esa-sceva/satcom-chunk-collection huggingface dataset:
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Machine Learning and Deep Learning-Based Techniques
#### 3.7.3 Maximum Likelihood Classifier  
The maximum likelihood classification (MLC), initially suggested by German mathematician [PERSON] in 1821 for the normal distribution, is one of the most popular supervised classifiers in remote sensing [75; 76; 77]. This method, which is based on the assumption that the probability density function for each class is multivariate, assigns an unknown pixel to the class to which it has the highest possibility of belonging [75; 78]. MLC is a widely used multivariate statistical classification technique that is included in a number of image processing software programs, including PCI, ERDAS, and ENVI. This classifier was applied to many datasets for lithological categorization in this work using ENVI 5.3 software, which was provided by Exelis Visual Information Solutions in the Boulder, CO, USA.
## 3 Proposed Model  
The data flow of the proposed architecture FCA is simple. The user submits the application to the system, based on different profiling technologies where tasks are sequenced according to estimation time in the master node. Figure 2 illustrates this. The master node in the proposed architecture is the FCA central controller, which is responsible for managing the priority of task sequencing and performance in the system. The data processing of tasks on devices and fog resources is scheduled by a scheduler. The scheduler maps their sources to the remote tasks on different heterogeneous VMs and local tasks are mapped on the devices. However, the data dependencies incur some extra communication cost due to the computation being held in different places. For instance, local tasks are computationally executed on devices and remote tasks are computationally executed on the fog VMs.
## 5 Conclusions  
In summary, we drew the following conclusions: (1) In terms of hardness, SEM, EDS, ATR-FTIR, and WCA, PCUs with the same hardness processed through IM, CM, and 3D exhibited similar comprehensive performance. That is, given a reasonable temperature setting, all three molding methods could be applied for the processing and molding of PCU pellets. (2) Regarding mechanical properties, PCUs with 85A and 95A hardness, processed through IM, CM, and 3D methods, demonstrated tensile and compressive properties that were more akin to those of natural intervertebral discs. However, there remained a gap between their performance and that of natural discs, necessitating further improvements in material properties before they could be fully utilized in intervertebral disc products as replacements for natural discs.  
Therefore, the ultimate conclusion of this study was that PCU materials with 85A and 95A hardness, processed by IM, CM, and 3D, exhibited comprehensive properties more consistent with the requirements of natural intervertebral discs, making them the most promising candidates for replacing natural discs in intervertebral disc products. Based on the current research findings, further performance optimization studies on PCU are still needed in the future to fully align its properties with those of the natural intervertebral disc, thereby achieving an effective replacement of the natural disc with artificial intervertebral disc products made of PCU material.  
Conceptualization, J.Z. and W.L.; methodology, J.Z. and Y.C.; software, J.Z. and Y.C.; validation, J.Z.; formal analysis, J.Z.; investigation, J.Z. and Y.C.; resources, W.L.; data curation, J.Z. and S.W.; writing--original draft preparation, J.Z. and S.W.; writing--review and editing, J.Z., Y.C. and W.L.; visualization, S.W.; supervision, S.W. and W.L.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020B1515120082).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
The authors declare that they have no competing interests in this manuscript. No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript.
## 5 Equivalent Circuit Analysis  
Creating an equivalent circuit of lumped components for such a design analyzed in this paper is not an easy process. First, the antenna real and imaginary input impedance must be monitored to predict the best resonator to be used and whether they have common behavior or not. As can be noticed from Figure 6, the first six resonances have an impedance close to 50 \(\Omega\) near the resonating frequency, while for the last wideband, the impedance is widely spanned near the 50 \(\Omega\) line. An assumed circuit model is displayed in Figure 7, where the inductance (Lf) represents the input feedline and the first parallel RLC model to match the shorting pin used in the design; then, several series resonators are employed to create the first six bands, which are representing the meandered bowtie arms. Finally, the partial ground plane, which has a major effect in the wideband of the seventh band, is modelled by the rest of the circuit.  
Figure 5: Time-domain results of the proposed antenna showing (**a**) input pulse, (**b**) input spectrum, (**c**) Fourier transform of the input signal, and (**d**) output signals.  
Figure 6: Real and imaginary impedance for the designed model.  
The proposed circuit model is optimized using an ADS environment and has an approximated S-parameter result matched favorably with the simulated and measured results discussed before, and it is shown in Figure 8. The optimized values of the lumped circuit components are summarized in Table 3.  
Figure 8: Comparison between simulated and calculated reflection coefficient.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Notch 1** & **Notch 2** & **Notch 3** & **Notch 4** & **Notch 5** & **Notch 6** \\ \hline \(Q\) Simulated & 3 & 9.47 & 7.51 & 21.28 & 19.76 & 7.37 \\ \hline \(Q\) Calculated & 4.53 & 6.92 & 9.33 & 11.17 & 18.5 & 3.55 \\ \hline \(ROC\) Simulated & 7.68 & 11.32 & 8.09 & 12.43 & 9.71 & 4.11 \\ \hline \(ROC\) Calculated & 8.1 & 8.23 & 10.45 & 7.25 & 8.57 & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the simulated and calculated Q and ROC of the proposed antenna.  
Figure 7: Equivalent circuit model.  
Two elements, the quality factor (\(Q\)) that represents the ratio of the stored to the dissipated energy in each cycle, and the roll-off criteria (\(ROC\)) which represent the variation in the reflected signal versus frequency, are completely linked to the selectivity and sharpness of the rejection mechanism. For the frequency rejection bands, the quality factor of the RLC series and parallel resonators can be calculated as [26].  
\[Q=\frac{f_{c}}{BW_{-3 dB}} \tag{4}\]  
\[ROC=\frac{\Delta S_{11}(dB)}{\Delta f(GHz)} \tag{5}\]  
where \(BW_{-3 dB}\) represents the notch bandwidth at a level of 3 dB below its maximum, and \(\Delta S_{11}\)(dB) is the variation in the reflection coefficient within the frequency band represented by \(\Delta f\)(GHz). The calculated quality factor and \(ROC\) of the six rejection bands based on Equations (4) and (5) are summarized in Table 4.
# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
###### Abstract
The autoregressive use of GCC-FLOW is able to generate arbitrarily long length of sea clutter sequences with approximately the same statistical characteristics of the given sea clutter data. Furthermore, by using the global condition variable of the GCC-FLOW for different sea states, the proposed method is capable of generating diverse sea clutter data with the interpolated sea state features. The rest of this paper is organized as follows: In Section 2, the architecture of the proposed GCC-FLOW is described in detail. In Section 3, the training sea clutter data and the hyperparameter settings for the GCC-FLOW to generate sea clutter data are described. In Section 4, the experimental results are presented in terms of ensemble distribution and time correlations of the generated sea clutter compared to those of the training data. Finally, a conclusion is drawn in Section 5.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Methodology
#### 2.3.2 Yield Estimation  
We converted the GPP to crop yield using the following relationship:  
\[\rm Yield=GPP\times AR\times\alpha\times\frac{1}{1+RS}\times HI\times\frac{1}{ 1-MC} \tag{5}\]  
where Yield is the estimated maize yield in an administrative area. GPP represents the average of the accumulated GPP over an administrative area in the summer maize growth stage. AR indicates the fraction of the GPP remaining after autotrophic respiration, which is set to 0.53, according to [PERSON] et al. [47]. RS is the root-to-shoot ratio with a value of 0.18 [48], which was used to obtain the above-ground biomass. MC, the grain's moisture content at harvest, has a value of 0.11 for maize [49]. HI represents the harvest index, measuring the percentage of the above-ground biomass that is converted to economic yield. Moreover, \(\alpha\), with a value of 2.22, is the conversion factor of carbon content to dry matter [50].
## 2 Problem Formulation  
In the EBE context, consider a dynamic system whose state is measured by multiple sensors. For instance, several sensors located in different locations on the ground can simultaneously observe an object in a larger space area. Each sensor can be positioned far or near the object, and the transmission of information can be fast or slow. Furthermore, once the sensor has collected its data, this data must be delivered to the fusion center for further processing.  
Therefore the following open-loop state-space model is considered which used to model the estimation problem:  
\[x_{k} = \mathbf{F}_{k}x_{k-1}+\mathbf{w}_{k} \tag{1}\] \[z_{k} = \mathbf{H}_{k}x_{k}+\mathbf{v}_{k} \tag{2}\]  
where \(k\) denotes an iteration index; \(x_{k}\in\mathbb{R}^{n_{x}}\) denotes the state vector; and \(z_{k}\in\mathbb{R}^{n_{z}}\) represents the sensor's observations. Functions \(\mathbf{H}_{k}\) and \(\mathbf{F}_{k}\) show the observation and state models, respectively. Uncertainties in the observation and state models are represented by \(w_{k}\) and \(v_{k}\), respectively. These uncertainties are considered mutually uncorrelated white Gaussian noises having covariances \(\mathcal{Q}_{k}>0\) and \(\mathcal{R}_{k}>0\), respectively. The state-space model represented in Equations (1) and (2) is based on the development presented by [PERSON] and [PERSON] [23]. An important constraint in event-based communication is that the sensors have inadequate power supplies, while the fusion center has enough power to perform complicated computation algorithms. Hence, a sensor obtains each measurement based on an activation mechanism, and after that, it decides, through a triggering mechanism, whether to keep or transmit the measurement to the estimator. In traditional EBE, a binary triggering mechanism is used for local decision making on the sensor side, denoted by \(\gamma_{k}\), defined as  
\[\left\{\begin{array}{ll}\gamma_{k}&=1:\text{Measurement occurs, communication triggered}\\ \gamma_{k}&=0:\text{No measurement occurs, no communication}\end{array}\right.\]  
According to the above mentioned framework, the set of observations, including that of iteration \(k\), is defined as \(\mathbf{Z}_{k}=\left\{\gamma_{0},\mathbf{z}_{0},\ldots,\gamma_{k},\mathbf{z}_ {k}\right\}\).
## 1 Meteorological and pollution data  
The first meteorological dataset is the CAMS (for Copernicus Atmosphere Monitoring Service) global reanalysis (EAC4) data coming from the Atmospheric Data Store (ADS) of ECMWF (for European Centre for Medium-Range Weather Forecasts).  
The reanalysis works on data assimilation principle, through which previously captured forecasts are integrated with newly available observations to produce a relevant estimation of the atmosphere conditions across time. The data points of the EAC4 reanalysis are set on an 80 (km)-spaced grid across the globe. The dataset employed in this paper is the PM\({}_{10}\) (for Particulate matter d \(<\) 10 \(\upmu\)m) concentrations, with records taken on a time distance equals roughly to 3-hourly from \"00:00\" to \"21:00\".  
We selected a rectangle that roughly incorporates the territory of Italy as the covered study area for the data. To do so, we join georeferenced mobility data with neighborhoods (i.e., polygon) data of municipalities in Italy, we then extract minimum and maximum latitude/longitude values from the resulting dataset.  
We extracted data for a time interval that roughly equals to one month in 2014 to match the corresponding time interval in the mobility data. This results in 3-h-spaced tuples for the bounding rectangle covering Italy, which amounted to circa 21,500 tuples.  
From the resulting dataset, we selected latitude/longitude coordinates, the timestamps, in addition to the target variable, specifically the concentration of PM\({}_{10}\) in the 3 h span measured in kg/m\({}^{-3}\).  
The file format for this pollution data source is GRIB, downloaded through the ADS API and transformed into CSV by the data collector component of the EMDI system.  
We capture the second meteorological dataset from the analysis of Urban SIS (Sectoral Information System) [44], a demonstrator project that aims at developing, demonstrating and testing a method to downscale climate indicators to an urban scale (specifically a grid size of roughly 1 km\({}^{2}\)) for practical smart city applications. The dataset contains daily concentrations of PM\({}_{10}\) pollutant in major cities of Bologna, Modena, Reggio Emilia, and Ferrara in Italy. The file format for this dataset is NetCDF, which is then transformed into CSV by our data collector.  
From this dataset, we extract latitude/longitude coordinates together with the target value. The target value includes the 90 th percentile concentration of PM\({}_{10}\) of daily averages for one year. Values are measured in \(\upmu\)g/m\({}^{-3}\) and limited to maximum 50 \(\upmu\)g/m\({}^{-3}\); a value that is imposed by the European Union.  
All the datasets were imported as a CSV file format using Jupyter notebooks and the latitude/longitude coordinates were transformed into geospatial data structures.
## 4 Experimental Results and Discussions
### Datasets and Evaluation Metric
#### 4.1.2 Evaluation Metric  
Like most other object detection methods, mean Average Precision (mAP) was adopted as the evaluation metric. In object detection, mAP is the average of the APs in multiple categories. The AP depends on the precision and recall score and is calculated as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{13}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{14}\]  
\[AP=\int_{0}^{1}precision(r)dr \tag{15}\]  
\[mAP=(\frac{1}{N}){\sum_{i=1}^{N}AP_{i}} \tag{16}\]  
where \(N\) means the number of all the classes. \(AP\) is computed as the area under the precision-recall curve. \(FP\) indicates the false positives; \(TP\) indicates the true positives, i.e., IoU > 0.5; FN indicates the false negatives.
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 1 Introduction
However, every explanation is set within a context that depends on what is expected of the AI system. For in-vehicle monitoring, anomaly identification should be one of the main points of interest, and it should generally be paired with anomaly classification. The detected anomalies should be coupled with cues that demonstrate why a specific data instance is abnormal. The simplest implementation of this practice is to spatially identify the anomaly in a frame (e.g., with a bounding box or a GradCAM activation map). However, most anomaly detection studies focus on detection performance only, ignoring the capability of illustrating the identified anomalies. The complexity of the anomalies calls for developing visually interpretable anomaly detection models, as the provided cues could be essential to identify problems such as under-represented groups. That said, this work appears as the first aggregated critical review on the applicability of deep video anomaly detection to in-vehicle monitoring, making the following three major contributions:  
* Review of a large number of state-of-the-art methods for deep video anomaly detection, aiming to explain their framework and implementation, thus providing a deeper understanding of potential issues raised by the new scenario that these methods did not contemplate. Benchmarks for their performance were compiled as well as publicly accessible source codes to evaluate the ease of applicability;
* Review of a large number of datasets with real anomalies that are used to benchmark state-of-the-art models, investigating if any portion of the available datasets is representative of the real-world settings for in-vehicle monitoring or if the sequences can be repurposed for this matter. As public datasets dedicated to in-vehicle monitoring are lacking, this analysis is vital;
* This work initiates an important discussion on application-oriented issues related to deep anomaly detection for in-vehicle monitoring. Other surveys and reviews have disregarded this scenario and its specificities, despite its relevance, as shown by the listed funded projects that seek application-oriented solutions for in-vehicle monitoring. Possible solutions were proposed, aiming to follow up on future work. This document is organised as follows: Section 2 presents the working principles, different approaches, and state-of-the-art works in deep anomaly detection for video sequences. Section 3 reviews currently used datasets to train and benchmark models for anomaly detection. Moreover, Section 4 discusses funded projects that seek the exploration of in-vehicle monitoring, listing available resources that serve as a starting point for developing application-oriented solutions. Section 5 examines the challenges that this new scenario of application faces as well as the opportunities that arise with its exploration. Finally, Section 6 presents the main conclusions of this review.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 2 Two-Way Ranging (TWR) Overview
### Timing Model  
Two-way ranging (as the name implies) involves a cooperative exchange between at least two users. Consider two users labeled \(A\) and \(B\) separated in space and operating with independent, imperfect clocks. At any given instant \(n\), there is an offset between these clocks labeled \(T^{(n)}\) and a distance between them \(d^{(n)}\). If these users are interacting using over-the-air electromagnetic waveforms, then each transmission takes some time \(\tau^{(n)}=d^{(n)}/c\) to propagate between the platforms, where \(c\) is the speed of light. These interactions are depicted in Figure 2. The clock offsets \(T\) and propagation delays \(\tau\) are the fundamental quantities of interest and can be estimated by measuring the time-of-arrival (ToA) of each reception and applying an appropriate time-of-flight (ToF) estimation algorithm.  
User \(A\) transmits a waveform at time instant \(n\), labeled \(t^{(n)}_{A,Tx}\). This waveform takes a small amount of time to propagate to user \(B\), labeled \(\tau^{(n)}\). User \(B\) receives the waveform at time \(t^{(n)}_{B,Rx}\). If the clocks were perfectly aligned, the receive timestamp would simply be \(t^{(n)}_{A,Tx}+\tau^{(n)}\). Because the clocks are misaligned, user \(B\) measures the event earlier with respect to clock \(B\), thus the received timestamp, as perceived by user \(B\), is  
\[t^{(n)}_{B,Rx}=t^{(n)}_{A,Tx}+\tau^{(n)}-T^{(n)}. \tag{1}\]  
Figure 2: Depiction of two interactions between radios \(A\) and \(B\). Interactions are indexed by \(n\). The time offset is labeled \(T^{(\cdot)}\) and the propagation delay is labeled \(\tau^{(\cdot)}\). Transmit events are labeled with up arrows, and corresponding receive events are labeled with down arrows. The clocks are misaligned, so the receive timestamps \(t^{(\cdot)}_{(\cdot),Rx}\) follow Equations (1) and (2).  
At some later time instant \(n+1\), user \(B\) transmits a waveform to user \(A\). The received timestamp, as perceived by user \(A\) is  
\[t_{A,Rx}^{(n+1)}=t_{B,Tx}^{(n+1)}+\tau^{(n+1)}+T^{(n+1)}. \tag{2}\]
## 3 Results
### Prototype Test and Data Synchronization  
In this subsection we present the collected data from an approximately 25 min of prototype test (see Figure 12). The subject has been asked to wear the prototype on her left wrist and not covering the surface, do not interrupt the measurement unless it occurs due to the technical issue generated from the prototype. The subject did not have information on the functionality of the prototype and only was trained to switch on, configure and at the end of the experiment disconnect and switch off the prototype. The subject was a healthy female with 28 years old. According to the protocol reported by the subject, she has started the experiment in the morning at 9:55, 3 min before leaving her office, and it was completed at 10:19 at the other side of city ending to the other indoor location. Based on the protocol, the user spends three minutes in her indoor workplace. During this time, she experiences exposing the loudness of 32 to 57 dB, a room temperature of approximately 26 \({}^{\circ}\)C with the humidity range of 60% to 63% RH. During this period, as we expect the prototype does not detect any UV and \(NO_{2}\). Skin temperature is in the range of 19 to 23 \({}^{\circ}\)C. The subject continue to her daily routine by walking outdoor heading to train station. In this period, loudness, UV, air temperature, air humidity, and skin temperature are in the range of 32-59 dB, 0-5 index, 26-28 \({}^{\circ}\)C, 63-56 RH, and 18-28 \({}^{\circ}\)C, respectively. Interestingly, the 0.7 ppm has been recorded for \(NO_{2}\). We can indicate this event as a waiting time behind a red traffic light on a cross section in a close distance to vehicles. This period lasts 8 min approximately. Riding a train for less than four minutes is the next activity of the subject. The loudness is significantly reduced (32-53 dB) and UV is detected in the range of 0-3 index. This may be considered as the indication of sitting the subject in window seat, which is comply with her written protocol. The subject spends the next 7 min walking from the train station to her target location. We perceive increasing the average of loudness as expected. While the highest UV index (5) is recorded in this period, the longest zero value of UV also is observed. This indicate the diversity of the route that user has been under gone. The last four minutes of this experiment has been recorded in indoor, again. Zero UV index and low average intensity of loudness can be considered as the location change. The subject, did not report interruption in data transmission nor any malfunctioned. As a result of this experiment, we have successfully tested the whole prototype for testing data transmission, sensor adequate functionality, and data synchronization.