esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Development of 3 SMVI Software and Integration
### Image Analysis and Segmentation  
Image analysis is the initial stage of the process of image segmentation in the computer vision process. The image is fed into the system, and features are then extracted from it. Pixels that have similar characteristics are grouped together into an area, and regions with linear structures and 2D shapes are separated based on the success or failure of the computer vision-based process that relies on efficient segmentation. Industrial machine vision is one example of a typical application where the assembly of gadgets is monitored, and objects are automatically recognized and tracked. Apart from that, image segmentation has been used in traffic control, content-based image retrieval, video investigation, and sports arenas. The process of extracting meaningful information and insights from images is referred to as image analysis.  
In the segmentation process, the system uses either of two features of an image separation procedure that relies on controlling the direction of illumination as part of the discontinuity. There are defined mechanisms like thresholding, region splitting and merging, and region growth used in the similarity approach to partition the image into comparable areas. Thresholding is used to separate the foreground from the background by selecting a threshold value. A binary image is generated from the gray-level image, with the threshold value being the average value of all pixels. Binary images have the advantage of minimizing data complexity and simplifying the process of image recognition and segmentation, but setting a threshold value can be difficult due to its sensitivity to noise. Clustering is the process of categorizing objects based on their characteristics. Following segmentation, models are extracted using edge detection algorithms such as Canny edges, as illustrated in Figure 10.  
The process of measuring the roundness error of a Delrin workpiece involves capturing images, isolating the edge pixels of the object, and applying an edge detection algorithm. Typically, the frame grabber captures color images. However, for this particular application, binary images featuring only two colors, black and white, are required. To achieve this, a gray image must first be generated from the color image using the following formula:  
\[Gl=0.299 Rc+0.587 Gc+0.114 Bc \tag{3}\]  
where \(Gl\) describes the pixel's grey level and \(Rc,\;Gc,\;and\;Bc\) are the red, green, and blue components of the pixel color, respectively. Then, the histogram of each gray level is determined to compute a threshold value using the following:  
\[T=Abs((Gl_{max}1-Gl_{max}2)/2) \tag{4}\]  
where \(Gl_{max}1\) and \(Gl_{max}2\) are the two grey levels with the highest frequencies, and \(T\) is the threshold value. All pixels with grey levels above the threshold are converted to black pixels to generate the binary image, and all other pixels are converted to white pixels.
## 3 Proposed Approach
### Multi-Objective Loss Function  
For medical image segmentation tasks, dichotomous classification (0-1) is typically employed, with training methods targeting pixel categories. This approach is suitable for medical images with extensive lesion areas, akin to skin disease segmentation where the majority of images contain positive samples. In contrast, ICH segmentation entails smaller lesion areas, leading to a significant imbalance between positive and negative sample proportions. Utilizing a binary cross-entropy loss function in this scenario would result in negative samples dominating training, culminating in suboptimal network performance.  
We encountered a serious imbalance between the proportion of positive and negative samples in our intracranial hemorrhage segmentation task due to the small size of the lesion area. The use of binary cross-loss entropy loss function, in this case, will result in a predominance of negative samples in the training position, resulting in poor network performance.  
To address this issue, the focal loss function can be employed as an alternative to the traditional binary cross-entropy (BCE) loss function. In our work, focal loss may be a better choice since it is specifically designed to address class imbalance, whereas BCE loss is a standard loss function suitable for binary classification but lacking inherent mechanisms for handling imbalanced datasets. Furthermore, to effectively mitigate class imbalance, we adopted a multi-loss function training approach, primarily comprising Dice loss [39] and focal loss [40]. Focal loss, a cross-entropy function variant, remains constant for  
Figure 7: The structure of the MRHDC module.  
misclassified samples but decreases for accurately classified samples. Overall, this equates to an increased weight for misclassified samples within the loss function. By adjusting the proportion of the two loss functions, an optimal segmentation effect can be achieved.  
Here, the total function is defined as follows:  
\[D\text{ice}=\frac{2|X\cap Y|}{|X|+|Y|} \tag{7}\]  
\[D\text{iceLoss}=1-Dice \tag{8}\]  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\log(p_{t}) \tag{9}\]  
\[L_{total}=\partial L_{fl}+\beta L_{Dc} \tag{10}\]  
where X represents the pixel label of the real segmented image, Y represents the pixel category of the image predicted by the model. \(|X\cap Y|\) for prediction of image pixel and real label image pixels, the dot product between the dot and the result was added, \(|X|\) and \(|Y|\) are approximately added to each pixel in their respective images. \(\alpha\) weights help to deal with category imbalances. \((1-p_{t})^{\gamma}\) is the regulating factor, \(\gamma\geq 0\) is the adjustable focusing parameter. When \(\gamma=0\), this expression degrades into cross entropy.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.1 Optimization Problems
The proposed approach is compared with other different models to show its effectiveness. In [104], the authors propose a DRL based model to maximize downlink SNR in Intelligent Reflecting Surface (IRS) communications. Simulations results show that the system can, not only achieve almost the upper bound of received SNR, but also reduce the time consumption. In [105], a DRL actor-critic based model is used for resource allocation optimization and to solve the joint network control challenge in IoT systems. The actor-critic based algorithms reduce the data rate assigned to each IoT network and IoT devices. The algorithm also chooses whether transmission will be in space or terrestrial network. The proposed model outperforms conventional approaches with different network parameters and metrics. In [106], a Single-Agent Q-learning (SAQ-learning) algorithm is proposed for resource allocation using historical experience with satisfactory result. In the same paper, a Bayesian Learning Automated (BLA) Multi-Agent Q-learning (MAQ-learning) algorithm is proposed for task offloading decision. The effectiveness of the proposed algorithm is confirmed from the comparison with the results of conventional algorithms in various network scenarios.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 4 Security Analysis
### Non-Repudiation  
In the proposed scheme, a digital signature is used to achieve nonrepudiation for the EMR compiled by a doctor. In the initial treatment authentication and communication phase, hospital A uses the private key to sign the patient's EMR, and then the signed message is transmitted to the patient. The patient uses the public key of hospital A to verify the signed message. In the inter-hospital authentication and communication phase, hospital A uses the private key to sign the patient's EMR, and then the signed message is transmitted to hospital B. Hospital B uses the public key of hospital A to verify the signed message. Then, hospital B uses the private key to sign the patient's EMR, and the signed message is transmitted to the patient. The patient uses the public key of hospital B to verify the signed message. Thus, the proposed scheme achieves nonrepudiation for EMR established by hospital A or hospital B. Table 1 shows the non-repudiation of the proposed scheme.
# Recent Developments in Materials Science for the Conservation and Restoration of Historic Artifacts
## 6 Smart Materials--A Potential Breakthrough in Heritage Science
### Current Challenges and Future Potential of Smart Materials in Heritage Science  
The application of smart materials in the restoration and conservation of cultural heritage artifacts is a promising field with transformative potential, yet it faces significant challenges. Smart materials, which respond to environmental changes like temperature,humidity, or light, offer the ability to monitor and even self-regulate conditions around or within an artifact, presenting new pathways for preventive conservation.  
Integrating smart materials with diverse historical substrates, such as stone, wood, or canvas, requires ensuring they do not chemically or physically damage the original material. Unintended reactions, color changes, or mechanical strain can affect an artifact's integrity, making compatibility testing essential but complex. Many smart materials, particularly those based on advanced polymers or responsive coatings, are still unproven for longevity. They need to demonstrate stability and reversibility over time, as non-reversible additions may conflict with ethical conservation practices. Many smart materials involve high production costs, limiting their accessibility for widespread application. Additionally, specialized knowledge is required to apply and maintain these technologies, which can be a barrier in smaller or resource-limited institutions.  
Future developments in self-healing polymers could revolutionize conservation by allowing materials to repair minor physical damage autonomously. This approach is especially relevant for outdoor sculptures or architectural heritage exposed to environmental stressors. Smart coatings that adjust their permeability to control moisture levels show promise in regulating conditions for sensitive artifacts. By keeping microclimates stable, these materials can reduce degradation rates without the need for constant human intervention. Smart glass or photochromic materials that adjust opacity or color based on ambient light can protect light-sensitive works by dynamically controlling light exposure, potentially extending the display life of delicate pieces.
# Injuries and Pain Associated with Goalkeeping in Football--Review of the Literature
## 4 Discussion
### Goalkeepers' Injuries of Upper Limbs
#### 4.4.3 Shoulders  
A number of shoulder injuries is an increasing problem in football, whereas GKs are even more exposed to shoulder disorders than other field players [23; 25]. Reports from European biggest football tournaments, perceptions and procedures of physicians of football best European representations and studies analyzing electromyographic muscles activity were analyzed to design the upper limbs injury prevention program in football GKs, which was named Fifa11+ Shoulder (Fifa 11+ S) [23].  
GKs' actions include different dynamic movements of shoulders, such as throwing a ball, diving and deflecting a ball with one or two hands, catching and punching a ball which can lead to muscle and tendon injuries. Repeated interventions ending in landing on the side of the body on the ground also evoke injury risk in the shoulder area. The research of [PERSON], [PERSON] and [PERSON] showed that the most common overuse injuries are located in the shoulder area [35]. The analysis of shoulder and elbow injuries in GKs versus field players in NCAA from season 2009-2010 to 2013-2014 shows that GKs suffer more elbow and shoulder injuries compared to field players--8.3 vs. 1.8 incidents respectively per 10,000 athletic exposures (AE). GKs face even more risk of elbow and shoulder injuries during practice (21.3-fold) and during preseason (16.1-fold) than field players. Female GKs have 7.7-fold higher injury incidents in this location than female field players and 1.9-fold higher than male GKs. The most frequent injuries were acromioclavicular joint injuries, rotator cuff tears/sprains and elbow and shoulder instability [25]. The literature provides evidence that muscles of the shoulder girdle can suffer tear during GKs' dynamic actions, such as ball throwing, punching, or diving. A case study of a football GK with musculotendinous rupture of the distal biceps brachii describes the mechanism of the trauma occurred during an overhead ball throw with a single arm [33]. The case study published by [PERSON] describes a case of a GK with the teres major tendon rupture [34]. The case study of [PERSON] presents an injury of latissimus dorsi tear during match play [63]. Though the type of injury is rare, the mechanism is frequent in GKs play and training. Study analyzing GK actions during gameplay in different training games conditions showed that the ball was played with hands by the GK in 58,9% of offensive actions [10]. GKs' diving causes hazardous falling on the forearm, which can lead to the shoulder dislocation [23; 25; 54]. This type of injury can bring wide complications because of numerous muscles and tendons surrounding the shoulder area, as well as the joint complex structure. Complete return to sport can be extended to a year when complications occur [64].
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 5 Mechanical Properties of Foam Concrete
### Elasticity Modulus  
The elasticity modulus of foam concrete varies between 1 and 12 \(\mathrm{kN/m^{2}}\) for a dry density of 500 to 1600 \(\mathrm{kg/m^{3}}\). Generally, the elastic modulus of foam concrete is lower than normal concrete. It was found to be 25% less than that of normal concrete. The reason for this can be attributed to the fact that coarse aggregate is not used in foam concrete production [13,45].  
It was reported that the modulus of elasticity increases up to 400 \({}^{\circ}\)C and then decreases; similar behavior was observed for compressive strength. The increase in foam ratio decreases the modulus of elasticity [94]. Generally, compressive strength and modulus of elasticity have a linear relationship. Methods that increase the compressive strength in foam concrete generally increase the elasticity modulus. The use of fiber improves the compressive strength as well as the modulus of elasticity. However, depending on the type of fiber used, the increase in elasticity modulus may vary. This may be thanks to the fibers having different stiffness [62]. Similarly, the change in the aggregates used in foam concrete affects the elastic modulus. The use of low elastic modulus aggregate affects the overall material hardness. It was reported that rubber parts replacing sand caused the elastic modulus to reduce [92].  
Using FA as a fine aggregate showed a lower elasticity coefficient than sand [45]. In one study, using RCA instead of river sand caused a decrease in the dynamic elastic modulus value. This was attributed to the increased porosity with the use of RCA [51]. In the study carried out by [PERSON] and [PERSON], the modulus of elasticity gradually increased with the increase in the dry density of foam concrete prepared with MPC. It was reported that higher elastic modulus was obtained compared to foam concrete prepared with OPC with the same dry density [41].
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer
### Overlap between Two Spectra  
Overlap between two spectrums is a problem that needs to be paid attention to in dual-gratings imaging spectrometers. Compared with traditional imaging spectrometers, double gratings mean that there must be two overlapping energy bands. If the bands are not restricted, there must be spectral overlap. From the point of view of the generation of spectral overlap there are mainly two aspects. One is the overlap between the dual spectra produced by the dual gratings, and the other is the overlap between high-order spectra and imaging bands.  
1. For overlap between double spectra, this article has two ideas; the first one is the traditional method, adding filters in front of the gratings to prevent aliasing by constraining the imaging bands. The second is to use the response range of the detector. Imaging the second waveband in the unresponsive area of the first waveband can reduce one filter as shown in Figure 6. The second scheme is suitable for situations where the imaging band is close to the edge of the detector's response band.
2. For the overlap between high-order spectrum and imaging bands, there is another way to be considered, that is, one grating works at +1 order, and the other grating works at \(-1\) order. In this way, the high-order spectral imaging area can be staggered from the imaging area as shown in Figure 7.  
Figure 5: The position of the slit and the two spectra.  
Figure 6: Image 2 is imaged in the unresponsive area in the ultraviolet band of Image 1, which can reduce one filter.  
When using the filters, the actual situation of the filter must be considered. The first case is that the center wavelength of the filter is usually shifted by a few nanometers. The second case is that usually the bandpass of the filter is expressed in the form of FWHM. However, in actual use, in order to avoid overlap between spectra, the bandpass whose response is less than 50% of the peak value should also be considered. For example, a piece of filter FB390-10 from the Thorlabs company. The center wavelength is 390 \(\pm\) 2 nm and FWHM is 10 \(\pm\) 2 nm. The transmission data is shown in the Figure 8. Assuming that the filter is applied to the above H-line and K-line, it can be seen from the data that within the drift of the center wavelength of \(\pm\)2 nm, the H-line and K-line can be guaranteed to be within the response range. For the entire imaging plane, in order to ensure that the spectrum does not overlap, it is necessary to leave space for low response bands on the image plane. However, there is no need to consider the two edges of the filter's response band. Leaving imaging space for one of the edges allows the dual spectrum to not overlap.
# Multi-Objective Parameter Optimization of Flexible Support System of Optical Mirror
## 2 Modeling of Motion Resistance  
### Motion Resistance Modeling of FSS  
As shown in Figure 1, the FSS consists of 36 support cylinders and is divided into three sectors on average. The upper and lower chambers of the cylinders in each sector are connected through hydraulic lines, which ensures equal pressure in the upper and lower chambers in the same sector. The rigidity of the support cylinder can be adjusted by varying the pressure values of the upper and lower chambers, and the support height of the cylinder can be adjusted by varying the pressure difference between these chambers. Therefore, the posture of the optical mirror can be fine-adjusted by varying the support parameters of the cylinders in the three sectors. The internal resistance of the support cylinder would affect the force output consistency and support stiffness of the FSS. The main components that cause the internal resistance of the support cylinder are the beryllium bronze Feed (BBR) and rolling diaphragm (RD). The structural parameters of the BBR and RD need to be optimized and analyzed to reduce the internal resistance of the FSS.  
The structure of the support cylinder is shown in Figure 2. There is no direct contact between the piston and cylinder, and the BBR is used for radial support to prevent frictional resistance between the piston and cylinder wall during movement. The small RDs of the upper and lower chambers isolate these chambers from air, and the large RDs separate these chambers. Therefore, two closed spaces are formed. When the support cylinder moves, the BBR and RD would deform elastically, and thereby cause the support cylinder to generate internal resistance.  
Figure 1: Topology of the flexible support system.  
Because the FSS is composed of three separate liquid supply sectors (which do not interfere with each other), the internal resistance of the FSS can be divided into three components:  
\[F_{total}=\left[\begin{array}{cccccccc}F_{a}&F_{b}&F_{c}\end{array}\right]^{T} \tag{1}\]  
where \(F_{a}\), \(F_{b}\), and \(F_{c}\) are the internal resistances of Components 1, 2, and 3, respectively.  
\[\left[\begin{array}{c}F_{a}\\ F_{b}\\ F_{c}\end{array}\right]=\left[\begin{array}{cccccccccccc}H_{1}^{1}&\cdots&H_{1 }^{12}&H_{2}^{1}&\cdots&H_{2}^{12}&P_{1}^{1}&\cdots&P_{1}^{12}&P_{2}^{1}& \cdots&P_{2}^{12}&P_{3}^{1}&\cdots&P_{3}^{12}\\ H_{1}^{13}&\cdots&H_{1}^{24}&H_{2}^{13}&\cdots&H_{2}^{24}&P_{1}^{13}&\cdots&P_{ 1}^{24}&P_{2}^{13}&\cdots&P_{2}^{24}&P_{3}^{13}&\cdots&P_{3}^{14}\\ H_{1}^{25}&\cdots&H_{1}^{36}&H_{2}^{25}&\cdots&H_{2}^{36}&P_{1}^{25}&\cdots&P_ {1}^{36}&P_{2}^{25}&\cdots&P_{2}^{36}&P_{3}^{25}&\cdots&P_{3}^{36}\end{array} \right] \tag{2}\]  
where 1-36 are the numbers representing the support cylinders; \(H_{1}\) and \(H_{2}\) are the internal resistances caused by the elastic deformation of the BBRs in the upper and lower chambers, respectively, of the support cylinder; and \(P_{1}\), \(P_{2}\), and \(P_{3}\) are the internal resistances caused by the elastic deformation of the upper chamber RD, lower chamber RD, and middle RD, respectively.
## 3 Results and Discussion
### Marbles
Combining minero-petrographic features and isotopic data (Table 2), different provenance for marbles used in the San Giovanni Baptistery can be identified:  
* Carrara marble (Italy), white and veined, with a typical homeoblastic fabric, a mosaic-polygonal microstructure, with straight grain boundaries, MGS ranging from 150 to 500 micron, and isotopic ratios of C from 1.63 to 2.35 and O from \(-\)3.25 to \(-\)1.16, is used in the original and substituted portions of monument (Figure 8a,b);
* Hymettus marble (Greece), likely coming from the spolia of Fiesole Roman Theatre, Capitolium, and Thermae [9] is a white bluish marble characterized by grey/blue veins. The fabric is heteroblastic, with mosaic, lineated microstructure, curved grain boundaries, and an MGS ranging from 1 mm and 2 mm. The isotopic ratio varies from 3.35 to 2.33 for C and from \(-\)3.15 to \(-\)1.97 for O (Figure 8c,d);
* Lasa marble, used in the 20 th century for the substitution of the most weathered slabs, comes from Laas (Lasa, South Tyrol, Northern Italy), and was selected by restorers for the macroscopic similarities to Hymettus marble (Figure 8e) [34]. It is a pure calcitic marble, containing small amounts of quartz, with a homeoblastic fabric and mosaic microstructure. The grain boundaries are prevalently curved (Figure 8f). The isotopic ratios of C and O fall in the range of data published on Lasa marbles [35];
* Other ancient marbles are most probably re-used marbles (i.e., Paros, Pentelicum) for some decorative elements (Figure 4). Some uncertainties remain for some samples due to insufficient material available to perform all of the necessary analyzes. In fact, for M3 and M7 samples, obtaining uncontaminated powders for isotopic analyzes was impossible. The analytical data obtained on provenance of marbles were used to upgrade the database of materials presented in [9; 36].