esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Constellation Design
### Constellation Technical Characteristics
#### 2.2.4 Onboard Image Processing Technology and Dual-Line Array Delay System for Ship Detection  
The remote sensing of ocean areas faces a fundamental challenge posed by seawater background information, which not only occupies data transmission bandwidth but also adds noise to the collected data. The integration of onboard image processing technology of the HN-1 01 satellite addresses this issue by facilitating ship detection and information acquisition, leading to a reduction in downlink data. The inclusion of a dual-line array delay imaging system enhances moving target extraction, streamlining the search for key targets. Users can opt to transmit only sliced images containing identified ships, instead of whole-track images, thereby strengthening the efficiency of ship identification and information transmission to ground stations.  
Simulation has substantiated that, with a 30\({}^{\circ}\) inclination and a 500 km orbit height, three networking satellites can achieve daily coverage of the entire Hainan Province, which increases to 2-3 times a day in the key areas. Deploying 10 networking satellites with a wide swath width enables daily, high-coverage observations of low-latitude regions. Given the frequently cloudy and rainy weather conditions, the payloads of these satellites should include multispectral, hyperspectral, and SAR sensors [43].  
A medium spatial resolution of 5 m proves sufficient for identifying ships exceeding 20 m in size. In the panchromatic spectrum, 20 m is in a critical state. Ships over 60 m can be detected in the panchromatic spectrum in calm sea conditions and ships over 100 m can be effectively detected.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 5 Analysis of Performance  
We performed the analysis of the multi- authority CP-ABE access control method in this section. The content of the analysis is expanded from three aspects: flexibility analysis, calculation overhead, and efficiency analysis.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 2 Related Work
### Basic CCN [2]  
When a node requests content in CCN, it sends an interest packet containing the content name towards the content provider. If a node receiving the interest packet has the requested content, it replies with a data packet containing the content. Otherwise, it records the content name and the ID of the requesting node in its Pending Information Table (PIT) and forwards the interest packet towards the content provider according to its Forwarding Information Base (FIB). When a node receives a data packet, it stores the content included in the packet in the Content Store (CS) and forwards the packet towards the requesting node according to its PIT. Generally, CCN was designed for wired networks and has no node mobility considerations.
## 5 The Proposed Hybrid Scheme  
In the proposed hybrid scheme, two key short-range communication technologies, Wi-Fi (802.11) and Bluetooth (802.15.1), are interconnected. The major reason behind designing such a scheme is to utilize the strong features of both of the technologies so as to attain features such as low-cost, low-power, high range, and high speed. We consider multiple representative classes of UAV: Bluetooth slave (BS), Bluetooth master (BM), and Gateway (GW). At first, with the help of beacon signals, discovery function is performed to establish a connection between the associated UAV. Once the connection is established, data are transferred among the BM-UAV and BS-UAV using 802.15.1, while the data communication between the GW-UAV and GS is established through 802.11. Further details of the proposed scheme are given in the following subsections.
# Android-Based Audio Video Navigation System Forensics: A Case Study
## 4 Android-Based AVN System Forensics
### Timeline Construction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Event** & **Artefact (Location and Name)** & **Description** \\ \hline
6/3/2019 06:45:38 & Vehicle ON & \
\begin{tabular}{c} Navigation App \\ (/data/data/com.mnsoft.navi/UserData \\ /KOR/startlog\_lepochtime).txt) \\ \end{tabular} &
\begin{tabular}{c} Driver starts \\ the engine \\ \end{tabular} \\ \hline
6/3/2019 06:45:45 & Destination search & \
\begin{tabular}{c} Navigation App \\ (/data/data/com.mnsoft.navi/databases \\ /Navi.vr.db) \\ Select the one registered in the info\_name \\ field of Destination\_table \\ \end{tabular} &
\begin{tabular}{c} Chooses \"office\" \\ as destination. \\ (Address) \\ \end{tabular} \\ \hline
6/3/2019 06:45:45 & \
\begin{tabular}{c} Navigation App \\ (/data/data/com.mnsoft.navi/Userdata \\ /KOR/FavoriteDest.bin) \\ \end{tabular} &
\begin{tabular}{c} Time to \\ search the \\ destination \\ \end{tabular} \\ \hline
6/3/2019 06:52:47 & \
\begin{tabular}{c} Bluetooth App \\ (/data/data/com.android.providers. \\ bluetooth/databases/BST setup.db) \\ \end{tabular} &
\begin{tabular}{c} MAC address \\ and name of \\ the smartphone \\ \end{tabular} \\ \hline
6/3/2019 06:52:47 & \
\begin{tabular}{c} System Log \\ (/ivilog/dropbox/trace\_log.txt) \\ \end{tabular} &
\begin{tabular}{c} Bluetooth \\ connection time \\ \end{tabular} \\ \hline
06:53:15 & Downloading 06:53:15 & \
\begin{tabular}{c} Bluetooth App \\ (/data/data/com.android.providers. \\ bluetooth/databases/BTContacts.db) \\ \end{tabular} &
\begin{tabular}{c} phonebook \\ (names of people, \\ phone numbers, etc.) \\ \end{tabular} \\ \hline
6/3/2019 07:39:18 & \
\begin{tabular}{c} Downloading \\ (/data/data/com.android.providers. \\ bluetooth/databases/BTCallHistory.db) \\ \end{tabular} &
\begin{tabular}{c} Recent call history \\ (names, phone numbers, \\ time of the call) \\ \end{tabular} \\ \hline
6/3/2019 07:39:18 & \
\begin{tabular}{c} Bluetooth App \\ (/data/data/com.android.providers. \\ bluetooth/databases/BTCallHistory.db) \\ \end{tabular} &
\begin{tabular}{c} Call info (name, \\ phone numbers, \\ time of the call) \\ \end{tabular} \\ \hline
6/3/2019 07:39:18 & \
\begin{tabular}{c} Bluetooth App \\ (/data/data/com.android.providers. \\ bluetooth/databases/BTCallHistory.db) \\ \end{tabular} &
\begin{tabular}{c} Call info (name, \\ phone numbers, \\ time of the call) \\ \end{tabular} \\ \hline
6/3/2019 18:40:54 & \
\begin{tabular}{c} Navigation App \\ (/data/data/com.mnsoft.navi \\ /UserData/KOR/GPStack.dat) \\ \end{tabular} &
\begin{tabular}{c} GPS tracklogs \\ (longitude, latitude, \\ Korea area code) \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 11: Timeline of the driver’s events/activities constructed from forensic artefacts of the Kia NIRO EV. 41) in the GPSTrack.dat file which stored GPS tracklogs. The offsets, in turn, represent the longitude, latitude, and Korea area codes. Using the Talmap tool, we found that the offsets represent the address of 500, Sorae-ro, Namdong-gu, Incheon, Republic of Korea.
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 2 The System Model  
Aiming at the possible scenario of multiple simple IOT devices communicating with the reader in the future, this paper designs a system model in which multiple tags communicate with the reader in the ABC environment, while the eavesdropper tries to monitor the data sent by tags, as shown in Figure 1. Each passive tag modulates symbol information on the RF signal in the environment by adjusting its antenna impedance, and then sends the modulated signal to the reader. In addition, because of the broadcast nature of the wireless communication channel, the eavesdropper will also receive modulated signals. Suppose the channel parameters between the tags and the RF signal source, the tags and the reader, the tags, and the eavesdropper are \(h_{k},d_{k},f_{k}\) respectively.  
Where \(h_{k}\sim\mathcal{CN}(0,\sigma_{h}^{2}),d_{k}\sim\mathcal{CN}(0,\sigma_{d}^{2})\) and \(f_{k}\sim\mathcal{CN}(0,\sigma_{f}^{2})k\in\{1,\ldots,K\}\), \(\sigma_{h}^{2},\sigma_{d}^{2},\sigma_{f}^{2}\) represent the distance between the tags and the RF signal source, the tags and the reader, the tags and the eavesdropper, respectively. Moreover, we define both the channel between the RF signal source and the reader, and the channel between the RF signal source and the eavesdropper as Rayleigh fading channel with zero mean and unit variance, i.e., \(h_{r}\sim\mathcal{CN}(0,1),h_{e}\sim\mathcal{CN}(0,1)\), The signals received by reader and eavesdropper can be respectively expressed as:  
\[y_{r}(n)=h_{r}s(n)+\sum_{k=1}^{K}h_{k}d_{k}\eta_{k}s(n)B_{k}(n)+\omega_{r}(n) \tag{1}\]  
Figure 1: The Ambient backscatter communication system model.  
\[y_{e}(n)=h_{e}s(n)+\sum_{k=1}^{K}h_{k}d_{k}\eta_{k}s(n)B_{k}(n)+\omega_{e}(n) \tag{2}\]  
where \(s(n)\) represents the complex equivalent signal of the RF signal, \(B_{k}(n)\) represents the symbol sent by the \(k\)-th tag by changing the antenna impedance, \(\eta_{k}\) represents the signal attenuation factor inside the tag, \(\omega_{r}(n)\) and \(\omega_{e}(n)\) represent the additive white Gaussian noise with zero mean and unit variance.
## 6 Simulation-Based Implementation, Experimental Results and Discussion
### Simulation of Abnormalities and Attack Countermeasures  
Figure 6 shows the countermeasures taken against abnormalities detected in a WSN with the aid of the proposed mechanism. A packet from malicious node 31 is received and dropped, as shown in Figure 6. The encryption/decryption mechanism is the main process that strives to achieve confidentiality, which is the primary requirement for security. Therefore, all sent packets are encrypted, and hence, all received packets must be decrypted. For example, when the sink (or an intermediate node) receives a packet, it will execute the decryption process. If the received packet is decrypted with a legal output, then the sink will consider this packet to be a legal packet originating from a legitimate node. Otherwise, the sink (or intermediate node) will regard this packet as an attack packet. Subsequently, if and when the same node has launched a certain number of attacks at the sink (or intermediate node), then that node will be regarded as an adversary and removed from the neighbor table. Hence, the random-key-based efficient encryption mechanism secures the network from the time of deployment.
# Research on Compressed Sensing Spectrometry Based on Electro-Optical Transmittance Coding
###### Abstract  
To develop fast and integrated spectrometry, we present a new method of compressed sensing spectrometry based on electro-optical transmittance coding. A LiNbO\({}_{3}\) electro-optic modulator was applied to a directly compressed measurement code in the spectral dimension. Subsequently, a sequential forward floating selection algorithm was employed to select the coding measurement matrix, and sparsity adaptive matching pursuit was used as a solver algorithm. The principle was analyzed, and the measurement system was built for verification experiments. The experimental results reveal that the spectral reconstruction relative error is in the order of \(10^{-2}\), and the full width at half maximum of the spectral measurement is as high as 1.2 nm. The spectral resolution can reach approximately 0.4 nm in the visible light band range (0.38-0.78 \(\upmu\)m) with 1024 spectral channels. The compressed ratio of the compressed sensing spectrometry is up to 1:29. Furthermore, the spectral signal measurement time is only 0.25 ms. The study demonstrates a novel method, which exhibits high precision, speed, high compressed ratio and hyperspectral resolution.  
compressed sensing; spectrometry; electro-optical modulation; transmittance coding +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: email: [EMAIL_ADDRESS]  
2
Footnote 2: email: [EMAIL_ADDRESS]
## 3 Background  
The CubeSat program is designed to standardize and reduce development costs of nanosatellites, as well as ensuring the safety of the launch vehicle. This is done with the aim of improving the accessibility of space exploration for a range of organizations including academic institutions, governmental divisions, and the corporate sector through education and by establishing a baseline  
\begin{table}
\begin{tabular}{c c} \hline
**Operational Requirement** & **Stated Value** \\ \hline Operational Altitude & Average 600 km, min value 550 km \\ Mission Duration & Minimum 6 Months–1 year \\ Payload Mass & 1 kg \\ Payload Power Draw & 1.9 W \\ Payload Dimensions & 100 \(\times\) 100 \(\times\) 100 mm (1U Dimensions) \\ Inclination & 98 degrees \\ \(\beta\) & 0 degrees \\ \hline \end{tabular}
\end{table}
Table 1: Summarizes the key numerical values presented in Section 2.4, Mission and Payload Specification.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value at 600 km (Low Solar Activity)** \\ \hline Background Space Temperature & 3 K \\ Density & 1.03 \(\times\) 10\({}^{-14}\) kg/m\({}^{3}\) \\ Pressure & 1.09 \(\times\) 10\({}^{-8}\) Pa \\ Molar Weight & 5.5149 kg/mol \\ \hline \end{tabular}
\end{table}
Table 2: Atmospheric Properties at a 600 km Altitude [10].  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value at 600 km (Mean Solar Activity)** \\ \hline Background Space Temperature & 3 K \\ Density & 1.56 \(\times\) 10\({}^{-13}\) kg/m\({}^{3}\) \\ Pressure & 1.09 \(\times\) 10\({}^{-8}\) Pa \\ Molar Weight & 13.0389 kg/mol \\ \hline \end{tabular}
\end{table}
Table 3: Atmospheric Properties at a 600 km Altitude with mean solar activity [10].  
specification. The 3U CubeSat has nominal dimensions of 100 mm by 100 mm by 340.5 mm [1]. Figure 4 illustrates 1U and 3U CubeSat developed by CalPoly. Pre-Assembled flight-ready structures are available at a premium from specialist suppliers; however, the design specification developed by CalPoly is designed to help those looking to construct a CubeSat independently. Upon launch the CubeSats are loaded into a Poly Picosatellite Orbital Deployer (P-POD) [1]. The requirements for insertion into the P-POD are also specified in the design guide. Only one 3U CubeSat may be loaded into the P-POD at a time, any larger craft than a 3U would require the development and approval of a custom fabricated deployer.
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 2 Materials and Methods
### Other Regression Methods
#### 2.4.3 Deep Artificial Neural Networks  
Deep Artificial Neural Networks (DANN) are very popular in many fields, and have been used in wind farms for failure detection before [20,21]. Among the inconveniences when using DANNs, we have to note their structure itself (number of hidden layers, number of units in each hidden layer), the activation function and the optimisation algorithm (several options available) and the computational time required for the training step.  
Another important problem is related to the overfitting effect, which may appear when the structure has a lot of parameters to adjust and a small number of training samples. To avoid overfitting, dropout will be implemented [22] in the experiments of our DANN with 3 hidden layers, used as a regression model. The main parameters of the DANN are: Adam optimizer, max epochs 50, minibatch size 128. The structure of the DANN is as follows: input layer (6 neurons); hidden layer 1 (20 neurons) with ReLu as the activation function and a dropout percentage of 20%; hidden layer 2 (10 neurons) with ReLu as the activation function and a dropout percentage of 10%; hidden layer 3 (5 neurons) with ReLu as the activation function; output layer (1 neuron).
## 3 Cryptographic Preliminaries
### Homomorphic Encryption
However, bootstrapping is commonly considered as a costly procedure, so one usually tries to avoid it as much as possible [14]. **Security of HE**. An essential attribute of the CKKS is its semantic security. This ensures that a computationally bounded adversary cannot distinguish whether a given ciphertext corresponds to the encryption of one of two different messages. Intriguingly, even if two messages are identical (i.e., \(\mathbf{x}=\mathbf{y}\)), their respective ciphertexts will differ (i.e., \([\mathbf{x}]\
eq[\mathbf{y}]\)). This is due to random noise introduced during the encryption process. The robustness of CKKS's semantic security is based on the Ring Learning with Errors (RLWE) assumption [62]. The RLWE problem has undergone extensive scrutiny within the cryptographic community. Significantly, it is deemed a computationally hard problem, resistant to quantum attacks, thereby positing RLWE-based schemes like CKKS as viable options in the quantum era.
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 2 Background  
### Motivation  
Phishing remains one of the most prevalent and damaging forms of cyberattacks, with increasingly sophisticated techniques that challenge traditional detection systems. Although several phishing detection tools are currently in use, many struggle to detect advanced tactics such as Zero-Font phishing and other obfuscation-based methods. Traditional filters, which are highly dependent on static rules or superficial content analysis, often fail to capture the nuanced and deceptive strategies employed by modern attackers. These limitations create vulnerabilities that expose users to data breaches, financial losses, and a growing erosion of trust in digital communication.  
In this context, machine learning presents a powerful solution for enhancing phishing detection. Unlike conventional tools, machine learning algorithms--such as Random Forest, Decision Trees, and Support Vector Machines--can process large datasets to identify subtle patterns and features indicative of malicious behavior. These models learn from historical data, adapt to evolving threats, and offer real-time classification of phishing emails with high accuracy. By leveraging techniques like feature extraction and pattern recognition, machine learning significantly improves detection speed, accuracy, and resilience against emerging attack vectors.  
Integrating machine learning into phishing detection frameworks not only addresses the limitations of traditional methods but also enables more proactive defense strategies. As cyber threats continue to evolve, machine learning becomes increasingly vital for protecting sensitive information and maintaining trust in digital ecosystems. Its adoption is not only advantageous; it is essential to build robust and adaptive cybersecurity defenses.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Ref.** & **Country, Year** & **Technology Used** & \multicolumn{2}{c}{**Physiological Signals**} & **Type of Device** & **Key Findings** \\ \hline
9. & [46] & India, 2020 & IoT, wearable sensors, cloud computing & Heart rate, blood pressure, temperature & Wearable devices & Cloud-based health monitoring systems for elderly care offer scalable and efficient solutions. \\ \hline
10. & [95] & USA, 2020 & Wearable sensors, IoT, cloud computing & Heart rate, blood pressure, temperature & Wearable devices & Wearable devices & Cloud-based health monitoring systems offer \\ \hline
11. & [54] & China, 2020 & Wearable sensors, IoT & Heart rate, blood pressure, temperature & Wearable devices & Wearable technology and IoT provide continuous and real-time health data for elderly care. \\ \hline
12. & [47] & Portugal, 2020 & Wearable sensors, IoT & Heart rate, blood pressure, temperature & Wearable devices & Wearable technology and IoT improve elderly health monitoring. \\ \hline
13. & [90] & Iran, 2020 & IoT, wearable sensors & Heart rate, blood pressure, temperature & Wearable devices & Wearable technology and smart health monitoring systems enhance elderly care through real-time data. \\ \hline
14. & [103] & Canada, 2021 & CNN, phase-based video motion processing algorithm & Heart rate, breathing rate & Android application & Higher accuracy and efficiency in estimating HR and BR compared to traditional methods, practical applications in healthcare, and disease prevention for elderly individuals. \\ \hline
15. & [102] & Indonesia, 2021 & Signal conditioning circuit, ATMega32 SP microcontroller, Android application & Heart rate & Heart rate monitoring device, Android application & Efficiently monitors and retrieves patient data, providing a customizable alert mechanism through the mobile application. \\ \hline
16. & [58] & USA, 2021 & IoT, Wearable sensors & Heart rate, blood pressure, temperature & Wearable devices & IoT-based health monitoring systems enhance elderly care through real-time data collection and analysis. \\ \hline
17. & [29] & China, 2021 & IoT, cloud computing, wearable sensors & Heart rate, blood pressure, temperature & Wearable devices & Integrating IoT and cloud computing enhances the efficiency and accessibility of health monitoring systems. \\ \hline
18. & [27] & France, 2021 & IoT, cloud computing, wearable sensors & Heart rate, blood pressure, temperature & Wearable devices & Wearable devices \\ \hline
19. & [114] & China, 2021 & Wearable sensors, IoT & Heart rate, blood pressure, temperature & Wearable devices & Warable devices and cloud computing significantly improve healthcare monitoring and patient management. \\ \hline  & & & & & & Current technologies in wearable health monitoring systems offer improved care for elderly individuals. \\ \hline  & & & & & & Enhances communication, continuous patient health monitoring, real-time analysis, and alerts for detected anomalies. \\ \hline  & & & & & & \\ \end{tabular}
\end{table} 
Table **Table A1**. _Cont._
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
###### Abstract  
In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems present several advantages over conventional methods (e.g., GC-MS and others), including faster analysis, lower costs, ease of use, and portability. Additionally, they enable non-destructive testing and real-time monitoring, making them ideal for routine screenings and on-site analyses where effective detection is crucial. The collected data underwent rigorous analysis through multivariate techniques, specifically principal component analysis (PCA) and linear discriminant analysis (LDA). The application of machine learning (ML) algorithms resulted in a good outcome, achieving high accuracies in identifying fruits contaminated with pesticides and accurately determining the concentrations of those pesticides. This level of precision underscores the robustness and reliability of the methodologies employed, highlighting their potential as alternative tools for pesticide residue detection in agricultural products.  
E-tongue; E-nose; fruit pesticides; pattern recognition; machine learning Article