esa-sceva/satcom-chunk-collection huggingface dataset:
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation
### Evaluation in Real Systems
#### 7.2.1 Comparison with Interpolation  
Interpolation is a straightforward method for data reconstruction. We compare the effectiveness of LESS (with random sampling) with two common interpolation methods: cubic spline and cubic polynomial (SP and CP in Figure 11) with different sampling rates based on the experiment results in the Zhoushan system. Figure 11 illustrates the CDF of absolute estimation error values with sampling rates at 50%, 60% and 70%. We find that LESS outperforms SP and CP with all sampling rates. Specifically, when sampling rate is 0.6, 93% of the error is less than 20 in LESS while it is only 80% for SP and 77% for CP. Besides, only 2% errors exceed 50 in LESS while the percentage is around 14% for SP and 12% for CP. LESS has better performance as it involves the spatial-temporal and system knowledge in data processing.
# Towards Incidence Management in 5G Based on Situal Awareness
## 4 Information Security Architecture for 5G
### Monitoring and Correlation  
The monitoring module collects the information provided by lower layers (Virtualized Infrastructure and Sensors) and applies aggregation/correlation techniques to simplify the further analysis tasks:  
* _Monitoring (Data Collection)_. The main objectives are the gathering and management of the information from all data sources, and facilitating their access to upper layers. Monitoring tasks would be able to actively poll different sources to collect real-time statistics, providing highly accurate and low overhead traffic measurement [41]. This module also controls the registration and access process of new sensors. The collected information is organized in efficient data structures, taking into account the large amount of data to be processed. In this regard, two scenarios were considered. In the first scenario, the sensor sends a report to the monitor when it detects relevant information (alerts, link failures, memory or CPU overload). In the second scenario, the monitor requests information (whenever necessary) to the sensors in order to facilitate the aggregation and analysis tasks (virtual topology, available links, among others).
* _Correlation_. It is responsible for the first abstraction level of information processing, in which, in order to have a global view of the network status, correlation and aggregation processes are executed. Information considered as redundant or non-sensitive is discarded. As an example, in case of multiple alerts received from each device belonging to the same affected area, a single alert is displayed with the affected topology. Due to the dynamism offered by virtual environments, in contrast to the rigidity of the physical elements, network topology is expressed as an extended or increased graph (\(G_{a}(V_{a},E_{a})\)), which models virtual nodes (\(V_{a}\)) and links (\(E_{a}\)) located in the physical infrastructure [42; 43]. Likewise, as a result of correlation and aggregation operations, the received low-level metrics can be expressed or translated into high-level metrics, also known as Health of Network (HoN). For example, transmission data rate (Mbps), delay (ms) and jitter (ms) of data in streaming video, collected by the sensors at different points in the network, can be expressed as an overall perception of quality of service QoS/QoE, quantified by the measurement of the Mean Opinion Score (MOS).
## 3 Proposed Cumulative QoE Model
### Memory Effects
#### 3.2.3. Forgetting Curve and Repetition  
Due to the significant impact of the negative experience caused by distorted events, the primacy and recency effect can be neglected under repeated bitrate switches or rebuffering [22]. In such situations, forgetting behavior and repetition should be taken into account. The forgetting behavior, in other words, forgetting curve characteristic [21], is a natural process, describing the exponential loss of memory over time. As shown in Figure 3(a), when information is learned, its memory retention declines at an exponential rate. Accordingly, any occurred events can be exponentially forgotten by time if there is no attempt to retain it. The level of remaining memory about such events at a specific time point depends on:  
* The strength of memory (memory intensity): The durability that memory traces in the brain. The more annoying the event is, the stronger the user memorizes it and the longer it lasts.
* The time that has elapsed since the occurrences of events: As shown in Figure 3(a), the user will forget an average of 60% of what they experience within the first period of time [20; 21].
* Repetition: The more frequently an event occurs, the more likely it sticks to the user memory (as shown in Figure 3(b)).  
In a typical streaming session, an interruption (bitrate switching or rebuffering) can happen regularly. When an event, especially rebuffering repeatedly occurs, the strength of memory of those events will tend to increase [21], negatively influencing the perceived video quality. Consequently, as the number of negative events increases, QoE will recover at a slower pace after the occurrence of each event. Such memory characteristics can be formulated as the following equation [43]:  
Figure 4: Examples of forgetting curve and repetition.  
Figure 3: A typical U-shaped curve combined primacy and recency effects.  
\[f_{RP}(t)=exp(-\frac{\alpha_{RP}}{NR(t)}*TR(t)),\quad 0\leq t\leq L \tag{5}\]  
where \(NR(t)\) is the number of rebuffering events occurring until the time \(t\), \(TR(t)\) is the time elapsed since the last video impairment, and \(\alpha_{RP}\) is the intensity of memory related to a rebuffering event. The ratio \(\frac{\alpha_{RP}}{NR(t)}\) determines the retention of the user's memory after the \(NR(t)\)th rebuffering. Accordingly, the lower \(\frac{\alpha_{RP}}{NR(t)}\) is, the higher retention rate, making \(f_{RP}\) declines at a lower rate.
## 4 Material and Methods
### Spiramycin Production Assay  
Spiramycin production by _S. ambofaciens_ broth cultures was assessed by high-performance liquid chromatography-electrospray ionization-mass spectrometry (HPLC-ESI-MS) as described [83]. At different time intervals, supernatants were filtered through Phenex-RC membrane (0.45 mm; Phenomenex). Five uL of a solution of erythromycinomycin (1 mg/mL) (Sigma-Aldrich) in 30% v/v aqueous acetonitrile plus 210 uL of acetonitrile were added to 500 uL of filtrated samples; mixture samples were vortexed, centrifuged at 4 degC for 5 min, and then 2 uL of the supernatant were injected for spiramycin determination.  
The high-performance liquid chromatography (HPLC)-ESI-MS apparatus consisted of a Surveyor MS quaternary pump coupled to a Finnigan LCQ Deca XP Plus mass spectrometer (ThermoFisher, Monza, Italy) equipped with an ESI source and a quadrupole ion trap analyzer. Xcalibur software (ThermoFisher) was used for instrument control and data analysis. The spectrometer was calibrated externally with a mixture of caffeine, MRFA and Ultramark (ThermoFisher) [84]. The mass spectrometer operated in the positive-ion mode with the following settings: sheath gas, 60 units; auxiliary gas, 20 units; spray voltage, 1.5 kV; capillary temperature, 325 degC; and capillary voltage, 10 V. Mass spectra were recorded in full-scan MS in the _m/z_ range 400-1100. Spiramycin I, II and III were resolved on a BioBasic C-18 analytical column (150 \(\times\) 2.1 mm, particle size: 5 um) (Thermo Scientific, Monza, Italy), which was eluted with 5 mM ammonium formate, pH 7.0 (solvent A) and LCMS-grade acetonitrile (solvent B), at a flow rate of 200 uL/min, applying the following gradient of solvent B: 0 min, 15%; 1 min, 15%; 15 min, 70%; 20 min, 70%; 21 min, 15%; and 25 min, 15%. To minimize source fouling during analysis, the eluent was directed to the source only during erythromycinycin and spiramycin elution by using the divert valve on the mass spectrometer. Calibration curves were made based on peak area ratios of analyte/internal standards vs. analyte concentrations, using 6 point calibration standards in the range of 0.1-5.0 ug/mL (0.1, 0.2, 0.5, 1.0, 2.5, and 5.0 ug/mL). The recoveries of the method, evaluated by spiking the samples with low (200 ng) and high (2 ug) levels of the analyte, were 97 and 93%, respectively.  
The method limits of detection (LODs) and limits of quantification (LOQs) were determined using the samples fortified at the lower validation level. LODs and LOQs determined at S/N ratios of 3 and 10 for spiramycin were 20 and 67 ng/mL, respectively.
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 2 Literature Review
The experimental results show that YOLOv3 not only beats the standard one-stage object detector in terms of speed, but also equals the accuracy of the two-stage object detector. For tiny aircraft detection, the YOLOv3 demonstrated good detection accuracy while little processing time.
# Substrate-Driven Atomic Layer Deposition of High-\(\kappa\) Dielectrics on 2D Materials
## 1 Introduction
As an alternative to surface pretreatments of 2D materials, some research groups recently considered the possibility of exploiting their peculiar interaction with the underlying substrate as a way to enhance the ALD nucleation process. As an example, [PERSON] et al. [21] demonstrated the direct ALD growth of uniform Al\({}_{2}\)O\({}_{3}\) layers on 1 L graphene as grown by CVD on metal substrates (Cu and Ni-Au). More recently, [PERSON] et al. [22] showed the deposition of highly homogeneous and ultra-thin Al\({}_{2}\)O\({}_{3}\) films by direct thermal ALD on 1 L MoS\({}_{2}\) supported by a gold (Au) substrate. In both cases, the high-quality ALD growth was explained by the enhanced adsorption of the ALD precursor molecules on 1 L graphene or MoS\({}_{2}\) surfaces during the nucleation stage. This was ascribed, in part, to the action of electrostatic fields induced by charges at the interface with the metal substrate [23; 24], as well as the partial transparency of the atomically thin 2D materials to the electric fields [25]. Such a beneficial role of 2D material and substrate interaction on the high-\(\kappa\) nucleation in ALD processes is not limited to the case of metal substrates. In fact, the direct ALD growth of uniform Al\({}_{2}\)O\({}_{3}\) films has also been recently reported for 1 L epitaxial graphene on SiC (0001), and it has been ascribed to the electrostatic effect of positively charged dangling bonds related to the buffer layer at the graphene/SiC interface [26; 27]. While the state of the art of ALD on 2D materials has been the object of different comprehensive review papers in recent years [28; 29; 30], a dedicated review article on the direct ALD growth of high dielectrics driven by the 2D material and substrate interaction is still missing. The aim of the present work is to provide an overview in this research topic. After a discussion on the advantages and disadvantages of the conventional approaches (seed layer and pre-functionalization treatments) used to promote ALD growth on graphene and TMDs, this paper will focus on the direct thermal ALD of high-\(\kappa\) dielectrics (mainly Al\({}_{2}\)O\({}_{3}\) and HfO\({}_{2}\)) on CVD graphene on Cu (or Ni), on epitaxial graphene on 4H-SiC (0001), and on exfoliated MoS\({}_{2}\) on gold. The role of the interaction with the underlying substrate and the relevant process parameters (i.e., the ALD temperature and number of cycles) on the coverage will be discussed. Furthermore, the morphological and electrical properties of the grown high-\(\kappa\) layers will be illustrated. Finally, the current status and open challenges for applications of the high-\(\kappa\)/2 M material systems will be critically examined.
# Cement Pastes with Hygyroscopic Polymeric Additions for Potential Building Applications
## 3 Results and Discussion
### Thermal Measurements
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & \multicolumn{2}{l}{**Thermal Conductivity**} & \multicolumn{2}{l}{**Thermal Diffusivity**} & \multicolumn{2}{l}{**Specific Heat**} & \multicolumn{2}{l}{**k/a\({}^{0.5}\)**} & \multicolumn{2}{l}{**a\({}^{0.5}\)/k**} \\  & & **(k)** & **(a)** & **(M/m\({}^{3}\) K)** & **(m\({}^{-2}\) K\({}^{-1}\) s\({}^{0.5}\) W)** & **(m\({}^{2}\) K s\({}^{-0.5}\) W\({}^{-1}\))** \\ \hline \multirow{4}{*}{No Treatment} & CS & 0.880 & 0.304 & 2.892 & 1.596\(\cdot 10^{3}\) & 6.267\(\cdot 10^{-4}\) \\  & SA-PA & 0.711 & 0.599 & 1.187 & 9.191\(\cdot 10^{2}\) & 1.088\(\cdot 10^{-3}\) \\  & NY & 0.800 & 0.329 & 2.431 & 1.395\(\cdot 10^{3}\) & 7.171\(\cdot 10^{-4}\) \\  & NY/SA-PA & 0.850 & 0.356 & 2.387 & 1.424\(\cdot 10^{3}\) & 7.022\(\cdot 10^{-4}\) \\ \hline \multirow{4}{*}{200 \({}^{\circ}\)C (1 h)} & CS & 0.394 & 0.720 & 0.547 & 4.640\(\cdot 10^{2}\) & 2.155\(\cdot 10^{-3}\) \\  & SA-PA & 0.351 & 0.314 & 1.118 & 6.258\(\cdot 10^{2}\) & 1.598\(\cdot 10^{-3}\) \\ \cline{1-1}  & NY & 0.326 & 0.352 & 0.926 & 5.491\(\cdot 10^{2}\) & 1.821\(\cdot 10^{-3}\) \\ \cline{1-1}  & NY/SA-PA & 0.341 & 0.756 & & 3.918\(\cdot 10^{2}\) & 2.552\(\cdot 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thermal properties of the 28-day-cured samples acquired with the hot disk instrument in addition to the values of ka\({}^{-0.5}\) and a\({}^{0.5}\)k\({}^{-1}\), which indicate the material’s eligibility for heat storage and insulation, respectively. must comprise interconnected pores, so that the water vapor can be distributed. The bimodal pore size distribution of the SA-PA samples suggests that small pores, optimizing thermal diffusivity, are numerous and interconnected by a few macropores. Accordingly, the specific heat is low (Table 4). As already discussed, polyamide fibers create large voids at the fiber-matrix interface, along which water vapor comes out easily. Among the samples treated at 200 \({}^{\circ}\)C, CS and NY/SA-PA showed the highest value of diffusivity and the best behavior in heating cycle insulating. All the treated samples, having already lost a lot of water during the thermal treatment, exhibited a lower specific heat than the same samples not thermally treated. Table 3 also shows the percentages of total porosity, which increased after the heat treatment, although the absolute values cannot be directly compared. As already known [15; 16; 20], the percentage of porosity alone is not sufficient to describe the behavior of a cementitious material as much as the pore size distribution, along with the overall water content. The NY/SA-PA sample, having lost one percentage point less than the other samples, contained moisture that must still have been able to move, benefiting from the macroporosity.
# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 3 Proposed Strategy  
The proposed strategy for reconfiguring the SNs aims to synchronize their activation and hibernation periods with the UAV flight. It permits SNs to transmit for the shortest time, thus reducing the energy consumption from their batteries, and maximizing their lifetime [8; 21; 24; 30]. UAVs, like SNs, use timeslots as a time reference for the management of their states.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 1 Introduction  
Designing systems composed of autonomous units that can coordinate their activities will likely be the next step in the IoT technological revolution. Progress is conditioned on the availability of reliable communication between the nodes of such a system. In most cases, advanced wireless communication infrastructure (5G and beyond) provides sufficient communication bandwidth with negligible overhead for securing communications with standard cryptographic tools (such as message authentication codes and digital signatures). In addition, the computational effort needed to perform cryptographic operations is acceptable thanks to advances in computational power and dedicated embedded hardware support.
## 2 Materials and Methods
### Materials
The PPP software runs in real time using data obtained from the GNSS receiver and RTCM, respectively, to compute the precise positioning of the platform. At each system startup, it creates a TCP server and, once the required precision has been achieved, publishes the solution at a fixed frequency of 1 Hz. The IMU software, for its part, receives accelerometer, gyroscope, and magnetometer readings to calculate its attitude and publishes it on another TCP server to be available to any client. The interconnection between the main processor unit, the GNSS receiver, and the IMU sensor is provided through an UART port.
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 3 Results
### Related Work on Spine
For the segmentation task of the spine, we performed the same task as the multi-organ segmentation task described in the previous section, and the results of the spine segmentation experiments for each network are shown in Table 8.  
Figure 16: The segmentation prediction results generated by CI-UNet. The results of the spine segmentation experiments are shown in Table 8. We found that the mIoU and mPA metrics of the VGG backbone are higher than the ResNet50 backbone metrics in the spine segmentation experiments. Compared with the double convolutional backbone UNet, CI-UNet is 12.35 percentage points higher in the mIoU metric and 4.76 percentage points higher in the mPA metric. Compared with the MobileNetV2 and ResNet50 backbone PSPNet, CI-UNet is 13.2 and 9.9 percentage points higher in mIoU metrics and 5.25 and 4.23 in mPA metrics, respectively. Compared with M-FCN, it is 6.76% higher in the mIoU metric and 2.74% higher in the mPA metric. Compared to U-Net_based, it is 6.2% higher in the mIoU metric and 2.34% higher in the mPA metric. The total amount of data of the spine dataset of the Guizhou branch of Beijing Jishuitan Hospital is 200, and the ratio of the training set and validation set is 9:1. Due to the small amount of data, the experimental results may have large fluctuations. We can see from the comparison of the  
Figure 17: The predicted image Cobb calculation results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Attention** & **mIoU (\%)** & **mPA (\%)** & **PA (\%)** \\ \hline \multirow{3}{*}{UNet} & Double Conv & None & 75.62 & 88.72 & 91.16 \\  & VGG16 & None & 85.31 & 91.31 & 95.89 \\  & ResNet50 & None & 82.26 & 90.5 & 94.78 \\ \hline \multirow{3}{*}{Attention} & VGG16 & SE & 85.44 & 92.18 & 95.95 \\  & ResNet50 & SE & 82.21 & 91.03 & 94.46 \\ \cline{1-1}  & VGG16 & Cbam & 86.59 & 92.95 & 96.30 \\ \cline{1-1}  & ResNet50 & Cbam & 83.33 & 91.03 & 95.15 \\ \cline{1-1}  & VGG16 & PSA & 86.45 & 92.46 & 96.21 \\ \cline{1-1}  & ResNet50 & PSA & 84.18 & 91.42 & 96.10 \\ \hline \multirow{3}{*}{PSPNet} & MobileNetV2 & None & 74.71 & 88.23 & 91.47 \\ \cline{1-1} \cline{2-6}  & ResNet50 & None & 78.07 & 89.25 & 93.05 \\ \hline Deeplabv3+ & MobileNetV2 & None & 83.63 & 91.75 & 95.09 \\ \hline M-FCN & None & None & 81.21 & 90.74 & 94.89 \\ \hline U-Net\_based & None & None & 81.77 & 91.14 & 95.07 \\ \hline CI-UNet & VGG16 & PSA & 87.97 & 93.48 & 96.46 \\ \hline \end{tabular}
\end{table} 
Table 8: The results of the spine segmentation experiments. above results that the evaluation indexes of CI-UNet designed in this paper are superior compared with other networks.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 6 Conclusions  
This article focuses on the application background of laser guidance for ship's wake bubbles. It studies the laser backscatter characteristics and illumination imaging characteristics of ship's wake bubbles with different distances, scales, number densities, and thicknesses. The results indicate that as the target size of the bubbles, the thickness of the bubble layer, and the bubble number density gradually increase, the peak amplitude of the bubble echo signal gradually becomes stronger. However, when it increases to a certain extent, a broadening phenomenon occurs. Additionally, the photons within the receiving lens area of the bubbles become more concentrated, and the number of photons in the circumferential area increases. The position of these photons varies more randomly. The echo characteristics of ship's wake bubbles differ between the two detection methods, each having its own advantages that can complement each other. Integrating these two detection modes can further enhance their benefits.  
A dual-mode fusion detection experimental platform for laser detection of ship's wake bubbles has been established. Methods based on multi-timescale hierarchical signal processing and algorithms based on time differential cumulative gating image processing have been proposed. Tests and verification analyses have been conducted on the bubbles. The results show that the detection SBR under the laser adaptive detection mode can reach up to 3.26, but it cannot distinguish false wakes. The SBR under the imaging mode can reach approximately 16, and when a false wake appears, the maximum detection SBR can reach 31. The experiments demonstrate that the dual-mode fusion detection of ship's wake bubbles using lasers can effectively enhance the detection SNR and effectively counter ship-maneuver evasion methods to a certain extent. It achieves effective detection and real-time signal processing of ship's wake bubble targets of different magnitudes with high sensitivity, a large dynamic range, high resolution, and a large field of view against a strong reverberation background. This verifies the feasibility of the dual-mode fusion detection mode.  
Figure 17: Change in maximum gray value. (**a**) unprocessed image; (**b**) gaussian filtering adaptive thresholding; (**c**) multi-frame accumulation; (**d**) final processing diagram.  
In future work, control experiments or simulations under different lighting conditions will be conducted to address issues related to image color degradation.  
Conceptualization, writing--review and editing, X.Z.; investigation, writing--original draft, X.Z.; methodology, S.Z.; supervision, Z.D., S.Y. and B.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.