esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### About Validation  
The results presented here show that water reflectance in the green, red and NIR bands as function of turbidity can be retrieved accurately from Pleiades imagery (Figure 5) in site 1. In site 2, the green and red reflectances compare well between OLI and Pleiades. The comparison between the MODIS and Pleiades products is not as good, especially in the green band. This worse performance can be explained by  
(i) the time differences between the images: 2 hour 25 minutes between MODIS/Aqua and 45 minutes between MODIS/Terra and Pleiades, and 1 hour 40 minutes between MODIS/Aqua and Terra.  
Figure 15: Comparison between Pleiades turbidity in Nephelometric Turbidity Unit (NTU) (7 April 2017) resampled to different spatial resolutions: (**a**) maps at 2, 20 and 250 m, and scatter plots comparing (**b**) 2 and 20 m, (**c**) 2 m and 250 m, (**d**) 20 m and 250 m turbidity values. The black square shows the area of interest considered in the scatter plots and the color bar denotes pixel density.  
In the macro-tidal Gironde Estuary, such time differences induce significant turbidity variations [20; 21]. When comparing MODIS/Terra and MODIS/Aqua satellite products (same sensor and atmospheric correction), differences consistent with those from Pleiades are observed (Figure 7a,b). The advection of water masses and sinking of suspended particles between two image acquisitions explain a significant part of the observed differences. Changes in the atmosphere may also occur during this time difference. The good agreement between Pleiades and OLI products is likely caused by the short time difference (22 minutes) between the satellite data acquisitions.  
(ii) the different spatial resolutions of the three sensors result in different sensitivity to fine turbidity features (e.g. fronts), especially for the MODIS green band at 500 m, (iii) the different spectral responses of the sensors, and the applied corrections including radiometric calibration and atmospheric correction methods result in inevitable differences between the Pleiades, OLI and MODIS products.  
Red and NIR band reflectances are used to derive turbidity products from Pleiades imagery. The turbidity for test site 1 corresponds well with autonomous in situ measurements for a wide range of turbidity (Figure 8b). The DSF atmospheric correction hence is appropriate for the retrieval of turbidity in the turbid and extremely turbid waters of the GE, similar to previous results obtained in coastal and estuarine waters [33; 46].
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 5 Discussion
### Relevance and Lessons  
The emergence of the CIH Virus, also known as Chernobyl, in 1998 created a paradigm shift in malware development. This was one of the first instances where malware was designed to directly target and cause irreversible damage to hardware components.  
In 2017, BrickerBot brought a new wave of concerns as it thoroughly targeted the rapidly growing Internet of Things (IoT) ecosystem. This malware, known for its capacity to irreparably damage IoT devices, accentuated the critical need for enhanced security measures within this domain. BrickerBot underscored the importance of reinforcing IoT security through practices like changing default credentials, securing access points, and maintaining regular firmware updates. Silex, in 2019, highlighted the dynamic and constantly evolving landscape of PDoS threats. By targeting both Linux systems and IoT devices, Silex demonstrated a significant escalation in PDoS attacks.  
Mamba, the upgraded version of Phobos ransomware, employs whole-disk encryption to render systems inoperable. As opposed to the standard file-specific encryption, data backup and recovery protocols, in the face of such sophisticated attacks, are needed.  
The case of PaperW8 demonstrated the increasing complexity and ingenuity of PDoS attacks, especially in exploiting vulnerabilities within multiple IoT devices using the same malware. This malware's approach, involving multiple steps, from gaining access to executing destructive actions, illustrates the multifaceted nature of modern cyber threats. CVE-2022-23968 highlighted the vulnerability of even well-established hardware to PDoS attacks.  
Collectively, these case studies not only enhance our understanding of the methodologies and impacts of various PDoS attacks, but also stress the imperative need for adaptive and forward-thinking cybersecurity strategies to protect against these continually evolving threats.
## 4 Drug Delivery Systems--Applications in Infectious Diseases
### Inhibiting Bacterial Adherence  
Adherence is the first and most important step in establishing bacterial colonization of host tissues. Only after bacteria have successfully adhered to tissues do they start to replicate and then activate their virulence traits or their biofilm-forming ability. By blocking adhesion, infections can be prevented or treated and, therefore, anti-adhesion therapeutics have been intensely studied.  
N-ethyl maleimide and its analogs were tested together with protamine sulfate to ascertain their inhibiting activity on the bifunctional enzyme N-acetylglucosamine-1-phosphate uridyltransferase (GlnU), thus, preventing bacterial adhesion of Gram-positive germs, such as _S. aureus_ or _Enterococcus_ spp., by interfering with peptidoglycan and lipopolysaccharidesaccharide biosynthesis [49].  
For Gram-negative germs, an example is the application of high-affinity mannoside antagonists of type 1 pilus adhesin FimH against uropathogenic _E. coli_ strains, with promising results in treating urinary tract infections by blocking the binding of the FimH adhesin to the mannose on the bladder's epithelium and by reducing the intestinal reservoir of uropathogenic _E. coli_[50].
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 4 Results and Analysis
### Result from TL Methods
#### 4.2.1 Results from TL Method Based on 1D-CNN  
Figure 12 shows the accuracy of the 1D-CNN-based TL solution in the target domain testing set against the number of cases in the target domain training set for ECS and APU, using the fuel system as the source domain. The non-TL baseline result was plotted on the same graph for comparison. From Figure 12, it can be observed that the 1D-CNN-based TL solution significantly improves the accuracy in almost all 50 scenarios for the ECS compared to the non-TL baseline; yet, the same trend is not seen in APU. For the APU, the TL methods with 1D-CNN performed worse than the non-TL baseline in general.  
For the same reason as described in Section 4.1, the average predictive accuracy for the target domain testing set was calculated for the first 30 scenarios for ECS and all 50 scenarios for APU. Regarding ECS, the average predictive accuracy following the TL solution with 1D-CNN was 84.98%, compared with non-TL baseline predictive accuracy of 76.48%. The significant improvement in ECS diagnostic accuracy with TL from the fuel system demonstrates the effectiveness of the 1D-CNN-based TL solution on this dataset. The positive transfer should be attributed to the ability of the TL method to transfer pattern recognition capacity from the fuel system dataset to the ECS dataset, which shows positive knowledge transfer across the two systems. In contrast, the average accuracy of the APU following the TL method was found to be 52.84%, which is 4.08% lower than the non-TL baseline. Hence, a negative transfer took place from the fuel system to APU.  
The opposite behaviour of the TL solution based on 1D-CNN was to be expected, given the fact that the three systems discussed in this work were vastly different, which means finding similar patterns between them is not always guaranteed. In terms of the principle of the 1D-CNN-based TL solution, it would only work if there was an order of target domain features that produced similar patterns for certain classes of data compared to the source domain. However, for distinct systems, such an order may not exist, which could explain the result in APU. Additionally, the current method of rearranging target features may not be the optimal way to seek similar patterns in source and target domains,  
Figure 12: Result from TL solution based on 1D-CNN for (**a**) the ECS case; (**b**) the APU case.  
which is also a potential reason for the negative transfer case for APU. In summary, given the improvement in ECS and degradation in APU, the TL solution based on 1D-CNN can be concluded as effective under certain source and target domains but not for all combinations of source and target systems. Therefore, an alternative TL solution must be considered for the distant source and target domains of the ECS and APU problem, such as the TL solution based on 2D-CNN introduced in Section 3.3.
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 5 Discussion  
In this paper, a multi-UAV cooperative search algorithm based on RL was proposed to solve the problem of multi-UAV cooperative dynamic target searching in unknown sea areas. According to the comprehensive efficiency function, a reward and punishment function was designed. At the same time, the target probability map and certainty value map were mixed to describe the unknown sea area, and the territory awareness information map was introduced to coordinate the cooperation between multiple UAVs. The extended search map can be updated online according to the search situation of multiple UAVs. The simulation results showed that the algorithm was effective, and the multi-UAV cooperative dynamic target search was verified by comparative analysis, which was more effective than the original search method.  
This paper did not consider issues induced by the communication network shared by the UAVs. In practice, the communication networks may have constraints such as transmission delays, packet dropouts, and bandwidth issues, which may degrade the performance. Exploiting the effect of the communication constraints to derive effective algorithms for multi-UAV cooperative searches is an interesting topic worthy of investigation in the next step of research work.  
Conceptualization, W.Y.; methodology, W.Y. and X.G.; validation, W.Y. and L.W.; investigation, Y.W.; writing--original draft preparation, W.Y. and X.G.; writing--review and editing, L.W.; supervision, W.Y.; project administration, W.Y.; funding acquisition, W.Y.  
This research was funded by the Natural Science Foundation of China (grant number 61703072), the Dalian Science and Technology Innovation Fund (grant number 2019)12 GX040), the Fundamental Research Funds for the Central Universities (grant number 3132019355), the Liaoning Natural Science Foundation Project (grant number 20180551118), and the Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission (grant number MD-IPAC-201901).  
We would like to thank [PERSON] from Dalian University of Science and Technology for his help with UAV tests.  
The authors declare no conflicts of interest.
## 3 Results and Analysis
### Analysis of the Water and Salt Migration Results for Saline Soil under Freeze Thaw Cycles  
#### 3.2.1 Changes in the External Water Replenishment Amount  
In the freeze--thaw cycle process, a pressureless water replenishment method was used for the saline soil column where the liquid water was labeled with a fluorescent tracer. The variation curve of external water replenishment with a freeze--thaw cycle duration is shown in Figure 9. The external water replenishment amount under freeze--thaw cycles exhibited an increasing trend over time, and the rate of increase gradually decreased. The maximum replenishment amount was 15.20 L. The external water replenishment curve also showed a certain periodic pattern with freeze--thaw temperature changes. With decreasing temperatures, the external water replenishment amount increased, and with increasing temperature, the external water replenishment amount decreased. The process of external water replenishment lagged behind the temperature change.  
The analysis revealed that a decrease in the temperature led to the formation of phase change crystals in the pores of saline soil, which in turn drove the migration of water and saltwater, resulting in an increase in external water replenishment. However, after heating,  
Figure 8: Isothermal distribution in saline soil under freeze—thaw cycles.  
Figure 9: Variation curve of external water replenishment with freeze—thaw cycle duration.  
the crystallization-dissolution thawing process reduced the vacuum suction. Owing to the periodic changes in external water replenishment under freeze--thaw cycles, the rising height of fluorescein-labeled liquid water also changed accordingly. The liquid level heights at different times are shown in Figure 10.  
The figure shows that the overall height of the fluorescein liquid surface generally increased, but it may also decrease. The increase in the liquid water surface height occurred mainly at the heating stage, whereas the decrease in the liquid surface height occurred largely at the cooling stage. This is opposite to the change in the water replenishment amount with temperature, mainly because, although the amount of water replenishment increases during cooling, the increase in the freezing depth causes the liquid water surface to decrease, and the height of the fluorescein liquid surface decreases accordingly. Although the amount of water replenishment decreases in the heating process, the external water replenishment entering the soil in the previous cooling process increases with the increasing freezing front, causing the height of fluorescein-labeled liquid water to rise. However, the height of the liquid level gradually decreases after each decrease, which is related to the gradual decrease in the rate of increase in external water replenishment.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 3 System Architecture
### LoRa Field Packet Generator
#### 3.2.2 Hardware Realization  
The packet generator is built around an ATSAMD21G18A, a 32-bit ARM Cortex\({}^{\alpha}\) M0+ microcontroller by Microchip clocked at 48 MHz and paired with 32 kB SRAM Memory and 256 kB in-system self-programmable Flash. It also includes six serial communication modules (SERCOM) configurable as UART/USART, SPI or I2C, three 16-bit timer/counters, 32-bit real-time clock and calendar, 20 PWM channels, one 14-channel 12-bit ADC, one 10-bit DAC. The prototype device integrates a LoRa RN2483 radio module, a low-power long range transceiver operating in the 433 MHz and 868 MHz frequency bands for its communication. The LoRa module by microchip embeds a SX1276 transceiver, but also a MCU, by making available, through simple AT commands, the setting of PHY layer parameters and the ABP/OTAA registration methods necessary to join LoRaWAN infrastructures. In this way, the ATSAMD21G18A is only responsible for generating packets and handling the user interface via graphic display or USB connection to a terminal. The RN2483 is connected to the main microprocessor via a secondary UART on GPIO pins. Figure 5 shows the prototype device including the packet generator and the LoRa module, while Figure 6 presents the block diagram of the radio module.  
Figure 4: Repeller board sketch (**a**) wide open (**b**) in an enclosure.
# Can Self-Similarity Processes Be Reflected by the Power-Law Dependencies?
## 1 Introduction and Formulation of the Problem  
The recent publication of the book [1] where it was demonstrated that \"fractal models\" should not be confounded with \"fractal\" behaviors, which are seen in many physical phenomena, substantially influenced the creation of this paper. As an illustration of this point of view, several alternative modeling tools are proposed in [2, 3] to model real power-law behaviors (also denoted \"fractional behaviors\"). Below, we will always refer to \"fractal\" activity as \"power-law\" behavior. It is impossible to explicitly link this power-law behavior with real and complex-conjugated exponents to the suggested models. The authors provide the self-similarity principle (SSP) [4], which frequently results in the expected power-law behavior and can be tested on actual data, in an effort to support this fundamental separation from another angle. In the power-law behavior that results from the SSP, log-periodic oscillations are always present. These oscillations ought to be viewed as organic additions, augmentations or components. In paper [4], we considered many complex systems that can be described by a combination of power-law functions with complex additions expressed in the form of log-periodic oscillations. In this paper, we consider the _nonlinear_ case, and based on [PERSON]'s inequality [5], we prove that the global minimum in the fitting of power-law functions corresponds to the generalized geometric mean (GGM) and _not_ the arithmetic mean that is conventionally used in modern signal processing theory.  
Let us imagine that we have a measured curve (obtained from available measured data) and preliminary information about its possible self-similarity and power-law behavior is _absent_. Is it possible to find some additional evidence about its self-similarity at least on the qualitative level? One can suggest a criterion (described in Section 3) that proves theself-similar behavior of many random curves. The basic problem that is solved in this paper can be formulated as follows. One can prove that there is a broad class of self-similar curves that have a power-law behavior described by the real part together with complex-conjugate adding. This class is tightly connected with the SSP, and in many cases, the fitting function corresponds to the GGM if the contribution of each self-similar part is properly weighted.  
The content of the paper Is organized as follows. In Section 2, we consider the basics of the theory. In Section 3, the data and proposed algorithm are described. Verification on available data is also considered in Section 3. The final part, Section 4, contains an analysis of the results obtained.
## 4 Experiments
### Effect of a GCN Layer Number  
In this section, we examine how the number of GCN layers in our model, MHAKEGCN, affects its performance on four datasets. We conducted experiments by setting the number of GCN layers to 1 to 8 and evaluated the model's accuracy and macro-F1 scores. The experimental results are presented in Figures 2 and 3.  
The figures show that both the accuracy and macro-F1 scores reach their highest values when the number of GCN layers is two. When the number of GCN layers is one, the model does not adequately learn the sentiment dependence of specific aspects, resulting in poor performance. On the other hand, when the number of GCN layers exceeds two, the performance drops as the number of layers increases. This suggests that increasing the model's parameters sharply reduces its efficiency.
## 3 Results
### Analysis of Research Fields  
#### 3.4.1 Research Field and Content  
As an interdisciplinary technology, Unmanned Aerial Vehicle Remote Sensing (UAV-RS) research outputs are widely distributed across multiple academic fields. Overall, UAV-RS studies are highly concentrated in application-oriented and technological disciplines, particularly in Remote Sensing, Environmental Sciences, and Imaging Science (Table 5).  
Specifically: (1) Remote Sensing leads, with 2107 published papers, ranking first among all disciplines. This field encompasses Earth observation, data acquisition and processing, and information extraction. The development of UAV-RS has significantly advanced both the depth and breadth of research and applications in remote sensing. (2) Environmental Sciences follows closely, with 1947 papers. Researchers in this domain utilize UAV-RS for ecological conservation, pollution monitoring, and climate change impact assessment. The flexibility and high-resolution imaging capabilities of UAVs make them powerful tools in environmental monitoring and evaluation, underscoring their growing importance in this area. (3) Imaging Science and Photographic Technology ranks third, with 1707 papers. Research in this domain typically focuses on the design of UAV-mounted imaging systems, the development of image processing algorithms, and the enhancement of image quality. UAV-RS has not only driven advancements in imaging and photographic technologies, but has also provided high-quality data sources for other disciplines. (4) Geosciences Multidisciplinary is ranked fourth, with 1559 papers. UAV-RS technologies have found widespread applications in geological exploration, topographic  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Subject Category** & **Number of Publications** \\ \hline
1 & Remote Sensing & 2107 \\
2 & Environmental Sciences & 1947 \\
3 & Imaging Science Photographic & 1707 \\  & Technology & 1559 \\
4 & Geosciences Multidisciplinary & 883 \\
5 & Engineering & 883 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top 5 discipline categories of UAV-RS publications.  
mapping, and natural disaster assessment. The high-resolution 3D data provided by UAVs are crucial for understanding surface processes and features of the Earth.  
Additionally, the Engineering field has contributed 883 papers, indicating UAV-RS applications in civil engineering, mechanical engineering, and related areas of engineering design and implementation.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Energy Management  
In order to build an industrial-level smart grid energy metering environment based on IoT, network control, load adjusting, remote observing and estimation, transformer wellbeing checking, and observation of wind plants and sunlight-based power establishments are a few significant variables [13; 25]. Long range, low power, robust QoS, and excellent readability are requirements for high-level smart grid and energy metering. NB-IoT is, hence, better suited for usage in this application. Additionally, at the point where the power distribution section consists of individual commercial and residential appliances, LoRaWAN technology simplifies the transition to the private area network (PAN) and home area network (HAN) [111]. This includes smart electric bill meters, smart hazard alarm systems, etc. A case study was conducted in Lebanon to observe the performance and feasibility of deploying LoRaWAN for smart metering at a low cost and long range in a real-world scenario [112]. This solution offers an open-source, inexpensive, energy-smart metering system requiring little intervention in an already-existing electrical installation. In addition, the energy sector has been altered by LPWAN technology, and efficient sensor monitoring systems have reduced factory energy use [113]. As a result, the industrial energy system has become a major part of the IIoT. Furthermore, IIoT technologies have improved the efficiency of modern energy systems. For example, advanced control systems, predictive maintenance, and remote monitoring can improve smart energy management [114]. Software-defined machines, big data analytics, and smart sensors are emerging technologies that are steadily improving the system's operating performance [115]. Smart power grids are also able to utilize RedCap's low latency and high data rate IoT services [22].