esa-sceva/satcom-chunk-collection huggingface dataset:
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 5 Future Development Considerations  
Further development of RMCSat rests in detailed assessments of the payload antennas as satellite subsystems do not present any problems at this stage. Securing a suitable launch is another critical element as the final launch orbit dictates whether design changes to the CubeSat platform or operations plan are necessary.  
The radio technology of the helical antenna shows that it is suitable for the CubeSat mission, however, the physical characteristics of the antenna require detailed engineering analysis and design. The issue of antenna rigidity and potential distortion during spacecraft maneuvers must be addressed in subsequent design discussions. Similarly, the deployable nature of the antenna means that it must deploy reliably and achieve the correct geometry after deployment. Any change in the geometry through compression or lateral deformation alters the pitch angle and spacing of the wire turns, and hence the gain pattern of the antenna. A stabilization mechanism, such as a deployable tape, has been suggested to improve rigidity without affecting antenna performance [29].  
The beamwidth of the secondary patch antenna also requires further analysis. An HPBW of 70 degrees is provided by the antenna under consideration, but additional investigation is needed to confirm if it is sufficiently broad to capture and replicate the exposure field of the primary antenna [27].  
Finally, the proposed CubeSat design is founded on a noon-midnight SSO. If this orbit, or one close to it, is not achievable, the complexity of the solar observations increases significantly along with the layout of the two payload antennas as discussed in Section 3. The inclusion of any consequential physical shielding for the primary antenna also has implications for the mass budget of the satellite.
## 1 Introduction  
With the advancement of computer-aided design and micro/nano-fabrication technologies [1], optical elements with functional micro-/nano-structures have been successfully used to improve the performance of components or modules, such as light-emitting diodes [2; 3], photodetectors [4], solar systems [5; 6], displays [7], and glass components [8]. The anti-reflective optical film having subwavelength structure arrays on its surface, and replicated by the use of a roll to roll micro-replication process is numerically and experimentally investigated [9; 10]. The experimental results show that the Fresnel reflection on the interface surface is obviously suppressed when the subwavelength structure arrays exist on the surface of the optical film. In addition, metal films or doped silicon wafers with subwavelength hole arrays (SHAs) have been proposed to enhance the transmission [11; 12; 13; 14; 15]. [PERSON] et al. discovered the optical transmission of subwavelength cylindrical cavities in metal films could be significantly enhanced [11]. For such optically thick metal films, the zero-order transmission spectra are clearly related to the geometry of the hole array [11; 12]. The transmission of terahertz radiation through highly doped silicon wafers with SHAs has been experimentally investigated [14; 15]. It was found that the transmission is significantly enhanced, and the enhancement is related to the hole size and array thickness. We demonstrate extraordinary THz transmission of an array of subwavelength apertures patterned on ultrathin highly doped silicon by reactive ion etching. Additionally, several subwavelength hole arrays structured in the active area of the complementary-metal-oxide-semiconductorCMOS compatible thermopile are investigated [16]. It is numerically and experimentally shown that the measurement results are consistent with that of the simulation results, and the infrared absorption efficiency (IAE) is significantly enhanced. There is an interesting phenomenon to be discovered; the rectangular column (RC) or ellipse column (EC) in a rectangular hole, which can be enhanced by the IAE of that thermopile for the best case in [16] (the rectangular type with the hole length 15 \(\upmu\)m, the hole width 3.5 \(\upmu\)m, and the array thickness 3.5 \(\upmu\)m).  
In this study, we investigated several special subwavelength columnar structures in the rectangular hole of the best case of [16], to enhance the infrared absorption efficiency of CMOS compatible thermopiles. Using the finite-difference time-domain (FDTD) method, we researched the best geometry of the extra subwavelength columnar structures (ESCS). It was obtained that for the three types of RCs and three ECs, the IAE enhancements of those thermopiles are 14.40% and 15.21%, respectively.
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 2 Materials and Structural Design
### Structural Design
#### 2.2.1 Rigid-Soft Hybrid Structure  
The rigid-soft hybrid design is a structure in which reinforced islands are embedded in an elastic layer. This design also is referred to as the hybrid design. Each reinforced island and each elastic layer are composed of two materials that have different moduli of elasticity [71]. When deformed, the rigid, reinforced island regions support the active parts of the electronic device without deformation, and the elastic layer resists mechanical deformation by effectively dispersing the strain. [PERSON] et al. developed a hybrid substrate composed of rigid, reinforced islands with photo-patternable polymer (SPC-414; E = -360 MPa) and an elastic layer with silicone elastomer (elastofilcon A; E = -0.09 MPa), as shown in Figure 3a[15]. For the stretching test, when the external strain was applied (0% and 30%), there was no discernible change in the gap in the interface. This was because the elastic layer of the hybrid substrate provides sufficient stretchability to protect the rigid reinforced regions from mechanical deformation. High transparency (93% in the visible light regime) and low haze (1.6% in the visible light regime) were achieved by using a hybrid substrate, which is suitable for soft, contact lenses.
## 2 Theory and Model  
### Low Rank of Vibration Source Signals  
Because the working process of rotating machinery is periodic, the vibration signal excited by the internal excitation source has repetitiveness. For example, the vibration signal generated by a bearing outer ring crack fault is a series of repeated impact waveforms.  
The vibration signal generated by a rotor eccentric fault is a series of repetitive harmonic waveforms. The recurring waveform is called the characteristic waveform in this study. The matrix composed of related data is low rank [15]. Because the repeated characteristic waveforms are correlated, the matrix composed of the characteristic waveforms in the vibration signal from a fault is a low-rank matrix in an ideal state.  
In the case of engineering applications, the rank of the matrix obtained from the vibration signal containing noise is higher, or full rank. Low-rank restoration is carried out to eliminate the noise in the vibration signal, making the rank of the matrix lower.
## 3 Current Research and Applications  
The Department of Transportation (DoT) identifies six research areas in the ITS Strategic Plan 2020-2025 [83]. One of them is emerging and enabling technologies, which focuses on innovative technologies that facilitate ITS advancements. This section will discuss the current state-of-art of UAV research and applications in the transportation sector that complement the DoT ITS Strategic Plan.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### UAV Handover Scenarios  
In 3D space, UAVs typically fly much faster than the average BS. Additionally, the UAV can function as either a standard mobile UE or a fly BS. Different types of HO scenarios emerge for two cases based on these arguments.
# Extracting the Secrets of OpenSSL with RAMBleed
## 5 Analysis of OpenSSL and Apache
### Investigating Whether Physical Memory is Allocated for Secret Information
#### 5.2.4 How to Perform a TLS Handshake with a New Child Process  
For the physical memory page allocation to \(p\) and \(q\) to occur in the actual process of communication, the attacker must cause the server to create child processes and perform a TLS handshake with one of the newly created child processes. In this section, we describe a method for achieving this by utilizing the behavior of the pre-fork model. The procedure is as follows.  
**step 1 (Send MaxSpareServers TCP requests.)**: The server has a maximum of MaxSpareServers idle processes. Therefore, when a MaxSpareServers TCP request is sent, the server allocates the request to existing idle child processes, and if there are insufficient child processes, it spawns child processes until MinSpareServers processes are idle. This enables at least MinSpareServers new child processes to be created. The client that causes the creation of these new child processes is called a dummy client;
**step 2 (Send a TLS request).**: In Apache, a parameter called timeout defines the amount of time a child process waits between receiving a TCP packet from a client and then receiving the next TCP packet, during which time the child process is in a non-idle state. Unless a dummy client sends a FIN packet, the existing child process will not become idle; therefore, the only idle process on the server for the time set by Timeout will be the new child process. If an attacker sends a TLS request within the duration set by Timeout, there is a high probability that a TLS handshake can be executed with the new child process. When a parent process creates and terminates multiple child processes, it must wait at least 1s after sending a request from a dummy client before sending a TLS request because only one process is handled per second.  
Using this method, the attacking process can perform a TLS handshake with the newly created child process and generate physical memory allocations for \(p\) and \(q\) with high probability. Figure 5 illustrates how the server creates a new child process when MaxSpareServers=5 and MinSpareServers=1.
## 2 Study Site and Available Data
### Available Data
#### 2.2.2 Proximal and Remote Sensing Observations  
* Terrestrial photography from a camera, a CC640 Campbell Scientific, installed at the Refugio Poqueira weather station, was used in this study at the plot scale to improve the understanding of wet-snow dynamics. The images acquired by the camera cover an area of about 30 m \(\times\) 30 m (equivalent to the spatial resolution of the Landsat TM sensors). The camera takes 5 pictures a day, every 2 h from 8:00 AM to 16:00 PM, with a resolution of 640 \(\times\) 504 pixels. Following the methodology proposed by [3], snow cover area (SCA) time series were derived and used in this study. Automatic inspection was carried out to remove those images of bad quality due, for instance, to cloud cover, fog, or rain.
* Sentinel-1 SAR imagery  
The S-1 two-satellite constellation was used in this study. It can collect C-band SAR imagery with different polarizations and pixel resolutions with a revisit time of 6 days. In our analysis, the interferometric wide swath mode was used with a 3.5 m \(\times\) 22 m resolution. The Sierra Nevada mountains are covered by two specific satellite orbits: orbit 1 in the afternoon and orbit 81 in the morning. These orbits result in a local incidence angle range of approximately 35\({}^{\circ}\) (in the afternoon) and 36\({}^{\circ}\) (in the morning). This range is crucial because it falls within the threshold where backscatter signals, specifically the co-polarized and cross-polarized backscatter (VV and VH), exhibiting optimum results according to both backscatter data and theoretical considerations [68; 69]. This study has focused on the afternoon images as they offer a clearer identification of the snowmelt phases while the morning acquisitions can be affected by a melt-freeze component. The local acquisition time is approximately 18:10 GMT+1. In total, 30 scenes in the 2016-2017 period, 51 scenes in the 2017-2018, 42 scenes in the 2018-2019 period, and 49 scenes for the last year were processed and analyzed.  
* Optical MODIS data: MOD10A2 Snow product  
The MOD10A2 version 006 product was used to assess snow dynamics at the catchment scale. This product has an 8-day composite and 500 m \(\times\) 500 m spatial resolution.  
This MODIS product was selected because it offers the possibility of a long and consistent time series. Despite the spatial resolution being coarser than other optical sensors, for instance Sentinel 2 or Landsat, using the MOD10A2 guarantees the availability snow cover information every 8 days. This temporal resolution is close to the 6 days revisiting time of S1-SAR. A total of 156 scenes were employed in the study period from 2016 to 2020. Furthermore, the MODIS snow products have undergone comprehensive global validation, achieving an overall accuracy of 94.2% when MODIS daily snow products were compared to ground data [70; 71]. Studies conducted in mountainous regions, such as those by [72; 73], suggest that the accuracy levels are not influenced by land cover or topography. Instead, the errors tend to be higher in winter compared to spring and summer periods.
## 1 Introduction
### Related Works
#### 1.1.1 IoU (Intersection over Union)  
Computational vision is being advanced by CNNs, and various studies are being conducted to detect objects in images. The most basic element used to detect objects in an image is the BB. Object detection uses the BB for the entity and the predicted BB to predict the object in the image and detects the entity by finding and correcting for losses. IoU is the most popular way to compensate for this loss [28]. Extensions to this technique include Generalized IoU (GIoU), Distance IoU (DIoU), and Complete IoU (CIoU), which are basically based on IoU [28; 29].  
Figure 1 compares the difference between IoU and GIoU presented in the GIoU paper in a situation where the values of \(l_{2}\)-Norm and \(l_{1}\)-Norm are the same. \(l_{2}\)-Norm is the distance between the upper right point (\(x_{1}\), \(y_{1}\)) of the green box corresponding to BBGT in (a) and the upper right point (\(x_{2}\), \(y_{2}\)) in (a), and \(l_{1}\)-Norm is the distance between the two boxes in (b), with (\(x_{c}\), \(y_{c}\)) as the center of the two boxes and w, h as the width and height of the boxes; (a) shows a case where two BBs have the same \(l_{2}\)-Norm, but different IoU and GIoU values, and (b) shows that the same \(l_{1}\)-Norm can result in different IoU and GIoU values.  
To express this in a formula, we call the green region \(A\), the black region \(B\), and the region \(C\) that minimally combines the two regions. _IoU_ and _GloU_ are as follows in Equation (1).  
\[IoU=\frac{\left|A\cap B\right|}{\left|A\cup B\right|},\ GloU=IoU-\frac{\left| C\smallsetminus(A\cup B)\right|}{\left|C\right|} \tag{1}\]  
In this paper, to create a path using Wi-Fi signals, all data points, which are Wi-Fi signals, are treated as images. Each point is represented as a BB, and IoU is used as a criterion for merging and extending neighboring BBs.
## 4 Simulation Analysis  
In order to explore the separation effect of the multi-low-rank constrained vibration source separation method, the simulation signals of the bearing inner ring and outer ring crack faults were constructed. The impact caused by a bearing crack fault can be modeled by the single-degree-of-freedom mass-spring-damper impact response [21; 22].  
\[s(t)=A\mathrm{e}^{-at}\sin\omega_{r}(t)\mathrm{u}(t) \tag{17}\]  
The fault-bearing vibration signal model can be expressed as [23; 24]:  
\[x_{\mathrm{b}}(t)=\sum_{m=1}^{M}A_{m}e^{-\alpha(t-mT_{\mathrm{P}})}\sin(2\pi \omega_{r}\big{(}t-mT_{\mathrm{P}}\big{)})\mathrm{u}\big{(}t-mT_{\mathrm{P}} \big{)} \tag{18}\]  
Figure 2: The process of the vibration source signal separation method with multiple-low-rank constraints.  
where \(A_{m}\) is the amplitude of the \(m\)-th fault impulse, and u(\(t\)) is the unit step function. \(T_{\text{P}}\) is the spacing of adjacent impulses caused by bearing fault, \(\alpha\) is the structural damping factor, \(\omega_{\text{r}}\) is the resonance frequency induced by the bearing fault.  
The simulation signals of the bearing inner ring and outer ring crack faults can be defined as:  
\[x(t)=x_{\text{bo}}(t)+x_{\text{bi}}(t)+ns(t) \tag{19}\]  
where \(ns(t)\) is white noise, and \(f_{\text{s}}\) is the sampling frequency.  
The parameters of the simulation signal are listed in Table 1. The characteristic frequency of the crack fault of the bearing outer ring \(f_{o}\) is 79.2 Hz, and the characteristic frequency of the crack fault of the bearing outer ring \(f_{i}\) is 138.9 Hz from Table 1.  
The simulation signal is shown in Figure 3. The impulse waveform caused by the inner ring and outer ring faults was not observed in the mixed signal from Figure 3.  
The vibration source signal separation method with low-rank constraint was used to separate the bearing fault vibration source signal in the simulation signal. The separation result is shown in Figure 4. The crack fault signals of the inner ring and outer ring are separated, and the pulse waveform can be clearly observed from Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(A_{mo}\) & 1.5 V & \(A_{mi}\) & 1 V \\ \(\alpha_{o}\) & 1200 & \(\alpha_{i}\) & 800 \\ \(T_{po}\) & 1/79.2 s & \(T_{pi}\) & 1/138.9 s \\ \(\omega_{r}\) & 2800 Hz & \(f_{s}\) & 24 kHz \\ \(f_{o}\) & 79.2 Hz & \(f_{i}\) & 138.9 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 3: Time domain waveforms of the simulated signal.  
In order to observe the components of the separated inner and outer ring crack fault signals, their envelope spectrum was calculated separately, as shown in Figure 5. There were peaks at the first harmonic and the second harmonic of the fault characteristic frequency in the envelope spectrum, as can be seen in Figure 5. This verified that this method can effectively separate the inner ring and outer ring crack fault signals.
## 5 Numerical Model and Validation  
The PPA 1001 unimorph cantilever is composed of five layers of different materials, see Figure 9. The active layer is made of PZT 5H (0.15 mm), it is partially covered by a copper electrode (0.03 mm) and by a polyester layer (0.05 mm). The structure is an AISI 304 stainless-steel layer (0.15 mm), the bottom layer is made of polymide (0.03 mm).  
A numerical FE model was developed with the COMSOL software (Version 5.2, COMSOL Inc., Burlington, MA, USA, 2015). The piezoelectric material was modeled as transversely isotropic linear elastic material with a polarization axis perpendicular to the layer. The other materials were simulated as linear elastic materials with isotropic properties. The characteristics of the harvester's materials can be found in [(39)]. A mapped mesh of second order hexahedral elements with 27 nodes was used. The piezoelectric layer was modeled by means of 5 elements in the thickness direction and by 30 elements and 10 elements in the direction of length and width respectively. Geometrical details, like the edges of the structural material that surround the piezoelectric material, were taken into account.  
The steady-state harmonic response was calculated in the multi-physics domain, which includes mechanical, electrostatic, and electrical equations. In the simulations, a hysteretic damping model with \(\eta=2\zeta=0.017\) (where \(\zeta\) is damping ratio) was used, in agreement with the experimental results of Table 2.  
First, the numerical model was validated by comparing the numerical FRFs between generated voltage and base acceleration with the experimental FRFs. Both open circuit condition and optimal load resistance were considered.  
Figure 10 shows very small differences both in the values of the resonance frequency and in the values of the resonance peak. In open circuit condition the numerical resonance peak is 1.82 V/ms\({}^{-2}\) and takes place at 125.6 Hz, the experimental values being 1.85 V/ms\({}^{-2}\) at 126.4 Hz. With optimal load resistance (12.7 k\(\Omega\)) the numerical resonance peak is 0.60 V/ms\({}^{-2}\) and takes place at 124.5 Hz, the experimental values being 0.64 V/ms\({}^{-2}\) at 125.6 Hz.  
Then, the numerical model was extended in order to simulate the harvester equipped with A1. A beam of hexahedral elements simulated the steel wire of A1 and a lumped mass simulated the brass disk.  
Figure 11 shows both the numerical and experimental FRFs between generated voltage and base acceleration of PPA 1001 equipped with A1. In open circuit condition there is a small difference (about 7%) in the height of the first resonance peak. With optimal load resistance the difference is smaller.  
Figure 9: Layers of PPA 1001.
## 4 Discussion
### Limitations and Prospects  
Figure 9 presents a strategy for using geometric elements to characterize river bedform features. In this respect, the crest, trough, flat bed, and scour pit are considered. Many of the geomorphic elements shown in Figure 9 are indicative of special geographic units in other subaqueous environments; for example, peak and pit can be used to map seafloor hills and depressions [25]. Further studies are required to determine the indicative functions of the geomorphic elements for subaqueous objects;  
for example, for erosional holes, erosional flutes, sand mining holes, and sinking boats, which relate to the anthropogenic drivers of subaqueous topographical changes [18], and for bank scarps, toes, and failure, which relate to bank slope stability.  
The spatial scales of ripples in a bedform generally have lengths in the order of 0.05-0.50 m [46] and cannot be detected when using a cell size of 1 m. The bathymetric data used in this study were at a resolution of \(1\times 1\) m, which means that the ripples between subaqueous dunes and the flat bed were inevitably ignored. Geometric elements mapping using higher resolution bathymetric data are required in further studies to ameliorate this limitation. In addition, dunes often show superimposed relationships under various bedform scales [3; 47], because of the effect of complex flow dynamics on different temporal and spatial scales. The bathymetric data cell size may thus need to be adjusted to identify superimposed regions on different scales.  
Height, length, and direction of bedforms are important parameters relating to the dynamic features of flow [2]. Although the geographic elements map is not suitable for use in calculating these parameters, it provides the potential to narrow the analysis range by separating the dunes area from the relatively flat bedforms. Previous studies have inferred that subaqueous geomorphic elements have statistical relationships with the category of landforms [40; 42]. Some studies have found that the spatial distribution of geomorphic elements presents a certain pattern that represents the type of landform [48; 49]. As in the relationship between landform elements and landscape, all of the geomorphic elements listed in Figure 9 are meaningful for comprehensively depicting the pattern of elements and regionalizing the bedform areas; however, the development of a more detailed method remains the subject of further research. In addition, the generation of a geomorphic elements map has the great potential to be used in auto-tracking the movement of bedforms, which is significant for assessing bedform instability [50; 51].