esa-sceva/satcom-chunk-collection huggingface dataset:
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Satellite Design
#### 2.3.1 Mechanical Structure  
The mechanical structure of ESTCube-2 was developed to accommodate the compact design of the avionics stack systems, side panel systems, and various payloads, including the IPB payload. The ESTCube-2 structure consists of primary and secondary structures to which all systems are attached. The primary structure consists of two U-shaped frames, to which the avionics stack electronics structure and side panel (SP) assemblies are attached (see Figure 7). The secondary structure consists of two internal payload blocks and two external deployable structures. Two payload blocks surround the avionics stack electronics on the Z axis of the local coordinate system (see Figure 7).  
The mass budget of ESTCube-2 is visible in Table 1 in Appendix A.
_Proteomes_**2013**, \(1\), 254-274; doi:10.3390/proteomes1030254  
_proteomes_ ISSN 2227-7382  
www.mdpi.com/journal/proteomes  
PAPE (Prefractionation-Assisted Phosphoprotein Enrichment): A Novel Approach for Phosphoproteomic Analysis of Green Tissues from Plants  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,* and [PERSON] 1**  
1 Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle/Saale, D-06120, Germany;  
E-Mails: [PERSON] (I.L.); [EMAIL_ADDRESS] (D.S.)  
2 Riboxx GmbH, Meissner Str. 191, Radebeul, D-01445, Germany;  
E-Mail: [PERSON]ESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +49-345-5582-1410; Fax: +49-345-5582-1409.  
_Received: 15 October 2013; in revised form: 28 November 2013 / Accepted: 28 November 2013 / Published: 5 December 2013_  
**Abstract:** Phosphorylation is an important post-translational protein modification with regulatory roles in diverse cellular signaling pathways. Despite recent advances in mass spectrometry, the detection of phosphoproteins involved in signaling is still challenging, as protein phosphorylation is typically transient and/or occurs at low levels. In green plant tissues, the presence of highly abundant proteins, such as the subunits of the RuBisCO complex, further complicates phosphoprotein analysis. Here, we describe a simple, but powerful, method, which we named prefractionation-assisted phosphoprotein enrichment (PAPE), to increase the yield of phosphoproteins from _Arabidopsis thaliana_ leaf material. The first step, a prefractionation via ammonium sulfate precipitation, not only depleted RuBisCO almost completely, but, serendipitously, also served as an efficient phosphoprotein enrichment step. When coupled with a subsequent metal oxide affinity chromatography (MOAC) step, the phosphoprotein content was highly enriched. The reproducibility and efficiency of phosphoprotein enrichment was verified by phospho-specific staining and, further, by mass spectrometry, where it could be shown that the final PAPE fraction contained a significant number of known and additionally novel (potential) phosphoproteins. Hence, this facile two-step procedure is a good prerequisite to probe the phosphoproteome and gain deeper insight into plant phosphorylation-based signaling events.  
**Keywords:** phosphoproteomics; LC-MS; _Arabidopsis thaliana_; phosphoprotein enrichment
## 7 Conclusions  
This study presents a rock blasting and fragmentation effect simulation and analysis method. The accuracy and validity of this method were confirmed through onsite experiments, and this study also provided valuable recommendations for optimizing rock blasting parameters. The useful conclusions that can be drawn are as follows:  
1. A new simulation and analysis method for rock blasting fragmentation is proposed. The finite element analysis software LS-DYNA was used to simulate rock blasting fragmentation, and the resulting fragmentation images were imported into WipFrag for statistical analysis. This approach offers valuable insights for optimizing blasting parameters.
2. Due to the directional effects along the cylindrical charge, the explosive loading on blast hole wall first increases and then stabilizes. Furthermore, the detonation processes are different, with varying charging structures. Therefore, it is essential to select charge structures based on the specific site conditions.
3. The proposed simulation and analysis method was validated though onsite blasting experiments. The fragmentation effects from the simulation and image analysis were used to provide recommendations for optimizing rock blasting parameters.  
It is important to note that this study aims to propose a simulation and analysis method for rock blasting fragmentation that overcomes the limitations of existing numerical simulation methods. Further research is needed to explore the variation trends of rock fragmentation in the presence of joint surfaces and to examine fragmentation characteristics in areas with uneven rock distribution.  
Methodology, Q.Y.; Validation, Q.Y.; Formal analysis, H.Z.; Investigation, X.G.; Data curation, W.J.; Writing--original draft, Q.Y. and H.Z.; Writing--review and editing, Q.Y., H.Z., X.M., Q.G. and Y.J.; Supervision, Q.G.; Funding acquisition, Q.G. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (52379096), the State Key Laboratory of Precision Blasting, Jianghan University (BL2021-08), Research Funds for the Interdisciplinary Projects, CHU (300104240925), the Innovation Capability Support Program of Shaanxi (2023-CX-TD-35), and the Key Research and Development Program of Shaanxi (2023 KJ-159).  
Figure 28: Grading curves of rock fragments under different blast hole spacings.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors state that they have no conflict of interest with respect to the research, authorship, and publication of this article.
## 4 Hydrodynamic Characteristics of FOWT-SFFC
### Natural Periods and RAOs of Motions
The natural heave and pitch periods of FOWT-SFFC without mooring forces are listed in Table 6. They are 26.85 s and 29.36 s respectively, consistent with the values in Table 5, though slightly larger. As shown in Figures 5 and 6, though the heave RAO of FOWT-SFFC at its natural period is the largest among three concepts, it is smaller than OC3 Hywind and OC4 DeepCwind at wave periods ranging from 5 s to 20 s. The main wave energy in nature is contained in this range and therefore no significant heave motion will be induced for FOWT-SFFC. By contrast, the natural heave period of OC4 DeepCwind is at about 17.5 s, thence a large heave is prone to be excited by long waves like swell. As for pitch, the RAO of FOWT-SFFC is the smallest among all three concepts, not only at the natural period, but also at the periods ranging from 5 s to 15 s. The pitch RAO of OC4 DeepCwind is the smallest when the wave periods are between 15 s and 20 s. It is emphasized that both heave and pitch natural periods (26.85 s and 29.36 s) are well outside the dominant wave frequency range. Overall, the seakeeping performance of FOWT-SFFC is the best among three concepts. It should be mentioned that the RAOs in Figures 5 and 6 reflect the linear input-output relationship of waves and heave/pitch response in the frequency domain. Therefore, the power spectrum of heave/pitch response under waves can be established in terms of wave spectrum and RAOs (Equation (3)). Nevertheless, this relationship is approximate because the floating system actually consists of slender members like side columns and braces (Table 1) subject to nonlinear Morison wave force [46]. \[S_{response}(f)=S_{wave}(f)|\text{RAO}|^{2} \tag{3}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Heave** & **Pitch** \\ \hline Natural Period (s) & 26.85 & 29.36 \\ Natural Frequency (Hz) & 0.0372 & 0.0341 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Damped natural period of the FOWT-SFFC without mooring forces. Figure 5: Heave response amplitude operators (RAOs) of three concepts of offshore wind turbines.
## 4 Data Preparation  
### Tunnel Lining Apparent Data Collection  
A metro tunnel inspection vehicle independently developed for data collection by the research team was used to obtain images of metro tunnel linings with apparent defects. This equipment consists of a FARO 3D laser scanner (Figure 9a, Artec 3D, Luxembourg), an MDC mobile detection rail car (MDC Aluminium Foot Rest Railway Components, Phagwara, Punjab) (with dimensions of 1620 mm \(\times\) 565 mm \(\times\) 540 mm, suitable for a track gauge range of 1425-1445 mm, and weighing approximately 30 kg, with a cruising speed of 2 km/h in this experiment), a speed sensor, an industrial computer, a power supply, and other components. During the operation phase, the rotating lens built into the laser scanner sensor emits laser pulses at the target object during high-speed rotation. The laser receiver captures the reflected information, including the spatial coordinates of the point cloud and RGB information. The apparent orthophoto grayscale map of the metro tunnel lining is then obtained via operations such as point cloud projection, interpolation calculations, and grayscale conversion. Additionally, the pulse information sent by the speed sensor is converted into speed and mileage information by the mileage recording module and synchronously transmitted to the industrial computer to obtain the corresponding positional information of the image data.  
The data depicting apparent defects were collected by the research team from two sections of metro tunnels in Shenzhen, Guangdong Province, China. Both sections were constructed using the shield tunneling method, with the tunnel structures designed as single-track circular tunnels, each with a diameter of 5.4 m. The tunnel linings were made of reinforced concrete segments, and the tunnels were buried at depths ranging from 25 to 35 m. The surrounding geological environment includes residual cohesive soil and fully weathered tufaceous sandstone, with overall poor stability of the surrounding rock. The strata in the tunnel areas are highly permeable with good water-bearing properties. The field experiment is shown in Figure 9b. The total length of the collected data segment is 2.4 km, and apparent orthophoto grayscale images (Figure 9c) of the metro tunnel linings were obtained. These images were used as raw data for subsequent defect detection and extraction.
## 3 The Conceptual Framework Proposed
### Analyzing the Real-Use Environments and Implementing Living Labs
#### 3.2.2 Technological Context  
The technological context of product use refers to the technical environment in which a product is used or operates. Recently, many new products such as NCPs operate in conjunction with other devices or systems to provide enhanced functionality, thanks to the advancement of network technology. It is thus more important than ever to understand the technological real-use environment of a product.  
As seen in Figure 2, when a product is used in connection with other products, the product is either a finished product or a part. Certainly, there are many finished products that are used independently of other products, such as electric shavers, respirator masks, and curtains. On the other hand, more and more products are being connected with other products or devices, working dependently. For example, unlike in the past, Bluetooth speakers have spread worldwide. Home appliances such as washing machines and refrigerators are now connected to mobile devices through IoT technologies.  
Using the framework in Figure 2, the key elements of the technological real-use environment of a product can be drawn. In the case of a product that requires compatibility with a specific technology, the compatible products are an important element of the real-use environment of the product. Consider the case that computers and beam projectors are connected by the USB-C to HDMI cable. Also, some products are only compatible with certain brands. Next, for the products with higher connection levels, such as IoT-based home appliances and sensor-based measuring devices, those products function like a single system with other products or devices. Thus, the technological real-use environments of those products often consist of sensors, network devices, and other peripherals. Lastly, the framework also suggests that the information about the specification of a product and its technical statues (e.g., working, defected) is also important to understand the technological real-use context of the product. Such information is a common requirement for the understanding, no matter whether the product operates dependently or independently.  
Figure 1: The Framework for the Context Analysis of a Product (User Context).
## 2 Problem Description and Model
### Notations and Variables  
The inputs of the SAOSSP contain task requirements and satellite parameters, and the parameters of VTWs are calculated according to the target location and the orbital parameters of the AOS. The output solutions contain the permutation of executable tasks and the corresponding observation action sequence of the AOS. The variables which are used to describe the SAOSSP are defined in Table 1.  
In addition to the above variables, several other concepts and variables are involved in this problem. Firstly, the attitude adjustment on each axis can be simplified as \"constant acceleration--constant speed--constant deceleration\" or \"constant acceleration--constant deceleration\" [38], and the transition time \(tranT(\cdot)\) is formulated as below:  
\[tranT(\Delta A)=\left\{ \begin{array}{c}0,\Delta A=0\\ 2\cdot\sqrt{\frac{\Delta A}{a}}+ST,0<\Delta A\leq\frac{\omega^{2}}{a}\\ \frac{\Delta A}{\omega}+\frac{\omega}{a}+ST,\Delta A>\frac{\omega^{2}}{a}\\ \end{array} \right., \tag{1}\]  
where \(\Delta A\) is the adjusted angle value on one axis, \(a\) is the angular acceleration value, \(\omega\) is the maximum angular velocity value, and \(ST\) is the stability time after adjustment. If the roll angle changes by \(\Delta r\) and the pitch angle changes by \(\Delta p\), the total transition time \(\Delta T\) will be \(\Delta T=tranT(\Delta r)+tranT(\Delta p)\). Secondly, in the VTW \(w_{i}^{k}\), the relationship between the pitch angle \(pa_{i}^{k}\) and the observation start time \(ost_{i}^{k}\) can be formulated as a linear function [39]:  
\[pa_{i}^{k}=a_{i}^{k}\cdot ost_{i}^{k}+\beta_{i}^{k}, \tag{2}\]where \(a_{i}^{k}=\frac{-2\cdot p_{max}}{e_{i}^{t_{i}}-d_{i}-st_{i}^{k}}\), and \(\beta_{i}^{k}=\frac{et_{i}^{k}-d_{i}+st_{i}^{k}}{ed_{i}^{t_{i}}-d_{i}-st_{i}^{k}} \cdot p_{max}\). Thirdly, the image quality is determined by the observation start time [4], so we respectively define \(est_{i}^{k}\) and \(lst_{i}^{k}\) as the earliest start time and the latest start time meeting the minimum image quality requirement in the VTW \(w_{i}^{k}\). In addition, the earliest start time \(est_{i}^{k}\) corresponds to the maximum pitch angle \(px_{i}^{k}\) meeting the minimum image quality requirement, and the latest start time \(lst_{i}^{k}\) corresponds to the minimum one \(px_{i}^{k}\). All of them can be formulated as below:  
\[est_{i}^{k}=st_{i}^{k}+(q_{i}-1)\cdot\frac{et_{i}^{k}-d_{i}-st_{i}^{k}}{18}, \tag{3}\]  
\[lst_{i}^{k}=et_{i}^{k}-d_{i}-(q_{i}-1)\cdot\frac{et_{i}^{k}-d_{i}-st_{i}^{k}}{ 18}, \tag{4}\]  
\[px_{i}^{k}=a_{i}^{k}\cdot est_{i}^{k}+\beta_{i}^{k}, \tag{5}\]  
\[pn_{i}^{k}=a_{i}^{k}\cdot lst_{i}^{k}+\beta_{i}^{k}. \tag{6}\]  
Finally, three decision variables are defined to establish the mathematical model of the SAOSSP, which is shown in the following formulae:  
\[x_{i}^{k}=\left\{ \begin{array}{c}1,t_{i}\text{ is successfully executed in }w_{i}^{k}\\ 0,\text{otherwise}\end{array}
## 5 Test Experiments
### Transit Offset Error Calibration  
Tests were carried out according to the calibration process mentioned in Section 4.2. The telecentric measurement system was placed as shown in Figure 10, and the emitter and receiver were vertically placed. As an explanation of the process, the transmitter emits collimated lighting with a green LED to project a shadow on the CMOS sensor in the receiver, and the receiver captures the image of test rod A. The initial position of the central axis of test rod A is recorded as \(E\). The standing axis rotates \(180^{\circ}\), and the green LED light emitted from the transmitter is projected to test rod B; the position of the central axis of test rod B is recorded as \(F\). The offset (\(EF\)) between the central axis of test rod A and B before and after the standing axis rotation was 0.074 mm. Therefore, the transit offset error (\(t\)) was 0.04 mm according to Equation (7).
## 3 Forecasting Approach
### The Proposed Hybrid Methodology
#### 3.1.1 Step I: SOM  
A self-organizing feature map (SOM) is first applied to the data in order to split the database into several disjoint groups, called clusters, with similar statistical distribution. The assumption is that, in the next step, a forecasting technique can predict more accurate each cluster instead of the whole database. Each cluster works independently in the second and third step. In such cases, a single SARIMA and SVR models are applied independently after decomposing the heterogeneous data into smaller homogeneous regions. A predefined number of clusters, \(c\), are selected, before starting the SOM algorithm, to assess and compare the performance of different solutions.  
An experimental framework was developed in order to select the optimal number of past values (\(nc\)) of the input vector (the number of input neurons). Thus, a database of inputs was designed as in Equation (7):  
\[x_{i}=\left[y_{t},y_{t-1\cdot k},y_{t-2\cdot k},\ldots,y_{t-nc\cdot p\cdot t} \right]^{T}, \tag{7}\]  
where \(t\) is each sample in the daily time series from 2010 to 2014 and \(k\) means the sample period explained graphically in Figure 1 (\(k=1\) or \(k=7\) days). Each row is arranged recursively using different lagged terms (as in an autoregressive window).  
A large number of different experiments were performed modifying the input vector size and the number of iterations. In order to consider the inherent randomness of the process, 20 repetitions of each experiment were carried out.  
Figure 2: The overall process scheme of the SOM-SARIMA-SVR approach.
## 4 Role of URLLC in Operating IoT
### URLLC and Massive Device Connectivity  
Present mobile services and specifications are not completely equipped to deliver URLLC cost-effectively at scale [35]. Furthermore, they lack the capacity to deliver a reliable low latency communication to multiple users at the same time. It is particularly difficult to ensure link-level reliability and latency over a wide area and in a remote scenario, as shown in Figure 4. As wide-area  
Figure 8: Basic IoT operation over tactile internet on massive multiple-input multiple-output (MIMO).  
Figure 7: Options of the 5G architecture, multiple radio access technologies (RATs) [28].  
cases involve many elements such as transitional nodes, backhaul, core/cloud, and fronthaul, they can play a vital role in degrading latency.  
However, the resources such as energy and computing power of IoT devices also play a vital role when operating over URLLC. To meet the latency requirements for URLLC, the IoT devices are forced to utilize excessive power and processing ability that is not appropriate for the life span of IoT devices. However, most of the IoT devices have limited resources [36].
## 2 Materials and Methods
### Method Validation
#### 2.7.2 Linearity and Sensitivity  
System and method linearity were tested for all phytoestrogens covering a wide range, from 0.16 to 16,000 ng/mL, due to the different concentrations expected in food, serum and urine samples. The final concentration of the standards ranged from 0.008 to 1200 ng/mL in food and urine and 0.02 to 2000 ng/mL in serum. Linearity was performed three times, and each level of concentration was injected in triplicate. Area, phytoestrogens concentrations and correlation coefficients were obtained from the generated chromatograms. Peak area of the phytoestrogens and standards were plotted against the nominal concentrations of the calibration standards. Linear regression analysis generated calibration curves with the equation:  
\[y=mx+b, \tag{1}\]  
where \(y\) is the peak area, \(x\) the concentration of the targeted phytoestrogen, \(m\) the slope and \(b\) the intercept of the regression line. Linearity was evaluated by observing correlation coefficients (\(t^{2}\)) for the system and method. The limits of detection (LOD) and quantification (LOQ) of the method were defined as the lowest concentration with a signal-to-noise ratio of 3 and 10, respectively.
## 3 Lightweight Cryptography for Smart Objects
### Lightweight Cryptographic Hash Functions
#### 3.3.4 Quark  
The QUARK [51] hash family comes with three instances: U-QUARK, D-QUARK, and S-QUARK, with hash sizes of 136, 176, and 256 bits, respectively. QUARK, like PHOTON and SPONGENT, is based on a sponge construction. The QUARK hash family is optimized for hardware implementation and, as stated by the authors, software implementations should instead rely on other designs. QUARK has a bigger footprint than PHOTON and SPONGENT, but shows higher throughput than SPONGENT and higher security than PHOTON.
## 3 Results
### Transmon Spectroscopic Characterization
We obtain \(\omega_{01}/2\pi=v_{01}=6.4296\) GHz and hence \(\Delta_{01}/2\pi=v_{01}-v_{r}=-839\) MHz. We calculate the coupling \(g_{01}\), considering Equation (3) for \(n=0\) obtaining \(g_{01}/2\pi=92.5\pm 1\) MHz. Since \(g_{12}=\sqrt{2}g_{01}\)[18], using Equation (3) with \(n=1\), we can calculate \(\Delta_{12}/2\pi=-1260\pm 40\) MHz. We extract the system anharmonicity as \((\Delta_{01}-\Delta_{12})/2\pi=v_{01}-v_{12}=\alpha=421\pm 84\) MHz. At this point it is straightforward to calculate the capacitance of the transmon inverting [18]:  
\[h\alpha=E_{c}=\frac{e^{2}}{2C} \tag{5}\]  
where \(h\) is the Planck constant, \(E_{c}\) is the charging energy, \(e\) is the electron charge, and \(C\) is the capacitance. We obtain \(C=46\pm 5\) fF. From the charging energy, we estimate the critical current of the Josephson junction using [18]:  
\[\hbar\omega_{01}=\sqrt{8E_{c}E_{f}} \tag{6}\]  
This couples with a Josephson energy \(E_{f}=\Phi_{0}l_{c}/2\pi\). The value obtained by inverting is \(l_{c}=24.7\) nA and a Josephson inductance \(L_{J}=\Phi_{0}/(2\pi l_{c})=13\) nH. The Hamiltonian parameters extracted by the analysis of the experimental data are in good agreement with the output values of the electromagnetic simulation of the qubit-resonator system (see Section 4). We used ANSYS 2023 R1 software to simulate the qubit-resonator coupling factor \(g_{01}/2\pi\) and the qubit capacitance. We obtain \(g_{01}^{sim}/2\pi=97\) MHz and \(C^{sim}=57\) fF. A collection of the experimental parameters reported in this and the following section is reported in \[\chi=\chi_{01}-\frac{\chi_{12}}{2} \tag{2}\]0