esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix
## 2 The Alternating Nonnegative Least Squares for NMF
#### The Symmetric Case
```
functionSym_IMF(A,W_0,H_0,\(\alpha_{0},\
u_{\max}\))
```  
```
\(v=0\); \(\epsilon_{0}=\|A-W_{0}W_{0}^{T}\|_{F}^{2}\); repeat \(\
u=\
u+1\); \(\{W_{v},H_{v},\alpha_{v}\big{\}}=\texttt{Sym\_ANLS}\big{(}A,W_{v-1},H_{v-1}, \alpha_{v-1}\big{)}\); \(\epsilon_{v}=\|A-W_{v}W_{v}^{T}\|_{F}^{2}\); \(cond=|\epsilon_{v}-\epsilon_{v-1}|/\epsilon_{v}\leq\eta_{2}\); until\(cond\ ||\ (v\geq\
u_{\max})\); return\(\big{(}v,\ W_{v},\ H_{v},\ \alpha_{v},\ cond\big{)}\);
```  
The effective dimension \(k_{e}\) of \(\Pi\) can be smaller than the expected \(k\), because void clusters may turn out (see the following function Partition, where \(\{\ \}\) denotes the void set). ```
functionPartition(W)
```  
```
\(\pi_{r}=\{\ \}\)for\(r=1,\ldots,k\); for\(i=1,\ldots,n\) let\(j\) be such that \(w_{i,j}=\max_{r=1,\ldots,k}w_{i,r}\); append\(i\)to\(\pi_{j}\) endfor; \(\Pi=[\pi_{1},\ldots,\pi_{k}]\); \(k_{e}=k\); for\(r=1,\ldots,k\) if\(\pi_{r}=\{\ \}\)then discard\(\pi_{r}\); \(k_{e}=k_{e}-1\) endfor; return\(\big{(}\Pi,\ k_{e}\big{)}\);
```  
When applying Sym_IMF we expect that, together with the stabilization of the sequence of the residuals \(\epsilon_{v}=\|A-W_{v}W_{v}^{T}\|_{F}^{2}\), a form of stabilization of the corresponding clustering should also take place. Actually, we have verified experimentally that, in general, the stabilization of the clusterings proceeds irregularly, before the residuals \(\epsilon_{v}\) show a substantial reduction. This is the reason why Sym_NMF does not rely on early clustering stabilization, which could produce incorrect results, and exploits the decrease of the residuals.
# A Comparative Performance Analysis of Four Wind Turbines with Counter-Rotating Electric Generators
## 4 Numerical Simulations and Discussions  
The _WSCG_ analytical model, described by the relations in Tables 1-3, contains eight independent parameters: the interior kinematic ratios \(i_{01}\) and \(i_{02}\), the interior efficiencies \(\eta_{01}\) and \(\eta_{02}\), the primary wind rotor power parameters (\(\omega_{R1}\) and \(T_{R1}\)) and the ratios \(k_{\omega}\) and \(k_{t}\). The numerical simulations are based on the input data from Table 4, where \(i_{01}\), \(i_{02}\), \(\eta_{01}\) and \(\eta_{02}\) are constant parameters of the mechanical transmission, \(k_{\omega}\) and \(k_{t}\) are independent parameters used as variable simulation ratios whose values can be controlled by appropriately adjusting the pitch angles of the wind rotor blades.  
The numerical simulations are performed under the following assumptions:  
* Both secondary rotors (\(R2\) and \(R3\)) rotate in opposite directions to the primary rotor \(R1\) (by exception, rotor \(R2\) may also have the rotation direction of rotor \(R1\), a situation highlighted in Section 4.1);
* The powers of rotors \(R1\), \(R2\) and \(R3\) are input powers for the transmission, i.e., \(P_{R1}>0\), \(P_{R2}>0\) and \(P_{R3}>0\) (but there are also exceptions in which rotor \(R2\) becomes the brake, i.e., \(P_{R2}<0\), as highlighted in Section 4.2);
* The counter-rotating electric generator \(G\) is characterized by the operating speed \(\omega_{G}=\omega_{GR}-\omega_{GS}\), the torque \(T_{G}=T_{GR}=-T_{GS}\) and by the mechanical power \(P_{G}=T_{G}\omega_{G}\) (considered as the transmission output power, i.e., \(P_{G}=T_{G}\omega_{G}<0\), Figure 2);
* In order to simplify the comparative energy analysis of the selected cases, the mechanical characteristics related to the wind rotors and the generator are disregarded; as a result, the power of the primary rotor \(R1\) will be further considered as the reference power, and the other powers will be expressed by ratios of the type \(P_{x}/P_{R1}\), whatever the \(x\) shaft, called reduced powers (at the primary rotor).
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 6 Transportations in 5G and Beyond
### Challenges
Traffic management and optimization based on big data and artificial intelligence [39] improve latency and increase the performance of traffic flow. According to [40; 41; 42; 43; 44; 45], many new challenges will be possible to enhance the QoS and EE through efficient, intelligent techniques integrated within the proposed framework. These enhancements will certainly improve future ITS and C-ITS. For instance, medical testing of passengers and their responses before starting a journey will be a potential challenge. All ADVs face many complicated issues such as response time of results, the accuracy of early detection techniques, and energy management, which are potential problems. The proposed model will be flexible and enhance the VCS with the emerging 5G+ technologies to improve the solutions to these issues. The importance of UAV-assisted ITS is vital in densely populated areas, where on-road delivery is not a perfect solution for delivering services in emergencies. Regarding the VCS, our proposed framework supports UAV-assisted ITS. Although we have studied the various framework for improving transportation facilities, emerging technologies based on SDN allowed us to propose a flexible framework. The challenge of this framework is to adapt the features of new user requirements considered in the 5G+ systems. Decision-making of ADV via network calculus can be possible in this framework, and it supports analyzing performance with quick decision-making. In future transportation systems such as ADV, multiple services can be improved with the results obtained from this research. Hence, ADV needs massive wireless connectivity considered in 5G and 5G+ wireless networks.
## 5 Energy Efficiency of the MHCP-MHCP Network
### Total Power Consumption  
In this paper, the total power consumption of all the BSs in the MHCP-MHCP network is defined as  
\[P_{total}=\sum\
olimits_{k=1}^{2}A\lambda_{k-MHCP}P_{k} \tag{18}\]  
where \(P_{k}\) represents the power consumption of a BS in the \(k\)-th tier. In general, the BS consists of static power consumption and transmit power consumption [46; 47]. In this paper, we use the linear power consumption model given in [42], which is  
\[P_{k}=N_{TRk}\cdot P_{Ck}+\theta_{k}P_{Tk} \tag{19}\]  
where \(N_{TRk}\) is number of the transmit antenna elements of a BS in \(k\)-th tier, \(P_{Ck}\) is the static power expenditure, and \(\theta_{k}\) is the slope of power consumption related to load. Combining BS density and area of the MHCP-MHCP network, the total power consumption of all BSs in the MHCP-MHCP network can be written as  
\[P_{total}=A\lambda_{1-\text{MHCP}}(N_{TR1}\cdot P_{C1}+\theta_{1}P_{T1})+A \lambda_{2-\text{MHCP}}(N_{TR2}\cdot P_{C2}+\theta_{2}P_{T2}) \tag{20}\]  
Substituting (17) and (20) into (10), the energy efficiency of the MHCP-MHCP network can be expressed as  
\[\eta_{EE}=\frac{R_{total}}{P_{sum}}=\frac{\frac{B\pi\lambda_{u}}{ C(\alpha)}\sum\limits_{k=1}^{2}\frac{\lambda_{k-MHCP}P_{Tk}^{2/\alpha}(\beta_{k}/G_{k} )^{-2/\alpha}\log_{2}(1+\beta_{k})}{\sum\limits_{k=1}^{2}\lambda_{k-MHCP}P_{ Tk}^{2/\alpha}\left(1+1.28\frac{\lambda_{u}P_{Tk}^{2/\alpha}(\beta_{k}/G_{k})^{-2/ \alpha}}{\sum\limits_{k=1}^{2}\lambda_{k-MHCP}P_{Tk}^{2/\alpha}(\beta_{k}/G_{ k})^{-2/\alpha}}\right)}}{\lambda_{1-MHCP}(N_{TR1}\cdot P_{C1}+\theta_{1}P_{T1})+ \lambda_{2-MHCP}(N_{TR2}\cdot P_{C2}+\theta_{2}P_{T2})} \tag{21}\]
## 6 Data Transmission
### Data Transmission Path  
**Theorem 1**: _In Figure 3.2, if \(d\leq R_{t}<\sqrt{3}d\), the transmitter can only transmit the sensed DATA to its neighboring sensors centered at the surrounding grids, and the DATA are transmitted from outside to inside layer-by-layer._  
**Proof 1** (Proof of Theorem 1).: __  
As shown in Figure 3.2 (part of Figure 3.2), Sensor A can transmit its data to other sensors only if the sensor transmission range exceeds the distance between neighboring sensors, _i.e._, \(d\leq R_{t}\) (see Equation (3.2)). As mentioned in Equation (3.2), to guarantee that data collected or received by Sensor A can only be transmitted to its neighbors, \(R_{t}<\sqrt{3}d\) should hold to make non-neighboring sensors out of its transmission range, since the distance from Sensor A to its second closest sensor is \(\sqrt{3}d\). According to the GPSR protocol, since the sensors in the inner layer are closer to the base station, the transmitter can only send the sensed DATA to an inner layer that is next to it.  
Hence, when \(d\leq R_{t}<\sqrt{3}d\) holds, the sensor in layer \(i\) can only receive the data from the nearest sensor in layer \((i+1)\) and send the data to the nearest node in layer \((i-1)\). The assumption that data are transmitted from the outside to the inside, layer-by-layer, is widely used in WSN lifetime or energy optimization problems. Readers are referred to [11, 12, 13, 14, 15], _etc_.  
Because of the symmetry of a regular hexagon, we only need to study one-sixth of the whole network, _i.e._, a regular triangular area, to obtain the data transmission path to the base station (expressed by red lines in Figure 1). In the figure, the circles identify the sensor deployment locations. The position of a sensor is numbered by \((i,j)\), which indicates that the sensor is located at the \(j-\mathrm{th}\) position of the \(i-\mathrm{th}\) layer. After obtaining data transmission paths for all sensor locations in one regular triangular area, since the data transmission paths for the other five triangles are the same as the one studied, the amount of data transmitted and received at each sensor location in the target area can then be calculated.  
Theorem 1 shows that the sensed data will be transmitted to the neighboring sensors in an inner layer. Then, we can find the data transmission path of WSN by computing the distance between neighboring sensors and the base station. For sensors located at \((2x,x+1)\) (\(x=1,2,\ldots\), e.g., (2,2), (4,3), (6,4), \(\ldots\)), from a vertical line of the triangle, there are two next-hop possibilities since two reception sensor locations in the next inner layer are sharing the same distance from the base station. We assume that if  
Figure 4: Data transmission path.  
Figure 3: Data transmission analysis.  
a sensor has two transmission possibilities, the chance of choosing either path is 0.5. For other sensors, there is only one possible next hop, as we can only find one sensor location close to the base station in the next inner layer.
# Automated Exploratory Clustering to Democratize Clustering Analysis
## 4 Experimental Works
### Clustering Algorithms  
To generate different clusterings with different characteristics, we used four clustering algorithms. These algorithms all have particular intrinsic notions of a clustering. We selected these algorithms to represent different notions of clusterings. An overview of these algorithms is given in Table 3. All algorithms have parameters, which we fine-tuned using the Particle Swarm [63] method to optimize the DBCV value. We used the implementation of [64] for the hyperparameter optimization. We used a swarm size of 10 and a generation number of 3, leading to 30 different clusterings per dataset and algorithm, out of which we pruned double clusterings.  
The classical k_Means_[11] algorithm produces clusterings consisting of spherical, convex clusters without noise. \(k\) centroids are determined, and every object is assigned to the closest centroid, resulting in \(k\) clusters. The centroids and assignments are updated iteratively until convergence. We used the implementation in [65] and set the bounds for k to the interval \([2,30]\).  
_HDBSCAN_[13] assigns clusters to objects based on the distance to other objects. The approach is linkage-based and finds clusterings of arbitrary shapes. In this paper, we used the implementation of [66], where we tuned the parameter min_cluster_size in the interval \([1,30]\).  
The _Menshift_[14] algorithms sets centroids in the mean of a region of points. We used the implementation of [65], where we set the bounds for bandwidth to the interval \([0.1,50]\).  
The _OPTICS_[30] algorithm is a density-based approach similar to HDBSCAN. When all reachabilities are computed, a cut on these is made to generate the clusters. We used the implementation in [65] and set the bounds for xi to \([0.025,0.5]\) and for min_samples to \([3,20]\).
## 8 Conclusions and Future Work  
The irregularity of the field, driver's behaviour and number of field-runs by machinery has a direct impact on the work area calculation. By getting the machinery work area we can perform the authentication of machinery usage, allocate resources efficiently, calculate the yield per unit area, measure the cost of machinery operation and study the driver's behaviour. The GPS and AI are good at finding the precise work area of agriculture farm machinery. The convex shape algorithms like Delaunay were good at finding areas of regular shapes but gives inaccurate numbers when dealing with irregular shapes.  
The concave hull algorithms like KNN was good in detection of both regular and irregular shapes, however it was computationally expensive for larger data sets. KNN when combined with FLANN showed good accuracy and was computationally cheaper compared to other algorithms. The KNN is a very good solution, but it could show incorrect results when the study is performed around the perimeter only. The functionality of KNN was further extended to irregular and sparse work using Contact-based algorithm. The combination also helped find the number of field-runs.  
The Contact-based area calculation uses a distance mechanism and was helpful finding the area and number of field-runs. The mechanism expects that the field was covered in good proportion in all regions. If the work was performed around the boundaries (as is the case in Figures 2 and 4), the Contact-based mechanism will give incorrect results. Therefore, KNN, Delaunay, KNN with FLANN and Contact-based mechanism are useful only when used in conjunction.  
As part of future work, the work can be extended to show a relationship between number of GPS samples of data points and precision of work area calculation. The boundary detection for a small number of points is difficult to achieve, a technique could be derived to deal with this problem. We can integrate the system in this paper, with a billing system to estimate the cost of machinery operation using the calculated work area. By measuring the cost of machinery and the net yield produced, we can effectively allocate the resources to increase the production of farms. Studying the driver behaviour, we can highlight the practices that cause low yield and effectively introduce the best agriculture machinery operation practices.  
Conceptualization, M.W., T.K. and A.K.; methodology, M.W., T.K., T.-W.U. and A.K.; software, M.W. and A.I.; validation, T.K., T.-W.U. and A.K.; formal analysis, T.K., T.-W.U. and A.K.; investigation, M.W., T.K. and A.I.; resources, T.K. and T.-W.U.; data curation, M.W. and A.I.; writing--original draft preparation, M.W.; writing--review and editing, M.W., T.-W.U. and A.K.; visualization, M.W., T.-W.U. and T.K.; project administration, T.-W.U.; and funding acquisition, T.-W.U. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.2018R1A2B2003774). The authors declare that they have no conflict of interest.
Regarding these misalignments in the setup, the diffraction equation in Equation (2) for the first-order diffracted beams can be modified as follows:  
\[P_{pos\_i} =\frac{\lambda}{\sin\theta_{\mathrm{in}}+\sin\theta_{\mathrm{ diff}}}=\frac{\lambda}{\sin\bigl{(}\Delta\theta_{+1}-\frac{\alpha}{2}\bigr{)}+ \sin\bigl{(}\Delta\theta_{+1}+\frac{\alpha}{2}+\beta\bigr{)}}=\frac{\lambda}{2 \cos\bigl{(}\frac{\alpha-\beta}{2}\bigr{)}\sin\Bigl{(}\Delta\theta_{+1}+\frac {\beta}{2}\Bigr{)}} \tag{4}\] \[P_{neg\_i} =\frac{\lambda}{\sin\theta_{\mathrm{in}}+\sin\theta_{\mathrm{ diff}}}=\frac{\lambda}{\sin\bigl{(}\Delta\theta_{-1}+\frac{\alpha}{2}\bigr{)}+\sin \bigl{(}\Delta\theta_{-1}-\frac{\alpha}{2}-\beta\bigr{)}}=\frac{\lambda}{2 \cos\bigl{(}\frac{\alpha-\beta}{2}\bigr{)}\sin\Bigl{(}\Delta\theta_{-1}-\frac {\beta}{2}\Bigr{)}} \tag{5}\]  
where \(\lambda\) is the wavelength of the measurement beam, \(\Delta\theta_{\pm 1}\) is the angular position of the spindle, \(\alpha\) is the angular misalignment of the measurement laser beam about the \(Z\)-axis with respect to the \(X\)-axis, and \(\beta\) is the angular misalignment of the optical axis of the detector unit about the \(X\)-axis with respect to the \(Z\)-axis. Based on Equations (4) and (5), the influences of \(\alpha\) and \(\beta\) on measurement of the pitch deviation are estimated quantitatively. Figure 4a,b show the difference between the calculated pitch \(P_{\mathrm{c}}\) obtained based on Equations (4) and (5) and the nominal pitch \(P\) due to the angular misalignments \(\alpha\) and \(\beta\), respectively. Calculations are carried out under the condition of \(P=833.33\) nm and an angular position of the spindle of \(28^{\circ}\). The influence of \(\alpha\) is estimated to be approximately \(-32\) pm under the condition of \(\alpha=1^{\circ}\), while that of \(\beta\) is estimated to be approximately \(13.7\) nm under the condition of \(\beta=1^{\circ}\). From these results, it can be concluded that the corrections of \(\alpha\) and \(\beta\) are necessary for accurate pitch measurement. Figure 3: Optical configuration for measurement of the grating pitch; (**a**) Without the angular misalignments; (**b**) With the angular misalignments.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 2 Network Slicing in 5G
### Resource or Infrastructure Layer  
It comprises of actual physical and virtual resources of the RAN, networking nodes, cloud nodes, and associated links. Through virtualization, the flexibility of network resources can be achieved, which helps to create the core network slices. Spectrum resources can be shared and flexible management of these resources can be achieved by slicing the RAN. Scheduling technique can be useful to fulfill the task of radio resource sharing. It helps to dedicate the resources according to the application requirements.  
In addition to the layers, NGMN also described the E2E management and orchestration concept for NS. For a specific service, it called upon the request of applicable network  
Figure 1: 5G network slicing architecture.  
function, manages the physical and virtual resources, allocates the relevant functions, and monitors each slice via application programming interface (API). It orchestrates the virtual resources and maps them to physical infrastructure resources.
# Beyond Correlation: Mutual Information to Detect Damage in Nonlinear Systems
## 6 Discussion of Results
### Statistical Significance
To investigate the effect of noise on the \(p\)-values, the bootstrap analysis described above was repeated with noisy acceleration signals. Zero-mean Gaussian white noise was added to the acceleration signals at levels corresponding to 30 dB, 25 dB, and 20 dB signal-to-noise ratios. Even at the 20 dB level, \(p\)-values did not show noticeable deviations from the values depicted above. While we cannot fully explain this behavior, we offer the following observations. First, the presence of noise mostly affects the high frequency range, leaving the transmissibility coherences in the low frequency range essentially unchanged. For the ATC metric, using an upper limit of 80 Hz in the integration greatly reduces the effect of noise. For the TMAC metric, which measures the correlation between two transmissibility coherence vectors, many of the values are close to 1 regardless of the presence of noise; any noise-related changes in TMAC are not large enough to modify the significance testing results. That is, statistically significant observations in the noise-free case remain statistically significant after the addition of noise. We note that correlation is generally considered a noise-sensitive metric. However, the effect of noise added to the acceleration signals is largely diminished during the transmissibility coherence calculations. If we were to artificially introduce changes to the transmissibility coherences instead of adding white noise in the time domain, the TMAC values would decrease. The lack of sensitivity of the MI metric is expected not only due to the lack of large differences in the transmissibility coherences compared to the noise-free case, as in the TMAC case, but also the intrinsic robustness of the mutual information measure to noise.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 3 Physical Layer Aided Authentication and Key Agreement (PL-AKA)
### Physical Layer Challenge-Response Authentication (PHY-CRAM)  
In this subsection, we briefly introduce a PHY-CRAM, which utilizes channel phase information to encapsulate a secret key for authentication in multicarrier systems. The PHY-CRAM is integrated into a conventional AKA in the proposed protocol for which details will be discussed in the following Section 3.2.2. The PHY-CRAM [8] is divided into three steps: the physical layer challenge, response, and verification as follows.
## 6 Results and Discussion
### Convergence Analysis  
The convergence of the BEA-SSA technique compared with GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20] for diverse iterations is  
Figure 8: RSSI analysis using BEA-SSA compared with the other models [20; 22; 26; 22; 22] for node counts of (**a**) 100 and (**b**) 200.  
Figure 7: Residual energy analysis using BEA-SSA compared with the other models [20; 22; 26; 22] for node counts of (**a**) 100 and (**b**) 200.  
Figure 9: PDR analysis using BEA-SSA compared with the other models [20; 22; 26; 22] for node counts of (**a**) 100 and (**b**) 200.  
shown in Figure 10. As needed, the BEA-SSA has achieved the least cost values with an increase in iterations for both nodes. From Figure 10a, from the 7 th to the 10 th iteration, the cost has reduced to 0.435 for 100 nodes. Likewise, in Figure 10b, from the 4 th to the 10 th iteration, the cost has reduced to 0.19 for 200 nodes. The other evaluated schemes, GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20], reveal a comparatively elevated cost value. The energy level is predicted using an enhanced Deep Convolutional Neural Network (DCNN), which is used to select the best CH in WSN. The BEA-SSA is used in the WSN to select the best CH. The RSSI, PDR, and residual energy are all high for the Bald Eagle Assisted SSA (BEA-SSA) model. The implementation of the BEA-SSA-based CHS and the enhanced energy evaluation and prediction utilizing DCNN are responsible for these advancements. Thus, with the BEA-SSA-based optimization, improved results are obtained for CHS.