esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Comparison of Privacy Protection Methods  
In the realm of blockchain transaction privacy protection, each privacy protection mechanism exhibits unique advantages and limitations in terms of privacy preservation and processing efficiency.  
Coin mixing technology, by amalgamating transactions from multiple users, complicates the tracing of transaction origins. However, this method may decrease system efficiency, particularly when processing a large volume of transactions. The stealth address scheme generates the unique address for each transaction, offering a higher degree of privacy protection, and is relatively more efficient, especially in comparison to coin mixing. However, the key to stealth address schemes is that they can only conceal the identity of the receiver while exposing other aspects of the transaction (such as the sender's identity and amount). The ring signature technology, by obscuring the true sender within a group of users, provides robust anonymity for the sender but may incur increased complexity in the verification process and a reduction in system efficiency. In contrast, direct privacy protection schemes such as zero-knowledge proof technology provide the highest level of privacy protection, which enables the validation of transactions' legitimacy without disclosing any specific details. However, this elevated level of privacy protection is often accompanied by heightened computational complexity and reduced transaction processing efficiency.  
In summary, selecting an appropriate privacy protection mechanism necessitates finding a balance between privacy and efficiency. While the coin mixing scheme and the stealth address scheme provide effective privacy protection to a certain extent, each has its limitations. The ring signature scheme and the zero-knowledge proof approach offer a deeper level of anonymity but may come at the cost of reduced processing efficiency. Therefore, in practical applications, it is crucial to choose the most suitable privacy protection strategy based on the specific context and requirements.
## 2 Pressure Inversion Model for Gob Areas Driven by Optical Fiber Monitoring Data
### The Inversion Method for Pressure Distribution in the Gob Area
After the key stratum fracture, considering the stress concentration effect at the fractured surface, a mapping relationship between fiber geometric deformation and stratum subsidence is established based on the masonry beam structure characteristics. By establishing quantitative relationships between stratum subsidence characteristics and goaf pressure distribution in these two stages, a complete pressure inversion theoretical system is formed. This theoretical innovation not only enables accurate prediction of pressure distribution in the goaf area but also provides important theoretical support for the development of real-time monitoring technology. To verify the reliability and practicality of this theoretical system, we designed a physical model experiment based on Brillouin Optical Time Domain Analysis (BOTDA) technology. This experiment not only comprehensively monitors the deformation process of the key stratum but also obtains critical parameters of stratum fracture through high-precision fiber sensing technology, providing an important experimental foundation for the practical application of the theoretical model. Next, let us examine in detail the specific design and implementation process of this physical model experiment.
# Design and Calibration of a Sensory System of an Adaptive Gripper
## 3 Results
### Calibrating the Slip Sensor
The PID controller receives three input values: Error signal (e): It represents the difference between the desired value of the gripping force (setpoint) and the current value of the force measured by the force sensor. In the case of slip detection, the error signal reflects the difference between the desired position of the slip sensor stylus and its current position. Derivative of the error signal (de/dt): It represents the rate of change in the error signal. A high rate of change indicates a fast slip. Integral of the error signal (\(\int\)e dt): It represents the sum of the errors in the past. A high integral indicates a persistent error. Based on these three values, the PID controller calculates an output signal that adjusts the gripping force: output signal = Kp * e + Ki * \(\int\)e dt + Kd * de/dt, where Kp (proportional gain) determines the immediate response of the system to the error. A large Kp leads to a fast response, but can cause oscillations. Ki (integral gain): Compensates for persistent errors. A large Ki reduces static error, but can cause overshoot. Kd (derivative gain): Dampens oscillations and improves system stability. A large Kd reduces overshoot, but can make the system slow.
# Topological Qubits as Carriers of Quantum Information in Optics
## 4 Multiports and Winding Number
Such winding number-polarization entangled states can be achieved in practice by making the phase shifts in the diamond graphs polarization-dependent [20]. In this way, if a photon is prepared in a polarization superposition state  
Figure 7:4  
and placed into the chain, it will give rise to the winding number superposition state  
Figure 7:5  
where the 0 and 1 represent winding number values [20]. Interpreting these winding numbers as bit values, the photons now form a winding number _qubit_, directly analogous to those of Equations (8) and (13), and spanning the full set of quantum states in a two-dimensional complex Hilbert space. The topological stability of the winding number state suppresses bit flips, but since each winding number is unambiguously associated with a given value of a more easily measured variable (polarization), the bit value can easily be read out by polarization measurements. The multiport system produces winding number without the disadvantages of the previous examples: the qubits can be readily produced, manipulated, and detected. Examining Figure 7, it can be seen that there are no ports open to the vacuum at any of the beam splitters; as a result, no vacuum noise is introduced, in contrast to other quantum optical approaches. Further, since the qubit values in this system depend only on the discrete topological class of the energy band, small perturbations of any kind generally introduce no errors; thus normal levels of noise in the system have little effect, with robust topological protection of the qubits.
## 3 System Architecture
Customers can access their electricity bills whenever necessary, and an administrator can check the electricity meters' status and modify the parameters of each electricity meter if needed. Hand-held devices are also included and used as \"mobile data concentrators\" in the EMMNet. If any fault occurs in the fixed data concentrators, the hand-held devices can still be used to collect data in a timely manner. Hand-held devices can communicate with other devices in four different ways: 470 MHz wireless channel, lrDA, RS232, and RS485. In addition, data collectors and data concentrators can be reprogrammed by hand-held devices if the requirements of the automatic electricity-meter-reading change.
# Haptic Glove TV Device for People with Visual Impairment
## 6 Analysis and Evaluation of Results
### Usability and User Experience Results
Although in reality, there was a small gap that some people did manage to perceive. This small gap occurred mainly because the tests were performed on a 2.4 Ghz WiFi network, open and shared to replicate a real user scenario, where packet loss and delays due to channel congestion could occur. It is also important to mention that the hardware processing times could be controlled by software by adjusting the delays, but the adjustment was manual until proper synchronization was achieved. Another factor that could have generated synchronization errors was the fact that the test video sequences sampling was done manually so that some human error could have been generated. Figure 22, question 7, reveals that the device, for the vast majority, was effortless to understand since they consider it intuitive. Taking into account that they had a brief explanation and training stage to perform the glove test. Figure 22, question 8, also reveals that the training stage was necessary where they identified the vibrations and became familiar with the glove. Figure 22, question 9 and Figure 24, show the most crucial question validating the hypothesis that our work with the glove does have utility and provides a solution for these people who have been excluded and cannot enjoy this information on TV. Of the 15 people evaluated, 14 gave a high and very high evaluation. The only one gave a low rating, as he never felt comfortable with the glove and never really showed interest because he did not like sports, perhaps because of his condition, as he is a person who was blind from birth. The mean, median, and mode are at a very high value. The value of kurtosis also shows a high value because of the concentration of information at 5 (very high). The skewness value is negative since the mean is lower than the mode and the tail of the distribution is longer for values below the mean. Figure 23: To what extent were you able to locate the soccer ball through the vibrations generated by the glove motors? Additional questions were asked to determine the user's satisfaction and experience, which are shown in Figure 22, questions 10, 11, and 12. It can be determined that most participants consider that it is not tiring to use the device, that they had an enjoyable experience, and that most would like to use the device again.
# Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on [PERSON] Shi
## 4 Implementation of Weight Calibration and Weighted Addition
We also change the optical crosstalk in the chip to see the impact of the optical crosstalk on the weighted addition. The blue, red, yellow and violet lines show the error changes when the crosstalk is set at \(-15\) dB, \(-20\) dB, \(-25\) dB and \(-30\) dB, respectively. The shaded area shows the error range obtained from the experiments for two-channel addition. The error variation is attributed to the calibration of the weight control, as already anticipated in the weight factor curve in Figure 3c. The error related to the weighted addition operation increases when the induced optical crosstalk is greater, as a high level of crosstalk eventually results in a lower dynamic range. The same high crosstalk level enhances the peak power oscillation recorded for generating the weight control curve, resulting in severe error variations, as already anticipated in Figure 3d. Nevertheless, this fits perfectly within the error variation window we found for our experimental results (see the dashed box in Figure 4a). The same analysis is done while changing the number of channels added to the WDM input. Figure 4b,c plot the resulting errors for three- and four-channel weighted additions, respectively. A visibly smaller error is presented for three-channel and four-channel weighted additions. The effect of the optical crosstalk is reduced as the oscillation power caused by the optical crosstalk is relatively smaller with respect to the dominating signal power coming from the addition of all the input multiple signals. This suggests that the higher the number of inputs into the neuron, the better the accuracy when operating within the available power budget. Finally, Figure 4d summarizes the obtained results, by plotting the maximum errors versus the number of channels in weighted addition.
## 4 Case Study
### Design of Reinforcement Ratio  
Although the survey data indicate that the lateral pressure coefficient for this case is 0.9, the spatial variability of the rock mass means that it cannot be guaranteed that all  
Figure 14: Configuration of the segmented lining: (**a**) the preset seam; (**b**) retractable joints; (**c**) the state of the joint when the working is inflated; (**d**) the state of the joint when the working is deflated.  
Figure 15: Load–displacement relationship of the joints.  
strata in the area will have a lateral pressure coefficient of 0.9. From an engineering design perspective, a more conservative lateral pressure coefficient of \(\lambda\) = 0.39 should be used. After setting six preset seams with a maximum seam opening displacement of 1 cm, the design with a reinforcement ratio of 2.0% results in a maximum crack width that meets the crack control requirements. The design results are shown in Table 4  
Table 4 compares the maximum reinforcement stress with and without retractable joints. With six preset seams, the maximum tensile stress in the inner reinforcement without retractable joints is 145.3 MPa, while the outer reinforcement has a maximum stress of 60.8 MPa. In the case of retractable joints, the maximum tensile stress in the inner reinforcement is 240.6 MPa, and in the outer reinforcement, it is 212.3 MPa. The use of retractable joints results in a slight increase in reinforcement stress compared to the structure without joints. However, the overall difference in stress levels remains small. This is because the joints at the seams help control circumferential displacement. Moreover, the reinforcement joints promote a more balanced stress distribution between the inner and outer layers. This suggests a reduction in bending moments and improved structural stability of the lining.
## 3 Methodology
### Hydrological Model  
The hydrological model used in this study was the Variable Infiltration Capacity (VIC) model [60; 61], which is a grid-based distributed hydrological model maintained by the University of Washington. The distinguishing characteristic of the VIC is that it incorporates land-cover vegetation heterogeneity, multiple soil layers with spatially varying infiltration capacity, and base flow as a non-linear recession curve [62]. The model also considers dynamic changes of both water and energy balances over a grid mesh. For a more detailed description of the VIC model, the reader is referred to [PERSON] et al. [63] and [PERSON] et al. [64]. Presently, the VIC model is widely applied in hydrological simulations and evaluations of satellite precipitations over different basins [65; 66; 67]. Here, the VIC model was implemented at a spatial resolution of 0.1\({}^{\circ}\) for the upper Yellow River basin. The daily streamflow observations were used to calibrate and validate the VIC model. Three statistical indices (Nash-Sutcliffe efficiency (NSE), RB and RMSE) were used to assess the result of hydrologic simulation validation (Table 2).
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 4 Numerical Validation
### Modeling of the Academic Electromagnetic Device
#### 4.2.1 Computational Configurations  
A sinusoidal voltage was applied between the terminals of the winding marked in red, as shown in Figure 9. The frequency interval was [0:10\({}^{5}\)] Hz. To consider the skin effects in the winding for high frequencies, the mesh used for the model featured 14,443,563 tetrahedrons, including 2,466,531 nodes and 16,911,563 edges. The mesh of the winding and the magnetic core are presented, respectively, in Figure 11a,b. For the EOS model, the number of DoFs is 2,466,002 and the computational time for one frequency takes about two hours, while it takes about 75 hours for 19,316,839 DoFs in the MQS model. The computing time using Darwin formulation takes about 86 hours for 19,373,155 DoFs. It should be recalled here that the matrix system associated with MQS is symmetric, while this is not the case for the Darwin model. In the general case, the computational cost for the MQS model is less expensive than Darwin's due to the ill-conditioned matrix of the Darwin model, and different preconditioners can be applied for MQS and Darwin cases.  
Figure 10: Schematic representation of the electromagnetic device.  
Figure 9: Geometry of the electromagnetic device.  
Moreover, the computational cost for the EQS model is relatively low in comparison with the two other models.  
Additionally, It should be mentioned that the condition number of the matrix depends on the frequency for both MQS and Darwin models. Consequently, the number of iterations associated with the iterative solver, which is required to achieve the solution's convergence, is not the same. For example, for \(f=10\) kHz and with a fixed stopping criterion, the number of iterations associated with BiCGSTAB is 14 k using the MQS model, while it takes about 17 k for the Darwin model, as shown in Table 1.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 6 Results
### FIDO2 CAP: A Novel Protocol for Captire-Portal FIDO2 Authentication  
In Section 4, we define FIDO2 CAP: FIDO2 Captive-portal Authentication Protocol. This is the main result of this paper. The proposed novel protocol is based on captive portals defined by RFC 8952 [8], and adapted to support FIDO2 user authentication. The main advantage of this approach is that any captive portal compatible with the RFC could be modified with the architecture of FIDO2 CAP to support this new authentication protocol.  
The FIDO2 CAP architecture is shown in Figure 2, where we have included some elements, like the User Database and the WAWA. On the other hand, we have considered the two FIDO2 ceremonies: authentication and registration. The message flows are presented in Figure 3 for authentication, and Figure 4 for registration. In both flows, we take into account discoverable and non-discoverable credentials, which ensures compatibility with different FIDO2 authenticators.
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 5 Process Flow
### Creating the Time-Series  
The module responsible for generating the time series iterates through the filtered data, taking into account the values of the individual sectors. The sector can be conceptualized as a matrix, where each cell element is assigned a time series (except, of course, if no measurement occurred in a given sector). In addition, the number of measurement points is recorded. The formation of the time series proceeds as follows. If a particular sector contains GPS coordinates where a \"jam\" value has been recorded, the value calculated based on the congestion strength is added to the appropriate hour value of the time series. The procedure for calculating the curve value for a given hour is as follows:  
\[\text{curve value}=\sum 1-\frac{1}{2^{\text{jam\,level}}} \tag{1}\]  
where:  
* jam level: the automatically recorded jam level in a scale from 1 to 5.  
The algorithm assigns appropriate congestion level values to each hour, which are then added to the corresponding hour of the time series. At the onset of congestion, a lower level of congestion results in a higher value being added, and as congestion intensifies, higher levels of congestion are represented by lower values. This attempts to account for the relationship in which each additional vehicle exponentially increases the strength of congestion. Finally, to ensure comparability, the module standardizes individual results on a unified scale (0-1000). This enables the distance measurement module to gauge the behavioral \"distance\" value between these time series.
## 3 Results
### Effect of SJFE on Cell Viability and NO Production in LPS-Stimulated RAW 264.7 Cells  
SJFE did not show any effect on the cell viability of RAW 264.7 cells up to 500 ug/mL. Quercetin and rutin also showed no effect [Figure 2A]. However, SJFE effectively suppressed the generation of NO in LPS-stimulated RAW 264.7 cells, and this inhibition was dose-dependent [Figure 2B]. At a concentration of 500 ug/mL, SJFE caused a 50% reduction  
Figure 1: HPLC chromatograms of rutin, quercetin, kaempfer, and isorhamnetin identified in SJFE. (**A**) rutin, (**B**) quercetin, kaempfer, and isorhamnetin. The conditions of HPLC were described in MATERIALS and METHODS.  
in NO production. Notably, quercetin demonstrated an even more potent inhibition at just 10 \(\upmu\)g/mL compared to SJFE. On the other hand, its glycoside form, rutin, did not exhibit any inhibitory effects even at a concentration as high as 100 \(\upmu\)g/mL [Figure 2B].
## 3 The Jointed Rock Slope Models
### Numerical Model Based on Digital Image Processing  
Images can provide information on the joint distribution of jointed rock masses. They can also be a basis for modeling jointed rock masses. The PFC2D model can be generated based on according to the digital image using the RDFN model. The procedure consisted of the following steps:  
1. The image was uploaded into the MATLAB 2023b program, and the RDFN gray digital image was established using the principle of gray image recognition, as illustrated in Figure 3a;
2. The various types of pixel points will be classified based on gray image pixel recognition. In this case, 251 corresponds to white pixels and 0 corresponds to the black pixels. After classification, the spatial distribution coordinates of the bedrock model and joint model will be obtained.
3. The joint and rock models will be grouped and assigned based on the PFC code. The mechanical modeling of the RDFN model will then ultimately be carried out, as shown in Figure 3b.  
Import the RDFN and original slope models into CAD and combine them to obtain the jointed slope model. In the resulting model, the white parts represent the rock mass, the black parts represent the excavation unit, and the red parts represent the joints, as indicated in Figure 4. Rock elements and joint elements are components of the slope, while excavation elements are used to maintain the stable shape of the slope before the model iteration. The actual excavation and slope release process is simulated by deleting all excavation elements at once. Multiple sets of joint planes are generated as required, and each set of joints is composed of different joints located at a definite distance. In some actual civil engineering scenarios, the rock joints are unevenly distributed. As a result, the joint angles need to be calibrated to ensure that multiple dip joints are generated within the same model. For example, in the slope model with a joint inclination of 45\({}^{\circ}\), the joint inclination follows a normal distribution that has an average of 45\({}^{\circ}\) and fluctuates between 30\({}^{\circ}\) to 60\({}^{\circ}\). The resulting slope model will comprise a single set of joint dip angles, which will increase gradually from 0\({}^{\circ}\) to 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\).  
Figure 3: The principle for establishing numerical model according to digital image approach. (**a**) DIC digital image recognition; (**b**) Particle flow modeling.
###### Abstract  
The evaluation of the fault diagnosis capability of a data center network (DCN) is important research in measuring network reliability. The g-extra diagnosability is defined under the condition that every component except the fault vertex set contains at least g+1 vertices. The t/k diagnosis strategy is that the number of fault nodes does not exceed t, and all fault nodes can be isolated into a set containing up to k fault-free nodes. As an important data center network, BCube Connected Crossbars (BCCC) has many excellent properties that have been widely studied. In this paper, we first determine that the g-extra connectivity of \(BC_{n,k}\) for \(0\leq g\leq n-1\). Based on this, we establish the g-extra conditional diagnosability of \(BC_{n,k}\) under the MM* model for \(1\leq g\leq n-1\). Next, based on the conclusion of the largest connected component in g-extra connectivity, we prove that the t/k-diagnosability of \(BC_{n,k}\) under the MM* model for \(1\leq k\leq n-1\). Finally, we present a t/k diagnosis algorithm on BCCC under the MM* model. The algorithm can correctly identify all nodes at most k nodes undiagnosed. So far, t/k-diagnosability and diagnosis algorithms for most networks in the MM* model have not been studied.  
Keywords:data center networks; BCCC; fault tolerance; MM* model; g-extra diagnosability; t/k-diagnosability +
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