esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Filter-Integrated Antenna Realized and Tested to Observe Channel Selectivity
(**c**) S\({}_{11}\) and S\({}_{21}\) when the setup places the BPF in the feed for the TX antenna. As an extension of the over-the-air test, since the simulation of the three-dimensional structure is time-consuming, simple numerical experiments are conducted. Figure 12a has the frequency responses of the passband of the filter on the TX-side, which is combined with the antenna. Figure 12b means the received signal has a plot presenting the frequency shift in S\({}_{21}\) as the result of the frequency shift occurring to the TX side. This kind of test makes the proposed work different from other suggestions [18; 19], where the over-the-air transmission is not investigated. Table 9 compares their works with this work from various view-points. As in Table 9, the features of the latest articles showing the filter combined with an antenna and this work are dealt with in the aspects of operation frequency, bandwidths, orders of the filter, waveguide cross-section sizes and lengths. The frequency and bandwidth of [18] is relatively easy to achieve, but that of this work has the highest level of difficulty in  
Figure 12: RF\(-\)link and channel selectivity test which assumes a frequency change in the bandpass filter block. (**a**) S-parameters of filter\(-\)combined antenna with the frequency of the BPF before the change. (**b**) S–parameters of filter\(-\)combined antenna with the frequency of the BPF after the change. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(f_{0}\) & **Bandwidth** & **Order of** & **WG** & **Resonator** & **Filter** \\  & **(GHz)** & **(GHz)** & **the Filter** & **Flange** & **Length (\(\lambda_{g}\))** & **Length (\(\lambda_{g}\))** \\ \hline
[18] & 32 & 2 GHz & 4 & WR-28 & 0.59 & 5.18 \\
[19] & 10 & 0.2 GHz & 4 & WR-90 & 0.61 & 2.85 \\ This work & 7.5 & 0.5 GHz & 8 & WR-112 & 0.05 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparing the characteristics of the proposed filter and others’ filters with antennas. design from the stand points of fractional bandwidth and skirt slope. The orders of filtering of [18; 19] are much lower than that of this work. However, the proposed structure is the most compact in spite of the highest order of filtering. In other words, the size-reduction effect of the waveguide CRLH geometry is superior to that of the references.
# Orbit Determination of Korean GEO Satellite Using Single SLR Sensor
## Abbreviations  
AC Analysis Center  
AO Adaptive Optics  
AGI Analytical Graphics Inc.  
ALOS Advanced Land Observing Satellite  
AMI Advanced Meteorological Imager  
BDS BeiDou Navigation System  
BOL Beginning of Life  
CCR Corner Cube Retro-reflector  
DLT Debris Laser Tracking  
EOL End of Life  
ICESat Ice, Cloud, and land Elevation Satellite  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Direction** & **Radial (m)** & **In-Track (m)** & **Cross-Track (m)** \\ \hline Overlap errors & 3.747 & 22.375 & 22.406 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Orbit overlap differences of Compass-G1.
## 2 Related Work
A user side secure sub-carrier allocation has been studied in Reference [50]. Unmanned aerial vehicle (UAV) secure offloading for single antenna mobile edge systems have been studied in Reference [51]. A block chain based cooperative computation offloading and resource allocation scheme considering security and privacy have been discussed in Reference [52]. These works have mainly focussed on offloading with security without considering privacy and other design challenges (except the last work [52] that considers both security and privacy). The literature review shows that the focus of the existing approaches is on improving or optimizing one or two of the design parameters while we propose to holistically optimize QoS, energy efficiency, security, and privacy. Also, the way our proposed framework UbiPriSEQ adaptively and dynamically deals with the environment by continuous learning is not seen before. Moreover, the use of DRL to provide adaptive intelligence in 5G networks is also novel. Finally, no work similar to UbiPriSEQ exists that have used a real-life application (SpMV) to implement and study the system performance.
## 5 Results
### U-Net Segmentation  
The ROC curve was monitored to set the number of epochs needed for each experiment. Figure 8 shows how the ROC-AUC changes over epochs for each experiment. The training was stopped when it was possible to notice a decreasing tendency of the ROC-AUC value. The training run achieved a mean speed of around 900 ms/step. The dataset for evaluation consisted of 108 images of only the MR type. Models from the best epochs were chosen for evaluation: the 30 th epoch for MR, the 52 nd epoch for augmented MR, the 23 rd epoch for MR with CT, and the 58 th epoch for the augmented MR with CT. Augmentation of the MR dataset led to noticeable improvements in loss, MSE, and ROC-AUC. For MR with CT, dataset augmentation improved only the Mean IoU, giving worse results for other metrics. This allows us to conclude that augmentation did not improve overall model performance and only vastly increased training time. Enlarging the MR dataset with CT improved the loss, accuracy, MSE, and ROC-AUC and gave a slightly lower Mean IoU. A best fold model was chosen for each experiment and evaluated on a test dataset to further examine the effectiveness. The result of the U-Net algorithm is a probability that the pixel will be classified as a prostate for a given threshold. Thirteen (13) different threshold levels were defined and the Dice coefficient was calculated. The results in the form of ROC-AUC are presented in Table 1.  
Maximum values were achieved for the highest threshold for each experiment. The best model should have the highest mean value for the highest possible threshold. It is possible to notice that datasets without augmentation perform better; augmentation lowers the threshold for achieving the highest mean value. The distribution of the Dice coefficient showed more false-positive values for the augmented dataset. Enlarging the MR dataset with CT, on the other hand, influenced training positively and allowed us to achieve a model with the best performance from all experiments. Furthermore, compared to the MR dataset, it increased the threshold value for the best value from 0.4 to 0.6 and lowered the threshold value for the highest possible threshold.
## 2 Basic Theories  
### Multiverse Optimizer Algorithm [19]  
Traditional VMD performs signal decomposition by setting decomposition parameters, which may struggle to fully optimize different objectives. By using the MVO optimization algorithm, a balance between multiple objectives can be achieved, thereby improving the accuracy of the noise reduction effect. The Multiverse Optimizer (MVO) is a population intelligence optimization algorithm proposed jointly by [PERSON], [PERSON], and [PERSON] in 2015, based on the multiverse theory in physics. It simulates the movement behavior of populations in a multiverse under the influences of white holes, black holes, and wormholes. The algorithm is divided into exploration and exploitation phases, where white holes and black holes operate during the exploration phase, while wormholes operate during the exploitation phase. The specific process is as follows:  
The production of the initial population:  
\[U=\begin{bmatrix}x_{1}^{1}&x_{1}^{2}&\cdots&x_{1}^{4}\\ x_{2}^{1}&x_{2}^{2}&\cdots&x_{2}^{2}\\ \vdots&\vdots&\vdots&\vdots\\ x_{n}^{1}&x_{n}^{2}&\cdots&x_{n}^{d}\end{bmatrix} \tag{1}\]  
Then, according to the roulette wheel mechanism, each object in the multiverse system undergoes transitions through the trajectories of white holes and black holes. The process is expressed as follows:  
\[x_{i}^{j}=\left\{\begin{array}{l}\left\{\begin{array}{l}X_{j}+TDR\times \left(\left(ub_{j}-lb_{j}\right)\times r4+lb_{j}\right),\ r3<0.5\\ X_{j}-TDR\times\left(\left(ub_{j}-lb_{j}\right)\times r4+lb_{j}\right),\ r3 \geq 0.5\\ x_{i}^{j},\ r2\geq WEP\end{array}\right.\end{array}\right. \tag{2}\]  
In the equation, \(X_{j}\) represents the j object of the current optimal universe; \(lb_{j}\) and \(ub_{j}\) are the lower and upper bounds of \(x\), respectively. \(r2\), \(r3\), and \(r4\) are random numbers in the range [0, 1]. \(WEP\) (Wormhole Existence Probability) and \(TDR\) (Traveling Distance Ratio) are two important parameters, and their update principles are described by the following equations:  
\[WEP=WEP_{\min}+l\times\left(\frac{WEP_{\max}-WEP_{\min}}{L}\right) \tag{3}\]  
\[TDR=1-\frac{l^{1/p}}{L^{1/p}} \tag{4}\]
## 2 Test Equipment and Test Methods  
The use of square samples with a high-speed camera allows for better monitoring of sandstone deformation, as monitoring with circular samples does not effectively capture the formation of a two-dimensional planar deformation map. The study selected sandstone from a specific mine, which was processed into cubic specimens measuring 50 mm \(\times\) 50 mm \(\times\) 100 mm. A prefabricated fracture was created in the center of each specimen, with a length of 10 mm, a width of 2 mm, and an inclination angle of 45\({}^{\circ}\). Rock porosity was measured using a rock AniMR-150 nuclear magnetic resonance (NMR) meter. The cyclic loading and unloading tests were conducted using a WHY-300/10 microcomputer-controlled electro-hydraulic servo universal testing machine. During the tests, the PCI-II Acoustic Emission (AE) system and a high-speed camera were used for monitoring. The experimental equipment is shown in Figure 1. The AE threshold was set at 40 dB, and the CCD camera could capture images at a maximum rate of 20 frames per second. The tests were controlled by force, with a loading rate of 0.5 kN/s. The loading sequence was as follows: 0 kN \(\rightarrow\) 11 kN \(\rightarrow\) 1 kN \(\rightarrow\) 21 kN \(\rightarrow\) 1 kN \(\rightarrow\) 31 kN \(\rightarrow\) 1 kN \(\ldots\), as depicted in Figure 2.  
Figure 1: Test flow chart.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 5 Simulation Results and Performance Evaluation
### Simulation Results  
In this section, we have presented and discussed the simulation results for both FLMPC-One and FLMPC-Two. Both schemes are compared with LMPC in terms of active nodes, delay, energy consumption, required packet error rate and PRR. The detailed discussion of each parameter along with it's results is presented as follows:
# A Quantitative Detection Method for Surface Cracks on Slab Track Based on Infrared Thermography
## 3 Quantitative Detection Method of Crack Area
### Pixel Length of the Crack  
Since the slab-surface cracks appear uneven in the thickness direction, Zhang-Suen thinning method [23] is used to refine the extracted crack area into a crack skeleton composed of single-layer pixel points, as shown in Figure 6. The pixel length of the crack can then be calculated. Based on the skeleton graph, the pixel points on the crack skeleton are coded by 8-direction chain code proposed by [PERSON] [24]. The chain code is a method to describe a curve by using the coordinates of the starting point of the curve and the direction of the boundary point [24]. The coding process starts from the endpoint of the crack-skeleton map, and the pixel points in the crack skeleton are encoded according to the direction in Figure 7 until all the pixels on the skeleton have been scanned.  
Figure 6: Crack-edge-detection process of infrared image of track-slab-surface crack. (**a**) Extracted crack area. (**b**) Crack-skeleton graph.  
According to the 8-direction chain code of pixels on the crack-skeleton graph, the length of the crack skeleton, i.e., the pixel length of the crack, can be obtained as:  
\[Len_{crack}=wei_{even}Num_{even}+wei_{odd}Num_{odd} \tag{16}\]  
where \(Num_{even}\) and \(Num_{odd}\) are the numbers of even chain codes and odd chain codes in pixel direction coding of the crack-skeleton image, respectively. In Figure 6, the direction of the even chain code is vertical or horizontal, and the segment length of the crack at the pixel point is the number of pixels, with a weight of \(wei_{even}=1\). The crack direction in the odd chain code is \(\pm 45^{\circ}\), and the segment length of the crack at the pixel point is \(\sqrt{2}\) times the number of pixels, with a weight of \(wei_{odd}=\sqrt{2}\).
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 2 Related Work
### Orbit Determination and Time Synchronization Method  
The precise ODTS use GNSS observation data to solve the precise orbit and clock offset of navigation satellites. This method generally uses ionospheric combination observations, and the error equation of its phase and pseudo-range observations can be, respectively, expressed as [18; 19]  
\[v_{k,\Phi}^{j}(i)=\Delta t_{k}(i)-\Delta t^{j}(i)+\rho_{k}^{j}(i)/C+\delta\rho _{k,trop}^{j}(i)/C+\lambda\cdot N_{K}^{j}/C+e_{k,\Phi}^{j}(i)-\lambda\cdot\Phi_ {k}^{j}(i)/C, \tag{3}\]  
\[v_{k,p}^{j}(i)=\Delta t_{k}(i)-\Delta t^{j}(i)+\rho_{k}^{j}(i)/C+\delta\rho_{ k,trop}^{j}(i)/C+e_{k,p}^{j}(i)-P_{k}^{j}(i)/C, \tag{4}\]  
where \(j\) is the satellite number; \(k\) is the station number; \(i\) is the corresponding observation epoch; \(\Delta t_{k}(i)\) represents the receiver clock offset; \(\delta\rho_{k,trop}^{j}(i)\) is the effect of tropospheric delay; \(\Delta t^{j}(i)\) is the satellite clock offset; \(e_{k,\Phi}^{j}(i)\) and \(e_{k,p}^{j}(i)\) are error influences from multi-path and observation noise; \(P_{k}^{j}(i)\) and \(\Phi_{k}^{j}(i)\) are the combined observations of the corresponding satellite, station, and epoch; \(L\) is the corresponding wavelength; \(\rho_{k}^{j}\) is the geometric distance between the satellite position and the receiver position at the moment of signal transmission [21].
## 2 The Design of the Control System
### Fuzzy Logic
#### 2.4.3 Fuzzy Rule Design  
The rate of change in acceleration is most pronounced when the driver has an aggressive driving style and is more similar for different slope conditions. The driver's output increases from 0 (Z) to positive (PB) in the acceleration rate of change domain when driving uphill or downhill, depending on their speed change, where a more conservative output is favorable if the slope is gentle. The effect of speed variation is more pronounced when the driver is a normal type of driving style. Drivers in this category usually have limited acceleration variation, i.e., they do not accelerate or decelerate as often, and are considered to be operating more steadily up and down hills, so their output is maximized only at high slopes, high speeds, and high rates of changes in acceleration. When the driver has a conservative driving style, in order to show the difference from a normal driver, it is assumed that he or she will adopt the most conservative operating strategy in the uphill and downhill conditions and will usually adopt a low speed and low change in acceleration strategy to pass, so the output usually varies from negative big (NB) to positive small (PS). Similar to the moderate driver, the maximum is only reached at high slope, high speeds, and high rates of changes in acceleration. Based on the above design of the fuzzy rules, a total of 135 fuzzy rules were designed and Figure 7 shows the design surface corresponding to the fuzzy rules.  
Figure 6: Plot of affiliation functions for fuzzy logic input and output.  
Figure 7: Cont.  
According to different driving styles, slope changes, speed, and rates of changes in acceleration, the value of the error weight \(Q\) is adjusted in real time and the optimization problem can be solved through quadratic planning, so that the design of the model prediction control model based on driving style is completed.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 3 The Proposed Scheme
### Verification Phase (Consumer-End)  
Sometimes consumers may doubt the legality of the tobacco products that they purchased from the retailer, so they can use the mobile application to check the legality of the tobacco products. Figure 12 shows the consumer checking flowchart where the detailed steps are as follows:  
1. Consumers are required to provide the consumption information by mobile application such as the tobacco ID (TID), retailer ID, purchase ID, and the timestamp purchase that is printed in the invoice.
2. The application executes a chaincode \"GetTobaccoInfo\", as shown in Algorithm 6.
3. If the TID is valid, the BCC returns the tobacco detailed information. Otherwise, the BCC returns that the TID is not valid, so we can determine that this is an illegal tobacco product or trade.
4. The mobile application shows the results of the chaincode, where consumers can obtain the legality and logistics status of the tobacco products.  
Figure 12: Consumer checking tobacco in the verification phase.  
```
funcGetTobaccoInfo(TIDstring,RetailerIDstring,PurchaseIDstring,PurchaseDateTimetime.Time)(resultstring){ index=SearchTID(TID) ifindex>=0 &&TP[index].RetailerID==RetailerID &&TP[index].Purchase_ID==PurchaseID &&TP[index].Payment_Datetime==PurchaseDateTime{ result=TP[index]'sinformation }else{ result=\"Tobaccoinvalid\" } returnresult } } funcVerification(stringID_AP,stringTID,RoleTypetype)(Is_legalbool){ index:=SearchTID(TID) iftype==RoleType.Manufacturer{ IfID_API=TP[index].Manufacturer_ID{ returnfalse } } last_index:=len(TransportRecord)-1 IfID_API=TP[index].TransportRecord[last_index].Recipient_ID{ returnfalse } } returntrue }
```  
**Algorithm 6** Chaincode verification of the proposed scheme.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 2 Telecommunication Systems in an Isolated and Constrained Environment
### Optical Fiber
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Coarse WDM** & **Dense WDM** & **Ultra-Dense WDM** \\ \hline Number of & \multirow{2}{*}{\(<\)17} & \multirow{2}{*}{8 to 128} & \multirow{2}{*}{\(>\)400} \\ wavelengths & & & \\ \hline Channel spacing & 20 nm to 25 nm & 0.4 nm to 1.6 nm & 0.08 nm \\ \hline Spectral width & 1260 nm to 1620 nm & 1500 nm to 1600 nm & 1500 nm to 1600 nm \\ \hline Flow by wavelength & 1.25 to 2.5 Gbits/s & \multirow{2}{*}{10 Gbits/s} & \multirow{2}{*}{\(>\)40 Gbits/s} \\ \cline{4-4} \cline{5-5}  & & & \\ \end{tabular}
\end{table} 
Table 1: Comparison of the different WDM multiplexing.
## 4 Performance Evaluation
### Experiment Setting  
The proposed CDS-C scheme is evaluated using Matlab, which consists of three components including the preprocessor, POS tagging API, and Bayes-based text classifier. The preprocessor is performed to remove stop-words and punctuations, and normalize the training dataset into a customized form, which makes it accessible by a POS tagger and features extractor. A Matlab-based function is utilized to implement the Stanford POS tagger [52; 53], which offers the API that is used to obtain the POS tag for each word in the training dataset. The Bayes-based text classifier is implemented to apply the [PERSON]'s theorem in the analysis of the orientation of the sentiment and prediction of the polarity (positive/negative) of the tested document. In this paper the MNB classifier is utilized as the text classifier with the assumption of feature independence. The workload in the experiments is extracted from Sentiment 140, which is a benchmark dataset derived from Twitter API, and widely adopted in the sentiment analysis [54]. The workload is composed of 1,600,000 documents of labeledtweets where one half is labeled as positive and the other is labeled as negative. The data of Sentiment 140 covers a wide range of domains and data characteristics [55], and two instances are shown in Figure 3. Each data consists of six attributes: the polarity indicator (0 = negative, 4 = positive), user id, posting time, query condition, username, and text of the tweet. Four metrics are adopted to evaluate the performance of feature weighting in sentiment analysis: _accuracy_, _precision_, _recall_, and _F1-score_[19].
## Abbreviations  
\begin{tabular}{l l} Symbol & Definition \\ \(a_{1},a_{2},a_{3}\) & Dimensions of the superquadric on the various axes \\ \(\mathbf{a_{j}}\) & Intrinsic and extrinsic camera parameters of image \(j\) \\ \(\mathbf{b_{i}}\) & 3D point \(i\) \\ \(\mathbf{d}\) & Vector of two points \\ \(E\) & Reprojection error \\ \(\mathbf{E}\) & A set of similarity values \\ \(e\) & Similarity of the edges \\ \(F(x,y,z)\) & Function to determine whether a given point is on the inside or outside of the superquadric \\ \(l_{i}\), \(l_{j}\) & Internal difference of a component \\ \(\mathbf{K}\) & Intrinsic parameter matrix of the camera \\ \(k\) & Number of top eigenvectors \\ \(l\) & Mean distance between the point and the center \\ \(\mathbf{n_{i}}\), \(\mathbf{n_{j}}\) & Normal directions of two points \\ \(\mathbf{P}\) & Points in a 3D space on the image plane \\ \(\mathbf{p_{i}}\), \(\mathbf{p_{j}}\) & Centers of the supervoxels \\ \(p_{x,y}\),\(p_{y}\),\(p_{z}\) & Parameters of the translation matrix \\ \(\mathbf{Q(a_{j},b_{i})}\) & Projection point of object point \(\mathbf{b_{i}}\) under camera parameters \(\mathbf{a_{j}}\) \\ \(\mathbf{s}\) & External product of the normal directions \\ \(s\) & Scaling rate \\ \(|S|\) & Number of points within point group \(S\) \\ \(S_{i}\) & A single group \\ \(s_{x}\),\(s_{y}\),\(s_{z}\) & Scaling ratios \\ \(\mathbf{u_{i}}\),\(\mathbf{v}\) & Coordinates of the image plane \\ \(\mathbf{v_{1}}\),\(\mathbf{v_{2}}\),\(\mathbf{v_{3}}\) & Eigenvectors of the point group \\ \(V_{i}\) & A supervoxel point \\ \(v_{i}\), \(v_{j}\) & Supervoxels \\ \(x\),\(y\),\(z\) & Coordinates of a point on the superquadric \\ \(\widetilde{\mathbf{X}}\) & Center of the point group \\ \(\mathbf{X_{c}}\), \(Y_{c}\), \(Z_{c}\) & Camera coordinate of points \\ \(\mathbf{x_{ij}}\) & Point \(i\) seen in image \(j\) \\ \(x_{j}\) & \(j\)th piece of image sample \\ \(\mathbf{X_{w}}\), \(Y_{w}\), \(Z_{w}\) & World coordinate of points \\ \(\alpha_{i}\), \(\alpha_{j}\) & Angles used to define the type of adjacency between points \\ \(\delta\) & Threshold value used in image segmentation \\ \(\delta_{depth}\) & Threshold value used in image matching \\ \(\varepsilon_{1}\), \(\varepsilon_{2}\) & Shape of the superquadric \\ \(\eta\),\(\omega\) & Surface parameters of superquadric \\ \(\theta\),\(\psi\) & Parameters of the rotation matrix \\ \(\theta_{thresh}\) & Threshold value used to determine the type of stair-like adjacency \\ \(\tau\) & Maximum degree of internal similarity \\ \(\omega_{ij}\) & Degree of similarity between two neighboring points \\ \end{tabular}
## 4 Security Approach
### Detecting Malicious Regions  
With the security approach presented, clients have to query multiple key holders in order to resolve one routing request. Depending on the setup of the Threshold Cryptography algorithm, the minimum number of regions that have to be queried \(k\), varies. As soon as the client has at least \(k\) resolves conducted, it can decrypt the secret \(s\). However, \(s\) can only be retrieved if all resolves are correct. If one  
Figure 5: Key Piece Storage Location.  
of them has been modified, e.g., to disturb the whole mapping system, the secret will be corrupted. In this case, a client can alter the regions which are taken into account for one query. In a further step, the client could also rotate these regions to determine which region has provided an invalid response. This can then be reported to a central entity, e.g., the global authority (GA). We propose that random probes are also conducted by the GA periodically to monitor all regions and the operation of the overall system. This again should be indicated to clients in order to give a hint on each region's credibility.