esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### Inertial Navigation Systems  
INS are based on translational and rotational velocity, and how these two variables can be used in combination with GNSS to track and locate objects in the field [2]. GNSS signaling varies with topography, forest conditions, and other factors, but INS can fill in positioning information between known GNSS coordinates [2]. Using a combination of gyroscopes, accelerometers, and magnetic sensors, INS continually calculates mobile device location by dead reckoning. Dead reckoning is a navigation technique that estimates current position by summing sequential vector headings and estimated speed or distance from a known starting point, such as coordinates obtained initially from GNSS [102]. This approach is particularly useful in areas where individuals or equipment may move from areas with suitable GNSS accuracy to areas with degraded GNSS quality, such as traveling from a large meadow or clearcut into a dense forest stand (Figure 6). GNSS-INS accuracy varies with GNSS receiver quality, but these systems are generally capable of sub-meter positioning, with 4-5 cm possible using PPP (Table 1) [103; 104; 105; 106]. Micro-electric mechanical systems (MEMS), which use miniaturized sensors, can reduce the size and cost of INS navigation, but MEMS accelerometers and gyroscopes generally exhibit higher errors than traditional INS. Calibration of these sensors is recommended for increased positioning accuracy [107].
## 4 Interference Detection Based on the Reassigned Spectrogram
### Generalized TF Distribution  
All of the existing TF distributions can be written in a generalized form, which can be used to facilitate the design of desirable TF transforms. This class of transform is known as [PERSON]'s class [11], and the definition of [PERSON]'s class of bilinear (or quadratic) TF distributions can be written as follows:  
\[\begin{split} C(t,\omega)&=\frac{1}{2\uppi}\int_{- \infty}^{\infty}\int_{-\infty}^{\infty}AF(\uppi,\theta)g(\uppi,\theta)e^{-j( \theta t+\omega\uppi)}\text{d}\theta\text{d}\uptau\\ &=\frac{1}{2\uppi}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty} \int_{-\infty}^{\infty}y_{a}(s+\frac{\uppi}{2})y_{a}^{*}(s-\frac{\uppi}{2})g( \uppi,\theta)e^{-j[\theta(t-s)+\omega\uppi]}\text{d}s\text{d}\theta\text{d} \uptau\end{split} \tag{28}\]  
where \(AF(\uppi,\theta)\) is the ambiguity function defined in Equation (22); and \(g(\uppi,\theta)\) is a two-dimensional parametrization function defined in the ambiguity function domain, which is called the kernel function of [PERSON]'s class. This kernel function determines the properties of the bilinear TF distribution, which is often a low-passing function and normally serves to mask out the interference in the original Wigner-Ville representation. When considering \(g(\uppi,\theta)=1\), WVD can be obtained.  
In [PERSON]'s class, there exists a kernel formalism that allows one to interpret each element of the TF distribution as the convolution product of a central distribution (the WVD) with a parameterizing two-dimensional kernel. Therefore, [PERSON]'s class in Equation (28) can be rewritten as the two-dimensional convolution of the WVD of the signal \(y_{a}(t)\) and a two-dimensional smoothing function, provided as follows:  
\[\begin{split} C(t,\omega;\Pi)&=W_{y_{a}}(t,\omega )**\Pi(t,\omega)\\ &=\frac{1}{2\uppi}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty }\Pi(t-s,\omega-\theta)W_{y_{a}}(s,\theta)\text{d}s\text{d}\theta\end{split} \tag{29}\]\[\Pi(t,\omega)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}g(\uppi,\uptheta)e^{-j (\uppi\uppi+\uptheta t)}\upalpha\uppi\uptheta \tag{30}\]  
where (\(\ast\ast\)) denotes the two-dimensional convolution operation, \(\Pi(t,\omega)\) is a two-dimensional smoothing function and \(W_{y_{a}}(s,\uptheta)\) is the WVD of the signal \(y_{a}(t)\). Different TFRs can be obtained from the fundamental WVD by applying a different smoothing function \(\Pi(t,\omega)\). Theoretically, these TF distributions suffer from a limitation inherent in the convolution product, since the revolutions of time and frequency are fixed, regardless of the local geometry of the signal signature. In many cases, especially when the TF readability is of primary importance, the kernel function \(\Pi(t,\omega)\) is well designed to achieve some kind of TF smoothing; on the other hand, this smoothing leads to a less accurate TF localization of the signal components. Its shape and spread should be properly determined to achieve a suitable trade-off between good cross-term mitigation and good TF energy aggregation in the TF plane.
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 4 Acute Life-Threatening Asthma Advanced Management in the ICU
### Intubation  
Approximately 2% of all acute severe asthma patients will go on to require intubation and mechanical ventilation [7]. Among ICU patients, nearly 36-46% may require invasive mechanical ventilation within 24 h of admission [64; 6]. The decision to intubate is a clinical one, albeit considering other investigations and the projected deterioration of the patient's condition. Although variable depending on a combination of factors, the clinical indications for intubation in acute severe asthma are detailed below [65; 66; 67] (Table 1).  
Intubation in the context of these relative indications can be challenging. The intubation of such patients requires rapid sequence induction with close monitoring of cardiovascular status. Preoxygenation may not be straightforward due to air trapping. A larger endotracheal tube is preferred, as it may help with subsequent bronchial toileting with the use of a bronchoscopy [68]. The induction agents commonly used are propofol and ketamine in combination with opiates and a paraslving agent. There are several potential complications that may arise during endotracheal intubation. Some are specific to asthma patients. These include worsening bronchospasm and significant air trapping during and after intubation [65]. Cardiovascular collapse from depleted intravascular volume status, vasodilation from anaesthetic medications, and increased intrathoracic pressure leading to reduced cardiac output need to be anticipated and managed effectively to prevent cardiac arrest [65; 69; 70] (Table 2).  
\begin{table}
\begin{tabular}{c c} \hline
**Relative Indications** & **Immediate Indications** \\ \hline Progressive exhaustion & Cardiac arrest \\ \hline Increasing use of accessory muscles or change in rate/depth of respiration & PaO\({}_{2}\) \(<\) 8.0 kPa and/or PaCO\({}_{2}\)\(>\) 6.5 kPa \\ \hline Change in posture or speech & Severe obtundation or coma \\ \hline Failure to reverse severe respiratory acidosis despite intensive therapy &
\begin{tabular}{c} Impending respiratory failure with gasping or \\ inability to speak \\ \end{tabular} \\ \hline Altered sensorium & Respiratory arrest \\ \hline Severe hypoxemia with maximal oxygen delivery \\ \hline Silent chest \\ \hline \end{tabular}
\end{table}
Table 1: Relative and immediate indications for intubation [65; 66; 67].  
\begin{table}
\begin{tabular}{c c} \hline
**Potential Complications during Intubation** \\ \hline Laryngosquym & \\ Worsening bronchospasm & Significant air trapping \\ \hline \end{tabular}
\end{table}
Table 2: Potential complications arising from endotracheal intubation in an asthmatic patient [65; 69; 70].
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 6 Conclusions  
To solve the problems of low efficiency in the detection of oceanic phenomena in SAR images, we propose a detection network called the MFNN based on ResNet-50 and ASPP. Different from the existing methods in the current research, MFNN can detect the location and category information of multiple oceanic phenomena by extracting and fusing the multilevel features and multiscale features of SAR images. Moreover, by improving multiscale feature extraction on the network structure and adding weights during parameter optimization, it makes the detection accuracy of linear oceanic phenomena, such as oceanic fronts and ship wakes, better than other detection methods.  
We constructed a sample dataset using SAR images from the Sentinel-1 satellite and used this dataset to train and test the MFNN. The experimental results show that the MFNN proposed in this paper realizes the detection of five types of oceanic phenomena, namely, oceanic eddies, rain cells, oceanic fronts, ship wakes, and oil spills in SAR images and obtains an average detection accuracy of 91%.  
[PERSON] conceived and performed the experiments; [PERSON] supervised and designed the research and contributed to the article's organization; [PERSON], [PERSON] and [PERSON] provided the data for sample dataset construction. [PERSON] and [PERSON] drafted the manuscript, which was revised by all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank the anonymous reviewers for their constructive comments and recommendations.  
The authors declare no conflict of interest.
## 5 Summary  
We used the NDVI and NPP products from the MODIS sensors to estimate the fuel loads on a typical shrubland/grassland ecosystem in the western USA. Fuel loads on shrublands and grasslands were characterized by large intra- and interannual variability. Climate and disturbance history are the two major factors affecting this variability in vegetation conditions, which in turn impact fuel loads. Our study demonstrated that the variability in fuel loads can be effectively quantified using high-temporal-resolution MODIS products.  
We also simulated the fire behavior and spread with the dynamic fuel loads. Our simulation results indicated that employing the newly derived biomass information into fire spread models not only alters the values of commonly used fire behavior indices but also changes the simulated amount of area burned. These findings suggested that accurately predicting fire behavior and spread will require detailed information on the fuel loads dynamics, which currently is not provided by existing static fuel loads information. Future work will focus on improving the fuel loads dynamics information using better spatial resolution data and more validation information, as well as integrating the information into fire models for various fire applications.  
[PERSON], [PERSON], and [PERSON], and [PERSON] conceptualized the research; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper; [PERSON], [PERSON], and [PERSON] conducted data analysis and designed the graphics; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The funding for this research was provided by the National Aeronautics and Space Administration (NASA) Applied Science Program (A-35 Wildland Fires). Part of the funding for this research was provided by the LANDFIRE Program and by the U.S. Geological Survey (USSG) Climate and Land Use Mission Area through the National Assessment of Ecosystem Carbon Sequestration and Greenhouse Gas Fluxes (LandCarbon) project. [PERSON]'s the work was performed under USGS contract 613 PC00028. Any use of trade, firm, or product names is for descriptive purpose only and does not imply endorsement by the U.S. Government.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 1 Introduction
#### Some Examples to Illustrate PIN Functionality
If the links are limited, but as long as there are sufficient resources to process the intermediate nodes, it is reasonable to consider the possibility that the network performs certain tasks as the packet is transmitted. The network could be responsible for processing the original image or a video to suit different QoS requirements and could deliver the data to each user depending on their specific QoS requirements and network conditions at any given time. The above descriptions definitely raise three very different scenarios but have an important element in common, which is the possibility of providing value through an implementation of PIN. It is possible that the functionality that is implemented in each case could be very different (an aggregation of abundant and widely dispersed information, a simplification of the information as it travels over the network, filtering, and enrichment of information, image processing or video to adapt to different QoS requirements); however, in all cases, an important contribution of value is identified. The benefits that are achieved may be different in each case (e.g., the elimination of servers, a reduction in the network load, enriched information and more appropriately, facilitation of collaboration in environments with severe restrictions on links); however, in all of these situations, it appears reasonable that the network can perform processing.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 2 Materials and Method
### GNSS Base Station and Satellite Ephemerides Data  
There is a UK Ordnance Survey (OS) geodetic GNSS site located at Blackpool airport (WGS: 53 deg46'36.9'' N, 03 deg02'05.9'' W, British National Grid: SD 31891 31673), 14.5 km to the south of the deployment site; a source of research-quality GNSS data which can be applied to the post-processed kinematic (PPK) solution. Initial processing used base station data at 30 s intervals from the Ordnance Survey. Once the 1 Hz data became available (after a delay of 45 days) these were used in the final positioning solution instead. The archived 1 Hz GNSS data were obtained from the British Isles continuous GNSS facility (BIGF) [28].  
To improve the overall solution, satellite ephemerides for precise orbit corrections and satellite and station clock error corrections were applied. These were obtained from the Crustal Dynamics Data Information System [29; 30]. The corrections are delivered with increasing accuracy in the form of 'ultra', 'rapid' and 'final' correction files; available  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Liverpool**} & \multicolumn{2}{c}{**Blackpool**} & \multicolumn{2}{c}{**Heysham**} \\ \hline  & **Time** & **Ht (m)** & **Time** & **Ht (m)** & **Time** & **Ht (m)** \\ \hline Wed 28 & 15:52 & 2.00 LW & 15:38 & 2.17 LW & 15:51 & 1.97 LW \\ August & 21:32 & 8.92 HW & 21:23 & 8.87 HW & 21:42 & 9.10 HW \\ \hline Thu 29 & 04:30 & 1.49 LW & 04:14 & 1.60 LW & 04:29 & 1.49 LW \\ August & 10:00 & 9.12 HW & 09:55 & 9.01 HW & 10:11 & 9.33 HW \\  & 16:48 & 1.49 LW & 16:32 & 1.61 LW & 16:45 & 1.49 LW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tidal predictions for Liverpool (Gladstone Lock), Blackpool and Heysham (from POLTIPS 2009) for 28–29 August 2019.  
between 3-9 h, 17-41 h and 11-17 days, respectively. The results in this paper use the final products.
## Conclusions: Low Temperature
### Intermediate Temperature: \(22\,^{\circ}\)C-\(32\,^{\circ}\)C
#### 7.2.3 A2t-Ftsp  
The results obtained with A2T-FTSP can be studied in Figure 5. Once again, the temperature variation oscillates between \(22\,^{\circ}\)C and \(32\,^{\circ}\)C.  
A2T-FTSP has a great performance from the beginning of the test. For the first \(1,000\) s of the experiment, the average synchronization error is about \(1.123\)\(\mu\)s, with an almost completely constant behavior during all of this period. As is programmed, at instant \(1,000\) s, a temperature variation is created, going from \(22\,^{\circ}\)C to \(32\,^{\circ}\)C. At instants \(1,200\) s and \(1,500\) s, a slight increase in the average synchronization error is observed. However, once A2T-FTSP reaches instant \(1,500\) s in the test, a great improvement in the average synchronization error can be observed, ending the test with an average synchronization error of \(1.712\)\(\mu\)s, which is \(109.96\%\) better than FTSP.  
**Conclusions: Intermediate Temperature**  
Analyzing the results with a temperature variation between \(22\,^{\circ}\)C and \(32\,^{\circ}\)C, it can be concluded that:  
* As Figure 6 shows, the use of temperature-based synchronization protocols improves the average synchronization error compared to FTSP under the same conditions.  
**Figure 6. Behavior study: \(22\,^{\circ}\)C-\(32\,^{\circ}\)C.*** The behavior differences between the new proposals are minimal (see Figure 5). This is mainly due to the thermal interval used, which is really close to the nominal frequency of the _CMR200T_ oscillator (\(25\,^{\circ}\)C). As a consequence, the frequency variations are minimal, obtaining similar results between the new methods.
* By using both AT-FTSP and A2T-FTSP, an improvement in the average synchronization error is obtained, ranging from \(55.60\%\) to \(109.22\%\) compared to FTSP.
## 6 Conclusions and Future Perspectives
### Challenges
#### 6.2.4 Limited Explainability  
Despite the impressive performance of DL models, complex architectures such as CNNs and Transformers are often criticized for their lack of interpretability. In ocean and climate research, understanding why a model makes a specific prediction is as important as the prediction itself, especially in safety-critical applications where transparency is required. However, the black-box nature of DL models makes it difficult to determine which input variables contribute most to the model output. The high dimensionality and spatiotemporal complexity of remote sensing and oceanographic datasets further amplify this challenge. For instance, in sea level anomaly prediction, DL models have shown improved temporal forecasting accuracy. However, it remains unclear whether the anomaly is primarily influenced by ocean circulations, thermal expansions, or seasonal trends. This lack of interpretability hinders the deployment of DL models in real-world ocean systems.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications
#### 3.4.9 WiSense  
The WiSense module is another popular commercial mote that consists of two printed circuit boards (PCBs). One PCB houses the microcontroller, while the other houses the CC1120 radio. The mote is mainly targeted for very-low-power wireless applications including remote sensing. The module was built around a 16-bit ultra-low-power microcontroller (MSP430G2955) by Texas Instruments, containing 56 kB flash, 4 kB SRAM, 128 kB EEPROM and a 16 MHz clock. Regarding communication capabilities, a sub-1 GHz transceiver (CC1120) incorporated in a PCB separate from that of the microcontroller was utilized. It can communicate in the 865-867 MHz band and can achieve communication ranges of over 1 km line-of-sight at 1.2 kbps. The output power can reach up to +13 dBm. The data rate can vary from 0 to 200 kbps. In addition, an on-chip temperature sensor is accommodated in the sensor node.  
The operating voltages of the nodes vary from 1.8 V to 3.6 V. A 3 V lithium coin cell or a series connection of two 1.5 V AA/AAA batteries can be used to power the nodes. A battery holder should be bought separately. An option is also available for a combination of a solar panel and a Li-ion battery for powering the mote. Concerning power consumption, the standby current (in LPM3) of the mote is as low as 1 \(\upmu\)A.
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.1 AI Integration in 6G Networks
Challenges in ISAC research include the development of high-precision measurement equipment, the design of reasonable measurement scenarios, the selection of efficient transmission frequency bands, and the assessment of the relationship between communication channels and sensing. Additionally, determining precise ISAC channel models is essential. In order to overcome these obstacles, hardware architecture, system design, waveform design, and anti-jamming signal processing must integrate communication and sensing requirements while avoiding interference and collisions. The growing attention to ISAC is evident, with the establishment of IEEE 802.11 bf in 2020 and 3 GPP SA1 initiating a study item on ISAC in March 2022.
## 4 Experimental Verification and Results Analysis
### Experimental Steps  
The procedure for attitude measurement based on a rotary table system is outlined as follows:  
Step 1: Establish a dynamic attitude measurement system platform by positioning the laser tracking device (total station) and camera at predetermined locations. Conduct calibration for the IMU error model and the camera intrinsic parameter model.  
Step 2: Mount a cooperative target equipped with an IMU onto a precision two-dimensional rotary table, ensuring that the target coordinate system aligns with the rotary table coordinate system. Through calibration, ascertain the rotational relationship between the laser tracking device's coordinate system and the camera's coordinate system, as well as the rotation relationship between the IMU and the rotary table's coordinate system.  
Step 3: Utilize an upper computer to control the precision two-dimensional rotary table for pre-set single-axis rotations. Set the azimuth angle rotation range from 15\({}^{\circ}\) to 55\({}^{\circ}\), starting at 15\({}^{\circ}\), with intervals of 10\({}^{\circ}\) for a total of 5 sets, each repeated 10 times. For the pitch angle, the rotation range is set from 10\({}^{\circ}\) to 50\({}^{\circ}\), with 10\({}^{\circ}\) intervals across five sets, also repeated 10 times per set. At measurement distances of both 4 m and 8 m, continuously capture images of the moving cooperative target using camera control software while simultaneously recording IMU data.  
Step 4: Transform the visual information and IMU-acquired data into attitude angles within the laser tracking device's coordinate system. Following the preprocessing of the visual and IMU data (including solving for attitude angles from both sources and time synchronization of the collected data), employ an improved backpropagation algorithm for data fusion to derive the attitude angles in the laser tracking device's frame of reference.
## 4 Analysis Example: A Role-Based Interorganizational Workflow for a Construction Project
### Calculation of Workflow Performance Indicators  
For the example of a prefabricated building project, the role-based interorganizational workflow is analyzed, according to matrix \(R\) in Figure 7.  
\[R=\left[\begin{array}{cccc}0&0.133&0&0\\ 0.4536&0&0.167&0.2136\\ 0&0.2331&0&0.06\\ 0.06&0.3731&0&0\end{array}\right] \tag{16}\]  
The matrix \(R\) is decomposed. The eigenvalue (\(\Lambda\)) and eigenvector (S) matrices are shown below.  
\[\Lambda=\left[\begin{array}{cccc}0.4379&&&\\ &-0.0152\;+\;0.0147{\rm i}&&\\ &&&-0.0152\;-\;0.0147{\rm i}&\\ &&&-0.4075\end{array}\right] \tag{17}\]  
\[\text{S}=\left[\begin{array}{cccc}0.1994&-0.1643\;-\;0.1111{\rm i}&-0.1643\;+ \;0.1111{\rm i}&0.2276\\ 0.6566&0.0311\;-\;0.0055{\rm i}&0.0311\;+\;0.0055{\rm i}&-0.6973\\ 0.4299&0.8853\;+\;0.0000{\rm i}&0.8853\;-\;0.0000{\rm i}&0.3098\\ 0.5867&-0.3452\;+\;0.2384{\rm i}&-0.3452\;-\;0.2384{\rm i}&0.6049\end{array}\right] \tag{18}\]  
Since the maximum eigenvalue (max\(\left\{\left|a_{ij}\right|\right\}\)) \(<\) 1, the iteration of the role-based interorganizational workflow is convergent.