esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Comparison between GRASPSPP and Branch and Bound Algorithm  
This section shows the comparison between GRASPSPP and a Branch and Bound algorithm (B&B) presented in 2003 [19] by [PERSON] et al, where the authors executed their approach with a time limit of one hour and reported the time in which they found their best result. For this comparison, we executed the GRASPSPP algorithm with a time limit of 5 min in an attempt to produce an equivalent computational effort because our processor is an Intel Core i5 at 2.50 Ghz, which outperforms the authors' Pentium III at 800 Mhz processor.  
Figure 10 shows the percentage error (see Equation (11)) comparison between GRASPSP (2.62 average, 7.17 standard deviation) and B&B (0.97 average, 1.79 standard deviation). We obtain the _%error_ as follows:  
\[\%error=(100\times(obtainedHeight-bestHeight))/bestHeight \tag{11}\]  
Here, GRASPSPP outperformed B&B in eight instances; while B&B outperformed GRASPSPP in 11 instances. Among those 11 instances, three of them presented an extremely  
Figure 8: Average errors (errAvg) between GRASPSPP and IS.  
Figure 9: Time comparison between GRASPSPP and IS algorithm.  
high %_error_ in GRASPSPP; therefore, we require further analysis of the properties of those instances to identify the cause and implement corrections; those instances were ht4, ht7, and ht9. Additionally, the Wilcoxon test showed that the GRASPSPP and B&B are statistically equivalent.  
Figure 11 shows the computational time comparison between GRASPSPP (0.000 average, 0.001 standard deviation) and B&B (1116.30 average, 1632.09 standard deviation). These values are the computational times of each instance set. Here, the B&B algorithm presents some instances with the highest possible values, because they reached the time limit of 3600 s. On the other hand, the proposal processes the instances with times closer to one second.  
A visual comparison shows that the GRASPSPP algorithm is faster than B&B. Additionally, the Wilcoxon test corroborates that the GRASPSPP outperforms B&B statistically with a \(p\)-value of 0.001.
# Active Control of Low-Frequency Noise through a Single Top-Hung Window in a Full-Sized Room
## 3 Results
### Passive and Active Attenuation Performance  
As the ANC system is designed to minimize the sum-of-the-squared sound pressure levels at the error microphone positions, the active control performance of the four primary noise types (see Table 2) is first evaluated at the seventh observation microphone, placed near the error microphones. The active and passive attenuation at the seventh observation microphone, \(\alpha_{active,Error}\) and \(\alpha_{passive,Error}\), respectively, are summarized in Table 3. Across the four primary noise types, active attenuation was somewhat consistent (8 dB to 12 dB), whereas the passive attenuation was about 10 dB, with the exception of compressor noise. The passive attenuation of the compressor noise was only 6.5 dB, which was about half that of the active attenuation at 12.4 dB.  
To evaluate the global control effectiveness of the ANC system, the active and passive attenuation, calculated with the energy-average SPL of microphones one through six (see Figure 2) are also presented in Table 3. The active and passive attenuation (i.e., \(\alpha_{active,EA}\) and \(\alpha_{passive,EA}\)) were almost 4 dB lower than at the error microphone position, except for the compressor noise. However, the trend across the noise types was similar to that of the attenuation at the error microphone position. Overall, the ANC system on a fully opened top-hung window offered similar attenuation to the passive insulation provided by fully closing the window. More importantly, active attenuation outperformed passive attenuation significantly for compressor noise.
## 4 Performance of KF Used for Parameter Estimation in RTHS
### Parameter Study of KF
#### 4.3.2 Effect of Initial Covariance Matrix P\({}_{0}\) and R  
The relationships between the \(RMSE\) of the compensated system and the initial covariance and measurement noise covariance are shown in Figure 8. The figures show that for a specific displacement input, as the value of \(W_{\text{f}}\) increases, \(RMSE\) remains almost the same. Moreover, with an increase in the stop frequency, the \(RMSE\) increases. However, the maximum value of the \(RMSE\) is less than 5%, indicating that the proposed method is still effective with varying initial guess values.  
The relationships between the converged value and the factor \(W_{\text{f}}\) are shown in Figures 9 and 10. Similar to that used in Figure 7, the true values and the error threshold of 1% are also given in the figure. It can be seen that for all of the considered cases, regardless of which of the three variables changes, i.e., \(R\), \(\mathbf{P}_{0}\), or \(W_{\text{f}}\), the converged value remains at almost the same level. The maximum relative error between the convergency value and the true value is within 1%, indicating that the parameters of the control plant are improved notably, and the parameter estimation method exhibits strong robustness.
## 5 Proposed Predictive Maintenance (PdM) Structure Using Internet of Things (IoT)
### Generating Data for Predictive Model Construction  
Noticing the unavailability of the equipment performance data, we developed a data collector that was used to collect the real time data to be used in the development of predictive analytics model. The collected data helped to observe data changes versus components health performance with the  
Figure 4: Long Short Time Memory (LSTM) Cell structure.  
purpose of classifying the operating component health status. Figure 5 illustrates the main components schematic of the data collector that was used to gather and transmit data.  
For our experiment, temperature was highlighted as main defect symptom of the selected equipment's components. The developed embedded device was made up of temperature sensors for collecting real time temperature, a microcontroller for processing data, and a communication module for transmitting data to the database.
## 7 Conclusions  
This paper proposes a 6 TiSCH adaptive slotframe allocation technique AAS for IIoT applications. This technique uses the node's MAC addresses and hops distance from the root to schedule the cell within a slotframe. The number of cells to be scheduled is estimated by monitoring the pending packets in a queue and the PDR of the link. By scheduling optimal cells, AAS improves the end-to-end latency and network lifetime. Mathematical analysis shows the performance of the AAS technique over current schedulers. Results show that the end-to-end latency of the AAS is up to 63% lower than the ALICE and 32% lower than the A\({}^{3}\). The PDR of AAS is 27% higher than A\({}^{3}\) for a packet interval of 2 s and 11% higher than ALICE for a packet interval of 10 s. The network lifetime with the AAS is up to 23% longer than ALICE and 10% longer than the A\({}^{3}\) scheduler. The results demonstrate that the proposed technique outperforms the existing approaches in terms of end-to-end latency, PDR, and network lifetime. This technique will significantly improve IIoT network performance.  
**Author Contributions:** Conceptualization, N.P., B.S.C. and P.D.K.; methodology, N.P., B.S.C. and P.D.K.; software, N.P., B.S.C. and P.D.K.; validation, N.P., B.S.C. and P.D.K.; formal analysis, N.P., B.S.C. and P.D.K.; investigation, N.P., B.S.C. and P.D.K.; resources, N.P., B.S.C. and P.D.K.; writing--original draft preparation, N.P.; writing--review and editing, N.P., B.S.C. and P.D.K.; visualization, N.P., B.S.C. and P.D.K.; supervision B.S.C. and P.D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
Figure 9: Network lifetime versus slotframe length.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 3 Results
### Experimental Calibration
#### 3.2.1 Case of One Single Filled Joint
As the water content increases, \(T_{\mathrm{P\to p}}\) decreases evenly and related curves become divergent when \(\theta\) is smaller than \(83^{\circ}\); When the water content increases from \(0\%\) to \(5\%\), both \(R_{\mathrm{P\to p}}\) and \(R_{\mathrm{P\to sv}}\) have an obvious increase while they have very limited changes as the water content increases from \(5\%\) to \(10\%\); \(T_{\mathrm{P\to sv}}\) is basically independent on the water content of the filled medium. Figure 6: Variation of reflection–transmission coefficients versus the wave frequency \(f\) when a P wave impinges a filled joint with a thickness of 20 mm. The incident wave has the incident angle of \(20^{\circ}\) and the frequency of 50 Hz. Figure 8 shows the reflection -transmission coefficients versus the quality factor when a P wave impinges on a single filled joint with a thickness of 40 mm. For convenience, a variable \(Q\) is introduced to express \(Q_{\rm p}\) and \(Q_{\rm s}\) as \(Q_{\rm p}=Q\) and \(Q_{\rm s}=2/3Q\), respectively. To only investigate the influence of the quality factor of the filled medium, the density, thickness and wave velocity of the filled medium were fixed as constant values (\(V_{\rm p}\)P = 185 m\(\cdot\)s\({}^{-1}\), \(V_{\rm s}\)P = 123 m\(\cdot\)s\({}^{-1}\) and \(\rho_{\rm f}=1807\) kg\(\cdot\)m\({}^{-3}\)). It shows that \(T_{\rm p\to p}\) decreases abruptly with \(Q\) increasing, after achieving a minimum value, it increases with an increment of \(Q\) instead and eventually it basically keeps invariant. \(T_{\rm p\to sev}\) does not change much with increasing \(Q\). Both \(R_{\rm p\to p}\) and \(R_{\rm p\to sev}\) increase rapidly with \(Q\) increasing and then they increase gently, and they basically keep invariant as \(Q\) is larger than 20. Generally, \(Q\) has a more significant effect on \(T_{\rm p\to p}\), \(R_{\rm p\to p}\) and \(R_{\rm p\to sev}\) than \(T_{\rm p\to sev}\). Figure 8: Reflection–transmission coefficients of a P wave impinging on a single filled joint versus the quality factor \(Q\). The incident P wave has the frequency of 50 Hz and an incident angle of 20\({}^{\circ}\). Figure 7: Reflection–transmission coefficients versus the incident angle \(\theta\) when the P wave impinges on a single filled joint with a thickness of 10 mm. The incident wave has the frequency of 50 Hz. Due to the viscosity of the filled medium, the wave energy dissipated after propagating through the filled joint, which can be defined as follows [21; 40]:  
\[E_{loss}=1-T_{p\to p}^{2}-R_{p\to p}^{2}-T_{p\to\mathrm{sep}}^{2}-R_{p\to \mathrm{sep}}^{2} \tag{41}\]  
Figure 9 shows the energy loss versus the quality factor \(Q\) corresponding to Figure 8. It displays that the energy loss \(E_{\mathrm{loss}}\) first increases abruptly with the increase in \(Q\). After achieving a peak, \(E_{\mathrm{loss}}\) decreases rapidly with an increment of \(Q\). When \(Q\) is larger than 25, \(E_{\mathrm{loss}}\) changes gently with \(Q\) increasing.
## 7 Conclusions  
The method presented in this paper was devised with the aim of creating a novel secure watermarking scheme for digital images based on the SVD transform. Our approach was influenced by the work by [PERSON] et al. [14], which states that the original media is not required in the detection process for resolving legal ownerships. This disallows the usage of a non-blind watermarking method to attest the possession of the original digital image or, more generally, of a multimedia content.  
The proposed scheme is based on singular vectors, therefore it does not suffer the majority of the problems related to SVD watermarking schemes which use the singular values in the embedding process. Moreover, our scheme preserves the orthogonality property of the singular vectors during the watermark embedding step, which permits a smooth detection of the watermark in case of no-attack.  
Although our solution has been devised and tested using the right singular vectors, it can be applied to the left singular vectors as well (\(U\) matrix), or extended to use both right and left singular vectors. Indeed, enlarging the embedding space improves the security and provides the possibility to insert the watermark sequence redundantly. However, extra care must be taken in order not to degrade the quality of the watermarked image during the embedding process. This is one of our future research directions, with the aim of increasing the performance of the method with respect to robustness to attacks and further reduction of the false positive and false negative occurrence.  
Given the interesting results presented, we are also going to test the application of the SVD (using singular vectors) to other type of media that in general require copyright protection.
# Triangular Mesh Surface Subdivision Based on Graph Neural Network
## 4 Experimental Analysis
### Experimental Comparison of Extreme Input Mesh Data  
Neural subdivision is based on a data-driven approach, which improves the shortcomings of the traditional subdivision algorithm's over-smoothing. However, it is also limited by the features of the training data. For some extreme input meshes, such as when the resolution is much lower or much higher than the training mesh, the triangle density is too large, the number of mesh vertices is low, and the angle between triangles is too small, etc., unexpected subdivision failures will occur.  
In the fish model, where the density of the back isoplane is large and the angle is too small, the neural subdivision fails, as shown in Figure 7. In our method, dynamic graph convolution is introduced, which connects non-adjacent graph nodes and enriches the receptive domain of graph nodes so that the overall network can learn the relationship between adjacent nodes, capture the dependencies between non-adjacent nodes, and learn the global features of the mesh through stacking. In addition, the average area of a neighborhood triangle and the number of neighborhoods of each vertex are introduced into the node feature to enhance the robustness of the neural network against extreme features.  
We implement the method on the same extreme input mesh, and the results are subdivided in Figure 8. Experimental results show that GNS can adapt to the subdivision task of extreme input meshes and achieve smooth and satisfactory subdivision results.  
Figure 6: The subdivision effect on cartoon elephant and Bob using NS and GNS. (The first line is the input coarse mesh, the second is the effect of neural subdivision, and the third is the effect of GNS).  
Figure 7: Subdivision effect of the neural subdivision on the fish model. The fish model has many subdivision artifacts on the tail, back, and fins.
# A Simplified Methodology to Evaluate the Design Specifications of Hydraulic Components
## 3 Pressure Specifications  
Commonly, the market defines machine life expectancy using different modes, but always specifying working hours, such as 5000 h without failures. More often, statistical concepts are considered, so the life expectancy is expressed as \"5000 h \(\beta_{10}\)\" [17]. The latter means that when a sampling of 100 machines are tested over 5000 h, only 10 of them (10%) are expected to fail before reaching those working hours. Therefore, the damages generated during the useful life of the machine, when the identified tasks are performed by the machine, cannot generate a failure before the life of the objective \(T\). This can be stated as  
\[\phi_{eq}T=\sum_{1}^{n}\phi_{k}\pi_{k}\cdot T=\alpha\text{ and }N=\alpha P^{-m} \tag{8}\]  
which is analogous to the Basquin equation. Then,  
\[T\sum_{1}^{n}\phi_{k}\pi_{k}=\alpha=N_{eq}P_{eq}^{m} \tag{9}\]  
where \(\alpha\) is the Basquin coefficient and \(N_{eq}\) is the equivalent number of cycles at a pressure level \(P_{eq}\) under constant amplitude conditions.  
The pair of values (\(N_{eq}\), \(P_{eq}\)) defines a line in logarithmic coordinates with slope \(m\) (for steel \(m=3\)). From this curve and analogous to what is proposed in standard ISO 13445-3 [9], the curve (\(N_{eq}\), \(P_{eq}\)) is identified by equivalent pressure value corresponding to \(2\times 10^{6}\) cycles, which constitutes the class curve \(P_{\varnothing eq}\):  
\[P_{\varnothing eq}=\left[\frac{1}{2\cdot 10^{6}}T\phi_{eq}\right]^{\frac{1}{3}} \tag{10}\]  
\(P_{\varnothing eq}\) values also follow a log-normal distribution,  
\[\begin{array}{c}D\Big{(}\log P_{\phi_{eq}}\Big{)}=D\big{[}p_{\phi}\big{]}=N \big{[}m_{\phi eq},\;s_{\phi eq}\big{]}\\ D\Big{(}\log P_{\phi_{eq}}\Big{)}=N[5.38,\;0.085]\end{array} \tag{11}\]  
At this step, once the statistical distribution of load is known, according to the desired or accepted reliability value, the machine manufacturer may define hydraulic cylinder pressure life specification (laboratory). As an example, if we considered a load reliability of 90%, then (see Figure 4),  
Figure 3: Statistical distribution of damage factors.  
\[P_{S}=P_{\Phi_{eq}(90\%_{s})}=243\text{ bar} \tag{12}\]  
The specified pressure \(P_{S}\) defines the equivalent damage produced in laboratory test. So, laboratory test parameters (\(P_{eq}\), \(N_{eq}\)) that produce an equivalent damage in the cylinder are any values according to Equation (13):  
\[N_{eq}P_{eq}^{3}=2\cdot 10^{6}\ P_{s}^{3} \tag{13}\]  
Figure 5 shows that any values of (\(P_{eq}\), \(N_{eq}\)) fulfills the specification conditions and produces equal damage to the cylinder (i.e., 386 bar over 500,000 cycles).
###### Abstract  
In order to study the axial compression performance of steel pipe concrete short columns filled with steel slag micronized ultra-high-performance concrete (UHPC), this paper designs 27 steel pipe UHPC short columns for axial compression test and compares and analyzes the axial compression performance of the specimens in terms of the damage mode, the deformation curve, and the coefficient of strength enhancement, which is aimed at investigating the differences in the actual load-bearing performance of steel pipe UHPC short columns through changes in the aspect ratio, concrete type, and steel content rate, and so on. The purpose of this paper is to compare and analyze the axial compressive performance of the specimens in terms of damage mode and strength enhancement factor in order to investigate the difference in the actual bearing capacity performance of steel pipe UHPC short columns through the changes in length-to-diameter ratio, concrete type, and steel content. The test results show that the axial compressive performance of steel slag powder steel pipe UHPC short columns is greatly affected by the L/D ratio and steel content; the specimen bearing capacity increases with the increase in the wall thickness of the steel pipe and decreases slightly with the increase in the L/D ratio, and the steel fibers can effectively improve the deformation of the concrete so as to enhance the composite effect with the steel pipe; the contribution of the core UHPC to improve the value of bearing capacity accounts for a higher percentage when UHPC with 1% steel fiber dosage and 20% coarse aggregate dosage gave the best uplift with no change in the type of steel pipe. In this paper, the axial compression test bearing capacity results of steel slag micro powder steel pipe UHPC short column are compared with the calculated bearing capacity results of domestic and international specifications and analyzed from the perspectives of perimeter compression strength, steel fiber mixing of core concrete, and the relevant parameter design suggestions for high-strength steel pipe concrete specimens are put forward.  
ultra-high-performance concrete; steel slag powder; round steel pipe; axial compression
# Internet of Drones Intrusion Detection Using Deep Learning
## 5 Simulation Results
### Performance Analysis
#### 5.2.2 LSTM_RNN Performance over KDDCup 99 Dataset  
Another set of experiments conducted over the KDDCup 99 Dataset to examine the performance of LSTM_RNN was compared to KNN and LR. Again, three sets of experiments were conducted, including the binary, multiclass, and detailed classifications. Figure 15 shows the algorithms' performance-based binary classification. As can be seen, LSTM_RNN shows a performance increase, on average, of 5% and 8% accuracy over KNN and LR, respectively. At the same time, LSTM_RNN outperformed both algorithms, KNN and LR, in precision, Recall, and F-score. It is worth mentioning that LSTM_RNN produced almost 94% and 95% True Positive and True Negative, respectively.  
Figure 14: UNSW-NB15 Multiclass classification details accuracy comparison.  
Figure 15: KDDCup 99 Binary classification performance comparison.  
Figure 16 shows that LSTM_RNN produced almost 91% accuracy, 83% precision, and 99% recall. On the other hand, the KNN result shows 88% accuracy, 81% precision, and 96% recall. Similarly, LR gave 88% accuracy, 87% precision, and 99% recall. Therefore, it seems that LR outperforms the KNN due to the high false-negative rate of KNN. Applying the three algorithms for detailed classification over the KDDCup 99 Dataset, Figure 17 shows that LSTM_RNN and KNN performed well, on average, 100%, 100%, and 97% classifying U2L, U2R, and Probe attacks classification, respectively. Simultaneously, LSTM_RNN accuracy was 90% and 98% in classifying DoS and Normal attacks, respectively. Compared to KNN and LR, LSTM_RNN performed much better than both algorithms. However, it was noticed that KNN had 63% and 65% accuracy performance for DoS and Normal attacks, which are the lowest accuracy performance.  
Figure 16: KDDCup 99 multiclass classification performance comparison.  
Figure 17: KDDCup 99 detailed classification details accuracy comparison.
## 4 Results
### Visual Assessment of DEM Quality  
Hillshaded images of the DEMs resulting from different correlation algorithms and parameters (Table 1) differ greatly (Figure 3). For either algorithm, increasing CKs correspond to a smoother surface. At the same time, voids in the output are reduced as larger and thus more distinct CKs aid the matching process. Reduced CK sizes appear to preserve sharper features in the landscape, which may be of interest to geomorphic studies looking at sharp ridge crests and valley bottoms. However, this occurs at the cost of a rising number of holes and apparent DEM noise due to false matches.  
DEMs generated with the BM algorithm are characterized by a surface with regular horizontally and vertically oriented artifacts, giving it a linen-cloth-like structure (Figure 3B-D). MGM artifacts can be described by a salt-and-pepper, or sandpaper, structure that is particularly apparent at low CK sizes (Figure 3F-H). Based on field knowledge, our hillshades should show smoothly varying topography (Figure 1C) and not the rough sandpaper-like appearance that becomes prominent with our smallest CK used for the MGM algorithm (5 \(\times\) 5 CK size; Figure 1F). Such artifacts can be partially smoothed out by gridding the point cloud of pixel correspondences to a lower resolution (i.e., 6 m; Figure 3E), but this comes at the expense of losing smaller details and mixing DEM noise with reliable matches. A larger CK size also smooths these sandpaper-like artifacts, but as noted, this also smooths real sharp features (i.e., ridges and valleys).  
A key difference we note between the BM and MGM algorithms is that the MGM results appear to better capture the particularly sharp (incised or eroded) portions of the study area, even at large CK sizes. In Figure 3, the deeply incised valleys are more clearly represented by the MGM DEMs, whereas they are increasingly smoothed out when using BM. This may be partially due to the larger CK sizes that the BM algorithm requires (CK sizes \(<\)15 \(\times\) 15 were initially tested but led to many holes in the output DEM).