esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Power Reserve Control (PRC)
For this reason, many studies have assessed the impact on the frequency stability in small island power systems [29; 31]. In this work, a generic small island power system has been considered, i.e., the most challenging situation, to evaluate the coordinated effect on frequency stability of photovoltaic modules equipped with a power reserve control and wind turbines with inertial control enabled. The models described in the following have been developed using the Matlab/Simulink software, which allows for easily the observation and study of a large variety of scenarios. This section starts with an overview of the complete model to offer a general introduction to the problem and it continues with the detailed description of the different subsystems of the model.
## 4 Experimental Results
### Parameter Settings  
The main objective of our study is to develop a malware detection framework using a Transformer-based model with the MalCode2 Vec embedding method. The analysis of a HWP document structure was performed at both the stream and file levels. At the stream level, streams were classified as either malicious or benign (i.e., binary classification). Furthermore, file level performance was determined based on the output of the binary classification. If any stream within a file was identified as malicious, the entire HWP file was classified as malicious (see Figure 1). To achieve this, each stream originating from the same file was assigned a unique label. For both encoding methods (integer encoding and MalCode2 Vec), features were in word- and character-level embeddings. Character embedding facilitated the construction of the word even for out-of-vocabulary terms, while word embedding was limited to the seen words.  
Figure 4 illustrates an overview of the proposed classification approach. In the character-level embedding approach, the vocabulary was built with characters and a  
punctuation, while for word-level embedding, the vocabulary was built with words (including characters). The vocabulary size was 76 for character-level embedding, while for word-level embedding, it was built with 236,820 tokens. The generated representations served as input to the Transformer model, which learned them and incorporated the positional information into each token within the sequence. The Transformer received these representations and used a self-attention mechanism to capture the correlation between each pair of tokens.  
The proposed architecture was developed through iterative running of different HWP document structures with different parameter configurations to determine the most effective settings. Table 3 presents the optimal parameters identified for the classification models. For integer encoding, we set the embedding size to 50, utilized three attention heads, and configured the Transformer's feed-forward network with a hidden layer size of 32. The model incorporates a GlobalMaxPooling1D0 layer, followed by two or three fully connected layers with ReLU activation [32]. Optimization was handled using the Adam optimizer [33] with an initial learning rate of 0.001. A dropout rate of 0.2 [34] was applied to the fully connected layers, and early stopping was implemented to prevent overfitting. The sequence lengths for character-level and word-level embeddings were set to 6400 and 270, respectively. We evaluated the performance for both phases using accuracy, precision, recall, and F1 score. All experiments were conducted using six-fold cross-validation for robust evaluation.
## 4 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Parameter** & **Unit** & & & **SG1** & & & **SG2** & & & **SG1/CNT** & & & **SG2/CNT** & \\ \hline Temp. & K & 298 & 313 & 333 & 298 & 313 & 333 & 298 & 313 & 333 & 298 & 313 & 333 \\ \(Q\) & wt.\% & 33 & 345 & 34.4 & 35 & 32.8 & 29.6 & 29.2 & 30.8 & 23.8 & 31.1 & 26.8 & 23.8 \\ \(SSA_{BET}\) & m\({}^{2}\)/g & 584.8 & 673.5 & 560.5 & 458.9 & 358.8 & 362.9 & 840.7 & 997.4 & 640.8 & 742.7 & 517.1 & 424.8 \\ \(C\) & - & 5.17 & 4.11 & 6.12 & 5.95 & 5.44 & 5.06 & 1.46 & 1.39 & 0.92 & 0.66 & 1.45 & 1.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters from fitting equilibrium adsorption data with adsorption isotherms. The material's other properties should also be considered. According to the literature, the addition of CNT can improve the bed-to-wall heat transfer coefficient. This results from the high conductivity of carbon nanotubes and small particle diameters, with conduction as the main mechanism of heat transfer [61]. Finally, the discussed issue should also be considered regarding the innovative idea, of the fluidized bed of sorbent application, instead of the conventional, fixed bed, commonly used nowadays in the adsorption chillers. Mixing different materials of different fluidization properties, e.g., silica gel and CNT, may facilitate fluidization and increase the bed-to-wall heat transfer coefficient [61; 62]. To summarize, besides adsorption properties, other properties should also be considered when discussing the performance of adsorption cooling and desalination systems. The efficiency of devices using physical adsorption depends primarily on the heat and mass transport parameters of the adsorbent bed. Conceptualization, J.K., N.S. and A.P. ; methodology, A.P., W.K., K.B. and J.L. ; software, N.S. and M.K. ; validation, N.S. and M.K. ; formal analysis, A.P., W.K. and K.B. ; investigation, A.K., A.P. and J.L. ; resources, K.S. ; data curation, K.S. ; writing--original draft preparation, A.P. and A.K. ; writing--review and editing, A.P., A.K. and M.K. ; visualization, M.K. ; supervision, J.K. and N.S. ; project administration, A.P. ; funding acquisition, J.K., A.P. and M.K. All authors have read and agreed to the published version of the manuscript. This research was funded by National Science Center, Poland, grant number 2018/29/B/ST8/00442 and the Strata Mechanics Research Institute of the Polish Academy of Sciences as a part of statutory projects. Not applicable. The authors declare no conflict of interest.
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 4 The Architecture and Complexity Analysis of the Proposed Decoder
### Architecture of the Candidate Sequence Generator  
The candidate sequence generator can generate the candidate information sequences and corresponding \(k\)-dimensional correlation discrepancy for subsequent modules, according to the channel observation values \(\mathbf{r}^{i}\). As shown in Figure 4, the systolic array architecture consists of \(k\) cascaded PEs working in a sequential manner based on Algorithm 2. For the candidate sequence generator architecture, the \(j\)-th components of the received soft information sequence are connected to the \(j\)-th PE and the overall generation starts with the first PE corresponding to \(m_{0}\). The input of the first PE is the preset initial value, and the output is connected to the next PE. For the \(d\)-th iteration (\(2\leq d\leq k\)), the \(d\)-th PE has two inputs, the first \((d-1)\) components of the soft information sequence \(m_{d-1}\) with its hard decision result \(u_{d-1}\), and the list \(\varphi_{d-1}\) including the intermediate d-dimensional candidate sequence with its correlation discrepancy from the \((d-1)\)-th PE. For the output, the \(d\)-th PE generates the list \(\varphi_{d}\) and transfers it to the \((d+1)\)-th PE of the next stage, until the output of the \(k\)-th PE constitutes the generator output, where \(k\) is the information sequence length.  
Figure 4: The architecture of the candidate sequence generator.  
The PE is the core of the candidate sequence generator. It consists of two expansion modules, two First-In-First-Out (FIFO) memories, and one comparator, which selects the minimum of the two FIFO outputs. The \(d\)-th PE is shown in Figure 4, where the input and output represent the intermediate candidate sequence generation. The \(d\)-th PE serially receives the ordered list \(\varphi_{d-1}\) from \((d-1)\)-th stage one by one. The first step is the expansion of \(\varphi_{d-1}\) into \(\varphi_{d}^{0}\) and \(\varphi_{d}^{1}\), as described in the previous section. Once the first elements \(\varphi_{d}^{0}\) and \(\varphi_{d}^{1}\) are stored in the FIFOs, the merging process starts. In each clock cycle, the outputs of the two FIFOs are compared, and the one with the smaller correlation discrepancy value is selected to be fed into the list \(\varphi_{d}\). Simultaneously, the pull signal is set to 1 to allow a new couple at the output of the FIFOs.  
The setting of the depth of two FIFOs per stage considers the worst case, that is, all couples of \(\varphi_{d}\) are retrieved from one FIFO. Therefore, the depth of the FIFO in the \(d\)-th PE is equal to the number of couples in the output \(\varphi_{d}\) of the stage. According to the previous section, if the stage label \(d\) satisfies \(2^{d-1}>C\), the depth of the FIFO is set to \(C\), otherwise, the depth of the FIFO is \(2^{d-1}\). After the last PE outputs \(C\) candidate information sequences, all of the FIFOs will be cleared and the candidate sequence generation is completed.
# Numerical Investigation of the Relationship between
###### Abstract  
Bypass graft failures are linked to hemodynamic disturbances resulting from poor design. Several studies have tried to improve graft patency by modifying conventional graft designs. One strategy being employed is to induce spiral flow in bypass grafts using an internal ridge which has been proposed to optimize blood flow. However, there is still no study focusing on how the anastomosis angle can affect the hemodynamics of such a design despite its huge influence on local flow fields. To fill this gap, we aimed to understand and optimize the relationship between anastomosis angle and ridged spiral flow bypass graft hemodynamics to minimize disturbances and prolong graft patency. Steady-state, non-Newtonian computational fluid dynamics (CFD) analysis of a distal, end-to-side anastomosis between a ridged graft and idealized femoral artery was used to determine the anastomosis angle that would yield the least hemodynamic disturbances. Transient, pulsatile, non-Newtonian CFD analysis between a conventional and ridged graft at the optimal angle was performed to determine if such a design has an advantage over conventional designs. The results revealed that smaller anastomosis angles tend to optimize graft performance by the reduction in the pressure drop, recirculation, and areas in the host artery affected by abnormally high shear stresses. It was also confirmed that the modified design outperformed conventional bypass grafts due to the increased shear stress generated which is said to have atheroprotective benefits. The findings of the study may be taken into consideration in the design of bypass grafts to prevent their failure due to hemodynamic disturbances associated with conventional designs and highlight the importance of understanding and optimizing the relationship among different geometric properties in designing long-lasting bypass grafts.  
hypas graft; spiral flow; hemodynamics; computational fluid dynamics Article
## 2 Materials and Methods
### Statistics  
Non-parametric Kruskal-Wallis followed by [PERSON]'s multiple comparisons test and Mann-Whitney-U test with the software GraphPad Prism version 6.05 for Windows (GraphPad Software, La Jolla, CA, United States) were conducted on the \(p\)-values \(<\) 0.05. Results are presented in mean \(\pm\) sem (standard error of the mean) for actin filament (Table 2) and cell area values (Figure 1). An unpaired \(t\)-test was performed for cell edge height values (Section 3.3) presented in mean \(\pm\) SD (standard deviation).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**1 h** & **Ti** & **P PAAm** & **PDADMA** \\ \hline Filament number & 62.3 \(\pm\) 5.1 b  & 148.3 \(\pm\) 10.7 a  & 51.4 \(\pm\) 6.3 b  \\ Total filament length [um] & 386.5 \(\pm\) 35.3 b  & 1050.0 \(\pm\) 150.3 a  & 241.6 \(\pm\) 29.4 b  \\ Mean filament length [um] & 6.2 \(\pm\) 0.5 & 6.6 \(\pm\) 0.3 & 4.3 \(\pm\) 0.3 a  \\ Maximum filament length [um] & 27.6 \(\pm\) 2.7 & 35.6 \(\pm\) 3.1 & 19.3 \(\pm\) 2.6 b  \\ \hline
**24 h** & **Ti** & **P PAAm** & **PDADMA** \\ \hline Filament number & 45.5 \(\pm\) 4.9 & 48.9 \(\pm\) 6.0 & 28.6 \(\pm\) 4.9 b  \\ Total filament length [um] & 830.5 \(\pm\) 70.0 & 863.8 \(\pm\) 99.0 & 138.4 \(\pm\) 33.1 a,b  \\ Mean filament length [um] & 19.0 \(\pm\) 1.6 & 18.0 \(\pm\) 1.9 & 4.3 \(\pm\) 0.3 a,b  \\ Maximum filament length [um] & 88.8 \(\pm\) 7.0 & 81.2 \(\pm\) 9.1 & 14.1 \(\pm\) 1.2 a,b  \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantification of actin filament formation in MG-63s on charged titanium surfaces after 1 and 24 h. Data are exported from FilaQuant software for automatic filament recognition, covering the majority of filaments (\(n=10\) cells per surface; mean \(\pm\) sem; Kruskal-Wallis followed by [PERSON]’s multiple comparisons test).  
Figure 2: **(a)** In the vertex model, an osteoblastic cell is represented as a polygon with six sides and is specified by the surrounding vertices, where the length of the side between two neighboring vertices \(i\) and \(j\) is \(l_{ij}\). The cell shape is determined by quasi-static simulations in which a force-balanced state is achieved using an algorithm that performs a Monte Carlo-based minimization of Equation (3). In the final force-balanced state, the shape of the cell is defined by its area \(A_{0}\) and perimeter \(P_{0}\). **(b)** Earlier studies of the vertex model have shown that when the shape index factor \(p_{0}=P_{0}\) / \(\surd{A_{0}}\) is below a critical value \(p_{0}^{*}\), the stable configuration is the ground state and has the shape of a regular polygon, i.e., with higher circularity value. However, when the value of \(p_{0}\) is higher than \(p_{0}^{*}\), the ground state is degenerated and the polygon has an irregular shape, which is characterized by a lower circularity value.
## 2 Blockchain Structure Design Based on Multi-Ship Collision Avoidance Communication Scenario
### The Blockchain Structure Design for Multi-Ship Collision Avoidance Scenarios  
The service layer provides services to the contract layer for interacting with the blockchain, such as the trusted sending of messages (proving that a message was sent at a certain point in time), the trusted receiving of messages (proving that a message was received at a certain point in time), data uploading (storing data on the blockchain), and validation (proving that data are complete, untampered with, and trusted). To implement the above services, the service layer invokes the underlying components of the blockchain facility layer, such as cryptography, communication, storage, and a consensus module, in order to synchronize the blocks of this node with the blocks of other nodes, which are all illustrated in Figure 2.  
Figure 2: Hierarchical architecture design of ship collision avoidance decision system based on blockchain approach.  
By design, the application layer interacts only with the service layer, and the service layer interacts only with the application layer and the facility layer.  
Due to the excessive size of the complete block data stored in the blockchain, the above figure omits the timestamps, signatures, consensus proofs, and other parts and only shows the data part about the CA communication, which is shown in Figure 3. Transactions contain the information sent and received in the trusted communication phase of the CA communication process and the algorithm execution results in the algorithm execution phase. In block h, Pre.Hash is the hash value of block h \(-1\), while the Hash of block h is the result of hashing all data in the current block. The special point to note is that these data also include Pre.Hash.  
Therefore, when the data in the transactions of block h \(-1\) change in any way, the Hash of block h \(-1\) will definitely change. At this point, a comparison of the Pre.Hash in block h reveals that block h \(-1\) has been tampered with. If you try to disguise the tampered block h \(-1\) by changing the Pre.Hash in block h to a new Hash of the tampered block h \(-1\), then the Hash of block h will also be changed, and in turn, you need to change the Pre.Hash of block h + 1. It follows that if you tamper with the data of block h \(-1\) and do not want to be detected, then you need to change all the blocks in the local blockchain for block h and after.  
In addition, each block is synchronized to other nodes of the blockchain through the consensus mechanism of the blockchain, so such tampering can be discovered by any of the nodes in the blockchain. That is, in order to achieve data tampering without detection, one would also have to tamper with the blocks of most of the nodes in the blockchain at the same time, which we generally consider unattainable (or far more costly than beneficial). In other words, a correct block can be verified by any node in the blockchain, thus achieving trustworthy, complete, and tamper-proof data and thus achieving trustworthy CA communication.
## 4 Multiple Services Set Identifier (MSSIDs) Technique  
In the past, a number of separated APs were required to present a multiple services network in a specific area to deploy for Wi-Fi users. Each AP advertises single SSID on a specific frequency channel. The raising number of APs in a particular area leads to a channel congestion issue. As a result, the Wi-Fi service will be inefficient.  
Recently, most of the APs are configured to work with MSSID and feature simultaneously, as shown in Figure 2. MSSID services advertise various SSIDs within one AP instead of a group of separated APs [10]. This configuration is commonly used to advertise various networks for various kinds of users, where each kind of user requires different settings to each SSID, such as firewall, key, bandwidth, privacy, and security. MSSIDs' technique uses the same frequency channel. In order to avoid a collision or frame loss, MSSIDs utilizes a Clear Channel Assessment (CCA) protocol [36]. This protocol is used to investigate the channel state and whether it is used by another SSID. The channel is utilized by another SSID when the channel is clear. Therefore, the probability that all SSID signals are affected by the fading issue will be low [11]. In this article, we use this benefit to mitigate the variation of RSS signals by using a fusion approach for MSSIDs' signals.
# Optimization Method for Stiffened-Plate Layout in Box Structures Based on Load Paths
## 4 Verification of Optimization Result  
In order to verify the optimization design effect of the gantry stiffened plate layout guided by the force flow, the new model is taken as the research object, and the same boundary conditions as Figure 7 are used to carry out the finite element analysis here, and the displacement and stress distributions obtained are shown in Figure 15. On the other hand, modal analysis of the optimized gantry structure yields the mode shapes as shown in Figure 16.  
A detailed comparison of data before and after optimization is presented in Table 3. The maximum stress after optimization is 53.115 MPa, which is reduced by 14.9% compared to the original gantry. The maximum displacement of the gantry after optimization is 0.165 mm, which is reduced by 32.95% compared to the original gantry. Moreover, its distribution has shifted from the original top connection position to the left and right by some distance. In comparison, the deformation at the most critical top connection position has decreased by 40% to 75% compared to the original gantry. This signifies an enhancement in the accuracy of the spindle, which serves as the primary machining component. The overall mass has decreased by 8887 kg, which is a 14.4% reduction compared to the original model. This significant weight reduction demonstrates noticeable lightweighting effects while ensuring the rigidity of the gantry.  
Regarding dynamic performance, the optimized gantry shows a slight increase in the first two natural frequencies that are closest to the forced frequency range. The remaining natural frequencies are slightly lower than those of the original model, but still significantly  
Figure 15: The FEA results of the optimized gantry. (**a**) Displacement distribution. (**b**) Stress distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Original Gantry** & **Optimized Gantry** & **Rate of Change (\%)** \\ \hline Max displacement (mm) & 0.2461 & 0.165 & \(-\)32.95 \\ Max stress (MPa) & 62.43 & 53.115 & \(-\)14.9 \\ Mass (kg) & 61656 & 52769 & \(-\)14.4 \\ First order natural frequency (Hz) & 46.213 & 46.641 & +0.93 \\ Second order natural frequency (Hz) & 54.884 & 56.525 & +2.99 \\ Third order natural frequency (Hz) & 74.058 & 65.394 & \(-\)11.70 \\ Fourth order natural frequency (Hz) & 79.395 & 67.186 & \(-\)15.38 \\ Fifth order natural frequency (Hz) & 96.24 & 68.946 & \(-\)28.36 \\ Sixth order natural frequency (Hz) & 101.13 & 73.394 & \(-\)27.43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of gantry mechanical properties before and after optimization.  
higher than the forced frequency. This indicates that the dynamic performance of the overall gantry is relatively stable after optimization.
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
### Superimposition of EMG and ECG Signals ct of 50 Hz and Higher-Order Components  
It has already been mentioned above that the ECG signal significantly interferes with EMG recordings. This strongly depends on the type of muscles being diagnosed and the location of the electrodes attached to the skin. To evaluate the contribution of cardiac activity in the presented experiments, only ECG signals were registered first. Next, the spectrum was calculated and compared with typical EMG and ECG signals superimposed on each other (see Figures 3 and 4).  
Figure 3: ECG signal measured on the right abdominal muscle, (**a**) raw signal (**b**) after removing low-frequency part using 3 rd order Butterworth HP filter, \(f_{c}\) = 2 Hz.  
Figure 2: EMG signal with high impact of power line interferences, (**a**) after removing low-frequency part using 3 rd order Butterworth HP filter, \(f_{c}\) = 2 Hz, (**b**) spectrum for \(f_{\rm S}\) = 2 kHz.  
As one can notice, the spectrum of ECG and EMG signals are overlapping. The main part of the ECG signal energy lies in the low-frequency band below 20 Hz. Fortunately, the EMG data has dominant spectral components around the 40 Hz range. This allows for the reduction of the impact of ECG disturbance signal on the EMG signal.