esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Framework for Securing the Smart City Communication Networks
## 3 Description of Our Proposed Cyber Security Framework for Smart Cities
### Key Exchange Scheme  
A cryptographic key exchange system is a method of exchanging cryptographic keys. We modified the (D-H) key exchange technique, which enables two-way communication between transmitters with no prior knowledge of one another to join and construct a shared secret key across unsecured network communications. As a result, using a symmetric key, this key may be used to encrypt subsequent transmission, which the destination can then decrypt to safely receive communications. The collaboration of [PERSON] and [PERSON] produced the first practical technique for establishing shared secret keys between two-side communications (such as A and B) via an unsecured communication network, as shown in our Algorithm 1. The modification of the D-H keys exchange technique is illustrated in the Algorithm 1. In this pseudocode, '\(p\)' denotes a prime number and '\(g\)' indicates a primitive root such that \(g<p\). As '\(g\)' is a primitive root of '\(p\)', the numbers \(g\ mod\ p\), \(g^{2}\ mod\ p\), \(\ldots\), \(g^{x}\ mod\ p\) will produce all numbers from 1 to \(p-1\). A and B are denoted as [PERSON] and [PERSON], respectively, and \(K1\) and \(K2\) indicated the secret key of each. They select the arbitrary numbers 't' and's' such that 0 < t, s < \(p\). Now, the first user selects a random number (\(x\)) as its private key. Its public key is calculated as \(g^{x}\ mod\ p\). Similarly, the second user user selects a random number (\(y\)) as its private key. Its public key is calculated as \(g^{y}\ mod\ p\). The public keys are exchanged over a public channel in order to calculate \(K1\) and \(K2\). Both users are now required to choose \(m\) as a secret number. Thereafter, both users exchange their final keys, and then shared the symmetric keys.
###### Abstract  
Massive multiple input-multiple output (M-MIMO) is a promising and pivotal technology in contemporary wireless communication systems that can effectively enhance link reliability and data throughput, especially in uplink scenarios. Even so, the receiving end requires more computational complexity to reconstitute the signal. This problem has emerged in fourth-generation (4G) MIMO system; with the dramatic increase in demand for devices and data in beyond-5G (B5G) systems, this issue will become yet more obvious. To take into account both complexity and signal-revested capability at the receiver, this study uses the matrix iteration method to avoid the staggering amount of operations produced by the inverse matrix. Then, we propose a highly efficient multi-user detector (MUD) named hybrid SOR-based Chebyshev acceleration (CHSOR) for the uplink of M-MIMO orthogonal frequency-division multiplexing (OFDM) and universal filtered multi-carrier (UFMC) waveforms, which can be promoted to B5G developments. The proposed CHSOR scheme includes two stages: the first consists of successive over-relaxation (SOR) and modified successive over-relaxation (MSOR), combining the advantages of low complexity of both and generating a better initial transmission symbol, iteration matrix, and parameters for the next stage; sequentially, the second stage adopts the low-cost iterative Chebyshev acceleration method for performance refinement to obtain a lower bit error rate (BER). Under constrained evaluation settings, Section (Simulation Results and Discussion) presents the results of simulations performed in MATLAB version R2022a. Results show that the proposed detector can achieve a 91.624% improvement in BER performance compared with Chebyshev successive over-relaxation (CSOR). This is very near to the performance of the minimum mean square error (MMSE) detector and is achieved in only a few iterations. In summary, our proposed CHSOR scheme demonstrates fast convergence compared to previous works and as such possesses excellent BER and complexity performance, making it a competitive solution for uplink M-MIMO B5G systems.  
Keywords:massive multiple input-multiple output (M-MIMO); fourth-generation (4G); beyond fifth-generation (B5G); multi-user detector (MUD); orthogonal frequency-division multiplexing (OFDM); universal filtered multi-carrier (UFMC); successive over-relaxation (SOR); modified successive over-relaxation (MSOR); Chebyshev acceleration method; hybrid SOR-based Chebyshev acceleration (CHSOR); Chebyshev successive over-relaxation (CSOR)
# Ship Network Traffic Engineering Based on Reinforcement Learning
## 4 Discussion and Conclusions
ZYGX2022 YGRH016); Interdisciplinary Crossing and Integration of Medicine and Engineering for Talent Training Fund, West China Hospital, Sichuan University under Grant No. HXDZ22010; the Municipal Government of Quzhou (Grant 2022D018, Grant 2022D029, Grant 2023D007, Grant 2023D015, Grant 2023D033, Grant 2023D034, Grant 2023D035); Zhejiang Provincial Natural Science Foundation of China under Grant No. LGF22G010009; and Guiding project of Quzhou Science and Technology Bureau (subject No. 2022005, 2022K50, 2023K013, 2023K016). **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request. **Conflicts of Interest:** The authors declare no conflicts of interest. **Nomenclature**  
The following terms are used in this manuscript: \begin{tabular}{p{142.3 pt} p{284.5 pt}} Ship Network & A ship network refers to the communication and data transmission infrastructure deployed on a vessel. \\ Traffic Engineering & Traffic engineering focuses on optimizing the performance, reliability, and efficiency of communication networks. \\ Reinforcement Learning (RL) & Reinforcement learning enabling agents to adapt to complex and dynamic environments and achieve sophisticated goals autonomously. \\ Quality of Service (QoS) & Quality of Service (QoS) refers to the set of metrics and mechanisms used to measure and ensure the performance, reliability, and availability of network services. \\ \end{tabular}
## 3 Results and Discussion
### Effect of DB and LTGN on the Microstructure
The local nitrogen content,  
Figure 3: Microstructure near the surface layer after turning, polishing and LTGN: (**a**) affected layer; (**b**) EDX outcomes. measured at a point, is 17.45 at% (Figure 5b) and is slightly higher than that of the second sample, but this is at the expense of the \(\varepsilon\)-phase, which is of greater intensity. The degree of tetragonality of the S-phase (a = 2.964 A, c = 3.888 A and c/a = 1.3117) is less than that of the first two samples. Based on the results of the phase and microstructural analyses, the following conclusions can be drawn:  
* The plastic deformation of the surface layer introduced before LTGN by static cold working with sliding friction contact (i.e., by DB) diminishes the diffusion process aiming at nitrogen enrichment of the surface layer and the closely located subsurface layers, which is confirmed by EDX analyses. Thus, as the degree of surface plastic deformation increases, the quality of the S-phase formed by LTGN deteriorates. The preliminary impact through severe surface plastic deformation reduces the intensity of the S-phase but increases the intensity of the \(\varepsilon\)-phase and leads to the appearance of a new (also hard) phase: stabilized nitrogen-bearing martensite. * This conclusion is generally supported by [PERSON] et al. [47], who, for 316L steel, found that the average depth of the nitrogen-rich layer is 24.8 \(\upmu\)m (425 \({}^{\circ}\)C/20 h plasma nitriding), but after preliminary surface mechanical attrition treatment, it is only 5 \(\upmu\)m.
* Ignoring the negligible amount of \(\gamma\)-phase in the first two samples, the ALs of all three samples contain hard phases in different proportions. A significant increase in microhardness and surface residual stresses can be anticipated for each of the three processes: (1) turning and LTGN; (2) smoothing DB and LTGN and (3) hardening DB and LTGN. Figure 4: Microstructure near the surface layer after LTGN and smoothing DB: (**a**) affected layer; (**b**) EDX outcomes.
## 3 Results and Discussion
### Identification of Multiple Instances of Damage for an OWT Substructure  
We employed three cases (the calculated \(DI\) was determined by the data set recorded within eleven seconds) to investigate damage identification in multiple locations. In Case 1, damage occurred at Locations 3 and 4, which were on the same pipe. In Case 2, damage occurred at Locations 2 and 4, which were on different pipes. In Case 3, damage occurred in different pipes to differing degrees. The numerical results of Cases 1 and 2 and Case 3 are presented in Figures 5 and 6, respectively.  
The results in Figures 5 and 6 were in excellent agreement with the conditions presented in Table 2. When the severity of the damage was increased (from Case 1-1 to Case 1-3), the \(DI\) at Locations 3 and 4 increased from 2 and 3.5 to 5 and 12 (Figure 5a), respectively. In Figure 5b, Case 2, representing multiple instances of damage in different pipes, also demonstrated the same trend as for Case 1. Furthermore, the \(DI\) values in locations with minor damage were at least 100% greater than the value in the undamaged section (Cases 1-1 and 2-1). Likewise, the \(DI\) values in locations with moderate and severe damage were at least 250% and 900% larger, respectively, than those in undamaged sections (Cases 1-2, 1-3, 2-2, and 2-3). The results indicated that the proposed approach can effectively identify damage severity in multiple damage sites irrespective of the pipe in which damage occurs. Two extremely high \(DI\) values (\(>\)10) were obtained in Cases 1-3 and 2-3 (the cause of extremely high values discussed in Section 3.1.3.); therefore, an extremely large \(DI\) could be only obtained in numerical calculations.  
Regarding Case 3, Cases 3-1 and 3-2 represented different severities of damage in the same pipe, whereas Case 3-3 represented different severities of damage in different pipes. The \(DI\) values clearly identified severity in the two scenarios. Similar to those for Cases 1 and 2, the \(DI\) values in locations with minor and severe damage for Case 3 were at least two and ten times greater, respectively, than the value in the undamaged section. The results for Case 3 suggested that the approach could identify differing damage severity in different pipes. The results for all cases preliminarily confirmed the applicability of the method to an OWT jacket substructure.
# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
## 3 Results and Discussion
### Principal Component Analyses
In the PC\({}_{2}\) loading line, the peak of 1450 nm could correspond to the O-H stretch first overtone of water [8]. Moreover, the wavelength of 1930 nm is attributed to a stretching and bending combination for the band of water [12]. The wavelengths of 1382 and 1696 nm are assigned to the C-H combination of -CH\({}_{2}\) and the C-H stretch first overtone of -CH\({}_{3}\), respectively. Hence, the characteristic wavelengths selected in the PC\({}_{1}\) loading line were likely relevant to the lipid and protein which might help to analyze the mixed soy content in meat. In addition the wavelengths in the PC\({}_{2}\) loading line were mainly related to water and hydrocarbon in samples. As a comparison, Figure 5b indicates the important wavelengths found in the PC loading lines based on the FT-NIR spectral data. The general trend of the PC\({}_{1}\) and PC\({}_{2}\) loading curves is similar to the PC loading lines in Figure 5a. In addition, the two peaks of 1410 and 1882 nm correspond to the C-H combination and the -OH stretch of water, respectively. As expected, the bands of 1190, 2058, 2182, and 2296 nm are also seen in the PC\({}_{1}\) loading line. The peak at 1361 nm could be assigned to the C-H combination of -CH\({}_{3}\), and 1900 nm corresponds to the C=O stretch second overtone of fatty acids. Figure 5: Characteristics of loading line plots based on PCA. (**a**) First three PC loading lines based on dispersive NIR data; (**b**) First two PC loading lines based on FT-NIR data. The information of the other two wavelengths of 1444 and 1696 nm represented is consistent with the peaks of 1450 and 1696 nm in the PC\({}_{2}\) loading line in Figure 5a.
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 2 Materials and Methods
### Study Area  
The field-measured hyperspectral samples were collected in four cruises in Hongze Lake, Taihu Lake, Qiandao Lake, and Hangzhou Bay, respectively. The varied hydrology conditions of the water body provide diversified hyperspectral \(R_{\text{rs}}\) curves.  
Hongze Lake is located in the lower reaches of Huai River in the western Jiangsu Province. It is the junction point of the middle reaches of the Huai River, the tributaries, and the downstream rivers. It plays an important role in the flood regulation of the region [38].  
Taihu Lake, located between the Jiangsu and Zhejiang Provinces, is a large shallow lake with an area of \(\sim\)2338 km\({}^{2}\) and a mean depth of \(\sim\)1.9 m [39]. The lake suffers from water quality deterioration and also shows high turbidity in some areas, due to a large quantity of sediment resuspension. These factors have led to the presence of complex water conditions [40].  
Figure 1: Spatial distribution of sampling stations.  
Qiandao Lake is the largest freshwater, man-made reservoir in the Yangtze River Delta of China. It is a large (580 km\({}^{2}\)), deep (mean depth = 30 m), and slightly turbid reservoir that supplies freshwater to more than 10 million residents in the surrounding cities [41].  
Hangzhou Bay is the largest bay along the southeastern coast of China, which is located in the northeast Zhejiang Province. It is a wide, shallow, and funnel-shaped estuary with approximately 8500 km\({}^{2}\) area, 86 km length, and 95 km wide at the mouth [42]. The water and sediment loads from the Yangtze River into the Hangzhou Bay profoundly influence its hydrodynamics and sedimentation [43].
## 3 Results
### Quantitative Perspective Results
#### 3.1.2 Professors and Managers  
A total of 46 (60%) of professors surveyed declared that they had enough digital technology (computers and internet access) to continue the teaching and learning process from their own homes. However, 24% of them stated that they did not know how to use the educational platform used in the university (Moodle and Google Classroom).  
According to Figure 2, the most used technology as a means of educational interaction was a combination of the following technological platforms: Google Classroom (24%), Moodle (26%), and Zoom (22%). Email was also recorded as a means of communication used by 9% of professors (see Figure 3).  
Figure 2: Technological tools in remote learning (students).  
Professors used digital technology for various activities (respondents were able to choose more than one answer in this question). Three educational practices stood out synchronously: explanations through Zoom (64%), chatting with students (34%), and 21% debates (through Zoom, Meet) among group participants.  
Asynchronously, technology was used mainly for sending and receiving information (84%) and for submitting assignments (84%). Discussion forums were used by 40% of participants, and online tests were used by 18%.  
Digital technology was also used by professors to create their own teaching materials. A total of 56% of participants stated that they used technology to design materials, 53% stated they used it to search for information, and 40% used it to produce and edit videos. All recorded uses of technology by professors are presented in Figure 4.  
Although all professors used digital technology to continue with remote classes, a significant percentage of them (24%) stated that they did not know how to use the educational platforms (learning management systems). That is to say, that there was no appropriation of educational technology among this percentage of respondents. In the same vein, 58% of professors indicated that they required short courses on specific topics to solve their doubts about technology, and 31% asked for online help to explain specific doubts. This suggests that the appropriation of educational technologies is at different levels, according to the roles and experiences of the academic actors.  
Figure 4: Uses of technology by professors.  
Figure 3: Technological tools in remote learning (professors).  
The school culture at the UAM-C was transformed by the pandemic. From the students' perspective, the educational objective was achieved by the majority, although this knowledge was in varying degrees of appropriation, as shown in Table 2.
## 3 Literature Review
### Real-Time Processing: A Bottleneck and Enabler for AI Agents  
In autonomous maritime systems, real-time data processing is not merely a technological feature; it is a critical enabler of intelligent behavior. For AI agents operating in dynamic, safety-critical maritime environments, the ability to perceive, interpret, and act upon data streams in milliseconds is essential for operational reliability, legal compliance, and collision avoidance.  
Autonomous vessels continuously gather vast amounts of data from multimodal sensors, including radar, LIDAR, AIS, sonar, and visual cameras [58]. AI agents must process this information in real time to maintain situational awareness, detect anomalies, execute navigational decisions, and communicate with external systems and operators. The timeliness and accuracy of this processing directly affect the vessel's safety and responsiveness (Figure 1).  
The maritime environment imposes several technical and operational challenges on real-time AI processing, including the following:  
* _High Data Volume and Velocity._ Sensor fusion from multiple, high-frequency data streams generates a continuous influx of unstructured data. Without optimized infrastructure, AI models can experience latency that renders decisions obsolete by the time they are made [59].  
B. _Latency Constraints._  
Delays in perception-to-action cycles are unacceptable in safety-critical systems. AI agents must operate within strict latency budgets, often in the range of milliseconds, especially in near-collision scenarios or in unpredictable weather conditions. C. _Edge Computing Requirements._  
Given the limited bandwidth and intermittent connectivity at sea, cloud-based processing is often insufficient. Instead, edge computing--processing data directly on-board--is increasingly adopted to reduce reliance on external servers and improve real-time responsiveness [59]. D. _Sensor Noise and Data Uncertainty._  
Harsh maritime conditions introduce significant noise into sensor data. AI agents must include robust filtering, data cleaning, and uncertainty quantification mechanisms to maintain high decision confidence [60]. E. _Scalability and Future Readiness._  
As autonomous vessels adopt more sophisticated sensors and algorithms, real-time processing systems must be scalable. Architectures must accommodate modular expansion without compromising timing guarantees. F. _Cybersecurity and Integrity._  
Real-time data pipelines must be secured against tampering, spoofing, or corruption. AI agents must verify data integrity and maintain secure execution pathways to ensure trustworthy operations [62].  
In summary, real-time processing is both a technical bottleneck and a critical enabler for AI agents in maritime autonomy. Without timely and accurate data interpretation, the promises of AI--intelligent, safe, and autonomous behavior--cannot be realized. In the following section, we explore how these real-time constraints are reflected in the design of multi-layered, reactive AI agents operating on autonomous vessels.
## 3 DR-integrated Framework for Optimized Design
### Entropy-Based Correlation Coefficient (ECC)  
Step 2 of the proposed framework involves the DR process. In the high dimensional system analysis, it is required to make an obvious guideline to decide which DR method (i.e., FE or FS) should be accurately employed. In the framework, an explicit criterion is presented to properly select either FE or FS. The criterion can be exploited in accordance with a status of random variables' correlation. As a conventional method, linear correlation estimation [45] measures the degree of the correlation between the data sets. But the approach cannot be supported once features contain non-Gaussian distributions, non-linear correlation, or correlation coefficient of 0.5. To overcome these challenges, it is inevitable to provide a distinct criterion corresponding to an entropy-based correlation coefficient (\(\mathit{ECC}\)) denoted by \(e\)[46]. A joint entropy, \(H_{XY}\), is calculated by  
\[H_{XY}=\sum_{i=1}^{n}\sum_{j=1}^{n}p_{joint}(x_{i})p_{joint}(y_{j})\log_{2} \frac{1}{p_{joint}(x_{i})p_{joint}(y_{j})} \tag{15}\]  
where \(p_{joint}(x_{i})\) and \(p_{joint}(y_{j})\) are the joint probability distributions of \(x_{i}\) and \(y_{j}\), which are the random variables. As a basic concept of mutual information (MI), entropy can be used to measure dependence, but it does not have a satisfactory scale as the maximum value depends on the size of samples [47]. Hence, the ECC from the normalized MI rescales the MI values to be between 0 and 1. The status of the dependence between the random variables is then calculated by \(e\):  
\[e=\sqrt{\frac{H_{X}+H_{Y}-H_{XY}}{H_{X}+H_{X}}},\quad 0\leq e\leq 1. \tag{16}\]  
Here, 1 refers to heavy correlations, and vice versa. On the basis of two different ranges of \(e\), \(0\leq e\)\(<\) 0.5 and \(0.5\leq e<1\), the proposed framework can provide a guidance to use either FE or FS. The first range indicates that the behavior of the variables is independent and uncorrelated. Thus, the FS will minimize the size of the multi-dimensional data. The second range, on the other hand, means that there is a correlation between multivariate data. Hence, FE will be suitable to reduce the redundancy of the data, \(R_{e}\), originated from the strong correlation between multivariate data. When the value of \(e\) in Equation (16) is close to 0.5, it suggests engineers may employ both DR methods. After the reduction proceeds, the comparison of \(R_{e}\) values calculated by Equation (7) is required to confirm whether or not sparse features are sufficiently acquired. If the raw features still include higher \(R_{e}\) compared with the gleaned new features, an additional DR process might be required. Thus, the combination of \(e\) with can be considered as a reliable solution that can draw independent and sparse features by reducing the computational cost.