esa-sceva/satcom-chunk-collection huggingface dataset:
# A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
## 4 System Measurement
### Transmitter Power  
The transmitter power is measured by using a single-tone quadrature signal, varying frequencies from 100 MHz to 1.3 GHz, with a 100 MHz frequency step. The measured power is plotted based on four-channel modulation and mirrors for the same frequency offset, as shown in Figure 8. The measurement is taken at a 1 m distance with an additional 3 dB amplifier G\({}_{\text{PA}}\) between the receiver horn antenna and the FSW67 spectrum analyzer. An additional 60 GHz amplifier is added, as the received signal power is too low for FSW67 to demodulate. Based on the setup and considering the free-space link loss G\({}_{\text{pl}}\) to be 68 dB, the gain of the onboard transmitter antenna G\({}_{\text{tx}}\) and horn antenna G\({}_{\text{rx}}\) are 12 dBi and 17 dBi, respectively. Also, the total loss for the 60 GHz cables and adaptors G\({}_{\text{cable}}\) is 8.7 dB. The transceiver output power is 16.7 dB higher than the directly measured value, as estimated in (4). Figure 8 plots the transmission power of the transceiver chip based on measurement results.  
\[G_{\text{loss}}\left(\text{dB}\right)=\left(G_{PA}+G_{tx}+G_{rx}\right)-G_{pl} -G_{cable}=-16.7\left(\text{dB}\right) \tag{4}\]  
Figure 8: The transmission power on measurement.  
Figure 7: Measurement setup of the proposed system.  
It clearly shows that channel 2 has the highest output power compared with other channels, and all four channels have less than 1 dB channel flatness within the 1.76 GHz bandwidth. The saturated output power for the transceiver is around 2.7 dBm. The power significantly drops for out-band signals because the baseband DVGA in the transmitter chain filters the unwanted baseband to meet the spectrum mark requirements.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 2 Basic Theory
### Coherent State  
This paper considers a quasi-Bell state that has been constructed using coherent states that are nonorthogonal quantum states. The coherent state is known as the most fundamental quantum state of light, and this state is very important because it can be realized approximately using laser light. The coherent state is expressed as  
\[|\alpha\rangle=\sum_{n=0}^{\infty}\frac{\alpha^{n}}{\sqrt{n!}}\mathrm{e}^{-\frac {1}{2}|\alpha|^{2}}|n\rangle, \tag{3}\]  
where \(\alpha\) is the complex amplitude of the coherent state, and the average number of photons of the state is \(\langle n\rangle_{\mathrm{Coh}}=|\alpha|^{2}\).  
The inner product of the two coherent states corresponding to the amplitudes \(\alpha\) and \(\beta\) is  
\[\langle\alpha|\beta\rangle=\mathrm{e}^{\alpha^{*}\beta-\frac{|\alpha|^{2}}{2} -\frac{|\beta|^{2}}{2}}. \tag{4}\]  
If both \(\alpha\) and \(\beta\) are real numbers, then the value of \(\langle\alpha|\beta\rangle\) is also a real number. In addition, the coherent state that is reflected from a beam splitter with reflectivity \(R\) is subjected to energy attenuation, causing it to become another coherent state, represented by \(|\sqrt{R}\alpha\rangle\).
## 2 Materials and Methods  
### Soil Sampling, Preparation, and Selected Physico-Chemical Analyses  
The soil used in this study was Ultisols, _Typic Paleudults_ (Bekenu Series). This soil is fine loamy, siliceous, isohyperthermic, red-yellow to yellow. It has an argillic horizon with fine sandy clay loam texture. The structure is generally weak medium to coarse sub-angular blocky and friable [18]. Although Bekenu Series is less fertile, proper fertilization and management can improve its fertility to support a wide range of crops such as rice, oil palm, rubber, and fruit trees [18]. The soil was sampled at 0 to 25 cm in an uncultivated area of Universiti Putra Malaysia Bintulu Sarawak Campus, Malaysia. Thereafter, the soil samples were air-dried, crushed, and sieved to pass a 2 mm sieve for the adsorption and desorption studies. Soil texture, field capacity, and bulk density were determined using the method described by [PERSON] [19]. pH of the soil was determined in a ratio of 1:2 (soil: distilled water suspension) using a pH meter. Soil total C and N were determined using Leco CHNS Analyzer (LECO Truspec Micro Elemental Analyzer CHNS, New York). Afterwards, the soil C was divided by 0.58 to obtain soil organic matter.  
Available P of the soil was extracted using the double acid method [19] followed by the molybdenum blue method [20]. Exchangeable Ca, Mg, and K were extracted using the leaching method [21] after which the contents of these soil cations were determined using Atomic Absorption Spectrophotometry (Analyst 800, Perkin Elmer, Norwalk, CT, USA). Cation exchange capacity was determined using the leaching method [19] followed by steam distillation. The method of [PERSON] and Nelson [22] was used to extract exchangeable NH\({}_{4}\)+ and available NO\({}_{3}\)- after which the concentrations of these soil ions were determined using the steam distillation method. The selected soil properties were comparable to those reported by [PERSON] [18] except CEC, sand, silt, and clay contents. The selected chemical and physical properties of the soil are summarized in Table 1.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Property** & **Current Study (\(\pm\)S.D)** & **Range * (0–36 cm)** \\ \hline pH & 4.41 (\(\pm\)0.01) & 4.6–4.9 \\ EC (\(\upmu\)S cm\({}^{-1}\)) & 53.90 (\(\pm\)0.10) & NA \\ Bulk density (Mg m\({}^{-3}\)) & 1.16 (\(\pm\)0.03) & NA \\ Total organic carbon (\%) & 1.43 (\(\pm\)0.11) & 0.57–2.51 \\ Organic matter (\%) & 2.47 (\(\pm\)0.19) & NA \\ Total N (\%) & 0.08 (\(\pm\)0.00) & 0.04–0.17 \\ Exchangeable NH\({}_{4}\)+ (mg kg\({}^{-1}\)) & 21.02 (\(\pm\)0.00) & NA \\ Available NO\({}_{3}\)– (mg kg\({}^{-1}\)) & 7.01 (\(\pm\)0.00) & NA \\ Available P (mg kg\({}^{-1}\)) & 2.28 (\(\pm\)0.48) & NA \\  & **cmol(+) kg\({}^{-1}\)** & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected physical and chemical properties of Bekenu Series.  
The CLB used in this study was imported from Australia and its selected chemical properties are listed in Table 2.
# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 4 Experiments
### Evaluation Metrics  
In this study, we evaluated the proposed de-identification methods by comparing the face-region similarity, using SSIM and PSNR against the existing research [9; 18; 19]. SSIM assesses structural similarity, measuring how de-identification minimizes identifiable information in comparison to the original image. PSNR indicates pixel-level differences between the original and de-identified images. Additionally, we utilized cosine similarity to compare features extracted from the entire image, including faces before and after de-identification, observing the impact of the face-region ratio on the overall performance. For image-copy-detection tasks, evaluation followed the de-identification procedure described in Section 3.1, compared to the existing research [24], using metrics such as the \(mAP\) (mean average precision), \(\mu AP\) (micro average precision), _Acc@1_ (accuracy@1), and _Rcl@p90_ (recall@p90). The \(\mu AP\) represents the average precision across all copy-detection results, while the Acc@1 measures the accuracy of the highest-probability-class prediction, and the Rcl@p90 indicates the accuracy rate of the model for the top-90%-probability classes. For the video-copy-detection task, we applied the \(\mu AP\) metric to evaluate the performance after de-identification, to compare the performance before and after de-identification with the study [25; 26].
###### Abstract  
Many path planning algorithms developed for land or air based autonomous vehicles no longer apply under the water. A time-optimal path planning method for autonomous underwater vehicles (AUVs), based on a Markov decision process (MDP) algorithm, is proposed for the marine environment. Its performance is examined for different oceanic conditions, including complex coastal bathymetry and time-varying ocean currents, revealing advantages compared to the A* algorithm, a traditional path planning method. The ocean current is predicted using a regional ocean model and then provided to the MDP algorithm as a priori. A computation-efficient and feature-resolved spatial resolution are determined through a series of sensitivity experiments. The simulations demonstrate the importance to incorporate ocean currents in the path planning of AUVs in the real ocean. The MDP algorithm remains robust even if the ocean current is complex.  
Keywords:path planning; autonomous underwater vehicles; Markov decision process; ocean current +
Footnote †: journal: Applied sciences
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 3 Transceiver and Receiver Coil Geometries and Performance Analysis  
### Transceiver and Receiver Coil Geometries  
The geometries of \(Rx_{1}\), \(Rx_{2}\), and \(Tx\) were found by repeatedly executing the optimization technique depicted in Figure 4. The diameter of the \(Rx_{1}\) coil, \(d_{O1}\), varied with regard to \(n_{1}\), \(w_{1}\), and \(p_{1}\). Similarly, the diameter of the \(Rx_{2}\) coil, \(d_{O2}\), varied with regard to \(n_{2}\), \(w_{2}\), and \(p_{2}\), which determined the \(d_{O1}\) and \(d_{O2}\) as 34 mm. In order to identify optimal turns, \(n_{1}\) and \(n_{2}\) were initially set to 2 and then they were varied between 2 and 6 with respect to \(w_{1}\), \(p_{1}\), and \(w_{2}\), \(p_{2}\). With \(n_{1}\) = 3, the maximum \(Q_{1}\) and self-inductance were 117.55 and 222.08 nH, respectively. Similarly, \(Q_{2}\) and \(n_{2}\) = 3 accomplished the same result. The values \(Rx_{1}\) and \(Rx_{2}\) were simultaneously tuned to maximize \(M_{12}\). The value \(D_{3}\) was always set  
Figure 3: Flowchart of the optimization procedure.  
to 50 mm since the average distance between the heart and the exterior of the chest is 50 mm for practically every human [17; 18; 19; 20; 21]. The value \(M_{12}\) was greatest at \(D_{12}=0\) mm and gradually dropped as \(D_{12}\) increased. The values \(Rx_{1}\) and \(Rx_{2}\) weighed 0.1 g when \(n_{1}=3\) and \(w_{1}=1\) mm. Due to being lightweight, \(Rx_{1}\) and \(Rx_{2}\) could be effectively administered to the heart. Even if \(Tx\) was located above the chest, it is essential to build a small coil. Initial plans for \(Tx\) included two turns, but the resulting \(M_{13}\) and \(M_{23}\) were low. Consequently, \(n_{3}\) was set to 1. The value \(Q_{3}\) and the self-inductance of \(Tx\) were 123.2 and 225.1 nH, respectively. Table 1 shows the optimized parameters for \(Rx_{1}\), \(Rx_{2}\), and \(Tx\) at 13.56 MHz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Symbol** & \(Rx_{1}\) & \(Rx_{2}\) & \(Tx\) \\ \hline Self inductance (nH) & \(L\) & 222.0 & 222.0 & 123.2 \\ Self resistance & \(\Omega\) & 0.16 & 0.16 & 0.047 \\ Q-Factor & \(Q\) & 117.5 & 117.5 & 225.1 \\ Outer diameter (mm) & \(d_{O}\) & 34 & 34 & 61.5 \\ Inner diameter (mm) & \(d_{I}\) & 10 & 10 & 58.5 \\ Number of turns & \(n\) & 3 & 3 & 1 \\ Width (mm) & \(w\) & 1 & 1 & - \\ Spacing (mm) & \(s\) & 3 & 3 & - \\ Pitch (mm) & \(p\) & 1 & 1 & - \\ Weight (g) & - & 0.1 & 0.1 & - \\ \hline Distance (mm) & & \(D_{12}=10\) to 60 & \(D_{3}=50\) & \\ \hline Wire type & & Copper & & \\ \hline Frequency (MHz) & & 13.56 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transceiver and receiver coil specifications at 13.56 MHz.  
Figure 4: (**a**) Receiver \(Rx_{1}\) and \(Rx_{2}\); (**b**) transceiver \(Tx\); (**c**) the transceiver coil and receiver coils in the heterogeneous phantom in the simulation study.
# Lactic Acid Bacteria as Biocontrol Agents against Potato (_Solanum tuberosum_ L.) Pathogens
## 3 Results and Discussion
### In Situ Test of Biocontrol Potential of L. plantarum KB2 LAB 03 against Pathogens on Potatoes
The most satisfactory results were achieved for bacterial inhibition--85 and 90% for _S. scabiei_ and _P. carotovorum_, respectively (Table 4, Figure 8). _A. solani_ and _C. cocodes_ development was inhibited by 75%. In addition, plant growth-promoting properties of _L. plantarum_ were confirmed for chilli seeds contaminated with _Colletotrichum gloeosporoides_, another _Colletotrichum_ strain responsible for anthracose and black dot disease of several plants [57]. The lowest reduction of potato infestation by _L. plantarum_ KB2 LAB 03 was observed for _R. solani_, the most susceptible mould in the in vitro assay (Table 4, Figure 8). The potential of _L. plantarum_ application as a biocontrol agent was also previously described for _Pseudomonas syringae_ (kiwifruit disease), _Xanthomonas fragariae_ (strawberry disease) and _Xanthomonas arboricola_ (plum disease) [58]. Our work demonstrated that the mechanism of the antimicrobial action of the LAB treatment on potato pathogens is related to the production of organic acids. The determined lactic and acetic acids exhibit antimicrobial properties confirmed in numerous studies [46]. Proteinaceous antifungal compounds, the release of reuterin and hydroxy fatty acids may also be responsible for antimicrobial efficacy of LAB [59]. Other possible mode of action our treatment work is competition for nutrients [44]. Our suggestion is that the effectiveness of LAB as a biocontrol agent against potato pathogens is the result of the synergistic action of the described mechanisms.
## 5 Results and Discussion  
### Results in Standard Chinese Dataset  
We first conducted experiments on the standard Chinese dataset, where OntoNotes and MSRA are flat datasets and Weibo and GA contain a large amount of nested entities. The experimental results are shown in Table 3.  
From the results in Table 3, the MPBCNER model performed better than baselines on both the standard Chinese flat and nested. The average lead was 1-2% in the Chinese flat dataset and 3-5% in the standard Chinese nested dataset. The results indicate the efficacy of our methodology in incorporating entity boundary information. This capacity facilitates the model's enhanced precision in handling nested entity data.  
It can especially be seen that different PLMs affected the results to some extent. RoBERTa uses more than ten times the data and longer training time than BERT does, which improves model robustness and performance. ERNIE additionally introduces a knowledge-enhanced pre-training task and enhances the model's ability to learn about the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **OntoNotes** & **MSRA** & **Weibo** & **GA** \\ \hline BERT-CRF & 87.62 & 89.56 & 70.13 & 78.23 \\ SpERT & 88.47 & 92.26 & 79.03 & 83.14 \\ BERT-MRC & 84.61 & 90.11 & 75.27 & 82.42 \\ BARTNER & 88.24 & 91.16 & 79.75 & 83.86 \\ TemplateNER & 84.65 & 90.32 & 76.32 & 79.65 \\ PromptNER & 88.71 & 92.86 & 78.26 & 82.16 \\ \hline MPBCNER (BERT) & **89.12** & **92.14** & **81.35** & **85.27** \\ [PERSON] (RoBERTa) & 90.23 & 92.75 & 82.16 & 85.92 \\ MPBCNER (ERNIE) & 92.16 & 93.01 & 83.74 & 88.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results in standard Chinese flat and nested dataset (F1%).  
understanding of words and entities by integrating external knowledge such as knowledge graphs. This feature gives ERNIE a clear advantage in Chinese NLP tasks.
## 4 Case Studies  
### Validation of Power-Flow Calculation Method  
To verify the proposed power-flow calculation algorithm, this case study calculates the power flow of the IEEE 14-bus system in Figure 9[43; 44]. The system includes 14 buses, 1 slack bus (1.0\(\cdot\)0\({}^{\circ}\)), 4 P-V buses (a magnitude of voltage of 1.0 p.u.), 11 loads (in total: \(P+jQ=259+j73.5\) MVA), 1 shunt capacitor (j0.19 p.u.), 1 generator (with 40 MW), and three tap-changing transformers (buses 4-7, 4-9, and 5-6). The other detailed system data are available in [43; 44].  
The results produced by the proposed bus impedance, Newton-Raphson, Gauss-Seidel, and decoupled power-flow calculation methods implemented in MATLAB show consistency with one another. The detailed voltage profile data are presented in the Appendix A. For example, a tap-changing transformer between buses 4 and 7 (with a transformer turns ratio of 0.978) modeled by the proposed power-flow method increases the primary side voltage of 0.95433\(\sim\)11.645\({}^{\circ}\) p.u. to the secondary side  
Figure 8: Workflow of the proposed method.  
voltage of 0.98343\(\angle\)\(-\)15.117\({}^{\circ}\) p.u. Figure 10 presents a convergence curve of the voltage of bus 7, or the secondary side voltage of the tap-changing transformer, which reaches 0.98343\(\angle\)\(-\)15.117\({}^{\circ}\) p.u.  
Figure 10: Convergence of the voltage of bus 7.  
Figure 9: IEEE 14-bus system [43; 44].  
Figure 11 shows the IEEE 30-bus test system [44; 45]. The system includes a slack bus, five P-V buses, two shunt capacitors, twenty-one loads, and four tap-changing transformers. The detailed system data are available in [44; 45]. Tables A1 and A2, presented in the Appendix A compare the power-flow calculation results determined by the proposed method to those produced by the Newton-Raphson, Gauss-Seidel, and decoupled power-flow calculation methods implemented in MATLAB. The results show good consistency with each other. As the third validation, Figure 12 shows the power-flow calculation results of the IEEE 57-bus test system, determined by the proposed method, to those produced by the Newton-Raphson and Gauss-Seidel methods. The detailed system data are available in [44; 46]. The results show consistency with each other.  
Since the test systems contains typical transmission system elements (i.e., loads, slack, P-V, P-Q buses, shunt capacitors, generators, and tap-changing transformers), the proposed power-flow method can be integrated into the proposed GA.  
Figure 11: IEEE 30-bus system [44; 45].
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 2 Methodology
### Interference Thresholds
At this time, the 16 aircraft outside the runway will also generate interference simultaneously, thus affecting the threshold. Their interferences will be lower compared to the scenario where the WCLS is not considered.
## Data, Methodology and Results
### Data and Model
#### 2.1.3 TPXO8 Model  
The TPXO8 model is a global ocean tidal model developed by [PERSON] and [PERSON] of Oregon State University. The model provides tides as complex amplitudes of earth-relative sea-surface elevation for eight primary, two long period and three non-linear harmonic constituents [28; 29; 30]. This study discusses the influence of tidal height variation in offshore shallow waters on the correction, requiring the support of high-accuracy and high-spatial-resolution tidal predictions in offshore where tides change complexly. TPXO8 model has strengthened the accuracy and the spatial resolution for offshore in two aspects: 1) TPXO8 has performed harmonic analysis along track on the altimetry data of satellites such as TOPEX/Poseidon and Jason1/2, and added data of tide gauge, Envisat and ERS in the shallow water area. The non-linear 1/4 day period tidal constituent has also been considered to improve the accuracy in the offshore. 2) 33 high-resolution regional assimilation models of 1/30\({}^{\circ}\) are developed and added to TPXO8 global model calculation result with resolution of 1/6\({}^{\circ}\). These regions are mainly closed and semi-enclosed oceans and most of the continental shelve coastal areas. Additionally, TPXO8 uses the General Bathymetric Chart of the Oceans (GEBCO) 1\({}^{\prime}\) bathymetric data in available offshore areas to improve accuracy and spatial resolution.
## Materials and Methods  
### Subjects and Prototypes  
In this study, nine healthy male participants (years: 25 \(\pm\) 8; body mass: 71 \(\pm\) 12.4 kg; height: 176 \(\pm\) 10 cm) volunteered to take part in the study. None of the participants reported muscular-skeletal disorders in the previous three months. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was reviewed and approved by the Ethics Committee of the Southwest Jiaotong University (approval number 2017_001).  
A powered augmentation exoskeleton prototype, which was developed by the Southwest Jiaotong University, was used (as shown in Figure 1). The exoskeleton was described in detail by [PERSON] and [PERSON] et al. [17; 18]. The experimental prototype is the third generation prototype. Its mechanical structures consist of a backpack and two powered legs which power the knee joints via two hydraulic actuating cylinders. Using the servo valve to realize the servo-control for displacement of the hydraulic cylinder, the feedback Proportion-Integral-Derivative (PID) control law of the hydraulic cylinder displacement was designed to carry out force-assisted demands in a portable system under changing conditions of different weights and different poses. Each link could be adjusted to suit for the length of the human extremities. Additionally, the whole exoskeleton could be adjusted to suit human dimensions ranging from 165-185 cm, covering 95% of the anthropometry population. Each leg has 6 degrees of freedom (DOFs): three hip joint DOFs, one knee joint DOF, and two ankle joint DOFs. The backpack is connected to the human trunk by shoulder girdles and a flexible waist bandage. The lower exoskeleton links are connected to the thighs by flexible bandages. Additionally, the operator's feet are put into the exoskeleton shoes.  
The exoskeleton is composed of rigid parts, two hydraulic cylinders, and some damping components, which are implanted in the exoskeleton to reduce vibration impacts. The Rigid-Flexible Coupling System has intrinsic compliance due to damping parts which allow for, and passively counteract, deviations from a target position in a safe way. Subjects were strapped to the exoskeleton by flexible, adaptable bandages, allowing free movement at the human joints [17].  
The whole system control strategy was a kind of combination of position control and ZMP (zero moment point) control. The control scheme was shown in Figure 2. There are five different control strategies corresponding to five walking modes: level walking, level running, climbing slope, ladder walking, and standing. In order to study the basic muscle activities and subjective discomfort, we selected the level walking control mode to mimic free treadmill walking while carrying a load.  
Figure 1: The argumentation exoskeleton prototype for load carriage experiments.
# Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators
###### Abstract
All of the above-mentioned studies used the sweep flow rate as the manipulated variable. In this work, we compare three different control algorithms for reference tracking of the CO\({}_{2}\) partial pressure in a membrane oxygenator using both the sweep flow rate and blood flow rate as manipulated variables. To that end, first, a compartmental model of the blood membrane oxygenator is presented, detailing the CO\({}_{2}\) kinetic and transport. Next, the three control algorithms are introduced--a PI feedback controller, a non-linear model predictive controller (NMPC), and a deep reinforcement learning (RL) controller. Their effectiveness in tracking setpoints and rejecting disturbances is presented. Finally, based on the results of the experiments, the advantages and disadvantages of each control algorithm are discussed.