esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 3 Combining Knowledge Graph and Collaborative Filtering Recommendation Algorithm
### Similarity Fusion  
In the similarity fusion method, linear models are favored for their interpretability and scalability. Utilizing a linear model allows for an intuitive understanding of the influence of various features on the recommendation results. Hence, this paper opts for a linear model. The similarity obtained from the knowledge graph, \(simA(x,y)\), is integrated with the similarity derived from item-based collaborative filtering, \(simB(x,y)\), through a weighted fusion approach. The optimal outcome is attained by iteratively adjusting the weights. The computational formula is presented in Equation (10):  
\[sim=(1-\alpha)\cdot simA(x,y)+\alpha\cdot simB(x,y) \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) & \(\ldots\) & \(X_{u}\) \\ \hline \(Y_{1}\) & \(M_{11}\) & \(M_{21}\) & \(M_{31}\) & \(\ldots\) & \(M_{u1}\) \\ \(Y_{2}\) & \(M_{12}\) & \(M_{22}\) & \(M_{32}\) & \(\ldots\) & \(M_{u2}\) \\ \(Y_{3}\) & \(M_{13}\) & \(M_{23}\) & \(M_{33}\) & \(\ldots\) & \(M_{u3}\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \(Y_{v}\) & \(M_{1v}\) & \(M_{2v}\) & \(M_{3v}\) & \(\ldots\) & \(M_{uv}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: User behavior matrix.  
The parameter \(\alpha\) signifies the fusion weight ratio, with \(\alpha\in[0,1]\) governing the balance between item-based collaborative filtering and TransD in the fusion process. By varying the value of \(\alpha\), we can assess how different fusion ratios influence the performance of the recommendation algorithm and identify the optimal \(\alpha\) value. Once the similarity results are obtained, a Top-K sorting is conducted to produce the final recommendation list.
## 2 Methodology and Data
### Reference Coordinates  
PPP quality assessment often focuses on accuracy and convergence time, which in turn strongly depends on the adoption of reference coordinates treated as 'truth'. Additionally, in recent years, a significant development of multi-GNSS positioning is observed. Therefore,one of the most important goals of the MGEX project is to prepare products for official multi-GNSS-based combination solutions, which will be official IGS products in the future [66].  
The multi-GNSS solutions computed by different ACs use different models than the official IGS, even for the same GNSS system [15; 67]. There is already research on obtaining official combined solutions from all ACs, where discrepancies between different ACs are still obtained [13; 14; 15; 67]. These differences are the consequences of using different models to determine the products, as well as the need to estimate parameters such as ISB [68] or using different receiver antenna models for Galileo observations [69; 70]. Therefore, it is important to adopt appropriate reference coordinates in multi-GNSS analyzes.  
Before beginning the principal analyses, we verified how the adopted reference coordinates affected the analyses performed (Figure A1). The following coordinates were calculated as reference coordinates: weekly from the G solution, weekly from the E solution and weekly from the GE solution, using the sine function as a function weighting the observations (Function_7). In addition, daily coordinates calculated by AC CNES/CLS from MGEX were also used. Any 3D errors were analyzed. From the obtained results, it can be seen that using only Galileo reference coordinates causes a bias of about 1 cm for GPS calculations. Also, the use of GPS reference coordinates produces a bias of approximately 1 cm for Galileo calculations. As shown in [69], the use of Galileo phase center corrections (PCC) models for Galileo observations causes a bias of -8 mm with respect to GPS results. The use of PCC GPS models for Galileo observations results in greater consistency of the results between Galileo and GPS solutions. For the results in relation to the daily reference coordinates calculated by AC CNES/CLS, the biggest errors were obtained for all analyzed solutions. The best results were obtained for reference coordinates calculated for weekly GPS+Galileo solutions, which eliminate systematic errors between GPS and Galileo. These coordinates are adopted as 'truth' coordinates, allowing the best possible analysis to be made.
## 2 Numerical Model
### Formulations
#### 2.1.3 Time-Domain
The time step used to obtain the solution at the next time instant \((k+1)\) is obtained from the one chosen at the previous instant \((k)\), based on the maximum local relative variation of the MVP:  
\[\Delta t^{(k+1)}=\frac{\Delta t^{(k)}}{\delta/\delta_{\text{rel}}+\delta_{ \text{abs}}}, \tag{10}\]  
where \(\delta_{\text{rel}}\) and \(\delta_{\text{abs}}\) are selected by the user to set the sensitivity of the adaptivity to temporal variations of the solution. The variable \(\delta\) provides an indication of the maximum relative change of the solution in the computational domain:  
\[\delta=\max\Biggl{[}\frac{|[A_{z}]^{(k)}-[A_{z}]^{(k-1)}|}{0.5|[A_{z}]^{(k)}+ [A_{z}]^{(k-1)}|+\varepsilon}\Biggr{]}. \tag{11}\]  
The constant quantity \(\varepsilon\ll\delta_{\text{abs}}\) is an arbitrary small quantity needed to avoid a zero-division in the above expression. Inverse-Laplace Transform Method  
As anticipated, FLARE integrates a Matlab translation of the Fortran 90 routine for the numerical inverse-Laplace transform developed by [PERSON] and colleagues [34, 40]. This allows one to compute the time-evolution of one of the quantities yielded by the FEM solver, using solutions computed in the complex domain. This formulation is closely related to the quasi-stationary formulation, substituting the purely imaginary \(j\omega\) with the complex variable \(s\). Details of this approach can be found in [31] and are briefly summarized below. The Laplace transform \(\mathcal{L}\{f(t)\}\) of a generic function of time \(f(t),t\geq 0\) is [41]:  
\[F(s)=\int_{0}^{\infty}e^{-st}f(t)\;dt, \tag{12}\]  
The analytical inversion of the Laplace transform can be obtained via the Bromwich (or Riemann) inversion theorem:  
\[[K][A_{z}]+[S]\left[\frac{dA_{z}}{dt}\right]=[T], \tag{4}\]0  
where \(\gamma\)--the real part of \(s\)--is such that the contour of integration is to the right of any singularities of \(F(s)\) greater or equal to the abscissa of convergence \(\sigma_{0}\). Unfortunately, the analytical evaluation of (13) is only straightforward for a limited number of cases. For this reason, great research efforts have been dedicated to the development of numerical inversion algorithms. Notably, at the present stage, it is not possible to define the best strategy for all possible cases, and the performance of different inversion algorithms depends on the particular application [42]. Due to this, we provide three different inversion techniques in FLARE. These are compared for the case considered in this work in Section 3.6.
# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 1 Introduction
### Methods for Fault Detection and Classification
However, it is important to understand that each approach comes with its own advantages and drawbacks. In addition, in recent years, different artificial intelligence techniques have been accepted as the basic methods for fault detection [30]. Studies in this area mostly include Neural Networks [31], Convolutional Neural Networks [32], Support Vector Machine [33], k-Nearest Neighbor, Decision Tree, and Fuzzy Logic. On the other hand, computer vision applications have an important impact and represent efficient findings for classification of and localizing faults for PV systems, as in other engineering fields, such as the motor system. [PERSON] et al. proposed a motor fault diagnosis method based on visual knowledge for improving the feature extraction ability, showing that the limitations of traditional signal analysis have been overcome [34]. In another study aiming to eliminate the limits in existing studies, a continual learning framework was developed to diagnose the electric motor faults. Experimental results showed the effectiveness and superiority of the proposed approach for class-added fault diagnosis [35]. We organized our paper as follows. Firstly, in Section 2, we provide brief information about the status of the PV generation in the world and the significance of the maintenance issue. Also, thermal imaginary is introduced. Section 3 is the main part of the paper and describes methods for PV placement, thermal imaging, image processing, and fault detection and classification. In this section, the results of fault detection and classification using inverter data are also given and discussed. Section 4 constitutes the closing part of the study, and in this chapter, information about the study is given and the main findings obtained are stated.
## 1 Introduction  
Medicinal plants have been used, since the Hippocrates era, for healing a wide range of diseases including infectious, systematic and inflammatory diseases [1; 2]. In contrast the current antimicrobial drugs are used for the treatment of human and animal infections for just less than a century now [3; 4; 5].  
In addition, the development of drug-resistant pathogens appeared quickly, while the emergence of multi-drug resistant strains has increased exponentially during the recent years [6].  
Methicillin-resistant _Staphylococcus aureus_ (MRSA), vancomycin-resistant enterococci (VRE), _Streptococcus pneumoniae_, _Mycobacterium tuberculosis_, _Enterobacteriaceae_ producing extended spectrum b-lactamases (ESBLs) and carbapenemases, multidrug-resistant isolates causing severe hospital infections in immunocompromised individuals such as _Pseudomonas aeruginosa_ and _Acinetobacter baumannii_ are increasingly reported from both the human and veterinary medicine practice and the aquatic environment as well [4; 7; 8; 9; 10; 11; 12; 13; 14].  
With the emergence of drug resistance and the shortage of effective therapeutics to treat infectious diseases, medicinal plants attracted the attention of researchers looking for new compounds with potent antimicrobial activity. Recent studies suggest plant-essential oils to be associated with sufficient antimicrobial properties against several foodborne pathogens, indicating good prospective for use in food preservation [15; 16; 17; 18; 19; 20; 21]. Moreover, there are published data on their use in farm animals for prophylaxis from infectious diseases and as safer substitutes for the antibiotics used for growth promotion [22; 23; 24; 25; 26].  
Among the actions targeting to reduce and solve the drug-resistance problem is the search for novel antibiotic substances; hence essential oils and their compounds appear to be encouraging replacements. However, most of the published research in this field concerns either reference bacterial strains or non-pathogenic bacteria. Even though there are a few papers concerning clinical isolates [27; 28; 29; 30; 31], there is limited information on the essential oils' effect on drug-resistant strains [32; 33; 34; 35]. The goal of the present study was to explore the antibacterial effectiveness against multi-drug resistant Gram-positive bacteria isolated from waste water treatment plants and clinical specimens, of five essential oils, which though being widely used in previously conducted studies, their effectiveness against drug-resistant strains is barely addressed in the relevant bibliography.
## 3 Analytical Methods and Applications
### Basic Analytical Methods  
Basic analytical methods involve spatial analysis of point patterns and clusters, kernels and density analysis and estimation, cartographic modeling and models of spatial processes. In geospatial IoT research, mostly density analysis has been observed. Density analysis takes known quantities of some phenomenon measured at each location or area and examines the spatial relationship of the locations of the measured quantities [31]. Density surfaces show where point or line features are concentrated. An example is illustrated in Figure 4, visualizing the density of crime incidents in the city of Chicago [31].  
Figure 4: An example of density analysis (adapted from [31]).  
Figure 3: An example of buffering analysis (adapted from [31]).  
Density analysis has been used in tourism recommendations [37], discovery of attractive areas using collections of geo-tagged photos [38], identifying groundwater potential zones in hard rock terrain [39], monitoring the seasonal variation of physicochemical parameters of an urban water stream [40] and the analysis of how alcohol outlets affect the possibility of violence, in association with specific socio-demographic groups [41].  
A particularly common output of density analysis is a heat map, where each raster cell is assigned a density value and the entire layer is visualized using a temperature gradient metaphor, as shown in the example of Figure 5. More vivid colors usually represent high concentration of measurements or large values of some measured quantity.  
Heat maps in IoT have been used for traffic flow visualization [42], for optimizing public transportation by analyzing taxi rides [43] and for predictive policing [44], where police departments and emergency institutions analyze past and real-time data to identify crime or violence hot-spots, and manage their physical resources. In addition, heat maps are commonly used in mobile sports tracking applications [45], for understanding greenhouse emissions form livestock agriculture [35], for visualizing urban air quality [46] and in modeling traditional and genetic biodiversity [47]. Finally, an interesting application of heat maps based on kernel density estimation was for the monitoring of dengue fever [48].
## 2 Materials and Methods
### Charge Controller  
It prevents overcharge and over-discharge of the battery. In lead-acid batteries, over-charge is avoided by charging batteries in three stages (bulk, absorption, or boost and float stages, Figure 2). Absorption includes battery equalization in some cycles, periodically. During the second and third stages, the current is limited using the pulse with modulation (PWM) technique. The limits between stages are voltage setpoints, while advanced controllers compare the minimum battery state of charge (\(SOC\)) of the previous discharge with SOC setpoints to determine the charge stages to be applied. In this work, SOC controllers performed the boosting stage if the \(SOC\) during the last discharge was lower than a specific value (usually 70%), and equalization was performed if battery \(SOC\) during discharge was lower than another specific value (usually 40%).  
Lithium batteries require a specific charge controller, as there is a very little voltage difference for a high \(SOC\) difference (state of function is estimated in Li-ion batteries), and they cannot accept overcharge; therefore, there is no equalization stage and no float stage, just a constant voltage/constant current (CV/CC) charge algorithm. Some controller models are programmable and can be used for lead-acid or for lithium batteries.  
The charge controller prevents over-discharge by disconnecting the battery when a specific setpoint voltage (or \(SOC\)) is reached and reconnecting after partial recharging when another higher specific setpoint voltage (or \(SOC\)) is reached.
# Augmenting Environmental Interaction in Audio Feedback Systems+
## 3 System Design and Validation  
Based on the above notions, we designed an audio feedback system in which control of the signal-processing parameters depends on recognized features of the feedback sound, aiming in particular for sensitivity to information it carries regarding the acoustic environment. These dependencies are used to shape musical tendencies in the sonic output, with analogical approximation to the following composed interactions:  
1. The amount of reverberation in the space determines a tempo-scale characteristic
2. The system will change its output level depending on the volume level of ambient noise
3. A prominent resonance determines a timbre characteristic
4. The distribution of room modes determines a timbre characteristic, energy of higher frequency partials in particular  
Figure 3 shows the overall diagram for simulating the effects (tempo, volume and timbre) of audio feedback and validating the mapping. The amplifier is adaptively controlled based on the peak amplitude of the input signal. Convolution of an impulse response simulates the room acoustic response, which is shaped by propagation and reflections in physical space. We used impulse response data from [PERSON] [19] and [PERSON] [20]. A noise function simulates room ambient noise, used as an excitation energy source in the feedback loop. We used a biquad high-pass filter with a cutoff frequency below 80 Hz to remove excessive amplification of low frequency feedback, which is uncommon in a real acoustic environment. A low-pass filter, similar to the damping loop filter of a waveguide model, is also used to reduce the howling that can be introduced by excessive amplification.  
Table 1 compares the approximation methods and control parameters to implement the proposed mappings. It is inevitable that we can attain only approximate information about the acoustic environment from the real-time audio input, since complete real-time segregation of ambient noise and acoustic reflection information from the received feedback sound is practically impossible. Accepting this limitation, we have designed our system to infer what properties it can have, and use them to augment the specificity of the result.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
The results are competitive compared with existing models like MOTR and TransTrack on both validation and test sets. Overall, our contribution can be summarized into the following three aspects:  
1. We propose a joint track-detection decoder to fuse information between the track and detection branches, improving the performance for both tasks. 2. We propose a score-guided multi-level query fuser to extract instance representation for long-term tracking by fusing multiple intermediate queries based on the confidence score. 3. We implement FusionTrack on top of the modules and perform comprehensive experiments on the DanceTrack dataset, achieving competitive results.
## 2 Materials and Methods
### Flux Measurements and Data Processing  
As an indispensable part of the global network of micrometeorological flux observations, Swiss FluxNet comprises a collection of research sites that were constructed to better understand the exchange of carbon dioxide and other gases between the atmosphere and terrestrial ecosystems, as well as the impacts of climate change [3]. At each flux site, an open-path infrared CO\({}_{2}\)/H\({}_{2}\)O gas analyser (Li-7500, LI-COR, Lincoln, NE, USA) and three-dimensional (3D) sonic anemometer (models Solent R3-50 and HS, Gill Instruments, Lymington, UK) are jointly used as the EC-based micrometeorological measurement system. An automatic meteorological station (AWS) is also installed on the flux tower and continuously measures the local meteorological variables, namely the incoming shortwave radiation solar radiation (Rg), near-surface air temperature (Ta), relative humidity, the vapour pressure deficit (VPD), soil temperature and volumetric soil water content (Ts and SWC), and the amount of precipitation (Pre). In this study, we used the variability in the VPD to represent atmospheric dryness, which is defined as the difference between saturated water vapour pressure and actual water vapour pressure [27; 28]. Thus, the VPD means how far the air is from thermodynamic equilibrium, with higher VPD related to stronger atmospheric demand for water from the land surface.  
The raw flux data are recorded at 10 Hz. After the flux measurements are taken, the data must be processed to correct for errors and calculate the fluxes. Eddy covariance fluxes were calculated using the free and open source software EddyPro (V6, LI-COR) and then made available as part of the ICOS Ecosystem Thematic Centre (ETC) dataset [29; 30]. The data processing techniques followed the FLUXNET data processing pipeline: (1) the sonic anemometer data are rotated to account for any misalignment between the anemometer and the vertical axis; (2) the data are filtered to remove any high-frequency noise and periods of unstable atmospheric conditions; (3) the data are checked for errors and outliers, and any suspect data are removed; (4) the fluxes of carbon dioxide, water vapour, and energy are calculated using standard equations; and (5) any missing data are gap-filled by using interpolation or other techniques. In this study, the standardised gap-filling and partitioning  
Figure 1: Location of the flux sites used in this study. The full names of CH-Dav, CH-Oe1, and CH-Cha are Davos (evergreen needle-leaf forest), Oensingen1 (grassland), and Chamau (grassland), respectively. The base map is derived from the AVHRR 1 km land-cover map.  
procedures of CO\({}_{2}\) fluxes were performed using the method proposed by [PERSON] et al. (2011), with the marginal distribution sampling (MDS) gap-filling algorithm and flux partitioning methodology based on a short-term temperature-dependent approach by separating NEE into GPP and Re. More details about the data processing can be found in [PERSON] et al. (2013).