esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Path Tracking Control Based on States Estimation
### The Measurement Noise  
In the terms of position and attitude measurement of the roadheader, [PERSON] [25] designed and built an ultra-wideband (UWB) pose detection system, which consists of four UWB positioning base stations and three local positioning nodes on the roadheader. Using the UWB P440 modules from the company \"Time domain\", together with the micromechanical gyroscope and shaft encoder developed by the company \"Action\", a prototype of the roadheader pose detection system was built, as shown in Figure 8. Using 12 sets of distance measurements, a time-of-arrival (TOA) positioning model [26] was established to calculate the 3D coordinates of three positioning nodes on the roadheader fuselage, and the three attitude angles (heading, pitching, and rolling angles) of the roadheader were then solvable according to the concept of inertial navigation system (INS).  
The roadheader pose detection experiments were carried out in a narrow tunnel. In the experiments, the base stations were immobile while the roadheader was moving slowly, and the pose measurement accuracy was different as the distance between the base stations and nodes changed in scale.  
Figure 9 presents the error frequency histogram of the ranging involved in the pose detection system based on ultra-wideband (UWB) within different distance scales, i.e., 20 m, 35 m, 57 m, and 90 m. It shows that the distance errors comply with the normal distribution and have a standard deviation less than 10 mm and a mean value between \(\pm 5\) mm. The 3D coordinates and heading angle of the roadheader were calculated using the measured distances, and according to the experimental results, the mean error and standard deviation of the calculated coordinates are below 2 cm and of the heading angle are below 2.5\({}^{\circ}\).  
Recall that the pose of the roadheader is used for a subtraction with the desired ones along the pre-designed path to form the pose deviation \(\mathbf{p_{e}}=(x_{e},y_{e},\theta_{e})^{T}\) in the basic path tracking control model in Section 2.1. It is reasonable to assume that the measurement errors of the coordinate deviation \(x_{e}\), \(y_{e}\) obey a normal distribution with the mean value of 0 and a standard deviation of 4 cm, and the direction angle deviation \(\theta_{e}\) obeys a normal distribution with a mean value of 0 and a standard deviation of 5\({}^{\circ}\).
## 2 Materials and Methods
### GC-MS Conditions  
An Agilent 6890 GC coupled with an Agilent 5975 Mass Spectrometry (Agilent Technologies, Santa Clara, CA, USA) was used to analyze volatile compounds in these wine samples according to our previously published methods [37; 38]. A 60 m \(\times\) 0.25 mm HP-INNOWAX capillary column with 0.25 \(\upmu\)m film thickness (J&W Scientific, Folsom, CA, USA) was used to separate these volatile compounds. Helium was used as the carrier gas and the flow rate was set at 1 mL min\({}^{-1}\). The injector temperature was kept at 250 \({}^{\circ}\)C in the splitless mode (0.75 min). The temperature program of the oven was as follows: 50 \({}^{\circ}\)C for 1 min, heated to 220 \({}^{\circ}\)C at 3 \({}^{\circ}\)C min\({}^{-1}\) and kept at 220 \({}^{\circ}\)C for 5 min. The temperature of the ion source and quadrupole were 250 and 150 \({}^{\circ}\)C, respectively. The MSD transfer line heater was set at 250 \({}^{\circ}\)C. The electron ionization voltage was set at 70 eV and a full mass scan ranging of \(m/z\) 30-350 was recorded. A C\({}_{6}\)-C\({}_{30}\)\(n\)-alkane series (Supelco, Bellefonte, PA, USA) under the same chromatographic conditions was used to calculate retention indices. For the volatiles with their reference standard, volatile compounds were identified by comparing their mass spectrum with the Standard NIST11 library and further confirmed by matching their retention indices with the reference standard. Regarding the volatiles without an available standard, they were tentatively identified by comparing their mass spectrum with the Standard NIST11 library and retention indices with the literature. Quantitation of these volatile compounds in the wine sample followed our published method [6]. Briefly, a synthetic wine matrix (14% ethanol and 5 g/L tartaric acid with pH adjusted to 3.8 using 5 M NaOH solution) was generated based on the average alcohol degree and total acidity of the wine samples in the present study. All the standard solutions were prepared using the synthetic wine matrix, and then diluted to fifteen levels. The extraction and analysis of the standard solution was as the same as the wine sample. For the volatiles with their reference standard, they were quantified using the peak ratio of the reference standard over the internal standard versus the concentration of the reference standard. Regarding the volatiles without an available standard, the quantitation was carried out using the standard that had same carbon atom or similar structure of the volatiles. The concentrations of volatile compounds in both clone wines are listed in Table 2.
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 2 Related Work
### Recommendations Using Socially-Generated Knowledge from Flickr  
The tags that have been added by the users have been considered as valuable information for the extraction of the underlying semantics of a photo. When they are accompanied by geo-tags they may be efficiently used in several location-based recommendation algorithms. The work of [PERSON] and [PERSON] [11] focused on the detection of events using tags, date information and geo-tags. Their goal was to group photos based on the event they depict, in presence of noise, i.e., photos that do not depict any kind of event. To this goal, they extracted and analyzed temporal and locational distribution patterns of tags. In order to suppress noise, they chose to apply a wavelet transform, thus providing a multi-resolution analysis of the aforementioned tag distributions. Since periodic events are characterized by certain patterns in tag distributions, their approach produced significantly better results in this case. Discovering trends for tourist attractions was the goal of [PERSON] et al. [12], whose recommendation system adopted a probabilistic approach, ranking places of interest according to their popularity and user-related temporal information. More specifically, they clustered geo-tags, in order to discover POIs. They tackled the recommendation problem as a ranking problem, i.e., they ranked POIs based on their popularity and the context in which users select POIs. They also experimented with the hour, day and month of the visits, and also with their combination. Their results indicated a small improvement to baselines techniques, suggesting that the semantics of POIs should be involved in the process. [PERSON] et al. [13] also aimed to identify events and ranked places of interest, without any prior knowledge. They also applied clustering, so as to discover the spatial aspects of POIs and then, for each cluster they used time-series analysis, to discover its temporal dynamics. Their findings indicate four main types of spatio-temporal clusters, namely stationary, reappearing, occasional and regular moving. A tourism recommendation system was also the work of [PERSON] et al. [14]. They also applied a clustering algorithm and extracted a set of representative images and tags for each cluster. They used visual features on images and number of occurrences on tags. Their system responded with suggestions to users' photo queries and was evaluated on a set of topics. Finally, [PERSON] et al. [15] proposed an algorithm that aimed to predict the location where a person'sphotos were taken, thus provide recommendations concerning their geo-tagging, without relying on the use of geospatial metadata, but rather utilizing solely the textual tags that people use to describe a given location. To this goal, their approach estimated a language model, which relied on the analysis of the terms that have been used to describe photos that had been taken at this location.
# A Dual-Polarized Omnidirectional Rectenna Array for RF Energy Harvesting Harvesting
## 5 Conclusions  
A dual-polarized omnidirectional rectenna array with a hybrid power combining scheme is proposed in this paper. The circularly arranged antenna elements and dipole elements are used to form horizontally and vertically polarized subarrays, respectively. The dual-polarized omnidirectional antenna array is realized by uniting the two subarrays. An RF power-combining network is designed by using the structure of 1-to-2 and 1-to-4 Wilkinson power dividers and a 3-dB hybrid coupler. The designed antenna array, RF power combining network, and rectifiers are fabricated and measured. The impedance matching, isolation performances, and radiation patterns of the antenna array were measured. In addition, the PCEs of the rectifiers and the performances of the RF combining network were measured to verify the validity of the design. Furthermore, the combined dual-polarized omnidirectional rectenna array was also measured. The measured results show that the omnidirectional-recycling capability of the rectenna array can be achieved. The PCEs of the rectenna array in different reception scenarios are over 44% when the input power density is less than 400 \(\upmu\)W/cm\({}^{2}\). On the other hand, the utilization of the RF power combining network enables the rectenna array to maintain excellent stability when receiving EM waves at different incidence and polarization direction angles. Its overall PCE fluctuations are below 10%. Therefore, the proposed rectenna array is suitable for RF energy harvesting applications.  
Conceptualization, H.S. and Y.W.; methodology, H.S. and Y.W.; software, Y.W.; validation, R.R.; investigation, R.R.; resources, N.L.; writing--original draft preparation, H.S. and Y.W.; writing--review and editing, H.S., Y.W., N.L. and R.R.; visualization, R.R.; supervision, H.S.; funding acquisition, H.S. and N.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities under Grant No. B220202083.  
Not applicable.  
The authors declare no conflict of interest.
# Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite
## 4 In-Orbit Performance Evaluation Results  
The Luojia-1A satellite was launched into space on 2 June 2018. Then, the in-orbit performance evaluation was carried out for one month and Wuhan University started freely distributing the night light image data from Luojia-1A satellite from 9 July. During this period, the LEO navigation augmentation system is evaluated from the three aspects: the orbit, the clock, and the signal quality.
# Recent Strategies for Environmental Remediation of Organochlorine Pesticides
## 3 Methods for Removal of Organochlorine Pesticides from the Environment  
The removal of pesticides from the environment has been a subject of intensive research, and this has remained for a period of time. Several techniques have been used to either degrade or remove the organochlorine pesticides from water, soil, food and air. [PERSON] [22] reported in 1969 that peeling, cooking, washing and blanching of food will reduce the organochlorine pesticides in food [23]. The study also reported that feeding animal with charcoal could lower organochlorine pesticides' toxicity in animals. However, these methods cannot completely remove the organochlorine compounds due to their stability and persistence. Burial in deep well, landfill, use of cement kiln, land cultivation, evaporation ponds and open burning [7; 24; 25] have also been used for disposing organochlorine pesticides, but these pose serious environmental and health challenges [3]. Furthermore, land requirement, leaching, slow decomposition and varying climatic conditions are part of the factors that limit the use of these methods [3]. Due to these limitations, new strategies are now being adopted for the removal of organochlorine pesticides. These various methods that have been developed are the focus of this review. The advantages and disadvantages of each of the different techniques are presented in Table 1. It is important to note that most of these remediation strategies are still at pilot scale. Thus, further investigations are required in order to advance these strategies to full (commercial) scale [26]. However, strategies such as Fenton oxidation, adsorption, incineration, bioremediation and phytoremediation has been used on site. One of the methods that has been used for the removal of organochlorine is phytoremediation. Phytoremediation is commonly carried out either by using plants or through the introduction of bacteria into the system. It is important to note that rhizoremediation, phytotransformation and
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Intensity Interval**} & \multirow{2}{*}{**Comparative**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**Hedge’s \(g\) (95\% CI)**} \\ \cline{3-3} \cline{5-5}  & & & & \\ \hline \multirow{3}{*}{V\({}_{\rm MAX}\) (m\(\cdot\)s\({}^{-1}\))} & Int 1: 0.773 \(\pm\) 0.138 & 1 vs. 2 & **\textless{}0.001 \(\ast\)** & 1.51 (0.92; 2.09) \\ \cline{2-5}  & Int 2: 0.585 \(\pm\) 0.104 & 1 vs. 3 & **\textless{}0.001 \(\ast\)** & 3.24 (2.46; 4.03) \\ \cline{2-5}  & Int 3: 0.403 \(\pm\) 0.076 & 2 vs. 3 & **\textless{}0.001 \(\ast\)** & 1.97 (1.34; 2.60) \\ \hline \multirow{3}{*}{V\({}_{\rm MEAN}\) (m\(\cdot\)s\({}^{-1}\))} & Int 1: 0.472 \(\pm\) 0.088 & 1 vs. 2 & **\textless{}0.001 \(\ast\)** & 1.16 (0.60; 1.72) \\ \cline{2-5}  & Int 2: 0.379 \(\pm\) 0.069 & 1 vs. 3 & **\textless{}0.001 \(\ast\)** & 2.70 (1.99; 3.41) \\ \cline{2-5}  & Int 3: 0.272 \(\pm\) 0.054 & 2 vs. 3 & **\textless{}0.001 \(\ast\)** & 1.7 (1.10; 2.30) \\ \hline \multirow{3}{*}{A\({}_{\rm MAX}\) (m\(\cdot\)s\({}^{-2}\))} & Int 1: 3.580 \(\pm\) 0.981 & 1 vs. 2 & **\textless{}0.001 \(\ast\)** & 1.32 (0.75; 1.89) \\ \cline{2-5}  & Int 2: 2.514 \(\pm\) 0.549 & 1 vs. 3 & **\textless{}0.001 \(\ast\)** & 2.06 (1.42; 2.69) \\ \cline{2-5}  & Int 3: 1.889 \(\pm\) 0.595 & 2 vs. 3 & **\textless{}0.001 \(\ast\)** & 1.08 (0.53; 1.63) \\ \hline \multirow{3}{*}{A\({}_{\rm MEAN}\) (m\(\cdot\)s\({}^{-2}\))} & Int 1: 1.989 \(\pm\) 0.612 & 1 vs. 2 & **\textless{}0.001 \(\ast\)** & 1.22 (0.66; 1.78) \\ \cline{2-5}  & Int 2: 1.321 \(\pm\) 0.460 & 1 vs. 3 & **\textless{}0.001 \(\ast\)** & 2.32 (1.65; 2.99) \\ \cline{2-5}  & Int 3: 0.711 \(\pm\) 0.465 & 2 vs. 3 & **\textless{}0.001 \(\ast\)** & 1.30 (0.73; 1.87) \\ \hline \end{tabular} SD: standard deviation; V\({}_{\rm MAX}\): maximum velocity in m\(\cdot\)s\({}^{-1}\); V\({}_{\rm MEAN}\): mean velocity in m\(\cdot\)s\({}^{-1}\); A\({}_{\rm MAX}\): maximum acceleration in m\(\cdot\)s\({}^{-2}\); A\({}_{\rm MEAN}\): mean acceleration in m\(\cdot\)s\({}^{-2}\). \(\ast\) Significant differences for \(p\)-value \(\leq\) 0.05 are highlighted in bold.  
\end{table} 
Table 4: Pairwise comparisons between the different load conditions for V\({}_{\rm MAX}\), V\({}_{\rm MEAN}\), A\({}_{\rm MAX}\) and A\({}_{\rm MEAN}\) during the concentric phase of the bench press exercise.
###### Abstract  
Fish as well as birds, mammals, insects and other animals are capable of emitting sounds for diverse purposes, which can be recorded through microphone sensors. Although fish vocalizations have been known for a long time, they have been poorly studied and applied in their taxonomic classification. This work presents a novel approach for automatic remote acoustic identification of fish through their acoustic signals by applying pattern recognition techniques. The sound signals are preprocessed and automatically segmented to extract each call from the background noise. Then, the calls are parameterized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC), Shannon Entropy (SE) and Syllable Length (SL), yielding useful information for the classification phase. In our experiments, 102 different fish species have been successfully identified with three widely used machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 95.24%, 93.56% and 95.58%, respectively.  
biological acoustic analysis; bioacoustic taxonomy identification; fish acoustic signal; hydroacoustic sensors; species mapping
## 3 Results
2002 (0.42 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)). In fall (DOY 249-335), the first period is still influenced by summer discrepancies between the two datasets which progressively reach the same final GPP pattern with a smooth decrease finally reaching the low winter values (around 1 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)). Indeed, MODIS GPP was fairly low-correlated with EC GPP for the summer season, with correlation coefficient varying between 0.07 in 2007 and 0.12 in 2008. In winter (DOY 0-64 and 336-366), both MODIS GPP and EC GPP have similar low values with a higher correlation coefficients, except for the year 2006 (R = 0.25). We will note here that the MODIS GPP product consistently underestimated GPP during the winter season compared to the other seasons. As we observed significant differences in the seasonal pattern of MODIS GPP compared to measurements from the tower, we explored the potential candidates for this bias based on variables used in Equation (2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline Year & 2001 & 2002 & 2003 & 2004 & 2005 & 2006 & 2007 & 2008 & 2009 & 2010 \\ \hline R & 0.59 & 0.66 & 0.77 & 0.87 & 0.66 & 0.36 & 0.70 & 0.79 & 0.66 & 0.69 \\ \hline \end{tabular}
\end{table}
Table 1: Correlation coefficients (R) between 8-day means of MOD17A2 GPP and EC GPP from 2001 to 2010 from 2001 to 2010.  
Figure 1: Comparison between the flux-tower GPP and MOD17A2 GPP from 2001 to 2010 (points represent mean \(\pm\) standard-error). Blue lines represents MODIS GPP, red lines represents EC GPP and the dashed lines are the dividing lines of the seasons: spring (DOY 65–152), summer (DOY 153–248), autumn (DOY 249–335) and winter (DOY 0–64 and 336–366).
## 2 Materials and Methods
### Stimuli  
The proposed test consists of monosyllabic Arabic words spoken by a male talker and recorded by audio software (Audacity Platform, Oak Park, MI, USA) via a commercially available microphone (Saramonic SR-ULM5, White Plains, NY, USA). Furthermore, 48 monosyllabic, phonetically balanced Arabic words were selected for test development. The Arabic words were randomly divided into six word lists (each with eight monosyllabic words). The words in the first three lists were embedded in white background noise (24 monosyllabic Arabic words), and the words in the remaining lists were embedded in multi-talker babble background noise (24 monosyllabic Arabic words).  
Seven SNR conditions were presented for each word list in both noise types. The first word in each list was presented without background noise, and the remaining seven words were delivered with different SNR conditions. The SNR variations of the proposed Arabic word-in-noise screening test were +15, +10, +8, +6, +4, +2, and 0 dB (Figure 1). All the audio files, consisting of both the speech and background noise, have a duration of 8 s with a sampling frequency of 44,100 Hz.
# Recovery Motion Analysis for False Ceiling Inspection Robot
## 2 Background
### Problems Encountered During Indoor False Ceiling Maintenance  
Concealed false ceilings have the issue of being neglected as the components are hidden from view. Problems when false ceilings are left unattended over long periods of time include, but are not limited to, (a) the presence of pests and rodents that enter false ceilings through crevices or gaps, (b) ventilation and plumbing system leakage that leads to hazardous chemicals and vapours circulating through the building, (c) mould or stains on false ceiling panels, and (d) the eventual damage or breakdown of infrastructure due to neglect and deterioration. These problems can escalate in severity and incur large repair costs over time or even cause health ailments for occupants. To counter such issues, Facility Management (FM) inspection works and examinations of the structural integrity of buildings themselves are often conducted regularly for the inspection of support structures that reside above false ceilings.  
Moreover, removing a single false ceiling panel is unlikely to enable a human worker to view the entire false ceiling plenum since, without the aid of additional equipment such as expensive and bulky endoscopic cameras, the worker's field of view would likely be obstructed by the complex and non-systematic arrangement of infrastructure within the false ceiling plenum. Hence, the removal of multiple panels at different points of the false ceiling is necessary to comprehensively inspect a false ceiling interior. This process of removing and replacing panels requires the constant and cumbersome shifting of equipment to allow a human worker to reach a false ceiling at a height; this also adds to the wear and tear of false ceiling access panels. These issues could be mitigated if robot-inclusive principles were implemented for false ceiling plenums [22].  
These robot-inclusive principles would be cumbersome to implement retroactively for existing buildings. Mobile robots could enable automated robotic inspections within false ceilings for buildings. The dimensions of the robot would need to account for a site's layout in terms of robot-inclusive Accessibility and Observability principles. Thus, these robots need to be designed as small and lightweight to nimbly navigate within constrained spaces, while still accounting for the robotic components for inspection tasks. For other sites, spatial information of false ceilings would be needed to design a customised robot suited for the site that could move efficiently through the zone for comprehensive scans of the false ceiling.
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved Wavelet Threshold Denoising Algorithm  
Since [PERSON] et al. proposed the hard threshold function [12] and the soft threshold function [13], the wavelet threshold denoising algorithm has been widely used. However, the discontinuity of the hard threshold function will cause the reconstructed image signal to oscillate, and the fixed deviation of the soft threshold function will result in blurry or excessively smooth borders of the denoised image. Based on the soft and hard threshold functions, this algorithm proposes an improved threshold function that overcomes the discontinuity of hard threshold and the fixed deviation of soft threshold, reduces the compression of wavelet coefficients, and solves the problems of image oscillation and boundary blur to a certain extent. At the same time, the new threshold can change with the noise level of the high-frequency components in each layer of wavelet decomposition, which has strong flexibility and adaptability.