esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modeling Method  
Partial least squares (PLS) is a commonly used multivariate statistical method, which extracts the most comprehensive variables and identifies the noise by decomposing and filtering the data in the system [33; 34]. In the PLS model, the spectral matrix is decomposed first and the main principal component variables are obtained. Then, the contribution rate of each principal component is calculated. Based on the accumulative contribution rate of principle components, the principal components are selected as input to establish a mapping relationship with chemical indicators. Generally, the flexibility of PLS makes it possible to establish a regression model in the case that the number of samples is less than the number of variables [35].
# Photon-In/Photon-Out X-ray Free-Electron Laser Studies of Radiolysis
## 4 Conclusions and Outlook  
XFEL-based photon-in/photon-out spectroscopies represent an emerging and unique opportunity to study radiolysis in the sub-picosecond physico-chemical time range. For studies in the liquid phase, the photon-in/photon-out methods provide substantially relaxed vacuum requirements relative to those for the photoelectron-based spectroscopies. The versatile two-color, sub-femtosecond pulse generation capabilities over a wide energy range (250-1600 eV) at LCLS allow selection of initial ionization conditions and selective probing of ionization-generated chemical species by transient absorption. Similar capabilities are currently being considered at other facilities [85; 86]. Advanced beamsplitter techniques that simultaneously provide sample and reference probe beams onto a single 2D-detector promise to further enhance the sensitivity for transient absorption studies [87; 88]. The incorporation of RIXS further discriminates transient species with overlapping absorption resonances, and while statistically limited at present 120 Hz repetition rates, will become more routine at the MHz repetition rates soon expected. Such experimental studies can provide missing input into the challenging theoretical problem associated with the description of coupled electron-nuclear motion on highly excited electronic states associated with radiolysis in both simple and complex systems.  
Conceptualization, L.Y., E.T.N., J.A.L., and C.I.P.; methodology, L.Y., G.D., D.K., S.H.S., and A.M.M.; writing--original draft preparation, L.Y., E.T.N., D.K., J.A.L., and C.I.P. writing--review & editing, S.B.C., T.M.O. All authors have read and agreed to the published version of the manuscript.  
This research was supported by IDREAM (Interfacial Dynamics in Radioactive Environments and Materials), an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES). PNNL is a multiprogram national laboratory operated for DOE by Battelle Memorial Institute under Contract DE-AC05-76 RL0-1830. DK,GD,AMM,SHS were supported by the U.S. Department of Energy, Office of Science, Basic Energy Science, Chemical Sciences, Geosciences, and Biosciences Division under Contract No. DE-AC02-06 CH11357.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We are grateful for technical discussions on the performance of LCLS operating modes with [PERSON], [PERSON], on potential beamline and liquid jet endstation configurations with [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]. We appreciate ongoing scientific discussions with [PERSON], [PERSON] and theoretical support from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 1 Introduction
Such interference severely degrades the spectral efficiency (SE) performance in user-centric cell-free massive MIMO systems [7; 13]. Therefore, to minimize severe pilot contamination and achieve load balancing for the APs, each AP has a limit on the number of UEs it can support [12]. Cell-free massive MIMO, in which a large number of APs with one or multiple antennas support UEs coherently, can ensure high SE and uniformly good service to UEs [14]. However, in cell-free massive MIMO systems with a large number of APs, the number of radio frequency (RF) chains also increases. The high-resolution analog-to-digital converters (ADCs) in the RF chains become impractical due to high power consumption and significant hardware costs [15; 16; 17; 18]. Additionally, it is necessary to consider the impact of hardware impairments to account for realistic environments [19; 20]. As the number of users increases, the required fronthaul capacity also increases. Therefore, in massive access scenarios, it is essential to reduce the fronthaul capacity.
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 3 Error Transmission Theory  
In the localization process of UAVs, a variety of error factors will have an impact on the results. Given that coordinate transformation is essentially a nonlinear process, the error will undergo a corresponding nonlinear transformation when it is transmitted. In  
Figure 1: Space rectangular coordinate system.  
Figure 3: Airframe coordinate system.  
Figure 2: Geographic coordinate system.  
order to effectively analyze this process, it is first necessary to linearize the errors using Taylor series and then further analyze the transmission of errors. In view of these errors, this section will use the principle of Taylor expansion to discuss the transfer mechanism of errors in depth in the process of target localization.  
For vector \(y\), after a random vector \(x\) undergoes a nonlinear transformation \(f\),  
\[y=f(x)=f(x_{1},x_{2},\cdots x_{n})=f(\overline{x}+\Delta x) \tag{1}\]  
where \(\overline{x}\) represents the mean of the random vector \(x=(x_{1},x_{2},\cdots x_{n})\); \(\Delta x\) denotes the random error variable, with mean 0 and covariance \(P_{x}\).  
The first-order Taylor expansion of the above equation at \(x\) is obtained:  
\[y\approx f(x)+d_{\Delta x}f \tag{2}\]  
where \(d_{\Lambda x}f\) represents the full differential of the function \(f(\bullet)\) at \(\overline{x}\) with respect to \(\Delta x\).  
The random error variable is nonlinearly coordinate-transformed to obtain a variance of  
\[\begin{array}{ll}P_{y}&=E[(y-\overline{y})(y-\overline{y})^{T}]=E[\Delta x \
abla f^{T}(x)\Delta x^{T}\
abla f(x)]\\ &=J_{\overline{x}}P_{x}J_{\overline{x}}^{T}\end{array} \tag{3}\]  
where \(J_{\overline{x}}\) represents the Jacobi matrix of \(f(\bullet)\) at \(\overline{x}\).  
The UAV target localization process is affected by a variety of errors, so this study mainly analyzes the UAV position and attitude angle and pod heading and pitch angle errors; all of the above errors are random and obey normal distribution, and we assume that they are independent of each other.
## 5 Literature Review by Domain Area
### Healthcare and Medical Applications  
AR is proving to be an exciting development in the healthcare and medical sector, offering a multitude of applications that have the potential to transform medical processes and improve patient outcomes [75]. One area where AR is making a significant impact is surgery. Surgeons are now able to visualize the internal anatomy of a patient without the need for large incisions. This is achieved by superimposing a 3D model of a patient's anatomy onto their body, effectively providing the surgeon with a kind of \"X-ray vision\". This aids both in the planning of complex procedures and during the surgery itself, resulting in less invasive operations and quicker patient recovery times [76].  
In the field of rehabilitation, AR is being used to make physical therapy more engaging and enjoyable for patients. AR games can be developed that transform exercises into fun activities, which can be particularly beneficial for children or those undergoing long-term rehabilitation [77]. Finally, AR is even influencing the world of prosthetics and orthotics. It can assist in the design and fitting of these devices, increasing comfort and usability for patients [75]. These are some of the most recent developments in the \"healthcare and medical applications\" domain:* **AccuVein:** AccuVein is a handheld device that uses AR to visualize veins in real-time, making it easier for healthcare providers to insert needles for IV placement and blood draws. It illuminates the skin to show vein structures and improves the accuracy of vein puncture [78].
* **ProjectDR:** Developed by researchers at the University of Alberta, ProjectDR is an AR system that projects medical imaging data, like computed tomographies and magnetic resonance imaging datasets, onto a patient's body in real-time. This technology could help surgeons see beneath a patient's skin without the need for incisions [79].
* **HoloAnatomy:** Developed by Case Western Reserve University and Cleveland Clinic in collaboration with Microsoft, HoloAnatomy uses Microsoft's HoloLens AR headset to teach medical students about human anatomy. It allows students to visualize and interact with life-sized 3D models of the human body [80].
###### Abstract  
The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band's settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R\({}^{2}\) (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R\({}^{2}\) = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.
## 5 Digital Health Passports: Enablers of Workplace POCT  
The integration of digital health passports with workplace POCT represents a powerful evolution in health management strategies. Originally developed in response to the COVID-19 pandemic, digital health passports provide secure, verifiable platforms for individuals to manage and share personal health information, including diagnostic test results, vaccination records and screening outcomes [13; 14; 15; 16].  
At their core, digital health passports are mobile applications or web-based interfaces linked to encrypted databases, often incorporating blockchain technology to ensure data integrity and tamper resistance [71]. These platforms empower individuals by granting them ownership and control over their health data while facilitating selective disclosure of information to employers, healthcare providers or public health authorities as needed.  
In the context of workplace health programs, the integration of POCT could result in digital health passports allowing real-time, decentralized health monitoring. After a POCT is performed onsite, results can be securely uploaded to an individual's digital health record, immediately accessible via smartphone or other devices. Employees retain the right to manage data-sharing permissions, thereby preserving autonomy and confidentiality.  
Furthermore, real-time data aggregation at the population level--conducted in a de-identified and compliant manner--can enable organizations to monitor workforce health trends, anticipate healthcare resource needs and design targeted wellness initiatives. Such integration transforms episodic health screening into a dynamic, continuous process aligned with the broader shift toward personalized and predictive healthcare models [72, 73, 74].  
Technological enablers critical to this integration include blockchain for secure and decentralized data management, artificial intelligence (AI) for predictive analytics and mobile health (mHealth) platforms for user engagement and remote access [75, 76].  
Advantages and challenges related to POC testing and Digital Health platforms in the workplace are summarized in the following Table 3.
## 4 Experiments and Discussion
### Datasets
#### 4.1.2 RSDD Dataset  
RSOD [50] contains 976 images that have been clipped into approximately 1000 \(\times\) 1000 pixels, and the spatial resolution of these images ranges from 0.3 m to 3 m. There are 6950 object instances in this dataset in total, covered by 4 common classes in ORSIs, including 4993 aircraft, 1586 oil tanks, 180 overpasses and 191 playgrounds. Each instance of classes is shown in Figure 11.  
In addition, instances in RSOD dataset are under various scenes, including urban, grasslands, mountains, lakes, airport, etc. Although the scale of RSOD is not as large as that of DIOR, the characteristics of images in optical remote sensing object detection task can also be reflected by RSOD dataset. Therefore, we further analyzed the effectiveness of YOLO-DSD compared with YOLOv4 in RSOD dataset.
## 3 Results
### Performance Evaluation
#### 3.1.2 Drying Curves  
The variation of the moisture content during the drying process of maize in the three batches with use of ISD and direct sun drying is shown in Figure 3.  
The total drying time for the maize in the ISD was 19.9 h and 10.3 h distributed over 3 days for batches 1 and 2, respectively. For batch 3, it required only 9.5 h to dry the maize to the required moisture content. The starting moisture content for the ISD was 26.6% for batch 1 and 23.6% for batch 2, and the final moisture content was 11.9% and 12.3% for batch 1 and 2, respectively.  
For batch 3, the starting moisture content was 17%, and a final moisture content of 11% was reached in 2 days. A small re-humidification of the maize in batch 1 was observed on the third day of the experiment, as a result of the humid conditions prevailing during the night on that day. Due to the difference in the initial moisture content, the moisture ratio was used for a better comparison of the different treatments.  
Table 1 shows the \(t\)-test comparison of the moisture content of the samples dried by both methods among the three batches.  
The starting moisture content for maize dried in the ISD was 23.5% for batch 1 and 21.3% for batch 2, and the final moisture content was 11.9% and 13.2% for batches 1 and 2, respectively. For the maize in batch 3, the starting moisture content was 15.1%, and a final moisture content of 12.5% was reached in 2 days. The significance \(t\)-test did not show a difference in the moisture content among the different drying methods (\(t\)-test, \(\alpha\) = 0.05), except for batch 3. The drying temperature was sufficient to reduce the moisture content to the recommended level within a reasonably short period [33].  
Figure 2: Temperature and relative humidity (**a**) and absolute humidity (**b**) in the inflatable solar dryer (ISD) and for direct sun drying (DSD), shown exemplarily for batch 1 of the drying experiment.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs in the mmWave Frequencies
#### 3.5.5 mmWave Antenna Evaluation  
mmWaves are one of the key enablers of next-generation communications, as they provide the great advantage of high data transmission rates. However, the people working on them have to deal with inherent challenges (easily absorbed by the atmosphere and blocked by obstacles such as walls and even rain). In particular, the 30 GHz frequency has the minimum propagation losses compared to other frequencies, and therefore, it is a potential candidate for 6G communications, especially in connecting users with the BS. On the other hand, the 60 GHz frequency suffers from high attenuation yet enjoys a much larger bandwidth. Frequencies around 100 GHz show some attenuation, especially at 120 GHz, where we have a local maximum (ref. [101]). This frequency band is recommended to deliver high data rates for reduced coverage applications, such as indoor/outdoor wireless local networks and integrated sensing (ref. [117]).
# Adaptive Quick Reduct for Feature Drift Detection
## 3 Results
### Test 5. Classification  
In order to further asses the potentialities of AQR, the protocol proposed in [17] and based on prediction accuracy has been adopted. Because AQR is not a classifier but a feature selector, it was paired with \(k\)-Nearest Neighbor (kNN) to classify the instances in the current window, obtaining the kNN-AQR combo (very similar results however have been obtained in combination with the Naive Bayes classifier). The accuracy is computed accordingly to the Prequential or interleaved-test-then-train scheme [24], where each instance is first used to test and then to train the model.  
Figure 5: Test 4. Selected features at each iteration: (**a**) without drift (**b**) with drift. \(y\) axis represents the iteration number; \(x\) axis the number of the feature.  
The chosen datasets are Electricity [25] and Kaggle's Give Me Some Credit (GMSC) [26], whose characteristics are summarized in Table 3.  
The proposed algorithm is compared with the following competitors: kNN, Naive Bayes, kNN with feature weighting (kNN-FW) [17], Naive Bayes with feature weighing (NB-FW) [17], Very Fast Decision Tree (VFDT) [20] and a Hoeffding Adaptive Tree (HAT) [21].  
In Table 4, comparative performances are reported. On the Electricity dataset, the best performing algorithm is kNN-FW that however has the drawback of an higher memory footprint if compared to kNN-AQR (the latter only needs to keep in memory the last computed reduct). Still, kNN-AQR yields a prequential accuracy slightly inferior to HAT, that is one of the best performing algorithms for stream classification with feature drifts. On the other side, on the GMSC dataset, kNN-AQR resulted the best performing algorithm, proving its stability on the long run.
## 2 Materials and Methods
### Theoretical Assumptions for Cant Calculation  
The difference between the rail heights is reflected in the angle by which a moving vehicle turns. The relationship between cant and the vehicle tilt angle is described by the following Formula:  
\[D=e\cdot\sin(\alpha) \tag{4}\]  
where:  
\(D\): - Cant [mm],  
\(e\): - Centreline spacing of the rails [mm],  
\(\alpha\): - Vehicle's angle of inclination in relation to the plane [rad].  
In a vehicle that is stationary (or is moving along a perfectly straight track in the horizontal plane), the cant can be measured using the change in the standard gravity component value measured transversely to the car (Figure 3b).  
\[a_{x}^{(g)}=g\cdot\sin(\alpha) \tag{5}\]  
where:  
\(a_{x}^{(g)}\): - Acceleration transverse to the car body [m/s\({}^{2}\)],  
\(g\): - Standard gravity [m/s\({}^{2}\)].  
In a moving vehicle, during the movement along a circular curve without cant, an additional transverse acceleration component (centrifugal) will appear, as shown in Figure 3a. Where a vehicle is regarded as a point particle, the relationship between the velocity, curve radius, and the centrifugal acceleration is as follows:  
\[a_{x}^{(C)}=\frac{v^{2}}{R} \tag{6}\]  
where:  
Figure 2: A graph of the repeatability test of the angle measurement using a gyroscope.  
\(a_{x}^{(C)}\) - Centrifugal acceleration [m/s\({}^{2}\)],  
\(v\) - Vehicle's linear velocity [m/s],  
\(R\) - Curve radius [m].  
When a vehicle is moving along a curve with a cant, the above-mentioned states are combined (Figure 3c). The measured acceleration transverse to the car body will depend on the components derived from the gravity and centrifugal acceleration:  
\[a_{x}^{(r)}=g\cdot\sin(\alpha)-a_{x}^{(c)}\cdot\cos(\alpha) \tag{7}\]  
Given the small values of the angle \(\alpha\) (in practice not exceeding 6 degrees), it can be assumed approximately that its cosine is equal to 1. Such an assumption simplifies the calculation process. Basing on Formulas 4-7, the cant can be calculated using the following relationship:  
\[D=\frac{1}{g}\cdot\left(a_{x}^{(r)}\cdot e-v^{2}\cdot e\cdot K\right) \tag{8}\]  
where:  
\(K\) - Curvature [rad/m]  
In the presented relationship, the unknowns include the transverse acceleration, the vehicle movement velocity, and the curve radius (in general--curvature). The simultaneous determination of these three values requires the application of a complex measurement technique.  
In order to solve the defined problem for existing tracks, the authors developed an algorithm which uses signals from GNSS and INS measurements. The analytical form of the algorithm and its operation will be illustrated in next sections.
# Joint Power Allocation and Link Selection for Multi-Carrier Buffer Aided Relay Network
## 1 Introduction
To ensure the long lifetime of the system, energy harvesting (EH)-based source and relay nodes were considered in [20]. Authors solved the joint source and relay power optimization along with an adaptive link selection by an iterative spatial branch-and-bound-based method.