esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method  
### System Architecture  
Figure 3 is the system architecture diagram.  
In this study, we use the Elliptic Curve Digital Signature Algorithm (ECDSA), blockchain, and smart contracts to design a traceable authorization mechanism for the museum's digital content resource. There are six parties involved in this study: Museum (M), Content Administrator (CA), Licensee (L), Blockchain Center (BCC), Proxy (P), and Bank (B).  
1. Museum (M): The museum is the owner of the digital content. The museum collects the cultural relics and is responsible for the generation and management of the museum's digital content resource. The digital content resource is classified and protected by the museum.
2. Content Administrator (CA): The CA is a cloud platform of the museum. It is responsible for reviewing the Licensee's request to determine 'allow or not' to access the digital content resource.
3. Licensee (L): When citizens or institutions want to access the digital content resource of the museum, the Licensee should pay a premium to the museum.
4. Blockchain Center (BCC): This center records the access information of the digital right resource for the Licensee. The BCC accepts the parties' registration and issues the identity certificate and public/private key pair to each party.
5. Proxy (P): The proxy is an agency of the museum. After CA authenticates the Licensee's identity, P is responsible for actually cloud authorization for the Licensee to access the museum's digital content resource.
6. Bank (B): Bank is authorized by a Licensee to pay a premium to the museum. We briefly illustrate the scenarios in the following steps.
7. Step 1: Registration phase: Museum, Licensee, Proxy, and Bank need to register with Blockchain Center; the Blockchain Center issues the identity certificate and public/private key pair to each party.
8. Step 2: Digital content production phase: The DCA classifies the museum's resources, encrypts these resources into a protected digital resource, and then stores it in the CA. The CA also uploads the detailed categories into the Blockchain center.
9. Step 3: Authentication phase and issuing invoice phase: After the Licensee proposes to access digital resource requests, the CA reviews the Licensee's qualifications and then issues the invoice.
10. Step 4: Payment phase:  
Figure 3: The system architecture.  
After payment, the Licensee requests the Bank to issue a certificate for the museum to authenticate this payment. The Content Administer then authenticates the Licensee's identity. The Content Administer performs one of the following cases.  
Case 1: Generates the authorized key to the Licensee directly.  
Case 2: Generates a proxy key to the Agency, and the Agency transfers it to the Licensee.  
* Step 5: Digital content browsing phase:  
After the Licensee receives the authorized key, the Licensee uses it to decrypt the protected digital content. The digital content can be read (or played) normally.
## 2 Network Piping System; Flow Distribution Calculation  
### Topology of the Network  
The first step in solving a pipe network problem is to make a network map showing pipe diameters, lengths and connections between pipes (nodes). Sources of natural gas supply and consumption rates have to be assigned to nodes. For convenience in locating pipes, code numbers are assigned to each pipe and closed loop of pipes (represented by roman numbers for loops in Figure 1). Pipes on the network periphery are common to one loop and those in the network interior are common to two loops. Figure 1 is an example of a pipe network for distribution of natural gas for consumption in households.  
The next step is to write the initial gas flow distribution through pipes in the network. This distribution must be done according to [PERSON]'s first law. The choice of initial flows is not critical, and the criterion should satisfy [PERSON]'s first law for every node in the network [3].  
Figure 1: The network of pipes for natural gas distribution for domestic consumption.  
The total gas flow arriving at a node equals the total gas flow that leaves that node. The same conservation law is also valid for the whole network in total (except for gas input and output nodes that cannot be changed during calculations; see consumption nodes in Figure 1). The sum of pseudo-pressure drops along any closed path must be approximately zero for the network to be in balance according to [PERSON]'s second law. In this paper, the flow distribution, which satisfies both of [PERSON]'s laws, will be calculated using the Hardy Cross iterative method.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 3 Stratifying Privacy-Preserving Solutions  
Privacy and security in IoT-based healthcare systems are deeply interconnected, yet they address distinct concerns. Security focuses on protecting data from unauthorized access, tampering, and cyber threats, employing measures such as encryption, authentication, and intrusion detection. Privacy, on the other hand, ensures that individuals retain control over their personal health information, dictating how, when, and by whom data can be accessed. While security is a prerequisite for privacy, it is not sufficient on its own; privacy requires additional mechanisms, such as data minimization, anonymization, and user-controlled access. This review examines privacy-preserving solutions in IoT-based healthcare while acknowledging their dependence on robust security measures.  
The integration of the IoT in healthcare solutions has highlighted the importance of complying with privacy regulations and preserving patients' privacy, especially in relation to their sensitive data. This review presents a structured framework derived from the existing literature, summarizing the techniques that preserve the confidentiality, integrity, and access control of patient data. The framework helps guide the design and implementation of a privacy-preserving IoT-based healthcare system by presenting five elements of robust privacy-preserving solutions, as shown in Figure 2. These include Data Protection Mechanisms, access control and authorization, Privacy-Preserving Data Sharing, privacy policies and governance, and User Awareness and Empowerment. Furthermore, we present a classification of these core elements in Figure 3.
## 3 Results and Discussion
### Uncertainty Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Impact Category** & **Unit** & \begin{tabular}{c} **Minimum** \\ **Result** \\ \end{tabular} & **Average Result** & \begin{tabular}{c} **Maximum** \\ **Result** \\ \end{tabular} & **Medium** & \
\begin{tabular}{c} **Standard Devi** \\ **(\(\sigma\))** \\ \end{tabular} &
\begin{tabular}{c} **Coefficient of** \\ **Variation (\%)** \\ \end{tabular} \\ \hline Acidification & kg SO\({}_{2}\) eq & 0.026 & 0.039 & 0.058 & 0.0382 & 0.0072 & 18.50 \\ \hline Eutrophication & kg PO\({}_{4}\) eq & 6.19-10\({}^{-3}\) & 8.25-10\({}^{-3}\) & 1.13-10\({}^{-2}\) & 0.0084 & 0.0013 & 15.61 \\ \hline Global Warming & kg CO\({}_{2}\) eq & 3.442 & 4.303 & 5.379 & 4.268 & 0.4886 & 11.35 \\ \hline Photochemical & kg NMVOC & 0.0355 & 0.0525 & 0.0775 & 0.0513 & 0.0103 & 19.76 \\ \hline Abiotic & kg Sb eq & 1.99-10\({}^{-6}\) & 2.41-10\({}^{-6}\) & 3.02-10\({}^{-6}\) & 2.44-10\({}^{-6}\) & 2.51-10\({}^{-7}\) & 10.23 \\ \hline Depletion, fossil & MJ & 84.85 & 106.06 & 133.10 & 104.98 & 12.121 & 11.43 \\ fuels & & & & & & \\ \hline Water Scarcity & m\({}^{3}\) eq & 3.755 & 12.515 & 19.398 & 12.72 & 3.970 & 31.46 \\ \hline Ozone Layer & kg CFC-11 eq & 6.84-10\({}^{-7}\) & 1.37-10\({}^{-6}\) & 3.18-10\({}^{-6}\) & 1.22-10\({}^{-6}\) & 5.945-10\({}^{-7}\) & 43.80 \\ Depletion & kg CFC-11 eq & 6.84-10\({}^{-7}\) & 1.37-10\({}^{-6}\) & 3.18-10\({}^{-6}\) & 1.22-10\({}^{-6}\) & 5.945-10\({}^{-7}\) & 43.80 \\ \hline Land Use & m\({}^{2}\) crop eq & 0.0414 & 0.0243 & 0.0609 & 0.0243 & 4.86-10\({}^{-3}\) & 19.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Exposure of variability of results due to uncertainty in the different impact categories. Figure 3: Results uncertainty analysis as a function of error values for each environmental impact category. 31.46%, respectively, the CV is 14.48% for the impact categories. This is a value that reflects that the data used in the research are not distorted, which justifies that the results obtained in Section 3.1 Environmental Results are considered correct and in accordance with the reality of NCA production in Spain.
## 3 Analytical Model of the HIL Equipment
### Proportional Valves  
The flow proportional valves are modeled according to the ISO 6358, thus they work as pneumatic nozzles characterized by a sonic conductance \(C\) and a critical ratio \(b\). Considering \(P_{\rm A}\) and \(P_{\rm B}\), respectively, as the upstream and downstream absolute pressures, and \(\rho_{0}\) as the air density at 25 \({}^{\circ}\)C, the flow rate \(G\) was calculated as:  
\[G=\rho_{0}P_{\rm A}C\ \text{for}\ 0<\frac{P_{\rm B}}{P_{\rm A}}\leq b,\ \ G=\rho_{0}P_{\rm A}C\sqrt{1-\left( \frac{P_{\rm B}/P_{\rm A}-b}{1-b}\right)^{2}}\ \text{for}\ b<\frac{P_{\rm B}}{P_{\rm A}}\leq 1 \tag{1}\]  
Equation (1) is used to calculate the flow rate through each valve: it is considered positive when the flow enters a cylinder's chamber, and vice versa. The conductance \(C\) is not constant, but depends on the sectional area, which can be varied by means of a command signal. Assuming the variation of the conductance linear with respect to the voltage command \(V_{\rm ref}\), the following simplified relation is considered:  
\[C=K_{\rm v}\ V_{\rm ref} \tag{2}\]  
in which \(K_{\rm v}\) is the static flow gain of the valve, equal to:  
\[K_{\rm v}=\frac{C_{\rm max}}{V_{\rm max}} \tag{3}\]  
To consider the dynamic effect of each valve on the system, a first order dynamics was assumed, as reported in Equation (4):  
\[C=\frac{K_{\rm v}}{1+\tau s}V_{\rm ref} \tag{4}\]
## 4 Discussion and Validation
### Intelligent Maintenance (Scenario 5) vs. Corrective Use Case (Scenario 4)  
The final results of this research clearly demonstrate tempting lifetime benefits during 20 years of operation. In comparison, the intelligent maintenance system is expected to improve the operational availability by 0.268% by replacing 2.721% ((4183/16181) \(-\) (3724/16101) = 2.721%) of the corrective maintenance workload with intelligent maintenance. In workload, it equals replacing 459 h of corrective maintenance which corresponds to a reduction of 11% ((4183 \(-\) 3724)/4183 = 11%) of the total corrective maintenance workload. Specifically, the intelligent maintenance system reduced the unintended corrective maintenance visits by 20 (26.316%), whereas a reduction of 5 (62.500%), 8 (61.538%), and 7 (24.138%) corrective maintenance visits are traced back to the rotor, bearing, and seal, respectively. Following, these 20 corrective maintenance events were replaced by intelligent maintenance which leverages the PdM capabilities into opportunistic maintenance intervals and thereby does not affect the operational availability.
# Efficient Low-Complexity Turbo-Hadamard Code in UAV Anti-Jamming Communications
###### Abstract  
Unmanned aerial vehicle (UAV) systems undergo a period of rapid development in both civil and military scenarios. A major challenge in the malicious jamming environment is to guarantee the reliability of UAV communications links. Frequency hopping (FH) is one of the most commonly used means of combatting the influence brought about by jamming. In this paper, we integrate low-rate codes into an anti-jamming FH communications system, and propose an efficient and low-complexity turbo-Hadamard code scheme. Tail-bitring is applied to design the component convolutional-Hadamard codes, and a corresponding decode algorithm is used for implementation in the UAV hardware platform. Numerical simulation results demonstrate that the anti-jamming performance of this method is improved as compared with conventional concatenated codes. Finally, we compare the complexity and transmission efficiency of the proposed algorithm with the algorithms implemented on the field programmable gate array (FPGA) platform in detail.  
unmanned aerial vehicles; anti-jamming; low-rate codes; turbo-Hadamard codes Article
# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 1 Introduction
Second, a new algorithm was developed to detect logos in television broadcast content, even in the presence of partial occlusion and without making use of prior data. The algorithm was assessed over the datasets demonstrating superior capacity with regards to the state-of-the-art. The remainder of this article is structured as follows. Section 2 describes and compares advertisement detection methods from the literature, providing an analysis of the problems that are generally found. In Section 3, we detail the created television dataset, providing an analysis of the different features that can be found on advertisements and programs as well as an accounting of the most relevant aspects that can be used to differentiate advertisements from program content. Section 4 presents the proposed and implemented methodology for an automatic advertisement detection. The evaluation methodology and the obtained results are discussed on Section 5. Finally, Section 6 details the conclusions and potential future work.
## 3 Overview of HO Management in 5G UDSC Network
### HO Management in 5G UDSC Network
(2) decision: in this step HO process is initiated. If the initiation occurs in inaccurate time, then there is increase in call drop and thus reducing the QoS. (3) execution: to improve the QoS, decision should be executed at the right time to bypass the irrelevant HO. When both the second and third stage of HO is operated by mobile station or any other controller, then HO process is classified according to controller basis such as network controlled HO (NCHO), mobile controlled HO (MCHO) and mobile assisted HO (MAHO) [74]. Following the guidelines in [75], Table 3 illustrate the HO information gathering (network and mobile terminal related) and decision making (criteria based and strategy based) categories. There are three parameters considers for the HO procedure i.e., system parameters, control parameters, performance parameters. First, Reference Signal Received Power and signal to noise and interference ratio comes under the system parameters. Second RSS HO Margin (Hysteresis), Time-to-Trigger (TTT) comes under the control parameters category. Third, HO failure ratio (HPIHOF), ping-pong HO ratio (HPIHPP), and call dropping ratio (HPIDC) comes under performance metrics [76].
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 3 Bending Analysis  
Bending analysis of a flexible jeans-based antenna involves a meticulous examination of the antenna's behavior when subjected to various curvatures or bends. This evaluation is pivotal in assessing the antenna's performance, durability, and signal integrity in real-world scenarios. From an electromagnetic perspective, the bending analysis delves into how curvature affects the antenna's radiation pattern, resonant frequency, bandwidth, and impedance matching. Moreover, the resonance frequency of the antenna can experience deviations as the curvature induces variations in the electrical length of the radiating element.  
Figure 3 depicts the flexible configuration of the antennas under consideration in case (i) R = 60, theta = 40, and case (ii) R = 50, theta = 45. While the impedance matching remains consistent during operation, the resonance frequency experiences a slight rightward shift owing to the curvature of the structure. Notably, even with further bending, the antennas consistently retain their higher frequency band across all scenarios. In conclusion, the impedance, resonance frequency, and bandwidth of the novel bent antenna exhibit strong performance, underscoring its suitability for real-world communication scenarios. Figure 4 depicts three scenarios concerning S-parameters.  
Figure 3: Bending analysis of the antenna.(**a**) Theta = 0 (i.e., flat). (**b**) Theta = 40. (**c**) Theta = 45.
## 2 Materials and Method
### 3D Printing Cylindrical LW-PLA Samples for Radiological Characterization  
In this study, a Raise3D Pro2 Plus printer (Raise3D, Irvine, CA, USA) was utilized with a 0.4 mm diameter nozzle. Prior to the printing process, ideaMaker 4.3.2 (Raise3D, Irvine, CA, USA) was used as the slicing software to generate the G-code for the cylindrical models. All models had identical dimensions of 1.5 cm diameter and 2 cm height (Figure 1). Samples were printed with a 1.75 mm diameter LW-PLA filament from Colorfabb in Belfeld, Netherlands. This filament is special for its foaming at around 220 \({}^{\circ}\)C and above, which allows for a reduced density at the nozzle exit. The ability to control the density of the filament material specifically at the nozzle exit is crucial for having prints at lower densities, without making any changes (i.e., reduction) to the infill rate.  
Cylindrical samples were printed in six groups of printing temperatures, ranging from 200 \({}^{\circ}\)C to 250 \({}^{\circ}\)C in increments of 10 \({}^{\circ}\)C. Each group was printed with 5 specific flow rate settings, as detailed in Table 1. Other main printing parameters, such as the infill rate, print speed, layer thickness, and fan speed, were held constant across all printing jobs, set at 100%, 33 mm/s, 0.15 mm, and 50%, respectively. The infill pattern was chosen as 'lines'.  
For temperatures of 220 \({}^{\circ}\)C and above, the maximum applied flow rate was deliberately decreased in increments of 10%. This adjustment aimed to prevent any printing artifacts, such as swelling of the print due to overexpression induced at high printing temperatures. This phenomenon is clearly demonstrated in Figure 2, which depicts two samples: one printed with LW-PLA and the other with regular PLA (Porima, Turkey). Both samples were printed at a temperature of 250 \({}^{\circ}\)C and a flow rate of 100%. As is evident, the LW-PLA sample exhibited overexpression, resulting in printing artifacts, whereas this is not observed in the sample printed with regular PLA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Temperature (\({}^{\circ}\)C)** & \multicolumn{4}{c}{**Flow Rate (\%)**} \\ \hline
200 & 100 & 90 & 80 & 70 & 60 \\ \hline
210 & 100 & 90 & 80 & 70 & 60 \\ \hline
220 & 90 & 80 & 70 & 60 & 50 \\ \hline
230 & 80 & 70 & 60 & 50 & 40 \\ \hline
240 & 70 & 60 & 50 & 40 & 30 \\ \hline
250 & 60 & 50 & 40 & 30 & 20 \\ \hline \end{tabular}
\end{table}
Table 1: Printing temperature and flow rate settings for 30 cylindrical LW-PLA samples.  
Figure 1: Preview of the printing setup in ideaMaker for a group of cylindrical samples. Each setup features a fixed printing temperature but varying flow rates among the samples.  
In addition to the cylindrical LW-PLA samples, a set of cylindrical regular PLA samples were printed with identical dimensions to serve as blank controls. The printing temperature was chosen as 230 \({}^{\circ}\)C, which was the maximum value recommended by the vendor. Flow rates were set as 100%, 80%, 60%, 40%, and 20%. All other printing parameters were kept constant.
# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 4 Experimental Results and Analysis  
Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Building Index (NDBI), etc. are all manifestations of the interaction between bands in a ratio [32]. Among them, NDVI is expressed as \(\text{(NIR - R)}\)/(NIR + R), which is related to the reflection value of the NIR band and the R band. The above indexes are obtained through a large number of experiments in the laboratory. This article aims to use a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. These features used as new indexes are compared with the indexes obtained by the traditional method. This experiment only takes vegetation and water body as objects for analysis, and subsequent research can be further extended to more types of ground features.  
The interaction relationship between original features is expressed as new features by multiplication or division operation, which can be used as a new index for extracting ground feature information. When considering the interaction between bands in the experiment, the following methods are used to reduce data redundancy and make the amount of calculation controllable: considering that in hyperspectral data, adjacent bands have similar spectral characteristics, it is calculated once every 9 bands The median value is used as a representative. It should be noted that only the median value in the odd number of bands can retain the information characteristics of the original band. If the average value or the median value in the even number of bands is taken, the original band needs to be averaged, which will destroy the characteristics of the original band. The reason why we chose 9 bands has been illustrated in Section 4.4. In addition, continuous strengthening of L1 regularization will make the feature matrix sparse. When automatically learning the interaction relationship, if the feature dimension is greater than the number of training samples, the sparsity of the L1 norm is required to reduce the feature dimension, so the use of the interaction between bands to determine the type of features needs to rely on the sparsity of the L1 norm.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Case Studies  
Bluetooth low-energy (BLE) technology finds extensive application in wearable devices. For example, Fitbit's wearable health tracker employs lightweight encryption to safeguard the transmission of user data, utilizing low-power Bluetooth (BLE) technology for synchronizing data with smartphones. Sports watches offer high-precision GPS tracking while maximizing battery usage through dynamic adjustments to the GPS frequency and the use of energy-efficient secure communication protocols.  
Lightweight encryption algorithms are widely used in smart home devices, especially in resource-constrained devices, to strike a balance between security and energy efficiency. For example, the Advanced Encryption Standard (AES) is a symmetric block cipher scheme used to protect sensitive data [99], which is broadly employed in smart door locks. A smart door locks automatically enters sleep mode when the door is closed, and it wakes up only upon receiving a legitimate unlocks command [100]. SimpliSafe, one of the most popular home security systems on the market today, employs secure TLS and elliptic curve encryption to protect video transmissions. Its cameras reduce power consumption by entering a low-energy standby state when no motion is detected [101].  
Industrial sensors reshape the \"sensing system\" in Industry 4.0; improving the energy efficiency and security capabilities of sensing systems greatly stimulates IoT systems to progress toward the goal of energy-aware security. Intelligent industrial sensing has the capability to analyze, evaluate, and even adaptively learn to perform complex tasks, including image recognition, feature detection, and multi-dimensional detection. For example, [PERSON] et al. [102] proposed a sensor interface device crucial for sensor data acquisition in industrial wireless sensor networks (WSNs). This device utilizes a complex programmable logic device (CPLD) as the core controller application. CPLDs as the core controller greatly streamline the design of peripheral circuits and reduce the current consumption. Moreover, by adhering to the IEEE 1451.2 Smart Sensor Interface Specification standard [102], it ensures the secure and efficient acquisition of data from a variety of sensors, which is crucial for maintaining safety and promoting energy efficiency in industrial automation processes.  
Figure 10: The structure of federated learning.