esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated IR Modulator with a Quantum Cascade Laser
## 5 Laboratory Tests of the IR Modulator  
The preliminary tests of the IR modulator were performed using the lab setup presented in Figure 5. The modulator was placed on the test board to supply all signals (the voltage supply of the switching module, the differential triggering signals, and the biasing of the QC laser). The device was switched using the AFG 3252 model generator (Tektronix, Beaverton, OR, USA). The light pulses were registered with the PVI-4 TE-10.6 (VIGO System S.A., Warsaw, Poland) detection module with a responsivity of \(2.5\times 10^{4}\) V/W and bandwidth of 500 MHz. An oscilloscope of the MSO 6 model (Tektronix) with the active probe (TDP-1500, Tektronix) visualized the voltage biasing of the laser structure. There was no technical possibility of registering current pulses (the laser wire was directly bonded on the driver signal pole) and analyzing their shapes.  
Figure 6 presents some shapes of the registered voltage signals and radiation pulses for different pulse time durations at a frequency of 1 MHz. The voltage signal came -5 ns faster than the light one, and the laser biasing voltage limited its amplitude. It agrees strongly with the data described in [28].  
Some fluctuations caused by impedance mismatching of the signal interface were also observed. The high similarity of the shape of voltage signals obtained during reference measurements using a resistor (Figure 4b) and the QC laser is notable. But these shapes differed from those of the light pulses. A slower rising edge and laser pulse oscillations were noticed.  
Figure 5: View of the testing setup and lasing device placed on the test board irradiating detection module.  
Some virtues of the experimental and simulation results are shared, e.g., inflection in the rising slope, oscillations at the top signal, and some peaks at the falling edge (Figure 7). These effects were not registered for the tests with resistances, indicating that their source is a powered laser structure. This is a new aspect for analyses of QC laser module construction for applied science. Experimental results of laser biasing voltage and light pulse also defined the time delay of \(\sim\)5 ns. This phenomenon was analyzed during modulation simulations.
## 3 Results and Discussion
### Effect of Different Calculation Approaches for Gas Flux Estimation
#### 3.3.2 Relationship of O\({}_{2}\) and CO\({}_{2}\) Fluxes  
O\({}_{2}\) and CO\({}_{2}\) fluxes fell close to the diagonal \(-\)1:1 line (Figure 10), which means O\({}_{2}\) fluxes and CO\({}_{2}\) fluxes were well counterbalanced. Individual data points from winter and spring deviate substantially from the 1:1 line. This can be a result of a failure in the estimation of the O\({}_{2}\) gradients, or can be an actual ongoing process [115]. The ratio of the soil CO\({}_{2}\) efflux to the soil O\({}_{2}\) flux can be addressed as apparent respiratory quotient (ARQ) [119]. ARQ is often close to 1 [115; 120] if transport mechanisms like the export of dissolved CO\({}_{2}\) or other relevant processes such as intensive weathering of minerals in the soil can be ruled out. Studies found ARQ values \(<\) 0.7 in Mediterranean ecosystems [119] and that even up to 2/3 of the soil respiration can be lost via subsurface processes [121]. At the ES site soil respiration seemed to be well counterbalanced by an equimolar O\({}_{2}\) flux so that CO\({}_{2}\) losses due to subsurface processes can be ruled out. Dissolution of soil CO\({}_{2}\) in soils with pH \(>\) 7 can play an important role [9; 119; 83]. Yet, the soil at the ES site is acidic and dissolution and export of soil CO\({}_{2}\) via percolating water plays only a negligible role.  
Figure 10: Relationship of O\({}_{2}\) and CO\({}_{2}\) fluxes at the ES spruce site. CO\({}_{2}\) emission and O\({}_{2}\) consumption fall close to the diagonal 1:\(-\)1 line in most of the cases and were highest in summer (yellow) and lowest in winter (blue).  
Future detailed analysis of soil gas monitoring data will allow studying ARQ to evaluate the relevance of subsurface processes at the different sites. Studying the temporal evolution of ARQ and O\({}_{2}\) concentrations might give new insights into processes at temporarily waterlogged soils which may become anoxic for a certain time [120].
## 4 Experimental Results
### Evaluation Metrics  
The evaluation metrics mean squared error (MSE), root mean squared error (RMSE), and R squared (R2) were used to assess the performance of the predictive models [33]. MSE measures the average squared difference between the predicted values and the actual values, providing an overall indication of model accuracy. RMSE is the square root of the MSE and provides a more interpretable measure in the same units as the target variable. R2, also known as the coefficient of determination, represents the proportion of the variance in the dependent variable that is predictable from the independent variables. It indicates the goodness-of-fit of a model, with higher values indicating better model performance.  
Furthermore, the execution time was employed as an evaluation metric, quantifying the duration required to execute the model inference. It is measured in seconds and provides an indication of the computational efficiency of a model. Table 5 displays the evaluation metrics, accompanied by the equations employed for their computation.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Season** & **Statistical Features** & **Luminance: Living Room** & **Luminance: Double Room** \\ \hline \multirow{6}{*}{Fall} & Mean & 278.541 & 318.84 \\ \cline{2-4}  & Std. deviation & 310.54 & 369.566 \\ \cline{2-4}  & Min. & 0 & 0 \\ \cline{2-4}  & Max. & 658.56 & 786.45 \\ \cline{2-4}  & Start date & 09/01 & 09/01 \\ \cline{2-4}  & End date & 11/30 & 11/30 \\ \hline \multirow{6}{*}{Winter} & Mean & 12.22 & 11.73 \\ \cline{2-4}  & Std. deviation & 20.45 & 21.40 \\ \cline{2-4}  & Min. & 0 & 0 \\ \cline{2-4}  & Max. & 378.04 & 453.45 \\ \cline{2-4}  & Start date & 12/01 & 12/01 \\ \cline{2-4}  & End date & 02/28 & 02/28 \\ \hline \multirow{6}{*}{Spring} & Mean & 350.45 & 340.45 \\ \cline{2-4}  & Std. deviation & 217.54 & 210.51 \\ \cline{2-4}  & Min. & 0 & 0 \\ \cline{2-4}  & Max. & 900.46 & 867.64 \\ \cline{2-4}  & Start date & 03/01 & 03/01 \\ \cline{2-4}  & End date & 05/31 & 05/31 \\ \hline \multirow{6}{*}{Summer} & Mean & 335.12 & 370.80 \\ \cline{2-4}  & Std. deviation & 284.55 & 336.90 \\ \cline{1-1} \cline{2-4}  & Min. & 0 & 0 \\ \cline{1-1} \cline{2-4}  & Max. & 938.22 & 955.73 \\ \cline{1-1} \cline{2-4}  & Start date & 06/01 & 06/01 \\ \cline{1-1} \cline{2-4}  & End date & 09/31 & 09/31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset statistics.
## 4 Proposed HF-Based Security Architecture
### Overview of an IoMT-Based Health Monitoring Systems  
In this Section, we present the architecture of an IoMT-based health monitoring system. Typically, the architecture of an IoT-based health monitoring system is divided into three domains: (1) perception domain; (2) network domain; and (3) cloud domain, as shown in Figure 2.  
(a) Perception Domain: It is possible to understand this as the device layer in the ITU-T standard model [40] and as the IoMT edge network of the IoMT-based health monitoring system. The perception domain interacts with objects (e.g., physical things) via the IoMT devices (e.g., sensors, actuators, etc.) that are located on the IoMT edge network. The primary function of the perception domain is to connect things to the IoMT edge network. It is also responsible for measuring, gathering, and managing the information that is provided by these devices via IoT devices transmitting the information that has been gathered to higher domains through domain interfaces. Finally, the perception domain is home to the biosensors that are in charge of gathering the user/patient's vital signs as well as the context-ware sensors that are in charge of gathering context information from the user/patient's surrounding environment (e.g., air pressure, humidity, sound, etc.).  
(b) Network Domain: This is the transmission domain, and in the IoMT-based health monitoring system architecture described above, it is implemented as the middle domain. The function of the network domain is to collect the data that has been obtained by the perception domain and to establish the routes that will be taken in order to send the data  
Figure 2: IoMT-based health monitoring system.  
that has been received to the Cloud domain via integrated networks. This particular sphere incorporates a wide range of tools and methods of communication, including Wi-Fi, 4G/5G, and the Internet, amongst others. In addition, it is the responsibility of the network domain to supply the relevant data services, such as data aggregation and fog computing.  
(c) Cloud Domain: This then makes use of the data it has received from the network domain in order to give relevant cloud-based services or operations to the user/patient, to healthcare professionals, or to other authorized individuals (e.g., authorized relatives of the user/patient). For instance, the provided cloud-based services may include services such as the monitoring and assessment service of the user/patient's health status on the side of the healthcare professionals, the storage service where the received data are stored to databases in the cloud domain, and the analysis service to evaluate received data in order to provide predictions about the future state of sensing devices at the perception domain.
## 5 Discussion
### Application Example of a CICS-SMM Controller  
In application example 2, a SICS-SMM controller controls a working cell, consisting of two processing and test stations, and a loading robot. Figure 16 shows the technological structure for this example.  
Two SICS controller instances control the two processing stations. Another SICS instance is responsible for coordinating the robot with the two stations. The connection of the respective SICS router instances to the devices of the two stations takes place via a universal gateway as a web connector [18]. The Modbus TCP interface of the robot is connected to the Internet via WebSocket using a device gateway, realized by means of Node-RED. The application was successfully presented at exhibition SPS/IPC/Drives 2016 in Nuernberg (Germany).  
In contrast to example 1, the application example with the SICS SMM controller can be configured completely in the IoT platform. Additional administration tools are not required.  
Using the clearing portal described above, the runtime of the control instances for the operation of the system can be fine-tuned analyzed and billed.  
The possible realization of the further requirements, which are necessary for future industrial 4.0 solutions, was also demonstrated with the application examples.  
* Orchestration of heterogeneous industrial control systems: Easy connection with robot control, proprietary I/O modules and servo drive from different manufacturers.
* Fast reconfiguration and agility (plug and work): The activation of completely new control programs and the I/O configuration is possible in a few seconds on the fly. Studies on synchronization and \"safe state\" issues are still pending.
* Remote control and improved manufacturer service: Remote control is an integral part. All data are already available in the cloud for diagnostic purposes without additional effort.
* Simulation support for planning and process optimization: Simulation and visualization models of the systems can be easily connected via the cloud.
* Application of smartphone and tablet PCs: Through browser technology and responsive design, all devices are ready to be used for operation.
# Side-Channel Power Analysis Based on SA-SVM
## 2 Materials and Methods
### Model Evaluation  
The accuracy and guessing entropy [34] of the model were used in this study to evaluate its performance.  
Accuracy (Acc) is the proportion of properly classified predicted results to the total samples. The probability vector is the probability of the SVM output categorization. We describe the probability vector of power traces Q as \(\textbf{prob}_{i}=\left[\mathbb{P}_{i,c_{1}},\mathbb{P}_{i,c_{2}},\dots, \mathbb{P}_{i,c_{z}}\right]\), \(i=1,2,\dots,Q\). \(\widehat{H(k^{*})}\), which is the predicted classification and is given by  
\[\widehat{H(k^{*})}=\underset{\{c_{1},\dots,c_{z}\}}{\text{arg}\,max}\,\textbf {prob}_{i} \tag{16}\]  
Figure 2: Flowchart of SA.  
Accuracy is defined as follows:  
\[Acc=\frac{1}{\mathcal{Q}}\sum_{i=1}^{\mathcal{Q}}\begin{cases}0\widehat{H(k^{*})} \
eq H(k^{*})\\ 1\widehat{H(k^{*})}=H(k^{*})\end{cases} \tag{17}\]  
Guessing entropy (GE) is used to determine how many power traces are required to decrypt the key. We define the vector of each guessing key \(k\) as follows:  
\[\mathbf{g}_{k}=\log\prod_{i=1}^{N_{k}}\underbrace{\mathbf{prob}_{i}}_{\{c_{1},\dots,c_{\mathcal{C}}\}}=\sum_{i=1}^{N_{k}}\log\underbrace{\mathbf{prob}_{i}} _{\{c_{1},\dots,c_{\mathcal{C}}\}} \tag{18}\]  
where \(k=1,\ 2,\ \dots,\ K\) denotes the key space size, and \(N_{k}\) is the number of power traces corresponding to the guessing key. \(\mathbf{g}_{k}\) is used in descending order to obtain \(G=[\mathbf{g}[1],\mathbf{g}[2],\cdots,\mathbf{g}[K]]\). \(\mathbf{g}[1]\) is the candidate key with the greatest probability, and \(\mathbf{g}[K]\) is the candidate key with the lowest probability. When the candidate key of \(\mathbf{g}[1]\) is the real key, the guessing entropy is considered to be zero. The fewer the necessary power traces for the recovery of a single key byte, the better the model's performance.
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## 3 System Performance Analysis
### Probability of SND in SPNC Protocol  
We need to analyze the probability of SND for the system. The probability of SND in SPNC protocol is written as:  
\[P_{snd}=\int_{z^{snd}}f(h,g)\mathrm{d}h\mathrm{d}g \tag{31}\]  
Substituting Equation (8) into (31), we can obtain the exact expressions of average end-to-end throughput for SPNC protocol. Furthermore, we can obtain the numerical results with the help of Mathematica.
## 1 Introduction  
The jet formation process of shaped charges, including both linear and conical, has been a major research area for over a half century and has been aimed at the increase in penetration capability for many different applications. However, due to a few numbers of applications for military purposes and a multi-dimensional complex geometry, a comparatively small amount of study has been done in the field of linear shaped charges.  
There have been few publications about the motion of the LSC liner since the initiation of the Birkhoff theory [1] including the development of a numerical simulation code, LESCA, specifically designed for LSCs applications which involve a combination of numerical approaches and a series of analytical approximations [2]. Traditionally, the jet formation process of LSCs has been assumed to be the same process as that of CSCs based on the Birkhoff theory. The jet is created by a gradual collapse of the liner caused by the detonation of a high explosive charge. The jet is then elongated due to the velocity gradient between the tip and base of the jet [3]. However, in case of the LSC, the initiation point is not along the top of the liner as in the case of a CSC. Due to this, it is anticipated that the jet formation process of a LSC is somewhat different from that of a CSC. The laterally long high explosive charge along the length of LSC has to be initiated from one end in common practices, creating a gradual flat liner collapse in a side direction [4,5]. Because a LSC liner consists of two flat metallic components instead of a curved liner as in CSCs, different jetting properties during penetration would be present and a more detailed investigation regarding the jet formation or behavior is necessary to understand the penetration performance of LSCs.  
The main objective in this series of preliminary simulations is to identify both the jetting behavior during the detonation of a LSC and specific flight formation of the jet before impacting a target. During the course of the project, a preliminary computational study of LSC jet impacts on a mild steel target is investigated and the impact formation to the target is identified as well. The impact configurations are changed by varying the standoff distance, and a series of hydrocode simulations using AUTODYN(r) is completed to validate the results.
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 4 Energy Efficiency Optimization  
### Problem Formulation  
It can be seen from (12) that the EE is a function w.r.t. the number of antennas, the total transmit power, and the power allocation coefficients. In this section, we aim to determine the optimal system parameters \(N\), \(P\), and \(\theta\). The optimization problem can be formulated as  
\[\begin{split}\mathcal{P}_{1}:&\max_{N,P,\theta}& \tilde{\eta}_{\text{EE}}\\ & s.t.& N>N_{\text{th}}\\ & P>0\\ & 0\preceq\theta\preceq 1\\ &\theta_{1}+\cdots+\theta_{k}+\cdots+\theta_{K}\leq 1,\end{split} \tag{16}\]  
where \(N_{\text{th}}\) is a predefined threshold of the massive MIMO system. The power allocation vector \(\theta\) contains \(K\) dependent variables. Thus, the exhaustive search has exponential complexity w.r.t. the number of UEs, which leads to computational infeasibility. In the following subsections, we aim to propose an optimal algorithm with low complexity to solve the NP-hard non-convex problem.
## 3 Results and Discussion
### Analysis of Biological Samples  
The method was applied to simulated body fluid in order to assess potential matrix effects and identify the appropriate sample pre-treatment conditions. Using simulated blood plasma (for details, see the Experimental section), we investigated the reduction of oxidized thiols (i.e., cystine), as the major biothiol species in biofluids, and the conditions for precipitating proteins, which carry a significant amount of disulfide bonds and hence may produce false-positive results.  
The reduction of biothiols with TCEP was optimized through trial-and-error tests in simulated blood plasma (SBP) solutions containing 250 uM of cystine, 250 uM of cysteine and no cysteine as control. We found that the TCEP concentration in the final solution that is used for analysis should be <0.05 mM. At higher concentrations, a positive interference was observed because an excess of residual (unreacted) TCEP could reduce the gold ions to metal gold. Therefore, the reduction of cystine to cysteine was performed with 1.0 mM of TCEP as reducing agent for 30 min before protein removal. The final sample was then diluted as appropriate to adjust the concentration of TCEP in the final extract below the tolerance limit.  
Despite the lack of interference observed in the selectivity study, the application of the method to the simulated body fluids showed recoveries that varied from 67 to 76% in simulated urine and 94 to 123% in simulated blood plasma (Table 3).  
Student's _t_-test analysis in the recovery values revealed the presence of matrix effects in the urine samples but not in the blood plasma (i.e., the calculated _t_-value in the simulated urine and simulated blood plasma was higher and lower than the critical _t_-value at the \(p\) = 0.05 probability level, respectively). Since no AuNP formation was observed (i.e., no absorbance peak at 500-540 nm appeared), this interference was not caused by the matrix components present in the real samples that could reduce gold ions. Therefore, to mitigate potential matrix effects, the method of standard additions was also examined for analysis of biological samples. The spiking levels were 2, 4 and 10 times higher than the expected concentration of the analyte, which ensured good linearity and a zero intercept (i.e., the confidence internal of the intercept b \(\pm\) t\({}_{\text{sb}}\), passed through zero) [41]. The recovery of Cys in the biological samples ranged from 103.8 to 115%, which offered an improvement compared to the direct application of external calibration (the calculated t-value was lower than _t_-critical at \(p\) = 0.05).
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 4 Conclusions  
This paper emulates and compares the RF-impairments of the OTFS waveform to OFDM impairments using SDR. The experiments were conducted in a real indoor wireless environment, where the metallic structure of the building introduced enough multipath, the Doppler shift was induced by the stirrer, and the impairments were either inherently produced inside the devices or added before transmission. The BER performance of the OTFS modulation was superior to that of the OFDM under doubly dispersive channel. The CCDF of the PAPR of OTFS is shown to vary with the lattice structure in the delay-Doppler domain, and under the \(M>N\) condition, OTFS provides a better PAPR compared to OFDM. In addition, the I/Q-imbalance and DC-Offset impairments were explored, and the results showed that OTFS and OFDM are impacted in a manner that is approximately identical to one another. In addition, phase noise mitigation and OTFS give a higher level of phase noise resistance in comparison to OFDM. These findings provide an understanding of how and when to choose the waveform that is best appropriate for the characteristics of a particular channel.  
In future work, the practical pulse shaping strategies should be taken into consideration, and methods should be provided to minimize the impacts of each RF impairment on the OTFS waveform. Furthermore, channel estimation and coding overhead, complexity, and performance gain are critical issues that need to be taken into consideration with a detailed analysis for different waveforms.  
Conceptualization, A.A., S.E.Z., A.Y. and H.A.; formal analysis, A.A. and S.E.Z.; methodology, A.A., S.E.Z., A.Y. and H.A.; resources, H.A.; software, A.A. and S.E.Z.; writing--original draft preparation, A.A.; writing--review and editing, A.A., S.E.Z., A.Y. and H.A. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 120C142.  
**Informed Consent Statement**: Not applicable.  
Figure 16: Comparison of the effect of the phase noise on the BER performance for both OTFS waveform with \(M=32,N=32\) and OFDM waveform with \(N=1024\) at 2.4 GHz carrier frequency.  
Data Availability Statement: Not applicable.
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Mode Selection (MS)  
MS methods to determine the best mode for each user are necessary due to the availability of multiple communication modes, as illustrated in Figure 7. To achieve the best connection of cellular and D2D networks, MS methods have been proposed. However, current MS methods cannot implement connections from opportunistic cellular networks into the selection method. We covered the most recent papers in MS methods and presented a summary of these studies in Table 4.