esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Impact of Initial Energy  
To evaluate the impact of initial energy, we ran simulations to determine the number of rounds of data gathering when 1%, 20%, 50%, and 100% nodes die using Direct, PEGASIS, PBDAS, and CBDAS with each node having the same initial energy level. Figures 5a-c show the results of simulations with initial energy per node of 0.25 J, 0.5 J, and 1.0 J, respectively. In CBDAS, the sensor filed was partitioned into a grid of 10 \(\times\) 10 cells. As the results show, CBDAS is better than the other approaches. On average, CBDAS has approximately 1.6 times more rounds compared to PEGASIS when 1%, 20%, 50%, and 100% nodes die. This is because the distances between nodes become longer and the nodes have to become leaders more frequently with a larger percentage of node deaths.
# Path Loss Measurement of Outdoor Wireless Channel in D-band
## 5 Conclusions  
This paper presents a D-band MMW transmission measurement campaign in a long-distance outdoor line-of-sight environment with a measurement distance range of 100 m to 800 m. We fit the path loss modeling results at seven frequencies using path loss samples. The performance of the two models is similar in outdoor long-distance scenarios and the path loss exponent for each model differs slightly from the free space path loss exponent. This work provides a reference for future exploration of communications in the D-band frequency range at longer distances. In our future work, we plan to investigate other outdoor scenarios and the propagation characteristics of D-band MMW signals in non-line-of-sight environments.  
Conceptualization, C.B.; methodology, C.B.; validation, W.L. and M.W.; formal analysis, C.B.; investigation, C.B.; data curation, W.L.; writing--original draft preparation, C.B.; writing--review and editing, W.Z.; visualization, X.W. and Y.W.; project administration, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2018 YFB1801004) and NNSF of China (Grant number 61720106015, 61835002 and 62127802).  
Not applicable.  
The authors declare no conflict of interest.
# Evaluation of the Nutritional Quality and Shelf Life of
## 4 Discussion  
### Processing of the Salami-Type Fermented Sausages  
pH values before fermentation remained close, not showing any significant difference. After maturation, an increase in pH values was observed. Those values are in line with the ones shown in a study using sausages inoculated with _Lactobacillus plantarum_ BG 112 [22]. In this stage, the culture starter plays an important role, since it is responsible for fermenting the sources of carbohydrates in the medium. In the case of the formulations in this research, sucrose was used, releasing lactic acid and reducing pH in the medium in the first days. In addition to that, metabolites that help in the development of technological and sensory characteristics and the microbial quality of the product can be produced [23; 25].  
In the formulations from the S2 and S3 assays, pH values had an even greater increase when compared with the standard assay S1, which only had the starter culture, while the others had _Lactobacillus_ in them. The pH values during the first days of fermentation and maturation guarantee the quality of the salami because of the inhibition of unwanted microorganisms, like _E. coli_ and _S. aureus_[1].  
At the end of maturation, the acidification kept hitting pH levels between 5.0 and 5.3, which coincides with the isoelectric point of meat proteins, causing a faster water release in the product, bringing on a reduction in the capacity of the meat to retain water and facilitating the drying process of the final product [22; 26].  
Finally, in the storage process a change in the product occurs, which depends mostly on the initial acidity of the product, considering that the pH itself will take a fall in the fermentation and maturation stages. Those changes also depend on storage temperature as well as on the acidifying power of the starter culture and on the cooling process of the product, which is responsible for stopping fermentative activity in inappropriate cases [25].
## 2 Materials and Methods  
### Sample Preparation and Storage  
Broiler chicken thighs (weight 400 \(\pm\) 50 g each, 600 thighs were tested in total corresponding to 150 thighs per experimental group) were provided by a local producer (Evv oia, Greece) and received at the Laboratory of Food Chemistry and Technology (NTUA) within one day after cutting and packaging. Upon receipt, samples were immersed into a film-forming solution for 10 min at 5 \({}^{\circ}\)C for the application of the edible coating on their surface. The tested edible coatings were pectin (2% \(w/w\) aqueous solution) enriched with 1% organic extract of citrus bioflavonoids (Flavomix 14 PO, Polypan Group, Moschato, Greece, coded as flavomix) or 0.5% glucono-\(\delta\)-lactone (g-\(\delta\)-1), and coated samples were subsequently stored aerobically at isothermal conditions (0, 5 and 10 \({}^{\circ}\)C). Additionally, untreated thighs were also stored aerobically (control) or under MAP (42.7% O\({}_{2}\), 18.5% CO\({}_{2}\)) in high-density polyethylene (HDPE) pouches (Boss NT42N, Bad Homburg, Germany, 2 thighs per package). All samples were stored in high-precision (\(\pm\)0.2 \({}^{\circ}\)C) low-temperature incubators (Sanyo MIR 153, Sanyo Electric, Ora-Gun, Gunma, Japan). The temperature in the incubators was continuously recorded using electronic, programmable miniature dataloggers (COX TRACER(r), Belmont, NC, USA). Sampling took place in appropriate time intervals for the efficient kinetic modelling of quality deterioration.
## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Application and Evaluation of Blending Ensemble Model
#### 4.3.2 Weight Analysis of Landslide Predisposing Factors and Model Reliability Testing
Following the same steps, the predisposing factor of rainfall was reintroduced and weight analysis of the 16 landslide predisposing factors was conducted by using the prediction results from the three base learners as input features to train the meta-learner. And the final prediction was derived by applying the logistic regression model, thereby obtaining the importance coefficients of the 16 predisposing factors in the Blending model, as is illustrated in Table 10. A comparison of the ROC curves of each of the models can be made, as is shown in Figure 14.  
Similarly, 100 landslide data points were randomly selected for prediction. Among the 56 landslide points, 55 were correctly predicted, achieving an accuracy of 98.2%. For the 44 non-landslide points, 42 were correctly predicted, resulting in an accuracy of 95.5%. The model showed significant improvement compared to that without taking the factor of rainfall into account. As can be seen through a comparison of the statistics in Tables 9 and 11, \begin{table}
\begin{tabular}{l l} \hline
**PF** & **Importance Coefficients** \\ \hline Elevation & 0.155971 \\ NDVI & 0.116825 \\ DSN & 0.078708 \\ Slope & 0.074366 \\ YSC & 0.065887 \\ Plan curvature & 0.064489 \\ Distance to faults & 0.061751 \\ Profile curvature & 0.059429 \\ Aspect & 0.054520 \\ YSEB & 0.053629 \\ Distance to roads & 0.052439 \\ Road density & 0.051704 \\ Soil types & 0.039704 \\ YSD & 0.036049 \\ Distance to rivers & 0.034530 \\ \hline \end{tabular}
\end{table} 
Table 8: Importance coefficients of predisposing factors (PF) in Blending ensemble model. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Models** & **AUC** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Random Forest & 0.8841 & 0.8045 & 0.8049 & 0.8390 & 0.8216 \\ XGBoost & 0.8572 & 0.7955 & 0.7874 & 0.8475 & 0.8163 \\ CatBoost & 0.8787 & 0.8000 & 0.7846 & 0.8644 & 0.8226 \\ Blending Classifier & 0.8784 & 0.8045 & 0.8000 & 0.8475 & 0.8230 \\ \hline \end{tabular}
\end{table} 
Table 9: Evaluation metrics for binary classification of various models.
# Networking of Multi-Robot Systems: Architectures and Requirements
## 4 Communication Issues and Requirements for MRS Systems
### Robot Operating System Networking Services  
ROS (Robot Operating System) is a well-known open-source development environment for robotics applications [43]. The ROS environment provides abstractions and visualization for robotics hardware, devices, and motors. This enables ROS to be used for various types and brands of robotics. In addition, it offers many libraries and tools that simplify the development process of robotic applications such as message-passing among processes and package management. A graph architecture is used for executing groups of ROS-developed processes. These processes are executed in nodes that can deal with different types of messages such as communication messages, sensor messages, control messages, state messages, and actuator messages. Some MRSs applications and solutions were developed with ROS. One example is WAMbot, which was developed to use visual object recognition to navigate, discover and map a large urban environment [44]. Another example is a ROS-based  
Figure 4: The networking protocols at the various layers of the nodes using robot-to-infrastructure (R2I) communication.  
multi-robot architecture for ambient assisted living [45]. Furthermore, a collision avoidance technique with localization uncertainty for MRSs was developed with ROS in [46]. The main advantage of using ROS for developing these solutions is using the various available ROS tools and libraries to improve and shorten the development processes. However, these systems, applications, and solutions were developed using some available libraries such as message passing, these libraries are not fully optimized to deal with different networking architectures and requirements in MRSs. More work is needed to create more libraries to provide advanced and optimized communication services that can deal with different networking architectures and requirements.
# Wearables for Industrial Work Safety: A Survey
## 3 State-of-the-Art Techniques in the Field of Industrial Wearables
### Data Transmission
Its extended range can provide wearable devices with seamless connections in challenging environments like industrial setups and, being backward compatible, is expected to allow seamless integration with higher energy efficiency [142]. Further, and on top of the cellular IoT standards for wearable mMTC, certain industrial wearable applications can have requirements [143] that are similar to the other two 5G service classes, namely enhanced mobile broadband (eMBB) and ultra-reliable and low latency communications (URLLC) [144]. For instance, AR and VR-based applications require high data rates, high reliability, and low latency and can utilize the millimeter wave (mmWave) 5G technology [145]. Other potential mmWave and terahertz technologies for industrial high-end wearable applications include IEEE 802.11 ad (also called WiGig) and Visible Light Communications (VLC), respectively.
# Design of a SIW Variable Phase Shifter for Beam Steering Antenna Systems
## 3 SIW Beam Steering Antenna System
### Vivaldi Antenna  
An antipodal Vivaldi antenna is employed as the radiating element for this antenna system. It is designed based on the parametric study in [24], and then fine optimized [25] for performance in the required frequency band of 16 GHz to 18 GHz. The Vivaldi antenna is horizontally polarized, and vertical polarization is blocked by the comb-like corrugations on two arms of the antenna. Therefore, the corrugations improve cross polarization. The proposed antenna has a wide bandwidth with a reflection coefficient better than -15 dB in the entire operating bandwidth, as presented in Figure 9a. Its end-fire radiation pattern with high directivity is displayed in Figure 9b. This single antenna provides about 9.6 dB gain at mid-band frequency. The proposed Vivaldi antenna has a very directive radiation pattern with 50\({}^{\circ}\) HPBW, which consequently limits the scan range of the beam steering system to about 50\({}^{\circ}\).  
Figure 8: Insertion loss of the SIW variable phase shifter for five different biasing voltages.  
Figure 7: Phase variation of the SIW variable phase shifter for five different biasing voltages.
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 2 Materials and Methods
### Data Processing  
In order to more accurately capture the activity level characteristics of horses, this experiment adopted the method of combined acceleration instead of relying on single-axis acceleration. Single-axis acceleration can be affected by slight horse vibrations, neck movements, and circuit noise, leading to inaccurate results [20]. The combined acceleration integrates acceleration data from each axis, providing a more comprehensive metric reflecting the activity level characteristics of horses. As seen in Figure 5, the introduction of combined acceleration has provided a more stable measurement standard (Figure 5d). Table 2 displays the raw data for the three activity levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activity Levels** & **X-Axis** & **Y-Axis** & **Z-Axis** & **ACC** \\ \hline \multirow{6}{*}{low activity} & \(-5.89\) & \(6.8\) & \(-4.5\) & \(10.05\) \\ \cline{2-5}  & \(-4.85\) & \(7.43\) & \(-5.55\) & \(10.46\) \\ \cline{2-5}  & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \cline{2-5}  & \(-4.92\) & \(7.51\) & \(-4.22\) & \(9.92\) \\ \cline{2-5}  & \(-4.93\) & \(5.99\) & \(-5.3\) & \(9.39\) \\ \hline \multirow{6}{*}{medium activity} & \(8.98\) & \(9.58\) & \(-5.84\) & \(14.37\) \\ \cline{2-5}  & \(-1.09\) & \(11.57\) & \(-5.41\) & \(12.81\) \\ \cline{2-5}  & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \cline{2-5}  & \(-3.45\) & \(4.9\) & \(-5.41\) & \(8.07\) \\ \cline{2-5}  & \(4.67\) & \(-2.01\) & \(-6.09\) & \(7.93\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Motion data for the horse during low activity, medium activity, and high activity, with each row containing raw accelerometer data for the x, y, and z axes, along with the combined acceleration (ACC).  
Figure 5: Shows the comparison between three-axis acceleration and combined acceleration under low activity: (**a**) x-axis acceleration, (**b**) y-axis acceleration, (**c**) z-axis acceleration, (**d**) combined acceleration.  
The experiment collected three-axis acceleration data, represented by acceleration values on each axis as \(a_{x}\), \(a_{y}\), and \(a_{z}\). The formula for calculating the combined acceleration is presented in Equation (1).  
\[ACC=\sqrt{a_{x}^{2}+a_{y}^{2}+a_{z}^{2}}. \tag{1}\]
# How Does C-V2X Help Autonomous Driving to Avoid Accidents?
## 3 Key Technology for C-V2X VRUCW  
### C-V2X  
In our VRUCW solution, Qualcomm 9150 commercial chipset [20] is installed in both RSU and OBU to provide C-V2X PC5 interface function. According to free space path loss model in [21], the dBm value of received signal strength (RSS) can be given in Equation (1)  
\[R=10 lg(\frac{16\pi r^{2}}{\lambda^{2}}d^{\alpha}) \tag{1}\]  
where \(R\) is received signal strength; \(d\) is the distance between sender and receiver; path loss exponent of \(\alpha=2.2\) is assumed; \(\lambda\) is the wavelength, can be derived from Equation (2):  
\[\lambda=\frac{c}{f} \tag{2}\]  
\(c\) is light speed; \(f\) is the frequency of radio wave, we use \(f=5.850\)-\(5.925\) GHz, so when \(R\geqslant-125\) dBm, \(d\leqslant 1000\) m as concluded in [22], after evaluation, one RSU is sufficient to cover both intersection 1 and 2 in our solution. Taiwan National Communications Commission (NCC) is planning to reserve 5.850-\(5.925\) GHz for Intelligent Transport Systems (ITS) development when writing this paper. 5G Automotive Association (5 GAA) [23] has called on national and regional regulators to make spectrum 5.855-\(5.925\) GHz band available for global harmonized use. The U.S. Federal Communications Commission (FCC) [24] selected the frequency range 5.850-\(5.925\) GHz to improve automotive safety. The European Telecommunications Standards Institute (ETSI) harmonizes the 5.875-\(5.905\) GHz frequency band for ITS applications. While China are using 5.905-\(5.925\) GHz for ITS trials and considers C-V2X the only option. Though most countries and regions make the spectrum technology neutral, C-V2X is favored by almost all major telecommunication operators, auto makers, and technology vendors. ITS Spectrum for V2X in global is summarized in Table 5.
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
###### Abstract  
Floods are the most destructive, widespread, and frequent natural hazards. The extent of flood events is accelerating in the context of climate change, where flood management and disaster mitigation remain important long-term issues. Different studies have been utilizing data and images from various types of sensors for mapping, assessment, forecasting, early warning, rescue, and other disaster prevention and mitigation activities before, during, and after floods, including flash floods, coastal floods, and urban floods. These monitoring processes evolved from early ground-based observations relying on in situ sensors to high-precision, high-resolution, and high-coverage monitoring by airborne and remote sensing sensors. In this study, we have analyzed the different kinds of sensors from the literature review, case studies, and other methods to explore the development history of flood sensors and the driving role of floods in different countries. It is found that there is a trend towards the integration of flood sensors with artificial intelligence, and their state-of-the-art determines the effectiveness of local flood management to a large extent. This study helps to improve the efficiency of flood monitoring advancement and flood responses as it explores the different types of sensors and their effectiveness.  
floods; sensors; remote sensing; flood losses +
Footnote †: journal: Sensors
# Design and Experimental Study of a Down-Drive Piezoelectric High-Frequency Fatigue Testing Machine
## 2 Materials and Methods  
### Structural Design  
The structure of the piezoelectric fatigue testing machine is shown in Figure 1, which mainly consists of a piezoelectric vibrator, preset load adjusting mechanism, force detecting mechanism, clamping mechanism, specimen, dynamic load adjusting mechanism, and fixing mechanism, as shown in Figure 1a. The piezoelectric vibrator provides the exciting force for the system and consists of a circular substrate and two circular piezoelectric certificates. Among them, the upper and lower circular piezoelectric ceramics are polarized in opposite directions, and under the action of external alternating voltage, the upper and lower circular piezoelectric ceramics will vibrate due to the inverse piezoelectric effect, as shown in Figure 1b. The dynamic load adjustment mechanism is set up to adjust the resonance frequency and force of the resonance system, which mainly consists of a vibration transmitting plate, flexibility loop, lower support plate, vibration transmitting rod, driving and counterweight parts, and each part is connected together by the vibration transmitting rod, as shown in Figure 1c. While the core part of the dynamic load adjustment mechanism is the drive and counterweight part, the piezoelectric actuator is mounted in the lower part of the fatigue tester and transmits the excitation force to the elastic ring through a connecting shaft with a diameter of 5 mm. The piezoelectric actuator consists of a double-layered piezoelectric vibrator with a sandwich, in which the piezoelectric ceramic and metal are bonded together with epoxy resin. The inverse piezoelectric effect of the piezoelectric vibrator generates alternating excitation forces for the actuator, which are amplified by two elastic rings. In addition, in order to vary the driving frequency, two identical semi-cylindrical steel mass blocks were mounted on the upper plate of the piezoelectric actuator and secured with bolts of 3 mm in diameter, as shown in Figure 1d.  
Figure 1: Structure of fatigue testing machine. (**a**) Fatigue testing machine system structure diagram (200 mm \(\times\) 45 mm \(\times\) 600 mm); (**b**) Three-dimensional schematic of a piezoelectric vibrator; (**c**) Three-dimensional structure of dynamic load adjustment mechanism; (**d**) Three-dimensional structure of piezoelectric actuator.
## 2 Data and Methods
### Datasets
Grain yields and cropland area data recorded from 2001 to 2019 were acquired from China's agricultural statistical data and the 60 years' agricultural statistical data of the Ministry of Agriculture and Rural Affairs of China ([[http://zdscxx.moa.gov.cn:8080/nyb/pc/index.jsp](http://zdscxx.moa.gov.cn:8080/nyb/pc/index.jsp)]([http://zdscxx.moa.gov.cn:8080/nyb/pc/index.jsp](http://zdscxx.moa.gov.cn:8080/nyb/pc/index.jsp)), accessed on 21 October 2021) and the China Statistical Yearbook published by the National Bureau of statistics ([[http://www.stats.gov.cn/tjsj/ndsj/](http://www.stats.gov.cn/tjsj/ndsj/)]([http://www.stats.gov.cn/tjsj/ndsj/](http://www.stats.gov.cn/tjsj/ndsj/)), accessed on 21 October 2021). To ensure consistency among the spatiotemporal resolutions of the SIF, NDVI, and GPP data, the GPP data were resampled to a spatial resolution of 0.05\({}^{\circ}\), and the 8-day GOSIF and GPP data were resampled to the monthly scale [32].
## 4 Attitude Control Experiments
### Posture Control Experiment  
The posture control experiment was conducted in an experimental water tank, shown in Figure 17. The AUV's states (roll, pitch, yaw, and depth) were transmitted through the underwater visible light system with a communication speed of 1 Mbps and a loop time of 0.01 s. The sensor output data were stored every 0.1 s. Table 4 shows the experimental results. Figures 19-21 show the respective attitude and depth sensor data graphs in the order of roll, pitch, and depth. Yaw data were not recorded because the AUV was rotated to obtain camera footage from multiple directions.  
The results of the control experiment in Table 4 show the mean error values for attitude control by receiving the attitude information of the AUV through underwater optical communication. In the experiment, it was not easy to control the roll and pitch attitude since the AUV was small and light, but the roll and pitch were effectively controlled within 5\({}^{\circ}\) and 2.5\({}^{\circ}\) in real-time, respectively, demonstrating stability even under external disturbances such as minor water currents and thruster-induced vibrations. Particularly, the error in depth was less than 2 mm even at the shallow depth of the water tank, indicating an effective control. Ultimately, this experiment demonstrated that it was possible to monitor images of the AUV from the ground in real time through optical wireless communication and to control the attitude of the AUV in real time based on the attitude control command. Overall, the consistent stability observed across all three parameters emphasizes the robustness of the developed hovering control system and the practical applicability of UWOC-based real-time control.  
Also, while the AUV is under motion control, the video images from the AUV can be transferred through the developed UWOC system. To visually verify the image changes, a photo simulating the underwater environment was attached to the wall of the tank and filmed by the AUV. While controlling the AUV, the video data from the underwater image with 1 Mbps equivalent to 21.2 FPS with VGA (640 \(\times\) 480) resolution and the roll and yaw motion data of the AUV were successfully transferred to the over-water control station. Two captured motion pictures from the real-time video images of the AUV in the water tank with 1 Mbps are presented in Figure 22.  
Figure 21: Depth result of posture control.  
Figure 20: Pitch result of posture control.