esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.13. Telemedicine in Pediatric  
The evolution of telemedicine in pediatrics has accelerated owing to COVID-19, focusing on the remote management of complex diseases, impact assessments, and cross-border collaborations. Clinical Case Reports and Treatment Advances (2024) have highlighted the treatment of infantile hemangioma with \(\beta\)-blockers via telemedicine, demonstrating the feasibility of managing complex conditions remotely [172]. The pandemic's impact on pediatric healthcare and service adaptation (2020-2022) examined COVID-19's effects on pediatric hospital activities, emphasizing disruptions and strategies for resuming services [173]. The focus was on pediatric epilepsy and asthma, assessing clinical changes and satisfaction with telemedicine [174; 175]. Reviews have addressed pediatric hepatitis C management during pandemics [176]. Surveys have documented telemedicine use among pediatric providers, offering insights into acceptance and barriers [177]. Telemedicine Network Development (2023-2024) reported a cross-border network for pediatric hematology and oncology, emphasizing specialist access [178]. Studies have evaluated healthcare providers' and patients' perspectives on teleconstulations in Poland [179]. COVID-19 has accelerated the adoption of telemedicine in pediatric care, particularly in chronic conditions. Telemedicine has proven effective for managing complicated pediatric cases. Surveys have shown a growing acceptance among providers and patients while identifying challenges. Cross-border collaborations demonstrate the expanding scope of specialized care. The field has evolved from pandemic-driven adoption to sustainable models supported by evidence and feedback.
## 5 Conclusions  
The results of this study suggest that SERS spectra of urine from patients with breast cancer display distinguishable features compared to controls, providing a strategy with potential application for breast cancer screening. By employing silver nanoparticles activated with Ca(NO\({}_{3}\))\({}_{2}\) (10\({}^{-4}\) M), the SERS spectra of urine metabolites were obtained without any filtering steps. The overall accuracy yielded by the PCA-LDA model based on the SERS spectra of urine samples was 88%.  
Although much more work is needed for understanding the compounds in urine responsible for the SERS spectra and for homogenizing the experimental setups employed between laboratories, the promising results obtained so far raise a hope that SERS-based screening strategies on biofluids such as serum or urine will be successfully translated in the clinic in the near future.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/4/806/s1](http://www.mdpi.com/2076-3417/9/4/806/s1)]([http://www.mdpi.com/2076-3417/9/4/806/s1](http://www.mdpi.com/2076-3417/9/4/806/s1)). Table S1: The age and stage of breast cancer patients and controls; Figure S1: The SERS spectrum of uric acid 10\({}^{-4}\) M before (black) and after activation with Ca\({}^{2+}\) 10\({}^{-4}\) M (blue); Figure S2: The explained variance by the first 7 principal components (PCs); Figure S3: The Hotelling T\({}^{2}\) values of n = 22 samples in the control group corresponding to the first 7 principal components (PCs); Figure S4: The
## 3 System Design and Methods
### Network Architecture Design
#### 3.1.4 The Optimization Objective of the Transfer Learning Network  
The overall loss function of our proposed model consists of the classification loss and the MMD-based distribution alignment loss (including MMD loss for both unconditional and condition distributions). The optimization objective is to jointly minimize the classification loss of labeled data and minimize the distribution distance in terms of MMD. Therefore, the optimization objective can be expressed as:  
\[\min_{f_{s},f_{t},f_{c}}L_{c}\left[\mathbf{Y},f_{c}(\tilde{\mathbf{X}})\right] +\alpha(Q_{m}\left[\tilde{\mathbf{X}}_{s},\tilde{\mathbf{X}}_{t}\right]+Q_{c} \left[\tilde{\mathbf{X}}_{s},\tilde{\mathbf{X}}_{t}\right]) \tag{15}\]  
where \(\alpha\) is a coefficient to adjust the relative importance of classification accuracy of labeled data and the distribution alignment across domains. During the training procedure with stochastic gradient descent-based methods, we iteratively update parameters of the feature projection networks and the classification network.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 4 Performance Analysis
### The SK/PK Upper Bound
#### 4.2.2 Upper Bound on PK Capacity  
The expression of \(C_{p}^{U}\) in (25) can be calculated as  
\[\begin{split}& I\Big{(}\widehat{Y}_{A};\widehat{Y}_{B},\widehat{ Y}_{C}\Big{|}\widehat{Y}_{R}^{(1)},\widehat{Y}_{R}^{(2)}\Big{)}\\ &=I\Big{(}\widehat{Y}_{A};\widehat{Y}_{B},\widehat{Y}_{C},\widehat{ Y}_{R}^{(1)},\widehat{Y}_{R}^{(2)}\Big{)}-I\Big{(}\widehat{Y}_{A};\widehat{Y}_{R}^{(1)},\widehat{Y}_{R}^{(2)}\Big{)}\\ &=h\Big{(}\widehat{Y}_{A},\widehat{Y}_{R}^{(1)},\widehat{Y}_{R}^{(2)} \Big{)}+h\Big{(}\widehat{Y}_{B},\widehat{Y}_{C},\widehat{Y}_{R}^{(1)},\widehat {Y}_{R}^{(2)}\Big{)}-h\Big{(}\widehat{Y}_{R}^{(1)},\widehat{Y}_{R}^{(2)} \Big{)}-h\Big{(}\widehat{Y}_{A},\widehat{Y}_{B},\widehat{Y}_{C},\widehat{Y}_{ R}^{(1)},\widehat{Y}_{R}^{(2)}\Big{)}-h\Big{(}\widehat{Y}_{A},\widehat{Y}_{B}, \widehat{Y}_{C},\widehat{Y}_{R}^{(1)},\widehat{Y}_{R}^{(2)}\Big{)}\\ &=\log_{2}\left|\frac{\mathbf{C}_{\widehat{Y}_{A}\widehat{Y}_{R}^{(1)} \widehat{Y}_{R}^{(2)}}\Big{|}\big{|}\mathbf{C}_{\widehat{Y}_{R}\widehat{Y}_{C }\widehat{Y}_{R}^{(2)}\widehat{Y}_{R}^{(2)}}}{\mathbf{C}_{\widehat{Y}_{R} \widehat{Y}_{C}\widehat{Y}_{R}^{(1)}\widehat{Y}_{R}^{(2)}}\right|},\end{split} \tag{31}\]  
where \(\mathbf{C}_{\widehat{Y}_{A}\widehat{Y}_{R}^{(1)}\widehat{Y}_{R}^{(2)}}\), \(\mathbf{C}_{\widehat{Y}_{R}\widehat{Y}_{C}\widehat{Y}_{R}^{(1)}\widehat{Y}_{ R}^{(2)}}\) and \(\mathbf{C}_{\widehat{Y}_{A}\widehat{Y}_{R}\widehat{Y}_{C}\widehat{Y}_{R}^{(1)} \widehat{Y}_{R}^{(2)}}\) are given in (23), (27), and (28), respectively, and \(\mathbf{C}_{\widehat{Y}_{R}^{(1)}\widehat{Y}_{R}^{(2)}}\) can be given by  
\[\mathbf{C}_{\widehat{Y}_{R}^{(1)}\widehat{Y}_{R}^{(2)}}=\left[\begin{array}[] {cc}p_{A}+p_{B}+\sigma_{2}^{2}&p_{A}\\ p_{A}&p_{A}+p_{C}+\sigma_{3}^{2}\end{array}\right]. \tag{32}\]  
**Theorem 1**.: _In the high SNR scenario, when \(T_{1}=T_{2}=T_{3}\), the upper and lower bounds maintain a fixed gap of \(\log_{2}\left(\frac{3}{2}\right)\) BPCM for both the SK and PK._  
Proof of Theorem 1.: Please refer to Appendix A.
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
###### Abstract
Digital signature schemes are used in electronic funds transfer, data exchange, and software distribution. Compared with physical signatures, they cannot be changed nor copied by someone else, and the signers cannot repudiate signatures later [15; 16]. In asymmetric encryption, digital signature schemes are based on the public key. The output of the signature process is called the digital signature, a cryptographic value calculated from the data and a private key. The signer's private key is used to sign the data, while the signatory's public key is used to verify the signature by the recipient [17; 18]. In cryptography, asymmetric techniques have gained significant importance due to their capability to secure digital communication channels. These techniques use different keys for encryption and decryption, and their security relies on the computational complexity of certain mathematical problems, such as factoring large numbers or discrete logarithms. However, some lightweight asymmetric techniques do not offer additional security such as the digital signature. Despite lacking a digital signature, these lightweight asymmetric techniques are ideal for use in resource-constrained environments, such as embedded systems and mobile devices. They provide basic encryption and decryption functionality while minimizing computational resources, enabling them to be used in low-power devices with limited processing capabilities. One common approach to creating a public key in such lightweight asymmetric techniques is to use a key of the form \(k+r\times p\), where \(p\) is a large prime number and \(k\) and \(r\) are random integers. This approach has led to the development of numerous asymmetric encryption techniques, but none include a corresponding digital signature. To address this gap, the paper proposes a technique for creating digital signatures for these lightweight asymmetric encryption techniques. The proposed method uses the private keys \(k\), \(r\), and \(p\) to generate a signature that can be verified using the public key \(k+r\times p\). By providing a digital signature for these encryption techniques, this new method enhances the security level and opens up new possibilities for their use in applications that require stronger security guarantees. The rest of the paper is organized as follows: Section 2 discusses and reviews the relevant works and presents the encryption techniques that use \(k+r\times p\) as a public key. Section 3 explains the proposed scheme. In Section 4, we perform an analyses against attacks. Section 5 presents the experimental results and discussions. Finally, the conclusion is presented in Section 6.
# Throwaway Shadows Using Parallel Encoders Generative Adversarial Network
## 5 Comparison and Discussion
### Visual Comparison for Facial Images  
Figure 2 shows a comparison of our model results with current state-of-the-art representative methods such as EdgeConnect [10], Partial Convolution [14], Gated Convolution [15] and Ghost-free Shadow removal [6]. To make the comparison fair, we train these methods on our synthetic shadow database. Examples in the first couple of rows are the real test images (no ground truth), while the other two rows show the result for the synthetic test sample.  
As can be seen in Figure 2, our technique plausibly removes shadows from the facial images for both complex real and synthetic test samples. On the other hand, all other representative methods struggle to produce reasonable results. EdgeConnect [10] struggles to produce a proper edge map for a large damaged region of the face resulting in artifacts. Partial Convolution [14] produces sharp results as compared to EdgeConnect and Gated Convolution but still shows artifacts especially at the borders of damaged and undamaged  
Figure 2: Visual comparison of shadow removal. **(a)** Input image, **(b)** EdgeConnect [10], **(c)** Partial Convolution, [14], **(d)** Gated Convolution [15], **(e)** Ghost-free Shadow removal [6], **(f)** Ours, **(g)** Ground truth. **Note:** There is no ground truth for the first couple of rows since these samples are real world shadow images collected from the Internet. The last two samples are from our synthetic database.  
regions. Ghost-free shadow removal [6] plausibly removes the shadow but is unable to produce natural looking face semantics due to the complex nature of the face semantics.  
On the other hand, our model combines the benefits of vanilla and partial convolution encoders. This helps our model in removing the shadow and learning the well-incorporated and artifact-free semantics of the face under the shadow.  
Figure 3 shows additional qualitative results of our model for complex and large size shadow samples in our synthetic database. The first column shows the input image, second and third columns show the segmentation map of the object shadow and shadow free image generated by our model, respectively. Last column represents the ground truth for input images. The results show that our model effectively removes different types of complex, large and challenging shadow occlusions from the face images. The last input sample contains shadows created by a lightning effect (not by an occluded object); thus, our model is unable to produce the accurate segmentation mask of the shadow region and a plausible shadow free output.
Sensors_**2010**, _10_, 6361-6376; doi:10.3390/s100706361  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
Article  
QCM-Arrays for Sensing Terpenes in Fresh and Dried Herbs via Bio-Mimetic MIP Layers \({}^{\dagger}\)  
**[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Analytical Chemistry, University of Vienna, Waehringer Strasse 38, A-1090 Vienna, Austria; E-Mails: [EMAIL_ADDRESS] (N.I.); [EMAIL_ADDRESS] (G.M.); [EMAIL_ADDRESS] (A.R.); [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (B.N.); [EMAIL_ADDRESS] (P.A.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +43-1-4277-52317, Fax: +43-1-4277-9523.  
\({}^{\dagger}\) Dedicated to Professor [PERSON] on the occasion of his 60 th birthday.  
Received: 10 May 2010; in revised form: 25 May 2010 / Accepted: 15 June 2010 /  
Published: 28 June 2010  
**Abstract:** A piezoelectric 10 MHz multichannel quartz crystal microbalance (MQCM), coated with six molecularly imprinted polystyrene artificial recognition membranes have been developed for selective quantification of terpen emanated from fresh and dried Lamiaceae family species, _i.e._, rosmarinus Officinalis \(L\).), basil (_Ocimum Basilicum_) and sage (_Salvia Officinalis_). Optimal e-nose parameters, such as layer heights (1-6 KHz), sensitivity <20 ppm of analytes, selectivity at 50 ppm of terpen, repeatability and reproducibility were thoroughly adjusted prior to online monitoring. Linearity in reversible responses over a wide concentration range <20-250 ppm has been achieved. Discrimination between molecules of similar molar masses, even for isomers, e.g. \(\alpha\)-pinene and \(\beta\)-pinene is possible. The array has proven its sensitive and selective properties of sensor responses (20-1,200 Hz) for the difference of fresh and dried herbs. The sensor data attained was validated by GC-MS, to analyze the profiles of sensor emanation patterns. The shelf-life of herbs was monitored via emanation of organic volatiles during a few days. Such an array in association with data analysis tools can be utilized for characterizing complex mixtures.  
keywords:quartz crystal microbalance; chemical sensor; molecular imprinting; herbs; terpenes +
Footnote †: journal: Journal of Chemical Physics
## 4 Security Threat Landscape in 5G Networks
### Security Solutions for mMTC
Lightweight cryptographic methods, for instance, minimize power and computational overhead but may compromise on the level of security provided. Distributed security mechanisms enhance resilience against attacks such as DoS and DDoS but necessitate complex coordination among devices or nodes. Intrusion detection and prevention systems offer real-time threat monitoring and proactive defenses but require frequent updates and may generate false positives. Machine learning and AI-based solutions stand out for their adaptive capabilities but require extensive training data.
## 3 Results Analysis
### AvB Estimation Results
#### 3.3.1 Experimental Results in 300 Mbps Network  
The results of the experiments of AvB estimation in different virtual environments are depicted in Figure 16, where OS indicates estimated results obtained with system timers and _HPT_ indicates those with _HighPerTimer_.  
Figure 16: _Cont._  
As can be seen, MAD and median were calculated for six types of cross traffic on a 300 Mbps link for 250 estimation trials of the tool for each type. They show a strong decreasing tendency of the variance, and accordingly the median, of the estimated AvB with the growth of cross traffic presence in a path; however, in the case with standard timer acquisition, it always has higher variance in comparison to _HPT_. In the case of spare link, when AvB is equal to the path capacity, all types of virtualization have the highest impact on AvB estimation accuracy. Results with VM running on Xen virtualization have the highest variance. Such behavior was repeated several times when the tests were reloaded.
## 3 Results and Discussion
### Deformation Analysis of Coal Samples with Different Gas Pressures  
#### 3.3.1 Analysis of Compression Deformation of Coal Samples with Different Gas Pressures  
The deformation characteristics of coal samples vary with different gas atmospheres. This difference is closely related to the adsorption characteristics of gases. Due to different adsorption characteristics, the deformation rate of coal samples and the maximum compression deformation variables are different during the charge compression process. During the process of compression and deformation of the coal sample during the charging process, accompanied by gas migration, a part of the gas molecules enter the pores of the coal body. As a result, the coal matrix adsorbs gas to expand and deform. Since CO2, CH4, and N2 have different adsorption characteristics, they exhibit different adsorption expansion deformations under the same gas pressure. It can be seen from Figures 10-12 that as the gas pressure increased, the maximum compressive deformation increased. When the gas pressure increased from 0.4 MPa to 0.8 MPa, their corresponding maximum compressive strains in the CO2, CH4, and N2 atmospheres increased by 101 \(\mu\)\(\varepsilon\), 243 \(\mu\)\(\varepsilon\), and 335 \(\mu\)\(\varepsilon\), respectively. When the gas pressure increased from 0.8 MPa to 1.2 MPa, their corresponding maximum compressive strains under the CO2, CH4, and N2 atmosphere increased by 84 \(\mu\)\(\varepsilon\), 347 \(\mu\)\(\varepsilon\), and 56 \(\mu\)\(\varepsilon\), respectively. Under the pressure of 0.8 MPa and 1.2 MPa, N2 exhibited the most significant compression deformation, followed by CH4 and CO2, among which CO2 showed the minimum deformation. The compressive deformation of the coal sample under the atmospheres of CH4 and N2 varied considerably with the pressure change, whereas the influence of pressure change under the CO2 gas atmosphere was relatively insignificant. At the same time, the difference of the maximum compressive deformation was insignificant with the increase of CO2 pressure. By linearly fitting the gas adsorption equilibrium pressure with the maximum compressive deformation, the gas adsorption characteristics of the different gases were evident. According to the  
Figure 8: Damage diagram of coal samples under 0.8 MPa gas pressure instantaneous pressure relief test.  
Figure 9: Damage diagram of coal samples under 1.2 MPa gas pressure instantaneous pressure relief test.
## 3 Results and Discussion
### Characterization of the Parylene C Coating on Cylindrical Zn Stents
#### 3.2.3 Contact Angle Measurements of Uncoated and Parylene C coated Zn Samples.  
The hydrophobicity of the tested samples was estimated by conducting contact angle measurements. Initially, a droplet of 1 \(\upmu\)L of deionized water was applied to the surfaces. The contact angle on a Parylene C-coated Zn sample was measured and compared with that on an uncoated Zn sample. As displayed in Figure 13a, the contact angle on the Parylene C-coated Zn surface was \(\theta=95.05\pm 4\) (n = 5). This value is close to that reported by [PERSON] et al. [26], where the contact angle of Parylene C on machined medical steel was \(94.25^{\circ}\) and on polished medical steel was \(92.16^{\circ}\). We believe that there is still room for improvement since the contact angle of an untreated Parylene C film can reach \(117^{\circ}\), as demonstrated by [PERSON] et al. [46]. The contact angle on the uncoated Zn sample was \(\theta=84.2\pm 2\), as shown in Figure 13b. From these measurements, it can be concluded that the Parylene C coating on the Zn surface increased the contact angle and therefore the hydrophobicity of the coated samples. An increase in hydrophobicity can be beneficial for avoiding thrombosis on the stent surface in a physiological environment.  
Figure 12: Surface morphology viewed by SEM; (**a**) Secondary electron image identified crevices in the Parylene C coating on an expanded Zn stent (crevice is lighter gray); (**b**) Back-scattered electron image (crevice is white); (**c**) Back-scattered electron image of a strut from an unexpanded stent showing a uniform coating of Parylene C, free of crevices.
# BODIPY-Based Nanomaterials--Sensing and Biomedical Applications
## 1 Introduction
### Nanoparticles Involved in the Photodynamic Therapy
* To the advantages of combining nanoparticles and photosensizers in PDT belong increased nanocarrier surface, better solubility of PS, and better permeability of nanoparticles due to the EPR effect and dual mechanism of action.
## 2 Application Scenarios and Key Concepts/Enablers for RF Hardware in JCRS
### Scenarios
#### 2.1.3 V2X Communication  
V2X communication using JCRS is an emerging technology that enables vehicles to communicate with various objects in their environment, including other vehicles, pedestrians, and infrastructure to share the information collected from onboard sensors. This improves the road safety and traffic system resilience. V2X communication is crucial for autonomous vehicles. It allows for autonomous cars to share their intent with other road users, making their behavior more predictable and fostering a smoother integration with human-driven vehicles. Ref. [13] elaborated a system model and key parameters for JCRS in FR2 mmWave band. Resource sharing can be implemented by embedding one of existing modulation schemes into frequency modulated continuous wave (FMCW) waveforms, thus enabling low data rate communication between the vehicles. Sharing the relative position and velocity information of the neighboring objects will increase the situation awareness  
Figure 1: Traffic monitoring scenario.  
on a local scale. The same information communicated through a cellular network from other traffic entities (with a cyber presence) will be processed as well.
# SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
## 2 Overview
### DSP and Apache Flink  
**DSP program**. Typically, one DSP program consists of the combination of operators (high order functions) and UDFs that are implemented under the constraints of operators. These operators, together with UDFs, implement the program logic and are chained in tandem. One typical DSP application is the composition of some DSP APIs and user-defined logic within them from the developers' view. DSP APIs, through which we can interact with DSP operators, and user-defined logic are two blocks for one DSP application. Our study will mimic the DSP APIs and transform them into DSP-free code so that a third-party symbolic execution engine can analyze them as regular Java code.  
**Apache Flink**. SPOT targets Apache Flink, called the 4G of Big Data. It is a real-time framework for processing stream data in a high-performance, scalable, and accurate fashion. The stream data is abstracted as DataStream in Flink, representing an infinite list of data in some specific type. New streams are derived from the upstream data during the data transformation with a transformation happening in the upstream operators, which are high-order functions, together with UDFs working within. We targeted Flink as our subject platform and developed a symbolic analysis tool called JPF-Flink, specifically for the programs implemented with the Flink framework.  
**JDU path coverage**. JDU (Joint Dataflow and UDF) path coverage is introduced by [PERSON] et al. [13], which consider the paths thoroughly along with the operators and the internal paths in their UDFs. Their experimental results demonstrate that JDU path coverage is directly related to improvement in fault detection.