esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Recommendation Pipeline  
DataToCare takes as input a state vector \(\mathcal{S}[\hat{\vec{p}}]\) representing a target patient \(\hat{\vec{p}}\)'s current medical state. \(\mathcal{S}[\hat{\vec{p}}]\) contains (i) demographic information such as the target patient's ethnicity, age, gender, and insurance, (ii) the diagnostic group to which the target patient belongs, (iii) lab results and measurements obtained up to the time of state \(\mathcal{S}[\hat{\vec{p}}]\) such as a target patient's latest blood glucose level or blood pressure reading, and (iv) for each treatment the target patient received since admission, how recently and how many times it was administered.  
To form treatment recommendations for the target patient \(\hat{\vec{p}}\) at some state \(\mathcal{S}[\hat{\vec{p}}]\), DataToCare uses the MIMIC-III dataset to find a set of relevant patients, \(\mathbb{P}(\mathcal{S}[\hat{\vec{p}}])\), who have similar abnormal lab results or measurements at any time during their stay. We choose \"any time\" rather than the \"same time\" to allow a comparison of a target with similar patients who might be in a similar situation at various times during their Intensive Care Unit stay. We carry this out for two reasons: (i) the timing of the evolution of the illnesses of different patients may differ significantly, so the treatments to a patient who has similar abnormal measurements to the target patient on a different day may still give insight to the desired treatments of the target patient, and (ii) pragmatically, the larger pool gives us a larger effective data size.  
For each treatment \(r\in\mathds{R}\) prescribed to any patient \(p\in\mathbb{P}(\mathcal{S}[\hat{\vec{p}}])\) and a user-specified time horizon \(h\), DataToCare builds an independent treatment classifier. This random forest classifier is trained on all the time-varying state vectors, \(\{\mathcal{S}[p,t]\}\), of the patients \(p\) in \(\mathbb{P}(\mathcal{S}[\hat{\vec{p}}])\) from admission to discharge or death. The goal of the classifier is to predict whether \(r\) was given within the following \(h\) hours. Here, \(h\) should be no smaller than the minimum response time in an ICU (one hour) and no larger than the maximum time of utility (24 h). We focus our effort on \(h=2\), because patient conditions change frequently in the ICU, so near-term predictions are the most useful.  
DataToCare feeds the target patient state \(\mathcal{S}[\hat{\vec{p}}]\) into the treatment model for each treatment \(r\) to determine whether \(r\) should be given within the \(h\) hours following \(\mathcal{S}[\hat{\vec{p}}]\). Figure 1 illustrates the simplified flow of the treatment prediction pipeline. The next subsections present detailed explanations of the processes.
## 3 Data Enhancement Methods
### SMOTE Data Enhancement  
The third data enhancement technique was the synthetic minority oversampling technique (SMOTE) [5]. In other words, the data of the minority samples were repeatedly sampled or synthesized to generate new sample data. For example, Figure 2 shows some two-dimensional sample data of known human heights and weights. The main idea of  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Generator Model**} \\ \hline
**Layer** & **Hyperparameter Values** \\ \hline Input Layer & Shape: 1024 \\ \hline Dense & Shape: 2, 2, 256 \\ \hline Batch Normalization & Default \\ \hline Leaky ReLU & Alpha: 0.2 \\ \hline Reshape & Shape: 2, 256 \\ \hline UpSampling2D & Up size: 2, 2 \\ \hline \multirow{4}{*}{Conv2D} & Kernels: 128 \\  & Kernel size: 3, 3 \\ \cline{1-1}  & Strides: 1, 1 \\ \cline{1-1}  & Padding: same \\ \hline Batch Normalization & Default \\ \hline Leaky ReLU & Alpha: 0.2 \\ \hline UpSampling2D & Up size: 2, 2 \\ \hline \multirow{4}{*}{Conv2D} & Kernels: 1 \\  & Kernel size: 3, 3 \\ \cline{1-1}  & Strides: 1, 1 \\ \cline{1-1}  & Padding: same \\ \hline Batch Normalization & Default \\ \hline Sigmoid & Default \\ \hline Cropping2D & Cropping: 1, 1, 0, 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _WGAN-GP_ generator model.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Discriminator Model**} \\ \hline
**Layer** & **Hyperparameter Values** \\ \hline  & Kernels: 64 \\  & Kernel size: 3, 3 \\ \cline{1-1} Conv2D & Strides: 2, 2 \\ \cline{1-1}  & Padding: same \\ \cline{1-1}  & Use bias: true \\ \hline  & Kernels: 128 \\ \cline{1-1}  & Kernel size: 3, 3 \\ \cline{1-1} Conv2D & Strides: 2, 2 \\ \cline{1-1}  & Padding: same \\ \cline{1-1}  & Use bias: true \\ \hline Leaky ReLU & Alpha: 0.2 \\ \hline Dropout & Rate: 0.3 \\ \hline Flatten & Output shape: 512 \\ \hline Dropout & Rate: 0.2 \\ \hline Dense & Output shape: 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._SMOTE is to select one data item from a small number of samples, such as the red data point A in Figure 2. The distance between all of the data points and the selected red data point A is calculated according to the k value (Figure 2, assuming k is equal to 3), and then the nearest k data points in its vicinity are found. As shown in Figure 2, the data points in the circle include the selected red data point A, and the three blue data points closest to the red data point are B, C, and D. Then, a random data point is selected from the three closest data points. As shown in Figure 2, data point B is the randomly selected data point among the three data points. Then, n synthetic data are randomly generated between the originally selected data point A and the randomly selected data point B. As shown in Figure 2, suppose n is equal to 2; that is, two new data points, S\({}_{1}\) and S\({}_{2}\), are synthesized between the red data point A and the blue data point B.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
###### Abstract  
We provide a broad overview of current quantum communication by analyzing the recent discoveries on the topic and by identifying the potential bottlenecks requiring further investigation. The analysis follows an industrial perspective, first identifying the state or the art in terms of protocols, systems, and devices for quantum communication. Next, we classify the applicative fields where short- and medium-term impact is expected by emphasizing the potential and challenges of different approaches. The direction and the methodology with which the scientific community is proceeding are discussed. Finally, with reference to the European guidelines within the Quantum Flagship initiative, we suggest a roadmap to match the effort community-wise, with the objective of maximizing the impact that quantum communication may have on our society.  
quantum communication; quantum key distribution (QKD); optical telecommunication
## 9 Thermal Properties of Asphalt Mixture Components
### Thermal Conductivities of Asphalt Mortar and Aggregate  
As for the thermal conductivity of asphalt mortar and aggregate, existing research conclusions [36] show that when the temperature changes, the thermal conductivity of any material will change, and the formula between thermal conductivity and temperature is satisfied as to the followed formula.  
\[\lambda=\lambda_{0}(1+bT) \tag{8}\]  
where \(b\) is the temperature coefficient, which is usually constant; \(\lambda_{0}\) is the thermal conductivity of material at 0 \({}^{\circ}\)C; and \(T\) is temperature. In a wide temperature range, this linear formula can be used for calculating thermal conductivities of asphalt and aggregate.  
According to existing research [37; 38], \(\lambda_{0}\) and \(b\) of basalt aggregate and asphalt are shown in Table 4.  
Similar to the specific heat capacity calculation process, the thermal conductivity of asphalt mortar also needs to be calculated. [PERSON] [39] studied the thermal conductivity of the asphalt mixture in 1972 and put forward the calculation formula as follows:  
\[k_{m}=(k_{a})^{m}\cdot(k_{b})^{n}\cdot(k_{b})^{p}\cdot(k_{w})^{q} \tag{9}\]  
where \(k_{m}\), \(k_{a}\), \(k_{b}\), \(k_{e}\), \(k_{w}\) are the thermal conductivities of the mixture, aggregate, asphalt, air and water, respectively, and \(m\), \(n\), \(p\), \(q\) represent the volume contents of aggregate, asphalt, air and water, respectively.  
It can be considered that there is almost no air and water in asphalt mortar, so only the effects of asphalt and aggregate should be considered in the formula above. Combining the density of each component, the volume contents of each component in mortars can also be calculated according to the mixture gradations shown in Table 1. All volume contents are expressed in percentages, as shown in Figure 7.  
The thermal conductivities of asphalt mortars can be calculated according to the volume contents of each component in Figure 7 and the thermal conductivities of each component in Table 4, as Figure 8 shows:  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Mixture Components**} & \multicolumn{2}{c}{**Parameters**} \\ \cline{2-3}  & \(\lambda_{0}\) **(W/(m\(\cdot\)\({}^{\circ}\)C)**)** & \(\mathbf{b}\) \\ \hline Basalt aggregate & 2.4757 & \(5.22\times 10^{-3}\) \\ Asphalt & 0.1227 & \(1.2\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermal conductivities of asphalt mixture components.  
According to the results shown in Figure 8, the thermal conductivities of asphalt mortar are linearly related to temperature. Because the volume contents of components are very close, the thermal conductivities of AC-13 and AC-25 were almost the same. Due to the thermal conductivity of asphalt being much smaller than that of aggregate, the thermal conductivity of PAC-13 mortar with higher asphalt content was lower. The straight lines shown in Figure 8 were fit by linear equations and input into ABAQUS software for simulations.
# A Review of Sharp-Switching Band-Modulation Devices
## 2 Basic Characteristics  
### Operation in DC Mode  
The band-modulation effect gives rise to a sharp transition, from low to high current, in both output \(\mathrm{I_{A}-V_{A}}\) and transfer \(\mathrm{I_{A}-V_{FG}}\) characteristics. The \(\mathrm{I_{ON}/I_{OFF}}\) ratio can exceed 8 decades. Figure 5a reproduces the typical hysteresis in \(\mathrm{I_{A}-V_{A}}\) curves recorded by sweeping the anode voltage. The hysteresis results from an S-shaped characteristic that can be measured by setting the anode current with a current source (Figure 5b). For low \(\mathrm{V_{A}}\), the barriers induced by the gates keep the \(\mathrm{Z^{2}-FET}\) in the OFF state. When \(\mathrm{V_{A}}\) reaches a turn-on value \(\mathrm{V_{ON}}\), the positive feedback mechanism makes the injection barriers collapse, and the current abruptly attains the normal \(\mathrm{I_{ON}}\) value of a forward-biased PIN diode (ON state) [22]. The switching mechanism is related to the change in the minority carrier concentrations (holes in the gated N-region and electrons in the ungated P-region). In the hysteresis window, two different currents are measurable for the same value of \(\mathrm{V_{A}}\); they can be viewed as states '1' and '0' in a static memory (1T-SRAM) or in a dynamic memory (1T-DRAM), as discussed in Sections 5.1 and 5.2.  
In Figure 5a, the hysteresis window and the turn-on voltage are enhanced by reinforcing the barrier heights, especially via \(\mathrm{V_{FG,BG}}\). For a reverse \(\mathrm{V_{A}}\) sweep, the device remains in the \(\mathrm{I_{ON}}\) state until the point (\(\mathrm{V_{A}}\approx 0.7\) V) where it turns off. Two equilibrium states, '0' and '1', are shown in Figure 5a. The hysteresis can be achieved with \(\mathrm{V_{A}}\) and \(\mathrm{V_{FG}}\) of around 1 V, making the \(\mathrm{Z^{2}-FET}\) a promising device for low-power circuits and embedded memory. Regarding the transfer \(\mathrm{I_{A}-V_{FG}}\) characteristics, the threshold voltage is tunable by the anode voltage and/or device dimensions.  
Figure 5: Experimental sharp-switching characteristics of various \(\mathrm{Z^{2}-FETs}\) in quasi-steady-state operation. Output \(\mathrm{I_{A}-V_{A}}\) curves for different gate voltages, measured by (**a**) scanning the anode voltage and (**b**) forcing the anode current. (**c**) Transfer \(\mathrm{I_{A}-V_{FG}}\) curves for various anode voltages \(\mathrm{V_{A}}\).  
Unlike the ultrathin FD-SOI transistors [23], the \(Z^{2}\)-FET is free of supercoupling [24] and operates successfully, even in sub-10 nm thin films. Supercoupling is ineffective in \(Z^{2}\)-FETs because the populations of electrons and holes are separated _literally_ (in the gated and ungated regions respectively), rather than _vertically_, as in ultrathin FD-SOI MOSFETs.
## 6 Research in Ransomware Detection
### Ransomware Analysis
#### 6.1.2 Dynamic Analysis
A pre-encryption ransomware detection system was proposed in [97] which utilized the API for the detection purpose. The ransomware samples were analyzed in cuckoo to extract the API calls. This detection system first compared the under-examination sample with already available sample repository. In the second stage the samples were run in cuckoo to observe its behavior. A hardware assisted ransomware detection technique was proposed in [100] named RanStop. This system was able to detect the ransomware in 2 ms before encryption happens. Hardware program counter (HPC), a monitoring micro architectural unit of modern processor was used to collect and dynamically analyzed the running events. However, it has limitation as the hardware data processed in runtime can also be corrupted. A deep learning study analyzing the ransomware samples dynamically was proposed in [108]. This method was proposed for the edge computing devices to reduce the latency of data transmission. The behavioral analysis was carried out in cuckoo to extract the features for training. The deep learning classifier autoencoder was used to train on the extracted data and perform the detection. An adaptive ransomware detection framework was proposed in [16]. This framework detected the ransomware by performing the dynamic analysis on a set of benign and ransomware. The dynamic analysis was performed for 4 to 9 min. The proposed deep learning semi supervised approach extracted the runtime behavioral patterns. Hence it was able to detect the zero-day ransomwares. A scalable ransomware detection framework was proposed in [109] to detect the attack on electric vehicle charging stations (EVCS) controlled by supervisory control and data acquisition (SCADA) system. The dynamic binary analysis was performed in virtual unit via PIN framework. The proposed framework performed the analysis in two phases. Assembly frequency analysis was conducted offline on stored data in one phase while in online fashion using the real time data in another phase. Both features extraction and selection were performed by using the frequency analysis of assembly instructions. A ransomware detection system named DeepGuard was proposed in [110] which detected the ransomware by observing the user file interacting pattern. A boundary was defined between legitimated user behavior and ransomware activities. Different ransomware families were executed in the sandbox to log the malicious activities and the file interaction patterns. The deep autoencoders were trained on the log data of user activities patterns. A detection model [113] named Dynamic Ransomware Detector based on the improved TextCNN (DRDT) utilized the dynamic analysis to detect ransomware in TextCNN. The model was trained by using the API calls. This detection model improved the performance of TextCNN by using the API calls, NLP, and max pooling. Sandbox was used to process the API calls.
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 4 Vehicular Environment Identification Methodology
### Dataset Generation  
During the training process, five types of vehicular environments under rainy conditions are considered: Rural LOS, Urban LOS, Urban NLOS, Highway LOS, and Highway NLOS. Each rainy environment is modeled through a wireless channel based on the vehicular environment's delay, gain, and Doppler frequency. Each environment is assigned a label corresponding to the class output of the convolutional neural network model, as shown in Table 2.  
When constructing the dataset samples, a half-duplex vehicle-to-vehicle (V2V) communication system based on Orthogonal Frequency Division Multiplexing (OFDM) technology was used. To simulate a diverse vehicular communication environment, we used MATLAB's V2 VChannel framework for wireless channel model simulation, version R2023a, was employed, as detailed in reference [27]. In the V2V channel model, we added a new multi-path component to simulate the vehicular environment under rainy weather conditions. During the research process, numerous data packets conforming to the 802.11p standard were transmitted through various channel models. For each specific environment, data packets were tested for transmission at different signal-to-noise ratios (SNR), with the SNR values set to range from 15 dB to 40 dB, increasing in increments of 0.5 dB. This process is repeated 400 times for each environment, employing different releases of the channel model to enhance the diversity and robustness of the data. During each transmission process, we record the received packets and compute the Channel State Information (CSI), thereby extracting 128 feature symbols corresponding to each environment  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicular Environment** & **Label** & **Speed Limits** \\ \hline Highway NLOS & 0 & 110 \\ Highway LOS & 1 & 110 \\ Rural LOS & 2 & 70 \\ Urban LOS & 3 & 30 \\ Urban NLOS & 4 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vehicular environment labels and speed limits.  
The sequence feature \(F_{t}\) used to store the CSI can be represented by the following formula:  
\[F_{t}=\left[\left[A(1),A(2),\ldots,A(N)\right]\right] \tag{16}\]  
where \(F_{t}\) is the CSI sample. At the end of this process, the dataset contains CSI samples, with 80% allocated for training data and the remaining 20% used as a validation dataset.
## 3 Results and Discussion
### Characterization of Nanocontainers
#### 3.1.4 X-ray Diffraction Analysis  
The XRD pattern in Figure 4 demonstrates the titanium oxide, cerium oxide and cerium titanium oxide phases that were formatted and consisted of the nanoccontainer's shell. Taking into account that the peaks are very narrow, it can be assumed that the nanocontainers are crystalline. The position of the peaks indicate that they consist of the types: TiO\({}_{2}\) anatase (89-4921, syn), Ti\({}_{0.936}\)O\({}_{2}\) rutile (89-0554, syn), cerium (38-0765), Ce\({}_{11}\)O\({}_{20}\) cerium oxide (89-8435), CeO\({}_{2\text{-x}}\) cerium oxide (49-1415) and Ce\({}_{2}\)TiO\({}_{5}\) cerium titanium oxide (49-1606). Considering the aforementioned types, it may be remarked that the produced nanocontainers consist of cerium titanium oxide phases together with cerium oxide and titanium oxide. This result can be assigned to the different reactivity of the inorganic precursors of TTIP and Ce(NO\({}_{3}\))\({}_{3}\) during their hydrolysis and condensation reactions.
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 3 Proposed Method
### Enhanced Loss Function with Perceptual Loss  
The generator introduces three losses, as shown by Equation (4). Namely, \(L_{adv}\) is the adversarial loss, \(L_{pixel}\) is the pixel loss, \(L_{perceptual}\) is the perceptual loss and \(\mu\) is the weight of the perceptual loss.  
\[L_{all}=L_{adv}+L_{pix}+\mu L_{perceptual} \tag{4}\]  
where \(L_{adv}\) adopts the adversarial loss of GAN, as in Equation (5). Here, \(x\) is the input hazy image, \(P_{data}(x)\) is the dataset of \(x\), \(G\) is the generator and \(D\) is the discriminator:  
\[L_{adv}=E_{x\in P_{data}(x)}[\log(1-D(G(x)))] \tag{5}\]  
The pixel loss was originally used to compute paired image-to-image transfer tasks, but now it is used to compute the L1-Norm between a generated image and a really clear image (ground truth):  
\[L_{pix}=-E_{x\in P_{data}(x),y\in P_{data}(y)}[\|y-G(x)\|_{1}] \tag{6}\]  
where \(y\) is the ground truth and \(P_{data}(y)\) is the dataset of \(y\). However, this only computes the loss of a style transfer between the generated image and the really clear image, which is not sufficient to restore all texture information, because most of the hazy images are very blurred. Therefore, we added a perceptual loss function to maintain the original image structure by extracting high-level and low-level feature information from the second and fifth pool layers of the Visual Geometry Group 16 (VGG16) [27] architecture. The expression of the perceptual loss function is as follows:  
\[\begin{array}{ll}L_{perceptual}(\hat{y},y)&=\frac{1}{C_{i}H_{i}W_{i}}\|\phi_ {i}(\hat{y})-\phi_{i}(y)\|_{2}^{2}\\ &=\frac{1}{C_{2}H_{2}W_{2}}\|\phi_{2}(\hat{y})-\phi_{2}(y)\|_{2}^{2}+\frac{1}{ C_{5}H_{5}W_{5}}\|\phi_{5}(\hat{y})-\phi_{5}(y)\|_{2}^{2}\end{array} \tag{7}\]  
where \((\hat{y},y)\) is a pair of the generated images and the really clear image, \(i\) represents the \(i\)th layer of VGG16 [27], \(\phi_{i}(\hat{y})\) and \(\phi_{i}(y)\) are the feature maps of layer \(i\) of VGG16 induced by the network output and the really clear image and \(C_{i}H_{i}W_{i}\) is the size of the feature map in the \(i\)th layer. In this way, we can compare the image in the feature space instead of the image in the pixel space and use the generated image and the really clear image to reconstruct the features in the two spaces for comparison to maintain a higher definition of the image that is similar to the really clear image.
## 6 Performance and Implementation
### Performance Analysis  
We summarize the computational and the communicational cost of our protocol as follows.  
**Computational cost:** For simplicity, we denote by \(\text{Exp}_{\text{G2}}\) and \(\text{Exp}_{\text{G2}}\) the exponentiations in \(G_{1}\) and \(G_{2}\), by \(\text{Mult}_{\text{G1}}\) and \(\text{Mult}_{\text{G2}}\) the multiplication in \(G_{1}\) and \(G_{2}\), by \(P\) the pairing computation, and by \(H\) the map-to-point hash function, respectively. The original hash function, PRF and PRP operation, addition and multiplication on \(Z_{q}^{*}\), and so on are omitted in our evaluation, because the computational cost of them is negligible. Suppose the data is split into \(n\) blocks. The main computation of the group manager is generating system parameters and partial private key for each group user. Thus, the main computational cost is \(2\text{Exp}_{\text{G1}}+H\). The primary computation of group users is generating tags for data blocks, which is the most expensive operation in our protocol, but fortunately part of it can be done offline. The cost of group users is \((2n+1)\)\(\text{Exp}_{\text{G1}}+nH\). The dominated computation of the TPA is generating a challenge and checking the validity of a proof. We suppose all the group users have generated tags and the challenge involves their corresponding tags. Thus, the cost for the TPA is \(zP+z\text{Exp}_{\text{G2}}+\text{Exp}_{\text{G1}}\) at most for one challenge. When checking a proof, the cost for the TPA is \(2\text{Exp}_{\text{G1}}+cP_{\text{G1}}+cH+(c-1)\text{Exp}_{\text{G2}}\). The main computational cost for the CSP is to generate a proof for a challenge, and the total cost is \((2c-1)\)\(\text{Mult}_{\text{G1}}+c\text{Mult}_{\text{G2}}+c\text{Exp}_{\text{G2}}+c\text{Exp}_{ \text{G1}}+P\). We make a detailed comparison with [PERSON]'s protocol [30] in Table 1.  
**Communicational cost:** In the challenge phase, the TPA summits \(\mathit{chal}=\{Q,T_{1},T_{2}\}\) to the CSP. The CSP responds with a proof to the TPA that is a hash value from \(H_{3}\). Thus, the communicational cost is \(2c\log_{2}\lambda\ +\ (c\ +\ 1)\log_{2}q\ +\ l\).