esa-sceva/satcom-chunk-collection huggingface dataset:
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 2 Serum and Plasma Metabolome as a \"Snapshot\" of a Human Biochemistry
Plasma and serum metabolites are comparable in \"width\": the average number of unique metabolites identified in plasma and serum routinely using GC-TOF-MS equipment was 178 and 170, respectively, and 80% of this metabolic pool was present in both types of biomaterial [38]. Notably, deltas between the signal intensities of several metabolites in serum and plasma persisted. This observation means that plasma and serum offer similar analytical opportunities. Moreover, the experiments performed on plasma can be compared with those on serum and vice versa, which may facilitate the metabolic comparison of samples obtained using different protocols. Nevertheless, the choice of the sample may significantly affect the results of experiments for several molecules of interest. Therefore features of different routinely applied protocols should be considered when comparing plasma and serum tests [39; 40; 41; 42]. For example, the profiles of amino acids in plasma and serum differ because several amino acids are prone to conversion during clotting at room temperature (e.g., asparagine converts to aspartic acid and glutamine converts to glutamic acid) [43]. On the other hand, the serum is a more \"metabolite-rich\" matrix: the concentration of most metabolites in it is generally higher [42]. For example, the platelet activity marker thromboxane B2 (TXB2) concentration in plasma is 0.2-2 ng/mL, and in serum, it is already 2-178 ng/mL [44]. The volume displacement effect can partially explain this phenomenon. It is assumed that after serum deproteinization, the remaining low molecular weight components are distributed in a smaller volume, which increases their concentrations. Perhaps this was the reason for the identification of more potential biomarkers in serum than in plasma in the study of various phenotypes [45]. It is, in any case, evident that there are still more similarities between plasma and serum than differences, and it is impossible to make a clear choice in favor of one or another liquid. Fortunately, there is no need to do this since both plasma and serum are excellent matrices for metabolomic studies with broad applicability.
## 6 Experimental Study
### Network Types  
The networks used during the experiments were power-law and random networks, created with the Barabasi-Albert (_BA_) [14] and Erdos-Renyi (_ER_) [15] procedures, respectively, having mean degree and initial (joining) degree of \(0.05n\) and \(0.05n\), respectively (where \(n\) is the network size). These networks were selected for their different node degree distributions: in power-law networks, the vast majority of nodes has a very low degree, and few nodes have a very high degree (power-law distribution), while in ER random networks, all nodes have comparable degrees (binomial distribution). In fact, high degree nodes in BA networks are connected mostly with many low degree ones, which leads to the creation of very different neighborhoods around individual nodes for these two network types. This difference, coupled with the AdaptiveLID algorithm's ability to perform local repairing operations, leads to the varying behaviors that can be seen in the experiments below.  
On the other hand, nodes of both network types had full preference lists (consisting of all their neighbors), but ranked uniformly at random and with a desired number of connections equal to half their own degree. Focus was given on random preference lists, since previous research [4, 5] showed that: (a) a strict matching solution cannot always be found when they are used; and (b) the measured satisfactionof unconverged instances can be relatively low. These characteristics make random preferences a challenging test case to evaluate the performance of the algorithms.
# A Self-Testing Platform with a Foreground Digital Calibration Technique for SAR ADCs
## 2 Proposed Self-Test Platform
### Processing Flow of the Proposed Self-Testing Platform  
Figure 12 shows the processing flow of the self-testing platform method. According to the block diagram, it starts from a sine wave generator with a frequency condition setting, and then a 12 bit DAC output signal is fed into the target SAR ADC. Next, the RDFT processor is used to detect the third harmonic tone and the proposed calibration formula is employed to calculate the error factor. Finally, we can use this error factor to compensate for the ADC error using the re-coding method.
## 4 Methodology
Repeat the steps 1, 2 and 3 to get another copy of \(\left|uv\right\rangle\) because \(M_{z}\) operator needs two copies to quantify the degree of concurrence in between (see Section 4). Remark: this step does not violate the non-cloning theorem [60] because when steps 1, 2 and 3 are repeated, a new different system is initialized in the first step and when the second and third steps are applied then a new copy of \(\left|uv\right\rangle\) is created, independently, without cloning the original state. 5. Apply the operator \(M_{z}\), shown in Figure 4, on the two copies of the state \(\left|uv\right\rangle\) and quantify the probability of the state \(\left|0000\right\rangle\), \(\left|0100\right\rangle\), \(\left|1010\right\rangle\) or \(\left|1110\right\rangle\) and use Equation (7) to quantify the concurrence value \(C\). if \(C=0\) then \(\text{inp}\in\text{class label \"0\"}\). Else if \(C>0\) then \(\text{inp}\in\text{class label \"1\"}\). ] End. ```  
**Algorithm 1** The proposed Quantum Classification Algorithm based on Competitive Learning and Entanglement Measure (QCPNN). In step 4, steps 1, 2 and 3 are repeated to generate a duplicate of the two-qubit system \(\left|uv\right\rangle\) that does not violate the non-cloning theorem [60], because \(M_{z}\) operator requires two copies to quantify the degree of concurrence between the qubits \(\left|u\right\rangle\) and \(\left|v\right\rangle\) as explained in Section 4. Finally, in step 5, the \(M_{z}\) operator is applied on the two copies of \(\left|uv\right\rangle\) to test the presence of entanglement between \(\left|u\right\rangle\) and \(\left|v\right\rangle\) by estimating the probability of the state \(\left|0000\right\rangle\), \(\left|0100\right\rangle\), \(\left|1010\right\rangle\) or \(\left|1110\right\rangle\) and use Equation (7) to quantify the concurrence value \(C\). If the concurrence is \(C=0\), then the winning class is \"0\". On the other hand, if the value of the concurrence is \(C>0\), then the label of the winning class is \"1\".
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 4 Data Analysis
### Radiation Performance
Also in this scenario, Boeing-1 ADC clearly outperforms the rest of the _selection_: its \(\text{FOM}_{SET}\) (\(3.37\times 10^{7}\) levels/mg) is around two orders of magnitude above the second best value: \(5\times 10^{5}\) levels/mg obtained by AD9246S. In a closer look to these two best values, both have similar \(\text{FOM}_{W}\) but the radiation performance of Boeing-1 ADC is clearly better than those of AD9246S (\(\text{LET}_{th}\): \(30.9>10\) MeV\(\cdot\)cm\({}^{2}\)/mg, \(\sigma_{sat}\): \(1.3\times 10^{-7}<2.5\times 10^{-6}\) cm\({}^{2}\)/device). Considering  
Figure 7: \(\text{FOM}_{TID}\) against power consumption. only upsets, the best \(\text{FOM}_{SEU}\) is obtained by ADC128S102 QML-SP with \(2.7\times 10^{4}\) levels/mg; its good performance is not only due to a good \(\text{FOM}_{W}\) (second best with 831 f/level), but also to a reduced \(\sigma_{sat}\) (\(4.14\times 10^{-5}\) cm\({}^{2}\)/device). Since Boeing-1 has both the best \(\text{FOM}_{TID}\) and the best \(\text{FOM}_{SET}\) (both several times better than the second classified), we can conclude that it is the most power efficient randenv-ADC of the _selection_.
# A Compact Monopole Wideband Antenna Based on DGS
## 2 Antenna Design Methodology and Optimization Process
### Parametric Analysis
#### 2.5.3 Effect of Spacing of L-Slot Horizontal (S)  
Changing S to 12.13, 15.13, and 17.13 mm achieved maximum bandwidth and improved impedance matching at S = 17.13 mm. The simulated S\({}_{11}\) for various S values is shown in Figure 7c. Smaller spacings led to decreased bandwidth.
## 1 Introduction
For a 5G NR FR1 256-quadrature amplitude modulation (QAM) 100 MHz bandwidth test signal, the average power output for the split design PA was 31.8 dBm, and for the stack design PA, it was slightly lower at 31.5 dBm. The experimental findings are summarized and then compared with the results obtained from studies that have been previously published.
## 4 Clinical Experimental and Validation
### Results
#### 4.5.2 Characteristics of Patients' Movement  
As shown in Table 2, the independent-samples \(t\) test showed that there were significant differences of interactive force, movement deviation, movement range and movement smoothness between stroke patients and healthy subjects.  
(1) Movement velocity and acceleration  
The changes in the movement velocity and acceleration curves represent the participant's ability to control movement stability. The movement velocity fluctuation curves and acceleration curves of patients and healthy subjects are shown in Figures 9 and 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Healthy Subjects** & **Stroke Patients** & \multirow{2}{*}{\(\mathbf{p}\)} \\  & **(\(\mathbf{M\pm STD}\))** & **(\(\mathbf{M\pm STD}\))** & \\ \hline Interactive force & \(8.94\pm 2.10\) & \(6.18\pm 2.77*\) & 0.031 \\ Movement velocity & \(41.51\pm 8.85\) & \(40.62\pm 19.17\) & 0.901 \\ Movement deviation & \(2.19\pm 0.46\) & \(5.48\pm 2.39*\) & 0.003 \\ Movement range & \(0.98\pm 0.01\) & \(0.95\pm 0.03*\) & 0.016 \\ Movement smoothness & \(0.60\pm 0.05\) & \(0.49\pm 0.11*\) & 0.018 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative parameters during circular trajectory tracking movement.  
Figure 8: The characteristic parameters of movement in five different stiffness parameters: (**a**) interactive force of patients in five different stiffness parameters; (**b**) movement deviation of patients in five different stiffness parameters; (**c**) movement smoothness of patients in five different stiffness parameters; (**d**) movement velocity of patients in five different stiffness parameters. * \(p<0.05\).  
patients was greater than that of the healthy subjects: the mean trajectory deviation of the hemiplegia patients was 5.48 mm and the standard deviation was 2.26 mm during the circular trajectory. The mean deviation and standard deviation of healthy subjects were 2.19 mm and 0.44 mm. Most of the deviations and variances of the patients' movement trajectory reflect the weak ability to control the accuracy of movement.
###### Abstract  
This paper presents the results of the application of four different tools (tourist card, questionarie, GPS and NFC) with the objective to study the movement of tourists in a tourist historic city (Popayan, Colombia). Given the need for these types of cities to manage tourism in a sustainable way, and considering that the management of tourist flows is a key aspect to achieve this, the aim was to find out which of the tools applied provides more precise data on the movement of tourists in the destination. For this, information was collected on the movement of tourists with four different tools, applying each tool in four different years (2011, 2012, 2013 and 2015) during the same time period (Holy Week). For the analysis of tourist movements, the Markov chain was obtained for each period. In order to study the generation of routes geo-location was used in each case. The results show that even though GPS technology provided more information on the visited places, NFC technology facilitates more extensive information. In addition, NFC technology allowed the extraction of important information about the places visited, showing a wide number of sites visited and, therefore, providing greater value for the study. Finally, the results of the study provide a better understanding of how destination management organizations could develop more suitable alternatives of the customer services systems, the delivery of tourist information and the identification of sites with heavy use. Conclusively, this study helps to identify how to take better advantage of the marketing strategies through different tools that analyses tourism movements.  
tourist movement; GPS; NFC; tourist card; questionarie; tourism destination; historic city 2019 11 5265 10.3390/sul11195265
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 6 Conclusions  
This paper presents the Mobility-Aware Client Selection (MACS) strategy to enhance federated learning (FL) in dynamic IoT environments. MACS improves client selection by integrating mobility prediction and resource-aware evaluation, ensuring stable participation under varying network and computational conditions. Simulation results demonstrate that MACS outperforms Random selection, Static selection, Reinforcement Learning-based FL (RL-based), and Deep Learning-based FL (DL-based) in key metrics such as selected clients, network stability, and reduced latency. While MACS significantly enhances selection efficiency, the introduction of mobility prediction adds some computational overhead. This overhead may need optimization, especially for low-power IoT devices. Maintaining stable client participation remains particularly challenging in high-mobility environments like vehicular networks. To address these challenges, shorter prediction intervals or hierarchical selection strategies could improve performance in such scenarios. Future work will explore integrating deep learning models, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), and Transformers, to refine mobility prediction by analyzing sequential patterns. Additionally, Federated Reinforcement Learning methods, such as Deep Q-Networks (DQN) and Actor-Critic models, could be considered to refine further client selection strategies based on real-time conditions. These advancements would enhance MACS's adaptability while keeping its computational demands practical for IoT deployments. An interesting direction for future research is the integration of lightweight reinforcement learning techniques with MACS. By integrating the strengths of both approaches, it is possible to achieve more efficient and adaptive client selection strategies.  
This combination could improve performance while keeping computational requirements manageable, making it more suitable for resource-constrained IoT devices. MACS offers a robust solution for improving federated learning in dynamic IoT environments. While there are challenges related to computational overhead and high-mobility scenarios, potential optimizations and enhancements promise to make MACS an even more effective tool for client selection in federated learning applications.  
The author expresses her sincere gratitude and appreciation to Onaizah Colleges, Saudi Arabia, for supporting this research.  
The data presented in this study are available upon reasonable request from the corresponding author.  
The author declares no conflict of interest.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## 2 Proposed System Model and Techniques
### Transceiver's Components Linearization
#### 2.1.1 IQ Mixer Linearization
\begin{array}{cc }G_{1,RX}&0\\ 0&G^{*}{}_{1,RX}\end{array} \right]\]  
Here: The first one is the leakage matrix, and the second one is the scaling matrix. The leakage matrix is subjected to IQ mixer imbalance compensation and estimation, whereas the scaling matrix can be cancelled through channel estimation, and \(\hat{G}_{q}=\frac{G_{2,RX}}{G^{*}{}_{1,RX}}\). OFDM symbols can be reconstructed by using the matrix inversion property, as in realistic case, \"G\" can never be singular for imbalance OFDM symbols. Here the principal or critical point is gaining knowledge about mixing matrix \"G\". For the estimation of the \"G\" matrix and compensation, two probability statistics are used for parameter calculation: (i) cross-correlation b/w OFDM symbols \"\(E\left\{O_{m}(n)O_{-m}(n)\right\}\)\", and (ii) power of sum of OFDM symbols \"\(E\left\{\left|O_{m}(n)+O^{*}{}_{-m}(n)\right|^{2}\right\}\)\". Here an assumption is made. _Assumption:_  
\(E\left\{\hat{y}_{IQ,RX}(n)\hat{g}_{-IQ,RX}(n)\right\}=0,\) _i.e., uncorrelated OFDM symbols with zero_  
_Further:_  
\(G_{1,RX}+G^{*}{}_{2,RX}=G_{2,RX}+G^{*}{}_{1,RX}=1\)IQ imbalance parameter estimation through BA algorithm can be expressed as:  
\[G_{1,RX}G_{2,RX}=\frac{\sum_{mem}\sum_{ne_{1}}E\{O_{m}(n)O_{-m}(n)\}}{\sum_{mem }\sum_{ne_{1}}E\{|O_{m}(n)+O^{*}_{-m}(n)|^{2}\}} \tag{4}\]  
And for \(\hat{G}_{q}\) calculation:  
\[\hat{G}_{q}=p_{q}\left(\frac{\sum_{mem}\sum_{ne_{1}}E\{O_{m}(n)O_{-m}(n)\}}{ \sum_{mem}\sum_{ne_{1}}E\{|O_{m}(n)+O^{*}_{-m}(n)|^{2}\}}\right) \tag{5}\]  
where,  
_np: Non-negative Subcarrier indices_  
_np: sample time indices_  
New estimated gain \(\hat{g}_{RX}\) and phase \(\hat{\phi}_{TX}\) can be defined as:  
\[\hat{g}_{RX}=\sqrt{1-4 Re\{G_{1,RX}G_{2,RX}\}}\]  
and:  
\[\hat{\phi}_{TX}=sin^{-1}\bigg{\{}\frac{-2}{\hat{g}_{RX}}Im\{G_{1,RX}G_{2,RX} \}\bigg{\}}\]  
Now the compensation matrix can be written as:  
\[\widetilde{G}^{-1}=\frac{1}{Adj\widetilde{G}}|\widetilde{G}|=\frac{1}{\left| \widetilde{G}_{1,RX}\right|^{2}-\left|\widetilde{G}_{2,RX}\right|^{2}} \Bigg{[} \begin{array}{cc}\widetilde{G}^{*}_{1,RX}&-\widetilde{G}_{2,RX}\\ -\widetilde{G}^{*}_{2,RX}&\widetilde{G}_{1,RX}\end{array} \Bigg{]}\]  
Compensated OFDM symbols:  
\[\left[ \begin{array}{c}\widetilde{y}_{IQ,RX}(n)\\ \widetilde{y}^{*}_{-IQ,RX}(n)\end{array} \right]=\widetilde{G}^{-1}\widetilde {G}\left[ \begin{array}{c}\hat{y}_{IQ,RX}(n)\\ \hat{y}^{*}_{-IQ,RX}(n)\end{array} \right], \tag{6}\]  
The imbalance between the I and Q branches of Tx and Rx chain mixer increases with an increase in carrier frequency. A higher constellation is required for achieving high data rates and high operating SNR. To achieve all the above, a high complex analog domain circuit is needed. Hence, to eliminate the IQ imbalance in wireless transceivers with low complexity, digital compensation techniques are developed.
## 2 Small-Scale Model Test
### Layout of Optical Fiber Sensor  
The layout and protection of the optical fiber sensor is important for data acquisition and reliability. The surface of the concrete is rough; therefore, the optical fiber cannot be adhered to directly and tightly. During the process of burying and loading of pile, the optical fiber suffers the friction from surrounding soil, which may cause it to break off. A feasible method of material packaging and protection was developed after several attempts (see Figure 5). The main steps are introduced in detail, as follows.  
A line was drawn along the axis of the pile, and the notch was cut along the line at a depth of 2-3 mm. The notch protected the optical fiber from breaking off due to friction and collisions during the process of backfilling soil and loading. The notch was cleaned with a brush and blower, because dust and particles in the notch might cause damage. After cleaning, the optical fiber was laid and gently stretched; it was fixed in the notch at fixing several points with a quick drying adhesive, similar to glue 502. A thin layer of epoxy resin was smeared into the notch to make the surface smooth; complete solidification of the epoxy resin takes 24 h. The surface of the epoxy resin was ground to reduce the friction between the resin and soil during the process of loading. The optical fiber left exposed was protected by a special protective sheath of 0.9 mm in diameter. The optical fiber was laid along the pile in a U shape. The advantage of this was that data can be obtained from both ends of the optical fiber. In case one end is damaged during the experiment, the data can be obtained from the other; the symmetrical layout of optical fiber allowed us to check whether the load is applied with eccentricity or not.
## 5 Conclusions  
In this research, the low-speed stability of a scooter-type motorcycle is studied using theoretical and experimental methods. The regions for the stability of the motorcycle are determined from the theoretical method. Subsequently, experiments are conducted on a straight path with professional riders at speeds 3, 5 and 10 km/h. A detailed analysis of experimental data is performed using statistical methods. Maximum correlation coefficient and lead time between input and output parameters are calculated and compared to identify the important output parameters to achieve the stability of the motorcycle at low speeds. Further, the appropriate input parameter is identified from a linear multiple regression analysis between the identified output parameters and the input parameters. Following conclusions are drawn from the study:  
1. The experimental results validate that the theoretical method of analysis can be used to find the regions of low-speed stability of a motorcycle.
2. The roll angle and roll rate are important output parameters to be assessed to achieve low-speed stability.
3. The steering angle is the appropriate input parameter over steering torque for low-speed stability because the regression correlation for the steering angle is significantly stronger than the steering torque, with the roll angle and the roll rate.  
In future, the results obtained from this research are to be used for developing a controller that can enable a motorcycle in attaining the low-speed stability, which is to be validated by simulations and experiments. Also, the theoretical model is to be improved by including other forces and parameters such as overturning moments, tire thickness and pitch motion of the motorcycle.  
Conceptualization, S.S., I.K. and V.M.K.; methodology, S.S. and I.K.; validation, S.S. and I.K.; formal analysis, S.S. and I.K.; writing--original draft preparation, S.S.; writing--review and editing, S.S., I.K. and V.M.K.; supervision, I.K. and V.M.K.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
MCC Maximum Correlation Coefficient  
MRA Multiple Regression Analysis  
avg Average  
SD Standard Deviation
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
2D & Two-Dimensional \\
3D & Three-Dimensional \\
5G & Fifth Generation \\
6G & Sixth Generation \\ ABER & Average BER \\ ADC & Analog-to-Digital Converter \\ ADT & Adaptive Detection Threshold \\ AF & Amplify-and-Forward \\ AGC & Automatic Gain Control \\ AI & Artificial Intelligence \\ AMC & Adaptive Modulation and Coding \\ ANN & Artificial Neural Network \\ AO & Adaptive Optics \\ APDs & Avalanche PDs \\ APSK & Amplitude PSK \\ ASC & Average Secrecy Capacity \\ ASK & Amplitude Shift Keying \\ AT & Atmospheric Turbulence \\ AWGN & Additive White Gaussian Noise \\ BA & Buffer-Aided \\ BER & Bit-Error Rate \\ BF & Buffer-Free \\ BICM & Bit-Interleaved CM \\ BPOLSK & Binary POLSK \\ BPSK & Binary PSK \\ BSI & Buffer State Information \\ CNNs & Convolutional Neural Networks \\ CPAT & Coarse PAT \\ CSI & Channel State Information \\ CSPR & Carrier-to-Signal Power Ratio \\ DAC & Digital-to-Analog Converter \\ DD & Direct Detection \\ DF & Decode-and-Forward \\ DFB & Distributed Feedback \\ DL & Deep Learning \\ DM & Deformable Mirror \\ DMT & Discrete Multi-Tone \\ DNN & Deep Neural Network \\ DoF & Degrees of Freedom \\ DPSK & Differential PSK \\ DQPSK & Differential Quadrature PSK \\ DRE & Digital Resolution Enhancer \\ DRL & Deep Reinforcement Learning \\ DSP & Digital Signal Processing \\ e2e & End-to-End \\ ECC & Error Correction Code \\ EGC & Equal Gain Combining \\ EW & Exponentated Weibull \\ FDT & Fixed Detection Threshold \\ FEC & Forward Error Correction \\ FER & Frame Error Rate \\ FFT & Fast Fourier Transform \\ FoV & Field-of-View \\ FP & Fabry-Perot \\ \end{tabular}