esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 6 Seismic Hazard Monitoring
#### Example Case-Study: 2016 Mw 6.0 Petermann Ranges Earthquake (Northern Territory)  
Access to pre- and post-earthquake imagery from Sentinel-1 and ALOS-2 facilitated observations of the 2016 Mw 6.0 earthquake in the Petermann Ranges, Northern Territory (for location, see Figure 2A). In this case, we formed co-seismic interferograms using post-earthquake SAR images that were acquired by ALOS-2 and Sentinel-1 (see Table S2 for sensor details) only upon request to the satellite operators (Figure 9). For both ALOS-2 and Sentinel-1, the time separation between the pre- and post-earthquake images was significant (182 and 228 days, respectively).  
Due to the longer wavelength of the sensor (L-band: \(\lambda\) = 22.9 cm), the ALOS-2 co-seismic interferogram (Figure 9A) still has excellent coherence and was particularly useful in the period after the earthquake to remotely map the complex surface rupture pattern and enable field teams to focus mapping efforts on the ground [64]. In contrast, the co-seismic interferogram from Sentinel-1 (C-band: \(\lambda\) = 5.6 cm) is heavily de-correlated (Figure 9B), with coherence also lost due to the large magnitude of near-field displacements close to the surface rupture trace (the black line in Figure 9). At the time of the earthquake, Sentinel-1 had not yet commenced a regular acquisition strategy over the region. The now regular acquisition interval of Sentinel-1 is a game-changer in this regard and will enable more Australian earthquakes to be detected and analysed with InSAR in the future. This includes international efforts for the automated delivery of co-seismic InSAR map products. Whilst this has previously been limited to large magnitude events (e.g., NASA Jet Propulsion Lab's Advanced Rapid Imaging and Analysis (ARIA: [69]), the LiCSAR platform [70] delivers interferometric products for all events above Mw 5.5 identified in the United States Geological Survey Comprehensive Earthquake Catalog. The results will complement Australian seismic monitoring, which currently relies upon a sparse sensor network (typical station spacing of \(>\)1000 km), by providing additional constraints upon epicentre location and source parameters for events that result in surface displacements.  
Figure 9: Wrapped phase co-seismic interferograms of the 2016 Petermann Ranges earthquake from ALOS-2 and Sentinel-1. (**A**) Ascending-pass, 182-day ALOS-2 L-band interferogram. (**B**) Descending-pass, 228-day Sentinel-1A C-band interferogram exhibits lower coherence. The dashed black line marks the mapped surface ruptures after [64].
## 1 Introduction  
Stingers, also known as \"burners,\" are defined as transient sensory and motor losses of one upper extremity due to a stretch or compression injury to the brachial plexus or the exiting cervical nerve roots (Figure 1) [1; 2; 3; 4]. Stingers most commonly occur during contact sports like American football and rugby, but other sports like wrestling, boxing, cycling, and horseback riding have been implicated as causes [1; 2; 3; 4]. The symptoms can persist from a few seconds, quickly resolving on the field, to several hours. In severe instances, they can take up to 2-6 weeks to completely resolve [3; 5; 6; 7]. The athlete typically complains of sharp pain in the supraclavicular area, followed by unilateral arm pain that may sometimes adhere to a dermatomal distribution that reaches the fingertips [6; 8; 9]. Other symptoms include associated paresthesia, tingling, numbness, decreased cervical range of motion, and extremity weakness resulting in mild to complete loss of strength and a \"dead-arm\" sensation [3; 5; 6]. All nerve roots in the brachial plexus are susceptible to injury. However, the upper roots (C5 and C6) are most frequently affected due to underlying anatomical features and susceptibility to hyperextension and direct compression, resulting in stingers [5] (Figure 2). Stingers are a common occurrence in contact sports [1; 7]. In today's highly competitive and career-oriented athletic culture, decisions regarding the health of athletes are critical to both the player and the team. Thus, there is a persistent need to understand the pathology, clinical features, investigative modalities, novel radiologic measurements, management, rehabilitation, and return-to-play decision protocol for stingers, especially among health professionals involved in caring for athletes. This study aims to provide a concise review of our current understanding of the clinical, diagnostic, and therapeutic aspects of stingers, including rehabilitation and prevention measures.  
Figure 1: Anatomic illustration of the brachial plexus. All nerve roots are prone to injury. However, C5 and C6 are most frequently involved. Created in BioRender. [PERSON] (2024) [[https://BioRender.com/g33z849](https://BioRender.com/g33z849)]([https://BioRender.com/g33z849](https://BioRender.com/g33z849)), accessed on 22 March 2025.
###### Abstract  
Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.  
wireless sensor networks; medium access control; routing protocols; cross-layer protocols; energy consumption +
Footnote †: journal: Sensors  
0000-0001-5897-4600][PERSON]
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 5 Earthquakes
### IoT Architectures for Earthquake EW Systems  
Table 4 summarizes the technologies used on each layer of the reference architecture for the Earthquake EW use cases reviewed. Most of the reviewed systems monitor ground motion to detect earthquakes, and as such accelerometers are the most widely used sensors. Other methods like using GPS displacement data can be also useful for real-time monitoring and early warning [6]. Structural Health Monitoring systems that use MEMS sensors to measure building motion can also be used as data sources for the evaluation of ground motion and its effects on buildings at an affordable price [71; 72]. MEMS sensors are widely spread and cheap solutions and, while the quality of data they provide is less accurate than dedicated seismic devices, they can be used to collect large amounts of data from different sources and they allow to make predictions with the right data processing and conditioning. Since denser networks proved to be more effective in predicting earthquakes, using MEMS sensors can be a valuable asset to be able to install more sensors while keeping low costs. The reviewed solutions use both short range protocols such as Wi-Fi and long range cellular protocols, while some solutions also use wired local area communication solutions (Ethernet), even though wireless communications have proven to be the most effective in case of emergencies [38]. With the advent of 5G, communications could be further improved, and URLLC services satisfy two of the main requirements of Earthquake EW, latency and data reliability. Earthquakes can be particularly damaging to network infrastructures, cutting off entire areas from communication networks and making it difficult to send data from wireless sensor networks, that could also be damaged by the earthquake itself. Moreover, network traffic usually rises after an earthquake, requiring operators to employ schemes to handle congestion and high traffic. Satellite communication provides wide coverage and it is not affected by ground disasters, so they can guarantee good communications in cases of paralysis of primary communication networks [28].
## 1 Introduction  
A severe thunderstorm is a major meteorological phenomenon that may bring about windshear, turbulence and microbursts to the aircraft operating at Hong Kong International Airport (HKIA). In particular, microbursts, as characterized by strong downdrafts within a thunderstorm, can generate significant windshear or a gust front on the surface, which are aviation hazards [1]. In Hong Kong, microbursts can be triggered by convection or terrain-disrupted airflow [2]. The Hong Kong Observatory (HKO) operates a WindShear and Turbulence Warning System (WTWS) to provide alerts of low-level windshear and turbulence at HKIA based on data from a C-band terminal Doppler weather radar (TDWR), Doppler light detection and ranging (LIDAR) systems and a network of anemometers around HKIA [1, 3, 4].  
In terms of pilot reports of windshear at HKIA, microbursts and gust fronts only account for about 10% of windshear encounters. The seemingly low percentage is mainly related to the timely alerting of thunderstorm-related windshear by the above suite of meteorological equipment operated by the HKO at HKIA, notably the microwave radar systems.  
However, though rather rare, encountering a microburst could happen at HKIA, especially during unsettled weather conditions in Hong Kong in the summer time. The present paper documents the meteorological observations of two microburst encounters by an aircraft operating around HKIA in the evening of 30 April 2024. Aircraft data were also obtained for that case, so that the aircraft-based measurements of wind, particularly the vertical velocity, and the eddy dissipation rate (EDR) could be compared directly with the radar-based observations. As such, the present article is novel, as it is the first time microbursts at HKIA have been documented with the inclusion of aircraft data. It is hoped that the materials in this paper could be a useful reference for aviation weather forecasters in Hong Kong and around the world and stimulate convection-resolving simulation of similar microburst events.
## 4 Conclusions  
This paper reviewed the literature on blockchain in LSCM, with a focus on extant and emerging organizational theories that can be used to expand understanding and knowledge for blockchain literature in the LSCM field. We found that researchers in their studies of blockchain in LSCM have addressed a range of topics, but the majority of the papers have no theoretical foundations, which limits not only opportunities to strengthen and further develop theories in LSCM, but also neglects evidence-based theories for the investigation of blockchain technology and its potential applications.  
This paper allows us to draw a number of conclusions regarding the integration of the literature. First, organizational theories provide a valuable source of theoretical foundations and underpinnings for examining and expanding research in blockchain in LSCM. Second, we found significant questions exist that require examination, and thus provide plentiful opportunities for future research, in particular, given the infancy of blockchain technology.  
Third, given that there is still only limited blockchain literature in LSCM which is based on the use and applications of theories, the domain of blockchain may offer an opportunity to engage in more interdisciplinary research, thereby strengthening the theoretical basis, and finding new ways to look at innovation and technology beyond organizational boundaries.  
This paper contributes to the blockchain discourse by providing a well-structured foundation for those who aim to apply organizational theories and widen research in LSCM. A summary of the blockchain-related research questions and associated investigation opportunities per theory can be found in Appendix A. The authors strongly believe that significant opportunities exist to understand the nexus between LSCM and blockchain by applying theoretical foundations. However, the paper did not elaborate and discuss related tools and methodologies that may help to understand the linkage between LSCM and organizational theories.  
**Author Contributions:** S.K., D.M.H. and M.D. contributed to the manuscript by providing the initial concept development. M.D., J.M., N.S., D.M.H. and S.K. were involved in the data collection and analysis. All authors participated in writing and editing the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Detecting a Drowsy Driver
### Pose Extended-Active Shape Model  
In general, it is known that the head pose of a driver changes continuously since one drives a car while watching the side mirrors and rear mirror in turn. In this study, the range of head movement occurred while driving is utilized to determine whether the driver is drowsy or not. In particular, the change of head pose is significant when the driver is looking at the side-mirror to the right rather than the side-mirror to the left. The change of the driver's head pose and its frequency become an important factor in determining drowsiness of the given driver.  
Among many, we can think of two statistical face models, favored in the computer vision community, for the present purpose. One of them is Active Shape Model (ASM) and the other is Active Appearance Model (AAM). Given that the head poses of the normal driver are very diverse and some of them are obviously extreme, the face model certainly needs to deal with such extreme pose cases. However, it is well known that these face models have been effective in estimating the head pose, which is less than about 30\({}^{\circ}\), called the average face shape. Therefore, they cannot deal well in the cases where the head pose is higher than 30\({}^{\circ}\), called the extreme head pose cases.  
The reason is that some of extreme shape vectors are lost in carrying out the Principal Component Analysis (PCA) process. Moreover, since the locations of the average face shape significantly differ from those of the extreme shape, it is difficult to correct the fitting error. For instance, DB-ASM model typically uses the average shape model even when the head pose has increased more than a certain angle. In such case, the shape fitting becomes difficult and the fitting error is increased.  
In this study, we have extended DB-ASM to include the extreme pose cases as illustrated in Figure 2, called it Pose Extended ASM (PE-ASM), where six extreme head poses are embedded into the basic model. On the right of the same figure, the center is the average shape model, and the other six shape models correspond to the extreme head pose cases, respectively. In other words, we have trained seven different DB-ASMs including the frontal face model. We have used the threshold values, as indicated in Table 1, in categorizing the head pose of the current face. In Section 3.1, it is shown that the POSIT algorithm is used in estimating the head pose of the given face. According to the estimated head pose value, the corresponding shape model is assigned among the seven head pose categories. Therefore, our face model is a head pose-specific model. Once the shape model is assigned, the face fitting starts with it and iterates until the error converges to a certain threshold.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Critical Techniques in Integrated Design
#### 4.2.3 Unified Frame Structure Design

Table 5: Summary of distributed cooperative sensing technology in satellite–terrestrial ISAC systems. is necessary to consider the new impact of the future ISTN architecture on ISAC signal transmission. In the network, there are a large number of ISAC devices, and sensing frames must be included to realize the sensing function. Considering that the addition of sensing will introduce more information parameters, including the target's position, motion state, surrounding environment, etc., the transmission of sensing parameters will greatly increase the load in a single frame and reduce the space for communication information. Consideration can be given to adding sensing-related information into the existing frame structure in the form of multiplexing to alleviate the reduction in communication resources. In addition, for long-distance communication transmission and sensing from air to ground, there will be a large loss of signals due to atmospheric attenuation and long-term transmission, so it is necessary to consider adding a self-verification scheme to achieve a low number of retransmissions for long-distance transmission.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 2 Forecasting Method and Application
### Evaluation Criteria for Forecasting Models  
#### 2.5.1 Accuracy Test of Grey Prediction Model  
After the grey prediction model is established, the model can be used to predict effectively only after the accuracy test is qualified. The posteriori error test is adopted in this paper. The equations of posteriori error test are as follows.  
The posteriori error ratio C:  
\[C=\frac{S_{1}}{S_{2}} \tag{14}\]  
where, \(S_{1}\) is the standard deviation of the original sequence and \(S_{2}\) is the standard deviation of the residual sequence.  
The small error probability \(P\):  
\[P=P\Big{\{}\big{|}\varepsilon(i)-\overline{\varepsilon}\big{|}<0.6745S_{1} \big{\}},(i=1,2,\ldots,k) \tag{15}\]  
where, \(\varepsilon(i)\) is the residual error and \(\overline{\varepsilon}\) is the residual average.  
The smaller the value of \(C\) and the greater the value of \(P\), the higher the grade of the grey prediction model. Generally, the accuracy grade of the grey prediction model can be divided into four levels which are shown in Table 2.
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 2 Modeling and Analysis of Corner
### Modeling the Leakage of Core Window Inner Corner  
The core window inner corner area, Part D as shown in Figure 3, is square and its side length is the distance between the winding end and the core. The energy in Part D accounts for about 20% of the total leakage energy. With the increased current, the leakage of the core window inner corner rises, especially when the core is saturated. Therefore, in order to calculate the inductance accurately in the saturation situation, the leakage flux of this part cannot be ignored.  
In this work, we chose the Sudhoff method [20] to establish the equivalent magnetic reluctance of the core window inner corner which can be calculated in Equation (6).  
\[R_{wi}=\frac{\pi}{\mu_{0}\cdot l_{c}\cdot\ln(1+\frac{\pi w_{c}}{4c})}, \tag{6}\]  
where \(e\) is a small value to reflect the process air gap or the demolding process gap at the corners.
# Eco-Efficient Mobility in Smart City Scenarios
## 4 Proposed Solution
### Prototype  
The implemented OBU prototype is illustrated in Figure 4. As described above, it is composed of two principal components that are shown in Figure 4a. The main board is a Raspberry Pi Zero W, with a 1 GHz ARM CPU and 512 MB of RAM. In turn, the communication concentrator has been designed from scratch to integrate the desired transceivers. This board mounts an FTDI FT4232H chip to interface four serial ports from the USB interface provided by the Pi Zero. Therefore, the GPS receiver (Lantronix A2235-H), the LoRaWAN transceiver (Murata CMWX1 ZZABZ-093), and the NB-IoT modem (Qucet BC95) are integrated into this board by using RS-232 ports. Typical 868 MHz and multiband LTE stick antennas are attached to LoRaWAN and NB-IoT transceivers, respectively. An OLED display (SparkFun LCD-14532) is connected as well for outputting debugging and status messages, as can be seen in Figure 4b.  
The prototype also includes a range of sensors attached to the Raspberry Pi Zero to enable smart city and C-ITS tracking and monitoring applications: an infra-red thermal array sensor (CO\({}_{2}\)), an environment (pressure, humidity, temperature and CO\({}_{2}\)) unit (CCS811 and BME280) and a 9 DOF inertial sensor (LSM9 DS1).  
As shown in Figure 4b, the communication concentrator is placed over the main board and the sensors are distributed around the principal elements. Finally, a lithium power bank (RS PRO PB-A5200) with 5 Ah of capacity has been attached to the unit and placed below the boards. In our experiments, we have obtained a battery life time of more than two days, working at 5V and reporting data regularly. The dimensions of the whole set are 15 \(\times\) 7 \(\times\) 5 cm, and an adequate enclosure has been designed and 3D-printed for its easy integration on-board a bike or moped, or using a personal mobility device.
## 2 Materials and Methods  
### NP Solution Preparation and Chemical Agents  
Fe\({}_{3}\)O\({}_{4}\) NPs were obtained from the Toda Kogyo Corporation (Otake, Hiroshima, Japan), and the partial characteristics of these NPs were reported previously [12]. The characterization of Fe\({}_{3}\)O\({}_{4}\) NPs by X-ray diffraction (XRD) (RINT-2500, Rigaku, Akishima, Tokyo, Japan) and transmission electron microscopy (TEM) (JEM-1200 EX, JEOL, Akishima, Tokyo, Japan) were as follows: The mean XRD size in powder was 9.3 nm, and the mean size of the spherical NPs was about 10 nm, as determined by TEM. Fe\({}_{3}\)O\({}_{4}\) NPs showed a hydrodynamic diameter of 83.4 \(\pm\) 16.2 nm, possessed a negative surface charge, and exhibited a zeta potential of \(-\)40 mV at pH 9.2 and a polydispersity index of 0.19 in deionized water. After ultraviolet-sterilization of the particles, Fe\({}_{3}\)O\({}_{4}\) NPs stock suspensions were prepared by suspension of particles in RPMI-1640 (Thermo Fisher Scientific, Waltham, MA, USA) at each concentration of interest (1, 10 and 100 \(\mu\)g/mL), followed by sonication at 30 W for 10 min with an Ultrasonic HomogenizerVP-050 (TAITAEC, Koshigaya, Saitama, Japan). The mean hydrodynamic diameters of Fe\({}_{3}\)O\({}_{4}\) NPs were 196.9, 199.5 and 244.7 nm at 1, 10 and 100 \(\mu\)g/mL in culture medium at pH 7.4, respectively. Docetaxel and rapamycin were purchased from Sigma-Aldrich (St. Louis, MO, USA), and carboplatin was purchased from Bristol-Myers Squibb (Tokyo, Japan). These compounds were dissolved in dimethyl sulfoxide (DMSO) (Nacalai Tesque, Kyoto, Japan). The DMSO concentration in the cell culture did not exceed 0.1%.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 3 Results and Discussion
### PFCs Removal Efficiency Through Pilot Test  
We conducted a pilot test on the influent to the treatment plant to determine the removal efficiency. In particular, we examined the changes in the removal by coagulation-sedimentation, ozone oxidization, chlorine injection, and activated carbon adsorption.