esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description and Model Formulation
### Description of Multi-Stage Lifelong Multi-AGV Path Finding
#### 2.1.2 Basic Assumptions  
1. Transportation tasks are assigned dynamically, and future tasks cannot be predicted at the current time;
2. AGV locations and motion states are known;
3. Partial obstacle information is known, including factory layout and pre-mapped equipment, while unknown obstacles (e.g., debris, human workers) must be detected by AGVs;
4. AGVs can detect obstacles within a limited range around themselves;
5. AGVs can communicate, broadcasting their operational states and planned trajectories;
6. Time is discretized. At each timestep, AGVs can take an action.
## 4 Case Study
### Data Preparation
#### 4.2.2 Traffic Flow Data  
The initial traffic flow data for the case study area were obtained from the Tibet Autonomous Region Department of Transportation, based on daily average traffic volume statistics collected at various traffic flow survey stations from January 2021 to 2023. The dataset includes station name, date, vehicle types (passenger cars, trucks, tractors, motorcycles), and vehicle count. A sample of the statistical data is presented in Table 2, and the data collection process at the survey stations is illustrated in Figure 8. The initial traffic volume for each road segment was calculated based on these statistical records.  
\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Site Name** & **Anduo** & **Bange** & **Nagqu** & **Xijiao** & **Naiji** & **Sigong** \\ \hline Date & 1 January & 1 March & 1 December & 1 July & 5 January & 1 June \\  & 2021 & 2021 & 2021 & 2022 & 2023 & 2023 \\  & Light Trucks & 69 & 175 & 40 & 359 & 65 & 48 \\  & Medium Trucks & 15 & 1140 & 15 & 166 & 13 & 26 \\  & Heavy Trucks & 9 & 36 & 5 & 129 & 16 & 14 \\ Vehicles & Extra-Heavy Trucks & 866 & 317 & 749 & 944 & 1146 & 1513 \\ (units) & Containers & 0 & 0 & 0 & 0 & 0 \\  & Small Passenger Vehicles & 464 & 560 & 254 & 1239 & 1337 & 695 \\  & Large Passenger Vehicles & 11 & 8 & 8 & 33 & 18 & 23 \\  & Subtotal & 1434 & 2236 & 1071 & 2870 & 2595 & 2319 \\ Motorcycles (units) & 28 & 18 & 17 & 73 & 13 & 22 \\ Tractors (units) & 2 & 0 & 1 & 0 & 0 & 0 \\ Total (units) & 1464 & 2254 & 1089 & 2943 & 2608 & 2341 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample of daily average traffic volume.  
Figure 8: Traffic flow data collection diagram.  
Figure 7: Study area and road network distribution.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters
### Universal Assumptions  
Many universal assumptions are used in the literature [118; 119; 120; 121; 122; 123; 124], before deriving the quadcopter's model equations to simplify its nonlinearities and address mathematical complexities while keeping the model as accurate as possible. Most of these assumptions are summarized as follows:  
* The quadcopter's body structure is considered rigid;
* The quadcopter's body frame is symmetric, leading to a diagonal inertia matrix;
* The drag and thrust factors are proportional to the square of the rotors' speeds;
* The center of gravity and the quadcopter's body mass are considered unified. However, this assumption might be affected by the load distribution on the quadcopter; therefore, the load should be distributed symmetrically;
* The quadcopter's principal body axes and body frame are aligned.
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Performance Comparison  
Specifically, without loss of generality, we conducted experiments with different numbers of nodes and varying UAV flight speeds. The number of nodes was set from \"C10N100R30\" to \"C40N400R30\", with a maximum data transmission radius of \(R_{d}=10\) m, a maximum charging radius of \(R_{c}=30\) m, and a maximum flight speed ranging from 15 m/s to 20 m/s. With an increasing number of nodes, both the energy consumption and data volume of CH nodes increase within the same period. A larger flight speed allows the UAV to reach nodes faster, but it also increases energy consumption. On the other hand, a smaller speed can save energy but may sacrifice some throughput and result in a certain number of dead nodes, thereby affecting UAV throughput, the number of dead nodes, and time utilization. Additionally, as the number of nodes increases, in our proposed approach, the UAV flight time and distance are influenced by the increasing number of CH nodes, which also affects flight energy consumption. Based on these hypotheses, we conducted comparative experiments.  
Figure 5: Learning curve with the episode = 400.
## 3 Observation of Topological Defects on Different Timescales
### Inhomogeneous Defect Dynamics: Intrinsic and Extrinsic  
The coherent response of the AM can be used to investigate the incoherent defect dynamics. [PERSON] et al. [24] showed that the linewidth of the AM can be used to determine the dynamics of topological defects that relax incoherently. The main problem with such experiments is that the heating caused by the D pulse also causes broadening and a frequency shift on the AM. A significant effort was necessary to eliminate heating artefacts from the analysis as described in [24].  
Figure 2: Coherent generation and annihilation of domains in photoexcitation experiments. **(a)** The calculated order parameter (in color depicts the AM power spectrum) shows domains parallel to the surface as a result of inhomogeneous pumping. The domain wall annihilation is visible at t = 4.5 ps, resulting in the generation of A-waves. The A-wave that travels towards the surface can be detected in the optical response of the A mode frequency. **(b)** The power spectra of the oscillatory response for the collective mode in DyTe\({}_{3}\) as a function of time after the phase transition at \(t=0\). The soft-mode hardening is indicated schematically by the dashed line. The insert shows the anomalous behavior of the frequency as the A-waves emitted after domain wall annihilation reach the surface. (The arrow points to the anomalies).  
Two timescales were observed in the relaxation of topological defects created during a rapid quench of a charge-density-wave system through the electronic ordering transition. The defects were attributed to the Kibble-Zurek mechanism [24]. Using a three-pulse femtosecond optical spectroscopy technique and careful consideration of thermal processes, intrinsic topological defect annihilation processes were identified on a time scale of \(\sim\)30 ps, and a possible signature of extrinsic defect-dominated relaxation dynamics was found at longer time scales. The non-thermal linewidth for the case of relaxation in two different samples of TbTe\({}_{3}\) is shown in Figure 3. The modulation of the AM due to the defects that annihilate incoherently is not detected directly by the stroboscopic technique. However, the incoherent topological defect dynamics give rise to a spatial inhomogeneity of the order parameter and a decoherence of the AM oscillations leading to an increased linewidth \(\gamma_{AM}\) for \(t<30\) ps. Concurrently, the defects give rise to a softening of the collective mode frequency \(\omega_{AM}\) because of the suppression of the order parameter that they cause [24]. The increase of the effective damping at shorter times, as shown in Figure 3, is attributed to the inhomogeneity of the order parameter. As the experiments are stroboscopic, no detailed information beyond the relevant timescales is available, and no information on the defect structures could be deduced.
## 2 Material and Methods
### Hydrogen Plant Modeling  
Hydrogen is produced by splitting water through the PEM water electrolysis process. The electrochemical reactions are described by the following expressions [35]:  
\[\text{Anode}:\ 2H_{2}\mathcal{O}\to\mathcal{O}_{2}+4H^{+}+4e^{-} \tag{18}\]  
\[\text{Cathode}:\ 2H^{+}+2e^{-}\to H_{2} \tag{19}\]  
The overall reaction of hydrogen production is described below [35]:  
\[2H_{2}O_{(I)}\to 2H_{2(g)}+O_{2(g)} \tag{20}\]  
The previously mentioned procedure must feed with electrical power to be achieved. The required electricity input is provided by the ORC generator; therefore, a share of the electricity production is exploited for hydrogen production via the PEM water electrolyzer (\(P_{el,PEM}\)). Furthermore, the incoming water is not used as received from the network but is preheated at a temperature level of about 80 \({}^{\circ}\)C [36]. The water preheater is located after the ORC heat recovery system. In general, the total energy (\(\Delta H\)) needed for the water electrolysis is defined by the following expression [37]:  
\[\Delta H=\Delta G+T\cdot\Delta S \tag{21}\]  
(\(\Delta G\)) is [PERSON]'s free energy and (\(T\cdot\Delta S\)) is the thermal energy demand.  
The electricity input to the electrolyzer is defined below [36]:  
\[P_{el,PEM}=J\cdot V_{cell} \tag{22}\]  
where (\(J\)) is the current density, and (\(V_{cell}\)) is the electrolyzer cell voltage. An analytical representation of the electrolyzer model based on the current-voltage (\(J\)-\(V_{cell}\)) equations is included in Ref. [36]. Then, due to the low hydrogen density at the atmospheric pressure level, the produced hydrogen amount has to be compressed up to 100 bar, to increase its density [38]. The electricity input to the hydrogen compressor, which also comes from the ORC electricity generation, is presented below:  
\[P_{el,comp}=\frac{\dot{m}_{hydrogen}\cdot(h_{d,is}-h_{c})}{\eta_{is,comp}\cdot \eta_{mg}} \tag{23}\]  
The compressor isentropic efficiency (\(\eta_{is,comp}\)) is considered at 85% [38], while the electromechanical efficiency (\(\eta_{mg}\)) is equal to 97% [30]. Due to compression, except for the pressure increment, the temperature also increases; therefore, the hydrogen amount has to be cooled down to the temperature of 25 \({}^{\circ}\)C. Finally, hydrogen fuel at a pressure of 100 bar and a temperature of 25 \({}^{\circ}\)C, is stored in tanks, so that it can be transported and utilized.
# A Survey of Candidate Waveforms for beyond 5G Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DFT & Discrete Fourier Transform \\ KPI & Key Performance Indicator \\ eMBB & Enhanced Mobile Broadband \\ mMTC & Massive Machine-Type Communication \\ CCDF & Complementary Cumulative Distribution Function \\ URLLC & Ultra-Reliable and Low-Latency Communication \\ GFDM & Generalized Frequency Division Multiplexing \\ FDE & Frequency Domain Equalization \\ TIBWB-OFDM & Time Interleaved Block Windowed Burst OFDM \\ MIMO & Multiple-Input-Multiple-Output \\ MMSE & Minimum Mean Square Error \\ IBIC & Inter-Block Interference Cancellation \\ ISC & Interference Successive Cancellation \\ AWGN & Additive White Gaussian Noise \\ ICI & Inter-Channel Interference \\ SRRC & Square Root Raised Cosine \\ IB-DFE & Iterative Block Decision Feedback Equalizer \\ DSSIC & Double Sided Interference Cancellation \\ PAPR & Peak-to-Average-Power-Ratio \\ IFFT & Inverse Fast Fourier Transform \\ WTO & Windowing Time Overlapping \\ FDD & Frequency Division Duplex \\ ITSBI & Inter Sub-Band Interference \\ OFDM & Orthogonal Frequency Division Multiplexing \\
3 GPP & 3 rd Generation Partnership Project \\ S/P & Serial to Parallel \\ DL & Downlink \\ ISI & Inter-Symbol Interference \\ PSD & Power Spectral Density \\ IoT & Internet of Things \\
5G & Fifth Generation \\ UL & Uplink \\ F-OFDM & Filtered-OFDM \\ UE & User Equipment \\ CFO & Carrier Frequency Offset \\ OOB & Out of Band \\ AP & Access Point \\ mm & Milli-Meter \\ PHY & Physical Layer \\ BS & Base Station \\ SNR & Signal-to-Noise Ratio \\ LTE & Long Term Evolution \\ BER & Bit Error Rate \\ MF & Match Filtering \\ ZF & Zero-Forcing \\ SC & Single-Carrier \\ TDD & Time Division Duplex \\ MC & Multi-Carrier \\ CP & Cyclic Prefix \\ INSBI & Inner Sub-Band Interference \\ \end{tabular}
## 5 Particle Detectors
### Mass Spectrometry
#### 5.2.4 ToF-SIMS  
Time of flight-secondary ion mass spectrometry (ToF-SIMS) is a surface-sensitive technique providing elemental and molecular information from the uppermost one or two monolayers of solid surfaces, with limits of detection down to \(10^{-12}\) and less (in the ppb- sub ppb range). It enables the detection and localization of all elements, molecules, and their isotopes in the mass range 1-12,000 AMU. This is obtained by raster scanning a nano-focused primary ion beam over the surface of the sample. The impact of the primary ions of some keV energy (30 keV with a Bi Liquid Metal Ion Gun) on the target atoms generates a collision cascade, with the consequent emission of particles; most of them are neutral, only a very low percentage (1-3%) is positively or negatively charged (secondary ions). These charged particles can then be extracted, collected, and identified by a ToF detector. In the case of TOF.SIMS5 (IONTOF,Munster, Germany) operating at the LASR3 Surface Laboratory Roma Tre (INFN-CHNet and Roma Tre University), the voltage for the secondary ion extraction is 2 \(\div\) 3 kV. The ToF detection system exploits the combination of a linear drift path and an ion mirror. The latter, a non-linear field reflectron doubling the pathlength run by the extracted ions and curving the trajectory, enables reaching a mass resolution M/\(\Delta\)M > 30,000. At the end of the flight path, the secondary ions are then post-accelerated using a 10 kV voltage onto a Multichannel Plate, where a scintillator and a photomultiplier are located. The scheme of the instrument is reported in Figure 16.  
Chemical maps with a lateral resolution of less than 50 nm can be obtained, correlating mass spectra to each pixel of the image as a function of position. Combining the primary ion beam with a sputter ion beam (dual-beam mode) in repeated cycles, the in-depth distribution of elements and molecules can be also determined, with a nominal lateral resolution of less than 1 nm along the \(z\)-axis. In the last decade, ToF-SIMS technique was used in both the non-destructive (static) and destructive (dynamic) modes, taking advantage of the possibility of obtaining chemical maps of organic [281] and inorganic [282, 283] materials with high lateral resolution. In the INFN-CHNet experience, ToF-SIMS was employed to investigate dyes and pigments, metal layers and plates, corrosion phenomena and degradation products on both inorganic and organic samples [284, 285, 286, 287, 288, 289, 290, 291, 292]. Very recently, ToF-SIMS has also been used to identify biomarkers and reveal their spatial localization in organic archaeological remains.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 50 cm Depth in Bangladesh  
In this subsection, based on the model performance of the testing dataset, we explored the optimal combination of tree-based models, time windows, and input scenarios in forecasting soil temperature at the depth of 50 cm (ST50).
# A Low Phase Noise Dual-Loop Dual-Output Frequency Synthesizer in SiGe BiCMOS
## 2 Design and Implementation of the Dual-Loop Dual-Output Frequency Synthesizer
### High-Speed Prescaler and Double-Balanced Down-Conversion Mixer  
The synthesizer Loop1 outputs a fixed frequency signal at 12.96 GHz. The corresponding prescaler consists of two current-mode logic (CML) divide-by-2 stages [17], as shown in Figure 5a, which divides the 12.96 GHz VCO output to 3.24 GHz signal. CML divider has the advantages of small area, compared to the injection-locked frequency divider. Circuit parameters are optimized for each CML stage to obtain better RF performance and lower power consumption for input signals at 12.96/6.48 GHz.  
The synthesizer Loop2 outputs Ku-band signal covering 14.9175-17.55 GHz. Normally, a high-speed MMD needs to be used after VCO to achieve the reference frequency step. This step introduces high-power consumption and in-band phase noise degeneration with 20 lg(N), where N is the integer division ratio. In this work, instead, a down-conversion mixer is used to down-convert the Ku-band VCO signal from \(\sim\)16 GHz to \(\sim\)3 GHz with the 12.96 GHz LO signal from synthesizer Loop1. In this way, the power hungry high-speed prescaler or MMD can be avoided, and in-band phase noise can be improved. The double-balanced structure of down-conversion mixer reduces the LO and RF to IF port feed-through, as shown in Figure 5b. The mixer gm-stage is resistively degenerated to improve linearity. To make trade-off between the operating bandwidth and power-handling capacity, the transistor sizes are optimized for the switching quads (8 \(\upmu\)m) and gm stage (16 \(\upmu\)m).  
Figure 4: Schematic of (**a**) the Ku-band Colpitts VCO with 3-bit cap-bank; (**b**) the Ka-band doubler.
## 2 Audio Data Acquisition
### Recording Locations  
The recording of the database was carried out placing the device at two different locations in order to capture different sound sources. In both locations, the device was mounted on the facade of a building at an approximate height of 4 m over the street level.  
The first location was in the city centre of Algorta (Biscay). It was chosen with the aim of collecting sounds related to traffic, street works and usual sounds of an urban area. The equipment was in use during 31 days between May and June of 2021, recording 600 h of data.  
The second location was in Portuguese(Biscay), and it was intended to record urban sounds related to leisure. Between the end of June and the beginning of August 2021, the device was deployed for 31 days, capturing 687 h in total. NoisenseDB was built from selected audio segments from this large set of recordings, as it is explained in the next section.
# Identification of Ratholes in Desert Steppe Based on UAV Hyperspectral Remote Sensing
## 3 Data Processing  
### Data Pre-Processing  
The collected raw data were first screened via manual visual inspection, and the over-exposed or underexposed data were removed. The remaining spectral images with good shooting results were corrected for reflectance using SpecVIEW (version number 2.9.1.8), which transformed the DN value of the original spectral data into spectral reflectance.  
Noise is an unavoidable interference factor in the process of spectral data acquisition. Due to the limitations of the equipment itself, the hyperspectral data acquisition process is affected by the equipment sensor accuracy, dark currents, and the data transmission process, which leads to the contamination of hyperspectral data with various types of noise, including Gaussian noise, streak noise, and impulse noise [18]. The presence of noise will affect the real validity of the spectral data. Therefore, data smoothing and noise reduction are important steps in hyperspectral data processing. In this study, the Savitzky-Golay smoothing filter was selected to smooth and reduce the noise of the spectral data, and the effect of smoothing and noise reduction was achieved without changing the trend of the spectral variation.
## 6 Architecture Preliminary Validation via Prototype  
In order to provide a preliminary validation of the proposed approach we implemented a prototype that provides a subset of the functionalities of the service-oriented architecture presented above. The prototype consists of an SDN controller enhanced with orchestration capabilities implemented on top of a Floodlight controller [46] to establish dynamic chains of network functions (e.g., middlebox). Basically, the prototype plays the role of an orchestrator for dynamic service chaining operating over SDN networks, _i.e.,_ SDN orchestrator. In particular, the SDN orchestrator addresses the second service scenario envisioned in the Section 3, _i.e.,_ \"flow service with custom data treatment\", and performs functions both at the \"Network Service Provision and Control\" layer and at the \"Infrastructure Control and Management\" layer.
# EPTlib: An Open-Source Extensible Collection of Electric Properties Tomography Techniques
## 3 EPT Techniques
### Summary of the Implemented Techniques  
Table 1 summarizes the input requirements and the output capabilities of the EPT techniques implemented in _EPTlib_, distinguishing between possible phase-based and magnitude-based approximations.  
Moreover, it is worth recalling the main hypotheses on which the three techniques are based:  
1. Helmholtz-EPT assumes that the EPs are locally homogeneous, leading to large systematic errors at the tissue boundaries.
2. The complete versions of both Helmholtz-EPT and convection-reaction-EPT estimate the Tx phase \(\varphi^{+}\) as half of the measurable TRx phase \(\varphi^{\pm}\). This is a reasonable estimation when the same RF coil is used both in transmission and in reception with a polarization switch [5], or when special care is taken in the phase acquisition, like for example adopting a CLEAR reconstruction [46].
3. Both convection-reaction-EPT and gradient-EPT assume that the longitudinal component of \(B_{1}\) has a negligible gradient. This assumption limits the applicability of these techniques around the mid-plane of longitudinally symmetric sources, which are usually adopted for volume coils.
4. Gradient-EPT requires acquisitions performed with a pTx system with at least five Tx channels. Currently, this kind of equipment is mainly employed in the research of ultra high field scanners.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Input**} & \multicolumn{2}{c}{**Output**} \\ \cline{2-7}  & **Tx Chan.** & **Rx Chan.** & **Tx Sens.** & **TRx Phase** & \(\sigma\) & \(\varepsilon_{\text{r}}\) \\ \hline
**Helmholtz-EPT:** & & & & & & \\ Complete & 1 & 1 1  & & & & \\ Phase-based & 1 & 1 1  & & & & \\ Magnitude-based & 1 & 1 1  & & & & \\
**Convection-reaction-EPT:** & & & & & \\ Complete & 1 & 1  & & & & \\ Phase-based & 1 & 1  & & & & \\
**Gradient-EPT:** & & & & & \\ Complete & \(\geq\)5 & 1  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the input requirements and output capabilities of the EPT techniques implemented in _EPTlib_.
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments  
### Databases and Experimental Protocols  
To assess the performance of MT-IRN, comprehensive experiments are performed on both synthetic and authentic databases containing various types of distortions. The synthetically distorted databases include LIVE [47], CSIQ [48], TID2013 [49], and KADID-10k [50], while the authentically distorted databases include LIVEC [51] and KonIQ-10k [52]. The details of the databases are summarized in Table 2.  
For synthetically distorted databases, 80% of images are used as a training set and the remaining 20% as a testing set, divided by the reference images, to avoid image content overlap. For authentically distorted databases, the training and testing sets are directly divided into 80% and 20% proportions. For each dataset, the random partitioning process is repeated 10 times according to the aforementioned rules, and the median of the results from the 10 experiments is taken as the final result. Spearman's rank correlation coefficient (SROCC) and Pearson's linear correlation coefficient (PLCC) are used to evaluate the performance of MT-IRN. The SROCC measures the monotonicity between the predicted scores and the ground truth scores, while the PLCC measures the linear correlation between them [10]. Both SROCC and PLCC have a range of [\(-\)1, 1], with a larger absolute value indicating better performance of the model.