esa-sceva/satcom-chunk-collection huggingface dataset:
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Space Exploration
#### 3.4.2 Swarm-Based Construction  
* Habitat Construction [31]: Swarm robots can construct habitats and other structures on planetary surfaces. By coordinating their actions, the robots can build complex structures using local materials or pre-fabricated components. Benefits: Enhances construction efficiency, reduces the need for human labor, and ensures precision and robustness in habitat construction.
* Infrastructure Deployment [33]: Robots can deploy and assemble infrastructure such as solar panels, communication arrays, and scientific instruments. The benefits are that it enables rapid deployment of essential infrastructure, supports the establishment of permanent bases, and ensures redundancy and resilience through distributed systems.
# GNSS Multipath Detection Using Continuous Time-Series C/N0
## 2 Features of Measured C/N\({}_{0}\) in Dense Urban Areas
### Measured C/N\({}_{0}\) in Dense Urban Areas
This phenomenon is also illustrated in case 2 in Figure 1. Regarding the C/N\({}_{0}\) of GPS 30, the signal was often completely lost owing to the tall building between GPS 30 and the antenna, which is exactly the situation of case 1 in Figure 1.  
Figure 2: Target location for investigating carrier to noise ratio (C/N\({}_{0}\)) in dense urban areas. Figure 4: Temporal C/N\({}_{0}\) mainly receiving line-of-sight (LOS) signals. Figure 3: Sky plots of all satellites and their conditions (8:25:00, 462,300 s).
# Numerical and Experimental Study of a Large-Scale Natural Circulation Helium Loop
## 2 Experiment  
Experimental data were gathered from tests conducted in an experimental facility built in Trinava, Slovakia (Figure 1). It is a model of an emergency cooling system for generation IV gas-cooled fast reactors (GFRs), and it was designed as a natural circulation loop. The loop was filled with helium, which could achieve a pressure of up to 7 MPa and a temperature of up to 520 \({}^{\circ}\)C. The heat was generated by a vertically oriented electric heater and was removed by a water cooler with a maximum design capacity of 250 kW. The main advantage of this device is that it is a large-scale device with parameters that are more similar to those of future devices intended for emergency cooling. Helium loops with such parameters are rare, which is mainly because of the investment and operating costs; however, they have the potential to provide unique and important knowledge of the behavior of such systems.  
There were three main quantities that were measured along the loop that allowed us to investigate its behavior: pressure, temperature, and the flow rate of the cooling water. The pressures and temperatures of the helium were measured at points placed at the heater's and the cooler's outlets (see Figure 1). The heat generated by the heater was estimated from the electric input of the coils. The heat removed by the cooler was determined from the measured cooling-water flow rate and the measurement of the water inlet and outlet temperatures. These data allowed the determination of the density and, consequently, the mass of the helium in the loop. Some of these quantities were also used as inputs for the simulation model.  
The runs of the experiments were guided by scenarios that defined the time courses of the input parameters, i.e., the heating power and cooling-water flow rate. In addition, the maximum pressures and temperatures of helium to be reached during the experiment were defined. Each scenario had three main parts. The first was a heating phase in which the helium was brought from an initial state (filling pressure, ambient temperature) to a state where a constant power was transmitted at a constant helium temperature and pressure for 30 min (steady state--second phase). Several steady states could be achieved during one experiment according to the requirements of each scenario. The last phase was aftercooling, where the heating power was reduced either gradually or stepwise. The duration of the experiment typically ranged from 5 to 8 h. All of the measured quantities were recorded continually with a frequency of 1 Hz.  
More information about the experimental device's setup can be found in [18].
## 2 Theoretical Background
### Model Performance Evaluation Metrics  
Several metrics are utilized to evaluate the accuracy of forecasting results in scientific research. In this study, we assessed our predictions using two commonly employed metrics: root mean square error (RMSE) and mean absolute percentage error (MAPE). The following formulas depict these metrics.  
\[\text{MAPE}=\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}\frac{|\text{y}_{ \text{i}}-\text{y}_{\text{i}}^{\prime}|}{\text{yi}}\times 100\% \tag{16}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}\left(\text{ y}_{\text{i}}-\text{y}_{\text{i}}^{\prime}\right)^{2}} \tag{17}\]  
where n is the data number in total; y\({}_{\text{i}}\) is the actual load value and y\({}_{\text{i}}^{\prime}\) is the forecasted load value at time t.
## 4 Experiments
### Loss Function  
The loss function curve during the iterative training process is shown in this section with the performance analysis of the proposed segmentation algorithm. As described in Section 3.3, the shadow edge detection loss and shadow region segmentation loss are summed to obtain the multi-task supervision loss, and then the multi-task supervision loss and intro task consistency loss are added to obtain the final total loss.  
The student is trained with supervised segmentation loss and unsupervised consistency loss compared to teacher outputs. We integrate the consistency loss for segmentation and regression tasks. As shown in Figure 7, we focus on the _edge_con_ and _seg_con_ during the model training process.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **Dice** & **Sen.** & **Spec.** & \(S_{\mathbf{\kappa}}\) & \(E_{\mathbf{\phi}}^{mean}\) & **MAE** \\ \hline \multirow{4}{*}{COVID-HSSCritical} & U-Net [25] & 0.284 & 0.363 & 0.762 & 0.613 & 0.662 & 0.019 \\ \cline{2-7}  & AttU-Net [26] & 0.231 & 0.301 & 0.732 & 0.583 & 0.651 & 0.018 \\ \cline{2-7}  & Inf-Net [50] & 0.330 & 0.601 & 0.793 & 0.575 & 0.621 & 0.053 \\ \cline{2-7}  & Trans-Inf-Net [19] & 0.561 & 0.734 & 0.971 & 0.670 & 0.692 & 0.028 \\ \cline{2-7}  & transYLmtMT & **0.823** & **0.862** & **0.987** & **0.924** & **0.903** & **0.015** \\ \hline \hline \end{tabular}  
* **Red** represents the best, blue represents the second best.  
\end{table}
Table 4: Result on COVID-HSS critical dataset.  
Figure 7: Loss function during the training process.  
According to the curve graph of the loss function, it can be intuitively observed that the function begins to converge when the loop is trained approximately 1000 to 4000 times, and it tends to stabilize when the loop is trained approximately 4000 to 5000 times. After 10,000 iterations of training the algorithm model in this article, the loss value reached a relatively stable state, and was considered to have achieved an ideal segmentation effect.
# Detection of Volatile Constituents from Food Lures by Tephritid Fruit Flies
## Materials and Methods
### Oflactometer Experiments  
Two-choice olfactometer experiments were conducted in a Y-tube bioassay apparatus, placed in a box made of white fabric (40 cm \(\times\) 40 cm \(\times\) 50 cm) to disperse light more evenly and to avoid disturbances by movements of the experimenter (Figure 1). The Y-tube was made of a borosilicate glass tube with an id of 3.1 cm. The length of the upstream arms was 16 cm each and the common arm was 14 cm long. The upstream end consisted of glass odor source chambers (8.5 cm and 2 cm id) containing a stimulus (10 \(\upmu\)L) on a WhatmanTM, grade 1, circular filter paper (20 mm diam.). An air flow was created using an Elite 802 pump (Rolf C. Hagen U.K. Ltd., Castleford, UK), pumped through a teflon tubing and passed through series of glass wash bottles for activated charcoal filtering and humidification. The air leaving the wash bottles was split into two teflon tubings, each adjusted using a flow meter (BA-4 AR, Kytola instruments, Muurame, Finland, Figure 1), and then entering the glass odor source chamber with an airstream of 0.5 L min\({}^{-1}\). Experimental flies were starved for 5 h prior to experiments; individual flies were transferred to vials and moved to a climate chamber (25 \({}^{\circ}\)C, 65% RH) 1 h prior to experiment to acclimatize flies.  
Behavioral experiments assessed attraction of female _B. dorsalis_ to 1. a mix of five commercial baits in equal ratios (mix of baits) 2. the pooled headspace extract of the five lures, 3. the 13-component synthetic blend and 4. the _B. dorsalis_-based synthetic blend (dorsablend). Following these experiments, female _B. zonata_, _C. capitata_ and _Z. cucurbitae_ were tested towards zonablend, capiblend and cucublend respectively (Table 1). Flies were released individually at the entrance of the Y-tube's common arm, and were considered to have made a choice when the female reached the odor source chamber  
Figure 1: Schematic design of the Y-tube apparatus used in the 2-choice olfactometer experiments. The Y-tube bioassay apparatus was placed in a box made from white fabric to disperse the light more evenly and to avoid disturbances induced by movement. Pumped air was cleaned in a series of bottles with activated charcoal and distilled water. An airflow of 0.5 L min\({}^{-1}\), adjusted using two flowmeters, reached via Teflon tubing, the glass odor source chambers at each side of the upstream tube. Treatments loaded on filter paper were placed inside the odor source chambers. Flies were released individually at the entrance of the Y-tube common arm, and were considered to have made a choice when the female reached the odor source chamber.  
(Figure 1). Flies that did not enter the odor source chamber within 10 min were considered non-responders. The positions of the stimuli were rotated and odor sources were replaced with new stimuli on clean filter paper after every five flies. Forty flies were released for each experiment. Glass tubes were cleaned and rinsed with 70% ethanol and dried in an oven (350 \({}^{\circ}\)C) for 8 h.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 3 Research Design
### Sentiment Analysis  
Following the collection of relevant data, the next stage is to ascertain people's perceptions of Microsoft and its products or services. So, we use SA to try to create insights from Twitter and StockTwits data. We use two SA tools, TextBlob and VADER, for both Twitter and StockTwits to check out which produces the best outcomes. The data are then preprocessed so that they may be used to develop ML models. The aim is to combine stock prices and sentiment scores into a single dataset and enhance the possibilities of anticipating Microsoft stock prices by employing ML classification methods on these data.
## 2 Preliminaries
### Graph Theory  
Suppose that there are N vehicles participating in a cooperative-attack mission. The information communication among multiple vehicles can be described with an interaction digraph [22]. Let \(G\) (\(M\), \(E\), \(A\)) denote a communication graph. \(G\) (\(M\), \(E\), \(A\)) consists of a node (\(M=\{M_{1},M_{2},M_{3},\ldots,M_{N}\}\)), an edge (\(E\subseteq\left\{(M_{i},M_{j});M_{i},M_{j}\in M\right\}\)), and the weighted adjacency matrix (\(A=\left[a_{ij}\right]\in R_{n\times n}\)). The adjacency element (\(a_{ij}\)) satisfies \(a_{ij}>0\) if and only if information is exchanged between \(M_{i}\) and \(M_{j}\). If there is a directed path between any two distinct nodes, the directed graph is strongly connected. The in-degree of the node (\(v_{i}\)) is defined as \(d_{i}=\sum\limits_{j=1}^{n}a_{ij}\). The diagonal matrix obtained from the \(d_{i}\) as diagonal entries is called the diagonal in-degree matrix (\(D\)). Finally, the graph Laplacian matrix is obtained as \(L=D-A\). The Laplacian matrix (\(L=\left[l_{ij}\right]\in\mathbb{R}^{n\times n}\)) of the directed graph (\(G\)) is defined by:  
\[l_{ij}=\begin{cases}\sum\limits_{k=1}^{n}a_{ik},&j=i\\ -a_{ij},&j\
eq i\end{cases} \tag{1}\]  
where \(\mathbb{R}\) denotes the fields of real numbers.
## 3 Materials and Methods
### Instrumental Set-Up  
GC-MS analyses were performed on an Agilent 6890 series gas chromatograph equipped with an Agilent 7683 automatic liquid sampler and interfaced to an Agilent 5973N mass selective detector (Agilent Technologies, Little Falls, DE, USA). A split/splitless (S/SL) injector operating at 250 \({}^{\circ}\)C was used in two inlet configurations: manual injection in the S mode (ratio of 1:40) for the HS-SPME/GC-MS assays; automatic injection in the SL mode (injecting a volume of 1 \(\mu\)L) for the HS-BA\(\mu\)E/GC-MS assays. A fused silica capillary column (30 m length \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\mu\)m film thickness (Zebron ZB-5; 5% diphenyl, 95% dimethyl polysiloxane; Phenomenex, Torrance, CA, USA)), was used. The oven temperature was programmed to start at 50 \({}^{\circ}\)C followed by a 1 min isothermal, and then heated up to 90 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\) (held isothermally for 1 min), heated up to 200 \({}^{\circ}\)C (20 \({}^{\circ}\)C min\({}^{-1}\), held for 2 min) and, finally, heated up to 300 \({}^{\circ}\)C (25 \({}^{\circ}\)C min\({}^{-1}\), held for 5 min), which led to a total run time of 27 min. Helium was used as the carrier gas in the constant pressure mode (14.3 psi). For all analyses a solvent delay of 3.5 min was used. The transfer line, ion source and quadrupole temperatures were maintained at 280 \({}^{\circ}\)C, 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. In the full-scan mode, electron ionization mass spectra in the range of 45-550 Da were recorded at 70 eV of energy. The acquisition data and instrument control were performed by the MSD ChemStation software (G1701 CA, version E.02.02.1431; Agilent Technologies, Santa Clara, CA, USA). The identity of each compound was assigned by the comparison of their retention indices (RI) relative to a mixture of C\({}_{10}\)-C\({}_{40}\)\(n\)-alkane homologs [28] and the characteristic features of their mass spectra in comparison with the Wiley's library spectral data bank (G1035B, Rev D.02.00; Agilent Technologies, Santa Clara, CA, USA). The calculations of each assay were performed by a comparison of the average peak areas of the extracted compounds with the IS peak area.
## 2 Experiment  
The fuel cell stack performance and temperature characterization experiments are conducted on a high-power laboratory test platform with a 140-cell stack. Figure 1a shows the schematic diagram of the test system, and Figure 1b presents the experiment setups. The test system comprises subsystems of gas supply, thermal management, exhaust gas collection, and signal acquisition. The gas supply system uses a mass flow controller to provide the stack of nitrogen, hydrogen, and air at the set flow rate. The thermal management system controls the stack temperature by heating and cooling the coolant. The exhaust gas collection system collects the exhaust gas and liquid water from the cathode and anode outlets. The signal acquisition system tests and records pressure, temperature, and 140-cell voltages with a sampling time of 1 s.  
Table 1 lists the stack's technical specifications. In the stack performance experiment, its temperature is controlled at 65 \({}^{\circ}\)C, and it is autonomously humidified by a membrane humidifier to an internal humidity of 95%. The hydrogen stoichiometry ratio is 1.3 without humidification. The air stoichiometry ratio is 2 with cathode off-gas humidification. The stoichiometric ratio is the ratio of the amount of reactant supplied to the amount of reactant required for the electrochemical reaction. The stoichiometry ratio is kept constant throughout the experiment, and the current sampling points are set in the range of 0-520 A in steps of 20 A, with a 3 min dwell at each point. The signal acquisition system records the cell voltages and other information at a sampling rate of 1 Hz.  
Table 2 lists the parameters of the stack temperature characterization experiment. To analyze the heat production and stack temperature response time, the target temperature and maximum temperature of the coolant are set to 65 \({}^{\circ}\)C and 68 \({}^{\circ}\)C, respectively, and the stack operates at three steady-state currents of 467 A, 487 A, and 507 A. The ambient temperature is 20 \({}^{\circ}\)C in the lab. The temperature is controlled close to the target temperature through the radiator, and the coolant temperature changes are recorded. Thermographic images at the stages of start-up, 467 A, and 507 A are taken to visualize the temperature distribution on the stack surface. The stack surface is divided into 25 segments labeled A-Y from the inlet end to the outlet end.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Stack cell number & 140 \\ Material of bipolar plate & Graphite \\ Active area & 406 cm\({}^{2}\) \\ Thickness of PEM & 0.0018 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stack technical specifications.  
Figure 1: (**a**) Schematic diagram of the fuel cell test system; (**b**) experiment setups.
## 4 Experiments
### Performance Evaluation
#### 4.4.1 Comparison with Baseline Methods

Table 2: Average performance comparison for our dataset. Regarding the graph-based methods, ASTGNN maintains its dominance over all other baseline methods. Specifically, concerning inflow and outflow predictions in the Hangzhou dataset, our STGNN model demonstrates notable performance over ASTGNN. Precisely, in the context of 30 step predictions, STGNN outperforms ASTGNN by 10.5% and 8.6% in terms of RMSE and by 9.1% and 6.5% in terms of MAE for inflow and outflow, respectively. Moreover, for 60-step predictions, STGNN exhibits enhancements over ASTGNN by 8.5% and 12.1% in terms of RMSE and by 7.8% and 8.0% in terms of MAE for inflow and outflow, respectively.
## 1 Introduction
In this paper, a nonlinear observer for the state estimation of a tethered ROV system in slowly varying ocean currents is proposed. The observer will estimate the velocity state, the external current velocity, and the cable disturbance. The rest of this paper is organized as follows. In Section 2, the kinematic and dynamic models of ROVs are introduced and formulated. In Section 3, numerical methods for discrete model solutions are given. In Section 4, a nonlinear observer is proposed for estimating velocities, unknown ocean currents, and the cable disturbing force, and the stability properties of the observer are analyzed. Then, in Section 5, the performance of the proposed observer is illustrated through case studies. Finally, the conclusions are presented in Section 6.
## 4 Results and Discussion
### Solar Radiation
#### 4.2.3 The Effect of Different Air Pressures on Radiant Heat Errors  
We analyzed the effect of solar radiation on the temperature rise in the sensor under different air pressure conditions during the soundings from high to low altitudes. The ambient temperature was set at 233.15 K, the air speed was 100 m/s, the initial temperature  
Figure 8: Temperature variation of Pt thin film under different radiation conditions.  
Figure 7: Effect of different radiation-proof coatings on the temperature of temperature sensors.  
of the temperature sensor was 238.15 K, and the solar radiation was 1000 W/m\({}^{2}\). The air pressure was set to \(5\times 10^{0}\) Pa, \(5\times 10^{1}\) Pa, \(5\times 10^{2}\) Pa, \(5\times 10^{3}\) Pa, \(5\times 10^{4}\) Pa, and \(5\times 10^{5}\) Pa.  
As seen in Figure 9, the sensor's temperature first falls sharply and then slowly. In the first part, the temperature drop rate of the sensor increases as the air pressure increases. In the second section, the sensor temperature drop rate decreases as the air pressure rises, and the steady-state temperature difference becomes smaller and smaller. Overall, the steady-state temperature becomes closer to the ambient temperature as the air pressure increases over a certain period of time, i.e., the temperature error decreases.  
Similarly, as shown in Figure 10, the sensor's temperature rises sharply and then slowly. In the first section, the temperature drop rate of the sensor increases as the air pressure increases. In the second section, the sensor temperature rises rate decreases as the air pressure increases, and the steady-state temperature difference becomes smaller and smaller. Overall, the steady-state temperature becomes closer to the ambient temperature as the air pressure rises over a certain period of time, i.e., the temperature error decreases.  
Figure 10: Changes in the heating of Pt thin film under the influence of radiation at different air pressures.  
Figure 9: Changes in cooling of Pt thin film under the influence of radiation at different air pressures.  
Therefore, the higher the altitude and the lower the air pressure, the less effective the heat transfer and the greater the temperature error value.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 5 Discussion  
We propose a semantic-guided adaptive bias calibration approach to alleviate the bias towards seen classes in generalized zero-shot point cloud segmentation. We explore the visual-semantic correlation through the spatial-channel attention mechanism and utilize visual-semantic alignment to effectively distinguish different category samples in the feature space and generate auxiliary predictions to counteract the bias. We adaptively adjust the fusion weights between the main prediction from the primary segmentation branch and the auxiliary prediction from the visual-semantic fusion branch, providing auxiliary guidance preferentially for low-confidence samples while suppressing unnecessary corrections to high-confidence predictions. The experiments on common benchmarks confirm that our approach has significantly enhanced zero-shot segmentation performance. Despite these advances, our method still faces challenges in aligning visual and semantic information. This method aligns point cloud features of the same class with a single semantic embedding, which, in practical applications, may lead to segmentation misjudgments due to differences in object morphology, similar to other methods. In the future, we plan to explore multi-scale alignment methods between visual and semantic information further to improve the performance of zero-shot point cloud semantic segmentation.  
Conceptualization, Y.W.; methodology, Y.W. and M.Q.; software, Y.W.; validation, Y.W. and M.Q.; formal analysis, Y.W.; investigation, Y.W.; resources, M.Q.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W. and M.Q.; visualization, Y.W.; supervision, M.Q.; project administration, M.Q.; funding acquisition, M.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61902225) and the Joint Funds of Natural Science Foundation of Shandong Province under Grant (ZK2021 LZ1011).  
**Institutional Review Board Statement** Not applicable.  
Figure 5: Effect of hyperparameters: confidence weight \(\beta\) on S3 DIS (**a**) and branch balance parameter \(\lambda\) on ScanNet (**b**); \(\beta=1.2\) and \(\lambda=0.7\) show the best results.  
Informed Consent Statement: Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.