esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Evaluation  
We evaluate our method on the OSDaR23 dataset [3], which provides LiDAR point clouds as ground truth. To the best of our knowledge, this is the only dataset suitable for this task, offering synchronized camera and LiDAR data from railway scenes, along with ground truth extrinsic calibrations. The dataset consists of 45 unique scenes with approximately 1600 images in total. From this, we identify 21 scenes containing approximately 870 recordings that meet the preliminary requirements (see Section 3.1) for every recording within the scene.  
We assume LiDAR provides higher accuracy than camera due to its native 3D sensing, avoiding the inaccuracies caused by the camera's reduction to 2D. To align with camera data, we remove LiDAR points outside the camera's field of view and limit evaluations to a range of 50 m ahead of the train.  
We use the Chamfer distance as the evaluation metric, which quantifies the discrepancy between two point clouds in a metric-based manner. Given the point cloud from our method \(\mathbf{P}_{c,m}\) with \(N\) points and the respective LiDAR point cloud \(\mathbf{P}_{l}\) with \(M\) points, the Chamfer distance is defined as:  
\[CD(\mathbf{P}_{c,m},\mathbf{P}_{l}) =\frac{1}{2}\left(\frac{1}{N}\sum_{l=0}^{N}\abs{x_{i}-NN(x_{i}, \mathbf{P}_{l})}_{2}\right.\] \[+\left.\frac{1}{M}\sum_{j=0}^{M}\abs{x_{j}-NN\big{(}x_{j},\mathbf{P}_ {c,m}\big{)}}_{2}\right), \tag{21}\]  
where  
\[NN(x,\mathbf{P})=\argmin_{x^{\prime}\in\mathbf{P}}\abs{x-x^{\prime}}_{2}. \tag{22}\]  
To meet legal requirements for vegetation clearance, we aim for an average Chamfer distance below 1 m.
# An Overview of CubeSat Missions and Applications
## 8 The Use of CubeSats in Novel Technology Demonstration Missions
### Novel Mission Architectures and In-Orbit Demonstration  
In this final section, CubeSat demonstration missions with novel mission architectures will be discussed. One type of novel mission architecture is the asteroid exploration. One such mission is the NEA Scout, as previously mentioned, that intended to travel slowly past and near-Earth asteroid, utilizing a solar sail, in order to demonstrate solar sailing feasibility and gather detailed images from the asteroid. Despite its unsuccessful mission, it paved the way for the upcoming missions of HERA and Juventas, both aiming for asteroid exploration. One other notable mission is Brokkr-1, a pioneering mission from AstroForge (Huntington Beach, CA, USA) [321]. Launched in April 2023, this mission's primary objective was to demonstrate the feasibility of asteroid mining, using in-space refinery technology. This mission was not successful since the establishment of a command uplink to activate the refinery payload was not achieved. However, building upon the lessons learned, AstroForge initiated their second mission, Odin, but this time, they did not select a CubeSat platform for this demonstration. Another novel mission architecture is that of MakerSat-0 [322]. Launched in November 2017, this 1U CubeSat was developed by Northwest Nazarene University and it was designed to study how the 3D printedpolymers are degrading over time in space. Given the rise of 3D printing technology and the expansion of its potential applications in space, the commonly used materials' degradation rate is a topic that needs to be investigated. MakerSat-0 contained two main experiments, the polymer degradation experiment, to assess mass loss and erosion on ABS, Nylon12, PEI/PC and PLA samples, and an ionizing radiation particle counter, to measure the in-orbit radiation level. From the results, it was found that PLA was the most robust polymer, with no measurable mass loss, while Nylon12 was the least robust, losing approximately 40% of its mass. The lessons learned from this mission also were utilized in the follow up mission MakerSat-1 [323]. It was launched in December 2019 and delivered to the ISS, but contrary to any other CubeSat, MakerSat-1's structure was 3D printed in ISS using the on-board Additive Manufacturing Facility (AMF). The CubeSat was then snap-assembled and deployed in a 480 km orbit. Its scientific mission was to test the durability of PLA, PEI/PC, and ABS samples exposed in the space environment with harsh temperatures and radiation conditions. Once again, PLA was the most resilient of them with 9.9% mass loss while ABS performed the worst with 26.5% mass loss. MakerSat's successful demonstration set a milestone for in-space manufacturing, assembling and testing, while the results have influenced future 3D printing in the ISS.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
###### Abstract  
The integrated satellite-terrestrial network (ISTN) provides a promising solution to achieve high-data-rate and ubiquitous connectivity in next-generation communication networks. Considering the scarce spectrum resources and unevenly distributed traffic demands, we investigate the resource allocation algorithms for ISTNs, where the beam-hopping (BH)-based satellite system and terrestrial systems share the same frequency band. Taking advantage of the scheduling flexibility of BH technology, the dynamical protection zones are constructed to avoid co-channel interference and improve the spectrum efficiency. Since both spectrum efficiency and user fairness are the key optimization indexes in practical systems, two resource allocation problems are formulated to maximize the weighted sum of capacity (MWSC) and maximize the minimum capacity-to-demand ratio (MMCDR) of ISTNs, respectively. By reformulating the problems as mixed-integer linear programming problems, optimal solutions are obtained. To reduce the computational complexity, two greedy suboptimal algorithms are proposed for the MWSC and MMCDR, respectively. The simulation results show that the proposed algorithms achieve higher spectrum efficiency and guarantee fairness between the satellite and terrestrial systems. It is also shown that both the greedy algorithms perform similarly to the optimal algorithms while having much lower complexity.  
integrated satellite-terrestrial network; spectrum sharing; resource allocation; beam-hopping 202424699. [[https://doi.org/10.3390/s24144699](https://doi.org/10.3390/s24144699)]([https://doi.org/10.3390/s24144699](https://doi.org/10.3390/s24144699))
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 3 Modeling Platform, Tools, and Research Methodology Used
### Tools Used  
The tools used were as follows:  
* Kali Linux (2024.2): this operating system and the multitude of different built-in tools were used for various tests/studies related to determining the level of network security and vulnerability testing [48];
* Wireshark (version 4.0.7): this network protocol analyzer can \"capture\" all exchanged packets between network devices in an IP network [49]. Due to its integration with GNS3, all nodes in the modeled network can be monitored through Wireshark. This tool \"captured\" all packets that were exchanged between Asterisk and the users;
* Colasoft Capsa Free (version 11.1): this network analyzer was used to monitor the traffic by displaying information about generated traffic, number of TCP packets, traffic generated by certain protocols, and other traffic-related factors [50];
* Colasoft Ping Tool (version 2.0): a tool that can be used to measure in real time the value of the round-trip delay [51]. The results of the measurement can be used to make graphs to show how the round-trip delay changed over a given period of time.
## 3 Nature of Available Datasets
St-Pierre-le-Jeune Catholic Church, Strasbourg, France (\"St-Pierre\"): The St-Pierre-le-Jeune Catholic Church was built between 1889 and 1893 in Strasbourg during the German era. The church is located in a UNESCO-listed district, the Neustadt, which comprises some other historical buildings of interest such as the Palais du Rhin, formerly the Imperial palace during the German Reichsland era between 1871 and 1918. It is an example of neo-Romanesque architecture crowned by a 50 m high and 19 m wide dome. The neighborhood around the church was used as a case study in the research along with its interior. The church's surroundings was scanned by aerial LIDAR in 2016 by the city's geomatics service; the point cloud data have since been published as open data ([[https://data.strasbourg.eu/explore/dataset/odata3d_lidar](https://data.strasbourg.eu/explore/dataset/odata3d_lidar)]([https://data.strasbourg.eu/explore/dataset/odata3d_lidar](https://data.strasbourg.eu/explore/dataset/odata3d_lidar)), retrieved 24 January 2020). The exterior of the church was also recorded using drones in May 2016 to get a larger-scale and thus more detailed data, while the interior was scanned using a TLS in April 2017. The supporting datasets are as follows:  
1. _Valentino Castle, Turin, Italy (\"Valentino\")_: The Castle of Valentino is a 17 th century edifice located in the city of Turin, Italy. It was used as the royal residence of the House of Savoy and was inscribed into the UNESCO World Heritage list in 1997. Today, the building is used by the architecture department of the Polytechnic University of Turin. The particular \"Sala delle Colonne\" or Room of Columns inside the castle was used in this study. This point cloud has been graciously shared by the Turin Polytechnic team for our experimental use. The Valentino dataset is used exclusively for the pillar detection part of the research. 2. _Haut-Koenigsbourg Castle, Orschuiller, France (\"HK-Castle\")_: The Haut-Koenigsbourg is a medieval castle (dated to at least the 12 th century) located in the Alsace region of France. Badly ruined during the Thirty Years' War, it underwent a massive, if somewhat controversial, restoration from 1900 to 1908. The resulting reconstruction shows the romantic and nationalistic ideas of the German empire at the time, the sponsors of the restoration. The castle has been listed as a historical monument by the French Ministry of Culture since 1993. In this research, only a part of the timber beam frame structure of the castle scanned using a TLS was used to perform tests on the beam detection algorithm. The beams are mostly oblique and distributed in the 3D space. The beams are of very regular shape and relatively unbroken [55]. The HK-Castle dataset is used exclusively for the beam detection part of the research.
## 3 Results  
For this study, the best R\({}^{2}\), adj R\({}^{2}\) and RMSE achieved for predicting chlorophyll-a using support vector machine regressor, random forest regressor, and neural network multi-layer perceptron regressor are presented in Table 5. In a combination of hyperparameters tuning and feature selection, the models showed improvement compared with the default models (no feature selection, no tuning) for the test datasets. Comparing the algorithms, SVR reached the best R\({}^{2}\) (0.78) and RMSE (1.113 \(\upmu\)g L\({}^{-1}\)), however, these were only slightly better results (MLP = 0.76; 1.144 \(\upmu\)g L\({}^{-1}\) and RF = 0.75; 1.189 \(\upmu\)g L\({}^{-1}\)). The algorithms presented good performances for the subsets of training dataset during the cross-validation step (Figure 4). In addition, the predicted values were close to the observed data (Figure 5). All the ML algorithms were better than the linear base model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Hyperparameter** & **Selected Value** & **Default** \\ \hline \multirow{4}{*}{SVR} & kernel & rbf & rbf \\  & C & 3 & 1 \\  & gamma & 0.01 & 0.1 \\  & Epsilon & 0.1 & 0.0001 \\ \hline \multirow{4}{*}{MLP} & max\_iter & 90 & 200 \\  & hidden\_layer\_sizes & 80 & 100 \\  & activation & logistic & relu \\  & solver & adam & adam \\  & Alpha & 0.5 & 0.0001 \\  & warm\_start & True & False \\ \hline \multirow{4}{*}{RF} & bootstrap & True & True \\  & max\_depth & 11 & None \\  & max\_features & 14 & auto \\  & min\_samples\_leaf & 13 & 1 \\  & min\_samples\_split & 2 & 2 \\  & n\_estimators & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameters tested in GridSearchCV and those applied to each ML algorithms. The algorithms gave a good performance for the training dataset and allowed a good generalization for the test dataset, as it can be seen from how close the predicted values are from those observed in Figure 5. Using all of the 211 features and the default hyperparameters, the results in the test data were not as good as those from the optimized models (Table 5), mainly due to overfitting, when the models are more complex than necessary and the fitting in the training dataset is affected by noise [44]. Considering the features used as inputs in each of the algorithms, the Recursive Feature Elimination was implemented by combining Ridge and SVR linear estimators and selecting a maximum number of 17 predictors. This generated the following result: ('SD, 'SST', 'Salinity', 'SD_-1', 'SST_-1', 'SST_-2', 'SST_-9', 'SST_-12', 'SST_-13', 'SST_-14', 'SST_-15', 'Salinity_-1', 'Chl_-1', 'Chl_-4', 'Chl_-5', 'Chl_-7', 'Chl_-8'), with the negative numbers in the codes (Table 2) representing the applied lag in days. The adj R\({}^{2}\) results, which are sensitive to the number of used predictors, showed improvement from 0.02 to  
Figure 4: Boxplot of accuracy in the 10-fold cross-validation training step for the SVR, MLP and RF models, showing the mean and the number of folds (n) or subsets in the training data used to define the best hyperparameters.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 6 Conclusions  
The EECP-MFO model as an efficient and optimized clustering algorithm for FANETS was proposed in this study. Fast-moving nodes have two significant drawbacks: limited energy and inefficient routing. By adjusting the transmission range and correctly clustering the network, we can improve routing and conserve energy on UAVs. The EECP-MFO approach proposed an evolutionary technique for cluster optimization to address the FANET routing problem. Using the EECP-MFO method for FANETs is a cost-effective way to reduce the number of clusters as it cuts down on the amount of unneeded broadcasting and decreases the cost of routing and speed of routing. The efficiency of the proposed EECP-MFO algorithm was assessed and validated as the experiments on the simulations were  
Figure 20: Comparison using various metrics.  
performed and monitored with variable transmission ranges of SNs. EECP-MFO gave near-optimal solutions in the FANETS topological restrictions and created minimum clusters in the search space. GWO, PSO, and ACO are all well-known evolutionary algorithms. However, EECP-MFO is the best solution for the challenges under investigation. It is the clear winner in cluster longevity and the number of clusters; however, EECP-MFO also succeeds in time and energy consumption in establishing clusters. In our proposed algorithm, the UAVs can process identical data packets, which exhausts the UAV's limited resources and degrades network performance. A packet scheduling system is required to solve these difficulties and improve packet management in FANETs. In the future, we will focus on this research area for congestion control in resource constraint UAV networks.  
Conceptualization: [PERSON]. ([PERSON]), S.S.; methodology: [PERSON]. ([PERSON]), [PERSON] ([PERSON]), M.S.; validation: [PERSON]. ([PERSON]), [PERSON] ([PERSON]), [PERSON], [PERSON]; formal analysis: [PERSON], M.S.; investigation: [PERSON]. ([PERSON]), [PERSON], M.S.; resources: [PERSON]. ([PERSON]), [PERSON], M.K.I.R.; data curation: [PERSON], [PERSON] ([PERSON]); writing original draft preparation: [PERSON]. ([PERSON]); writing review and editing: [PERSON]. ([PERSON]), [PERSON], [PERSON], [PERSON] ([PERSON]); visualization: [PERSON]. ([PERSON]); supervision: [PERSON], [PERSON]; project administration: [PERSON]. ([PERSON]), [PERSON]; funding acquisition: [PERSON], M.K.I.R. All authors have read and agreed to the published version of the manuscript. This research work received no external funding.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
###### Abstract  
Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)--whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)--is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.  
c 2018 8 1754 10.3390/app810754 12 July 2018; Accepted: 20 September 2018; Published: 28 September 2018
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Information
#### 6.3.3 Key Findings  
PIS tools improve passenger experience while allowing for offering informed choices, determining the status of main facilities or using innovative solutions of smart ticketing. A FIS also improves the labor utilization and productivity, and nowadays is widely adopted by logistics companies for better customer support and loyalty. The following are the main advantages of FIS for railways: improved dynamic train performances; real-time information provision, which is especially important in the case of hazardous goods and to plan revenue management; it enables the intercommunication and exchange of information from train-to-ground; and remote real-time diagnosis using sensors embedded into wagons.
## 3 Results
### Physical Properties
#### 3.3.3 Electrical Conductivity  
The electrical conductivity (EC) values in Table 8 show that the parameter rose with the inclusion of GRC in both the NA and MRA mixes. That observation is related to the greater ion mobility resulting  
Figure 4: **(a)** Open porosity; (b) dry density; and (c) electrical conductivity as a function of the GRC replacement ratio.  
from a more inter-connected saturated pore structure induced by the addition, which in turn raises electrical conductivity [56]. The graph in Figure 5 shows a direct linear relationship (R\({}^{2}\)\(\geq\) 0.978) between EC and P\({}_{\text{o}}\) or the volume of permeable pores in the NA and MRA mixes. The use of GRC raised EC linearly (R\({}^{2}\)\(\geq\) 0.980) (Figure 4c)) by 7.1% in N10/0 and 16.8% in N25/0 relative to NAC and by 3.0% in R10/50 and 10.9% in R25/50 relative to R0/50. Using 50% MRA raised EC by a mean of 7.2% relative to the mixes with 100% NA.  
All the increases in EC observed lie within the 7% to 24% range reported in the literature for concrete mixes with 25% or 50% coarse fired clay aggregate [53] and mixes with 25% or 50% RCA fines [57].
## 4 Result and Discussion
### Influence of Structural Parameters of Heat Exchanger on Heat Transfer Performance
#### 4.4.1 Evaporator Side Structure  
(1) Width of evaporator flat tube.  
In this paper, the width of the flat tube in the evaporator structure was 24 mm, and the influence of different flat tube widths on heat transfer performance was simulated through the model. Other structural parameters were kept unchanged to increase the width of the flat tube from 20 mm to 28 mm. As can be seen from the Figure 14, the heat transfer increased with the increase of the width of the flat tube, and the increase gradually decreased. With the increase of the width of the flat tube, the direct heat exchange area between the refrigerant and the flat tube also increased, so the heat transfer gradually increased. The internal pressure drop of the evaporator decreased with the increase of the width of the flat tube. When the width of the flat tube increased from 20 mm to 28 mm, the internal pressure drop of the evaporator decreased from 26.51 kpa to 14.36 kPa. Because the effective flow area of the refrigerant increased, the hydraulic diameter increased, the refrigerant velocity decreased and the pressure drop decreased.  
(2) Number of evaporator slices.  
In evaporator micro-channel structure that includes a flat tube with multiple slices, the slices can increase the flat tube fluid disturbance, making the flat tube flow channel structure more compact, but the slices number also will increase the resistance of fluid in the flat tube. The research evaporator  
Figure 14: Heat exchange capacity and pressure drop in the evaporator as a function of flat tube width.  
structure adopted in the experiment included 20 slices, and the model simulated the effects of different numbers of the wafer heat-exchange performance.  
As can be seen from the Figure 15, keeping other structural parameters unchanged, the number of slices was increased from 16 to 24 per flat tube. The chart indicates that while the number increased from 16 to 20, the heat was on the rise, and as slices continued to increase in number, the heat transfer fell sharply. As the slices number increased, the pressure drop in the evaporator increased. Because the flat tube slice number increased, refrigerant circulation area decreased; correspondingly, the hydraulic diameter was reduced, the refrigerant flow rate increased, the pressure drop increased.