esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensing Hydrogen Seeps in the Subsurface for Natural Hydrogen Exploration
## 3 Results and Discussion  
### Sensor Response and Stability  
Although the GA5000 gas analyser was developed as a portable instrument that does not require frequent calibration, very little information exists regarding its stability and suitability over extended periods of operation and measurement. To investigate its long-term analytical performance, the instrument was tested at regular intervals, noting that the same sensor was used throughout the entire study. The GA5000 was calibrated with a multicomponent standard containing 89 \(\pm\) 4 ppm hydrogen and the same standard was then measured over time. The results obtained are shown in Figure 3. The hydrogen gas readings varied between 85 \(\pm\) 5 ppm over a seven-day period, which represents a variation of about 6%. Significant drift in the sensor actually occurred immediately after the calibration, but then become more stable with time. Over the total period, the response changed from 89 ppm to 81 ppm, which represents a sensor drift rate and stability less than 1 ppm per day. Evidently, the response to hydrogen gas did not deviate considerably with time, suggesting that long-term exposure does not pose any major problem for the sensor. This study shows that the GA5000 gas analyser does not undergo any significant deterioration in response over a seven-day period and that the sensor may be suitable for long-term monitoring studies in the field.  
The response of the analyser/sensor was investigated against hydrogen standards generated with the mass flow controllers, including different levels of methane. It was  
Figure 3: Evaluation of GA5000 sensor stability as a function of time. The readings were obtained by exposing the sensor to a known hydrogen gas concentration.  
shown that the sensor responded to the hydrogen gas mixtures and Figure 4 shows a typical calibration curve where methane was also present at around 10,000 ppm. Regression analysis revealed that the sensor gives a linear response to hydrogen over the 0 to 400 ppm concentration range. Interestingly, the hydrogen concentration reading obtained from the GA5000 sensor was slightly higher than the expected/known value. However, given that the uncertainty in the original standard is 4.5%, the uncertainty of standard preparation (both cylinders B and C) is 5%, and the instrument is known to drift by around 6%, then the total uncertainty is around 20.5%. The readings from the GA5000 are therefore within the error bars.
# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
## 2 Materials and Methods  
### Study Area and Models  
The study area encompasses the MDGV and approximately 20 km beyond its boundary (Figure 1). Receptors were placed on a 4 km grid spacing that matches the meteorological grid. Meteorology-Chemistry Interface Processor (MCIP 4.4) uses output files from Weather Research and Forecasting (WRF; [39]) meteorological model (Version 3.9.1.1) to generate meteorological data, which are used by both emissions and chemistry models. The meteorology used in this study was obtained from the Alberta provincial air quality photochemical modelling study [10]. Emissions modelling utilized the Sparse Matrix Operator Kernel Emissions (SMOKE 4.8.1) modelling system.  
SMOKE modelling provides the initial spatial and temporal allocation of emission inputs, as well as any chemical speciation required for each hour. The emissions inputs were generated for three mechanisms (CB6K3, SAPRC07, and RACM2). Simulations with different chemical mechanisms were performed using the Community Multiscale Air Quality (CMAQ 5.3.2) model, which is a 3D grid-based air quality model developed and maintained by Community Modeling and Analysis System. CMAQ simulates the formation of ozone, photochemical oxidants, and secondary particulate matter and the deposition of pollutants such as acids, toxic pollutants, and nitrogen species. It is an active open-source project that continuously enhances the accuracy and efficiency of photochemical modelling, by taking advantage of state-of-the-art multi-processor computing techniques. Table 1 summarizes the CMAQ model configurations used for all simulations in this study. The initial and boundary conditions were obtained from the Alberta provincial air quality photochemical modelling study [10]. The simulations were conducted for one month in summer (July 2013) and one month in winter (December 2013).  
Figure 1: Study area showing model domain, grids, and monitoring stations.
# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 2 Overview of Healthcare Applications
### Telehealth
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Work Description** & **Results** & **Technologies Employed** \\ \hline
[33] & \begin{tabular}{c} Adoption of voice interface \\ technology for patients with \\ heart failure \\ \end{tabular} & \
\begin{tabular}{c} Higher remote engagement between patients \\ and providers for better heart failure prevention; \\ data from 47 patients; challenges: engagement \\ and ease of use. \\ \end{tabular} &
\begin{tabular}{c} Technology based on \\ Amazon’s Alexa voice \\ assistant (Alexa+) with Echo \\ Dot devices; Avatar tablet \\ application (Avatar); \\ \end{tabular} \\ \hline
[34] & \begin{tabular}{c} Review on mobile health use \\ in atrial fibrillation \\ \end{tabular} & \
\begin{tabular}{c} Expert claims ECG, PPG \\ (photoplethysmography) and MCG \\ (mechanocardiography) use in medicine can \\ reduce morbidity. \\ \end{tabular} &
\begin{tabular}{c} Wearables for PPG and ECG; \\ handheld devices for MCG, \\ PPG and ECG; remote \\ monitoring \\ \end{tabular} \\ \hline
[35] & \begin{tabular}{c} Boamente, a suicidal \\ prediction mobile application \\ \end{tabular} & \
\begin{tabular}{c} Identifies suicidal ideations from texts \\ originating from a virtual keyboard; dataset built \\ using Twitter API and labeling tweets by \\ psychologists; dataset sharing restricted by \\ Twitter’s policy. \\ \end{tabular} &
\begin{tabular}{c} Artificial intelligence/deep \\ learning, neural language \\ processing, digital \\ phenotyping \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._a Raspberry Pi, an Arduino and the e-Health Sensor Platform v1.0. The e-Health sensor platform allows an Arduino or the Raspberry Pi through an adaptor shield to collect data from blood pressure monitors, pulse oximeters, galvanic skin response, airflow, and temperature sensors. Previously described works and others can be found in Table 3, where we can discover telehealth applications, their purpose, and the technologies they employ.
## 5 Conclusions and Outlook  
In real-time simulations with nonlinear component models, numerically varying system matrices occur and require the recomputation of the LU decomposition in each simulation step. It is shown that the employment of the NICSLU method accelerates the refactorisation computation in comparison with both the SuperLU and KLU methods. In addition, we propose the integration of a partial refactorisation approach into the NICSLU method by adopting a factorisation path algorithm and show that an additional speedup was achieved in all considered use cases.  
In the presented performance analysis, we cover use cases involving system matrices of different sizes, a variety of sparsity patterns and different ratios of numerically varying matrix entries. This allows us to conclude that the proposed high-performance LU decomposition method can help to meet computation time requirements in a variety of real-time simulation scenarios.  
Furthermore, the application of the NICSLU method leads to a significant speedup of the solution vector computation in comparison to SuperLU when applying it in a scenario within the real-time simulator DPsim. Such speedup of the solution step is of particular relevance in real-time simulation scenarios in which the underlying system matrix remains constant.  
In future work, the proposed method can be extended by a partial RHS solving routine, which uses a suitable dependency path algorithm similarly as for partial refactorisation to speed up the solution vector computation. Furthermore, the method can be applied to various use cases and scenarios for a broader analysis of its benefits and limitations. The knowledge gained from the analysis can be used for further conceptual and software-technical improvements of the method and its implementation.  
Conceptualisation, methodology, investigation, J.D., L.S. and L.R.; software, visualisation and validation, J.D. and L.S.; data curation, J.D. and L.S.; writing--original draft preparation, J.D., L.S. and L.R.; writing--review and editing, J.D., L.S. and L.R.; resources, supervision and project administration, J.D. and L.R.; funding acquisition, A.B. and A.M. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON], [PERSON] and [PERSON] is supported by the Helmholtz Association under the Joint Initiative Energy Systems Integration.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to unpublished results in this project.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMD & Approximate Minimum Degree Ordering Algorithm \\ BTF & Block triangular form \\ CSR & Compressed sparse row \\ DAE & Differential-algebraic system of equations \\ DCIM & Delayed-current injection method \\ EMT & Electromagnetic transient \\ HiL & Hardware-in-the-loop \\ LES & Linear equation system \\ MNA & Modified nodal analysis \\ NR & Newton-Raphson \\ \end{tabular}
# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 2 Materials and Methods
### Water Cloud Model for Winter Wheat Water Content Inversion.  
The water cloud model was originally proposed by [PERSON] and [PERSON] in 1978 [21]. It assumes a vegetation layer as homogeneous scatterers, removed the scattering information between vegetation layers and ground surfaces, and then the entire vegetation coverage area was simplified. The model, Total backscattering, including the body scattering term reflected by the vegetation and the ground backscattering term through crop double attenuation, was expressed by the model (Formulas (4)-(9)):  
\[\sigma^{0}=\sigma^{0}_{\text{veg}}+\tau^{2}\sigma^{0}_{\text{soil}} \tag{4}\]  
\[\sigma^{0}_{\text{veg}}=\text{AV}_{1}\text{cos}\,\theta(1-\text{exp}\,(\frac{-2 \text{BV}_{2}}{\text{cos}\,\theta})) \tag{5}\]  
\[\tau^{2}=\text{exp}\,(\frac{-2\text{BV}_{2}}{\text{cos}\,\theta}) \tag{6}\]  
\[\text{V}_{1}=\text{L}^{\text{E}_{1}} \tag{7}\]  
\[\text{V}_{2}=\text{L}^{\text{E}_{2}} \tag{8}\]  
\[\sigma^{0}_{\text{soil}}=\text{CM}_{\text{v}}+\text{D} \tag{9}\]  
where \(\sigma^{0}\) is total SAR backscatter, \(\sigma^{0}_{\text{veg}}\) is backscatter from vegetation, \(\sigma^{0}_{\text{soil}}\) is backscatter from soil, \(\tau^{2}\) is the two-way attenuation effect of the vegetation layer, \(\text{V}_{1}\) and \(\text{V}_{2}\) are canopy descriptors, A, B, D, E\({}_{1}\), and E\({}_{2}\) are the model parameters, L is leaf area index, and \(\theta\) is the incidence angle. \(\text{M}_{\text{s}}\) is soil moisture and was later replaced in the final wheat water content inversion model by the enhanced SAR polarization index that was most sensitive to soil moisture. C is the sensitivity of the radar to soil moisture content.  
In this study, \(\text{V}_{1}\) was set to 1 and L represents crop water content. Therefore, we have:  
\[\sigma^{0}=\text{A}*\text{cos}\,\theta\Big{[}1-\text{exp}\Big{(}\frac{-2\text {BL}}{\text{cos}\,\theta}\Big{)}\Big{]}+(\text{C}+\text{D}\text{m}_{\text{s}} )*\text{exp}\Big{(}\frac{-2\text{BL}}{\text{cos}\,\theta}\Big{)} \tag{10}\]  
\[\text{L}=-\frac{\text{cos}\,\theta}{2\text{B}}\,\text{ln}\,\frac{\sigma^{0}- \text{A}\text{cos}\theta}{\text{C}+\text{D}\text{m}_{\text{s}}-\text{A}\text {cos}\theta} \tag{11}\]
# Predicting the Evolution of Syntenies--An Algorithmic Review
## 5 Accounting for Gene Trees
### Evolution of Tandemly Arrayed Gene Clusters
Then, reinserting gene order and sign information on the leaves of \(S\), the order and sign of genes at the internal nodes of \(S\) are inferred by traversing \(S\) bottom-up and applying DILTAG on each branch \((x,y)\) of \(S\), with the exception that instead of reaching a single gene, the algorithm stops when it reaches the expected number of gene copies. All orders leading to the optimal solution are then conserved in a set \(\mathcal{S}_{x}\). The set \(\mathcal{S}_{A}\) of a leaf \(A\) is just the TAG cluster corresponding to \(A\). Now, denote by \(\Gamma(x)\) the set of speciation vertices of the lca-reconciliation of \(T\) mapping to \(x\). For example, in Figure 7, \(\Gamma(D)=\{d_{1},d_{2},d_{3}\}\). Let \(s\in\{l,r\}\), denoting the left or the right child of a node. The pre-speciation genome set \(PG(x_{s})\) is the subset of \(\Gamma(x)\) with a child in the branch \((x,x_{s})\), i.e., the genes in \(\Gamma(x)\) that have not been lost after speciation on the branch going to \(x_{s}\). For example in Figure 7, \(PG(D_{l})=\{d_{1},d_{2},d_{3}\}\) while \(PG(D_{r})=\{d_{1},d_{2}\}\). The set \(\mathcal{S}_{x}\) at each internal node \(x\) of \(S\) is computed by MultiDILTAG as follows: (1) For each of \(s\in\{l,r\}\), DILTAG is executed on each element of \(\mathcal{S}_{x_{s}}\), and stops as soon as the attained gene order contains \(|PG(x_{s})|\) genes. The set of all ancestral gene orders obtained (output of DILTAG) form an initial _pre-speciation_ set \(\mathcal{PS}_{x_{s}}\) (further refined by removing the elements that do not lead to a minimum cost). For example, in Figure 7, \(\mathcal{PS}_{D_{l}}=\{(d_{1},-d_{2},-d_{3})\}\) and \(\mathcal{PS}_{D_{r}}=\{(d_{1},d_{2})\}\); (2) For each of \(s\in\{l,r\}\), construct the set \(\mathcal{PS}^{\prime}_{x_{s}}\) by reinserting, in each gene order, and in all possible orders, the genes lost on the branch \((x,x_{s})\). Then, \(\mathcal{S}_{x}=\mathcal{PS}^{\prime}_{x_{s}}\cup\mathcal{PS}^{\prime}_{x_{s}}\). For example, in Figure 7, \(\mathcal{S}_{D}=\{(d_{1},-d_{2},-d_{3}),(d_{1},d_{2},d_{3}),(d_{1},d_{2},-d_{3} ),(d_{1},d_{3},d_{2}),(d_{1},-d_{3},d_{2}),(d_{3},d_{1},d_{2}),(-d_{3},d_{1},d _{2})\}\). During the execution of the algorithm, a solution graph is incremented by adding the appropriate \"speciation edges\" from the elements of \(\mathcal{S}_{x}\) to those of \(\mathcal{PS}_{x_{l}}\) and \(\mathcal{PS}_{x_{r}}\) giving rise to the minimum distance. Although showing good performance in inferring the total number and size distribution of duplication events on simulated datasets, a limitation of the MultiDILTAG heuristic is however in dealing with multiple gene deletions, as the algorithm is highly exponential in this case, and becomes quickly intractable.
## 3 Proposed Model
### User-Item Click Frequency Table  
Figure 3 shows user click item time-based sequence records. These records show the item click sequences of each user in a session. The product IDs are encoded. We calculate the click frequencies of each user for each product across all the sessions to generate a user-item click frequency table of size m \(\times\) n. Each user is represented as Fij, i.e., in terms of their clicks' frequency on an item. Fij represents the click frequency on item j by user i. Each row represents a user, and each column represents an item. If user i has not clicked an item j, then Fij = 0. The symbol m represents the total number of users, and the symbol n represents the total number of products.
# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 2 Materials and Methods
### Yield Data  
The high-resolution yield data used to train and test the regression machine learning algorithm belong to the 13 wheat plots of farmer 1 (Figure 1B, red dots). Data were collected by combining a yield monitor with a GPS mounted on a John Deere T560 combine harvester. The working width of the harvester was 6.1 m. The GPS receiver works with RX corrections, allowing it to work with a 15 cm precision. Wheat yield data were acquired during the harvest period of 2019, between 23 July and 9 August. Before being usable, the yield data had to be pre-processed to remove inaccurate grain yield measurements. The cleaning process consists of applying different rules to eliminate outlier measurements. The measurements located at a distance shorter than 15 m from the plot edge were eliminated to avoid the border effect. Finally, using the ordinary kriging technique, the data were interpolated. A more detailed explanation about the process followed to pre-process the dataset can be found in [PERSON] et al. (2019). The average yield of the 13 plots obtained after performing this process ranged between 4.6 and 8.6 t ha\({}^{-1}\) (Table 1).
# UAV Communication Recovery under Meteorological Conditions
## 2 System Model  
We incorporate specific attenuation into the air-to-ground (A2G) propagation loss model from [17]. The propagation model for communications between a UAV and ground users under various weather conditions can be represented as  
\[PL_{\text{max}}=P_{Lo5}(\theta)\times PL_{Lo5}+(1-P_{Lo5}(\theta))\times PL_{ NLLo5}+\frac{(\beta+\gamma)d}{1000}, \tag{1}\]  
where \(d\) denotes the distance between the UAV and the receiver in meters. The above model consists of three critical components: the LoS probability expression (\(P_{Lo5}(\theta)\)), the propagation path loss from the UAV to a ground receiver in LoS or non-line-of-sight (NLoS) conditions (\(PL_{Lo5}\) or \(PL_{NLo5}\)), and the specific attenuation caused by weather and gas (\(\gamma\) and \(\beta\)). We discuss these three components in detail in the following subsections.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods
### CNN-BiLSTM-RF Anomaly Detection Model
#### 2.2.2 The Bidirectional Long Short-Term Memory Network Layer  
The bidirectional long short-term memory network layer is used to capture long-term dependencies in time series data [24]. Given the temporal correlations and sequence dependencies in electricity load data, the Bi-LSTM layer helps the model understand and learn the time patterns in the data. The bidirectional structure considers past and future information, enhancing the model's comprehension of time series data.  
As illustrated in Figure 2, the BiLSTM consists of two distinct LSTM units: one propagating forward in time and the other backward. The outputs from these two LSTMs are concatenated at the end. This design accommodates past and future contextual information, integrating insights from both directions into the hidden state at each time step. As a result, it can extract more nuanced semantic features, enhancing the model's representative capability.  
Figure 2: Bi-LSTM architecture diagram.  
In the Bi-LSTM neural network, forward information update is:  
\[\overset{\rightarrow}{h_{t}}=H(W_{\overset{\rightarrow}{x\,h_{t}}}x_{t}+W_{ \overset{\rightarrow}{h\,h}}\overset{\rightarrow}{h_{t-1}}+b_{\overset{ \rightarrow}{h}}) \tag{3}\]  
Backward information update is:  
\[\overset{\leftarrow}{h_{t}}=H(W_{\overset{\leftarrow}{x\,h_{t}}}x_{t}+W_{ \overset{\leftarrow}{h\,h}}\overset{\leftarrow}{h_{t+1}}+b_{\overset{ \leftarrow}{h}}) \tag{4}\]  
Combining forward and backward neural networks update is:  
\[y_{t}=W_{\overset{\rightarrow}{h\,y}}\overset{\rightarrow}{h_{t}}+W_{ \overset{\leftarrow}{h\,y}}\overset{\leftarrow}{h_{t}}+b_{y} \tag{5}\]  
where \(t\) is the time sequence, \(x\) is the input at the corresponding time index \(t\), \(y\) is the output at the corresponding time index \(th\) is the hidden vector at the corresponding time index \(t\), \(W\) represents the weight matrix at the corresponding index, \(b\) is the bias function at the corresponding index, and \(H\) is the activation function.  
Hence, the Bi-LSTM layer can be viewed as a mechanism for comprehensively understanding the input sequence. It takes into account the contextual information at each position within the series. It amalgamates these data into a unified feature representation, aiding the model more effectively in identifying anomalies within electricity load data.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## Appendix A Details of the Derivation of the Fisher Information Matrix  
### Prior Calculation on Fourier Transforms  
In order to compute the FIM we need to know the Fourier transform of a set of functions. First, remembering that the signal is band-limited of band \(B\leq F_{s}\), we have,  
\[s(t)\rightleftharpoons S(f)=\left(\frac{1}{F_{s}}\sum_{n=N_{1}}^{N_{2}}s(nT_{s}) e^{-j2\pi f/nT_{s}}\right)1_{\left[-\frac{F_{s}}{2};\frac{F_{s}}{2}\right]}. \tag{1}\]  
In order to tackle the issue that may come from the spectral shift due to the Doppler effect, we simply need to take \(F_{s}\) large enough so that \(\frac{F_{s}}{2}\geq\frac{B}{2}+f_{c}\text{max}\left\{|b_{0}|,|b_{1}|,|\Delta b |\right\}\) (\(\Delta b=b_{1}-b_{0}\)),  
\[s(t)e^{j2\pi f_{c}bt}\rightleftharpoons S(f-f_{c}b)=\left(\frac{1}{F_{s}}\sum_ {n=N_{1}}^{N_{2}}s(nT_{s})e^{-j2\pi(f-f_{c}b)nT_{s}}\right)1_{\left[-\frac{F_{ s}}{2};\frac{F_{s}}{2}\right]}. \tag{2}\]  
Let \(s_{0}\) be defined by \(s_{0}(t;\tau)=s(t-\tau)\), then we have that,  
\[(t-\tau)s_{0}(t;\tau)=ts_{0}(t;\tau)-\tau s_{0}(t;\tau), \tag{3}\]  
and  
\[(t-\tau)s(t-\tau) \rightleftharpoons\frac{j}{2\pi}\frac{\text{d}}{\text{d}f}\left( S(f)e^{-j2\pi f\tau}\right)-\tau S(f)e^{-j2\pi f\tau}\] \[\rightleftharpoons\frac{j}{2\pi}\frac{\text{d}}{\text{d}f}(S(f)) e^{-j2\pi f\tau}. \tag{4}\]  
Now let \(s_{1}\) be defined by \(s_{1}(t;b)=s(t)e^{j2\pi f_{c}bt}\). We have the known result that,  
\[ts_{1}(t;b)\]  
and therefore,  
\[ts(t)e^{j2\pi f_{c}bt} \tag{5}\]  
On the other hand,\[s_{1}^{(1)}(t;b)=s^{(1)}(t)e^{j2\pi f_{c}bt}+j2\pi f_{c}bs_{1}(t;b)\] \[\Leftrightarrow\;s^{(1)}(t)e^{j2\pi f_{c}bt}=s_{1}^{(1)}(t;b)-j2\pi f _{c}bs_{1}(t;b),\]  
then, we directly have that,  
\[s^{(1)}(t)e^{j2\pi f_{c}bt}\rightleftharpoons j2\pi\left(f-f_{c}b\right)S(f-f_ {c}b). \tag{100}\]  
Finally if we now define \(s_{2}\) as \(s_{2}(t;b)=ts(t)e^{j2\pi f_{c}bt}\),  
\[s_{2}^{(1)}(t;b)=s_{1}(t;b)+ts^{(1)}(t)e^{j2\pi f_{c}bt}+(j2\pi f _{c}b)s_{2}(t;b)\] \[\Leftrightarrow\;ts^{(1)}(t)e^{j2\pi f_{c}bt}=-s_{1}(t;b)+s_{2}^{(1)} (t;b)-(j2\pi f_{c}b)s_{2}(t;b),\]  
therefore,  
\[ts^{(1)}(t)e^{j2\pi f_{c}bt}\leftrightharpoons-S(f-f_{c}b)+j2\pi(f-f_{c}b)\frac {\mathrm{d}}{\mathrm{d}f}\left(S(f-f_{c}b)\right). \tag{101}\]