esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Sensor Data Imputation: OWA-Based Model Aggregation for Missing Values
## 2 OWA Methodology
### Ordered Weighted Averaging  
The OWA operators were originally introduced in [26] to create a function to aggregate data associated with multiple criteria. Subsequently, this technique has been used as a popular aggregation operator for many different types of applications [27; 28]. The primary objective of implementing these aggregation operators is to establish a suitable methodology for assigning importance weights to data from various sources. In particular, there are different OWA families, such as max, min, and average. Thus, these operators are extremely flexible for practitioners to utilize in various applications. These operators can be defined as follows:  
Definition: OWA operators are a means to associate a set of n numbers with a single numerical value or equivalently real-valued functional over a finite set S = \(s_{1},\ldots,s_{n}\). In other words, the OWA method is a mapping function (\(F\)) from multiple dimensions to one dimension, \(R^{n}~{}\rightarrow~{}R\), by using an associated weighting value for each observed value as follows:  
\[\mathbf{w}=\begin{bmatrix}\mathrm{w}_{1}\\ \mathrm{w}_{2}\\ \vdots\\ \mathrm{w}_{n}\end{bmatrix} \tag{1}\]  
where  
\[\sum_{j=1}^{n}\mathrm{w}_{j}=1 \tag{2a}\] \[0\leq\mathrm{w}_{j}\leq 1, \tag{2b}\]\[F(a_{1},a_{2},\ldots,a_{n})=\sum_{j=1}^{n}b_{j}\mathbf{w}_{j} \tag{3}\]  
where \(\mathbf{F}\) is a column vector consisting of \(n\) observation elements with associated weighted after sorting the observation data, and \(a=(a_{1},a_{2},\ldots,a_{n})\) represents the unordered arguments' values, \(\mathbf{b}=(b_{1},b_{2},\ldots,b_{n})^{T}\). b can be rewritten in a different way, such as \(\mathbf{b}=(a_{\gamma 1},a_{\gamma 2},\ldots,a_{\gamma n})\), by sorting the arguments with \(b_{1}\geq b_{2}\geq\cdots\geq b_{n}\), where \(\gamma\) is considered as a permutation of the arguments on (1, 2,..., \(n\)), and \(\mathbf{w}=(\mathbf{w}_{1},\mathbf{w}_{2},\ldots,\mathbf{w}_{n})^{T}\) is the OWA weighting vector.  
Now, it can be seen that the essential key of this operator is ordering the argument values. In addition, this operation is idempotent, commutative, and monotonic. In this work, we explain the methodology for incorporating importance into OWA aggregation applications by utilizing a transformation operator. The implementation of various aggregation operators can be achieved by considering the magnitude of the generality of the weights and determining the appropriate emphasis on the arguments based on the order of the weight positions in \(\mathbf{w}\). By placing high values with the weights, higher aggregation scores can be generated. On the other hand, lower scores in the aggregation can be achieved by setting lower values of the weights. The challenge associated with this problem lies in the extensive range of operators represented by the OWA operator. Specifically, the various components of the OWA family, such as max, min, and average, employ distinct methods for including priority; for more detail, please see [29].
# Efficient Zero-Sequence Impedance Measurement in Autotransformers Using Low-Voltage Excitation
## 2 YNa Autotransformer Zero-Sequence Impedance Field Test Method
### Two-Phase Disconnection Test on the Low-Voltage Side  
The two-phase disconnection test on the low-voltage side requires the application of a three-phase power supply on the low-voltage side, open circuiting the two-phase winding on the low-voltage side and short circuiting the winding on the non-power side to ground. The corresponding compound sequence network is the same as Figure 7.  
The phase sequence voltage relationship is shown in Equation (11):  
\[\left\{\begin{array}{l}\dot{V}_{a}=V_{1}+V_{2}+V_{0}\\ \dot{V}_{b}=\left(-\frac{1}{2}-j\frac{\sqrt{3}}{2}\right)V_{1}+\left(-\frac{1 }{2}+j\frac{\sqrt{3}}{2}\right)V_{2}+V_{0}=0\\ \dot{V}_{c}=\left(-\frac{1}{2}+j\frac{\sqrt{3}}{2}\right)V_{1}+\left(-\frac{1 }{2}-j\frac{\sqrt{3}}{2}\right)V_{2}+V_{0}=0\end{array}\right. \tag{11}\]  
Based on Equation (11), it can be found that \(V_{1}=V_{2}=V_{0}\). Therefore, the current-voltage vector diagram for each phase sequence is shown in Figure 8. The corresponding compound sequence network is a three-sequence series connection. Given this, the sequence current relationship is the same as Equation (9), which can be substituted into Equation (10) to obtain the zero-sequence impedance of this autotransformer.  
\[Z_{\rm{H}(L||g)-0}=\frac{V_{1}+V_{2}-2l_{1}Z_{\rm{HL}-1}}{l_{0}}=\frac{2V_{a} -2l_{a}Z_{\rm{HL}-1}}{l_{a}} \tag{12}\]  
Figure 8: Phasor diagram of secondary voltage/currents for the low-voltage Two-Open-Phase test. (**a**) Currents. (**b**) Voltage.  
Figure 7: Sequence network of the YNa autotransformer high-voltage two-Open-Phase test.  
The sequential voltage expressions in the above tests are universal. The difference in them lies in the different angles of the different disconnection tests. For example, in the two-phase disconnection test on the high-voltage side, the negative-sequence voltage \(V_{2}\) is in the opposite phase to the positive-sequence voltage \(V_{1}\) due to three equal sequence currents, and this phenomenon makes the angle between \(\dot{V}_{ab}\) and \(\dot{V}_{1}\) increase.
# BVLOS Unmanned Aircraft Operations in Forest Environments
\begin{table}
\begin{tabular}{c c c} \hline \hline
**VLOS Range** & **Approximate Square Area** & **Approximate Circle Area** \\ \hline
200 m & 8 ha & 13 ha \\
500 m & 50 ha & 79 ha \\
1 km & 200 ha & 314 ha \\
2 km & 800 ha & 1257 ha \\
5 km & 5000 ha & 7854 ha \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Approximate maximum operating areas based upon VLOS limitations, where VLOS range is equal to the distance between the centre and corner of the calculated square areas and the radius of the calculated circular areas. the operational efficiencies associated with higher altitudes (including a greater field of view for sensors and associated increase in flight line spacing), in addition to the enhanced VLOS that this creates for the pilot above the forest canopy. The anecdotal experience of the authors suggests operations between the altitudes of 400 and 1500 ft above ground level (AGL) are relatively common over forest environments. However, to operate above 400 ft, in most jurisdictions, requires regulatory approvals, such as licensing or certification. For this reason, it is critical that operators check aeronautical information as part of mitigating airborne risk and maintaining situational awareness [21]. Listening to air band radio, after receiving appropriate training to understand standard aviation phraseologies, is also strongly advised to enhance situational awareness. While EVLOS methods can be employed to overcome the visual occlusion caused by forests, the dense foliage can also cause signal attenuation for radio waves. This has a strong limiting effect on the range of the command and control link (C2) between the craft and the operator [22; 23]. Another associated issue with operation in forest environments is that they are often remote and occupy land that would be otherwise unsuitable for high value crops. For this reason, operators often have to contend with steep and hilly terrain, which have a similar potential for obstructing VLOS as trees do, combined with the trees themselves. The remoteness can also present issues with accessing operating areas, due to the absence of roads, open spaces to set up ground operations, and other infrastructure, particularly in natural forests where harvesting is not permitted.
## 4 Conclusions  
Impact analysis of multi-time-delays on the performance of an AMD system is completely conducted. For the negative effect of multi-time-delay in each subsystem, a new compensation controller is designed using an \(\mathrm{H}_{\infty}\) control law. Finally, based on the results of an experimental system, some concluding remarks are drawn.  
(1) The maximum strokes of the system with multi-controllers are similar to those of the system with a single-controller when there is no time-delay effect. The sum of the control forces of the sub-controllers is larger than the overall control forces of the latter system, indicating that the output of s the former system is relatively higher than that of the latter system.  
(2) In the system with multi-controllers, as no time-delay effect or an equal time-delay effect is considered, the differences between its strokes and control forces are relatively close to zero, which means the multi-controllers operate synchronously.  
Figure 12: The structural responses and the control parameters of the experiment under the measured results, (**a**) the displacements and (**b**) the accelerations of the 2 nd floor, (**c**) the displacements and (**d**) the accelerations of the 4 th floor, (**e**) the forces and (**f**) the strokes.  
(3) Equal time-delays reduce the performance of the systems with multi-controllers or with a single-controller, and have little effect on the feedback gains to the displacements of the auxiliary mass and the synchronization of the systems with multi-controllers.  
(4) When there are unequal time delays in the system with multi-controllers, as the time-delay differences increase, the amplitude of the control forces becomes larger, while the amplitude of the strokes maintains stable, which shows that the multi-controllers become relatively independent.  
(5) The control system without compensation has a negative impact on the reductions of the structural responses. When there are multiple time delays in a control system, the efficiency of PA compensation system is lower than that of H\({}_{\infty}\) compensation system. The H\({}_{\infty}\) compensation system greatly improves the performance of the multi-time-delay systems, and its auxiliary mass runs smoothly and the control forces are stable.
## 2 Materials and Methods
### Flammability Experimental Setup  
The fire testing was carried out by means of a flammability test facility (FTF) in compliance with the ASTM D6413 international standard. The FTF, fabricated by Govmark Testing Services, was realized in a ventilate stainless steel chamber (see Figure 1). Inside the chamber, a stationary burner with a gas pilot tube is allocated. The flame is adjustable by two gas inlet controlling valves (one on the methane gas cylinder and the other outside the chamber). A flame controller was placed on the left side of the cabinet, with a timer for the measurement of the flame time, the after-flame and after-glow time (see definitions below). The evacuation flow rate was 2 m\({}^{3}\)/h, and the chamber was placed in a hood in order to evacuate eventual burning smokes after the test execution. The samples subjected to the flammability test were 6 mm-thick rectangular slabs (dimensions: 300 \(\times\) 64 mm\({}^{2}\)); one test series was carried out on two-layered samples, to assess the influence of thickness. The test method approached the procedure of ECSS-Q-70-21A standard [8]. The samples were conditioned in a clean room, at 23 \({}^{\circ}\)C, for at least 24 h; then the test was performed within 4 min after the removal of the sample from the clean room. Flame height and position (centered at the bottom edge of the tester) were adjusted before the test (i.e., without the sample), and the pilot flame was left lighted. The sample was then positioned vertically to the flame. The purpose of the test is to determine the material's flammability characteristics by the exposure to a flame applied at the bottom edge of the material samples [9; 23; 24; 25; 26; 27; 28]. The flame-time was currently set to 12 s for different materials comparison; the reference thermoplastic was also tested at several flaming time intervals, in order to gain further knowledge about the fire-retardant effective dynamics. The testing recorded temperature (measured by K-thermocouples) was around 450 \({}^{\circ}\)C. The main measured parameters were as follows:  
* Char length--distance from the sample edge directly exposed to the flame up to the furthest point of visible damaging;
* After-flame time--time of sample flaming after the ignition source has been removed;
* After-glow time--time of sample glowing after ignition source removal and flame extinction.  
The standard typically adopted for acceptance or rejection is that the material has to be non-combustible or self-extinguishing. That means that the combustion zone has to propagate less than one half of the height into the sample (150 mm in this case) and that the burning time has to not exceed 10 min. Moreover, there should be no sparking, sputtering or dripping of flaming particles from the test sample. Finally, all the samples (three is the minimum number required) must show no failure, in order to fulfill the standard requirements.
## 3 Synthesis of Transition-Metal-Sulfide-Based Nanomaterials
### Thermal Decomposition of a Sulfur Source  
One of the more traditional methods for the synthesis of MoS\({}_{2}\) nanoparticles and TMS-MoS\({}_{2}\) composite nanomaterials is through the thermal decomposition of ammonium tetrathiolmolybdate (ATM). This method is commonly applied to the synthesis of ammonium tetrathriotungstate (ATT) [118, 119]. ATM can be formed in a basic aqueous solution by the reaction of (NH\({}_{4}\))\({}_{2}\)S or H\({}_{2}\)S with ammonium molybdate as shown below:  
\[4\text{S}^{2-}+\text{MoO}_{4}^{2-}\rightarrow\text{MoS}_{4}^{2-}. \tag{8}\]  
The ATM can be crystallized or further reacted with TM depending on the desired product such as the generation of CoMoS\({}_{2}\), NiMoS\({}_{2}\), or MoS\({}_{2}\)[120, 121, 122]. For MoS\({}_{2}\) nanomaterials, the synthesized ATM is crystallized and placed into a tube furnace with an inner atmosphere containing H\({}_{2}\) gas [122]. The furnace is then heated to the desired temperature, typically 350 \({}^{\circ}\)C or above, which can be used to control the crystallinity of the nanomaterial.  
In fact, higher temperatures result in much more stacking in the TMS. The heated sample goes through the chemical reaction shown below:  
\[\text{MoS}_{4}{}^{2-}+\text{H2(g)}\rightarrow\text{MoS}_{2(g)}+\text{H}_{2} \text{S}_{\text{(g)}}. \tag{9}\]  
An alternative synthesis method reported recently showed that elemental sulfur can be used to synthesize CoMoS\({}_{2}\)[72], while MoS\({}_{2}\) was synthesized by precipitating CoMoO4 on top of elemental sulfur through a reflux synthesis. The CoMoO\({}_{4}\)/S material was converted thermally using Ar/H\({}_{2}\) (90:10) at 350 \({}^{\circ}\)C. The final material synthesized through this method was the Co\({}_{5}\)S\({}_{-}\)MoS\({}_{2}\) HDS catalytic material.  
[PERSON] and [PERSON] synthesized Fe\({}_{7}\)S\({}_{8}\) and Fe\({}_{3}\)S\({}_{4}\) nanosheets using single source precursors [123]. The authors prepared Fe(diethyldithiocarbamate)\({}_{2}\)(1,10-phenanthroline) and Fe (diethyldithiocarbamate)\({}_{3}\) using solvothermal synthesis in oleylamine. The Fe(diethyldithiocarbamate)\({}_{2}\)(1,10-phenanthroline)-based precursors were decomposed at temperatures between 240 and 320 \({}^{\circ}\)C, which showed that at 260 \({}^{\circ}\)C, the product was identified as monoclinic Fe\({}_{7}\)S\({}_{8}\). At 320 \({}^{\circ}\)C, the product exhibited diffraction features of hexagonal troilite FeS. When the Fe(diethyldithiocarbamate)\({}_{3}\) complex was decomposed, the sample showed the presence of Fe\({}_{3}\)S\({}_{4}\). [PERSON] et al. decomposed ammonium thio salts for the synthesis of MoS\({}_{2}\) and WS\({}_{2}\) catalysis [69]. The authors prepared ATT and ATM materials and pressed each at 350 and 700 MPa while decomposing the catalysts at 400 \({}^{\circ}\)C, in a H\({}_{2}\)S/H\({}_{2}\) atmosphere. The higher pressures resulted in a catalyst with higher surface area.
###### Abstract  
Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT's prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.  
optical camera communication (OCC); internet of medical things (IoMT); LED detection; YOLO +
Footnote †: journal: Applied Sciences
# HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition
## 2 Related Work
### Few-Shot Action Recognition  
Few-shot action recognition handles complex video data rather than 2D images. Mainstream networks for video feature extraction are based on 2D or 3D CNNs. Two-dimensional CNNs, such as ResNet [36] and InceptionNet, focus on mining information at the image level. Owing to effective pre-training on the large-scale ImageNet dataset, ResNet and InceptionNet are capable of efficiently and accurately extracting image features. They are currently adopted by many SOTA methods [20; 23; 24; 37]. For instance, [PERSON] [23] utilized ResNet-50 [36] to extract features from each frame of the image, designed frame tuples for temporal alignment, and achieved excellent performance; however, when the video or frame tuple is long, the computational efficiency significantly decreases. Compared with 2D CNNs, 3D CNNs, such as C3D [38] and SlowFast [39], can more accurately capture information in time and space, recognizing actions and changes in videos. Currently, the mainstream few-shot action recognition methods [11; 15; 17; 35] that adopt 3D CNNs are trained from scratch directly on the target dataset. The focus of our work is not on 2D or 3D CNNs, but on hierarchical task knowledge mining. We comprehensively and fairly compare existing methods by using two different embeddings: a 3D CNN without pre-training and a 2D CNN pre-trained on ImageNet. In feature matching, distance functions are divided into mathematical functions and deep-learning-based metric networks. Mathematical functions such as Euclidean distance or cosine distance are intuitive, but their accuracy is low because of the non-prominent key feature information. Regarding this, HyRSM [24] measured the distance between the query and support videos from the perspective of set matching and designed a bidirectional mean Hausdorff metric to enhance the resilience to misaligned instances. The attention mechanism enhances key information by setting different weights within the features to strengthen critical parts and weaken irrelevant parts, thereby highlighting the key areas of visual representation. For instance, ARN [11] uses temporal and spatial attention modules to strengthen the key information in the time and space of features. SoSN [17] enhances by introducing second-order feature statistics. Metric networks, such as a relation network [35], concatenate support-set and query-set features and input them into the network to calculate similarity, which is currently the mainstream way to match features. The proposed matching module is based on cosine distance and attention mechanism. In contrast to conventional methods, our method focuses on mining the key spatiotemporal information of features and parts with strong correlation between features.
compositions containing bioferments may positively affect the condition of problematic skin and improve the natural balance of the skin microbiome [3; 4].  
Disruption of the skin barrier is a common challenge for cosmetologists and dermatologists. This problem concerns skin affected by dermatoses, from the use of inappropriate cosmetics, but also by the aging processes occurring in the body. In recent years, a huge variety of products for the care of hypersensitive skin has appeared, some of them concern cosmetics with bioferments intended for people with a damaged skin barrier [4]. Currently, studies are focused on obtaining new bioferments from various plant species and testing them for their anti-wrinkle, antioxidant, UV protective, and anti-inflammatory activity [5; 6; 7; 8; 9; 10]. Additionally, bacterial ferment lysates are also used in cosmetic products and have scientifically proven anti-aging, anti-inflammatory, or melanogenesis-inhibiting effects (Figure 1) [11; 12; 13; 14; 15].  
Another interesting product used in modern cosmetics is the surfactants obtained through the fermentation process. Surfactants are the main ingredients of most household chemicals (washing and cleaning agents) as well as products for hygiene and body beautification. About 50% of chemical surfactants on the market are of petrochemical origin [16]. Currently, efforts are being made to replace synthetic surfactants with their ecological (natural) equivalents. Biosurfactants reduce the surface and interfacial tension of liquids, undergo micellization above critical micellar concentration (CMC) and, importantly, are produced from renewable raw materials (oil-contaminated waste, agro-industrial waste, dairy and sugar industry waste, and animal fat) by bacteria, yeasts, fungi, or directly by plants (e.g., saponins) [17]. The physicochemical properties of biosurfactants are analogous to the synthetic surfactants, but they are characterized by high biodegradability, lower toxicity, low irritation potential, and better compatibility with the skin. The production of biosurfactants uses less water and produces less carbon dioxide. The product has greater foaming capacity, starts working at lower temperatures, does not contain preservatives, and does not cause allergies. Post-production waste can be used as feed additives [17; 18]. The term \"biosurfactants\" is also used for surfactants obtained through enzymatic synthesis. They are used in oil extraction, cosmetic applications, pharmaceutical formulations, agrochemistry, land bioremediation, and household chemicals (Figure 1) [17; 19].  
Figure 1: Comparison of the use of bioferments and biosurfactants.  
This review summarizes the information on the history, preparation, and properties of bioferments and biosurfactants, with particular emphasis on collecting current knowledge about their scientifically proven properties and potential use in cosmetic preparations.