esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Outage and Error Probability
## 4 Simulation Results and Discussions
### Network Performance Parameters--Discussion
#### 4.2.3 Throughput  
Figure 10 shows the performance of ACE and E-ACE in terms of throughput. E-ACE has 69% more throughput as compared to CARQ and ACE has 63% more throughput as compared to CARQ. Difference in throughput of E-ACE and ACE is less because third retransmission is rarely invoked. Most of the time, two retransmissions are enough to achieve acceptable BER. CARQ and EEDBR shows less throughput as compared to ACE and E-ACE, because network conditions (noise, fading, etc.) may not be suitable to achieve acceptable BER even after retransmissions in CARQ. CARQ does not combine the source signal with the relayed signal to reduce BER. When direct transmission is not successful, it discards the erroneous data and rely only on the retransmitted data packet from relay.  
Table 2 shows throughput vs. dead nodes comparison of the four protocols. It shows that initially E-ACE has more throughput than ACE, however, this difference is very small. The performance of ACE and E-ACE is better than EEDBR till the death of 130 nodes. It is observed that throughput can  
Figure 11: Packets dropped.  
Figure 9: Total energy consumption of the network.  
be increased by using more cooperative nodes. However, with more retransmissions, more energy is consumed. After 230 s, ACE, E-ACE and CARQ have less throughput as compared to EEDBR because they have less number of alive nodes available to send data to sink, whereas, EEDBR has more alive nodes. More alive nodes can send more data to sink and increase throughput. Since the load on nodes is equally distributed, therefore, in sparse network, nodes may not find relays or next destination to send data to sink and die due to idle sensing. This is the main reason of low throughput and low packet acceptance ratio as shown in later time slots of Figure 12.
## 5 Technical Challenges and Ethical Issues
### The Path Forward  
To realize the full potential of DT technology, the following actions are crucial:  
* Standardization and Collaboration: Develop universal standards for data representation and interoperability. Collaboration among healthcare organizations, big data companies, and biomedical research centers is necessary to guide model development and ensure seamless integration.
* Enhanced Data Handling Capabilities: Invest in infrastructure and processes to manage vast datasets efficiently, ensuring high data quality and integrity.
* Privacy and Security Frameworks: Strengthen regulatory and cybersecurity measures to protect patient data and build trust among stakeholders.
* Scalable Solutions: Design cost-effective and scalable DT platforms to make the technology accessible to organizations of varying sizes.  
By addressing these issues, DT technology can move beyond its current limitations and unlock transformative opportunities in healthcare.
## 3 Results  
### Organ Equivalent Dose  
Table 2 summarized the organ equivalent dose for the selected organs in protocols P1-P6. As observed, the highest organ-absorbed dose was found in the liver and the lowest was presented in the thyroid for 100 kVp with a mean value of 1.22 \(\pm\) 0.13 mSv (P1) and 0.31 \(\pm\) 0.07 mSv (P3), respectively. However, for 80 kVp, the highest organ-absorbed dose was found in the stomach compared to the other organs with a mean value of 0.72 \(\pm\) 0.01 mSv. Figure 3 presents the mean organ equivalent doses of standard pediatric CT CAP examinations with varying tube potential and tube current. As a result, the value of doses reduced as tube voltage and tube current decrease in P2 to P6. In comparison between 100 and 80 kVp, the results of organ-absorbed dose were decreased with the reduction of tube voltage. Overall, this study found that the organ dose measurements in thyroid, lungs, liver, stomach, gonads, and skin were reduced by almost 50% with lessening tube voltage from 100 to 80 kVp.  
Figure 2: Example of objective image quality analysis of CT number (mean), image noise (SD), SNR, and CNR by putting the region of interest (ROI) in the lungs and soft tissue field.  
Table 2 and Figure 3 present the impact of standard protocol, tube voltage, and tube current on organ equivalent dose during the examination. Table 3 summarizes the influence of increased slice collimation (P7), reduced pitch factor (P8), and disabling the TCM function (P9) on organ dose. The doses were slightly reduced when slice collimation was increased. However, by reducing the pitch value, the organ dose was slightly increased. Furthermore, the organ equivalent dose was increased by more than 50% in P9 after disabling the TCM function. Figure 4 shows the highest organ dose was observed in the liver followed by skin, stomach, lungs, gonads, and thyroid in all protocols. The highest organ dose was detected in P9. Therefore, the application of TCM in pediatric patients offers a big consequence in CT radiation dose due to different in-patient body habitus and thickness.
# Ant-Balanced Multiple Traveling Salesmen: ACO-BmTSP
## 1 Introduction
The primary purpose was to find the minimum distance traveled by using the minisum number of salesmen. [PERSON] et al. [26] implemented a hybrid algorithm, GABC-LS, to solve the minisum mTSP based on a genetic algorithm, an artificial bee colony, and two local search methods (move1-within and two-opt). This was a small-sized instance. Table 1 indicates the pros and cons of the reviewed algorithms. Considering balanced tours, most works used the minimax function. Therefore, the minimization of the maximum tour value may neglect variations in smaller tours. This phenomenon implies a loss of the algorithm's sensibility and compromises the feedback clues that the optimization function gives to the search. On the other hand, combining ant and other optimization algorithms in a hierarchical structure leads to a long computation time. Keeping these ideas in mind, the following research questions were formulated:  
1. Is it possible to solve the mTSP by using optimization functions without dead zones? 2. Could the mTSP be solved by using ant colony optimization with minor modifications? This paper proposes a novel algorithm based on ACO for the mTSP with several depots. The main contributions are the following: (i) an innovative objective function that enables simultaneous achievement of two goals--minimal distance and balanced tours; (ii) a refined version of ACO that includes a proposal for an ant-picket algorithm instead of an ant finding a complete path alone, and the solution is subdivided into several tours to be searched by a group (picket) of ants. For the experimental assessment in this proposal, a group of instances proposed by [PERSON] and [PERSON] [27], by [28], and by [29] were used to validate the proposed algorithm. This paper is organized as follows: The multiple traveling salesman problem used in the experimental study is presented in Section 2.1. Section 2 presents the ACO algorithm and associated concepts, describes the application of ACO to the mTSP, and presents the proposed approach. Section 3 presents the simulation results. Section 4 discusses the advantages of the proposed algorithm. Finally, the main conclusions and suggestions for future studies are presented in Section 5.
# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 3 Experimental Setup
### Evaluation  
Other than validation during the training phase, we implemented two more approaches to evaluate our model generalization. First, as explained in Section 3.1, we created a new dataset for testing purposes only. Second, we made use of RIM-ONEr3 [28], a publicly-available dataset of fundus images, to evaluate our model generalizability. Evaluation results obtained for both datasets are reported in the following section using several metrics: accuracy, recall (sensitivity), precision, F1 score, specificity, and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). These metrics were calculated as follows:  
* Accuracy = \(\dfrac{\text{tp}+\text{tn}}{\text{tp}+\text{fp}+\text{tn}+\text{fn}}\)
* Recall (sensitivity) = \(\dfrac{\text{tp}}{\text{tp}+\text{fn}}\)
* Precision = \(\dfrac{\text{tp}}{\text{tp}+\text{fp}}\)
* F1 score = 2 \(\cdot\dfrac{\text{recall}\cdot\text{precision}}{\text{recall}+\text{precision}}\)
* Specificity = \(\dfrac{\text{tn}}{\text{tn}+\text{fp}}\)  
In the above, true positive (tp) denotes the number of glaucomatous images that have been classified as glaucomatous and true negative (tn) denotes the number of normal images that have been classified as normal. Similarly, false positive (fp) denotes the number of normal images that have been classified as glaucomatous, and false negative (fn) denotes the number of glaucomatous images that have been classified as normal. The first four metrics were used to evaluate the robustness of the deep learning models. The recall, specificity, and ROC-AUC were used to evaluate the models from a clinical point of view.
## 3 Methods  
The contribution of our modeling approach is the straightforward relationship between the extensional stiffness \(E\) and the bending stiffness \(B\). As shown below, this is especially practicable when the robot has an elementary design: axial measurements are sufficient in order to identify all parameters necessary for three-dimensional simulations. Restriction to axial measurements is of particular interest to adopters with limited testing equipment aiming to simulate their prototypes.  
The experimental validation of the model approach is conducted with an actuator, Figure 3, developed in a preliminary study [19]. Its design is modular, i.e., individual components can be adapted in order to build a custom configuration. Furthermore, the design is advantageous for modeling since the three chambers are purely cylindrical and bonded by end caps and linking elements, simplifying the kinematics. The reinforcement of the chambers consists of rings instead of a fiber, which reduces manufacturing effort but increases nonlinearity of stretching. All components except the chambers can be stiff 3D-printed or made of silicone that must be stiffer than the chambers. Figure 3 shows a configuration of the actuator with two modules stacked. Although individual pressurization of each module would be possible, throughout this study stacked chambers are supplied with the same pressure.  
Referring to Figure 4, the procedure of validating the model consists of several steps. First, the beam parameters are identified experimentally. Since two modules stacked tend to buckle when performing axial force measurements, we conducted the experimental identification in Section 3.1 with two prototypes of an individual module. For the three-dimensional measurements described in Section 3.2 and thus for validation, we used bending actuators with two modules stacked to increase the leverage of external loads. However, the axial behavior, which is investigated for the beam parameter investigation, is independent from the number of modules stacked. Conducting the beam parameter identification and the validation with different prototypes is in contrast to the usual procedure [3, 8, 10, 12]. We chose the procedure to demonstrate that three-dimensional simulations can be performed with limited testing equipment. All actuators used in this study were poured from the same silicone, and with exactly the same time in vacuum and curing temperature.  
Figure 4: Procedure of validation with corresponding sections.  
Figure 3: Modular three-chamber bending actuator consisting of two modules stacked with names of its components. Stiff components are white, flexible chambers are red.
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 5 Progress in Biomass Chemical Looping Gasification
### Thermodynamic Analysis and Kinetic Studies
The kinetic behavior and reaction performance of lignin pyrolysis and gasification was evaluated through a TGA-MS system. According to the results, BaFe\({}_{2}\)O\({}_{4}\) and CaFe\({}_{2}\)O\({}_{4}\) showed good syngas selectivity, while BaFe\({}_{2}\)O\({}_{4}\) exhibited excellent reactivity and regeneration in the air after reduction.
###### Abstract  
The number of applications where fluidic oscillators are expected to be used in the future, is raising sharply, then their ability of interacting with the boundary layer to modify forces on bluff bodies, enhancing heat transfer or decreasing noise generation, are just few of the applications where fluidic oscillators can be used. For each application a particular pulsating frequency and amplitude are required to minimize/maximize the variable under study, force, Nusselt number, etc. For a given range of Reynolds numbers, fluidic oscillators present a linear relationship between the output frequency and the incoming fluid flow, yet it appears the modification of the internal fluidic oscillator geometry may affect this relation. In the present paper and for a given fluidic oscillator, several performance parameters will be numerically evaluated as a function of different internal modifications via using 3D-CFD simulations. The paper is also evaluating the relation between the momentum applied to the mixing chamber incoming jet and the oscillator output characteristics. The evaluation is based on studying the output mass flow frequency and amplitude whenever several internal geometry parameters are modified. The geometry modifications considered were: the mixing chamber inlet and outlet widths, and the mixing chamber inlet and outlet wall inclination angles. The concept behind this paper is, to evaluate how much the fluidic oscillator internal dimensions affect the device main characteristics, and to analyze which parts of the oscillator produce a higher impact on the fluidic oscillator output characteristics. For the different internal modifications evaluated, special care is taken in studying the forces required to flip the jet. The entire study is performed for three different Reynolds numbers, 8711, 16034 and 32068. Among the conclusions reached it is to be highlighted that, for a given Reynolds number, modifying the internal shape affects the oscillation frequencies and amplitudes. Any oscillator internal modification generates a much relevant effect as Reynolds number increases. Under all conditions studied, it was observed the fluidic oscillator is pressure driven.  
fluidic oscillators design; Computational Fluid Dynamics (CFD); flow control +
Footnote †: journal: Applied Physics Letters
## 2 Related Works
### Literature Review
#### 2.2.1 Blockchain-Based Approaches  
Blockchain-based identity systems have been extensively explored for their ability to enhance security and privacy [12]. The Bring-Your-Own-Device-IDentity (BYODID) concept offers a semi-decentralized approach to identity management in the IoT [13]. It introduces blockchain-based digital identities to ensure unique, global device identities with features such as identity creation, ownership transfer, and portability across networks. The architecture uses blockchain principles to maintain a global registry of immutable records with unique identifiers for each device, enhanced anonymity, and minimal reliance on third parties. Device manufacturers generate cryptographic keys and a blockchain address during production to create a unique digital identity. This identity is securely stored on the blockchain using smart contracts, enabling ownership management and ensuring trust and security. In addition, BYODID enables the secure transfer of ownership between users while maintaining device security and traceability. This ensures that the identity remains unique, portable and globally recognized across networks.  
The authors of [14] proposed a blockchain-based identity management scheme that uses a proxy service to minimize the computational power and bandwidth required for blockchain transactions on IoT devices, while preserving transaction signatures generated directly by the device. In this approach, each IoT device is assigned a blockchain identity issued by a Certification Authority (CA) acting as an identity provider, which is used to sign transactions sent to a blockchain proxy (specifically using Fabric Proxy). However, the proposed architecture relies on a centralized identity management system by the identity provider, which conflicts with the principles of decentralized or Self-Sovereign Identity [15].  
The paper in [16] introduces the concepts of Self-Sovereign Identity (SSI) and Decentralized Identifiers (DIDs) as tools to enhance the security of IoT devices and improve data privacy and integrity by reducing reliance on external certification authorities. Building on these concepts, the authors of [17] apply them using a permissioned blockchain approach, allowing device owners to create and fully control their credentials without requiring authorization from intermediaries. The authors also propose that a device's identity should be established by the manufacturer during production in a secure execution environment, with the private key securely stored in an embedded secure chip.  
[PERSON] et al. [18] outlined the potential of blockchain to establish Self-Sovereign Identity (SSI) ecosystems, providing devices with autonomous and verifiable digital identities. Similarly, [PERSON] et al. [19] analyzed the integration of SSI with blockchain, highlighting its ability to overcome the challenges of centralized systems, especially in resource-intensive IoT applications. However, scalability and cost constraints often hinder the feasibility of blockchain in high-volume IoT environments.
## 4 Experiment and Result Analysis  
### Experimental Design and Environment  
The hardware platform used in this experiment is a desktop computer with 128 GB of memory, a 16-core, 32-thread CPU with a frequency of 3.40 GHz, and an NVIDIA (Santa Clara, CA, USA) A6000 GPU with 48 GB of VRAM. The operating system is Windows 10 64-bit Professional. Anaconda3, PyCharm 2020, and Python 3.8 are used as the data processing and deep learning platforms.  
The compression ratios of the model were set as 1%, 4%, 10%, 25%, 30%, 40%, and 50%, and the sum of reconstruction error and constraint loss were taken as the loss function. The Adam optimizer updated the network parameters, and the learning rate was set to 0.0001. The parameters of the CBAM_ISTA\({}^{+}\) are quoted from the paper published by [PERSON] et al. [23]. The peak signal-to-noise ratio (PSNR) was used as the evaluation metric to measure the quality of image reconstruction, defined as the inverse of the Mean Squared Error (MSE) between the original and reconstructed images. The formula is as follows:  
\[\text{MSE}=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\Bigl{[}I(i,j)-K(i,j) \Bigr{]}^{2} \tag{15}\]  
\[\text{PSNR}=10\times\log_{10}\biggl{(}\frac{\text{MAX}_{I}^{2}}{\text{MSE}} \biggr{)} \tag{16}\]  
where \(\text{MAX}_{I}\) is the maximum possible pixel value of the image.
## 2 Data Transmission in a Wireless Seizure Detection System  
A generic wireless EEG system is comprised of two subsystems: a wireless EEG sensor node and a data server. The sensor node captures the EEG signals and transmits them to the data server via a wireless link. The sensor node consists of a data acquisition module, a microcontroller, a flash memory module, a wireless transmitter and a battery pack. The data server, typically a standard personal computer, receives the signals and processes them. The wireless transmission of the EEG signals from the sensor node to the data server constitutes a major source of power consumption on the sensor node. By performing data compression or feature extraction on the raw EEG data on the sensor node, the amount of data that needs to be transmitted and, hence, the required power consumption can be substantially reduced. In the following, we describe the conventional approach of transmitting the entire EEG signals and two different data reduction methods, namely EEG compression and feature extraction, in the context of a wireless seizure detection system (see Figure 1).  
Figure 1: Transmission of electroencephalogram (EEG) in a wireless seizure detection system. (**Top**) The entire EEG signals are transmitted. (**Middle**) The EEG signals are compressed and transmitted. (**Bottom**) Features that are pertinent to seizure detection are extracted from the EEG signals and transmitted.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 4 Dimensioning Tools: Interference and Fading Characterization
### Single-User Case with Multiantenna Receiver
#### 4.2.1 Antenna Selection  
Given a node position \(r=|x|\), the SIMO fading distribution with antenna selection averaged on the random noise and interference, \(A_{f}=\max(F_{1},\ldots,F_{n_{r}})\) is distributed as the maximum between \(n_{r}\) i.i.d exponential variables. The CDF function is then,  
\[\text{Prob}\{A_{f}\leq u\}=(1-e^{-u})^{n_{r}}=\sum_{p=0}^{n_{r}}(-1)^{p}\binom{ p}{n_{r}}e^{-pu}. \tag{34}\]  
**Proposition 6** (Antenna selection).: _The interference Laplace transform with antenna selection is identical to the SISO case in (32),_  
\[\mathcal{L}_{I}^{\text{scl}}(s)=\mathcal{L}_{I}(s). \tag{35}\]  
_Given a node position \(r=|x|\), the SIMO fading distribution with MRC averaged on the random noise and interference,_  
\[\mathbb{E}_{I,P_{n}}\ \mathbb{E}_{A_{f}}[\mathds{1}_{\{A_{f,k}\leq A_{f}<A_{ f,k-1}\}}]=\sum_{p=0}^{n_{r}}(-1)^{p}\binom{p}{n_{r}}\Big{[}\mathcal{L}_{P_{n}} (ps_{k-1})\mathcal{L}_{I}(ps_{k-1})-\mathcal{L}_{P_{n}}(ps_{k})\mathcal{L}_{I }(ps_{k})\Big{]} \tag{36}\]  
Proof.: The interfering signal is received on a random fading coefficient that is exponentially distributed as in the SISO case. The identify in (36) can be deduced from the distribution of \(A_{f}\) given in (34).
# Size Effect of Silver Nanoparticles Derived from Olive Mill Wastewater in THP-1 Cell Lines
## 1 Introduction  
Silver nanoparticles (AgNPs) are broadly applied in the biomedical field for their antibacterial and anticancer properties [1, 2, 3, 4]. Frequently, the use of toxic solvents could increase the NPs toxicity since the reaction residues or toxic molecules can remain adhered to the surface of the AgNPs [5]. Therefore, recent research has focused on alternative methods that require the use of extracts from agricultural wastes such as leaves or tree parts [6]. However, in the Mediterranean area, the most abundant waste is represented by the residue of oil production, namely OMWW [7]. It is a serious problem, since OMWW is characterized by a high organic pollution load, namely Chemical Oxygen Demand (COD), polyphenols and suspended solids. For this reason, this waste cannot be disposed in the environment as irrigation water [8, 9]. However, the high concentration of polyphenols could be advantageous with the aim of obtaining safe metallic NPs, since these biomolecules act as reducing agents [10]. In this experimental work, we achieved AgNPs with two sizes (30 nm and 50 m), using two distinct concentrations of silver salt and OMWW. The NPs were characterized by TEM, DLS, zeta potential, UV-VIS and FTIR to know their size, morphology, surface charge and UV-Infrared absorption. Then, we used two differentconcentrations, 3 \(\upmu\)M and 5 \(\upmu\)M, of AgNPs (30 nm and 50 nm) to test their capability to trigger an inflammatory response in vitro, using macrophage cell lines (THP-1). Compared with other kinds of AgNPs achieved by the standard chemical route, our method allowed us to obtain safe AgNPs, useful for biomedical application from agricultural waste. The experimental results obtained in this work showed that both the sizes of AgNPs did not activate THP-1 in a significative manner; however, the larger size of the AgNPs was more suitable for application in vivo due to their lower capability to induce the inflammatory response. This conclusion is important because the low activation of the immune system is desirable in order to have nanocarriers which are not destroyed by immune cells once they enter into the circulatory system [11].