esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of the As(V) Removal Capacity of the Ion-Exchange Fabric
The hybrid process showed the highest removal rate at solution pH 4, and the removal rate decreased with increasing solution pH, which is consistent with the As(V) adsorption data described in the previous section. In particular, the removal rate of As(V) dropped rapidly at solution pH values of 8 and 10 during the hybrid process, and the flow rate also decreased more rapidly than with the Zn + Kaolin solution. This is because the As(V) precipitate formed at the given pH condition covered the surface pores of the membrane. The flow rate and As(V) removal capacity were recovered successfully through the backwashing process with 0.1 M NaCl solution. Finally, the removal efficiencies of heavy metals and turbidity by the hybrid ion-exchange fabric/ceramic membrane system from the As + Zn + Kaolin solution were evaluated (Figure 7). Zn(II) was completely and reliably removed by the hybrid process at solution pH values of 8 and 10, while the As(V) removal rate was drastically reduced. This was because more fractions of Zn(II) had been removed through phase change, as previously mentioned in Figure 2 and Table 1. Meanwhile, As(V) and Zn(II) were steadily removed to over 80% at solution pH 6, which was the highest As(V) removal rate among all of the solution pH conditions. The flow rate reduction at solution pH 4 was the lowest, but the As(V) and Zn(II) removal rates were significantly lower than under the other solution pH conditions. The decreased flow rate and As(V) removal capacity could be recovered through the backwashing process during continuous treatment of the As + Zn + Kaolin solution with the hybrid ion-exchange fabric/ceramic membrane system. Figure 6: Effect of solution pH on the flow rate and As removal rate during the hybrid ion-exchange fabric/ceramic membrane processing of the As + Kaolin solution: (**a**) pH 4, (**b**) pH 6, (**c**) pH 8, and (**d**) pH 10.
## 4 Evaluation
### Results and Discussion
#### 4.2.5 Handoff Latency  
Handoff latency is defined as the period between the time when the MP loses the connection with its current PoA and the time when the MP receives the first Inpt from the new PoA. Handoff latency comprises two types of latency: (i) layer 2 latency: this delay represents the time required to complete the handoff, which is set at 100 ms in our scenario; (ii) mobility management latency: this delay is defined as the time required for the MP receive the Inpt after the handoff is completed. Figure 12 shows the handoff latency measured in milliseconds for the MAP-ME, multi-RV KITE, and DMMS schemes against the producer speed. As we can see, the highest handoff latency was for the multi-RV KITE scheme. This increase in latency was primarily attributed to the mobility management delay resulting from the exchange of TI/TD messages between the MP and the RV server, which is necessary for creating the trace path, in addition to the time required to update the MP's information on all RV servers. MAP-ME, on the other hand, achieved a lower handoff latency, where the Inpt only needs to wait for the update of the intermediate routers and reach the old PoA, which is nearly in close proximity to the new PoA in our scenario. Therefore, the handoff latency in MAP-ME is dependent on the distance between the old PoA and the new PoA.  
The DMMS with high producer's location prediction accuracy achieves the lowest handoff latency thanks to its proactive mechanism, which informs the Anchor about the expected location of the MP as soon as the handoff process is initiated. In other words, the DMMS reduces the handoff latency by overlapping the mobility management latency with link-layer latency, providing the quickest response to the producer's handoff event.
# Mycobacterial Cell Wall: A Source of Successful Targets for Old and New Drugs
## 4 New Generation of Cell Wall Inhibitors
### Target: Arabinogalactan/LAM
#### 4.2.5 TBA-7371  
_Discovery._ TBA-7371 was developed in collaboration between Astra Zeneca and the TB Global Alliance. TBA-7371 is a derivative of 1,4-azaindoles developed through scaffold morphing of an imidazopyridine compound with good MIC but low minimum bactericidal activity (MBC) against _M. tuberculosis_[234]. SAR on the hit azaindole compound led to TBA-7371.  
_Activity._ TBA-7371 is bactericidal against _M. tuberculosis_, with a MIC ranging from 0.78 to 3.12 \(\upmu\)m against drug-susceptible and drug-resistant clinical isolates of _M. tuberculosis_[235]. Solubility of TBA-7371 is 170 \(\upmu\)m. TBA-7371 does not exhibit toxicity in THP1 cell line up to concentration of 0.1 mM [235].  
_Mode of action._ TBA-7371 inhibits DprE1 with an IC\({}_{50}\) of 10-30 nm but, unlike BTZ043 and PBTZ169, TBA-7371 is a non-covalent inhibitor of DprE1 [234].  
_Resistance._ Resistance to TBA-7371 was observed in strains overexpressing _M. tuberculosis dprE1_ or carrying the mutation Y314H in _dprE1_, but not in strains with a mutated Cys387 in _dprE1_[234].  
_Clinical trial._ TBA-7371 is in phase I clinical trial (NCT03199339) for safety, tolerability and pharmacokinetic studies in healthy adults. No results have been posted.  
DprE1 is a successful drug target. Since the discovery of BTZ043 and its target DprE1 [225], numerous groups have uncovered novel molecules that target DprE1. DprE1 inhibitors can be divided into two groups depending on whether or not they bind covalently to DprE1. The presence of the nitro group is a requirement for the covalent binding to the Cys387 amino acid of DprE1. Among the covalent inhibitors are benzothiazethionine [236], dinitrobenzamides [237], nitroquinoxalines [238] and nitroimidazoles [239]. Several non-covalent DprE1 inhibitors have been identified with various chemical structures: azaindoles [234], benzothiazoles [240], pyrazolopyridones [241], aminoquinolone piperidine amides [242], carboxyquinoxalines [243], pyrrole-benzothiazzinones [244], pyridobenzimidazole [245], sulfonylpiperazin-benzothazinones [246] and piperidinopyrimidines [247]. These compounds inhibit DprE1 through electrostatic or hydrophobic interactions with different sites of DprE1. Most of these compounds have low MICs (in the nM range), are active against MDR and XDR TB as well as in a mouse model of _M. tuberculosis_ infection. Considering the promiscuity of the DprE1 target, better DprE1 inhibitors might still be discovered to improve TB treatment.
## 3 Results and Discussions
### Determinations of Triterpenoids by GC-MS  
The pentacyclic triterpenoids unsolic and oleanolic acids, which were previously isolated from ethyl acetate and chloroform fraction [13; 17], and identified by 1D NMR spectroscopy [25] were used as standards. After methylation, the sample was subjected to GC-MS producing three peaks at 16.734, 32.093 and 33.185 min corresponding to the molecular-ion peaks at _m/z_ 74.0 (100%), 203.1 (100%) and 203.1 (100%), respectively. Structural assignments were based on analysis of fragmentation pattern of mass spectra, direct comparison of mass spectral data with profiles in the National Institute of Standards and Technology (NIST2) library, and comparisons of mass spectra with data published in the literature (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline
**RT, min** & **Compounds** & _m/z_ **Fragmentation** & **Contents, \%** \\ \hline
16.734 & & 74 (100\%); 87 (50\%) & 1.93 \\ Palmitic acid (methyl ester) & & & \\ \hline
32.093 & & 203 (100\%); 262 (90\%); 133 (20\%) & 18.15 \\ HO & & & \\ \hline \end{tabular}
\end{table}
Table 1: The chemical composition of the total triterpenoids of _D. heterophyllum_.  
Figure 4: UV-Visible spectrophotometer absorption spectrum of triterpenoids, extract from _D. heterophyllum_ (**A**), total triterpenoids solution from _D. heterophyllum_ (**B**), \(\lambda\), 547 nm.  
Two triterpenic acids, including unsolic acid (79.9%) and oleanolic acid (18.1%), and one fatty acid, palmitic acid (1.9%), were the major components of the purified total triterpenoids of the aboveground part of _D. heterophyllum_.
## 4 Experiments
### Accuracy Evaluation  
To estimate the accuracy of the proposed method, the obtained results are compared with those of the method developed in Ref. [14]. The utilized strategy is based on the CRIP illustrated in Figure 11. First, a chessboard is placed inside the measured volume. The grid line \(l\) of the chessboard in the horizontal direction intersects the laser stripe at point \(D\), while points \(A\), \(B\), and \(C\) represent the corner points on \(l\). The coordinates of \(A\), \(B\), and \(C\) are known in the CCS, and the pixel coordinates of \(a\), \(b\), \(c\), and \(d\), which correspond to the pixel points \(A\), \(B\), \(C\), and \(D\) in the CPCS, respectively, are extracted. According to the CRIP, the following relations hold true:  
Figure 10: Experimental setup.  
\[\left\{\begin{array}{l}CR=\frac{(u_{a}-u_{c})(u_{b}-u_{d})}{(u_{b}-u_{d})}=\frac{ (v_{a}-u_{c})(v_{b}-u_{d})}{(v_{b}-u_{c})(v_{c}-v_{d})}\\ CR=\frac{(X_{a}-X_{c})(X_{b}-X_{d})}{(X_{b}-X_{c})(X_{a}-X_{d})}=\frac{(Y_{a}-Y_{ c})(Y_{b}-Y_{d})}{(Y_{b}-Y_{c})(Y_{a}-Y_{d})}=\frac{(Z_{a}-Z_{c})(Z_{b}-Z_{d})}{(Z_{b} -Z_{c})(Z_{a}-Z_{d})}\end{array}\right. \tag{16}\]  
where \(CR\) is the cross-ratio. (\(u_{a}\), \(v_{a}\)), (\(u_{b}\), \(v_{b}\)), (\(u_{c}\), \(v_{c}\)), and (\(u_{d}\), \(v_{d}\)) represent the pixel coordinates of \(a\), \(b\), \(c\), and \(d\), respectively. (\(X_{A}\), \(Y_{A}\), \(Z_{A}\)), (\(X_{B}\), \(Y_{B}\), \(Z_{B}\)), (\(X_{C}\), \(Y_{C}\), \(Z_{C}\)), and (\(X_{D}\), \(Y_{D}\), \(Z_{D}\)) denote the coordinates of \(A\), \(B\), \(C\), and \(D\) in the CCS, respectively. Using the strategy described, the coordinates (\(X_{D}\), \(Y_{D}\), \(Z_{D}\)) of \(D\) can be determined. Therefore, the distances \(d_{t,AD}\) (from \(A\) to \(D\)), \(d_{t,BD}\) (from \(B\) to \(D\)), and \(d_{t,CD}\) (from \(B\) to \(D\)) are considered the ideal evaluation distances. The coordinates of testing point \(D\) in the CCS are calculated using the proposed method and the method developed in Ref. [14]. The distances from testing point \(D\) to \(A\), \(B\), and \(C\) in the CCS are measured distances. The distances \(d_{m1}\) are obtained using the method described in Ref. [14], while the distances \(d_{m2}\) are determined using the proposed method. The 3D coordinates of the ideal points obtained by the principle of cross-ratio invariability and testing points computed by different calibration techniques are listed in Table 1, while the calibration accuracy analysis data are presented in Table 2.
## 4 The 3D Array: Methodology and Numerical Results
### The Faceted Linear Array
Indeed, increasing the conformity of the structure (e.g., increasing \(N_{f}\)) keeps the radiated beam constant over a larger angular range, leading to a clear reduction in the maximum achievable value. As proof, in the conformal scenario \(N_{f}=\infty\), directivity FoV is again above the \(\pm 90^{\circ}\) range, the axial ratio FoV is \(\pm 87^{\circ}\), and the maximum achievable directivity is reduced to \(\Gamma=4.51\) dB. Regarding the decrement in the axial ratio FoV for \(N_{f}=4\) and \(N_{f}=\infty\), this is easily explained by Figure 10b. By looking at \(N_{f}=3\) (blue line), \(N_{f}=4\) (magenta line) and \(N_{f}=\infty\) (cyan line), the conformal geometry is the one exhibiting a better uniformity along the elevation steering range. The axial ratio starts to diverge only close to the horizon level (\(\theta_{s}=90^{\circ}\)) as expected. Concluding, in Figure 11, directivity and axial ratio FoV (on the left y-axis in red) are depicted vs. the number of facets \(N_{f}\) (x-axis). On the right y-axis in blue, directivity loss \(\Gamma\) is shown. The best architecture is the one which satisfies the multiple objectives: maximization of directivity and axial ratio FoV, minimization of the number of facets \(N_{f}\) (which is translated in manufacturing complexity) and minimization of directivity loss \(\Gamma\). Among all the possible solutions, trapezoidal geometry (\(N_{f}=3\)) is selected for the expansion to the 3D case by weighting the different objectives considered.
# Efficient Detection Approach for DTMF Signal Detection
###### Abstract  
A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a dual-tone multi-frequency (DTMF) signal detection. The idea is that an optimal phase search is performed over the frequency band of interest in each tone detection, and then the optimal frequency response of a detector is built accordingly. In this manner, a DTMF detection task is done following one-time detection computation. This proposal demonstrates an overall computational load reduction of 80.49% and 74.06% in comparison with a discrete Fourier transform (DFT) approach and the Goertzel algorithm, respectively. This detection complexity reduction is an advantage and an important issue for applying DTMF detection technique to embedded devices.  
Keywords:dual-tone multi-frequency (DTMF); remote control; tone detection; Goertzel algorithm; discrete Fourier transform (DFT) +
Footnote †: journal: Applied Physics Letters
###### Abstract  
In this paper, a novel ultra-wideband (UWB) printed antenna with quadruple band-notched characteristics is proposed and investigated. The quadruple band rejections are achieved by etching two interdigital capacitance slots on each side of the ground plane, embedding a curled C-shaped slot on the circle patch, and adding a curled split-ring resonator on the backside of the antenna. Interdigital inductance slots can obtain a narrower notched band than general structures due to their high inductance, thereby preserving some valuable frequencies. Adjusting the tail branch's length and distance of the curled C-shaped slot and the curled split-ring resonator can control the notch frequency and width. Finally, the proposed antenna operates from 2.9-11 GHz (VSWR \(<\) 2) with four band stops (VSWR \(>\) 2) for rejecting WiMAX, WLAN, and downlink of X-band satellite communication. Furthermore, the difference between the experimental results and the expected value is less than 3%. The proposed antenna can accurately filter out narrow-band signals.  
ultra-wideband (UWB); band-notched; narrow-band signals; printed antenna Article  
Design of a Quadruple Band-Notched Ultra-Wideband (UWB) Antenna Using Curled C-Shaped Structures and Interdigital 3949. [[https://doi.org/10.3390/electronics1123949](https://doi.org/10.3390/electronics1123949)]([https://doi.org/10.3390/electronics1123949](https://doi.org/10.3390/electronics1123949))
## 4 The Proposed DC-CAAE Method
### The Benchmark Methods
#### 4.7.3 DC-Based Methods  
Due to the low efficiency of the similarity measurement methods used, the performance of traditional clustering methods on high-dimensional data is poor, and the computational complexity on large-scale datasets is high. The dimensionality reduction and clustering of high-dimensional data are often two separate parts, and such a structure can lead to mismatches between the dimensionality reduced data features and the clustering method, never reducing the accuracy of clustering. The research shows [31] that the performance of a feature extractor can be greatly improved by jointly optimizing the data dimension reduction and clustering tasks. This method is based on the premise that the sample data are obtained through a potential feature transformation, called deep clustering [32]. Deep clustering connects data dimensionality reduction and clustering through deep learning, and the joint training enables the clustering and feature extraction to be carried out within a unified framework.
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 5 Experiments
### Overall Performance
#### 5.4.2 Results on Large-Scale Dataset  
The large-scale dataset OGB-MAG is considered to have a more realistic value due to its size and number of classes that are closer to industrial application scenarios. Results on OGB-MAG are listed in Table 3. Since some of the compared baselines use additional training skills on the OGB-MAG dataset, we split the experiment into three parts according to different training settings for fair comparison. The symbol \"-\" means we follow the base training settings. \"Label input\" means label information is treated as model input,while \"label input + multi-stage\" denotes that multi-stage pseudo label training is adopted after \"label input\". The details of these skills are described in Section 4.7. We report the results on both validation and test sets.  
From the table, we can make three observations: (1) Additional use of label information does have a positive effect on GNNs. (2) HGPN invariably achieves better performances under three different training settings, improving 2.07% on the base setting, 1.6% on the \"label input\" setting and 2.22% on the \"label input + multi-stage\" setting. Such improvement can be attributed to HGPN's ability to purify both structure and feature noises. (3) Decoupled methods (NARS, GAMLP and HSAGN) are not necessarily superior to traditional sampling-based methods on large-scale graphs. Previous researchers [37; 41] claim that the decouple GNN methods are more suitable to large-scale graphs because they can have a larger receptive field without the limitation of graphic memory. However, our HGPN with only 2 layers of sampling nodes beats all the decoupled methods, which demonstrates that sampling-based methods with a proper design can also become very competitive in graphs with millions of nodes.
## 1 Introduction  
Face attributes are useful to achieve detailed description of human faces (e.g., smile, gender, age, etc.). Face attributes prediction has applications in human-computer interaction, face verification [1; 2] and video surveillance [3; 4]. Face variations in pose, illumination, scale and occlusion increase the difficulty of face attributes prediction. The performance of face attributes prediction has been improved by using deep convolutional neural networks (DCNNs) [5; 6; 7; 8; 9; 10]. Face attributes prediction is trained separately in these networks, but the inherent correlation between the face attributes has been ignored.  
Various studies show that dependencies exist in face attributes [11; 12; 13; 14; 15]. Multi-task learning networks can improve the performance of individual tasks by jointly learning correlated tasks. In traditional multi-task learning architectures, the shared layers learn general representations for all the tasks by parameter sharing while the following task-specific representations are learned in the task-specific layers. However, the dependencies between the tasks have been ignored in the task-specific layers. Accordingly, further improving the performance of individual tasks by using task dependencies among face attributes in the task-specific layers of the multi-task learning architecture is a challenge problem.  
We propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. Our proposed architecture splits into two task-specific branches after the shared layers. In the task-specific branches, we establish the task dependencies in the task-specific layers by incorporating attention mechanism. The fully connected layers in the task-specific layers are transformed by using the designed attention modules for learning task-dependent disentangled representations, where the task-dependent disentangled representations denote the representations [16; 17] of one task that are disentangled [18] by depending on another task. The transformed fully connected layers that contain task-dependent disentangled representations are fed into softmax layers to predict the final face attributes. In experiments, we demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on FotW and LFWA datasets.  
The rest of this paper is organized as follows: Section 2 briefly reviews related works. Section 3 describes the proposed multi-task learning using task dependencies architecture in detail. Section 4 describes the experimental configuration; the results on FotW and LFWA datasets are also presented and discussed in Section 4. Section 5 concludes the paper.
## 5 Results and Discussion
### Discussions on the INPPCS  
With the aid of the proposed system in the NPD process, particularly the NPPM, its usefulness and feasibility have been verified through case studies on industrial printing equipment manufacturing companies. The case company specializes in offering industrial printing solutions, such as thermal printers for logistics operations and point-of-sales systems, which can be continuously evolved along with the recent technological advances, such as cloud computing and robotic process automation. Therefore, such a company is required to frequently launch NPD projects to revamp existing printing solutions and invent new equipment for industries. In order to manage a large set of NPD projects, an effective NPPM solution is needed to configure the product portfolios and make go/kill decisions, balancing the resource allocation and commercial value of NPD projects.  
In addition to verifying the feasibility and usefulness of the proposed system, a number of commercial values can be obtained, including customer satisfaction, return on investment, and market penetration. To assess the commercial value of these new products, a cross-sectional study, in which the proposed system and a traditional approach are implemented, can be conducted for a given period of time. Consequently, the impact of the proposed system can be quantified and benchmarked with existing approaches.