esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Deep Learning Model for Protein-Protein Interactions Extraction from Biomedical Literature
## 3 The Model Description
### Model Input  
In the expression of PPIs, most of the interaction words are verbs and nouns, and thus dependency parsing is particularly well-suited for relation extraction because the dependency grammar (DG) views the verb as the structural center of all of the clause structure. Dependency parsing has been well used for PPI extraction in previous studies [25; 26; 33]. Given an input sentence, we extract the SDP (shortest dependency path) by dependency parsing. Figure 2 shows the dependency parsing graph (Stanford parser [36] and a visualization tool [37] are utilized).  
The extracted SDPs are treated as texts for the embedding. Embedding is a feature learning process where words from the vocabulary are mapped to vectors of real numbers in a low-dimensional space relative to the vocabulary size. For notation, we use \(D\in R^{|V|\times d}\) to represent the pretrained embedding, where \(V\) is the vocabulary and d is the embedding dimension. Specifically, for word-embedding, \(V\) is composed of the words from the input sentences and for SDP embedding, \(V\) is composed by the symbols from the input SDPs.  
When we assign each word (or symbol) in an input sentence (or SDP) with a corresponding row vector from \(D\), we would get a matrix representation \(P\in R^{N\times d}\) for an input sentence (or SDP). The word-embedding and SDP embedding are two \(\mathrm{c}\times\mathrm{N}\times\mathrm{d}\) tensors, where \(c\) is the input size of the channel, \(N\) is the max length of input sentences, and \(d\) is the embedding dimension.  
Figure 1: Architecture of the hybrid deep learning model. The input of the model is a sentence which is through two embeddings (word-embeddings and shortest dependency path (SDP) embedding). The two embeddings are separately fed to a bidirectional long short-term memory (LSTM) network followed by a convolutional neural networks (CNN) network, as two separate channels. The outputs of the CNN networks of the two channels are concatenated together for classification.  
Figure 2: The dependency parsing graph for the sentence “alpha-catenin inhibits beta-catenin (Entity 2) signaling by preventing the formation of a beta-catenin T-cell-factor (Entity 1) DNA complex” (dependency relations are shown by the arrow lines). The shortest dependency path extracted between the target entity pairs is “T-cell-factor (Entity 1) compound complex nmod formation dobj preventing advcl inhibits dobj signaling compound beta-catenin (Entity 2)”.  
The input of the model is a sentence that goes through two embedding channels, namely: (1) pretrained word embedding trained on the PubMed abstract corpus [38] and (2) SDP embedding trained on the shortest dependency paths extracted between target entity pairs.
## 3 Case Study
### Data Processing  
There are many factors affecting prediction accuracy, such as data quality, data characteristics, and model selection, etc. However, the quality of traffic volume data is one of the main factors [26]. Therefore, the processing of the abnormal data including missing data and erroneous data appears to be crucial in traffic volume prediction [30]. To repair abnormal data, the adjacent completion method is adopted and its function is shown in Equation (14):  
\[x(t)\ =\ [x(t-w)+x(t-w-1)+\cdots+x(t-1)]/w \tag{14}\]  
where \(w\) denotes the number of data to be repaired.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Data Resource** & **Mean** & **Variance** & **Maximum** & **Minimum** & **Skewness** & **Kurtosis** & **Non-Stationarity** \\ \hline A & 70.184 & 20,710 & 188 & 3 & 0.0183 & 1.6495 & Strong \\ \hline \end{tabular}
\end{table}
Table 1: Data characteristics (A).  
Figure 3: Traffic volume time series (A).  
Figure 2: Location of the intersection in Chongqing.
## 2 Transcriptional Regulation in Integrons
### The Role of Pc Promoters on Gene Cassette Transcription  
#### 2.1.1 Pc Promoters in Class 1 Integrons  
As aforementioned, the gene cassettes captured by integrins are mostly devoid of promoter sequences, in a way that their transcription is driven by the Pc promoter embedded in the integron 5\({}^{\prime}\) conserved segment (Figure 1) [3]. The Pc promoter is embedded in class 1 integrase gene, 223 to 252 bp from the _attI1_ recombination crossover point [2; 3]. The asymmetric nature of cassette recombination involving the folded bottom strand of the _attC_ site leads to cassettes insertion in the correct orientation relative to Pc promoter [25]. The _intI1_ sequences present more than 99% nucleotide conservation with differences mostly found within the Pc promoter region, indicating the existence of distinct Pc variants [2; 3; 26]. Such variants were defined based on polymorphisms in the canonical Pc promoter (named Pc Strong), which presents an identical -35 and highly conserved -10 hexamers relative to \(\sigma^{70}\) consensus promoter sequences [3]. Until now, 13 Pc and four P2 variants, as well as a diversity of Pc + P2 combinations, have been identified in naturally occurring integrons by in vitro and in silico analyses [2; 3; 26; 27; 28; 29] (Table 2), and it can be expected that new ones will continually emerge by random point mutations in Pc promoter sequences. Indeed, new Pc variants arise in response to the need of expressing gene cassettes more efficiently due to stronger antibiotic selective pressure, leading to the selection of more efficient Pc sequences. The more frequently identified Pc variants in both clinical and natural environments are the Pc Strong (PcS), Pc Weak (PcW), Pc Hybrid 1 (PcH1), and Pc Hybrid 2 (PcH2) [2; 3; 30; 31; 32; 33; 34]. They were the first Pc variants characterized and their classification was based on the relative transcription strengths, as detailed below. The PcW variant presents polymorphisms in both -35 and -10 hexamers relative to the canonical PcS, while PcH1 and PcH2 contain the -35 and -10 hexamers of PcW and PcS in inverse combinations (PcH1: -35 of PcW and -10 of PcS; PcH2: -35 of PcS and -10 of PcW) (Table 2). Although the unique difference between PcH1 and PcS is one nucleotide in the -35 region, a remarkable difference in their transcription strength was observed (Table 2) [3; 28]. These findings indicate the relevance of the nature and position of polymorphisms in the determination of promoter activity, contributing significantly to variations in transcription strengths. Other Pc configurations have been identified, but in very low frequencies, such as PcLn42, PcLn116, PcPUO, reported in the class 1 integrins In42, In116 and in plasmid PUO901, respectively, and the Super Strong Pc (PcSS) [26; 35; 36; 37; 38]. The PcSS emerged due to a C to T replacement of the fifth base of the PcS -35 sequence. This unique point mutation led to a remarkable increase in the expression of the integer-associated \(bla_{\text{IMP-5}}\) gene cassette and on imipenem resistance, suggesting that PcSS was significantly stronger than PcS [38].
## 2 MINLP Formulation
### Objective Function Formulation  
The objective function of the optimization problem corresponds to the minimization of the annual operative costs attributed to the grid energy losses added to the annualized investment costs in D-STATCOMs. The objective function and its components are presented in Equation (1).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Optimization Technique** & **Refs.** \\ \hline Particle swarm optimization & [6,16,17] \\ Genetic algorithms & [14,18,19,20] \\ Cuckoo search algorithm & [21,22,23,24] \\ Immune algorithm & [25] \\ Harmony search algorithm & [26,27] \\ Imperialist competitive algorithm & [28] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Meta-heuristic approaches applied to the optimal location and sizing of D-STATCOMs in electric distribution networks.  
\[f_{1}=C_{\text{KWh}}T\sum_{h\in\mathcal{H}}\sum_{k\in\mathcal{N}}\sum_{m\in \mathcal{N}}Y_{\text{Km}}V_{kh}V_{mh}\cos(\delta_{kh}-\delta_{mh}-\theta_{km}) \Delta_{h},\]  
\[f_{2}=T\bigg{(}\frac{k_{1}}{k_{2}}\bigg{)}\sum_{k\in\mathcal{N}}\bigg{(}\alpha \bigg{(}Q_{k}^{\text{D-STATCOM}}\bigg{)}^{2}+\beta Q_{k}^{\text{D-STATCOM}}+ \gamma\bigg{)}Q_{k}^{\text{D-STATCOM}},\]  
where \(A_{\text{cost}}\) corresponds to the objective function value \(f_{1}\), which is the component of the objective function associated with the cost of the grid energy losses, \(f_{2}\) is the component of the objective function related with the annualized investments costs in D-STATCOMs, \(C_{\text{KWh}}\) is the average energy costs; \(T\) is a constant associated with the planning horizon (i.e., 365 days), \(Y_{km}\) is the magnitude of the component of the nodal admittance matrix associated with nodes \(k\) and \(m\), which have an angle \(\theta_{km}\), \(V_{kh}\) and \(V_{mh}\) are the magnitudes of the voltage variables associated with buses \(k\) and \(m\) and the period \(h\), with angles \(\delta_{kh}\) and \(\delta_{mh}\), respectively, \(\Delta_{h}\) is the time associated with the power flow evaluation (for this research it is assumed to be 1 h), \(k_{1}\) and \(k_{2}\) are positive constants of annualization of the investment costs and the life-time of the D-STATCOM, respectively, \(\alpha\), \(\beta\) and \(\gamma\) are positive constants associated with variable costs of installation of the D-STATCOM with nominal reactive power generation capacity \(Q_{k}^{\text{D-STATCOM}}\). Observe that \(\mathcal{H}\) and \(\mathcal{N}\) are the sets that contain all the periods of time and all the buses of the network, respectively.
## 3 Security Analysis
### Collective Attack  
Based on the polynomial and Lagrange interpolation, we find that restoring the secret requires \(t\) legitimate participants. We assume that the dealer and one of legitimate participants are honest. The rest of participants are illegal user in this secret sharing in the large group. However, \(t-1\) dishonest legitimate participants can not obtain the secret \(S\) by using collective attack, since they cannot carry out collective attack. The above-mentioned situation is similar to GMCS-based quantum cryptography. Suppose that the dealer demands \(t-1\) legitimate participants to publish their private key while the last participant holds its Gaussian random numbers and \(f(x)\) from the dealer. Therefore, the last participant owns all data of legitimate participants to recover the secret key. Since the dealer is honest and attempts to generate a secret key against all the other \(t-1\) participants. In this case, it is the same as the GMCS-based quantum key distribution (QKD) and the secure key rate of the GMCS-involved CVQSS protocol can be evaluated by standard security proofs of QKD [25; 26; 27]. Because the secure key of CVQSS ought to secure against any group of \(t-1\) participants, the dealer needs to select the smallest one among secure key rates of legitimate participants and the dealer. The secure key rate of the GMCS-based QKD can be calculated between the dealer and a selected participant while other \(t-1\) legitimate participants are dishonest [24; 34]. In order to show the performance of the CVQSS protocol, we execute simulations using concrete parameters and analyze the results in the next section.  
Figure 2: The data-processing of legitimate \(t\) participants and the dealer. L is laser, M represents modulator, BS is beam splitter, DHD expresses double homodyne detector, \(\{x_{j},p_{j}\}\) are independent Gaussian random numbers retained by participant \(B_{j},\{x_{j},f(x_{lj})\}\) is the private key of \(C_{lj}\), \(l\in\{1,2,\cdots,t\},j\in\{1,2,3,\cdots,n\}\).
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 2 Configuration of the Proposed Grid Impedance Compensator  
### Review of the Grid Impedance Compensator  
A simplified circuit structure of single-phase grid-connected voltage source inverter (GC-VSI) is given in Figure 2, where \(Z_{1}(s)=sL_{1}\), \(Z_{2}(s)=sL_{2}\), and \(Z_{C}(s)=1/sC_{f}\). The corresponding control block diagram with the grid current control loop can be presented in Figure 3a, where \(G_{d}(s)\) means the digital delay, and \(G_{C}(s)\) is the current controller. To simulate the worst case, no resistive parts in the filter parameters and equivalent grid impedance \(Z_{g}\) are considered. Such an assumption has no effect on the analytical results in the following studies. For simplicity, the inverter gain is assumed to be unity.  
In terms of ac small signal, the cancellation of the grid impedance is to decouple the injected disturbance signal \(\Delta U_{\mathrm{pcc}}\). As shown in Figure 3b, use of a unit PCC voltage feedforward can counteract the effect of grid impedance, where the PCC voltage is inserted to the inverter output at the node  
Figure 3: Control block diagram of single-phase GC-VSI. (**a**) Single-loop grid current regulation; (**b**) Active cancellation of grid impedance in reference [34].  
Figure 2: A simplified single-phase GC-VSI.  
C. In [34], Figure 3b is realized through an active compensator of grid impedance as illustrated in Figure 4, in which an LV-VSC is in series at the inverter output to provide the required injected voltage, \(u_{\text{o,C}}=u_{\text{pcc,h}}\). \(u_{\text{pcc,h}}\) is the unit PCC harmonic voltage extracted from the PCC voltage \(u_{\text{pcc}}\). Because the LV-VSC is in series at the inverter output as shown in Figure 4, the cost and power loss of the circuit increases with the inverter output power.  
Therefore, in order to inherit the grid impedance cancellation function and sustain small power consumption of the compensator, it is necessary to have a deeper insight in the rationale of grid impedance cancellation.
## 2 Materials and Methods  
### Data Collection  
Twenty recreationally active men with a mean age, height, and weight of \(21\pm 1\) years, \(1.72\pm 0.07\) m, and \(69\pm 7.5\) kg, respectively, participated in this research. Subjects signed a written consent before starting the experimental procedure. Subjects were confirmed to be healthy and uninjured. Subjects wore tight outfits and were barefoot to avoid errors or noise during data recording due to the movement of clothing. A total of 16 reflective markers were pinned to the clothing or taped to the skin at locations of anatomical landmarks of the subject's lower extremity: left heel (LHEE), right heel (RHEE), left toe (LTOE), right toe (RTOE), left ankle (LANK), right ankle (RANK), left tibia (LTIB), right tibia (RTIB), left knee (LKNE), right knee (RKNE), left thigh (LTHI), right thigh (RTHI), left posterior superior iliac (LPSI), right posterior superior iliac (RPSI), left anterior superior iliac (LASI), and right anterior superior iliac (RASI), as shown in Figure 1. Reflective markers with a medium size of 14 mm (in diameter) were used.  
This research received approval from the Research Ethics Committee of Universiti Kebangsaan Malaysia (UKM PPI/111/8/JEP-2016-612). Three Vicon Nexus infrared cameras (Vicon Motion Systems Ltd., Oxford, UK) comprising 1 MXF20 and 2 MX3+ models were calibrated at 100 Hz before the movement of reflective markers was captured during AKS. Subjects warmed up and familiarized with AKS before the actual experimental process. Cyclical data have low variability [29; 30; 31]. In addition, collecting more than three repetitions of data would decrease the standard deviation [30]. For these reasons, the data were recorded cyclically, and the subjects were asked to perform two sets of eight repetitions of AKS with a 16 kg kettlebell (Maxx Arc Kettlebell). Subjects were given two to five minutes of resting time between each set. The recorded data were digitized using a Vicon Nexus motion analysis system software version 1.5.2. The data were filtered using a Woltring filtering routine [32] built in the system software with a predicted mean squared error (MSE) of 10 mm\({}^{2}\)[33] to attenuate the noise before being exported to Microsoft Excel for further analysis.  
Figure 1: The placement of reflective markers at the anatomical landmarks of each subject’s lower extremities (Source: Vicon Motion System Ltd., Oxford, UK).
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 2 Materials and Methods  
### Study Area  
We carried out the study in the Kerian River (Figure 1) that flows through the northern states of Peninsula Malaysia. The Kerian River is a meandering river of approximately 90 km length, originating in the Bintang Range in the northern state of Perak. This river serves as a border between the state of Kedah and Perak for approximately 73 km until it enters the Malaysian state of Penang for its remaining 17 km, where it finally discharges into the Strait of Melaka. The study region lies downstream of the Kerian River at the location 5\({}^{\circ}\)7\({}^{\prime}\)31\({}^{\prime\prime}\) N, 100\({}^{\circ}\)29\({}^{\prime}\)50\({}^{\prime\prime}\) E in Bandar Baharu, Kedah, where the width of the river measures 34.51 m. The width of the mouth of the Kerian River is approximately 166.71 m.
## 5 Challenges and Future Research Direction
### Flow Rule Update Operation  
TCAM support exact and wildcard matching rule, in contrast to exact, wildcard rule makes multiple flows aggregately, thereby reusing entries among different flows, hence, minimized number of entries and reduce overhead for frequent flow setup request. However, TCAM is slow in flow entries update operation. As a result, packets incurred significant delays, especially in large-scale networks. For example, failure recovery has a strict recovery delay requirement to adhere to the carrier-grade network. As such, minimum number of rule updates are preferable in network with a large number of devices. Single switch rules update has received some attention; however, multi-switch flowtable update received little attention. A further analytical model is required to investigate and reduce the packet processing delay mainly caused by the update operation. It will be an interesting research to leverage on multiple flow table pipelining features to flexibly devise a multi-switch flowtable rule update optimization scheme to further reduce the update delay.
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs
### Generalized Frequency Division Multiplexing (GFDM)  
Like FBMC, GFDM is a multi-carrier waveform based on subcarrier filtering, where every subcarrier is shaped by a circular filter. The total number of mapped QAM symbols is arranged into \(K\) subcarriers and \(M\) subsymbols. Therefore, the total number of data symbols is \(N=MK\)[26]. Next, every subcarrier is upsampled, filtered, and shifted to its carrier frequency. Then, these subcarrier signals are added, with a CP included at the end of each resulting block of subsymbols to avoid ISI [27]. Unlike OFDM, CPs are added per block (set of subsymbols) and not per symbol. Therefore, GFDM spectral efficiency is higher compared to OFDM [27], although with a high latency due to processing large blocks at a time.  
As GFDM is not orthogonal, additional techniques must be implemented to properly recover the signal. There exist two main techniques: (1) interference cancellation scheme [28], and (2) OQAM [29], the latter with less complexity. Such techniques make GFDM receivers more complex. Moreover, GFDM is weaker than OFDM in terms of carrier frequency offset (CFO) [26].
# Frequency-Domain Characterization of Finite Sample Linear Systems with Uniform Window Inputs
## 6 Conclusions  
We discuss finite samples of discrete-time LTI systems notated as FS-LTI(\(L,\ M,\ N\)), where \(L\) is the width of the uniform window function, \(M\) is the number of output samples, and \(N\) is the zero-padded length of the output. If the output after \(M\) samples is indeed zero, the corresponding IS-LTI system is a marginally stable system, whose impulse response function oscillates after an initial transition time. Such a system presents a challenge in frequency-domain processing, which offers many advantages in digital signal processing. One specific issue encountered is that the system's IRF, \(h[n]\), cannot be obtained directly from \(H_{0}[k]\), which is the ratio of DFT of the zero-extended output to input. To obtain the IRF, we reset the poles of \(H_{0}[k]\) to zero to obtain \(H_{1}[k]\), whose inverse DFT is \(h_{1}[n]\). Although \(h_{1}[n]\) is not \(h[n]\), it contains the full information to determine \(h[n]\), which can be obtained from a linear combination of \(h_{1}[n]\) and its time-shift. We discuss two situations: (1) \(L\) and \(N\) are coprime and \(N\geq\max(M,\ L)+1\), and (2) \(N\geq L+M+1\). Under the condition \(N\geq\max(M,\ L)+1\), we show that the minimum \(N\) to recover \(h[n]\) is not larger than the coprime of \(L\). If \(L\) is a prime number, we just need to zero-extend the output by one element in frequency-domain processing. If \(N\) is larger than \(L+M\) in the second situation, we show that \(h[n]\) can always be recovered from \(h_{1}[n]\).  
The discussion here applies to all realistic discrete-time LTI systems, where the number of available samples is always finite. Any realistic IS-LTI system is necessarily truncated to an FS-LTI system. While one can use time-domain methods to recover \(h[n]\) up to the effective number of samples, the frequency-domain method discussed here mitigates the effect of noise and interference in many situations. One application lies in radar remote sensing where the system's best range resolution cannot resolve the targets' spatial details. In demonstrating the theorems, we have utilized two DFT pairs, whose frequency-domain expressions have only a few discrete values. Interesting trigonometry identities can be derived from the DFT pairs.  
This work was supported in part by the U.S. National Science Foundation under Grant AGS-2152109.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the author.  
The author declares no conflicts of interest.
### Innovative and Sustainable Extraction Methods
#### 2.2.6 High-Pressure Homogenization (HPH)  
Another emerging extraction assistance technology based on the breaking of the organic cell wall is high-pressure homogenization (HPH). Also considered as a pretreatment or an assistance for solvent extraction, this method provokes the rupture/modification of the cell walls, facilitating the migration of the solvent into the cellular membrane of the matrix by high-intensity mechanical stress. This is possible because the liquid (extraction solvent) flows through the homogenization chamber at high pressure (40 or 350 MPa), generating high intramaterial shear forces that disintegrate the plant matrix [81].  
HPH is widely used as a treatment against microbial contamination, similar to a nonthermal pasteurization [82]. Some studies have investigated the combination of both effects of the technique in a variety of organic matrices. In a study about the quality of pomegranate juice, HPH was compared to thermal pasteurization techniques, and their effects on microbial, nutritional, and organoleptic properties were reported. The total polyphenol content and antioxidant activity were also measured, and, in both treatments, they were higher [83].  
The negative effects of this technique have been also reported in the literature: for instance, it has been observed to negatively affect the stability against the sedimentation of strawberry juice and to increase its viscosity. However, even if color and anthocyanin content were slightly affected by the high pressure levels, the total polyphenol concentration was increased up to 30% compared to the control juice (filtered) [84]. In a more recent work, blackcurrent was treated with HPH at different values of pressure, number of passes, and inlet temperature to investigate the effect on total phenolic content and other properties. The results showed a significant degradation of vitamin C and anthocyanin [85].