esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Ablation Analysis  
To investigate the importance of each model component to the overall model performance, we exclude each significant element in turn and observe how it affects model performance on the experimental dataset.  
**Adversarial training.** In the experimental dataset, we compared the entire model to a model with no adversarial training, i.e., no _Decoder_2. Figure 3 shows the F1 scores of the CAT-IADEF and CAT-IADEF (no adversarial training) models on four experimental datasets.  
As shown in Figure 3 the performance of CAT-IADEF (no adversarial) has declined on the four datasets, with a minimum drop of 0.11% and a maximum drop of 2.72%; this means that adversarial training enables the model to identify \"slight\" anomalies by magnifying errors.  
**Dynamic threshold.** We remove the dynamic threshold plate on CAT-IADEF and train it on the experimental dataset. Figure 4 shows the F1 scores of CAT-IADEF and CAT-IADEF (no dynamic threshold) on our four experimental datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**P1 (SMAP)**} & \multicolumn{2}{c}{**T4 (MSL)**} & \multicolumn{2}{c}{**Valve1-4 (SKAB)**} \\ \cline{2-6}  & **Dimension 1** & **Dimension 2** & **Dimension 1** & **Dimension 2** & **Dimension 1** & **Dimension 2** \\ \hline MAD\_GAN-EF & 5 & 6 & 0 & 5 & 7 & 1 \\ OmniAnomaly-EF & 5 & 6 & 0 & 5 & 4 & 7 \\ USAD-EF & 5 & 6 & 0 & 5 & 1 & 7 \\ TranAD-EF & 0 & 3 & 0 & 5 & 1 & 7 \\ CAT-IADEF & 5 & 6 & 0 & 5 & 7 & 1 \\ \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Valve1-14 (SKAB)**} & \multicolumn{2}{c}{**T1 (SMAP)**} & \multicolumn{2}{c}{**D15 (MSL)**} \\ \cline{2-6}  & **Dimension 1** & **Dimension 2** & **Dimension 1** & **Dimension 2** & **Dimension 1** & **Dimension 2** \\ \hline MAD\_GAN-EF & 7 & 4 & 0 & 5 & 0 & 5 \\ OmniAnomaly-EF & 4 & 7 & 0 & 5 & 0 & 5 \\ USAD-EF & 2 & 7 & 0 & 5 & 0 & 5 \\ TranAD-EF & 7 & 2 & 0 & 5 & 0 & 5 \\ CAT-IADEF & 7 & 1 & 0 & 5 & 0 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation of anomaly explanation.  
Figure 3: CAT-IADEF and CAT-IADEF (no adversarial training) performance comparison.  
As shown in Figure 4, the POT dynamic selection threshold positively impacts the model. The most significant improvement is in the D15 dataset, from 0.73 to 0.993, representing 36.0% progress. The tiniest improvement is in the P1 dataset, from 0.85 to 0.923, representing an increase of 8.6%.
## 2 Stability and Controllability Analysis
Different possible controllability Gramian-based metrics exist, such as the trace of \(\mathbf{W}_{c}\), which measures the average controllability of the system across all state-space directions; the determinant of \(\mathbf{W}_{c}\), representingthe volume of the reachable state set with unit input energy; the minimum eigenvalue of \(\mathbf{W}_{c}\), which indicates the least controllable direction and serves as a worst-case metric; the largest eigenvalue of \(\mathbf{W}_{c}\), reflecting the best-case controllability in the most easily controlled direction, and the sum of the two minimal eigenvalues, which quantifies the combined controllability of the two most challenging state directions [20,21].
## 3 Results  
### Foliage Projected Cover  
#### 3.1.1 Comparison to Landsat  
To determine how the new SPOT5 FPC map compared to the previous Landsat FPC map, both maps were degraded to 100 m pixels and a sample of areas selected using a regular 1 km grid across NSW. These areas were then sampled using a stratified random approach, to ensure that each vegetation formation class was evenly sampled, and only pixels classified as tree cover in both products were used. An orthogonal distance regression model was fit between the SPOT5 and Landsat FPC values, resulting in a very strong relationship (\(r^{2}\) = 0.93, RMSE = 6.27) (Figure 4). The results varied by vegetation formation, with SPOT5 overestimating FPC compared to Landsat in some vegetation types (Dry sclerophyll forests (Shrub/grass subformation), cleared, Semi-arid woodlands (Shrubby subformation), Dry sclerophyll forests (Shrubby subformation)), and underestimating in Arid shrublands (Acacia subformation). More information is available in the supplementary material.  
Figure 4: Density plot of foliage projective cover (FPC) derived from multi-temporal SPOT5 imagery compared with that from multi-temporal Landsat imagery, after both maps were resampled to 100 m pixels (darker points have a greater density of observations). The dashed line and equation represent the line of best fit derived from orthogonal distance regression (appropriate when both x and y have error), which shows an excellent agreement with low root mean square error (RMSE) and high correlation (\(r^{2}\)).  
The results are useful as they indicate that the lower spectral resolution of the SPOT5 data was capable of modelling FPC, although the close relationship is not surprising given the SPOT5 FPC model was trained using the Landsat FPC data. However, the comparison does not assess the application of the model at 5 m resolution, which assumes a linear relationship between FPC at the different scales. This assumption is the focus of ongoing research.
## 3 Method
### Adaptive Retrieval
#### 3.2.2 Multi-Step Strategy  
Simple queries are relatively straightforward, but complex queries require more unknown information. If the query is used directly for retrieval, it is difficult to find all the relevant documents. Complex queries can be broken down into multiple simple queries. To retrieve useful knowledge and filter out irrelevant information, we use a small model to break down the complex query into two sub-questions, \(q_{1}\) and \(q_{2}\), and then perform multi-stage retrieval to gather as many relevant documents as possible.  
After decomposing the complex query using a small model, two logically progressive simple queries are formed, namely \(q_{1}\) and \(q_{2}\). For each query, as shown in Figure 2, we first use \(q_{1}\) to retrieve \(n\) relevant documents, i.e., \(\{d_{1},\dots,d_{n}\}\). At this point, it is still insufficient to answer the query, so we perform a second retrieval using \(\{q_{1},q_{2},d_{i}\}\) as a whole to obtain \(m\) relevant documents, i.e., \(\{d_{1},\dots,d_{m}\}\). Compared to the first, the second retrieval introduces more information and retrieves some documents more closely related to the query, but it also returns irrelevant and redundant documents. Irrelevant information can affect the large model's ability to find answer clues and impact the question-answering process.  
\[RD^{1}=Retriever^{1}(q_{1})\]  
\[RD^{2}=Retriever^{2}(q_{1},q_{2},d_{i}),d_{i}\in RD^{1}\]  
To remove irrelevant documents, we filter the relevant documents by calculating whether \(d_{j}\) contributes to answering the query. As illustrated in Figure 1, we have developed a balanced dataset in which each entry comprises a question, relevant texts, and irrelevant texts designed to train the relevance model for information filtering.  
\[rc=Classifier(q,d_{j}),\ d_{j}\in RD^{2},\ rc\in\{0,1\}\]  
where 0 indicates that document djs relevant to qj and 1 indicates it is not relevant. The classifier utilized here adopts the same methodology as the query classifier but is built upon a different base model. Notably, the query classifier and the relevance classifier are designed to fulfill distinct purposes.
## 6 Evaluation
### Evaluation Perspectives
#### 6.1.4 Route Conflicts Ratio  
(Eval. 4) We clarify that the \"route conflicts ratio\" decreases as the relay node density increases depending on the number of UAVs, and clarify a sufficient relay node density to avoid too many \"route conflicts\". It is calculated by (the number of conflicted routes)/(the number of all routes), and a lower value is better.
## 3 Methods  
### Method Overview and Assumptions  
The proposed method was designed to efficiently eliminate indoor clutter objects from the point cloud data obtained from a construction site. It is based on the assumptions that the structural elements are connected from the floor to the ceiling and that indoor clutter objects exist mostly on the floor and are not connected from the floor to the ceiling. The proposed method used voxel-grid downsampling, DBSCAN clustering, and the SOR filter to accurately identify and remove indoor clutter while preserving the structural elements in the point cloud.  
Figure 1 illustrates the framework of the proposed method, which consists of seven steps (a to g). First, the proposed method receives an original point cloud as input data, as shown in Figure 1a. Subsequently, the point cloud near the floor and the ceiling is eliminated, as shown in Figure 1b. This is done to ensure that the method focuses on the indoor clutter on the floor, as the structural elements are assumed to be connected from the floor to the ceiling. The voxel-grid downsampling is then applied to generate a uniform point-cloud density, as shown in Figure 1c. This process reduces the computational burden and allows for faster processing. The x- and y-coordinates are then extracted from the point cloud that was downsampled with the voxel grid, as shown in Figure 1d.  
Figure 1: Proposed method framework.  
Next, the extracted x- and y-coordinates of the structural element candidates are clustered through DBSCAN, as shown in Figure 1e. DBSCAN is used to group the points with similar spatial coordinates into clusters, which helps identify the structural elements. The SOR filter is then applied to obtain more accurate structural element candidates, and the indoor clutter objects are removed, as shown in Figure 1f. The SOR filter is used to smooth the surface of the structural element candidates and remove the remaining noise data. Finally, the obtained structural element candidates are used to search the structural elements in the point cloud using an unsupervised radius-based nearest neighbor search algorithm, as shown in Figure 1g. Once the voxel size is determined, the proposed method operates automatically, except for the SOR filtering step. The parameters required for each step are automatically determined based on the voxel size. The details of these steps are explained in Section 3.2.
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
###### Abstract  
It is widely acknowledged in communication theory that polar codes have been proven to achieve channel capacity across a range of communication channels. However, their exceptional performance is usually evaluated through simulations or analyses conducted under the assumption of infinite precision, i.e., floating-point arithmetic, which represents an ideal numerical computation. To address this implementation challenge, this work proposes a min-sum successive cancellation (MS-SC) polar decoder employing all-integer quantization to improve practicality in real-world scenarios. To balance the trade-off between practicality and decoding performance, we investigate whether 5-bit all-integer quantization is the optimal choice for the MS-SC polar decoder. Moreover, the simulation results over fading channels show that the proposed decoder achieves a performance almost equivalent to the high-precision successive cancellation (SC) decoder. The integer-based calculation for the MS-SC polar decoder reduces computational complexity by 75% compared to the conventional SC decoding algorithm with infinite-precision computation.  
Keywords:polar codes; SC decoding; min-sum approximation; integer quantization
###### Abstract
The kinetic model can be simplified to:  
\[\ddot{x}_{c}(t)+\frac{k_{A}+k_{B}}{M}\cdot x_{c}(t)=\frac{1}{M}\cdot F_{c}(t) \tag{2}\]  
Knowing that the mass of the slide is \(M\) = 28 kg and that the stiffness of the aerostatic bearing \(k_{A}\) = \(k_{B}\) = 625,000 N/mm, the natural frequency of the slide can be obtained as \(\omega_{n}\) = 207.02 Hz. The structure of the vacuum-preloaded porous aerostatic bearing consists of a rectangular vacuum cavity in the center of the bearing and a porous restrictor surrounding the vacuum cavity. Therefore, the interference force acting on the slide mainly includes three components, one of which is generated by the vacuum cavity. The second is the disturbance of the bearing thrust caused by the fluctuation of the air supply pressure, and the third is a disturbance outside the system (such as cutting force). The direction of action of the interference force at the center of mass of the slide is the normal direction of the thrust surface of the bearing. Assuming that the \(X\)-axis slide is only subjected to external sinusoidal disturbances from the vertical direction,  
\[F_{c}(t)=F_{0}sin\omega t \tag{3}\]  
Assuming \(t\) = 0, \(x(0)\) = \(x_{0}\), and \(v(0)\) = \(v_{0}\), the solution for the forced vibration of the slide is:  
\[x_{c}=x_{0}cos\omega_{n}t+\frac{v_{0}}{\omega_{n}}sin\omega_{n}t-\frac{B \lambda}{1-\lambda^{2}}sin\omega_{n}t+\frac{B}{1-\lambda^{2}}sin\omega t \tag{4}\]  
where \(\lambda=\frac{\omega}{\omega_{n}}\). \(B=\frac{F_{c}}{K_{A}+k_{B}}\). According to the above dynamic model of the slide, assuming that the initial speed of the slide is \(x(0)\) = \(x_{0}\) = 0 and \(v(0)\) = \(v_{0}\) = 0, the solution for the forced vibration of the slide can be simplified as:  
\[x_{c}=-\frac{B\lambda}{1-\lambda^{2}}sin\omega_{n}t+\frac{B}{1-\lambda^{2}} sin\omega t \tag{5}\]  
When \(\omega<<\omega_{n}\), Equation (5) can be simplified as:  
\[x_{c}=Bsin\omega t \tag{6}\]
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 3 Propagation Attenuation Map
### Parabolic Equation Method
From the viewpoint of the proposed PAM solution, any PEM implementation can be used. Considering additional factors (e.g., the height of buildings, and vegetation) only influences the accuracy of the estimation of field strength or signal attenuation, but it does not matter for the implementation of our PAM algorithm. Analyzing the literature, we see some similarities of the PAM algorithm to the 3D PEM approach presented in [48; 49]. However, it should be highlighted that we do not determine the field strength distribution in the 3D space, but only 2D attenuation distribution (i.e., PAM) at the specific height of the receiving antenna. Such PAM as a heat map allows determining the radio coverage or the range of individual MANET nodes. In both cases, i.e., in the PAM and 3D PEM, radial rays from the TX (i.e., terrain profiles for which field distributions are determined) are applied. These rays are determined for specific azimuth directions with a given step.
# Branching Densities of Cube-Free and Square-Free Words
## 4 Positions Fixed by Small Cubes
### Lower Bounds on Branching Density  
We implemented the above algorithm and ran it for all \(i\leq 18\); for \(i=18\), the memory required for the table \(C(j,v)\) is over \(1\) GB. The results are as follows.  
**Lemma 6**.: _One has \(d_{1}=d_{2}=1/2\), \(d_{3}=\cdots=d_{11}=7/13\), \(d_{12}=d_{13}=d_{14}=13/24\), \(d_{15}=d_{16}=d_{17}=d_{18}=53/96\)._  
Now we are ready to prove our first main result.  
**Theorem 3**.: _The branching density of an infinite binary cube-free word is at least \(3509/9120\approx 0.38476\)._  
Proof of Theorem 3.: Let us fix some integer \(h\geq 2\). Theorem 1 and Corollary 1 together imply that the density of fixed positions in an infinite cube-free word is upper-bounded by \(d_{h-1}+\frac{6}{5h}\). Trying all values of \(d_{i}\) from Lemma 6, we get the maximum at \(h=19\):  
\[d_{18}+\frac{6}{5\cdot 19}=\frac{53}{96}+\frac{6}{95}=\frac{5611}{9120}.\]  
The statement of the theorem now follows.  
In the same way, we can get the lower bound for the ternary square-free language \(\mathsf{SF}\). From [19] (Lemma 5), we have the upper bound \(\frac{2}{h}\) for the density of positions fixed by squares of periods \(\geq h\). Lemmas 4 and 5, and Corollary 1 have direct analogs for ternary square-free words; Proposition 1 and the algorithm inside remain valid for any automaton having, at most, two outgoing edges for each state. Running the algorithm for the regular approximations of \(\mathsf{SF}\) up to \(i=30\), we obtained the correspondent numbers \(d_{i}^{\prime}\). Taking \(h=31\) and adding \(\frac{2}{h}\) to \(d_{30}^{\prime}=19/28\), we arrive at the following bound.  
**Theorem 4**.: _The branching density of an infinite ternary square-free word is at least \(223/868\approx 0.25691\)._  
Recall that the _growth rate_ of a factorial language \(L\) over the alphabet \(\Sigma\) is the limit \(\lim_{n\to\infty}|L\cap\Sigma^{n}|^{1/n}\). The growth rate \(\beta\) of \(\mathsf{CF}\) is known with quite high precision [9]: \(1.4575732\leq\beta\leq 1.4575773\). In terms of the prefix tree, this means that for big \(n\), the number of nodes at the \((n+1)\)th level is approximately \(\beta\) times bigger than the number of nodes at the \(n\)th level. This fact makes \(\beta-1\) a lower bound on the fraction of branching nodes at the \(n\)th level (because this level also contains nodes having no children). In Theorem 5 below, we use Proposition 1 and Remark 3 to prove that there exist infinite cube-free words with the branching density strictly smaller than \(\beta-1\).  
The above considerations can also be applied to the growth rate \(\gamma\) of \(\mathsf{SF}\), \(1.3017597\leq\gamma\leq 1.3017619\)[9]. However, it is still open whether an infinite square-free word can have the branching density smaller than \(\gamma-1\). The method of Theorem 5 would not work for \(\mathsf{SF}\) because the obtained values of \(d_{i}^{\prime}\) are too small.
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 3 Fabrication Techniques of Graphene-Based Membranes
### Chemical Vapor Deposition  
CVD is a chemical technology, which mainly uses one or more gas-phase compounds or elemental substances containing thin-film elements to perform chemical reactions on the surface of the substrate, to form thin films. CVD is an efficient method for the mass production of high-quality, graphene-based membranes. Different types of CVD methods are available, such as plasma-enhanced CVD, thermal CVD, thermal/cold wall CVD and many others. CVD is an advantageous method that enables the continuous preparation of graphene films by growing graphene on various substrates (nickel, copper, platinum, ruthenium or iridium) [32]. Due to the chemical inertness of graphene, the transfer of graphene from the growth substrate to other substrate can be difficult, and may cause defects and wrinkles in the graphene membrane during the transfer process. Thermal fluctuations can also affect the stability of the grown material [38]. Using this method, the complex transfer process of graphene growth on Cu or Ni substrates is unavoidable. On such substrates, high synthesis temperatures (>1200 \({}^{\circ}\)C) or high costs are required and often degrade the electrical and optical properties of graphene. [PERSON]'s group reported a new method for the direct synthesis of graphene on various insulating substrates (SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), etc.) by the remote catalysis of copper nanoparticles by atmospheric pressure CVD, which provides a low-defect preparation method. This is an efficient method for the production of large-area graphene membranes [47].
## 3 Results and Discussion
### Scenario Analysis
#### 3.4.2 Scenario 2 (4 Access Points)  
In the second scenario, we propose to cover the campus with four base stations. We obtained the results shown in Figure 9, where 36.4% of the campus surface is covered by an excellent level of field greater than \(-\)70 dBm, and 45% of the surface is covered by a signal level between \(-\)80 dBm and \(-\)70 dBm (Figure A4 in Appendix A). Four access points are active at a duration of 10 h per day and consume an amount of electricity of 480 Wh. This energy consumption produces 200.64 g of CO\({}_{2}\) if the source of production of the electricity is based on natural gas and 26.4 g of CO\({}_{2}\) if the source of electricity production is solar.
## 2 Materials and Methods
### Material
#### 2.1.4 Metrics  
Several metrics are used in this study to measure the performance of the AF detector, such as sensitivity, specificity, and accuracy. Sensitivity is the proportion of actual AF rhythm that is correctly identified as AF, and specificity is the proportion of normal rhythm that is correctly detected as normal. Finally, accuracy represents the overall accuracy of the method used [52]. In detail, the formulas of sensitivity, specificity, and accuracy can be seen in Equations (1)-(3), as follows:  
\[Sensitivity=\frac{\text{TP}}{\text{TP}+\text{FN}}*100\%, \tag{5}\]  
\[Specificity=\frac{\text{TN}}{\text{TN}+\text{FP}}*100\%, \tag{6}\]  
\[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}*100\%, \tag{7}\]  
where TP is true-positive, TN is true-negative. TP can be defined as the number of AF beats that is correctly detected as AF. Meanwhile, TN is the number of normal beats that is correctly identified as normal. In those formulas, FP (false-positive) is the number of normal beats that is incorrectly detected as AF, and FN (false-negative) is the number of AF beats that is identified incorrectly as normal. Furthermore, confusion test metrics are presented in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline  & **Actual AF** & **Actual Normal** \\ \hline Predicted AF & TP & FP \\ Predicted Normal & FN & TN \\ \hline \end{tabular}
\end{table}
Table 2: Confusion matrix TP, TN, FP, FN.
## 7 Experimentation and Analysis
### Validation of Resource Optimization Methods
#### 7.2.4 Impact of Number of Tasks on System Overhead  
Figure 13 compares the changes in total system overhead of the four algorithms when the task volume increases. When the task volume is small, the difference between the four algorithms is not significant because the terminal side where the task area is located can meet the task processing demand, and when the task volume increases, the terminal devices in the task area are close to full capacity, so the GLSOAG advantage is not obvious. As the task volume continues to increase, when the large volume tasks such as intelligence analysis exceed the processing capacity of the server on the edge side, the task data need to be shunted to the center cloud. The GLSOAG can still maintain its advantage in convergence speed and accuracy in the face of complex multi-dimensional problems, and the advantage gradually expands when the task volume is greater than 40.  
Figure 12: Effect of the number of iterations of different algorithms on the system overhead.  
Figure 13: Impact of task volume on system overhead.