esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Time-Frequency Image Feature-Based HI  
In this section, we present the deep learning-based HI for RUL prediction. The procedure of the CWTCNN-HI-based method is shown in Figure 1. This method is composed of three stages: image feature extraction, CWTCNN-HI construction, and RUL prediction. In the feature extraction stage, image features are extracted from raw vibration signals using CWT. The image features of the training dataset are utilized to construct deep learning-based HI in the CWTCNN-HI construction stage. The CWTCNN-HI model learns how frequency changes with the machine health condition from training image features. The trained CWTCNN-HI model estimates the HI by inputting the image features of the testing dataset. In the RUL prediction stage, the remaining lifetime is derived by assuming that the failure occurs at the time the predicted HI reaches the threshold. The Gaussian process regression (GPR) algorithm is used to predict HI and future degradation trends based on the estimated HI. Then, to specify the threshold for RUL prediction, the testing datasets are clustered considering the similar degradation trend. Finally, the RUL is predicted by calculating the difference between the time at which the predicted HI reaches the threshold and the current time. More details about each of the stages in the proposed prognostics method are presented as follows.
## 3 Proposed Methodology
### The Simulation of Tunnel Excavation  
#### 3.1.1 The Simulation of Releasing Load on Excavation Boundary  
The simulation of one-step excavation is realized by releasing excavation load on the excavation boundary step by step. After one-step excavation, as shown in Figure 4, firstly, the equivalent nodal load \(\mathbf{f}(\sigma)\) on the excavation boundary, initially equal to the in-situ stress \(\mathbf{f}(\sigma_{0})\), will be extracted and gradually released with the deformation of rock mass increasing. To achieve the simulation of excavation unloading, \(\mathbf{f}(\sigma_{0})\) will be equally divided into n releasing steps. Therefore, the increment of releasing load \(\mathbf{\Delta}\mathbf{f}(\sigma)\) is \(\mathbf{f}(\sigma_{0})/\mathbf{n}\) in each step. Moreover, the excavation releasing ratio \(\mathbf{r}\), defined as the proportion of released load in the total load, can be calculated as the ratio of \(\mathbf{i}\) to \(\mathbf{n}\), where \(\mathbf{i}\) is the count of releasing step and ranges from 1 to \(\mathbf{n}\). Then, the opposite force \(\mathbf{f}(\sigma_{r})=-\mathbf{r}\mathbf{f}(\sigma_{r})\) will be applied on the excavation boundary as the released load to realize the process of unloading. After cycling to equilibrium, this releasing step is accomplished. The point safety factors of each monitoring objective are calculated by Equation (3) and denoted as \(\mathbf{F}_{1}^{i}\), \(\mathbf{F}_{2}^{i}\),..., \(\mathbf{F}_{k}^{i}\).... To evaluate the overall surrounding rock and avoid the specificity from the selection of several monitoring objectives, the geometric mean of all point safety factors are denoted as the global safety factor, \(\overline{\mathbf{F}_{i}}\) in Equation (9):  
\[\overline{\mathbf{F}_{i}}=\sqrt{\mathbf{F}_{1}^{i}\mathbf{F}_{2}^{i}\cdots\mathbf{F}_{n}^{i}} \tag{9}\]  
Similarly, the dimensionless displacements of each monitoring objectives can be calculated and denoted as \(\mathbf{\Lambda}_{1}^{i}\), \(\mathbf{\Lambda}_{2}^{i}\),..., \(\mathbf{\Lambda}_{k}^{i}\).... Then, the geometric mean of all dimensionless displacements is calculated as the global dimensionless displacement, \(\overline{\mathbf{\lambda}}\) in Equation (10):  
\[\overline{\mathbf{\Lambda}_{i}}=\sqrt{\mathbf{\Lambda}_{1}^{i}\mathbf{\Lambda}_{2}^{i} \cdots\mathbf{\Lambda}_{n}^{i}} \tag{10}\]  
In this way, the curve of the global safety factor with the excavation releasing ratio, \(\mathbf{r}-\overline{\mathbf{F}}\), and the curve of global dimensionless displacement with the excavation releasing ratio, \(\mathbf{r}-\overline{\mathbf{\lambda}}\), can be plotted. The internal stress on the excavation boundary is \(\mathbf{f}(\sigma_{i})=(1-\mathbf{r})\mathbf{f}(\sigma_{0})\). If \(\mathbf{f}(\sigma_{i})\) is not equal to zero, it represents that the unloading should be continued until the stress on the excavation boundary decreases to zero.
## 2 Background and Key Features
### UAV Communication
#### 2.2.1 Radio Frequency Communication  
Radio frequency (RF) communication is the most traditional and widely used method for UAV communication. RF communication has strong penetration and anti-interference capabilities over short distances, making it suitable for the local control of small UAVs. With the widespread application of UAVs, spectrum resources are becoming increasingly scarce, making the optimization of channel selection and the improvement of spectrum efficiency crucial. [PERSON] et al. [31] proposed a deep reinforcement learning (DRL)-based interference management scheme for RF communication in UAVs connected to cellular networks, aiming to reduce interference in multi-UAV scenarios and enhance spectrum efficiency. In [32], the authors established an RF communication path loss model between UAVs and ground stations and analyzed the impact of signal propagation from different heights and distances. They proposed the optimal flight altitude suitable for RF communication, thereby maximizing ground coverage and enhancing communication reliability. With the increase in communication needs, optical communication and millimeter wave communication have become important directions for future high-speed communications. For example, free space optical communication is being widely studied in short-distance, high-bandwidth-demand scenarios.
## 2 Materials and Methods
### Characterization of Silver-Nanowire-Embedded Flexible Transparent Electrodes  
To evaluate the thickness uniformity of the silver-nanowire-embedded flexible transparent electrodes as a function of the polymer viscosity, we measured the thicknesses of flexible electrodes that were prepared with prepolymer solutions of different viscosity, coated at different spinning speeds. The thickness was measured at the center and the edge of the electrodes using a digital Vernier caliper (Slyzone, UK) and normalized to the edge film thickness of each sample to compare different films easily. Additionally, we constructed organic solar cells made of poly [4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b\({}^{\prime}\)]dithiiophene-2,6-diyl] [3-fluoro-2-[(2-ethylhexyl)carbonyl]thien[3,4-b]thiophenediyl] ((C\({}_{41}\)H\({}_{53}\)FO\({}_{4}\)S\({}_{4}\))n, PTB7): [6,6]-phenyl-C\({}_{71}\)-butyric acid (C\({}_{82}\)H\({}_{14}\)O\({}_{2}\), PC\({}_{71}\)BM) to investigate the effect of the thickness difference on  
Figure 1: (a) Schematic illustration of the fabrication of silver-nanowire-embedded flexible transparent electrodes, displaying the thickness measurement locations. (b) A representative SEM image of silver-nanowire-embedded transparent electrodes.  
the device performance. Specifically, a zinc oxide precursor solution was spin-coated on a silver-nanowire-embedded transparent electrode. After its hydrolysis process, the cosolution of PTB7:PC\({}_{71}\)BM was spin-coated on a zinc-oxide-coated transparent electrode to form a photoactive layer. Spin-coating of a diluted poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) solution in isopropyl alcohol and deposition of a 100 nm thick silver electrode were completed to construct PTB7:PC\({}_{71}\)BM organic solar cells. The details were described elsewhere [28]. The device performance was evaluated by measuring the current density-voltage (J-V) characteristics under 100 mW cm\({}^{-2}\) illumination at air mass coefficient AM 1.5 G using a solar simulator (ORIEL LCS-100, Newport) and a Keithley 2400 source meter.
## 4 Results and Discussion
### Influence of Cutting Parameters on the Sizes of Edge Defects  
Figure 6 shows the change law of the edge defect size at the workpiece exit end with the depth of cut, spindle speed, and feed. Figure 6a shows the influence of the depth of cut on the edge defect size at the exit end when the spindle speed and feed were respectively 500 r/min and 0.237 mm/r. It can be seen that the length and height of edge defects increased with the depth of cut. While the depth of cut was below 2.5 mm, the size of micro edge defects showed a slow increase. However, as the depth of cut continued to increase, the edge defect sizes increased very quickly. This is mainly because as the depth of cut increased above some value, the resultant higher cutting forces brought about severe and unstable cutting movement, which further promoted the formation of large edge defects at the exit end. Additionally, when the depth of cut and spindle speed were kept constant, the influence of the feed on the edge defect size is illustrated in Figure 6b. From the figure, it can be noted that as the feed rate increased, the size of micro edge defects at the exit end significantly increased, and there was a linear increasing relation between edge defect size and feed rate. Figure 6c presents the dependence of edge defect size on spindle speed, and it can be seen that the spindle speed had a lesser influence on the size of the exit edge defect. This is because the brittle SiC particles in the 2009 Al matrix led to no significant softening effects in the 17 vol.% SiC\({}_{\text{p}}\)/2009 Al composites. Similar observations were also reported in [10; 11] for the machining of SiC\({}_{\text{p}}\)/6061 Al composites under different cutting parameters. Based on the above conclusions, to achieve high-quality turning operation with less edge defects and high dimensional accuracy, a smaller depth of cut and feed rate should be adopted in the turning of 17 vol.% SiC\({}_{\text{p}}\)/2009 Al composites, but in contrast, a high a spindle speed as possible should be employed.  
From Figure 6a-c, it is very remarkable that for edge defect size, the curves of simulation results were smoother than those of experimental observation, and the simulation data were slightly less than the experimental ones. This can be explained by the simplification of finite element modelling for oblique cutting. In the present study, 17 vol.% SiC\({}_{\text{p}}\)/2009 Al composites were considered to be homogeneous and isotropic, and to have no defects. In fact, besides the tool wear and machine  
Figure 6: Influence of cutting parameters (**a**) depth of cut, (**b**) feed rate, and (**c**) spindle speed on edge defect sizes.  
vibration, large numbers of micro defects in real 17 vol.% SiC\({}_{\text{p}}\)/2009 Al composites could give rise to larger edge defects in the turning experiment than that in the simulation calculation.
## 3 Results and Discussion
### Analysis of the Variation Mechanism According to the Relationship between the CNT and RVE Lengths
To identify the geometrical relationship between the effective properties of the RVE and the RVE and CNT lengths, the length fraction of CNTs greater than 2 \(\upmu\)m according to the RVE length is shown in Figure 9. In Figure 9a (3 \(\upmu\)m RVE), the CNT length fraction sharply increased at a CNT length of 1.8 \(\upmu\)m and then decreased. Moreover, the proportion of CNTs greater than 2 \(\upmu\)m in length was 10.46%. In Figure 9c (10 \(\upmu\)m RVE), the CNT length fraction also sharply increased at a CNT length of 1.9 \(\upmu\)m and then decreased, and the proportion of CNTs with lengths greater than 2 \(\upmu\)m was 33.37%. This result indicates that even when the RVE length was varied, the largest proportion of CNTs occurred near an average CNT length of 2 \(\upmu\)m. Combining the above results, the length fraction of CNTs greater than 2 \(\upmu\)m according to the RVE length was analyzed and is shown in Figure 2:0 As the RVE length increased, the length fraction of CNTs greater than 2 \(\upmu\)m increased. To analyze the variations in the effective properties with increasing RVE length, the rate of change was examined based on the effective properties observed at 30.84 MPa in the 3 \(\upmu\)m RVE, as shown in Figure 2:1 First, the rate at which the effective properties varied with the RVE length is depicted in Figure 11a. When the rate of change in a 3 \(\upmu\)m RVE at 30.84 MPa was 100%, the rate of change gradually increased as the RVE length increased and reached 118% at 10 \(\upmu\)m RVE. For a more detailed analysis, Figure 11b shows the rate of increase. From 3 to 4 \(\upmu\)m RVEs, this rate increased by approximately 5%. However, as the RVE gradually increased, this rate gradually decreased. From 9 to 10 \(\upmu\)m RVEs, the change was 0.87%. This finding demonstrates patterns similar to the variation and fraction of CNTs larger than 2 \(\upmu\)m, as shown in Figure 2:2 Because this property is affected by the length of the CNTs inside the RVE, the reason was identified to be the geometrical relationship between the effective properties and CNT length. Figure 2:3 Variation in the effective properties according to the RVE length. Figure 2:4 CNT length fraction greater than 2 \(\upmu\)m according to the RVE length. Figure 2:5 CNT length fraction according to RVE lengths of (**a**) 3, (**b**) 5, (**c**) 8, and (**d**) 10 \(\upmu\)m.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Simulation Metrics Evaluated  
Network performance was evaluated by means of the following metrics:  
* End-to-end goodput: calculated as the average number of bits of application layer messages received per unit time in the coordinator. This value is normalized and expressed as a percentage of the transmission rate used in the PHY layer.
* End-to-end message loss rate: percentage of application messages generated in devices not received by the coordinator. Packets are not received by the coordinator due to the following reasons: because there is an overflow in the queue storing the messages to be transmitted by the MAC layer, due to channel access failures (when _macMaxCSMABackoff_ limit is exceeded), or due to frame transmission failures (when _macMaxFrameRetries_ limit is exceeded).
* CSMA/CA statistics: obtained from the execution of the random access algorithm in each device. Example statistics are: the percentage of successfully transmitted frames, the percentage of collisions (ACK frames not received), the percentage of channel access failures and the percentage of frame transmission failures.
* Energy consumption: calculated from consumed current by the optical transceiver in each device. Consumption depends on the states through which the transceiver passes along simulation time.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 5 Conclusions
This research was mainly funded by Centre National de la Recherche Scientifique (CNRS, France), UVSQ (Universite de Versailles Saint-Quentin-en-Yvelines, France), Agence National de la Recherche (ANR, France), Office National d'Etudes et de Recherches Aerospatiales (ONERA, France), Developpement economique-Saint-Quentin en Yvelines (78, France), and Departement des Yvelines (78, France). Not applicable. The INSPIRE-SAT7 team acknowledges support from the Universite de Versailles Saint-Quentin-en-Yvelines (UVSQ, France), the Office National d'Etudes et de Recherches Aerospatiales (ONERA, France), the Universite Paris-Saclay (France), the Sorbonne Universite (SU, France), the Centre National de la Recherche Scientifique (CNRS, France), the Centre National d'Etudes Spatiales (CNES, France), the Departement des Yvelines (78, France), the Laboratory for Atmospheric and Space Physics ([PERSON], LASP, USA), the National Central University ([PERSON], NCU, Taiwan), the Nanyang Technological University ([PERSON], NTU, Singapore), the South African National Space Agency ([PERSON], SANSA, South Africa). The authors also thankfully acknowledge the Minister's de l'Enseignement superieur, de la Recherche et de l'Innovation (MESRI, France) for their support. This work is supported by the Programme National Soleil Terre (PNST, France) of CNRS/INSU (France) co-funded by Centre National d'Etudes Spatiales (CNES, France) and Commissariat a l'energie atomique (CEA, France). The authors declare no conflict of interest. List of acronyms  
**Acronym** **Definition**  
ADC  Analog-to-Digital Converter  
ADCS  Attitude Determination and Control Subsystem  
AMSAT  The Radio Amateur Satellite Corporation  
ASIC  Application-Specific Integrated Circuit  
BOL  Beginning Of Life  
CAN FD  Controller Area Network Flexible Data-Rate  
CDHS  Command and Data Handling Subsystem  
CUIONO1  Charge Utile IONOsphirique 1 (Ionospheric payload)  
DB  DaughterBoard  
ECMWF  European Centre for Medium-Range Weather Forecasts  
ECV  Essential Climate Variable  
EEI  Earth's Energy Imbalance  
EEPROM  Electrically-Erasable Programmable Read-Only Memory  
ERB  Earth's Radiation Budget
# Diagnosing the Machine Set Rotor Using Its Relative Vibrations
## 4 Measurements of Relative Vibration
### Measurements of the Displacement of Rotors' Relative Vibration
#### 4.2.1 Measurements of Journal Eccentricity in the Shell  
In the case of radial vibration, the rotor axis is determined by the rotor shaft journal generatrixes. The position of the journal center relative to the shell can be measured in the plane perpendicular to the shaft axis by two displacement sensors X and Y placed at the right angle to each other. In steady-state conditions and a constant load, the trajectory of the journal center in the rectangular coordinate system XY is a point. The load with a variable component results in a plane figure (Lissajous curve) observed in the XY system. The center and radius vector of that figure should be determined. Systems for radial vibration measurement are shown in the standard [10].  
The trajectory of the journal center of a radial slide bearing of the rotor kit oil whirl/whip option from Bently Nevada was examined. The rotor was removed and replaced by a shaft, with a diameter inscribed in the shell hole. Eddy current sensors mounted in the shell were set to indicate the same value of voltage. The rotor was mounted again, and the real clearance circle of the bearing was determined by manual rolling of the rotor journal on the inner side of the shell (Figure 13). Then, after engine start-up, the trajectory was recorded at the speed of 2000 rpm (points 2 and 1) and at its change to 260 rpm (Figure 13). The geometric center \(K\) of the clearance circle was determined (Figure 13). The distance (measured in (V)) between points \(K\) and 2 and between points K and 1 is the value of absolute eccentricity \(e_{1}\) at 260 rpm and \(e_{2}\) at 2000 rpm (Figure 13).  
The standards do not indicate a measurement method for absolute eccentricity of a journal in the shell. To measure the journal-in-shell eccentricity:  
* eddy current sensors can be placed in the holder and mounted to the machine body. The sensors in the holder form a measuring head. The sensor axes in the head should be perpendicular to the axis/to the generatrixes of the journal;
* the head on the machine body must be mounted so that the sensor axes intersect with the axis of the shell hole in the machine body;
* the measurement plane should be in a place where the journal is not subject to wear;
* the measuring head must be calibrated, i.e., a specific value of the displacement (displacement signal) measured by the head for the journal located in the center of the shell hole must be specified.  
Figure 13: The clearance circle and the trajectory of the journal center at the speed of 2000 rpm and at its change to 260 rpm: K—center of clearance circle, 1—position of journal center for 2000 rpm, 2—position of the journal center for 260 rpm, \(e_{1}\)—value of eccentricity for point 1, \(e_{2}\)—value of eccentricity for point 2.  
The measuring head cannot be calibrated on the machine to be tested. This can be performed only by using a special auxiliary device--a calibration bashing. The head calibration method for the sleeve bearing is described in Refs [51; 58].
## 3 Gray Value Compression
### Range Coding  
Range coding also belongs to the family of entropy coders and is very similar to arithmetic coding, with the main difference being the choice of the interval within which to search for a number that can represent a range of data as in [43]. In arithmetic coding, the interval within which a codeword is searched is \(\langle 0,1\rangle\), while in range coding an interval is defined at \(\langle 0,N\rangle\), where \(N\) is the arbitrarily chosen upper bound of the interval.  
Looking at the results presented in Figure 6, we can see that the quality of the reconstructed image has not improved, i.e., the direct indicator--AAE--is not lower at any compression ratio. The EEDC with Huffman coding for gray value compression is better than the EEDC with range coding.
## 2 Tumor Neoantigen Vaccines
### Dendritic Cell Vaccines  
Dendritic cells, first discovered by the Canadian scientist [PERSON] in 1973, are the most functional specialized APCs in the organism and play an important role in regulation, maintaining innate immune responses, and inducing adaptive immune responses [12]. The modified dendritic cells can express neoantigens and co-stimulatory molecules, which are processed and then presented to T cells to activate immune responses. However, in vitro expansion of DCs is more complex and costly, and the number of DCs required for vaccine production is large. To combat these issues, researchers have proposed the use of cell enrichment methods, sorting of fluorescently labeled cells, differentiation, and isolation from monocytes or leukocytes to improve the generation of isolated DCs [13]. Neoantigens can be transfected, pulsed, or electroporated onto DCs [14; 15; 16].  
[PERSON] et al. first treated cancer patients with cancer neoantigen DC vaccines, which had previously been unrecognized human leukocyte antigen (HLA) class I-restricted neoantigens [17]. Through genomic analysis of tumor tissue samples from melanoma patients, functionally mature autologous interleukin (IL)-12p70-producing DC vaccines were subsequently utilized. In vivo, injection enhanced the specific recognition and response of T cells to neoantigens such as TMEM48 F169 L, SEC24 A P469 L, and others. Vaccination significantly increased the frequency of TCR\(\beta\) clonotypes and expanded the diverse neoantigen TCR repertoire.  
In another study, the stimulating factor CAIX was found to be delivered to autologous dendritic cells via adenovirus (Ad), supplemented by the immune-enhancing cytokine granulocyte-macrophage colony-stimulating factor (GM-CSF). Vaccination triggers a specific response against renal cell carcinoma (RCC) expressing the CAIX antigen. Combining ICIs weakened tumor immunosuppression to enhance vaccine efficacy. However, this study also reflects DC vaccines' limitations in weak immune activation and the need for repeated vaccinations [18].  
In addition to the above, the immunosuppressive environment in vivo and factors such as low specific T cell affinity, weak targeting ability, and tumor microenvironment can affect the effectiveness of DC vaccines [19; 20]. The practical capability of homing to the lymph nodes and efficiency of antibody release from DCs are also limitations of DC vaccines, so the objective tumor response rate rarely exceeds 15% in clinical response [21].  
In addition to having an important role in antitumor immunotherapy, DC vaccines have shown good progress in anti-infection [22], organ transplantation, allergic diseases, and autoimmune diseases [23].