esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Data Hiding Based on Block Truncation Coding
## 3 Survey of Related Works
### Reversible Data Hiding
#### 3.2.2 Error Correction Coding  
[PERSON] et al. [52] proposed a two-layer DH scheme to enhance the EC without causing increasing computation complexity for AMBTC. For the first layer, four disjoint sets using different combinations of the mean value (AVG) and the standard deviation (VAR) are derived from the combination of secret bits and the corresponding bitmap. For the second layer, these four disjoint sets are extended by adding or subtracting 1 according to a matrix (HC (7,4)) code. To maintain reversibility, we must return the irreversible block to its previous state, which is the state after the first layer of data is embedded. Then, to losslessly recover the AMBTC-compressed images after extracting the secret bits, we  
Figure 6: Embedding procedure using histogram shift based on \(4\times 4\) BM: (**a**) histogram generation of a BM (\(4\times 4\)), (**b**) histogram after embedding bits, and (**c**) extraction of hidden bits and reconstructed histogram.  
use the continuity feature, the parity of pixels value, and the unique number of changed pixels in the same row to restore AVG and VAR. According to the experimental results, this method was measured to have high EC and good image quality. However, the compression rate deteriorates by using 2 bits for all pixels of the 2-bitmap. In addition, the decoded pixel uses eight types of pixels, which is differentiated from the two types of pixels of the traditional AMBTC.  
[PERSON] et al. [53] proposed a two-layer RDH method based on AMBTC applying HC (7.4). It is mainly composed of two parts: the data embedding phase, and the data extraction and image recovery phase. In the data embedding phase, the embedding method has two layers. The first layer is based on [PERSON] et al.'s method. The second layer is based on matrix embedding with HC (7.4). In order to restore the original quantization levels \(\mu_{0}^{i}\) and \(\mu_{1}^{i}\), a location map is needed in order to record some multi-solution cases. During the extract phase, it restores the original \(\mu_{0}^{i}\) and \(\mu_{1}^{i}\) and extracts confidential messages.
###### Abstract  
This study determined the effect of fermentation by Fresco DVS 1010 starter culture with added probioticatic strain _Lactobacillus rhamnos_ GG and potentially probiotic isolate _Lactobacillus plantarum_ HMI, at fermentation times (0, 8 h) and cold storage period (24 h, 4 th day, 7 th day, 14 th day), on microbial parameters, pH changes, total phenolic content, phenolic compounds profile, and antioxidant activity of buckwheat water- and milk-based mashes, flavored with cranberries and unflavored. The tested starter Fresco culture effectively fermented the buckwheat products and the viable cell counts of potentially probiotic bacteria were sufficient to demonstrate the health-promoting properties of final products. Lactic acid bacteria had a positive impact on total phenolic compound content, total flavonoid content, and antioxidant activity of buckwheat mashes, whereby final values (14 days) were higher by about 16.9-130.8%, 13.4-37.7%, and 14.5-145.9%, respectively, in comparison to initial values (0 h). Seven phenolic acids (gallic, protocatechuic, vanillic, syringic, caffeic, \(p\)-coumaric, and ferulic) and two flavonoids (rutin and quercetin) in buckwheat mashes were measured during the experimental period. The content of quercetin, gallic, and protocatechuic acids increased and, conversely, \(p\)-coumaric acid decreased, in all products. Prepared buckwheat fermented products have the potential to meet the criteria for potentially functional foods.  
fermanation; lactic acid bacteria; probiotic properties; buckwheat; cranberry; phenolic compounds; antioxidant activity; _Lactobacillus plantarum_; _Lactobacillus rhamnos_
## 3 Results and Discussion
### Physical-Chemical Analysis of Fruits
#### 3.2.6. Weight Loss Percentage  
Monitoring the weight change during the storage period indicates the effectiveness against moisture of the coating. It is considered that the weight changes in the fruits depend almost exclusively on the water loss since the loss of volatile compounds from the fruits like aroma and flavor molecules and metabolic gas residues are practically undetectable in weight [58]. The results in Figure 2 show weight loss in the fruit. However, the nuts that were coated had a lower weight loss compared to the control fruit (Figure 2).  
Figure 2: The behavior of the weight loss percentage during the 12 days of evaluation in cape gooseberries coated with the treatments. Note: The superscript letter (a–d) refers to the significant differences (\(p<0.05\)) between each treatment.  
The lower weight loss could be explained by the barrier action exerted by chitosan and RGEO against the diffusion of water vapor. When pressure steam of the external environment is lower than that of the internal fruit, it results in a decrease in weight over time, thus causing a reduction in the appearance, firmness, and nutritional quality of the fruit [59]. The multifactorial statistical analysis of type III showed that the factors Days/Treatment (\(p<0.05\)), have a statistically significant effect concerning the response variable at a 95% confidence level. T3 had a considerable difference compared to the control indicating that the incorporation of RGEO has a positive effect concerning this parameter.
## 4 Methodology and Modeling
### Results for Bioclimate Zone I/ASHRAE 3A (Curitiba, PR)
#### 5.1.2 South Facade (Thermal Zone 1)  
When looking at Figure 12, as in thermal zone 3, for the typical meteorological year, Figure 12a, and for the 2050 climate, (b), the thermal zone loses heat intensely, with a minimum of approximately \(-6\,\mathrm{kW\,h\,m^{-2}}\) in June of the TMY. Likewise, as in thermal zone 3, heat loss through the wall- in cases with PCM--decreases in the winter months, denoting the reduction in demand for spontaneous cooling of the thermal zone.  
Due to heat loss through the envelope on the south facade when no insulating material is applied, the heating load overlaps the cooling load, justifying the high-EUI period for this application during colder months. Annual heat losses during the typical meteorological year reach almost \(-49\,\mathrm{kW\,h\,m^{-2}}\). During winter, the envelopes with phase change material and thermal insulation present similar EUI and net heat balance on the south-oriented wall. Outside this period, stressing the climate projections, the PCM envelope has a better capacity to remove heat from the thermal zone, reducing the cooling load: annually, the PCM facade presents average heat losses of \(-16.45\,\mathrm{kW\,h\,m^{-2}}\), while the thermal insulator is \(7.28\,\mathrm{kW\,h\,m^{-2}}\), around \(56\%\) lower. The impact of the absence of solar radiation is easily observed through the monthly heat losses through the facade without insulating material.  
This can be justified by the low heat fluxes during this interval, given the low outdoor temperature and the proximity of the internal temperature to the PCM operating point. In this sense, the core of the material functions as a barrier for heat transfer between the outdoor environment and the internal environment during phase change.
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 2 Materials and Methods
### Data and Model
#### 2.2.2 The HEC-RAS Model and DEM Data  
The US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) was applied to the study region to estimate the extent--and damage--of the 2014 flood. The n-values of Manning's coefficient required by the model were based on the LULC type, with the LULC classification identified using the Landsat images mentioned above.  
The HEC-GeoRAS GIS extension of the model comprises a set of procedures and tools for preparing geometric data for import into the HEC-RAS model to give a 3D view of the river basin, thereby allowing for an estimation of the spatial extent of a flood and its depth [28; 29]. This import file requires a DTM represented by triangulated irregular networks (TIN). Each triangle represents a uniform slope steepness and flows direction. The DTM was created using data from the Shuttle Radar Topography Mission (SRTM) at 30 m resolution as distributed by the Consortium for Spatial Information (CSI). The study area in the dataset fell within one single tile. Several studies have used SRTM data to extract the boundary of a watershed and its drainage network [30; 31]. This approach was followed, with the watershed delineated using the following steps in the GIS software ArcMap 10.6: i) importing the SRTM data, ii) creating a depression-less DTM over the study region, with the \"Fill\" method used to remove all sinks to create an accurate representation of flow direction, iii) determine the flow path for each cell in the DTM using a technique named \"Flow Path,\" and (iv) calculate the flow aggregation value at each cell given the number of upstream cells flowing into it. Depressions, also referred to as sinks, have no drainage due to being surrounded by cells at higher elevations and are often the result of DTM processing.  
Figure 1: The subdivisions of Muzaffargarh District in the Punjab province of Pakistan.  
To represent the DTM as a TIN, points were extracted around the edge of the DTM spaces, and then, the surface was filled for each gap, creating a filled surface from the DTM. TINs were calculated from these points and converted to a base surface. The filled base surface from the TINs was subtracted from the filled surface from the DTM, and the result was added to the DTM base surface. The contour intervals were made over the area of interest, and the TIN was generated using these contours to create the geometric data.
# A Survey on Satellite Communication System Security
## 5 Defense in Satellite Communication Systems
### Defense Methods for Confidentiality
that could achieve complete security purely through entanglement without the need for a trust relay [75]. Moreover, research incorporating various technologies like twin fields and technologies that are capable of much longer distances has also been conducted [76].
## 2 Preliminaries
### Graph Neural Network
#### 2.1.1 Gcn  
Inspired by convolutional neural networks, GCN is an efficient variant of CNN that operates directly on graphs. Figure 1 depicts the architectural framework of GCN. The core formula lies in the propagation between layers, calculated by:  
\[H^{(l+1)}=\sigma(\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}}H^{( l)}W^{(l)}) \tag{1}\]  
where \(\tilde{A}=A+I\) represents the adjacency matrix of the graph plus self-connections, \(A\in\mathbb{R}^{n\times n}\) and \(I\) the adjacency matrix and identity matrix; \(\tilde{D}\) represents the degree matrix, with \(\tilde{D}_{ii}=\sum_{j}\tilde{A}_{ij}\); \(H\) represents the features at each layer; \(W^{(l)}\) is the weight matrix for the \(l\)th layer; \(\sigma\) is a nonlinear activation function, such as \(Sigmoid\), \(ReLu\).  
Figure 1: The GCN architecture.  
Given the feature representation of node \(i\) at layer \(l-1\), \(h_{i}^{l-1}\), the output, which is the feature representation of node \(i\) at layer \(l\), \(h_{i}^{l}\), is obtained by:  
\[h_{i}^{(l)}=\sigma(\sum_{j=1}^{n}A_{ij}W^{(l)}h_{j}^{(l-1)}+b^{(l)}) \tag{2}\]  
where \(W^{(l)}\) is the weight matrix; \(b^{(l)}\) is the bias vector; \(h_{i}^{(0)}\) represents the initial input of feature \(x_{i}\), with \(x_{i}\in R^{d}\), and \(d\) the dimension of the input features.
# The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming
## 3 Geometric Semantic GP with Multi-Generational Selection
### Multi-Generational Selection for GSGP  
One interesting aspect to notice in the \"fast\" GSGP implementation is that we are able to access all the intermediate populations at zero cost: they are already stored in the data structures that we use, and their output is already computed to obtain the outputs of the current population. Hence, it is natural to ask if we can use this additional \"free\" information to improve the performances in GSGP. In particular, we propose allowing \"older\" individuals to be selected in the tournament selection.  
Concerning the implementation details, as one can observe in Figure 2 at the bottom, the pointers can go back for multiple generations (two in the example figure) instead of a single one, with no additional overhead. The pseudocode for the multi-generational selection is presented in Algorithm 1. As it is possible to notice, since all the information is already available, there is no significant additional computational cost compared to standard GSGP.  
```
functionMulti-generational-selection(\(P\), \(n\), \(f\), \(t\), \(D\))  Tournament\(\leftarrow\varnothing\)\(\triangleright\) Individuals selected for the tournament for\(1\leq i\leq t\)do\(\triangleright\) Repeat for the tournament size \(t\) \(\ j\gets n-\) extract from \(D\)\(\triangleright\) Select the generation \(k\leftarrow\) uniform random integer between \(1\) and \(|P[j]|\)\(\triangleright\) Select the individual Tournament \(\leftarrow\) Tournament \(\cup\{P[j][k]\}\)\(\triangleright\) Add the individual to the tournament endfor best \(\leftarrow\)\(\operatorname*{arg\,max}_{x\in\operatorname*{Tournament}}f(x)\)\(\triangleright\) Find the best individual in the tournament return best endfunction
```  
**Algorithm 1** The pseudocode of the multi-generational (tournament) selection algorithm, where \(P\) is a two-dimensional array of individuals of \(n\) rows (generations), where \(P[i][j]\) is the \(j\)-th individual in the \(i\)-th generation, \(f\) is the fitness function, \(t\in\mathbb{N}\) is the tournament size, and \(D\) is a distribution.  
In what follows, we describe how this selection can be performed since multiple possibilities are available. In particular, we propose a uniform and a geometric selection strategy.  
Figure 2: A visual representation of how GSGP can be implemented in an efficient way, sharing subtrees between individuals. At the top, the standard implementation where the parents can be selected only from the previous generation is shown. At the bottom, parents can be selected uniformly at random from the previous two populations. Notice how no additional storage is required.
## 3 Results and Discussion
### Relating Samples to VOCs by Univariate Analysis of Variance (ANOVA)
#### 3.3.2 VOCs Common to Several Spoilages/Normal Fermentation  
The models of some VOCs received significant-high contributions from several spoiled samples. In this line, F282 and F283 (affected by butyric spoilage) and F508 (sulfidic spoilage) contributed to the models for 1-butanol (V30), styrene (V36), and benzoic acid (V91), indicating that they could be produced in both spoilages.  
Most of the compounds found in only one sample with butyric fermentation (F2082 or F283) were also related to sulfidic (F508), although in marked lower values. They were butyl acetate (V21), methyl pentanoate (V23), propyl butanoate (V27), ethyl pentanoate (V29), butyl butanoate (V34), (Z)-3-hexenyl acetate (V40), (E)-3-hexen-1-ol (V46), and 3,4-dimethylbenzaldehyde (V71). Thus, they could be related to both spoilages.  
Sulfidic (F508) and putrid (F562) spoilages significantly contributed to the following compounds' models: ethanol (V10), 2-methyl-3-buten-2-ol (V19), isopentanol (V33) and, at lower level, to acetone (V3), methyl acetate (V4), 2-pentanone (V11), and 2-pentanol (V28). They could then be associated with both spoilages.  
Also, preenol (V42), (Z)-3-hexen-1-ol (V47), dimethyl sulfoxide (V60), methyl hydrocinanate (V73), and 3-hydroxy-2,4,4-trimethylpentyl 2-methylpropanoate (V77) could be associated with normal fermentation because of their significant-moderate standardised coefficient values in at least eight out of the 10 sample VOCs' models. Lastly, hexanal (V22), preenol (V42), (E)-linal oxide (V52), propanoic acid V(58), o-guaiacol (V76), or p-creosol (V81) were present in at least 5 out of the 6 samples from industry A. However, no VOCs could be related exclusively to industry B, probably because of its high variability in processing conditions. Overall, the ANOVA was generous in associating VOCs with spoilages and habitual fermentations, but could not derive a threshold for establishing an unambiguous relationship between components and type of sample.  
Four sensory descriptors (lactic, lupin, wine, and alcohol) were related to VOCs in normal Spanish-style table olives using partial least squares (PLS) regression [29]. The correlation was as follows: lactic odour with methyl propanoate, methyl hydrocinanmate, methyl (E)-3-hexenoate, 1-propanol, and propanoic acid; lupin descriptor with ethyl propanoate, methyl (E)-3-hexenoate, acetic acid, methyl propanoate, and 3-methyl-2-buten-1-ol; wine descriptor with methyl hexanoate, 3-methyl-3-buten-1-ol, geraniol, benzyl alcohol, and \(\beta\)-citronellol; and the alcohol attribute with geraniol, 1-hexanol, methyl hexanoate, ethanol, and benzyl alcohol.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 2 Services and Requirements for F6G  
### Classification of Services  
According to the F5G group timeline [7, 8], today we have entered the 5 th generation of fixed networks (F5G), which paves the way for various fresh services, such as augmented and cloud virtual reality, smart city, ultra-high definition (UHD) video streaming, etc., and discussions about the 6 th generation (F6G) are already taking place. F6G is expected to majorly transform the overall network ecosystem in various dimensions, in order to underpin the stringent requirements of the forthcoming F6G services. Key components for F6G are expected to be (a) the industrial sector, e.g., through digital representation of the physical world (digital twin), (b) the field of entertainment/communications, e.g., through holograms, internet of senses, and mixed reality, and (c) the transportation sector, mainly through the use of ultra-smart infrastructures, such as airports, ports, highways, cities, and railways. To further understand the evolution of services, we illustrate the three key services for each of the six generations of fixed networks in Figure 1.  
The main difference in F6G services compared with F5G ones, which poses major challenges to network providers, is that they need various strict requirements to be satisfied _concurrently_ in order to ensure a seamless service operation during the entire service life cycle. For example, industrial services such as digital twins require high densification, high bit rate, low latency, and high reliability at the same time. In order to ensure this, a significant amount of research work needs to be performed, and the network needs to be transformed accordingly in order to attain (based on the service) increased capacity, lower latency, higher reliability, greater availability, higher densification, lower jitter, etc., and all  
Figure 1: Representative services over the six fixed network generations as a result of 3 rd and 4 th Industrial Revolutions.  
these using low-cost and low-power-consumption platforms. These requirements can be satisfied through many options, each with its own merits; some indicatives are new optical wavelength bands, space division multiplexing, high-capacity and ultra-low latency optical switching, free-space optical communications, new integration schemes, and the use of all-optical processing techniques in Tx/Rx, as well as the integration of Artificial Intelligence (AI)/Machine Learning (ML) with the overall network ecosystem. These options and their feasibility, along with their advantages, are discussed in the current work.  
In the next sub-sections of this chapter, we elaborate on the F6G services and their requirements, then we present our vision of the F6G ecosystem and, finally, we analyze the network transformation that needs to be realized in order to support all the new types of services with their requirements.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.13 Intraocular Pressure Sensors  
Intraocular pressure monitors are designed to measure the pressure inside the eye. Elevated intraocular pressure is a significant risk factor for glaucoma, a group of eye conditions that can lead to optic nerve damage and vision loss if left untreated. These sensors are crucial for diagnosing and monitoring glaucoma as well as for assessing the effectiveness of treatment interventions. One example of an intraocular pressure sensor is the Sensimed Triggerfish.  
Figure 2 illustrates popular IoMT wearable devices categorized by the region of the body where they are used. In the head region, devices such as hearables, smart glasses, and smart helmets can be worn. Hearables or smart earbuds provide functionalities such as real-time translation, biometric monitoring, and voice-activated controls. Smart glasses offer augmented reality (AR) experiences, navigation assistance, and hands-free notifications, while smart helmets are equipped with features such as collision detection, health monitoring, and communication systems for enhanced safety. For the upper limbs, wearables include a variety of health monitoring devices, such as wearable ECG monitors that track heart activity and detect anomalies. Temperature monitors continuously measure body temperature, while gesture control devices allow users to control other devices through hand movements. Fitness trackers monitor physical activity, sleep patterns, and other health metrics, while smart watches offer multi-functional capabilities including notifications, health tracking, and mobile payments. Smart rings, though smaller in size, provide similar functionalities to smartwatches, often focusing on health metrics and contactless payments. In the lower limbs, specifically the legs, wearable devices include EMG monitors to measure muscle activity aiding in physical therapy and sports training. Rehabilitation devices assist in recovery from injuries by providing data and support for therapeutic exercises. Body composition analyzers measure metrics such as body fat and muscle mass, helping users to maintain their fitness goals, while posture correctors alert users to improper posture, promoting better spinal health. Compression devices improve circulation and reduce swelling, which is particularly useful for individuals with conditions  
such as varicose veins. Activity trackers and GPS trackers monitor movements and location providing valuable data for fitness and safety. Additionally, smart socks enhance activity tracking and comfort, and are often equipped with sensors to monitor gait and balance. Detailed information about body sensors, including sensor type, description, data type, benefits, challenges, and integrations, is provided in Table 2.