esa-sceva/satcom-chunk-collection huggingface dataset:
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 1 Introduction
En-route filtering schemes, notably, DEF and CCEF, result in a better security at the expense of network lifetime. Reference [13] has proposed to increase network lifetime by improving routing. There is a need to extend network lifetime in filtering schemes which often ignores underlying routing. Additionally, we proposed pre-deterministic key distribution, dynamic path routing, and dynamic response to attack frequency changes instead of a fixed probabilistic approach. A number of research works [14; 15; 16; 17; 18; 19; 20] emphasize attacks and countermeasures. In the past, energy-hole problem mitigation was discussed in Reference [21]. MICA2 [22] sensor modes sensing platform was used in the experiments. Sensor clocks are synchronised using the energy-efficient time synchronization protocol (ETSP) [23]. Recent work [24; 25; 26; 27; 20] also highlighted countermeasures against attacks in en-route filtering with an emphasis on network lifetime. Several work focused on network lifetime extension [10; 28; 29; 30; 31], energy efficiency and network lifetime [32; 33; 34], and energy efficiency [35; 36; 37; 38; 39] and privacy [40; 41; 42] using various techniques. In this paper, we aim to improve network lifetime without compromising detection capacity and energy efficiency in en-route filtering schemes. The proposed scheme is adaptive since it is based on energy-aware dynamic path routing and has the ability to counter variation in attacks on WSNs. Simulation experiments validate the efficacy of our scheme. AEF shows an average of 2.64 times (50% false traffic ratio (FTR)) and 2.63 folds (70% FTR and 2.61 (90%). In comparison with CCEF, we observe averages of 1.14 (50%), 1.10 (70%), and 1.05 (90%) times gain. The detection capacity and energy-efficiency performance are similar to the existing schemes. Our main contribution in this work are as follows:  
* Introducing dynamic security response to counter attack-intensity variations, i.e., higher security for larger attacks and vice versa. This is achieved by pre-deterministic key-distribution which allows different paths with a variable number of verification nodes and hence security. * Proposing energy-aware dynamic routing that helps load balancing over a larger group of participating nodes. * Mitigating the energy hole and significantly extending network lifetime by using the aforementioned methods. * In summary, despite a reasonable volume of related research to improve network lifetime in the routing layer, this work focuses on prolonging network lifetime in security schemes.
## 2 Materials and Methods
### Data Sets  
#### 2.2.1 Vegetation Index Dataset  
Satellite-derived Normalized Difference Vegetation Index (NDVI) data at a spatial resolution of 250 m and a temporal resolution of 16 days were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, distributed by NASA's Earth Observing System Data and Information System ([[https://reverb.echo.nasa.gov/](https://reverb.echo.nasa.gov/)]([https://reverb.echo.nasa.gov/](https://reverb.echo.nasa.gov/))). The time span of the MOD13Q1 NDVI products (Collection 6, tile h08v05) cover from 18 February 2000 to 22 April 2017 [59]. The data after spring 2017 were excluded from this study, because of the extreme fire seasons in 2017 and 2018 (e.g., the Thomas Fire [60] and the Woolsey Fire [61]). These fires abruptly decreased the NDVI of large areas and hindered the analysis of climatic and drought effects.  
We conducted some preprocessing for the NDVI data to ensure the reliability of this study. The pixels with negative NDVI values, which denote little or no vegetation (e.g., completely built-up urban areas), were removed before analysis. As snow tends to lower the NDVI [62], MODSCAG snow covered-area data [63], provided by NASA Jet Propulsion Laboratory, were applied to mask the NDVI pixels that had average snow fractions of more than 1% during a 16-day period. To match the temporal resolution of the drought index, the 16-day NDVI time-series were resampled to monthly time-series, by averaging the available tiles in each calendar month at each pixel.
## 5 Conclusions  
To establish a mapping relationship between the formation process of slug flow in pipeline segments and the pressure inside the pipeline, a multi-faceted approach was taken in this study. Firstly, an experiment was constructed to investigate the slug flow formation process under different liquid accumulation volumes and inclination angles using air and water as flowing media. Secondly, OLGA simulation software was implemented to verify the flow regime, pressure, and slugging speed of the slug flow in the experiment, demonstrating a high level of agreement between the simulation and experiment. Finally, high-speed cameras were utilized to capture the formation process of slug flow and compared with the OLGA simulation. The experimental and simulation results indicate that the formation of slug flow was a complex process that involves three distinct stages: the growth of slug flow, the ejection of slug flow, and the flow back of slug flow. Through the analysis of the experimental and simulation studies, the relationship between the formation process of slug flow and pressure inside the pipeline was explained, revealing the sensitivity of the appearance of slug flow to pressure. Specifically, the findings allow for an enhanced understanding of the intricate relationship between the formation process of slug flow and pressure. These outcomes provide valuable theoretical support for identifying slug flow through pressure signals and improving our understanding of the safe operation of underwater compressed gas energy storage system pipelines.  
Conceptualization, C.L., Z.W. and W.X.; methodology, C.L., H.W. and W.X.; formal analysis, C.L., H.W. and Z.W.; investigation, C.L., H.W. and Z.W.; resources, W.X.; writing-original draft preparation, C.L. and H.W.; writing-review and editing, Z.W. and W.X.; visualization, C.L. and H.W.; data-curation, C.L. and H.W.; supervision, W.X.; funding acquisition, W.X. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 52075065 and 51905066.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Liquid Volume (mL)** & **Angle (\({}^{\circ}\))** & **OLGA (m/s)** & **Experiment (m/s)** & **Error (\%)** \\ \hline \multirow{4}{*}{300} & 5 & 2.56 & 2.34 & 8.59 \\  & 10 & 3.04 & 2.91 & 4.28 \\  & 15 & 3.88 & 3.96 & 2.06 \\  & 20 & 4.91 & 5.12 & 4.27 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._
## 2 Materials and Methods
### System Implementation
#### 2.3.4 Online Control Loop  
The online BCI for controlling the ARM was designed to have a selection mode, relying on a discrete control command, and a self-paced control mode, offering continuous control of the robot's motion, as seen in Figure 2.  
During selection, the system cycles through the targets sequentially in 3-second intervals. Targets are highlighted on the monitor and presented through its unique audio cue. Each target corresponds to the cartesian direction commands for the end effector (movement along the x, y, and z-axis) and a gripper state control switch (open or closed). Accordingly, a target's unique audio cue corresponds to a word that describes the command it pertains to (\"up\", \"down\", \"left\", etc.). Auditory cues were included so subjects could focus on the task without needing to check the screen.  
To select a command, the user must wait for its cue. Selections are triggered by high alpha activity, meaning the subject must close their eyes in time with the auditory cue. Upon selecting a target, a confirmation tone plays, and the system enters a tolerance period, which lasts 4 s. The user must open their eyes to proceed with the current selection and can henceforth attend to the robot's movement. If the system detects the occurrence of high alpha waves during the last 2-second epoch, it will return to the selection menu. Consequently, to interrupt selections, the user must keep their eyes closed. A different tone plays when cancelling the selection. Returning to the menu at this point will not reset the cycle, leaving the user at the current target.  
Entering asynchronous control, the robotic arm performs the chosen motion continuously, as long as the subject exhibits low alpha waves. The only exception is the gripper command, which will close or open the fingers immediately upon selection and return to the menu. When high-intensity alpha is detected, a stop command is issued, and the system enters the tolerance period again. Users can evaluate if they wish to continue performing that motion or return to the selection menu. High alpha waves will return the system to the selection menu, while low alpha waves will reissue the command, continuing the current motion. Returning to the menu after entering continuous control will always reset the selection menu cycle. The grip command does not include this tolerance phase.
## 2 Related Works  
### Field Measurement at the Runyang Suspension Bridge (RSB) Site during Typhoon Wipha  
The Runyang Yangtze River Bridge connects Yangzhou city and Zhenjiang, and is composed of the Runyang Suspension Bridge (RSB) (south side) and the Runyang Cable-stayed Bridge (north side). The main span of the RSB (see Figure 1) is 1490 m. Each two side spans are 470 m. The RSB is a hinged and simply supported steel-box-girder bridge. As a national key engineering project, the RSB is located on the Southeast coast of China. The meteorological survey indicates that this region belongs to subtropical monsoon climate. The cold and dry northern wind from the mainland is the main strong wind in winter, while the typhoon dominates in summer.  
Figure 1: Location of the Runyang Suspension Bridge (RSB).  
Every year, the RSB is attacked by typhoons. The ultrasonic anemometer was installed on south tower (210 m in elevation) to collect wind data using a cylindrical coordinate system for further study of the typhoon characteristics at the RSB site. In September 2007, Typhoon Wipha passed through Jiangsu Province, the 3D strong wind data was collected by the anemometer. The sampling frequency was set as 20 Hz. The detailed information about the field measurement can be found in [PERSON] et al. [20]. The sampling number in time was 40960 for better use of the FFT. Data collected from 20:00:00 to 20:34:08 on 19 September were utilized to estimate the EPSD. Daubechies wavelet (Db10) with a decomposition level of 11 was selected to separate the time-varying mean wind speed [21].  
The stationarity of the turbulent wind flows was verified by the run test method with a significance level of 0.05 [22]. The field measured wind data and its time-varying mean are shown in Figure 1(a,c). The turbulence is shown in Figure 1(b,d).
## 6 Discussion
### Data Integration and Calculation
Four cross sections of the landslide (considering the movement direction) were traced on the area, and two others with the oblique north to south direction (see Figure 9 to identify its position). Figure 11 shows, from left to right, upper sections to lower ones. They show an important deepening at the center of the landslide area, and at Section 3 (over the intermediate position of the access road), a V-shaped area deeper than the other ones can be seen. The same deepening zone can be observed in oblique sections (see Figure 12) where the V-shaped sediment geometry is wider, but in all cross-sections, one of the flanks of that deepening area is sharply steep. The possible presence of a fault structure in the basement, corroborated by low values in resistivity observed in VES-5, can be considered. Now, this depressed area can correspond to a ravine or temporary torrential channel (marked by the low values in sediments resistivity). The cross-Sections 2, 3 and 5 obtained are concordant with the seismic refraction profiles interpreted (considering that seismic profiles were applied in different directions). The graphical distribution of the value of the Vulnerability Index (\(K_{g}\)) is presented in Figure 13. It shows peak values in the area around VES-5 and H9 survey points and coincides with the greatest movement and the area of action carried out by the company E.P. CELEC with the construction of the reinforced earth wall. In addition, in this area, according to communication from the company's technicians, the movement continues to occur today. Figure 10: Simplified longitudinal sections of the investigated area are displayed, showing the landslide rupture surface position. Organized from south to north (left to right), the mobilized materials (soft sediments) over the basement are exhibited (Displaying scale 1H:2V). This parameter is related to the ground shear strain or effective stress [46] and can be correlated with the potential ability of materials to move (sliding susceptibility); therefore, it can be recognized as a tool for assessing said capacity in landslides. In this study, in Figure 13, extremely high values appearing under the actual moving area (remarked by a dark blue continuous-line oval) and high values towards the upper area from the last one described (remarked by a dark blue dashed-line oval) can be observed. The rest of the points can be considered as low capabilities concerning sliding or as currently stable. Figure 11: Cross sections from the upper (**left**) to the lower zone (**right**) showing sliding sediments over the basement (Drawing Scale relation: 1H:2V, keeping vertical relation of elevation and Figure 10 scale). Figure 12: Oblique sections through the north–south direction represent the sliding sediments (in movement) over the basement where the rupture surface geometry is delineated (Drawing Scale relationship: 1H:2V, keeping the vertical relationship of elevation and Figure 10 scale).
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 2 Related Work  
This section discusses the interoperability characteristics of the Inter-Blockchain Communication (IBC) protocol, which enables communication and value transfer between various blockchains within the Cosmos ecosystem. The section also emphasizes how Zones and a Hub help to facilitate scalability and interoperability while preserving the autonomy of individual blockchains. The transport and application layers of the IBC protocol are presented, displaying its capacity to provide coordinated and safe data transmission between heterogeneous chains.
## Materials and Methods  
### Study Area and Biome Classification  
The geographical region of interest for this study covers the Meteosat viewing disk from \(-65^{\circ}\) S to \(65^{\circ}\) N in latitude and \(-65^{\circ}\) W to \(65^{\circ}\) E in longitude (Figure 1).  
The study is performed for land surfaces within the African continent, southern Europe, part of the middle East, and north-eastern Brazil. As mentioned above, one of the objectives of this study is to assess variations of the contributions of thermal stress and vegetation state to VHI over different biomes. The land biome classification used here is a variant of the 2006 GlobCover product [50] developed for the Advanced Along-Track Scanning Radiometer (AATSR), with a spatial resolution of \(1/120^{\circ}\) re-gridded from the original \(1/360^{\circ}\) GlobCover resolution [51]. This land cover dataset is stratified into a total of 27 land and inland water/coastal water biomes and one open ocean biome and takes into consideration changes in emissivity among biomes.
# Refractive Index Sensing through Surface Plasmon Resonance in Light-Diffusing Fibers
## 4 Conclusions  
An LDF has been proposed as host material for SPR-based refractive index sensing. The realized SPR sensor shows a high sensitivity to the external refractive index (about 4000 nm/RIU for an SRI of 1.394). A tentative model has been developed in terms of power distribution within the fiber, which has been shown to accurately describe the changes of transmitted power along an unclad LDF. The proposed sensor does not require any specific processing of the fiber, apart from a metallization of the probe region, and exhibits a better sensitivity than similar SPR sensors based on a multimode optical fiber. In contrast, the SNR is lower than the HCS fiber, and much lower than single-mode fiber SPR sensors. However, we believe that the observed SNR is still sufficient to allow biochemical applications as demonstrated in the literature with other multimode SPR configurations. The sensor is easily repeatable, disposable, free of labeling, and has a potential for real-time detection, so it is ideally suited for biochemical sensing and environmental monitoring.  
[PERSON], [PERSON] and [PERSON] conceived, designed and realized the SPR sensor device. All the authors realized the experimental measurements, analyzed the results and contributed to the writing of paper.  
This research received no external funding.  
The authors declare no conflicts of interest.
# Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning Models
## 4 Results
### Behavior and Emotion Detection Using Optimal Models  
The experimental results indicate that the proposed CNN model outperforms the other employed models in behavior detection. Similarly, ResNet50 excels in emotion detection, making these two models the preferred choices for detecting the respective underlying features from the testing data. Table 7 presents the evaluation results for behavior and emotion detection from the corresponding testing datasets, measured in terms of Precision, Recall, and F-measure. Behavior is classified as engaged or non-engaged using the binary CNN classifier, while emotions are classified as Happy, Sad, Angry, and Neutral using the multiclass ResNet50 classifier. The testing results reveal that the CNN achieves an F-measure of 83% for both categories of behavior detection. In the case of the four emotion categories, ResNet50 provides varying F-measure results: it achieves the highest F-measure of 86% for detecting the Neutral category, followed by the Sad category at 83%, the Angry category at 81%, and the Happy category at the lowest, at 79%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detection Type** &
\begin{tabular}{c} **Testing** \\ **Accuracy** \\ \end{tabular} & **Mod** & **Precision** & **Recall** & **F-Measure** \\ \hline Behavior & & Engaged & 0.84 & 0.82 & 0.83 \\ detection using & 0.83 & Non-Engaged & 0.82 & 0.84 & 0.83 \\ CNN & & Happy & 0.80 & 0.78 & 0.79 \\ Emotion & & Sad & 0.85 & 0.82 & 0.83 \\ detection using & 0.82 & Angry & 0.82 & 0.80 & 0.81 \\ ResNet50 & & Neutral & 0.82 & 0.89 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation metrics for behavior and emotion detection from testing dataset.  
Figure 9: Inter-comparison of emotion detection models: (**a**) training accuracy; (**b**) training loss; (**c**) validation accuracy; and (**d**) validation loss.
## 2 The Principle and Element Design of the CPFRA
### Geometry and Performance of the Metasurface-Based Element on the Primary Reflector Surface  
#### 2.2.1 Geometry of the Metasurface-Based Element  
In order to realize the architecture proposed in Section 2.1, a metasurface-based element on the primary reflector surface is designed. Figure 3a shows a 3D view of the  
Figure 1: Architectures of (**a**) the traditional circularly polarized RA and (**b**) the proposed circularly polarized FRA.  
Figure 2: Three-dimensional structure and exploded view of the proposed antenna.  
proposed element. The metasurface-based element is composed of two dielectric layers and three metal layers. The top layer is a \(2\times 2\) square patch, the middle layer is a metal ground layer with a cross-shaped slot, and the bottom layer is a microstrip line layer. The dielectric substrate of the metasurface-based element is made of Rogers Duroid 4003 material (with \(\varepsilon_{r}=3.55\) and tan\(\delta=0.0027\) at 10 GHz). The thicknesses of the upper substrate and the lower substrate are 0.508 mm and 0.254 mm, respectively. Figure 3b shows the top view of the metasurface-based element. The length of the side of the element is 5.8 mm. The length of the square patch is \(sur\_w=2.2\) mm. The distance between two adjacent patches is \(sur\_s=0.3\) mm. Because of the introduction of the metasurface, broadband performance can be achieved. In addition, a reflective phase of more than \(360^{\circ}\) can be adjusted by changing the length of the microstrip line (L). The detailed working principle and design steps can be found in [18].
###### Abstract  
With the advent of NextG wireless networks, the reliance on centralized identity and service management systems poses significant challenges, including limited interoperability, increased privacy vulnerabilities, and the risk of unauthorized tracking or monitoring of user activity. To address these issues, there is a critical need for a decentralized framework that empowers users with self-sovereignty over their subscription information while maintaining trust and privacy among network entities. This article presents a novel framework to enable Self-Sovereign Federated NextG (SSFXG) wireless communication networks. The SSFXG framework separates identity management from the service management layer typically controlled by network operators to foster interoperability functionalities with enhanced privacy and trace-resistant assurances in the NextG landscape. The proposed model relies on blockchain technology as an infrastructure to enable single-authority-free service provisioning and boost mutual trust among federated network components. Further, the SSFXG framework facilitates subscribers' self-sovereignty over their subscription information while ensuring anonymity and enhanced privacy preservation, avoiding unnecessary network activity monitoring or tracking. Preliminary evaluations demonstrated the effectiveness and efficiency of the proposed framework, making it a promising solution for advancing secure and interoperable NextG wireless networks.  
Keywords:wireless communication networks; next generation (NextG); self-sovereign identity (SSI); blockchain +
Footnote †: journal: Information
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 4 Performance Evaluation
### Evaluation Assumptions  
In the following analysis, we systematically determine each of the assumptions that were used to evaluate the effectiveness of the suggested categorized topic structure. Listed below are the assumptions used in this study:  
* The MQTT brokers examined in this study are Mosquitto 1.4.15, Beyywise MQTT Route 2.0 (known for scalability), ActiveMQ 5.15.8, and EMQ X 4.0.8 (known for non-scalability). The performance of our suggested system was evaluated by using the values of messages in three different QoS zones of the MQTT protocol. This evaluation was conducted based on the analysis presented in the paper by [25].
* In one second, a process comprising four payloads is transmitted from the greenhouse gateway to the MQTT broker.
* In the conventional MQTT protocol, the structure of every payload is defined by five topic levels. Each topic level is allocated a data volume of 20 bytes, resulting in a total data volume consumption of 100 bytes for each payload. In contrast, the proposed structure of each MQTT payload designates its allocation to a distinct topic level, with a fixed data size of 20 bytes.
* The design proposal in our study receives environmental parameters and events from sensors located throughout the greenhouse. These data are relayed to an MQTT broker, which acts as a gateway for our greenhouse structure.