esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Approach
### Design Methodology
#### 4.2.5 Interoperability and Standardization  
Interoperability and standardization are crucial design considerations to ensure seamless integration with diverse technologies and systems. Developers must assess and evaluate the compatibility of various technologies, ensuring that the solution can adapt to different environments and platforms. To achieve this, our solution adheres to the following methodologies and standards:  
* Syntactic Interoperability: AURORA MINDS utilizes JSON-LD (JavaScript Object Notation for Linked Data) for structured data exchange, ensuring compatibility across systems that use linked data principles. This approach facilitates the integration of data from various sources while maintaining consistency and adaptability. The system also employs OAuth 2.0 and JWTs (JSON Web Tokens) for secure and efficient token-based authentication, ensuring that users can securely interact with the system across different platforms. These widely adopted standards streamline the integration of external applications while ensuring robust data protection.
* Semantic Interoperability: Our solution focuses on privacy and secure data handling, leveraging Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to provide secure, privacy-preserving data exchanges. Although our solution does not explicitly rely on specific healthcare ontologies like Healthcare Level Seven (HL7) in its core architecture, the modular design of the p-ABCs (privacy-enhancing Attribute-Based Credentials) seamlessly integrates with these ontologies. This ensures that data exchanged between systems is meaningful, standardized, and understandable, particularly in health data exchange scenarios.
* AURORA MINDS incorporates several interoperable data formats and communication protocols in its implementation to ensure security and privacy while enabling seamless integration with existing identity management systems. The interoperability standards employed in our solution are the following:  
* OAuth 2.0 for secure authorization and access control.
* JSON Web Tokens (JWTs) for securely transmitting claims between parties.
* OpenID Connect and SAML for authentication and identity management.
* RESTful APIs for standardized communication between systems.
* Distributed Password Authentication and Digital Signatures to ensure data integrity and authenticity.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 4 Jamming Utilization Method Based on IMSFH
### Frequency-Detection Network  
With the development of artificial intelligence technology, pattern recognition has been widely applied in many fields, such as speech recognition and image classification. Pattern recognition involves processing collected information and classifying objects with  
Figure 4: IMSFH receiver framework.  
similar characteristics based on specific rules using classifiers. In this paper, in order to effectively perform frequency detection, we utilized the time-frequency information of the environment, which is represented as a spectral waterfall. We input the spectral waterfall into a neural network to transform the frequency-detection problem into a supervised classification problem, which is a common method in the field of pattern recognition. Specifically, during the training stage, since we already know the synchronization signal and its frequency, we can use the spectral waterfall corresponding to different frequencies as different category labels for classification training through supervised learning methods. After the training stage is complete, during the communication stage, the network determines the current user's communication frequency by analyzing the spectrum waterfall of the incoming complete communication stream. The core advantage of this method is that it allows the network to not only learn frequency characteristics, but also effectively utilize the reactive jamming rule learned during the training stage, thereby improving the accuracy and efficiency of communication. Through this approach, we can effectively utilize neural networks to complete frequency-detection tasks, enhancing our adaptability to dynamic communication environments.
# Shotkuseki Extract Promotes Keratinocyte Differentiation Even at a Low Calcium Concentration
## 1 Introduction  
The epidermal layer consists mainly of keratinocytes, and only cells in the basal layer divide. Keratinocytes change their morphology and properties through differentiation, and through this differentiation process all organelles disappear to form the corneocytes and stratum corneum. The cornified cell envelope, which covers keratinocytes, binds to intercellular lipids in the stratum corneum to form a tough insoluble membrane that acts as the stratum corneum barrier. In addition, keratinocytes expressed epidermal differentiation markers such as keratin 5 (K5) and 14, keratin 1 and 10 (K10), involucrin, liorcrin, filaggrin, and transglutaminase during the differentiation process [1, 2, 3, 4, 5]. Epidermal growth factor, transforming growth factor, and calcium play important roles in regulating keratinocyte differentiation [6, 7, 8]. Minerals are important components of living organisms. Various minerals are present in living organisms and are essential for life activities and the maintenance of organisms. Minerals are closely related to physiological actions and enzymatic reactions. In epidermal cells, calcium promotes differentiation [8] and regulates hyaluronic acid synthesis [9], magnesium improves barrier functions [10, 11], and zinc promotes cell proliferation [12].  
In recent years, it has been reported that the application of extracts from natural resources such as hot spring water and deep sea water, which contain several minerals,can improve barrier function and heal and prevent inflammation in atopic dermatitis and psoriasis [11; 13; 14; 15; 16]. In this study, we focused on the effects of Shotokuseki extract (SE), which contains various minerals, on epidermal differentiation. SE is an extract from Shotokuseki, a type of sedimentary rock that forms from sediments of once-living organisms such as plankton and marine plants accumulating on the seabed and contains plenty of minerals. Therefore, various minerals are also likely to be present in SE. SE is used as a material for cosmetics, such as a lotions and creams, and it has been used for over 40 years. The effect of SE on the skin and its mechanism have not been investigated.  
To examine the effects of SE to the epidermis, keratinocytes were treated with SE, and the expression levels of epidermal differentiation marker genes and proteins were measured.
# Lightweight Transformer Network for Ship HRRP Target Recognition
## 2 Proposed Model
### Group Linear Transformations (GLTs)
The right is the Transformer encoder combined with GLTs. GLTs combine the output \(\mathbf{Y^{l-1}}\) with the input \(\mathbf{H}\) through the mixer _MIX_ to mix the features and extract more stable features of the target, which can also avoid the problem of gradient vanishing. \[MIX\left(H,Y^{l-1}\right)=shuffle\left[H,Y^{l-1}\right] \tag{9}\]  
Due to the superior performance of GLTs, Self-Attention is used instead of Multi-head Attention to obtain the same attention effect and achieve a better recognition accuracy using smaller dimensions and fewer operations to calculate the attention scores and carry out the assignment of weights:  
\[\mathbf{u}=self\_Attention\left(\mathbf{Y^{l}}\right)=Attention(\mathbf{Q},\mathbf{K},\mathbf{V}) =softmax\left(\frac{\mathbf{Q}\mathbf{K^{T}}}{\sqrt{d_{k}}}\right)\mathbf{V} \tag{10}\]  
where \(\mathbf{Q}\), \(\mathbf{K}\), and \(\mathbf{V}\) are three key-value vectors, which are abbreviations of _Query_, _Key_, and _Value_ vectors, respectively. \(d_{k}\) is the dimension of the \(\mathbf{K}\) vector, and \(\mathbf{Q}\) and \(\mathbf{K}\) are obtained by linear transformation of the output of the previous layer, and the final attention values are obtained by calculating the attention distribution between \(\mathbf{Q}\) and \(\mathbf{K}\) and attaching it to \(\mathbf{V}\). The feedforward neural network layer contains two linear layers and a nonlinear activation function for feature transformation of the attention scores, where the nonlinear activation function is using GELU and the dimensionality of the linear layer is changed by first downscaling to one-fourth of the input dimension and then upscaling to the original dimension:  
\[\mathbf{v}=Feedforward(\mathbf{u})=((\mathbf{u}\mathbf{W_{1}}+\mathbf{b_{1}})\times\phi(\mathbf{u} \mathbf{W_{1}}+\mathbf{b_{1}}))\mathbf{W_{2}}+\mathbf{b_{2}} \tag{11}\]  
where \(\phi(x)\) is the cumulative distribution function of the normal distribution, \(\mathbf{W_{1}}\), \(\mathbf{b_{1}}\) are the weights and biases of the linear layer of the reduced-dimensional part, and \(\mathbf{W_{2}}\), \(\mathbf{b_{2}}\) are the weights and biases of the linear layer of the raised-dimensional part. Supposing that the Transformer encoder module with GLTs has \(M\) layers, the output \(\mathbf{v^{M}}\) obtained after the last layer is decoded by a network containing a single linear layer, and finally normalized by the SoftMax function to obtain the final output. \[\mathbf{y}=Linear\left(\mathbf{v^{M}}\right) \tag{12}\]  
\[\mathbf{y^{{}^{\prime}}}=softmax(\mathbf{y}) \tag{13}\]  
where \(\mathbf{y^{{}^{\prime}}}\) denotes the probability vector that the sample belongs to each class of ship targets.
# Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360
## 2 Methodology
### Traditional Machine Learning  
This paper applied five traditional machine-learning models to implement binary classification tasks, including LR, DT, KNN, GNB, and RF [10].  
**LR** is a statistical technique employed to model the probability of a binary outcome. It finds widespread application in machine learning for scenarios involving binary classification, aiming to predict whether an instance belongs to one of two classes. The posterior probability of class \(c_{1}\) is expressed through a logistic sigmoid applied to a linear function of the feature vector \(\phi\),  
\[p(c_{1}|\phi)=y(\phi)=\sigma(w^{T}\phi), \tag{5}\]where \(p(c_{2}|\phi)=1-p(c_{1}|\phi)\) and \(\sigma(\cdot)\) is the logistic sigmoid function.  
**DT** is a data mining technique employed to establish classification systems using multiple covariates or to create prediction algorithms for a target variable. This method organizes a population into branch-like segments, forming an inverted tree structure with a root node, internal nodes, and leaf nodes. Notably, the algorithm is non-parametric, allowing it to effectively handle large and complex datasets without imposing a rigid parametric structure [11]. However, one limitation of DT lies in its reliance on hard splits in the input space, where a single model is responsible for predictions for any given value of the input variables.  
**KNN** is a supervised machine-learning algorithm used for classification and regression tasks. It is a type of instance-based learning, where the model makes predictions based on the similarity of new data points to existing labeled data points in the training set [12; 13].  
**GNB** is a straightforward classification algorithm [10]. Its primary principle involves assigning labels to classes by maximizing the posterior probability for each sample. This method operates under the assumption that voxel contributions are conditionally independent and follow a Gaussian (normal) distribution. The discriminant function is formulated as the sum of squared distances to the centroid of each class across all voxels in the search-light. This sum is then weighted by the variance and the logarithm of the a priori probability, computed in the training set using Bayes rule. In essence, GNB provides a probabilistic approach to classification, leveraging assumptions about the distribution of features to make predictions.  
**RF** has proven to be remarkably successful as a classification and regression method. This approach involves the combination of multiple randomized decision trees, and it aggregates their predictions through averaging. Notably, it has demonstrated exceptional performance in scenarios where the number of variables is significantly greater than the number of observations. Its versatility extends to large-scale problems, making it adaptable to various ad hoc learning tasks. Additionally, it provides measures of variable importance, adding to its utility and interpretability [14].
## 2 State of the Art  
In this area, the efforts are focused on the use of alternative energies for the power supply of new lighting technologies [5; 6; 7], which allow obtaining considerable energy savings. Within lighting technologies, Light Emitting Diodes (LEDs) assure the possibility of switching on the lamp without the preheating typical of halogen ones; a very high lighting efficiency; low power consumption; a superior life time and quick switching times not comparable to those of older technologies (only incandescent lights have a lower lighting time, but with very big power consumption and the shortest time life); less sensitivity to transient phenomena, which have a big impact on other technologies, allowing thousands of lightings without the risk of lamp failure. These innovative characteristics allow development of a new remote-control system based on intelligent lamp posts that also send information to a central control system in order to simplify management and maintenance issues [1; 2; 3; 4] also using holistic and bottom-up design strategies [1]. Within these researches, a relevant topic is the use of new sensor networks (wireless and not) and communication technologies, both to locally manage the lamp posts, but also sensor networks, and to send data towards a remote center [1; 2; 3; 4; 5; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17].  
Based on the best technologies and on the SG paradigm, our proposal describes a solution for an extremely diffused hostile scenarios, often neglected by the municipalities, but which have an enormous social relevance, and where both the mains and the internet signal provided by the most diffused communication technologies (ADSL and 3G) are not available.
# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
## 1 Introduction
In [12], the authors also use the information on collided preambles for the last three slots, and use heuristics in devising algorithm to find the number of devices, and in turn the optimal transmission probability. But the algorithm fails if the heuristics show slight deviation. In [13] the authors use information on success and idle preambles and employ pseudo Bayesian algorithm to find the number of devices in the network. The authors develop a simple update rule to estimate the number of devices, which can be implemented easily in real time scenario. Figure 1: Internet of things (IoT) devices in billions over the years. In EAB, each device is assigned a class number and eNodeB only allows transmission from devices of only one class. The devices from other classes are forced to wait until their class is unbarred. In [9], the authors take into account the specifications suggested by 3 GPP and presented analytical model for EAB and its performance. In [14], the authors study the parameters of EAB and compares its performance with ACB. The authors find out that ACB has higher collision probability than EAB while EAB has higher access delay than ACB. More number of collisions means more retransmissions and in turn more energy usage. Thus the devices that are delay constrained cannot use EAB while ACB is used by the devices that are not energy contrained. So we need to devise a strategy which can be adopted by both delay and energy constrained IoT devices. In [15], we presented a combined access barring (CAB), a combination of ACB and EAB, in which ACB is applied within EAB. CAB presents less collision probability than ACB and less access delay than EAB. In this paper we extend our analysis and improve the performance of the proposed technique in [15] by introducing practicality. In [15], it is assumed that the number of devices in the network is known which is not true in real time scenarios. Moreover, the number of devices used in the algorithm are the devices present in the whole network. However, the number of devices that need to be estimated is the devices within a class that is allowed to transmit. The rest of the paper is organized as follows. In Section 2, we present the random access process in LTE-A and associated ACB and EAB process. The proposed technique CAB, is presented in Section 3. Performance evaluation and discussion are presented in Section 4 and finally the paper is concluded in Section 5.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 3 Problem Description
If the quality of these fused data is beyond the predefined threshold, \(v_{i}\) will stop the propagation of this data and fuse them into a piece of valuable information \(I_{h}\) with a credible confidence about a target \(T_{k}\). Otherwise, \(v_{i}\) will add the data into the pending queue \(\textit{pendingQue}i_{i}\), and make communication decisions for all data in this queue. ```
1:while true do
2:\(L_{i}\leftarrow\) data received or sensed by \(v_{i}\)
3:for all data \(d_{j}\) received or sensed by \(v_{i}\)do
4: Try to fuse it with relevant data in \(L_{i}\);
5:if the quality of the fused data meets the threshold then
6: Fuse them into a piece of information;
7: Inform other nodes data \(d_{j}\) is outdated;
8:else
9:\(\textit{PendingQue}_{i}\gets d_{j}\);
10: Make communication decisions for each data in \(\textit{PendingQue}_{i}\);
```  
**Algorithm 1** Distributed data relay process. The objective of data relay is to aggregate relevant data to some single node to fuse more high quality information while minimizing the energy consumption of the network. However, sensors cannot take optimal actions since they do not have global view of the network. They make data communication decisions based on local knowledge to maximize incremental quality of relevant data set of neighbors in local data set, and minimize the energy cost. For data \(d_{j}\), there are two communication choices, broadcast \(act_{v_{i}}^{d_{j}}=1\), or not \(act_{v_{i}}^{d_{j}}=0\). If broadcasting, it is not absolutely sure that all the neighbors will receive this data because of the uncertainty of network connection. We can explain the objective function of data relay as following:  
\[\operatorname*{argmax}_{act_{v_{i}}^{d_{j}}}act_{v_{i}}^{d_{j}}\times(\sum_{v_ {k}\in N(v_{i})}P(v_{i},v_{k})\times\Delta Q(d_{j},L_{k})-\beta\times\textit {Energy}(v_{i},d_{j})) \tag{1}\]  
where \(\Delta Q(d_{j},L_{k})\) is the incremental quality of knowledge base \(L_{k}\) after receiving data \(d_{j}\). \(\textit{Energy}(v_{i},d_{j})\) is the energy consumption on transmitting \(d_{j}\), and \(\beta\) is a coefficient to balance the energy cost and information cost in decisions.
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 4 Results and Analysis
### Discussion  
From the results, it is clear that the proposed protocol outperforms the other two schemes in terms of the utilization and successful block transmission percentage. The major reason for this improvement is the adaptive nature of the protocol that adjusts the slot sizes as per the data requirements. Moreover, a priority is assigned to the high-risk patient data. As a result, the proposed technique has the highest successful block transmission percentage for high-risk patients' data. This improvement comes at the expense of lower successful percentage for the low-risk patient data. Since these data have low priority with low latency requirements, it does not impact the application performance. Therefore, the proposed technique is suited for the transmission of blockchain data to the mining nodes for high-risk patients.
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 1 Introduction  
With the growth and application of wireless communication technology, it is crucial to ensure the reliability and stability of the frequency source's output signal [1, 2, 3]. A frequency synthesizer is the core component of the RF front end in a satellite radio communication system. It can provide a precise and stable frequency as the local oscillation signal for wireless communication devices [4]. RF engineers face design difficulties such as high stability and low phase noise frequency sources. Frequency synthesis techniques include direct analog frequency synthesis, indirect frequency synthesis, and direct digital frequency synthesis [5, 6, 7]. The direct analog frequency synthesis technique is realized by multiplying, dividing, and mixing one or more base signal sources to generate many discrete frequency output signals [8]. A crystal oscillator generates the reference signal. The direct digital frequency synthesis technique employs digital sampling and storage technology to perform frequency synthesis using the phase concept. As an indirect frequency synthesis technique, the phase-locked frequency synthesis is realized by locking the frequency of the voltage-controlled oscillator (VCO) to a specific frequency through a phase-locked loop (PLL), and the required frequency is generated and output by the VCO [9, 10]. Compared with the other two techniques, the indirect frequency synthesis technique can appropriately select the target frequency signal without many filters. It also has a simple structure, low power consumption, and high output signal purity, which are conducive to miniaturization and integration [11].  
This article employs the phase-locked frequency synthesis approach to design a frequency synthesizer operating in the L-band. The phase-locked loop system is introduced in detail. A passive fourth-order RLC loop filter is proposed, and its performance is compared with the passive fourth-order RC loop filter in terms of phase noise, loop capture time, and spur suppression. The test results give the output frequency of 1426 MHz at the detection frequency of 50 MHz. The design provides a reference significance to the design of the L-band frequency source.
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 4 Discussion and Results
### Time Synchronization Accuracy  
There should be a common time reference for field devices and the communication endpoints in the network. Time synchronization is a fundamental requirement for many of the TSN protocols. Reliable and accurate communication in industrial use cases require accurate time synchronization. As described in Section 3, we have used the NetTimeLogic PPS analyzer for precise time synchronization accuracy measurements of the prototype 5G-TSN integrated setup. The onboard FPGA on the PPS analyzer computes the time difference between the PPS signals at the two endpoints of the setup of 5G integrated with TSN, as shown in Figure 8 with nanosecond accuracy. We have carried out PPS measurements for the time synchronization error of the 5G system integrated with TSN for over 6 h. As shown in the histogram in Figure 11, the mean time synchronization accuracy in the setup of 5G integrated with TSN remains below 3 us, while the maximum error remains below 8 us. This level of time synchronization accuracy in the integrated setup (including over-the-air time synchronization in the URLLC test system) is sufficiently well suited for the IEEE 802.1 Qbv based gating scheme implemented in our prototype setup for de-jittering purposes, as described in the next section.