esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup  
### Materials  
In the experimental system, a water tank was primed with deionized (DI) water (18.2 M\(\Omega\)) or NaCl solution. DI water was produced using a laboratory water purification system (Smart series, HETAI, Shanghai, China), and the NaCl solution was prepared by diluting a certain amount of NaCl (99.5% purity, Tianjin Zhiyuan, Tianjin, China) in DIwater. The copper working electrode and the sensing electrode are both cylindrical with a diameter of 1 mm. The sensing electrode is connected in series with an electrometer (6517B, Keithley, Cleveland, OH, USA). A homemade NI LabVIEW 2018 program was used to record the real-time ionic current signals measured with the electrometer. Gold (Au), platinum (Pt), copper (Cu), aluminum (Al), iron (Fe) and zinc (Zn) were used as the metal sheets (10 mm \(\times\) 15 mm \(\times\) 1 mm) in the experiments. None of the metal sheets were further treated regarding the oxidation layers.
# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 3 Proposed Methods
### Detailed Construction of Network Architecture  
The MLP layer is illustrated in Figure 3. It converts the input encoded position vector into a feature tensor and usually contains a collection of several linear and activation layers, which utilize GELU activation. In the network, the MLP module appears only after the position embedding block. Since the vectors after position encoding usually have small dimensions, the MLP serves as a dimension expansion tool.  
The NeRV block is the essential structure in FIO-F and JSCIFI-F. The network architecture is illustrated in Figure 4.  
Figure 3: The structure of the MLP Layer. GELU denotes the Gaussian Error Linear Unit.  
Several NeRV blocks express the input video stream as a neural network consisting of multiple convolution operations and output the corresponding three-channel frame information. The PixelShuffle layer [35] is used for upsampling, and higher-quality images can be obtained. Indeed, it is the parameters within all NeRV blocks that store the mapping between positional feature vectors and image features.  
For the fusion layer, which takes \(E_{m}\) and \(F_{2}(\mathcal{P}(t))\) as inputs, unlike the spatial-temporal features obtained with pixel multiplication, this feature fusion serves as a distributional shift for \(E_{mbedding}\)[16].  
\[N_{E}=Norm(\text{TF}(\gamma)) \tag{20}\]  
\[\mu,\sigma=div(F_{2}(\mathcal{P}(t))) \tag{21}\]  
\[F_{fuse\_out}=(N_{E}*\sigma)+\mu \tag{22}\]  
where \(N_{E}\) denotes the normalized embedding, and \(div(\cdot)\) is the decomposition operation, which is performed in the channel dimension. \(\mu\) and \(\sigma\) denote the bias vector and the scale vector, respectively.  
JSCIFI-F only uses a simple version of the transformer block where Q, K, and V illustrate the query, key, and value tensor in domain transformation, since the number of parameters determines the compression ratio. Smaller networks also play a role in domain migration, while having a more beneficial effect on hyperspectral image compression.  
The architecture of the transformer layer and the attention block are illustrated in Figure 5.
## 5 Biosensor Application of Electrodeposited Composite Nanostructures
### Composite Electrodes with Silica  
A sensor for superoxide was made by electrodepositing Au nanoparticles on ITO glass, and then modifying them with cysteine and superoxide dismutase in a silica gel layer [141]. The Au nanoparticles were deposited by cyclic voltammetry between \(-\)0.2 V and \(-\)1.3 V for 20 cycles at 50 mV sec\({}^{-1}\) in 0.1 mM of KAu(CN)\({}_{2}\) in 0.05 M of pH 7.5 phosphate buffer. The Au nanoparticles had an average size of 28 nm. Then, the Au nanoparticle-coated ITO glass was immersed into a solution of superoxide dismutase, cysteine, and silica gel. The direct electron transfer of superoxide dismutase was observed by cyclic voltammetry, and the peak current increased for up to 20 cycles of electrodeposition, which was used to form the nanoparticles. Superoxide was generated by the reaction of DMSO and NaOH. A linear response to superoxide was observed over the range of 0.08 to 0.64 \(\upmu\)M. In an earlier report, for which the silica gel layer was not included, a coverage of enzymes of 1.04 \(\times\) 10\({}^{-10}\) mol cm\({}^{-2}\) was estimated [142].  
The performance of glucose oxidase immobilized on electrodeposited Au, CdS, or ZnS nanoparticles on ITO glass and covered by a silica layer were compared [143]. The Au nanoparticles were deposited from 0.2 mM of HAuCl\({}_{4}\) in 0.02 M of pH 7 buffer by applying 20 cycles between 0.1 V and \(-\)0.9 V (versus SCE) at 50 mV sec\({}^{-1}\). Cadmium sulfide nanoparticles were deposited by applying 40 potential cycles between \(-\)0.4 V and \(-\)0.8 V at 50 mV sec\({}^{-1}\) at 50 \({}^{\circ}\)C in 0.8 mM of Na\({}_{2}\)S\({}_{2}\)O\({}_{3}\) and 0.05 M of CdCl\({}_{2}\) at pH 2-3. Zinc sulfide nanoparticles were prepared by applying 40 cycles at 50 \({}^{\circ}\)C between \(-\)0.4 V and \(-\)1.0 V at 50 mV sec\({}^{-1}\) from 0.8 mM of Na\({}_{2}\)S\({}_{2}\)O\({}_{3}\) and 0.05 M of ZnCl\({}_{2}\) at pH 4. Then, the solution of glucose oxidase and silica sol and polyvinyl alcohol was deposited over the nanoparticles. It was found that the electroactivity of the glucose oxidase was greatest on ZnS nanoparticles, and that these sensors also showed the greatest sensitivity to glucose.
## 6 Experimental Results
### Experiment 2: Feature Importance Using Chi\({}^{2}\)  
In this experiment, the Chi\({}^{2}\) feature selection method is applied to the eight ML algorithms. The experiments are conducted again based on the FFF-SA algorithm where each experiment starts by modeling the ML algorithm with the accumulative set of features \(k\). As explained in Figure 5, the experimental results show dispersed results from most ML algorithms except the NB and SVM algorithms that achieved the best results. The NB algorithm recorded the best accuracy of 83.76% with the accumulative feature \(k=2300\). With the accumulative feature \(k=2400\), the accuracy decreased very slightly and recorded 83.62%. The remaining accumulative \(k\) features continued in slight decreases, forming a straight line to the end of the accumulative feature with \(k=4900\) that recorded 82.91%. The second-best results are achieved on the SVM algorithm with the accumulative feature \(k=2200\) that recorded an accuracy of 81.49%. On the accumulative feature \(s\)\(k=2100\) and \(k=2300\), the SVM recorded accuracies of 81.21% and 80.98%, respectively.
## 4 Tensile Sensing Test of the PP Based OFBG Strain Sensor  
The tensile sensing tests were carried out using an universal testing machine. The load measurement precision is 0.001 kg. A 50 mm extensometer was used to measure the tensile strain of the sensor. The strain measurement precision is 0.001 mm. The calibration test is shown as Figure 10. The wavelength of OFBG was measured with the FBG-SLI Interrogator SI720 of Micron Optics. The load, tensile strain, and wavelength of OFBG were recorded, as shown in Figures 11 and 12. Because the PP matrix is in an elastomeric state with a low modulus like asphalt at normal atmospheric a temperature, time-lag phenomenon appears, but the repetitive properties are very good, so it can be used in monitoring low modulus materials or structures.  
Figure 10: Strain-load relationship of PP-OFBG sensor.  
Figure 9: Strain-time relationship of OFBG.  
It can be seen in Figure 11 that the sensing coefficient of this new sensor is about 0.85 pm/\(\upmu\)e. The calculation result in Section 3.1. is about 1.04 pm/\(\upmu\)e. The sensibility factor of OFBG is 1.20 pm/\(\upmu\)e. The difference among them is due to the difference between the micro-mechanics interface strain transfer performance and the materials' actual performance. To verify the repetitive properties of this new kind of sensor, the specimens were subjected to several loading and unloading cycles. The test results reveal that PP-OFBG strain sensor can work very well in load-unload loops because it is in the elastic range, as shown in Figure 12.
## 2 Critical Communications Issues In Microgrids
### Wireless Access Network Power Consumption  
As BS consumption is the highest in the wireless access network, its influence on energy expenditure needs to be evaluated. The BS architecture consists of two main use parts: load and non-load dependent [39]. Load dependent devices consume around 60% of the total power consumption [40], and its consumption varies depending on the network traffic load. The traffic load changes over time, representing the number of active users in a defined period. Devices such as the digital signal processor (DSP), the transceiver (BB) and power amplifier (PA) are load dependent; only the PA consumes about 30% of the energy. Non-load dependent devices consume 40% of the total energy consumption, and its devices are standard for all sectors. The air conditioner (AIR) is the highest consumer in non-load dependent devices, consuming around 25% of the total power, followed by the rectifier (RECT) and the backhaul link, which are independent of the number of users [41]. The total power consumption of a base station is proposed in [39] and is written as:  
\[P_{total}=P_{(non-load-dep)}+P_{load}L_{factor}, \tag{1}\]  
where \(P_{total}\) is the total BS power consumption, \(P_{non-load-dep}\) and \(P_{load}\) are, respectively, the non-load dependent and load dependent parts from the BS and \(L_{factor}\) is the load factor.  
As a way to measure the impact of energy consumption on a geographical area, the energy efficiency factor evaluates the total power consumption per square kilometer [42]. The energy efficiency \(\Omega\) in \((W/km^{2})\), is written as  
\[\Omega=\frac{\sum_{1}^{m}P_{total}}{A}, \tag{2}\]  
where \(m\) is the number of available base stations and \(A\) is the total area in km\({}^{2}\).
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 2 Proposed Methodology
### System Architecture and Design  
The retransmission mechanism to recover transient errors is popularly used in communication [12; 14]. The proposed retransmission mechanism generates a fault-tolerant scheduling framework by transmitting the same message repeatedly, as shown in Figure 1.  
The retransmission mechanism (i.e., duplicating messages) is based on time redundancy mechanisms in the static segment of FlexRay. In the example, we assume that the probability of  
Figure 1: Diagram of message re-transmission mechanism.  
failure (\(PF_{i}\)) of \(M_{2}\) is bigger than \(M_{1}\). When the process occurs without a message re-transmission mechanism, the values of the re-transmission times (\(RT_{i}\)) of \(M_{1}\) and \(M_{2}\) are both 0. For a system with a message re-transmission mechanism, \(M_{2}\) should be retransmitted because \(M_{2}\) has a lower reliability than \(M_{1}\). We now modify \(RT_{1}\) and \(RT_{2}\) to be 0 and 1, respectively.  
The message will be successfully delivered with the retransmission mechanism to avoid transient failure. The reliability of each message (\(M_{i}\)) is closely related with the retransmission times (\(RT_{i}\)) and probability of failure (\(PF_{i}\)). Message reliability is estimated according to Equation (1), and is equal to that shown in [8]. According to the global success reliability (\(GS\)), obtained by multiplying the probability of each message, increasing the probability of the successful delivery of each message will help to further improve the reliability of the system. In the fault-tolerant scheduling framework, \(M_{i}\) means the retransmitted message, where \(i\) = 1, 2,..., \(n\), and \(j\) are the retransmission times to identify whether a message type is a copy or new, and its range is 1 to the total number of slots in the static segment to ensure that there are enough slots for retransmission:  
\[(1~{}-~{}PF_{i}^{RT_{i}+1})^{\frac{\tau}{t_{i}}} \tag{1}\]  
The proposed FRSA architecture and parameters according to the above retransmission mechanism are shown in Figure 2. It is composed of three units: the message generator and the message reliability processing and scheduling output modules. The FlexRay parameters and lookup table are some parameters required for the system and corresponding new \(GS_{mi}\) values, respectively. In addition, there are two methods for the handling of a large number of messages in the proposed algorithm. First, each message is sorted by its \(GS_{mi}\) in descending order; this step excludes the messages that do not need to be retransmitted. Second, in order to meet reliability and deadline constraints in the case of a greater number of messages, computation is performed using a lookup table method instead of an iterative solution. We describe the proposed algorithm in more detail in the next section.
# Through-Floor Vital Sign Searching for Trapped Person Using Wireless-Netted UWB Radars
## 5 Conclusions  
A novel multiple-observation-points-based vital sign searching concept using wireless-netted UWB life-detection radars has been developed for the searching and rescuing of through-floor trapped persons. Note that the echoes and detection results collected from different observation points need to be processed following an associated means of vital sign search procedure, and the selecting and optimization of the searching path are presented. By adopting the proposed vital sign searching procedure, the radar system is able to locate the spatial position of the trapped person by way of associating the diversity of detection results from different observation points. Furthermore, simulation and experiment were conducted, showing the validation of the proposed vital sign searching path concept.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Searching Path** & **Observation Point 1** & **Observation Point 2** & **Observation Point 3** \\ \hline  & (0.8 m, 1.2 m, 3.75 m) & (\(-\)0.4 m, 0 m, 3.75 m) & (\(-\)0.4 m, 0.8 m, 3.75 m) \\ \hline No.1 radar result & & & 3.86 \\ No.2 radar result & & & 3.77 \\ No.3 radar result & & 3.87 & \\ No.4 radar result & & 4.12 & \\ No.5 radar result & & 3.79 & 3.79 \\ No.6 radar result & & 3.88 & 3.87 \\ No.7 radar result & 4.29 & & \\ No.8 radar result & 4.51 & & \\ No.9 radar result & 0 & & \\ No.10 radar result & 4.91 & & \\ Output & (\(-\)0.38 m, 0.02 m, 4.19 m) & (\(-\)0.45 m, 0.85 m, 3.76 m) & (\(-\)0.47 m, 0.79 m, 3.76 m) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental result for the vital sign searching path.  
Figure 9: The experimental result of searching for a trapped person along a searching path.  
**Author Contributions:** The research was performed by the authors as follows: conceptualization, Z.X. and S.W.; methodology, Z.X. and S.W.; software, S.W. and X.L.; validation, Z.X. and S.W.; formal analysis, S.W. and X.L.; investigation, Z.X. and H.S.; resources, S.J. and T.P.; data curation, X.L. and S.W.; writing--original draft preparation, X.L. and S.W.; writing--review and editing, X.L. and Z.X.; visualization, X.L. and S.W.; supervision, S.J. and T.P.; project administration, Z.X. and H.S.; funding acquisition, Z.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was supported by Innovation Funds of Equipment Pre-Research under Grant JZXY20190253040601 and JZXY20190253036001. The authors wish to express their gratitude to the editor and the anonymous reviewers.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods  
### PEMF Product  
The electric circuit device that generates the PEMF was manufactured in a cage shape so that the magnetic field was focused on the leg joints of the rats. When 220 V AC power was applied, AC was converted into 48 V DC power through a switching mode power supply (SMPS), and energy in the form of pulses was transferred to the PEMF-generating coil (Figure 1). The pulse was output at 28 Hz (28 times per second), and a driving pulse in the form of a square wave was generated using a microprocessor (ATtiny2313A, Microchip, Chandler, AZ, USA) with a duty ratio of 50%, which is the on-time to off-time ratio. A 3 V driving pulse was generated, and current amplification was required owing to insufficient current energy for driving the coil. Thus, a solid-state relay (SSR) for the direct current was used. The SSR plays a key role in transferring energy to the magnetic field coil by amplifying the 3 V pulse signal generated by the microprocessor to 48 V.  
The pulsed magnetic field coil uses an enameled copper wire (with a diameter of 0.3 mm) with an insulating coating, and has a cube shape of 350 (width) mm \(\times\) 300 (length) mm \(\times\) 30 (height) mm. The total number of turns is 100. Finally, the driving current is adjusted so that the intensity of the output magnetic field becomes approximately 20 G at the center of the coil. The pulse field measuring device is a Hall element-type Gauss meter (Model 3251, Yokogawa, Tokyo, Japan), and an oscilloscope (XDS3062A, Tektronix, San Jose, CA, USA) is used to set the pulse frequency.  
The fabricated PEMF device was finished in a cage shape to conduct the experiment in the general breeding environment of the rats (Figure 2A). At the lower end of the cage, a box case with a built-in pulse magnetic field controller was placed, and the cage body made of transparent acrylic was located at the upper end of the pulse magnetic field controller. Sawdust was laid inside the pulsed magnetic field coil so that the rats could conduct normal activities, and a feeding or water container was mounted on the upper part of the cage. The conditions were the same as the general breeding environment of the rats.  
Eight cages with a built-in pulse magnetic field device were manufactured, and four cages each were employed for the normal and experimental groups.  
Figure 1: Diagram of the PEMF device.
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 5 Conclusions  
This paper investigated the semantic segmentation method of real-time 3D point clouds for unmanned vehicle systems. We proposed LFNet, a lightweight, fully convolutional network based on an attention mechanism and a sparse tensor, which learns global contextual features by designing GFLM and achieves the effective fusion of feature information at different scales by designing MFFM. LFNet reduces unnecessary operations and memory occupation by using a sparse tensor. This improves the adaptability of the model to the limited computational resources of unmanned vehicle systems.  
The experimental results showed that LFNet improves the mIoU evaluation metric by 6.4% and 5% over existing models for the SemanticKITTI and nuScenes datasets, respectively. For data acquired by HDL-64E LiDAR on the SemanticKITTI dataset, our LFNet efficiency can reach 10 fps on the GTX 3090 experimental platform. Thus, our model can meet the real-time application of unmanned vehicle systems. With our attention module, our method can significantly improve the accuracy of semantic segmentation.  
Conceptualization, W.Z.; methodology, F.W.; software, Y.Y.; validation, F.W., Z.W. and J.Z.; resources, J.Z.; data curation, Z.W.; writing--original draft preparation, F.W.; writing--review and editing, Y.Y. and W.Z.; supervision, W.Z.; project administration, F.W. and W.Z.; funding acquisition, F.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the Postdoctoral Science Foundation of China, grant number 2021M690502, and the Dalian Excellent Youth Talent Fund Project, grant number 2022 RY23. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 1 Introduction
[19] outlines potential factors that determine agility. These factors are the ability to process visual information in the sport and competitive environment, the capacity to anticipate events that influence movements, and the ability to recognise sport-specific patterns and knowledge based on previous experience of the game or sport [19]. Therefore, reactive agility comprises many components present in downhill mountain running, such as the correct positioning of the foot, the cognitive components of recognition and anticipation, the speed and technique of the change of direction, and the muscular qualities of the lower limb [18]. [PERSON] et al. [20] showed in their study that RA performance is associated with both motor and perceptual-cognitive factors, and [PERSON] et al. [21] determined that cognitive factors had a greater influence on RA than physical factors. Therefore, RA tests have also been used to discriminate different levels of athletes in different sports such as rugby and football [22; 23]. However, in the current literature, there are no studies that assess the possible relationship between the individual's reactive agility, the performance of runners in trail running descents, and the influence of the biomechanical parameters of the athlete. Understanding this relationship may be of particular interest to coaches and athletes, as this information can be used to improve sporting performance by optimising the training of these variables. Therefore, the main objective of this research was to evaluate the correlation between reactive agility and performance in downhill sections of amateur trail running runners. Thus, the hypothesis was that the existence of better reactive agility values is related to better performance values in the downhill sections during a trail running test. Likewise, the specific objective was to evaluate the type of RA against numerical stimuli (cognitive stimuli) and sport-specific stimuli, assessing its influence on the possible correlations of the variables extracted during the tests. The hypothesis in relation to this objective is that RA against sport-specific stimuli will have a greater correlation with performance than RA against a cognitive stimulus.
## 4 Material and Methods
### Antibacterial Activities  
_B. cereus_ (ATCC 10.876, Gram+), _K. marina_ (DSM 16.420, Gram+), _A. bochemicus_ (DSM 102.855, Gram+) and _E. coli_ (ATCC 25.922, Gram-) and _P. fluorescens_ (ATCC 13.525, Gram-) bacterial strains were used to evaluate the antibacterial activity of the extracts.  
Lysogeny Broth agar containing 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl, and 15 g/L agar was sterilized and used to maintain all the bacterial strains at 26 \({}^{\circ}\)C for _B. cereus_, _A. bochemicus_ and _P. fluorescens_, and 37 \({}^{\circ}\)C for _K. marina_ and _E. coli_. Fresh culture plates inoculated with each different strain were prepared the day before the analysis.  
The MIC was determined according to the microdilution method [80].  
Each bacterial strain suspension (10\({}^{6}\) CFU/mL) was treated by adding 12 dilutions of extracts in LB (from 4 mg/mL to 0.002 mg/mL). Negative, positive, and solvent controls were obtained using medium, medium with gentamicin (from 100 \(\upmu\)g/mL to 0.05 \(\upmu\)g/mL) and medium with DMSO (from 5% to 0.002%), respectively. After 24 h of incubation, 10 \(\upmu\)L of MTT (0.5 mg/mL) was added to visualize bacterial growth. Each assay was repeated in triplicate. After 24 h, the MBC was determined for the dilution samples in which no bacterial growth was observed.