esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
###### Abstract  
In this paper, a novel mechanical structure for a gamma-ray monitor (GRM) is designed for a small satellite payload. Its structural stiffness, strength and dynamic response are directly related to the performance of the novel GRM, which must meet the static and dynamic characteristic requirements of the structure in a harsh vibration environment. The static and dynamic simulation of the structure are carried out by finite element method (FEM), and the mechanical structure response laws of the novel GRM are  
satellite to verify the feasibility of the structural design. [PERSON] [15] used MSC.NASTRAN software to analyze the structure of Hubble Space Telescope's Wide-Field/Planetary camera (WF/PC). [PERSON] [16] performed part-level structural analysis on Soft X-ray Telescope (SXT). [PERSON] [17] estimated the mechanical performance parameters of the micro-perforated optical plate of the WXT payload on EP satellite, and carried out simulation and verification by finite element analysis. [PERSON] [18; 19; 20] performed a detailed mechanical analysis of the focusing mirror module on NASA's International X-ray Observatory.  
In order to meet the requirements of a small satellite payload mission, in this paper, a silicon photomultiplier tube (SiPM) is proposed to replace the traditional photomultiplier tube (PMT). A novel GRM mechanical structure with smaller volume and lighter weight is designed by adopting the design scheme of a central support tube structure.
## 3 Methodology
### Time-Frequency Spectrum Rearrangement Algorithm  
Time-frequency energy is seriously diffused near the real energy axis as with the Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) methods. As a result, an inaccurate understanding of the time-frequency energy signal is developed. The rearrangement algorithm rearranges and compresses the time spectrum energy of signals according to their energetic center of gravity, thus obtaining more intensive energy [54]. The rearrangement steps are as follows:  
Step 1: The expression for calculating the rearrangement operator \(<\hat{\tau}_{f},\hat{\omega}_{f}>\) is:  
\[\hat{\tau}_{f}(\omega,t)=t-\frac{1}{2\pi}\hat{\omega}_{\omega}\text{arg}W_{f} (\omega,t) \tag{2}\]  
\[\hat{\omega}_{f}(\omega,t)=\frac{1}{2\pi}\hat{\omega}_{\omega}\text{arg}W_{f} (\omega,t) \tag{3}\]  
where \(\hat{\tau}_{f}\) can be regarded as group delay and \(\hat{\omega}_{f}\) can be regarded as instantaneous frequency.  
Step 2: After the rearrangement operator is obtained, rearrange the time spectrum energy, namely \(<\omega,t>\rightarrow\)\(<\hat{\tau}_{f},\hat{\omega}_{f}>\), which can be expressed by the following formula:  
\[R_{f}(\omega,t)=\int_{R}\int_{0}^{+\infty}\Bigl{|}W_{f}(\omega,t)\Bigr{|}^{2} \delta\Bigl{(}\omega-\hat{\omega}_{f}\Bigr{)}\delta\Bigl{(}t-\hat{\tau}_{f} \Bigr{)}d\omega dt \tag{4}\]  
The original energy axis will get closer to the real energy axis through the Rearrangement Algorithm, which can better reflect the time-frequency characteristics of non-stationary signals. From Formula (4), it can be noted that the spectrum will lose its phase information and the reconstruction of the signal will fail after rearrangement, which is one of the significant defects of this method.
# Review of V2X-IoT Standards and Frameworks for ITS Applications
## 3 Commercial V2X Products
### Commercial V2X Devices  
As V2X systems are only beginning to develop more intensely, there are not many commercial devices on the market yet. Tables 3-5 contain comparison of the currently available commercial V2X devices. The data collected show that these devices are basically divided into three types, usually of different purpose: devices for communication with the road side unit (RSU), which are most often mounted on streets, traffic lights or other near the street infrastructure; the on-board unit (OBU); and small-scale mobile devices or smart device attachments whose primary purpose is to inform pedestrians, cyclists and motorcyclists of current and potential traffic accidents. However, the market is also trying to create universal devices, combining RSU and OBU into one system. An example of such a system could be the Commsignia V2X Evaluation Kit. Another important aspect can also be seen in this comparative analysis. In practice, almost all commercial V2X devices have communication modules only for DSRC communication technologies and consists of two main parts: (1) a radio frequency (RF) transceiver, mainly implementing the IEEE 802.11p standard with the PHY layer, and (2) a connection processor that implements the standard IEEE 802.11p/IEEE 1609.4 MAC layer and other features as well as launching the platform operating system, supporting security, various interfaces and network services in addition to allowing for the installation of user-generated V2X applications, etc. Some devices use a separate application processor to implement the latter functions. Meanwhile, data protection functions are implemented in two ways: using software or hardware-based security solutions. Furthermore, all platforms have GPS modules that are designed for localization, time and synchronization settings. In addition to the hardware of the V2X devices, the software is just as important. Most of these devices are based on Linux operating systems with the SSH protocol for safe data exchange between the platform and the user. In addition, some manufacturers also provide a graphical user interface for the device and real-time monitoring. To facilitate the development of V2X consumer programs, some manufacturers devices also provide an application development kit (SDK) which, together with application programming interfaces (APIs), allows the user to programmatically implement various V2X devices functions. **Note:** The “-” symbol in the cells indicates that data could not be found or are not in the system. \end{table}
Table 3: Commercial V2X devices currently on the market—Part I.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 4 Security Analysis
### Mutual Authentication
#### 4.1.1 CS Verifies the Identity of Ui through Checking D3? = h(IDi\(\|\)PIDi\(\|\)ru)  
In the user registration phase, \(CS\) computes \(A_{i}=h(PID_{i}\|ID_{cs}\|x)\) and \(B_{i}=h(ID_{i}\|x)\) for \(U_{i}\), and two parameters are only known by \(CS\) and \(U_{i}\). When \(U_{i}\) uses \(A_{i}\) to hide the random number \(r_{u}\) in the authentication phase, i.e., \(D_{1}=A_{i}\oplus r_{u}\), \(CS\) can use \(h(PID_{i}\|ID_{cs}\|x)\) to extract \(r_{u}\). Finally, \(CS\) can verify the identity of \(U_{i}\) by equation \(D_{3}=h(ID_{i}\|PID_{i}\|r_{u})\).  
1.2 CS Verifies the Identity of Sj through Checking D7? = h(SIDj\(\|\)PSIDj\(\|\)PSIDj\(\|\)PSIDj\(\|\)rs\(\|\)D6)  
In the cloud server registration phase, \(CS\) computes \(A_{j}=h(PSID_{i}\|ID_{cs}\|x)\) and \(B_{j}=h(SID_{j}\|x)\) for \(S_{j}\), and two parameters are only known by \(CS\) and \(S_{j}\). When \(S_{j}\) uses \(A_{j}\) to hide the random number \(r_{s}\) in the authentication phase, i.e., \(D_{4}=A_{j}\oplus r_{s}\), \(CS\) can use \(h(PSID_{j}\|ID_{cs}\|x)\) to extract \(r_{s}\). Finally, \(CS\) can verify the identity of \(S_{j}\) by equation \(D_{7}=h(SID_{j}\|PSID_{j}\|PSID_{j}\|r_{s}\|D_{6})\).  
4.1.3 Sj Verifies the Identity of CS through Checking D10? = h(SKs\(\|\)D8\(\|\)D9\(\|\)Bj)  
Because \(B_{j}\) is only shared between \(S_{j}\) and \(CS\), they only have the capability of computing \(h(SK_{s}\|D_{8}\|D_{9}\|B_{j})\). Therefore, \(S_{j}\) can verify the identity of \(CS\) by equation \(D_{10}=h(SK_{s}\|D_{8}\|D_{9}\|B_{j})\).  
1.4 Ui Verifies the Identity of CS through Checking D14? = h(SKu\(\|\)D12\(\|\)D13\(\|\)Bi)  
Because \(B_{i}\) only shares between \(U_{i}\) and \(CS\), they only have the capability of computing \(h(SK_{u}\|D_{12}\|D_{13}\|B_{i})\). Therefore, \(U_{i}\) can verify the identity of \(CS\) by equation \(D_{14}=h(SK_{u}\|D_{12}\|D_{13}\|B_{i})\).
# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## Materials and Methods
### Cotton-Specific Photosynthetic Parameters  
Cotton leaf is characterized by a very high light saturation point (LSP). In well-watered conditions, the cotton LSP can reach 2304 \(\mu\)mol m\({}^{-2}\cdot\)s\({}^{-1}\), and the maximum CO\({}_{2}\) assimilation rate can reach 43.1 \(\mu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) according to the experiments by [PERSON] et al. [62]. In photosynthesis model, V\({}_{\rm cmax}\) and J\({}_{\rm max}\) are two important parameters to describe the photosynthetic rate at the leaf level. V\({}_{\rm cmax}\) is the maximum rate of carboxylation for Rubisco-limited (or light-saturated) conditions, and J\({}_{\rm max}\) is the maximum rate of electron transport for the light-limited condition [60]. According to the measurements of by [PERSON] et al. [63], cotton has a temperature optimum near 40 \({}^{\circ}\)C for V\({}_{\rm cmax}\) and 35 \({}^{\circ}\)C for J\({}_{\rm max}\). The V\({}_{\rm cmax}\) and J\({}_{\rm max}\) at the optimum temperatures are four and two times higher than their respective values at 25 \({}^{\circ}\)C [64]; therefore, the optimum temperatures for maximum carboxylation and electron transport were set to 313.15 K and 308.15K, respectively, in BEPS. The V\({}_{\rm cmax}\) at 25 \({}^{\circ}\)C varies with leaf nitrogen and phosphorus [65; 66]. Since such nutrition information is not reliably available from Sentinel-2, the cotton V\({}_{\rm cmax}\) at 25 \({}^{\circ}\)C was set to 120 \(\mu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) to be consistent with our previous modeling for crops [52]. For BEPS simulation, the CO\({}_{2}\) concentration data were obtained from [[https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html](https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html)]([https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html](https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html)).
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 4 Proposed DEOR Algorithm
### Construction of Candidate Forwarding Set
```
Input:\(C_{\text{up}}(i)=\varnothing\) Output: The candidate forwarding set \(C_{\text{up}}(i)\)
1:for each node \(n_{i}\in N\)do
2: Define the Forwarding Zone \(F_{i}\) using Equation (5)
3: Get the subset \(Z_{i}\) using Equation (6)
4:endfor
5:for each node \(n_{j}\in N_{i}\)do
6: Get the subset \(N(m_{i})\) using Equation (7)
7: Get the subset \(N(\varepsilon_{i})\) using Equation (8)
8:if\(n_{j}\in Z_{i}\)&&\(n_{j}\in N(m_{i})\)&&\(n_{j}\in N(\varepsilon_{i})\)
9:then add \(n_{\
u}\to C_{\text{up}}(i)\)
10:endif
11:endfor
12:if\(C_{\text{up}}(i)!=\varnothing\)
13:then\(C(i)=C_{\text{up}}(i)\)
14: switch to Algorithm 3
15:else
16: switch to Algorithm 2
17:endif
```  
**Algorithm 1**Construct the Candidate Forwarding Set
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 3 Parameter Identification Method for the Hyper-Viscoelastic Model  
In the previous section, the parameter identification method for the hyperelastic model was explained in detail, this model is suitable for the numerical simulation of natural rubbers with low damping. As for the high damping rubbers, they can be simulated by combining the Ogden model with the generalized Maxwell model. In the following, theories of the hyper-viscoelastic model and the parameter identification method for the model were expounded.
## 3 Results and Discussion  
### Design and In Silico Evaluation of a Paneibacillus_ sp. _Specific Probe_  
To detect/identify _Paenibacillus_ sp. in a reliable way is essential to design a DNA-FISH probe with a good performance, for it is required that the probe holds a low probability of hairpin and self-dimer formation and a high affinity and level of specificity and hybridization efficiency [34; 35]. The affinity of the DNA-FISH is the propensity of the probe to bind to its target under given hybridization conditions (melting temperature, GC content and probe length [36]). The DNA-FISH probe specificity is its suitability to bind only to its target.  
To design a DNA-FISH probe for _Paenibacillus_ sp., the DECIPHER program was used and the ten best candidates in terms of score and specificity given by the DECIPHER program (data not shown) were selected for their in silico evaluation (Table 1).  
Taking into consideration all the criteria described above for obtaining a DNA-FISH probe with a good performance, the Pab489 probe proved to be the best candidate to identify _Paenibacillus_ sp., i.e., its GC content was shown to be less than 60% (58.8%), nopossibility of hairpin and self-dimer formation was predicted, and it presented the highest theoretical hybridization efficiency (99.99%), combined with a high number of matches for _Paenibacillus_ sp., 444 matches out of 500 (Table 1).
## 7 Conclusions  
This study proposes a methodology for the analysis of sEMG data on different patients with an innovative methodology that integrates an electronic system and soft-computing techniques. In particular, the realization of the electronic circuit allowed the acquisition of the information of superficial electromyography using a non-invasive approach on several healthy subjects and subjects with potential neuropathy. The sEMG data from the electrodes placed on the patient's biceps are appropriately collected from the acquisition circuit. The acquired signals are sent via the GPIO protocol to a Raspberry Pi processor, which has the task of converting files to.txt format and sending them to a cloud platform through communication with a GSM module. Subsequently, the properly pre-processed and normalized data are used for the training of an advanced 1D-CNN, which aims to identify the trend of signals and classify healthy subjects and subjects with potential neuropathy. Overall, the method proposed achieved a good result in terms of accuracy.  
Future improvements could lead to studies that include collaborations with medical institutions to test the model on real clinical data and evaluate its effectiveness in a clinical context. Integrating other signal modalities, such as electrocardiograms (ECGs) or electroencephalograms (EEGs), could provide a more comprehensive view of the neuromuscular status of the subject and further improve the diagnostic precision of the model.  
Conceptualization, F.L. and D.P.; methodology, S.A.P. and G.O.; software, D.P. and G.A.; validation, F.L. and M.V.; formal analysis, S.A.P.; investigation, D.P.; resources, F.L.; data curation, G.A.; writing--original draft preparation, F.L. and D.P.; writing--review and editing, G.A. and M.V.; visualization, M.V.; supervision, F.L.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study did not require ethical approval. Review and ethical approval were waived for this study because it did not involve the administration of tests or experiments on humans, but only measurement campaigns on sEMG signals.  
The data are contained within this article.  
The authors declare no conflicts of interest.
# A Two-Step High-Order Compact Corrected WENO Scheme
## 2 Numerical Methods
### Conservative Scheme  
To obtain the derivative at the \(j\)-th point (or \(x_{j}\)), the numerical flux \(\hat{f}\) defined at the midpoints are used in the following equation to ensure the conservation:  
\[\hat{f_{j}}=\frac{\partial}{\partial x}f(x_{j})=\frac{\hat{f_{j+\frac{1}{2}}}- \hat{f_{j-\frac{1}{2}}}}{h_{j}} \tag{2}\]  
The flux function can be calculated using the values of original function \(f\) through an implicit definition as follows:  
\[\hat{f_{j}}=\hat{f}\big{(}u\big{(}x_{j},\ t\big{)}\big{)}\ \equiv\frac{1}{h_{j}}\int_{x_{j-\frac{1}{2}}}^{x_{j+\frac{1}{2}}}f( \xi)d\xi. \tag{3}\]  
where \(x_{j-\frac{1}{2}}\) is the midpoint between \(x_{j-1}\) and \(x_{j}\) and \(h_{j}=x_{j+\frac{1}{2}}-x_{j-\frac{1}{2}}\).  
We can further define a primitive function \(H\) of \(f(\xi)\), and its value at the midpoints can be calculated by  
\[H_{j+\frac{1}{2}}=H\Big{(}x_{j+\frac{1}{2}}\Big{)}=\int_{-\infty}^{x_{j+\frac {1}{2}}}f(\xi)d\xi=\sum_{i=-\infty}^{j}\int_{x_{i-\frac{1}{2}}}^{x_{i+\frac{1} {2}}}f(\xi)d\xi=\sum_{i=-\infty}^{j}\hat{f_{i}}h_{i}. \tag{4}\]  
Then, the derivative of the primitive function at the midpoint equals the flux function value; in other words,  
\[H^{\prime}_{j+\frac{1}{2}}=\hat{f_{j+\frac{1}{2}}}. \tag{5}\]  
Substituting Equation (5) into Equation (2) yields  
\[\hat{f_{j}}=\frac{H^{\prime}_{j+\frac{1}{2}}-H^{\prime}_{j-\frac{1}{2}}}{h_{j }}. \tag{6}\]  
More details regarding the conservative difference schemes and the flux functions can be found in Reference [4].
# Virtual Replication of IoT Hubs in the Cloud:
## 3 Architecture
### Operational Scenarios
#### 3.2.3 Pushing Resources  
Sometimes, the memory constraints of smart objects make it unfeasible to let them act as CoAP servers. In this case, described in Figure 5, the smart object acts as a CoAP client and sends CoAP requests (POST and PUT) to the IoT Hub, which plays the role of origin server, by maintaining resources on behalf of the clients. If the smart object is a CoAP client, according to the semantics of CoAP methods, the following message flow takes place: (1) the smart object sends CoAP POST  
Figure 4: Message flow for the Observing scenario: a CoAP client requests a resource to the Cloud platform using the CoAP Observe option. The Cloud platform internally selects a suitable IoT Hub replica and forwards the request. The observe request is then forwarded to the IoT Hub and to the smart object thus creating an “observe chain”. Resource updates are then sent from the smart object back to the IoT Hub, then to the replica, and finally to the CoAP client. Other replicas (which are not in the path of the request) are kept in sync using the synchronization protocol.  
requests to the IoT Hub in order to create resources and CoAP PUT requests in order to change their value. When the IoT Hub receives POST and PUT requests, after properly handling these requests it accordingly stores them and (2, 2\({}^{\prime}\) and 2\({}^{\prime\prime}\)) synchronizes them on the replica. When (3) an external client sends an HTTP or a CoAP request to the Cloud platform front-end, it (4) the latter forwards the request to one selected replica of the IoT Hub. Since the replica is synchronized with the IoT Hub, (5, 6) the replica can respond immediately with the stored representation of the resource.
## 1 Introduction
The desire advance in the actual knowledge is to produce a trustworthy and with a small grid-spacing weather forecast, including precipitation. At this scope, in the present work, a valley slope in the Northern Ossola Valley (Lepontine Alps) was selected. The Ossola region is characterised by heavy instability phenomena and recurrent debris flow events, due to meteorological conditions coupled with geological features (e.g., widespread loose debris and fractured rocks) and hydrographic basins morphology ([23], and reference therein). Particular attention was paid to the last two debris flow events affecting the Croso stream basin in the Cairasca Valley in 2000 and 2019. Concerning the meteorological background, the 2000 and 2019 events were triggered by different kinds of rainfall episodes. In the first case, prolonged and heavy rainfalls occurred, while during the second one, intense and localised precipitations affected the investigated area. During the most recent one, happened on the 12 August 2019 a series of debris flows affected the slopes of the Cairasca Valley, provoking temporarily interruptions along the Cairasca stream. The local road was also interrupted, fortunately without any casualty. The Croso stream basin was hence characterised also from a morphometric, geological and geomorphological perspective to understand the predisposing and preparing factors inducing debris flows affecting the slope. In more detail, the investigated slope was analysed from a morphometric point of view, and the Italian Web-GIS based database and historical archives on hydrogeological instabilities were also examined. The main aim of the present work is analysing the precipitations that triggered the two debris flow events within the Croso stream basin, using the WRF model adequately adapted to work at high-spatial-resolution (500 m of grid-spacing) in mountainous terrain. The WRF meteorological model was set-up as previously tested by [24], and applied to the two episodes. The simulated results were compared with observations at five meteorological stations in the studied area and, finally, related to the geological background of the slope.
# X-ray Photoelectron Spectroscopy in Mineral Processing Studies
## 4 Towards in Situ XPS
### Sampling and Exploring Natural Mineral Dispersions  
The majority of XPS studies have been conducted with single minerals or their simple mixtures, whereas real ores, industrial products, and their pulps are very complex in terms of chemical and phase composition, with low concentrations of valuable sulfides and abundant gangue minerals, wide particle size distribution, and presence of solutes and colloids. These entities can interact with sulfide mineral surfaces modifying their characteristics and behavior, and could essentially complicate XPS studies by screening the surface of interest and causing inhomogeneous electrostatic charging. [PERSON] [17] has published a sampling procedure aimed at prevention of ex situ oxidation of the mineral surfaces and rejection of pulp solutes and slimes. Slurry specimens were purged with nitrogen, followed by their sealing and freezing to arrest possible reactions until examination. After thawing, the sample was deslimed through several steps of sonication/decantation, and the aqueous solution was removed after filtering. Then the thickened slurry was loaded into the spectrometer and remaining water was evaporated without contact with air during the pumping. This, or a slightly modified protocol, has been utilized in numerous studies on sulfide minerals and ores, and their interaction with flotation reagents. It can be combined with freeze-drying or fast-freezing techniques [171] to retain volatile species.  
Meanwhile, ultrasonic treatment and discarding the slimes may result in the loss of important information about real mineral surfaces in their dispersions. In particular, it was demonstrated that smaller sulfide mineral particles are more oxidized and attract more ferric hydroxides after the comminution in steel mills than coarser ones [192]. The grinding of sulfide ores was found to produce ultrafines in quantities comparable with, or exceeding the amounts of flotation reagents [193; 194], particularly serpentine gangues [194; 195; 196] which bear positive charge and adhere to negatively-charged sulfide minerals, suppressing their flotation. Thus, correct procedures of sampling and handling of mineral dispersions for XPS exploration still need to be elaborated.  
The heterogeneous electrostatic charging can emerge as a serious problem when studying multiphase particulate dispersions with abundant dielectric phases. Usually, an electron flood gun is utilized to eliminate the sample charging [197], but this could be insufficient when bright synchrotron radiation is applied in HAXPES or high lateral resolution XPS and SPEM. Strong charging effects were also observed for lab-based cryo-XPS of moderately hydrophobic particles of single minerals [102; 103]. Therefore, more sophisticated methods such as a high-energy electron gun [198], low-energy ion beam or gas ionization in AP-XPS [172; 173; 174; 175] would be necessary.  
Table 1 briefly summarizes the pros and cons of the photoelectron spectroscopy techniques and examples of their utilization in studies on specific sulfide minerals.