esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 7 Cold Stress Response of Antarctic Fungi and Adaptation
### Cold-Active Enzymes  
The survival of fungal strains in conditions of extremely low temperatures is also promoted by so-called cold-active enzymes, which favor their metabolic processes, substrate uptake, development, and reproduction [6]. These enzymes are known to increase the affinity for the substrates by decreasing the activation energy required for the enzyme-substrate complex formation. This is facilitated by a greater flexibility in the enzyme's structure, either in part or as a whole, which enhances the rate of enzymatic reactions at lower temperatures, thereby contributing to reduced energy usage [194].  
The International Commission of Enzyme Nomenclature classified the cold-active enzymes that have been discovered thus far into six principal categories: hydrolases, oxidoreductaseases, transferases, lyases, isomerases, and synthetases. [PERSON] et al. [9] reviewed the ability of fungi and yeasts from terrestrial and marine Antarctic settings to synthesize such enzymes as part of their survival strategy. The authors presented a wide variety of fungal genera belonging to the phyla _Ascomycota_ and _Basidiomycota_, producing enzymes with a low temperature optimum. The analysis encompassed a variety of hydrolases, such as \(\alpha\)-amylase, cellulase, chitinase, glucosidase, invertase, lipase, pectinase, phytase, protease, subtilase, tannase, and xylanase, in addition to oxidoreductase, specifically laccase and superoxide dismutase.  
Fungi from Antarctic soil and moss samples obtained from various sites in Victoria Land (continental Antarctica) were evaluated for their ability to produce cold-active enzymes, such as lipases, polygalacturonase, pectin lyase, amylase, cellulase, chitinase, phosphatase, glucose oxidase, urease, protease, and RNase [46]. Among these, the psychrotolerant strain of _Lecanicillium muscarium_ (CCFEE 5003) emerged as a notable candidate for the production of cold-tolerant extracellular enzymes, particularly those that hydrolyze chitin.  
A total of 27 filamentous fungi were isolated from marine sponges obtained from King George Island, Antarctica, and subsequently evaluated as potential sources of cold-active pectinases [223]. Of these, eight demonstrated pectinolytic activity at a temperature of 15 \({}^{\circ}\)C. The most effective producer identified was _Geomyces_ sp. F09-T3-2, which exhibited optimal enzymatic activity at a temperature that is 10 \({}^{\circ}\)C lower than that of commercially available mesophilic enzymes. [PERSON] et al. [224] identified the psychrophilic fungal strain _Truncatella angustata_ BPF5 as a promising candidate for the production of pectinase, which holds potential applications in the wine industry. The presence of cold-active enzymes, including SOD [225], catalase [51; 53], sialidase [226], lipases [227], invertase [228], chiti-nolytic enzymes [48], etc., has been detected in fungal strains isolated from Antarctica. The application of metagenomic techniques facilitates the discovery of a greater number of cold-active enzymes [229].
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 5 Role of Pulsed Light Technology in Food Safety
### Microbial Decontamination in Food Products
With the successful decontamination of clear liquids by PL, more research has to be extended towards turbid and opaque liquid foods.
## 3 Results and Discussion  
### Microstructure and Phase Characterization of the HPT Processed Composite  
#### 3.1.1 XRD  
The indexed XRD patterns of the Al, Mg and Al-Mg nanostructured composites after HPT and following annealing at 275 \({}^{\circ}\)C are shown in Figure 1. The HPT samples were primarily characterized by the presence of fcc Al (92.4 wt.%) and hcp Mg (7.6%) (see Figure 1). Al\({}_{3}\)Mg\({}_{2}\) and Al\({}_{12}\)Mg\({}_{17}\) intermetallic phases, alongside Al and Mg, were observed in the HPT sample after annealing (see Figure 1). The sample's surface phase composition was found to be as follows (wt.%): 74.3 Al, 3.2 Mg, 19.1 Al\({}_{3}\)Mg\({}_{2}\) and 3.4 Al\({}_{12}\)Mg\({}_{17}\). A detailed analysis of the intermetallic growth kinetics during annealing in the Al-based metal-matrix composites fabricated with HPT is provided in the authors' previous works [14].  
Our observation is consistent with the results of explosion welding Al-Mg claddings, where, in the state immediately after explosion welding, intermetallic phases were not observed, and the use of annealing led to the formation of the intermetallic compounds Al\({}_{3}\)Mg\({}_{2}\) and Al\({}_{12}\)Mg\({}_{17}\)[38].  
An analysis of the Al XRD profiles broadening is summarized in Table 1. It can be seen that the lattice parameter in the as-deformed condition is larger than that of undeformed pure Al (i.e., a = 4.0494 A). Pure Al is widely known to be subject to dynamic recrystallization under severe strains due to its high stacking-fault energy and high deformation-to-melting ratio (homologous) temperature, and one can conclude that the HPT-induced increase in the Al lattice parameter in the composite was caused by the formation of an  
Figure 1: XRD patterns of Al (a), Mg (b) and Al-Mg nanostructured composites under HPT conditions (c) and after further annealing at 275 \({}^{\circ}\)C (d).  
Al-Mg solid solution. In general, the value of the coherent scattering domain, as well as the values of the lattice parameters and microstrains, corresponds to the values obtained in the HPT-processed pure Al. Annealing at 150 \({}^{\circ}\)C led to some relaxation of the Al matrix structure--the lattice parameter was partially restored, and the level of microstrains was decreased. Annealing at 275 \({}^{\circ}\)C resulted in the lattice parameter being almost restored, and the microstress value came to an equilibrium.
## 4 Is There a Chromosomal DnaA Hyperstructure?  
The chromosomal _datA_ site is a 1 kb region that contains binding sites for DnaA and that helps in its inactivation [27]. An excess of _datA_ sites results in a delay to the initiation of replication, whereas the lack of _datA_ results in extra initiations, and it was originally suggested that the binding of DnaA to newly duplicated _datA_ during _oriC_ sequestration could help prevent premature reinitiation when _oriC_ is desequested [27]. When _datA_ is negatively supercoiled, DnaA-ATP oligomers are stabilised, and _datA_-IIHF interactions and DnaA-ATP hydrolysis are promoted [28]. These results are consistent with a _datA_-based chromosomal hyperstructure helping regulate initiation. The two chromosomal intergenic regions, DnaA-reactivating sequence 1 (DARS1) and DnaA-reactivating sequence 2 (DARS2), each contain a cluster of DnaA binding sites; these sites promote regeneration of DnaA-ATP from DnaA-ADP by nucleotide exchange, and thereby help to promote the initiation of replication [29]. (Note that DnaA-ADP is mainly monomeric and unable to go from high-affinity binding sites to nucleate polymerisation at the low-affinity binding sites in the origin of replication.) The reactivation of DnaA by DARS2 is coordinated by the site-specific binding to DARS2 of IHF and Fis; this binding of IHF is temporally regulated during the cell cycle [30], as is the binding of Fis, which occurs specifically prior to initiation [31]. It is thought that DARS1 is mainly involved in maintaining the origin concentration, whereas DARS2 is also involved in maintaining single cell synchrony [32].  
After initiation, the ATPase activity of DnaA is stimulated by the regulatory inactivation of the DnaA (RIDA) complex composed of the Hda protein interacting with the DNA-loaded \(\beta\)-clamp [33]. Recently, it has been found that only the disruption of RIDA has a major effect on initiation (since DARS and _datA_ can compensate for one another) [34]. All of this raises the question of whether the inactivation of DnaA takes place within a chromosomal DnaA hyperstructure.  
DnaA is also a sequence-specific transcriptional regulator. Such regulators bind to sites that are distributed on the chromosome with a periodicity consistent with a solenoidal-type organization [35]; this organization would bring together a regulator and its sites into a hyperstructure. If this hyperstructure does indeed exist, what is its relationship with the _datA_ and the DARS sites? Do the above constitute separate hyperstructures and, if so, how do they interact? Or are they all part of an initiation hyperstructure?
## 2 System Model  
### System Description  
As shown in Figure 1, we consider the downlink of a distributed cache network consisting of one MBS, UAVs and ground users. The set of UAVs is denoted by \(\mathcal{M}=\{1,2,\ldots,M\}\), and the set of users is denoted by \(\mathcal{N}=\{1,2,\ldots,N\}\). Assume that the number of antennas for each UAV is the same, and the number is \(N_{T}\). Consider that there are \(K\) popular content, denoted by set \(\mathcal{K}=\{1,2,\ldots,K\}\).  
Compared with MBS, UAVs are usually closer to users, so they provide higher transmission rates for related users. To simplify the discussion, it is assumed that the service rate of an MBS for each user is the same. The service rate of an MBS is set as a constant, which is lower than the average of UAVs.
# Optimal Gliding Trajectories for Descent on Mars
## 2 Formulation of the Optimal Control Problem
### Path Constraints  
It is assumed that the spacetime begins its atmospheric descent at 92.0 km of altitude, with a negative value of the flight path angle, meaning that one component of its velocity vector is pointing towards the center of the planet. This part of the descent begins after orbital decay, which could be calculated to be in a specific region, also with aerodynamic control; an example is presented in [25]. Due to the nature of the descent, where the spacecraft loses its mechanical energy due to drag, it is expected to have a decrease in velocity. For those reasons, the constraints on the state vector, the control vector, and the path, in an interval of time between (\(t_{0}\)) and (\(t_{f}\)), are selected to be as follows:  
\[\begin{array}{c}t_{0}<t\leq t_{f}\\ 50\leq t_{f}\leq 800\;\mathrm{s}\\ R_{Planet}\leq R(t)<R_{Planet}+92\;km\\ 0.0\;m/s\leq V(t)\\ -180\;deg\leq\theta(t)\leq 180\;deg\\ -90\;deg\leq\varphi(t)\leq 90\;deg\\ -20\;deg\leq\gamma(t)\leq 20\;deg\\ 0\;deg\leq A(t)\leq 359.9\;deg\\ -4\;deg\leq\alpha(t)\leq 28\;deg\\ -180\;deg\leq\hat{\beta}(t)\leq 180\;deg\\ \dot{\alpha}(t),\;\hat{\alpha}(t),\;\hat{\beta}(t),\hat{\beta}(t)=\text{free} \end{array} \tag{9}\]
## 2 System Model
### Simulation Setup  
MATLAB 2018a simulator was employed to execute extensive simulations that assess the performance of the routing approaches with various device speed scenarios [51, 52]. In this simulation, the scenarios used randomly distributed 49 devices in an area of \(500\times 500\) m. The mobile device moves in the network according to the RWP mobility model with the speed from 10 to 60 m/s. Constant Bit Rate (CBR) is set to 20 packets per second that correspond to each source and destination devices flow that generates universal datagram protocol packets by 512 bytes size at the source device. The radio transmission distance is set at 270 m. The 802.11b radio data link layer and channel capacity are set to 11 Mbps. The simulation run time is assigned to 200 s and each simulation cycle is repeated 200 times  
Figure 4: Flow chart of optimal route selection with EMBLR routing scheme.  
to get the value of the average results. The 2.4 GHz wireless channel frequency is set for the simulation. The other parameters value are presented in Table 2 as follows:
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
###### Abstract  
With the development of science and technology, the traditional industrial structures are constantly being upgraded. As far as drones are concerned, an increasing number of researchers are using reinforcement learning or deep learning to make drones more intelligent. At present, there are many algorithms for object detection. Although many models have a high accuracy of detection, these models have many parameters and high complexity, making them unable to perform real-time detection. Therefore, it is particularly important to design a lightweight object detection algorithm that is able to meet the needs of real-time detection using UAVs. In response to the above problems, this paper establishes a dataset of six animals in grassland from different angles and during different time periods on the basis of the remote sensing images of drones. In addition, on the basis of the Yolov5s network model, a lightweight object detector is designed. First, Squeeze-and-Excitation Networks are introduced to improve the expressiveness of the network model. Secondly, the convolutional layer of branch 2 in the BottleNeckCSP structure is deleted, and \(3/4\) of its input channels are directly merged with the results of branch 1 processing, which reduces the number of model parameters. Next, in the SPP module of the network model, a \(3\times 3\) maximum pooling layer is added to improve the receptive field of the model. Finally, the trained model is applied to NVIDIA TX2 processor for real-time object detection. After testing, the optimized YOLOv5 grassland animal detection model was able to effectively identify six different forms of grassland animal. Compared with the YOLOv5, EfficientDet-D0, YOLOv4 and YOLOv5s network models, the mAP_0.5 value was improved by 0.186, 0.03, 0.007 and 0.011, respectively, and the mAP_0.50.95 value was improved by 0.216, 0.066, 0.034 and 0.051, respectively, with an average detection speed of 26 fps. The experimental results show that the grassland animal detection model based on the YOLOv5 network has high detection accuracy, good robustness, and faster calculation speed in different time periods and at different viewing angles.  
U  
2022  
2022
###### Abstract  
In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.  
Keywords:non-intrusive load monitoring; power consumption pattern; load disaggregation; low sampling data; optimized support vector regression +
Footnote †: journal:
# Computation of the Hausdorff Distance between Two Compact Convex Sets
## 2 Materials and Methods
### Convergence  
Because this topic has been covered in previous studies [36; 37; 21; 38; 39], we give an abbreviated treatment here. Each algorithm is summarized by a closed algorithm map \(\mathbf{x}_{n+1}\in M(\mathbf{x}_{n})\) that increases the objective \(f(\mathbf{x})\). The limit points of the map occur among the stationary points of \(f(\mathbf{x})\). By definition, a stationary point \(\mathbf{x}\) satisfies \(d_{\mathbf{v}}f(\mathbf{x})=df(\mathbf{x})\mathbf{v}\leq 0\) for all tangent vectors \(\mathbf{v}\) at \(\mathbf{x}\). The set of tangent vectors \(\mathbf{v}\) is the closure of the set of points \(c(\mathbf{y}-\mathbf{x})\) with \(\mathbf{y}\in C\) and \(c>0\). This is a place where the convexity of \(C\) comes into play. Hence, \(\mathbf{x}\) is a stationary point if and only if \(df(\mathbf{x})\mathbf{x}\geq df(\mathbf{x})\mathbf{y}\) for all \(\mathbf{y}\in C\). With this distinction in mind, we state our basic theoretical findings for the Frank-Wolfe method. Homotopy is omitted in these considerations.  
**Proposition 1**.: _The limit points of the Frank-Wolfe iterates (2) are stationary points of the objective \(f(\mathbf{x})=\min_{\mathbf{y}\in B}\|\mathbf{x}-\mathbf{y}\|\) on \(A\). Furthermore, the bound_\[\min_{0\leq k\leq n}\max_{\mathbf{y}\in A}df(\mathbf{x}_{k})(\mathbf{y}-\mathbf{x}_{k}) \leq \frac{1}{n+1}[\max_{\mathbf{x}\in A}f(\mathbf{x})-f(\mathbf{x}_{0})]\]  
_holds. Thus, the stationary condition \(\max_{\mathbf{y}\in A}df(\mathbf{x})(\mathbf{y}-\mathbf{x})\leq 0\) is reasonable to expect at a limit point \(\mathbf{x}\) of the Frank-Wolfe algorithm._  
Here is the corresponding finding for projected gradient ascent.  
Proposition 2: _The limit points of the projected gradient ascent iterates (3) are also stationary points of the objective \(f(\mathbf{x})=\min_{\mathbf{y}\in\mathcal{B}}\|\mathbf{x}-\mathbf{y}\|\) on \(A\). Furthermore, the bound_  
\[\min_{0\leq k\leq n}\|\mathbf{x}_{k+1}-\mathbf{x}_{k}\| \leq \sqrt{\frac{2}{(n+1)}[f(\mathbf{x}_{0})-\min_{\mathbf{x}\in A}f(\mathbf{x})]}\]  
_holds._  
Although the convergence rate \(O(\frac{1}{\sqrt{n}})\) of projected gradient ascent is slower than the corresponding slow convergence rate \(O(\frac{1}{n})\) of the Frank-Wolfe method, in practice, both algorithms usually converge in fewer than 100 iterations. In the case of the Frank-Wolfe algorithm, each iterate is an extreme point. Many convex sets possess only a finite number of extreme points, and convergence to one of them is guaranteed. Unfortunately, the converged point often provides just a local maximum.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation  
### Data Validation: Comparison with Laboratory Data  
To ensure the accuracy and reliability of the collected water quality data, a comparative validationwas conducted between the multi-sensor Raspberry Pi-based monitoring system and standard laboratory methods employed by the Macao Municipal Laboratory. The validation process involved quantitative statistical analysis using the Bland-Altman method, which is widely used for assessing the agreement between two measurement techniques.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 4 Numerical Simulation
### Interaction and Boundary Conditions  
Figure 10 depicts the setup where the reference point of the rigid body is constrained against all translational and rotational movements. An upward displacement was imposed on this reference point to simulate the pull-out action. To precisely simulate the connection between the steel beam and perfobond connectors in the finite-element model, tie-binding constraints were applied. At the interfaces between the concrete and both the perfobond connectors and the rebar, generalized contact interactions were established. To prevent interpenetration, a \"hard\" contact condition was specified in the normal direction. In the tangential direction, a \"penalized\" friction model was implemented to account for the sliding resistance. To simplify the model's actual loading boundary conditions, fixed constraints were applied to the upper surface of the concrete slab, creating a base rib. This approach effectively replicates the experimental setup within the numerical simulation.
## 5 Discussion
### Glass Bead Characteristics  
The glass beads' shape has a strong impact on the initial retroreflectivity of the pavement markings [8]. Regarding the coefficients of the properties of shape, both bl\({}_{50}\) and SPHT\({}_{20}\) present a positive impact on the retroreflectivity; however, the SPHT\({}_{20}\) has a stronger contribution to the retroreflectivity improvement than bl\({}_{50}\). In addition, the glass beads evaluated in this research present poor shape properties considering the threshold of sphericity to classify the particles as round (SPHT > 0.93), while a much larger percentage of particles for all glass beads may be classified as round considering the thickness-to-length ratio (b/1 > 0.85). Therefore, considering the glass beads evaluated herein, they meet the requirements of shape, b/1 > 0.85, more easily than the requirements for sphericity, SPHT > 0.93. Moreover, SPHT is harder to achieve and has a higher impact on the retroreflectivity. Thus, it is recommended that the glass beads evaluated herein should present better shape characteristics, mainly sphericity.  
The previous study reporting the impact of glass bead gradation on pavement markings' retroreflectivity also discretized the size distribution, but evaluated it as a rank between the samples evaluated [8]. This research calculated and attributed two coefficients commonly used to classify granular materials in soil mechanics, the coefficients of uniformity (C\({}_{\text{U}}\)) and curvature (C\({}_{\text{C}}\)), to characterize the size distribution of each glass bead sample. The results of both coefficients are in a short range, which might indicate the need of proposing a specific classification range for glass beads. Ultimately, the estimates of the model's coefficients showed that the improvement of glass beads' gradation by C\({}_{\text{C}}\) is more important than the uniformity (C\({}_{\text{U}}\)). The coefficients could evaluate, discretize, and differentiate the glass beads' size distributions.  
Based on the results, the expected coefficient of uniformity for glass beads must range around 1.5 to 1.9 to guarantee the absence of fine particles that reduce the retroreflectivity or larger particles that will cause a lack of embedment depth and premature failure of pavement markings. Regarding the coefficient of curvature, the results obtained range around 1.05 to 1.20; this may be an adequate range to guarantee a well-graded glass bead to improve R\({}_{\text{L}}\). It is important to highlight that these ranges are only premises based on the results obtained herein. However, it is recommended to calculate the C\({}_{\text{U}}\) and C\({}_{\text{C}}\) for other glass beads to observe how they impact the retroreflectivity and evaluate whether the coefficients can be used to create a new range or not.