esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Monitoring of Torpedo Car Shells Based on Equal Angle Scanning
## 2 Theory and Method  
### Infrared Radiation Theory  
According to [PERSON]'s law of black body radiation [12], as long as the temperature of any object in nature is higher than absolute zero (\(-273.15\)\({}^{\circ}\)C), it will emit energy in the form of electromagnetic radiation in a very wide range of wavelengths, generating electromagnetic waves (radiation energy). Different materials, different temperatures, different surface brightness, different colors, etc., emit different infrared radiation intensity. [PERSON]'s radiation law gives the specific spectral distribution of blackbody radiation. At a certain temperature, the energy radiated by a blackbody per unit area in unit time, unit solid angle, and unit wavelength interval is:  
\[\mathrm{B(\lambda,T)}=\frac{2\mathrm{hc^{2}}}{\lambda^{5}}\cdot\frac{1}{\exp (\frac{\mathrm{hc}}{\mathrm{kT}})-1} \tag{1}\]  
\(\mathrm{B(\lambda,T)}\)--Blackbody's spectral radiance (\(\mathrm{W-m^{-2}-S_{r}{}^{-1}-\mu m^{-1}}\))  
\(\mathrm{c}\)--Speed of light \(\left(2.998*10^{8}\mathrm{m\cdot s^{-1}}\right)\)  
T--Absolute temperature of blackbody \(\left(\mathrm{K,T=t+273}\right)\)  
\(\mathrm{h}\)--Planck constant \(\left(6.626*10^{-34}\mathrm{J\cdot s}\right)\)  
K--Boltzmann constant \(\left(1.380*10^{-23}\mathrm{J\cdot K^{-1}}\right)\)  
At a certain temperature, the spectral radiance of a black body has an extreme value, and the position of this extreme value is related to the temperature. This is the Wien displacement law (Wien) [13]:  
\[\lambda_{\mathrm{m}}\times\mathrm{T}=2898(\mu\mathrm{m}\cdot\mathrm{K}) \tag{2}\]  
\(\lambda_{\mathrm{m}}\)--Wavelength at maximum blackbody spectral radiance  
T--Absolute temperature of blackbody \(\left(\mathrm{K,T=t+273}\right)\)  
The normal temperature range of the shell of the torpedo car is 100-500 \({}^{\circ}\)C, so we choose a medium-wave infrared detector (3-5 \(\mu\)m), probing to achieve the maximum signal-to-noise ratio.  
According to [PERSON]'s Formula (1), we put forward the voltage response model of the infrared detector (3):  
\[\mathrm{U}=\frac{\mathrm{A}}{\exp(\frac{\mathrm{B}}{\mathrm{t+273.2}})-1}-\mathrm{C} \tag{3}\]  
Here, t is the target radiation temperature, U is the voltage signal output by the detector, and A, B, C are the correlation coefficients.
# Storing Set Families More Compactly with Top ZDDs
## 4 Top ZDD
### Details of the Data Structure
#### 4.1.3 Information on Complement Edges  
Complement edges are divided into two groups: those stored in the root of the top DAG and those stored in other vertices. We represent them differently.  
First, we explain the data structure for storing complement edges in the root of the top DAG. Let \(E_{root}\) be the set of all complement edges stored in the root. We sort edges of \(E_{root}\) in the preorder of their starting point. Orders between edges with the same starting point are arbitrary.  
For complement edges stored in the root, we store the preorder ids of their starting point using a bit vector \(B_{src\_root}\), the preorder ids of their ending point using an array \(dst\_root\), and edge labels 0/1 using an array \(\textit{type\_root}\). The cluster corresponding to the root of the top DAG is the spanning tree of the ZDD. For each node \(v\) of the spanning tree, we represent the number of complement edges in \(E_{root}\) whose starting point is \(v\), using a unary code. Unary code is a method of representing a natural number by the length of continuous 1's. We concatenate and store them in preorder in the bit vector \(B_{src\_root}\). For edges in \(E_{root}\) sorted in preorder of the starting points, we store the preorder id of the ending point of the \(i\)-th edge in \(dst\_root[i]\), and set \(\textit{type\_root}[i]=0\) if the \(i\)-th edge is a 0-edge, and set \(\textit{type\_root}[i]=1\) otherwise.  
Next, we explain the data structure for storing complement edges in vertices other than the root. Let \(E_{in}\) be the set of those edges. We sort the edges as follows.  
1. We assign each edge of \(E_{i}n\) to a cluster such that it contains both its starting and ending points. Then, we divide \(E_{i}n\) into several groups, each corresponding to a certain cluster. We sort these groups by the preorder id of the top tree vertex corresponding to the cluster.
2. Inside each cluster \(cl(x)\), we sort the edges of \(E_{in}\) in preorder of starting points of the edges. For edges with the same starting point, their order is arbitrary.  
We store the sorted edges of \(E_{in}\) using a bit vector \(B_{edge}\) and three arrays \(\textit{src\_in}\), \(dst\_in}\), and \(\textit{type\_in}\). The bit vector \(B_{edge}\) stores the numbers of complement edges in vertices of \(T^{\prime}\) by unary codes. The arrays \(\textit{src\_in}\), \(dst\_in}\), and \(\textit{type\_in}\) are defined as: \(\textit{src\_in}[i]=(\text{the local preorder id of the starting point of the $i$-th edge inside the cluster})\), \(dst\_in}[i]=(\text{the local preorder id of the ending point of the $i$-th edge inside the cluster})\), \(\textit{type\_in}[i]=0\) if the \(i\)-th edge is a 0-edge, and \(\textit{type\_in}[i]=1\) otherwise.  
Table 1 summarizes the components of the top ZDD.
# Decentralized Public Transport Management System Based on Blockchain Technology +
## 3 System Design and Implementation
### Data Collection and Transmission  
To operate the system and maintain the technical conditions of a bus, it is necessary to obtain actual information about its operation, e.g., the manufacturing year and fuel type, as well as the features that the manufacturer lay down. All this information may be obtained through the internal CAN bus from the vehicle's onboard computer. Part of the analytics takes place directly on the vehicle, so various messages can light up on the driver's dashboard.  
Information from existing sensors, as well as from additionally installed ones, is sent to the onboard computer. From there, it must be taken into the system using the cellular network [45]. Further, information about technical conditions when entering the blockchain is stored in the transaction history of the vehicle.  
The amount of transmitted data depends on the following:  
* The type of vehicle;
* The number of IoT sensors per vehicle;
* A variety of options for the transmitted information;
* The frequency of data transmission.  
All information is transmitted in the form of time series. On average, a bus may contain up to 20 IoT sensors covering all the following main technical nodes: engine, suspension, turbine, fuel system, cooling system, etc. The primary task is to collect information from them, aggregate it, and transfer it to the cloud. For one value, it is enough to allocate 16 characters, that is, 128 bits of information. Accordingly, for one bus, 2560 bits are transmitted every minute. This frequency is sufficient for analyzing technical conditions, even when discussing autonomous transport. This is enough, since the model does not use complex sensors like a Lidar or a camera.  
For one bus per day, this volume is 3,684,400 bits, and for a system with 500 buses, this is already 1,843,200,000 bits per day for analysis. Honestly, this is a tiny amount to analyze. This volume must be efficiently processed and stored. For this, it is important to determine the compression algorithm and the structure of the blockchain. The actual amount of data will depend on the specific situation, and this calculation is indicative. Possible types of data and their transmission frequencies are presented in Table 4.  
Figure 1: Proposed integration model.  
Rush hours and the day of the week affect the stored data volumes. The morning and afternoon hours are the densest. The average distribution of passengers for one bus is shown in Figure 2. This distribution is taken from the empirical data of a public transport company. The graph shows peaks in the morning and evening hours. However, the actual distribution may differ between specific conditions and routes, and also strongly depends on the day of the week.
# A Robust Security Architecture for SDN-Based 5G Networks
## 5 Performance Evaluation
### Simulation Set up  
We realize our scheme by Mininet emulator and OpenDaylight as shown in Figure 7. The OpenDaylight is used to perform an SDN controller. The SGW and LSA used in our scheme are modeled by OpenHIP. The hash value in the authentication scheme are generated by SHA-1. For each kind of attack, we test three kinds of settings--_sparse_, _moderate_ and _dense_--with respect to the amount of hosts used in the network. There are four hosts in _sparse_, 50 hosts in _moderate_ and 500 hosts in _dense_. In this work, we estimate the performance of our work by using the disconnect rate, which is derived as:  
\[r=num_{dis}/100, \tag{1}\]  
where \(num_{dis}\) is the number of disconnects in our simulations. In this paper, we compare the performance of our scheme with the state-of-the-art \(TFSv1\)[24] and \(SDecurity\)[25]. The \(TFSv1\) is widely used in today's commercial SDN Network but is vulnerable to the three kinds of attacks mentioned since it is lack of authentication. \(SDecurity\) is a typical Software Defined Security Experimental Framework. In our simulation, we assume that the attacker has the prior knowledge of our authentication process, and generate random secrets instead of using the outdated secret obtained by eavesdropping, namely, in this worst case, the attacker can have the chance to break through the security architecture when they guess the right secret key, and we assert that this rate will be much better when deployed in practice.
## 2 Fundamentals of TEM Based on TDFEM
### Verification of Algorithm Accuracy  
To verify the accuracy of the finite unit algorithm, the geoelectrical model proposed in this paper is based on a 3D sphere model considering the good symmetry of a sphere. The sphere model has a radius of 180 m, and the 18 m thick spherical shell at its outermost edge is set as the infinite-element domain. The geoelectric model is built inside, as shown in Figure 4a. The procedure is as follows: A circular coil with a radius of 1 m and a cross-sectional radius of 25 mm is placed at the center of the sphere, with a coil emission current waveform of 2 A, a shutdown time of 20 \(\upmu\)s, a transmit backline of 60, and a receiving line of 40. Using non-uniform meshing, it is divided into 114,834 tetrahedron meshes. The analogue time starts at 0 s, and the current starts to shut down at 0.5 ms, with a shutdown time of 20 \(\upmu\)s. The 1.5 ms analogue time ends when the current is switched off. The method of local-refinement time-step sectioning is applied, where a time step of 2 \(\upmu\)s is used in the current shutdown process and in the range of 20 s before and after, and a time step of 20 \(\upmu\)s is used for the rest of the duration. This can also ensure the calculation accuracy and significantly improve the computational efficiency.  
Figure 5a,b compares the numerical values obtained from the full-space model and the analytical solution. The compared parameter is the induced electromotive force at the center of the coil. The full-space numerical simulation results agree the analytical solution in each period, and the relative error in the induced electromotive force is below 4.5%.  
Figure 4: Results of non-uniform grid subdivision.  
Figure 5: Comparison of numerical and analytical solutions.
# Surface-Modified Compounds Improve the Detection Sensitivity of Terahertz Metasurface Biosensors
## 4 Discussion  
To clearly discussion the experiment result, there need to compared the results of the above three parts. When there had not modification on the sensor, the detection of CEA, NSE and SCC did not show high sensitivity, only about 0.53-0.57 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)), and there was no obvious linear relationship between the change of the concentration and the frequency shift. After the surface of the sensor was modified with WS\({}_{2}\), the detection effect of the three markers was significantly improved, and the sensitivity reaches 0.72-0.85 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)). There was also an obvious linear relationship between the concentration and the frequency shift. Meanwhile, the detection results after the sensor are modified by HNT, the sensitivity reaches 1.17-1.26 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)), which has a significant improvement, and also keeps a good linear relationship. So, the sum up, the modification of metasurface sensors using HNT will lead to higher detection sensitivity and linearity of the sensors for cancer markers such as CEA, NSE, and SCC. The reason for this effect may be that HNT has a larger specific surface area than the blank sensor surface and WS\({}_{2}\), which can provide more attachment points for biomarkers. Meanwhile, HNT also has a better effect on surface local electric field enhancement.
## 2 Material and Methods  
### Cell Lines and Reagents  
MV4-11, MOLM13, HL60, and OCI-AML3 cells were obtained from the German Collection of Microorganisms and Cell Lines (DSMZ) and cultivated in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 100 \(\mu\)L Plasmocin(tm) per 500 mL, and passaged every 2-3 days. FLT3-ITD-expressing 32D cells and Ba/F3 cells were obtained as described earlier [28; 29; 30] and cultivated in RPMI 1640 medium supplemented with 10% FBS, 2.5 ng/mL murine IL-3, and 1 mM sodium pyruvate. HEK293 cells with tetracycline (tet)-inducible NOX4 expression [31] were kindly provided by Prof. [PERSON] (University of Geneva, Geneva, Switzerland), and cultivated in DMEM medium supplemented with 10% FBS. Cell lines were routinely checked for absence of mycoplasma by PCR assay.  
To obtain FLT3-ITD/MA9 or MA9 cells, 2 \(\times\) 10\({}^{6}\) bone-marrow (BM) cells of C57/BL6 _Flt3_(TID/ITD knockin [32] or C57/BL6 wildtype mice were transduced by standard techniques (details available on request) with pMSCV-MLL-AP9/GFP (retroviral vector expressing the human MLL-AF9 fusion gene and an IRES-GFP, kindly provided by Prof. [PERSON], Dana-Farber Cancer Institute, Boston, MA, USA). In case of FLT3-ITD cells, 5 \(\times\) 10\({}^{4}\) GFP\({}^{+}\) cells were transplanted two days later via lateral tail vein injection into sublethally irradiated (7 Gy) C57 BL/6 'incubator mice'. Moribund mice were sacrificed and BM cells were isolated and cultured in RPMI medium without any cytokines. MA9 cells were directly obtained from pMSCV-MLL-AF9/GFP-transduced BM cells by cultivation in medium supplemented with IL3. Primary human AML cells were obtained with informed consent of patients from peripheral blood as described earlier [25] (permission ethic board of Jena University Hospital 4653-04/16).  
Setanaxib (GKT137831) was obtained from Selleckchem (Munich, Germany), or MedChemExpress (Monmouth Junction, NJ, USA). Daunorubicin, doxorubicin, and cytarabine were obtained from the Pharmacy, Jena University Hospital (Jena, Germany). AC220 was a kind gift from Prof. [PERSON] (University of Regensburg, Regensburg, Germany). Midostaurin was from [PERSON] (Taufkirchen, Germany). Concentrated compound stock solutions were generated in DMSO and diluted with medium as required, the final DMSO concentration in the cell culture was 0.05%.
## 4 Results and Discussion
### Packet Delivery Ratio  
In the following Equation, the packet delivery ratio (_PDR_) is expressed as follows:  
\[PDR=\frac{NP_{\textit{PRTx}}}{NP_{\textit{PktTx}}}\times 100\% \tag{12}\]  
In this case, the values of \(NP_{\textit{PktTx}}\) and \(NP_{\textit{PktTx}}\) reflect the total number of data packets received and transmitted, respectively.  
As may be seen in Figure 14, the PDR for each protocol is shown. The results show that EQPD-MAC protocol outperforms RMP-MAC and MPQ-MAC protocols by up to 25% and 14.5%, respectively. In MPQ-MAC and RMP-MAC protocols, the sensor nodes become unresponsive for long periods of time when the buffer limit is exceeded, which results in data packets being dropped. It is also worth noting that in RMP-MAC, the PDR drops considerably as the number of senders increases, indicating that the retransmission limit has been exceeded. The proposed protocol delivers consistently around 100% PDR, like PMME-MAC, for any number of sensor nodes deployed in the network. The first reason is that EQPD-MAC does not encounter any network disruptions, and the sensor nodes are always ready to receive packets from sensor nodes. Secondly, all of the sensor nodes involved in packet routing are synchronized. To prevent packet loss, the sensor nodes check the TA as soon as it receives the Tx-beacon if it has enough time to successfully receive a packet and sensor nodes can extend the TA if required.  
Figure 14: Packet delivery ratio (PDR).
## 2 Materials and Methods
### Support Vector Machines  
SVMs take a different approach to classification, employing linear algebra to find a separation between the classes. This is done by fitting a hyperplane that best separates the clusters, such that the margin between the closest data point from each class and the hyperplane is maximized. These closest points are called support vectors. If a direct separation in the dimensionality of the data is not possible, the data is transformed to a higher dimension, where the fitting is carried out. This is done through the kernel-trick. A commonly used kernel is the radial basis function (RBF) kernel [25].  
We also used the scikitlearn-library for the implementation of the model [24].  
Just like in the case of RFs, for SVMs we also performed a grid search for the optimal parameters for a given data set. As a kernel, we used the RBF kernel. We performed a grid search on the following inputs to determine the best-performing SVM:  
1. C: regularization parameter for the \(L_{2}\)-regularization.
2. gamma: controls the shape of the decision boundary.
## 4 Results and Discussions
### Effect of Pile Inclinations  
To quantitatively analyze the effect of pile inclinations under different surcharge magnitudes on the responses of pile and soil with the least figures, example cases of two surcharge magnitudes (\(p\) = 34.375 and 84.375 kPa) were given to avoid repeated data disclosure because of similar tendencies. Some data of the other surcharge cases (e.g., \(p\) = 134.375 and 184.375 kPa) will be mainly discussed in the following sections.
## 3 Results and Discussion
### Contact Angle Measurement
Surface roughness is also important in determining the surface wettability. The wettability of the surface increases as the contact angle decreases [11; 23; 39; 41; 49; 56]. Rough surface substrate has smaller contact angle, meaning that the water droplet was thinner, indicating the surface is hydrophilic [17]. As discussed in the previous section, the rubber of 7% and 10% NaOH treatment has a rougher surface compared to 1% and 4% NaOH treated rubber (Figures 7 and 8), which contributed to the lower contact angle measured in this work. The surface energy of the rubber is equally important in affecting the surface wettability. Surface energy increases with increasing surface roughness, the surface with higher surface energy has a smaller contact angle, thus the surface can be wet well by the liquid [49]. The surface with more functional groups has higher polarity, which also provides the rubber with higher surface energy. As referred to the FTIR analysis in the previous section, treatment with higher NaOH concentrations facilitated oxidation on the rubber surface which generated more functional groups [11; 22; 23; 24; 31; 49; 50; 55; 57]. Figure 16 clearly demonstrated the correlation between the composite tensile strength and the contact angle of the rubber surface. Higher concentration NaOH sufficiently altered the rubber surface and obtained the maximized surface properties. Thus, it increased surface wettability which allowed better matrix coating on the rubber, enhanced the rubber-polyester interface bonding and improved the composites tensile strength.
# Leveraging Static and Dynamic Wear Leveling to Prolong the Lifespan of Solid-State Drives
## 3 Greedy with Migration Pool
### Data Structure  
The Greedy-MP policy categorizes blocks that become candidates for victim selection during garbage collection, specifically blocks with no clean pages, into two pools: the normal pool and the migration pool. The normal pool comprises blocks with relatively low erase counts, while the migration pool contains blocks where excessive erasures have occurred, leading to the migration of cold data.  
Initially, whenever a block exhausts its clean pages, it is inserted into the normal pool. However, if a specific block exhibits an excessively high erase count compared to others, cold data migration is performed on that block, and the migrated block is then inserted into the migration pool. Consequently, the migration pool remains empty until the first execution of migration. However, as the frequency of migration increases over time, the size of the migration pool gradually expands while the size of the normal pool diminishes.  
Both the normal pool and the migration pool, as depicted in Figure 1, consist of \(N+1\) linked lists each, where \(N\) represents the number of pages within a block. Specifically, blocks with zero valid pages are linked to the header with index 0, while blocks with one valid page are linked to the header with index 1, and so forth. Thus, when blocks are inserted into each pool, they are added to the corresponding list based on the number of valid pages they contain. Additionally, as pages within a block become invalidated, the block is moved to a different list within the pool based on the number of valid pages. Managing each pool with individual lists based on the number of valid pages offers the advantage of quickly finding the block with the highest invalidation ratio during garbage collection [21].  
In other words, in the garbage collection process, the system checks each pool starting from index 0 to see if there are any blocks linked to that index. For example, if there is a block linked to index 0, it means that the block has no valid pages, making it a candidate for the victim block. If there are no blocks linked to index 0, the system then checks index 1 to see if any blocks are linked there. If a block is linked to index1, it is selected as a candidate for the victim block.