esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Novelty and Contributions  
The major contributions of this work are as follows:  
* We propose a framework for SMIS that incorporates a newly personalized customization factory.
* We propose a secure IoT-enabled NBMS against the black hole attack, using NTRUEncrypt cryptography and the AODV routing protocol.
* We enable manufacturing firms to implement secure IoT-enabled subtractive and additive manufacturing via MiRs.
* We simulate the network using NS2 to evaluate three different networks: the healthy network, malicious network, and an NTRUEncrypt-secured network.
# Deep-Learning Software Reliability Model Using SRGM as Activation Function
## 3 Software Reliability Model
### Software Reliability Model Using Deep Learning  
#### 3.2.1 Deep Neural Network  
An artificial neural network is organized through nonlinear connections and modeled after the biological system of human thinking. Based on neurons (nodes), the most basic unit of information, the artificial neural network comprises input, output, and hidden layers. The input layer takes the information, the output layer considers or makes judgments based on the information received, and the hidden layer transmits the information between the input and output layers. It not only transmits information but also performs information transmission through a nonlinear combination. After the weights and biases of each layer are combined, along with the information obtained, the sum and product are combined, and the result is derived through a nonlinear transformation using the activation function. When there are many hidden layers in the structure, it is a DNN, as shown in Figure 1. The circles in the figure represent neurons. The data put to the input layer is informed to the next hidden layer through a combination of sums and products with weights, biases, and activation functions. This process is repeated until the output layer [42; 43].  
When transferred from layer to layer, the sigmoid function, hyperbolic tangent function, or rectified linear unit (ReLU) function is used to convert the nonlinear functions. In addition, the result transmitted to the output layer uses a sigmoid or softmax function for categorical variables and an identity function for continuous variables. In the field of software reliability, the sigmoid function is used because the software failure itself is a structure that increases in a sigmoid form, and the identity function is used because the dependent variable is a continuous variable. If the final predicted value \(y\) is obtained through the above process, a loss function representing the difference between the actual value and the predicted value is learned based on the partial derivative value for a specific parameter, where \(\theta\) represents the weight and bias parameters, and \(\alpha\) represents the learning rate. It is expressed as Equation (5).  
\[\theta_{t+1}=\theta_{t}-\alpha\frac{\partial}{\partial\theta}Loss(\theta) \tag{5}\]
# UVCS: Unit Virtual Coordinate System for UAV Intra-Swarm Routing in GPS-Denied Environment
###### Abstract
The main contributions of our study are as follows:  
* we propose an approach for self-organization and topology support in drone swarms in the complete absence of external positioning information using the developed Unit Virtual Coordinate System;
* we propose metrics to analyze the characteristics of intra-swarm routing on top of the developed Unit Virtual Coordinate System;
* we carry out a comparative analysis of the characteristics of self-organization according to the topology formed by the developed algorithm, with the topology obtained with complete knowledge of the network. Methods of queuing theory are used to carry out the analysis. The rest of the paper is organized as follows. Section 2 reviews the current state of the problem of connectivity support and routing in autonomous swarms. In Section 3, we introduce an algorithm for assigning virtual coordinates based on the locally available knowledge. In Section 4, we specify the mathematical model. Section 5 introduces the metrics of interest. The numerical results are presented in Section 6, and conclusions are drawn in the last section.
## 4 Conclusions  
In summary, a TO lateral shift apodized sampled waveguide grating device based on FBPA-PC EP and FSU-8 was designed and fabricated by the metal-printing technique. This device realizes the effective combination of multiple functions, including periodic filtering, wide-spectrum filtering, and high side-lobe suppression. The 3-dB bandwidth and wavelength spacing can broaden to 4.8 nm and 9.7 nm. This device has a large SLSR due to the location of the ridges with a Gaussian form. The side-lobe suppression ratio can reach 22.6 dB, much larger than that of traditional Bragg grating (6.1 dB), which is the most important aim in this design. In addition, the metal-printing technique can reduce the power consumption for TO tunable devices. The rise and fall response times of this device are about 1.508 ms and 630.5 \(\upmu\)s. The applied powers of 42.4 mW and 87.2 mW led to reflection spectrum shifts of 1.8 and 3.5 nm in the test, which can be attributed to the larger TO coefficient and the metal-printing structure. This proposed lateral shift apodized sampled grating will have an extensive application in the WDM system owing to its multiple functions and simpler fabrication process for metal-printing.  
Conceptualization, J.W.; methodology, D.Z.; software, J.W.; validation, C.C., C.W. and R.C.; formal analysis, J.W.; investigation, D.Z.; resources, J.W. and D.Z.; data curation, J.W. and Y.Y.; writing--original draft preparation, J.W.; writing--review and editing, J.W., X.Q. and F.W.; visualization, J.W. and Y.Y.; supervision, F.W., C.C. and D.Z.; project administration, X.W. and X.S.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2019 YFB2203001), National Natural Science Foundation of China (61875069, 61605057, 61675087) and Science and Technology Development Plan of Jilin Province (20190302010 CX).  
Figure 12: (**a**) Schematic diagrams of the measurement system; (**b**) TO switch responses obtained by applying a square-wave voltage at frequency of 300 Hz; (**c**) Reflection spectrum of traditional Bragg sampled grating; (**d**) Reflection spectrum of proposed Bragg sampled grating and its thermo optic tunable characteristic at \(\Delta T\) = 0 \({}^{\circ}\)C, 10 \({}^{\circ}\)C, and 20 \({}^{\circ}\)C.
## Dynamic Matching Strategies in Two-Sided Markets
### The Greedy and the Patient Matching Strategies
#### 5.2.1 Greedy Algorithm  
A greedy algorithm means that any node tries to find a match as soon as possible when it arrives at the market. If no match can be found, the node will remain in the market. Figure 9a shows our greedy algorithm's processing snapshots, with four continuous-time units, in a market. When \(n_{3}\) arrives at the market, it immediately matches \(n_{1}\) and leaves the market. Therefore, any node that remains in the market will have not have a link with any others; and they can only wait for matches with nodes arriving at the market later. In other words, nodes in the market get matched when they become critical and leave the market in pairs. The market size will be highly concentrated on a fixed range if we continuously apply a greedy algorithm to the market. This phenomenon implies that a balance between the arrival rate and the departure rate is reached. The departure nodes include those leaving the market immediately after they found matches successfully and those leaving without a match because of lifecycle expiration.  
Figure 8: Structural issue of the matching process: the node’s connecting structure depends on the market’s thickness. All other modes can still find matches even if node N\({}_{1}\) matches any of its connected neighbors immediately.  
Equation (1) can be understood as any node getting a match when it arrives at the market, while the nodes added to the market are those who did not find matches with the other nodes in the market. On the other hand, we can express the departure rate in the market as:  
\[r_{y,y-1}\ =\ y+n\bigg{(}1\ -\ \bigg{(}1\ -\ \frac{d}{n}\bigg{)}^{y}\bigg{)}. \tag{2}\]  
Equation (2) can be understood by considering the following two cases:  
(i) Leave without matched: suppose we use \(T_{a}\) as a time unit in the equation. After a \(T_{a}\), there are \(n\) nodes, on average, arriving at the market. However, as all nodes are assumed to not have found matches, all the in-system nodes will leave the market after a \(T_{a}\) is passed. Therefore, the number of departure nodes equals the number of nodes \(n\) in the market. (ii) Get a match, then leave: When the node arrives at the market and gets a match immediately, the number of nodes in the market will be reduced accordingly because the matched node will leave the market right away. Therefore, the balance equation of the size of a stable market, under the greedy matching algorithm, can be written as:  
\[f(y)\ =\ n\ \bigg{(}1\ -\ \frac{d}{n}\ \bigg{)}^{y}-\bigg{(}y\ +\ n\ \bigg{(}1\ -\ \frac{d}{n}\ \bigg{)}^{y}\bigg{)}\bigg{)} \tag{3}\]  
By solving Equation (3), we can find the abovementioned highly concentrated market size that is:  
\[N^{*}\ =\ \frac{n}{(2d+1)}. \tag{4}\]  
While the _loss_ of the market under the greedy algorithm can be defined as the ratio of the node arrival rate to its departure counterpart, that is:  
\[\textit{Loss}\ =\ \frac{1}{(2d+1)} \tag{5}\]  
Figure 9: Dynamic matching market under (**a**) the greedy algorithm and (**b**) the patient algorithm.
## 4 Discussion
### Implications for Medical Training and Certification  
The results have important implications for medical education and certification processes. The ability of LLMs to achieve high accuracy on low-difficulty, recall-driven items supports their potential as educational aids for medical education. This aligns with the findings of US-based studies in which LLMs were used to generate clinical vignettes, assist in self-testing, and explain complex topics [30].  
However, the observed drop in performance on higher-difficulty and high-discrimination items underscores the limitations of LLMs as autonomous reasoning agents. In high-stakes clinical environments, especially in specialties like anesthesiology, where decisions are time-sensitive and safety-critical, AI should augment--not replace--human judgment [34].  
Domain-Specific Performance Patterns: Cognitive Architecture Differences Between Human and Artificial Intelligence  
The moderate positive correlation (r = 0.567, \(p\) = 0.0346) between human and LLM accuracy across the 14 anesthesiology knowledge domains reveals fundamental differences in cognitive architecture while suggesting partial alignment in difficulty patterns. Domains in which humans struggle moderately (HTM: 17.2%, Monitoring: 40.5%) also challenge LLMs, but to a lesser extent (HTM: 55.6%, Monitoring: 65.3%). This indicates that LLMs maintain baseline competency through systematic knowledge access, even in complex areas that require hemodynamic interpretation and physiological monitoring integration. Conversely, the dramatic LLM advantage in Volume Replacement and Transfusion Therapy (100% vs. 45.5%) reflects LLMs' superior ability to access and apply algorithmic, protocol-driven knowledge compared with humans' more variable recall of specific transfusion guidelines and fluid management protocols.  
The differential performance profile indicates systematic differences in knowledge processing: LLMs excel in domains that require factual recall and standardized protocol application (Cardiopulmonary Resuscitation, Volume Replacement) but show more modest advantages in areas that demand contextual clinical judgment and experiential reasoning (Complications of Anesthesia, Airway Management). This cognitive complementarity supports the development of hybrid decision-support systems that utilize systematic AI knowledge retrieval for protocol-driven domains while preserving human oversight in complex diagnostic reasoning and complication management. Recent experimental evidence from endoscopic decision-making has shown that effective human-AI collaboration can lead to superior outcomes through the weighted integration of complementary expertise, where clinicians appropriately follow AI advice when it is correct while maintaining independent judgment when AI recommendations are flawed [35].
# Cross-Sectional Shape Optimization of Cylindrical Elastomer Spring for Sensitive Cargo Container
## 4 Conclusions  
In this study, a nonlinear structural analysis and an optimal design of the polyurethane spring were performed to improve the safety of the cylindrical rubber spring inside the container of a transporting sensitive cargo that is under development. The conclusions obtained from this study were as follows:  
1. As a result of the equi-biaxial displacement control test of the natural rubber specimen, it was found that the hyperelastic behavior equation with the highest correlation coefficient for the stress-strain curve was the Yeoh model. Therefore, the material constants of polyurethane were predicted using this equation and used for NFEA;
2. The conceptual design was carried out to satisfy the basic design requirements of the spring, and a nonlinear structural analysis of six-shaped rubber springs was performed. As a result, a weak point could be identified in the center inside the spring. Through NFEA, the shape with the lowest MPS (shape 6) was selected as the original model, and the optimal design of the polyurethane spring was carried out;
3. Using the original model obtained from the conceptual design, the optimal design was performed using three optimization algorithms: the OLH, HJA, and MIGA. As a result of performing optimization on the polyurethane spring, an optimal cross-section that satisfies the design requirements through changes in height, external diameter, and internal diameter can be obtained. The stiffness was 166 N/mm, an increase of 61% over natural rubber, satisfying the high-weight condition. The required frequency for resonance prevention also met 5 Hz or less. In addition, the MPS was 0.244, which is about 39% lower than the original natural rubber type, and the fatigue life was expected to be longer;
4. A compression displacement control test was performed on the optimal shape prototype to obtain a correlation coefficient between the analysis and the measured value. The correlation coefficient was 0.928, and it was confirmed that the verification result of the optimal shape spring made of polyurethane was valid.  
Since this research was conducted limited to detailed spring design, further research on temperature, durability, etc., shall be conducted through a train operation test for future research.  
Conceptualization, K.-S.K. and C.-S.K.; validation, K.-S.K. and C.-S.K.; writing--original draft preparation, K.-S.K. and C.-S.K.; writing--review and editing, C.-S.K.; visualization, K.-S.K. and C.-S.K.; project administration, C.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure, and Transport (Grant RS-2020-KA162811).  
Not applicable.  
This work was supported by Korea Agency for Infrastructure Technology Advancement (KAIA). The authors would like to express their gratitude.  
The authors declare no conflict of interest.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 2 Related Work
### Lightweight Authentication Protocols  
Authentication is a key security element in IoD systems. [PERSON] et al. [44] proposed a lightweight mutual authentication scheme for drones using physically unclonable functions (PUFs), exhibiting resistance to replay and man-in-the-middle attacks with low computational overhead; nevertheless, their method needs dedicated hardware that adds costs to drones. [PERSON] et al. [45] proposed a lightweight authentication and key agreement protocol for UAV communication networks with lower computational complexity compared to traditional public key mechanisms but equal security levels. [PERSON] et al. (2018) also proposed a certificateless signcryption scheme for drone communication with no certificate management infrastructure, hence reducing communication overhead. For computational resource-limited drone systems, [PERSON] et al. (2018) designed an ultra-lightweight nonce-based bitwise operation authentication protocol that reduced computational needs but provided little defense against advanced attack channels. To overcome this limitation, [PERSON] and [PERSON] (2018) offered a provably secure anonymous IoD authentication protocol that satisfied security strength and efficiency in computation. Recent research has involved RAPID-Auth, which attains 40% reduced energy consumption with elliptic curve cryptography ([PERSON] et al., 2018), and context-aware authentication that adjusts security levels according to environmental conditions ([PERSON] et al., 2018). Our framework builds on these by integrating behavioral analysis with cryptographic mechanisms without usual energy costs.
## 5 Parasitic Effects and Compensation Techniques
Figure 7 depicts the strategic combination of transduction mechanisms in hybrid resonators, which significantly enhances the signal-to-noise ratio by fully leveraging the individual strengths of both capacitive and piezoelectric transducers. As shown in Figure 7a, by employing two piezoelectric electrodes (ports 3 and 4) for actuation and two capacitive electrodes (ports 1 and 2) for sensing, this design scheme not only minimizes cross-talk between input and output ports but also enhances the suppression of spurious modes. This hybrid configuration promotes a cleaner resonance signal response, as capacitive transducers are particularly effective in selectively detecting the response of the targeted resonance mode in a spurious-mode free fashion. Conversely, Figure 7b illustrates another operation scheme by this exact hybrid resonator, where the two capacitive electrodes are used for actuation, and the two piezoelectric electrodes are employed for sensing (detection of the resonance response). This setup further isolates the input signal from the output signal as the capacitive transducers surrounding the resonator body are shielded from the piezoelectric transducer electrodes that are separated by the ground electrode and piezoelectric transducer film in between two kind of transducer electrodes, thereby reducing cross-talk and leveraging the distinct isolation properties of the piezoelectric transducer components (ground shielding).
# Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands +
## Abbreviations  
3D three-dimensional  
3 GPP 3 rd Generation Partnership Project  
5G fifth-generation  
AOA  angle of arrival  
AOD  angle of departure  
CDF  cumulative distribution function  
DL  downlink  
GBM  geometry-based model  
gNodeB 5G base station  
ITU  International Telecommunication Union  
LOS  line-of-sight  
MIMO  multiple-input-multiple-output  
mmWave  millimeter-wave  
MPM  multi-ellipsoidal propagation model  
NLOS  non-line-of-sight  
NR  New Radio  
PAS  power angular spectrum  
PDP  power delay profile  
Rx  receiver  
SIR  signal-to-interference ratio  
TDL  tapped delay line  
Tx  transmitter  
UE  user equipment  
UE-I  interfering UE  
UE-S  serving UE  
UMa  urban macro  
UL  uplink  
**Symbols**  
\((\theta,\phi)\)  AOA of individual propagation path  
\((\theta_{T},\phi_{T})\)  AOD of individual propagation path  
\(|g(\theta,\phi)|^{2}\)  normalized power pattern of receiving antenna  
\(|g_{I}(\theta_{T},\phi_{T})|^{2}\)  normalized power pattern of interfering transmitting antenna  
\(|g_{S}(\theta_{T},\phi_{T})|^{2}\)  normalized power pattern of serving transmitting antenna  
\(\alpha_{R}\)  direction of receiving beam  
\(\alpha_{RI}\)  direction of interfering receiving beam  
\(\alpha_{RS}\)  direction of serving receiving beam  
\(\alpha_{T}\)  direction of transmitting beam  
\(\alpha_{TI}\)  direction of interfering transmitting beam
###### Abstract  
The modeling of conduction and switching losses for insulated gate bipolar transistors (IGBTs) and free-wheeling diodes (FWDs) in automobile applications is becoming increasingly important, especially for the improvement of the system efficiency and the reliability prediction. The traditional modeling of conduction and switching losses based on the space vector pulse width modulation (SVPWM) is not applicable in practice due to the complex curve-fitting and the computation demands. In this paper, a simple and practical losses model for IGBTs and FWDs is proposed based on the SVPWM algorithm. Firstly, the traditional power losses model is introduced briefly. Then, the piecewise linear switching losses model and the conduction losses model based on the equivalent three-order harmonic model of the duty cycle are proposed. The comparison of experimental results between the traditional model and the proposed model is presented in the experiment validation. Furthermore, the power analyzer is adopted to measure the inverter losses, and the chips losses are further validated when other extra losses are considered. The proposed model shows good modeling accuracy with the large benefit of smaller measurement and lower computation requirements.  
insulated gate bipolar transistors (IGBT); free-wheeling diode (FWD); space vector pulse width modulation (SVPWM); electric drives; permanent magnet synchronous motor (PMSM) Article
# The Emerging Role of Cyclodextrin Metal-Organic Frameworks in Ostheotherapetics
## 3 Cyclodextrin-Based MOFs  
\(\gamma\)-CD-MOFs are comprised by \(\gamma\)-CD molecules as the organic linker and an alkaline metal, Na\({}^{+}\), K\({}^{+}\), Rb\({}^{+}\) or Cs\({}^{+}\), as the metallic centre [11; 32]. These materials are isostructural, with the following description being focused on the \(\gamma\)-CD-MOF with K\({}^{+}\), the first to be isolated: a set of six \(\gamma\)-CD units forms a cubic-shaped arrangement, held together by four K\({}^{+}\) ions (Figure 2a). Each K\({}^{+}\) ion is eight-coordinated, embracing two primary OH groups and two glycosidic ring oxygen atoms, as well as four secondary OH groups. The centre of each (\(\gamma\)-CD)\({}_{6}\) cube comprises a quite large spherical pore with an average 1.7 nm diameter (Figure 2b). Pores are interconnected by a series of windows of roughly 0.78 nm in diameter, defined by the \(\gamma\)-CD tori at the faces of the cube (Figure 2c), to form a tri-dimensional framework with an estimated total pore volume of 54% [11; 32].  
The unique geometry and porosity of \(\gamma\)-CD-MOFs translates into an exceptional loading ability, which has been used to incorporate a variety of anti-inflammatory and anti-septic agents useful in the contexts of osteoarthritis and dental repair, respectively. Details are given in the following subsections.