esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing
### Data Pre-Processing
#### 3.1.2 Latency Error Correction  
All gaze-contingent paradigms are always subject to a lag between where the participant's current eye position is and where the rendering of the scotoma is shown on display. This latency is related to the system's processes to display the image based on the eye position. First, it needs to record the eye position and transmit it to the computer; the computer would receive it and shift the scotoma position, render the new image, and finally, it displays the new image on the headset screen [38]. This delay in the scotoma presentation means for the current experiment that the scotoma might not cover the exact 6\({}^{\circ}\) radius of the central vision at all time.  
To account for the latency error between the actual eye's position and the actual recording of the eye, the target position was used as an indicator of where the recording of the eye should have been. This error can be approximated in the distance between the gaze and target positions measured during the normal condition.  
For every frame, the normalised target (re-referenced to the eye) and the dominant eye's normalised gaze were transformed into two-dimensional Cartesian coordinates. Then, the eye-target distance was calculated using the Pythagorean theorem. For every target data sample, twenty-one eye data points (ten previous to the matching timestamp and ten forward) were registered. The median in the distances between these 21 points and the target position was considered the system standard error.  
A mean and the standard deviation [39] for this error were calculated for each subject and assumed as the time delay between the recorded eye position and the actual eye location.
## 2 Materials and Methods
### Antibacterial Activity Test  
The antibacterial activity of the biosynthesized ZnO NPs against pathogenic microorganisms, namely Gram-positive _Bacillus cereus_ (KNIH28) and _Staphylococcus aureus_ (ATCC6538 P), and Gram-negative _Escherichia coli_ (_E. coli_)ATCC27853), was carried out on Muller-Hilton agar dishes using the disk diffusion method [45]. Nutrient agar broth (Himedia, Bombay) was used as bacterial culture medium in the bacterial assays. Pure cultures of the microorganisms were provided by the Alborz hospital, Karaj, Iran. The bacterial pathogens were freshly incubated for 4 h to standardize the culture to McFarland standards (10\({}^{6}\) CFU/mL).  
All of the chemicals used for the cell culturing were of analytical grade. Cells were grown in agar media and humidified incubator at 37 \({}^{\circ}\)C under 5% CO\({}_{2}\). The concentration of bacterial cells was adjusted to 10\({}^{6}\) CFU/mL, and ciprofloxacin (10 ug/disc) was used as a standard. Paper disks (6 mm diameter, Whatman No. 1 filter paper) infused with 100 ug/mL concentrations of prepared ZnO NPs and pure extract (10 ug/mL) suspended in deionized water were used to ascertain the antibacterial activity, followed by incubation at 37 \({}^{\circ}\)C for a period of 24 h. Inhibition zones, that formed around the disk, were investigated by measuring the corresponding diameter. The antibacterial experiments were repeated three times.
# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
## 3 An Energy Consumption Optimization for ICN
### Problem Transformation  
In the content centric network, higher utilization and lower energy consumption are pursued under given cache capacity constraints. Generally speaking, the lower the cache utilization is, the higher the average delay will be, and the more energy will be consumed in the network. Due to the complexity of multi-objective optimization, network energy consumption is considered as the optimization objective of cache placement strategy in this paper. The problem of selecting appropriate cache placement nodes from the source node to the destination node is studied in this paper. Obviously, the expected energy saving in the cache policy needs to be obtained to ensure that the content is cached at the energy-efficient node, causing the content to be transmitted along the optimal energy efficiency path to the destination node. The goal of the caching placement decision is to select a node and place the content on this node so that it can save the most energy compared with other nodes. This problem is called the maximization problem, and the optimal stopping rule is used to solve the optimization problem [30].  
In ICN, content is transmitted through the connections between the nodes, and the distribution energy needs to be consumed. Content of the same size is transmitted between different nodes with different energy consumption, and the energy consumption between nodes mainly depends on the distance between nodes. Therefore, in order to save the distribution energy consumption, the distance of distribution must be reduced. In this paper, the distance random variable is continuously observed by the user, and then the energy consumption sequence is obtained. The expected energy consumption is used as the basis for deciding whether to select the observed node as the cache placement node. The core idea is that the node is compared with the energy consumed by the current detection and the first time. If the condition is satisfied by the currently observed node, it is selected as a cache placement node, otherwise it is abandoned and the next node continues to be observed. Therefore, the problem of users selecting the best cache placement node is converted into an optimal stopping rule problem. The corresponding relationship between the elements of the problem and the optimal stopping problem is shown in Figure 1.
# Hide45: A Method for Optimal Payload Data Hiding in Base45 Encoded Strings
## 4 The Base45 Encoding  
The Base45 encoding [21] is defined in an RFC that proposes a method for encoding pairs of binary octets using an alphabet \(A\) of forty-five printable symbols mainly aimed at representing binary data stored into QR-codes. The Base45 alphabet is composed of the ten decimal digits, the twenty-six letters of the English alphabet, the space, and other eight special characters (listed in the following double quoted string \"\(\%\)\"+\(-\)/.\").  
Every pair of octets is considered to represent a sixteen-bit binary number which is converted in Base45 and expressed with three symbols from the alphabet \(A\). In case of a binary string having length an odd number of octets the last octet is interpreted as an eight-bit binary number, converted in Base45 and encoded using two symbols from \(A\).  
In the present context the main thing to note is that with three Base45 symbols it is possible to represent \(45^{3}=91\),\(125\) sequences that are much more than the \(2^{16}=65\),\(536\) required to encode two octets. The following section will briefly present the encoding and data embedding proposed in [20], discuss the improvement based on pairs of octets statistics developed in [23], and introduce the novelty of the present paper with the enhancement obtained by an optimized bit assignment.
## 5 Applications
### In Vivo Characterization of Biological Tissues
Instead, for thinner subcutaneous adipose tissue, the absorption properties also depend on water (at 980 nm) and blood (at short wavelengths) of the underlying muscle. On the other end, the contribution of the skin also needs to be considered, as it also shows marked intersubject variability, and possibly age dependence. Thus, a layered model, accounting for skin, adipose tissue layer and underlying muscle, is needed to allow a proper investigation of the adipose tissue.
## 3 Results
### Defect Reactions versus Individual Defects
The volume change for the reaction (Equation (14)) is then given as:  
\[\Delta\Omega_{V_{\text{U}}}^{\prime\prime\prime\prime}=\Delta\Omega^{\prime} \big{(}V_{\text{U}}^{\prime\prime\prime\prime}\big{)}+4\Delta\Omega^{\prime} (h.)+4\Omega_{\text{U}\text{O}_{2}}-\Omega_{\text{U}\text{3}\text{O}_{8}} \tag{15}\]  
involving individual charged-defect relaxation volumes \(\Delta\Omega^{\prime}\big{(}V_{\text{U}}^{\prime\prime\prime\prime}\big{)}\) and \(\Delta\Omega^{\prime}(h.)\), calculated again from separate supercell calculations. The overall computed reaction volume \(\Delta\Omega_{V_{\text{U}}}^{\prime\prime\prime\prime}\) is \(-10.8\) A\({}^{3}\), which again corresponds to contraction of the lattice due to the formation of uranium vacancies. These findings are in good agreement with the experimental results [73] and with theoretical prediction [74] for the contraction of the UO\({}_{2}\) lattice due to the formation of the hyper-stoichiometric phase UO\({}_{2+x}\) (with \(x<0.5\)). The sum of the relaxation volumes of individual charge defects (first two terms in Equation (15)) is about \(-6.26\) A\({}^{3}\), similar but not exactly equal to that predicted by the relaxation of neutral U vacancy (\(\Delta\Omega^{\prime}\big{(}V_{\text{U}}^{\times}\big{)}=-9.07\) A\({}^{3}\)) in a DFT supercell calculation. Our results comparing the relaxation volume of the neutral defects with the summed relaxation volumes of the charged defects calculated from separate supercells calculations are shown in Table 6. There is good agreement with regards to the sign of the lattice relaxation (expansion or contraction) due to the formation of an overall neutral defect reaction with the two approaches. The difference in the predicted magnitude can be attributed to the interaction (both elastic and electrostatic) between the individual charged defects. Thus, while the relaxation volumes of the charged defects have large magnitudes, they to a great extent cancel when summed, leading to values that are in reasonable agreement with corresponding neutral defects. Similarly, while defect reactions, such as those in Equations (12) and (14), contain components corresponding to charged defects, once these are summed in a net neutral reaction, the large volumes of the charged defects cancel, leading to relatively small volumes for the reaction itself. In a recent study by [PERSON] et al. [9], similar conclusions were also reached, where authors showed that, using a net neutral defect reactions model, relaxation volumes can reproduce the experimental observed lattice-constant variation with stoichiometry.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions
### Ablation Studies
#### 4.4.1 Selecting the Number of Iterations  
According to (a)-(f) in Table 5, increasing the number of iterations gradually improves the model's performance, except for time efficiency. Significant enhancements in model performance were observed within the first eight iterations; however, subsequent iterations showed diminishing improvements and increased processing time. Considering both the model's performance and the quality of recovered images, we selected the model with eight iterations.
# Complete Genome Report of a Hydrocarbon-Degrading _Sphingobium yanoikuyae_ S72
###### Abstract  
_Sphingobium yanoikuyae_ S72 was isolated from the rhizosphere of sorghum plant in Mexico and we evaluated its survival and role in the degradation of some selected monoaromatic hydrocarbons and polycyclic aromatic hydrocarbons (PAHs) using minimal medium (Bushnell Hass medium (BH)) in which each of the hydrocarbons (naphthalene, phenanthrene, xylene, toluene, and biphenyl) served as sole carbon source. Gas column chromatography-mass spectrometry analysis was used to evaluate the effect of S72's growth in the medium with the hydrocarbons. The genome of the S72 was sequenced to determine the genetic basis for the degradation of the selected hydrocarbon in S72. The genome was assembled de novo with Spades assembler and Velvet assembler and the obtained contigs were reduced to 1 manually using Cussed software. Genome annotation was carried out Prokka version 1.12, and gene calling and further annotation was carried out with NCBI PGAAP. Pangenome analysis and COG annotation were done with bacteria genome analysis tool (BPCA) and with PATRIC online server, respectively. S72 grew effectively in the culture medium with the hydrocarbon with concentration ranging from 20-100 mg/mL for each hydrocarbon tested. S72 degraded biphenyl by 85%, phenanthrene by 93%, naphthalene by 81%, xylene by 19%, and toluene by 30%. The sequenced S72 genome was reduced to 1 contig and genome analysis revealed the presence of genes essential for the degradation of hydrocarbons in S72. A total of 126 unique genes in S72 are associated with the degradation of hydrocarbons and xenobiotics. S72 grew effectively in the tested hydrocarbon and shows good degradation efficiency. S72 will therefore be a good candidate for bioremediation of hydrocarbon contaminated soil.  
nphthalene; phenanthrene; GC-MS; biphenyl; _Sphingobium_; toluene; genome; sequencing 10.390/app1226201
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 3 Multi-UAV-Based Network Infrastructure Analysis  
This section addresses the mathematical representation of the multi-UAV-based network infrastructure. Our approach is the establishment of the generic expression of the network throughput with variable parameters depending on the network protocol. We firstly present the assumptions and the notations for the analysis of the multi-UAV-based network infrastructure, and then propose the network throughput estimation equation for the mobile devices and the UAVs.
## 1 Introduction
In this paper, we present a low-cost, low-SLL, differential-fed, SIW-based SAA with no beam squinting. The antenna consists of a \(6\times 16\) slot array, which is differentially fed by two identical six-way unequal PDs from both ends of the SIW slot array. Then, a two-way hybrid WG-to-SIW \(E\)-plane PD is designed to provide equal amplitude and out-of-phase excitation for two six-way unequal PDs. Metallic decoupling walls are installed between two adjacent linear slot arrays to suppress the external mutual coupling in the \(E\)-plane. We apply Taylor distribution to reach an SLL of \(-30\) dB in both the \(E\)-plane and \(H\)-plane at the designed frequency. Simulation is performed using the commercial software ANSYS HFSS. To verify the proposed antenna, a prototype is fabricated and tested. The experiment reveals that the proposed antenna achieves the measured \(-10\) dB reflection bandwidth of \(7.15\%\) (\(9.57\)-\(10.28\) GHz), a peak gain of \(21.92\) dBi, and a stable boresight beam with a low SLL of \(<\)\(-23.4\) dB across the entire operating band.
## 2 Materials and Methods
### S3 Instrumentation
* The analysis timeline can be divided into three different steps (Figure 2):
* Pre-injection/Before: At this step, a continuous flow of previously filtrated chromatographic air passes by the sensor chamber. * Injection/During: The sample HS is flowed in the sensor chamber. * Restoration/After: It starts when the injection period is finished, during this step filtered chromatography air is flowed into the sensor camber. In this time, the sensor restores the original condition of the base line. These steps are preceded by a step of warm-up that allows the achievement of the base line for the entire system. The air is taken into the sensor chamber using a pump through a needle valve. That is used to adjust the total airflow, which is measured by a flowmeter downstream of the pump. The chamber is also monitored with a thermostat to prevent the temperature of the surrounding environment from affecting the sensor responses. The volume of the chamber should be as small as possible. In this case, 20 mL to avoid the concentration gradient of the volatile fraction inside the chamber. The chamber, and the connection between the element tires are made using steel pipes, in order to obtain a lower absorption of volatile substances that may then be released during subsequent measurements. Figure 2: Graph showing the three steps of a measurement done with a MOX Nanowire SnO\({}_{3}\) sensor, with before step in blue, during step in green, and after step in red. In the picture are represented two measurements of two cheese samples with 12 months of ripening and undegraded quality. The X-axis represents the time in seconds and the Y-axis the ohmic resistance. For S3 analysis, 2 g of grated cheese were taken in a 20 mL chromatographic vial for each sample, sealed with Silicon-PTFE septum, crimped with an aluminum crimp and then subjected to analysis. The equilibrium of the HS was reached thanks to the oven where the samples were shaken for 10 min at 40 \({}^{\circ}\)C.  
To visualize the quantified complex data matrix acquired by the S3 in terms of resistivity changes and the relationships between different cheese samples and their volatile composition, quality, and ripening time, a multivariate statistical approach involving principal component analysis (PCA) was carried out. It was operated with a user-friendly Data Editor software and data were processed by EDA software developed in MATLAB at SENSOR laboratory. PCA is a linear combinatorial process whose core function is to reduce the complexity of the dataset by allowing the information to be shown in a narrow dimension. The result is derived from the variance of the dataset without any information on the classification of samples.
## 4 Experimental Validation
### Description of the System  
The system used to measure the accuracy of the proposed method was composed of a brushed dc motor, a current sensor, a data acquisition card, and a computer. Figure 8 shows the experimental setup.  
Figure 8: Scheme of the system.  
A universal machine set, H-REM-120-CM Universal Laboratory Machine (ULM) from Hampden(r) Engineering Corporation (East Longmeadow, MA, USA), was used for the tests. This machine set is coupled with a swinging frame dc dynamometer. The universal machine stator is composed of 24 slots with 12-turn coils. The rotor of the universal machine is a 36-slot rotor with a two-pole, full-pitch dc winding composed of 72 four-turn coils connected to 72 commutator segments. This machine can be connected in many different configurations, such as in single-phase induction, three-phase induction, synchronous, and a dc machine with series or shunt excitation. For this work, the machine was connected with the topology of a brushed dc shunt motor. In this configuration, the motor has a rating of 1 kW at 110 V.  
The current sensor used was an AYA IBP 200:1 (Pulse Electronics, San Diego, CA, USA). This sensor has a current transformer (CT) connected to a 2 \(\Omega\) precision resistor (Caddock, Roseburg, OR, USA). The resistor leads convert the current to a voltage signal in order to be able to input it into the data acquisition system. The current sensor was connected to the armature circuit near the motor brushes, as Figure 8 shows. Note that only the armature current was measured. A current transformer was intentionally selected because it does not transform the dc current. This selection provides better precision in the measurement of the ac component. The data acquisition card used was a NI USB-6356 from National Instruments (Austin, TX, USA). It has eight simultaneous analog inputs at a sample rate of 1.25 MS/s/ch, two analog outputs at 3.33 MS/s, and 24 digital I/O. One analog input was connected to the current sensor. The data acquisition card was connected to a computer.  
The computer used was an ASUS Notebook K72 Jk Series (Taipei, Taiwan) with an Intel(r) Core(tm) i3 CPU M350 processor (Santa Clara, CA, USA), 4 GB RAM, and a 500 GB hard disk. The operating system was Windows 7, and the development environment was LabVIEW 2010 from National Instruments (Austin, TX, USA). The computer was configured to work in soft real time, and the data acquisition card was configured to acquire samples in a continuous mode.  
It was necessary to measure the actual speed to compare the accuracy of the proposed method. The actual speed was measured with an encoder. The encoder used was a Monarch Instrument ACT 3 Tachometer/Totalizer/Ratemeter (Amherst, NH, USA). This encoder was configured to output an analog voltage proportional to the motor speed. The offset value and full scale speed values of the encoder were programmed to values close to the motor operating speed to achieve high resolution in the speed measurement. The encoder and its connections are not included in Figure 8.
## 2 Materials and Methods
### Powder Characterization  
Physiochemical Properties  
Analysis of the particle morphology was performed utilizing SEM microscope TM-3000 (HITACHI, Tokyo, Japan) at 500\(\times\) magnification. Prior to analysis, samples were prepared by metalizing the surface of the sample placed on double sticky tape by coating with a layer of gold using automatic coater Cressington 108 auto (EO Elektronen-Optik-Service GmbH, Dortmund, Germany).  
Particle size distribution was carried out using laser diffraction on apparatus 1190 (CILAS, Orleans, France). Powders were suspended in ethanol at the obscuration of 10%. The results were presented as particle size distribution diagrams and expressed as the median diameter \(D_{50}\).  
Moisture content (MC) was determined using an oven method: approximately 1 g of powder was dried at 105 \({}^{\circ}\)C for 4 h.  
Water activity (\(a_{w}\)) was determined on apparatus Hygro Lab1 (Rotronic, Bassersdorf, Switzerland) at 24 \(\pm\) 2 \({}^{\circ}\)C.  
Hygroscopicity (H) was analyzed as a weight gain of 1 g powder samples incubated at 25 \({}^{\circ}\)C, 75% RH (saturated NaCl solution) during 144 h. Results were calculated as the amount of water absorbed per 100 g powder solids and expressed in percent.  
Powder loose bulk density (\(D_{L}\)) was determined as the weight of powder that occupied a 25 mL cylinder. The automatic tapper STAV 2003 (Engelsmann AG, Ludwigshafen, Germany) was employed to measure tapped bulk density (\(D_{T}\)) after 100 taps and determine the volume occupied by the powder. The Hausner ratio (HR) was calculated to evaluate powder flowability and was expressed as HR = \(D_{T}/D_{L}\).  
Apparent density (\(D_{ap}\)) was carried out on helium pycnometer Stereopycnometer (Quantachrome Instruments, Boynton Beach, FL, USA). Loose bulk porosity was calculated as \(\varepsilon_{L}=1-(D_{L}/D_{ap})\).  
The Colorimeter CR-5 (Konica-Minolta, Tokyo, Japan) was applied to determine color in CIE \(L*\)\(*\)\(*\)\(*\) system.  
Prior to determination of antioxidant activity, the extracts of buckwheat honey and obtained powders were prepared in duplicate. Samples were diluted in distilled water and filtrated at ambient temperature using Chemland medium filter papers (125 mm) and rinsed twice with 10 mL of distilled water. Antioxidant activity was analyzed using an ABTS**+ (2,2'-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) radical cation method with minor modifications [24]. The ABTS**+** solution was diluted with distilled water, and it was measured at 734 nm using a spectrophotometer Helios Gamma (Thermo Fischer Scientific, Waltham, USA) to establish 0.7 \(\pm\) 0.1 absorbance. A total of 3 mL of ABTS solution was added to an adequate volume of honey and powder extracts. The samples were stored in darkness at ambient temperature for 6 min. The absorbance was measured at 734 nm against distilled water. Each sample was prepared in duplicate. The antioxidant activity was expressed as EC\({}_{50}\) ABTS.