esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 5 Overview on Semi-fragile Watermarking Algorithms
### Recoverable Semi-Fragile Watermarking Algorithms  
The recoverable semi-fragile watermarking algorithms not only can detect and locate the tampered areas, but also can restore them. Such algorithms generally use the compressed version of the image itself or feature data obtained from the image to generate the recovery watermark. Since there are few studies on the spatial domain methods, we will make a review of the semi-fragile watermarking algorithms in transform domains.
# An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
## 2 Related Works
### Farming Knowledge Systems and Expert Systems for Plant Disease Diagnosis
Still, there exist various expert systems for plant disease diagnosis. An android-based application in [34] is developed to identify fruit diseases from photos by using K-Means to segment each damaged area, using an artificial neuron network (ANN) to classify the diseases. A desktop application for diagnosing six papaya plant diseases [35] is developed as an IF-THEN rule-based system, using CLIPS and Delphi language. In [36], a fuzzy logic rule-based web application for diagnosing banana diseases and pests contains 37 symptoms for 5 pests and 4 diseases of banana plants. In [37], a web-based system for managing nine rice diseases and pests is introduced, covering 32 damages for a rice plant. Several expert systems [14; 38; 39; 40] utilize ontologies and semantic web technologies for crop planning and diagnosis of plant diseases. An expert system in [38] employs an ontology and rules for recommending rice varieties and theirs corresponding crop calendar based on geographic and temporal information. Its knowledge-base and rules cover rice varieties, disease and pest resistance, soil types, and ecosystems. For disease diagnosis purposes, ref. [39] presents phytopathology ontology covering plants, diseases, causes, a comprehensive set of symptoms, recommendations, etc. Its knowledge base is modeled as individuals (ABox) with rules developed based on SWRL. On the other hand, refs. [14; 40] RicoDO and TreatO ontologies model rice diseases, abnormalities, and controls as TBox axioms using the description logic-based formalism. This paper aims to improve RiceDO and TreatO to meet the accepted standards and utilizes both ontologies for identifying rice diseases and controls with the developed expert system called RiceMan.
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
## 5 Study of Sand Emergence Phenomenon Based on In Situ Reservoir Samples
### Analysis of Results
Figure 11:8 Temperature and pressure change curves of single-group experiments and partial enlargement. (**a**) Pressure-temperature variation in a single set of experiments. (**b**) Pressure drop followed by a slow decrease. Figure 11:9 Measurement of sand output before and after the experiment. large, the sand production is also large, indicating that the water production is the main controlling factor leading to the sand production in the process of hydrate extraction, and the effect of sand carrying is not to be neglected when the gas production reaches a certain scale. With the gradual slowing down of the rate of pressure reduction, the overall trend of water production and sand production first increases then decreases, then increases and gradually stabilises. It is shown that there are optimal buckling intervals under different buckling schemes, which are related to the mechanisms of stratigraphic deterioration. Based on this water production and sand output model, a straight well is exploited as an example, with a 20 m sand-proof section, a 9-5/8\({}^{\prime\prime}\) screen pipe completion and a pressure reduction rate of 0.5 MPa/h. The inner radius of the filled sample area is 1.3 cm, the outer radius is 5.0 cm, the volume \(V_{1}\) is 87.9 cm\({}^{3}\), and the water yield is \(M_{1}\). The corresponding scale of influence of the straight well is 0.47 m in radius, \(V_{2}\) in volume, and \(M_{2}\) in water yield, according to the principle of similarity:  
Figure 13.0  
where, \(V_{1}\) experimental model volume, m\({}^{3}\); \(V_{2}\) mining well model volume, 12.97 m\({}^{3}\) (outer radius of 0.47 m, inner radius of 0.12 m, length of 20 m of the annulus), m\({}^{3}\); \(M_{1}\) predicted model water production, which is 0.10357 kg by checking Figure 23; \(M_{2}\), mining well model water production, which is 15.28 \(\times\) 10\({}^{3}\) kg, i.e., 15.2 m\({}^{3}\) of water. Similarly, the sand output is 0.63 \(\times\) 10\({}^{3}\) kg, which is the original sand output without sand control measures. According to the experience of sand control design, the sand output after sand control is generally 1/10 of the original sand output, so the final sand output from the wellbore is 63 kg. Through exploratory experiments on water production and sand production, as well as the formation of search charts, support can be provided for the development of later hydrate trial production, plugging and permeability enhancement processes, and pressure reduction plans. Figure 13.1 Quantitative experimental data and phenomena of water and sand production from natural gas hydrate.
## 3 Results
One direction that is gaining large momentum in the field is the utilization of hot-electron optoelectronic nano-devices. This concept relies on the fact that metals have an abundance of free electrons, so they can nicely capture light if they are fashioned as a nanoscale optical antenna. In addition, employing metals is a necessary part of electronic circuits, e.g., as electrical contacts. Therefore, if meticulously designed, a nano-metal with optical antenna properties can be used as an electrical contact as well; therefore, one can access the hot electrons in metals for optoelectronic applications [16]. Such hot carriers can enable the extremely fast switching of electrical signals [17]. In a capacitor configuration, such high-energy hot electrons have the possibility of being transported over a Schottky barrier in a very short timeframe. The injection of hot carriers into a dielectric/oxide/air material will change its conductivity in a very short timescale, thereby allowing for ultrafast optoelectronic switching, which is inherently a frequency down conversion process. The beauty of this technique is hidden in the fact that hot-electron optoelectronic systems do not rely on the absorption of light in a semiconductor. Therefore, practically any oxide or metal combination can be employed to produce a hot-electron optoelectronic convertor without being concerned about the intrinsic properties of employed materials or a capacitive nature of the circuit, which limits the bandwidth. Initial demonstrations of such switches were proposed recently [18; 19]. However, practical devices are yet to be demonstrated.
# Multi-Level Control and Utilization of Stormwater Runoff
## 3 Results and Discussion
### Comprehensive Analysis of the Benefits of PBT-GR  
#### 3.5.1 Water Quality Safety Assessment  
The quality of the effluent of the PBT-GR system was examined to evaluate the direct impact of the effluent on the surrounding environment and explore the feasibility of recycling rainwater resources for irrigation purposes. The quality of the effluent was evaluated according to the Environmental Quality Standard for Surface Water (GB 3838-2002), [33] the Groundwater Quality Standard (GB/T 14848-2017) [33] and the Water Quality Standard for Farmland Irrigation (GB 5084-2005) [34]. Figure 7 shows the effluent quality of the PBT-GR system according to the main water quality variables assessed. The concentrations of all pollutants in the effluent of the PBT-GR system fell within the class V surface water and irrigation water standards. The effluent produced by the PBT-GR can therefore be used for landscape water, industrial and agricultural applications discounting other requirements, and the concentrations of heavy metals in the effluent did not pose a short-term toxicity risk to surrounding waters. The concentration of SS in the effluent of the PBT-GR was far below the limit of the water quality standard for farmland irrigation, whereas the TN concentration exceeded the class V surface water and groundwater limits, indicating a limited capacity of the PBT-GR system to remove N pollutants >2.8 mg/L. Therefore, the effluent of the PBT-GR system was found to be unsuitable for direct release into groundwater and surface water resources, and should only be used as irrigation water. The concentrations of Pb and Zn in the PBT-GR system were 0.1 mg/L and 0.3 mg/L, respectively, which were within the irrigation water standard and the concentration of Zn and appropriate for plant growth.  
Figure 7: Event mean concentrations (EMCs) of pollutants in effluent produced by different components of the PBT-GR system. Abbreviations: SS, suspended solids; TN, total nitrogen; TP, total phosphorus (n = 6).
# Active Electromagnetic Clutch for Crankshaft Decoupling from a Belt Drive System
## 5 Experimental Validation
### Clutch Functionality Tests  
Figure 8 depicts a typical actuation task for the electromagnetic clutch. The system is normally closed. A disable (DIS) signal is received at \(t\approx 0.3\) s. The coil current PI control is set to track a pull current reference of 5 A. When this setpoint is reached, the clutch command is set to zero. The current generates a magnetic force that pulls the mover and opens the clutch. Strong disturbances in the current are appreciated at the instant of the impact. This is due to the fast variation of the hub speed when it reaches the mechanical stroke limits. After a time interval of 0.2 s, the coil current setpoint is lowered to a hold value of 2 A to keep the clutch in open mode. The value of the current is lower in this case because the electromagnetic force counteracts only the elastic reaction of the conical spring.  
Figure 8: Clutch operation time history: Electromagnetic voltage (**top**), current (**middle**), and clutch command (**bottom**). Current perturbations due to back-EMF are highlighted during closing and opening with arrows.  
Figure 7: Layout of the clutch control unit (CCU).  
At 1.6 s, an enable (EN) command is received. The current reference is set to zero. Thus, the return spring releases its energy to set back the mover. When the mover reaches its end stroke, a back-EMF disturbance yields a variation in the coil current, which then approaches zero.  
A realistic working condition is reproduced by means of the described test bench in Figure 9. During the initial phase, the clutch is closed and transmits power to the belt starter-generator through the belt transmission. This is shown by the angular speed of the machine and the null current in the clutch coil. At 10 s, when the vehicle has already stopped, the clutch is opened: A pull current is briefly applied and then lowered to a holding value. Subsequently, the electric machine powers the AC compressor while the ICE is decoupled and off. Finally, the electric machine stops and the clutch is closed to restore BDS functionality.  
Note that when the engine is decoupled, the belt starter-generator electric machine (EM) is driven up to maximum speed. To re-establish a traditional BDS, the EM is stopped at max. deceleration and the clutch is closed again. In this worst-case condition, each of these two phases has a maximum actuation time requirement of 100 ms fulfilled in both cases.
# The Effect of LEDs on Biomass and Phycobiliproteins Production in Thermotolerant _Oscillatoria_ sp.
## 3 Results
Figure 5: The concentration of phycocyanin (C-PC) (**a**), allophyocyanin (APC) (**b**), and phycerythrin (PE) (**c**) and their purity (**d–f**) under different intensities of white/red:blue LEDs. Figure 6: Biomass concentration under different photoperiods of white/red:blue LEDs. The effect of the LEDs' photoperiod on the concentration and purity of phycobiliproteins can be found in Figure 7. In the case of C-PC, APC, and PE, more extended light periods substantially increased these proteins' final concentrations and purities, with higher values using 18 h light and 6 h dark of photoperiod; however, when the flask was exposed to continuous light (24 h), the concentration and purity in all the evaluated phycobiliproteins significantly diminished compared to the control and the other photoperiods.
## 3 Coordinate-Based Machine Learning Models  
Several mathematical models, including neural networks, were trained on the database of marker kinematics, which were obtained after postprocessing.  
We started by calculating more characteristics to increase the classification accuracy.  
The first characteristics we used were the vector breathing rates in projections on the coordinate axes. We selected the velocity of the thoracic marker relative to the dorsal marker and the velocity of the abdominal marker relative to the dorsal marker because they conveyed meaningful information. The velocity of the thoracic marker relative to the abdominal marker or vice versa can be ignored because they move in the same direction during breathing. According to the obtained vector velocities, the corresponding scalar velocities were calculated for each unit of measurement time between the above markers.  
We also calculated pairwise distances between all three markers. The distances between the markers were calculated as the length of the vector according to the markers' coordinates. Graphs of changes in the distances between markers during the measurement process for various types of breathing are presented in Figure 16.  
Another important characteristic is the average respiratory rate. It was calculated as the number of inhalation/exhalation cycles' per time interval divided by that time interval. The distance between two adjacent maxima was considered for the inhalation/exhalation cycle. The seasonality function of the time series was used to calculate it. We also calculated the mean breathing amplitude as the arithmetic average of the distances between the maxima and the minima closest to them [32].  
The next step was selecting the machine learning models and parameters for them. The parameters were selected by applying the cross-validation method [33] and manually adjusting them. It required splitting the original sample into \(N\) subsamples. The model was trained \(N\) times on \(N-1\) subsamples and tested on the remaining subsample, which was different each time. Machine learning was performed for each combination of parameters, and the results were compared to find the best-performing parameters. We performed cross-validation using the GridSearchCV class from the scikit learn (sklearn) Python library.  
Figure 15: Participant 1’s cinegrams of the markers’ movements recorded during chest-abdominal breathing at a frequency of 0.156 Hz with different coherence values. The reference point for constructing mutual spectra is the Y coordinate of the first marker.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 6 Comparison between Proposed Techniques for Flexible Reconfigurable Antennas
The antennas based on this technique are more flexible and durable compared to other techniques, due to the use of soft materials. In addition, their efficiency is higher compared to antennas based on electrical switches due to higher conductivity and lower dielectric loss of the materials. On the other hand, there are more limitations in the range of materials that can be used. It is very challenging to design a complex structure with this technique especially compared to their rigid counterparts. In an antenna which uses conductive fluid as a switch, the reconfigurability is mostly limited to two states. Origami based antennas as a flexible structures do not use electrical switches. They offer a complexity reduction due to their method of reconfigurability based on changing the antenna's shape. The efficiency of these antennas is higher compare to textile or fabric-based antennas with electrical switches. This is primarily due to employment of copper tape, and other solid conductors in their structures. As they primarily use paper or copper in the form of thin foil in their structure, they are not robust or durable for continuous deformation compares to the other presented methods. In addition, there is a challenge of holding the antenna's shape upon folding and unfolding, therefore in some cases, an actuator is employed in the antenna structure. Furthermore, in some types of origami-based structure such as a magic cube, the reconfigurability is mostly limited to two states.
## 4 Result Analysis
### Dynamic Response Analysis under Moving Loads
#### 4.4.2 Dynamic Response of the Partially Covered Plate for Various Boundary Conditions  
Under three boundary conditions (CFCF, SFSF CFSG), the response of the sandwich plate with an optimized first four order damping loss factor under moving loads is studied in this section. The load (\(F\) = 981 N) moves along a line (\(y_{L}\) = \(W/2\)) of the partially covered plate at 50 m/s and the size of the patch is 0.08 m \(\times\) 0.08 m. Other parameters are the same as before. Figure 8a-c shows the dynamic responses of the center point of the partially covered plate with patch size 0.08 m \(\times\) 0.08 m after the first four order damping loss factor optimizations for CFFF, SFSF, CFSG. Blue, red, pink and black solid lines represent the results of the first, second, third and fourth order damping optimizations, respectively.  
From Figure 8a-c, it is easy to see that the response of the system will decrease with the enhancement of boundary constraints. Meanwhile, the best optimization strategy is consistent with Section 4.4.1. Therefore, it can be concluded that the dynamic response of the system will decrease with the decrease in the order of the optimal damping loss factor, regardless of the boundary conditions of the system.  
Figure 8: Dynamic response of partially covered plate with patch size 0.08 m \(\times\) 0.08 m after the first four order damping loss factor optimizations for CFFF, SFSF, CFSG. (**a**) CFCF; (**b**) SFSF; (**c**) CFSG.
## 4 Discussion  
An interactive experience not unlike Pokemon GO, such as the [PERSON] AR use case reviewed above, can be imagined, where visitors embark on an interactive cultural journey that begins at home and continues beyond their trip. Pokemon GO pioneered AR-based gaming on a large scale and demonstrated the potential of blending virtual and physical spaces into hybrid experiences that integrate entertainment, education, and real-world exploration--principles now inspiring CH XR applications.  
Before their visit, tourists can explore a metaverse version of the site, completing AR or VR treasure hunts to learn about historical landmarks. By solving puzzles or interacting with virtual guides, they can collect NFTs, which could represent ownership of digital artifacts like ancient coins, statues, or historical documents. The NFTs are stored securely on the blockchain, ensuring proof of unique ownership. Upon arrival at the cultural site, these NFTs could unlock on-location experiences such as exclusive AR quests, live performances, or behind-the-scenes access to museum artifacts. Families can use AR-enabled devices to follow themed trails, where children and parents work together to solve mysteries, meet historical figures in mixed reality, and earn additional points or NFTs that can be redeemed for physital souvenirs (a physical artifact paired with digital information and animations). Hospitality providers and local shops participate by offering discounts, meals, or custom gifts tied to NFT rewards.  
Post-visit, the experience continues with a digital memory book, compiling virtual artifacts, photos, and videos captured during the trip. Enthusiasts can trade, display, or reuse their NFTs in shared XR worlds, contributing to crowdsourced knowledge preservation. Amateur cultural heritage explorers upload their own 360-degree scans or MR recreations of uncovered artifacts, enriching the shared digital archive. This system not only enhances cultural learning and engagement but also creates new economic opportunities for local businesses, tourism providers, and edutainment creators, transforming cultural heritage into a sustainable, interactive ecosystem. Additionally, it can potentially lead visitors off the beaten track to lesser visited areas, thus creating a more distributed visitor experience, a larger area for creating economic opportunities, and a more spread-out eco-footprint.
# Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
## 3 Damage Identification Methodology and Their Fundamentals
### Shannon Entropy  
Entropy is commonly associated with the amount of disorder or order; the greater the entropy, the greater the disorder or chaos [58]. The Shannon entropy index (SEI) is a nonlinear measure developed by [PERSON] [57], the father of the entropy theory, and serves to quantify the complexity and uncertainty found in a time signal. In particular, the SEI has demonstrated suitability for analyzing signals in the time domain with noise and non-stationary properties, such as those measured in civil structures, especially large ones [59]. This makes it a reliable tool with which to evaluate the health status of civil structures, largely since its value can vary depending on the alterations or changes measured in the dynamic response [60].  
To measure the complexity of a timing signal \(X\), the SEI is calculated as follows:  
\[SEI(X)=-\sum_{i=1}^{K}p(x_{i})\log_{2}[p(x_{i})], \tag{1}\]  
where \(p(x_{i})\) represents the probability that a time signal \(X\) can take according to its results, \(x_{1}\), \(x_{2}\), \(x_{3}\), \(\ldots\), \(x_{K}\).  
As an illustrative example, the SEI can be obtained via a Bernoulli trial [61], which is a randomized experiment named in honor of [PERSON], in which only two results can be obtained, being labeled as either a success or a failure, see Figure 6. From the point of view of probability theory, these trials are modeled via a random variable \(x\) that can take only two values, \(0\) and \(1\). Typically, \(1\) is used to represent success. Therefore, if \(p\) is the probability of success, then the entropy depends on the probability \(p(x=1)\) that \(x\) can take the value \(1\). When \(p(x=1)=0.5\), all possible outcomes are equally probable, meaning the result is not very predictable, and that the entropy is maximum.  
In the last decade, the Shannon entropy has been applied for different purposes, including the evaluation of the health condition of structures [44; 45]. The Shannon entropy measures the uncertainty in a random variable [57]. Therefore, this can be a reliable indicator to identify damage in a civil construction, since the vibration signals change according to the damage level, consequently generating a variation in the Shannon entropy magnitude [44; 45]. Therefore, since the Shannon entropy is associated with chaos, this  
Figure 6: Representation of the SEI.  
helps to determine in which parts of a signal there is more or less chaos, associating high levels of chaos or entropy with an important phenomena or critical situations in the signals.