [38] & 6 & 49 \\ Phase separation (24 h/48 h) & \({}^{\circ}\)C & ABNT NBR 15166 [40] & 1.0/- & 0.5/0.8 \\ Apparent viscosity & & & & \\ at 135 \({}^{\circ}\)C (_spindle_ 21, 20 rpm) & & & & \\ at 150 \({}^{\circ}\)C (_spindle_ 21, 50 rpm) & & & & \\ at 175 \({}^{\circ}\)C (_spindle_ 21, 100 rpm) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characterization of asphalt binders 3% NC and 3% NC + 2% SBS.  
comparison, it can be noted that the results are below the maximum limit (5 \({}^{\circ}\)C) established by the Brazilian Specification for Asphalt-Polymer [50].  
In the same way, the results obtained for the modified binders' viscosity also met the limits established by the Brazilian Specification for Asphalt-Polymer [50] despite the considerable increases in comparison to CAP 50/70 (conventional).
## 3 Results
### Inversion Using Spectral Subsets from Sentinel-2  
RapidEye had only five spectral bands in which none returned a large AAE. Therefore, we only examined the effect of spectral subsets on LAI retrieval using Sentinel-2 data. The significance of using four spectral subsets was assessed using R\({}^{2}\), RMSE, and NRMSE between the retrieved and measured LAI. As such first, the spectral bands which resulted in an AAE larger than 0.02 (see Figure 5) were selected assuming these are the bands with large errors [27]. These were the NIR (842 nm, 865 nm) and the red edge (783 nm) bands. Accordingly, the LUT of Sentinel-2 was inverted discounting these spectral bands (subset A). Next, the spectral bands form Sentinel-2 which were comparable to those from RapidEye data were considered. These spectral bands (490 nm, 560 nm, 665 nm, 705 nm, and 783 nm) were consequently used in the inversion process (subset B). Third, the combination of NIR (842 nm, 865 nm) and SWIR (1610 nm 2210 nm) spectral bands were considered and used in the LUT inversion (subset C). Finally, the three red-edge bands (705 nm, 740 nm, 783 nm) were taken and employed for the inversion of Sentinel-2\({}^{\prime}\) LUT (subset D). Retaining the four subsets from Sentinel-2 spectral data decreased the LAI accuracy with respect to using all spectral bands (Table 4). However, when the NIR and SWIR spectral bands were used (subset C), LAI was estimated with moderate accuracy (R\({}^{2}\) = 0.44, NRMSE = 0.21).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Spectral Subset** & **R\({}^{2}\)** & **RMSE** & **NRMSE** \\ \hline Spectral subset A (783 nm, 842 nm and 865 nm** **are excluded)** & 0.24 & 1.38 & 0.26 \\ Spectral subset B (490 nm, 560 nm, 665 nm, 705 nm, and 783 nm) 1 & 0.10 & 2.58 & 0.49 \\ Spectral subset C (842 nm, 865 nm, 1610 nm, 2210 nm) & 0.44 & 1.14 & 0.21 \\ Spectral subset D (red edge bands: 705 nm, 740 nm, and 783 nm) & 0.26 & 2.5 & 0.48 \\ \hline \hline \end{tabular}  
* These spectral bands have central wavelengths close to those from RapidEye.  
\end{table}
Table 4: R\({}^{2}\), RMSE, and NRMSE (m\({}^{2}\)/m\({}^{2}\)) between measured and retrieved LAI based on selected spectral subsets from Sentinel-2 (n = 20).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RapidEye 2015 (n = 30)**} & \multicolumn{3}{c}{**Sentinel-2 2016 (n = 20)**} \\ \hline
**Solution** & **R\({}^{2}\)** & **RMSE** & **NRMSE** & **R\({}^{2}\)** & **RMSE** & **NRMSE** \\ \hline Best fitting spectra & 0.50 & 1.27 & 0.27 & 0.55 & 0.87 & 0.17 \\ Mean of first 10 & 0.51 & 1.10 & 0.24 & 0.59 & 0.84 & 0.16 \\ Mean of first 50 & 0.48 & 1.17 & 0.25 & 0.52 & 0.90 & 0.17 \\ Mean of first 100 & 0.48 & 1.18 & 0.25 & 0.52 & 0.90 & 0.17 \\ \hline \hline \end{tabular}  
* \end{table}
Table 3: R\({}^{2}\), RMSE and normalised RMSE (NRMSE) (m\({}^{2}\)/m\({}^{2}\)) values between measured and estimated LAI in Schiermonnikoog using RapidEye data in 2015 (n = 30) and Sentinel-2 data in 2016 (n = 20) and LUT inversion (utilising the minimum RMSE criterion, mean of the best 10, best 50 and mean of the best 100 solution.
## 3 Simulation Results
### RDA Compensation Simulation
Smaller \(\tau_{p}\) values could improve the compensation efficiency of the TMC algorithm, and target scatters were easier to identify (see the first row), as illustrated in Figure 7. However, there was not much difference between the ISAL images when the ISAL imaging time \(\tau_{T}\) became smaller. Compared with the upper and lower parts of Figure 7, \(\tau_{p}\) was the key parameter for mitigating the adverse effects of atmospheric turbulence on ISAL imaging. In conclusion, turbulence-induced phase fluctuation in the range dimension is the key ingredient for causing a degradation in the ISAL imaging quality. In addition, TMC can only overcome the adverse effects of phase fluctuation at different pulses. Hence, two important conclusions can be drawn: (1) For a certain range resolution, increasing the chirp rate \(\gamma\) means reducing the pulse duration time \(\tau_{p}\), and thereby the adverse effect of atmospheric turbulence can be overcome, and (2) there is no relationship between the compensation efficiency of TMC and the ISAL imaging time \(\tau_{T}\). Thus, decreasing the ISAL  
Figure 6: ISAL imaging with atmospheric turbulence in different range and cross-range dimensions (**upper** and **lower** rows: ISAL imaging with phase fluctuation at different pulses and entire ISAL process; **left** to **right**: without compensation, TMC, and TMC + PFA). imaging time \(\tau_{T}\) reduced the cross-range resolution and could not overcome the adverse effect of the turbulence.
## 4 Asset Digitalization Use Case: IoT and AHI App for a Bridge Maintenance
### Conclusions of the Use Case  
This use case demonstrates the potential of a model-based approach for bridge digitalization to significantly enhance bridge maintenance and management. It shows how by leveraging IoT technologies and advanced analytics, infrastructure asset managers can gain real-time insights into bridge performance, prioritize maintenance efforts, and optimize resource allocation. The integration of short-term monitoring models with long-term predictive models such as AHI enables proactive maintenance strategies, ensuring the resilience and sustainability of bridge infrastructure in the face of evolving operational conditions and challenges.
# A 3D Object Detection Based on Multi-Modality Sensors of USV
## 4 Experiments and Results
### Evaluation on the Offshore Marine Environments Dataset  
We captured many images and point cloud data from a USV in a real offshore marine environment. We divided the sample data into training set and verification set in a 1:1 ratio. Training set data included cruise ships, sailboats and fishing boats. After marking the ground truth of each image, random sampling was applied to generate positive samples and negative samples. When the IoU of random sampling was greater than 0.5, a positive sample was generated. Similarly, negative samples were generated in the same way, while IoU was less than 0.3 and had basic facts. Considering the symmetry of the object, mirror samples were generated for each positive sample and negative sample. For validation set data, the scenarios that USV faces in a real-world natural environment were included. Finally, we tested our algorithms on these challenging datasets to demonstrate the efficiency and accuracy of our network.  
We applied the network to our own dataset of maritime ships, as shown in Figure 7. In the case of 300 proposals in one figure, we drew recall under different IoU thresholds. In particular, when the IoU was 0.25, recall reached 84%. When the IoU was 0.5, ourrecall reached 67%, which indicates that the image and radar fusion learning network had a huge advantage in the case of large numbers of invalid marine image information.  
It can be concluded from the BEV picture (Figure 8) and 3D detection picture (Figure 9) that our method could obtain accurate three-dimensional position, size and direction. Finally, we compared our network with DPM algorithm with regard to accuracy and time, as shown in Table 6. The statistical results show that our network haD higher accuracy rate and lower false alarm rate, while maintaining higher efficiency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **Time** \\ \hline DPM & 54\% & 16 s \\ Ours & 78\% & 120 ms \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of accuracy and time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Easy** & **Moderate** & **Hard** \\ \hline Modified Resnet-50 +early & 75.55 & 66.41 & 58.77 \\ VGG16+ Deep Fusion & 88.29 & 77.49 & 76.41 \\ Modified Resnet-50+Deep Fusion & 88.73 & 79.07 & 77.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average precision (AP) (in %) for BEV boxes for ablation studies.  
Figure 7: From left to right: recall vs. IoU; and recall vs. proposals at IoU threshold of 0.25 and 0.5.
## 4 Results and Discussion
### Future Trends in IoT Adoption in Silicon Mountain and Central Africa  
While this study primarily focuses on current challenges in IoT adoption, it is equally important to consider longer-term trends that could significantly influence IoT uptake in the Silicon Mountain tech ecosystem and the broader Central African region. These trends include future technological advancements, evolving workforce skills, and policy changes, all of which are important for shaping the future of the IoT in the region.  
The development of technologies such as 5G and edge computing will play a transformative role in enhancing IoT infrastructure and capabilities. In terms of this, 5G will provide the high-speed, low-latency connectivity necessary to support large-scale IoT deployments across sectors such as healthcare, smart cities, and agriculture. Moreover, the advent of artificial intelligence (AI) and machine learning (ML) integration with IoT devices will enable more intelligent automation, predictive analytics, and real-time decision-making, offering immense potential for innovation in sectors like manufacturing and transportation. Edge computing will also mitigate data privacy concerns, thereby positively influencing IoT uptake. Preparing for these advancements will be key to ensuring the successful scaling of IoT applications in the Silicon Mountain tech ecosystem.  
One of the significant barriers to IoT adoption in the region identified by this study is the lack of skilled labor in fields such as IoT development, data science, and cybersecurity. However, as IoT technologies continue to evolve, there will be an increasing demand for a workforce proficient in managing and maintaining complex IoT systems. Educational institutions will need to adapt by incorporating the IoT, AI, and data analytics into their curricula, while industry partnerships can play a vital role in providing hands-on training and certifications. Government and private-sector initiatives to foster IoT skill development will be critical to ensure that the workforce is equipped to meet the demands of an IoT-driven economy. This training of next-generation IoT professionals will facilitate IoT uptake.  
For IoT adoption to thrive in the Central African region, supportive regulatory frameworks are essential. Governments must develop policies that address data privacy, cybersecurity, and interoperability to create a stable environment for IoT deployment. Additionally, policymakers can incentivize IoT innovation by providing tax breaks, grants, and funding opportunities for startups. The establishment of IoT regulatory bodies to oversee compliance with global standards and to facilitate public-private collaboration will also help accelerate IoT adoption across various industries.
## 3 Tree Compatibility and Incomplete Directed Perfect Phylogeny
### Incomplete Directed Perfect Phylogeny  
Assume \(X=\{x_{1},\ldots,x_{n}\}\), and let \(C=\{c_{1},\ldots,c_{k}\}\) be a set of _characters_. A _character matrix_ is an \(n\times k\) matrix \(A=[a_{ij}]\), where \(a_{ij}\in\{0,1,?\}\). Entry \(a_{ij}\) is called the _state of taxon \(x_{i}\) on character \(c_{j}\)_. For each \(j\in[k]\) and each \(s\in\{0,1,?\}\), the \(s\)-_set of character \(c_{j}\)_ is the set of taxa \(\sigma_{j}(A,s)=\{x_{i}\in X:a_{ij}=s\}\).  
A _completion_ of a \(\{0,1,?\}\)-matrix \(A\) is a \(\{0,1\}\)-matrix \(B\) obtained by replacing all the \(?\)s in \(A\) by zeroes and ones (thus, \(\sigma_{j}(B,?)=\emptyset\) for all \(j\in[k]\)). A _perfect phylogeny_ for a completion \(B\) of \(A\) is a phylogenetic \(X\)-tree \(\mathcal{T}\) such that \(\sigma_{j}(B,1)\in\operatorname{Cl}(\mathcal{T})\) for every \(j\in[k]\). If such a tree \(\mathcal{T}\) exists, we call it a perfect phylogeny for \(A\) as well.  
The input to the _incomplete directed perfect phylogeny problem_ (IDPP) is an \(n\times k\)\(\{0,1,?\}\)-matrix \(A\). The problem is to find a perfect phylogeny for \(A\) or report that no perfect phylogeny exists.  
For each \(j\in[k]\), we say that column \(j\) of \(A\) is _trivial_ if \(|\sigma_{j}(A,1)|<2\) or \(|\sigma_{j}(A,0)|<1\). Let \(A^{\prime}\) be the matrix obtained from \(A\) by striking out all trivial columns. It is straightforward to show that \(A\) has a perfect phylogeny if and only if \(A\) does. Thus, in the the rest of the paper, we assume that \(A\) contains no trivial columns.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 4 System Design  
The main objective of any IoT application is to allow its end users to access services and information that are specific to their time, environment, and location. Therefore, devices and sensors must be aware of their surroundings as well as the main objective of the underlying application. For the purpose of facilitating data dissemination in an IoT environment, this paper proposes a communication mechanism, based on the conventional gossiping protocol, that utilises both the content of the distributed messages and the network context. Designing an efficient mechanism for communication among sensors in an IoT application involves several challenges related to the following concerns:  
* Power consumption
* Storage capacity
* Bandwidth limitations  
These challenges are addressed in the proposed protocol by allowing the sensors used in an IoT application to communicate effectively using a multi-factor weighting function (MFWF). The proposed content- and context-aware protocol can be simply described as follows:  
1. When a source node has new data to send to the sink node, it will first communicate with its neighbouring nodes to gather information related to their energy levels and physical locations. 2. Once the node has gathered the neighbouring nodes' information, it will assign a weight value to each neighbouring node. The weight value (\(w\)) is calculated for each neighbouring node as follows: \[w=\sum_{i=1}^{n}f_{i}\cdot p_{i}\] (1) where \(n\) is the total number of parameters used in the function and \(f_{i}\) is the weighting factor for parameter \(p_{i}\). The value of the weighting factor can be set to either 0 or 1. If it is set to 1, then the corresponding parameter is considered in the calculation; otherwise, it is not. 3. The next node to be selected will depend on the weight value computed. The node with the highest weight value is selected, and the message is forwarded. If multiple neighbouring nodes have the same highest weight value, then the source node will randomly select one node from among them. This procedure will continue at every selected node, i.e., sender node, until the message is received by the sink node. The main objective of designing the MFWF as shown in Equation (1) is to allow the impact of each parameter on the efficiency of the underlying IoT application to be investigated individually. Moreover, the MFWF is scalable, as new parameters can be added in the future to further improve efficiency, thus ensuring the continued growth of the proposed protocol to maximise its robustness and flexibility. The detailed procedure is shown in Algorithm 1. The weight function includes the following parameters: residual energy, next-node distance, sink distance, node density, and message priority. The rest of this section describes each of the parameters considered in the MFWF.
## 3 Results and Discussion
### Response of Urban Heat Island Effect to Land Use/Land Cover Change
#### 3.3.1 Spatial Correlation between LULC Types and NLST Levels
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **NLST** & **Farmland** & **Greening Land** & **Water Bodies** & **Construction Land** \\ \hline \multirow{4}{*}{1987} & MIN & 0.00 & 0.00 & 0.00 & 0.12 \\  & MAX & 0.69 & 0.59 & 0.73 & 0.94 \\  & MEAN & 0.28 & 0.27 & 0.13 & 0.43 \\  & STDEV & 0.09 & 0.11 & 0.09 & 0.08 \\ \hline \multirow{4}{*}{1996} & MIN & 0.00 & 0.00 & 0.00 & 0.00 \\  & MAX & 0.98 & 0.97 & 0.96 & 0.99 \\  & MEAN & 0.36 & 0.32 & 0.10 & 0.64 \\  & STDEV & 0.12 & 0.11 & 0.10 & 0.11 \\ \hline \multirow{4}{*}{2007} & MIN & 0.00 & 0.00 & 0.00 & 0.00 \\  & MAX & 0.94 & 0.99 & 0.99 & 0.99 \\  & MEAN & 0.42 & 0.38 & 0.13 & 0.65 \\  & STDEV & 0.13 & 0.12 & 0.15 & 0.12 \\ \hline \multirow{4}{*}{2016} & MIN & 0.07 & 0.06 & 0.00 & 0.00 \\  & MAX & 0.90 & 0.69 & 0.89 & 1.00 \\ \cline{1-1}  & MEAN & 0.52 & 0.46 & 0.12 & 0.64 \\ \cline{1-1}  & STDEV & 0.12 & 0.23 & 0.15 & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Normalized land surface temperature in different LULC categories, minimum (MIN), maximum (MAX), average (MEAN) and standard deviation (STDEV). Figure 6: Area proportion of different LULC types for each NLST level during 1987 (**a**), 1996 (**b**), 2007 (**c**) and 2016 (**d**).