esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optical CAM Technology
### Ternary CAM Cell and Matchline Architecture
As can be noticed, this is a multilevel signal, with every different power level corresponding to a different number of bit-level search misses. When all T-CAM cells match the four bits of the incoming _search-input_ signal, no optical power is recorded at the AWG output. A successful ML operation of the complete T-CAM row can be verified for the entire pulse traces used as the four parallel _search bit_ sequences. Three representative examples at the timeslots #1, #9, and #27 have been highlighted in order to facilitate the understanding of the T-CAM row performance in different situations. In the example of timeslot #1, all four T-CAM cells are in the \"X\" state since all respective XFF outputs are equal to \"0\", which finally results in an XOR output of \"0\", regardless of the TCFF and _search-bit_ values. As expected, the final ML signal at timeslot #1 is also equal to \"0\", corresponding to a complete match between the T-CAM row and the _search-input_ contents. Within timeslot #9, none of the T-CAM cells are in the \"X\" state since all XFF outputs are equal to a logical 1. For T-CAM cell #1, the XOR output is equal to a logical \"0\" since the TCFF and the _search-bit_ content are equal. However, for the remaining three T-CAM cells, the respective XOR outputs are equal to a logical \"1\", denoting the different content between the corresponding TCFF and _search-bit_ signals. The presence of three optical pulses at different wavelengths but within the same timeslot #9 designates that the optical power obtained at the AWG output will equal the sum of the power levels of the three individual pulses, obviously leading to an ML signal with non-zero power that indicates a non-perfectly matched search operation. In the example of timeslot #27, T-CAM cells #2 and #4 are in the \"X\" state since their XFF content equals a logical \"0\". As such, the respective XOR outputs are also equal to \"0\" and this happens even in the case of T-CAM cell #4, where the TCFF content and its respective _search-bit_ are different. Regarding cell #3, the XOR output equals \"0\" because both the TCFF content and the _search-bit_ are equal. However, cell #1 has an XOR output of \"1\" since TCFF content and the respective _search-bit_ have different values. This single optical pulse obtained as the result of the comparison along the entire T-CAM row is then also translated into a non-zero power level at the AWG output, again suggesting a non-matched row, while Figure 9j presents clearly open eyes for all four T-CAM cells with an ER of 13 dB. The energy efficiency of the TCAM cell is 300 pJ/bit.
## 3 Methodology
### Data Transformation  
Another important point that we want to highlight in this section is that of data transformation and in particular data decomposition. Through experimentation we have found that by using STL decomposition [7] on the time series, we can improve our forecasting performance [8].  
STL decomposition can handle any type of seasonality and has the nice property of transforming data so that forecasting approaches are more robust to outliers. More precisely, if the time series contains unusual observations such as spikes and anomalies, forecasting results will not be severely  
Figure 4: The scatter plot above shows the relationship between observation \(y\) at time \(t\) and that at time \(t+1\) for all the cells in the grid. The relationship is mainly linear, but non-linear behaviors can also be noticed.  
Figure 5: Graphics showing for every time step the degree of auto-correlation in the data from \(t_{0}\) to \(t30\) which appear to be strong up to 18 steps. The gray area shows the uncertainty on the degree of auto-correlation, which remains low.  
affected. The process of STL decomposition is shown in Figure 6, where each observation \(y_{t}\) is decomposed in trend (\(T_{l}\)), seasonal (\(S_{t}\)) and residual (\(I_{t}\)) components:  
\[y_{t}=T_{t}+S_{t}+I_{t} \tag{1}\]  
We use the decomposition process to perform forecasting on each component separately, and then sum up the three forecast values in order to compute the final prediction as in the following equation.  
\[\widehat{y}=\widehat{T}_{t}+\widehat{S}_{t}+\widehat{I}_{t} \tag{2}\]  
Another kind of data transformation we applied is differentiation, in order to make the time series stationary. The process simply consists of subtracting the observation at time \(t+1\) from that at time \(t\). Thus, a differentiation-based data transformation is needed in order to apply statistical forecasting methods such as ARIMA for example.  
Moreover, in order to apply deep neural networks forecasting methods on our uni-variate time series data, we had to transform it into a supervised learning dataset, by considering a rolling window and a number \(n\) of lagged variables. Figure 7 shows an example, where \(S\) is the uni-variate time series, while \(X\) and \(y\) are obtained by lagging \(S\) for a given number of steps. From a learning perspective, \(X\) is the set of features and \(y\) contains the labels (supervisions), in this case for a 6-steps-ahead forecasting scenario.
# Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres
## 4 Conclusions  
The present study focuses on developing a new low-cost biosorbent material for removing Cr (VI) ions from aqueous solutions by extracting cellulose fibres from pine cone sawdust using an acid hydrolysis treatment. SEM-EDS, XRD, TEM, FTIR, TGA, and DTA analyses verified the modifications in the cellulose fibres extracted from pine sawdust. The evidence of the successful extraction of cellulose fibres was indicated by a slight shift in the wavenumbers of functional groups such as (-OH), (-C-H), and (-COOH), as revealed by the FTIR spectra. Furthermore, changes were observed for the bands at 3338.18 cm\({}^{-1}\), 2977.77 cm\({}^{-1}\), 2901.16 cm\({}^{-1}\), 1166.32 cm\({}^{-1}\), and 1054.06 cm\({}^{-1}\) after metal biosorption. The hydroxyl and carboxylic groups showed a shift, which indicated that they were involved in the adsorption of chromium ions. This study showed that the adsorbent's uptake of Cr (VI) ions increased with an increasing initial concentration, and the highest uptake was observed in the solution with the highest initial concentration. The adsorption processes for Cr (VI) best fit the Langmuir isotherm model. Pseudo-second-order kinetics was the better fitting kinetic model for Cr (VI) adsorption onto cellulose fibres. The results of the error analysis supported these findings. The economic value of the adsorbent was evaluated, and its potential for reusability and regeneration was examined. The results showed that the adsorbents could be reused for at least three cycles.  
Figure 18: Reusability studies of adsorbents for removing hexavalent chromium ions on cellulose fibres (CF).  
**Author Contributions:** Conceptualization, N.N.M.; investigation, K.M.M. ([PERSON]) and K.M.M. ([PERSON]); supervision, N.N.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The Vaal University of Technology funded this research and the publication of this article.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors acknowledge the support of the Department of Chemistry, Vaal University of Technology, Vanderbijllpark, South Africa, for granting the usage of its facilities.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Related Work
### Comparison of Characteristics and Criteria When Choosing the Main DEA Models: CCR and BCC  
As a summary of the basic models of CCR and BCC, we present the following comparative scheme, which includes the main characteristics of these models of DEA. Figure 1 below demonstrates the comparative overview of the basic DEA CCR and BCC models.  
Thus, the basic DEA models can be classified according to the following criteria [20]:  
* Type of production function;
* Selected orientation (input- or output-oriented models, as well as models without orientation);
* Acceptance of a constant (CRS) or variable return to scale (VRS).  
As described above, CCR and BCC models are models with orientation to either the input or the output. The constant scale effect is applied to the model CCR. In BCC models, variable economies of scale are considered. The CCR and BCC models are partially linear models.  
The BCC model is useful for variable returns to scale and measures only the net technical efficiency of each DMU [60].  
The CCR model studied above considers the aggregate (technical and scale) efficiency, whereas the BCC model decomposes the total aggregate efficiency of a unit into its purely technical and scale efficiency.  
To evaluate production functions with the help of DEA, it is especially important to choose a suitable DEA model for each specific case. Table 1 shows a comparison of the CCR and BCC models.  
Figure 1: Comparative overview of the basic DEA CCR and BCC models.  
Thus, Table 1 shows that the CCR and BCC models are used to measure technical efficiency. For all basic models, efficiency is measured in the range from 0 to 1.  
In scientific articles, CCR and BCC models have a wide range of practical applications. They have been used to evaluate the efficiency of enterprises in economics, banking, agriculture, medicine, industry, trade, transport, and many others. However, the use of DEA models in the fuel and energy complex has not been investigated.  
We next take a closer look at the main characteristics that need to be considered when choosing a DEA model.
## 6 DRRF and Real-time Traffic
### Scheduling Algorithms with Enforced Deadline  
For real-time traffic, video traffic in particular, received packets with the huge delay or over the deadline are not useful. Since the deadline or average delay is negotiated during the connection setup, we can use the deadline information at the scheduler by dropping the packets that are over the deadline, and then save the bandwidth. Therefore, for all three algorithms described above, packets are dropped if they will not meet the deadline after transmission. We also analyze cases without this option; however, the results showed worse performance with a large fraction of packets being discarded at the destination due to exceeding the deadline. We conclude that given the resource constrained nature of a wireless medium, any reasonable implementation should minimize waste by discarding late packets before transmission.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 2 System Model  
### Network Topology  
A HetNet system deployed in a bounded area, \(A\subset\mathbb{R}^{2}\), with randomly placed BSs forming K tiers is considered. Each tier is distinguished by a unique transmit power, density (the average number of BSs per unit area), and antenna configuration providing service to same coverage area in the same frequency band. The spatial locations of the base stations of each tier are represented with a stochastic two-dimensional point process. For the tractable analysis, the spatial locations of the BSs of each tier ae modeled with an independent Poisson point process (PPP). \(\Phi_{k}(A)\sim\text{PPP}(\lambda_{k})\), \(k=1\), \(2\), K, where \(\lambda_{k}\) is the density of the \(k\)-th tier, which captures the worst-case scenario [25]. In the given bounded area, \(L_{k}\) numbers of \(k\)-th tier BSs are being deployed with a uniform transmitting power, \(P_{k}\). This work considers K = 2, representing macro-base stations (MBS) acting as an umbrella cell, under which many low-powered pico-base stations (PBS) are being deployed in the vicinity of user hotspots in order to provide uniform service. The BSs of each tier are equipped with massive-MIMO transmission systems capable of serving multiple users in same time-frequency grid, considering intra-cell SDMA. Each BS of the \(k\)-th tier is equipped with \(N_{k}\) transmit antennas, serving \(R_{k}\) single antenna active users in the same time-frequency grid (\(R_{k}<N_{k}\)). The schematic representation of the given model is presented in Figure 1.  
Figure 1: Schematic representation of a typical massive MIMO-enabled two-tier heterogeneous network (HetNet).
## 4 Numerical Simulation of Roof Cutting Parameters
### Numerical Simulation of borehole Spacing  
After determining the parameters of the roof cutting height and roof cutting angle, the influence of different borehole spacings on the effect of roof cutting and pressure relief of dense drilling is compared and analyzed, and the optimal borehole spacing is further determined. The weakening effect of dense boreholes on the roof is affected by the advance abutment pressure. The plastic zone distribution between holes is shown in Figure 13 when the hole spacings are 150 mm, 200 mm and 300 mm, respectively, in the range of 15 m in the advance working face along the cutting line. The penetration rate \(\zeta_{\rm p}\) of plastic failure between dense drilling on the roof is used as the evaluation index to analyze the influence of hole spacing on the roof cutting effect. The ratio of the area \(S_{\rm p}\) of the plastic failure area to the total area \(S_{\rm c}\) of the borehole cutting section is defined as the penetration rate.  
\[\zeta_{\rm p}=\left(S_{\rm p}/S_{\rm c}\right)\times 100\% \tag{34}\]  
The number of pixels of tensile and shear failure in the plastic zone cloud map is counted, and the plastic penetration rate between holes with different hole spacing is counted by Equation (34). As shown in Figure 14, when the hole spacing is 150 mm, the plastic zone between holes is the most developed, and the penetration rate \(\zeta_{\rm p}\) is 72.9%. When the borehole spacing is 200 mm, the penetration rate \(\zeta_{\rm p}\) is reduced to 69.1%. When the borehole spacing is 300 mm, the development degree of the plastic zone between boreholes is the lowest, and the penetration rate \(\zeta_{\rm p}\) is 39.0%. With the increase in hole spacing, the degree of weakening of roof strength by roof cutting drilling decreases, and  
Figure 13: Plastic failure law in boreholes with different borehole spacing.  
the proportion of tensile failure is greater than that of shear failure, indicating that the process of weakening the roof by dense drilling is dominated by the tensile strength of the rock mass.  
Through the above analysis, it can be concluded that when the aperture is determined, the smaller the hole spacing is, the higher the development degree of the plastic zone between the holes is, and the better the weakening effect of the borehole on the roof is. However, the excessive weakening of the roof strength has higher requirements for the roof support at the end and the support of the leading section of the roadway, and the reduction of the hole spacing greatly increases the strength of the on-site construction. Therefore, the optimal hole spacing is 200 mm.
# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 3 Simulation and Experimental Setup
### Design of Setup 1
#### 3.1.1 Case Study 1  
To demonstrate the effectiveness of the proposed DHVS method in singular configurations, this case study has been designed in which the robot with attached marker moved to a pre-defined position and another robot, equipped with the camera, tracked the visual features. The position was set in which the desired features rotate 90\({}^{\circ}\) around the \(Z\)-axis. Deliberately, the position was defined in which the robot encounters singularities in order to investigate if the proposed DHVS method has capability of avoiding such singularities. In addition, the camera calibration was intentionally degraded by 20% to evaluate the performance of DHVS in an imperfect calibrated condition.
## 3 Analysis of Experimental Results
### Shear Stress-Shear Displacement Curve  
Reciprocating shear tests were conducted on argillaceous siltstone rockfill materials with different coarse grain contents, and the test data were compiled according to Figure 4. Similarities were found in the development pattern of the shear stress-shear displacement curves of the argillaceous siltstone rockfill materials with different coarse grain contents under different vertical pressures. Using the data at 500 kPa as examples, the characteristics of the shear stress-shear displacement curves of the argillaceous siltstone rockfill materials with different coarse grain contents have been analyzed (Figure 5).  
As shown in Figure 5, the soils with different coarse grain contents under the reciprocating shear have strain-hardening-type stress-strain curves. The shear strength of the soil materials gradually increases with the coarse grain content. The reason is as follows. The skeleton is the main stress-bearing structure in the soils. At low contents, the coarse grains in the soil are in suspension, with insufficient interlocking among them. Thus, the ability to resist external force-induced deformation is relatively weak. With the gradual increase of coarse grains in the soil, the interlocking and contact among the block grains are  
Figure 5: Reciprocating shear stress-shear displacement curves under different coarse particle contents.  
gradually enhanced. Since the soil skeleton is mainly formed by mutual contact among the coarse grains, the soil can resist greater shears, and the shear strength gradually increases. In addition, the shear strength of soil samples with the same coarse grain content varied in different reciprocating shear processes and decreased with the number of reciprocating shears, \(N\). Specifically, when \(N=1\), the shear strength reduction of the soil samples is significant and greater than that during the other reciprocating shears. The reason is as follows. As the soil material undergoes the first reciprocating shear, the soil grains on the shear surface mutually compress, interlock, and closely contact each other, rendering it difficult for frictional sliding and tumbling of the soil grains. As the number of reciprocating shears increases, the grains on the shear surface are gradually rounded, and some grains fragment. As a result, the interlocking among the block grains decreases, and the friction characteristics weaken, gradually decreasing the shear strength of the soil material.
# Stability of MSW Landfill Slopes Reinforced with Geogrids
## 3 Results and Discussion
### Unreinforced Analyses
#### 3.1.2 Effect of the Internal Friction Angle  
The effect of the internal friction angle (\(\phi\)) on slope stability analyzed for different \(\phi\) values of the MSW landfill. In the analyses, the other parameters were chosen as c = 10 kPa, \(\psi\) = 0\({}^{\circ}\), E = 6500 kN/m\({}^{2}\), \(\
u\) = 0.45, \(\gamma\) = 9 kN/m\({}^{3}\), and \(\beta\) = 18.43\({}^{\circ}\) (1V/3H). The FS values for different internal friction angle values obtained as a result of the FEM and LEM analyses can be seen in Table 3 and Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Cohesion, c (kPa)**} & \multicolumn{2}{c}{**Factor of Safety (FS)**} \\ \cline{2-3}  & **FEM** & **LEM** \\ \hline
5 & 1.23 & 1.25 \\
10 & 1.52 & 1.55 \\
15 & 1.77 & 1.80 \\
20 & 1.99 & 2.03 \\
25 & 2.22 & 2.23 \\
30 & 2.46 & 2.42 \\
35 & 2.64 & 2.60 \\
40 & 2.79 & 2.77 \\
45 & 2.95 & 2.94 \\
50 & 3.11 & 3.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis results for different cohesion values.  
Figure 4: Variation in FS with c.  
From the analyses, it was observed that the FS of the slope increased with increasing \(\phi\) values, as expected. When Figure 5 is examined, it is clear that the FS of the slope increases approximately linearly with the increase in the \(\phi\) value. Although the reason for the increase in the FS value is similar to the cohesion effect, higher FS values are obtained due to the increase in \(\phi\). In addition, it is seen that the FS values obtained by LEM are slightly higher than the values obtained by FEM.
## 4 Results
### Results on Synthetic SAR Images
CKS-FCM only uses non-local spatial information to suppress noise, SVM-CK lacks generalized spatial information and does not use feature fusion, and MWSG has limited expression of local spatial information, and these methods do not enhance local spatial consistency except MAKC. In Figure 4b3, the synthetic SAR image Syn3 consists of eight classes, and the gray-levels of eight classes in Figure 4a3 come closer to each other, which will increase difficulty for the accurate classification and recognition. It is clear that some local patches are misclassified as other classes in the homogeneous areas in Figure 4c3. In Figure 4d3,e3, the speckle noise in the Syn3 cannot be suppressed by CKS-FCM and CKFF, and the pixels in gray and in white of classification results are mixed together significantly. For the classification results in Figure 4f3,g3, though these two algorithms can divide eight areas roughly, the edges are rugged and some local regions are still not well identified. They cannot produce accurate and clear classification for the Syn3. Fortunately and obviously, the proposed method MAKC in Figure 4h3 achieves the best classification result, not only in region consistency but also in boundary separability. This is because MAKC captures the most information and considers local consistency. Table 1 lists the average overall accuracy of classification results over ten independent runs, and Table 2 shows the Kappa coefficients of different algorithms on the synthetic SAR images, which is consistent with the visual results. The results show that the proposed MAKC method achieves better classification performance in terms of OA and Kappa than the other state-of-the-art methods. The accuracy of the MAKC method reaches 99% on the synthetic images, especially on Syn2, where the Kappa coefficient reaches 1. The reason for this is that MAKC adopts a variety of features and makes full use of the neighborhood spatial information, the composite kernel fully expresses the nonlinear relationship, and local consistency optimization compensates for feature defects. Similarly, SVM-CK also employs nonlinear and context spatial information but without local consistency optimization and generalized spatial information to implement the classification.
## 2 Materials and Methods
### Global Digital Controller
#### 2.2.3 Wireless Data and Power Telemetry  
Near-field communication is used for wireless communication. Inductive coils, which are standard mechanisms for transmitting wireless data and power, are used in wireless telemetry systems [28]. The proposed system involves a Class-E power amplifier, an amplitude-shift keying modulator circuitry, and a current-sensing loop for back-telemetry-recovering data. The implanted device contained a rectifier, regulator, over-voltage protection circuit, demodulator, and reverse telemetry controller. The data were modulated on the carrier wave, which was then conveyed through an induction link, and a 2 MHz carrier frequency drove the Class-E amplifier. The impulse response was rectified before being transferred to the regulator, which generated dual-polarity voltage levels in the implanted device. The global digital controller began to operate when clock and data signals arrived from the demodulator.