esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Quality of Service Routing Protocol (QoSRP) in UWSNs
### Network Model  
The network model used in the design, development, simulating and testing the QoSRP scheme is illustrated in Figure 1. The proposed architecture consists of a sea surface sink, base station (BS) and ASNs. The randomly deployed ASNs in a geographic area of interest over the ocean bottom are equipped with the main functions of sensing, sampling, and acoustic transmitters. These location-aware ASNs (computed by using self-localization scheme in [32]) deployed for continuous oceanographic data collection are equipped with omnidirectional acoustic transceivers have identical communication range and asymmetric communication links. In UWEs, the ASNs due to constraints of limited residual energy and short communication range transmit packets to the sink in a multiple hops manner. Thus, the ASNs die once the energy runs out. In addition, the ASNs employing different channels on multiple paths with different lengths have different propagation period, the angles of arrival (AoA) and the angle of departure (AoD) in UWEs. In addition, the ASNs move passively with water currents in vertical and horizontal directions with a velocity of 0 to 0.8 m/min and 0 to 1 m/min, respectively. This extremely slow movement in both upward and parallel directions is assumed negligible in the UWEs. On the other hand, the sink floats on the sea surface and is equipped with both acoustic modem and radio modem aiming to gather data from ASNs through acoustic signals and forward the gathered data using radio signals to the offshore BS and then the remote user/s for monitoring purposes. The sea surface sink is embedded with a global positioning system (GPS) and periodically updates the BS about its location information by disseminating periodic beaconing in the network. The energy of both sea surface sink and BS is assumed to be unlimited since they can be charged by suitable green energy resources, such as solar energy, etc. The technology such as carrier sense multiple access (CSMA) is assumed to avoid data packets collision during transmission and reception in UWSNs. Finally, we assume that the remote user/s can configure, control and monitor ASNs by connecting to the BS through one of the highly stable communication technology, such as satellite or cellular. The following section explains the working procedure of QoSRP protocol in detail.
## 2 Methodology
### Bayesian Binary Search (BBS)  
#### 2.2.1 Binary Search as Bayesian Inference  
The classical binary search/bisection algorithm can be formally framed within the context of Bayesian inference. Consider a sorted search space represented by the interval \([a_{0},b_{0}]\). In the absence of prior knowledge about the location of the target value \(x\), we define a uniform prior probability density function (PDF) over this initial interval:  
\[P(x)=\begin{cases}\frac{1}{b_{0}-a_{0}}&\text{if }a_{0}\leq x\leq b_{0}\\ 0&\text{otherwise}\end{cases}\]  
Let \([a_{k},b_{k}]\) denote the search interval at the \(k\)-th step of the binary search. At each step, we probe the midpoint \(m_{k}=\frac{a_{k}+  
Thus, standard binary search can be viewed as a specific instance of Bayesian inference employing a uniform prior and a deterministic observation model that halves the search space at each step. This provides a foundational perspective for understanding how Bayesian Binary Search generalizes this approach by incorporating learned priors and probabilistic probing strategies.
## 4 Results and Discussion  
In this segment, we present the outcomes of our experiments and provide a detailed analysis and discussion of the obtained results. We also delve into the computational cost associated with each component of the LVAE approach.  
1. Experimental Outcomes and Analysis: To evaluate the effectiveness of our LVAE technique, we conducted several experiments. The results clearly demonstrate that our approach is capable of accurately detecting unknown types of attacks. By carefully selecting and fine-tuning the model parameters, we ensure its success against new threats. Furthermore, we improved the overall performance of our method by carefully determining the optimal hyperparameters through experimentation. The specific configuration of these hyperparameters is detailed in Section 3.3.
2. Computational Cost Analysis: In order to identify novel or unknown attacks, we integrate eight different techniques in the integrated classifier section. Each technique has a set of carefully chosen parameters that maximize performance. While each approach incurs a different computational cost, we took measures to ensure efficiency without compromising accuracy. The specific configuration of these hyperparameters is detailed in Section 3.3.  
Overall, our LVAE approach exhibits promise in detecting unknown attacks while considering the computational cost of each component. This makes it a viable option for practical applications where effectiveness and accuracy are crucial considerations.
## 3 Methodology
### Resilience of Air Flights
#### 3.1.3 Resilience of Airport Flight Operation  
Based on the flight operations in the airport during an operational day, once a delay occurs, the airport suffers from profit losses until the delay dissipates. This duration is quantified by the resilience of flights on route \(k\) over periods in one operational day. An example of the resilience of delay time is shown in Figure 1.  
Real-time flight information primarily refers to the planned departure time, planned arrival time, actual departure time, and actual arrival time. The origin-destinations (ODs) of each flight and the airport's flight volume can be statistically obtained using real-time information. The method for calculating the operational resilience of flight delays is detailed as follows:  
\[\mathrm{R}^{k}_{\mathrm{delay}_{i}}=\gamma_{k}\frac{\mathrm{MAX}\Big{(}delay_{ i,a(\mathrm{max})}\Big{)}(t_{i+1}-t_{i})-\sum s_{a}\Big{(}delay_{i,a(\mathrm{max})}>0 \Big{)}}{\sum_{\alpha=1}^{\mathrm{N}_{i}+2}s_{a}},i=1,2,\cdots\mathrm{T} \tag{14}\]  
\[s_{a}=\int_{t_{i,a}}^{t_{i+1}}\Big{[}a_{ia}t+delay_{i,a(\mathrm{max})}-a_{ia}(t _{i,a+1}-t_{i,a})\Big{]}\mathrm{dt}+\int_{t_{i,a}}^{t_{i+1}}\Big{[}\beta_{ia}t+ delay_{i,a(\mathrm{max})}-\beta_{ia}(t_{i,a+1}-t_{i,a})\Big{]}\mathrm{dt} \tag{15}\]  
where, \(s_{a}\) is the cumulative delay from \(t_{i,a}\) to \(t_{i,a+1}\) in the statistical period \([t_{i},t_{i+1}]\); \(t_{i,a}\) is the time when the flight delay reaches an inflection point or zero during the statistical period \([t_{i},t_{i+1}]\); \(delay_{i,a(\mathrm{max})}\) is the maximum delay from \(t_{i,a}\) to \(t_{i,a+1}\) in the statistical period \([t_{i},t_{i+1}]\); \(\alpha_{ia}\) is the recovery rate of delay during the period \(t_{i,a}\) to \(t_{i,a+1}\) in \([t_{i},t_{i+1}]\); \(\beta_{ia}\) is the interruption rate of the delay rate during the period \(t_{i,a}\) to \(t_{i,a+1}\) in \([t_{i},t_{i+1}]\); \(MAX\Big{(}delay_{i,a(\mathrm{max})}\Big{)}\) is the maximum delay in \([t_{i},t_{i+1}]\); \(N_{i}\) is the number of flights that are not delayed or advanced in \([t_{i},t_{i+1}]\).  
The calculation procedure of the operational resilience of flight delays is shown in Figure 2.  
As mentioned in Section 3.1.1, the resilience of flight operations involves metrics for operations, economics, and capacity. In calculating these metrics, this study uses Equations (11) and (12), in which only the parameters related to delays must be replaced.  
Figure 1: Airport flight resilience calculation using delay as an example (The gray area represents recovery capacity, and the hatched area represents resistance capacity.).
## 2 Related Work
Sparse broadcasting of VMS data intrinsically requires interpolation of trajectories for future prediction of ship position, heading, and speed changes. Such interpolation unavoidably contains some errors or noises and its spatial resolution is sparse. The VMS data-based activity prediction methods usually are applicable to fishing activity identification but not applicable to the identification of the fishing gear type. That is, the VMS data-based method is effective for discriminating between fishing activities but is not effective for determining the type of fishing. Hence, it is hard to detect illegal fishing activities from VMS data. Nowadays many countries pay attention to illegal fishing monitoring and systematic analysis of fish catch. It is necessary for their maritime administrative party to keep monitor and analyze the fishing states for each fishing gear type. It is hence important to determine the fishing gear types from monitored trajectories data. This paper introduces a new deep-learning based identification of fishing gear types from ship trajectories data.
## 3 Emission Reduction Measures at Ports
### Landside Industry and Support Facility
In October 2023, Nokia and EGC deployed a private wireless network at a Caribbean port to provide connectivity and enable digital solutions (www.portstrategy.com, accessed on 5 January 2024). The concept of a smart port aims to utilise smart technologies and digitisation to achieve port sustainability considering economy, climate, and people simultaneously. The port of Antwerp developed a digital platform to support innovation and collaborations across start-ups, scale-ups, accelerators, investment funds, government, and knowledge institutions (www.portofantwerpbruges.com, accessed on 5 January 2024). The port of Singapore initiated the Maritime Data Hub, which acts as a one-stop data repository and centralised data exchange platform in order to enable the integration of Singapore with the global trade ecosystem [30]. In terms of policy and collaboration measures, Taiwan Environmental Protection Agency (EPA) established a set of regulations and counselling methods to enforce the new manufacturers entering the port area or existing industries in the port area to meet low-carbon requirements [54]. The Port of Long Beach initiated a community program to mitigate port-related air pollution. The program provided grants to support renewable energy, replace traditional equipment, and use energy-efficient lighting. Moreover, the Port of Long Beach also launched an energy policy to collaborate with port tenants, utilities, city departments, industry stakeholders, labour unions, and the Port of Los Angeles in order to achieve a more sustainable and resilient supply of energy and match the increasing power demand at the port [46]. The Port Community System (PCS) can be regarded as a collaboration measure. It is defined as an open electronic collaborative platform that connects ports' various stakeholders such as port operators, customs agencies, ocean carriers, logistics companies, and freight forwarders to enable operational data exchange and consolidation within the port network, which can lead to quicker decisions and streamlined operations. For example, Dubai's PCS helped avoid 12.7 million physical visits of logistics service providers at the port and save over 1.7 million kg CO\({}_{2}\) (www.worldbank.org, accessed on 5 January 2024). International Association of Ports and Harbours (IAPH) is a non-governmental organisation (NGO) representing over 170 ports in 84 countries. One of its aims is to act as the industry reference for sharing best practices, such as innovations in energy transition, emission reduction measures at ports, and ship-to-shore interfaces (www.iaphworldports.org, accessed on 5 January 2024). To provide a clearer academic landscape on the predominant themes and methodologies in the current research, we summarise the reviewed studies on port decarbonisation in Table 9 by six solution measure categories.
###### Abstract  
Advances in information technology have made data accessible anytime and anywhere. Currently, data confirmation is a popular area of research. Many current approaches to data confirmation rely on submitting certificates of ownership, embedding digital watermarks, or using blockchain. However, none of these approaches can avoid exposing source data to third parties that are not fully trusted. To address this issue, this paper proposes a new data confirmation method based on ciphertext policy attribute-based encryption (CP-ABE), which is widely used in cloud storage environments. The unique identifier of the data owner is encrypted by Paillier encryption and embedded into the ciphertext, so that the ownership corresponding to the plaintext is converted to the ownership corresponding to the ciphertext. During the entire confirmation process, third-party organizations cannot access the source data, reducing the risk of source data leakage. Finally, the feasibility of the scheme is proved by security proof and experiment comparison.  
CP-ABE; cloud storage; data confirmation; cryptography; Paillier encryption +
Footnote †: journal: Applied sciences
## 4 Results of Experiment and Simulation
### Description of De-Coupling Effect
#### 4.2.2 Experiment Results  
Decoupling effect experiments are carried out in this section. The results are shown in Figure 11. As shown in Figure 11, the proposed BNNN-based NFTSM performs better than the other three methods. The \(p\)-\(p\) value of \(\Delta\theta_{a}\) is \(0.162^{\lx@math@degree}\) when the PID controller is applied; the \(p\)-\(p\) value of \(\Delta\theta_{a}\) is \(0.12^{\lx@math@degree}\) when the NFTSM controller is applied; the \(p\)-\(p\) value of \(\Delta\theta_{a}\) is \(0.077^{\lx@math@degree}\) when the RBFNN- based NFTSM is applied; the \(p\)-\(p\) value of \(\Delta\theta_{a}\) is \(0.051^{\lx@math@degree}\) when the BNNN-based NFTSM is applied.
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 2 Materials and Methods
### Interrogation Unit  
The sensors were interrogated by the SENSeOR wideband radio-frequency transceiver WR D005. This unit operates in the [430.5, 449.5] MHz frequency band. It provides digital RS232/USB and analogue outputs. The maximum sampling rate/sensor is, according to the data sheet, 150 Hz (depending on the application). The dimensions of the interrogation unit are 18.4 \(\times\) 10.9 \(\times\) 3.0 cm\({}^{3}\).  
Once the interrogation unit is turned on, the user is prompted to choose a configuration. Different types of measurements are possible: quarter-bridge, half-bridge, half-bridge with temperature compensation or temperature alone. After the configuration has been chosen, the user may configure the filtering and recording parameters and correct the frequency offset.  
After this configuration, the user can start acquiring data. The \"Oscilloscope\" mode is similar to the display of a spectrum analyzer, where the frequency response of the sensors is shown. As an example, Figure 2a shows the response of sensor SSE E015 (peak centered at 433.95 MHz) and the two resonance peaks of sensor TSE F162 (435.88 and 436.58 MHz). The left most resonance is related with the response of a 3 rd sensor and falls outside the visible window. Typical temperature and strain output windows are depicted in Figure 2b.  
Figure 2: (a) “Oscilloscope” mode of interrogation unit, showing the response of sensor SSE E017 and the two resonance peaks of sensor TSE F162; (b) typical temperature and strain output windows.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 5 Future Opportunities
### Structure-Dynamics Correlations  
Understanding the correlation of structure and dynamics is crucial for many scientific question. For instance, the glass transition of many materials is governed by a marked slowing down while the ensemble-averaged structure remains unchanged [124; 127]. Instead, the increase of the relaxation time is believed to be connected to higher-order structural correlation, such as bond ordering [202]. Such information can be obtained by X-ray cross correlation analysis (XCCA) [203; 204; 205], probing spatial correlation in speckle patterns. Similar to XPCS this technique will significantly benefit from the increased coherent flux at the next-generation X-ray sources. In particular, studies combining higher-order structure and dynamics will become feasible for many applications, down to resolution of few nm using nanofocused X-ray beams [206].  
A recent example is given in [207], where the orientational order of colloidal hard spheres was studied in the vicinity of the glass transition. Here, a linear increase of structural higher-order correlations was observed while the static structure did not change. This was accompanied by an exponential slowing down close to the glass transition. Different from optical microscopy, a wide range of time scales can be probed in such experiments, which corresponds to different phases from liquid, supercooled liquid, to glasses and gel phases in such systems.
## 4 Comparison between Calculation Models and Full-Scale Measurements  
Based on the estimated coefficients, the accuracies of the IMCRI and existing models could be analyzed with full-scale measurement data. As shown in Table 1, the scattering coefficients estimated from rectangular structures (S\({}_{\rm r}\)) and hemisphere structures (S\({}_{\rm h}\)) are very similar, and are both smaller than the scattering coefficient estimated from the groove structures (S\({}_{\rm g}\)). Therefore, only S\({}_{\rm r}\) and S\({}_{\rm g}\) are shown in Figures 6 and 7 to analyze the accuracy of IMCRI. Two existing models, the ISM and the ARM were also compared, which model the sound propagation process only by specular reflections and diffuse reflections, respectively. For SPL simulation, curves predicted by the Hopkins-Stryker equation are also shown as a reference.
## 3 Results and Discussions
### Temperature Homogeneity Analysis
The study uniquely combines these material variations with both conventional (water) and advanced (nanofluid) cooling fluids, enabling comprehensive understanding of their interactive effects. Additionally, research presents a novel approach by integrating experimental measurements with numerical simulations under turbulent conditions, validating effectiveness of TPMS structures in practical cooling applications. Examination of temperature gradients and homogeneities along TPMS structure, particularly in combination with nanofluids, represents another innovative aspect of this study. These findings not only advance understanding of heat transfer mechanisms in TPMS structures but also provide practical insights for optimization of cooling systems in various engineering applications.
# Microbiological and Sensory Quality of Artisanal Sour Cream
## 3 Results and Discussion
### Sensory Analysis of Sour Cream Samples
#### 3.3.5. Taste Properties  
The results presented in Figure 4 indicate considerable variability in the taste properties of cream, including intensity of sweet, salty, sour, bitter, and astringency.  
Sweetness and saltiness were weakly expressed, with sweet taste scores ranging from 0 to 3 and salty taste from 0 to 1. As expected for fermented dairy products, acidity emerged as the dominant sensory attribute, exhibiting greater variability with scores between 1 and 5. This pronounced sour taste is the result of lactic acid production by lactic acid bacteria and is significantly influenced by the bacterial culture used and the timely termination of acidification [39]. All samples exhibited high yeast concentrations, which can affect taste, flavour, and appearance, as shown in Table 4. Bitterness, often associated with yeast presence, ranged from 0 to 4, with sample 12 displaying the highest levels. The occurrence of bitterness can be explained by microbial proteolysis, which increases the content of hydrophobic peptides and certain glycerides resulting from fat breakdown [40]. Astringency, reflecting oral mucosa dehydration, was rated from 0 to 4, while sweetness, saltiness, and bitterness were less pronounced overall. Research by [PERSON] et al. [36] confirms that soreness is the dominant sensory characteristic of sour cream and emphasises that heat treatment before or during fermentation further shapes its sensory profile. Additionally, fat content plays a crucial role in taste perception; full-fat sour cream tends to exhibit lower off-flavour intensity compared to reduced-fat varieties. These differences can be attributed to the interaction of milk fat with aromatic compounds such as acetaldehyde, 2,3-butanedione, and acetoin, with low-fat products releasing volatiles more rapidly, thereby influencing the sensory experience [41].  
Figure 4: The taste profile of sour cream samples.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 4 Paradigm Shift: Bulk Hydrogels to Hydrogel Micro/Nanoparticles
### Hydrogel Microparticle Synthesis
#### 4.2.3 Sonication  
Sonication methods leverage sound waves to agitate particulates or to induce cavitation, the formation and collapse of microbubbles in a liquid medium, by utilizing the wave frequencies ranging from 15 kHz to 400 kHz [232, 233]. This process generates high shear spots, where a liquid phase is temporarily disrupted into dispersions (bubbles) or a binary mixture of immiscible fluids into relatively stable emulsions. Emulsions produced from sonication require secondary stabilization such as chemical crosslinking or gelation, and the size of emulsion is controlled by the frequency, power input, and time of sonication, with a general trend of a smaller emulsion size being produced as more powerful and longer sonication is applied [232, 234].  
Natural polymers like polysaccharides or synthetic polymers could simply be applied to this approach by utilizing certain functional groups and also undergo physical fragmentation during this high-energy process, which could be useful to form smaller-sized particles. For example, thiol groups could help to simplify the stabilization step, by directly forming disulfide bonds which serve as crosslinking points, as demonstrated by [PERSON] et al. In this approach, a thiolated heparin and PEG matrix formed a complex of a polymer-rich phase, followed by sonication-aided disulfide linkage formation to stabilize the crosslinked gel matrix [235]. [PERSON] et al. reported that high methoxyl pectin (HMP) is directly applicable to the sonication method for controllable synthesis. HMP with a high degree of esterification exhibits increased hydrophobic interactions and hydrogen bonding and reduced ionic interactions, and in this case, hydrogen bonding serves as a crosslinking point. Hence, high-intensity ultrasonic waves are used to form droplets of HMP, while dimethyl sulfoxide is used to enhance polymer chain mobility as well as to reduce solution viscosity, and extensive sonication time helped to facilitate the size controllable synthesis. During this process, high shear and localized heating also caused the breakdown of high-molecular-weight polymer chains, allowing HMP microgels of 10-50 \(\upmu\)m in diameter to be formed while maintaining a high encapsulation efficiency of 85% [236].  
Ultrasonication is a facile method to produce micro- and nano-emulsions with minimal use of a surfactant via cavitation, but it continuously disrupts the existing emulsions and generates new ones, and prolonged treatment could cause degradation of the polymer under high shear environments. Nanoscale particles can still be formed by repetitive ultrasonication, but this poses a risk of thermal damage to bioactive materials or drugs as high-power sonication could rapidly increase the solution temperature in a few minutes even under cooling [53, 234].