esa-sceva/satcom-chunk-collection huggingface dataset:
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 2 Background
### Subjective and Objective Methods of Assessing Audio Quality  
Subjective methods of assessing audio quality and localization accuracy are time consuming and resource intensive. There have been various recent studies in the area, e.g., [12; 13] including some of particular relevance [8; 14] but objective measures are needed for large scale complex streaming services. Objective methods using computer models to assist in rapidly predicting speech and audio quality have been developed. Objective metrics exist for speech (e.g., POLQA [15; 16]) and audio quality (e.g., PEAQ [17; 18]) but no objective metrics are agreed upon for compressed spatial audio quality evaluation. Although early work to extending PEAQ to spatial audio was published [19] it did not yield a recommendation. Audio quality can be evaluated using subjective testing in the absence of objective assessment methods. Accepted subjective testing methods for assessing speech and audio quality include, ITU Rec. ITU-T P.800 for speech [20], ITU-R Rec. BS.1534-3 [21] and BS.1116-3 [22], and the recently published P.1310 for Spatial audio meetings quality [23].
## 2 System and Channel Model
### System Model  
In our system, both the HDT and HHT protocols utilize time division multiplexing technology. We assume that the channel variation between any two sensor nodes and the BS follows Rayleigh block fading and is affected by additive white Gaussian noise. We also assume that the channel state remains unchanged during a fixed period of energy harvesting and packet transmission, and that each transmission time is relatively independent. Rayleigh fading has been widely accepted as a reasonable signal propagation model and an effective model for complex environments. Given the complexity of our environment, we have chosen to model the channel fading between the sensor nodes and the BS in our system using the Rayleigh communication model. After conducting a rigorous analysis, we have considered a relatively reasonable system model, as shown in Figure 1. Our system model comprises a BS and multiple sensor nodes \(N_{k}\) that are responsible for both energy harvesting and information transmission. These nodes harvest energy from the BS and use it to transmit packets to the BS, thus completing the system model. To optimize the energy harvesting and information transmission processes, we install \(M+1\) antennas for the BS. \(M\) antennas are used for uplink data packet transmission, while the remaining antenna is dedicated to downlink energy harvesting, which enables the BS to obtain energy from the nodes.  
In our system, we have information transmission in the uplink and energy harvesting in the downlink. We use \(G_{k1j}\) to represent the channel gain of the uplink information transmission process from sensor node \(N_{k}\) to the base station (BS), where \(\forall k\in\{1,2,\ldots,K\}\) and \(\forall j\in\{1,2,\ldots,M\}\). Similarly, we use \(G_{k2j}\) and \(G_{k3j}\) to denote the channel gain of energy harvesting for the HDT and HHT protocols, respectively, i.e., the channel gain of downlink energy harvesting. Since the channel state change is random, we model the channel gain of the system as a random variable that follows the exponential distribution. As a result, based on the concepts of probability density function (PDF) and cumulative distribution function (CDF), we can define the PDF and CDF of the channel gain as follows:  
\[f_{X}(x)=\frac{1}{\theta_{k}}Exp(-\frac{x}{\theta_{k}}), \tag{1}\]  
Figure 1: Wireless power communication system model. The dotted line is used for downlink energy harvesting, and the solid line is arranged for uplink packet transmission.  
\[F_{X}(x)=1-Exp(-\frac{x}{\theta_{k}}), \tag{2}\]  
where \(X\) is a random variable about the channel gain, and \(\theta_{k}\) is the average channel gain of the random variable \(X\). In this system, the channel mean gains of random variables \(G_{k1}\), \(G_{k2}\) and \(G_{k3}\) are represented by \(\omega_{k}\), \(\Theta_{k}\) and \(\Phi_{k}\), respectively.
## 2 Results and Discussion
### Mechanistic Discussion  
To explain the stereochemical outcome in line with antecedent studies and our previous experimental findings [33; 34; 35; 36; 37; 38; 39; 40; 41], we propose in Scheme 4 a plausible mechanism for the regioselective and stereoselective formation of the spiro[oxindole-2,3\({}^{\prime}\)-pyridine].  
First, the in situ decarboxylative condensation of isatin derivatives **1** with _L_-valine **2** generates the (_Z,E_)-dipole **3**. Then the azomethine ylide subsequently undergoes a 1,3-dipolar cycloaddition reaction across the dipolarophile **4**. _A priori_, two regioisomeric and two stereoisomeric pathways can be taken in consideration. The _exo_- and _endo_-approaches refer to the approach of the dipolarophile with its electron-withdrawing group (carbonyl group) oriented toward the EWG group of the dipole (carboxamide group of oxindole) or away from it, respectively. The presence of a secondary orbital interaction (SOI) [42], via path \(A\), favors more the formation of the _exo_-regioisomer **5**, which occurs between the oxygen atom of the carbonyl of the diketone and the carbon atom of the carbonyl succinimide acting as dipolarophile according to Scheme 4. The formation of the alternative regioisomers or diastereoisomers **5\({}^{\prime}\)**or **6\(/\)6\({}^{\prime}\)** via pathways \(B\), \(C\) and \(D\) is probably less favorable because of steric or electronic repulsion occurring in their corresponding transition states. We are aware that, apart from SOI interactions, other parameters, such as reaction temperature [35; 40] or the choice of the solvent [43], may also impact the stereochemical outcome. To confirm our mechanistic proposal, DFT computing is intended in prospective work.
## 3 Performance Analysis
### Trajectory Stability  
At the SRVL stage, the flight quality specifies some requirements on the trajectory stability. The level-1 flight quality [6; 30] requires that the derivatives of the flight path angle versus the airspeed, \(\frac{d\gamma}{dV_{t}}\), should satisfy \(\frac{d\gamma}{dV_{t}}<0.032^{\circ}/(km\cdot h^{-1})\), and the \(d\gamma/dV_{t}\) difference between airspeed at the minimum approaching speed, \(V_{\text{tmin}}\), and \(V_{\text{tmin}}-2.572m/s\) should be less than \(0.027^{\circ}/(km\cdot h^{-1})\). \(\frac{d\gamma}{dV_{t}}\) can be acquired by Equation (4).  
\[\frac{d\gamma}{dV_{t}}=-\frac{\rho S}{2 mg}\Big{(}2V_{t}C_{D}-4 AV_{t}C_{L}^{2} \Big{)} \tag{4}\]  
The trimming points can be evaluated according to the stable gliding constraints set by this work (given in Equation (5)).  
\[\begin{array}{l}\psi=0,\phi=0,\beta=0\\ p=0,q=0,r=0\end{array} \tag{5}\]  
where \(\psi\), \(\phi\) and, \(\beta\) are the yaw angle, roll angle, and sideslip angle, respectively, and \(q\) and \(r\) are the pitch angular rate and yaw angular rate.  
As the flight path angle determines the gliding speed, given the control inputs, the airspeed satisfying the level-1 flight quality was evaluated by giving different flight path angles and trimming the vehicle. The results are shown in Figure 8, where the region between the red dashed lines and the black dashed line is the level-1 flying quality region,  
Figure 7: Deceleration performance analysis diagram: (**a**) \(\theta=5^{\circ}\) Forward flight speed control effectiveness; (**b**) \(\theta=3^{\circ}\) Forward flight speed control effectiveness; (**c**) \(\theta=5^{\circ}\) Sink rate control efficiency; (**d**) \(\theta=3^{\circ}\) Sink rate control efficiency.  
and the green dashed line is \(d\gamma/dV_{t}|_{V_{t}=V_{\min}-2.572}\). The velocity area of the green dashed line distributed in the level-1 flight quality region is the airspeed ranges stratifying the level-1 flight quality requirement, which is \(V_{t}>20m/s\).
## 1 Introduction  
Since ancient times, humans have developed natural products from plants, marine organisms, and microorganisms for various applications. Interest in natural resources goes back to over 1000 years [1]. Medicinal plants have been used for centuries as remedies, and continue to provide alternative agents to fight various devastating diseases [2; 3]. Numerous drugs are derived from natural sources including medicinal plants, which can be available in the form of food supplements, nutraceuticals, and complementary alternative medicine [1]. Plants synthesize secondary metabolites with various chemical structures including tannins, terpenids, alkaloids, and flavonoids, which are involved in several therapeutic pharmacological properties like antimicrobial, anti-oxidants, anticancer, and anti-inflammatory activities [2; 4]. Medicinal plants are characterized in priority as an exhaustive source of bioactive compounds that are used in drug development [4; 5].  
The genus _Berberis_ belongs to the family Berberidaceae with about 500 species. _Berberis_ possesses many medicinal properties since it has been used in the treatment of diseases including leishmaniasis, heart disease, cholecystitis, hypertension, colds, cholelithiasis, dysentery, gallstones, digestive ailments, jaundice, malaria, ischemic heart disease, cardiomyopathy, and urinary tract problems [6; 7; 8; 9; 10]. The Spanish barberry is used in traditional medicine to cure gastrointestinal stones, inflammation, liver, and biliary disorders [11]. Genus _Berberis_ is rich in compounds including tamarixetin, rutin, caffe, and chlorogenic acids with bioactivity potential. However, the available results on its chemical composition are more limited [10; 12]. These compounds are responsible for _Berberis_ biological activities such as antidepressant, antinociceptive, and immunomododulation effects [11; 13].  
Several works have reported that reactive oxygen species (ROS) are involved in cancer, meanwhile, antioxidant agents are used to counteract them. Plants have been found to contain significant ROS scavenging and antiproliferative activities toward cancer cells. Several studies have shown that plants serve as anticancer agents through apoptosis in numerous cancer cell lines [14].  
The goal of this work was to study the phytochemical composition, antioxidant, and antiproliferative activities of _Berberis hispanica_ Boiss. & [PERSON]. (_B. hispanica_). These goals may open new approaches to valorize this species as a source of promising agents to fight such diseases.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 2 Related Work
### Character Recognition in Different Characters Using YOLO  
#### 2.1.1 YOLO for Chinese Character Recognition in Ancient Books  
Recognising rare handwritten Chinese characters in historical manuscripts presents considerable challenges due to the complexity of ancient scripts and the scarcity of annotated data. A recent study addressed this by constructing a custom dataset containing 290 character classes and 71,277 annotated instances, sourced from the Guofeng chapter of the Book of Songs [3]. The YOLOv7 model was employed, with performance enhancements achieved through preprocessing techniques such as grayscale conversion, binarization, and refinements to bounding box annotation layouts. Additionally, an Efficient Channel Attention (ECA) module was integrated into the YOLOv7 architecture to reduce computational complexity without sacrificing accuracy. Experimental results demonstrated that YOLOv7 effectively manages the nuanced visual structures of ancient Chinese scripts. Future research directions include expanding the dataset, enhancing real-time detection capabilities, and exploring cross-linguistic applications to further improve model adaptability and performance.
### Demand-Side Management and Demand Response Management  
The art that is used to manage consumers' energy consumption from suppliers end is named the demand response (DR) while managing the energy consumption at the consumers' premises according to the consumers' priorities and decision is known as DSM [52]. In the DR, the supplier imposes certain limitations on the consumers' energy consumption in order to stabilize the power system. Moreover, in the DR, the supplier does not care about the consumers' preferences and priorities. The DR is normally designed for the supplier's benefit. The DR and its application from the supplier's perspective are summarized by [PERSON] in [26]. The survey in [26] is based on various optimization algorithms and pricing methods. The suppliers propose various DR schemes for consumer energy management. These DR schemes are based on multiple scheduling algorithms and different pricing schemes to manage consumers' electricity consumption. The proposed DR scheduling algorithm is based on a different optimization procedure through which the consumers are motivated to participate and adopt the given DR schemes. On the other side, DSM is an art that can be utilized at the consumers' premises by consumers to manage energy consumption. In DSM, the consumers' benefits are the primary objectives. The DSM scheme is valuable both for the consumers as well as for the supplier. The benefits of DSM are summarized in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Consumer Benefits** & **Supplier Benefits** \\ \hline Required demand will be fulfilled & Generation, transmission, and distribution costs will be reduced \\ Reliable service & Less capital will be required for generation, etc. \\ Reduction in electricity bills & System will be more efficient and flexible \\ Improved lifestyle & Better services for consumers \\ \hline \end{tabular}
\end{table}
Table 5: Benefits of DSM.
# Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions
## 2 Satellite Constellation Players
A major satellite ground service provider such as KSAT is already investing for infrastructure enlargement [15]. At the same time, constellation management shall be enhanced to make an efficient use the new infrastructure. This requires new operational architectures towards higher automation, either onboard or on ground, involving, for instance, artificial intelligence and virtual reality [16; 17]. A second concern involves the communication, with the RF spectrum becoming possibly overcrowded and the required data-throughput increasingly larger. Lastly, but probably most importantly, the space traffic and debris issues, which may prevent the safe and successful operation of spacecraft [17]. Figure 1: Percentages of proposed constellations in different market fields. Figure 2: Expected time evolution of in orbit spacecraft due to constellations. Colored histogram bars represent the estimated number of satellites launched yearly (see Table A1 in Appendix A for details). Black, dotted line represents cumulative sum. Not-classified (NC) stands for companies that have not published expected time-to-service. As far as the size is concerned, the proposed constellations are quite widespread. According to Figure 3, roughly half of the constellations are relatively small, containing less than 50 elements. Constellations with numbers of elements between 50 and 150 elements appear to be quite appealing as well, with a 22% share. Larger size slots are instead of particularly low interest. Finally, a considerable 17% features non-declared sizes, as anticipated. Satellite sizes show a clear bias towards micro and nano classes, reflecting the trend towards the miniaturization of satellite platforms in general (Figure 4). Although 35% of the constellation projects do not declare size, 32% belong to nano-class and 18% to micro-class. Pico, mini, medium and large classes reflect the minority, with a total share of 15%. This means that constellations are going to be composed of satellites weighting mainly from 1 to 100 kg. Figure 4: Constellations by satellite sizes: mass \(<\) 1 kg = pico; 1 kg \(<\) mass \(<\) 10 kg = nano; 10 kg \(<\) mass \(<\) 100 kg = micro; 100 kg \(<\) mass \(<\) 500 kg = mini; 500 kg \(<\) mass \(<\)1000 kg = medium, mass \(>\) 1000 kg = large. Figure 3: Constellations by satellite numbers.
# Intermediate Isolation System with Nonlinear Lower Structure and Isolation System
## 6 Time History Analyses on 3D Models and Comparison to IIS Design Spectrum
### Linear and Nonlinear THA: The Base Shear Ratio  
In Figure 11, the _IIS design spectrum_ is compared to markers, representing the average peak results of the seven THAs on the design solutions in the case in which masonry structure and isolation system are both assumed to be elastic (Figure 11a), or the masonry structure is assumed to be elastic and the isolation system inelastic (Figure 11b). In particular, the markers are depicted both in terms of displacement ratio and base shear ratio, and the RSA curves for \(\overline{d}\) and \(\overline{v}\) are overlapped since they represent results in normalized form. As can be seen in Figure 11, the THA values of the base shear ratio are slightly larger than the displacement ratio counterparts, both neglecting or considering the nonlinear behaviour of the isolation system.  
This comparison is only reported for the analyses where the LS is linear, since the base shear ratio is not indicative of the IIS response when the hysteretic behaviour of the masonry structure is considered. As shown in [31], by comparing the push-over curves and the global hysteretic loops under selected seismic inputs, it clearly appears that the former are the backbones of the latter. Exceeding the elastic limit of the masonry structure, the top displacement increases under an almost constant value of base shear. For this reason, when the existing structure undergoes inelastic deformations, in the as-is configuration and/or in the design solution, the base shear ratio is not representative of the seismic response of the IIS.
## 2 Material and Methods
### Data Pre-Processing, Feature Selection and Cross-Validation  
All measured pH and MC values were normalized for training and testing. Spectral reflectance, pH, and MC data were normalized using the Z-score normalization method according to Equation (2):  
\[x_{i}^{\prime}=\frac{x_{i}-\overline{x}}{\sigma_{x}} \tag{2}\]  
where \(x_{i}\), \(\overline{x}\), and \(\sigma_{x}\) represent the value of the \(i\)th observation, mean, and the standard deviation of the \(x\)-variable, respectively.  
The feature selection process in this study was conducted using embedded methods, as described in [33]. Specifically, the Bootstrap Random Forest technique was employed as a machine learning method for bagging ensemble learning. This involved training multiple Random Forest trees on different subsets of observations using bootstrap sampling, where each tree is trained on a different subset of observations. The remaining observations, known as the out of bag (OOB) sample, are then used to estimate the model's performance. The final model predictions are generated by averaging the outcomes of all the trees. This process is designed to reduce variance and improve the model's accuracy.  
To identify the most important features for predicting pH and MC values, 100 different spectrum features were extracted for each wavelength at each maturity stage. The features that best estimated each output value were then selected. Model performance was evaluated using k-fold cross-validation, where k = 10 owing to the 10 subsets in our dataset. The training set was split into 10 sub-sets, with one subset reserved for testing and the other nine used for training in each iteration. This process was repeated 10 times to ensure the robustness of the results [37].
## 5 Future Work and Conclusions  
QS plays a prime function in regulating fish spoilage by _Pseudomonas_ spp. Thus a proper and in-depth understanding of the QS system in those bacteria is very much essential. Of particular interest are studies that will clarify the following issues related to QS inhibition: (i) what are the best conditions for QS inhibiting agents in controlling bacterial phenotypes? and (ii) whether QS inhibiting agents impact beneficial microflora in food ecosystems. In addition, more than one AHL-mediated QS system occurs in a particular strain, which are all involved in metabolic activity regulation. Some investigated QS inhibiting agents show high target specificity. These agents present a challenge in developing approaches that influence a broad range of AIs of _Pseudomonas_ spp. Further investigations should focus on studying the \"universal\" anti-QS agent that targets a broad range of AIs to inhibit QS activity efficiently. A combination of the anti-QS approach with other biocontrol treatments to obtain a synergistic effect is a promising strategy that could increase the susceptibility of pseudomonads to the conventional preservative treatment of seafood.  
In this minireview, a summary of the results related to the contribution of QS in the metabolic activities of _Pseudomonas_ spp. is provided. QS inhibiting agents with examples of their application in seafood products were also described. With the increasing amount of information available, QS-based strategies can be used more effectively to extend the shelf life of perishable fish products and as innovative strategies for controlling foodborne _P. aeruginosa_.  
[PERSON] and [PERSON] conceived the concept and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Science Centre, Poland (grant number 2016/23/D/NZ9/00028).  
Not applicable.  
The authors declare no conflict of interest.
# Real-Time Detection of At-Risk Movements Using Smartwatch IMU Sensors
## 3 Materials and Methods  
### Experimental Data  
This study conducted a comprehensive data collection to analyze various types of movements, specifically designed to distinguish between normal and at-risk activities. The dataset comprises a total of 5534 instances of motion data, segmented into four primary categories: walking, running, desk work, and at-risk situations. Each category includes approximately 1399 samples for walking and running, 1361 samples for desk work, and 1375 samples for hazardous situations.  
Data were collected from 12 female participants, each contributing to the dataset for durations ranging from 10 to 11 min. This setup allowed us to capture a wide range of naturalistic movements in a controlled environment. The participants were instructed to perform specific tasks associated with each category under monitored conditions to ensure data consistency and reliability. After converting the raw data collected for each class into one-dimensional feature vectors, the data representative of the behaviors are shown in Figure 1. The figure shows the results of converting the 3-axis acceleration data into one-dimensional feature vectors. The x-axis represents the time collected at 50 Hz for 6 s, and the y-axis represents the amplitude. The transformation process is described in more detail in Section 3.2.  
The entire dataset was divided into subsets for training and testing to evaluate the performance of the machine learning models developed in this study. Moreover, 90% of the data were used for training, allowing the models to learn and adapt to the patterns of motion associated with each category. The remaining 10% constituted the test set, used to assess the effectiveness of the models.