esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 DIC Used in Impact Studies to Measure Target Deformation
reported that the authors obtained the panels directly from a helmet manufacturer and did not receive information regarding the fibre directions of the panels. The authors did report that the panels had 23 plies and a thickness of \(8.5\pm 0.5\) mm. The authors used a combination of a Phantom v311 and Phantom v310 cameras (Vision Research Inc., Wayne, NJ, USA), running at 15,000 FPS, a resolution of \(512\times 384\) pixels and 4 \(\upmu\)s exposure time to capture the high-speed images of the back face. No information was provided regarding the 3D DIC processing software the authors used. While the initial 9 mm projectile shots were used to calibrate the 3D DIC system, the authors also conducted a small study on multiple hits on the panels (without overlapping areas of effect), looking to determine if a difference in the BFD occurs and if the 3D DIC was capable of capturing it. Their results show that there was no difference in the BFD between the first and second shot on the same plate. However, there was less oscillation on the target plate on the second shot if there is pre-damage on the plate. The impacts with the 7.62 \(\times\) 39 mm projectiles were conducted with a muzzle velocity of between 500 and 720 m/s and at an angle of obliquity of 70\({}^{\circ}\). The authors investigated two difference sized panels (\(200\times 300\times 8.5\) mm and \(100\times 300\times 8.5\) mm) for these tests, to determine if the distance to the boundaries affected the panel response to impact. The authors show that the region of plastic deformation was smaller in the smaller panel and the larger panel exhibited greater dynamic elastic deformation. The larger panels did exhibit larger displacement in the z-direction, showing that the data from the smaller and larger panels, for the same projectile and muzzle velocity, were not comparable [36]. As this section shows, 3D DIC is rapidly becoming a very effective tool for determining both the front and back face deflection during ballistic impacts on the flat and curved panels of a variety of materials.
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 5 The Proof of Concept: A Case Study on Oil Spill Detection
### Description of the Scenario  
The proactive detection of oil spills is an important means to minimize the damage of spills to the marine environment. Thus, oil spill detection is considered one of the use cases in the SWARMs project. A set of SWARMs vehicles could collaboratively detect the occurrence of oil spills based on contextual data they could obtain. After detecting oil spills in a specific marine region, it is important to predict the severity of the spilled region so that remedial measures (e.g., clean-up, containment) can be taken accordingly. The estimation of the severity level of the specific area depends on several available raw contexts, including the thickness of spills, the estimated size of spills, the weather condition, and the currents. In addition, the severity level of the specific area could be influenced by different spills that occurred in the same area at the same time. For instance, two oil spills (spill_1 and spill_2) are detected in a specific region of the ocean (region_1). Spill_1 is observed as being thick and large and spill_2 is detected as being thin and small. In addition, the weather conditions above the spilled marine region are known to be inclement. Currents in the region_1 fluctuate very strongly.
## 3 Materials and Methods  
In response to the growing challenges in detecting fake news across diverse platforms and domains, in our study, the aim was to advance current detection methodologies by leveraging state-of-the-art deep learning models. While existing models have demonstrated impressive accuracy, they often struggle with generalization to new types of misinformation, evolving content, and multimodal data. The performance of models can vary significantly depending on the dataset, context, and even the language in which the news is written. For instance, some models may perform well on certain platforms like Twitter or Weibo (as seen in [PERSON] et al. (2019) [11]), but their performance may degrade when applied to different domains, such as socio-political contexts ([PERSON] et al. (2023) [22]). Even though high accuracy rates are reported in some studies, many models still struggle with data imbalance and the bias inherent in the datasets. Some models may overly rely on specific features (e.g., textual or visual), which may not generalize well across all types of fake news.  
To address these challenges, we incorporated advanced techniques that offer both high performance and scalability. We chose to utilize LLMs such as GPT-4o and GPT-4o-mini, as they are pre-trained on large, diverse datasets and have demonstrated strong performance on tasks similar to fake news detection. These models possess a deep contextual understanding in the embedding space, making them highly effective for language tasks. Additionally, we employed CNNs to compare the performance of LLMs against traditional methods, providing valuable insights into their relative effectiveness for this specific problem. Finally, we used BERT as a cost-effective alternative to LLMs, as it can achieve strong results while requiring less computational power. To tailor each model to the unique requirements of fake news detection, we fine-tuned with few-shot learning all three approaches on our task-specific dataset, ensuring that they were optimized for this challenge. By comparing these models, we aimed to enhance the understanding of their relative strengths and weaknesses in detecting fake news across diverse platforms and contexts.  
To provide a clearer explanation of the procedure followed in this study, we created a flowchart using the Lucidchart web app, presented in Figure 1.  
Figure 1: Flowchart illustrating the dataset preprocessing, splitting, fine-tuning, predictions, and predictive evaluation process.
## 2 Materials and Methods
### Accuracy of the Method  
As mentioned before, to the best knowledge of the authors, in the literature, there are no deterministic ways to obtain the J-R model parameters for the real (measured) EEG signals. Therefore, it is impossible to compare parameters obtained via the proposed method to the \"real\" parameters of measured signals, as those are unobtainable. The accuracy of the method can be determined, however, using signals generated by the J-R model itself. This way, the original parameters of the signal are known and the difference between them and the parameters obtained by the proposed method could be obtained.  
Accuracy was defined as one minus the difference between the obtained parameter's value and the real parameter's value divided by the range of possible parameter's values (from Table 2). It was visualized in the right part of Figure 4. Accuracy defined that way is an 8-element vector with values ranging from 0 (when the given parameter is as wrong as it could be) to 1 (when the parameter's value is identical to the reference).
## 2 Results and Discussion  
### Characterization of CDSS in Rehabilitation  
There are two main categories of CDSSs: those oriented to _assessment_ and those oriented to _proposal_. The objectives of the assessment-oriented ones are: assessment of a patient's past, current and future status (this includes prognosis, _i.e._, the likely outcome of an illness); risk quantification; and classification of patients according to their functional diversity [8]. The objectives of proposal-oriented ones are: risk prevention; and definition of therapeutic goals.  
We designed and developed a CDSS oriented to _assessment_ and, more specifically, to prognosis, in a clinical-system environment which requires reasoning under uncertainty, with standard indicators (e.g., _Ingestion functions_--b510) used in the representation of cases. The way of **giving advice** can be a _push_ action, with warnings or alarms, or a _pull_ action, giving support when explicitly requested. In the intra-hospital stage, support is pulled during all the rehabilitation process and pushed in the initial assessment and at the end of each rehabilitation activity. In the extra-hospital stage, advice is pulled in the prognosis support and pushed at the end of the PIE.  
In the **style of communication and decision-making process** of CDSSs applied to rehabilitation, both a consulting model and a critiquing model are used. In our case, the consulting model is used; the system provides an assessment of health future status and risks; and the final decision is taken by the health professional. With respect to the CBR used for the decision-making process, the system proposed only includes the _retrieve_ step of the typical _retrieve_, _reuse_, _revise_ and _retain_ phases shown in [9].
## 2 Materials and Methods
### Fabrication and Experimental Setup  
The 80 nm-thick YBCO film with a 20 nm in situ Au film on top was deposited on the MgO substrate with a bicrystal misorientation angle of 24\({}^{\circ}\) and the thickness of 0.5 mm by pulsed laser deposition. The critical current density \(J_{\mathrm{c}}\) of the YBCO film is 2.5 mA cm\({}^{-2}\), and the critical temperature \(T_{\mathrm{c}}\) is 85.7 K. HTS YBCO JJ THz mixers were patterned by a standard photolithography and Ar-ion beam etching technologies. The 2 \(\upmu\)m-wide YBCO strips across the grain boundary (GB) form JJs were embedded into two antennas, respectively, as seen in the zoomed photo of Figure 4a. Then, the mixer chip was packaged into a sample holder. A Si hyper-hemispherical lens with parameters of \(\varepsilon_{\mathrm{r}}\) = 11.9, \(R_{\mathrm{L}}\) = 4.5 mm and the extension length of 0.7 mm is fixed on the back of the mixer chip with cryogenic glue to focus the THz signal on the chip surface where JJ is located. The DC bias lines on the mixer chip are connected to the DC bias pins on the holder through gold wires and silver epoxy. Intermediate frequency (IF) output shares the same path with DC bias lines and is isolated by two capacitors. A radio-frequency (RF) filter module consists of resistors and capacitors and is applied to the DC bias pins to isolate DC and RF signals. LO signal is radiated out by a 1.95 cm-long monopole antenna, 0.8 cm above the holder mounted in the Gifford-McMahon (GM) cryocooler, shown in Figure 4b.  
Measurements are carried out in a GM cryocooler at a bath temperature of about 4.5 K. The schematic diagram of the measuring setup for harmonic mixing is shown in Figure 4c. THz signal is generated from commercial VDI-Tx-S140 manufactured by VDI. Two off-axis parabolic mirrors are applied in the quasi-optical system to focus THz radiation on the Si hyper-hemispherical lens. The LO pump is produced from a microwave analog signal generator Agilent N5183A MXG and radiated through a frequency-dependent monopole antenna. Additionally, the radiation efficiency of the monopole antenna has been calibrated by a vector network analyzer. HP 8447D 011 Dual Amplifier, which has a gain of about 50 dB in the 0.1-1300 MHz frequency band, is used to amplify the down-converted IF output signal, finally recorded in an Advantest R4131D spectrum analyzer. In addition, a homemade current source and an automatic acquisition system of \(I\)-\(V\) characteristics (IVCs) are applied in the measurements.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 9 Patents  
Some of the results of this research have been filed as Japanese patents on 5 November 2019.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/electronics11071163/s1](https://www.mdpi.com/article/10.3390/electronics11071163/s1)]([https://www.mdpi.com/article/10.3390/electronics11071163/s1](https://www.mdpi.com/article/10.3390/electronics11071163/s1)), File S1: fft_analysis.m: MATALB code for FFT Analysis; File S2: TLE2 POS.py: Python code to calculate range distance to satellites and other information from TLEs; File S3: SDRSharp_20211215_061940Z_437375000 Hz_IQ.wav: IQ signal recorded by SDR#.  
**Author Contributions:** Conceptualization, D.N.; methodology, D.N.; software, D.N., T.L.D.M. and BIRDS-4 project; validation, D.N.; formal analysis, D.N.; investigation, D.N. and the BIRDS-4 Project Team; resources, T.Y., H.M., S.K. and M.C.; data curation, D.N.; writing--original draft preparation, D.N.; writing--review and editing, M.C.; visualization, D.N.; supervision, K.T. and M.C.; project administration, T.Y., H.M., S.K. and M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by JSPS Core-to-Core Program B: Asia-Africa Science Platforms (JPISCCB20200005).  
**Data Availability Statement:** Data are contained within the article or Supplementary Materials.  
**Acknowledgments:** The development and operation of the BIRDS-4 project was based on the results of the three generations of projects up to and including BIRDS-3. The authors would like to express gratitude to the BIRDS-4 engineering team members in particular: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments and Results  
### Hyperspectral Data Sets  
In this study, four supervised hyperspectral remote sensing data were used: Indian Pines, Pavia University, Salinas, and a local area zoomed image from the Salinas image (denoted as SalinasA). The four sets of data were used in experiments after correction and removal of their high noise bands. The single-band grayscale images are shown in Figure 3, and the display results of the hue category label are shown in Figure 4. The experiments were operated on a MATLAB platform using five kinds of manifold algorithms to compare the performance of the different algorithms.
# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
###### Abstract  
Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.  
flood mapping; damage assessment; SAR image; Sentinel-1; Landsat-8; Google Earth Engine; GEE; Bangladesh +
Footnote †: journal:  
1
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 4 Modeling of the Rain and Fog Effect in the Virtual LiDAR Sensor
### Calculation of Backscattered Coefficients  
A novel approach has been introduced to model the backscattering effect of rain droplets for the LiDAR sensor. First, the Monte Carlo Simulation generates all the rain droplets based on the DSD; then, a collision detection algorithm is applied between the transmitted ray of each scan point and the droplets. The collision detection algorithm determines the particles that collide with the virtual LiDAR ray by knowing the beam characteristics, such as beam volume, origin, and direction. These particles are described as a set of tuples:  
\[\mathcal{CP}=\{(R_{d},\theta_{az},\varphi_{el},D)\}, \tag{16}\]  
where \(R_{d}\) is the distance of the particle from the sensor, the azimuth angle of the raindrop is denoted by \(\theta_{az}\), and the elevation angle by \(\varphi_{el}\), while \(D\) is the droplet diameter. The backscattered coefficient \(\beta_{back,rain}\) is calculated for every droplet at the distance \(R_{d}\).  
\[\beta_{back,rain}=\frac{\pi}{4}Q_{back}ND^{2}R_{d}, \tag{17}\]  
where \(Q_{back}\) is the backscattered efficiency of the particle and \(N\) is the number of drops in \(1/\text{m}^{3}\)[31].
## 6 Discussion  
### Sensor Orientation of Deimos-2 Images  
The RMSE\({}_{\text{2D}}\) values attained from the direct geopositioning approach were always lower than the nominal circular error 90% (CE90) of 100 m (equivalent to approximately 65.9 m as RMSE\({}_{\text{2D}}\)) specified for Deimos-2 L1B products without GCPs' support [16]. However, these direct geopositioning errors provided by Deimos-2 were almost four times higher than those obtained for the Maxar's (old DigitalGlobe) VHR satellite constellation, where the CE90 for WV2 ORS2A product turns out to be 10.2 m (6.7 m in terms of RMSE\({}_{\text{2D}}\)) [46]. It is worth noting that this achievement is not really important in practical terms because the sensor orientation phase when working on VHR satellite images is usually supported by accurate GCPs [1; 6; 7]. As already mentioned, a zero-order polynomial adjustment (RPC0) is adequate for most of the modern VHR satellites when using the triangulation method developed by [PERSON] and [PERSON] [12]. However, according to [PERSON] and [PERSON] [13], when there are high order distortions in the images, adding terms to the image-to-space transformation by using RPC1 or RPC2 instead of RPC0 is required. In the light of these findings, Deimos-2 L1B single images showed important distortions that required applying the RPC1 sensor model and 12 GCPs for achieving the best orientation results. [PERSON] and [PERSON] [4] reported that the planimetric accuracy at the sensor orientation phase should be in the range of 0.5-0.7 pixels for VHR satellites when using refined RPCs supported with a few high-accuracy GCPs. In this way, the attained geometric accuracy for Deimos-2 images using RPC1 and 12 GCPs (RMSE\({}_{\text{2D}}\) value of 1.225 m or 1.225 pixels) can be considered as suboptimal results. These poor results were influenced by their very large off-nadir viewing angle (ranging from 34.32\({}^{\circ}\) to 36.45\({}^{\circ}\)). Note that [PERSON] et al. [7] reported RMSE\({}_{\text{2D}}\) values of 0.89 pixels and 1.10 pixels in the orientation phase of two WV2 ORS2A single images with an off-nadir angle of 10\({}^{\circ}\) and 22.4\({}^{\circ}\), respectively. In addition, and according to [PERSON] et al. [16], the corresponding ground spatial
# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
## 4 Results and Discussion  
In this study, the cyclical variation pattern of the cooling load in the previous several days is identified and that of the following day is predicted by LSTM. Based on the prediction results, the trends in cooling capacity supply and temperature changes before and after the adoption of the peak shifting strategy for a typical day are analyzed. A total of five consecutive working days are taken as a case study to illustrate the peak shifting effect and economics of the strategy.
# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 4 Experimental Results and Analysis
### Performance Comparison  
#### 4.2.1 Comparison with RGB-D SOD Models  
To verify the effectiveness of the model, we compared CMANet with 17 RGB-D saliency object detection models, namely CTMF\({}_{18}\)[27], PCF\({}_{18}\)[28], AFNet\({}_{19}\)[30], CPFP\({}_{19}\)[29], SSF\({}_{20}\)[31], CoNet\({}_{20}\)[13], D\({}^{3}\)Net\({}_{21}\)[8], DCMF\({}_{22}\)[32], DMRA\({}_{22}\)[34], MMNet\({}_{22}\)[33], CMINet\({}_{22}\)[52], DSU\({}_{22}\)[53], CFIDNet\({}_{22}\)[35], SPNet\({}_{23}\)[9], DAL\({}_{23}\)[36], EGA-Net\({}_{23}\)[37], and RD3D\({}_{24}\)[38].
## 3 Methods and Strategies for BeiDou-3 and BeiDou-2 Satellite POD  
The routine software package developed by the Analysis Centre at the Institute of Geodesy and Geophysics, Chinese Academy of Sciences has been used to process data for BeiDou-3 and BeiDou-2 satellites in this study. A two-step method is adopted in this study. In the first step, the stations for BeiDou-3 and BeiDou-2 satellite POD are embodied in a GPS POD process using the ionosphere-free linear combination (LC) of L1 and L2 phase observations with IGS rapid products. Then, in the second step, the relative accurate station coordinates (CRDs), earth rotation parameters (ERPs), and zenith tropospheric delays (ZTDs), which were obtained from the GPS POD process, are further fixed in the BeiDou-3 and BeiDou-2 satellite POD process using the ionosphere-free linear combination (LC) of B1 and B3 phase and code observations beginning with the broadcast ephemeris. In the second step, only BeiDou-3- and BeiDou-2-related parameters (i.e., six keplerian elements and five solar radiation parameters (Constants in the D-, Y-, and X-directions and periodic in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station Abb.** & **Location** & **County** & **Receiver** & **Antenna** \\ \hline BJF1 & Beijing & China & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ BRCH & Braunschweig & Germany & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ CLGY & Calgary & Canada & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ GUAI & Urumqi & China & GNSS\_GGR & RINT-SCH CETD \\ LHA1 & Lhasa & China & CETC-54 GMR-4011 & NOV570.R4 NOVS \\ TAHT & Tahiti & France & GNSS\_GGR & RINT-SCH CETD \\ WUH1 & Wuhan & China & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ XIA1 & Xian & China & GNSS\_GGR & RINT-SCH CETD \\ XHON & Antarctica & United Nations & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected tracking stations from the International GNSS (Global Navigation Satellite System) Monitoring and Assessment Service (iGMAS).  
Figure 1: Tracking stations in precise-orbit determination and satellite laser ranging (SLR) validation for BeiDou-2 and BeiDou-3 satellites. The stations are represented by red five-pointed stars, blue circles, and green diamonds for iGMAS, Multi-GNSS Experiment (MGEX), and SLR stations, respectively.  
X-direction), and pseudo-stochastic orbit parameters are estimated). The BeiDou-3 satellite antenna phase center (PCO) values are provided by the Operation Control Department, and no phase center variation (PCV) values are available. The estimated PCO and PCV for BDS IGSO and MEO satellites by [PERSON] et al. [18] are used for BeiDou-2 satellites. However, the PCO and PCV for ground antennas are not available. Important aspects of the selected processing strategy regarding the observation model and force model are listed in Table 3[19; 20].