esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Virtual Loudspeakers for Spatial Room Acoustics Reproduction with Headphones
## 2 Virtual loudspeaker Position Optimization
### Listening Test Method  
The listening test was executed as an ABX discrimination test. Subjects were asked to answer a question: \"Which one of the samples A or B is the reference X?\". Moreover, they were asked to write down the criterion that they used to discriminate the reference from the odd sample. If they could not discriminate the samples from each other, they were asked to choose their answer at random. Full comparison between four conditions forms six pairs, which were repeated four times. Thus, a total of 24 stimulus triplets were presented for both rooms. A preference test was also considered as an option for the listening test. However, the differences between some of the samples were found to be small during preliminary tests, which made the asking for a preference unfeasible. Preference is also a matter of taste, which would have required more participants in the test, in case there would have been more than one preference group.  
The listening test started with the participant reading and signing a paper of informed consent describing the test procedure, possible harm, and data policy. Then, the subject did both test sets in randomized order. Before each set, there were a training set of four ABX triplets, during which the listener was instructed to adjust the listening volume to a reasonable level. After the training set, the subject was asked to keep the volume at the same level during the test set. The subject was given an option to take a break between the test sets, and after completing the experiment, a small non-monetary compensation was offered to the subjects.
## 3 Proposed Methodology
### Least Square Error (L.S.) Estimation  
If 'Z' is sent through a channel '\(j\)' such that it can be written in matrix form as,  
\[\mathrm{y}=Z_{j} \tag{9}\]  
Then, the error 'e' can be defined as  
\[\mathrm{e}=\mathrm{x}^{\prime\prime}-\mathrm{x} \tag{10}\]  
where x is the anticipated result.  
The definition of the squared error (S) is  
\[\mathrm{S}=|\,\mathrm{e}\,|\,2\,\mathrm{S}=(\mathrm{x}^{\prime\prime}-\mathrm{ x})2\,\mathrm{S}=(\mathrm{x}^{\prime\prime}-\mathrm{x})*(\mathrm{x}^{\prime\prime}- \mathrm{x})\mathrm{t} \tag{11}\]  
where \"t\" in superscript denotes complex matrix transposition.  
\[\mathrm{S}=(\mathrm{x}^{\prime\prime}-\mathrm{Z}\mathrm{j})*(\mathrm{x}^{ \prime\prime}-\mathrm{Z}\mathrm{j})\mathrm{t} \tag{12}\]By taking the derivative with respect to \(\dot{\gamma}\)' and setting it equal to zero, we may minimize this equation. The result of the final equation is:  
\[\mathrm{j}^{\prime\prime}=(\mathrm{Zt}\,\mathrm{Z})-1\mathrm{Ztx} \tag{13}\]  
\[\mathrm{j}^{\prime\prime}=\mathrm{Z}-1\,\mathrm{x}\,\mathrm{j}\,\mathrm{ls}= \mathrm{Z}-1\mathrm{x} \tag{14}\]  
Both SISO and MIMO systems can use Equation (14).
## 3 Numerical Simulation
### Governing Equation
#### 3.1.2 Moisture Field Control Equation  
The migration of unfrozen water in permafrost obeys [PERSON]'s law. According to [PERSON]'s equation and considering the blocking effect of pore ice on the migration of unfrozen water during the frozen ice-water phase transition, the governing equation for water in permafrost is as follows:  
\[\frac{\partial\theta_{u}}{\partial t}+\frac{\rho_{I}}{\rho_{w}}\frac{\partial \theta_{I}}{\partial t}=\
abla[D(\theta_{u})\
abla\theta_{u}+k(\theta_{u})] \tag{2}\]  
where \(\theta_{u}\) is the volumetric water content, \(\rho_{I}\) is the ice density, \(\rho_{w}\) is the water density, \(D\) is the diffusion coefficient, and \(k\) is the permeability coefficient.  
In the above equation, the diffusivity of water in permafrost is calculated as follows:  
\[D(\theta_{u})=\frac{k(\theta_{u})}{c(\theta_{u})}\cdot I \tag{3}\]  
where \(k(\theta_{u})\) is the permeability of the soil, \(c(\theta_{u})\) is the specific water capacity, and \(I\) is the impedance factor [29], which is meant to describe the hindrance of water migration by the ice in the soil, and it is obtained via the following:  
\[I=10^{-10\theta_{i}} \tag{4}\]  
\(k(\theta_{u})\) is calculated using the following equation:  
\[k(\theta_{u})=k_{s}\cdot S^{l}\Big{(}1-\Big{(}1-S^{\frac{1}{m}}\Big{)}^{m} \Big{)}^{2} \tag{5}\]  
\(c(\theta_{u})\) is calculated using the following equation:  
\[c(\theta_{u})=\frac{a_{0}m}{1-m}\cdot(\theta_{s}-\theta_{r})\cdot S^{\frac{1} {m}}\Big{(}1-S^{\frac{1}{m}}\Big{)}^{m} \tag{6}\]  
where \(k_{s}\) is the coefficient of permeability of saturated soil; \(S\) is the relative saturation; and \(a_{0}\), \(m\), and \(l\) are intrinsic parameters that vary with the soil.  
Since volumetric water content and ice content variables exist between the heat transfer and water migration equations and the number of equations is less than the number of unknown variables, a coupling term must be introduced to link the hydrothermal equations. The solid-liquid ratio \(B(T)\) is invoked as this coupling term [30], which expresses the ratio of the volume of pore ice in the permafrost to that in the unfrozen soil. The calculation formula is as follows:  
\[B_{I}=\frac{\theta_{i}}{\theta_{u}}=\begin{cases}1.1\Big{(}\frac{T}{T_{f}} \Big{)}^{B}&-1&T<T_{f}\\ 0&T\geq T_{f}\end{cases} \tag{7}\]where \(T_{f}\) is the soil's freezing temperature, and \(B\) is a constant that varies with soil quality and salt content.  
Based on the Van Genuchten (VG) stagnant water model, the relative saturation \(S\) of permafrost is defined as follows:  
\[S=\frac{\theta_{u}-\theta_{r}}{\theta_{s}-\theta_{r}} \tag{8}\]  
where \(\theta_{r}\) is the residual water content, and \(\theta_{s}\) is the saturated water content [31; 32].
# Hybrid RF/VLC Network Architecture for the Internet of Things
###### Abstract  
In recent years, there has been a remarkable advance in monitoring technologies in many environments, be they urban or rural. These technologies, included in the Internet of Things (IoT) domain, allow remote control and acquisition of data from sensors for their subsequence analysis. All these systems are based on the interaction between sensors and actuators. To achieve this goal, it is necessary to provide a very high level of connectivity between the devices, especially as far as wireless systems are concerned. In this sense, there is a great variety of standards in the market of communication networks oriented to this end. One of the biggest challenges today is to allow inter-operability between these different technologies in order to homogenize this field. In addition to this, it is intended to introduce new communication techniques that can provide certain additional advantages to those already existing. The main idea is the creation of a cellular network where radiofrequency and optical technologies coexist, and whose link with the rest of the world is through long-range and low-consumption wireless technologies. The center of each cell, that is the lighting system, can be powered using solar panels, as can the existing systems in the market. The objective is that these panels are capable of providing the necessary energy to the rest of the necessary systems.  
Internet of Things; personal area networks; visible light communications; network architecture; LoRaWan +
Footnote †: journal: Sensors  
19 November 2019; Accepted: 13 January 2020; Published: 15 January 2020
###### Abstract  
This paper investigated the effects of varying noise levels and varying lighting levels on speech and gesture control command interfaces for aerobots. The aim was to determine the practical suitability of the multimodal combination of speech and visual gesture in human aerobotic interaction, by investigating the limits and feasibility of use of the individual components. In order to determine this, a custom multimodal speech and visual gesture interface was developed using CMU (Carnegie Mellon University) sphinx and OpenCV (Open source Computer Vision) libraries, respectively. An experiment study was designed to measure the individual effects of each of the two main components of speech and gesture, and 37 participants were recruited to participate in the experiment. The ambient noise level was varied from 55 dB to 85 dB. The ambient lighting level was varied from 10 Lux to 1400 Lux, under different lighting colour temperature mixtures of yellow (3500 K) and white (5500 K), and different background for capturing the finger gestures. The results of the experiment, which consisted of around 3108 speech utterance and 999 gesture quality observations, were presented and discussed. It was observed that speech recognition accuracy/success rate falls as noise levels rise, with 75 dB noise level being the aerobot's practical application limit, as the speech control interaction becomes very unreliable due to poor recognition beyond this. It was concluded that multi-word speech commands were considered more reliable and effective than single-word speech commands. In addition, some speech command words (e.g., land) were more noise resistant than others (e.g., hover) at higher noise levels, due to their articulation. From the results of the gesture-lighting experiment, the effects of both lighting conditions and the environment background on the quality of gesture recognition, was almost insignificant, less than 0.5%. The implication of this is that other factors such as the gesture capture system design and technology (camera and computer hardware), type of gesture being captured (upper body, whole body, hand, fingers, or facial gestures), and the image processing technique (gesture classification algorithms), are more important in developing a successful gesture recognition system. Some further works were suggested based on the conclusions drawn from this findings which included using alternative ASR (Automatic Speech Recognition) speech models and developing more robust gesture recognition algorithm.  
speech; visual gesture; unmanned aerial vehicle (UAV); multimodal speech and visual gesture (mSVG); aerobot
## 5 Experiments and Analysis of Results
### Evaluation Metrics  
To evaluate the classification performance of the proposed DOA-BPNN model, we employ four widely used metrics: Accuracy, Precision, Recall, and F1-score. Accuracy measures the overall correctness of predictions [36], while Precision and Recall reflect the model's ability to correctly identify positive instances and minimize false-negatives, respectively. F1-score provides a balanced measure by combining Precision and Recall. For the multi-class intrusion detection task, we report macro-averaged results across all classes to ensure fair evaluation, regardless of class imbalance.
# Optimizing Flight Delay Predictions with Scorecard Systems
## 5 Results
### Ranking of Flights and Further Research
#### 5.6.2 The Reporting Airlines  
The variable \"Reporting_Airline\" represents the airline responsible for operating the flight. This factor is crucial to understanding how different carriers perform in terms of punctuality and delay management. Table 16 provides a detailed category report for this variable.  
From Figure 9, it can be concluded that airline MQ, partial score 310, had the biggest DR in the past, with a later tendency towards decreasing DR, but in the last 10 years has been slightly increasing.
## 5 Experimental Evaluation
### Performance Comparison
#### 5.5.2 Experiment 2  
In this section, we present a systematic ablation study (Experiment 2) comparing the performance between APGCN-CF and its baseline variant APGCN-CF-B. The comparative analysis focuses on three key evaluation metrics: precision, recall, and F1-scores, assessed across two minor agricultural product datasets (garlic and scallion). Figure 4 presents a comprehensive performance comparison of the recommendation results, demonstrating the significant impact of integrating graph convolutional networks into the recommendation framework.  
The ablation study reveals the consistent superiority of APGCN-CF over APGCN-CF-B across both datasets, demonstrating the critical role of the graph convolutional component in enhancing recommendation performance.  
For the garlic dataset, APGCN-CF exhibits remarkable improvements in top-15 recommendations, achieving a precision of 0.030 and recall of 0.226, compared to APGCN-CF-B's 0.028 and 0.215, respectively. This performance gain can be attributed to the GCN module's capability to capture higher-order user-item interaction patterns and propagate behavioral information through the graph structure, effectively modeling both explicit and implicit relationships.  
Similarly, in the scallion dataset evaluation, APGCN-CF demonstrates notable advantages in top-20 recommendations, with precision of 0.034 and an F1-score of 0.061, significantly outperforming APGCN-CF-B's corresponding metrics of 0.031 and 0.044. These results underscore the effectiveness of graph convolution operations in addressing data sparsity through neighborhood information aggregation, leading to more refined node representations and, subsequently, more accurate recommendations.  
The empirical evidence conclusively establishes that the integration of GCN architecture serves as a crucial enhancement to the recommendation framework, particularly in addressing the inherent challenges of sparse data and complex user-item relationships in minor agricultural product scenarios. This is attributed to GCN's ability to capture higher-order connection patterns in user-item interactions through message-passing mechanisms, efficiently discovering implicit relationships that traditional methods struggle to identify; furthermore, GCN facilitates knowledge transfer from high-density regions to sparse areas through information propagation across the graph structure, generating more robust node representations that effectively resolve data sparsity issues in recommendation systems. GCN can also naturally incorporate multi-source heterogeneous information such as temporal and geographical data into a unified representation learning framework, which is essential for minor agricultural product recommendation scenarios characterized by strong spatio-temporal features.  
Figure 3: Comparative analysis of recommendation results between APGCN-CF and baseline methods on test sets.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 6 Measurement of Fruit Maturity
### Fruit Attributes  
The fruit attributes associated with harvest maturity vary with fruit commodity. The assessment of such attributes can be used to 'fine tune' the timing of harvest time first approximated with heat units. Relevant attributes include fruit shape, skin color, soluble sugar content (SSC), dry matter content (DMC), tittatable acidity, flesh color, firmness, and indices of combinations of these attributes, noting that only some of these attributes will be relevant for a given fruit type, where minimum levels on such attributes have been incorporated into wholesaler and retailer specifications (e.g., minimum DMC standards for mango cultivars, [119]).  
Skin color and fruit shape can be assessed visually, and thus are candidates for estimation in-orchard using a machine vision system. An example application is the assessment of the maturity of tomato and citrus fruit based on skin color.  
Other attributes related to harvest maturity have required a destructive assessment of fruit. However, two non-invasive techniques have seen adoption for these assessments over the last decade. Handheld near infrared spectroscopy is now used for the in-orchard non-invasive assessment of fruit SSC and DM content in thin-skinned fruit, e.g., in apple [120], avocado [121] and mango [122]; as recently reviewed [101]. For mango, the rate of increase in fruit DMC has been used to forward predict optimal harvest timing [122]. However, the level of storage reserves in a fruit, be that starch and sugar in a mango or apple fruit, sugar in a stone fruit or oil in avocado fruit, can be impacted by growing conditions within a season. Thus, the target reserve level for optimal harvest maturity should be established in association with other maturity parameters. For example, in mango, the DM associated with a desired flesh color can be established for a given growing condition.  
A related technology relates to pigment analysis, typically via a two-wavelength measurement of light transmission through part of the fruit. For example, the DA meter (TR Truoni, Italy) offers a non-invasive assessment of chlorophyll and anthocyanin content using a partial transmission optical geometry. As for the NIRS technique, however, the absolute levels of the DA index associated with harvest maturity should be established for a given growing condition. Other in-field devices are based on proximal fluorescence sensing, providing a non-invasive assessment of flavonoid and pigment content. For example, the Multiplex 2 (Mx) fluorescence sensor (Force-A, Orsay, France) has been used for mapping fruit ripening to support selective harvesting in precision vicitulture [123].
## 3 Proposed Method
### Cluster Head Selection  
If the cluster head is selected purely based on the relative distance between other vehicles, then due to the varying speed of the vehicles the cluster maintenance becomes tedious. Hence, cluster head and gateway selections are not only based on their relative positions, but also proportionate weighting is given for the speed of the vehicle. This reduces the frequent change in the cluster state. The neighborhood of a vehicle is determined by its relative mobility concerning the speed of the vehicles within its transmission range.  
When a vehicle \(u\) receives the beacon messages sent by other vehicles moving in the same direction within its transmission range and builds its neighborhood table (\(N_{u}\)). If the neighbourhood is empty, then it changes its colour to black and becomes the cluster head. Otherwise, it calculates the degree of cohesion (\(DC\)) as,  
\[DC_{u}=\frac{1}{|N_{u}|}\ \sum_{v\in N_{u}}\frac{rm_{uv}}{d_{uv}} \tag{1}\]  
where \(rm_{uv}=\frac{min(S_{u},S_{v})}{max(S_{u},S_{v})}\) is the relative mobility of the vehicles \(u\) and \(v\), \(S_{u}\) is the speed of vehicle \(u\), \(S_{v}\) is the speed of vehicle \(v\), and \(d_{uv}\) is the distance between vehicles \(u\) and \(v\). The distance between the vehicles may be approximately calculated by the received signal strength. The vehicles with yellow color, exchange their degree of cohesion within their 2-hop neighborhood. On receipt of the \(DC\) value from all vehicles with yellow color in its 2-hop neighborhood, the vehicle with the highest \(DC\) value changes its color from yellow to black. The other white/yellow vehicles upon receiving the updated beacon message from the selected cluster head change their color to blue and set it's \(C_{id}\) to the id of its cluster head.
# A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
## 4 Experimental Analysis
### Experiments on Home-Made Dataset  
In this section, the proposed method is compared with several advanced fingerprint identification methods proposed in other related papers on our home-made dataset. Meth  
Figure 4: Example images in verification sets. (**a**) is the original fingerprint image, (**b**) is the fingerprint image with 75% effective area, (**c**) is the fingerprint image with 50% effective area, and (**d**) is the fingerprint image with 25% effective area.  
Figure 3: Example images in verification sets. The fingerprint image is evenly divided into local blocks with a size of \(100\times 100\).  
ods include PFILD [35], RPFR [42], PFRA [38], ALFR [39], and FERM [23]. PFILD combines minutiae with texture features to recognize fingerprint images. RPFR describes a partial fingerprint identification algorithm based on SIFT. PFRA proposed a fingerprint image identification method based on deep learning. ALFR uses CNN to extract 3 templates for fingerprint retrieval. FERM improves the structure of the CNN network, and combines the incomplete fingerprint image and the fingerprint feature map as the input of the CNN network model. For evaluating different registration methods, we used a cumulative match characteristic curve (CMC) in the home-made dataset, as shown in Figure 5. It is obvious that the identification method based on deep learning (PFRA, ALFR, APFI, FERM) has better performance than the method based on minutiae (PFILD, RPFR). The method based on deep learning is more accurate for feature extraction of low-quality fingerprint images. For fingerprint images with 100% and 75% effective area, the identification rate of ALFR is slightly higher than that of this paper. For fingerprint images with an effective area of 100%, the rank-1 recognition rates of APFI and ALFR are 91.3% and 93%, respectively. Meanwhile, for fingerprint images with an effective area of 75%, the rank-1 recognition rates of APFI and ALFR are 89.8% and 91.4%, respectively.  
Figure 5: Matching performance of different algorithms on self-built datasets. (**a**) shows the identification performance of different methods on fingerprint images with an effective area of 100%. (**b**) shows the identification performance of different methods on fingerprint images with an effective area of 75%. (**c**) shows the identification performance of different methods on fingerprint images with an effective area of 50%. (**d**) shows the identification performance of different methods on fingerprint images with an effective area of 25%.  
For fingerprint images with 50% and 25% effective area, APFI achieves better performance. For fingerprint images with 50% effective area, the rank-1 identification rate is 83.2% and the rank-20 identification rate is 94.9%. For fingerprint images with 25% effective area, the rank-1 identification rate is 72.2% and the rank-20 identification rate is 88.9%. This shows that APFI has more advantages in the identification of partial fingerprints.