esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The sensed information can accurately reflect what happens in the fresh fruit cold chain by providing suppliers and distributors with continuous and accurate readings throughout the distribution process. Supply chain monitoring system is based on a smart logistic unit (SLU) which was originally built for strawberries [26, 31] and showed good potential for achieving food safety and shelf life together with logistic efficiency and system sustainability. The data acquired with SLU can be used for implementing a first-expired-first-out (FFFO) management strategy in order to optimize shelf life of the product in terms of market distance and product shelf life potential. Although currently applied in many cases, the FIFO management strategy has numerous disadvantages [10], but the most important are unpredictable shelf life potential and high possibility of postharvest loss. This study aims to monitor the temperature, relative humidity and CO\({}_{2}\) level and the quality of peach and nectarine cold chain by multi-sensors technology (WSN) in real time in order to improve the transparency and traceability of the cold chain. The cold chain process, variance analysis, and the evaluation of quality parameters (firmness, soluble solids concentration (SSC), and weight loss) were considered and implemented in the study methods.
## 3 Methods and Data
### GPS Surveys  
For the validation of the PS-MTI results, monthly and bi-weekly GPS surveys with an occupation time of at least 2 h were carried out with Leica GNSS GS10 coupled with an AS10 antenna starting in September 2013. In total, 46 GPS validation measurement campaigns were carried out covering a period from September 2013 to September 2015 (Table 2).  
In addition, three permanent GPS stations have been active since March 2014 (see Figure 1c). They were set-up with Leica GM10 receivers coupled with AS10 antennas in March 2014. Each station was equipped with a 140 W solar panel and a storage battery, as well as a GSM communication system to automatically submit the data to an FTP server. The permanent stations acquired observations stored in 1 h and a 24 h files at 0.2 Hz and 1 Hz sampling rates, respectively.  
The raw GPS phase data measured in the field were post-processed according to the standard differential mode (DGPS) using Leica GeoOffice with respect to the permanent station at Piz La IIa, located at a distance of 3 km and operated by the South Tyrolean Positioning Service (STPOS), using broadcast products (orbits and clocks). Differential post-processing consisted of two steps: at first, the positions of the three permanent stations were estimated with respect to Piz La IIa; secondly, the positions for the remaining 13 stations were computed with respect to these three permanent stations. Thereby, an average 2D standard deviation of 2 mm and an average standard deviation of 3 mm for the vertical component was reached. Following this workflow, position solution for all survey points and for all survey campaigns (Table 2) has been retrieved.
## 6 Designing a Smart Healthcare Reference Architecture
### Layer 3: Transformation of Collected Data at Edge/Fog Layer  
A tremendous amount of healthcare data is collected by the sensors that need to be processed. Sending this bulk data to the cloud consumes high bandwidth, resulting in ineffective computing. Thus, it is an optimal choice to process the data at the edge/fog layer [74]. Pre-processing, data element analysis, and transformation of sensor data regarding patients' health care are the primary responsibilities of this layer. These are analogous to the IWF Architecture's edge/fog computing layer.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### Artificial Intelligence and Cataract
#### 4.1.3 AI and Cataract Detection  
There are several published studies reporting algorithms for automatic cataract detection and grading/classification. These software programs are different in methodology, input data type, and use case scenario.  
Cataract Detection Based on Slit Lamp Photographs  
The studies in the table below exclusively use slit-lamp photographs for training data for algorithms for automatic detection and classification of nuclear cataracts (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Author** & **Data Source** & **Method** &
\begin{tabular}{c} **Definition of Gold** \\ **Standard/Ground** \\ **Truth** \\ \end{tabular} & **Performance** \\ \hline
2009 & [PERSON] et al. [62] & \
\begin{tabular}{c} Singapore Malay eye \\ study (SiMES), \\ 10,000 training, \\ 5490 testing \\ \end{tabular} &
\begin{tabular}{c} Modified ASM: Lens \\ structure detection \\ HSV model: Feature \\ extraction \\ SVM: Automatic \\ grading \\ \end{tabular} & Wisconsin cataract & Accuracy: 95\% \\
2013 & [PERSON] et al. [63] & \
\begin{tabular}{c} ACHIKO-NC (subset of \\ SiMES, \\ 10,000 training, \\ 5278 testing \\ \end{tabular} &
\begin{tabular}{c} Modified ASM: Lens \\ structure detection \\ BOF model: Feature \\ extraction \\ GSR: Automatic \\ grading \\ \end{tabular} & Wisconsin cataract & MAE: 0.336 \\ \hline
2015 & [PERSON]. et al. [64] & \
\begin{tabular}{c} ACHIKO-NC (subset of \\ SiMES), 10,000 training, \\ 5278 testing \\ \end{tabular} &
\begin{tabular}{c} CRNN: Feature \\ learning \\ SVM regression: \\ Automatic grading \\ \end{tabular} & Wisconsin cataract & MAE: 0.304 \\ \hline
2015 & [PERSON]. et al. [64] & \
\begin{tabular}{c} ACHIKO-NC (subset of \\ SiMES), 10,000 training, \\ 5278 testing \\ \end{tabular} &
\begin{tabular}{c} CRNN: Feature \\ learning \\ SVM regression: \\ Automatic grading \\ \end{tabular} & Wisconsin cataract & MAE: 0.304 \\ \hline
2019 & [PERSON] et al. [65] & \begin{tabular}{c} ACHIKO-NC (subset of \\ SiMES), 10,000 training, \\ 5278 testing \\ \end{tabular} & \
\begin{tabular}{c} CRNN: Feature \\ learning \\ SVM regression: \\ Automatic grading \\ \end{tabular} &
\begin{tabular}{c} Wisconsin cataract \\ grading system \\ \end{tabular} & MAE: 0.304 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Automatic detection and classification of nuclear cataracts.  
Based on Color Fundus Photographs  
Recently, the use of fundus photographs to screen for diabetic retinopathy [66] provides an occasion to use fundus images to detect other sources of visual impairment, such as cataracts (Table 2).
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Protocol Operation-Based Routing
#### 4.2.1 Swarm Intelligence Routing  
Particle swarm optimization (PSO)-based UAWSNs: In PSO-WSN-UAV [53], the issue of determining the network topology of a WSN and the use of UAVs for data collection are exploited. PSO is used to reduce the energy consumption, improve the communication quality, and reduce the traveling time, as shown in Figure 7.  
The topology is formed in a way that requires CHs to contact the UAVs. Typical WSNs use a clustering protocol, such as low-energy adaptive clustering hierarchy (LEACH), to conserve energy, but WSNs that are associated with UAVs and subject to large-scale deployment need a more complex and robust method. In Reference [53], a PSO-based method was presented, which works to find the optimal topology to decrease the energy consumption, UAV travel time, and bit error rate (BER).  
Figure 7: Particle swarm optimization (PSO)-based shortest-path selection.  
According to the simulation results, it outsmarts LEACH in terms of energy consumption and BER. The unique contribution of Reference [53] was to consider the wind effect in the travel time of UAVs.
## 1 Introduction
### Interaction of Wollastonite and CO\({}_{2}\)  
CO\({}_{2}\) has an impact on the stability of conventional hydrated cements. Therefore, the use of a cement, based on calcium silicate (CS) curing in the presence of CO\({}_{2}\) (\(\overline{C}\)), seems very promising [28; 29; 30]. Yet knowledge considering this approach is rather scarce.  
[PERSON] et al. [31] investigated the effects of high temperature (723-783 K) on calcium silicate-based cement (CSC), OPCs and OPCs with 20% fly ash. During the experiments, the OPCs showed a reduction of stiffness (60%), an increase of porosity (30%) and a loss of mass (13-15%) higher than the CSC (stiffness: 6%; porosity: 20%; mass loss: 3%). [PERSON] et al. [32] investigated the mechanisms of aqueous wollastonite carbonation, using three different grain sizes (<38 \(\upmu\)m, <106 \(\upmu\)m and <500 \(\upmu\)m) at short time intervals (5-60 min), a temperature range from 298 to 498 K and a pressure variation up to 4 MPa. A maximum conversion of 70% in 15 min at 473 K, 2 MPa CO\({}_{2}\) partial pressure and with a particle size of <38 \(\upmu\)m was achieved. They were able to show that the aqueous carbonation mechanisms of wollastonite, steel slag and olivine are generally similar, although they showed that wollastonite carbonation was rapid, relative to the carbonation of Mg-silicates. [PERSON] et al. [13] showed that water has a huge impact on the carbonation reaction of wollastonite. Without water the wollastonite sample carbonated on the surface rather fast but after a carbonate monolayer was formed, the reaction comes to a standstill. The presence of water completely changes the reaction and caused ongoing carbonation. [PERSON] et al. [33] investigated the carbonation reactions of wollastonite at conditions relevant to geologic CO\({}_{2}\) sequestration in subsurface environments. They performed experiments in batch reactors at 363 K and 25 MPa. After carbonation, they found two reaction products: Calcite and a silica-rich phase. No other carbonate polymorph was found. [PERSON] et al. [34] performed carbonation experiments on wollastonite at typical geological storage conditions (333 K and 10 MPa). They investigated the microstructure of the carbonated wollastonite and found that after 20 h the reaction was controlled by the diffusion of water-bearing scCO\({}_{2}\) across the product layer on wollastonite surfaces. A layer of amorphous SiO\({}_{2}\) covered the surface of the wollastonite and functioned as a barrier.
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
## 5 Evaluation and Results
### Effectiveness
#### 5.1.1 Preliminary Data Analysis
After obfuscating the traffic (Figure 2(b),d), the described range of each device starts to overlap, introducing uncertainty in the learning process for device classification.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Surface Model Generation
#### 2.2.4 DSM Resampling  
There exist different paradigms, like storing the 3D information as point clouds [83; 84] or as a DSM with given spacing. The first option is state-of-the-art in aerial photogrammetry while the second one is mostly used in satellite-based remote sensing. Additionally, in the second case, all information from a multi-stereo data set can simultaneously be used in the spatial point intersection to achieve a higher over-determination in the least-squares approach [36]. Another method is to extract DSMs from each stereo pair and then fuse those DSMs to a final DSM [85; 86; 87]. This method has the advantage that multiple stereo pairs can be processed independently in parallel (e.g., on multiple PCs), while only the fusion process has to access all data. There are several methods for mapping the 3D point cloud onto a DSM, for example, interpolation of a regular grid of height values and filling the remaining gaps by an appropriate interpolation mechanism [21] or by LiDAR-based approaches like that in Reference [88].
## 7 Commuters' Acceptance of the Multimodal Traffic Recommendation System  
### Pilot Study  
Assisted by the Nottingham City Council (NCC) and Sofia Urban Mobility Center (SUMC), several users were recruited in both cities to take part in a pilot study. The users utilized the EMRG app for their regular home-to-work and work-to-home journeys for two weeks. The analysis shows that 70 and 478 trips were completed for Nottingham and Sofia, respectively. In Nottingham, 4.4% of the journey requests were submitted for the fastest route (car only), and 95.6% were made for the green route (a mix of public transport, walking, and cycling), whereas in Sofia, 10.38% of the requests were submitted for the fastest route and 89.62% for the green route.  
Considering the reports of average emissions in different modes of transport [70], it is crucial to encourage commuting using public transport. For example, in the United Kingdom (Nottingham), the average emissions by private cars are estimated at 133.7 gCo2e/km for petrol cars and 133.3 gCo2e/km for diesel cars. In the same report, the average emission for buses is estimated as 111.6 gCo2e/passenger per km and 61.7 gCo2e/passenger per km for trans. Therefore, as the initial analysis suggests, 4.4% of 70 users produced \(3.08\times 133.5\) gCo2e/km or 411.18 gCo2e/km, whereas 95.6% of 70 users produced 66.92 \(\times\) 111.6 gCo2e/km or 7.468 KgCo2e/km, in the worst public transport option (buses) in terms of pollution. In comparison, if 100% of the commuters were using their private cars, the generated emissions could amount to 70 \(\times\) 133.5 or 9.345 KgCo2e/km. The difference of around 2 Kg of CO2 emissions per kilometer for just 70 commuters reveals the importance of encouraging public transport and utilizing green route recommendations, hence the importance of a route recommender that promotes CO2 emission awareness.
# Industry 4.0 Technologies for Manufacturing Sustainability:
## 5 Discussion on Review Results
### Main Technologies of Industry 4.0 for Achieving Sustainability
#### 5.2.10 Cyber Security  
Smart factories in Industry 4.0 can be subjected to vulnerability exploitation, device hacking, malware attacks and other common attacks which are also faced the other network [133]. The cyber-attacks in Industry 4.0 can disrupt manufacturing activities and will be difficult for manufacturers to detect and defend from cyber attacks [134]. Manufacturing in Industry 4.0 is based on cyber-physical production systems and IoT based network which are connected to many sensors and communication devices in the system [10]. At present, cyber security threats are major issues in Industry 4.0. Once malicious software or any virus attack enter a cyber physical production system they can communicate with different machines and layers through communication networks which can affect the manufacturing activities [135]. These viruses or malicious software can destroy the manufacturer data or change the manufacturing process which result in system breakdown or downtime. Industrial data is highly sensitive as it requires information related to products and other copyright details which can be affected by cyber attacks [136]. [PERSON] et al., and [PERSON] [133; 134] addressed the challenges related to cyber security for cyber physical based production systems in Industry 4.0 in which modification in firmware and malware intrusions are found to be major issues. [PERSON] et al. [137] presented an overview of current issues related to cybersecurity for manufacturing in Industry 4.0. [PERSON] [138] discussed the importance of cyber security-related issues for Industry 4.0 roadmap development. It is noted that cyber-attacks can disrupt the manufacturing activities. So, the industries need to work more on cyber security related issues. [PERSON] et al. [139] presented a new approach for cybersecurity issues in Industry 4.0 which limits the information flow to authorized and internal floor devices in a system. [PERSON] et al. [135] analyzed the cyber security-related threats in Industry 4.0 context.  
RQ2: How can Industry 4.0 technologies contribute to sustainability in manufacturing?
## 2 Materials and Methods
### In-Water Data Collection  
The in-water behavior was interpreted on the basis of kinetic or symmetry domains. In-water force was measured during the incremental protocol using a reliable [21] differential pressure system composed of two hand sensors (Type A, Swimming Technology Research, Richmond, VA, USA). Inside each sensor, there is a diaphragm that flexes which is sensed as an electrical signal that is proportional to the difference in the two pressures. Each sensor measures the pressure component acting perpendicular to it. Both sensors were positioned between the third and fourth metacarpals (Figure 1). It has been assumed that this place is a good proxy of the application of pressure vector on the hand. This system measures pressure differences between the palmar and dorsal surfaces of bothhands, and therefore the resultant hand force (in Newtons, N) is derived from the product of the differential pressures and the hand surface area (in cm\({}^{2}\)). The hand surface area of dominant and non-dominant limbs was measured by digital photogrammetry. Women were instructed to place each hand on a flat surface (i.e., scanning machine) with a 2D calibration frame (3 \(\times\) 3 cm), and from there all images were converted into FFD files and exported to a specific on-screen digitizer (Universal Desktop Ruler, v3.8, AVPSoft, Pittsburgh, PA, USA). The sensor's cable was placed on each woman with shoulder/arm elastic straps and connected to a two-channel A/D converter plugged into a laptop with the Aquanex software (v.4.1, Model DU2, Swimming Technology Research, Richmond, VA, USA). The system was calibrated as reported elsewhere [21], and the data were acquired with a sampling frequency of 100 Hz.  
Data were imported into a signal-processing software (AcqKnowledge v.3.7.3, Biopac Systems, Santa Barbara, CA, USA), and the signal was smoothed with a 5 Hz cutoff low-pass fourth-order Butterworth filter. The resultant peak force (F\({}_{\text{PEAK}}\), N) of the dominant (D) and non-dominant (ND) upper limb was defined as the maximum value obtained from the underwater force-time curves of the horizontal adduction. However, the first positive and negative peaks (one cycle) were discarded to avoid bias. In the case of symmetry, a Symmetry Index (SyI, %) was estimated as proposed by [PERSON], [PERSON], and [PERSON] [22]. The calculation gathers the force produced by the dominant and by the non-dominant upper limbs. The symmetry data were interpreted as if SyI = 0%, perfect symmetry; if 0% > SyI < 10%, symmetric motion; if SyI \(\geq\) 10%, asymmetric motion.
## 3 Results and Analysis
### Effects of Input Parameters on the Sidewall Penetration  
Figure 7 exhibits the influence of welding parameters on the depth of the sidewall penetration. Figure 7a shows that as the welding voltage increases from 21.5 V to 24 V, the penetration depth of the sidewall increases from 0.33 mm to 0.44 mm. This is mainly attributed to the increase of the welding voltage that increases the heat input per unit time. Meanwhile, as the welding voltage increases, the influence range of the welding arc  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F Value** & \(p\)**-Value (Prob \textgreater{} F)** & \\ \hline Model & 1.69 & 6 & 0.28 & 8.14 & \textless{}0.0001 & Significant \\ \(W_{f}\) & 0.15 & 1 & 0.15 & 4.47 & 0.0403 & \\ \(W_{s}\) & 0.65 & 1 & 0.65 & 18.79 & \textless{}0.0001 & \\ \(O_{W}\) & 2.543 \(\times 10^{-5}\) & 1 & 2.543 \(\times 10^{-5}\) & 7.08 \(\times 10^{-4}\) & 0.9789 & \\ \(\alpha\) & 0.11 & 1 & 0.11 & 3.21 & 0.0803 & \\ \(O_{W}^{2}\times\alpha\) & 0.45 & 1 & 0.45 & 12.90 & 0.0008 & \\ \(O_{W}^{2}\) & 0.33 & 1 & 0.33 & 9.49 & 0.0036 & \\ Residual & 1.49 & 43 & 0.035 & & & \\ Lack of Fit & 1.31 & 36 & 0.036 & 1.41 & 0.3361 & Not significant \\ Pure Error & 0.18 & 7 & 0.026 & & & \\ Cor Total & 3.18 & 49 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANOVA test results for the welding-height model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Squares** & **df** & **Mean Square** & **F Value** & \(p\)**-Value (Prob \textgreater{} F)** & \\ \hline Model & 12.31 & 8 & 1.54 & 11.81 & \textless{}0.0001 & Significant \\ \(W_{f}\) & 0.44 & 1 & 0.44 & 3.41 & 0.0719 & \\ \(V\) & 0.63 & 1 & 0.63 & 4.87 & 0.0330 & \\ \(W_{s}\) & 0.43 & 1 & 0.43 & 3.28 & 0.0776 & \\ \(\alpha\)  
increases. The influence range of the arc on the sidewall also increases, thereby increasing the penetration depth on the sidewall.  
Figure 7b shows that the sidewall penetration decreases with the increase in the welding speed. This phenomenon may be attributed to the decrease of the line energy originating from low welding speeds, which decreases the sidewall penetration depth.  
Figure 7c shows that the oscillation width of the welding torch obviously affects the sidewall penetration depth. It should be indicated that in a real welding process, an increase in the oscillation width directly expands the affected range of the welding arc on the sidewall. Subsequently, as the oscillation width of the welding torch increases, the corresponding penetration depth of the sidewall increases.
## 4 Conclusions  
In this study, a Modified REL propagation model and a data modeling approach based on piecewise are proposed for shore-to-ship communication channels. Comprehensive performance evaluations demonstrated that the developed Modified REL model was better adapted to environmental variables and increased propagation accuracy compared to the simplified REL model. In particular, the Rx antenna height, which is not constant due to fluctuations in the sea environment, was optimized for each measurement and included in the calculations, and the adaptability of the model to environmental conditions increased. In addition, the Proposed Piecewise model successfully modeled more complex marine environment conditions by dividing the propagation environment into two regions according to the \(d_{break}\) point calculated based on Fresnel zone theory. The first region is represented by the Modified REL model, and the second region is represented by the log-distance model, providing a higher accuracy that cannot be achieved with a single model. Tests conducted in four different shore-to-ship scenarios showed that the proposed models exhibited superior performance compared with existing models in terms of both accuracy and flexibility. In particular, the Modified REL model achieved an average decrease of up to 3% in RMSE values compared with the classical REL model and a significant increase in model fit (R\({}^{2}\)). In addition, the Proposed Piecewise model yielded more successful results than the Modified REL model, while R\({}^{2}\) did not change much for scenarios M1, M2, M3, and M4, the RMSE values decreased by 1.5%, 15%, 37.5%, and 35%, respectively, and an average improvement of 22.25% was achieved. These results reveal the importance of piecewise modeling strategies and approaches sensitive to environmental factors to increase the reliability and efficiency of shore-to-ship communication. In future studies, these models can be tested in a wider frequency range and different maritime environments, including the evaporation duct effect neglected in our proposed model. Thus, a more comprehensive performance evaluation and model recommendation can be made. This study contributes significantly to the development of advanced and adaptive propagation models in the field of marine communication.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.