esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Effectiveness on the Load-Aware Strategy  
We conduct another experiment to evaluate the attack performance where the controller adopts a feedback control to adjust the timeout setting. We use the classic feedback-control algorithm proposed in ITM [15]. Due to the performance concern, we use fewer attack flows (20 simultaneous attack flows) to launch the attack. We compare (i) the flow table usage, (ii) the max timeout values, and (iii) the packet-in rates in both scenarios.  
Figure 20 shows the flow table utilization. The flow table occupancy starts to reach its peak at around 443 s. Compared with the case without feedback control, benign flow entries occupies less space. Before the flow table is full, the number of benign flow entries is less than 400. The reason is that when the flow table occupation is beyond the \(FB_{start}\), the controller starts to reduce the timeout. With the smaller timeout, the number of benign flow rules is reduced. As shown in Figure 21, the max timeout value is adjusted according to the flow table occupation. At 270 s, the max timeout is decreased to 1 s, the minimum timeout value. In Figure 20, the number of benign flow flows declines sharply after 270 s.  
Figure 21: The value of the max timeout when the controller applies the load-aware adjustment.  
Figure 20: The number of flow rules when the controller applies the load-aware adjustment.  
We also evaluate the impact on the controller by measuring the number of packet-in messages. In Figure 22, the packet-in rate is significantly higher after the flow table is completely overflowed at 443 s. By saturating the flow table space, the attack can cause an additional packet-in rate of approximately 140%. We also notice that the packet-in rate is higher than that in the normal traffic scenario, even before overflowing the flow table. Between the 270 s and 440 s (the flow table is not full), the average packet-in rate is about 8% higher than that of the normal scenario. The reason is that the controller begins assigning smaller timeouts due to high flow table occupancy before the overflow happens. Thus, benign flows with large packet intervals trigger more packet-ins.  
Against the load-aware strategy, the attacker can only slightly degrade network performance by occupying flow table space and forcing the SDN controller to assign smaller timeouts. However, to cause severe performance degradation and disruption, the attacker must overflow the entire flow table.
# Editorial Board Members' Collection Series: Functional Foods
## 3 Future Research
### Green Extraction Technologies  
The rapid evolution of green extraction methodologies is set to revolutionize the development of functional foods by enabling the sustainable recovery of bioactive compounds. Modern techniques--such as supercritical fluid extraction using CO\({}_{2}\), microwave-assisted extraction, and ultrasound-assisted extraction--offer highly efficient alternatives to conventional methods, significantly reducing both energy consumption and the use of toxic organic solvents [13]. In parallel, innovative green solvents like ionic liquids, deep eutectic solvents, and other bio-based alternatives are proving invaluable; for example, supercritical CO\({}_{2}\) is celebrated for its low toxicity and excellent performance in extracting thermolabile compounds, while ionic liquids provide adjustable polarity for isolating both polar and non-polar bioactives [14]. Moreover, the sustainable extraction of phytochemicals using these eco-friendly approaches not only enhances the yield and purity of valuable nutraceuticals--such as polyphenols, tocopherols, carotenoids, and fatty acids--but also preserves their functional integrity [15]. Hybrid techniques pair ultrasound-assisted extraction with pressurized liquid extraction. They raise recovery rates and improve selectivity.  
They also cut solvent waste and protect sensitive compounds [16]. Cloud Point Extraction (CPE) is an eco-friendly sample-preparation technique that uses non-ionic surfactants dissolved in water. Each surfactant-water mixture has a specific \"cloud point\" temperature: when the solution is heated beyond this point, it turns cloudy and splits into two liquid layers--a tiny, surfactant-rich layer full of micelles and a larger, surfactant-poor aqueous layer. The target compounds move into the micelle-rich layer, concentrating them without large volumes of toxic solvents. By harnessing this surfactant-driven phase separation, CPE isolates and pre-concentrates analytes efficiently under mild conditions and using green solvents [17]. Together, these innovations shrink the environmental footprint of extraction processes. They align with circular-economy and sustainable-development goals. Ultimately, they offer scalable, commercially viable ways to produce nutritionally enriched, clean-label foods [18; 19; 20]. These sustainable strategies will anchor future functional-food development. They enable efficient recovery of natural antioxidants and bioactives and their integration into foods that support health and well-being.
## 4 Dataset  
We utilized four datasets, namely the MINIST dataset [11], the PTB dataset [12], the ETTm1 dataset [13], and the UCR dataset [14]. The MINIST dataset comprises 60,000 samples, with 50,000 in the training set and 10,000 in the test set. It is a classic handwritten digital image dataset commonly employed in image classification tasks in machine learning.  
The PTB dataset, short for the Penn Treebank dataset, is a classic language model dataset collected and curated by the linguistics department at the University of Pennsylvania as part of the Penn Treebank project. Widely used in language modeling, natural language processing, and other related fields of research, the PTB dataset consists of three text files serving as training, validation, and test sets, containing 929 k, 73 k, and 82 k words, respectively.  
The ETT (Electricity Transformer Temperature) dataset collects transformer data from two independent counties in China over two years. We selected the ETTm1 dataset at a 15-min granularity, totaling 69,680 data points, each comprising the target variable \"oil temperature\" and six power load features. Through sliding windows (which transform time series data into supervised learning datasets), we partitioned it into 48,627 training, 10,303 validation, and 10,303 test instances, training for a total of 20 epochs.  
The UCR (Univariate Time Series Classification Archive) serves as an open resource for storing and sharing time series classification datasets. Encompassing various domains and applications such as medicine, finance, and engineering, each dataset contains a set of labeled univariate time series samples, which are categorized based on their time series shapes and features. We selected three datasets from the UCR dataset, coffee, computer, and ECG 500, for training evaluation.  
Figure 4: Comparison of normalized and non-normalized weight variance and neuronal output variance; from left to right, the variance changes are plotted at learning rates of 0.005, 0.001, and 0.0005, respectively. Observation of its changes shows that its weight (_Wih_ and _Whh_) and neuron output (_at_) variance changes show a steady upward trend, while the weight and neuron output variance under unnormalized conditions are non-smooth upward curves. WGN is sensitive to the learning rate, and different learning rates have different improvement effects.
## 3 Results and Discussion
### Influence of Dry Density on Saturated Permeability  
The relationship between the permeability \(k_{\mathrm{s}}\) and dry density \(\rho_{d}\) of the test materials can be fitted by a power function \(k_{\mathrm{s}}=a\cdot\rho_{d}{}^{b}\), as shown in Figure 5, and the correlation coefficients all range from 0.987 to 0.99857, indicating that the fitting reliability is good. When the initial moisture content is the same, the permeability of the remolded loess gradually decreases with increasing dry density. When the dry density of the YA soil samples increases from 1.30 g/cm\({}^{3}\) to 1.70 g/cm\({}^{3}\), the saturated permeability decreases from 3.65 \(\times\) 10\({}^{-5}\) cm/s to 2.68 \(\times\) 10\({}^{-7}\) cm/s, a decrease of two orders of magnitude. When the dry density increases from 1.45 g/cm\({}^{3}\) to 1.67 g/cm\({}^{3}\), the saturated permeability decreases from 5.41 \(\times\) 10\({}^{-6}\) cm/s to 4.93 \(\times\) 10\({}^{-7}\) cm/s. The same phenomenon also applied to the QY and LL loesses. When the dry density was low, the actual measured saturation permeability was on the order of 10\({}^{-5}\) cm/s, and when the dry density was high, the permeability was on the order of 10\({}^{-7}\) cm/s, decreasing by two orders of magnitude. The slope of the curve in Figure 5 shows that the decrease rate of the saturated permeability gradually decreases. That is, when the dry density of the soil sample is less than 1.6 g/cm\({}^{3}\), the saturated permeability decreases significantly; when the density is greater than 1.6 g/cm\({}^{3}\), the rate of decrease in \(k_{\mathrm{s}}\) gradually reduces. Therefore, the correlation between permeability and dry density should be considered in the design and construction of loess structures or protection works, such as earth dams, embankments, roadbeds, slopes and loess foundations. Based on the characteristics of a structure, a reasonable dry density value should be designated for construction that can effectively reduce engineering geological problems such as the deformation and failure of the structure. If the structure requires low permeability, compaction should be performed under the optimal moisture content to the greatest extent possible to obtain a soil body with the maximum dry density, thus achieving infiltration prevention.  
Figure 4: Relationship curve of \(k_{\mathrm{s}}\) vs. time. (**a**) data for CH soil, (**b**) data for QY soil, (**c**) data for YA soil, (**d**) data for LL soil.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 4 The Design Procedure of Circuit Component Parameters
### Step 2: Calculating the Transformer Turn Ratio  
Following the determination of converter specifications, the proper transformer turn ratio was chosen immediately. For obtaining the flexibility of the tuning range, the transformer parameter design as expressed by (11) is based on the rated output voltage of the energy storage/supply port according to the battery nominal voltage.  
\[N=\frac{N_{P}}{N_{S}}=\frac{V_{DC\ Grid\_nom}}{V_{Bat\_nom}} \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline DC grid voltage \(V_{DC\_Grid}\) & 400 V \\ Battery port voltage \(V_{Bat}\) & 280–403 V \\ Max. conversion power \(P\) & 1000 W \\ Series resonant frequency \(f_{r1}\) & 100 kHz \\ Power switching range \(f_{s}\) & 70–150 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Converter specifications.  
Figure 9: The design flowchart of bidirectional symmetric resonant converter.
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Clutter Models
#### 2.3.3 Model 3: Targets Drowned in Usual Clutter Distribution  
In this case, a normal distribution with random variables of mean \(\mu\) and standard deviation \(\sigma\) models the clutter distribution. The probability density function is given by the following equation [14]:  
\[p(\
u)=\frac{1}{\sigma}e^{\frac{-(\
u-\mu)^{2}}{2\sigma^{2}}} \tag{17}\]
## 2 Study Area  
### Geological Settings  
The target area of this study is located at the Pohang Basin on the southeastern coast of South Korea (Figure 1). A CO\({}_{2}\) injection well was drilled, and about 100 tons of CO\({}_{2}\) were injected in 2017 as a field scale pilot test [40]. The average water depth of the target area is 15.0 m, and the depth at the injection well is 15.9 m [41]. Based on geological studies, it was found that the target formation contains coarse-grained conglomerate and sandstone at depths deeper than 740.0 m from the sea level. In addition, potential leakage of the injected CO2 in the upper direction can be prevented with an overlying caprock of 700.0 m or thicker [42] (Figure 2a). It was found that the target formation contains two aquifer layers that were technically available for CO2 injection and storage, aquifer A and B (Figure 2b). Aquifer A, which consists of alternating sandstone layers, has a thickness of 11.0 m, and an average permeability of 30 md. On the other hand, aquifer B contains commingled layers of sandstone, mudstone, and conglomerate. The thickness of aquifer B is 14.0 m with an average permeability of 11 md.
## 2 The Proposed CA System Architecture  
The CA system should be supported by vigorous algorithms to increase the airspace capacity. Figure 1 illustrates the detailed fundamental processes of the complete CA system, in which the input is the 4D (three dimensions (3D) position + time) trajectory information belonging to each UAV; a high speed processor equipped with CD algorithm deals with the trajectories and obtains relevant information to predict whether a collision is going to occur in the future. If a potential collision is detected, DDOA computes the feasible solutions and causal analysis selects the optimal one. The control command is sent to the involved UAVs to resolve current threats and the states improve online.  
The core of the operational CA system consists of two components:  
* The CD module that analyzes the flight information depending on 4D trajectory has two different roles: generating the intuitionistic representation of threats and informing the CR module.
* The CR module integrates CD and resolves the detected conflicts in two stages: DDOA aims to avoid collisions by generating alternative trajectories for each UAV with a local optimization scope; causal analysis focuses on exploring the emergent dynamics between the alternative trajectories generated by DDOA and the rest involved trajectories. Based on the detected threats, the CR should be informed with the geographical coordinates where the collision would occur, the time when the collision would occur, and the involved UAVs. Though CR is a combinatorial problem in exponential growth with the increasing number of UAVs, in our research several strategies such as setting constraints are needed to narrow the solution space progressively to get the final optimal solution. Figure 2 conceptually illustrates the solution procedure. The DDOA generates several locally-optimal CA trajectories considering different constraints, e.g., the two UAVs should meet the performance requirements, satisfy the state equation, and keep a safe distance, etc. This causal analysis allows taking into consideration all the possible solutions that ensure the completeness of the solution space, and then reduces the space once again based on some specific constraints (e.g., the least cost).  
The hardware architecture of the novel CA system consists of the following major components: CA processor--performs airspace surveillance, intruder tracking, own UAV state tracking, conflict detection and conflict resolution, and it uses discrete status inputs of involved UAVs to control the CA logic parameters that determine the protection volume around own UAV; State transmitter--is used to provide air-to-air data exchange between UAVs so that coordinated, complementary CA trajectories  
Figure 1: Functional architecture of the novel collision avoidance (CA) system.  
Figure 2: Step-by-step optimal process.  
can be issued when required; Control unit--guarantees the multiple cooperative UAVs following the generated optimal trajectory to resolve the conflicts.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 4 Measurements and Analysis
### Outdoor Received Power Measurements  
To provide more background to the outdoor measurements presented in this paper, coverage maps were generated using the free Radio Mobile RF propagation simulation software (version 03/04/2018) by [PERSON] [21]. These maps, which can be found in Figure 9, depict the expected coverage for the specific transmitter location discussed earlier for both the 434 MHz and the 868 MHz bands, for transmitting and receiving antennas at 20 m and 13 m above ground, respectively. In these maps, areas colored in yellow denote regions where the expected signal power is equal to the theoretical detection threshold of the system in its default configuration (\(-136\) dBm), while areas colored in green denote regions where a truly reliable connection could be established (threshold = \(-126\) dBm). Unfortunately, the 868 MHz band is not available for simulation in the tool used to make these predictions, hence the nearby 902 MHz band was used instead, as little difference exists between propagation at 868 MHz and 902 MHz. Note both receivers are situated well within the green area, with a good link budget. Links over a limited distance were selected in order to allow the verification of the correct operation of the automatically adjusting stepped attenuators.  
Figure 9: Simulation-based coverage maps for the 434 MHz (**upper map**) and 902 MHz (**lower map**) bands for the transmitter described in Section 3.1. Yellow areas denote the coverage with a reception threshold of \(-136\) dBm and green areas denote the coverage with a link budget that is 10 dB greater than this threshold. Maps generated using Radio Mobile RF by [PERSON] [21].
# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
###### Abstract
In the presented study here, we benefitted from ESA Earth observation missions--in particular the Sentinel series of the EU Copernicus program. The Sentinel satellites, currently consisting of six satellites in orbit, offer a rich multi-sensor Earth observation dataset. We tested an innovative multi-sensor approach, as a preliminary work, using both Sentinel-1 and Sentinel-2 data to evaluate the potential for monitoring the ephemeral Kuiseb River and the dynamics of its fluvial aquifers in Namibia. We extracted parameters such as flood extent, vegetative growth, and backscattering coefficient, which provide information on the subsurface properties and soil moisture, which were in archived time-series from at least three years of continuous acquisitions by the Sentinel satellites through Synthetic Aperture Radar (SAR) and multi-spectral sensors.
## 5 Performance Evaluation
### Average End-to-End Delay  
The average end-to-end delay is related to the transmission distance and the speed of the signal in an acoustic channel. As \(D_{p}=d/s\), where, \(d\) indicates the maximum distance between the source and destination points and \(s\) represents the speed of the signal which varies depending on the water depth. Figure 4 represents the end-to-end delay comparison of the proposed model with recent schemes, the effect of depth levels, the effect of node mobility, and the number of sinks. From Figure 3(c),d, it is quite clear that the end-to-end delay is decreased with the help of multi-sinks and with less mobility. Because in the case of multi-sinks, data can be forwarded towards any sink without causing a little delay. Furthermore, as the FN mobility increases, there are chances that the distance between the forwarder and the sink increase, which also increase the end-to-end delay. Sometimes the connection break occurs due to the mobility of the FNs, the re-establishment of the connection also takes time and produces delay. However, there are no special effects of depth level on the average end-to-end delay as mentioned in Figure 3(a). Figure 3(b) shows a comparison of end-to-end delay of the proposed model, AUV-PN [24], and H2-DAB [22]. The X-axis is fixed for number of nodes in the network; while on Y-axis average values for the end-to-end delay in seconds are plotted. H2-DAB has a higher end-to-end delay due to the involved number of layers for sending data to the sink. Lower layer nodes have to wait for a longer time until the courier node is reached at the closest DCP. In H2-DAB special FNs are pushed into the water with the aid of a mechanically designed element to reach the lower layer FNs to collect the data. Then, these FNs stop for a specific time interval and after that these FNs are pulled back towards the surface which also increases the end-to-end delay.  
In the case of AUV-PN, the end-to-end delay is less as compared to H2-DAB [22]. On the other hand, AUV-PN has the greater end-to-end delay than RTC because in the beginning of each round AUV partitioned the sensing area into clusters. Following that the CH selection process, the AUV collects the list of PNs from the newly chosen CHs. Then AUV visits PN individually, on the arrival of AUV the PN collects data from the MNs and conveys it to AUV after fusion. The AUV sends the collected data to the surface sink after a complete network tour. This whole process takes time and  
Figure 3: The effect of data similarity on the performance of our model.  
increases the end-to-end delay. While in the proposed approach, the MS travels in its belonging region after a regular time intervals without staying longer at DCPs to collect the data from the associated RHs, which reduces the end-to-end delay as compared to AUV-PN.
## 3 Results and Statistical Analysis
### Effect of Interaction of Parameters on Sa  
#### 3.2.1 Laser Power and Scan Speed  
\(\bullet\)\(45^{\circ}\) down-facing surface  
Looking at the two graphs of laser power and scan speed in Figure 8, it is clear that for any given laser power, the \(Sa\) value increases with an increasing scan speed. The rate of increase in \(Sa\) varies at different laser powers. At lower laser powers, there is a rapid increase in \(Sa\) with increasing scan speed, while at higher laser powers the increase is only gradual.  
The second graph depicts the decrease in \(Sa\) at different scan speeds when increasing the laser power up to a certain point, after which the \(Sa\) begins to gradually increase again. The laser power point at which the \(Sa\) begins to rise again varies amongst the different scan speeds.  
\(\bullet\)\(35^{\circ}\) down-facing surface  
The \(35^{\circ}\) down-facing surfaces depict a different effect from the \(45^{\circ}\) surfaces. In this case, as seen in Figure 9 at the different laser powers, the \(Sa\) increases up to a scan speed of 1000 mm/s after which it begins to reduce once again.  
Figure 7: Replicate plot for \(35^{\circ}\) down-facing surfaces: Surface roughness (\(Sa\)) for each trial number.