esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 3 Methodology Concept
### Applied Algorithms and Technologies in This Study
#### 3.3.2 Transformation Process  
To accurately transform each segmented vehicle boundary into real-world spatial polygons, a projective transformation method was employed, utilizing seven common reference points with known coordinates in both the image coordinate system and the EPSG:3857 (Web Mercator with WGS84 datum) system, as shown in Figure 4. This transformation enabled the identification of unoccupied spaces by subtracting the areas of occupied vehicles from predefined parking zones, which were stored as vector geometries in the database.  
This two-dimensional projective transformation maps points from one coordinate system to another by using eight parameters, allowing for the precise calculation of new point positions based on the reference coordinates. Given the oblique angles in the camera images, this method was chosen over simpler alternatives, such as Affine or Helmert transformations, to achieve higher accuracy. For a detailed explanation of the methodology, see [PERSON] [81]. The achieved root mean square error (RMSE) was 0.57 m, ensuring a high level of mapping precision.  
The transformation was performed once for each camera using a single frame from the video to calculate the transformation parameters. These parameters were then applied consistently to all subsequent frames, as the camera positions remained unchanged throughout the video sequence. This approach eliminated the need for recalculating the parameters for each frame, ensuring that the vehicle coordinates could be accurately mapped to real-world coordinates for the entire video.  
This transformation is essential for providing real-time parking availability to users via mobile applications. The process operates seamlessly in the background, continuously updating parking information without requiring users to understand or engage with the underlying technical details.
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 4 Object Model Analysis  
To manipulate a general-shaped object, we preprocess its 3D-shape model and obtain the necessary information for manipulation planning. We aim to determine the following:  
* A set of object vertices, which are the potential rotational vertices during pivoting;
* A set of object edges, which are the potential rotational edges during tumbling;
* A set of stable object placements;
* A set of grasp configurations for the dual-arm manipulator.  
Superimposed segments are implemented when preprocessing the 3D-shape model of the object ([PERSON] and [PERSON], 2018). These segments often generate numerous grasp configurations. However, as numerous grasp configurations increase the calculation load of the manipulation planner, we reduce the number of grasp configurations by checking the collisions with the EEFs and evaluating the grasp stability, as described in the next subsection.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
### Contributions  
In this work, we make two contributions:  
1. A deep learning pipeline for ship detection with few training examples. We take advantage of self-supervised learning to learn features on large non-annotated datasets of Sentinel 2 images and we learn a ship detector using few-shot transfer learning;
2. A novel Sentinel 2 ship detection dataset, with 16 images of harbours with a total of 1053 ship annotations at the pixel level.
## 4 Experiments
### Maturity Stage Classification Configuration  
As mentioned in Section 2, maturity stage classifications that are too rough or too fine have advantages and disadvantages. In this paper, the two maturity stages in Ref. [27] are merged into one maturity stage. In order to illustrate whether the fine class maturity stage is configured as in Ref. [27], we perform an experiment. We assume completely correct classification (100% accuracy) for these six stages, and then run the regression model independently. Its MAE is shown in the first row of Table 7, such an error is not much better than the performance of MSCS-CNN in this paper. Additionally, we have to consider that using a CNN model to perfectly separate the six mature stages for classification is a difficult task (as shown in Config 2 of Table 7, poor staging ability leads to the poor performance of bone age MAE), so we consider combining the two for one.  
In addition, since the accuracy estimation of the MSCS-CNN in the maturity stage is relatively loose, for example, the C stage can be identified in the BC stage or the CD stage, we think it is correct, so it is unfair to use the accuracy comparison. Therefore, we perform another experiment to compare whether our cross-stage strategy is really helpful for the bone age assessment task, and we also assume that Config. 1 mentioned in Section 4.3 has completely correct staging, its MAE is as shown in the second row of Table 8, and its MAE is not much better than the MSCS-CNN. Thus, we refer to Table 5. If the cross-stage strategy is not used, it is difficult to separate them clearly to achieve good maturity stage classification performance, which also leads to the terrible MAE of Config. 1 in Table 7. Config. 2 assumes that the maturity stages A-F are individually separated, as mentioned in Section 4.3. We assume that the MAE results under the perfect classification of Config. 2 are shown in the row of Config. 2 in Table 7. Compared with Table 7 Config. 1, there is not much benefit. This result shows that we do not need to separate the two adjacent maturity stages clearly. Thus, we use a cross-stage strategy as a compromise.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Females** & **Males** \\ \hline
3-stage classification config. 1 & 1.0 & 0.96 \\
3-stage classification config. 2 & 0.69 & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Two 3-stage classification accuracies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Females** & **Males** \\ \hline Config. 1 acc. & 0.76 & 0.79 \\ Config. 2 acc. & 0.42 & 0.41 \\ Config. 1 MAE & 1.299 & 1.412 \\ Config. 2 MAE & 1.436 & 1.478 \\ \hline \hline \end{tabular}
\end{table}
Table 7: With and without cross-stage strategy performance comparison.
## 4 Experiment and Results
### Generalization Experiments of SIT-Conversion
#### 4.2.2 Comparison of Different Transformer Models
The accuracy of the EdgeNeXt model remains unchanged before and after conversion. Although the EdgeNeXt, Next-ViT, and UniFormer models contained different numbers of Transformer blocks, the maximum conversion loss generated by SIT-conversion is only 0.24%, which is almost negligible. Furthermore, the energy consumption is reduced by 44.44% to 45.13% on the EdgeNeXt model, by 24.75% to 25.08% on the UniFormer model, and by 18.18% to 20.09% on the Next-ViT model, greatly reducing the energy required during the model inference process. The above results demonstrated that SIT-conversion exhibited excellent performance in the ANN-to-SNN conversion process of various Transformer variants, confirming the universality of the proposed method. Even with 10 Transformer blocks in the UniFormer model, the Spiking-Softmax method could still maintain the accuracy of the attention matrix, without resulting in significant performance loss in the SNNs generated by SIT-conversion, achieving nearly lossless conversion. Simultaneously, it greatly reduced the model's energy consumption.
## 3 Results
### Comparison between Metal Rock Bolts and FRP Rock Bolts  
A comparison was conducted between metal rock bolts and FRP rock bolts. First, the reinforcement performance of rock bolts when the EL was 1.5 m is shown in Figure 11. Apparently, the rock bolt type had a paramount influence on the reinforcement performance of rock bolts. The initial stiffness of metal rock bolts was much larger than that of FRP rock bolts. This indicated that the anchorage system reinforced with the metal rock bolts was more brittle than the anchorage system reinforced with the FRP rock bolts. Thirdly, the post-failure performance of metal rock bolts was apparently diverse from that of FRP rock bolts. For metal rock bolts, after the largest anchorage force, the anchorage force of metal rock bolts oscillated. By contrast, for FRP rock bolts, after the largest anchorage force, the anchorage force of FRP rock bolts decreased smoothly. Additionally, there was no oscillation in the anchorage force of FRP rock bolts.  
A comparison was also conducted on metal rock bolts and FRP rock bolts when the EL was 2.5 m. The results are shown in Figure 12. It is apparent that the stiffness of the rock bolt anchorage system was still larger than that of the FRP rock bolt anchorage system. When the steel rock bolt reached the largest anchorage force, the corresponding PO displacement was 4.59 mm. By contrast, when the FRP rock bolt reached the largest anchorage force, the corresponding PO displacement was 14.72 mm. This value was more than three times the corresponding PO displacement that occurred in the steel rock bolt test.  
Last, the reinforcement performance of metal rock bolts and FRP rock bolts was compared when the EL was 3.5 m, as shown in Figure 13. In this scenario, the anchorage system consistently failed by the tensile rupture of rock bolts. There was still a paramount difference in the reinforcement performance of metal rock bolts and FRP rock bolts. The initial stiffness of FRP rock bolts was much smaller than that of metal rock bolts.  
Figure 11: Comparison between the metal rock bolts and FRP rock bolts when the EL was 1.5 m.
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 2 Related Work
### Information-Centric Networking  
There have been various approaches based on the concept of ICN. Among them, TRIAD [4]-the first of its kind, Named Data Networking (NDN) [5], Data-Oriented Network Architecture (DONA) [6], Publish-Subscribe Internet Technology (PURSUIT) [7, 8], Publish-Subscribe Internet Routing Paradigm (PSIRP) [9, 10], Scalable & Adaptive Internet soLutions (SAIL) [11], Architecture and Design for the Future Internet (4 WARD) [12], COntent Mediator architecture for content-aware nETworks (COMET) [13], Network of Information (NetInf) [14], and CCN [2] are worth mentioning. Despite the central idea being the same for all these variants, which is to deliver the requested content as quickly as possible using a name-based communication, there are distinctions in actual implementations in terms of routing, naming, and caching mechanisms.  
CCN is one of the most popular variations that follows the ICN perception and uses the names of the requested content to locate and obtain the desired content. The basic CCN caches all the recently requested content at all the machines that fall within the transmission path for future reference. Therefore, the later requests for the same content can be satisfied with an intermediate machine rather than the remote server. Consequently, network congestion, traffic overload, and response time can be reduced significantly. In addition, CCN secures the content itself rather than the transmission link using packet-level security and supports basic user mobility. Hence, a shorter data transmission time, improved efficiency in network resource management, increased scalability concerning bandwidth demand, and better robustness in challenging communication environments are the anticipated benefits. However, further improvements are still expected in terms of content caching and content removal schemes, and mobility support.
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
## 3 Results
### Round Static Obstacle Placed So It Covers the Full Path of the Prototypes Detection Cone  
The prototypes were moved to all the already marked testing distances and a log file was saved. Analyzing all the log files for those prototypes, the accuracy of the prototypes can be seen in Figures 13 and 14.  
Figure 11: PCM prototype accuracy.  
Figure 12: WAVE prototype accuracy.  
The data behind these images are presented in Appendix C, Table A3.  
As noted, there has been an increase in the number of incorrect messages compared to the scenario involving a square obstacle for the same prototype. One contributing factor is the nature of the surface on which the wave reflects, coupled with the potential impact of a slight deviation from the center of the cylinder on the accuracy of distance readings.  
Upon analyzing these results, it becomes apparent that the PCM prototype achieves an average success rate of 83.5%. Similar to the prior test set, the precision of messages for critical distances stands at 75.3%. For example, when the obstacle is positioned at a distance of 140 cm, the user should ideally hear the \"Caution\" message. However, the analysis reveals instances where the heard message is \"Danger\", potentially prompting the user to prepare for obstacle navigation more swiftly than necessary. Conversely, a \"Clear\" message in such a scenario could pose a risk of accidents.  
An issue that did not reoccur in a subsequent round of tests for the 210 cm distance is the instance of 0% accuracy, where the user would hear \"Clear\" instead of the expected \"Caution\" message. Given that this problem did not repeat and considering the greater distance involved, it is regarded as an isolated incident. Consequently, this value has been excluded from the calculation of the average accuracy.  
In this context, a minor concern surfaced for obstacles with a very small diameter, less than 3 cm, situated at distances exceeding 200 cm. Occasionally, these obstacles remain imperceptible to the prototype. However, this is a minor issue, given the substantial distance between the prototype and the obstacle. Additionally, the user will receive at least one message describing the route's status before reaching a critical juncture.  
The WAVE prototype maintains a success rate very close to 100% in these conditions. Regrettably, an anomaly was detected at the 210 cm distance. In this instance, the user should have received a \"Caution\" message instead of \"Clear\". Nevertheless, the consider  
Figure 14: WAVE prototype accuracy.  
Figure 13: PCM prototype accuracy.  
able distance of 210 cm between the user and the obstacle means that there will be at least two additional route analyses before it becomes a concern.
## 5 Research Method  
The employed research methodology is of the empirical type, where we used experiments to process and transform the imbalanced dataset, applied imbalanced methods to balance the data, trained the AMiDS models using imbalanced learning, scored (tested) and evaluated the models using standard ML metrics as well as time metrics, analyzed the evaluated models, and compared the models' performances to the performances of the same AMiDS models trained and evaluated using regular learning. Figure 5 depicts the conceptual model based on the prescribed methodology.  
Given the rare event prediction case, we used an empirical study to quantitatively assess the impact of imbalanced learning on low-complexity AMiDS models. As a result, we designed and ran a set of experiments using the data sampling and data generative methods listed in Table 2 to manipulate the portion (80%) of the imbalanced dataset used to train the AMiDS models. Then, we used the manipulated training dataset to train a PCA- and oSVM-based AMiDS on a supervised binary classification, which we later evaluated using the remaining portion (20%) of the imbalanced dataset, which was set aside for testing the trained models.  
Furthermore, we used ML standard metrics as well as the training time and inference time as additional performance indicators. Further, we analyzed the performance of the AMiDS models trained using imbalanced learning with the AMiDS models' performances when trained using regular learning (i.e., we trained the models using the training dataset (as-is) without applying data sampling to balance the class distribution).  
The study presented in [69] provides detailed descriptions and performance analyses of the PCA- and oSVM-based AMiDS, referred to by the paper as AN-Intel-IDS models, using regular learning. To generate datasets for imbalanced learning using the sampling methods, we used the pre-processed IoT-Botnet (PiB) datasets used in [70]. The PiB dataset has two classes: the normal minority class and the anomalous majority class. The majority class contains DoS and DDoS intrusion data samples. According to the dataset statistics, there are 126.00 (6.3%) instances in the normal minority class and 1874.00 (93.7%) instances in the anomalous majority class.  
While there are several proposed imbalanced learning methods in the literature to choose from, we selected methods from four different categories to obtain an early idea of which categories of methods are more suitable to apply when using imbalanced and passive adversarial learning, given the domain of applications (CioT and Miot) and the rare event prediction case.
###### Abstract
In Thailand, only two-thirds of farmers have access to irrigation, while one-third do not, according to data obtained by the National Statistical Office (NSO) from the Agricultural census in 2003 and 2013 [31]. According to [PERSON] et al. [32], In 2017, 42% of agricultural households and small farmers had access to water resources, 26% through irrigation, 11% from homes, and 6% from natural/public water sources. Temperature increases caused by climate change have a substantial impact on agriculture. Occasionally caused by droughs or floods, unpredictability of the weather makes it more difficult for farmers to reap a successful harvest. Consequently, the climate change element should also be assessed for future improvements, since the use of increased water in conjunction with enhanced fertilization management could result in essential agricultural production benefits. Farmers and related stakeholders may face a number of obstacles while attempting to make the best selections from the vast array of available options. A DSS must be meticulously created with consideration for numerous criteria, employing well-defined data and a tool capable of transforming the data into a set of relevant recommendations. Farmers and the agricultural system will benefit from the development of DSS and mathematical optimization to determine the optimal crop planning and irrigation scheduling in respect of water availability. Consequently, the purpose of this work is to provide an integrated optimization scheduling DSS for farmers and related stakeholders to assist farmers and related stakeholders in making decisions based on evidence. For each module, farmers or decision-makers will select the optimal solutions, based on their future perspectives and experiences. As water availability and distribution are the primary focus of this study, the DSS includes an optimization module that matches crop type and water availability based on the availability of water. The results of the DSS will not provide farmers or decision-makers with clear instructions or directions. In this instance, farmers are in a position to make the final decisions. The agriculture system in Thailand's Tak Province is analyzed, and the DSS is offered to increase the system's efficiency. The remainder of the paper is organized as follows. In Section 2, we describe the DSS theoretical framework. The proposed method that incorporates solving the model is introduced in Section 3, for which the agriculture as study case and DSS capabilities are illustrated in Section 4. Section 4 also discusses the managerial implications, and Section 5 discusses the main findings and future lines of research.
## 5 Parametric Analysis  
To further investigate the performance of SFLC beams reinforced with GFRP bars, the FE model for SLC-G-8#13.77 was selected as the control sample to conduct the parametric analysis with respect to the section height \(H\), FRP reinforcement ratio \(\rho_{f}\), and clear span length \(L\).
## 3 Results and Discussion
### Strain Variation Characteristics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \(\sigma_{1}\) & \multicolumn{2}{c}{**Instantaneous Strain/10\({}^{-3}\)**} & \multicolumn{2}{c}{**Creep Strain/10\({}^{-3}\)**} \\ \cline{2-5}
**Number** & **/MPa** & **Axial** & **Lateral** & **Axial** & **Lateral** \\ \hline \multirow{6}{*}{R15-0} & 32.16 & 2.877 & 1.198 & 0.039 & 0.208 \\  & 48.24 & 1.301 & 1.268 & 0.173 & 0.362 \\  & 64.32 & 1.166 & 1.491 & 0.223 & 0.510 \\  & 80.40 & 1.193 & 1.778 & 0.244 & 0.514 \\  & 96.48 & 1.006 & 1.657 & 0.313 & 1.217 \\  & 112.5 & 1.397 & 3.846 & — & — \\ \hline \multirow{6}{*}{R15-7} & 32.16 & 2.840 & 1.119 & 0.143 & 0.159 \\  & 48.24 & 1.366 & 1.318 & 0.106 & 0.298 \\  & 64.32 & 1.253 & 1.689 & 0.178 & 0.401 \\  & 80.40 & 1.212 & 1.912 & 0.162 & 0.518 \\  & 96.48 & 1.187 & 2.049 & 0.357 & 1.273 \\  & 112.5 & 1.409 & 4.210 & — & — \\ \hline \multirow{6}{*}{R15-14} & 32.16 & 3.493 & 1.351 & 0.091 & 0.235 \\  & 48.24 & 1.421 & 1.477 & 0.229 & 0.445 \\ \cline{1-1}  & 64.32 & 1.356 & 1.996 & 0.242 & 0.5910 \\ \cline{1-1}  & 80.40 & 1.293 & 2.350 & 0.304 & 0.844 \\ \cline{1-1}  & 96.48 & 1.292 & 2.749 & 0.696 & 2.984 \\ \cline{1-1}  & 112.5 & 1.388 & 4.764 & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Creep strain for an initial confining pressure of 15 MPa. in the lateral restraint, and the sample produced axially oriented tensile cracks under the action of bias stress loading, which led to the release of surface stress and the structural relaxation of the sample. Then, the lateral structural weakening of the sample triggered a significant expansion phenomenon in the reloading creep test, which led to the lateral strain becoming greater than the axial strain. Comparing the results of the normal temperature test group to those of the freeze-thaw test group, it can be found that the freeze-thaw action intensifies the creep deformation characteristics of the unloaded sample and that the creep strain percentage increases continuously with the extension of the freezing period. For example, considering R15-0 and R15-14, the percentage of the transient strain decreased from 86% to 78% with the extension of the freezing period, and the difference in the axial and lateral creep strains was approximately 1.5-2 times those of the stresses at all levels.