esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Prediction of Human Drug-Drug Interactions
This is because, since a clinical DDI study has to study the drugs both alone and in combination, the number of arms in the study will substantially increase when studying three or more interacting drugs. The Greco model [142], which is derived from Loewe additivity, was developed to assess PD interactions. However, such a model suffers from being limited to interactions between only two drugs. On the other hand, the general pharmacodynamic interaction (GPDI) model overcomes this limitation, in addition to being flexible to different drug interaction data without requiring knowledge on the modes of action of the studied drugs [143]. The GPDI model-based approach proposes a PD interaction to be quantifiable, as multidirectional shifts in drug efficacy (E\({}_{\text{max}}\)) or potency (EC\({}_{50}\)) and explicates the drugs' role as victim, perpetrator or even both at the same time. The GPDI model has been utilized along with the MTP model [121] to develop a model-informed preclinical approach for the prediction of PD interactions [144]. The MTP-GPDI model has been further employed to successfully evaluate and quantify the PD interactions of anti-TB drug combinations in mice [145]. Furthermore, it has been demonstrated that the GPDI model outperforms conventional methods in the evaluation of PD interactions for TB drugs [146]. It is clear that the need for a combination therapy of TB could potentially result in DDIs in the clinic. It is therefore essential to quantitatively understand the DDIs, both PK- and PD-interactions, as early as possible in drug development. Utilizing data from _in vitro_ combination experiments combined with preclinical _in vivo_ data on the exposure-response relationships of the drugs in combination and early clinical data, will inform on which combinations of drugs at which doses are efficacious and safe for patients. This quantitative integration of data and translation to the clinic is possible through the MID3 model-informed framework.
## 4 Experiment Results and Analysis
### The Effect of the Cube Size  
To demonstrate the effect of the extracted cube size in terms of overall accuracy of spectral-spatial method based on PCA, during the experiment, \(3\times 3\times 9\), \(4\times 4\times 16\), and \(5\times 5\times 25\) cube data are extracted respectively and in each class, we selected 200 of samples randomly as training sets. The OA plot of the two datasets over the entire sample is shown in Figure 5. From Figure 5, we learn that the overall accuracy increases in both datasets with the increased cube size. This is because more contextual information, including spatial and spectral information, can be acquired with the increased  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Classes** & **Total Samples** & **Train Samples** \\ \hline
1 & Brocoli green weeds 1 & 2009 & 200 \\
2 & Brocoli green weeds 2 & 3726 & 200 \\
3 & Fallow & 1976 & 200 \\
4 & Fallow rough plow & 1394 & 200 \\
5 & Fallow smooth & 2678 & 200 \\
6 & Stubble & 3959 & 200 \\
7 & Celery & 3579 & 200 \\
8 & Grapes untrained & 11,271 & 200 \\
9 & Soil vinyard develop & 6203 & 200 \\
10 & Corn senesced green weeds & 3278 & 200 \\
11 & Lettuce romaine 4 wk & 1068 & 200 \\
12 & Lettuce romaine 5 wk & 1927 & 200 \\
13 & Lettuce romaine 6 wk & 916 & 200 \\
14 & Lettuce romaine 7 wk & 1070 & 200 \\
15 & Vinyard untrained & 7268 & 200 \\
16 & Vinyard vertical trellis & 1807 & 200 \\ \hline Total & & 54,129 & 3200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of training samples of the Salinas dataset.  
Figure 4: The overall accuracy (**a**) and testing time (**b**) affected by the number of components remained after PCA.  
cube size. In the experiment, both datasets achieve above 96% classification accuracy when the cube size is \(5\times 5\times 25\).
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 4. Valorization Approaches of Date Processing Waste
### Extraction of High-Value Compounds
#### 4.4.2 Dietary Fiber  
Date processing waste has high dietary fiber content, including cellulose, hemicellulose, and lignin. Studies have indicated that these types of fibers found in diets offer several health advantages, such as enhancing the digestive system and minimizing the probability of developing long-term illnesses [109, 110]. Several studies have investigated the extraction of dietary fibers from date processing waste using different methods, including enzymatic hydrolysis, acid and alkali treatments, and microwave-assisted extraction [78, 111]. The extracted dietary fibers can be used as functional ingredients in the food industry, such as in the production of high-fiber bread, pasta, and snacks, as well as in the development of dietary supplements [112].
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 2 Preliminaries
### Motivation  
LoRa deploys CSS modulation technology to scan across the entire bandwidth. The essence of LoRa is that the dechirp operation converges the energy of the entire bandwidth to a specific frequency point, at which a prominent single peak is formed, while the energy at other frequency points is 0. The distinction makes LoRa able to work under extremely poor Signal-to-Noise Ratio (SNR) conditions (\(-15\) dB\(\sim\!-20\) dB) [25]. However, it comes at the cost of lower bit rates (0.7324 Kbps\(\sim\)13.6719 Kbps at 250 KHz bandwidth). The low bit rate of LoRa increases the packet length, which aggravates the packet collision problem of LoRaWAN.  
Owing to the limited energy budget and low-cost hardware system, LoRaWAN has to adopt the simplest MAC layer protocol, ALOHA, to achieve shared channel access. It will lead to packet collisions as the number of LoRa nodes increases. Severe packet collisions result in a sharp deterioration in the LoRaWAN performance. [PERSON] et al. point out that in the uplink communication process of LoRaWAN terminal devices, the retransmission probability caused by packet collisions increases exponentially with the number of terminal devices [26].  
Suppose that two LoRa terminal nodes send data packets to the gateway independently. Let the monitoring time window length of the gateway be \(N\) points and the length of a LoRa chirp be \(L\) points, where (\(L\ll N\)). Then, the bit rate of the LoRa packet can be calculated as \(R_{b}=SF/L\). As we know, the collision interval of the two LoRa chirps is \(2L\) points, then the probability of packet collisions at the gateway can be given as  
\[P\!\!=\!\frac{2L}{N}=\frac{2 SF}{NR_{b}}=\frac{2^{SF+1}}{N\cdot BW} \tag{4}\]  
Equation (4) reveals that although increasing the SF will increase the bit rate, as well as the probability of collisions, which is the tradeoff problem. To this end, we propose QR\(-\)LoRa (The reason behind the name of QR\(-\)LoRa comes from QR(Quick Response) code and both of them contain information in two dimensions), a modulation scheme that modulates information in both the frequency and amplitude dimensions to improve the bit rate of LoRa transmission. The theoretical analysis and experiments prove that the bit rate gain of QR\(-\)LoRa can grow to \(2\times\) compared with the existing LoRa device.
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 4 Experimental Verification
### Experimental Results
#### 4.3.2 Experimental Results of the Proposed Control Strategy  
To validate the proposed load prediction and rated-output power control strategy, the boost duty cycle was initially set at 0.5. Online load identification involved calculating the RMS values \(U_{lin}\) and \(I_{T}\) at various loads using (14).  
Table 3 summarizes the experimental results for \(R_{L-est}\) and the corresponding error \(\Delta R(\Delta R=R_{L-est}-R_{L})\) at \(D=0.5\). The error \(\Delta R\) is less than 4.3% for adjacent coil coupling coefficients \(k=0.15\) and \(k=0.096\), which decreases with increasing \(R_{L}\). The average value of \(R_{L-est}\) can be used to reduce errors. The maximum error \(\Delta R_{\text{max}}\) is 4.10% for \(R_{L}=10\)\(\Omega\), and the minimum error \(\Delta R_{\text{min}}\) is 1.75%, corresponding to \(R_{L}=20\)\(\Omega\).  
Figure 15 shows the experimental waveform plots for different output parameter settings. Table 4 illustrates the experimental results of the rated output control at a rated output power of 48 W. The table indicates that the error of the output power control strategy is less than 4% for various adjacent coil coupling coefficients and loads. The system's output remained stable even with fluctuations in adjacent-coil coupling coefficients, confirming the accuracy of the constant-output control strategy.
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 5 AI-Powered VANET Solutions
### Integrated Architectures  
VANET technologies necessitate a diverse set of connectivity and computational skills for their effective setup and installation. Recent technologies have been incorporated into VANET systems [173]. Integrating these heterogeneous networks and hybrid systems poses several challenges, mainly regarding the delay in analyzing information and the hazards associated with protection [174]. The data undergo multiple analyzing processes as they pass across various systems built inside VANETs. DRL has emerged as a highly effective strategy for effectively handling variety in data transport facilitated by the flexible characteristics exhibited by convolutional layers [175]. One potential application of GANs is the reduced latency in transporting information among various levels of an integration architecture. This is possible when GANs have a predetermined number of repetitions for creating an illustration approach, regardless of the dimension or form of the information [176]. The KNN algorithm provides a viable method for identifying breaches owing to its notable precision in both node classification and regression tasks. Nevertheless, the EDLN has been found to exhibit greater effectiveness due to its ability to accommodate diverse data sources and heterogeneous types of information [177]. Within the context of a cloud-VANET information flow situation, it is possible to generate two distinct EDLNs using previous ordinary streams and assaults. Subsequently, an additional classifier can be employed to obtain a feature vector representation [178]. The prediction step of the framework facilitates the anticipation and identification of novel hostile behaviors, thus allowing prompt sorting, cessation of aberrant behaviors, or prevention of harmful vehicle movement before the occurrence of novel attacks. VANET designs increasingly incorporate future technologies such as DSRC, IEEE 802.11p, and LTE standards [179; 180]. SVM and CNN are robust AI methodologies that have demonstrated efficacy in enhancing medium control and assigning spectrum in diverse situations [50; 181]. These algorithms cater to a wide range of information and enable nonlinear classifications and grouping even without annotated input data.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 3 Results
### Comparison of the Wave Separation Capabilities of MC-ConvTasNet and Wave-U-Net  
We compare the wave separation performance of MC-ConvTasNet and Wave-U-Net. Wave-U-Net is constructed based on the encoder-decoder architecture proposed in [21], with the encoder comprising four layers. The number of filters doubles in each subsequent layer, specifically totaling 32, 64, 128 and 256. In the decoder, we utilize one-dimensional transposed convolutional layers to replace the upsampling method originally used in the paper. All other details of the Wave-U-Net remain consistent with the description in [21]. After training, the Wave-U-Net achieves a scale-invariant signal-to-noise ratio of 8.7 dB on the test set. Among the three models, namely, the hard-to-hard single-interface model, the soft-to-hard single-interface model and the double-interface model, Wave-U-Net exhibits a lower Rstd than MC-ConvTasNet. The Rstd of Wave-U-Net is 23.7 dB, 21.7 dB and 23.7 dB, respectively. In field logging data, the amplitude of the direct wave after suppression using Wave-U-Net is greater than that of MC-ConvTasNet (see Figure 13). Despite having a simpler structure compared to MC-ConvTasNet, Wave-U-Net exhibits an inferior performance and generalization capability when it comes to extracting reflected waves.
# Efficient DNN Model for Word Lip-Reading
## 1 Introduction  
This paper focuses on word lip-reading technology that estimates the utterance content from visual information only without audio information. This paper uses \"words,\" but more precisely, it includes both words and short phrases. This technology is expected to be used in the following cases where it is difficult to use audio-based speech recognition: it is used in noisy environments where it is difficult to obtain speech, in public places where it is difficult to speak, and used by people with disabilities who cannot speak due to laryngectomy. Since various problems can be solved using lip-reading technology, it is expected to be one of the next-generation communication tools.  
As an academic framework, lip-reading technology is classified as supervised learning for video data. There are several topics for research on lip-reading; shooting directions such as frontal and side [1], recognition targets such as single sound [2; 3], word [4; 5; 6; 7; 8; 9], and sentence [10; 11; 12]. Word recognition is an active research topic, and various algorithms have been proposed.  
Word lip-reading has been studied since the early days of lip-reading technology. However, research has become active with the release of datasets such as OuluVS [13], CUAVE [14], SSSD [15], LRW [4], CAS-VSR-W1k (LRW-1000) [16], and RUSAVIC [17], and the introduction of deep learning. In particular, research groups using LRW, one of the large-scale datasets, have been competing for several percent accuracies in recent years ([[https://paperswithcode.com/sota/lipreading-on-lip-reading-in-the-wild](https://paperswithcode.com/sota/lipreading-on-lip-reading-in-the-wild)]([https://paperswithcode.com/sota/lipreading-on-lip-reading-in-the-wild](https://paperswithcode.com/sota/lipreading-on-lip-reading-in-the-wild)), accessed on 26 April 2023).  
This paper explores various deep learning models and their effectiveness in word lip-reading. While many papers use one or two datasets, this paper conducts experiments on four publicly available datasets; LRW, OuluVS, CUAVE, and SSSD.  
This paper is organized as follows. Section 2 describes the related research. Section 3 summarizes the basic model of the deep learning model considered in this paper. Section 4 introduces the deep learning model investigated in this paper. Section 5 shows recognition experiments on four datasets, and Section 6 concludes this paper.
## Materials and Methods
### Data  
#### 2.2.1 Field Measurements and Reference AGB Map  
A field campaign was implemented from 14 to 19 August (day of year, DOY 226-231), 2017. Forty-four plots with areas of 100 \(\times\) 100 m were selected to perform the field measurement (Figure 1a). In each plot, three \(0.5\times 0.5\) m quadrats were selected. All plants in each quadrat were clipped at the ground surface, transported to the laboratory, and oven-dried at 80 \({}^{\circ}\)C for 48 hours until a constant dry biomass was obtained. The average AGB value of the three quadrats within one plot was used to represent the AGB at the plot scale. Landsat 8 Operational Land Imager (OLI) data was then involved in upscaling the sparse field measurements to a seamless AGB map (Figure 1b). The consistent adjustment of the climatology to actual observations (CACAO) method and Gaussian process regression (GPR) were employed, during the upscaling process, to copy with the spatial gaps that exist in the OLI data and fully exploit the multispectral data cube, respectively. The details regarding the field measurement and AGB mapping can be found in [6].  
Figure 1: Study area. (**a**) MCD43A4 image from August 13, 2017 (the R, G, and B color space correspond to bands 6, 2, and 1, respectively). Sampling plots for the field aboveground biomass measurements are displayed as black dots. (**b**) is the reference aboveground biomass (AGB) map used to validate the proposed method. The white pixels in (**a,b**) are bad-quality and non-grassland pixels, respectively. For details regarding the generation of (**b**), please see [6].  
The AGB map mentioned above was generated from field measurements and was seen as 'true values' to validate the physically-based method in this study.
# Dynamic Planning of Mobile Service Teams' Mission Subject to Orders Uncertainty Constraints
## 4 Problem Description  
### Assumptions  
The following assumptions are met:  
* Given is a network \(G=(N,E)\),
* Each node \(N_{\lambda}\in N\) is labelled with a fuzzy value \(\widehat{t_{\lambda}}\) (represented in terms of OFN) denoting the duration of node occupation (service);
* Each edge \(\left(N_{\beta},N_{\lambda}\right)\in E\) is labeled with a fuzzy value \(\widehat{d_{\beta,\lambda}}\) denoting the travel time between nodes \(N_{\beta}\) and \(N_{\lambda}\);
* Given is a set \(\mathcal{U}\) of MSTs, in which each MST \(U_{k}\) travels route \(\pi_{k}\) (\(\pi_{k}\in\Pi\));
* Each \(U_{k}\) offers the set of qualifications \(\Phi_{k}\) expected by the served customers;
* Node \(N_{1}\) representing a service point occurs uniquely in all routes;
* Customer assigned to the node \(N_{\lambda}\) (\(\lambda>1\)) expects services that require proper set of qualifications \(\psi_{\lambda}\);  
Figure 5: Fuzzy schedule for the implementation of maintenance services.  
* Each route \(\pi_{k}\) consists of nodes in which customers \(N_{\lambda}\) assigned to them expect services that require qualifications \(\psi_{\lambda}\) following \(\Phi_{k}\cap\ \psi_{\lambda}\
eq\varnothing\);
* Customers are serviced cyclically in time windows repeated with period \(T\);
* Customer assigned to the node \(N_{\lambda}\) is serviced by deadline \(\Delta_{\lambda}=[ld_{\lambda};ud_{\lambda}]\);
* Fuzzy beginning moments \(\widehat{y_{\lambda}^{k}}\) (represented as OFN) of the node \(N_{\lambda}\) occupation make up final fuzzy cyclic schedule \(\widehat{\mathbb{Y}}\);
* Disruption is understood as a change of the service deadlines from \(\Delta\) to \(\Delta^{*}\);
* Moment \(t^{*}\) determines the disruption occurring.  
It is also assumed that--values of the decision variables are represented as OFN, see Definition 1. Consequently, the OFN \(\hat{A}\) is described by sequences \(f_{A}{}^{\prime}\) and \(g_{A}{}^{\prime}\) containing the values of functions \(f_{A}\) and \(g_{A}\) obtained by discretization of the interval \([0,1]\):  
\[f_{A}{}^{\prime}=(f_{A}(0),\ f_{A}(dx),\ \ldots,\ f_{A}((M-1)\ dx),\ f_{A}(1)), \tag{3}\]  
\[g_{A}{}^{\prime}=(g_{A}(1),\ g_{A}((M-1)dx),\ \ldots,\ g_{A}(1 dx),\ g_{A}(0)),\ dx= \frac{1}{M}, \tag{4}\]  
where \((M+1)\) is the number of samples (Figure 1c).
# Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
## 2 Materials and Methods
### Our Approach
#### 2.3.1 Formal Overview  
We are given a dataset \(D\) of labelled (\(\{(x_{i},y_{i})|(x_{i},y_{i})\sim\mu(x,y),\ i=1\ldots n\}\)) or unlabelled (\(\{x_{i}\ |\ x_{i}\sim\mu(x),\ i=1\ldots n\}\)) examples. This dataset can be decentralised, in which case we would use FL (see the next subsection). Our task is to train a GAN, which consists of the generator \(\mathcal{G}\) and the critic \(\mathcal{C}\) (discriminator), to generate synthetic samples from \(\mu\).  
Our privacy mechanism follows the previous work on privacy-preserving GANs [25; 26]. More specifically, it applies the Gaussian mechanism (clip to norm \(C\) and add Gaussian noise with variance \(C^{2}\sigma^{2}\)) to discriminator updates at each step of the training. Privacy of the generator is then guaranteed by the post-processing property of BDP. It is worth mentioning, however, that clipping and/or adding noise to generator gradients should not be overlooked by developers either. It can be beneficial for training in some cases, as it helps keeping a better balance in the game between the critic and the generator.  
We choose not to implement more complicated schemes, such as PATE-GAN [28] or G-PATE [29], which use the PATE framework [13] to guarantee differential privacy for GANs. Our key rationale is that a more complicated structure of this solution could create unnecessary errors and additional privacy leakage (e.g., leaking privacy by back-propagating through the teachers' votes to the generator, thereby neglecting the added noise). Nevertheless, we show in our evaluation that due to the distribution-calibrated BDP accounting (and hence, less added noise) our GAN generates better quality samples compared to these more complex solutions.
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 4 Problem Formulation and Solving
### Stackelberg Game Solution
#### 4.2.1 Problem Analysis  
**Lemma 1**.: _The Stackelberg game \(\Gamma\) has and only has a Stackelberg equilibrium point._  
Proof.: First argue that the Stackelberg equilibrium of the seller exists. In the second stage of the game, \(RSU_{j}\) know that the seller needs to trade the amount of data \(U_{s_{k}}\), it can obtain the highest benefit. The second derivative of \(U_{s_{k}}\) derived from Equation (1) is \({U^{\prime}}_{s_{k}}=-2\alpha_{k}<0\) (\(\alpha_{k}>0\)). Thus, \(U_{s_{k}}\) is a strictly concave function of \(d_{k}\). There is a maximum value of relative to \(d_{k}\). Therefore, for any offer \(p_{i}\), the seller can find the best solution that exists only if it is proven.  
Secondly, the Stackelberg equilibrium for the argument buyer exists. After the second stage, the buyer's strategy is determined. For the determined offer \(p_{i}\), based on Formula (7), \(RSU_{j}\) determines \(s_{k}\) and the seller's shared data \(d_{k}\) amount, which means that the point that \(d_{k}\) makes the quadratic function \(U_{d_{k}}\) extreme, the first derivative is zero, as:  
\[\frac{\partial U_{s_{k}}}{\partial d_{k}}=0 \tag{7}\]  
Considering \(d_{k}\leq D_{k}\), the seller's best decision is given as:  
\[{d_{k}}^{*}=\min\biggl{\{}\frac{p_{i}D_{k}}{2\alpha_{k}},\;D_{k}\biggr{\}} \tag{8}\]  
After determining the value of \(d_{k}\), according to the buyer's utility function (2), we have:  
\[U_{b_{i}}(p_{i})=\eta_{i}\sum_{k=1}^{n}{d_{k}}^{*}-\beta_{i}p_{i}\sum_{k=1}^{n }{d_{k}}^{*} \tag{9}\]  
Make the obtained derivative \({d_{k}}^{*}\) to the above equation, and calculate the second derivative \({U^{n}}_{b_{i}}\leq 0\) of \(U_{b_{i}}(p_{i})\), then, the function image \(U_{b_{i}}(p_{i})\) will definitely be convex. Therefore, there is a maximum point, which can be derived as a strict concave function \(U_{b_{i}}\) of \(p_{i}\). The \(RSU_{j}\) can determine the best bid \({p_{i}}^{*}\) that the buyer has and only based on the seller's strategy.  
It can be seen that the game \(\Gamma\) strategy of the DSSC on \(RSU_{j}\) ensures the existence of the Stackelberg equilibrium point. \(\square\)