esa-sceva/satcom-chunk-collection huggingface dataset:
# MIMO Relaying UAVs Operating in Public Safety Scenarios
###### Abstract  
Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have several significant advantages: a high degree of mobility, flexibility, exceptional line of sight, and real-time adaptative planning. For instance, cell edge coverage could be extended using relay UAVs. This paper summarizes the sidelink evolution in the 3 GPP standardization associated with the usage of the device to device (D2D) techniques that use long term evolution (LTE) communication systems, potential extensions for 5G, and a study on the impact of circular mobility on relay UAVs using the software network simulator 3 (NS3). In this simulation, the transmitted packet percentage was evaluated where the speed of the UAV for users was changed. This paper also examines the multi-input multi-output (MIMO) communication applied to drones and proposes a new trajectory to assist users experiencing unfortunate circumstances. The overall communication is highly dependent on the drone speed and the use of MIMO and suitable antennas may influence overall transmission between users and the UAVs relay. When the UAVs relaying speed was configured at 108 km/h the total transmission rate was reduced to 55% in the group with 6 users allocated to each drone.  
U  
A 2021 22 April 2021  
26 April 2021  
###### Abstract  
Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have several significant advantages: a high degree of mobility, flexibility, exceptional line of sight, and real-time adaptative planning. For instance, cell edge coverage could be extended using relay UAVs. This paper summarizes the sidelink evolution in the 3 GPP standardization associated with the usage of the device to device (D2D) techniques that use long term evolution (LTE) communication systems, potential extensions for 5G, and a study on the impact of circular mobility on relay UAVs using the software network simulator 3 (NS3). In this simulation, the transmitted packet percentage was evaluated where the speed of the UAV for users was changed. This paper also examines the multi-input multi-output (MIMO) communication applied to drones and proposes a new trajectory to assist users experiencing unfortunate circumstances. The overall communication is highly dependent on the drone speed and the use of MIMO and suitable antennas may influence overall transmission between users and the UAVs relay. When the UAVs relaying speed was configured at 108 km/h the total transmission rate was reduced to 55% in the group with 6 users allocated to each drone.  
Keywords:UAV; drones; disasters; relaying; 4G; 5G; drone simulation; sidelink; NS3; MIMO +
Footnote †: journal:
# Calibration Method of Projectional Geometry for X-ray C-arm Fluoroscopy Using Sinogram Data
## 3 Experiments
### Data Acquisition  
The imaging device was used for the experiments, and the image data were acquired in DICOM file format, which is commonly used in medical applications. After securely positioning the calibration phantom on the table, CBCT data were collected, and the sinogram and reconstructed volume were saved to files. Following that, several 2D X-ray fluoroscopic images were captured at various X-ray gantry orientations. The metadata that contains the detailed information of the image data is stored with the image data for calibration. The dimensions of X-ray fluoroscopic image data are 2480 \(\times\) 1920, and the spacings of the x- and y-direction are 0.154 mm and 0.154 mm, respectively. This information is stored in the tags (0018, 1604), (0018, 1608), and (0018, 1164) of the metadata. The sinogram data are a stack of 469 images with a dimension of 1240 \(\times\) 960, and the dimension is stored in the same tags (0018, 1604) and (0018, 1608) as the X-ray data, and the number of images is stored in the tag (0028, 0008). Since the device that acquired the X-ray data and sinogram data is the same, the spacing of the pixels for the sinogram data is twice that of the spacing for the X-ray data. Each projectional image for the sinogram was acquired every 0.399 degrees, from \(-\)98.95 degrees to 99.04 degrees, and the angle information is stored in the metadata tags (0018, 1520). The geometric calibration was applied to 301 images ranging from \(-\)60.12 degrees to 59.85 degrees for the experiment.
# A Filter-Based and Parallel Unknown Tag Identification Protocol in Open RFID Systems
## 4 Filter-Based and Parallel Unknown Tag Identification Protocol
### Basic Idea  
Most of the existing unknown tag identification protocols are based on a slotted hash, and they have two drawbacks: large time consumption and low accuracy. Firstly, the  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbols** & **Descriptions** \\ \hline \(\mathbb{K}\) & A known tag set \\ \hline \(\mathbb{U}\) & An unknown tag set \\ \hline \(N\) & The number of known tags \\ \hline \(M\) & The number of unknown tags \\ \hline \(l_{fp}\) & The size of the fingerprint \\ \hline \(F_{R}\) & A real frame in the collection phase \\ \hline \(V_{C}\) & An address code vector \\ \hline \(l_{s}\) & The size of the address code vector \\ \hline \(m\) & The size of the address code \\ \hline \(n\) & The number of missing tags \\ \hline \(H(\cdot)\) & A hash function with a uniform random distribution \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 1: The system model of unknown tag identification.  
existing protocols have lower slot utilization due to slot collisions. In this case, they need multiple rounds to deactivate all known tags, which is time-consuming. Secondly, a false positive occurs when an unknown tag and a known tag pick the same slot, resulting in this unknown tag being deactivated.  
The idea of the FPUI is to identify unknown tags as quickly and precisely as possible by the following three design principles:  
(1) _Improving slot utilization_: In the slotted-ALOHA-based protocol, multiple tags may choose a slot to respond to the reader, causing slot collisions and reducing slot utilization. This protocol modifies the RSQF filter to reconcile the collision slots to single slots, thus increasing the number of useful slots.  
(2) _Eliminating false positives_: In real open RFID systems, unknown tags may select the same slot as known tags, which causes these unknown tags to be regarded as known tags and deactivated, i.e., a false positive problem. The key to tackling the problem is separating known tags from unknown tags thoroughly. In this paper, a unique fingerprint is provided for each known tag in a _run_ to distinguish known tags from unknown tags, thus eliminating false positives.  
(3) _Increasing the identification efficiency_: Traditional tag identification protocols adopt ALOHA-based or tree-based methods to collect the tag ID information serially, so the process is time-consuming. This paper proposes a parallel tag ID collection scheme to concurrently identify multiple unknown tags, thus increasing the identification efficiency.  
To achieve the above goals, the FPUI identifies unknown tags in two phases: the separation phase and the collection phase. In the first phase, an RSQF-based fingerprint filter is built to separate known tags from unknown tags. In the second phase, the IDs of unknown tags are concurrently collected by assigning a series of orthogonal address codes to multiple singleton unknown tags, which is similar to CDMA technology.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.1 Linear Regression Model
Moreover, by employing the DBSCAN clustering algorithm to segment the time, the model's performance further improved. In order to discuss the relevance of the characteristic variables and the degree of influence on the model, in 2010, [PERSON] et al. [41] proposed a statistical method to extract key variables from a large number of characteristic variables. The interactions between the factors affecting the taxi time were considered to form new variables with the factors themselves, and then the sequential forward floating subset selection method (SFFSS) was used to select the optimal set of variables from the combination of variables in order to construct the multivariate linear regression model. In 2016, [PERSON] et al. [48] used a log-linear regression analysis method to develop a predictive model for aircraft taxi time. The model considered two sets of independent variables: one set of route-specific (route-related) variables and one set of interaction-specific variables. The experimental results show that the route-related variables are effective characteristic variables and the inclusion of interaction variables can increase the predictive ability of the model; in addition, the route-related variables affect the model to a greater extent than the interaction variables affect the model. In 2018, [PERSON] et al. [53] used a multiple regression model to discuss the magnitude of the correlation between each influencing factor and taxi time. Through the analysis of the regression coefficients in the regression equation, it was found that the number of arrival and departure aircraft, the operation time period, and the taxi-out time had a significant correlation, and the correlation increased with the increase of the number of arrival and departure aircraft. In 2021, [PERSON] et al. [63] used the stepwise regression method to establish the taxi time prediction model to study the effects of these parameters on taxi time at different airports.
## 2 Latest Developed Biosensors for COVID-19
### Electrochemical Biosensor
#### 2.3.2 Advantages and Limitations  
The reviewed electrochemical sensors based on SPEs for SARS-CoV-2 detection show excellent portability, with true positive rates ranging between 46.2% and 100%. Typically, commercially available SPEs were used as they are easy to use and disposable. The most commonly used material for the assembly of the working electrode of SPE is carbon, gold and platinum ink. SPEs modified with a range of nanoparticles or nanomaterials are also commercially available. The modification of the sensing surface with a large active surface area (e.g., gold nanoparticles, graphene, carbon nanotubes) increases the number of immobilised biorecognition elements and thus the number of available analyte binding sites, which increases the detection sensitivity of the biosensor. Compared to conventional glassy carbon or carbon paste electrodes, SPEs do not require electrode polishing and electrochemical pre-treatment by electro-deposition. Furthermore, potentiostats are already available in miniaturised formats, which enable on-the-spot or point-of-care applications.  
At present, saliva tests offer a promising alternative to nasopharyngeal swabs for COVID-19 diagnosis, since collecting saliva is non-invasive and easy to self-administer [93, 94]. However, differences in the sample collection methods--such as cough out (without sputum), split (exclude bubbles) or drooling--can affect the salivary composition and sensitivity to SARS-CoV-2. Consequently, the matrix effect may result in an inaccurate measurement for viscous saliva without any pre-treatment (e.g., dilution with phosphate buffer). However, high dilution of saliva samples with low viral content may lead to a false-negative result. Recently, spike protein detection in saliva samples using an electrochemical biosensor was proposed in [39, 40]. It was suggested in [40] that using fresh saliva sampled after drinking a glass of water obviates the need for sample pre-treatment.
Sensors_**2009**, \(9\), 5878-5893; doi:10.3390/s90805878  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
Performance of a Protected Wireless Sensor Network in a Fire. Analysis of Fire Spread and Data Transmission  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
University of Corsica/UMR CNRS SPE, Quartier Grossetti 20250 Corte, France;  
E-Mails: [PERSON]DDRESS] (J.-F.S.); [PERSON]ADDRESS] (E.G.); [PERSON]ADDRESS] (X.S.); [PERSON]DDRESS] (F.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33(0)4 95 450 207; Fax: +33 (0)4 95 45 01 62  
_Received: 3 July 2009; in revised form: 20 July 2009 / Accepted: 22 July 2009 / Published: 24 July 2009_  
**Abstract:** The paper deals with a Wireless Sensor Network (WSN) as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. To provide reliable information in fire studies and support fire fighting strategies, a Wireless Sensor Network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; 3) tracking the fire spread during its spatial and temporal evolution. One of the great challenges in performing fire front tracking with a WSN is to avoid the destruction of motes by the fire. This paper therefore shows the performance of Wireless Sensor Network when the motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN is then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m\({}^{2}\).  
**Keywords:** wireless sensor network; thermal insulation; natural fire; fire spread
## 4 Simulation and Experiment Results and Discussion
### Simulation Results
Regarding MCT, DO exhibited the lowest temperature and FM showed the highest temperature value, mirroring the simulation results. The EL value, which indicates the charging efficiency, revealed the largest loss for the CC-CV method and the smallest for DO, reflecting an 18% reduction compared to the CC-CV method. Table 8 lists the MSCC values obtained from the experiments, which closely aligned with the simulation results. Table 9 displays the normalized CPEI values presented in Table 7, including the results for each normalized CPEI. In Table 9, the normalized CE values were derived from the reciprocal of the EL value, as all values were normalized against the EL value of DO. Higher normalized CE values signify lower EL. The individual values and the total sum of the normalized CPEI values are illustrated in Figure 20. In Figure 20, excluding the CT value where DO ranked second, DO achieved the highest values among the three indices. The results for the overall performance sums are shown in Figure 20e. As depicted in Figure 20e, the CC-CV method produced the lowest performance score, while the PSO, JSA, GWO, BWO, and LFDA algorithms exhibited similar performance levels. In contrast, the performance scores for FM and WSO were relatively low. Notably, DO achieved the highest overall score compared to the other methods, with an 18% higher score than that of the CC-CV method. Figure 9:0 Experimental results for the charging temperature. Figure 9:1 Charging configuration of DO. (**a**) Simulation. (**b**) Experiment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **FC** & **CT (sec)** & **MCT (\({}^{\circ}\)C)** & **EL (J)** \\ \hline
1C CC-CV & 0.2862 & 6016 & 30.25 & 2173 \\ FM & 0.2082 & 4828 & 30.41 & 2109 \\ PSO & 0.2034 & 5404 & 29.3 & 1915 \\ WSO & 0.2389 & 5379 & 30.4 & 1880 \\ JSA & 0.2052 & 5425 & 29.3 & 1843 \\ GWO & 0.2046 & 5364 & 29.1 & 1855 \\ BWO & 0.2041 & 5448 & 29.2 & 1848 \\ LEDA & 0.2071 & 5447 & 29.3 & 1836 \\ AGTO & 0.2037 & 5285 & 29.6 & 1837 \\ DO & 0.1815 & 5213 & 29 & 1801 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental CPEI results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **FC** & **CT (sec)** & **MCT (\({}^{\circ}\)C)** & **EL (J)** \\ \hline
1C CC-CV & 0.2862 & 6016 & 30.25 & 2173 \\ FM & 0.2082 & 4828 & 30.41 & 2109 \\ PSO & 0.2034 & 5404 & 29.3 & 1915 \\ WSO & 0.2389 & 5379 & 30.4 & 1880 \\ JSA & 0.2052 & 5425 & 29.3 & 1843 \\ GWO & 0.2046 & 5364 & 29.1 & 1855 \\ BWO & 0.2041 & 5448 & 29.2 & 1848 \\ LEDA & 0.2071 & 5447 & 29.3 & 1836 \\ AGTO & 0.2037 & 5285 & 29.6 & 1837 \\ DO & 0.1815 & 5213 & 29 & 1801 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Obtained experimental OCC values. Figure 20: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **\(\mathbf{I_{\text{cl}}}\) (A)** & \(\mathbf{I_{\text{22}}}\) (A)** & \(\mathbf{I_{\text{3}}}\) (A)** & \(\mathbf{I_{\text{4}}}\) (A) & \(\mathbf{I_{\text{5}}}\) (A)** \\ \hline
1C CC-CV & 2.9 & - & - & - &
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 3 OBFNs with TTD Architectures
### MRR Group Delay Architectures
Note that, the number of MRR is decreased from 12 to 8 compared to that in the architecture proposed in [61; 62], which can reduce the size of the architecture and electric control units, especially for application in massive PAAs. In addition, this architecture can better relax the complexity and cost of modulators compared to the architecture using single-sideband (SSB) suppressed carrier modulation [23]. To realize 2D optical beamforming, one MRR group delay architecture has been proposed, as shown in Figure 9[47]. This architecture is used as a receiver for 4 \(\times\) 4 PAAs, which shows the capability of horizontal and vertical beamforming via the use of a fixed-wavelength laser, 16 MZMs, 16 \(\times\) 1 MRR group delays and a PD. Among them, 16 \(\times\) 1 MRR group delays have a binary tree structure that consists of 20 MRRs and corresponding power supplies. Although this 2D OBFN architecture has a small-scale integration of delays, the numbers of MRRs and power supplies are huge when adopted in massive PAAs. In addition, MRR group delay architectures require an electric monitoring and control circuit for each MRR, thanks to the MRR being highly sensitive to temperature fluctuation. Therefore, MRR group delay architectures need a large amount of electric circuits to control the  
Figure 8: Schematic diagrams of MRR group delays at the (**a**) on-resonant wavelength [58] (Copyright 2017, IEEE, #1298491-1), and (**b**) off-resonant wavelength [31] (Copyright 2022, IEEE, #5491090143023). Figure 7: (**a**) Schematic diagram of MRR, and (**b**) time delay of MRR. time delays and operation points of MRRs. One approach to reduce the complexity of the MRR group delay architecture for 2D beamforming is to use the wavelength multiplexing technique which utilizes the wavelength multiplexing and frequency-periodic response of an optical ring resonator [63; 64]. Horizontal and vertical beamforming are realized using two cascaded 4 \(\times\) 1 MRR group delay architectures for the 4 \(\times\) 4 2D PAA. Note that, compared with the MRR group delay architecture using one wavelength, more than half of the total number of MRRs is reduced by that adopting multiple wavelengths.
## Data and Methods  
TCC observations with global coverage were taken from CLARA-A2 and ISCCP-H satellite datasets, while TCC observations were also taken from the European surface-based ECA&D dataset. From all datasets, monthly means were taken or created, and the gridded satellite observations were compared with each other and with the corresponding ECA&D data from the station contained in the grid cell. More details about the respective datasets are given in the remainder of this section.
## 3 Results and Discussions
### HRV Features
#### 3.2.3 Non-Linear Domain  
SD1 does not require any interpolation to handle missing values, while SD2 needs quadratic interpolation on time to obtain reliable result (see Table 3). To give an explanation of these results, in Figure 6 we provide an example of the relationship between \(RR-interval_{n}\) and \(RR-interval_{n+1}\) (i.e., Poincare plot) where SD1 and SD2 are extracted. This figure shows Poincare plots obtained after interpolating missing RR-intervals by using different interpolation method on both time and duration. This figure shows that when the missing values were interpolated on time, the variability of SD1 reduced as the percentage of missing values increased, while the SD2 remain constant. Differently, the interpolation on duration introduce error on both SD1 and SD2 increasing their variability as  
Figure 5: Frequency analysis of a user’s RR-intervals timeseries recorded in 5 min with different percentages of missing values (i.e., 0%, 30%, 50% and 70%) handled with different interpolation methods (i.e., nearest neighbor, linear, quadratic and cubic spline) on both time and duration.  
the missing values increase. Finally, in this figure it can be seen that no-interpolation and quadratic interpolation on time introduced less error compared to the other method on SD1 and SD2, respectively.
## 4 Discussion
While we did not directly quantify soft tissue artifacts, we attempted to minimize their influence by positioning the sensor over a bony landmark (lumbo-sacral junction), verifying sensor stability during trial preparation, and repeating trials when visible sensor shifts were noticed. Although no quantitative correction for soft tissue artifacts was applied, we acknowledge this limitation that might have potential implications in the generalization of our findings. Future studies are needed to consider biomechanical compensation methods or improved fixation systems to mitigate this issue. In addition, while functional calibration procedures and a quantitative assessment of belt fixation pressure were not implemented in the current protocol, we are aware of their potential to enhance anatomical alignment and data accuracy; these represent a valuable direction for methodological refinement in future investigations. Since this study exclusively focused on healthy participants, future research should investigate the performance of IMU sensors for trunk movements in individuals with musculoskeletal disorders, where compensatory movements and functional limitations might further require their use in the clinical field [52]. Lastly, refining the calibration procedures and algorithms for the IMU system may help reduce measurement discrepancies [53]. Future research should focus on expanding the scope of IMU-based evaluations to also include other acceleration-derived metrics (e.g., root mean square, improved harmonic ratio and jerk). These indicators might be indicative for postural stability, movement symmetry, and smoothness, which are critical for understanding trunk movement dynamics [54]. Investigating these parameters in populations with musculoskeletal disorders would provide further essential insights into their clinical relevance and the potential for targeted interventions [55]. Additionally, studies involving larger and more heterogeneous cohorts, including individuals with specific pathologies, are needed to further validate the utility of IMU sensors in complex clinical scenarios.