esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Technical Considerations
#### 3.3.6 Cryptographic Mechanism  
The PoW algorithm is utilized to build the consensus process in the public BC. Although the PoW mining method overcomes the problem of transaction steadiness, it wastes many resources. Mining incentives can also result in extremely concentrated mining pools. With just seven transactions per second, the decentralized architecture provides a long-term consensus period [76]. The BFT algorithm is used to create the consensus mechanism in the BC consortium, which overcomes the problem of the PoW method's low efficiency and excessive resource consumption. For complete P2P communication monitoring of aberrant activity, the BFT algorithm offers a high number of signature verifications. However, communication complexity is substantial, resulting in system overhead [76].  
When employing BC, the private key is considered as the IoT device's identification and security credential, produced and managed by the IoT device rather than third-party organizations. Because the Elliptic Curve Digital Signing Algorithm cannot create enough arbitrariness, it can lead to a vulnerability where an attacker may obtain the user's private key [88]. The user's private key cannot be restored after it has been lost. If the user's private key is obtained by attackers, the user's BC account is vulnerable to tampering. Because the BC is not reliant on any centralized third-party entities, it is impossible to follow the criminal's activities and retrieve the changed BC information if the user's private key is taken.  
Ethereum and Bitcoin, as the top BC platforms, are not well suited to meet the demands of a large number of users, since they can only guarantee a modest maximum number of transactions. As a result, sharding techniques, as well as off-chain and side-chain techniques, have already demonstrated a high level of potential for addressing scalability issues. For BC applications, privacy is also a sensitive and crucial subject. All transaction data are publicly accessible in public BCs; nevertheless, openness in BC must be balanced with the security of personal and sensitive data. BCs of the private and consortium types alleviate this problem; however, they limit user access, limiting decentralization. The security degree of BC is determined by the consensus technique employed. For example, PoW-based consensus is subject to a 51% attack, but BFT-based consensus is vulnerable to a 33% attack. Small BCs with fewer users are more vulnerable to these attacks, whereas large BCs can provide far greater security but suffer from power centralization and hashing. As a result, for certain use-cases, an optimal tradeoff should be implemented. The BC community is working on improving and adapting consensus procedures to make them more efficient and secure [44].
## 4 Experiment and Results
### Q-Network Algorithm
#### 4.2.2 Fall Module  
A key element in the algorithm is the capacity to detect the falling of a robot with the _fall module_. This module is a pre-trained MLP that allows one to identify whether the robot has fallen or not. Even when the NAO robot comes with a built-in module of this type, the answer is very slow, as it provides an answer in 3 seconds, while we needed it in less than 0.2 seconds. This is why the development of a new fall module was needed.  
In order to implement this module, we acquired data from the simulation and store in a dataset of 790 values, and we used that data in a previously labeled format to train a Multi-Layer Perceptron (MLP) with an input layer of ten neurons; from these ten neurons, eight belonged to the force sensors in the soles of the feet, and the other two belonged to the inertial unit of the NAO robot. This inertial unit told us the torso angle of the robot with respect to the ground; it was computed using the accelerometer and the gyrometer of the NAO (see Figure 12).  
The fall-module MLP has two hidden layers, one with twelve neurons with the ReLU activation function, and another with eight neurons with a sigmoid activation function. Lastly, there was an output layer with only one neuron, which outputs 0 if the robot fell down or 1 if it did not. This architecture is shown in Figure 13.
# A Programmable SRv6 Processor for SFC
## 5 Conclusions and Outlook  
This article extends the deparser module in the RMT architecture to a deparser plus with a complex instruction set design to support complex stateful packet processing. Ingress RMT and egress RMT, which are designed with a reduced instruction set, integrated with the deparser plus form a programmable architecture called the programmable SRv6 processor, for performing SRv6 packet processing. Deparser plus has been tested and only introduces moderate overhead to existing RMT pipelines. It also provides line-rate packet processing.  
Currently, we have defined seven instructions in deparser plus. These seven complex instructions, together with the reduced instructions in the RMT architecture, contain most of the operations required for complex packet processing. Future work could be aimed at  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Utilization** & **Deparser Plus** & **Corundum** \\ \hline CLB LUTs & 3281 (0.28\%) & 61,460 (5.20\%) \\ CLB Registers & 2297 (0.10\%) & 38,739 (1.69\%) \\ F7 Muxes & 495 (0.08\%) & 5315 (0.86\%) \\ CLB & 665 (0.45\%) & 7340 (4.97\%) \\ Block RAM Tile & 16 (0.74\%) & 315 (14.58\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Resource utilization of VCU118 FPGA.  
Figure 6: Results for performance. (**a**) Throughput of the SRv6 processor; (**b**) packet rate of the SRv6 processor.  
extending the complex instruction set in deparser plus by inserting new instructions into the action table with the existing alter instruction to support more complex packet processing.  
Conceptualization, Z.L. and G.L.; methodology, J.W.; validation, Z.L., X.Y. and J.W.; formal analysis, Z.L.; investigation, Z.L.; resources, Z.L.; data curation, J.W.; writing--original draft preparation, Z.L.; writing--review and editing, G.L.; visualization, X.Y.; supervision, G.L.; project administration, X.Y.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2020 YFB1805603.  
The authors declare no conflict of interest.
## 4 Discussion
### Future Perspectives and Possible Applicability of the Obtained Results  
As previously said, these are the results of a fundamental microbiological examination of the ability of clinical isolates of _A. salmonicida_ subsp. _salmonicida_ to produce biofilms in different (scarce) nutritional media and at low temperatures. It can be seen that the strains show different (individual) abilities to produce biofilm under different conditions and therefore the results cannot be generalized. However, in order to clarify the possible applicability of the obtained results, the following can generally be said: (a) At 25 \({}^{\circ}\)C, the largest number of isolates did not have the ability to produce biofilms in minimal media (inorganic salts, glucose as the only source of C atoms). Hypothetically, this would mean that in conditions of increased (summer) temperatures, it is most important to keep pond waters maximally clean, without organic matter pollution as much as possible, to avoid biofilm formation. (b) At temperatures of 16 \({}^{\circ}\)C, it was shown that the tested isolates are capable of forming biofilms on the surfaces (air-liquid interface) in media with increased inorganic salt concentration (ABTG) and with the addition of iron (FeCl\({}_{3}\)). In essence, it is very difficult to control the amount of iron in fish pond waters, and according to the literature data, there is always a lot of iron in fish ponds. It is well known that iron plays an important role for the virulence, including the formation of biofilms, in many types of bacteria, including members of the _Aeromonas_ genus [53]. Reducing available iron is a good defensive option against all bacterial pathogens. Some authors suggest increased aeration of ponds to eliminate iron, but in essence, new iron control strategies in pond waters should be applied as a way of fighting against the maintenance of _A. salmonicida_ subspecies _salmonicida_[54]. Also, water exchange should be accelerated, because pellicle is difficult to form in highly dynamic conditions. (c) At all temperatures in the TSB medium (rich medium) all isolates formed strong biofilms, which means that organic pollution is a crucial factor that enables biofilm formation in _A. salmonicida_ subsp. _salmonicida_ in fish ponds. (d) At low temperatures, almost all isolates showed an extraordinary ability to form biofilms at the bottom of wells in extreme, minimal nutritional conditions, which indicates the maintenance of this pathogen in the cold months and in clean (without organic pollutants) and dynamic waters.  
All this points to the necessity of applying an innovative pond-disinfection strategy. The disinfection protocol must be aimed not at a one-time, simple disinfection but at multiple times with the use of disinfection protocols and biocides that are proven to be able to eradicate biofilms.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 3 Antenna Design, Manufacture and Testing
However, low-temperature ceramic Co-fire (LTCC) is a popular method used in industrial manufacturing. Helical antennas built with conductive wires are generally wound by hand and coil-winder-machined with a counter for laboratory testing and industrial manufacturing, respectively. The manufactured antenna is first characterized in the air, and the measured results are compared with the EM software simulation outputs. In case of a major discrepancy, the researchers go back to the previous step and reiterate it, as shown in Figure 4. Otherwise, the in-body antenna is tested inside phantom (in vitro) and animal tissue (in vivo) successively.
# Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems
## 2 The Structure and Mathematical Model of the Quadrotor
### Kalman Filter  
The Kalman filter is a state estimation algorithm that provides estimates of the internal states of a process in a way that minimizes the mean of the squared error. It is particularly useful in quadcopters for filtering out sensor noise and providing a more accurate state estimate for control purposes.  
The basic Kalman filter equations for prediction and update phases are as follows:  
* Prediction: \[\hat{\kappa}_{k|k-1}=A_{k}\hat{\kappa}_{k-1}+B_{k}u_{k-1}\] (87) \[P_{k|k-1}=A_{k}P_{k-1}A_{k}^{T}+Q_{k}\] (88)
* Update: \[K_{k}=P_{k|k-1}C_{k}^{T}\Big{(}C_{k}P_{k|k-1}C_{k}^{T}+R_{k}\Big{)}^{-1}\] (89) \[\hat{\kappa}_{k}=\hat{\kappa}_{k|k-1}+K_{k}\Big{(}y_{k}-C_{k}\hat{ \kappa}_{k|k-1}\Big{)}\] (90) \[P_{k}=(I-K_{k}C_{k})P_{k|k-1}\] (91)  
where:  
\(\hat{\kappa}_{k}\): State estimation at time k;  
\(A_{k},B_{k},C_{k}\): System model matrices;  
\(u_{k-1}\): Control input at time k \(-1\);  
\(P_{k}\): State estimation covariance matrix;  
\(Q_{k}\): Process noise covariance matrix;  
\(R_{k}\): Measurement noise covariance matrix;  
\(y_{k}\): Measurement data at time k;  
\(K_{k}\): Kalman Gain, balancing between model estimation and measurement data.
## 4 Current Evidence and Applications
### Predictive Modeling in Orthopedics  
One of the key areas where AI has shown promise is in predictive modeling for orthopedic outcomes. Machine learning algorithms have been used to predict complications such as nonunion, infection, and the need for surgical intervention. In the context of fracture healing, the Machine Learning Consortium developed a machine learning model to predict nonunion in tibial shaft fractures by analyzing clinical data and surgical notes [36]. The model incorporated variables such as patient demographics, comorbidities, fracture characteristics, and intraoperative details. By processing both structured data and unstructured text through natural language processing techniques, the model achieved an accuracy of 85%. This study highlights the potential of AI to analyze complex and heterogeneous data sources to predict fracture outcomes. Similarly, in pediatric fracture management, [PERSON] et al. applied machine learning algorithms to predict the necessity of surgical intervention in children with supracondyl humerus fractures [37]. By considering factors like patient age, mechanism of injury, physical examination findings, and initial radiographic measurements, the model provided clinicians with a decision support tool to aid in treatment planning. Such predictive models can help optimize resource utilization, reduce unnecessary interventions, and improve patient care. In other medical fields, AI has been successfully applied to predict outcomes in conditions like coronary artery disease and COVID-19 [38], demonstrating the transferability of methods and reinforcing the potential in orthopedics.
# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 2 Related Work and Key Challenges  
### Industrial IoT System Architecture  
In the research on IoT systems, the classic three-layer architecture divides IoT systems into perception, network, and application layers. These are responsible for sensing and collecting data, connecting with other physical entities or systems, and providing end-to-end applications to users, respectively [12; 13; 14; 15; 16]. [PERSON] proposed a middleware-based architecture, isolating middleware as a separate layer and defining a coordination layer to handle the standardization of data [17; 18]. [PERSON]'s five-layer architecture also focuses on middleware, abstracting a processing layer composed of middleware specifically for connecting, organizing data, providing basic capabilities, and abstracting a business layer to include attention to actual business elements such as profit models and user privacy [16; 19]. There are also architectures with different specific layer definitions but similar concepts, such as the three-layer architecture described by [PERSON] [20], and the four-layer architecture described by [PERSON] [21]. With the introduction of fog computing (or edge computing) concepts [16; 22; 23], architectures have increasingly emphasized the concept of cloud-edge collaboration, introducing elements such as edge gateways and edge computing nodes [12; 24]. Some architectures incorporate sociological theories, defining basic components such as ID, metadata, service discovery, and studying objects in physical network systems as nodes in social networks [16; 25].  
As a subset of IoT systems, IIoT systems also need to face devices with limited resources and networks with limited transmission capacity. Compared to ordinary IoT systems, IIoT systems place more emphasis on machine-to-machine communication and coordination, and deal with larger volumes of data, requiring stricter standards for latency, jitter, reliability, etc. [20; 24; 26]. Germany's Electro and Digital Industry (ZVEI)'s Reference Architectural Model Industrie 4.0 (RAMI 4.0) presents a six-layer architecture: asset, integration, communication, information, functional, and business layers. It interprets IIoT systems by combining hierarchical architecture as one dimension with elements such as product lifecycle and system control segments [27; 28]. The Industry Internet Consortium (IIC) released a reference architecture document that introduces the issues that IIoT systems should focus on from a business viewpoint, usage viewpoint, functional viewpoint, and implementation viewpoint [29] and provides a specific system architecture.
###### Abstract  
At present, the surface quality of Yuba skin is determined by sensory methods. In order to realize the intelligent classification detection of Yuba skin quality, this study designed a system that automatically determines the quality of Yuba skin surfaces based on image processing and support vector machine (SVM) approaches. Specifically, the system uses image preprocessing to extract the grayscale eigenvalues, gray level co-occurrence matrix (GLCM) eigenvalues, and gray level run length matrix (GLRLM) eigenvalues of the sample image and uses them as input values for a quality grading system. Through model evaluation of three classification models, the SVM classification model was selected according to the evaluation results, and different kernel functions were used in the model for sample training. Based on Matlab, the quality grading software of Yuba skin was developed and designed. Intelligent detection and grading were realized through the radial basis kernel function support vector machine (RBF-SVM) grading model. The best penalty factor (c = 3.50) and kernel parameter value (g = 0.98) were obtained through cross-validation. The accuracy of the model was 95.31% and 94.16% for the training and test sets, respectively. The grading accuracy of the RBF-SVM grading system was 93.56%, and the error was less than 5% compared with the traditional sensory method of grading; thus, the quality classification method based on the SVM classification system for Yuba skin is feasible and can be used for quality detection.  
Yuba; multiple eigenvalues; RBF-SVM hierarchical model; quality detection +
Footnote †: journal: Applied Sciences
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 6 Algorithm Analyses
### Correlation Analysis  
To evaluate the performance of pixel correlation, we carry out some simulations. The correlation coefficient of each pair is calculated by:  
\[r_{\mathrm{x,y}}=\frac{E(x-E(x))E(y-E(y))}{\sqrt{D(x)D(y)}} \tag{13}\]  
Figure 5: Histograms: (**a**) plain images; (**b**) encryption image of the proposed algorithm; (**c**) encryption image of [PERSON]’s algorithm; (**d**) encryption image of [PERSON]’s algorithm; and (**e**) encryption image of [PERSON]’s algorithm.  
\[E(x)\ =\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{14}\]  
\[D(x)\ =\frac{1}{N}\sum_{i=1}^{N}(x_{i}-E(x))^{2} \tag{15}\]  
where \(E(\cdot)\) is the expectation function and \(D(\cdot)\) is the variance function.  
For the proposed algorithm, 5000 pairs of adjacent pixels are randomly chosen from Figure 4a-e. For Figure 4a,b, Figure 6 reflects their relevance for adjacent pixels in the horizontal, vertical, and diagonal directions, respectively.  
For the proposed algorithm, [PERSON]'s algorithm, [PERSON]'s algorithm, and [PERSON]'s algorithm have their correlation coefficients of Figure 4a-e given in Table 3. Experimental data display that  
Figure 6: Pixel relevance of Figure 4a,b: (**a**) horizontal direction in Figure 4a; (**b**) horizontal direction in Figure 4b; (**c**) vertical direction in Figure 4a; (**d**) vertical direction in Figure 4b; (**e**) diagonal direction in Figure 4a; and (**f**) diagonal direction in Figure 4b.  
the values of the plain image are close to 1, but the values of all the encrypted images are close to 0. Therefore, all these four algorithms can destroy the pixel relevance completely.
## 2 Pump Model and Numerical Setting
### Grid Independence Examination  
#### 2.3.1 Computational Domain and Boundary Conditions  
The MRF model embedded in ANSYS Fluent R16.0 was applied to model the rotation of the two impellers by decomposing the domain into two different frames, namely, the Lagrangian and the Euler frames. The former contained the rotating impeller, and the Eulerian frame of reference involved the stationary parts of the pump. The governing equations of the flow inside the rotating domain were solved in the frame of the rotating impellers, while those outside the rotating domain were solved in the stationary frame. In the numerical preparation, the interface between the rotating and stationary domains was predefined, and the continuity of velocity at the interface was ensured. This model is suitable for steady-state simulations and ensures an effective coupling at the interface between adjacent rotating and stationary domains.  
A three-dimensional geometric model of the pump was constructed using the commercial code Siemens NX. Hence, the geometric model of the flow domain was obtained through Boolean operation and a further processing of local structures such as fillets and corners. The obtained flow domain was composed of five subdomains, corresponding to the water inlet part, sand inlet part, primary impeller, secondary impeller, and volute, as shown in Figure 2. The water inlet subdomain was extended upstream to ensure a fully developed incoming water flow for the pump. The outlet segment was extended in the streamwise direction, as shown in Figure 2, which suppressed the reverse flow at the outlet of the pump. The velocity inlet boundary condition was set at the water inlet, and the velocity of the solid phase was set at the sand inlet, where the solid volume fraction was set to 1. At the outlet, the outflow boundary condition was defined. No-slip boundary conditions were set for all fluid-wetted walls.
## 5 Simulations
### Convolution Networks with SSTM
The network performance improvement with the addition of the CNN-based SSTM module is gradual,  
Figure 8: Overall classification accuracy with various offsets in SRO1 and CFO1 scenarios with the aforementioned methods. (**a**) CFO1; (**b**) SRO1. Figure 7: Overall classification accuracy of the classifier with SSTM for various training scenarios vs. frequency offset and SNR. (**a**) CFO1; (**b**) SRO1. at higher dynamic range symbol rates. We believe this is because CNN's limited spatial convertibility is unable to cope with a wide range of difference in signal captures, resulting in the inability to effectively train at larger symbol rates. The multilayer perceptron is close to the baseline performance. Overall classification accuracies of all methods at typical offsets are shown in Table 5, with SNR fixed at 20  dB. With all methods increasing with frequency or sampling rate offset, the classification accuracy is significantly reduced. The proposed method achieves optimal performance with frequency and sample rate shifts. Our method has a promotion of 51% at high sampling rate offset, which indicates that the application of SSTM corrected sample rate offset implicitly. Thereby, the signals of various sample rate offset are normalized. This processing reduces the classifying difficulty of the baseline classifier.