esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Production, Maintenance, and Quality Problem
### Notations  
The notations used in the model formulation are:  
\[\begin{array}{lcl}u(k)&:&\text{Production rate of processes during period }k,\,(k=\ 0,\ 1,\ \dots,\ H-1)\\ S(k)&:&\text{Inventory level at the end of period }k,(k=\ 0,\ 1,\ \dots,\ H-1)\\ S&:&\text{Principal store}\\ w_{i}(k)&:&\text{Inventory level of each warehouse }w_{i},(i=0,\dots L)\text{ at the end of period }k,\\ (k=\ 0,\ 1,\ \dots,\ H-1)\\ \tau_{i}&:&\text{Delivery time for each warehouse }w_{i}\\ Q_{i}(k)&:&\text{Delivery rate during period }k,(k=\ 0,\ 1,\ \dots,\ H-1)\text{ for each warehouse}\\ L&:&\text{Total warehouses number}\\ \Delta t&:&\text{Production interval/period length}\\ d_{i}(k)&:&\text{Average demand during period }k,(k:0,\ 1,\ \dots,\ H-1)\\ V_{di}(k)&:&\text{Demand variance over each period }k,(k:0,\ 1,\ \dots,\ H)\\ H&:&\text{Total number of periods in the production horizon}\\ \end{array}\]  
\begin{tabular}{l l} \(H\cdot\Delta t\) & : Finite planning horizon \\ \(Q_{pv}\) & : Delivery vehicle capacity \\ \(c_{p}\) & : Unit cost of production \\ \(c_{h}\) & : Unit inventory cost of one unit during each period \(k\): principal store \(S\) (manufacturing stock) \\ \(c_{hi}\) & : Unit inventory cost of one unit during each period \(k\) for each warehouse \(w_{lv}\) (\(i=0,\ldots L\)) \\ \(c_{d}\) & : Shortage cost of one unit during each period \(k\) \\ \(c_{l}\) & : Unit delivery cost \\ \(c_{o}\) & : Order preparation cost \\ \(c_{m}\) & : Total maintenance cost \\ \(c_{pmi}\) & : Unit cost of preventive maintenance activity \\ \(c_{cm}\) & : Unit cost of corrective maintenance activity \\ \(U_{max}\) & : Maximal rate of production \\ \(U_{min}\) & : Minimal rate of production \\ \(\theta_{i}\) & : Service level value corresponding to the satisfaction of each customer i \\ \(j\) & : Samples' average number to identify the 'out-of-control' state. \\ \(h\) & : Sampling interval \\ \(\delta_{j}\) & : The shift magnitude to the critical state \"out-of-control\" compared to centerline \\ \(n_{pc}\) & : Number of assignable causes \\ \(k_{p}\) & : Standard deviations number between the center line and the control limits of control chart \\ \(c_{i}\) & : Unit inspection cost \\ \(c_{r}\) & : Unit defective cost of one unit \\ \(ACQ\) & : Average cost of quality \\ \(c_{M1}\) & : First maintenance cost component \\ \(c_{M1}\) & : Second maintenance cost component \\ \(c_{MT}\) & : Total maintenance cost \\ \end{tabular}
## 3 Conversion Method for CCA-Secure KEM
### Kem
#### 3.1.2 Security Model of KEM  
Here, we define the IND-\(\{\)CPA,CCA\(\}\) security of KEM by referring to [12]. First, a security experiment of IND-CPA played between a challenger \(\mathcal{C}\) and an adversary \(\mathcal{A}\) is described as follows.  
\[\begin{array}{l}\textbf{Experiment}\ \textbf{Exp}_{\text{IND-CPA}, \mathcal{A}}^{\text{KEM},\mathcal{A}}(\lambda)\\ (\text{PK},\text{SK})\stackrel{{\$}}{{\leftarrow}}\text{KEM}. \text{Setup}(\lambda);\\ \mathsf{K}_{0}^{\text{\tiny$\frac{\xi}{\xi}$}}\ \mathsf{K};(\mathsf{K}_{1}^{*}, \mathsf{CT}^{*})\stackrel{{\$}}{{\leftarrow}}\text{KEM}.\text{ Encap}(\text{PK});\\ b^{\prime}\leftarrow\mathcal{A}\ (\text{PK},\mathsf{CT}^{*},\mathsf{K}_{b}^{*});\\ \text{Output}\ b^{\prime}.\end{array}\]  
The advantage of \(\mathcal{A}\) for breaking the IND-CPA security of KEM is defined as \(\textbf{Adv}_{\text{IND-CPA}}^{\text{KEM},\mathcal{A}}=\Big{|}\Pr\big{[}1 \leftarrow\textbf{Exp}_{\text{IND-CPA},\mathcal{A}}^{\text{KEM},\mathcal{A}}( \lambda)\big{]}-\Pr\big{[}1\leftarrow\textbf{Exp}_{\text{IND-CPA},\mathcal{A} }^{\text{KEM},\mathcal{A}}(\lambda)\big{]}\Big{|}.\)  
**Definition 3**.: _The KEM scheme is \((t,\epsilon)\)-IND-CPA-secure if for any polynomial time adversary \(\mathcal{A}\) that runs in at most time \(t\), we have \(\textbf{Adv}_{\text{IND-CPA}}^{\text{KEM},\mathcal{A}}<\epsilon\)._  
Now, the security experiment of IND-CCA, where the additional decapsulation oracle is given to an adversary \(\mathcal{A}\), is described as follows.  
\[\begin{array}{l}\textbf{Experiment}\ \textbf{Exp}_{\text{IND-CCA}, \mathcal{A}}^{\text{KEM},\mathcal{A}}(\lambda)\\ (\text{PK},\text{SK})\stackrel{{\$}}{{\leftarrow}}\text{KEM}. \text{Setup}(\lambda);\\ \mathsf{K}_{0}^{\text{\tiny$\frac{\xi}{\xi}$}}\ \mathsf{K};(\mathsf{K}_{1}^{*}, \mathsf{CT}^{*})\stackrel{{\$}}{{\leftarrow}}\text{KEM}.\text{ Encap}(\text{PK});\\ b^{\prime}\leftarrow\mathcal{A}\text{Deco}(\cdot)\ (\text{PK},\mathsf{CT}^{*}, \mathsf{K}_{b}^{*});\\ \text{Output}\ b^{\prime}.\end{array}\]  
The oracle \(\text{Deco}(\cdot)\) takes as input \(\mathsf{CT}\) and returns a key \(\mathsf{K}\leftarrow\text{KEM}.\text{Decap}(\text{PK},\text{SK},\mathsf{CT})\) with the condition that \(\mathcal{A}\) cannot query the challenge ciphertext \(\mathsf{CT}^{*}\). The advantage of \(\mathcal{A}\) for breaking the IND-CCA-security of KEM is defined as \(\textbf{Adv}_{\text{IND-CCA}}^{\text{KEM},\mathcal{A}}=\Big{|}\Pr\big{[}1 \leftarrow\textbf{Exp}_{\text{IND-CCA},\mathcal{A}}^{\text{KEM},\mathcal{A}}( \lambda)\big{]}-\Pr\big{[}1\leftarrow\textbf{Exp}_{\text{IND-CCA},\mathcal{A} }^{\text{KEM},\mathcal{A}}(\lambda)\big{]}\Big{|}.\)  
**Definition 4**.: _The KEM scheme is \((t,\epsilon,q_{d})\)-IND-CCA-secure if for any polynomial time adversary \(\mathcal{A}\) that runs in time at most \(t\) and issues at most \(q_{d}\) decapsulation queries, then we have \(\textbf{Adv}_{\text{IND-CCA}}^{\text{KEM},\mathcal{A}}<\epsilon\)._
# An Adaptive Model Predictive Voltage Control for LC-Filtered Voltage Source Inverters
## 4 Adaptive MPC
### Adaptive Observer  
In this subsection, an adaptive observer is presented to obtain the augmented model and estimate the lumped disturbances (\(w\)). The observer also helps the control system to compensate for delays due to digital implementation by estimating signals and disturbances one sample ahead. It also eliminates the load-current sensors, which increases system reliability by removing some physical components subject to damages. Finally, higher noise immunity and size and cost reduction are the other essential advantages of the observer's determined signals.  
An adaptive observer for inverter current dynamic can be constructed as follows:  
\[\left\{\begin{array}{l}\hat{t}_{f}(k+1)=A_{dn}(1,1)\hat{t}_{f}(k)+A_{dn}(1,2)v_{ o}(k)+B_{dn}(1,1)v_{inv}(k)+D_{dn}(1,1)\hat{w}_{1}(k)+G_{1}\left(\hat{t}_{f}(k)- \hat{t}_{f}(k)\right)\\ \hat{w}_{1}(k+1)=\hat{w}_{1}(k)+G_{2}\left(\hat{t}_{f}(k)-\hat{t}_{f}(k)\right) \end{array}\right. \tag{7}\]  
where, symbol \"\({}^{\cdots}\)\" denotes the estimated values, and \(G_{1}\) and \(G_{2}\) are the observer gain. The estimation error dynamics using (6) and (7) are:  
\[\left\{\begin{array}{l}e_{if}(k)=\hat{t}_{f}(k)-\hat{t}_{f}(k)\\ e_{w1}(k)=w_{1}(k)-\hat{w}_{1}(k)\end{array}\right. \tag{8}\]  
Using (6), (7), and (8) and neglecting the disturbance changes in each sampling period (\(w_{1}(k+1)\approx w(k)\)), the error dynamics can be rewritten as:  
\[\left[\begin{array}{c}e_{if}(k+1)\\ e_{w1}(k+1)\end{array}\right]=\underbrace{\left[\begin{array}{cc}A_{dn}(1,1 )-G_{1}&D_{dn}(1,1)\\ -G_{2}&1\\ \end{array}\right]}_{A_{ob1}}\left[\begin{array}{c}e_{if}(k)\\ e_{w1}(k)\end{array}\right] \tag{9}\]  
Therefore, the observer gains must be chosen such that the eigenvalues of the observer (eig(\(A_{ob1}\))) are placed inside the unit circle, which makes the estimation error dynamics of (9) asymptotically stable [12,24].  
The same procedure as done for the inductor dynamic observer, can be employed to design the capacitor dynamic observer. The final results are:  
\[\left\{\begin{array}{l}\hat{v}_{o}(k+1)=A_{dn}(2,1)\hat{t}_{f}(k)+A_{dn}(2, 2)\hat{v}_{o}(k)+B_{dn}(2,1)v_{inv}(k)+D_{dn}(2,1)\hat{w}_{2}(k)+G_{3}(v_{o}(k )-\hat{v}_{o}(k))\\ \hat{w}_{2}(k+1)=\hat{w}_{2}(k)+G_{4}(v_{o}(k)-\hat{v}_{o}(k))\end{array}\right. \tag{10}\]  
Moreover, the observer gains (\(G_{3}\) and \(G_{4}\)) can be calculated in such a way the following estimation error dynamic is asymptotically stable, and eigenvalues of the observer (eig(\(A_{ob2}\))) are located inside the unit circle  
\[\left[\begin{array}{c}e_{ro}(k+1)\\ e_{w2}(k+1)\end{array}\right]=\underbrace{\left[\begin{array}{cc}A_{dn}(2, 2)-G_{3}&D_{dn}(2,1)\\ -G_{4}&1\\ \end{array}\right]}_{A_{ob2}}\left[\begin{array}{c}e_{ro}(k)\\ e_{w2}(k)\end{array}\right] \tag{11}\]  
where the estimation error dynamics are:  
\[\left\{\begin{array}{l}e_{ro}(k)=v_{o}(k)-\hat{v}_{o}(k)\\ e_{w2}(k)=w_{2}(k)-\hat{w}_{2}(k)\end{array}\right. \tag{12}\]
## 3 Basic Principles of 3 MA
### Eddy Current (EC) Method  
A sinusoidal current with low amplitude, typically of the order of mA, and high frequency (f\({}_{\rm HF}\) = 10 kHz \(-\) 1 MHz) is fed into the transmitter coil with a diameter of only a few mm (see Figure 2, no. 7). In the specimen, this excitation results in a low level of induction of a few \(\mu\)T (Rayleigh domain). The magnetic field induces electrical currents (eddy currents) in the specimen according to Faraday's Law of electromagnetic induction. A second coil, the receiver coil (no. 8) is used to detect the magnetic flux through its windings as an induced voltage. Based on a signal analysis, the real and imaginary part, as well as the modulus and phase of the coil impedance, are recorded. The 3 MA system offers to apply up to four different EC frequencies in a single measuring cycle, resulting in 16 measuring parameters, as shown in Table 2. These measuring parameters are dependent on the frequency f\({}_{\rm HF}\) and on both the conductivity (\(\sigma\)) and the excitation-dependent permeability (\(\mu\)(H)) of the material.  
\begin{table}
\begin{tabular}{c c} \hline
**Measuring Parameter** & **Description** \\ \hline A\({}_{1}\) & Amplitude of the fundamental wave. \\ A\({}_{3}\), A\({}_{5}\), A\({}_{7}\), A\({}_{9}\) & Amplitudes of the 3 rd, 5 th, 7 th and 9 th harmonics. \\ P\({}_{3}\), P\({}_{5}\), P\({}_{7}\), P\({}_{9}\) & Phases of the 3 rd, 5 th, 7 th and 9 th harmonics. \\ UHS & Sum of all upper harmonics, \(UHS=A_{3}+A_{5}+A_{7}+A_{9}\) \\ K & Distortion factor, \(K=100\%*\surd\left(A_{3}^{2}+A_{5}^{2}+A_{2}^{2}\right)/A_{1}^{2}\) \\ H\({}_{\rm CO}\) & Coercive magnetic field from harmonic analysis \\ H\({}_{\rm ro}\) & Harmonic content of the magnetic field strength at zero crossing \\ V\({}_{\rm mag}\) & Final stage voltage of the electromagnet \\ \hline \end{tabular}
\end{table}
Table 1: Measuring parameters from Harmonic Analysis (HA).  
\begin{table}
\begin{tabular}{c c} \hline
**Measuring Parameter** & **Description** \\ \hline Re1–Re4 & Real parts of EC signals at frequencies 1, 2, 3 and 4. \\ Im1–Im4 & Imaginary parts of EC signals at frequencies 1, 2, 3 and 4. \\ Mag1–Mag4 & Signal magnitudes at frequencies 1, 2, 3 and 4. \\ Ph1–Ph4 & Signal phases at frequencies 1, 2, 3 and 4. \\ \hline \end{tabular}
\end{table}
Table 2: Measuring parameters from multi-frequency Eddy Current (EC).
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
###### Abstract  
Pervasive data sensing is a major issue that transverses various research areas and application domains. It allows identifying people's behaviour and patterns without overwhelming the monitored persons. Although there are many pervasive data sensing applications, they are typically focused on addressing specific problems in a single application domain, making them difficult to generalize or reuse. On the other hand, the platforms for supporting pervasive data sensing impose restrictions to the devices and operational environments that make them unsuitable for monitoring loosely-coupled or fully distributed work. In order to help address this challenge this paper present a framework that supports distributed pervasive data sensing in a generic way. Developers can use this framework to facilitate the implementations of their applications, thus reducing complexity and effort in such an activity. The framework was evaluated using simulations and also through an empirical test, and the obtained results indicate that it is useful to support such a sensing activity in loosely-coupled or fully distributed work scenarios.  
distributed pervasive data sensing; pervasive monitoring; mobile collaboration; software development framework +
Footnote †: journal: Sensors
## 3 Methodology
### Criteria for Comparison of Model Performance  
In this study, the forecasting accuracy of model was compared through a statistical analysis of the forecasting results after learning the input data through the flood forecasting model. The root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) were used as indexes for scrutinizing the accuracy of the model. Moreover, the peak error (PE) was also used to evaluate the performance of model. The error and coefficient calculation formulas are as follows [31]:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(O_{i}-P_{i})^{2}}{n}} \tag{15}\]  
\[\text{NSE}=\left(1-\frac{\sum_{i=1}^{n}(O_{i}-P_{i})^{2}}{\sum_{i=1}^{n}\! \left(O_{i}-\overline{O}_{i}\right)^{2}}\right)\times 100 \tag{16}\]  
\[\text{PE}=(O_{i,max}-P_{i}) \tag{17}\]  
where \(O_{i}\) and \(P_{i}\) are the observed water surface elevation and the forecasting water surface elevation for time t, respectively, \(\overline{O}_{i}\) is the average value of the observed water surface elevation, \(O_{i,max}\) is the  
Figure 1: Hybrid activation function (Dash line: hyperbolic tangent activation function, Line: ReLU activation function, Hatched Area: hybrid activation function).  
maximum value of the observed water surface elevation, \(i\) is the \(i\)-th observed data or \(i\)-th forecasting results and n is the total number of observed data.  
In the case of RMSE and PE, the closer the value is to zero, the better the forecasting value and the observed value are matched. When the forecasting value and the observed value are well matched, the value of NSE approaches 1.
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 7 Proposed 1D CNN Regression for Cutter Wear Prediction  
The CNN model is commonly used for classification tasks with category-based datasets. However, CNNs are rarely applied to numerical values and regression analysis. Therefore, the traditional CNN model was merged with the regression problem due to the numerical input and output data. The proposed 1D CNN regression architecture was used for high-dimensional regression analysis, and the 1D CNN model was inspired by [36; 52]. Moreover, to reduce training time and to obtain a shorter computational time for fully connected layers, the kernel size was set to 3 [53]. Furthermore, the filter size (count) is the power of two between 32 and 1024. Even though a high filter size can create a powerful model, it can lead to overfitting. For this reason, 32, 64, 128, and 256 filter sizes were chosen for our model. However, the proposed 1D CNN model architecture and hyperparameters were constructed to suit the EPB TBM numerical dataset using Python Keras-Tensorflow. The proposed 1D CNN model architecture is shown in Figure 14. Before model training, the data were split; 80% of the data were used for training, and 20% of the data were tested. The CNN model was created using four convolutional layers within four max-pooling layers. The 1D CNN architecture was obtained by trial and error on the basis of correlation coefficient and loss function mean square error. The first convolutional layer consisted of 32 filter sizes, 3 kernel sizes, and the ReLU activation function. The second convolutional layer included 64 filter sizes, 3 kernel sizes, and the ReLU activation function. The third and the fourth convolutional layers comprised 128 filter sizes, 3 kernel sizes, and the Leaky ReLU activation function, and 256 filter sizes and 3 kernel sizes within the Leaky ReLU activation function, respectively. After convolutional and max-pooling layers, the flatten layer was implemented to transform the data into a single vector. To avoid overfitting, the  
Figure 13: Cutter head rotation speed (rpm).  
dropout function was utilized with 20%. After dropout, there were two fully connected layers with 512 and 128 filter sizes and the Leaky ReLU activation function before the output layer. The output layer was one filter size with the linear activation function due to the one output. For the model compilation, the loss function was determined as the mean square error, and the model metric was selected as the mean absolute error. The [PERSON] Adam function was performed as a model optimizer. Moreover, the Adam function is a stochastic gradient-descent-based optimizer, and it provides avoidance of the local minimum trap. A summary of the proposed CNN model is shown in Table 5. The 1D CNN model has a total of 1,375,553 trainable parameters with 5 batch sizes and 100 epoch sizes. The proposed 1D CNN model computational time (wall time) is 3 min 22 s, so the deep learning model provides a fast calculation for a large dataset. The important CNN activation and loss functions are described in Sections 7.1-7.6.
# ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer
## 1 Introduction  
Human NTRK genes (_NTRK1_, _NTRK2_, and _NTRK3_) encode three neurotrophic receptors TrkA, TrkB, and TrkC, respectively. Normally, their activation is induced by the binding of specific neurotrophic factors or in response to G-protein coupled receptor signaling in various regulation processes of cell growth, survival, and differentiation [1]. For the nervous system, these processes are manifested mainly in the formation of axons, dendrites, and synapses [2]. In other organs, e.g., thyroid glands, their role is unclear, but their aberrant activation, which happens mainly through gene fusion with other constitutively expressed genes, may lead to the activation of the mitogen-activated protein kinase (MAPK), phosphatidylinositol-3-kinase (PI3K)/AKT, phospholipase C gamma (PLCy)-Ca\({}^{2+}\), nuclearfactor-kappa B (NF-\(\kappa\)B), and protein kinase C (PKC) pathways, and consequently, tumorigenesis [1]. Tumors developed via this mechanism are sensitive to NTRK-inhibitors, e.g., larotrectenib or entretcinib, which have a high response rate with low toxicity [3]. However, due to the low occurrence of NTRK-fusions (\(\sim\)0.3% in all solid tumors) [4], and relatively recent development of effective methods for NTRK rearrangement detection [5; 6], previous studies were mainly focused on the biochemical analysis of activated Trk partner proteins, or the properties of the fused proteins themselves.  
Simultaneously, the analysis of differentially expressed genes, with a focus on searching for key master regulators, may help in the identification of new targets for inhibition [7; 8]. Additionally, such computational predictions can be confirmed by in vitro studies [9], or even lead to new drug development [10]. Commonly, these analyses include the identification of differentially expressed genes, their prioritization, for example, by the presence in only one of the lists [11], gene network construction, and master regulator identification [12].  
Using TCGA data, we investigated genes which were differentially expressed in thyroid tumors containing and not containing NTRK-fusions versus normal thyroid gland data that was recently obtained by [PERSON] et al. [13]. As for NTRK-fusion events in other types of tumors, we can identify only a few in the TCGA database (1-4 samples per tumor type), limiting the possibility of performing a tumor-type independent analysis of NTRK-fusions. To our knowledge, this is the first study in which the NTRK-fusion activation mechanism was elucidated through gene expression data analysis.
## 4 Discussion
### The Influence of SNR  
Furthermore, the influence of SNR on the prediction results is also investigated. Figure 18 shows the prediction errors of the two methods based on the ARM with the variation of SNR at 300 Hz when 90 measurement points are used. The submerged depth is 0.2 m and the measurement system is optimized with the EFI.  
It could be seen that the prediction qualities of VR-ESM and CMESM become better with the increase of SNR. When the SNR is high enough, the prediction errors tend to be invariant. As the SNR is usually high enough in the real measurement of normal velocity, the method could be efficient in engineering.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 3 Multi-Phase Adaptive Recoding  
The idea of an \(S\)-phase transmission is simple: In phase \(i\), for every batch having innovative rank \(r_{i}\) at the beginning of this phase, we generate and transmit \(t_{i,r_{i}}\) recoded packets. For simplicity, we call this innovative rank the phase \(i\) innovative rank of the batch. Afterwards, the phase \(i\) transmission is completed. If \(i<S\), the next node calculates the total rank of the batch formed by all the received packets of this batch from phase \(1\) to phase \(i\). By sending this information back to the current node, the current node can calculate the phase \((i+1)\) innovative rank of the batch, i.e., the rank of the batch at the current node minus that at the next node. Then, the current node starts the phase \((i+1)\) transmission. We can see that besides \(S\), the main component under our control to optimize the throughput is the number of recoded packets to be generated and transmitted in each phase.  
For one-phase transmission, the problem reduces to the fire-and-forget TAP. In this case, the number of recoded packets can be obtained via the traditional adaptive recoding algorithm due to the fact that the phase \(1\) innovative rank of a batch is the same as the rank of this batch at the current node, because the next node knows nothing about this batch yet. In the following text, we investigate the number of recoded packets in multi-phase transmission.