esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Methods
### Proposed Method
#### 2.4.2 Local Structure Preservation Based on Dual-Graph Regularization  
With the advancement of manifold learning, autoencoders have also employed various methods to maintain the data structure. However, the current methods primarily concentrate on the intrinsic structure of the data, disregarding the characteristic manifold. The dual-graph regularization considers both data manifolds and characteristic manifolds, preserving more comprehensive data information. Dual-graph regularization has been applied to NMF, but autoencoders reconstruct data using neural network backpropagation, which is a different method from NMF. We introduce it into autoencoders for the first time.  
In combination with Section 2.2, in the autoencoder, for making the encoded low-dimensional representation, \(Y\) retains the original information of the data as much as possible; the original data points \(x_{i}\) and \(x_{j}\) are adjacent, and the corresponding low-dimensional representation \(y_{i}\) and \(y_{j}\) are similarly close, that is, to minimize the target:  
\[\min_{x}\frac{1}{2}\sum_{i=1}^{n}\sum_{j=1}^{n}\left\|y_{i}-y_{j}\right\|_{2} ^{2}\overset{n}{s}_{ij}=\min_{x}Tr\Big{(}Y\L^{n}Y^{T}\Big{)}=\min_{x}Tr\Big{(} W^{T}XL^{n}X^{T}W\Big{)} \tag{14}\]  
Similarly, to keep the structure of the feature space, we expect \(w_{i}\) and \(w_{j}\) in the weight matrix \(W_{1}\) that measures the significance of features. The association between the two is consistent with that between \(x_{i}\) and \(x_{j}\), that is, minimizing the goal:  
\[\min_{x}\frac{1}{2}\sum_{i=1}^{d}\sum_{j=1}^{d}\left\|w_{i}^{(1)}-w_{j}^{(2)} \right\|_{2}^{2}\overset{d}{s}_{ij}=\min_{x}Tr\Big{(}W_{1}L^{d}W_{1}^{T}\Big{)} \tag{15}\]In summary, the objective function of the unsupervised feature selection approach DGA presented in this research is as follows:  
\[\underset{W_{1},W_{2}}{\text{argmin}}\frac{1}{n}\|\hat{X}-X\|_{2,\frac{1}{2}}^{ \frac{1}{2}}+\lambda(\|W_{1}\|_{2,\frac{1}{2}}^{\frac{1}{2}}+\|W_{2}\|_{2,1})+ \beta\text{Tr}(YL^{n}Y^{T})+\alpha\text{Tr}(W_{1}L^{d}W_{1}^{T}) \tag{16}\]  
where \(\lambda,\alpha,\beta(>0)\) denote the parameter used to balance the regularization terms so that the model avoids overfitting and enhances robustness while retaining the inherent local geometry.
# Aortic Pressure Control Based on Deep Reinforcement Learning for _Ex Vivo_ Heart Perfusion
## 4 Discussion
The perfusion model in this paper focuses on perfusion conditions in the Langendorff mode. In the pump-supported working mode, a proportion of the blood is pumped to the left atrium and aortic pressure is maintained [18]. Therefore, by adding models such as the left atrium and the left ventricle, and by adjusting the direction of blood flow from the blood pump, the method can be extended to the conditions of the working mode. This paper employs mathematical modeling to examine the feasibility of deep reinforcement learning control methods as a preliminary investigation into improved pressure control methods for EVHP. The simulation results demonstrate the effectiveness and robustness of the method presented in this paper with regard to Langendorff perfusion. However, the model does not fully reflect the actual conditions of EVHP, including variations in blood biochemical parameters, markers of cardiac injury, neurohumoral regulation, and other factors that may reduce the reliability of control. It is recommended that the control of AoP should take into account cardiac metabolism, which is not considered in this paper due to the limitations of the model. In clinical applications, metabolism-related parameters can be input into the model as state values and the reward function can be optimized by weighted averaging. However, this approach increases the number of states, which increases computational cost and time. Alternatively, it can be considered to optimize the control strategy for aortic pressure by evaluating the relationship between the metabolic parameter and the target aortic pressure, so that the metabolic parameter is included in the model considerations. Therefore, the next step is to apply the algorithm to the mechanical circulatory system to verify its reliability. Although the maximum output flow rate of the pump is limited to ensure the safety of the control system, if there is a system failure during the clinical trials of the EVHP, attempts can be made to initialize the system or use manual control to ensure the safety of the system output.
## 4 Result and Discussion
### Packet Delivery Ratio  
PDR is determined by dividing the total number of data packets sent from source to destination node by the number of data packets delivered. Data communication technology that delivers most packets is deemed the best. The packet delivery ratio of the different method is mentioned in Table 4.  
In Figure 4, PDR during data transmission for different numbers of nodes is illustrated. PDR of the proposed approach is higher than existing approaches, namely DWMA, CBR-RPL, WEMER and PEGASIS.
###### Abstract  
Since fan blades are exposed to fatigue, and in some cases harsh loading conditions, they may exhibit fracture failures due to crack propagation, resulting in significant losses. Previous studies of crack propagation in blades are mainly confined to either simplified blade geometry or loads, resulting in a significant discrepancy between the simulated crack propagation and the real blade propagation behavior, while it is lacking for challenging shapes and loads. A co-simulation approach of FRANC3D and ABAQUS was developed to study the crack propagation of an axial-flow fan blade subjected to centrifugal, aerodynamic, and combined loads. The projected approach is validated with results obtained from analytical calculations and experiments. Meanwhile, making use of benchmarks, the Stress Intensity Factor (SIF) and the prediction of mixed-mode crack growth path are validated. Considering various loads, the crack propagation path response for the fan blade is computed for different growth steps. The results pinpoint that the crack propagation length of the crack tip center is maximum under centrifugal loading. However, the aerodynamic load led to a maximum propagation length of the crack tip endpoints. In addition, the combined force of centrifugal and aerodynamic loads limits the crack from growing.  
Keywords:axial-flow fan; crack propagation; mixed-mode crack; combined loads _Article_
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 6 Simulation Results and Discussion  
In this section, the BER performance of the proposed RLS channel estimator is provided for SM, GSM, and FGSM over underwater time-varying acoustic MIMO channel. The simulation outcomes are achieved for \(10^{6}\) symbols and transmitted over a regular underwater acoustic channel. For the transmission antennas, the total transmitting power is normalized. The uncorrelated data symbols are generated, and signal constellation has been chosen for data modulation. BER performance was computed for different SMTs such as SM, GSM, and FSGM using Monte Carlo iterations, where the channel is estimated by different pilot numbers compared with P-CSI. After channel estimation, the antenna indices and transmitted data symbols is estimated by using the ML optimal detector. For our computer simulations, two benchmarks are compared: (1) the BER performance in case of P-CSI; and (2) BER performance at a different number of pilots used for RLS channel estimation. The parameters chosen for our proposed RLS channel estimation methods are \(\mathbf{P}_{0}=\epsilon\mathbf{I}\)  
Figure 2: RLS Adaptive Channel Estimation Model.  
\(\epsilon=10^{6}\), \(\lambda=0.995\), number of pilots are 2%, 5%, 10% and 15%. The SNR is defined as \(E_{s}/\sigma^{2}\), \(E_{s}\) is energy per symbol and \(\sigma^{2}\) is noise variance assuming that each receiving antenna has same power. A 16-QAM modulation and the channel 4 \(\times\) 4 time-varying UWA-MIMO channel with normalized 0.15 Doppler-scaling factor is used for all SMT schemes. The spectral efficiency of the FGSM is 7 bpcu, and for SM/GSM it is 6 bpcu. In these simulation results, two scenarios are considered: the first is in the case of ignoring the effect of the Doppler-scaling factor of the UWA channel, and the second is holding the effect of the Doppler-scaling factor of the UWA channel. The MSE can be calculated as:  
\[MSE=\frac{\mathbb{E}\{\sum\limits_{n}|\widehat{\mathbf{H}}_{n}-\mathbf{H}_{n}| ^{2}\}}{\mathbb{E}\{\sum\limits_{n}|\widehat{\mathbf{H}}_{n}|^{2}\}}. \tag{33}\]
## 4 Improvement of Thermal Performance
### Thermal Conductivity Enhancement Techniques
#### 4.3.1 Enhancement of Thermal Conductivity by Encapsulation  
According to the literature, organic PCMs have low thermal conductivity, which does not allow their efficient use in several applications. The preparation of core-shell or form-stable composite organic PCMs increases the thermal conductivity, as well as the heat transfer rate, due to increment of the surface area. A very important factor for the improvement of thermal conductivity is the size of the microcapsules. The extreme reduction of capsule size increases the thermal resistance because of the high contact rate of the capsules [47]. Another notable factor is the shell-to-core ratio. A higher shell-to-core ratio causes more increment on thermal conductivity but reduces the rate of energy storage because of the smaller amount of PCM inside the capsule [46]. The optimum shell-to-core ratio considering thermal conductivity, thermal storage, and mechanical strength is around 1:3 [64].  
Inorganic shells, such as SiO\({}_{2}\) and CaCO\({}_{3}\), present better improvement on thermal conductivity than organic shells, such as PMMA, though they have decreased mechanical properties. For that reason, there has been investigation for fabricating core-shell PCMs with double shell: polymer shell for mechanical strength and inorganic for thermal conductivity enhancement [46]. Additionally, another point of view is the forming of a metallic second shell, which can improve thermal conductivity along with mechanical strength and agglomeration problems. Recent studies of micro/nanoencapsulation and form-stable composites are tabulated in Tables 5 and 6, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**PCM** & **Carrier** & **Preparation** & **Themal Conductivity of** & **Thermal Conductivity of Encapsulated PCM** & **Year** & **Refs** \\  & & **Method** & **Pure** & **PCM(W/(m.K))** & & **(W/(m.K))** & \\ \hline PEG 10000 & Graphene Aerogel/ & Vacuum- & & & & \\  & Melamine Foam & assisted & & & & \\  & & Impregnation & & & & \\ \hline Dodecane & Expanded graphite & Vacuum & & & & \\  & & & & & & \\ \hline Lauric Acid & & Carbonized & Vacuum & & & \\  & Stearic Acid & Corn cob & Impregnation & & & \\  & & & & & & \\ \hline PEG 1000 & & Halloysite & & & & \\  & NanoTube reinforced & & Vacuum & & & \\  & with Ag & Impregnation & & & & \\  & nanoparticles & & & & & \\ \hline Castor Oil & & Polyurethane- & & In situ & - & 2019 & [42] \\  & & Acrylate & polymerization & & & \\  & Oligomer & & & & & \\ \hline \(m\)-Erythirtl & & Sepiolite and & Vacuum & & & \\  & Exfoliated Graphite & & Infiltration & & & \\  & nanoplatelets & & & & & \\ \hline PEG 6000 & & Epoxy Resin & & & & \\  & porous Al\({}_{2}\)O\({}_{3}\) & temperature & & & & \\  & ceramic & & & & & \\ \hline \end{tabular}
\end{table}
Table 6: Recent applications of form-stable composites PCMs.
## 3 Problem Formulation and Solutions
### Solution: Hybrid Precoder
#### 3.2.2. Digital Precoder (DP)
(18)
5\(\mathbf{D}=diag(Diag(\mathbf{P}_{crf}))\)
6\(\mathbf{L}=-(tril(\mathbf{P}_{crf})-\mathbf{D})\)
7\(\mathbf{U}=-(triu(\mathbf{P}_{crf})-\mathbf{D})\)
8\([Nr,Nt]=size(\mathbf{P}_{crf})\)
9 Calculate \(a\) and \(\omega\)
10\(\mathcal{S}^{\circ}=eye(Nr,Nr)\)
11\(\mathbf{X}0=zeros(Nr,length(\mathcal{S}^{\circ}))\)
12\(i_{iter}=1\)
13\(\mathbf{X}2=\mathbf{X}0\);
14 while\(i_{iter}\leqslant N_{iter}\)do
15\(\mathbf{X}0=\mathbf{X}2\)
16\(\mathbf{X}1=(D-\omega*L)^{-1}*(\omega*\mathcal{S}^{\circ}+((1-\omega)*D+ \omega*U)*\mathbf{X}0)\)
17\(\mathbf{X}2=(D-\omega*U)^{-1}*(\omega*\mathcal{S}^{\circ}+((1-\omega)*D+ \omega*L)*\mathbf{X}1)\)
18\(i_{iter}=i_{iter}+1\)
19\(\mathbf{\hat{F}}^{BB}=\hat{\mathbf{H}}*\mathbf{X}2\)
20\(\mathbf{\hat{F}}^{BB}=[\mathbf{f}_{1}^{BB},../\mathbf{f}_{1}^{BB},...,\mathbf{ f}_{N_{RF}}^{BB}/\mathbf{f}_{N_{RF}}^{BB^{*}}]\)./ Element-wise dot operator and \(\mathbf{F}^{BB^{*}}=[\mathbf{f}_{1}^{BB},...,\mathbf{f}_{N_{RF}}^{BB^{*}}]\); where, \(\mathbf{f}_{n}^{BB^{*}}=reptant(\left\|\mathbf{F}^{RF}\mathbf{f}_{n}^{BB},N_{ RF},1\right\|_{2}),n=1,....,N_{RF}\)
21\(\mathbf{F}^{BB}=\mathbf{0}^{N_{RF}\times K}\)
22\([\mathbf{F}^{BB}]_{:,[\Gamma]_{:,1}}=\mathbf{\hat{F}}^{BB}\)
23 for\(b=1\) to \(N_{RF}\)do
24\(\Xi=[\Gamma]_{b}\). 25\(\Xi=non-zeros(\Xi)^{T}\); discard the zero element
26 for\(n=2\) to \(|\Xi|\)do
27\([\mathbf{F}^{BB}]_{:,\Xi_{n}}=[\mathbf{F}^{BB}]_{:,[\Gamma]_{b,1}}\)
```  
**Algorithm 3** Proposed SSOR-CRZF DP  
The CRZF filtering matrix is generated inStep 4, and Steps 5 to 18 have been utilised in the SSOR procedure for obtaining the required SSOR-CRZF precoder (\(\mathbf{\hat{F}}^{BB}\)). After \(N_{RF}\) iterations, the baseband DP (\(\mathbf{F}^{BB}\)) is constructed using Steps 23 to 27. As a result, adhering to the design for a hybrid precoder, an AP (\(\mathbf{F}^{RF}\)) has been created first, and the effective channel matrix (\(\mathbf{H}\)) has then been used to build a low-dimensional DP (SSOR-ZF).
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 5 Application and Evaluation
### Evaluation Scenario
The Port Operator and VTS Operator instances are located at the quay and are connected to each other via a local network. These machines have an Intel Core i7-8700T processor with 16 GB DDR4 RAM. The machine of the Berthing Vessel is located on board. It is based on an Intel Core i7-1185G7 processor with 16 GB DDR4 RAM. All machines have an LTE connection. The Public Trusted Entities Repository is available for all the participants. In our implementation, any entity (such as an organization, an authority, or a company) is allowed to publish their trusted organizations \(\phi\) and trusted TSAs \(\theta\). However, in reality, it is primarily organizations, authorities, and institutes that publish trust lists, as they have the resources to verify the trustworthiness of individual TSAs. In addition, these also serve as an anchor of trust so that many participants could also trust their trust lists. In the specific test scenario, the repository has three entries from BSH, DLR, and IALA (c.f. Figure 7). The parties are the public authority \"Federal Maritime and Hydrographic Agency of Germany (BSH),\" the intergovernmental organization \"International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA),\" and the research institute \"German Aerospace Center (DLR).\" Note that the entries in the Trusted TSA Repository are only an example and do not necessarily correspond to reality.
# Pore Structure Degradation of Different Cement
## 2 Materials  
Limestone sand and Portland cement were utilised for the mortars used in this study. The designation of the cement, according to the UNE-EN 197-1 standard [21], corresponds to a CEM I 52.5 (CEMEX, Madrid, Spain). The particle size distribution of the sand was analysed according to the UNE-EN 933-1 standard [22], obtaining a maximum size of 4 mm. The dosage employed consisted of a water-to-cement ratio of 0.45 and an aggregate-to-cement ratio of 3. Moreover, a superplasticiser (Sika Viscocrete-5980) was used to achieve adequate workability (1.5% of the cement weight). Mortar of zinc stearate (ZS) was obtained by adding 2% weight-by-weight (w/w) of ZS (Alfa Aesar, Ward Hill, MA, USA) to the cement weight. The mortar of nanosilica (NS) was obtained by adding 2% w/w of a suspension of amorphous nanosilica (BASF MasterRoc MS 685, BASF Construction Chemicals Spain, S.L., Barcelona, Spain) to the cement weight. The mortar of ethyl silicate (ES) was obtained by spraying one coat of ES (SilikatTes 40 WN, WackerChemie AG, Munich, Germany) onto the mortar surface with the aid of a nebuliser.  
Mineralogical analysis of the cement was conducted with X-ray diffractions (XRD) using a Bruker D8 Advance diffractometer (Bruker Espanola S.A., Madrid, Spain). The spectrum was registered with stepping intervals from 4\({}^{\circ}\) to 60\({}^{\circ}\) at 0.05\({}^{\circ}\) in the \(\Theta\)-\(\Theta\)mode (X-ray tube power: 40 kV and 30 mA). The XRD showed that the cement is composed of alite (C\({}_{3}\)S), belite (C\({}_{2}\)S), tetracalciumaluminoferrite (C\({}_{4}\)AF), calcite, and gypsum (see Figure 1). Moreover, the chemical composition of the cement was studied with X-ray fluorescence (XRF)with a Philips magic pro spectrometer (Philips Iberica, Madrid, Spain);the main elements are shown in Table 1.
## 3 Experimental Results Analysis
### Destruction Mode  
The ultimate failure mode of the experiment is characterized by flexural-shear failure near the location of the wet joint, which differs from the cracking pattern observed in the wet joint of equal cross-section bridge deck panels. Initially, cracks appear at the interface region under loading, followed by noticeable cracks at the mid-span of the specimen. Subsequently, cracks occur at corresponding positions of the loading point on the lower edge of the specimen. As the load increases further, multiple vertical and longitudinal cracks develop on the side and bottom surfaces of the pure bending section, continuously extending and propagating toward the upper edge of the specimen. The width of the cracks increases, and the mid-span displacement rapidly enlarges, ultimately resulting in the bending failure of the specimen [21; 22].
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
###### Abstract  
Named data networking (NDN) is a novel communication paradigm that employs names rather than references to the location of the content. It exploits in-network caching among different nodes in a network to provide the fast delivery of content. Thus, it reduces the backhaul traffic on the original producer and also eliminates the need for a stable connection between the source (consumer) and destination (producer). However, a bottleneck or congestion may still occur in very crowded areas, such as shopping malls, concerts, or stadiums, where thousands of users are requesting information from a device that resides at the edge of the network. This paper provides an analysis of content delivery in terms of the interest satisfaction rate (ISR) in ultra-dense network traffic situations and presents a final and an adequate statistical model based on multiple linear regression (MLR) to enhance ISR. A four-way factorial design was used to generate the dataset by performing simulations in ndnSIM. The results show that there is no significant interaction between four predictors: number of nodes (NN), number of interests (NI) per second, router bandwidth (RB), and router delay (RD). Moreover, the NI has a negative effect, and log(RB) has a positive effect on the ISR. The NN less than 10 has a significantly higher effect on the ISR compared with other nodes' densities.  
named data networking; ultra-dense networks; Internet of Things; four-way factorial design; main and interaction effects; multiple linear regression Article
## 4 Security Attacks
### Consensus and Incentive Layer
#### 4.3.5 Time Jacking  
In a time jacking attack [28; 29; 31], an adversary manipulates the perceived network time of a target node. This is typically achieved by having the target node connect to multiple malicious peers that intentionally report falsified timestamps. As a consequence, the target node adopts these inaccurate timestamps, which then influence its block validation process. When the node's internal clock is skewed, it may reject otherwise valid new blocks that appear to have timestamps outside a predetermined acceptable range, thereby disrupting the consensus mechanism.  
To mitigate time jacking attacks, several countermeasures can be implemented. These include using the node's system time as a trusted reference or enforcing strict limits on the acceptable deviation of incoming timestamps. In addition, securing time synchronization through network encryption, randomizing peer connections to reduce prolonged exposure to malicious nodes, increasing the diversity of the peer network, and monitoring round-trip times for anomalies can collectively enhance resilience against such attacks.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 4 Simulation
### Simulation Results
#### 4.2.2 Parameters of the K-Distribution  
Figure 8 shows the probabilities of detection according to the normalized target signal power \(S_{0}\), which are measured by using the proposed and conventional methods with Monte Carlo simulation. Note that the conventional method just uses matched filtering and threshold detection. For 5 dB signal-to-noise ratio (SNR) Gaussian noise, simulation was carried out 1000 times while changing the normalized target power \(S_{0}\) from \(-15\) dB to 20 dB with 1 dB interval. In this simulation, it was assumed that a target is randomly located in the range of 100 m to 200 m with uniform distribution. The threshold was fixed to a constant to observe the performance difference between the conventional and proposed methods under the same simulation environment. In this simulation, the shape parameters \(\alpha=1\) and 10 were applied. It is observed that the proposed one improved the detection performance by about 3 dB compared to the conventional one. However, this is smaller than that of the numerical comparison in Figure 2 because the threshold is not optimized. The performance improvement was achieved because the energy was focused on the target by using the time reversal technique and the peak was raised as shown in Figure 6. Furthermore, as observed in Figure 3, the larger shape parameter deteriorated the performance in Figure 8.  
Figure 8: The probability of detection by computer simulation for the proposed and conventional methods with the same threshold for \(\alpha=1\) and 10 with \(\lambda=1\).  
Figure 7: Matched filtering outputs obtained by the conventional approach without applying the time reversal technique.
###### Abstract  
As the designed feature size of integrated circuits (ICs) continues to shrink, the lithographic printability of the design has become one of the important issues in IC design and manufacturing. There are patterns that cause lithography hotspots in the IC layout. Hotspot detection affects the turn-around time and the yield of IC manufacturing. The precision and F1 score of available machine-learning-based hotspot-detection methods are still insufficient. In this paper, a lithography hotspot detection method based on transfer learning using pre-trained deep convolutional neural network is proposed. The proposed method uses the VGG13 network trained with the ImageNet dataset as the pre-trained model. In order to obtain a model suitable for hotspot detection, the pre-trained model is trained with some down-sampled layout pattern data and takes cross entropy as the loss function. ICCAD 2012 benchmark suite is used for model training and model verification. The proposed method performs well in accuracy, recall, precision, and F1 score. There is significant improvement in the precision and F1 score. The results show that updating the weights of partial convolutional layers has little effect on the results of this method.  
hotspot detection; optical lithography; transfer learning; convolutional neural network