esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
In addition, most existing methods can only achieve time-invariant formation configurations, whereas in practical applications, heterogeneous cluster systems must be able to dynamically adjust their formations in real time to cope with complex external environments and changes in tasks. In the past, scholars have mainly used two methods to establish the relative motion models of UGVs and UAVs. One method is to directly analyze the geometric relationship of the motion of a single vehicle in a two-dimensional plane and establish a relative-motion model in a two-dimensional plane. The other is to derive the difference between the different vehicle positions in an inertial coordinate system and then obtain an expression of the relative motion state in the inertial space. This description cannot directly reflect the motion characteristics of UGVs and UAVs in a relative coordinate system, and their relative motion processes are insufficiently clear. Based on the above research background, this study investigates the UAV-UGV formation-maintenance control problem for cooperative combat mission requirements. First, from the perspective of a UAV-UGV cooperative combat system, the architecture of the control system and the relationship between the sub-modules are analyzed. Subsequently, a relative motion model between the UGV and the UAV is established. The optimal UAV-UGV cooperative formation-maintenance control is then realized based on the proportional-integral (PI) optimal control theory. The main contributions of this study are as follows:  
1. By directly studying the motion relationship between a UGV and a UAV in a relative coordinate system, their relative equations of motion are established in three-dimensional space. It is possible to directly obtain the motion of the UGV and UAV in a relative three-dimensional coordinate system, thus clarifying the physical meaning of the relative motion between the two. 2. The PI optimal control theory is used to design an optimal formation-maintenance controller that can overcome the constant relative-motion perturbations, as well as the nonlinear-model linearization bias. This controller can potentially achieve fast and stable optimal control of UAV-UGV formation.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 5 Experiment
### Illumination Changes  
Illumination changes are the most critical factors affecting the visual loop closure detection. In order to test the robustness of the algorithm to illumination changes, we collected image sequences with different illuminations. We fixed a trajectory and then sampled image sequences at 12:30, 15:00, 17:30 and 19:00. Finally, four image sequences were obtained and named as VS1230, VS1500, VS1730 and VS1900 respectively, as shown in Figure 11. Each image sequence contained 200 frames and 20 closed loops.  
The tests were performed on these four image sequences, and the results are shown in Figures 12-15. Since the algorithms of spp1+, spp12+, spp124+ have been proven to be outstanding in 5.3, here we only compared them with FabMap.  
Figure 11: Sample images taken at four different times.  
Figure 12: P-R curves of four algorithms on VS1230.  
At 12:30, the P-R curves of the four algorithms are on the upper right of the coordinate system. However, as the time went by, all P-R values of the algorithms decreased, and the FabMap decreased more obviously because the illumination turned to dim. Especially in the VS1730 and VS1900 image  
Figure 14: P-R curves of four algorithms on VS1730.  
Figure 13: P-R curves of four algorithms on VS1500.  
Figure 15: P-R curves of four algorithms on VS1900.  
sequences, the accuracy of FabMap was drastically lower at high recall rates. In the VS1900 image sequences, at 100% recall rate, the precision of spp124+ was still as high as 70% or more. This shows that the proposed algorithm is more robust to illumination changes than the traditional BoW method.  
In general, for RGB-D SLAM, researchers prefer higher precision because the wrong closed loop leads to a completely wrong pose graph. Therefore, we also did statistical analysis about the average precision of spp1+, spp12+, spp124+, and FabMap on the four image sequences VS1230, VS1500, VS1730, and VS1900.  
As can be seen from Figure 16, the average precision of the four algorithms was above 90% at 12:30, and at 17:30, only spp124+ and spp12+ were still above 90% precision. At 17:30, the average precision of FabMap is only 70%. At 19:00, the differences are more obvious. At this time, the average precision of spp124+ was still as high as 90%, and the other three were below 90%, especially FabMap which had an average accuracy of only 42%. It can be concluded that the algorithm of this paper is more robust than the traditional BoW method when the illumination changes.
# Measurement of Visible Radiation through a _Sansevieria cylindrica_-Based \"Living Sensor\"
## 2 Materials and Methods
In order to implement the living sensor, its working principle, in accordance with the aforementioned, will be considered and, in particular, the root exudate production changes the soil redox potential at each dry condition, thus determining a measurable signal among cathode and anode depending on Vis-light intensity. To study and to characterize the sensing system, a suitable experimental setup was conceived. Figure 2 shows its schematization which includes:  
* A _Sansveieria cylindrica_ having 15 stems for which we assume the shape of a cone with radius and height of about, respectively, 6.5 mm and 30 cm, so a base area of about 132.6 mm\({}^{2}\) is estimated, and a total area of 6257 mm\({}^{2}\) for each stem;
* A visible light source with a spectrum typical of D65 CIE illuminant, composed of 4 neon lamps, each one of 18 W;
* A support system useful for changing the distance between source and plant and, as a consequence, the intensity of the light intensity on living sensor;
* A controlled chamber, ESPEC CORP, model SH-242 used to investigate the influence of temperature and relative humidity on the device response;* Two electrical contacts of Zn and Cu, placed inside the soil at a depth of 6 cm, 8 cm apart, that represent, respectively, anode and cathode of the device;
* A LabVIEW(tm) routine to acquire the output voltage coming from the soil-plant system;
* A NI USB-6366 DAQ board with a laptop, adopted to continuously acquire the data;
* MATLAB(tm) processing to elaborate the acquired experimental data.
## 3 Emission Reduction Measures at Ports
### Seaside
At the port level, individual ports have initiated incentive schemes, such as the Green Flag Program at Los Angeles and Long Beach, to give a 15-25% reduction in port fees if vessels reduce their speed to a certain level near the ports, and the Green Passports Scheme at multiple European ports, to give up to a 10% reduction in port fees to vessels meeting the Environmental Ship Index (ESI) threshold. Ports may collaboratively encourage vessels to adopt cleaner fuels, e.g., the World Ports Climate Action Program issued by 12 major ports across the world (www.hellenicishippingnews.com, accessed on 10 December 2023). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Measures** & **Cases and Effect** & **References** \\ \hline
**Operational** & \\ \hline Slowing ship speed & Container ships at Los Angeles and Long Beach; engine load factor reduced from 80\% to 10\%. & [15] \\ \hline Just-in-time arrival & 483 very large crude carriers; 7–19\% emission reduction & [16], bluevisby.com (accessed on 10 December 2023) \\ \hline Reducing port time & Dry bulk ships in North and Baltic seas; 2–8\% energy reduction & [17] \\ \hline Training ship crew and port pilots & Tanker vessel at Klaipeda port; 12.5\% emission reduction from ship manoeuvres & [18] \\ \hline
**Technical** & \\ \hline Hull design, e.g., shape, material, coating & Various vessels; 1–30\% CO\({}_{2}\) reduction & [19] \\ \hline Propulsion system, e.g., hybrid power, machinery, waste heat recovery & Various vessels; 1–45\% CO\({}_{2}\) reduction & [19] \\ \hline Orshore power & Many ports including Gothenburg; Seattle; Vancouver; Antwerp; Southampton; Port of Koper; 30\% emission reduction & [15; 23] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Categorised solution measures to reduce port seaside emissions.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 7 Tradeoff Between Privacy and Utility  
The previous section showed that increasing amounts of interference increasingly degrade Wi-Fi sensing accuracy. This poses a question for IoT users who intend to use interference to fend off attackers, as they need to determine the optimal amount of interference that degrades attack accuracy while still allowing normal IoT communications. This section accordingly presents experiments to determine how the level of interference affects the usable Wi-Fi bandwidth (BW).
## 3 Performance Simulation and Analysis
### ACM Transmission Efficiency
For a specific G/S, the ACM TEF decreases with the increase of minimum MCS. This is due to the fact that the number of available MCS is reduced, and without the lower MCS, it may not be possible to cope with the larger atmospheric attenuation. 2. For a specific minimum MCS, the ACM TEF decreases in the order of Kashgar G/S, Beijing G/S and Sanya G/S, mainly due to the differences of rainfall.
## 2 Data and Method
### Method  
#### 2.2.1 Selection and Annotation of Nighttime Light Imagery  
To develop a more accurate algorithm for detecting illuminated fishing vessels in nighttime imagery, we first compiled a dataset of labeled images containing these vessels. The nighttime images from the study area, obtained through GEE, were preprocessed by being logarithmically transformed and normalized between 0 and 1. These images were then resized to a uniform dimension of 640 \(\times\) 640 pixels, and those without any vessels were discarded. The remaining images were categorized into three classes: clearly visible and distinguishable (Figure 2A); not clearly visible but locatable and distinguishable (Figure 2B); and not clearly visible, locatable but indistinguishable (Figure 2C). For the first two categories, accurate quantification and location determination were feasible, whereas the third category did not allow for precise numerical differentiation but permitted the identification of the operational location. The images from the first two categories were annotated using LabelImg (1.8.2). Ultimately, we obtained a dataset comprising 541 images with 10,837 vessels. This dataset has been uploaded to an online repository, and it is available to the scientific community on demand.
## 6 Comparison of SHG Characteristics without Magnets and with Ferrite Magnets
When the car battery is discharged, the power supply of the excitation winding of the SHG without magnets is lost, and there is a risk that the generator cannot be started due to lack of excitation. In the undercar SHG with ferrite magnets, even in the absence of current in the excitation winding, some excitation is created by permanent magnets. Therefore, even with a discharged car battery, SHG with ferrite magnets can provide the car with energy and recharge the battery;
9. The total cost of the materials of the electromagnetic core (electrical steel, copper, ferrite magnets, non-laminated steel) for SHG with ferrite magnets is 341/244.7 = 1.4 times more than for SHG without magnets, because of adding the cost of ferrite magnets. In general, as a result of comparing the characteristics of the SHGs with ferrite magnets and without magnets, it can be concluded that the use of the SHG with ferrite magnets in this application is promising; since the efficiency of the generator has been improved significantly, the dimensions and weight have also been significantly reduced, with a relatively small increase in cost.
###### Abstract  
Head-related transfer function (HRTF), which varies across individuals at the same direction, has grabbed widespread attention in the field of acoustics and been used in many scenarios. In order to in-depth investigate the performance of individualized HRTFs on perceiving the spatialization cues, this study presents an integrated algorithm to obtain individualized HRTFs, and explores the advancement of such individualized HRTFs in perceiving the spatialization cues through two different binaural experiments. An integrated method for HRTF individualization on the use of Principle Component Analysis (PCA), Multiple Linear Regression (MLR) and Partial Least Square Regression (PLSR) was presented first. The objective evaluation was then made to verify the algorithmic effectiveness of that method. Next, two subjective experiments were conducted to explore the advancement of individualized HRTFs in perceiving the spatialization cues. One was auditory directional discrimination degree based on semantic differential method, in which the azimuth information of sound sources was told to the listeners before listening. The other was auditory localization, in which the azimuth information was not told to the listeners before listening. The corresponding statistical analyses for the subjective experimental results were made. All the experimental results support that individualized HRTFs obtained from the presented method achieve a preferable performance in perceiving the spatialization cues.  
HRTFs; individualization; spatialization cues; auditory perception; subjective experiment
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 3 Robustness of M-estimator
### Classical Contaminated Model  
We will now describe a classical statistical model in the presence of outliers, which is known as the contaminated model in robust statistics. Suppose we are interested in estimating a \(d\)-parameter family of probability distributions \(f_{\theta}\):  
\[M=\{f_{\theta}|\theta=(\theta_{1},\theta_{2},\ldots,\theta_{d})\in\Theta\}, \tag{9}\]  
where \(\Theta\subset\mathbb{R}^{d}\) is an open subset. We denote its cumulative distribution function (CDF) by \(F_{\theta}\). The simplest situation is when possible outliers are generated by a single probability distribution \(g\) whose CDF is \(G\). A probabilistic mixture of two probability distributions \(f_{\theta}\) and \(g\) gives the contaminated model:  
\[f_{\theta}^{\varepsilon}=(1-\varepsilon)f_{\theta}+\varepsilon g, \tag{10}\]  
where \(\varepsilon\in[0,1)\) denotes the strength of occurrence of outliers. The strength of the noise \(\varepsilon\) is usually referred to as the _contamination parameter_. A familiar and classical example of this kind is to consider the normal distribution \(f_{\theta}=f_{\mu,\sigma}^{\mathcal{N}}\) with the expectation value \(\mu\) and the standard deviation \(\sigma\) as the ideal probability density function. Outliers are generated by another normal distribution whose expectation value is orders of magnitude different from \(\mu\). This is equivalent to a statistical model of the form:  
\[M_{\text{Contaminated}}=\{f_{\theta}^{\varepsilon}|\theta\in\Theta, \varepsilon\in[0,1)\}. \tag{11}\]  
Note that the contamination parameter as well as parameters characterized in \(g\) are to be regarded as nuisance parameters of the model \(M_{\text{Contaminated}}\).  
The more general case of modeling outliers in the ideal case (9) is introduced as follows. Let \(\mathcal{G}_{\text{outlier}}\) be a set of all possible CDFs for generating outliers. In it, each element \(G\in\mathcal{G}_{\text{outlier}}\) corresponds to a different CDF generating different types of outliers. The general contaminated model with outliers is described by the CDF:  
\[F_{\theta}^{\varepsilon}=(1-\varepsilon)F_{\theta}+\varepsilon G\quad(G\in \mathcal{G}_{\text{outlier}}), \tag{12}\]  
where \(\varepsilon\) is the contamination parameter. In this general case, we do not need to assume specific forms of \(G\) in order to apply the method of M-estimators. This is in contrast to the setting of parametric models.
###### Abstract  
Audio event detection (AED) is a task of recognizing the types of audio events in an audio stream and estimating their temporal positions. AED is typically based on fully supervised approaches, requiring strong labels including both the presence and temporal position of each audio event. However, fully supervised datasets are not easily available due to the heavy cost of human annotation. Recently, weakly supervised approaches for AED have been proposed, utilizing large scale datasets with weak labels including only the occurrence of events in recordings. In this work, we introduce a deep convolutional neural network (CNN) model called DSNet based on densely connected convolution networks (DenseNets) and squeeze-and-excitation networks (SENets) for weakly supervised training of AED. DSNet alleviates the vanishing-gradient problem and strengthens feature propagation and models interdependencies between channels. We also propose a structured prediction method for weakly supervised AED. We apply a recurrent neural network (RNN) based framework and a prediction smoothness cost function to consider long-term contextual information with reduced error propagation. In post-processing, conditional random fields (CRFs) are applied to take into account the dependency between segments and delineate the borders of audio events precisely. We evaluated our proposed models on the DCASE 2017 task 4 dataset and obtained state-of-the-art results on both audio tagging and event detection tasks.  
audio event detection; weakly supervised learning; convolutional neural network; structured prediction; conditional random field +
Footnote †: journal: Applied Sciences
## 2 Latest Developed Biosensors for COVID-19
### Surface Plasmon Resonance (SPR)/Localised Surface Plasmon Resonance (LSPR) Biosensor
#### 2.2.2 Advantages and Limitations  
The SPR-based biosensors we have reviewed for SARS-CoV-2 detection have good sensitivity and reusability. However, they lack selectivity and the refractive index of the biosensors are also affected by temperature, non-specific or specific adsorption on the sensor surface, and changes in buffer concentrations [57]. Hence, the sensor surface needs to be modified with a ligand (e.g., antibody, DNA probe) to selectively capture the target analyte.  
On the other hand, SPR-based biosensors allow the integration of nanostructures or nanoparticles to enhance the detection sensitivity [58; 59]. The extent of improvement depends strongly on the shape of the nanostructure and the type of metal used [49; 59; 60]. As mentioned in the previous section, two-dimensional heterostructure [35] and AuNIs [37; 38] attached to the gold film in the sensor exhibited maximum sensitivity of 200\({}^{\circ}\) RIU\({}^{-1}\) and detection of sample analytes at an ultra-low concentration in the range of pM and fM, respectively. SPR-based sensors allow the tailoring of the sensing medium to improve sensor performance and sensitivity by using multi-layers for the medium or altering the medium thickness [61; 62].  
However, plasmonic virus detection from clinical specimens is still limited due to surface fouling of the receptor surface and interference of non-specific bindings. Surface fouling limits the application of plasmonic biosensors by blocking recognition element immobilisation and specific binding [63]. It can be prevented by selecting the appropriate sample dilution ratio or the design of ultra-low fouling surfaces with anti-fouling strategies using polymer-based surface chemistry or zwitterionic technology [64; 65].  
Biosensors with good reusability are highly desirable because it lowers the cost per test. The solvent environment is a key parameter that determines the binding of antibody-antigen. The reversible non-covalent interaction between antibody and antigen can be disrupted by high salt concentration, extreme pH and detergents [66]. Hence, chemical regeneration is the most widely used approach to regenerate the sensor by chemically altering the solvent environment with a regeneration solution (e.g., acid/base, detergent, glycine or urea). SPR sensor could be chemically regenerated for SARS-CoV-2 detection [67].
###### Abstract  
In this paper, Predictive Functional Control (PFC) is used for X-Y pedestal control for LEO satellite tracking. According to the nonlinear characteristics of the X-Y pedestal and pedestal model variation caused by its operating point change, the use of system identification algorithm, which is based on special types of orthonormal functions known as Laguerre functions, is presented. This algorithm is combined with PFC to obtain a novel adaptive control algorithm entitled Adaptive Predictive Functional Control (APFC). In this combination, Laguerre functions are utilized for system identification, while the PFC is the control law. An interesting feature of the proposed algorithm is its desirable performance against the interference effect of channel X and channel Y. The proposed APFC algorithm is compared with Proportional Integral Derivative (PID) controller using simulation results. The results confirm that the proposed controller improves the performance in terms of the pedestal model variations; that is, the controller is capable of adapting to the model changes desirably.  
adaptive predictive functional control (APFC); Laguerre functions; LEO satellite tracking; unstructured system identification; X-Y pedestal +
Footnote †: journal: Applied Sciences  
1
# Quality Enhancement of MPEG-H 3 DA Binaural Rendering Using a Spectral Compensation Technique
## 3 Proposed Spectral Compensation for the MPEG-H 3 DA BR
### Panning Gain Compensation (PGC)
Loudspeakers are located at azimuth angles 0\({}^{\circ}\), \(\pm\)30\({}^{\circ}\), \(\pm\)60\({}^{\circ}\), \(\pm\)90\({}^{\circ}\), \(\pm\)135\({}^{\circ}\), and 180\({}^{\circ}\) in the horizontal plane.
# Overview of an Experimental Program for Development of Yield Surfaces Tracing Method
## 2 Materials and Methods  
### Objectives of the Experimental Program  
The long-term objective of the Laboratory of Thermomechanics is to develop an accurate and reliable yield surfaces tracing method capable of capturing the yield surface shape and position in a particular stress space under various load conditions. This YST method requires identifying a series of representative yield points constituting a YS. For now, we focus on the axial stress--shear stress space and use hollow cylindrical test specimens. The specimens are subject to prescribed combinations of axial load (tension/compression) and torque. Identification of individual YPs within each of these sequences, being referred to as \"probes\", is achieved using an automatized procedure, performing continuous evaluation of prescribed experimental yield point definition. The procedure can not only be used to evaluate the initial YS (i.e., the YS in the as-manufactured state of material), but also to induce an artificial plastic prestrain and to perform tracing of the subsequent YS bearing information on the desired directional distortional hardening evolution.  
While prior experimental methods mainly used strain gages to measure the axial and shear strains (e.g., [28; 29; 30; 31]), the current method employs a contact axial/torsional extensometer (EXTM). Similar types of extensometers were also employed in [2; 4; 16; 27]. The decreased precision of the EXTM in comparison with strain gages is well compensated by the following advantages: a simplified and faster procedure to attach the measurement device to the test specimen surface; substantially elevated measurement range allowing the evaluation of multiple YSs without the necessity of frequent reattachment of the EXTM; and the possibility of more automatization of the YS tracing procedure.