esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
In our simulations, around 20% of the ports operate at 10 Mbps and around 20% at 100 Mbps which means that around 35%-40% ports operate in reduced data rates. This includes the impact of passive network components also which turn on only upon receiving the packets. The strength of Adaptive TrimTree mechanism resides in the use of simple yet collective methods to minimize energy consumption without affecting overall network performance. The techniques based on a single methodology for example link rate adaption can be suitable for use in Ethernet [15; 16; 17] however when used in data center networks, link rate adaption would give minimal energy saving because these networks are large in size and mostly provide resource redundancy which resultsin a large number of network devices consuming energy while in idle state. Similarly a resource consolidation technique based on multiple or complex heuristics would result in considerable energy saving, however it can suffer from a lack of responsiveness in cases of high traffic demand [11]. The schemes developed for data center network energy saving are mostly based on a single energy saving technique, and we are not aware of a unified or holistic method especially developed for data center networks. Adaptive TrimTree scheme is based on a novel concept of unified approach for energy conservation which serves as a foundation for further work in this direction. We plan to test it through testbed implementations. There are many areas for future research, the first and foremost being the implementation and testing of this scheme in a variety of sizes of data center networks. Subsequently a network profiler can be developed and/or implemented to fine tune tradeoffs based on diverse applications requirements. An interesting and important future direction is implementation and assessment of \"coordinated sleeping\" and \"uncoordinated sleeping\" schemes for power management of active and passive network subsets especially in subsequent management of passive network subset along with its coupling with routing.
###### Abstract  
This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final stages of machining. A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. The results show that neural networks achieved the highest accuracy (94.7%) and the fastest prediction time, thereby underscoring their efficiency in processing complex production data. The BT model demonstrated stability in its predictions with a slower prediction time, while the SVM model exhibited superior training speed, though with slightly lower accuracy. This article proposes that optimising key process parameters, such as temperature, machining speed, and the type of coolant used, can markedly reduce the prevalence of production defects. It also recommends integrating machine learning with traditional quality management techniques to create a more flexible and adaptive control system, which could help reduce production losses and enhance customer satisfaction.  
machine learning; SVM; bagged trees; NN model; quality management; metal manufacturing Article
## 4 BFS Technological Stack--Dual-Blockchain Framework
### Conceptual Decisions vs. Implementation Details  
The BFS system conceptually requires a dual-blockchain framework to address the need for both transparency and privacy in financial reporting. This is necessary for ensuring public verifiability and auditability of financial statements while maintaining the privacy and security of sensitive transactional data. The conceptual basis for the design, inspired by a dual-blockchain architecture, aims to ensure the data integrity of publicly available financial statements and facilitate the transactional privacy of business-sensitive information through point-to-point broadcast. This concept is not inherently tied to Bitcoin and Corda but rather to the need for combining public and permissioned blockchain systems. The BFS architecture leverages both Bitcoin and Corda blockchain components to achieve a balance between data integrity, transparency, and privacy. While Bitcoin and Corda were selected for their strengths in these areas, other blockchain technologies could potentially be used to implement the BFS framework. The critical requirement is to have one public blockchain system for transparency and another permissioned blockchain system for privacy.  
The specific choice of Bitcoin for the public blockchain and Corda for the permissioned blockchain is an implementation detail. The BFS system uses Bitcoin's UTXO (Unspent Transaction Output) model for its public blockchain component. This choice is based on Bitcoin's proven security and widespread adoption, which ensures the immutability and public accessibility of financial data. The public nature of Bitcoin is specifically leveraged for the publication and dissemination of publicly available end-of-accounting-period financial statements, such as balance sheets. For the permissioned blockchain component, BFS uses Corda's Transaction Vault model. Corda is chosen for its design, which emphasises privacy and the ability to handle complex transactions securely among known parties. The private,  
Figure 2: BFS ecosystem participants and their transactional interactions using point-to-point communication and transactional registers.  
point-to-point nature of Corda is necessary for the privacy of sensitive transactional data shared only between the counterparties of the transaction.
# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 3 The Proposed Deep Crack Segmentation Approach
### Segmentation-Based Variant U-Net Models
#### 3.4.2 ResUnet++  
The ResUNet++ architecture is a Deep ResUNet-based semantic segmentation deep neural network that combines the strengths of residual blocks, Atrous Spatial Pyramidal Pooling (ASPP), squeeze and excitation blocks, and attention blocks, as shown in Figure 6. As stated in [28], attention maps improve image classification by highlighting relevant and suppressing misleading information such as the background. The attention mechanism is widely employed in Natural Language Processing (NLP) and semantic segmentation tasks, such as pixel-wise prediction. It pays attention to the subset of its input feature map, where its task is to determine which parts of the network require more attention. The advantages of the attention mechanism are that it reduces the information encoding computational cost, is simple, and improves the results. The ASPP is a module having the ability to precisely capture multi-scale information and add more global contextual information for more robust and accurate classification. The ASPP module consists of several parallel atrous convolutions with different expansion rates to investigate its input feature map at a specific effective field-of-view for precisely extracting information at multi-scale. Moreover, the global average pooling adds more global contextual information [26; 27].  
Moreover, the squeeze and excitation block (SE) is an architectural unit constructed to enhance the network's representational power by enabling it to fulfill the dynamic recalibration of the channel-wise feature. It can learn to use global information to automatically acquire the importance of each feature channel. It then selectively emphasizes useful features and suppresses nonproductive ones. Meanwhile, the residual block comprises multiple combinations of convolutional layers, batch normalization, ReLU activation, and identity skip connections. It aims to ease the training process and improve the network's representation ability [29].  
Figure 5: Architecture of Deep Residual U-Net (ResU-Net).  
Figure 6: Architecture of ResU-Net++.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 3 An Open Service for Remote ECG
### Design Overview and Security Issues  
Figure 2 summarizes the previous discussion with a design overview.  
The right box represents the patient's side devices. The portable sensor hosts the analog stage with an operational amplifier and filters for ECG acquisition, a specialized MCU that encodes the analog signal for digital transmission on the internal bus, and another MCU that implements the HTTP User Agent (UA) and the WiFi interface. A nearby AP connects the portable ECG sensor with the Internet.  
The left box represents doctor's side devices: another WiFi AP links the smart device (a PC or smartphone) running a browser that displays the ECG.  
Regarding security, we highlight that only the routers inside the APs on both doctor's and patient's sides need to expose a public Internet Protocol (IP) address. So that, according to common practice, personal devices are protected by router Network Address Translation (NAT). Communication to and from the server can be encrypted using secure WebSockets.  
The cloud provider is responsible for server safety, of which only the doctor and the patient know the Fully Qualified Domain Name (FQDN) or IP address. Using an ephemeral microservice improves security, since the potential intruder has little time to identify and attack it, and cannot reuse gathered information.
# Numerical and Experimental Investigations of Laser Metal Deposition (LMD) Using STS 316L
## 5 Conclusions  
In this study, to investigate the quantitative relationship between the solidification parameters and the microstructures formed by heat accumulation owing to repetitive heat input in the multilayer deposition of STS 316L, a numerical model was developed and validation experiments were performed. This study makes the following contributions:  
(1) A numerical model has been developed to understand the heat transfer of the deposit in the LMD multilayer deposition process. To improve the accuracy of the numerical model, the temperature profile was obtained at a fixed point in the deposit during the experiment and the results were validated by using the numerical model. There was an average difference of \(\sim\)5% between the peaks of the two temperature profiles. However, except for the temperature near peaks that were above the melting point of STS 316L (i.e., 1400 \({}^{\circ}\)C), the profiles between the measured and the simulated temperature were almost identical. Therefore, the developed numerical model simulates the multilayer deposition process well and can be directly applied to the thermal history analysis of various types of deposits.  
(2) The relationship between the microstructure morphologies and the solidification parameters was investigated. In particular, the relationship between the microstructure morphologies was quantitatively analyzed by deriving G and R affected by the heat accumulation during multilayer deposition. In this process, the relationship between cellular and cellular dendrite formation according to G/R was proposed using a solidification map. Accordingly, the resulting microstructure can be predicted through a simulation model without the need to perform real depositions, and the predicted results are useful for process variable design.  
Conceptualization, J.P., J.-y.K. and S.H.L.; Funding acquisition, S.H.L.; Project administration, S.H.L.; Software, J.-y.K. and I.J.; Supervision, S.H.L.; Validation, J.P.; Visualization, J.P.; Writing--original draft, J.P.; Writing--review & editing, S.H.L. All authors have read and agreed to the published version of the manuscript. This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. 2020R1C1C1009519) and by Korea Aerospace University (no. 2019-01-006). The authors declare no conflict of interest.
## 3 Research of the Path Tracking Control Strategy  
### Establishment of the Path Tracking Model  
In this section, the simplified parking path tracking model of a vehicle is established, and the path planning and tracking for an automatic parking system is studied based on this model. As shown in Figure 8, the simplified path tracking model for parking is the cartesian coordinate system, \((x_{0},y_{0})\) and \(\theta_{0}\) are the current midpoint coordinate of the rear axle and the driving direction angle of the vehicle,  
Figure 6: Corrected Curvature with Sinusoidal.  
Figure 7: Steering Wheel Angle Change.  
respectively, while \((x_{d},y_{d})\) and \(\theta_{d}\) are the ideal midpoint coordinate of the rear axle and the driving direction angle of the vehicle. The simplified parking path tracking model can be described by the following nonlinear differential equations:  
\[\dot{m}=\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{array}\right]=\left[\begin{array}{cc}\cos\theta&0\\ \sin\theta&0\\ 0&1\end{array}\right]n \tag{11}\]  
In Equation (11), \(m=\left[\begin{array}{cc}x&y&\theta\end{array}\right]^{T}\!\!n=\left[\begin{array} []{cc}v&w\end{array}\right]^{T}\!\!,v\) refers to the speed in parking, \(w\) represents the vehicle angular velocity during parking, \(\theta\) is the angle between the driving direction of the vehicle and the \(x\)-axis, besides, \(v\) and \(w\) are the input variables controlled in the kinematic model.  
The vehicle kinematics model can be obtained from the automatic parking kinematics equation:  
\[\left\{\begin{array}{l}\dot{x}=v\cos\theta\\ \dot{y}=v\sin\theta\\ \dot{\theta}=w\end{array}\right. \tag{12}\]  
Based on the analysis, the kinematic equation has two degrees of freedom, and the vehicle kinematic model has three output variables. The number of input is less than that of output. Therefore, the kinematic model is a typical underdrive system. In the path tracking process, the control law \(n=\left[\begin{array}{cc}v&w\end{array}\right]^{T}\) can be designed to track the target coordinates \([x,y]\) during parking. At the same time, it can quickly converge and realize the tracking of the vehicle's driving angle \(\theta\)[32; 33].
## 4 Proposed Scheme
### Suppressing the Noise Using RIS  
If a sufficient number of meta atoms are available for each user belonging to a RIS-NOMA cluster, then meta atoms can be used as a tool to make the data rate less dependent on channel gain, total cluster power, and AWGN noise power for downlink RIS-NOMA communication. The mathematical derivation to prove the above-mentioned hypothesis is presented in this subsection. By putting \(h_{eq_{2}}=h_{eq_{2}}^{sat}\) in (8), \(\gamma_{il}\) becomes equal to \(\gamma_{il_{sat}}\), as shown below:  
\[\gamma_{il_{sat}}=\frac{p_{T}a_{il}|h_{eq_{2}}^{sat}|^{2}}{P_{T}(\sum_{v=1}^{i -1}a_{ql})|h_{eq_{2}}^{sat}|^{2}+\sigma^{2}}, \tag{10}\]  
where \(h_{eq_{2}}^{sat}\) represents the equivalent channel gain for \(D_{il}\) in saturation state, i.e., increasing channel gain beyond this point does not increase the SINR anymore for \(D_{il}\). Dividing numerator and denominator both by \(P_{T}|h_{eq_{2}}^{sat}|^{2}\) we get  
\[\gamma_{il_{sat}}=\frac{a_{il}}{\sum_{v=1}^{i-1}a_{ql}+\frac{\sigma^{2}}{P_{T} |h_{eq_{2}}^{sat}|^{2}}}, \tag{11}\]  
\[\gamma_{il_{sat}}=\frac{a_{il}}{\sum_{v=1}^{i-1}a_{ql}+\frac{1}{\rho_{1}|h_{eq _{2}}^{sat}|^{2}}}, \tag{12}\]  
where \(\rho_{i}=\frac{p_{T}}{\sigma^{2}}\). If \(\rho_{i}|h_{eq_{2}}^{sat}|^{2}\geq 1\) and \(\frac{1}{\rho_{i}|h_{eq_{2}}^{sat}|^{2}}<<\sum_{v=1}^{i-1}a_{vl}\) then (12) reduces to:  
\[\gamma_{il,sat}=\frac{a_{il}}{\sum_{v=1}^{i-1}a_{vl}}\;\forall\;i\in\{2,3, \ldots,n\} \tag{13}\]  
\[R_{il_{max}}=B\log_{2}\left(1+\frac{a_{il}}{\sum_{v=1}^{i-1}a_{vl}}\right)\; \forall\;i\in\{2,3,\ldots,n\}, \tag{14}\]  
For \(i=1\), interference signal is zero; hence, (8) becomes  
\[\gamma_{1l}=a_{1l}\rho_{1l}|h_{eq_{2}}|^{2} \tag{15}\]  
\[R_{1l}=B\log_{2}\left(1+a_{1l}\rho_{1l}|h_{eq_{2}}|^{2}\right)\;for\;i=1. \tag{16}\]  
The data rate function \(R_{1l}\) for the highest channel gain user is an increasing function. Hence, the upper bound on data rate for user 1 is decided by maximum power-handling capabilities of the user device. It is clear from the (12) that when an appropriate number of meta-atoms is allocated to a user in a downlink NOMA cluster, its SINR reaches the level of saturation, as calculated in (13). Beyond this limit, if we increase the number of meta atomsserving to the user, it will not change the SINR for a given set of power coefficients. Hence, the data rate of the individual user becomes saturated with respect to \(N_{i,l}\) and we may say that the channel saturation condition is achieved. At this point, maximum throughput is obtained for the user belonging to the RIS-assisted NOMA downlink cluster.
# Seismic Velocity Anomalies Detection Based on a Modified U-Net Framework
###### Abstract  
Accurate and efficient reconstruction of hidden geological structures under the surface is the main task of high-resolution Velocity Model Building (VMB). The most commonly used methods in practice are Tomography and Full Waveform Inversion (FWI), which rely heavily on the initial model. Recently, deep learning types of methods have received widespread attention and have performed well in many tasks such as image segmentation and classification. Therefore, it is of great significance to introduce deep learning algorithms into the VMB procedure to accelerate the production cycle, especially for the velocity anomalies detection, which is crucial for a high-resolution initial model. In this paper, a modified U-Net framework is proposed and applied directly on the seismic shot gathers to identify anomalies in the early stage of VMB, which can provide a suitable initial guess for the following large-scale VMB procedures such as FWI. The numerical examples show the power of the proposed method on synthetic data.  
velocity anomalies detection; deep learning; seismic data; geological structure; seismic velocity analysis +
Footnote †: journal: Applied Sciences
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 4 Case Study
### Preliminary Experiment of Threat Discretization  
The threat analysis results were clustered 10,000 times using the k-means algorithm. The mode of the clustering result was selected as the reference. If the clustering result was different from the mode of the clustering results, it was regarded as an error. The number of errors in the 10,000 clustering results was used to characterize the stability and accuracy of the clustering results. The higher the number of errors, the less stable and accurate the clustering is. In [24], the rationality of the empirical rule k \(\leq\sqrt{n}\) is proved theoretically, where \(k\) is the number of clusters and \(n\) is the number of samples. As the number of clusters is undetermined, there are 94 instances of the information system for \(n=94\) and \(2\leq\) k \(\leq\) 9. The results with 10,000 clusterings carried out by randomly selecting the initial cluster center are shown in Figure 2.  
It can be concluded that when \(k=3\), the number of errors is the lowest. However, the error is rather high, even for the best case, as the error proportion for \(k=3\) is nearly 37%. When \(k>3\), the proportion of errors is higher than 50%, up to 98%, and thus the clustering results are unstable. In this case, the data on HT threats cannot be discretized by the k-means algorithm directly, and further improvement is needed.
## 4 Applications of Intelligent Acoustic Monitoring
### Smart Cities and Urban Monitoring
#### 4.2.4 Noise Monitoring with 5G/IoT  
In [113], [PERSON] et al. focused on designing and implementing a comprehensive environmental noise monitoring system for smart cities, based on 5G technology and the Internet of Things (IoT). Their work showed that 5G capabilities could offload sensor computations and distribute psychoacoustic data processing between system elements. They developed different variants of dividing the functions of algorithms for calculating noise metrics. They placed them in different system locations (for example, at the network's edge, in the cloud, and in end devices). Finally, they compared the performance of these variants, analysing how they affected the efficiency and implementation possibilities of the entire system.
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 3 Proposed Algorithm for MIMO Detection
### Improved Jacobi Algorithm
The whole-correction method leads to a more accurate solution of the vector \(\bar{\mathbf{x}}\) from the approximate solution set of \(\bar{\mathbf{x}}_{i}\). Therefore, the updated solution \(\bar{\mathbf{x}}\) should satisfy \(\left\|\mathbf{b}-\mathbf{A}\bar{\mathbf{x}}\right\|_{2}<\min\limits_{1\leq i \leq m}\left\|\mathbf{b}-\mathbf{A}\bar{\mathbf{x}}_{i}\right\|_{2}\). We are now in position to determine the weight \(a_{i}\). Let's select \(q\in\{1,2,\cdots,m\}\) randomly. Then, we can obtain  
\[\bar{\mathbf{x}}=\bar{\mathbf{x}}_{q}+\sum\limits_{i\
eq q}a_{i}\left(\bar{ \mathbf{x}}_{i}-\bar{\mathbf{x}}_{q}\right). \tag{10}\]  
The residual vector regarding \(a_{q}\) is \(\mathbf{r}_{q}=\mathbf{b}-\mathbf{A}\bar{\mathbf{x}}_{q}\). Thus, the residual vector of the updated \(\bar{\mathbf{x}}\) is rewritten as  
\[\mathbf{r}\left(\bar{\mathbf{x}}\right)=\mathbf{b}-\mathbf{A}\bar{\mathbf{x} }=\left(\mathbf{b}-\mathbf{A}\bar{\mathbf{x}}_{q}\right)-\sum\limits_{i\
eq q }a_{q}\left(\mathbf{A}\bar{\mathbf{x}}_{i}-\mathbf{A}\bar{\mathbf{x}}_{q} \right)=\mathbf{r}_{q}+\sum\limits_{i\
eq q}a_{q}\left(\mathbf{r}_{i}- \mathbf{r}_{q}\right). \tag{11}\]  
In this case, the optimization problem can be rewritten as  
\[\left\|\mathbf{b}-\mathbf{A}\bar{\mathbf{x}}\right\|_{2}=\left\|\mathbf{r}_{ q}+\sum\limits_{i\
eq q}a_{q}\left(\mathbf{r}_{i}-\mathbf{r}_{q}\right) \right\|=\left\|\mathbf{r}_{q}+\mathbf{Q}_{q}\mathbf{y}_{q}\right\|_{2}=\min, \tag{12}\]  
where  
\[ \begin{array}{l}\mathbf{Q}_{q}=\left(\mathbf{r}_{1}-\mathbf{r}_{q},\cdots, \mathbf{r}_{q-1}-\mathbf{r}_{q},\mathbf{r}_{q+1}-\mathbf{r}_{q},\cdots, \mathbf{r}_{m}-\mathbf{r}_{q}\right),\\ \mathbf{y}_{q}=\left(a_{1},\cdots,a_{q-1},a_{q+1},\cdots a_{m}\right)^{T}\!. \end{array} \]  
The minimum norm solution of the optimization problem in Equation (12) is given by  
\[ \begin{array}{l}\mathbf{y}_{q}=-\mathbf{Q}_{q}^{+}\mathbf{r}_{q},\\ a_{q}=1-\sum\limits_{i\
eq q}a_{i}.\end{array}  \tag{13}\]  
Therefore, an improved solution of \(\bar{\mathbf{x}}\) can be achieved when the contribution ratio is obtained. It is verified later that this improved solution is more accurate than any arbitrary approximate solutions of the iterations. Based on the above discussion, the pseudo code of the proposed algorithm is presented in Algorithm 1, where the algorithm terminates at the \(K\)-th iteration. Note that an important issue of the proposed algorithm is related to a suitable choice of the number of approximate solutions \(m\), which highly influences the convergence rate and the computation complexity of the algorithm. In the next subsection, we will consider the value \(m\) from two aspects.