esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Block Diagram of the Simulator
#### 3.1.2 Task Trajectory Generator  
This block computes the desired azimuth and elevation trajectories of the antenna subject to the constrained virtual sphere, corresponding to the two angles of the slice of the projected path satellite, mapped to three ground stations; see Table 1. The data correspond to 13 January (T1) and 15 (T2 and T3), 2021; see Figure 1 and Table 2:  
* Large Millimeter Telescope (LMT) at (18 deg59'9'' W, 97 deg18'53'' W) in Atzitzintla (Mexico).
* Atacama Large Millimeter Array (ALMA) at (23 deg19.41'' S, 67 deg45'11.45'' W) in Atacama Desert (Chile).
* Itapeting Radio Observatory (IRO) at (23 deg11'6.72'' S, 46 deg 33'29.88'' W) in Sau Paulo (Brazil).  
Figure 6: Block diagram.  
The point-to-point task trajectories are approximated with cubic splines, where the central curve segment is computed with the azimuth and elevation data of Figure 7 at different time intervals for each geolocation as obtained from O Orbitron.
## 3 System Architecture
### System Implementation  
The architecture presented in this paper utilizes SDN technology. It is essential to highlight that the core concept of the SDN is the separation of the control plane from the data plane. In practical deployments, as the scale of the network expands, the logically centralized control plane can no longer be supported by a single physical device. Consequently, the control plane is evolving toward a multi-controller approach, significantly enhancing the scalability and versatility of the SDN. Therefore, this paper adopts a distributed SDN architecture similar to Kandoo [64] and divides the controller into a centralized controller and a local controller. One local controller can be connected to a single switch or to multiple switches. In addition, the deployment location of the local controller is relatively flexible. It can be directly located on the switch or outside the switch. Figure 3 is the corresponding schematic diagram. Considering that the application scenario of this paper is a delay-sensitive service, the local controller is deployed on the switch to better focus on providing scheduling functions. The remaining basic functions of the switch can be managed by the centralized controller in conjunction with the local controller.  
Figure 4 presents a network node architecture diagram with scheduling functions deployed on a local controller. The network node is divided into the control plane and the data plane. The control plane primarily involves computational and network information announcements, computation service scheduling, identification management, domain topology generation, and inter-controller interaction modules. The data plane employs a pipeline architecture with multi-level flow tables, enhancing processing efficiency. Additionally, it includes the state area for executing flow tables, enabling more intelligent data plane forwarding functions. This will be discussed in detail in subsequent chapters.  
Figure 3: The architecture of distributed SDN controllers.
## 4 Experiments and Analysis
### Model Comparison Experiment
#### 4.4.2 Comparison of Model Efficiency and Complexity
To comprehensively evaluate the performance of our proposed YOLO-AFR model, Figure 7 presents a comparative analysis against several other contemporary real-time object detectors--YOLO-SGC, YOLOv12n, YOLOv11n, and YOLOv10n--on the SfdDD dataset. As depicted in the figure, our YOLO-AFR model demonstrates a compelling advantage, achieving the highest detection accuracy while maintaining a high inference speed and, notably, the lowest GFLOPs, thereby underscoring its superior overall performance and efficiency.
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 5 Experiment Methodology
### Comparison Metrics  
We adopted two metrics for comparison: running time and relative error rate.
* Running time: Varying the tensor size and fixing other parameters, we measured the execution time of the CPU tensor sensing, unoptimized GPU tensor sensing, and optimized GPU tensor sensing. Finally, we calculated speedups as the running time of the CPU tensor sensing divided by the running time of GPU tensor sensing.
* Error rate: We adopted the metric relative square error, defined as \(RSE=\|\widehat{\mathcal{X}}-\mathcal{X}\|_{F}/\|\mathcal{X}\|_{F}\).
# Efficient Adsorbent-Desiccant Based on Aluminium Oxide
## 2 Methods of Obtainment of Aluminium Oxide Used in Industry  
As a rule, bauxite, alunite and repheline are used as raw materials for obtaining aluminium oxide. If the content of aluminium oxide in them is more than 6-7%, the production is carried out by the main method--the Baeyer method. If the content of the substance is lower, the method of sintering ore with lime or soda is used. The main  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title** & **Scope of the Review** & **Year of Publication** & **Ref.** \\ \hline Progressive development in solid desiccant cooling: A review & In this paper review, the solid and liquid desiccant cooling systems were discussed in terms of their progressive development and working principle. & 2019 & [25] \\ \hline Recent developments in solid desiccant coated heat exchangers & In this paper, the advantages of desiccant coated heat exchangers (DCHEs) over other dehumidifiers are established, and a comprehensive review of various desiccant materials and binders used in DCHEs was carried out. & 2018 & [26] \\ \hline Activated alumina preparation and characterisation: The review on recent advancement & Activated alumina particles (AAP), synthesised electrochemically from aluminium, are an inexpensive material that can be used for water filtration due to its active surface. & 2018 & [27] \\ \hline Rational design, synthesis, adsorption principles and applications of metal oxide adsorbents: a review & This review discussed the adsorption properties of several typical metal oxides and key parameters affecting adsorption performance and their applications for removing various inorganic and organic contaminants. & 2020 & [28] \\ \hline Applications of aluminium oxide and nano aluminium oxide as adsorbents: review & The aim of this review was to clarify the role of aluminium oxide and nano aluminium oxide in removing some chemicals contaminants that influence human health, such as dyes, antibiotics and heavy metals. & 2020 & [29] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Titles, scope and references of the main review, focusing on the topics of the adsorbents based on aluminium oxide.  
raw-material source of Al\({}_{2}\)O\({}_{3}\) is bauxite, which mainly consists of hydrated forms of Al\({}_{2}\)O\({}_{3}\) (gibbsite, boehmite, diaispore). On average, bauxite contains from 45 wt.% to 60 wt.% of Al\({}_{2}\)O\({}_{3}\) (in conversion from hydroxides), 10-30 wt.% of Fe\({}_{2}\)O\({}_{3}\) and varying amounts of SiO\({}_{2}\), CaO, TiO\({}_{2}\) and H\({}_{2}\)O.
## 4 Discussion
Apart from considerable inhibitory activity towards aldose reductase noticed for compound **2** isolated from _P. candicans_[9], compounds **1** and **2** were also found to exhibit moderate antimicrobial potential against _Bacillus subtilis_ and _Staphylococcus aureus_ with MIC values in the range of 22.54-50.83 \(\mu\)g/mL [13]. They were, however, not effective against _Escherichia coli_[13]. So far, there has been no research about their cytotoxic activity. Ellagic acid is a well-known chemopre agent protecting cells against DNA damage and mutations caused by various carcinogenic factors such as radiation, aflat, benzo[a]pyrene, and \(N\)-nitroobenzylmethylamine [7]. There are also plenty of data confirming its direct apoptotic and anticancer activity towards a number of human tumours including breast, colon, prostate, and oral carcinoma, although in most cases its cytotoxicity, with the IC\({}_{50}\) values estimated in the range of 10-350 \(\mu\)M, was moderate as compared with cytostatics used in clinical practice [16; 17]. This study's results (Table 4) obtained for the free acid from leukaemia and melanoma cell lines are consistent with the above findings. The data concerning substituted derivatives of ellagic acid in general are scarce and to some extent contradictory. [PERSON] et al. [30] found that methylation of hydroxyl groups in the molecule leads to reduction of the cytotoxicity towards mice macrophages and postulated it could be connected with substitution of hydroxyl groups at C-3 and C-3\({}^{\prime}\) positions, important for inhibitory activity towards topoisomerases [31]. The more polar glycosidic methylated derivatives were even less effective [31]. Similar findings were presented by [PERSON] et al. [32]--glycosylated and methylated derivatives had very weak cytotoxicity with IC\({}_{50}\) values in the range of 374.5-711.7 \(\mu\)g/mL (1.09-1.41 mM). On the other hand, 3,3\({}^{\prime}\)-dimethylellagic acid 4\({}^{\prime}\)-O-\(\beta\)-D-xylopyranoside from _Euphoria lylonoma_ has been claimed to be effective against HepG2 human hepatocellular carcinoma cell line [33]. As this research shows, methylation connected with sulphation has negative impact on ellagic acid cytotoxicity against leukaemia and melanoma cell lines, the present work generally confirmed the majority of previous studies. However, a more detailed study would be needed for a series of ellagic acid derivatives to differentiate activity effects between methoxy and sulphoxy groups at various positions. Nevertheless, the observed decrease in cytotoxicity might be indeed partly connected with blockage of hydroxyl groups responsible for intermolecular interactions and hydrogen bond formation. The difficulties in trans-membrane transport may also be the cause. As was found in the case of flavonoid derivatives, the conjugation with strong polar groups such as sugar or sulphate moieties decreases the lipid solubility, the partition coefficient and thus the probability of effective passage across cell membranes [34].
## 7 Conclusions  
We implemented the SIP-DRDoS attack, which exploits vulnerabilities in UDP-based SIP signaling, in a laboratory environment that simulated a real enterprise-grade SIP network. We showed that the attack bypassed the existing defense mechanisms in an SIP network such as firewall, IDS, IPS, black-list, and IP-address/packet-count-based rate-limiting. Furthermore, we introduced and implemented an effective mitigation mechanism against the SIP-DRDoS attack. When we applied the SIP-DRDoS attack and created around 1 Gbps of traffic on the target end by sending 10K INVITE requests continuously on the originating side, the CPU load of the SIP server increased significantly up to 74% in only 3 min. However, our novel mitigation mechanism effectively pulled the CPU load down to 17% within 6 min after the initiation of the attack.  
The results of our experiments demonstrate the effectiveness of the proposed approach in detecting and mitigating SIP-DRDoS attacks in real time. Our approach is based on a deep packet analysis and dynamic rate-limiting, and it is able to accurately detect and respond to SIP-DRDoS attacks while allowing legitimate traffic to pass through. Overall, our approach shows that it is possible to develop and implement effective strategies for protecting SIP systems against DRDoS attacks.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 3 Proposed Method
### Voxel Selection-Based Refining Segmentation (VS-RS)
#### 3.2.1 Initial Selection of Voxels  
All voxels containing at least one point in the interior are recorded as filled voxels, denoted as \(V_{i}\). The number of points projected onto each projection plane is counted separately, denoted by \(S_{i}(p_{i})\), where \(p_{i}\) represents a projection plane. Figure 4 shows an example of voxel interior point projection. There are a total of sixteen points in this voxel.  
\begin{table}
\begin{tabular}{c c} \hline
**Test Point Cloud** & **Unoccupied Area Ratio** \\ \hline \hline \begin{tabular}{c} Loot \\ Redandblack \\ \end{tabular} & 65.40\% \\ \begin{tabular}{c} Soldier \\ Longdress \\ \end{tabular} & 71.48\% \\ \begin{tabular}{c} Basketball\_player \\ \end{tabular} & 65.68\% \\ \
\begin{tabular}{c} Dancer \\ \end{tabular} & 68.65\% \\ \hline \hline
\begin{tabular}{c} average \\ \end{tabular} & 64.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unoccupied areas ratio of the occupancy map.  
Figure 3: The proposed VS-RS.  
Among them, five points are projected onto the posterior plane (red plane), four points are projected onto the left plane (yellow plane), and the largest number of points is projected onto the lower plane (green plane), which is seven. Therefore, the final projection plane of the voxel is the lower plane, and \(\underset{p\in K}{\text{max}}S_{i}(p_{i})=7\), where K represents the set of six projective planes.  
To solve the problem of refining redundancy caused by not considering the differences between voxels. The concept of the Projection Uniformity Index is proposed in this paper. It can indicate whether the projection planes of all points in the voxel are unified. The Projection Uniformity Index \(U_{i}\) for filled voxels is as follows:  
\[U_{i}=\frac{\underset{p\in K}{\text{max}}S_{i}(p)}{T_{i}}, \tag{1}\]  
where \(T_{i}\) represents the total number of 3D points in the voxel. Then, each voxel will be selected based on the value of \(U_{i}\). Only when the value of \(U_{i}\) is not 1 will the voxel be selected. That is, the voxels selected based on the Projection Uniformity Index are those with internal changes within the projection plane. The Projection Uniformity Index can measure the level of chaos within the internal points of a voxel. By default, voxels containing only one point are selected. Figure 5 shows several examples of voxels in the initial selection. The uniformity of these voxels is not affected by the neighboring voxels. Therefore, we employ a parallel computing method at this stage, allowing different filling voxels to calculate the Projection Uniformity Index simultaneously and can reduce the amount of time consumed.  
Figure 4: An example of the voxel interior point projection.  
Figure 5: Several examples of voxels in the initial selection voxels.
# Optimization Model for Selective Harvest Planning Performed by Humans and Robots
## 3 Problem Description and Formulation
### Dealing with Uncertainty  
The complex aspect of this formulation is to update the value of \(P_{d,t}\), which determines the harvested yield weight and the relation between \(H_{j,t}\) and \(P_{d,t}\). To update the values of \(Y_{d,t}\) between periods, the value of \(P_{d,t}\) must be known, as is shown in Equation (4).  
To determine the exact value of \(P_{d,t}\), the distribution of the harvested peppers (\(H_{j,t}\)) within the different DAAs must be known. Since it is impossible to know the actual DAA of a harvested pepper, a stochastic process must be considered. We considered four ways to deal with this problem:  
* Distribute the harvested peppers uniformly among the different DAAs.
* Distribute the harvested peppers in proportion to the available peppers of each DAA.
* Create a worst-case scenario in which the peppers with the lowest weight (lower DAA) available in a maturity class will be harvested.
* Create a best-case scenario in which the peppers with the highest weight (highest DAA) available in a maturity class will be harvested.  
For example, in a problem in which the second maturity level is defined as peppers of 5 to 7 DAAs, let us assume that in period 1, the peppers available are: \(Y_{5,1}=4\), \(Y_{6,1}=6,\ \ Y_{7,1}=2\). This implies that \(A_{2,1}=\sum_{k=5}^{7}Y_{k,t}=4+6+2=12\). Now, let us assume the algorithm decided to harvest six peppers from level 2 in period 1: \(H_{2,1}=6\).  
* The first method, distributing uniformly, will set \(P_{5,1}=2\), \(YP_{6,1}=2\), \(P_{7,1}=2\).
* The second method, distributing in proportion, will set \(P_{5,1}=\frac{4}{12}\times 6=2\), \(P_{6,1}=\frac{6}{12}\times 6=3\), \(P_{7,1}=\frac{2}{12}\times 6=1\).
* The third method, the worst-case scenario, will set \(P_{5,1}=4\), \(YP_{6,1}=2\), \(P_{7,1}=0\).
* The fourth method, the best-case scenario, will set \(P_{5,1}=0\), \(YP_{6,1}=4\), \(P_{7,1}=2\).  
All four ways suggested for dealing with uncertainty are a function of the decision variables and, therefore, create a nonlinear formulation.
## 5 Evaluation Experiments
### Training and Test Sets  
To carry out the first experiment (A), we used data divided by main intentions. We divided our dataset in two: a training set, made by the 75% of the whole collection of examples, and a test set made of the remaining 25% of the elements. Figure 4 presents an overview of the distribution of the intentions among the training and test sets.  
In Experiment (B), we used the same test set for nine different trials, in which we build nine subsets of the training set previously defined for Experiment (A). The first training subset was created by extracting 10% of the elements, keeping the same distribution of the intentions of the entire training set. The second one consists of 20% of the examples, keeping all the instances included in the first one and adding another 10% of examples for each intention. This process was repeated progressively, increasing by 10% of examples, until we reached the 100% of the initial training set. The composition of training subsets and test set is shown in Table 1.  
In Experiment (C), we used the dataset built for Experiment (A). While the training set remained unchanged, in order to evaluate the response time using a considerable amount of requests, we created an extended test set replicating our 132 instances several times in order to reach a total of 1000 test requests.  
As for Experiment (D), we also used the dataset built for Experiment (A). We used the training set to train the services, and we selected 25 test examples whose intentions were recognized correctly by all the platforms. With those elements, we created 30 new test sets: in the first new test set, we changed a character randomly for each example belonging  
Figure 4: Number of intentions that compose the training and the test sets in Experiment (A).  
to the test set. To build the second test set, we used the test set just to create and change another character randomly for each example belonging to the test set. In this way, we created 30 different test sets, where the last test set had, for each example, 30 characters changed compared to the initial test set.
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 4 The Implementation and Optimization of Efficient GPU Tensor Sensing
### Design and Implementation of the GPU Tensor Sensing
#### 4.1.3 Eliminating Explicit Transpose Operations  
After each least squares method, the transpose of the target matrix is obtained. The transpose operation of the matrix needs to be performed (Lines 6 and 12 in Algorithm 2). However, the transpose operation takes much computing time and resource. As the operation after transpose of the matrix is the multiplication of diagonal matrices, we eliminate the explicit matrix transpose by enabling the transpose option in the gemmStridedBatched() routine. In this way, the gemmStridedBatched() routine will perform matrix transpose implicitly and efficiently before the matrix multiplications.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### SMT-Based Scheduling Optimization
#### 7.3.2 Integration with Deep Learning  
* Fault probabilities \(P(M_{i},J_{j},t)\) predicted by the 1D-CNN are dynamically input into the SMT solver to guide job scheduling decisions.
* The SMT solver re-optimizes schedules when:- Predicted fault probability exceeds threshold
- New jobs are added to the queue
- Machine state changes significantly
## 2 Materials and Methods
### Statistical Analysis  
The purpose of this study is to evaluate the effects of three major variables--age, gender, and time of day--on attention span. To achieve this objective, first, we verified the significance of the changes in the PVT parameters using a general linear model with the gender and time of day as fixed factors and the age as a covariate. Next, we used multiple regression analysis to verify whether the PVT parameters could be explained by the gender, time of day, and age. The parameter input method used was the stepwise method. This allowed us to verify the extent to which the predictor variables could statistically explain the variation in the PVT performance indicators. The statistical analysis was performed using IBM SPSS (v28), with a significance level set at \(p\) = 0.05 and partial eta squared (\(\eta^{2}\)) values. \(\eta^{2}\) indicates the proportion of the variance of the dependent variable that each independent variable (gender, time of day) explains. Since it is difficult to understand whether the difference is due to chance, due to the large sample size or is truly meaningful when the \(p\)-value is small, calculating \(\eta^{2}\) makes it possible to judge the effect. The \(\eta^{2}\) values were calculated using the following formulas.  
\[\eta^{2}=\frac{ss_{effect}}{ss_{effect}\ +\ s_{error}}\]\(SS_{\textit{effect}}\) indicates the sum of the squares of the factors (gender, time of day, etc.), and \(SS_{\textit{error}}\) indicates the error sum of the squares.  
The normality of the main PVT indices was confirmed using the Shapiro-Wilk test and Q-Q plot, and the homogeneity of variance was examined using the Levene test.  
To examine the effects of age, sex, and time of day on sustained attention performance, we employed both a general linear model (GLM) and stepwise multiple regression analysis. These complementary statistical approaches were selected based on their suitability for both hypothesis testing and exploratory modeling, respectively. The GLM was used to test our a priori hypotheses regarding the main effects and potential interactions among the independent variables. This framework allows for the inclusion of categorical variables (sex, time of day) and continuous covariates (age) within the same model, enabling us to assess both the between-group differences and the continuous trends in performance. A GLM also facilitates the calculation of effect size estimates such as the partial eta squared (\(\eta^{2}\)), which enhances the interpretability of statistical findings beyond simple \(p\)-values. In contrast, the stepwise multiple regression approach was used to explore the relative predictive power of each variable (age, sex, time of day) on various PVT metrics, such as the reaction time (RT), minor lapses (MNLs), major lapses (MJLs), and false starts (FSs).