esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Basic Data Striping Schemes in Cloud-Based Systems
### Sharing Methods
#### 4.1.1 Perfect Secret Sharing Scheme (PSS)
  
Shamir's PSS is a promising approach to secret sharing for cloud storage, and provides many advantages, including:  
* **Secure**--Anyone with fewer than \(t\) shares has no extra information about the secret than someone with zero shares. * **Extensible**--When \(n\) is fixed, new shares can be dynamically added or deleted without affecting the existing shares. * **Dynamic**--With this it is possible to modify the polynomial and construct new shares without changing the secret. * **Flexible**--In organisations where hierarchy is important, it is possible to supply each participant different number of shares according to their importance. However, the PSS scheme also has the following drawbacks:  
* **Computational overheads**--The preparation of the polynomial, the generation of shares, and the reconstruction of the secret may consume significant computational resources. * **Storage and transmission overheads**--Given \(t-1\) shares, no information whatsoever can be determined about the secret. Hence, the final share must contain as much information as the secret itself, which means effectively each share of the secret must be at least as large as the secret itself. The storage and transmission of the shares requires an amount of storage space and network bandwidth equivalent to the size of the secret times the total number of shares.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 4 Skyband and Dominating Query Processing  
Our MapReduce-based algorithm for skyband and dominating queries consists of the following five phases:  
P1: Data map and ranking. Each distributed dataset was partitioned vertically. Then, each partition was dispatched to the map workers (mappers). Each map worker, next, generates \((Val,OID)\) pairs, where \(Val\) is the numeric value of the corresponding object in the attribute domain and \(OID\) represents the object \(ID\). After receiving \((Val,OID)\) pairs as input, each \"reduce\" worker (reducer) produced the \((OID,Rank)\) pairs for each object, where \(Rank\) is the rank value of each object in the attribute domain. P2: Shuffling. In this phase, each map worker outputs \((OID,AttrRank)\) pairs, where \(AttrRank\) is the attribute name with the corresponding attribute rank for each object in the attribute domain. Next, each \"reduce\" worker also produced \((OID,Ranks)\) pairs for each object, where \(Ranks\) is a list of attribute names and the respective rank value for each attribute. P3: Worst rank computation. The coordinator collected all \((OID,Ranks)\) pairs to reduce data transmissions from map workers to reduce workers. After rearranging the \((OID,Ranks)\) pairs, the coordinator found the worst attribute rank for each object. P4: DC sets computation. The coordinator sends the attributes with the worst ranks to the workers, which are responsible for \(DC\) set computation for each object. Each worker takes an attribute rank, and the corresponding attribute's worst rank as input and outputs \(DC\) sets for each object. P5: Skyband and dominating objects computation. At this stage, the map workers take \(DC\) sets as inputs and perform domination checks between the \(DC\) sets and corresponding objects. Finally, the reducer produced the \(SB\) score and the \(\mu\) score required to compute the skyband query and top-\(k\) dominating query, respectively.
## 5 Validation Procedures and Results
### Results and Analysis  
Figures 5 and 6 show the number of packets generated by the original and the proposed secondary authentication framework. In Figure 5, the distribution of traffic in the original scheme relatively weighted on the traffic of UE(\(n(T_{AS})\)), serving network(\(n(T_{SN})\)), and NAS area(\(n(T_{NAS})\)) and the amount of traffic in home network(\(n(T_{HN})\)) is much smaller than other traffics. On the other hand, in Figure 6, the amount of traffic in the proposed framework is concentrated in the area of the serving network(\(n(T_{SN  
Traffic in the UE area and serving network are considered as the traffic of client-side and affect the user experience. On the other hand, traffic in the NAS area and home network, considered as server-side traffic, affects the risk and possibility of NAS signaling storm. To concentrate on the point of NAS signaling storm and user experience, we calculated the amount of traffic in the AS and NAS domain. Figure 7 shows the number of packets generated in the AS domain(\(n(T_{AS})+n(T_{SN})\)). The result of the original scheme generated 30,742 packets for the communication of the AS and SN domain, while the proposed framework generated 34,555 packets. This result shows that the proposed framework generated 12.4% more packets in the AS and SN communication.  
On the other hand, Figure 8 shows the amount of traffic generated in the NAS and HN domain(\(n(T_{NAS})+n(T_{HN})\)). As a result of the simulation with 1000 steps, the original scheme generated 23,732 packets, while our proposed framework generated 14,377 packets. This figure shows that the proposed framework that includes SAF and ADMF, and reduces 39.42% of traffic in the NAS  
Figure 6: Number of packets generated by the proposed secondary authentication framework.  
Figure 7: Number of packets transmitted in the access stratum (AS) domain.  
and HN domain, mitigating the threat of NAS signaling storm. Also, Figure 9 shows the number of total packets generated in the whole secondary authentication process on each scheme. The original scheme generated 54,474 packets, while our proposed scheme generated 48,932 packets. Our proposed model reduced the total number of packets by 10.17%.  
Our simulation results show that the proposed framework generates about 12.4% more traffics in the AS and SN domain, which is related to the availability of users. However, the impact of traffic on the AS and SN domain could be alleviated through well-deployed medium nodes of the edge layer. Thus, the impacts on the user's availability could be considered as a minor problem. In contrast, the proposed framework dramatically reduced the amount of traffic in NAS and HN domain by 39.42%. This result can significantly mitigate the risk of the NAS signaling storm. Also, the proposed framework curtailed the overall traffics by 10.17%. Consequentially, the proposed framework can improve the availability and stability of the 5G network.  
Figure 8: Number of packets transmitted in the Non-Access Stratum (NAS) domain.  
Figure 9: Number of total packets generated in each scheme.
_Article_  
**Correlating Natural, Dry, and Saturated Ultrasonic Pulse Velocities with the Mechanical Properties of Rock for Various Sample Diameters**  
**[PERSON] *(c) and [PERSON]**  
Geology Department, College of Science, United Arab Emirates University, Al Ain P.O. Box 15551, UAE; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +971-50-112-1905 check for updates  
Received: 26 October 2020; Accepted: 26 November 2020; Published: 21 December 2020  
**Featurered Application: The ultrasonic pulse velocity test is a widely used, inexpensive, nondestructive, and modest test to define the dynamic properties of rocks in various geotechnical and mining engineering applications as well as in gas and oil explorations under various environmental conditions.**  
**Abstract:** P-wave velocity is employed in various fields of engineering to estimate the mechanical properties of rock, as its measurement is reliable, convenient, rapid, nondestructive, and economical. The present study aimed to (i) correlate natural, dry, and saturated P-wave velocities with the mechanical properties of limestone and (ii) investigate how the ultrasonic P-wave velocities and mechanical properties of limestone are affected by the sample diameter. This study reveals that P-wave velocities under different environmental conditions can be correlated with the mechanical properties of limestone. Further, the R-value variations with different P-wave velocities for a given sample diameter are (i) negligible in terms of the uniaxial compressive strength (UCS) excluding 63.2 mm, (ii) limited for the diametrical point load index (PLI\({}^{\text{D}}\)) except for 53.9 mm, (iii) perceived in case of the axial point load index (PLI\({}^{\text{A}}\)) for 47.7 mm, (iv) observed for the indirect tensile strength (ITS), but generally insignificant, and (v) detected in terms of Schmidt hammer value (SHV) except for 47.7 mm.  
**Keywords:** correlation; ultrasonic pulse velocity; natural P-wave velocity; dry P-wave velocity; saturated P-wave velocity; mechanical property; sample diameter; limestone
# An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application
## 4 Results and Discussion
### Recognizing the Existing Obstacles in the Development of SCs
#### 4.1.7 Smart Parking  
Parking occupies a large amount of the area in a city--81% of the city area in Los Angeles, 23% in Munich, 23% in Paris, 19% in Copenhagen, and 18% in Zurich and Hamburg. Therefore, cities must use of intelligent parking systems in order to reduce congestion and help drivers [56]. In this regard, IoT technology has built a special mobile application in order to solve vehicle parking problems and this has had a remarkable effect for drivers. Based on research, from 2013 to 2018, downloading of the mobile application, increased from 17 million downloads to 80 million, which shows the benefit of this application in solving problems related to parking [57]. Therefore, the importance of smart parking in SCs should be investigated by policymakers [58], considering that finding parking spots can improve the welfare of citizens [59]. This action can be achieved by utilizing GPS data from drivers' smartphones and road-surface sensors embedded in the ground of parking spots. As a result, drivers can be notified of occupied and vacant parking spots via a real-time parking map [60].
## 2 State-of-the-Art in Light Dimming and Lights-Off Visible Light Communications
### Related Work
#### 2.2.4 Coding Techniques  
There are two types of coding in VLC systems: line coding and Forward Error Correction (FEC) codes, such as Reed-Solomon codes. Technical design considerations for improving the physical layer of VLC systems by reviewing the latest developments in modulation and coding technologies were presented in [41]. The main technical challenges for improving the VLC physical layer under lighting limitations are also described.
## 5 Performance Analysis and Results
### Impact of Context on Prediction Accuracy  
Integrating context within query predictions is crucial for improving the virtual assistant's accuracy and reducing the rate of hallucinations. The model is aided by contextual data, which grounds responses in relevant information, thereby minimizing the potential for misleading answers. The RAG method is relied upon in this approach, with contextually relevant documents being retrieved from a pre-defined database. Testing revealed that relevant context dramatically enhances response accuracy, with 65 out of 74 questions (87.73%) correctly answered by the VA when pertinent context is provided through RAG, as can be seen in Figure 5:  
Without context, responses are often fabricated, or the VA introduces inaccuracies. A sharp increase in hallucinations was observed when responses were generated by the model without any supporting information, resulting in incorrect or misleading answers in 75% of cases. The importance of structured contextual data is underscored by this discrepancy, particularly for complex queries that demand a high degree of specificity, such as questions related to administrative procedures or eligibility criteria. In instances where queries were presented without contextual information, it was observed that the nuances were frequently not recognized by the model, leading to logical errors and deviations from expected responses.  
The consistency of responses is also impacted by context. The VA's consistency improvements across similar queries were observed with contextual support, particularly in tests involving enumerative or multi-step answers. The presentation of fragmented information or entirely irrelevant details was prone to occur without context regarding the VA. When context was provided by the RAG method, the VA could pull cohesive and correct information, even for complex procedural questions. The ability of the VA to provide logical and relevant responses was refined through this structured approach, while its capability to filter out unnecessary or incorrect information was effectively reinforced.  
Overall, it has been observed that the inclusion of context through the RAG method yields two primary benefits: the enhancement of factual accuracy in responses and the reduction of hallucinations. This result validates the hypothesis that context-based predictions substantially improve VA performance. Implementing context retrieval protocols in the VA's architecture is essential to maintaining high accuracy and ensuring user trust, particularly in environments where reliable information is paramount.
## 3 Experimental Verification
### Experimental Principles and Platform Construction
#### 3.2.3 Data Acquisition and Processing
The method can be expressed as follows:  
\[u_{1}^{(n+1)}=u_{1}^{(n)}+\frac{k\Big{(}B_{\text{ref}}-B_{\text{max}}^{(n)} \Big{)}}{B_{\text{ref}}}\Big{|}u_{1}^{(n)}\Big{|} \tag{16}\]  
where \(u_{1}\) is the primary side voltage of the sample, \(B_{\text{ref}}\) is the reference flux density waveform, \(B_{\text{max}}\) is the measured flux density waveform, \(n\) is the number of iterations, and \(k\) is the iteration coefficient. During the experiment, the voltage waveform is iterated according to the process in Figure 13, and the waveform after iteration will no longer be the standard PWM waveform; the difference between the PWM waveform before iterating the voltage and after iterating the voltage is shown in Figure 14. From the figure, it can be seen that compared to the PWM waveform before iteration, the PWM waveform after iteration introduces higher harmonics, which makes the controlled magnetic induction intensity waveform consistent with the selected reference waveform, which is one of the key tasks to accomplish accurate measurement. At this point, after the hardware design of the magnetic characterization experimental platform and the implementation of the functions of the modules in LabVIEW, all the preparatory work for the magnetic hysteresis loop measurement has been completed. Before the start of each experiment, it is necessary to run the demagnetization program, so that the initial working point of the grain-oriented silicon steel specimen is near the origin. Then, the appropriate PWM excitation waveform is set in the host computer, and after running the program and outputting it, the magnetic hysteresis loop obtained from the measurement can be displayed in the host computer.
## 4 Principle Laws  
Several fundamental laws or assumptions were used in existing spatiotemporal data fusion methods although they are not often clearly stated. This section highlights and discusses these laws in detail. The most common application scenario is that two coarse images at \(t_{1}\) and \(t_{2}\), and a fine image at \(t_{1}\) are collected. We want to predict a fine image at \(t_{2}\) by fusing the three collected images. Some notations and definitions are given here for convenience.  
m: the number of fine pixels (also named as subpixels) within one coarse pixel;  
(\(x_{i}\), \(y_{i}\)): coordinate index of the \(i\)th pixel;  
C\({}_{1}\)(\(x_{i}\), \(y_{i}\), \(b\)) and \(C_{2}\)(\(x_{i}\), \(y_{i}\), \(b\)): band \(b\) value of coarse pixel (e.g., MODIS) at location (\(x_{i}\), \(y_{i}\)) observed at \(t_{1}\) and \(t_{2}\) respectively;  
F\({}_{1}\)(\(x_{i}\), \(y_{i}\), \(b\)) and \(F_{2}\)(\(x_{i}\), \(y_{i}\), \(b\)): band \(b\) value of fine pixel (e.g., Landsat) at location (\(x_{i}\), \(y_{i}\)) observed at \(t_{1}\) and \(t_{2}\) respectively;  
F\({}_{1}\)(\(x_{ij}\), \(y_{ij}\), \(b\)) and \(F_{2}\)(\(x_{ij}\), \(y_{ij}\), \(b\)): band \(b\) value of the \(j\)th fine pixel within the coarse pixel at location (\(x_{i}\), \(y_{i}\)) observed at \(t_{1}\) and \(t_{2}\) respectively;  
_fc_(\(x_{i}\), \(y_{i}\)): the fraction of class \(c\) of the coarse pixel (\(x_{i}\), \(y_{i}\));  
_F_(\(c\), \(b\)): the band \(b\) value of endmember class \(c\) at fine scale;  
\(\Delta C\)(\(x_{i}\), \(y_{i}\), \(b\)): change of band \(b\) value of the (\(x_{i}\), \(y_{i}\)) coarse pixel between \(t_{1}\) and \(t_{2}\);  
\(\Delta F\)(\(x_{ij}\), \(y_{ij}\), \(b\)): change of band \(b\) value of the \(j\)th fine pixel inside the coarse pixel (\(x_{i}\), \(y_{i}\)) between \(t_{1}\) and \(t_{2}\);  
\(\Delta F\)(\(c\), \(b\)): change of band \(b\) value of class \(c\) at fine resolution between \(t_{1}\) and \(t_{2}\).
# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 3 Methodology
### Multi-Stage Non-Maximum Suppression Algorithm  
In the prediction module of the network, since a great deal of prediction boxes would be generated at each anchor, the detector deletes all the prediction boxes that IoU is less than the threshold when the original non-maximum suppression algorithm of the YOLOv5 network was used. Based on the above unsettled problems, an improved multi-stage adaptive NMS algorithm is proposed to improve the ability to detect the occluding insulators. The multi-stage penalty function is introduced, and the application of this function makes the detection box whose IoU value is greater than the threshold value not discarded. Additionally, the results of all prediction boxes are retained as far as possible to decrease the missed detection rate of insulators in the case of occlusion. The penalty function is shown in Equation (1):  
\[P_{i}=\left\{\begin{aligned} S_{i}\sqrt{1-IoU(M,b_{i})^{3}},IoU(M,b_ {i})<N_{1}\\ S_{i}[0.7-\lg(IoU(M,b_{i})+0.5)],\ N_{1}\leq IoU(M,b_{i})\leq N_{1}\\ S_{i}\sqrt{1-IoU(M,b_{i})^{2}},IoU(M,b_{i})>N_{2}\end{aligned}\right. \tag{1}\]  
where \(S_{i}\) represents the confidence score of the \(i\)-th prediction box; \(N_{1}\) and \(N_{2}\) represent the two thresholds of the penalty function, and \(0<N_{1}<N_{2}<1\). The pseudocode of the improved multi-stage non-maximum suppression algorithm is shown in Algorithm 1.  
```
Input:  Initial candidate box set B = [b1,..., bN]  Confidence set K = [K1,..., KN] Begin  Define empty set D while The candidate box set B is not empty do  Order by confidence score from highest to lowest  The candidate box M with the highest confidence is moved into set D and removed from set B for Candidate boxes in set \(bi\)do if0 < The IoU of candidate frame M and candidate frame bi < N1 then \(Si\) = [1 - IoU(\(M\),\(bi\))\({}^{3}\)]\({}^{1/2}\) elseifN1 \(\leq\) The IoU of candidate frame M and candidate frame bi \(\leqq\) N2 then \(Si\) = 0.7 - 1g(IoU(\(M\),\(bi\))) + 0.5 else\(Si\) = [1 - IoU(\(M\),\(bi\))\({}^{2}\)]\({}^{1/2}\)  Recalculate the confidence K\(i\)  end if  end Output: Set D and the confidence set K End
```  
**Algorithm 1** Multi-stage non-maximum suppression algorithm pseudocode.
## 3 Segment Optimization for Adaptive Driving Beam
### Optimization Algorithms and Constraints  
As shown by [PERSON], different possibilities and optimization algorithms for this kind of problem exist. The general idea for the proposed optimization is that each area that is illuminated by a segment of the adaptive driving beam should be switched on and off  
Figure 5: Density distribution of motor vehicles on motorways from dark blue (no vehicles) to bright yellow (normalized maximum number of vehicles).  
Figure 6: Relative frequency of frames containing cars (orange, dotted line) and traffic signs (red, solid line) at night.  
the same percentage of time as all other segments. Since most traffic, due to the relative angular velocity to the camera, is near the center of the light distribution (see Figure 5, slightly offset to the left due to right-hand side driving, those center pixels should be the smallest. Segments at the outer sides of those light distributions are only turned off very briefly since the angular velocity of oncoming and passing traffic under those angles is the highest; therefore, the duration for which a vehicle is present in those areas is the shortest.  
For this purpose, the data presented in the sections above were used. As already described in [22], a certain number of adaptive driving beam segments were taken and distributed in rows and columns. This was done for all possible combinations between one single pixel (no optimization possible) to 10,000 pixels.  
As a constraint, the segments in each column had to have the same width and each column had to contain the same number of rows. This was done to allow for a somewhat realistic configuration, which could at least in theory be manufactured.  
Since adaptive driving beam may not be used inside city limits, the baseline data for the optimization consisted only of the data obtained on country roads and motorways. The data shown in the following had a 60% weight factor for country roads and a 40% weight factor for motorways. This was done since the most common use of adaptive driving beam is on open country roads, where such a light function also is expected to provide the most benefit. However, any weighting of the data is of course possible.  
Furthermore, the traffic sign distribution was considered as well. At areas where a traffic sign was recognized by the camera, the segment was dimmed down to 75% intensity, since this was found to be the optimal average intensity [33].  
To achieve a segment distribution that could be used in real traffic, another condition was that the overall segment distribution should be symmetrical. This was achieved by additively mirroring the traffic and traffic sign data before optimizing the segment sizes.
## 2 Materials and Methods
### Plasma Treatment and Co-Culture  
Plasma treatment was performed using an atmospheric pressure non-thermal plasma released by the plasma jet kINPen (neoplas, Germany), which was shown to exert no mutagenic effects [37]. As feed gas, argon (99.9999% pure; Air Liquide, France) was used with a gas flow rate of 3 standard liters per minute (slm). Plasma was generated with a sinusoidal voltage of 2-6 kV\({}_{\text{PP}}\) with a frequency of \(\sim\)1 MHz. The plasma jet was operated for 30 min prior to experiments to reduce effects of residual tube humidity on the plasma species composition [38]. The melanoma cells and splenocytes, respectively, were treated directly in 24-well plates. The plasma treatment of one well did not \"spill over\" into an adjacent (e.g., control) well as determined via measuring deposition of oxidants in both the treated and adjacent untreated wells in preliminary experiments (data not shown). The cultured well plate was placed 20 mm distant from the jet nozzle. The distance from the nozzle does play an important role as outlined previously [38]. We adjusted the distance in a way that the jet is as close as possible to the treated solution without liquid spilling into the jet (due to vortexing of the solution by the gas flux) that would extinguish the plasma ignition. The evaporation of the liquid induced by the gas flow of the kINPen was compensated for using a pre-determined amount of double-distilled water (to maintain iso-osmotic conditions) as described before [10]. For co-culture experiments, 1-2 \(\times\) 10\({}^{6}\) splenocytes were added to each well one hour after plasma treatment of melanoma cells.