esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions Alongside Numerical Experiments
### LTN and DNN on CIC-IDS2017  
While KDD99 is a well-known and extensively used dataset for intrusion detection, it is nowadays fairly old and has received multiple critics regarding its inconsistency and the false representation of a modern real-world scenario [29; 30; 31]. To demonstrate the capabilities of LTN, we continued our experiments with a multiclass classification but, in this last case, made it single-label instead of multi-label. CIC-IDS2017 dataset aims to solve all the previous faults of intrusion detection datasets and provide some up-to-date attacks and a reshaped network traffic analysis. A comprehensive explanation of how the dataset was created, all the used attacks, and the feature importance for each one of them is offered in the related paper [32].  
This IDS Evaluation Dataset offers the labeled network flow for actual weekdays days when attacks happened and in this experiment, we decided to see how well the attacks that happened on Friday, 7 July afternoon, can be predicted using LTN. The connection types, in this case, were either DDoS, PortScan, or BENIGN (normal connection). A quick exploration after eliminating the redundancy in the dataset shows that there are 212,718 BENIGN, 128,005 DDoS, and 57,305 PortScan connections.  
For the neuro-symbolic model, data were batched into tensors and a set of axioms was implemented. The knowledge base in this scenario is simpler; we defined only that all the DDoS attacks should have a \"DDoS\" label and all BENIGN connections should have a \"BENIGN\" one and PortScan should have a \"PortScan\" one. There was no need for constraints since the number of classes was small. The function grounded in LTN semantics was kept the same (a neural network with no change in the shape of hidden layers) and the metrics measured were also kept, with the removal of \(\phi\) queries.  
As shown in Figure 6, there is a clear evolution in the accuracy of our LTN model, but there is also room for improvement. We reached an accuracy of 0.950, with levels of satisfiability being very close. Both graphs are very similar, as is expected with a simpler knowledge base as ours.  
Figure 5: Evolution of loss and accuracy in training.  
For the DNN part, the network was implemented almost identically to the one used in the LTN function before. The results can be observed in Figure 7, and it should be remarked that while the training accuracy is steadily improving, the accuracy for the validation data still has some drops in performance. As a direct comparison, the LTN model provides more constant results and is better at generalizing to new data with the help of the defined Knowledge Base.
## 2 Methodology
### Hyperparameters  
A search was made for the most appropriate hyperparameters using GridSearchCV [69], running 500 different combinations of the datasets. The scores function for choosing the best combination of hyperparameters were accuracy for classification and the coefficient of determination for regression. The algorithms and the set of verified hyperparameters can be found in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **Evaluated Hyperparameters** \\ \hline \multirow{4}{*}{Decision trees (classification)} & ‘**criterion**’: ‘gini’, ‘entropy’; ‘**splitter**’: ‘best’, ‘random’; ‘**max_depth**’: 5, 10, 15, \\  & None; ‘**min_samples_split**’: 2, 4, 6, 8; ‘**min_samples_leaf**’: 2, 3, 4, 5; \\  & ‘**max_leaf_nodes**’: None, 100, 50, 10; ‘**ccp_alpha**’: 0, 0.005, 0.015, 0.03 \\ \hline \multirow{4}{*}{Decision trees (regression)} & ‘**criterion**’: ‘squared_error’, ‘friedman_mse’, ‘absolute_error’; ‘**splitter**’: ‘best’, ‘random’; ‘**max_depth**’: 5, 10, 15, None; ‘**min_samples_split**’: 2, 4, 6, 8; \\  & ‘**min_samples_leaf**’: 2, 3, 4, 5; ‘**max_leaf_nodes**’: None, 100, 50, 10; ‘**ccp_alpha**’: 0, 0.005, 0.015, 0.03 \\ \hline \multirow{4}{*}{Random forests} & ‘**n_estimators**’: 100, 300, 500; ‘**max_depth**’: 5, 10, 15, 20, None; \\  & ‘**min_samples_split**’: 2, 5, 10; ‘**min_samples_leaf**’: 1, 2, 5, 10; ‘**max_features**’: ‘log2’, ‘sqrt’, None \\ \hline \multicolumn{2}{c}{K-nearest neighbours} & ‘**n_neighbors**’: 2, 4, 8, 16; **p**’: 2, 3 \\ \hline \multicolumn{2}{c}{Linear regression} \\ \hline Logistic regression & ‘**C**’: 0.001, 0.01, 0.1, 1; ‘**penalty**’: 11’, ‘12’ \\ \hline Support vector machines & ‘**C**’: 0.1, 1, 10, 100, 1000; ‘**gamma**’: 1, 0.1, 0.01, 0.001, 0.0001 \\ \hline \end{tabular}
\end{table}
Table 4: Evaluated hyperparameters.  
Figure 2: Error introduced by distance between inclusion and penetrometer.  
This computationally intensive process used the Magerit-3 supercomputer belonging to the Universidad Politecnica de Madrid.  
Once the best hyperparameters have been obtained, the behaviour of the resulting models is analysed. The algorithms using the best hyperparameters are trained with 70%  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Evaluated Hyperparameters** \\ \hline XGBoost & ‘eta’: 0.01, 0.025, 0.1; **gamma**: 0.05, 0.5, 1.0; **max_depth**: 5, 12, 25; \\ ‘min_child_weight’: 1, 3, 7; **subsample**: 0.6, 0.8, 1.0; **colsample_bytree**: 0.6, 0.8, 1.0; **’lambda**: 0.01, 0.1, 1.0; **’alpha**: 0, 0.5, 1.0 \\ \hline \multirow{3}{*}{Multilayer perceptron} & ‘activation’: ’relu’, ‘identity’, ‘logistic’, ‘tanh’, ‘solver’: Adam’, 'lbfgs’, ‘sgd’; \\  & ‘alpha’: 0.0001, 0.01, 0.1; **’learning_rate**: ‘constant’, ‘invscaling’, ‘adaptive’; **‘learning_rate_init’: 0.001, 0.1, 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 3: Number of samples for each blow count value for maximum distances of 2 m.  
of the samples and tested on the rest 30%. The values shown are the mean values from 500 different train-test splits.
## 3 Materials and Methods
### TinyML Platform
#### 3.4.2 Chosen Platform  
Considering all the factors, the STM32F411 chip was chosen as the best suitable microcontroller for this task. It is relatively cheap, widely available as a Blackpill evaluation board, and fast enough for computational tasks due to the fast ARM Cortex M4 core, while being the most energy-efficient option out of all candidates. It also contains sufficient onboard memory to have a big enough DMA-driven buffer connected to the external RAM, resulting in no impact on the computation time due to the fact that all needed data can be present in the internal RAM on demand.
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 2 Related Works
### Computation Methods Based on the Point Cloud Modality  
With the increasing adoption and extensive use of 3D laser scanning technology in the field of smart mining, researchers have started exploring the calculation of rock fragmentation using 3D point cloud modalities. [PERSON] et al. [19] proposed a method for calculating rock fragmentation based on the laser scanning of rock point clouds. The process involved rasterizing the 3D point cloud data of rocks and employing a combination of the watershed algorithm and morphological operators to segment the rock fragments. The positional spatial information of the edge points of rocks was then utilized to determine the degree of rock fragmentation. To address the challenges associated with perspective distortion and limited utilization of spatial information in image-based methods for calculating rock fragmentation, [PERSON] et al. [20] employed high-resolution 3D laser scanning to capture rock point cloud data. They utilized a combination of morphological edge detection and watershed segmentation algorithms to effectively segment the rock fragments from the point cloud. In another study conducted by [PERSON] et al. [21], high-resolution 3D laser scanning was utilized to obtain rock point cloud data. The point cloud underwent preprocessing using filtering and orthogonal projection techniques. Subsequently, the segmentation of rock fragments from the point cloud was achieved through morphological edge detection and the watershed algorithm. Finally, the segmented point cloud underwent inverse projection to compute the degree of rock fragmentation. To overcome the limitations and inaccuracies of image analysis methods, [PERSON] et al. [22] utilized laser scanning technology to directly measure the size distribution of rock fragments.
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 5 Virtual Network Function as A Service: Component RFBisation
### RFBisation of the CORD Platform  
In previous versions of the CORD platform (\(<\)6.0), there was a very tight integration between the different components of CORD, since the majority of them were executed either as native services or as standalone containers and virtual machines. This translated to a very rigid deployment process, with little to no flexibility. However, in the latest version (6.0), the deployment of CORD is now managed via higher-level tools like Kubernetes [35] and Helm [36]. Through these tools, deploying, managing and versioning the different CORD components is easier, more flexible and robust than before. This new deployment approach greatly facilitated the process of RFBising the CORD platform, mainly due to the central point of management offered by the Kubernetes and Helm tools. Deploying CORD via the RDCL 3D tool requires the definition of several RFBs, each of which implements a specific functionality during the CORD deployment process. However, despite their differences, all RFBs can be placed in one of three categories Pre-deployment configuration RFBs, Deployment RFBs and Post-deployment configuration RFBs. Table 3 provides details these categories. To reduce the complexity of the graph, only the Deployment RFBs are graphically illustrated by the RDCL 3D tool, while the rest are passed to the agent as metadata. Figure 13 illustrates the RFB graph that composes the CORD 6.0 platform in the RDCL 3D web interface. As can be observed in Figure 13, the RFBs have been grouped into five major categories as children of the root RFB, depending on the role of the component they are responsible for deploying. These five categories are (1) Message Brokers; (2) Storage Mechanisms; (3) Virtual Infrastructure Managers (VIMs); (4) Orchestration and (5) Service Models. Additionally, the VIM group of RFBs is also comprised of sub-groups, namely (1) Kubernetes (2) OpenStack and (3) ONOS. While this grouping does not serve any functional role, it can significantly simplify the composition of RFB graphs. All the remaining RFBs are leaf RFBs, meaning that they are directly mapped to the execution/deployment of a micro-service. It can also be observed that all leaf RFBs are associated with a priority value, ensuring that their  
Figure 12: Operation of the Network Policy Framework. deployment in the execution environment is done in a preferred order. Table 4 provides a detailed list of all leaf RFBs related to the deployment of the CORD platform.
###### Abstract  
We investigated the spatiotemporal variability of remotely sensed gross primary productivity (GPP) over Turkiye based on MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2 GPP products. The differences in various GPP products were assessed using Kruskal-Wallis and Mann-Whitney U methods, and long-term trends were analyzed using Modified Mann-Kendall (MMK), innovative trend analysis (ITA), and empirical mode decomposition (EMD). Our results show that at least one GPP product significantly differs from the others over the seven geographic regions of Turkiye (\(\chi^{2}\) values of 50.8, 21.9, 76.9, 42.6, 149, 34.5, and 168; \(p<0.05\)), and trend analyses reveal a significant increase in GPP from all satellite-based products over the latter half of the study period. Throughout the year, the average number of months in which each dataset showed significant increases across all study regions are 6.7, 8.1, 5.9, 9.6, and 8.7 for MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2, respectively. The ITA and EMD methods provided additional insight into the MMK test in both visualizing and detecting trends due to their graphical techniques. Overall, the GPP products investigated here suggest 'greening' for Turkiye, consistent with the findings from global studies, but the use of different statistical approaches and satellite-based GPP estimates creates different interpretations of how these trends have emerged. Ground stations, such as eddy covariance towers, can help further improve our understanding of the carbon cycle across the diverse ecosystem of Turkiye.  
remote sensing; gross primary productivity; innovative trend analysis; empirical mode decomposition; carbon cycle; Modified Mann-Kendall +
Footnote †: journal: Remote Sensing  
2024169944103399/rsc16111994
## 4 EMS Optimization
### Hydrogen Energy and Hydrogen Power
The EMS likely prioritizes releasing stored hydrogen energy to meet the grid's needs when solar input is unavailable, while also possibly storing excess grid energy when prices are low. Figure 7 underscores the EMS's crucial role in managing hydrogen power to stabilize the grid, with distinct patterns of power fluctuation aligned with day and night cycles. Understanding these patterns is essential for optimizing hydrogen energy use and ensuring efficient grid operation throughout the day. In the context of hydrogen power, negative power values represent periods when the hydrogen energy storage system (HESS) is using power rather than generating it. This typically happens during hydrogen production, where electricity is utilized for electrolysis to split water into hydrogen and oxygen. In the described scenario, hydrogen power ranges from \(-\)40 kW to 40 kW over a 24 h period. For the majority of the day, the power level is at 40 kW, indicating that the HESS is generating or discharging hydrogen power to meet energy needs. However, there is a brief period between the 12 th and 13 th h when the power level drops to \(-\)40 kW. This negative power value means that, during this time, the HESS is consuming 40 kW of power, likely for hydrogen production through electrolysis. This pattern of fluctuating power levels highlights several key aspects of HESS operation:  
Figure 7: Hydrogen Power. 1. Dynamic Energy Management: The system can switch between consuming power for hydrogen production and generating power from stored hydrogen based on real-time energy demands and supply conditions. 2. Balancing Supply and Demand: By using power to produce hydrogen during periods of low demand or excess energy generation, the HESS helps balance the overall energy system, storing energy as hydrogen for later use. 3. Operational Flexibility: The ability to quickly switch between positive and negative power values shows the system's flexibility in efficiently managing hydrogen production and consumption. Negative power values in the hydrogen power data indicate times when the HESS is actively consuming electricity for hydrogen production. This ability is essential for optimizing energy storage, balancing supply and demand, and integrating renewable energy sources into the grid. These figures provide essential insights into how hydrogen energy and power are managed within the energy management system (EMS). They aid in the comprehension of the dynamic behavior of hydrogen energy storage systems (HESSs) in reaction to supply and demand for energy. The optimization of the integration and operation of HESSs, the assurance of efficient energy storage and supply, and the improvement of the overall stability and efficiency of the energy system all depend on this analysis.
# A Literature Survey on Offline Automatic Algorithm Configuration
## 6 Multi-Objective META-EAs
### Racing Methods
#### 2.2.3 [PERSON] et al. [7] described the irace package, which implements the improved and extended version of the iterated F-race. They introduced recent extensions such as a restart mechanism, the use of truncated sampling distributions, and an elitist racing that tests parameter vectors on a problem set whose size is increased in every iteration of irace. Finally, they experimentally evaluated this recent version of irace on two configuration cases. It has been underlined that the parameter sampling method and the statistical assessment of the performance of different configurations have vital importance for guiding the iterated racing procedures to find the better configurations. [PERSON] et al. [63] is one of the recent studies that use irace to configure the newly designed multi-objective evolutionary algorithm. For an overview of the various and wide range of applications of the irace package (available at [[https://iridia.ulb.ac.be/irace/](https://iridia.ulb.ac.be/irace/)]([https://iridia.ulb.ac.be/irace/](https://iridia.ulb.ac.be/irace/)) (accessed on 10 June 2022)) in different studies, we refer to [7].  
The irace was developed for optimizing the obtained solution quality for a defined running time. However, when used to optimize the running time, irace cannot perform as well as other configuration approaches. The reason is that it spends too much time evaluating poor configurations. [PERSON] et al. [64] aimed to improve the performance of the irace method for running time minimization objective, and they added an adaptive capping method to the irace as ParamILS uses. Additionally, the suggested new irace uses a new technique, dominance elimination, which rejects poorly performing configurations. They showed that these improvements over irace lead to a high-performing irace that is competitive with state-of-the-art algorithm configuration methods (ParamILS and SMAC)whose objective is minimizing the running time. They also deeply analyzed the behavior of irace and compared different methods of integrating adaptive capping.  
[PERSON], [PERSON], and [PERSON] [65] investigated the use of random forests models, a common improvement technique for search methods when evaluations are computationally hard, as surrogates in irace. The usage of the random forest model is different from that used in SMAC and more similar to the use in GGA++.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
## 1 Introduction
The simulation results showed that the MIMO channel capacity decreases as the symbol baud rate increases and can be enhanced by increasing the average transmit power or adjusting detector FOV. Compared to SISO communication systems, MIMO communication systems extend the communication range and increase the channel capacity of systems [14]. Although the commonly used planar MIMO scheme raises the upper limit of the transmission rate to some extent, it requires maintaining strictly aligned links, similar to SISO communication systems. Therefore, developing a novel transceiver architecture for underwater wireless optical MIMO communication systems is crucial. In this paper, based on the common planar circular array MIMO communication systems, we propose a transceiver architecture design that effectively mitigates communication outages due to link misalignment. It can be assumed that within a certain range of transceiver jitter, each transmitter will always be approximately aligned with a single receiver, thereby achieving a larger communication coverage area.
# Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Ratios
## 1 Introduction
Sometimes, a complete system based on RGB-D cameras controlled by neural networks is used to evaluate feed intake by cows [36]. Some recent developed image processing methods allow to detect objects on RGB images with high accuracy. Those methods work well with objects on a uniform background, but have limitations in our case where objects of interest are mixed within a complex pattern [37; 38]. The present study proposes an elementary system for automatic control of the component composition of feed mixes and a data processing method to determine the setting of feed mixer operation modes based on an RGB camera, light filters, a diode lamp, and software. The applied software includes the Matlab Image Processing Toolbox and, in particular, the Color Thresholder app and Image Region Analyzer app. The proposed system performs the functions of determining:  
* geometric characteristics of mixed particles;
* color intensity for each pixel in the image;
* uniformity of color intensity distribution over each image channel. The described set will make it possible to evaluate the homogeneity of the resulting mixes and make recommendations on the operating time of mixing units, thereby improving energy efficiency by optimizing the operating time of feed mixers.
## 4 Discussion
An accurate reconstruction of the VSP in ClinCheck is one of the challenges combining an SFOA with CAT [32]. One method involves explaining the challenge through written instructions within the ClinCheck, but this approach can be labor-intensive, leading to confusion, and, therefore, is particularly challenging in SFOA cases. To the best of our knowledge, there is only one article that offers a thorough and effective solution to this issue, utilizing a free third-party software in an efficient manner [28]. In this case, our VSP that was sent to the ClinCheck corresponded to the post-surgical outcome, as shown in Figure 4.  
A decreased score from the initial to post-treatment in the OHIP-14 and QQOL questionnaires indicates an immediate positive impact in the patient's quality of life, and the reduction in the PAR score after treatment suggests a successful post-treatment occlusion. Her facial appearance after treatment was not evaluated with an index; however, the treatment goals of a more harmonious profile and less gingival display when smiling was achieved (Figure 5). An SFOA combined with CAT is a field that requires further research. The protocol presented in the case report appears to be an efficient way to combine these treatment modalities, leading to an objective PAR improvement in addition to an improvement in the patient's quality of life. Figure 8: Superimposition before (black) and after (red) treatment. **Author Contributions:** Conceptualization, M.S., I.L.S., S.V., K.E.H., K.D.K. and S.E.N. ; Methodology, K.D.K., T.K.P. and J.B.; Software, M.S., I.L.S., S.V., K.E.H., T.K.P. and S.E.N. ; Validation, I.L.S., S.V., K.D.K., T.K.P. and S.E.N. ; Formal analysis, T.K.P. ; Investigation, S.V. and J.B.; Writing--original draft, M.S., I.L.S. and S.V. ; Writing--review & editing, K.E.H., K.D.K., T.K.P., S.E.N. and J.B.; Visualization, K.E.H. ; Supervision, T.K.P. and S.E.N. ; Project administration, S.E.N. and J.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to the reason of that it is going under the Danish quality assurance assessment. **Informed Consent Statement:** Written consent for the use of digital materials and radiographs for publication were obtained by the patient. **Data Availability Statement:** The original contributions presented in the study are included in the article, additional data is unavailable due to privacy restrictions. **Acknowledgments:** The authors would like to express their gratitude to the clinical engineer, [PERSON], and the oral and maxillofacial surgeon, [PERSON], for their assistance during the surgery and with surgical planning. Sincerest gratitude from the authors goes to orthodontistis [PERSON], for his contribution with the tracings and superimposition. **Conflicts of Interest:** The authors declare no conflict of interest.