esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Geological Condition  
### Geological Conditions of the Working Face  
Shaanxi Province Cuijiagou Coal Mine is located in the south-central part of the Jiao Ping Mining Area of Huanglong Jurassic Coal Field, with a length of about 7.7 km from east to west, a width of about 5.6 km from north to south, and a well field area of 28.2008 km\({}^{2}\). The main coal seam of the mine is 4-2# coal, and the mine's production capacity is 1.95 Mt/a. The 2305 comprehensive cavity coal face is the continuous face of the mine's three-disk area, and it is also the fourth face mined in the three-disk area of the mine. It is located in the middle of the mining area, spread along the north-south direction, with a tendency advancing length of 1460 m, a working face length of 200 m, a thickness of the working face mining of 3.5 m, and a caving coal height of 8.5 m. The depth of the 4-2# coal seam in the third disk area is 400-700 m, and the inclination angle is about 5\({}^{\circ}\). The locations of the Cuijiagou coal mine and the 2305 working face are shown in Figure 1.  
According to the geological data of the coal mine, the geological conditions of the working face are simple, with no obvious tectonics or fault zones. The strata of the mine field from old to new and from top to bottom, are as follows: Triassic Upper Yongping Formation (T\({}_{3}\)y), Lower Jurassic Fuvian Formation (J\({}_{1}\)f), Middle Jurassic Yan'an Formation (J\({}_{2}\)y), Zhiluo Formation (J\({}_{2}\)z), Lower Cretaceous Yijun Formation (K\({}_{1}\)y), Luohe Formation (K\({}_{1}\)l), Huachi Formation (K\({}_{1}\)h), and Quaternary (Q) strata. The main roof of the coal seam is gray-white fine powder sandstone, the immediate roof is mainly powder sandstone, and there are medium-grained sandstone and coarse-grained sandstone in the local section. The floor of the coal seam is gray and gray-black mudstone, interspersed with fine sandstone. A comprehensive histogram of coal and rock of the 2305 working face is shown in Figure 2.  
Figure 1: Cuijiagou Coal Mine Location Map and 2305 Working Face Location Map.
## 6 Open Issues and Future Trends
### Involvement of Cloud Computing  
Cloud computing technology, due to its sharing and flexible reconfiguration of resources, can be leveraged into avionics systems [7; 78]. The implementation of the so called avionics cloud [78] via virtual machine (VM) management can have the following advantages. First, it can virtualize the avionics resources with arbitrary configuration to improve the resource utilization among different subsystems. Computing and networking resources can be dynamically allocated and deallocated. Second, by virtualization technology, the system can host critical and non-critical functions in a more robust manner. The virtualization technology guarantees that critical functions can achieve the desired Quality of Service (QoS) for execution without being affected by other hosted distributed functions [7]. Functions can be queued to be performed under the cloud-based management. As a result, cloud computing is becoming an approach to designing critical systems with integration of other non-critical functions [79].  
Under avionics cloud, as the computing power, memory and storage can be virtualized, we need to consider the mapping between VMs and hardware components. In this case, together with the hardware and software mapping, there will be three levels of mapping when designing the DIMA systems. As such, this will make the function mapping more complicated for both objective functions and constraints. The same optimization framework can still be adopted, but the system complexity will increase with more constraints and variables in the DIMA optimization. It will take more time to find the solution using conventional methods. Distributed optimization using cloud resources has the potential to improve the efficiency and solve this issue [80]. Therefore, such a consideration is important for avionics cloud in future design.
# Defense and Security Mechanisms in the Internet of Things: A Review
## 3 Materials and Methods
### Blockchain Technology  
Another robust and secure technology is blockchain technology. This is a type of distributed ledger technology where data are shared in a network of computers. It assures the correctness of data using safe methods like hashing and digital signatures. A block contains a group of transactions, a timestamp, and a secure link to the last block in a tamper-proof blockchain. Thus, having a central authority in this arrangement is not essential. Because of this, the arrangement makes things clear and builds trust between the parties involved [75; 76].  
Some of the major characteristics of this technology are the following [77]:  
* Decentralisation entails the use of many inter-connected computers or nodes in a network instead of a centralised server or a single administration in the decision-making process and data storage.
* Immutability of the data that are placed in the blockchain since it cannot be changed once added.
* Transparency where the transactions are visible to the blockchain participants, either public or private network, depending the on cryptographic process and the security type where the data are secured with the help of public and private keys for ensuring privacy, integrity, and authentication.
* Consensus mechanisms enable the nodes to agree on the state of the blockchain.  
Security technology also has many advantages. A blockchain provides the data mentioned above with integrity, which manifests itself in the immutability of records and prevents unauthorised changes. The second advantage of the technology is decentralised security. In these solutions, no central server reduces vulnerability to attacks from a single point. We provide increased privacy thanks to cryptographic methods and access with permissions (in private blockchains). In turn, transparent and verifiable records allow for easy audits, and smart contracts allow for the automatic execution of contracts, reducing the risk of fraud. As a result, blockchain technology can be used to secure identity verification systems to prevent identity theft and secure supply chains because immutable ledgers ensure the authenticity and traceability of goods, protect data, prevent fraud, and secure voting systems [78; 79].
## 4 Local Transmission Control: Redundancy-Based Transmission
### Local Route Establishment  
In this procedure, redundant local routes are established between hubs using route setup and reply processes.
## 6 Applications and Simulations
### Peer-to-Peer Greedy Routing
#### 6.1.1 Stretch Factor  
As proven in the previous sections, the proposed scheme guarantees successful data delivery between any pair of nodes. Therefore, we focus on the stretch factor in the performance evaluation. The stretch factor of a route is the ratio of the actual path length to the shortest path length. We randomly select 10,000 pairs of nodes to calculate the average stretch factor for each network model.  
While many greedy routing algorithms have been proposed for wireless sensor networks, few of them can be applied in a 3D setting. Moreover, we only focus on truly greedy routing algorithms with constant-bounded storage and computation complexity in 3D sensor networks in this research. Therefore, random-walk [6] is the sole comparable scheme, as discussed in Section 1. Under random-walk, a packet is greedily advanced to its destination. If a local minimum is reached, it escapes from the local minimum by a random walk on a local spherical structure. Note that random-walk does not ensure deterministic routing results.  
The average stretch factors of random-walk and our proposed algorithm are summarized in Table 1. As can be seen, the proposed scheme exhibits stable stretch factor and outperforms random-walk in all network models. In a contrast, the performance of random-walk heavily depends on the size of the hole and the shape of the network, experiencing a higher stretch factor under the network with a bigger hole (see Figure 6e Model 6 and Figure 8a Model 7) or heavy irregular network shapes (see Figure 8e Model 8).  
Figure 10 illustrates the distribution of the stretch factor based on Network Model 6. We observe that most routing paths under our proposed scheme have a low stretch factor. For example, 70% of routes have their stretch factor lower than 1.4. On the other hand, the distribution under random-walk has a considerable shift to the right side. Particularly, there are about 20% of routes experiencing a stretch factor of 2.0 or higher (which means a routing path at least twice as long as the shortest path).  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 5** & **Model 6** & **Model 7** & **Model 8** & **Average** \\ \hline Ours & 1.63 & 1.63 & 1.66 & 1.61 & 1.62 & 1.44 & 1.69 & 1.72 & 1.62 \\ Random-walk [6] & 1.83 & 1.70 & 1.73 & 1.84 & 1.89 & 2.12 & 2.16 & 2.91 & 2.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of stretch factors.  
It is also an interesting observation from Figure 10b that, with the increase of the actual path length between source and destination, the stretch factor decreases under our proposed scheme. This is in sharp contrast to random-walk, where two far-separated nodes are likely located on the opposite side of the hole, and accordingly, conventional node-based greedy routing between them may fail, leading to a long random walk path.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 5 Conclusions  
In this paper, the virtual linkage approach is presented to solve the formation control problem. Instead of treating the group of agents as a single rigid body, as with the virtual structure, the whole formation is designed to be a mechanical linkage in the proposed approach. The simulation results show the effectiveness of the virtual linkage approach. There are several advantages as compared to the virtual structure approach. Firstly, the hierarchical architecture does not require the transmission of all the agents' state information into a single location, as compared to centralized virtual structure approaches. In detail, the information of each agent is reported to the corresponding virtual link's controller, respectively, and the calculated state information of the virtual link is then transmitted to the virtual linkage formation controller. Meanwhile, the time complexity of the proposed algorithm is reduced since the fitting process is broken into individual parts which can be calculated simultaneously, which makes it faster and more suitable for real-time control. Last but not least, the virtual linkage is able to be configured into different formation patterns by only changing the virtual joint angle, which makes it suitable for situations when formation patterns need to be reconfigured frequently.  
In future work, more attention will be paid to the decentralization and formation feedback of the virtual linkage approach[PERSON] Furthermore, the dynamical formation pattern generation and route planning for unknown environments is also an attractive direction and is worth researching.  
[PERSON] conceived of the presented idea. [PERSON] and [PERSON] developed the theoretical formalism. [PERSON] and [PERSON] designed the simulation experiments and analyzed the data. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] discussed the results and wrote the paper.  
This work is based on work supported in part by the National Key Technology R&D Program of China under Grant 2013 BAK03B03 and National Defense Basic Research Project under Grant B220132014.  
The authors declare no conflict of interest.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 3 User-Based Integrity with LEO-PNT Formulation (Option 2)
### LEO-PNT as a Barrier  
The LEO-PNT barrier is located within the integrity GNSS FTA of the integrity concept. Ithaca also provides this tree at a convenient level of decomposition. The GNSS top event (loss of integrity) is broken down into two main branches: a _Fault-Free Branch_ that includes the events that may lead to a loss of integrity under fault-free conditions; a _Faulty Branch_ that further decomposes the events that may lead to a loss of integrity under failure conditions.  
The integrity monitor will act as a barrier against the failure mode \"Undetected (_MEO_) Satellite Feared Event\", which is located in the faulty branch. Underneath this failure mode node in the tree, we find events such as satellite clock jumps, pseudorange errors, etc. We can assume that between 10 and 20 events like these can be identified, leading to a split of the 1.5 \(\times\) 10\({}^{-6}\) per hour budget amongst 20 events (in the worst case scenario, assuming the probability is equal for every event), e.g., 7.5 \(\times\) 10\({}^{-8}\) per hour. This is usually split into the probability of the occurrence (per h) of the feared event and the probability of its missed detection, Pmd. We will assume that the MEO constellation has a probability of occurrence (per h) of 1 \(\times\) 10\({}^{-4}\) (per h) for each of these events, leading to a probability of missed detection, Pmd, of 7.5 \(\times\) 10\({}^{-4}\). A conservative value of 1 \(\times\) 10\({}^{-5}\) for the Pmd is selected to account for other allocations such those related to feared events occurring in the LEO-PNT satellite itself.  
The FTA for continuity is not addressed in the Ithaca report, but a budget is required to stablish the false alarm probability of the integrity monitor, the Pfa. We adopt a Pfa of 1 \(\times\) 10\({}^{-5}\) for this analysis, with the justification that this is of the same magnitude as the Pmd and the Gaussian assumptions can still be checked with these values.  
Finally, the magnitude of the feared event that is to be monitored is taken to be 2 m. The justification for this is made using the example of a satellite clock jump: this event is projected to the user in the same manner as it is to the LEO and it exists in the range domain; thus, when the resulting error is below 2 m in magnitude, it is expected to be covered by an AL of 3 m.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Alert Limit (AL)** & **Target Integrity Risk (TIR)** & **Continuity Risk** \\ \hline
3 m & 1 \(\times\) 10\({}^{-5}\) per hour & 1 \(\times\) 10\({}^{-5}\) per hour \\ \hline \hline \end{tabular}
\end{table}
Table 2: Target Integrity Risk (TIR) for road users.  
With these three values allocated, the LEO-PNT monitor will need to provide the following performances, expressed in terms of \(\mathbf{T_{mon}}\) and \(\mathbf{\sigma_{mon,i}}\) (see Table 3).
# High-Speed Welding of Stainless Steel with Additional Compensatory Gas Jet Blow Molten Pool
## 4 Results and Discussion
### Tensile Strength Test and Analysis  
An INSTRON 8801 hydraulic fatigue testing machine was selected to test the tensile strength of the weld. The device had an axial load capacity of \(\pm\)100 kN, a maximum load of \(\pm\)100 kN, a total actuator distance of \(\pm\)75 mm, and a maximum tensile space of 1480 mm. The test was performed at room temperature. A set of flat-plate surfacing tests and two sets of tensile tests on butt welds were conducted on the selected parent materials with 2 mm thickness. The compensatory gas jet flow rate was set to 15 L/min. Dimensions of the test samples for weld tensile strength are shown in Figure 10.  
Tensile properties of welded joints are shown in Figure 11. Figure 11A shows the tensile curves of the welded joints. Figure 11B,C show the tensile curves and result of tensile tests, respectively. The test base material is 304 stainless steel plates with the dimensions 200 mm \(\times\) 50 mm \(\times\) 3 mm. Each plate's tensile and yield strength is 838 and 730 MPa, respectively. The tensile strength of sample 1#S, 1#B, 2#S and 2#B is 717 MPa, 822 MPa, 726 MPa and 832 MPa, respectively. The elongation of sample 1#S, 1#B, 2#S and 2#B is 14%, 23%, 15% and 27%, respectively. Therefore, the tensile strength, and elongation of the weld joints with compensation are much higher than those without compensation.  
The welds formed with compensatory gas jets are significantly superior to those without compensatory gas jets, in terms of the index parameters such as maximum load, maximum displacement, maximum stress, strain displacement at the yield point, strain displacement at fracture, fracture stress, and fracture load. The fracture position with the compensatory jet flow was located in the parent material, while without compensatory jet flow it was located in the weld. The compensatory jet weld's fracture occurring far from the heat-affected zone indicates that the heat-affected zone formed in the high-speed welding of 304 stainless steel plates is very small, agreeing with other scholars' research results. It can be seen from Figures 5 and 6, of the two groups of butt welds, that the weld appearance is intrinsically correlated with the tensile strength. From the front view images, it can be seen that the morphology of the uncompensated segment is uneven, the molten width is low,  
Figure 10: Dimensions of tensile strength test specimens.  
and the residual height is high. The back view shows that the parent material is not fully penetrated. These apparent differences in appearance are the main reasons for the differences in tensile strength. Butt welds without jet compensation break from the center; this is because the molten droplets fail to fill the gaps and thus the weld becomes the weakest point in the specimen.
## Results
### Characteristics of Products after Storage  
#### 3.2.1 Oxidative Stability  
After 7 days of storage, the highest ORP values were obtained for sample C (with curing salt), and the lowest ones for samples produced using acid whey (SW and SWU; Table 4), probably as a result of the presence of antioxidant compounds of protein origin, since statistically significant (\(P<0.05\)) higher peptide content was recorded in these samples (SW and SWU) during this period (7 days;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Raw Material**} & \multirow{2}{*}{**pH**} & **ORP (mV)** & **TBARS (mg MDA kg-1)** \\  & & **Oxidation–Reduction** & **a\({}_{\text{w}}\)** & **Thiobarbituric Acid** \\  & & **Potential** & & **Reactive Substances** \\ \hline meat & 5.400 \(\pm\) 0.052 & 315.54 \(\pm\) 3.227 & 0.982 \(\pm\) 0.001 & 0.293 \(\pm\) 0.072 \\ acid whey & 4.340 \(\pm\) 0.026 & 388.50 \(\pm\) 5.292 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic technological parameters characterizing the raw materials (mean \(\pm\) standard deviation).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **Value (\%)** \\ \hline Water & 72.3 \(\pm\) 0.08 \\ Protein & 23.27 \(\pm\) 0.34 \\ Fat & 4.20 \(\pm\) 0.26 \\ Collagen & 1.04 \(\pm\) 0.08 \\ Salt [Cl–] & 0.63 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proximate composition of fresh meat (mean \(\pm\) standard deviation).  
Table 5). A decrease in the ORP values was observed along with the storage time in the batches with nitrates and sea salt (this tendency was not recorded in the SW and SWU batches).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Time (Days)**} & \multicolumn{2}{c}{**Parameter**} \\ \cline{3-5}  & & \multicolumn{2}{c}{**TBARS (mg MDA kg\({}^{-1}\))**} & \multicolumn{2}{c}{**ORP (mV)**} \\ \hline \multirow{3}{*}{**C**} & 7 & 0.490 \(\pm\) 0.031 \({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text  
An increase in TBARS was observed with prolonged storage time, especially between 7 and 21 days, which indicates intensified oxidation processes in the product. The lowest TBARS values were recorded in the samples with curing salt (C) after 7 days of storage, while the highest values were recorded in the SW samples (Table 4). After 42 days of storage, the inhibitory effect on fat oxidation processes in SW and SWU samples (average of 0.780 mg MDA kg\({}^{-1}\)) and C and samples with curing salt subjected to ultrasound treatment (CU batches) (average of 0.614 mg MDA kg\({}^{-1}\)) were noted. The comparison tests indicated that the inhibitory effect was higher in the sea salt (S and SU) batches, with an average of 1.145 mg MDA kg\({}^{-1}\). No effect of ultrasound treatment on the TBARS index (\(P>0.05\)) was detected (except for the S and SU trials between 21 and 42 days of storage; \(P<0.05\); Table 4).
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 3 Results
### Sea Surface Wind Speed (SSWS) Verification  
The normalized backscatter coefficients of sea surface can be obtained by satellite radar altimeter, and the backscatter coefficients are related to the sea surface roughness [35]. Since wind is the main factor affecting the sea surface roughness, a geophysical mode function can be established for the interrelationship between the backscatter coefficients and the SSWS, and this model function can be used to invert the wind speeds from the observed backscatter coefficients measured by satellite altimeters [28; 36]. The process of SSWS data verification in the study was consistent with the verification of SWH data, with the verification of SSWS based on the obtained buoy data and the multiple satellites tracking of dual-crossover points for the SSWS.