esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Experimental Results of Multi-Objective Optimization Scheduling  
This study employs two scheduling methods, namely \"Multi-Objective Optimization\" and \"First-In-First-Out (FIFO)\" to schedule edible oil transportation work orders, and compares the efficiency of the resulting schedules. In the multi-objective optimization approach, for each scheduling iteration, the A* and Dijkstra path planning algorithm are used to calculate the shortest path for each task. The pipeline node weights involved in transporting the edible oil are then aggregated to compute the corresponding cost function. Concurrently, a time function for each task is calculated based on the route and the type of edible oil. These cost and time functions are then used to generate a two-dimensional Pareto distribution plot, facilitating the identification of the Pareto front for each scheduling solution. This process iteratively optimizes the task prioritization to achieve improved scheduling. By contrast, the FIFO method strictly adheres to the first-in-first-out principle, executing two tasks per scheduling round. To prevent pipeline conflicts and oil mixing, each task must be fully completed before the next can begin.  
The experiments analyzed work orders under four different scenarios:  
1. Fixed edible oil weight: All variables remain constant, including edible oil weight and task priority;
2. Variable edible oil weight: Simulates the impact of different edible oil weights on scheduling. As the edible oil weight increases or decreases, the system dynamically adjusts pipeline resource allocation to meet varying production demands;  
Figure 9: Virtual–physical integration of the scheduling system and edible oil factory: (**a**) pipeline structure and 3D factory model; (**b**) human–machine interface (HMI).  
3. Fixed edible oil weight with emergency task constraints: Introduces emergency tasks with specific priorities and time constraints. The system must promptly adjust the existing schedule to prioritize completion of emergency tasks;
4. Variable edible oil weight with emergency task constraints: Combines edible oil weight fluctuations and emergency tasks. The system must complete emergency tasks for underweight constraints while maintaining the normal operation of other tasks.  
Each scenario involves eight tasks per work order, with task sequences randomly arranged. The source of each task contains specific edible oil types, while destinations are randomly matched to either temporary storage tanks or packaging plant tanks. The goal is to transport edible oil to the designated destinations until all tasks are completed.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 3 System Model
### Sparse Jamming Model
By exploiting the sparse nature of jamming, [PERSON] can successfully estimate and remove jamming from the received vector.
## 4 Simulation, Implementation, and Results
#### Fog Layer Implementation  
As shown in Figure 15, the fog implementation process starts with data collection and goes to data analysis with the help of Raspberry Pi and Node-RED.  
Data collected from a diabetic individual were employed to deploy the fog layer. Blood sugar level fluctuations are depicted in Figures 16 and 17. Throughout the observed days, the patient exhibited normal conditions with no indications of abnormal spikes or drops in sugar levels except for the second day, where the second blood sugar measurement reached 250 mg/dL. Notably, it is evident that the patient's normal blood sugar levels exceed the global standard. Therefore, comparing the blood sugar level to the global standard values may lead to false alerts within the system.  
As previously mentioned, we utilized Raspberry Pi 2 for the fog layer implementation. The patient data files were converted to.csv format, and the FTP tool integrated into FileZilla software (3.56.2) facilitated the transfer of these converted files to Raspberry Pi. Subsequently, Node-RED was employed to execute the transferred files and initiate the data processing tasks. The parameters selected for analysis included the blood sugar level and patient condition codes. These parameters were transmitted to the function node as inputs, where the process of abnormality detection was conducted, as illustrated in Figure 18.  
Figure 16: The first-day measurements.  
Figure 15: Fog implementation process.  
The KNN algorithm was executed within the function node using JavaScript, as depicted in Figure 19.  
The UI node visually displayed the two parameters alongside the condition results. Subsequently, two sample fasting datasets were employed: one representing normal conditions and the other exhibiting abnormal characteristics, as depicted in Figures 20 and 21.  
Data point #105 was established as the benchmark for normal blood sugar levels, while data point #250, indicating an anomalous blood sugar level, served as the threshold for triggering alerts. Consequently, the responses generated by Raspberry Pi were considered appropriate based on these criteria.  
Figure 17: The second-day measurements.  
Figure 18: The actual Raspberry Pi model used.  
Figure 19: Coding in Node-RED.  
Figure 20: Normal blood sugar.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 2 Objective of the NOMA Conception
When there are too many subscribers for the number of orthogonal cycles, NOMA technology comes into its own. If carriers are employed as the signal's origination point, as they would be in an OFDMA scheme, then a discrete Fourier transform block must be placed after the modulator. When comparing NOMA systems with code separation (WSMA scheme) to multi-user MIMO systems, it was reported that the gain in noise immunity is not very substantial for 5G situations. However, a NOMA receiver has a significantly higher complexity, and algorithms with a manageable level of complexity are required for its implementation.
## 4 Materials and Methods
### Data and Methodology  
As shown in Table 3, different clear sky models necessitate different sets of input parameters. There are a few input parameters that apply to all different models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & **WMO** & **Latitude** & **Longitude** & **Altitude** & **Koppen** & **Number of** & **Relative** & **Water** \\  & **Code** & **(\({}^{\circ}\) N)** & **(\({}^{\circ}\) E)** & **(m)** & **Climate Type** & **Clear-Sky** & **Humidity** & **Vapor** \\  & & & & & & & **Days** & **(\%)** & **(g/kg)** \\ \hline \multirow{2}{*}{Marrakech} & \multirow{2}{*}{60230} & \multirow{2}{*}{31.617} & \multirow{2}{*}{\(-\)8.033} & \multirow{2}{*}{466} & Mid-Latitude Steppe & \multirow{2}{*}{196 (24.5\%)} & Mid-Latitude Steppe & \multirow{2}{*}{55} & Mid-Latitude Steppe \\  & & & & and Desert & & & & \\  & & & & Climate (Bsh). & & & & \\ \hline \multirow{2}{*}{Fes} & \multirow{2}{*}{60141} & \multirow{2}{*}{33.933} & \multirow{2}{*}{\(-\)4.983} & \multirow{2}{*}{579} & Mediterranean & \multirow{2}{*}{138} & Mediterranean \\  & & & & Climate (Csa) & (17.25\%) & & & \\ \hline \multirow{2}{*}{Agadir} & \multirow{2}{*}{60252} & \multirow{2}{*}{30.383} & \multirow{2}{*}{\(-\)9.567} & \multirow{2}{*}{23} & Mid-Latitude Steppe & \multirow{2}{*}{130} & \multirow{2}{*}{61} & Mid-Latitude Steppe \\  & & & & and Desert & & & & \\  & & & & Climate (Bsh) & & & & \\ \hline \multirow{2}{*}{Tangier} & \multirow{2}{*}{60100} & \multirow{2}{*}{35.733} & \multirow{2}{*}{\(-\)5.803} & \multirow{2}{*}{21} & Mediterranean & \multirow{2}{*}{38} & Mediterranean \\  & & & & Climate (Csa) & (4.75\%) & & & \\ \hline \multirow{2}{*}{Ouarzazate} & \multirow{2}{*}{60262} & \multirow{2}{*}{30.933} & \multirow{2}{*}{\(-\)6.910} & \multirow{2}{*}{1140} & Tropical and & \multirow{2}{*}{232} & \multirow{2}{*}{59} & Mid-Latitude Steppe \\  & & & & Climate (Bwh) & & & & \\ \hline \multirow{2}{*}{Tantan} & \multirow{2}{*}{60285} & \multirow{2}{*}{28.437} & \multirow{2}{*}{\(-\)11.103} & \multirow{2}{*}{45} & Tropical and & \multirow{2}{*}{62} & \multirow{2}{*}{64} & Mid-Latitude Steppe \\  & & & & Climate (Bwh) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: A table showing study area: Latitude, longitude, altitude, climate region or climate type, mean annual values of relative humidity, water vapour, the total number of clear-sky days used per site where the percentage of the annual average share of clear-sky days is given in brackets.  
Figure 1: A map showing the climatic zones and solar intensities of the study sites. The map was sourced from: www.marocmeteo.ma (accessed on 16 March 2022) [81].
## 1 Introduction  
Based on the laws of quantum physics [1], quantum key distribution (QKD) can in principle provide unconditional security between two legitimate users ([PERSON] and [PERSON]) [2]. However, due to the loopholes of imperfect devices, the security of practical QKD systems are vulnerable to various attacks by an evil eavesdropper ([PERSON]) [3, 4, 5]. In order to resist the attacks, plenty of methods have been proposed, such as the decoy-state method [6, 7], the measurement device-independent QKD (MDI-QKD) [8]. Combined with the decoy-state method, MDI-QKD can resist the loopholes from detector side-channel attacks and multi-photon components in sources, and thus has attracted extensive attention [9, 10, 11, 12, 13, 14, 15].  
Meanwhile, how to implement the reference frame calibration is a significant challenge. Because of the phase fluctuation, the reference frame needs to be calibrated in a timely manner. Previously, an original approach is using scanning-and-transmitting program to calibrate the phase drift. [PERSON] scans his phase modulation voltage while [PERSON] fixes her phase voltage to ascertain the zero-phase voltage of the minimum count. Two users process feedback control according to the fitted zero-phase voltage. Consuming extra calibration time, the scheme keeps the stability of the QKD system at the cost of the so-called duty cycle [16], which refers to the ratio of the transmission time to the total time. Another widely used scheme is inserting a phase stabilization laser (PSL) which has the same wavelength as the signal laser's between the interferometers of two users with an additional fiber link, which increases the system complexity. In the network [17], the PSL and an extra interferometer are employed in the relay to calibrate the reference frame between each two users, increasing the system complexity. Recently, data-driven machine learning based on complex data analysis methods for quantum control have been proposed [18; 19; 20]. In order to overcome those shortcomings, we adopt long short-term memory network (LSTM) [21; 22] onto the MDI-QKD system and predict out the phase drift between two users in advance. Immediately, real-time phase compensations can be realized, drastically increasing the key transmission efficiency [23].  
This paper is arranged as follows. In Section 2, we introduce the details about our machine learning model, and in Section 3 we tell how to run it on the MDI-QKD system with biased base choice. In Section 4, experimental data are analyzed and discussed. Finally, summaries and outlooks are given in Section 5.
# Soft-Output Detector Using Multi-Layer Perceptron for Bit-Patterned Media Recording
###### Abstract  
As conventional data storage systems are faced with critical problems such as the superparamagnetic limit, bit-patterned media recording (BPMR) has received significant attention as a promising next-generation magnetic data storage system. However, the reduced spacing between islands at increased areal density causes severe intersymbol and intertrack interference, which degrade BPMR system performance. In this study, we introduce a soft-output detector using multi-layer perceptron to predict reliable information. A received signal is equalized and detected by the MLP detector. The MLP detector provides a well-estimated value by using the binary-cross entropy function as a loss function and the identity function as an activation function for the output layer of the MLP detector. This study investigates the received probability distributions out of the detectors and compares the performance of various versions against a conventional detector. Compared with the conventional detection, the proposed MLP detectors provide a small variance and better BER performance than the conventional detection. Simulations of MLP designs show an advantage over conventional detection. Moreover, the proposed MLP detectors with the demodulator exhibit better BER performance than the conventional detector with the demodulator.  
bit-patterned media recording; multilayer perceptron; signal detection; soft-output detector
## 4 Methodology  
In time-critical systems, it is important to have predictable event chains in order to model and improve performance more effectively. However, in real-time embedded multicore systems, there are challenges due to processing cores running in parallel with different initialization times, core frequencies, and operating systems. This parallelism affects the performance of the LET model, as it does not consider the concurrent execution of tasks. The time it takes between read and write operations can vary depending on each core's events, like initialization, interrupts, and preemption, leading to varying latency and reduced predictability of data flow. To address the issue of variable latency in inter-core communication, TDMA is proposed as a complement to the LET model. While the LET model handles read/write operations within a single processing core, TDMA can be used for inter-core data exchange, offering improved composability. TDMA is predictable and composable.  
Predictability is the ability to provide an upper bound on the timing properties of a system. Composability is the ability to integrate components while preserving their temporal properties. Although composability implies predictability, the opposite is not true. A round-robin arbiter, for instance, is predictable, but not composable. A TDM (Time Division Multiplexing) arbiter, on the other hand, is predictable and composable [31].
## 4 Proposed Protocol  
### Comparison to Traditional FL  
The traditional FL scheme depicted in Figure 1a is composed of a central server located in a cloud-based environment, and trainers interacting with the central server. When a component of the FL system fails, the entire system's performance is affected. An attacker can exploit this weakness by targeting the central server, preventing the scheme from being executed when the server goes down.  
Figure 1: _Cont._  
Figure 1(a) shows the relationship diagram of a common FL scheme, where trainers interact directly with the central server, while Figure 1(b), MAM-FL, where trainers and voters interact with the blockchain and decentralized storage to perform FL training. The conventional FL approach is prone to SPoF as it relies on a central server to manage communication between trainers. This increases the risk of models being compromised during data aggregation through the communication endpoint. To address this issue, MAM-FL is proposed, as shown in Figure 1(b). MAM-FL incorporates blockchain technology to mitigate the vulnerability of the centralized server. The immutable nature of the blockchain also enables it to function as a verification mechanism by storing hashes of the initial model and subsequent updates. However, due to the typically large sizes of models, storing them in the blockchain can be expensive and resource-intensive. To mitigate this challenge, a decentralized storage solution is added, which avoids the SPoF that would exist if a central storage solution were used instead. Overall, the MAM-FL architecture provides several enhancements over the traditional FL approach, including a reputation and voting system that ensures accountability and prevents malicious actors from compromising the FL process.
## 5 Survey from the Defender's Perspective
### Problem Characteristics  
Although the defender can control the internal network, the agent does not know whether the attack exists and the possible consequences of the attack. Therefore, its decision-making process still has the following challenges. **False Positive Rate:** In the abnormal detection process, the detection by the defender will cause a certain false positive rate due to the existence of the anti-detection technology of unknown and known threat samples. This will happen for the high degree of fitting between the existing samples and the malicious samples, or forged signals sent by the attacker to the defense detection algorithm. The existence of a false positive rate will further improve the complexity of learning, leading to the wrong judgment of defense decision. **Local Information Known:** Security experts find it difficult to grasp all the information of the network environment in real time due to the dynamic and huge scale of the network environment, though they have the authority to manage the network environment. Therefore, the defender can only manage and maintain the network environment of limited scale, and such local limitation can be alleviated by means of multi-agent cooperative communication, while increasing the difficulty of decision-making. **Constraint of Resources:** The attack may be launched at any time using different vulnerable points, and the protection, detection, response and recovery of defense will take up or consume certain resources. Given this, an unrestricted commitment of defense resources is impossible. These resources may be the cost of equipment deployment, or personnel, time and other cost factors. Hence, in a network defense scenario, the defender needs to respond as quickly as possible and cut the cost as much as possible to complete the task. Table 2 summarizes and compares representative research literature from the defender's perspective. Like the attacker perspective, we also classify these three tasks from the adopted decision-making model, and compared decision-making models, task, strategies used and descriptions of related research.
# Automatic Selective Encryption of DICOM Images
## 3 The Proposed Approach  
The fundamental concept of the adopted methodology in this paper is depicted in Figure 3. This diagram shows three main stages within the proposed approaches, namely: selection, compression, and encryption. The essential input of the presented approaches in this paper is a DICOM medical image, which is composed of patient data and pixel data. Additional user input might be required to select regions, as in the study [29] and the presented approach in Section 3.2. The expected output is a secured DICOM object which contains anonymized patient data and encrypted pixel data.  
In this paper, several selection mechanisms that aim to extract and categorize data prior to the encryption process are adopted. Pixel data are seg  
Figure 3: The fundamental concept of the proposed selective encryption process.  
Figure 2: An encryption flow chart that achieves confidentiality on certificate-based encryption.  
on their importance. The output of the segmentation process includes a segmentation map as side information that preserves the original pixels' positions. Then, the patient data and the segmentation map are extracted and combined as side information. For a multi-frame DICOM object, the first DICOM frame is selected as the most crucial frame, as described in [29]. Additionally, in the presented approach in Section 3.2 (multi-frame DICOM object) a combination between a pre-defined reference frame and automatic thresholding based on the two-region encryption approach [25] is adopted. On the other hand, the two-region encryption approach [25] and the presented approach in Section 3.1 (multi-region encryption approach) present automatic selection mechanisms, which rely on the statistical properties of medical images.  
Generally, the output from the selection process includes selected regions, a segmentation map, and patient data. In the presented approaches in Sections 3.1 and 3.2, the outputs from the selection mechanism are compressed using a lossless compression algorithm. While in studies [25; 29], the outputs from the selection mechanism are directly encrypted. The adopted lossless compression algorithm in this research was Lempel-Ziv 4 (LZ4), which is well-known for its high compression speed and acceptable compression ratio.  
In the encryption phase, important pixel data (compressed) were encrypted using AES with 256 key-length, while less critical data were encrypted using AES with 128 key-length, XXTEA, and XOR. The compressed side of information and patient data were encrypted using AES with 256 key-length. Finally, the encrypted data were appended within a secured DICOM object based on part 15 of the DICOM standard. It is worthwhile to mention that XXTEA is selected to demonstrate the proposed approach. However, there are several lightweight encryption methods that would be suitable to encrypt the ROB.  
The following Sections 3.1 and 3.2 present two proposed approaches for single-frame DICOM images and multi-frame DICOM objects, respectively.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 2 Bloch Sphere Quantum Genetic Algorithm
### Algorithm Steps  
In the ideal algorithm, the object is considered as a point without dimensions. The building as a whole is simplified by the grid method [40; 41] to obtain a grid of 21 \(\times\) 21 of equal size, and each grid is assigned a value, and the size of the value in the grid represents the height. The following are the steps of BQGA.  
Step 1--Population initialization, setting the population size _popsize_, the number of iterations _maxgen_, the corner step _shiftstep_, the variation probability \(p_{m}\), the maximum inertia weight \(\omega_{\text{max}}\) and the minimum inertia weight \(\omega_{\text{min}}\);  
Step 2--Three-stranded gene coding, encoding the chromosomal genes by expressions (1), (2), (3) and (4);  
Step 3--Spatial transformations solving. Combine the specific optimization problem with expression (5) to perform the solution space transformation and coordinate mapping to the solution space of the optimization problem. Then the obtained solution space coordinates are fit with (14);  
Step 4--Chromosome updating. Update the chromosomes by using the quantum rotation gate in expression (15) to obtain new populations;  
Step 5--Chromosomal variation. Correction of the direction of chromosome updating by (18) and (19);  
Step 6--Adaptation function for screening. The obtained chromosomes are used to calculate the fitness of individual chromosomes based on Equations (20) and (21), and the optimal chromosomes and optimal gene chains are also used as the historical optimal chromosomes and gene chains. If \(Fit(i)<Fit(i-1)\), the contemporary optimal solution is updated. Otherwise, the previous solution is kept unchanged;  
Step 7--Iterate. Retain the optimal solution of the algorithm. Determine whether the number of iterations reaches the set value. If yes, the iteration ends and stops, otherwise, go back to Step 3;  
Step 8: Algorithm termination. When the iteration reaches the maximum number of evolutionary generations, the algorithm stops iterating.