esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In our work, we investigated the enzyme-induced and acid coagulation of raw milk samples, and these processes were monitored by measuring the changes in the apparent viscosity and the dielectric constant of the fermentation media. Our results revealed that both the enzyme-induced and the acid coagulation process could be divided into two distinct phases in terms of dielectric (f = 400 MHz) and rheological behavior; however, the tendencies of these phases differ from each other due to the fundamental molecular differences in the two coagulation mechanisms. In the case of the enzyme-induced aggregation, during the first 10 min, neither the apparent viscosity nor the dielectric constant shows any observable change. After that, in the 10-30 min period, the dielectric constant and the apparent viscosity present a steep increase and reach their respective maximum values. This indicates that the point of clot solidification occurs roughly half an hour after the enzyme is added to the system. In the case of acid coagulation, the solidification takes considerably more time, and in the first phase of the process, a slight increase in viscosity, and a slight decrease in the dielectric constant can be observed. Afterward, at around 50-55 min, when the destabilization phase finishes, a steep increment can be seen in the values of the apparent viscosity and a sharp decrease in the dielectric constant.  
To determine whether the dielectric behavior and the physicochemical structure (represented by the viscosity) correlate, we constructed linearized functions for the two distinct phases of the investigated coagulation processes, i.e., plotted the 10-based log values of the dielectric constant in the function of log(n). Based on the obtained R\({}^{2}\)-values (0.9655 and 0.9767 for the first and second phase of the enzyme-induced process, respectively, and 0.992 and 0.9676 for the acid coagulation process), our results suggest that regardless of the molecular mechanisms that induce the coagulation itself, there is a strong correlation between the values of the apparent viscosity and the dielectric constant, thereby making the dielectric measurement a suitable alternative as a monitoring technique in dairy processing.  
Conceptualization, S.B. and Z.P.J.; methodology, S.B., J.C. and Z.P.J.; investigation, R.D.; data curation, S.B. and Z.P.J.; writing--original draft preparation, R.D. and Z.P.J.; writing--review and editing, S.B.; visualization, R.D. and J.C.; supervision, S.B.; funding acquisition, S.B., Z.P.J. and R.D. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the financial support provided by the New National Excellence Program, under project numbers, UNKP-22-2-SZTE-199, UNKP-22-5-SZTE-208 and UNKP-22-3-SZTE-204. This project was supported by Bolyai Janos Research Scholarship of the Hungarian Academy of Sciences (BO/00161/21/4).  
Not applicable.  
Research data is available on special request.  
The authors declare no conflict of interest.
# Characterizing Micro-Displacements on Active Faults in the Gobi Desert with Time-Series InSAR
## 4 Results and Discussion
### The Structural Geology of the Jiujing Region
#### 4.2.7 F\({}_{\rm I-I}\) as a Dextral Reverse Fault  
Horizontally, on the western side of F\({}_{\rm I-I}\) and F\({}_{\rm I-2}\), the PSI data in Figure 7 show that JJ16, JJ17, JJ18, and JJ19 are displacing towards the west by ca 0.10, 0.29, 0.48, and 0.54 mm a\({}^{-1}\), respectively; meanwhile, as mentioned above, JJ12 to JJ15 on the eastern side of F\({}_{\rm I-I}\) are displacing eastward by 0.12 to 0.29 mm a\({}^{-1}\). This kind of crustal divergence indicates F\({}_{\rm I-I}\) is a normal fault. This interpretation assumes that maximum horizontal displacement is homogeneous across the entire granite section. However, since the entire Jiujing granite pluton is dominantly affected by a NE-SW trended compression, the existence of extensional  
Figure 12: Vertical displacement distribution along cross-section CC’ in Figure 6. This cross-section is formed from 300 PS values sampled along the cross-section line and shows the annual relative movements within each fault block. The marked faults are interpreted from the PS, and they are illustrative, showing the sense of fault movement and general direction of faults.  
structures F\({}_{\text{l-1}}\) is kinematically and mechanically incompatible with the compressional structures, such as F\({}_{\text{m}}\) and F\({}_{\text{g}}\). In this way, F\({}_{\text{l-1}}\) is less likely to be extensional.  
A potential explanation is that F\({}_{\text{l-1}}\) is a dextral reverse fault, dipping to the southwest, as illustrated in Figure 13. This could be triggered by a NE maximum compression (\(\sigma_{\text{1}}\)) at a very low angle between the fault planes. The horizontal component (\(\sigma_{\text{H}}\)) leads to right-lateral sliding along the fault plane, where the east side (JJ12, JJ13, JJ14, and JJ15) and west side (JJ16, JJ17, and JJ19) are displacing towards the southeast and northwest, respectively. This, in turn, leads to a divergent pattern along F\({}_{\text{l-1}}\), shown in Figure 7. Vertically, the uplifting of JJ16, JJ17, and JJ19 and the relative subsidence of JJ12 to JJ15 (shown in Figure 6) indicate that the hanging walls lie to the west and the footwalls to the east of these reverse faults, respectively, which suggests F\({}_{\text{l-1}}\) is west-dipping.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 3 Related Work
### Classic DTN Routing Protocols  
Routing protocols for DTNs are classified into two main categories: _replication-based protocols_, which ensure better delivery ratios by allowing the packets to be duplicated in the network, and _forwarding-based protocols_, which never replicate the packets. In this section we describe the most representative DTN routing protocols, which we consider for our performance evaluation.  
A first example is _Epidemic_[10], a replication-based protocol, in which nodes continuously replicate and transmit the packets to newly discovered contacts. Epidemic is resource hungry, as it does not limit replications, in order to improve the chance to deliver packets. This strategy is effective when the opportunistic encounters are purely random.  
The _MaxProp_ protocol [11] is a flooding-based technique in which, if a contact is discovered, all the packets attempt to be replicated and transmitted to that contact. The difference with Epidemic comes in determining which packets should be transmitted first and which ones should be dropped first. In other words, MaxProp maintains an ordered-queue based on the destination of each packet, ordered by an estimated likelihood of the path.  
_First Contact_[12] is a forwarding-based routing protocol in which each node transmits the current owning packet(s) to the first encountered node. The node that receives the packet(s) acts by following the same procedure, waiting for the first available contact. The process goes on until the packet(s) arrive(s) to the destination.  
_Spray and Wait_[13] is a routing protocol that attempts to gain the delivery ratio benefits of replication-based routing, as well as the low resource use benefits of forwarding-based routing. Spray and Wait achieves resource efficiency by setting a strict upper bound on the number of copies per packet allowed in the network. The Spray and Wait protocol is composed of two phases: the spray phase and the wait phase. When a new packet is created in the system, a number L is attached to that packet indicating the maximum allowable copies of the packet in the network. During the spray phase, the source of the packet is responsible for _spraying_, or delivery, one copy to L distinct _relays_. When a relay receives the copy, it enters the wait phase, where the relay simply holds that particular packet until the destination is encountered directly.
## 5 Frequency Hopping Techniques
### Channel-Aware, Reduced-Hop-Set Techniques  
Frequency hopping techniques based on reduced hop sets select \(M\) channels from the available \(K\) channels, and hop among them in a pseudo-random manner.
## 4 Discussion
### Computational Resource Cost  
The primary objective of this study is to implement the proposed method in the practical application of vegetation area positioning within urban environments. A critical discussion on the feasibility of deploying this method at the edge in urban scenarios is essential. The challenges associated with model deployment and the computational resources consumed during operation are the key determinants of its deployment viability. Given that this research involves large-scale models, which are typically characterized by high computational demands, it is imperative to evaluate the computational resources required for operation. For this analysis, the arithmetic power of the NVIDIA RTX4090 (82.58 TFLOPS with FP32) was used as a benchmark.  
As illustrated in Table 10, we compared the number of parameters and GFLOPs of the model in this study with those of multimodal large models during the training phase. The results indicate that WRPM trains 34.4 M parameters and consumes 3.72 GFLOPs, whereas SCP-Net trains 1.7 M parameters and consumes 0.46 GFLOPs. These figures demonstrate significantly lower computational resource consumption compared to the training requirements of the three SAM weight variants, highlighting superior cost-effectiveness and enabling scalable, low-cost deployment of the algorithm in urban settings.Models based on the Transformer architecture have a large amount of computation due to the self-attention mechanism and generally have more than 100 M parameters, which is much higher than the computational cost of the UGSAM [33; 34].  
Table 11 presents the training time of the SAM, the WRPM and SCP-Net, as well as the time required for inferring a standard image. All computations were carried out on a single NVIDIA RTX4090 graphics card.  
It can be observed from the table that the total training duration for the entire UGSAM is approximately 16 h and 30 min. Additionally, the time required to infer a single image using the Vit-B weights is around 32 s. Compared to the extensive training periods lasting several months and the requirement for hundreds of high-memory GPUs associated with large models, the UGSAM introduced in this study demonstrates remarkable  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Params (M)** & **GFLOPs** \\ \hline WRPM (FFA-Net) & 17.6 & 1.75 \\ WRPM (LS-UNet) & 14.5 & 1.51 \\ WRPM (FIM) & 2.3 & 0.48 \\ SCP-Net & 1.7 & 0.46 \\ SAM (Vit-H) & 636 & 81.34 \\ SAM (Vit-L) & 308 & 39.39 \\ SAM (Vit-B) & 91 & 11.64 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The parameter quantity and computational power consumption of each module in the UGSAM.  
cost-effectiveness and efficiency, rendering it highly suitable for large-scale deployment in urban environments.
## 2 The Proposed Method for Integration of Uncertain Expert Knowledge to the System Model
### Parameter Estimation by Importance Sampling (IS)
#### 2.2.2 Parameter Resampling  
As the sample elements were weighted by importance sampling, new probability distributions of the parameters come up. New sample elements are drawn from these empirical distributions.  
The new elements are drawn from the old ones with the probability of their weights. First, the \(N\) random numbers (\(\{s_{n}\}_{n=1,2,\ldots,N}\)) are generated from the uniform distribution on \((0,1]\). Then, for every \(s_{n}\), the \(m\)th sample element is chosen if  
\[\sum_{j=1}^{m-1}w_{i}^{j}<s_{n}\leq\sum_{j=1}^{m}w_{i}^{j} \tag{11}\]  
where \(w_{i}^{j}\) represents the weights of the elements [27].  
Figure 5 introduces the resampling scheme.  
The new samples are handled with equal weights. In the present work, more parameters are needed to estimate; thus, all of them are resampled and assigned to the sample elements independently. Then, the results are calculated again for each sample element and the iteration seen in Figure 4 is executed until the empirical distributions converge.
## 2 System Design
### Hardware System Design
#### 2.4.2 Computation Modules Design  
The CK module is the computation-intense part of the system, which is pipelined to advance the operation speed. In the CNN software algorithm, we need to pad 0 before and after the input tensor. The RTL circuit design is shown in Figure 6. Only when the valid signal of input is high, will the input be selected as the value to participate in the calculation. While detecting the valid signal's negative edge, the value 0, not the input will be transmitted into reg_din.  
In the ARMP module, an accumulator is employed to execute the accumulation. Similar to the previous treatment, when detecting the negative edge of input's valid signal, it is the bias, not the input transmitted into reg_din. The ReLU is implemented by judging the sign bit of the data. Furthermore, the MaxPooling is realized by a comparator.  
In FC module, a multiplier and an accumulator are used to realize the formula (1). Unlike the above two calculations, the FC module has a timing requirement that the input data and weights must be transferred synchronously.  
Figure 5: CNN accelerator system architecture includes 3 types of computation module, system controller (SYS_CTRL), memory controller (MEM_CTRL), and Double-Data-Rate SDRAM controller (DDR_CTRL). The red arrows mean the direction of data flow.  
Figure 6: Convolution kernel (CK) design. (**a**) CK’s timing diagram. (**b**) CK’s (Register-Transfer-Level) RTL circuit design.
## 4 Experimental Validation
### Experimental Results
#### 4.1.1 Resolution Test USAF 1951  
The magnifications \(\rm M_{L}\) and \(\rm M_{T}\) as a function of the displacement distance are shown in Figure 12. The notable variation in the \(\rm M_{L}\) values observed in Figure 12a, compared with the simulation values showed in Figure 7a, could be attributed to the error introduced by the micrometer controlling the axial shifts in the linear translation stage. Consequently, variations become evident in the \(\rm\Delta z_{o}\) displacements. In contrast, the \(\rm M_{T}\) values exhibit greater stability, as is shown in Figure 12b. These values were calculated by dividing the width of five vertical bars (belonging to element 2, group 4) measured in the reconstructed image by the width of the same five bars specified in the test resolution specifications.  
Note that, on the one hand, the error bars in Figure 12a are associated with micrometer resolution of the linear translation stage \((\rm\Delta z\pm 20~{}\upmu m)\). The error bars are centered around the theoretical value of the longitudinal magnification, \(\rm M_{L}~{}=~{}-9.480\pm 0.1896\). On the other hand, the error bars in Figure 12b are attributed to the resolution of the precision ruler utilized for measuring the image distance, \(s_{i}\pm 1\) mm. In this case, the error bars are centered on the theoretical value of the transverse magnification, \(\rm M_{T}~{}=~{}-3.079\pm 0.01176\). For the sake of quantification, Table 2 presents the maximum and minimum values of the longitudinal and transverse magnifications, accounting for experimental errors.
# Dynamics Test Research of Bounce of Carrier-Based Aircraft Arresting Hook
## 3 Test Key Technology
### Simulation of Aircraft Horizontal Velocity  
The landing horizontal velocity of the carrier-based aircraft is an important input parameter in the test. In the laboratory environment, the simulation of the landing horizontal velocity of the aircraft is realized by the relative motion principle [16]. By using the rotating device, the large disc can be pre-rotated. The rotating direction is opposite to the aircraft heading, and the rotating velocity can be adjusted to control the horizontal velocity. The simulation scheme of horizontal velocity adopts the mode of motor driving with rotation, and the schematic diagram of the system with rotation is shown in Figure 6. The asynchronous motor is fixed on the ground, the motor drives the driving wheel to rotate, the driven wheel connected with the large disc is pulled by the driving belt to realize the rotation of the large disc, and the aircraft horizontal velocity is adjusted by adjusting the motor velocity.  
Inquire the mechanical design manual [17], confirm that the transmission ratio of driving wheel and driven wheel is 2:1,  
when the disc is rotating. It is controlled by the frequency converter to accelerate to the predetermined velocity within 2-3 min. At the same time, the emergency braking device needs to be installed.  
The motor adopts asynchronous motor, and the motor velocity is controlled by frequency converter. The velocity of large disc is monitored by laser velocity sensor. The tangent velocity of the impact point of the large disc driven by the rotating device is adjusted within the range of 0 km/h-250 km/h, and the velocity control error is less than 2%.
## 4 Proposed Algorithm for Reliability Computing of Chain Routing Protocols in WSN
### Chain Formation  
* Q-Chain formation: The distance between each node and the BS is calculated. The last node in the chain is selected as the farthest node from the BS. To select the previous node, the distances of all nodes to the last node are evaluated, and the nearest node to the last node is chosen as the previous node in the chain formation, and so on.
* D-Chain formation: The distance to the BS for each node is calculated. The first node in the chain is selected based on the node's nearest distance, which will be the CH. To select the next node in the chain, the distances of all nodes to the first node are evaluated, and the nearest node to the first node is chosen as the next node in the chain formation, and so on.  
The chain excludes any failure node and rearranges the parents and children of the previous node and the next node of the failure node in the chain, as illustrated in Figure 8. For more clarification, assume node C is the failure node, then, the parent of node C (node B) becomes the parent of node D and the children of node C (node D) become children of node B. Figure 9 illustrates different suggested models. The models under consideration are illustrated below:  
* Single D-Chain routing (SD-Chain): series configuration, where both the CH and sensor nodes are placed in series, as illustrated in Figure 10, where the red node is the CH and the blue nodes are the chain members (CM).
* Single Q-Chain routing (SQ-Chain): hybrid configuration, where the CH is placed in series with two subsystems of sensor nodes that are placed in parallel, as presented in Figure 11.
* Multi D-Chain single-hop routing (MD-Chain-SH): hybrid configuration, where the CHs of chains are placed in parallel to each other, while sensor nodes are placed in series with their CH, as shown in Figure 12.  
Figure 8: Chain formation.  
Figure 10: SD-Chain formation.  
Figure 9: Proposed models.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix
### Simulation Verification
#### 4.4.1 Branch Line Failure  
As shown in Figure 6, a fault occurs at the N2 -T2 branch 40 km from the N2 end of the line, and the line wave signal is obtained at each end of the line using a line wave sensor. The signal is subjected to symmetric component transformation to obtain the line mode component. The initial wave heads of the faulty traveling wave mode components are calibrated using the modified CEEMDAN-TEO to obtain the time of arrival of the \(\alpha\)-mode and \(\upbeta\)-mode components at each end of the fault, as shown in Table 4.  
Figure 6: Multi-terminal transmission line fault model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Line Parameters** & **r/(\(\Omega\)/km)** & **r/(\(\Omega\)/km)** & **g/(S/km)** \\ \hline Positive sequence parameters & 0.035 & 0.43 & \(1\times 10^{-7}\) \\ Zero sequence parameters & 0.3 & 1.21 & \(1\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of multi-branch grid-connected lines.  
The fault branch judgment matrix was constructed according to Equation (11)  
\[\begin{bmatrix}\text{M}_{1}&\text{N}_{1}&\text{N}_{2}&\text{N}_{3}&\text{M}_{2} \\ \text{M}_{1}&\times&0&>0&>0&>0\\ \text{N}_{1}&0&\times&>0&>0&>0\\ \text{N}_{2}&<0&<0&\times&<0&<0\\ \text{N}_{3}&<0&<0&>0&\times&0\\ \text{M}_{2}&<0&<0&>0&0&\times\end{bmatrix} \tag{32}\]  
it can be seen that the elements in the row corresponding to N2 are all less than zero, and the elements in the column corresponding to N2 are all greater than zero. This is consistent with the principle for determining a fault on a branch line proposed above, so it can be determined that the faulty branch is the branch where the N2 endpoint is located.  
The fault distance using the N2 end as the initial end can be calculated as:  
\[d_{N_{2}F}=\frac{(40.087+40.186+40.211.40.223)}{4}=40.177\ km \tag{33}\]  
This shows that the faulty branch is accurately judged, and the positioning error is 177 m.
## 3 Results
### Cell Observation with a High Time Resolution
(**b**) The orientation of the cell with respect to the stretching direction. Analysis results of images with an interval of 5 min. (**c**) Cell shape change with a time solution of 5 min. Figure 12: Analysis of the detailed cell change occurring suddenly from the real-time movie with 30 fps. (**a**) A cell–cell junction was observed before a sudden disruption between two cells. (**b**) Analysis of images in the middle of lengthening phases before and after the sudden disruption. The distance of the center of mass was used to demonstrate the drastic changes of cells induced by the disruption of the cell–cell junction. (**c**) Distance of the center of cells changed over a short period time. In order to classify the process of morphological change, cell morphology was further analyzed every 5 min as cell orientation and shape index with respect to the stretch direction. The outline of the target cells in each image was drawn manually, and then the cell areas, perimeters, and corresponding equivalent ellipse were obtained using a self-made macro language of software ImageJ (NIH, USA). Shape Index of a cell is expressed as Equation (4) [51]:  
\[\text{Shape Index}=4\pi\text{A}/\text{P}^{2} \tag{4}\]  
where A is the area, and P is the perimeter of a cell. Shape Index is a parameter demonstrating the cell elongation, which is one for the circle and zero for a line. In this study, the angle between the direction of the long axis of the equivalent ellipse and stretch direction was defined as the orientation angle of cells, which was also determined by using ImageJ. Then the time course of the morphological changes was drawn, as shown in Figure 11b,c. From the curve of the shape index, we can observe many sudden changes, and drastic shape change was observed at approximately 85 min after cyclic stretch exposure. Because the movie with a high time solution around 85 min was available, it is possible to investigate detailed information about the sudden change. As shown in Figure 12, a sudden and complete rupture of the intercellular junction between cell 1 and cell 2 during six stretching-holding-relaxation cycles was confirmed. These images were captured from the middle of several stretching phases around 85 min. There was a drastic morphological change. To demonstrate this change, the distance of the center of the two cell areas was calculated. As shown in Figure 12c, the distance of the center of cells changed for approximate 10 \(\upmu\)m during a time period of 30 s. The results demonstrate that the system can be used to record cellular changes even when the change cannot be predicted. Further, an on-line cell movie was also obtained using a lens with a magnifying power of 40 times, as shown in Figure 13 and Video S3. Figure 13: Images of cells exposed to cyclic stretch captured from an on-line movie.