esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Analyses
### Experimental Environment  
The UWAC channels are generated using the acoustic channel simulator in reference [21] to approximate the practical channel. We adopt the environmental settings for two experiments, namely the Surface Processes Acoustic Communications Experiment, conducted in 2008 (SPACE'08) [21], and the Mobile Acoustic Communications Experiment conducted in 2010 (MACE'10) [31]. The simulation parameters are mainly chosen from the above two channel models of the SPACE'08 and MACE'10 experiment settings, and other individual parameters in our simulations are listed in Table 3. Thus, they equivalently provide enough information for the channel verification of the channel models, noise characteristics, and so on, together. The acoustic channel simulator adopts a statistical channel model to incorporate physical laws of acoustic propagation (frequency-dependent attenuation, bottom/surface reflections) and the effects of inevitable random local displacements. Additionally, random displacements on two scales are employed in the acoustic channel simulator as small- and large-scale effects, with the distances on the order of a few wavelengths and many wavelengths, respectively. In summary, the main system setting of parameters is shown in Table 3 and they are responsible for the completeness of our experiment simulations plus the two above channel models.  
Figure 4 shows the simulated time-varying UWAC channels. Figure 4a,b show the ensembles of the simulated channel impulse response based on the SPACE'08 and MACE'10 experiment settings over a duration of 1 min. Figure 4a corresponds to the receiver with depths of 2 m and 6 m, respectively. Figure 4b corresponds to the receiver with depths of 20 m, 30 m, 40 m, and 50 m, respectively.
## 2 Related Work
They should take into consideration the end-user QoE improvement in the selection of the relay vehicles. However, there are some studies that focused on the user QoE in routing decisions for multimedia services. Distributed beaconless dissemination (DBD) is a geographic routing protocol for the dissemination of live video flows on multimedia highway VANETs [38]. It creates Vehicle to Vehicle (V2V) backbones for video packet relaying to enhance the end-user experience. QoE-driven and link-quality receiver based (QOALITE) is a protocol for live video dissemination with QoE in VANETs [39]. For relaying vehicle selection, it uses location and link quality. Distributed QoE-aware receiver based (DQORE) is a QoE-aware location-based protocol for the dissemination of real-time videos in VANETs [40]. It is a coupling of QoE-aware receiver based (QORE) mechanism and a distance-based statistical routing protocol. The election of relay nodes in video dissemination is based on QoE-driven parameters and vehicle position. These protocols are designed for video dissemination and cannot be used in unicast video services. The distinction between the proposed scheme and the other geographic routing protocols lies in the selection of the next forwarder vehicle. In GeoQoE-Vanet, this process is based in addition to the QoE measured based on packet loss rate, delay and jitter, on the prediction of the distance to destination calculated according to the position, speed and direction, stored in the table of neighbours. Moreover, a packet is sent twice using two different relays when the QoE of each neighbouring vehicle is low. For performance evaluation, three QoE objective metrics are used to measure the video quality level from the user point of view, which are peak signal-to-noise ratio (PSNR) [41], structural similarity (SSIM) [42], and, mean opinion score (MOS) [43].
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Inadequacy of TESLA for Low-Bandwidth High-Latency Communication Media  
While TESLA has remarkable advantages, including an elegant and flexible design, its applicability in the particular operating environment (see Section 1.6) is limited. Specifically, the following problems arise:  
1. If messages are asynchronous and not sent at a steady rate (e.g., they are triggered by external events), then certain epochs may pass without any workload messages. However, TESLA requires some technical messages in each epoch to operate properly.
2. Alternatively, if the epochs are driven not by the clock but by the number of messages sent (e.g., the epoch changes every \(v\) messages), then some messages can be left unauthenticated for a long time. On the other hand, when a burst of multicast messages occurs in a short time, the epochs will change quickly. This creates a security risk, discussed in Section 4.2.1.
3. The length of the clock-driven epoch should be adjusted to variable message delivery delays for different network nodes. Thus, the choice implies either long waits for authentication data or the security risk discussed in Section 4.2.1.
4. If messages are not known in advance, the immediate packet authentication mentioned above cannot be used. It seems that in the case of a network constrained in terms of communication, the opportunities for the messages to be known \(d\) epochs ahead of time will be rare; thus, in most cases the mechanism will not be applicable.
# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
## 3 Results and Discussion
### System Interface  
The ProFit module is available as an option in the main menu of the ifarma interface, specifically in the \"Management of works, inputs, and yields\" group. By clicking the ProFit button (which is set below the \"PreFer\" option), the input form for entering values for the required economic items is launched in a single web page (Figure 5). For convenience, the output fields are displayed in a different form after the execution of the calculations and can be exported to spreadsheets.  
Apart from the tabular output data (e.g., statistics per field and the entire cultivation), which are displayed on a different web page of the ifarma environment, the output maps are displayed within the PreFer map viewer. The options for displaying the cost, earnings, and profit maps for every cultivation are available inside the \"Performance\" group of options in the PreFer map viewer menu (which also includes the yield maps).
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Gaps in Underwater SLAM Research
#### 9.1.3 Resource-Constrained Real-Time Computation  
Underwater robots often operate with limited computational power and battery capacity, making it challenging to run resource-intensive SLAM algorithms in real-time. Deep learning-based methods typically demand high GPU processing, while energy availability is restricted for extended underwater missions. Consequently, approaches that excel in laboratory conditions may fail to meet real-time or energy requirements in the field. Methods such as neural network pruning, quantization, and model compression, along with optimized sensor scheduling, must be developed and standardized to enable practical, long-duration deployments. Further research is required to balance accuracy with computational feasibility, ensuring that underwater SLAM solutions can be effectively implemented on low-power, embedded systems.
## 3 Methodology and Models
### Data Collection and Preprocessing  
#### 3.1.1 Data Sources  
The primary datasets used in this study were collected from meteorological stations ([[http://www.emy.gr/emy/el/](http://www.emy.gr/emy/el/)]([http://www.emy.gr/emy/el/](http://www.emy.gr/emy/el/))) accessed on 20 May 2021, and located across various regions of Greece, including Agchialos, Aktio, Alexandroupoli, Arazos, Argos, Florina, Ioannina, Kalamata, Kavala, and Kozani. These datasets include key meteorological parameters--such as temperature, humidity, wind speed, and solar radiation--that are crucial for predicting renewable energy production. Additionally, historical energy production data ([[https://www.admie.gr/](https://www.admie.gr/)]([https://www.admie.gr/](https://www.admie.gr/))) accessed on 20 May 2021, measured in megawatt-hours (MWh), were obtained from regional energy authorities to serve as the target variable in the predictive modeling process.  
In Figure 2, Greece's map, along with the corresponding meteorological stations' locations, is presented. The map illustrates the whereabouts of the meteorological stations utilized for collecting input data for the analysis of renewable energy implementation in Greece. These stations supplied critical data on the wind speed, wind direction, temperature, and humidity, which are crucial for enhancing the positioning of RES. Furthermore, these sites correspond with the regions for which the strategic plan and placement recommendations were produced, based on the analysis performed utilizing the GREENIA framework. The green markers denote the locations of meteorological stations in areas including Kozani, Alexandroupoli, Nea Agxialos, Ioannina, Arazos, Aktio, Kalamata, Argos, Kavala, and Florina. The stations supplied the meteorological data utilized to evaluate the renewable energy potential in each region. The areas identified by the meteorological stations are the same regions from which strategic recommendations for renewable energy deployment have been derived. The strategic plan provides recommendations for the optimal placement of wind turbines, solar panels, and other renewable energy system technologies, based on the data collected from these areas. The map's terrain colors emphasize the elevation and geographical characteristics of the areas. Darker regions indicate elevated terrain (such as hilly locations in Florina and Kozani), which may be more conducive to wind energy initiatives. Brighter regions, such Kalamata and Nea Agxialos, indicate lower altitudes, rendering them more conducive for solar energy implementation. Through the analysis of meteorological data from strategically positioned stations, the GREENIA framework generated site-specific recommendations for the deployment of renewable energy sources, optimizing energy production while addressing sustainability and community requirements.
## 5 Results Analysis
### Comparative Analysis of Algorithm Performance
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Efficiency (Unit/h)**} & \multicolumn{2}{c}{**GA\_GA**} & \multicolumn{2}{c}{**IG+ECTT**} & \multicolumn{2}{c}{**GA\_FCFS**} \\ \cline{2-7}  & **Upstream Station** & **Downstream Station** & **Obj (h)** & **Obj (h)** & **Gap (\%)** & **Obj (h)** & **Gap (\%)** \\ \hline \multirow{3}{*}{[C1]} & 15 & 0 & 760.11 & 777.62 & 2.30 & 783.87 & 3.03 \\  & 30 & 0 & 755.02 & 765.71 & 1.42 & 770.56 & 2.02 \\  & 45 & 0 & 751.65 & 760.23 & 1.14 & 764.24 & 1.65 \\ \hline \multirow{3}{*}{[C2]} & 0 & 15 & 598.01 & 604.17 & 1.03 & 607.77 & 1.61 \\  & 0 & 30 & 504.82 & 509.03 & 0.83 & 511.63 & 1.33 \\ \cline{1-1}  & 0 & 45 & 445.05 & 447.46 & 0.54 & 449.21 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: The sensitivity analysis results of transshipment efficiency. this paper, Framework 2 is more effective than Framework 1. Although a little computation time is sacrificed, Framework 2 can significantly shorten makespan and improve emergency transport efficiency.
## 3 Results and Discussion
### Stiffness Degradation  
The secant stiffness at each load cycle can be calculated by Equation (2):  
\[\mathrm{K_{si}}=\frac{\mathrm{V_{max,i}-V_{min,i}}}{\mathrm{\delta_{max,i}- \delta_{min,i}}} \tag{2}\]  
where \(\mathrm{V_{max,i}}\) and \(\mathrm{V_{min,i}}\) are the maximum loads at cycle i; \(\mathrm{\delta_{max,i}}\) and \(\mathrm{\delta_{min,i}}\) are the lateral displacements corresponding to \(\mathrm{V_{max,i}}\) and \(\mathrm{V_{min,i}}\). The initial stiffness of wall \(\mathrm{K_{e}}\) was computed at the lateral load corresponding to 20% of the predicted cracking strength for masonry walls, where it is considered that the wall still maintains elastic behavior.  
Figure 9: Idealized equivalent elastic-plastic system.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  
PCMWT and CMWT had an initial stiffness (K0) of 630.7 and 630.6 kN/mm, respectively. PCMW and CMW had an initial stiffness fo 388.8 and 386.8 kN/mm, respectively. This indicated that a T-shaped cross section increased the initial stiffness of the specimens by 62% compared to that of rectangular walls. However, the construction method had no influence on that. This is because the initial stiffness is related to the cross-sectional inertia moment.  
Figure 10 shows the normalized stiffness degradation with respect to K0. In general, the secant stiffness decreased sharply at low values of drift and then dropped slowly at high values of drift. The secant stiffness dropped to 30% of the initial stiffness at 0.21% drift when the first major diagonal crack was observed. At the ultimate loading, the stiffness was approximately 5% of the initial stiffness. The stiffness of specimens PCMWT and CMWT with flanges decreased rapidly compared to that of the cast-in-place walls until the first major diagonal crack occurred. This illustrated that the flange of the wall increased the initial stiffness but lead to a rapid stiffness degradation.
## Materials and Methods
### Data Analysis
PCA can reduce the dimensionality of data by contracting data into a few descriptive dimensions, denoted principal components,which represent main variation in the data [34; 35]. It is also commonly used as an exploratory tool for data analysis due to the ability of each PC to be displayed graphically [36]. Kennard-Stone (KS) used in this study is a technique for calibration and validation subset partitioning. It is based on maximizing the Euclidean distances between spectral data of samples to cover the multidimensional space [37]. Support vector machines (SVMs) were used for classification modeling in the study. SVMs are proven to present good generalization performance and to be able to model complex nonlinear boundaries through the use of adapted kernel functions [38]. In this work, radial basis function was used and particle swarm optimization (PSO) based on fivefold cross-validation was adopted for optimization of the parameters in SVM [39; 40; 41].
# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 4 Building Data Set and Choosing Configurations  
### Damage Images and Annotations  
200 steel images and 500 rubber bearing images are collected from different bridges, under different illuminance conditions and at different shooting angles. Then the damage these images are manually marked by Image Labeler, which is built-in application in MATLAB. After labeling the damages and getting the annotation files, these annotation files are renamed as the same name as original images. After that, images are divided randomly for the next steps. For corrosion images, 80% of images are prepared for training and the other images are both for validation and testing. And for rubber bearing damage detection, 80% of images for training, 10% for validation and the other part is for testing. To compare the difference results of squashed full images and cropped images, images and annotations from the data set are copied and cropped into small images with size of 224 x 224 pixels for Cropping Segmentation, after getting the data set. Table 2 shows the detail of data set. Since not all rubber bearing images are with damages, the number in parentheses represents the quantity of images excluded non-damage images.  
To compare the influence of different weights of damage pixel in data set, Mean Damage Ratio (MDR), is widely used for evaluating the data set of segmentation. Damage  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Damage Class** & **Image Type** & **Training** & **Validation** & **Testing** \\ \hline \multirow{2}{*}{Corrosion on steel} & Squashed images & 160 & 40 & 40 \\  & Cropped images & 9728 & 2719 & 2719 \\ \hline \multirow{2}{*}{Damage on rubber bearing} & Squashed images & 400 (193) & 50 (22) & 50 (27) \\  & Cropped images & 34,626 (1968) & 5027 (409) & 4218 (261) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The proportion of data set after dropping background data.  
Figure 8: The predicting process of Damage Segmentation.  
Figure 7: The schematic of cropping.  
Ratio refers to the number of damage pixels in each image divided by the total number of pixels in the image. The average of these outcomes in the data set is MDR. By calculating, MDR of corrosions is about 30% while MDR of cracks is less than 2%. Thus, corrosion is a kind of large area damage, and crack is a kind of minor damages.
## 2 Application Scenarios and Key Concepts/Enablers for RF Hardware in JCRS
### Scenarios
#### 2.1.4 Other Scenarios--Today and Tomorrow  
The number of applications and services requiring JCRS are growing every day. Ref. [14] discussed the applications in both communication and radar sensing domains for different frequency bands. Sub-8 GHz bands are reserved for long-term evolution (LTE) and wireless local area network (WLAN) in the communication domain, whereas in these bands, many radar applications exist as well, e.g., moderate and long-range surveillance radar, air traffic control (ATC) radar, weather radar, and early warning radars. In the mmWave band, automotive and high-resolution imaging radars exist. In recent years, the potential usage of 60 GHz for IEEE 802.11 ad/ay [15] communications has reduced the gap between the two systems, with front-end, antenna, and packaging technologies that can address the requirements for both systems. From a localization perspective [6], the needs of sensor fusion, robot localization, object recognition, context awareness, AR/VR/mixed reality (MR), and beam search in order to enable low power directional transmission have been caused an exponential increase in interest regarding joint communication and sensing, and specifically JCRS.
## 8 Experimental Results
### Over-the-Air Demonstrations
#### 8.2.2 Longer-Range Tests  
To demonstrate that this system is not limited to extremely close ranges, we demonstrate ranging stability in two longer-range demonstrations. During the first test, the UAV flies 200 m down the runway, lands, an operator physically interacts with the drone, and the UAV flies back to the starting position. During the second test, the UAV flies 700 m down the runway, turns around, and returns to the starting position. The results of these demonstrations are depicted in Figure 22.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Unit: centimeter (cm)**} \\ \hline
**Antenna Pair** & **Ground** & **Ground** & **Ground** & **Ground** \\  & **Ant1** & **Ant2** & **Ant3** & **Ant4** \\ \hline Drone Ant1 & 16.59 & 14.12 & 11.62 & 11.24 \\ Drone Ant2 & 13.54 & 8.58 & 7.96 & 8.78 \\ Drone Ant3 & 11.39 & 8.91 & 8.43 & 9.11 \\ Drone Ant4 & 10.68 & 9.18 & 9.02 & 10.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average ranging performance for all antenna pairs in the VTOL test.  
Figure 21: CHP2 range estimates (red) for a single transmit-receive antenna pair versus a tachymeter reference (blue) during a simple VTOL maneuver.
# In-Situ Nanoindentation Surface Topography of Lead-Free Piezoelectric Thin Films
###### Abstract  
Surface roughness significantly affects the performance of microelectromechanical systems (MEMS) and piezoelectric films. This study investigates the impact of surface roughness on the mechanical properties of thin piezoelectric films using nanoindentation and scanning probe microscopy (SPM). Four piezoelectric films with different thicknesses (220, 350, and 450 nm) and substrate configurations (LNO/SiO\({}_{2}\)/Si or LNO/Si) were analyzed. A discriminant analysis revealed that the fractal dimension is more effective than the arithmetic mean height (Sa) for distinguishing surfaces, with only 2% misclassification versus 25% for Sa. A multiscale analysis identified the Smr2 parameter with low-pass filtering at 140 nm as highly effective for surface discrimination, achieving only 0.1% misclassification. The analysis of the roughness parameter Sa at various scales showed that band-pass filtering at 500 nm yielded a 0.7% misclassification rate, indicating its relevance for fractal roughness characterization. Most relevant roughness parameters for mechanical property correlation were found: Smr2 with low-pass filtering at 500 nm correlated best with hardness (R\({}^{2}\) = 0.82), and Vvc with low-pass filtering at 2 nm correlated best with reduced elastic modulus (R\({}^{2}\) = 0.84). These results demonstrate that surface roughness features like valley volume and voids significantly impact the apparent mechanical properties of piezoelectric films.  
nanoindentation; topography; piezoelectric
## 4 The Role of UAV-Base Station in Wireless Communications
### Coverage Extension/Relaying  
The fast growth of mobile devices, such as smartphones, tablets, and wearables, has increased the demand for high-speed wireless access. As a result, the capacity and coverage of existing wireless cellular networks have been overstretched, thereby leading to the development of new wireless technologies to address this issue [13]. Because of their vast range of use-cases, UAVs have sparked many attention [91]. The air-to-ground (AtG) dominant line-of-sight (LoS) link enhances the performance of UAV-enabled wireless communications [92], thereby resulting in reduced propagation loss and better link QoS. As a result, UAVs functioning as aerial BSs or relays are commonly employed to increase network capacity or achieve more flexible coverage [93; 94; 95].  
The authors in [96] investigated mobile relaying in wireless powered communication networks (WPCN), where a UAV is used to aid in the transportation of information from numerous sources to a destination with severely obstructed communication channels. The sources are low-power and do not have any energy source. The UAV serves as a hybrid access point (AP) that is used both as WPT to power the user devices and as a means of information transmission and reception. In [97], the authors consider the use of UAV as a relay to serve users at the cell edge by extending coverage of an existing network using non-orthogonal multiple access (NOMA) technology, thereby enhancing their QoS.