esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithms for Drug Sensitivity Prediction
## 4 Predictive Models  
Models capable of predicting the response of a tumor culture to a new drug (or drug combination) or the response of a new tumor culture to a commonly-used drug can be estimated from genomic, or functional characterizations, or a combination of both. We will first discuss techniques that are commonly used to estimate predictive models from genomic characterizations followed by predictive models designed from a combination of genomic and functional characterizations. The predictive models designed from genomic characterizations alone tend to predict the response of a new tumor culture or cell line to an existing drug that has been tested on a range of cell lines.  
A variety of methodologies has been proposed for drug sensitivity prediction based on genomic characterizations. The typical practice is to consider a training set of cell lines with experimentally-measured genomic characterizations (such as RNA-Seq, microarray, protein RPPA, methylation, SNPs, etc.) and responses to different drugs, to design supervised predictive models for each individual drug based on one or more genomic characterizations. The proposed modeling techniques have been diverse, involving commonly-used traditional regression approaches, such as linear regression with regularization or problem-specific new developments that incorporate prior biological knowledge of pathways. The majority of developments have considered steady state models due to the limited availability of time-series data following drug applications. The performance of a similar class of models can differ considerably based on the type of data pre-processing, feature selection, data integration and model parameter selection applied during the design process. Some of the key challenges of drug sensitivity model inference and analysis include (a) the extreme large dimension of features as compared to the number of samples; (b) limited model accuracy and precision; (c) data generation and noise measurement and (d) lack of interpretability of models in terms of the biological relevance of selected features.  
A recent study on the performance of modeling frameworks [52] using large-scale pharmacogenomics screen data considered over 110,000 different models based on a multifactorial experimental design evaluating systematic combinations of modeling factors, such as (i) the type of predictive algorithm; (ii) the type of molecular features; (iii) the kind of drug whose sensitivity is being predicted; (iv) the method of summarizing compound sensitivity values and (v) the type of sensitivity response whether discrete or continuous. Results from the systematic evaluation suggest that the model input data, specifically the type of genomic characterizations and choice of drug, are the primary factors explaining modeling framework performance, followed by the choice of algorithm. In this study, the choices of modeling algorithms that showed the best performance were ridge regression and elastic net. We next discuss the linear regression models to provide an overview of ridge regression and elastic net.
## 2 Problem Formulation
### Acoustic Equation: Galbrun Equation  
The fundamental relationship between Eulerian and Lagrangian perturbations is:  
\[\psi^{L}=\psi^{E}+\mathbf{w}^{L}\cdot\
abla\psi_{0} \tag{1}\]  
where \(\psi\) represents any physical quantity, so the pressure, density, and velocity can be written as follows:  
\[p^{L} =p^{E}+\mathbf{w}^{L}\cdot\
abla p_{0} \tag{2}\] \[\rho^{L} =\rho^{E}+\mathbf{w}^{L}\cdot\
abla\rho_{0}\] \[\frac{d^{2}\mathbf{w}^{L}}{dt^{2}} =\mathbf{v}^{E}+\mathbf{w}^{L}\cdot\
abla\mathbf{v}_{0}\]  
Substituting Equation (2) into LEE, the Galbrun equation that is based on the mixed Eulerian-Lagrangian disturbance of linearized [PERSON]'s equations can be written as follows:  
\[\rho_{0}\frac{d_{0}^{2}\mathbf{w}^{L}}{dt^{2}}-\
abla(\rho_{0}c_{0}^{2}\
abla \cdot\mathbf{w}^{L})+\left(\
abla\cdot\mathbf{w}^{L}\right)\
abla p_{0}-^{T} \
abla\mathbf{w}^{L}\cdot\
abla p_{0}=0 \tag{3}\]  
where \(d_{0}/dt=\partial/\partial t+(\mathbf{v}_{0}\cdot\
abla)\) constitutes the material convective derivative, and \(\mathbf{w}^{L}\) is the displacement of Lagrangian disturbance. The speed of sound, density, and pressure were normalized and dimensionalized with \(c_{0}\) (340 m/s), \(\rho_{0}\), and \(p_{0}\), respectively.  
The equation of Galbrun can also be written as follows:  
\[\begin{cases}\rho_{0}\frac{d_{0}^{2}\mathbf{w}^{L}}{dt^{2}}+\
abla p^{L}-\left( \
abla\cdot\mathbf{w}^{L}\right)\rho_{0}\frac{d_{0}\mathbf{v}_{0}}{dt}-^{T} \
abla\mathbf{w}^{L}\cdot\
abla p_{0}=0\\ p^{L}=-\
abla(\rho_{0}c_{0}^{2}\
abla\cdot\mathbf{w}^{L})\end{cases} \tag{4}\]  
where \(p^{L}\) is the Lagrangian disturbance pressure.  
Figure 1: Two-dimensional cavity-duct system.  
After the integration over domain \(\Omega_{1}\) and the consideration of the boundary conditions, the following formulation gives a mixed variational formulation:  
\[-\int_{\Omega_{1}}\biggl{(}\frac{1}{\rho\alpha c_{0}^{2}}p^{*}p^{L}+ \mathbf{w}^{*}\cdot\
abla p^{L}+\
abla p^{*}\cdot\mathbf{w}^{L}-\omega^{2}\rho_{ 0}\mathbf{w}^{*}\cdot\mathbf{w}^{L} \tag{5}\] \[-\mathrm{i}\omega\rho_{0}\mathbf{w}^{*}\cdot(\mathbf{v}_{\mathbf{ 0}}\cdot\
abla\mathbf{w}^{L})+\mathrm{i}\omega\rho_{0}(\mathbf{v}_{\mathbf{0}} \cdot\
abla\mathbf{w}^{*})\cdot\mathbf{w}^{L}-\rho_{0}(\mathbf{v}_{0}\cdot \
abla\mathbf{w}^{*})\] \[\cdot(\mathbf{v}_{\mathbf{0}}\cdot\
abla\mathbf{w}^{L})+\mathbf{ w}^{*}\cdot\
abla p_{0}(\
abla\cdot\mathbf{w}^{L})-\mathbf{w}^{*}\cdot({}^{T} \
abla\mathbf{w}^{L}\cdot\
abla p_{0})\biggr{)}d\Omega_{1}\] \[-\int_{L}p^{*}(\mathbf{w}^{L}\cdot\mathbf{n}_{\mathbf{0}})- \mathbf{w}^{*}\cdot\Bigl{\{}\rho_{0}(\mathbf{v}_{\mathbf{0}}\cdot\mathbf{n}_ {\mathbf{0}})(-i\omega\mathbf{w}^{L}+\mathbf{v}_{\mathbf{0}}\cdot\
abla \mathbf{w}^{L})\Bigr{\}}d\mathrm{L}\] \[=0\qquad\forall(\mathbf{w}^{*},p^{*}).\]  
where \(\mathbf{w}^{*}\) and \(p^{*}\) are the test functions associated with the Lagrangian displacement \(\mathbf{w}^{L}\) and the Lagrangian pressure \(p^{L}\), respectively [17].
## 3 Experimental Results and Discussion
### Effect of Water Content on the Conversion Coefficient  
For the convenience of analysis, the average peak strength, peak strain, and elastic modulus measured using five groups of soil bodies with different water contents are sorted, and the obtained data are shown in Figure 11.  
As observed in Figure 11, the sample presents different mechanical properties under different water contents, and the peak strain of the sample has no obvious change rule under different water contents and fluctuates irregularly between 0.015 and 0.035. However, the peak strength and elastic modulus of the sample first increased and then decreased with an increase in water content, and there is a \"convex peak\" phenomenon. That is, critical water content is observed. When the water content is less than the content of critical water, the peak strength and elastic modulus increase with an increase in water content; when the water content is greater than the critical water content, the peak strength decreases with an increase in water content. [PERSON] et al. [9], [PERSON] et al. [10], and [PERSON] et al. [11] have similar test results as those shown above.  
The phenomenon of \"convex peak\" shows that the mechanical properties of large samples are closely related to water content. Because the sample comprises viscous soil, when the moisture content is lower than the moisture content corresponding to the wave crest, the viscosity between the overly dry soil is not enough, and it disintegrates under low pressure. When the moisture content is higher than that corresponding to the wave crest, as long as the soil remains unsaturated, its saturation increases with an increase in moisture content. During this process, the intergranular suction of the soil also grad  
Figure 11: Change in soil stress characteristics under different water contents.  
Figure 10: Stress conversion coefficient and energy ratio at different strain rates.  
ually decreases [12; 13; 14], resulting in a decrease in the shear strength [15; 16] and tensile strength [17; 18] of the soil and further resulting in a decrease in peak strength.
## 3 Results and Discussion
### sEMG Recording  
Figure 3A shows the recorded sEMG signals from the eight different channels in the time domain. The time domain data were transformed to the frequency domain by applying fast Fourier transform (FFT) through MATLAB, as shown in Figure 3B. Each channel has a similar peak frequency that demonstrates the reliability of the microelectrode array. Using the fabricated microelectrode, we achieved an SNR of -21.4 dB with a 4.28 dB standard deviation, as calculated from Equation (1). The root mean square (RMS) voltage of the sEMG signal was 0.853 mV, whereas the baseline noise was 78.4 \(\mu\)V, meaning that we can clearly distinguish the signal from noise, as shown in Figure 3C.  
\[\text{SNR}=10\times\log\Biggl{(}\frac{signal\ power}{baseline\ noise\ power}\Biggr{)}. \tag{1}\]  
Figure 3: Recorded sEMG (surface electromyography) data from the 8-channel microelectrode array. **(A)** A set of sEMG signals in the time domain. **(B)** A set of FFT (fast Fourier transform) data in the frequency domain. **(C)** Signal to baseline noise ratio (SNR). **(D)** sEMG signal change depending on the increasing load for measuring the maximum voluntary contraction (MVC).  
Figure 2: Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) measurements. **(A)** Bode magnitude and phase plots for the fabricated Parylene C-based microelectrodes; (inset) equivalent circuit model used for electrode characterization. **(B)** CV results of the microelectrode array.  
We also performed the maximum voluntary contraction (MVC) experiment to verify the recording capability of the microelectrode array. The MVC experiment was conducted with the same electrode location as described in the method section, starting from the 0% MVC level and going to the 100% MVC level, each lasting 6-7 s [21]. According to [PERSON]'s size principle [22,23], more MUs start to fire as more load is added to the muscles. We observed the tendency of increasing signal with increasing voluntary contractions, which is consistent with the theory, as shown in Figure 3D.
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
###### Abstract  
A broadband generalized sidelobe canceler (Broadband-GSC) application for near-field beamforming is proposed. This approach is implemented in the wavelet domain. Broadband-GSC provides a set of complex, adapted apodization weights for each wavelet subband. The proposed method constrains interference and noise signal to improve the lateral resolution with only one single emission. Performance of the proposed beamforming is tested on simulated data obtained with Field II. Experiments have proved that the new beamforming can significantly increase the image quality compared with delay-and-sum (DAS) and synthetic aperture (SA). Imaging of scattering points show that Broadband-GSC improves the lateral resolution by 43.2% and 58.0% compared with SA and DAS, respectively. Meanwhile, Broadband-GSC reduces the peak sidelobe level by 11.6 dB and 26.4 dB compared with SA and DAS, respectively. Plane wave emission experiment indicates that Broadband-GSC can improve the lateral resolution by 44.2% compared with DAS. Furthermore, the new beamforming introduces the possibility for higher frame-rate and higher investigation depth with increased lateral resolution.  
adaptive beamforming; wavelet; phased array; lateral resolution +
Footnote †: journal: Applied
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 4 Experimentation Methodology
### Stop Criteria
#### 4.7.1 Pre-Computed Tables  
The first method to determine routing convergence is implemented by Algorithm 1, inspired by the _Sibyl RT Calculator_, part of the Sibyl framework available at [45].  
This algorithm is presented from the controller side, i.e., the computations are centralized in the controller, which can interact with the nodes of the topology, e.g., requesting information.  
Hence, the algorithm is quite simple: first, the controller, as an omniscient entity, pre-computes the forwarding tables of all the nodes of the topology (e.g., using an ECMP-aware Dijkstra algorithm) and secondly, requests the actual forwarding table to each node, comparing these tables with the expected ones. Notice that, with a decentralized approach, each node can compare the expected and actual tables and notify the controller only when they are equal.  
A hybrid variation of this strategy may be implemented, where the controller pre-calculates the tables, notifies the nodes, and then waits for an \"end message\" of each node. Of course, when the controller is aware that every node has reached the expected table, the emulation must be finished under any approach.  
Figure 4: Analysis flow.  
```
0:\(k\): factor; \(T\): Topology Result: True for\(i=1\); \(i=5k^{2}/4\)do \(x_{i}\gets f(T,i,k)\)\(\triangleright\) f could be Dijkstra endfor \(y_{i}\gets getTable(i)\) while\(y_{i}\
eq x_{i}\)do \(y_{i}\gets getTable(i)\) endwhile
```  
**Algorithm 1** Convergence with pre-computed tables (centralized approach).  
Building a representative network graph is necessary to implement this stop criterion. It can be used to run a centralized multi-path routing algorithm (such as an ECMP-aware Dijkstra) to obtain every node's expected forwarding tables. Python was chosen as the common programming language for all the environments, which allows the network to be abstracted from a graph using the NetworkX library [68]. This library also includes a function that calculates all the shortest paths between any source-destination pair. On the other hand, to get the actual forwarding tables, the emulation orchestrator must use the Python API provided by each emulator to query each node. Eventually, when all the pre-computed tables are equal to the actual tables, the orchestrator detects the routing protocol convergence and stops the emulation.
## 2 Materials and Methods
### Model Architecture  
This study proposes a neural network model called CNN-BiLSTM for reconstructing ECG signals, as illustrated in Figure 2. In Figure 2, \"Conv1D\" represents a one-dimensional convolutional layer; \"ReLu\" is the activation function used in the corresponding convolutional layer; the \"Maxpooling\" refers to a one-dimensional maximum pooling layer; \"BiLSTM\" refers to the bidirectional long short-term memory layer, and the \"Fully Connected\" represents the fully connected layer.  
In Figure 2, our proposed CNN-BiLSTM model consists of a one-dimensional convolutional neural network (CNN) structure and a bidirectional long short-term memory (BiLSTM) network. The model we propose mainly includes the input layer, the CNN layer, the BiLSTM layer, the fully connected layer, and the output layer. The input layer inputs the PPG signal segment. The CNN layer has two convolutional layers, the ReLU layer and the maximum pooling layer. CNN is the most popular technique in deep learning [27]. It is also known as a feature learner, and has great potential to extract useful features from the input raw data automatically. CNN is generally composed of the input layer, the convolution layer, the pooling layer, the fully connected layer, and the output layer. The convolution layer convolves the input signal with the convolution kernel, and then applies an activation function to introduce nonlinearity. The convolution layer and activation function enable the network to learn complex features. The pooling layer can reduce the computational complexity by reducing the size of the feature map. The pooling layer helps to extract the most important features. CNN is usually composed of multiple stacked convolutional layers and pooling layers to extract higher-level features. The advantage of choosing the  
Figure 2: The architecture of the proposed CNN-BiLSTM model.  
CNN model is its ability to perform perceptual tasks to learn ECG-related features from PPG signals. The kernel size, stride and padding of the convolutional layer were 3, 1, and 1, respectively. The BiLSTM layer contains two BiLSTM layers. The BiLSTM model was chosen because it can effectively solve sequential and time series problems [28; 29]. Long short-term memory (LSTM) and BiLSTM networks are well suited for solving time series problems. BiLSTM models take longer to reach equilibrium than LSTM models but provide better predictions [30]. There are two fully connected layers in the fully connected layer. The output layer outputs the reconstructed ECG signal segment. We combined CNN and BiLSTM to take advantage of CNN to extract the local features and BiLSTM to capture the global features of the sequence.
## 2 Federation Deployment Control Protocol
### Federation Closure  
The closure phase aims at returning the satellite to its initial state, releasing the services that were used during the federation. As this phase is triggered by one of the nodes of the federation, a three-way handshake is performed to close the federation. The exchange of close packets indicates the intentions to terminate the federation. This packet follows a similar structure to the previous ones (see Figure 4). The initial fields are the same as the other packets, changing the _type_ value according to a close packet. Additionally, this packet includes an _acknowledgment_ bit if it is required to acknowledge a previous message, such as a data packet. Therefore, the satellite that is initiating the closure phase can also notify the proper reception of the last packet with this bit. Seven reserved bits are included for future extensions.  
The packet exchange in the closure phase is performed as follows. A satellite can decide to close the federation because the service is no longer available (i.e., the provider decides) or the customer has already been satisfied (i.e., the customer decides). In both cases, the satellite that wants to terminate the federation transmits a close packet highlighting this intention to the other one. When the packet is received, the satellite starts releasing the resources allocated for the federation, and replies with another close packet which has the _acknowledgment_ flag activated. With this flag, the originator of the federation closure understands that the received close packet is at the same time the reply of the first one. Finally, it replies with the last acknowledgment packet (previously presented). As remarked previously, this process follows a connection termination based on a three-way handshake. This robust mechanism to close the federation ensures that both satellites can clean the internal context associated with the federation. If the communication is lost during all the three phases, each satellite will perform the process of releasing the resources and cleaning the context after detecting that the connection is broken.  
Figure 4: close packet structure (unit = 1 bit).
# An Overview of Fog Data Analytics for IoT Applications
## 5 Research Challenges
### Orchestration  
The fog paradigm has envisioned a distributed computing and allocation approach as opposed to the centralized approach of the cloud paradigm. Different orchestration techniques have been implemented in the cloud. Orchestration can be defined as a sole, uniform, and centralized approach that accounts for coordinating communications among different applications and services. Thus, orchestration is related to automating the interaction processes among various services, but applies to centralized systems. However, as we have seen, fog uses a decentralized approach with benefits. Moreover, fog has some other characteristics apart from its decentralized approach, such as resource constraints and heterogeneity. Thus, introducing orchestration into the fog paradigm is a challenge. In this context, a novel concept called choreography can be employed. Choreography refers to maintaining a global view of all applications or services across the edge devices through information sharing, thereby updating all local changes to the global view. Thus, choreography uses a distributed approach, performs orchestration-related functionalities, and could be our solution for implementing at the fog-edge interface/link or the southbound region.  
Many different orchestration agents have been proposed by different authors with their detailed architecture, which is summed up in the paper by [PERSON] et al. [107]. The concept of virtualization has been used by many authors for the same purpose [108], while some authors have also proposed containerization in orchestration in the fog environment [109]. Most authors also propose software-defined networking (SDN) for obtaining centralized control of the network and an automatic management of the global view of the network as soon as any local changes occur. However, [PERSON] [110] proposed an orchestration architecture based on network function virtualization (NFV). The general approach by many of the authors has been focused on sub-problems, and very few authors tend to choose a hybrid approach involving both orchestration and choreography, with orchestration at the north-bound region (between the cloud and the fog) and choreography at the southbound region (between the edge devices and the fog). Table 4 shows some related work in the field of orchestration.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Heterogeneous Networks
#### 4.1.3 Interference in HetNet  
The combination of such SCs grants offloading traffic from macro-cell and improves the experience of the network by associating UEs in SCs with minimum power transmission. However, this combination leads to significant ICI in networks, particularly for SC users at the cell edge. In general, macro-cells are often deployed in a cellular network by a reasonable network plan, whereas low-power small-cells are typically placed by the identification of coverage problems and traffic intensities (e.g., hotspots) in the network [61]. Various types of distribution scenarios are already available for HetNets. In a multi-carrier distribution, SCs use more various carrier frequencies than macro-cells. This method efficiently minimizes ICI but does not guarantee optimum spectrum utilization [62]. On the other hand, the co-channel distribution is used by utilizing the same carrier for macro-cell and SC, in which the spectrum efficiency is optimized through spatial reuse and a prominent distribution technique in HetNets. Even though the co-channel technique enables excellent spectral utilization, it results in great ICI among macro- and small-cells [63].  
Because of the synchronous operating of many SCs within these cells, different types of smart devices or small equipment are connected in an MBS in the environment of HetNet, resulting in co-tier interference, which is the interference between entities belonging to the same network or tier. In the case of a femto-cell network, the co-tier interference happens between nearby femto-cells. While the interference between entities belonging to diverse networks or tiers is referred to as cross-tier interference. Figure 6 depicts such interference between femto-macro and macro-femto networks [64; 65]. These interferences are especially common at big gatherings when numerous users demand high throughputs, such as heavy data applications, internet browsing, and downloading/uploading images and videos. Accordingly, the ICI management and minimization approach would be created for next-generation cellular communication. Furthermore, all other interferences must be canceled to provide user fairness and QoS in wireless cellular networks [66; 67].