esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
[24] considered a special type of DDoS attack using commonly available IoT devices such as smart lightbulb, IP cameras, and Raspberry Pi devices. Their work demonstrates how these IoT devices can be used in performing a DDoS attack. In [25], [PERSON] et al. further considered the vulnerability of the production lines that use industrial IoT devices. They analyzed the possible DDoS attacks on the production lines that can be perpetuated by an attacker through the Internet and proposed solutions to mitigate such attacks. Similar to that, [PERSON] et al. [26] also investigated DDoS attacks using a simulation model of the production lines and the IoT security system \"Fibaro\" to establish whether the network and IoT devices can be misused to compromise production machines in industries. [PERSON] et al. in [27] proposed a machine learning approach that is based on the use of sampled flow (network traffic samples) to detect and mitigate a DDoS attack using a neural network. Despite numerous works [28, 29, 30, 31, 32, 33, 34, 35, 36] on network and device vulnerability detection, network anomaly, intrusion detection, etc., IoT vulnerability and security concerns are still on the rise [37]. Moreover, most models trained based on these classifiers require that the anomaly traffic pattern be collected beforehand. Furthermore, the network environment changes from time to time and it may be difficult to collect such nonconforming samples effectively. In addition, various network scanning tools do not consider the effect of a wide-area network scan on the network. Therefore, it may lead to network congestion and degradation of the communication quality, especially in the narrow band networks in which IoT wireless devices join. Furthermore, performing a network scan during the active period of an IoT wireless device usage is not effective because the network can easily be overloaded due to the large amount of network scan traffic that may adversely affect regular communication. Moreover, the outcome of a network scan may be unreliable due to a lot of retries and retransmissions of scan packets. Therefore, a method to determine the cause of scan failure/delay is required. While the existing systems focus on vulnerability/intrusion detection, to the best of our knowledge, our method is the first research that considers effective network scanning by adopting a technique for identifying the cause of network scan failure/delay using our proposed multimodel-based approach. Our work differs from the existing methods in that the proposed method does not detect network vulnerability or network intrusion, but rather addresses the reason for the failure of various existing network scanning tools (i.e., intrusion and vulnerability detection systems) and whether it is in relation to the communication environment status. Moreover, the proposed method achieves high accuracy where the traditional machine learning classifier fails to generalize a new scan response sample.
# A Cost-Optimized Model for Dynamic Port Hinterland
## 4 Experimental Design and Results
### Calculating the \(H(x,y)|_{(p_{n},o_{n})}\)
#### 4.1.5 Tmc (Time Management Cost)  
The value of time can be quantified as the depreciation rate for each product. Following Salunkhe and Kadam, bananas have a life span of 3 weeks, with an initial 3-week period without value loss due to the fruit's ability to mature post-harvest [50]. Consequently, for bananas, the value of Time Management Cost (_TMC_) is expressed in Equation (19).  
\[TMC=\left\{\begin{array}{ccc}0\;if\;t\;(\text{days})&\leq 21\;\text{days}\\ \\ \frac{VAL\cdot(LT-21)}{21}\;if\;t\;(\text{days})&>21\;\text{days}\end{array} \right.. \tag{20}\]  
Conversely, oranges have a total lifetime of 12 weeks but begin losing value immediately post-harvest. Thus, the _TMC_ value for oranges is represented by Equation (20).  
\[TMC=VAL\cdot LT/84. \tag{21}\]  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Origin** & **Concept** & **Algeicras** & **Valencia** & **Barcelona** \\ \hline Durban & Price (EUR) & 1290.91 & 1748.18 & 1748.18 \\  & Duration (d) & 33 & 47 & 38 \\ Manzanillo & Price (EUR) & 3824.85 & 3503.64 & 3503.64 \\  & Duration (d) & 11 & 19 & 20 \\ Shanghai & Price (EUR) & 4453.37 & 3824.85 & 3825.85 \\  & Duration (d) & 35 & 38 & 43 \\ New York & Price (EUR) & 1679.57 & 1852.77 & 1878.97 \\  & Duration (d) & 17 & 19 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Price, Duration, and Rotation of 40 feet reefer freights from selected ports to reference ports. Source: Personal compilation from commercial data.  
In these equations, \(LT\) denotes the transit time, which is calculated as the sum of maritime transit time and terrestrial transit time. The latter encompasses road transportation, railway transportation (where applicable), border management procedures, and inspection time.
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 3 Problem Statement and Solution Method
### M1: Instant Response Model
Additionally, due to the highly variable demand for vehicles at tourist attractions, implementing an instant response model in this context can easily result in low efficiency, resource waste, and a failure to leverage modular vehicles' flexibility fully.
## 5 In-Line Application of LIBS in Industrial Production
### Welds  
Welding processes are one of the most commonly used joining technologies. Defects in the weld metal reduce the safety and integrity of a weldment. For the welding of stainless steel, the chemical composition of the weld metal determines the solidification of the steel and the weld metal quality. For the inspection of weld seams, various destructive and non-destructive methods are employed after completion of the welding process. Inspection during the welding process would save time and effort. LIBS can be used for in-situ weld pool monitoring during the welding process. Tungsten inert gas welding of austenitic stainless steel was monitored by LIBS to measure in-situ changes of the chemical composition [402]. Figure 23a shows the schematic of an in-situ LIBS monitor in a welding process.  
Figure 22: LIBS in-line measurement of polymers in a recycling plant. (**a**) Schematic of the LIBS sensor installed at a polymer extruder. (**b**) Detection of different types of polymer material at the extruder by means of LIBS intensities measured for Ti and Sb. Adapted from [418].  
The normalized intensities for elements Cr, Ni, and Mn recorded during welding at various positions on the weld metal are shown in Figure 23b. After solidification of the weld pool the intensity for Mn strongly dropped, whereas intensities for Cr and Ni were almost unchanged. The formation of Mn vapor above the weld pool and condensation of Mn on the weld metal surface was concluded from the measurements. The results proved that LIBS can be used in situ to inspect the TIG welding process.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
## 4 Experimental Research
### Comparison of Variety of Feature Extraction Algorithm Combined with WPD  
To verify the validity of WPD combined with dimension reduction technology, this paper integrates the time-frequency features extracted by WPD with the nonlinear features extracted by different dimensionality reduction methods, and the new hybrid features are input in the KNN classifier. A series of classification results is shown in Table 3, in which the parameters for all feature extraction methods have been optimized, respectively.  
It can be seen from Table 3 that when the WPD is combined with any one of the common dimension reduction algorithms, including PCA, ICA, MDS, LLE and SE-isomap, its classification result is superior to the corresponding dimension reduction algorithm alone. Additionally, the combination of WPD and SE-isomap, as a feature extraction method, yields the highest recognition accuracy and shows a good stability in this study. This also indicates the necessity and correctness of the combination of time-frequency information \(\mathbf{F}_{1}\) and of non-linear structure information \(\mathbf{F}_{2}\) in processing of the MI-EEG signal. In consideration of the results from the WPD + LLE and WPD + isomap methods being  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(k\)-Value & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline
**Accuracy (\%)** & 82.9 & 83.1 & 86.2 & 87.5 & 88.3 & 90.6 & **92.7** & 89.1 & 91.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with different parameter \(k\) values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Feature Dimension** & **Accuracy** & **Methods** & **Feature Dimension** & **Accuracy** \\ \hline PCA & 317 & \(68.1\pm 6.3\) & WPD + PCA & 96 & \(73.1\pm 10.3\) \\ ICA & 78 & \(85.9\pm 8.9\) & WPD + ICA & 42 & \(87.3\pm 6.2\) \\ MDS & 112 & \(73.6\pm 7.4\) & WPD + MDS & 84 & \(77.4\pm 12.8\) \\ LLE & 24 & \(86.3\pm 4.2\) & WPD + LLE & 33 & \(91.8\pm 3.5\) \\ SE-isomap & 16 & \(88.5\pm 6.5\) & WPD + SE-isomap & 25 & \(92.7\pm 3.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other feature extraction methods (%). MDS, multidimensional scaling: LLE, locally linear embedding.  
relatively close, a two-sample \(t\)-test, whose assumption is similar to the one in Section 4.3.1, was also implemented here to verify the statistical difference between the two feature extraction methods, and the \(p\)-value is 0.0497, which implies that the performance of WPD + isomap is significantly better than that of WPD + LLE.
# Communication Manager for Hyper-Connected RPAS Environments
## 4 Validation: From the Lab to the Air
### Flight Trials  
In order to test the communications CIM in real experiments, initial flight tests were conducted at Club Las Aguilas, Madrid, on 25 August 2022.  
The flights were performed with the DJI M300 and DJI M600 UAVs. INTA has selected both UAVs to carry out the Labyrinth project's use cases. In addition, both UAVs incorporated a DJI Manifold SBC as the UAV payload where the CIM software is installed and configured.  
Figure 7: Traffic captured at INTA’s RPS in the laboratory experiments.  
Currently, they have three different communication systems built in: (i) 4G communication between UAV and GCS, (ii) Wi-Fi communication between UAV and GCS, and finally, (iii) DJI's proprietary RF communication that communicates the UAV with the external pilot. The last link (iii) is not included in the CIM and is only used to control the aircraft with a manual DJI remote controller in an emergency. An additional RF link is expected to be available shortly (as introduced in Section 3), which will be used as a priority channel for telemetry and video transmission.  
Figure 8a,b show different pre-flight operations. Figure 8a shows the INTA's external pilot performing manual flight tests with the DJI's proprietary RF link. These flight trials aim to test that the UAV flies correctly with the payload incorporated for the project operations. On the other side, Figure 8b shows the payload included in the DJI M600 for hosting the CIM and enabling communications with the INTA RPS.  
Since these experiments were the first flight tests performed with the CIM software installed, it was decided only to use the CIM functionality to create and establish connectivity between links but not the channel selection functionality. Still, the same flying tests were repeated using Wi-Fi and 4G/LTE to check the correct operation of both communication alternatives. Both flights (Wi-Fi and 4G/LTE) lasted approximately 15 min. During this time, the telemetry information was received in the RPS with no incidence, and the flight proceeded normally.
# Research Progress in the Construction and Application of In Vitro Vascular Models
## 2 Construction of In Vitro Vascular Models
### Construction of In Vitro Vascular Model Based on Microfluidic Technology  
Microfluidics is a technique used for manipulating liquids and gases in channels with cross-sectional dimensions of 10-100 \(\upmu\)m, and microfluidic chips are the primary platform for utilizing microfluidics. In recent decades, the advancement of microfluidics technology has accelerated the development of extracorporeal vascular models due to their light weight, small size, portability, precise fluid control, and maneuverability [51]. Additionally, this technology can accurately control the physiological structure of blood vessels, which can be designed as straight lines, bifurcations, narrow structures, and other physiological structures that are difficult to achieve with traditional methods [52]. The small size of the microfluidic chips also reduces wastage of reagents and biological samples. These advantages over traditional in vitro models make microfluidic chips an ideal platform for constructing in vitro vascular models. This platform can be used not only to elucidate the mechanism of vascular formation and related factors but also as a platform for vascular disease and drug screening. We divide the construction methods of in vitro vascular models based on microfluidic technology into photolithography, soft lithography, and self-assembly. The principles, steps, advantages, and disadvantages of each method are presented below.
## 4 Experiments and Results
### Ablation Study on the LiTS Dataset  
To demonstrate the validity of the proposed model and loss, we designed an ablation study to discuss the effects of different configurations of loss and possible models. Specifically, in order to prove the validity of the 2.5D model, we modify the convolution  
Figure 6: The abscissa represents the number of training iterations, and the ordinate represents the corresponding loss value, Setting different \(\beta\) values can slightly affect the convergence degree of the boundary loss function, but the boundary loss can always converge to a similar value on the total. When the \(\beta\) value is set to 1, the loss can converge to the lowest point.  
block of the input of the proposed 2.5D model and obtain a 2D model. The 2D model is also a fully convolutional network. Different from the 2.5D network, the input feature dimension of the 2D FCN is modified as \(1\times 320\times 320\). Then, we use combined loss, contour loss and boundary loss to optimize the 2D FCN and 2.5D FCN on LiTS dataset, respectively and the results are shown in Table 1. When boundary loss is used to optimize 2D FCN for the segmentation of tumors, 2D FCN yield Dice Per Case and IoU scores of 73.7% and 58.3%, respectively. At the same time, the 2.5 FCN with boundary loss yield clear improvements of 0.9% and 1.1% in the Dice Per Case and IoU scores respectively, when compared to the 2D FCN with boundary loss. In addition, 2D FCN yield respectively Dice Per Case and IoU scores of 93.9% and 88.5% for the segmentation of liver. The 2.5 FCN with boundary loss yield clear improvements of 0.4% and 0.8% in the Dice Per Case and IoU scores respectively, when compared to the 2D FCN with boundary loss. Since the spatial dependent information between slices is considered in the input of 2.5D FCN and more spatial feature information can be explored in the subsequent feature exploration. So the 2.5D FCN can effectively improve the segmentation effect of 3D medical images.
## 2 Materials and Methods
### Preparation of ExperiencesSoil Tests And Sensing
#### 2.3.2 Experiments 2--Three-Layer Soil  
Experiment two was conducted in a plastic container measuring approximately 40 \(\times\) 60 \(\times\) 35 cm\({}^{3}\). This experiment aims to assess if the size or type of the container influences the readings and if the RADAR can measure the soil moisture at a deeper level.  
The soil is composed of three layers. The first layer (the top) is 21 cm high, with the same soil composition as the previous experiment. The second layer is 5 cm in height with fewer nutrients, a higher concentration of sand, and a much higher apparent or bulk density. The third layer (bottom) is 4 cm high and is equal to the first layer. The position of the sensors is shown in Figure 2. The ground-truth sensor is 2.3 \(\times\) 9.8 cm\({}^{2}\) in size, which makes it small compared to the volume of soil in the experiment. Thus, it is assumed that its presence is negligible in terms of general SWC; experiments demonstrate that the sensor is outside of the cone of RADAR detection.  
Two soil moisture sensors were inserted at different depths for experiments one and two, one at 2 cm and the other at 21 cm underground, as shown in Figure 2. The RADAR was 9 cm away from the soil. We used a sample period of 100 s for the soil moisture sensor and five minutes for the RADAR during 36 h in the same position. This way, there are two intermediate readings from the control signal between two sequential readings from the RADAR.
## 4 Experimental Process and Result Analysis  
To clearly demonstrate the performance of the proposed method, three types of experiments are conducted. First, the estimation accuracy of the presented method is comprehensively demonstrated. Then, the proposed method is compared with the most commonly used methods and the current state-of-the-art methods. Finally, an ablation experiment is performed to verify the effectiveness of the MAGU module.  
Two datasets were adopted in the experiments. The datasets were collected on tree-lined streets and roads with wide views. The data were collected dynamically with a collection interval of 1 s. The true position information of the receiver was obtained from the NovAtel SPAN system. The adopted dataset contains code measurements and does not contain carrier phase measurements. The test environment is Python 3.9. The hardware is supported by RTX 3090 GPU, whose total memory is 24 GB. The first dataset was collected and publicly released by [PERSON] et al., which consists of GNSS positioning data collected by multiple low-cost receivers in the San Francisco area of the United States [36]. The second dataset was collected and publicly released by [PERSON] et al. [27]. Both datasets were used for comparative experiments and ablation experiments. Applying the same dataset for comparison can improve the persuasiveness of the comparison experiment to a certain extent.  
The experimental process of employing the first dataset is mainly as follows. First, the dataset collected and publicly released by [PERSON] et al. was obtained. Secondly, the original positioning error of the receiver is obtained by the Kalman filter (KF) method. Then, the parameters that may affect the positioning error are extracted from the dataset as feature variables, and the obtained positioning error is adopted as the target variable. Finally, the feature variables and target variables are input into the improved MLP model together, and the estimated results of the positioning error are output after multiple iterations of training. Next, each step will be introduced in detail, and corresponding diagrams will be given.
## 3 Results
### Substrate Specificity of \(\beta\)-galactosidase from _T. uzoniensis_
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Substrate** & **Strain** & **Substrate (mM)** & **Product (mM)** & **Molar Yield (\%)** & **Productivity (mM/h)** & **Ref.** \\ \hline Soybean meal & _Alicyclobacillus_ sp. & dalalzin (NN*) & dalalzin (0.21) & NR & 0.10 & [39] \\ Soybean milk & Soybean & dalalzin (0.15) & genstien (0.32) & NR & 0.16 & [40] \\ Soybean flour & _Apergillus niger_ & genstien (NR) & genstien (NR) & 17 & NR & [41] \\ Soybean flour & _Apergillus niger_ & dalazin (NR) & dalalazin (0.27) & NR & NR & [41] \\  & & genistin (NR) & genstien (0.36 \({}^{1}\)) & NR & NR & \\  & _T. martinus_ & daladin (NR) & dalazin (0.35) & 86 & NR & [17] \\  & & genistin (NR) & genstien (0.38 \({}^{1}\)) & 94 & NR & \\ Soybean flour extract & Almond & dalazin (NR) & dalazin (NR) & 81 & NR & [42] \\  & & genistin (NR) & genstien (NR) & 79 & NR & \\  & _A. niger_ & genstien (NR) & genstien (9.38 \({}^{1}\)) & NR & NR & [43] \\  & _A. oryzae_ & dalazin (2.50) & dalazin (1.80) & 73 & 0.61 & [44] \\  & _A. oryzae_ & genstien (1.20) & genstien (0.54) & 45 & 0.11 & \\  & _A. oryzae_ & daladin (10.00) & dalazin (8.40) & 84 & 0.17 & [37] \\  & _Apergillus terras_ & daladin (0.21) & daladin (0.07) & 33 & 0.41 & [45] \\  & _Gengmaths_ sp. & daladin (0.50) & daladin (2.50) & 28 & 0.24 & \\  & _P. thermophila_ & genstien (4.10) & genstien (3.70) & 86 & 1.23 & \\  & _P. thermophila_ & daladin (NR) & daladin (NR) & 98 & NR & [36] \\  & & genistin (NR) & genstien (NR) & 95 & NR & \\  & _P. fissions_ & genistin (1.20) & genistin (1.00) & 100 & 1.00 & [13] \\  & Uncluratable microbes & daladin (NR) & daladin (1.74) & NR & 0.87 & [46] \\ Soy molasses & _Dalvanyomyces hansami_ & genstien (NR) & genistin (1.18) & NR & 0.59 & \\  & & _P._ sterling_ & genstien (0.07 \({}^{1}\)) & NR & NR & [47] \\  & _D. interpklam_ & daladin (1.20) & daladin (1.20) & 100 & 0.60 & [10] \\ Wild soybean seed extract & _T. znamiensis_ & daladin (2.76) & daladin (2.76) & 100 & 1.86 & this study \\  & & genistin (3.84) & genistin (3.84) & 100 & 3.30 & this study \\ Wild soybean root extract & _T. znamiensis_ & daladin (9.90) & daladin (9.90) & 100 & 3.30 & this study \\  & & genistin (1.68) & genistin (1.68) & 100 & 3.36 & \\ \hline \end{tabular}  
* NR, not reported.
* Disiden or genstien (np/ Soybean flour (g)  
\end{table} 
Table 3: Production of dalazin and genstien from complex substrates by glycosatases.
# Circularly Polarized High-Gain Fabry-Perot Cavity Antenna with High Sidelobe Suppression
## 2 Proposed FPCA Design and Operation  
The proposed FPCA model is shown in Figure 1. It consists of a PRS superstrate, an AMC substrate, and an \(x\)-polarized U-slot microstrip patch antenna (MPA), which is located in the middle of the FPCA. The PRS changes the \(x\)-polarized input to a circularly polarized output. A 1.52 mm thick Taconic substrate (RF-35) is employed for the fabrication of the FPCA, which has relative permittivity \(\varepsilon_{r}\) = 3.5 and loss tangent tan \(\delta\) = 0.0025.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Dataset/Provenance and Compliance Tags
#### 6.15.3 Multidimensional Class Evaluation  
According to Table A32, this class includes two papers focusing explicitly on general concept drift and data drift, uniquely highlighting dataset versioning and metadata tracking as direct mechanisms for detecting and explaining drift. The identified application contexts span healthcare, manufacturing, logistics, and general-purpose data management, demonstrating the critical role of provenance tracking in domains heavily regulated or operationally sensitive, where understanding data lineage directly supports regulatory compliance and transparency. The explanations provided are uniformly global, naturally emerging from dataset metadata changes or provenance shifts rather than fine-grained, instance-level detail, aligning with high-level compliance and governance objectives. The intended audience includes both data scientists and end-users, recognizing the direct importance of transparent data management and compliance practices to operational stakeholders and regulatory authorities. No explicit interpretability-related metrics are mentioned, suggesting that interpretability arises directly from clearly documented and transparent metadata or compliance annotations rather than formalized quantitative measures. Learning modes include both hybrid and offline methods, effectively supporting retrospective compliance auditing and ongoing metadata management in operational scenarios, ensuring continuous monitoring and traceability of drift origins.