esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 3 Watermark Embedding
### Method 4 (M4): Standard QIM Approach  
This method also involves the grouping of spectral coefficients. However, the embedding process is different, and its details are presented in [4]. It is based on the quantized projection embedding where randomly permutated columns of a Hadamard matrix are used as base vectors. The projection is quantized with a quantization step based on the default JPEG quantization table. Then, it is used to modify the vector of coefficients from the given block by applying the quantization operator, corresponding to a bit value of the embedded data, for the consecutive frequency bands.  
In order to reconstruct the image when all bits are embedded, the transform coefficients need to be reordered back to their previous position, and the inverse transform needs to be applied.  
In the extraction phase, all previous steps need to be performed in order to calculate the projection which is then rounded with respect to the given quantization step. When the result is an even number, then the recovered bit is '0'; otherwise, it is '1'.
# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation
## 3 Proposed Method
### Differential Attack  
A differential attack is a method of attacking an encryption scheme by comparing and analyzing specific variations in plaintext in relation to changes conveyed during encryption.  
The ability to withstand differential attacks is tightly linked to the plaintext image's sensitivity. Computing the encoded image's pixel change rate (\(NPCR\)) and the unified average change intensity (\(UACI\)) may also be used to assess the method's capacity to withstand differential attacks. NPCR and UACI analysis are the most frequent methods for determining plaintext sensitivity. The NPCR and UACI mathematical structures are stated as shown in (11) and (12).  
\[NCPR=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}K(i,j)\times 100\% \tag{11}\]  
\[UACI=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}\frac{|a_{1}(i,j)-a_{2}(i, j)|}{255}\times 100\% \tag{12}\]  
Table 1 displays distinct test scores such as, peak signal to noise ratio, mean square error, similarity index, and entropy. Table 2 shows NPCR, and UACI results for the input test images, and Table 3 depicts correlation coefficients. Finally, comparisons with other works of literature are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Images** & **PSNR** & **SSIM** & **MSE** & \
\begin{tabular}{c} **Entropy** \\ **Plain Image** \\ \end{tabular} &
\begin{tabular}{c} **Entropy** \\ **Encrypted Image** \\ \end{tabular} \\ \hline Lena & \(\infty\) & 1 & 0 & 7.5694 & 7.9621 \\ Man & \(\infty\) & 1 & 0 & 7.0097 & 7.8529 \\ Peppers & \(\infty\) & 1 & 0 & 7.5487 & 7.9533 \\ Barbara & \(\infty\) & 1 & 0 & 7.3410 & 7.9043 \\ Baboon & \(\infty\) & 1 & 0 & 7.3705 & 7.9578 \\ Boat & \(\infty\) & 1 & 0 & 7.1894 & 7.8794 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PSNR, SSIM, MSE, and Entropy values for the input test images.
## 3 Critical and Rare Metals in Greece  
### Antimony (Sb)  
Antimony is used for a number of industrial applications, as diodes and infrared detectors in the semiconductor manufacturing. It is alloyed with lead to increase its hardness. Antimony compounds are used in batteries, low friction metals, paints, ceramics, glass and pottery, as flame retardants in  
Figure 3: Simplified geological map of the Rhodope metallogenic province in Greece displaying the major Tertiary ore deposits and prospects [6,7 with modifications]. KmX: Kimmeria/Xanthi; Me: Melitena; Mr: Maronia; Ko: Konos/Sapes; SB: St. Barbara/Kassiteres; SD: St. Demetrios/Sapes; V: Viper; PH: Perama Hill; Mk: Mavrokoryfi; PR: Pagoni Rachi/Kirki; SPh: Agios Philippos/Kirki; Pf: Pefka; Ltr: Loutros; Kl: Kallyntirion; M-E: Myli/Esymi.  
adhesives, plastics, rubber and textiles. Stibnite (Sb\({}_{2}\)S\({}_{3}\)) is the predominant ore mineral of antimony. China supplies over 75% of the total world production and in 2010 introduced quantitative and export licensing restrictions. Therefore antimony was recently placed in the highest risk category for future supply disruption.  
In Greece, antimony is found in quartz veins mainly in the form of stibnite. One of the most important Sb occurrences is the low-sulfidation epithermal system of Kallyntirion prospect at the Rhodope metallogenic province (Figure 3), also including Pb, Zn, Cu, Au and Ag. Another significant stibnite mineralization is the shear zone-related Rizana/Lachanas deposit, Serbomacedonian province, which is related to sheeted quartz veins crosscutting enginses and amphibites. The mineralization is spread over an area 10 km long and 2-4 km wide, and is potential for future exploitation. In the years 1931-1938 mining operations produced 2.3 \(\times\)10\({}^{-6}\) kg of ore consisting more than 30% Sb [14]. The estimated reserves are over 5.0 \(\times\)10\({}^{-7}\) kg with 30%-35% Sb, in addition to small tungsten concentrations, associated with wolframite [14]. According to [15] the chemical analyses of enriched samples from Rizana/Lachanas revealed Sb content between 71.73 and 83.87 wt % (average 75.77 wt %). Other stibnite occurrences with relatively low Sb contents are located in Philadelphia/Thessaloniki, Chios Island and Gerakario/Kilkis [14].  
Except from stibnite, antimony is also incorporated in numerous sulfosalts (tetrahedrite, famatinite, chalcostibite, zinkenite, bournonite, boulangerite) which are common in porphyry-, epithermal- and intrusion-related systems at the Rhodope metallogenic province, such as in Maronia, Pagoni Rachi/Kirki, Agios Philippos/Kirki, Mavrokoryfi, Pefka, Perama Hill, Sapes, Kapsalina/Thasos, Pangeon Mt. In some cases mineralized samples contain over 0.2 wt % Sb [6,7,16,17]. In addition, the galena concentrates from Lavrion, Attica, contain from 0.3 to 0.7 wt % Sb [18].
## 4 Results
Here, however, the values are smaller, and the maximum wavefront distortion at 5 Hz is 12.92 \(\lambda\). In Figure 8, the Legendre polynomial coefficients are given, as obtained from Figure 7a, and the distribution is very similar to that determined for the with O-rings case (Figure 6). The NDSG sealing structure was compact. Thus, even if the O-rings were removed, there was still wavefront distortion caused by partial stress at the corners. Since there was cooling water flowing over both end surfaces of the NDSG rod, the thermal lens effect was weakened, but wavefront distortion caused by fluid motion appeared. Figure 5: Peak-to-valley (P-V) wavefront distortion with fixed O-rings. (**a**) Distribution at pump frequency 1 Hz. (**b**) Graph of maximum distortion versus pump frequency. Figure 6: 2D Legendre polynomial coefficients for wavefront distortion with fixed O-rings at 1 Hz. Figure 7: Peak-to-valley (P-V) wavefront distortion without fixed O-rings. (**a**) Distribution at pump frequency 1 Hz. (**b**) Graph of maximum wavefront distortion versus pump frequency. The thermal stress fracture limit of a rod-shaped gain medium is given by [12]  
\[\frac{P_{a}}{L}=8\pi R_{T}, \tag{4}\]  
where \(P_{a}\) is the limit of total heat accumulation, \(L\) is the length and \(R_{T}\) is the thermal shock resistance parameter. This shows that the thermal stress fracture limit is proportional to \(R_{T}\). NDSG had a high thermal shock parameter of 14.5 W/cm; thus, the thermal stress fracture limit should be very high. A thermal toughness test was carried out on NDSG for comparison with the theoretical parameter. The total excitation energy of the two LD pump sources was 7.79 J at 1 Hz. The pump frequency was gradually increased until it reached 25 Hz, when the pump power was about 194 W. At this time, the coatings on both end surfaces had melted, but the NDSG still did not crack. This showed, to a certain extent, that the thermal toughness of NDSG was very high, corresponding to its high thermal shock resistance parameter. This feature is very valuable in high-energy, high repetition rate laser amplification. The images displayed in Figure 9 show the material uniformity, with Figure 9a showing no gain medium in the optical path, Figure 9b showing the Nd:glass square rod, and Figure 9c showing the NDSG rod. The doping of the Nd:glass square rod was uniform, and the beam transmission uniformity was very good. However, the uneven distribution in Figure 9c shows many places where the beam failed to pass through the NDSG rod. This indicates that the Nd doping level in silica glass was not uniform, resulting in a cluster phenomenon, substantially reducing the amount of light passing through. Some parts of the beam diverged, but most were absorbed in the material, generating heat. This affected the gain and wavefront distortion.
## 3 Numerical Simulation of the Effect of Deep Excavation on Adjacent Tunnels
### Constitutive Model and Parameters of Material
#### 3.2.2 Constitutive Model and Parameters of Retaining Wall, Horizontal Struts and Tunnel  
The linear elastic constitutive model is used for the retaining wall, horizontal struts and adjacent tunnel made of reinforced concrete. The linear elastic model includes three parameters: \(\gamma\) (unit weight), \(E\) (elastic modulus), and \(\
u\) (Poisson's ratio). In this study, \(\gamma\) = 24.5 kN/m\({}^{3}\), \(E\) = 30 GPa, and \(\
u\) = 0.2 are adopted, respectively.  
In the model, the tunnel is established as a uniform circle ignoring joints, but in fact, the tunnel is made up of segments assembled by blots, leaving longitudinal and transverse joints. Therefore, a parameter \(\eta\), the effective stiffness ratio, is introduced to reflect the weakening effect of joints on tunnel stiffness, i.e., the equivalent stiffness of the tunnel is \(\eta EI\). Where \(EI\) is the bending stiffness of tunnel ignoring joints, and \(\eta\leq 1\). In this study, \(\eta\) = 0.75 is adopted, referring to some studies [33; 34; 35; 36; 37].  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**No.** & **Parameters** & **Definition** & **Values** \\ \hline
1 & \(\gamma\) & unit weight & 19.8 kN/m\({}^{3}\) \\
2 & \(e\) & void ratio & 0.6 \\
3 & \(c^{\prime}\) & effective cohesion & 14.0 kPa \\
4 & \(\varphi^{\prime}\) & effective friction angle & 25.7\({}^{\circ}\) \\
5 & \(\psi\) & dilatancy angle & 0 \\
6 & \(\mathrm{E}_{50}^{\mathrm{ref}}\) & secant stiffness in standard & 7.2 MPa \\
7 & \(\mathrm{E}_{\mathrm{ood}}^{\mathrm{ref}}\) & trained stiffness for oedometer & 5.1 MPa \\
8 & \(\mathrm{E}_{\mathrm{ur}}^{\mathrm{ref}}\) & primary loading & 36.8 MPa \\
9 & \(m\) & power for the stress-level & 0.8 \\  & & dependency of stiffness & \\
10 & \(\
u_{\mathrm{ur}}\) & \multicolumn{2}{l}{unloading-reloading} \\
11 & \(p^{\mathrm{ref}}\) & reference stress for stiffness & 100 kPa \\
12 & \(R_{\mathrm{f}}\) & failure ratio & 0.9 \\  & & stress ratio of the effective & \\
13 & \(K_{0}\) & \multicolumn{2}{l}{horizontal stress to the effective} \\  & & vertical stress in a normally & 0.57 \\  & & \multicolumn{2}{l}{consolidated state} \\
14 & \(G_{0}^{\mathrm{ref}}\) & \multicolumn{2}{l}{initial shear stiffness} & 99.3 MPa \\
15 & \(\gamma_{0.7}\) & \multicolumn{2}{l}{shear strain at 0.7 \(G_{0}^{\mathrm{ref}}\)} & 0.20 \(\times\) 10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of the HSS model for the soil [27; 32].
## 3 Method Implementation
### Dataset
#### 3.1.1 Introduction to Self-Built Datasets  
The self-built dataset established in this paper comprises two subsets: a training set and a testing set, which are utilized for model training and testing the reading results, respectively. These two datasets consist of subsets for pointer meters and digital meters, respectively. The images included in the training set are selected for their distinctive features and clear scales. The images of pointer meters in the training set encompass two types: circular and square pointer meters. Both the pointer meter and digital meter datasets consist of 1500 images each, totaling 3000 images. The images in the test dataset are randomly selected from the training set to evaluate the algorithm results, as illustrated in Figure 7.
## 2 The Sensing Theory of FBG Dynamometry Bolt
### The Working Principle of the FBG Dynamometry Bolt  
The bolt and the spring were simultaneously deformed, and the spring was stretched to generate stress. The spring stress was transferred to the fiber core in the form of shear stress through the adhesive. After the fiber core was stressed, the grating period and effective refractive index of the grating part were changed. The reflected optical signal carrying the bolt strain information was transmitted to the demodulator host through communication equipment, such as jumpers, optical splitters, and multi-core transmission optical cables. The optical signal was demodulated into electrical signal data and transmitted to the ground server for storage through the underground ring network. The staff accessed the data through the client of the optical fiber monitoring system to realize the real-time online monitoring of the force of the bolt body. The working principle of the FBG dynamometry bolt is shown in Figure 5.  
The bolt and the spring deform together, and the surface strain of the bolt is equal to the spring strain, and the relationship is as follows:  
\[\varepsilon_{\text{m}}=\varepsilon_{\text{s}}=\frac{L_{\text{s}}}{L_{\text{f}} }\varepsilon_{\text{h}}, \tag{4}\]  
where \(\varepsilon_{\text{m}}\), \(\varepsilon_{\text{s}}\), \(\varepsilon_{\text{h}}\), respectively, represent the strain of the bolt, the total strain of the spring, and the strain of the spring matrix of the section that overlaps with the fiber grating. \(L_{\text{s}}\), \(L_{\text{f}}\), respectively, indicate the full length of spring, when it is completely straightened, and the paste length of the fiber grating.  
The spring matrix strain cannot be completely transmitted to the fiber core and there is a loss in the process of transmitting. It is necessary to study the strain transfer efficiency. The efficiency value of the spring matrix strain transferring to the fiber Bragg grating is expressed by the following mathematical formula:  
\[\eta=\frac{\varepsilon_{\text{c}}}{\varepsilon_{\text{h}}}, \tag{5}\]  
Figure 4: Schematic diagram of the fiber grating arrangement on the bolt body.  
Figure 5: Data transmission schematic diagram of the FBG dynamometry bolt.  
where \(\eta\) represents the efficiency value of the spring matrix strain transferring to the fiber Bragg grating; \(\varepsilon_{\mathrm{c}}\) represents the strain sensed by the fiber Bragg grating.
## 4 Challenges and Limitations of Nanosensor Deployment
### Calibration and Sensor Reliability
#### 4.2.2 Real-Time Monitoring and Reliability Assurance  
Developing systems for real-time monitoring of the nanosensors themselves is vital to ensure the ongoing reliability of data and to promptly identify and address sensor drift or failure, which might be instigated by factors more subtle and rapid than those affecting conventional sensors [93,94].
# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 3 Review Papers
### Single-Model-Based Pain Recognition
#### 3.1.4 Other Indicators  
In 2017, there was an attempt [13] to use deep-learning methods for LBP recognition. Their methods were based on segmentation and classification of LBP X-ray data. They obtained tomography lumbar spine pictures from a meta picture (MHD) arrangement and gave them five vertebral levels. Then, using a deep-learning framework, they extracted the features and classified the LBP based on five severity levels (normal, mild, crush, wedge, severe and biconcavity). They reached around 65% of accuracy that needs further improvements. However, using X-ray data of patients is not enough to recognize LBP.  
Recently, [14] [PERSON] et al. proposed a deep-learning method to recognize the low back pain in the context of static standing. Their system depends on kinematic data that was acquired using three attached motion sensors on different places of human skin. After preprocessing, they used the data as an input to LSTM network. Using 22 healthy people and 22 LBP patients, they got 1073 time series for training and 107 time series for testing. Their results showed a high accuracy of 97.2% as they mentioned. The disadvantage of this study is the using of only kinematic data and ignores the EMG data, which is important to differentiate LBP patients.  
A later study to recognize the protective behavior was conducted by [PERSON] et al. in 2019 [15]. They focused only on the body movement data from available data set (emo-pain data set). In addition, they applied the attention mechanism to LSTM architecture to keep the relevant recognition of protective behavior. They used the sliding window with zero padding to segment the data. Furthermore, they augmented that data by combining the two methods of random discarding and jittering. After comparison with previous models, this model obtained high performance of 0.844 mean F1 score. They found also that bodily attention is more important than temporal attention, while the combination between them is provide the higher performance.
## 2 Materials and Methods  
The investigated data were a sub-set of those described in [2]. Two hundred and twenty-eight different compounds of pharmaceutical interest have been taken into consideration (a complete list of them is reported in Table 1). The GIT permeability of the molecules at four different pH conditions (pH = 3, 5, 7.4, and 9) was assessed by parallel artificial membrane permeability assay (PAMPA), as described in [2; 11]. The molecules' structures (as SMILES files) and their permeability capability were downloaded from the QSAR Data Bank repository [12], where the authors made the data available [2; 13]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Compound Name** & **Compound Name** \\ \hline
8-hydroxyquinoline & Fluvoxamine \\ Acebutolol & Furesemide \\ Acetanilide & Gemfibrozil \\ Activasitine & Guanabenz \\ Alfuzosin & Haloperidol \\ Alosetron & Hydralazine \\ Aminosalicylic acid & Hydrochlorothiazzide \\ Amodiaquine & Hydroxychloroquine \\ Bendrofluumethiazide & Imipramine \\ Benzocaine & Indomethacin \\ Brimonidine & Iporomide \\ Capecitabine & Irbesartan \\ Chloropyramine & Isoniazid \\ Chlorzoxazone & Isradipine \\ Chlorthalidone & Ketoconazole \\ Cicopirox & Ketoprofen \\ Coumarin & Ketotifen \\ Cyclobenzapine & Labetalol \\ Cyproheptadine & Lamivudine \\ Deferiprone & Lamotrigine \\ Dipyridamole & Lidocaine \\ Donepezil & Linezolid \\ Flupritine & Lomefloxacin \\ Galantamine & Marbfloxacin \\ Granisetron & Mazipredone \\ Hespertein & Medroxyprogesterone acetate \\ Hydrocortisone & Methylprednisolone \\ Isoepac & Metoclopramide \\ Levofloxacin & Metoprolol \\ Levosimendan & Metyrapone \\ Loxapine & Midazolam \\ Melatonin & Naftillin \\ Menadione & Naloxone \\ Mepivacaine & Naproxen \\ Mycophenolate mofetil & Naratriptan \\ Oxyburocaine & Nevirapine \\ Phenazopyridine & Nicotinamide \\ Phenol & Nicotine \\ Physostigmine & Norfolkoxin \\ Prilocaine & Officaxion \\ Primaquine & Omeprazole \\ Procainamide & Ondansetron \\ Procarbazine & Orbifloxacin \\ Proparacaine & Oxacillin \\ Protionamide & Oxcarbazepine \\ Pyridoxal & Oxypenol \\ Salicylamide & Pantoproazole \\ Succinylsulfathiazole & Papaverine \\ Sulpiride & Paroxetine \\ Zolmitriptan & Pentamidine \\ Tamsulosin & Pentoxifline \\ Tolmeti & Perphenazine \\ Trichlormethiazide & Pheniramine \\ Tripelenamine & Phenylbutazone \\ Vardenafil & Pindolol \\ Yohimbine & Pitofenone \\ Aciclovir & Prednisolone \\ Acitretin & Prednisone \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of the 228 investigated compounds.
###### Abstract
Alginate is an anionic hydrophilic heteropolysaccharide extracted from brown macroalgae (_Phaeophyta_), being among the most used polysaccharides due to its hydrocolloid behavior, emulsifying capacity, and potential nutritional benefits. Its use in coating matrices has been extensively studied [20]. For instance, [PERSON] et al. (2019) [21] demonstrated that alginate-based coatings help maintain the quality of seafood products by inhibiting bacterial growth, retaining moisture, and reducing oxidative reactions. Similarly, carrageenan is a well-known high molecular weight sulfated polysaccharide with gelling properties, extracted from red macroalgae (_Rhodophyta_). Its versatility makes it suitable for use both as a coating matrix and as a functional ingredient in various food formulations [22]. To enhance the oxidative stability of coated food products, antioxidant agents can be incorporated into edible coatings, as they help delay lipid oxidation. In this context, macroalgae have attracted increasing attention as a promising source of bioactive compounds. These marine organisms are naturally rich in essential minerals, trace elements, carotenoids, polyphenols, omega-3 polyunsaturated fatty acids, essential amino acids, and complex carbohydrates, many of which exhibit antioxidant activity [23; 24]. Among the various macroalgae, _Pelvetia canaliculata_ (a brown seaweed native to the rocky intertidal zones of Europe) shows strong potential as a source of valuable bioactive compounds, although it is not yet widely exploited for industrial applications [25; 26]. The phenolic content of _P. canaliculata_ is highly influenced by its exposure to cycles of immersion/emersion in the intertidal zone. In fact, these compounds are secondary metabolites linked to multiple stress responses to abiotic (such as UV light damage and air temperature fluctuations) and biotic (e.g., grazer and pathogen attack) factors [27]. [PERSON] et al. (2007) [28] studied the effect of both day/night and tidal cycles on the phenolic content and antioxidant activity of three brown seaweeds, among which _P. canaliculata_. Its phenolic concentration reached approximately 3% DW, being positively correlated with antioxidant capacity. Air temperature was shown to influence the phenolic content, with the highest value observed on a daytime scale. This diurnal pattern promotes phenolic production as a protective response to oxidative stress induced by sunlight during low tide. Based on this promising potential, the present study focused on the development of edible coatings enriched with _Pelvetia canaliculata_ extracts for application on mackerel fillets (_Somber scombrus_) prior to frying, with the aim of reducing fat absorption and minimizing both water loss and oxidative degradation during the cooking process. To evaluate the effectiveness of the coatings, several physicochemical parameters (namely, colour, total fat content, and fatty acid profile) were analyzed in the fried fillets, providing insight into their impact on the product's nutritional quality and oxidative stability.