esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bio-Radar Prototype Description  
The Bio-Radar system employed in this research utilizes a Continuous Wave (CW) Doppler radar. As the terminology suggests, this type of radar continuously transmits a sinusoidal carrier wave, which is digitally generated, and captures the signal reflected by the target. Due to the micro-Doppler effect, a phase shift occurs when the subject's chest wall moves towards or away from the radar, resulting in phase modulation in the received signal [32]. The Bio-Radar prototype used in this study, the operating principles of which are illustrated in Figure 1, was developed in a previous work [33].  
The primary hardware component of the prototype is the Universal Software Radio Peripheral (USRP) B200 [34], which operates within a Continuous Wave (CW) frequency range of 70 MHz-6 GHz. This platform enables the digital programming of Bio-Radar characteristics, such as the power of the transmitted wave, set at 2 dBm, and its sampling frequency. A carrier frequency of 5.8 GHz was selected, falling within the Industrial, Scientific, and Medical (ISM) dedicated band. The system also features two 2 \(\times\) 2 patch array antennas, with one designated for Transmission (Tx) and the other for Reception (Rx). The specific operational parameters of the Bio-Radar system utilized in this study are detailed in Table 2, providing a comprehensive overview of the system's technical specifications.  
Figure 1: Block diagram of the Bio-Radar system, adapted from [33].
# Structural Behaviour of Concrete Deep Beams Reinforced with Aluminium Alloy Bars
## 3 Results and Discussion
### Parametric Study
#### 3.2.2 Tension Reinforcement Ratio  
The ratio of tension reinforcement plays a critical role in governing the flexural capacity, ductility, and failure mode of reinforced concrete beams. The effect of the tension reinforcement ratio was investigated by taking into account different tension reinforcement ratios ranging from 0.5% to 6%. The produced beams for numerical analysis had the same dimensions, a compression reinforcement of four 14 mm diameter AA bars, and a shear reinforcement of 4 mm diameter AA bars spaced at a distance of 100 mm and 133 mm at shear span and midspan, respectively, but had different tension reinforcement ratios (from 0.5 to 6%) (see Figure 10). The concrete compression strength was taken to be 20 MPa for all numerically tested beams. All beams had an a/d ratio of 2.28. Figure 10 shows the relationship between the tension reinforcement ratio and the ratio of moment at failure (Mu)  
Figure 9: Comparison of AA bars with steel bars [45].  
to flexural capacity (Md). AA RC beams with low tension reinforcement ratios (less than 1.5%) indicated Mu/Md ratios close to 1. This is typical for the flexural failure of beams, where failure is governed by the yielding of the AA bars, enabling the redistribution of stresses and enhanced energy dissipation. The failure mode was changed once the Mu/Md was less than 1. It can be understood as a transition from ductile to brittle behaviour without warning before failure and with limited plastic redistribution. The failure mode and structural response of AA RC beams were exhibited to be altered by both an increase and a decrease in the tension reinforcement ratio, suggesting a strong relationship between the reinforcement ratio and failure mode.
## 2 Exosome Features
### Mechanisms of Exosome-Target Tissue Interaction and Exosome Transport
#### 2.3.3 Direct Membrane Fusion  
Exosomes can fuse directly with the plasma membrane of recipient cells, allowing their cargo to be released into the cytoplasm (Figure 1). This mechanism involves the fusion with the cell membrane, wherein the exosome releases its entire content directly into the cytoplasm of the recipient cell. This process occurs through the fusion of the exosome's lipid bilayer with the cellular membrane, facilitated by proteins and receptors located on the exosome's surface, alongside SNARE and Rab GTPases proteins [38; 39].
# Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
## 3 Methodology
### Experimental Tools  
We used the Tensorflow Keras library and the training was conducted on Google Colab Pro+. The loss function was categorical cross-entropy loss, and the optimizer was RMSprop with a learning rate of 0.0001. We used 100 epochs. The hyperparameters were chosen based on related works and a wide range of experiments. The dataset was divided, with 80% for training and validation and 20% for testing. A fixed seed number was used during data splitting to ensure that the data were divided in the same way each time and that splitting would not affect the testing of other hyperparameters. The performance was measured with accuracy and F1 scores, in addition to monitoring the confusion matrix and F1 of each class.
# Buckling of Composite Structures with
## Appendix A Mean Stresses and Mechanics of Fracture Criteria
### Criterion of Failure and Analysis of the Growth of Delamination  
Typically, damage by delamination is associated with the occurrence of the so-called mixed form of destruction, i.e., a combination of types I, II and III--see Figure 13 and the relation (A8). Due to this fact, the following form of the criterion of destruction of composite structures was proposed (cf. [PERSON], [PERSON] [77]):  
\[\left(\frac{G_{I}}{G_{IC}}\right)^{a}+\left(\frac{G_{II}}{G_{IIc}}\right)^{b}+ \left(\frac{G_{III}}{G_{IIIc}}\right)^{c}=RB_{del}, \tag{10}\]  
where \(G_{Lc}\), \(G_{IIc}\), \(G_{IIIc}\) define the critical values of the energy release factors for the first, second and third forms of failure. These values are determined experimentally and it is assumed that they do not depend on the width of the crack. The symbols \(a\), \(b\), \(c\) are fitted on the basis of experimental studies, although they are usually assumed to be equal to 1. The influence of their values on the failure curve was investigated in Muc [17; 75]. \(RB_{del}\) is the failure index in the event of delamination, and when it is greater than one, delamination develops.  
The computational procedure for analyzing the initiation and subsequent development of de-lamination can be represented as follows:  
1. Determination of the displacement and stress field at each point on the boundaries of individual layers in the laminate.
2. Assumption of the existence of an inter-layer gap with a given surface area A (length a).
3. Determination of the coefficients of energy release \(G_{I}\), \(G_{II}\), \(G_{III}\).
4. Calculation of the value of the failure index for the \(RB_{del}\) delamination--Formula (10): * if \(RB_{del}<1\) then delamination is not possible; * if \(RB_{del}=1\), we observe the appearance of delamination at a given point; * if \(RB_{del}>1\), the development of delamination takes place, and it is necessary to redefine the field of displacements and stresses taking into account the increase in delamination.  
It should be emphasized that the study of the development of delamination involves the simultaneous analysis of the possibility of other forms of damage, such as cracking of the first layer in the laminate, fiber damage or buckling. When analyzing the development of delamination in the case of 2D structures, it is necessary to use local wording at the sub-laminate or individual layer level. Moreover, due to the occurrence of local bending effects, both in the description of 2D and 3D, it is necessary to consider the geometrically non-linear theory--see [PERSON] et al. [27], [PERSON], [PERSON] [78], and [PERSON] [36].
## 7 Future Research Directions
### Hybrid Approaches and Integration with Other Optimization Techniques  
While GAs have shown promise in optimizing multi-objective message routing in EnFVs, there is still room for further advancements by exploring hybrid approaches and integrating them with other optimization techniques. Hybridization involves combining genetic algorithms with complementary optimization methods to leverage their individual strengths and improve the overall performance of the routing algorithms [22,35].  
One possible direction for future research is the integration of machine learning techniques with GAs [83,84]. Machine learning algorithms, such as neural networks or reinforcement learning, can be employed to model and predict the dynamic behavior of EnFVs, including traffic patterns, charging station availability, and flight restrictions. These learned models can then be used as heuristic guidance within the GA framework to enhance the decision-making process during message routing. For instance, neural networks can be trained to estimate energy consumption or predict congestion levels, which can inform the GA's fitness evaluation and help optimize the routing solutions. Such hybrid approaches have the potential to improve the scalability and adaptability of the routing algorithms in dynamic environments. Additionally, the integration of other optimization techniques, such as swarm intelligence algorithms or ant colony optimization with GAs can lead to hybrid algorithms that offer enhanced exploration and exploitation capabilities [85]. For example, swarm intelligence algorithms, inspired by collective behavior in natural systems, can be used to dynamically adapt the population size, mutation rates, or selection strategies in the GA based on the current state of the transportation system. This combination can provide a more robust and efficient search process, enabling the GA to overcome local optima and converge towards better solutions. Furthermore, the use of metaheuristic frameworks, such as genetic programming or memetic algorithms, can also be explored to develop not only the routing solutions but also the operators and parameters within the GA itself [68,86]. This allows for an automated adaptation of the GA's behavior during the optimization process, leading to improved performance and convergence.  
These are just a few potential avenues for future research in the domain of multi-objective message routing in EnFVs using genetic algorithms. Exploring hybrid approachesand integration with other optimization techniques can lead to novel algorithms that tackle the challenges of scalability, adaptability, and efficiency in the context of dynamic transportation systems.
## 2 Electroluminescence Technique
### EL Analysis Procedure  
The health state of a PV silicon panel could be determined using the EL technique by means of supplying current to the panel. Typically, the hardware needed to perform tests such as is a power supply able to provide the short-circuit current of the panel under test, \(I_{sc}\), and at a voltage level higher than the panel open circuit value at the rated irradiance, \(V_{oc}\).  
Consequently, current injection produces near-infrared (IR) light emission in the solar panel. In an industrial solar plant scenario, this operation is performed by qualified technical staff, taking the PV panel photograph in the plant-site, and conducting the post-image process and analysis of results afterwards. The image is taken with an appropriate digital camera, and focused in the wavelength of interest, paying special attention to avoid parasitic light and camera vibrations [16].  
The technical specification International Electrotechnical Commission (IEC) 60904 defines methods to capture images of PV panels by means of EL technique. It also defines postprocessing procedures to get quantitative descriptors and provide a guideline to understand the results. The standard is oriented to the measurement in a controlled scenario, forcing the current flow thanks to a voltage power supply in the panel under test terminals. In addition, captures with low signal-to-noise ratio (SNR) are provided by thermoelectric cooling of charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) cameras. Relevant parameters such as the number of pixels, noise, wide length, and dynamic range are taken into account in the selection of the right detectors. Moreover, the images should have enough quality, and therefore they should be obtained in dark scenarios, by means of walls, curtains or otherwise, in order to avoid the parasitic light [16].  
The EL technique is very useful in the manufacturing step of PV panels, where the controlled described test could be run easily. However, applying EL evaluation over existing USPVPs implies one of these alternatives: (i) to dismount, test and remount every single panel individually; (ii) to perform the EL images on the PV panels without dismounting them by means of an external power supply. In the first case, cell breaking due to transport is a very common issue. In the second case, it is necessary to carry an external power source to the plant site. Additionally, in both cases, the processes are invasive, so there is a potential danger of damaging facilities and workers.  
If the PV solar inverter was adapted conveniently to work in a bidirectional way, it would be possible to force the panels to work in the 4 th quadrant and apply EL recognition during low irradiation time slots (at night, sunrise and sunset) directly on the installed panels.
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 3 Experimental Results and Analysis
### Embedding Results of Secret Messages  
Figure 4 and Table 3 show the abstracted embedded outcomes of our test images, comprising five RGBE and five LDR images. In Table 3, the embedding capacity (EC) varies between 2.30 million bits (for LDR 5) to 33.50 million bits (HDR 4), while the embedding rate (ER) varies from 8.332 bpp (HDR 4) to 12.428 bpp (HDR 1). The PSNR values between the tone-mapped original abstracted HDR image and the tone-mapped stego HDR images are over 43 dB. In addition, the PSNR values between the original abstracted LDR image and the stego LDR images are all greater than 37 dB. These high PSNR values exhibit good image quality. Additionally, the structural similarity index measure (SSIM) values, which were derived on the similar definition for HDR and LDR images, are very close to 1.0 for the two types of test images. The statistics demonstrate that our scheme can convey a large number of secret messages while producing stego images with excellent image quality.  
Figure 3: The presentation of the original test images.  
Figure 4: The exhibition of abstracted stego images.  
It is worth noting that HDR images have a higher embedding rate and better image quality than LDR ones. This is because the abstracted HDR images have a great number of black edge pixels, where their exponent channel is \(P_{E}=0\). Our algorithm takes advantage of this phenomena to embed nearly 24 bpp using our proposed six-level adaptive embedding scheme. Additionally, the distortion caused by data embedding is significantly reduced after the tone-mapping operation. As a result, the embedding rate and quality assessment results for HDR images are better than those for LDR images, which only use a two-level adaptive embedding scheme without conducting any tone-mapping operation. However, the test image HDR 4 is an exception which offers a smaller embedding rate, because it has significantly fewer number of black edge pixels. Furthermore, the pixel distribution skews towards higher E-channel values. Consequently, the overall embedding rate for the test image, HDR 4, is smaller than that of other test HDR images.
## 2 Materials and Methods  
### Spectra Measurements  
The free ligands 6-methyl-2-thiouracil and 6-propyl-2-thiouracil were purchased from Aldrich Chem. The metal salts Cu(CH\({}_{3}\)COO)\({}_{2}\).H\({}_{2}\)O and Pd(NO\({}_{3}\))\({}_{2}\).H\({}_{2}\)O (Aldrich Chem) and solvents used for the synthesis of the complexes had a high purity that was generally equal to A.C.S. grade and were suitable for use in many laboratory and analytical applications. Absorption spectra were registered on a UV-30 SCAN ONDA UV/Vis/NIR Spectrophotometer from 200 to 1000 nm. The IR spectra of L1, L2, and their complexes were registered in KBr pellet on a Bruker FT-IR VERTEX 70 Spectrometer from 4000 cm\({}^{-1}\) to 400 cm\({}^{-1}\) at a resolution of 2 cm\({}^{-1}\) with 25 scans. The stirred crystals were placed in an aluminum disc and the Raman spectra of the compounds were measured on a RAM II (Bruker Optics) with a focused laser beam of Nd:YAG laser (1064 nm) from 4000 to 100 cm\({}^{-1}\) at a resolution of 2 cm\({}^{-1}\) with 25 scans. Additionally, ATR spectra of the complexes were measured (MIRacle Single reflection, PIKE technology) to check if the coordination water was present in them. The NMR spectra of the ligand were registered on a Bruker Avance II NMR spectrometer operating at 600.130 and 150.903 MHz for \({}^{1}\)H and \({}^{13}\)C, respectively, using the standard Bruker software v3.6.3. The NMR spectra of the metal complexes were measured on a Bruker Avance III HD spectrometer operating at 500.130 and 125.76 MHz for \({}^{1}\)H and \({}^{13}\)C, respectively, using the standard Bruker software v3.6.5. Solid-state NMR spectra were acquired on a Bruker Avance III HD 500 MHz spectrometer equipped with a 2.5 mm Cross-Polarization Magic Angle Spinning (CPMAS) probe head. CP MAS and Cross-Polarization with Polarization Inversion (CPPI) MAS spectra were recorded at a MAS speed of 15 kHz and \(\alpha\)-glycine was used as an external reference (\(\alpha\)-glycine carbonyl C-176.03 ppm). Measurements were carried out at ambient temperature.
## 4 Applications
### Concrete Fatigue in Axial Compression
#### 4.1.1 Conversion of Material Parameters  
Literature [42] did not provide monotonic loading constitutive relationships for specimens F1 and F3, and the equivalent fatigue damage model suggested in Section 2.3.5 was used. It should be noted that the concrete parameters (such as strength, peak strain, and tangent modulus) in constitutive equations suggested by code [35] are defined based on standard-size 150 mm \(\times\) 150 mm \(\times\) 300 mm prisms. Therefore, these parameters must be converted from cylindrical specimens based on empirical formulas [43].  
As the prismatic compressive strength \(f_{\rm c}\) increases monotonically with the cubic compressive strength \(f_{\rm cu}\), the ratio of \(f_{\rm c}\) to \(f_{\rm cu}\) grows with concrete strength grade. The proportional relationship between these two types of strength can be expressed by Equation (37):  
\[f_{\rm c}=(0.70\ \sim 0.92)f_{\rm cu}. \tag{37}\]  
In the literature [44], the modification factor for converting between \(\Phi\)100 mm \(\times\) H250 mm cylindrical compressive strength \(f_{\rm c}\prime\) and \(f_{\rm cu}\) was derived by an ultrasonic-rebound synthetic method as \(f_{\rm c}\prime=\overline{\eta_{\rm f}}f_{\rm cu}=0.85f_{\rm cu}\); and the relation between prismatic compressive strength \(f_{\rm ct}\) of size 100 mm \(\times\) 100 mm \(\times\) 300 mm and \(f_{\rm cu}\) expresses as \(f_{\rm ct}=\overline{\eta_{\rm 2}}f_{\rm cu}=0.81f_{\rm cu}\). Since \(f_{\rm c}\) decreases with the specimen's depth-thickness ratio increase, the conversion between \(f_{\rm c}\) and \(f_{\rm cu}\) is determined as \(f_{\rm c}=0.90f_{\rm cu}\) for \(\Phi\)100 mm \(\times\) H250 mm cylinders.  
The compressive peak strain of the prismatic specimen can be calculated by Equation (38), and the initial elasticity modulus is determined according to Equation (39) [45].  
\[\varepsilon_{\rm p,c}=(700+172\sqrt{f_{c}})\times 10^{-6}, \tag{38}\]  
\[E_{0}=\frac{10^{5}}{2.2+(33/f_{\rm cu})}. \tag{39}\]  
Figure 20: Comparison of compressive fatigue damage evolution patterns under different maximum stress levels obtained from fatigue test results in the literature [31; 40; 42] with varying specimen sizes: (a) \(S_{\rm max}=0.75\); (b) \(S_{\rm max}=0.85\).  
After combining Equations (37)-(39), the conversion from cylindrical parameters of specimens F1 and F3 into prismatic parameters that will be integrated into the constitutive equations suggested by code [35] is expressed in Table 6.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 5 Implementation and Performance Evaluation
### Experiments and Measurements
#### 5.2.3 Experiment 3: Throughput Comparison between Edge and Cloud Communications  
Another performance indicator is the throughput, which is critical as far as the expansion of massive IIoT deployments in factories is concerned. In fact, a single IIoT device cannot consume enough resources to be noticeable in current networks, but scaling this usage to thousands of devices could cause problems, especially when the traffic of other applications, such as video surveillance, are mixed in the same network. In fact, a single IIoT device could generate about 1.25 Kbps of data, as this general research article shows [31]. Thus, one thousand sensors would generate 1.25 MB/s, which is 10 Mbps of data rate just with MQTT communication. Furthermore, this transmission would consist of many small packets, which could be challenging to operate in a single broker because it is more demanding to process many small packets than fewer (but bigger) packets of the same total length.  
Therefore, in this experiment, we concentrate on the throughput generated by the aforementioned alarm generation system. As previously explained, this processing can be performed both at the SmartNIC or at the cloud broker, and the aim of this experiment is to compare the throughput generated in these two cases.  
Several tests have been performed with multiple percentages of original MQTT publish messages exceeding the threshold value, in order to analyze several possible cases: 1% (**test 3.1**), 5% (**test 3.2**), 10% (**test 3.3**), 15% (**test 3.4**), 20% (**test 3.5**), 30% (**test 3.6**), and 50% (**test 3.7**). Among these tests, those with lower probabilities have more relevance in a real-world environment, as it is not a common situation to exceed the threshold of an IIoT measured parameter. However, in order to have a broader set of results to analyze, this experiment shows the results for up to 50% of packets above the threshold. It is reasonable to expect an almost linear increment in the throughput with an incremental probability of exceeding the threshold value, as this probability is directly proportional to the number of alarm packets generated, and therefore to the throughput. However, edge communication will be much lower than the cloud communication due to the absence of TCP connection establishment messages in the first one, thanks to the SmartNIC filtering of only publication messages (specifically alarms). Therefore, a 50% probability of exceeding the threshold in publication messages will result in more than 50% of additional traffic in the cloud communication compared to the edge, even though exactly half of the packets are alarms. In order to capture the traffic in this experiment, the Wireshark network analyzer has been used.