esa-sceva/satcom-chunk-collection huggingface dataset:
# Approximate Sensory Data Collection: A Survey
## 5 Other Works
To overcome the problem that the global information loss rate cannot be guaranteed, the authors in [56; 57] proposed distributed algorithms to draw the dominant data set from big sensory data. The spatial correlations among different nodes are described by a correlation coefficient matrix. According to this matrix, the redundancy among data is reduced and the dominant data set can be extracted during transmission. For any given \(\epsilon>0\), it is proved that the proposed algorithm can return the dominant data set whose information loss rate is no larger than \(\epsilon\). Meanwhile, the distributed dominant data set drawing algorithm and the correlation coefficient maintaining algorithm have constant communication and computation complexities (\(O(1)\)) for each node. However, this algorithm also has several problems. First, it only reduces the amount of data in the spatial domain and does not consider the temporal domain. Furthermore, it is assumed that the sink has unlimited storage and processing ability, which may not be possible in some applications such as the ones employing smart phones or other hand-held devices for data collection.
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts  
### Concepts of Resilience in Its Various Contexts  
The term resilience can be understood as the ability to maintain the necessary capacity in the face of adversity [14]. It can also be understood as the \"ability to adapt and recover quickly from any known or unknown changes in the environment\" [15] or even as \"the capacity of a cyber-system to perform effectively, regardless of the hazards\" [5].  
Presidential Policy Directive No. 21, issued by the American Government on 12 February 2013, sets forth a national policy for the U.S. government about the security and resilience of critical infrastructure. Resilience, as defined in the directive, refers to \"the ability to prepare for and adapt to changing conditions and to withstand and recover quickly from disruptions\". Resilience includes the ability to withstand and recover from deliberate attacks, accidents, or natural threats or incidents [16].  
It can also be understood as the \"ability to withstand, absorb, recover from or successfully adapt to adversity or a change in conditions\", or in an expanded definition, it can also be understood as the \"ability of systems, infrastructures, government, businesses and citizens to withstand, absorb, recover from or adapt to an adverse event that could cause damage, destruction or loss of national importance\" [17].  
[PERSON] et al. [18] defines resilience as the \"intrinsic capacity of a system to adjust its functioning before, during or after changes and disturbances, so that it can sustain the necessary operations under expected and unexpected conditions\".
# Design of a Novel RFID Reader for Oilwell Downhole Applications
## 3 Design of Downhole RFID Sparse Solenoid Antenna  
### Magnetic Field Model of a Sparse Solenoid Antenna  
Due to the rapid movement of electronic tags in underground oil pipelines, to capture complete data, it is necessary to extend the length of the solenoid antenna. However, a longer solenoid antenna will lead to an increase in its inductance value. Since the resonant matching capacitance cannot be made very small, it is necessary to reduce the antenna inductance by using a sparse solenoid antenna. The structure of the sparse solenoid antenna is shown in Figure 6, in which \(l\) represents the total length of the solenoid antenna, \(\theta\) denotes the winding angle, \(a\) is the radial distance along the axis of the coil, and \(h\) is the spacing between adjacent windings.  
Figure 5: Decoding flowchart of tag data.  
Figure 6: Sparse solenoid antenna structure.  
According to Biot-Savart Law, the magnetic field distribution of a sparse solenoid coil antenna at any point in space can be derived as follows:  
\[\left\{\begin{aligned} H_{x}&=\frac{I}{4\pi}f_{0}^{2 \pi z^{\prime}/h}\frac{\left(az\cos\theta-a\frac{h}{2\pi}\theta\cos\theta-\frac{ h}{2\pi}y+a\frac{h}{2\pi}\sin\theta\right)}{\left((x-a\cos\theta)^{2}+(y-a\sin \theta)^{2}+\left(z-\frac{h\theta}{2\pi}\right)^{2}\right)^{\frac{3}{2}}}d \theta\\ H_{y}&=\frac{I}{4\pi}f_{0}^{2\pi z^{\prime}/h}\frac{ \left(\frac{h}{2\pi}x-a\frac{h}{2\pi}\cos\theta+az\sin\theta-a\frac{h}{2\pi} \theta\sin\theta\right)}{\left((x-a\cos\theta)^{2}+(y-a\sin\theta)^{2}+\left(z -\frac{h\theta}{2\pi}\right)^{2}\right)^{\frac{3}{2}}}d\theta\\ H_{z}&=\frac{I}{4\pi}f_{0}^{2\pi z^{\prime}/h}\frac{ a(a-y\sin\theta-x\cos\theta)}{\left((x-a\cos\theta)^{2}+(y-a\sin\theta)^{2}+ \left(z-\frac{h\theta}{2\pi}\right)^{2}\right)^{\frac{3}{2}}}d\theta\\ \end{aligned}\right. \tag{1}\]
## Challenges and Potential Solutions
### High Intelligence versus Privacy and Complexity  
The balance between privacy and intelligence would be significant in 6G networks, as they are humanoid networks. Conversely, AI algorithms must interact with private data and refine them to improve network functionality, alter network figures, and deliver superior quality services [10]. Therefore, privacy would be foregone to the detriment of superior intelligence. A candidate solution is to utilize an intermediary negotiator between the end-user data and AI algorithms. Such a negotiator should be a third party, independent if possible, and operate on a distributed fashion. All personal and sensitive data will be anonymized by an intermediary negotiator agent. Similarly, the superior intelligence offered by AI algorithms and smart nodes lowers the freedom offered to human beings. Most likely, the user inclination will not be constantly tuned with the improved alternative generated by AI algorithms. The conflicting condition becomes more complicated when numerous users are considered. This issue can be modelled as locating the mid-point between customization and intelligence in 6G communications [11]. Definitely, customization attracts increased attention, andIntelligence versussexorbitant routines and Intelligence versussub-routines are needed for AI algorithms and smart nodes. Such routines and sub-routines should be specified in the most fundamental procedures of 6G networks. By using this approach, intelligent portfolios can simply be delivered inside the permitted boundary.  
High intelligence comes at a cost in terms of network complexity [19] and could translate to higher network operators and gadget producer budgets. Such developments would lead to a higher device cost for consumers and negate the dream of providing affordable gadgets. To tackle this issue, technological innovations in intelligent structures are essential. More significantly, novel business plans are vital. Once the security, secrecy, and privacy are guaranteed, consumers are permitted to exchange the available anonymized data for a reduced data price. Analogous to this scheme is the smart grid concept, through which electricity consumers trade their own-produced electricity with electricity power firms, and which would be borrowed by 6G communication networks.
## 3 Results and Discussion
### Distribution of Oxygen Concentration at the Cathode Side  
Figures 8 and 9 show the oxygen concentration distribution at the interface between the cathode flow field and the gas diffusion layer and the oxygen concentration distribution from the inlet to the outlet along the cathode flow field, respectively, when the cell voltage was 0.6 V. As can be seen in Figures 8 and 9, due to consumption of the reactants by the electrochemical reaction, the oxygen concentration gradually decreased along the direction of the gas flow. This caused a low concentration of oxygen near the outlet, then leading to local reactant starvation and low fuel cell performance. It can be seen in Figure 8 that, with the increase in the rib groove rate from 0 to 1, the oxygen distribution in the serpentine flow fields became more and more uniform due to the gas pressure at the entrance being high, thereby allowing the rib groove structure on the flow field to alleviate the uneven distribution of oxygen in the flow field. Figure 9 shows that the oxygen concentration in the convention serpentine design flow field first drastically reduced and dropped slowly after the third corner, but for serpentine flow fields with rib grooves, the oxygen concentration in the flow field had no obvious corner characteristics, instead decreasing slowly and showing greater stability. The oxygen concentration distribution was similar when the rib groove rate was 2/3 and 1, both of which were better than that of 1/3 of the rib groove rate. The rib groove structure reduced the concentration loss caused by insufficient oxygen reactants in the catalyst layer and provided a more uniform current density distribution, thus improving the overall performance of the PEM fuel cell.  
Figure 7: Membrane current density when the cell voltage was 0.6 V. (**a**) Conventional design, (**b**) 1/3 of the rib groove, (**c**) 2/3 of the rib groove, (**d**) full rib groove.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 2 Background
### Related Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Cryptographic Approach** & **Key Features/Advantages** & **Limitations** \\ \hline [PERSON] et al. [36] & Authentication and key agreement (AKA) protocol & Early establishment of secure communication channels & Lacks anonymity and scalability. \\ \hline [PERSON] et al. [37] & ECC-based, X.1035 standard and Diffie-Hellman based protocol & Provides basic authentication and key agreement & Does not ensure user anonymity and untraceability. \\ \hline [PERSON] et al. [38] & Authenticated encryption with associative data (AEAD) primitives used as access control protocol & Efficient protocol design & Limited anonymity and vulnerability to certain attacks. \\ \hline [PERSON] et al. [39] & ECC-based authentication and key agreement (AKA) protocol & Enables secure registration over open channels; flexible design & Relies on a fully trusted registration center. \\ \hline [PERSON] et al. [40] & SM2-based authentication and key exchange & Provides user anonymity: exhibits low computational overhead & Requires secure channels during the registration phase. \\ \hline [PERSON] \& Pertificateless public & Eliminates the need for traditional certificates; efficient key management & Scalability issues: dependency on centralized key generation centers. \\ \hline [PERSON] et al. [42] & Certificateless aggregation with Paillier encryption & Enables secure data aggregation; reduces computational overhead for smart meters & Potential collision risks and implementation challenges. \\ \hline [PERSON] et al. [43] & Certificateless with blockchain enhanced transparency & Offers decentralization and overhead; less practical for resource-constrained devices. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of cryptographic protocols for smart grid security. To address the gaps in prior works related to smart grid security, our proposed work introduces a secure and lightweight cryptographic protocol to address the evolving security needs of smart grid infrastructure. The protocol ensures strong protection against potential security threats while maintaining system integrity and reliability. By evaluating the protocol with different computing platforms and benchmarking its performance, we demonstrate the applicability of our proposed protocol to different smart grid nodes.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 4 Discussion
### Future Considerations  
Addressing Sparsity of the Data  
In Section 2.4.1, we described the creation of 'divided' dataset. One of the reasons this step was necessary was the low amount of labeled objects in the images, with (relatively) big areas where nothing was annotated. This sparsity of the data can be addressed by applying a two-stage image processing procedure, which we plan to apply as future work. In the first step, a macro picture (from an extensive global scan of the sample) will be examined for spots of the high density of cells which are especially representative for potential pathological conditions. The detection algorithm (DL model) will be applied to these selected spots in the second step. Since we do not have access to a cytological scanner, and we intended our solution for regular clinics (which usually do not have an access to a costly scanner), we came up with a model which works well with images captured by physicians from the microscope. It is expected that a model trained on fragments selected by a scanner will have better performance than our current approach, in which the selection step for the training data was done manually by a physician.  
Applying Different Models  
The new versions of YOLO models (e.g., v4 and v5) can be adapted to our solution, and we may expect a slightly better performance based on the benchmarks. However, we are currently conducting intensive research on using models for semantic segmentation in this application since they address better-overlapping cells that are sometimes challenging to classify. In addition to this, we would also like to segment overlapping cell borders. We plan to apply a similar approach as the authors of [17].
## 4 Comparison of Electro-Optical Mixer Performances  
In the context of the approach called standard modulation, electro-optical mixing is conducted through both experimental and simulation setups, as depicted in the aforementioned results. In both setups, an additional IF signal is introduced at the electrical terminal of SOA1. This configuration was originally established as part of the innovative differential modulation [28]. A comparative analysis between these methods is presented in Table 1, which substantiates that the differential modulation exhibits superior efficiency and quality, as evidenced by enhanced conversion gains and reduced EVMs, respectively.  
While acknowledging the groundbreaking achievement of implementing the standard modulation approach in the electro-optical SOA-MZI design for the first time, it is essential to highlight the major advantages it offers in comparison to the previously explored differential modulation method [20; 23; 28; 33]. Firstly, standard modulation techniques are renowned for their simplicity and ease of implementation, making them particularly advantageous in scenarios where a straightforward system design is paramount. Furthermore, standard modulation schemes often demonstrate greater robustness against phase shifts, a critical factor for ensuring reliable communication. In the context of our all-optical and electro-optical systems, the differential approach may enhance the signal quality, thereby contributing to an overall improvement in data transmission performance. However, it is noteworthy that the stability achieved in the mixing operation is higher for the standard modulation, providing additional time for precise system performance measurements. Additionally, the widespread adoption of standard modulation techniques in the industry enhances the compatibility with existing systems and equipment. Despite the seemingly superior performance of the differential modulation approach in Table 1, the selection between standard and differential modulation hinges on specific system requirements, channel characteristics, and the desired equilibrium between complexity and performance.
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
###### Abstract  
The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.  
machine learning; decision tree algorithm; IoT; WSN; C4.5; congestion control; 5G network +
Footnote †: journal: Accepted: 24 May 2019; Published: 30 May 2019
## 4 Results and Analysis
### Comparison of Training Results Based on Neural Network
Net-2 had one FC layer, one BN layer, and one more ReLU layer than Net-1-M. Our model trained in the multi-branch method (Net-2-M) achieved better training effects--the smallest RMSE and a faster convergence (red curve in Figure 4).
# Isolation and Properties of _Enterobacter_ sp. LX3
## 3 Results
### Phylogenetic Analysis  
Sequencing of 16S rRNA gene gave a 1409-base sequence (accession MH701852, GenBank) for establishing phylogenetic position of strain LX3. Comparisons with all available sequences in GenBank database showed that strain LX3 had the most similarity in 16S rRNA gene sequence with genus _Enterobacter_ species [23]. Calculation using MegAlign program in DNAStar program package indicated that sequence similarity was from 96.8% to 99.8% between strain LX3 and its closer relatives. The closest relatives of strain LX3 were _E. cloacae_ ham3, _Enterobacter_ sp. CCBAU15492 and _Enterobacter_ sp. YT4 having 99.8%, 99.6% and 99.7% similarity in 16S rRNA gene sequence respectively. Strain LX3 also was close to other phylogenetic neighbors including _E. asburiae, E. cancerogenus, E. ludwigii_ and _Leclercia adecarboxylalta_, which has more than 98.7% similarity in the 16S rRNA gene sequence. A phylogenetic tree was constructed with neighbor-joining method and shown in Figure 2. It was clear to cluster strain LX3 into the clade of _E. cloacae_.
# A Novel GPU-Based Acceleration Algorithm for a Longwave Radiative Transfer Model
###### Abstract  
Graphics processing unit (GPU)-based computing for climate system models is a longstanding research area of interest. The rapid radiative transfer model for general circulation models (RRTMG), a popular atmospheric radiative transfer model, can calculate atmospheric radiative fluxes and heating rates. However, the RRTMG has a high calculation time, so it is urgent to study its GPU-based efficient acceleration algorithm to enable large-scale and long-term climatic simulations. To improve the calculative efficiency of radiation transfer, this paper proposes a GPU-based acceleration algorithm for the RRTMG longwave radiation scheme (RRTMG_LW). The algorithm concept is accelerating the RRTMG_LW in the _g-point_ dimension. After implementing the algorithm in CUDA Fortran, the G-RRTMG_LW was developed. The experimental results indicated that the algorithm was effective. In the case without I/O transfer, the G-RRTMG_LW on one K40 GPU obtained a speedup of 30.98\(\times\) over the baseline performance on one single Intel Xeon E5-2680 CPU core. When compared to its counterpart running on 10 CPU cores of an Intel Xeon E5-2680 v2, the G-RRTMG_LW on one K20 GPU in the case without I/O transfer achieved a speedup of 2.35\(\times\).  
Keywords:high performance computing; graphics processing unit; compute unified device architecture; radiation transfer +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Correlation between TPC and Antioxidant Methods  
Positive correlations were found between the results of the three methods used to study the antioxidant capacity of the samples. The FRAP and DPPH results showed high significant correlation, with an \(R\)-value of 0.793, whereas the ABTS results showed a lower correlation with the FRAP and DPPH results, with R values of 0.524 and 0.620, respectively. Additionally, significant positive correlations (\(p<0.05\)) were found between the DPPH, ABTS, and FRAP assay results with the TPC (Figure 3). The stronger correlation with the TPC suggests that the antioxidant activity of raspberries is derived mainly from the content of phenolic compounds, with significant and positive correlations (\(p<0.05\)), especially between the FRAP results (\(R=0.783\)) followed by the DPPH results (\(R=0.734\)). The lowest correlation was found between the TPC assay and ABTS results (\(R=0.576\)). This result may be, and is probably, related to the detailed structure of the specific compounds present in the samples. These results are in line with those previously reported in the literature [31], indicating that FRAP and DPPH are appropriate, little-time-consuming methods with high reproducibility for quickly determining antioxidant activity in raspberry fruit extracts.  
The antioxidant properties of phenolic compounds are directly linked to their structure. As previously reported in the TPC section, the major phenols of raspberries--cyanidin-3-glucose, catechchin, epicatechin and proanthocyanidin B1--are composed of several aromatic rings bearing hydroxyl groups potentially able to quench free radicals by forming resonance-stabilised phenoxyl radicals [29; 34].
## 5 Emerging Paradigms and Future Directions
### Neuro-Symbolic Integration  
The fusion of symbolic reasoning with neural architectures offers a pathway to enhance interpretability while preserving data-driven flexibility. The SyCo-PINN framework exemplifies this synergy by generating candidate differential equations via inductive logic programming:  
\[\mathcal{H}=\arg\min_{\mathcal{H}}\sum_{x_{i}}\lVert\mathcal{N}(u_{\theta}(x_ {i});\mathcal{H})\rVert_{2} \tag{12}\]  
where \(\mathcal{H}\) represents the hypothesis or model to be optimized, \(x_{i}\) represents individual data points or inputs from the dataset, \(u_{\theta}(x_{i})\) is the output of the model parameterized by \(\theta\) at input \(x_{i}\). while refining symbolic rules through physics-constrained backpropagation. In turbulence modeling, SyCo-PINN autonomously rediscovers missing vortex stretching terms with 92.7% accuracy, reducing Reynolds stress prediction errors by 41% compared to black-box models [(20)]. Despite these successes, challenges persist: the symbolic search space grows exponentially with equation complexity (\(O(2^{n})\))(\(n\) represents the number of candidate mathematical operators in the symbolic regression problem), and reconciling interpretability with numerical stability remains non-trivial.  
Integrating genetic algorithms with differential algebraic geometry verification tools could constrain the symbolic hypothesis space to \(10^{3}\) candidates, enabling efficient discovery of physically consistent equations. Open-source libraries like SymPINN [(11)] are pioneering this direction, offering pre-trained modules for turbulence closure and biochemical reaction modeling.