esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### ATR-FTIR Spectrometry  
Essential oils are concentrated solutions of volatile compounds, consisting of complex homogenous mixtures of various compounds, with more than 100 constituents present in each species (taxon).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample No** & **Extract** & **Species** & **Family** \\ \hline
1 & Frankinense carrier resin extract & _Boswellia carteri_ & Burseraceae \\
2 & Evening primrose seed extract & _Ontother biemis_ & Primulaceae \\
3 & Arnica flower extract & _Arniac montana_ & Asteraceae \\
4 & Echinacea root extract & _Echinacea pur pur purra_ & Asteraceae \\
5 & Marigold flower extract & _Calendula officinalis_ & Asteraceae \\
6 & Tagetes flower extract & Tagetes erecta/Tagetes patula_ & Asteraceae \\
7 & Chamomille flowers and flowering shoots extract & _Chammoilla recatta_ & Asteraceae \\
8 & Oregon phenol type leaf extract & _Origarum vulgare_ & Laminaceae \\
9 & Oregona terpinel type leaf extract & _Origarum vulgare_ & Laminaceae \\
10 & Sage leaf extract & _Salvia officinalis_ & Laminaceae \\
11 & Lemon myrtle leaf extract & _Backhousia citridorna_ & Laminaceae \\
12 & & _Thymus vulgaris_ & Laminaceae \\
13 & Rosemary extract, cincole type, and leaf extract & _Rosamirus officinalis_ & Laminaceae \\
14 & Rosemary plus, leaf extract & _Rosamirus officinalis_ & Laminaceae \\
15 & Peppermint leaf extract & _Mentla piperita_ & Laminaceae \\
16 & Lavender flower extract & _Lavendula angustifolia_ & Laminaceae \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concentrated essential oils obtained by supercritical fluid extraction (FLAVEX®).  
Their FTIR spectra are complex due to the spectra of individual components overlapping and the mixing of various vibrational modes. Although contributions of major components never exceed 25% of the total content, those compounds in essential oils that occur at low concentrations (<1%) do not influence the ATR-IR spectrum significantly. Thus, ATR-FTIR spectra obtained from the essential oil samples present characteristic spectral fingerprints that can be used to discriminate different plant species and chemotypes.  
ATR-FTIR absorption spectra of the concentrated essential oils show the expected characteristic C-H stretch (\(\sim\)2900 cm\({}^{-1}\)), C=O stretch (\(\sim\)1700 cm\({}^{-1}\)), broad O-H stretch (\(\sim\)3400 cm\({}^{-1}\)) and C-O stretch (\(\sim\)1100 cm\({}^{-1}\)) of terpenoid components present in the essential oils (Figure 1a,b). As expected, the FTIR spectra of these oils are dominated by vibrational modes from monoterpenes which are observed at 886, 1436 and 1644 cm\({}^{-1}\). Another useful band in terms of differential identification of the oils is the band for C=O stretching, which appears at 1740 cm\({}^{-1}\) in laverd, rosemary and sage oil, and is shifted to lower wavenumbers in lemon myrtle, oregano (terpineol type) and peppermint oil [22; 23].  
Figure 1: Attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) superimposed spectra of concentrated essential oil samples (**a**) and commercial-grade essential oils (**b**).
# AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching
## 4 Experimental Results
### Weight Approximation Pipeline  
This section quantifies the existing relationship between the prediction accuracy of ConvNets and the design knobs explored in the approximation pipeline. A first analysis shows how clustering (i.e., \(N_{cnv}\) and \(N_{fc}\)) affects the accuracy along with the benefits of the proposed approximate pattern matching strategy.  
The clustering effects are depicted in Figure 8 where the accuracy drop is linked to the number of clusters used in that stage. For graphical purposes, we just plotted the solutions where \(N_{cnv}=N_{fc}\), however, the tool can explore also uneven working points. Intuitively, the accuracy degradation decreases for larger values of \(N_{cnv}\) and \(N_{fc}\). This behavior shows the hidden relationship between the number of different parameters, i.e., the model complexity and the generalization ability of a ConvNet. The break-even (near-to-zero accuracy) configuration differs among benchmarks and it is strictly related to the model topology and the task complexity. In particular, for ConvNets with similar size (e.g., for OR and IC tasks), the number of clusters required are strictly related to the task difficulty (43 vs. 10 classes), when the number of weights grows up (wider architecture) more clusters are required to represent the entire weight space, as shown by the KS benchmark for instance. The results achieved are remarkable. It is possible to shift the complex high-dimensional information of a ConvNet model with no substantial loss in the prediction ability. For instance, negligible accuracy degradation is obtained with \(N_{cnv}=N_{fc}=64\) for OR, \(N_{cnv}=N_{fc}=16\) for IC and \(N_{cnv}=N_{fc}=512\).  
The _Abit_ is a key knob in our proposal, as it affects both accuracy and hit rate in the associative memory. Figure 9 shows the accuracy degradation trend for a different number of bits used to implement the approximate matching. In order to assess the impact due to _Abit_, we fixed the number of clusters (\(N_{w}\)) such a way that the accuracy drop is negligible: \(N_{w}=64\) for OR, \(N_{w}=16\) for IC and \(N_{w}=512\) for KS. Those points represent possible candidate solutions indeed. The accuracy drop decreases as _Abit_ gets larger. Although the initial error differs among benchmarks, the common ground is that it gets close to zero using 13 MSB over the whole 32-bit; for the FP16 format, 8 MSBs are just enough to achieve negligible accuracy losses (<1%). From Figures 8 and 9, it is also clear that _Abit_ is a finer knob when compared to the number of clusters, as it leads to minor accuracy variations.  
Figure 8: Accuracy drop (%) variation changing the weight clusters configurations (\(N_{cnv}\), \(N_{fc}\)). Results for 32 bit (FP32) and 16 bit (FP16) data. (a) OR; (b) IC; (c) KS.
## 3 Numerical Results  
In this section, we present the numerical results on the bit error rate (BER) performance of the proposed CPM-GFDM system for various values of \(\mathbf{h}\). Our simulations are based on the Matlab code available on the Vodafone Chair website [46]. We consider two channels, namely the additive white Gaussian noise (AWGN) channel and a frequency-selective channel. Moreover, we compare the performance achieved by the proposed system with PSK-GFDM and QAM-GFDM. The GFDM parameters used for the simulations are listed in Table 1, while the modulation indices (\(\mathbf{h}\)) used for the CPM mapper are shown in Table 2. By changing the numerator \(\mathbf{p}\) from 1 to 15, we were able to choose the values for the modulation index \(\mathbf{h}\). To ensure that \(\mathbf{p}/\mathbf{q}\) remained rational, we varied the denominator \(\mathbf{q}\)'s values from 2 to 16. There are 23 different values of \(\mathbf{h}\) as a result. We concluded that this set of variables is sufficient to assess the performance of CPM-GFDM because any other numbers that are outside of this range do not produce results that are any better than the ones produced by rational values of \(\mathbf{h}\). Additionally, using higher \(\mathbf{p}\) and \(\mathbf{q}\) numbers would increase the number of points in the constellation, which would result in a complex VD.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Value**} \\ \hline No. of subcarriers (\(K\)) & 128 & \\ \hline No. of sub-symbols per subcarrier (\(M\)) & 5 & \\ \hline No. of active subcarriers (\(Kon\)) & 128 & \\ \hline No. of active sub-symbols (\(Mon\)) & 5 & \\ \hline Cyclic prefix (CP) & 32 & \\ \hline Roll off factor (a) & 0.5 & \\ \hline Pulse shape & RRC & \\ \hline Mapper & QAM, PSK, CPM \\ \hline Sampling rate & 10 MHz & \\ \hline \hline \end{tabular}
\end{table}
Table 1: GFDM Parameters.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**No.**} & \multicolumn{1}{c}{\(\mathbf{p}\)} & \multicolumn{1}{c}{\(\mathbf{q}\)} & \multicolumn{1}{c}{\(\mathbf{h}\)=\(\frac{\mathbf{p}}{\mathbf{q}}\)} \\ \hline
1 & 1 & 2 & 0.5 \\ \hline
1 & 1 & 4 & 0.25 \\ \hline
2 & 1 & 5 & 0.2 \\ \hline
3 & 1 & 8 & 0.125 \\ \hline
4 & 1 & 10 & 0.1 \\ \hline
5 & 1 & 16 & 0.0625 \\ \hline
6 & 2 & 5 & 0.4 \\ \hline
7 & 3 & 4 & 0.75 \\ \hline
8 & 3 & 5 & 0.6 \\ \hline
9 & 3 & 8 & 0.375 \\ \hline
10 & 3 & 10 & 0.3 \\ \hline
11 & 3 & 16 & 0.1875 \\ \hline
12 & 4 & 5 & 0.8 \\ \hline
13 & 5 & 8 & 0.625 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CPM Modulation Indices.
## 4 Experimental Evaluation
### Benchmark Models and Evaluation Metrics  
To show the effectiveness of our proposed approach, we compare our method against several baselines:  
* Conv-SEG [40]: It integrates the convolutional neural model with word embeddings to fully use word information and capture rich n-gram features without any feature engineering;
* BERT [22]: A pre-trained language model. It has good performance in CWS with fine-tuning. The setting of fine-tuning BERT is constructed according to Tian [34];
* ZEN [32]: A Chinese pre-trained language model enhanced by representing n-grams;
* WMSEG [34]: It incorporates wordhood information with several common encoder-decoder combinations of CWS using a memory network.
* WeiDC [41]: It used a knowledge distillation based on BERT to improve the performance of CWS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Train** & **Dev** & **Test** \\ \hline sentence & 1098 & 366 & 366 \\ char & 60,789 & 20,618 & 20,449 \\ word & 21,385 & 7395 & 7406 \\ geological word & 6033 & 1974 & 2044 \\ complete geological word & 2196 & 742 & 745 \\ avg. length of word & 2.57 & 2.51 & 2.50 \\ avg. length of geological word & 4.81 & 4.71 & 4.67 \\ avg. length of complex & 7.32 & 7.16 & 7.10 \\ geological word & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset details.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Character-embedding dimension & 768 \\ Key-embedding dimension & 768 \\ Value-embedding dimension & 768 \\ Dropout rate & 0.1 \\ Learning rate & \(1\times 10^{-5}\) \\ Optimization name & Adam \\ Score threshold & 0.7 \\ Unlabeled data weight & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters settings.  
In terms of the evaluation metrics, we follow the approach of [PERSON] [16]. We use the F1-score for GEO Datasets. Moreover, we use the F1-score for geological words and complex geological words to highlight the performance of our method in the geological domain. These metrics are presented progressively, allowing for a more detailed evaluation of the method's performance across different levels of complexity. Specifically, the F1-score (all geological words) reflects the overall performance of all geological words, while the F1-score (complex geological words) focuses on a more challenging subset.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## 1 Introduction
### Gaps and Advantages of InSAR and Models  
Previous InSAR studies, both in this area of interest and globally, were focused on the detection of the subsidence and the validation of the obtained results with classical observations, with little relation to subsidence models. In this study, we provide high-frequency deformations from both DInSAR and prediction models. Our investigation is one of the first to study the location, extent, and velocity of 3D deformation in monthly intervals. Our goal is to provide a detailed analysis of the mine-subsidence life cycle based on the advantages of the Sentinel-1A and 1B satellite constellation. The results aim to improve the understanding of the behaviour and characteristics of the surface deformation and, in the future, will lead to the modification of subsidence model parameters. The model approach that is analysed in this study is widely applicable not only in the area of interest but also by some modifications in Germany, USA, China, and others. Yet, apart from the its advantages, the Knothe model suffers from some limitations due to the complexity of the shape of the deposits, their multi-seam exploitations, and the local geological and mining conditions. We focused our investigation on the coal seam 503 and longwalls No. 5 and 6 from the Bytom-III mining area belonging to the Bobrek-Piekary coal mine in the northern part of USCB. We applied the DInSAR technique to determine the surface subsidence in the area of interest for the time span of one year, and we compared it with the modelling approach based on the Knothe theory. The results were validated with geodetic levelling in-situ measurements conducted during the investigated period.
## 2 Materials and Methods
### Data Analysis  
The video recordings of the real-life measurements were screened and annotated for wheelchair-related activities. Any portion of data that contained wheelchair ambulation with some sort of support (being pushed, power assist devices, etc.) was excluded. The next seven basic WCMMs were derived from the setup with two IMUs: linear velocity of the wheelchair, distance covered, number and magnitude of turns, number and duration of pushes, and incline of the wheelchair.  
In preparation of the calculation of these WCMMs from IMU data, two specific periods of at least 10 s were selected from the IMU recordings: complete standstill at level surface, and straightforward wheelchair propulsion. Orientation of the IMU with respect to wheelchair frame was derived from the complete standstill and averaged over 10 s. The portion of gyroscope data from straightforward wheelchair propulsion was averaged over the 10 s to represent the wheel axis in the local coordinate system of the IMU at the wheel. Afterward, for the analysis of data collected over the day, any rotation around this wheel axis was attributed to distance covered, linear velocity, BoM, and used for push detection to count the number of pushes. Any deviation from this wheel axis, in terms of the angular velocity vector, as measured by the wheel IMU, was attributed to a turn. The assumption used here is that a wheelchair will only rotate around the wheel axis and the vertical axis, not around the frontal axis. The latter is only the case in sidewards rocking of the wheelchair or when entering or leaving a walkway with a sidewards inclination for rain-water drainage, which is usually around 3\({}^{\circ}\) and, as such, neglectable in comparison to the number and magnitude of turns made daily. Inclination was calculated in the sagittal plane as angle between the vertical from the wheelchair as measured during standstill and 10 s average of the current acceleration signal. For further details on the calculation of all variables, and the validation of the WCMM method, see [PERSON] et al. [46]. To enable comparison over participants, the resulting outcome variables were normalized to a time span of 8 h (to reflect a regular working day).  
Furthermore, an extended analysis was applied, in which several of the basic WCMM variables were combined:1. Bouts of mobility (BoM, according to the definition of [PERSON] et al. [41]): bout duration, mean distance and mean velocity per bout.
2. Distance covered over a range of positive inclines (2 to 10\({}^{\circ}\), which represents uphill wheelchair propulsion).  
Additionally, several groupings of variables were visualized in scatter and radar plots, to further explore the inter-individual diversity of mobility behavior observed, as an example of how such summaries of real-life data on wheelchair mobility behavior could support and enhance clinicians and their patients in clinical decision making.
## 3 Theory and Method
### The Calibration Method of the Gain  
The antenna maximum direction gain \(G\) can be easily calculated by using the following formula when we have the beamwidth of the antenna [25]:  
\[G=\frac{4\pi R}{\Omega_{D}C_{r}}, \tag{8}\]  
where \(\Omega_{D}\) is the solid angle of the sun, and \(C_{r}\) is the correction factor that accounts for the partial resolution of the disk image by the main lobe of the antenna pattern. We can ignore the effect of antenna efficiency, a misalignment between the horn and the parabolic reflector and the non-uniform distribution of the solar TB, and assuming a Gaussian antenna pattern of half-power beamwidth \(\theta_{A}\) and solar angular diameter \(\theta_{D}\), \(C_{r}\) can be closely approximated by  
\[C_{r}=\frac{1-\exp\Bigl{[}-\ln 2\left(\theta_{D}/\theta_{A}\right)^{2}\Bigr{]} }{\ln 2\left(\theta_{D}/\theta_{A}\right)^{2}},\quad if\ \theta_{D}<\theta_{A}. \tag{9}\]  
The ratio \(R\) of the measured power difference is defined as follows:  
\[R=\frac{\iint_{\Omega_{D}}G(x,y)d\Omega}{\iint_{4\pi}G(x,y)d\Omega}. \tag{10}\]  
At the same time, the antenna effective collecting area \(A_{e}\) in m\({}^{2}\) and the aperture efficiency \(\eta\) are calculated from [12]:  
Figure 3: **(a,b)** The elevation distortion arises from the rotation of the antenna in azimuth with constant elevation. The horizontal axis is the rotation angle of the GMR antenna in azimuth and the origin represents the center of the sun. The vertical axis is the elevation distortion in the sky-sphere, and the curves indicate the distortion at different antenna elevations.  
\[A_{e}=\frac{\lambda^{2}G}{4\pi}, \tag{11}\]  
\[\eta=A_{e}/A_{g}*100, \tag{12}\]  
where \(A_{g}\) is the antenna physical area, and the effective area of the antenna is typically around 50%-60% of the physical area, it is a function of the feedhorn and it is usually very difficult to determine [26]. The popular method is to use the identical antennas to calibrate and measure the effective area in a microwave anechoic chamber and it is complex. However, the effective area can be easily measured by the sun-based monitoring method in operational, field applications after installation, as described above.
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
### Metal-Organic Frameworks: Promising Materials for Lithium-Sulfur Batteries Applications  
MOFs typically consist of inorganic metal atoms, ions, or metal clusters serving as coordination centers, which interconnect with organic ligands through various coordination modes to yield highly ordered crystalline materials [32; 33]. These materials can exhibit extended, periodic structures in one-dimensional (1D), two-dimensional (2D), or three-dimensional (3D) configurations. The pores within MOFs can be classified as microporous (less than 2 nm in diameter), mesoporous (ranging from 2 to 50 nm), or macroporous (greater than 50 nm), with microporous MOFs being the most prevalent. Owing to their distinctive structures and properties, MOFs demonstrate considerable potential for applications across multiple fields and represent an active and significant research direction within materials science.
## Materials and Methods
### Experimental Setup  
To measure the angular velocity of the shoe in the frontal plane, a gyroscope (IDG-650, InvenSense, TKD, Tokyo, Japan, measurement range \(\pm\)2000 deg/s) in a small box was attached to the heel cap of the right shoe. The positive peak exversion velocity (evVel_Gyro) was defined as the maximum positive angular velocity from the gyroscope signal during ground contact.  
As reference, a light-weight electrogoniometer (Megatron MP10) was attached to the heel cap of the right shoe (potentiometer), next to the gyroscope box, and at the posterior lower leg parallel to the Achilles tendon (flexible arm) to measure the rearfoot motion of the shoe in relation to the shank in the frontal plane [5; 12]. The axis of rotation was adjusted to represent the approximate position of the subtalar joint axis [5; 12]. A neutral rearfoot angle was individually defined for each subject in each footwear condition during a static trial on a calibration plate [12; 15]. The angular velocity of the goniometer signal was obtained by simple numerical differentiation of the goniometer signal during ground contact. The maximum positive angular velocity was declared as evVel_Gonio. Furthermore, the elapsed time between initial ground contact and evVel_Gonio and evVel_Gyro were determined (t_evVel_Gonio and t_evVel_Gyro).  
Gyroscope and goniometer operated synchronously and data were recorded at 1000 Hz by a data logger, secured in a waist belt. Data logger and PC were connected via a 10-m cable. To confirm rearfoot strike pattern, running was controlled visually according to [PERSON] et al. [16]. The presence of the impact peak in vertical ground reaction data was checked. Ground reaction force measurements were simultaneously recorded using a force plate (9287 BA, 0.6 \(\times\) 0.9 m, 1000 Hz, Kistler, Winterthur, Switzerland) that was synchronized with the gyroscope and electrogoniometer. According to [PERSON] et al. [17], the threshold of the force plate was set at 10 N to determine foot touchdown. To remove noise, raw data were filtered with a zero-lag Butterworth low-pass filter (gyroscope and goniometer: 4 th order at 50 Hz; force plate: 2 nd order at 50 Hz). Data post-processing was done using MATLAB R2016b (MathWorksTM, Natick, MA, USA).
## 2 Multi-Scenario Generation Based on Wind Power Forecasting Errors
### Solution Method  
In this study, a mixed-integer linear programming (MILP) approach is adopted to solve the wind/storage power planning and scheduling model under multi-scenario wind power uncertainty. The YALMIP 20230622 toolbox in MATLAB R2021b is used as the modeling interface, and CPLEX 12.10 is selected as the solver. Although CPLEX provides robust and efficient solutions for MILP models, its computational efficiency may decline for very large-scale instances with extensive scenario sets. Therefore, the proposed method is most suitable for day-ahead scheduling problems with a moderate number of representative scenarios. The decision variables in the optimization model include the following: (1) wind power scheduling plan \(p_{j}(t)\); (2) energy storage charging and discharging power \(p^{c}(t)\) and \(p^{d}(t)\); and (3) initial state of charge of the energy storage system \(SOC_{0}\).
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 3 Methodology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Parameter** & **Specification/Value** & **Purpose/Note** \\ \hline \multirow{5}{*}{**Hardware**} & UAV Model & DJI Mavic 3 Enterprise & Commercial-grade drone \\  & Sensor Resolution & 5280 \(\times\) 3956 pixels (\(\sim\)20.9 MP) & High-resolution imaging \\  & Focal Length & 12.3 mm (24 mm equiv.) & Wide-angle lens suitable \\  & Aperture & F/2.8 (fixed) & Balance light intake and \\ \hline \multirow{5}{*}{**Camera Settings**} & Shutter Speed & 1/2000 s & Minimize motion blur \\  & ISO & 400 & during flight \\  & Color Space & sRGB & Standard color \\  & White Balance & Manual & Ensure color consistency \\  & Exposure Mode & Manual & Consistent exposure \\  & Exposure Compensation & 0 EV & across images \\  & Exposure Compensation & 0 EV & No automatic \\  & & brightness adjustment \\ \hline \multirow{5}{*}{**Environmental**} & Lighting & Overcast sky, \textless{}20,000 lux & Minimize shadows and \\  & & & harsh lighting variations \\ \cline{1-1}  & \multirow{2}{*}{Weather} & No direct sunlight, cloudy & Consistent ambient \\ \cline{1-1}  & & & lighting \\ \hline \multirow{5}{*}{**Data Acquisition**} & Target Building & Similar characteristics to & Representative structure \\  & Total Flight Paths & 126 & Covering diverse scenarios \\ \cline{1-1}  & Avg. Flight Time per Path & \textless{}10 min & \\ \cline{1-1}  & Total Images Collected & 13,416 & Sufficient data for analysis \\ \cline{1-1} \cline{2-2}
**Summary** & Image Format & JPEG & Common image format \\ \cline{1-1}  & Average File Size & \textless{}7.8 MB per image & \\ \cline{1-1}  & \multirow{2}{*}{Metadata Recorded} & Timestamp, GPS Coordinates, & Essential for processing \\ \cline{1-1}  & & Camera Model, Exposure Info, etc. & and analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Implementation Environment Definition.