esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Crack-Characteristic Stress
### Analysis of Crack-Characteristic Stress  
According to the content in the above section, each crack-characteristic stress is shown in Table 2. When the confining pressure increases from 5 MPa to 20 MPa, the crack closure stress \(\sigma_{cc}\) increases from 12.55 MPa to 79.52 MPa, which is 6.34 times the original; the crack initiation stress \(\sigma_{ci}\) increases from 44.71 MPa to 122.89 MPa, which is 2.75 times the original; the dilatancy stress \(\sigma_{cd}\) increases from 79.61 MPa to 153.25 MPa, which is 1.93 times the original; the peak stress \(\sigma_{p}\) increases from 96.86 MPa to 161.21 MPa, which is 1.66 times the original. By comparing the amplitude of crack-characteristic stress, it can be found that confining pressure greatly influences crack closure stress and crack initiation stress, but has relatively little influence on dilatation stress and peak stress. The reason is that the confining pressure limits the closure of the primary crack and the initiation of new cracks  
Figure 2: Stress–strain curves of sandstone (\(\sigma_{3}=10\) MPa).  
in the rock sample to a certain extent. However, when the dilatancy stress is reached, the rock sample enters the plastic yield state, and its deformation and damage are great.  
Figures 3 and 4 show the crack-characteristic stress and ratio of crack-characteristic stress of sandstone under different confining pressures, respectively. The crack-characteristic stress increases linearly, reflecting the obvious confining pressure effect. The crack-characteristic stress ratio increases with different amplitude; the ratio of \(\sigma_{cc}\) to \(\sigma_{p}\) greatly increases from 0.130 to 0.493, and the ratio \(\sigma_{ci}\) to \(\sigma_{p}\) greatly increases from 0.
## 5 Conclusions  
Efficient caching in the network with limited resources remains a significant challenge. The basic founding idea of PF-ClusterCache is to increase the storage available for caching without additional storage and to take into account the freshness and popularity of cached data content.  
PF-ClusterCache aggregates caching storage from individual nodes into a global shareable storage to enforce zero caching redundancy across any cluster of nodes. PF  
Figure 14: Average hop count for popular traffic.  
Figure 15: Eviction rate of content from popular flows.  
ClusterCache makes it possible to cache much more popular content in clusters and across the network. As a result, it achieved remarkable performance and conserved valuable network resources. Using a mixture of popular and unpopular traffic flows and a three-hierarchy topology, which is a close representation of the IoT network architecture, we compared the proposed scheme with a well-known scheme as LCE and two recent schemes, CFPC and PoolCache. The results showed that PF-ClusterCache outperformed all schemes in all metrics. Moreover, the use of the LPF replacement scheme maintained the efficiency of the caching process by reducing the eviction rate while keeping only the most popular content in the cache, even with a small popularity threshold.  
PF-ClusterCache, at the extreme when there is only one node per cluster, behaves similarly to the consumer caching strategy but with the added application of freshness and popularity. However, even with a small number of nodes within the cluster and a limited CS size, the scheme can retain good performance due to the clustering procedure and the added value of popularity. As the LPF replacement scheme keeps only the popular content within the cache, it can be used with other caching schemes to enhance their performance. We aim to study the impact of integrating LPF with caching schemes that can support producer mobility by keeping the most fresh and popular content closer to the mobile producer and reducing the handoff delay impact on the network performance.  
Conceptualization, S.A., A.B., A.G. and S.A.-A.; Formal analysis, S.A., A.B., A.G. and S.A.-A.; Writing--original draft, S.A., A.B., A.G. and S.A.-A.; Writing--review & editing, S.A. and A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the Deanship of Scientific Research at King Saud University for funding and supporting this research through the initiative of DSR Graduate Students Research Support (GSR).  
The authors declare no conflict of interest.
# Design of Robust Sensing Matrix for UAV Images Encryption and Compression
## 5 Conclusions  
An optimal CS framework with a new structure of SRE and design of a robust sensing matrix is proposed. The proposed structure of SRE is designed to eliminate groups of sparse representation and reduce the sparse representation error (SRE). With the lower SRE (LSRE), the model for optimizing the sensing matrix is designed by minimizing the averaged mutual coherence and the energy of the projected LSRE simultaneously, which can achieve a more robust sensing matrix. This new CS framework with the proposed sensing matrix can deal with images containing large noise, which is suitable for the application of UAV images. This designed sensing matrix is adopted in the compression and encryption of UAV images, which can lead to high accuracy recovery performance. This designed sensing matrix can guarantee efficiency and security during the transmission of UAV images.  
Conceptualization, Q.J.; methodology, Q.J.; software, Q.J. and H.B.; validation, Q.J. and X.H.; formal analysis, Q.J. and H.B.; investigation, X.H.; resources, X.H.; data curation, Q.J.; writing--original draft preparation, Q.J.; writing--review and editing, Q.J. and H.B.; visualization, X.H.; supervision, X.H.; project administration, Q.J.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Recovery results for the UAV image “friends” taken by ourselves with noise (\(SNR\) = 10 dB). (**a**) is the original image, (**a1**) is the image with noise, (**b**) is the \(CS_{RAN}\) algorithm, (**c**) is the \(CS_{LZY}\) algorithm, (**d**) is the \(CS_{LLL}\) algorithm, (**e**) is the \(CS_{HZ}\) algorithm, and (**f**) is the \(CS_{LESM}\) algorithm.  
**Funding:** This work was supported by the Natural Science Foundation of P.R. China (Grant: 622233016, Grant: 62222315, Grant: 61973274, Grant: 61873239, Grant: 61801159), Key Project of Natural Science Foundation of Zhejiang Province Foundation (Grant: LZ22F030007) and Key Research and Development Program of Zhejiang Province Foundation (Grant: 2020C03074).  
**Institutional Review Board Statement:** This study does not involving humans or animals.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Future Directions  
Ongoing research is needed to evaluate the efficacy of telerehabilitation, specifically for LVAD patients. Clinical trials assessing health outcomes, patient satisfaction, and long-term benefits of remote rehabilitation services are crucial. Additionally, the integration of artificial intelligence and wearable technology could further enhance telerehabilitation capabilities, providing real-time data analytics and personalized feedback to healthcare professionals.  
Telerehabilitation holds substantial promise for patients with LVADs, enhancing their rehabilitation experience and health outcomes. By harnessing technology to provide accessible, personalized care, healthcare providers can help LVAD patients lead healthier, more fulfilling lives. As the field evolves, it will be vital to address the challenges and optimize telerehabilitation practices to ensure that all patients receive the support they need in their recovery journey.
###### Abstract  
Anomaly detection is an active research area within the machine learning and scene understanding fields. Despite the ambiguous definition, anomaly detection is considered an outlier detection in a given data based on normality constraints. The biggest problem in real-world anomaly detection applications is the high bias of the available data due to the class imbalance, meaning a limited amount of all possible anomalous and normal samples, thus making supervised learning model use difficult. This paper introduces an unsupervised and adversarially trained anomaly model with a unique encoder-decoder structure to address this issue. The proposed model distinguishes different age groups of people--namely child, adult, and elderly--from surveillance camera data in Busan, Republic of Korea. The proposed model has three major parts: a parallel-pipeline encoder with a conventional convolutional neural network and a dilated-convolutional neural network. The latent space vectors created at the end of both networks are concatenated. While the convolutional pipeline extracts local features, the dilated convolutional pipeline extracts the global features from the same input image. Concatenation of these features is sent as the input into the decoder, which has partial skip-connection elements from both pipelines. This, along with the concatenated feature vector, improves feature diversity. The input image is reconstructed from the feature vector through the stacked transpose convolution layers. Afterward, both the original input image and the corresponding reconstructed image are sent into the discriminator and are distinguished as real or fake. The image reconstruction loss and its corresponding latent space loss are considered for the training of the model and the adversarial Wasserstein loss. Only normal-designated class images are used during the training. The hypothesis is that if the model is trained with normal class images, then during the inference, the construction loss will be minimal. On the other hand, if the untrained anomalous class images are input through the model, the reconstruction loss value will be very high. This method is applied to distinguish different age clusters of people using unsupervised training. The proposed model outperforms the benchmark models in both the qualitative and the quantitative measurements.  
anomaly detection; computer vision; surveillance; deep learning; generative adversarial networks +
Footnote †: journal: Applied Sciences
# Internet of Things in Marine Environment Monitoring: A Review
## 5 Major Challenges and Opportunities  
### Energy Management  
Because marine environment monitoring and protection systems work on hash environments as mentioned above, battery replacement is difficult and costly. It is therefore very important to have an efficient energy management system together and preferably with an energy harvesting capability.  
Typical energy options for sensor nodes include batteries, capacitors, fuel cells, and energy harvesting. A battery is widely used in sensor nodes, and in fact in over half of the systems reviewed under this paper. However, using batteries in sensor nodes has a number of issues [92] including the replacement difficulty, risk of losing power during operation, and environment contamination. It is therefore important to explore alternative power supply options for sensor nodes. Energy harvesting is a natural way to go. In marine environments, energy harvesting options including solar, wind, water waves, and currents. The most outstanding energy harvesting at the moment is photovoltaics (solar energy) [92].  
Usually, network energy consumption is increasing at a very high rate due to an increase in data rates, increase in the number of Internet-enabled services and rapid growth of Internet connected edge-devices [21]. The drastic increase in IoT devices requires efficient fabrication of batteries because of the uncertainty in battery dissipation. On the other hand, the devices in marine environment monitoring and protection systems are heterogeneous in nature, each with various capabilities and numerous requirements [93]. Hence, the requirement of cost effective and energy efficient routing strategy in terms of space and time arises in future wireless communication networks.
## 1 Introduction
Here, a pulsed Nd:YAG laser was used to generate surface acoustic waves, and a laser beam was irradiated onto the specimens through a line-arrayed slit mask to generate tone burst waves. In the contact method, the initial second harmonic frequency coming from the system, i.e., electronic devices and transducers, can pose a problem. However, in the proposed technique the initial second harmonic frequency can be easily suppressed by adjusting the duty ratio of the line-arrayed slit mask [17]. This characteristic is an advantage that may be difficult to obtain in the case of using other non-contact excitations, for example EMAT (electromagnetic acoustic transducer) or ACT (air-coupled transducer). For surface acoustic wave reception, a laser interferometer produced an output signal directly proportional to the out-of-plane displacement of the surface waves and maintained a constant VDC regardless of the used material. Furthermore, we established a measurement principle that applies the proposed non-contact technique to the indirect measurement of the absolute acoustic nonlinearity parameter using surface acoustic waves. This study differs from previous studies on longitudinal waves [14] in that additional compensation is required for the difference in wavenumbers in the test and reference materials. For experimental verification, aluminum and copper specimens were used as reference and test materials, respectively, and the relative acoustic nonlinearity parameters of the two materials were measured from the surface acoustic waves generated and received using lasers. The absolute acoustic nonlinearity parameter of copper was estimated from the ratio of relative acoustic nonlinearity parameters with the compensation of wavenumbers. Additionally, the absolute parameters of aluminum and copper were measured by longitudinal wave using a conventional direct measurement method, with the former being used as a reference value and the latter being used for comparison with the estimation result.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 2 Multi-Wavelength QKD with a PUF
### QKD with PUF  
#### 2.2.1 PUF Background  
Another modification to traditional QKD is the inclusion of a physical unclonable function (PUF). A PUF is a hardware device that generates a random output value (called the response) based on an input (called the challenge) [32; 33; 34]. Together, the input and output are called a \"challenge-response pair\". PUFs utilize random manufacturing defects, such as variations in length and doping concentration, gate oxide in integrated circuit chips. As a result of these random variations, the PUF response to a given challenge will be random, unique to the device, and consistent [33]. The PUF's unique response is analogous to a \"fingerprint\" that can identify the device [35]. The \"fingerprint\" can be used to authenticate devices and generate cryptographic keys. Since the randomness is a product of manufacturing defects, manufacturers cannot produce a clone device, because, by definition, those defects are outside of their control. Furthermore, hackers require physical access to the device in order to complete the protocol, adding a layer of security to the system. This is advantageous in QKD protocols, since an eavesdropper cannot obtain the final cryptographic key without access to the PUF.
## 4 UWOC-MIMO System Performance Analysis
### System Average BER Analysis
However, due to the long calculation time required for Equation (26), we adopt the solution method for the BER in this section and use the series expansion corresponding to Meijer's G-function (07.34.06.0006.01) from [29]. First, according to Equations (3) and (4), we can obtain the relationship between the PDF of \(\gamma_{mm}\) and the PDF of \(h_{mm}\), and \(f_{\gamma}(\gamma_{mm})=f_{h}(\gamma_{mm}/\overline{\gamma})/\overline{\gamma}\). Then, the relationship for the cumulative distribution function (CDF) for \(\gamma_{\rm MRC}\) at the output of the MRC combiner and the CDF for \(\gamma_{mm}\) can be obtained as [19]:  
\[F_{\gamma_{\rm MRC}}(s)=\left[F_{\gamma_{mm}}(s)\right]^{MN}, \tag{27}\]  
Next, using the Laplace transform, \(F_{\gamma_{\rm MRC}}(s)\) can be deduced as:  
\[F_{\gamma_{\rm MRC}}(s)=\left\{ \begin{array}{ll}\left[\sum \limits_{k=1}^{\beta}a_{k}\Big{(}\frac{a\beta}{\beta+\Gamma\gamma}\Big{)}^{- \frac{a_{k}k}{k}}\Gamma\big{(}k-\xi^{2}\big{)}^{MN}\left(\frac{A_{\rm E}}{2 }\frac{a\beta}{\beta+\Gamma\gamma}\frac{\Gamma(-\xi^{2})}{A_{\rm E}\gamma} \right)^{MN\xi^{2}}s^{-MN\xi^{2}},\ \xi^{2}<k\\ \left[\frac{A_{\rm E}^{2}}{2}\sum\limits_{k=1}^{\beta}a_{k}\Big{(}\frac{a \beta}{\beta+\Gamma\gamma}\Big{)}^{-\frac{a_{k}k}{k}}\frac{(A_{\rm E}\gamma)^ {-1}}{k(\xi^{2}-k)}\Gamma(\alpha-k)s^{-k}\right]^{MN},\ \xi^{2}>k\end{array} \right., \tag{28}\]The outage probability of the MIMO system with MRC can be obtained as [17, 30, 34]:  
\[P_{\text{MRC,out}}=\text{Prob}(\gamma_{\text{MRC}}\leq\gamma_{\text{th}})=\int_{0} ^{\gamma_{\text{th}}}f_{\gamma_{\text{MRC}}}(\gamma)d\gamma, \tag{29}\]  
where \(f_{\gamma_{\text{MRC}}}(\gamma)\) is the PDF of \(\gamma_{\text{MRC}}\) at the output of the MRC combiner. It can be obtained by applying the Laplace inverse transform to Equation (28). Moreover, for the same reason as for Equation (22), the outage probability can be deduced as:  
\[P_{\text{MRC,out}}\approx\left[\frac{2}{2}\sum_{k=1}^{\beta}a_{k}\left(\frac{ \alpha\beta}{g\beta+\Omega^{\prime}}\right)^{\xi^{2}-\frac{\alpha+2}{2}}\frac{ \Gamma\left(k-\xi^{2}\right)\Gamma\left(\alpha-\xi^{2}\right)}{\left[\Gamma( MN\xi^{2}+1)\right]^{1/MN}}\right]^{MN}\left(\frac{\gamma_{\text{th}}/\overline{ \gamma}}{A_{z}}\right)^{MN\xi^{2}},\,\xi^{2}<k. \tag{30}\]
## 3 Case I: Passive Eavesdropper
### MSE Performances  
#### 3.4.1 MSE at the Legitimate Receiver User A  
First, the MSEs of SI channel and main channel at user A are computed for different values of self-interference-to-noise ratio \(\rho_{AA}\). For instance, Figure 7a,b show the MSEs of the SI channel versus \(SNR_{A}\) of the legitimate receiver user A in the blind without feedback and blind feedback schemes, respectively. Similarly, Figure 8a,b illustrate the MSEs of the main channel versus the \(SNR_{A}\) at user A in the blind without feedback and blind feedback schemes, respectively. It can be seen that MSEs increase significantly as the self-interference-to-noise ratio of user A (\(\rho_{AA}\)) increases, and the blind feedback scheme outperforms the scheme without feedback. It can also be observed that the increase in the self-interference-to-noise ratio of user A has less effect on the blind feedback scheme than the scheme without feedback. For example, maintaining \(MSE_{AA}\) at \(10^{-3}\), when \(\rho_{AA}\) increases from 0 to 30 dB, requires an increase of \(SNR_{A}\) only around 2.5 to 3 dB in the blind feedback scheme. However, it requires an increase of roughly 10 dB in the scheme without feedback. Therefore, the use of the blind feedback scheme can improve the channel estimation processes at user A significantly.
## 2 Results
### Identification of Fruit Metabolism and Quality Genes  
To understand the altered metabolic profiles of the mutant fruit lines and their impact on tomato fruit quality, we investigated the expression of key genes within several crucial metabolic pathways. These pathways included the following: fructose metabolism, the tricarboxylic acid (TCA) cycle, organic acid metabolism, cell wall modification, ethylene biosynthesis, carotenoid biosynthesis, ascorbate production, fatty acid metabolism, and glycerolipid metabolism (Figure 7, Supplementary Table S4). Our analysis revealed DEGs in each of these pathways, suggesting significant disruptions in the metabolic processes. Specifically, within fructose metabolism, genes encoding the fructokinase (FRK) isoforms exhibited differential regulation, potentially affecting fructose phosphorylation and utilization (Figure 7). Similarly, genes that were associated with the TCA cycle and organic acid metabolism, such as _citrate synthase_ (_CS_), _aconitase_, and _malate dehydrogenase_ (_MDH_), showed altered expression. Such changes could modify the levels of citric and malic acids, thereby impacting the fruit flavor. Alterations in cell wall texture were suggested by the DEGs encoding the cell wall modifying enzymes, such as polygalacturonase (PG), pectin methylesterase (PME), and cellulose synthase (CESA). The genes that were involved in ethylene biosynthesis, namely _1-aminocyclopropane-1-carboxylate synthase_ (_ACS_) and _1-aminocyclopropane-1-carboxylate oxidase_, also showed differential regulation, potentially influencing the ripening process. Line M7_5 exhibited a complex pattern with upregulated ethylene receptors and downregulated ERF TFs, suggesting disrupted downstream signaling. Line M29_2 showed downregulated _ACS2_, potentially reducing ethylene biosynthesis. We further observed the altered expression of genes involved in carotenoid biosynthesis, particularly _pyttoene synthase_ (_PSY_), suggesting changes in the fruit color. Similarly, the genes that were involved in ascorbate biosynthesis, including _GDP-mannose pyrophosphorylase_ (_GMP_), _GDP-L-galactose phosphorylase_ (_GGP_), and _L-galactose-1-phosphate phosphatase_ (_GPP_), were differentially regulated, potentially impacting the vitamin C content. Finally, the DEGs related to fatty acid and glycerolipid metabolism suggested alterations in the lipid composition, which could affect the fruit's flavor, aroma, and cellular membrane integrity (Figure 7).  
Figure 7: Differentially regulated tomato fruit metabolism and quality genes. Fruit quality genes, including fructose metabolism, the tricarboxylic acid (TCA) cycle, organic acids, cell wall modification, ethylene biosynthesis, carotenoid biosynthesis, ascorbate production, fatty acid biosynthesis and degradation, and glycerollipid metabolism. On the heatmap, the first line is dedicated to the M7_5 data, and the second line is for the M29_2 data. Red color indicates upregulation and blue indicates downregulation compared to the WT.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 3 Biomarkers in Diabetics with Known HF
### Biomarkers of Inflammation and Fibrosis
#### 3.2.3 Soluble Suppression of Tumorigenicity-2  
Soluble suppression of tumorigenicity-2 (sST2) is an established biomarker of inflammation and fibrosis, having certain predictive potency [111]. ST2 interferes with interleukin-33 and thereby induces the up-regulated expression of the transmembrane isoform ST2 ligand on the surfaces of target cells, resulting in biomechanical myocardial stress, injury, and inflammation. Finally, sST2 exerts protective effects on the myocardium by suppressing fibrosis and attenuating hypertrophy, thus enhancing survival.  
Among inflammatory biomarkers, sST2 has been approved by the experts from the ACC/AHA/HFSA for risk stratification of patients with HF, because it has demonstrated high accuracy and reproducibility in serial measures at a reasonable cost and adds predictive value to NPs and cardiac troponins for HF [112]. In addition, sST2 demonstrates a high discriminative ability for predicting CV morbidity and mortality, HF occurrence and hospitalization independently from common CV, and metabolic comorbidities, such as diabetic kidney disease, renal dysfunction, and hypertension [113; 114; 115; 116]. sST2 can also stratify the patients with HFrEF at higher risk of sudden death [113]. Furthermore, elevated levels of sST2 provide additional prognostic information for different phenotypes of HF, exceeding the ability of NT-proBNP [117; 118]. Thus, sST2 appears to be a powerful predictor for clinical outcomes in HF patients, regardless of the presence of T2 DM, while high economic burden is considered as the main constraint for an implementation of single and serial sST2 measures in routine clinical practice [117].