esa-sceva/satcom-chunk-collection huggingface dataset:
# Organic Crystals for THz Photonics
## 3 THz-Wave Generation and Detection via NLO Processes
### THz-Wave Detection
The signal, proportional to the THz electric field \(E_{\mathrm{THz}}(t)\), is measured as a function of the delay between pump and probe pulses. This time-varying refractive index profile acts as a time-varying optical lens and leads to focusing/defocusing of the probe beam, which can be detected by various common optical detection techniques [121; 124]. For the above mentioned electro-optic-based detection methods, the electro-optic sampling and the THz-induced lensing, the same phase-matching considerations are valid as for THz-wave generation with the corresponding material, considering the THz frequency limitations and the probe beam wavelength; i.e., for efficient detection, the THz refractive index \(n_{\text{THz}}\) and the optical group index \(n_{g}\) for the detection pulse should match. In the same way as for detecting with the photoconductive antennas, by delaying the probe pulse with respect to the THz pulse, a time-domain trace of the THz electric field \(E_{\text{THz}}(t)\) is obtained by electro-optic sampling or THz-induced lensing (see Figure 7). Examples of the measured THz signals using different detection principles in systems based on optical rectification in organic crystals are presented in Section 4.2.
## 2 Study Area and Data Source
### Data and Processing
#### 2.2.3 Meteorological Data  
The climate dataset employed in this work to analyze the impacts on NPP variation, which covers the period of 2000-2007, includes monthly mean temperature, total monthly precipitation across greater Guangzhou. Specifically, some historical records of monthly temperature and precipitation were derived from 13 climatological stations from the online Guangzhou Statistical information [50] and relevant Statistical Yearbook. As an important interpolation method, kriging has been widely used for sampling at each pixel in terms of the climate factors from the site-based information.
## 2 Materials and Methods
### UPLC-HRMS/MS Analyses and Ion Identity Molecular Networking  
_P. mauritianum_ metabolite extracts were resolubilised in methanol at a 5 mg/mL concentration and were analysed on a WatersTM ACQUITY UPLC(r) I-class system (Waters(r), Milford, CT, USA) coupled to a Thermo ScientificTM Orbitrap ExplorisTM 120 mass spectrometer (Thermo Scientific(r), Bremen, Germany) using a Thermo Scientific OptaMax NG ion source with a heated electrospray ionization (HESI-II) probe, piloted by Xcalibur 3.1 (Thermo Scientific). The optimized HESI-II parameters were as follows in positive ionization mode: source voltage was set from 3.1 kV to 3.7 kV from 1 min to 6 min and maintained at 3.7 kV until 10 min; sheath gas flow rate (N2), 35 units; auxiliary gas flow rate, 10 units; sweep gas flow rate, 1.0. Ion transfer tube temperature and vaporizer temperature were both set to 320 \({}^{\circ}\)C. The mass analyser was calibrated using the Thermo Scientific EASY-IC ion source internal reference mass (fluoranthene). The mass spectrometer method was set to FullMS data-dependent MS2 (ddMS2) for a scan range between 100 to 1500 \(m/z\). FullMS were acquired at a resolution of 30,000 for an expected peak width of 16 s, the normalized AGC target was set to 350%, and injection time was set to 120 ms. In ddMS2, the resolution was 15,000, the normalised AGC target 50%, the isolation window 1.5 \(m/z\), and the stepped normalized collision energy 15/30/45. Injection time was set to 125 ms, and the parent ions were placed on the dynamic exclusion list for 2 s. The RF lens was set at 60%. Methanol was injected as blank. The chromatographic column was a 50 \(\times\) 2.1 mm, 1.7 \(\upmu\)m Waters(tm) ACQUITY UPLC(tm) BEH C18 column (Waters). The injection volume was set to 1 \(\upmu\)L. The mobile phases were (A) water and (B) acetonitrile, both with 0.1% of formic acid, and the gradient was set as follows: linear from 5 to 100% B over 7 min and 2 min of isocratic elution at 100% B, followed by 1 min at 5% B for column conditioning (ACN + 0.1% formic acid). The flow rate was set constant at 0.6 mL/min. Thermo raw MS data were converted in mzXML format using MS convert 3.0.24 software and processed with MZmine 3.2.8 software [8]. Intensity detection threshold for MS1 and MS2 was set to 7.5 \(\times\) 10\({}^{5}\) and 0, respectively. ADAP Chromatogram Builder [9] was used to reconstruct the extracted ion chromatogram with a minimum scan group size of 5. Isotope features were removed using the \({}^{13}\)C isotope filter module. Blank and _P. maurtianum_ features were aligned (Join aligner), and the blank features were subtracted (feature list blank subtraction) from the feature table, ensuring only plant features remained in the final molecular network. The features IDs were reset (feature list row filter). Finally, features were metacorrelated, and adducts were searched using the module ion identity networking. The feature quantification table was imported into GNPS and analysed as a feature-based molecular network. Collapsed ion identity network was retrieved; IMN graph was visualized and annotated with Cytoscape Software version 3.10.0 [10].
## 3 Experimental Research
### Step 3: Implementation of GS Designs  
In the third phase of the project, several GS configurations were implemented in the study soils (see Figure 1). The configurations were limited to three rods in soils 1, 2, 3, 4, 7 and 8 and six rods in soils 5 and 6. In addition, these configurations were chosen because measured GR values can be compared to calculations using equations previously developed and published in the literature [3; 24; 25].  
The code of each configuration is detailed below:  
* 1R: One vertical rod.
* 2 RP: Two rods connected in a parallel grid.
* 3 RP: Three rods connected in a parallel grid.
* 3 RT: Three rods connected in a triangular grid.
* 6 RR: Six rods connected in a rectangular grid.  
For each configuration, copper rods with a length (\(L_{r}\)) of 1.80 m \(\times\) 5/8\({}^{\prime\prime}\) and spaced at a distance (\(d\)) of 3.80 m were used. The #4 AWG stranded bare copper conductor was buried at a depth (\(h\)) of 0.25 m.
###### Abstract  
A versatile electrospray method was utilized for deposition of thin ZnO films doped with Co (5%) (CZO) or co-doped with Co (2.5%) and Al (2.5%) (CAZO). Thin polycrystalline films with approximate thickness of 200 nm and high transmittance (more than 80%) were obtained. No additional XRD peaks due to dopant impurities or dopant oxides were observed. The cobalt doping led to decrease in grain size and increase in crystallite size from 22 nm to 29 nm in the (101) direction. Smaller changes were observed for the CAZO films. Surface roughness of the films was measured using a 3D optical profiler. Surface roughness of the doped films was from 5 nm to 9 nm higher than that of the pure films. Refractive index, extinction coefficient and thickness of the films were calculated using ellipsometric measurements and were further used for determination of optical band gap and Urbach energy. Refractive index and optical band gap increased with doping from 1.86 and 3.29 eV for pure ZnO to 2.00 and 3.35 eV for CZO and 1.97 and 3.33 eV for CAZO films. Through calculation of Urbach energy (119 meV for pure ZnO, 236 meV for CZO and 138 meV for CAZO), it was demonstrated that doping leads to an increase in structural disorder, most pronounced in the case of Co doping.  
ZnO; cobalt and Co/Al-co-doped ZnO; electrospray deposition; ellipsometric measurements +
Footnote †: journal: Applied Sciences
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network
## 3 Experiment and Validation
### Gridding Schemes  
Before the determination of the appropriate gridding scheme, we need to define the boundaries of the tomographic region, including the ground and top boundaries. A plane with a height of 8 km is selected as the top boundary according to the principle proposed by [PERSON] et al. [23]. The \"tropospheric cuboid\" (i.e., tropospheric zone) is divided into 10 layers from bottom to top with thicknesses of 0.5, 0.5, 0.7, 0.7, 0.7, 0.9, 0.9, 0.9, 1.1, and 1.1 km. In general, the ground boundary only needs to cover the entire interest area, which excludes a large number of effective signals. The proposed improved method enables a larger ground boundary, although it may lead to more null-ray-passed voxels. Therefore, we gather the intersections of all receiver signal lines and 8-km-high tropopause at DOY 182, 2019 (1 July), presented in Figure 6. The general ground boundary (scheme I) only covers the experimental area, while the expanded boundary (scheme II) is extended by 0.25\({}^{\circ}\) in the longitude direction with respect to the general boundary. Compared to those in scheme I, the statistics in Table 2 show that the total number and utilization rate of the effective signal in scheme II are increased by 77,129 and 5.01%, from 1,443,492 and 93.56% to 1,520,621 and 98.57%, respectively. This reveals that scheme II is more suitable for this experiment than scheme I. Hence, we use scheme II for the ground boundary.  
As shown in Figure 7, we set an interval of 0.1\({}^{\circ}\) in both longitude and latitude directions to grid the research area, which yields a \(12\times 14\) grid surface layer. By combining vertical layering strategies, the whole tropospheric cuboid is discretized into \(12\times 14\times 10\) independent voxels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Period** & **Day-of-Year (DOY)** & **Weather Conditions** \\ \hline
1 & 182–184 & clear or cloudy \\
2 & 244–246 & rain \\ \hline \hline \end{tabular}
\end{table}
Table 1: Two periods and their day-of-years (DOY)s and weather conditions.  
Figure 5: Experiment area, including 20 GNSS stations (red triangles) and 1 radiosonde (RS) station (green circle).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Total Number** & **Utilization Rate** \\ \hline I & 1,443,492 & 93.56\% \\ II & 1,520,621 & 98.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total numbers and utilization rates of the GNSS signals for the two schemes.  
Figure 6: Intersection of the ray and 8-km-high tropopause. In scheme I, a general tomographic horizontal boundary covering only the Nanjing area is used, while in scheme II, the optimal horizontal boundary including the most signals is used.
# Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller
## 3 Modelling
### Simulink Modelling
#### 3.3.2 Case 2: Positive and Negative Road Disturbances  
For practical purposes, both positive and negative road disturbances are taken into account as shown in Figures 6 and 7 for both front and rear side respectively and then applied to the designed system. The Matlab function remains the same for both the cases except a product of \(-1\) in the later case.  
In Simulink, for designing the control law, R, E, I and H Blocks are coupled to the multiplexer in as per equations of control law \(u\) while \(K\) will be designed according to LQR Controller. Gain block is connected to demultiplexer as there are two outputs which will be the inputs of the those respective blocks of forced inputs \(u1\) and \(u1\). The forced inputs will then eventually linearize the system. Finally, connecting all the blocks to a main block of Half-Car model as its inputs which actually contains all state equations of the system. The outputs of the main block are the actual variables to be controlled. The outputs of Road Disturbance sub-system and control law are now the inputs of the main block. The Simulink block diagram is shown in Figure 8. For simulation, typical half car model parameters are used.  
Figure 5: Rear Road Disturbance.  
Figure 6: Front Road Disturbance PN.  
Figure 7: Rear Road Disturbance PN.  
Figure 11: Vertical Acceleration.  
Figure 12: Pitch Acceleration.  
Figure 13: Front Body Displacement.  
Figure 16: Pitch Displacement.  
Figure 17: Vertical Acceleration.  
Figure 18: Pitch Acceleration.  
Figure 15: Vertical Displacement PN.  
In the graph of front suspension travel shown in Figure 13, the peak of front suspension travel has gone to \(-0.014\) but now, the peak has reduced to \(-0.02\) while on the other side it is not \(0\) in rear suspension travel region but using controller it has reduced to almost \(0\) in that region.  
In the graph of rear suspension travel shown in Figure 14, the peak of rear suspension travel has gone to \(0.15\) but now, the peak has reduced to \(0.03\) while on the other side it is not \(0\) in front suspension travel region but using this technique it has reduced to almost \(0\) in that region.
# Review of Task-Scheduling Methods for Heterogeneous Chips
## 5 Research Status of Experiment
### Experimental Methods
#### 5.2.1 Model-in-Loop  
MIL simulation is a closed-loop pure numerical experimental method that connects the algorithm model with the controlled object model. This approach uses mathematical modeling to simulate heterogeneous chip behavior and scheduling methods, enabling developers to predict and optimize the performance of the system prior to heterogeneous chip development. In MIL simulation, commonly used models include DAG models, which can effectively represent the dependencies between tasks and their execution order, thus supporting the verification of complex scheduling logic. Moreover, cohort theoretical models are widely used to simulate task scheduling and resource allocation strategies to evaluate system performance and resource bottlenecks under different approaches.  
Usually, MIL simulations utilize powerful numerical computing and data processing environments such as MATLAB 2023 or Python 3.6 to implement [45]. MATLAB Is particularly favored for its extensive engineering support and built-in simulation framework. At the same time, Python is widely used in more customized simulation requirements for its flexibility and rich library support (such as NumPy 1.8.1 and SciPy 1.15.0). With these tools, developers can quickly implement model building, run simulations, and analyze results, thus effectively accelerating the development cycle and optimizing the overall performance of heterogeneous chip task scheduling.
## 6 Conclusions and Future Research  
In this work, we developed a model titled the attention-based latent information extraction network (ALIEN) for CTR prediction. ALIEN is designed for common scenarios, such as the recommendation of movies on a video streaming website or products on an e-commerce shopping site, where the user and item attributes and the user's past clicking decisions are typically available. Based on the experimental results, ALIEN resolves the issues of: (1) modeling high-order feature interactions, and (2) the explainability of the prediction. To achieve these two objectives, ALIEN (i) constructs the low and high-order feature interactions from user and item attributes, via the vector inner-product approach combined with a compressed interaction network (CIN) module; (ii) extracts latent information from feature interactions and the user's history interactions with two attention-based layers to enhance the performance of CTR prediction; and simultaneously (iii) provides explainability by interpreting the contributions of different feature and history interactions. We have conducted experiments on two real-world datasets and demonstrated considerable improvements over strong baselines. Moreover, our proposed model can provide reasonable explanations, even when attributes are quite scarce. For future research, we aim to model other sources of attributes, particularly on a knowledge graph, to better characterize users and items and therefore make even more precise predictions.  
**Author Contributions:** Conceptualization, R.H., S.M., M.S. and H.E.; methodology, R.H.; software, R.H.; validation, R.H., S.M., M.S., H.E. and Z.O.; formal analysis, R.H.; investigation, R.H.; resources, R.H., S.M., M.S. and H.E.; data curation, R.H.; writing--original draft preparation, R.H.; writing--review and editing, R.H., S.M., M.S., H.E. and Z.O.; visualization, R.H.; supervision, S.M., M.S., H.E. and Z.O.; project administration, M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Science and Technology Major Project (grant number 2018 YFB1403003).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Illustration of the influence of user behavior on the candidate item. The genre and release year of each item are provided. Generally, history items with high relevance to candidate item get high attention scores.
## 2 Biosensors in Patient Health Monitoring
### Diverse Biosensors Enable Comprehensive Health Monitoring  
This section delves into key biosensors crucial for comprehensive patient health monitoring, featuring cutting-edge technologies such as the AD8232 for electrocardiogram (ECG) monitoring, and the MAX30102 for heart rate and pulse oximeter monitoring. Both biosensors are examined in detail, elucidating technological advancements, underlying principles, specifications, features, and their pivotal roles in facilitating real-time patient care.
# Underwater Acoustic Target Tracking: A Review
## 6 Discussion, Challenges, Countermeasures, and Lessons Learned
### Lessons Learned  
Designing effective underwater target tracking algorithms should take many factors into account especially the effect of the marine environment. Therefore, many additional measures should be employed. Here we list some lessons based.  
The medium used in underwater target tracking is acoustic waves; however, the tracking instruments can be varied, as Section 3 introduces. Therefore, how to choose the tracking instrument according to the realistic application is important. In this paper, we classified the tracking algorithms based on the different tracking instruments and compared the strength and weakness of the three classical tracking instruments in Table 3. To sum up, the acoustic imaging sensor is usually used for tracking targets in close range and has the advantage of obtaining not just kinematic information, while having the drawback of limited FOV. TASA and USWNs can track targets whatever in close or distant range while having the problem of PS ambiguity and energy consumption respectively. Researchers can select the appropriate tracking instrument based on their advantages and disadvantages.  
Due to the complexity of the marine environment, errors are inevitable in tracking results. Designing a calibration method and choosing proper filter algorithms can reduce the error. The authors in [74] design a pre-processor to reduce the error of obtained bearing information, which makes a great improvement of the tracking performance. In addition, as Table 6 shows, almost every tracking algorithms use some methods for optimization. In Section 5, the tracking algorithms are classified based on the tracking optimization and the corresponding disadvantages are listed in Table 5. Researcherscan study these references to find some ideas. Except for improving tracking accuracy, the optimization methods can be designed from the constrained source.
## Abbreviations
\begin{tabular}{l l} ODB & Optical Duobinary \\ ODN & Optical Distribution Network \\ OFDM & Orthogonal Frequency-Division Multiplexing \\ OFDMA & Orthogonal Frequency Division Multiple Access \\ OLT & Optical Line Terminal \\ ONU & Optical Network Unit \\ Opex & Operational Expenditure \\ ORI & Open Radio Interface \\ OTN & Optical Transport Network \\ PA & Power Amplifier \\ PAM-4 & 4-Level Pulse Amplitude Modulation \\ PAN & Personal Area Network \\ PBS & Polarization Beam Splitter \\ PDA & Personal Digital Assistant \\ PDC & Personal Digital Cellular \\ PDCP & Packet Data Convergence Protocol \\ PHY & Physical \\ PLC & Power Line Communication \\ PON & Passive Optical Network \\ POTS & Plain Old Telephone Service \\ PS & Protocol Stack \\ PSTN & Public Switches Telephone Network \\ PtMP & Point-to-Multipoint \\ PTN & Programmable Transport Network \\ PTNI & PTN Interface \\ PtP & Point-to-Point \\ QoE & Quality of Experience \\ QoS & Quality of Service \\ RAN & Radio Access Network \\ RAT & Radio Access Technology \\ RE & Radio Equipment \\ REC & Radio Equipment Controller \\ RF & Radio Frequency \\ RLC & Radio Link Control \\ RoFSO & Radio-over FSO \\ RRH & Remote Radio Head \\ RT & Ray Tracing \\ RTMI & Radio Telefono Mobile Integrato \\ Rx & Receiver \\ SC & Single Carrier \\ SC-QAM & Single-Carrier QAM \\ SDN & Software-Defined Networking \\ SDSL & Symmetric DSL \\ SE & Spectral Efficiency \\ SiGe & Silicon Germanium \\ SISO & Single-Input Single-Output \\ SM & Spectrum Management \\ SMS & Short Message Service \\ SS & Spatial Streams \\ SSM & Static SM \\ SSMF & Standard Single-Mode Fiber \\ TACS & Total Access Communication System \\ TDD & Time Division Duplex \\ TDM & Time Division Multiplexing \\ TDMA & Time Division Multiple Access \\ TRx & Transceiver \\ TSN & Time-Sensitive Networking \\ \end{tabular}
## 3 The Human Role in Teleoperation
### Working Memory  
Spatial memory is part of our cognitive system. Those tasks, involving higher demands from human short- and long-term memory, typically result in higher mental workloads. _Working memory_ refers to the mind's capability to maintain and manipulate important information to perform complex tasks such as reasoning, comprehension, and learning. [PERSON]'s model [99] evolution proposes a four component model: a central executive system along with three short-term storage subsystems, the visuo-spatial sketchpad (processes visual-spatial information), the episodic buffer (with limited capacity), and the phonological loop (processes verbal-acoustic information). For example, route planning or thinking about time involve the visuo-spatial sketchpad subsystem and the central executive system. Maintaining information in the working memory involves a rehearsal process [100; 101]; however, working memory overload or significant distractions can interrupt this process. For example, operators that rely on working memory to label objects or to recall the spatial positions and orientation of the objects tend to experience higher cognitive workloads. Moreover, working memory data are necessary for mental transformations involving visual and proprioceptive frames of reference (rotations and translations). The human cognitive system uses the _working memory_ to retain new information temporally for processing or to store in long-term memory. It is shown that working memory has a limited capacity and holds the information for a short time (a few seconds). The manipulation of this information enables reasoning and decisions and shapes event behavior. [PERSON] [102] introduced the \"chunks\" concept, referring to any set of information to be associated with long-term memory. Moreover, he defended that human working memory can hold just \(7\pm 2\) \"chunks\" or \"items\" of information. More recent studies point to a number of \"chunks\" dependent on the type of information.  
Managing multiple and separate frames of reference, controlling multiple actuators, distributing attention over multiple instruments, memorizing the spatial position of multiple targets due the limitation of the field of view, register numbers with several digits, or changing sequential procedures frequently due to external unpredictable events are some examples that can compromise tasks. As a conclusion, teleoperation systems should avoid making a person recall more than \(7\pm 2\) \"chunks\" to evolve through a task.