esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Deconvolution with Scale Ambiguity
## 4 Discussion and Experimental Results
### Discussion  
#### 4.1.1 Determination on Parameter \(p\)  
In the proposed method, the parameter \(p\) plays a critical role as it implicitly influences the normalization of the blur kernel. It is also difficult to be determined theoretically. In order to choose a suitable \(p\), we carried out experiments on a synthetic dataset to test 19 different values of \(p\) varying from 0.5 to 5, the step size for \(p\) is 0.25. To generate blurred images, we randomly pick 100 images from the BSDS500 dataset [43] and blur them by 8 kernels introduced by [19]. Three commonly used evaluation criterions, kernel similarity, error ratio and PSNR, are utilized for evaluation.  
The kernel similarity measures the accuracy of the estimated kernel \(k_{est}\) over ground-truth kernel \(k_{GT}\). This metric calculates the maximum response of normalized cross-correlation between \(k_{est}\) and \(k_{GT}\) with some possible shift. The error ratio metric proposed by [19] is commonly used to evaluate the restored result, and the PSNR is commonly used metric to evaluate the quality of the recovered results in comparison with ground-truth images.  
Figure 3: The pipeline of the proposed algorithm. In this algorithm, \(u\) and \(k\) are solved iteratively for each image scale in a coarse-to-fine pyramid framework.  
The statistical results are summarized in Figure 4. In the subfigures, the three horizontal axes denote the values of \(p\), and the three vertical axes denote the average kernel similarity, average error ratio and average PSNR, respectively. As can be seen in Figure 3(a), in terms of the average kernel similarity, the optimal \(p\) lies in \([1.5,2]\). Figure 3(b) demonstrates that when \(p\in[1.75,2.25]\), the proposed method generates results with error ratio values smaller than 2. Other \(p\) values have relatively large error ratio values. In Figure 3(c), we observe that \(p\) values in \([1.5,3]\) achieve average PSNR higher than 25, and the highest one is reached when \(p=2\). Based on above observations, we set \(p=2\) in all of our experiments.
## 3 Models and Design Goals
### Design Goals  
Our objective is to create an efficient system to offer OLDs a secure environment to communicate and services such as avoiding risks. It will satisfy the following desirable properties. _Routing Plan_: Making use of the global ability, the control plane will generate the most suitable using plan for OLDs. _Emergency Handling_: When an emergency occurs, to avoid a second occurrence, the control system informs the impacted OLDs promptly via its multicast mechanism. Then it replans new routes for OLDs by balancing all network situations. _Secure Communication_: The most important factor is that all the messages sent by OLDs need to be trustworthy and factual. Considering that, the system should have the following security properties.  
1. Anonymity: OLDs in our system will not communicate with other entities with real identities. Based solely on the messages sent by OLDs and some public information, malicious users cannot ascertain the real identity of the sender. This way, OLDs are allowed to send messages without exposing sensitive privacy information.
2. Authentication and Privacy: Each interactive party in our system can authenticate the others to ensure reliability and legitimacy. In particular, in different areas, messages sent by OLDs should reflect the present LC's information without exposing them to adversaries, which ensures location privacy would not be compromised.
3. Traceability: We will not exclude the possibility of the existence of malicious entities. They aim to interrupt or interfere normal communications or spread false and deceptive messages to gain convenience and benefits for themselves. When misbehavior occurs, the controller plane should be able to trace the real identities and punish them by cutting services or submitting their information to related authorities.
4. Unlinkability: The proposed scheme would not enable third parties to link scattered messages to the same OLDs. That is to say, no third party could know one specific OLD's activities by analysing those intercepted messages.  
Figure 2: The process of subscribing.  
(5) Resistance to common attacks: The scheme must have the ability to effectively counter prevalent attacks that occur in conventional networks, including replay attacks, impersonation attacks, modification attacks and others.
# Optimized Travel Itineraries: Combining Mandatory Visits and Personalized Activities
## 3 Methodology
### Solution Representation  
The solution representation for the proposed problem is separated into two parts: Sol 1 and Sol 2 (Figure 1). Sol 1 is a route representation, represented by a permutation of \(|N_{c}|\) locations (\(1,\ldots,|N_{c}|\)), followed by \(|P|-1\) zeros. The zeros are used to indicate breaks or separations in the route for each day, indicating that the route ends and that the traveler should return to the origin location. The total dimension for the routes is \(|N_{c}|+|P|-1\). For example, if the problem consists of 25 locations (\(\left\{\,{{N_{c}}}\,\right\}=25\)), and three days (\(\left\{\,{P}\,\right\}=3\)), 27 dimensions are created in Sol 1 (Figure 2). Moreover, to represent the activity selected in each location, Sol 2 is created, and \(\left\{\,{{N_{c}}}\,\right\}\) dimensions are used.  
In Sol 2, the value in each dimension is assigned from 1 to \(2^{\left|{A}\right|}-1\), considering a maximum of four activities. Each number corresponds to a four-bit pattern, known as a binary-coded decimal (BCD), which represents a specific set of selected activities. Table 1 illustrates these activity selection patterns. For example, in Figure 3, there are 25 dimensions to represent the selected activities for 25 locations. The assigned number for each dimension indicates the set of selected activities. In this case, with four activities considered, the assigned number ranges from 1 to 15. The first dimension represents the set of activities for Location 1. If the assigned number is 7, referring to Table 1, the binary-coded decimal is [0, 1, 1, 1], meaning that in Location 1, Activities 3, 2, and 1 are selected.
## 3 Wireless Channel Analysis
### Leaky Waveguide System  
Based on the analysis of the DAS results in Section 3.1, the severe Doppler effect will cause a dramatic fast fading effect of the received signals especially in the vicinity of ground antenna. Consequently, it is not suitable to be implemented for the Hyperloop. The LWS and leaky coaxial cable (LCX) are widely used to provide stable and sufficient coverage in the confined space environment [31]. However, LCX cannot support the communication at a high frequency carrier [32]. As such, we utilize the LWS to provide effective radio coverage for the Hyperloop. Figure 3a depicts the access network architecture based on the LWS. The LWS is connected to the road side AAUs via the cable to avoid the penetration attenuation stemmed from the steel tube. A roadside AAU accounts for the signal processing of several leaky waveguides. It is worth noting that the conventional leaky waveguide emits a signal of which the equiphase surface direction is not perpendicular to train movement direction as shown in Figure 3b. This phenomenon is caused by the fact that the currently used leaky rectangular waveguide uses fast waves as the fundamental mode to generate radiation. The Doppler frequency shift of the LOS component can be expressed as \(f_{d}=v_{0}\cos\theta/\lambda\), where \(v_{0}\) is the velocity and \(\lambda\) is the wavelength. Obviously, the \(f_{d}\) keeps a constant as long as \(v_{0}\) does not change. Then, some off-the-shelf frequency compensation algorithms can be used to handle this situation directly. Note that the AAU connect to all leaky waveguides at the same side (left/right), otherwise, the Doppler shift will change from \(f_{d}\) to \(-f_{d}\).  
Figure 2: Wireless channel analysis results of the DAS: (**a**) the normalized CIR; (**b**) the Doppler power spectrums at different positions.
# A Simple Estimation Method of Weibull Modulus and Verification with Strength Data
## 4 Material Strength Data
### Metals and Alloys  
Table 3 lists the data of metals and alloys. For these datasets, the average of m\({}_{\text{obs}}\)/m\({}_{\text{est}}\) is 1.034, and the standard deviation is 0.093. The m\({}_{\text{obs}}\)/m\({}_{\text{est}}\) average is 2.4% higher than that for the whole set. This comes partly from 11 data points with m\({}_{\text{obs}}\)/m\({}_{\text{est}}>1.1\), but these large deviations are still within the expected behavior. Most metallic alloys exhibit good ductility, and Weibull analysis is usually not needed. It is often assumed that metal alloys have m values of over 100, but lower m values have been obviously observed. The top four rows are calculated from the results of the ASTM study at established  
laboratories in the United States [24], and the m values were 74 to 124 (Figure 1), as discussed in Section 3 above. The data for Nimonic(r) 75 Ni-base alloy from the European interlaboratory study [25] yielded m = 125 (Figure 1). Here, another Ni alloy, Inconel(r) 625 (American Special Metals, Miami, FL, USA) showed m = 55 [45]. These six datasets can be treated as the benchmarks for ductile steel, Al alloys, and Ni alloys. It is important to recognize that these studies used well-controlled sets of samples. This approach is usually replicated in research laboratories, but it is not representative of large sample data for design and reliability works, where mill practice, alloy chemistry, and structural shapes vary (see Section 5.2 for more discussion). Weibull data for metals and alloys are indeed scarce, and 22 of the 43 datasets in Table 3 showed m > 50. These were all ductile metals and alloys, including 18 Ni maraging steel, stainless steels, and Mg alloys. However, some ductile metals such as copper showed low m values of 12 to 16 as well. Sintered steel showed low ductility along with low m values below 30. More data with low m values are given in Section 5 using standard deviation (or CV), where one finds that m < 30 is common for large-scale industrial datasets. Brittle fracture data for steels at sub-zero temperatures should be assessed using Weibull analysis, but even here, the test results of repeated tests are difficult to find. Several datasets for cleavage fracture were analyzed and added to Table 3 at rows 9 to 12 [46]. Surprisingly, m values were 10 to 20 in a sharp contrast to the theoretical value of 4 predicted by [PERSON] [32; 33]. Another brittle material is nickel aluminide (NiAl). Monocrystalline NiAl showed consistently low m values of about 5 [47], while NiTi intermetallic showed a slightly higher m of 8.5 [46]. General trends can be viewed in Figure 5. No peculiar behavior is present.
# A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
###### Abstract  
The fifth generation (5G) communication has the potential to achieve ubiquitous positioning when integrated with a global navigation satellite system (GNSS). The device-to-device (D2D) communication, serving as a key technology in the 5G network, provides the possibility of cooperative positioning with high-density property. The mobile users (MUs) collaborate to jointly share the position and measurement information, which can make use of more references for positioning. In this paper, a GNSS/5G integrated three-dimensional positioning scheme based on D2D communication is proposed, where the time of arrival (TOA) and received signal strength (RSS) measurements are jointly utilized in the 5G network. The density spatial clustering of application with noise (DBSCAN) is exploited to reduce the position uncertainty of the cooperative nodes, and the positions of the requesting nodes are obtained simultaneously. The particle filter (PF) algorithm is further conducted to improve the position accuracy of the requesting nodes. Numerical results show that the position deviation of the cooperative nodes can be significantly decreased and that the proposed algorithm performs better than the nonintegrated one. The DBSCAN brings an increase of about 50% in terms of the positioning accuracy compared with GNSS results, and the PF further increases the accuracy about 8%. It is also verified that the algorithm suits the fixed and dynamic condition well.  
GNSS; device-to-device (D2D) communication; integrated positioning; clustering; particle filter (PF) +
Footnote †: journal: remote sensing  
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# Equilibrium Inefficiency and Computation in Cost-Sharing Games in Real-Time Scheduling Systems +
## 3 Monomial Cost Functions
### Increasing Cost Shares: \(d>1\)  
We now discuss the PoA for the case when the cost function equals \(x^{d}\) with \(d>1\). In this case, the cost share \(c(x)/x\) is strictly increasing. We observe that the general upper bound on the PoA of congestion games and an early lower bound designed for routing games which applies to our model yield the following theorem.  
Theorem 2 ([8] Theorem 4.3): _For games with cost function \(c(x)=x^{d}\) with \(d>1\), the worst case PoA is \(d^{\Theta(d)}\)._Note that the PoA is constant when \(d\) is fixed and is independent of the number of players \(n\), in contrast to the unit and \(d<1\) cases. In this work, we are mostly interested in this behavior of the PoA as a function of \(n\). However, observing the PoA as a function of \(d\) is also of interest and we note that the \(d^{\Theta(d)}\) expression hides a gap. For example, for \(d=2\), the lower bound of [8] is 2 and the general upper bound for congestion games is 5/2, however the correct value for our setting is 2.01 as given by bounds in [42; 43] (designed for the load balancing game but still apply for our setting). Identifying the precise PoA value as a function of \(d\) for more general values of \(d\) appears to be a challenging open problem that we leave as future work.
## 3 Results and Discussion
### Characterization of the Adsorbents
This area increased moderately upon acidic activation as occurred for AC2. Likewise, activation of SW also increased the surface area as happened for AC1 suggesting a crucial NO\({}_{2}\) role on the formation of a pore structure and hence a higher surface area. Interestingly, CM had the lowest surface area and the highest alkalinity which were reflected in a low CV adsorption. Conversely, the activation treatment increased the acidity of adsorbents rather than the surface area improving CV adsorption. This phenomenon could be explained by two main factors: (a) the high content of acidic moieties (i.e., carboxylic, carbonyl, and phosphate) and (b) an improvement of S\({}_{\text{BET}}\). In fact, the pyrolyzed product showed approximately half of SW S\({}_{\text{BET}}\), hence it exhibited a lower CV adsorption. Moreover, functionalization of the SW surface especially with carboxyl and phosphate moieties was essential for enhancing the adsorption capability. In turn, these acidic moieties contributed to the low PZC values. The PZC is the pH value where the net surface charge in the adsorbent particle is neutral. The lower the PZC value, the wider the pH range for the removal of a cationic adsorbate such as CV. Therefore, in a pH media higher than the PZC the adsorbent has a net negative surface charge and it is expected to attract the positively charged CV molecules by electrostatic attractions. Further, adsorbents that did not experience an acidic activation (i.e., SW and CM) showed PZC values larger than 2.0 and therefore, they showed inferior removal efficiencies of 85-95% due to a lower ability for creating electrostatic interactions with CV. At neutral pH CV (pKa\({}_{1}\) = 5.31; pKa\({}_{2}\) = 8.64) is partially ionized and at this pH the adsorption process is still influenced by the negative surface charge of the adsorbents. This suggests that CV adsorption was governed by a physical process, where hydrogen bonds and \(\pi\)-\(\pi\) interactions seems to prevail. The morphological features of the adsorbents are shown in Figure 1. As compared to the raw SW, CM had a rougher surface, and upon acidic activation the surface becomes more eroded and rougher showing big holes and cavities with tiny lumps. In fact, acidic activation of SW and CM increased the carbon and phosphorous content resulting in gas release and breaking of C-O-C and C-C bonds leading to the formation of a microporous structure. Further, the CM sample exhibited crystalline CaO particles on the surface and showed tiny mesopores (black dots) which contributed to the resulting low S\({}_{\text{BET}}\). On the other hand, CAC exhibited a rough surface mainly formed by micropores. Figure 1: _Cont._
# Evolution Model of Coal Failure Using Energy Dissipation under Cyclic Loading/Unloading
## 4 Analysis and Discussion
### Energy Driving Mechanism of Coal Fracture Process  
According to the laws of thermodynamics, the deformation and damage process of materials is essentially a state instability phenomenon driven by energy dissipation and release [9]. The rate of energy change becomes an important parameter to characterize the stable state of coal, which is of great significance to judge the energy evolution stage and damage mechanism [20]. As shown in Figure 9, to characterize the change in the energy growth rate of coal under axial loading, the concept of tangential factor of energy growth is proposed. The calculation formula is as follows:  
\[K_{TF}=\frac{dE_{i}}{d\sigma_{i}}=\frac{\Delta E}{\Delta\sigma} \tag{2}\]  
where \(K_{TF}\) is the energy tangent factor of coal, representing the energy change rate under the loading state in Figure 9, \(K_{TF}=K_{MP}\), the energy factor increment is \(\Delta E=E_{i+1}-E_{i}\), and the stress increment is \(\Delta\sigma=\sigma_{i+1}-\sigma_{i}\).  
Figure 8: Evolution of coal energy under graded cyclic loading/unloading.  
As shown in Figure 10, based on the change curve of the energy tangent factor, different stages of coal deformation and failure could be better distinguished, and the energy growth state of coal at different stress loadings could be obtained. In the I stage (0-4 MPa), the tangent factor of coal energy kept increasing. The low energy storage state of coal promoted the rapid increase of the elastic energy storage rate, which indicated the gradual transition of the coal from the compaction stage to the elastic deformation stage. In the II stage (4-16 MPa), the rate of coal energy growth changed steadily to a higher level. As the loading stress increased linearly, the internal crack of coal expanded stably, while its elastic energy and dissipated energy increased stably and greatly. In the III stage (16-18 MPa). When the energy storage mechanism and energy consumption mechanism of coal reached a dynamic balance, it became unstable and was destroyed.  
The rapid growth rate of the elastic strain energy indicates that elastic deformation is the principal part of coal failure process. As shown in Figure 10, the energy growth rate of coal is nonlinear with the change in axial stress. The tangential factor of energy growth increased rapidly in the initial loading stage, developed steadily after 20% of the ultimate strength, and decreased obviously after 90%. Through quantitative analysis, the corresponding relationship between the energy growth rate and stress-strain can be better described, and the energy storage level of coal at different deformation stages can be characterized.
## 3 Results
### Correlation Between Polyamine Levels in Peripheral Blood Cells and Plasma
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Spermidine**} & \multicolumn{3}{c}{**Spermine**} & \multicolumn{3}{c}{**N-Acetyl Putrescine**} & \multicolumn{3}{c}{**N-Acetyl Spemidine**} \\ \hline
**Mononuclear Cells** & **Control** & **AMI** & **CHF** & **Control** & **AMI** & **CHF** & **Control** & **AMI** & **CHF** & **Control** & **AMI** & **CHF** \\ \hline Putrescine & 0.558 *** & 0.425 * & 0.926 *** & 0.467 *** & & 0.3 *** & 0.467 * & 0.589 *** & \\ Spermidine & & & 0.887 *** & & 0.529 *** & & 0.445 *** & & \\ Spermine & & & & & 0.414 *** & & 0.394 *** & & \\ \hline Erythrocytes & & & & & & & & \\ \hline Putrescine & 0.747 *** & 0.829 *** & 0.450 *** & & 0.853 *** & 0.228 *** & 0.708 *** & 0.655 *** & 0.304 *** & 0.589 *** & 0.851 *** \\ Spermine & & & & & 0.254 *** & 0.696 *** & 0.798 *** & & 0.447 * & 0.558 *** & 0.345 *** & & 0.815 *** \\ Spermine & & & & & & & & & 0.68 *** & & 0.846 *** \\ \hline Plasma & & & & & & & & & & \\ \hline Putrescine & 0.445 * & 0.437 *** & & 0.81 *** & 0.663 *** & & & & 0.833 *** & 0.761 *** \\ Spermidine & & & & & 0.672 *** & 0.727 *** & & & & & \\ \hline \end{tabular} AMI: acute myocardial infarction; CHF: chronic heart failure. * \(p\) \(\leq\) 0.05, *** \(p\) \(\leq\) 0.01 and *** \(p\) \(\leq\) 0.001.  
\end{table} 
Table 2: Significant Spearman correlation coefficients (\(r_{s}\)) between polyamines in the peripheral blood cells and plasma of the different groups.
###### Abstract  
Conventional shared energy storage (SES) allocation and coordinated operation mechanism are mismatched with the actual time-varying demand of the distribution system, resulting in low utilization of energy storage and renewable energy sources (RES), which restricts the system operational efficiency and RES integration. To solve this issue, this paper proposes a centralized shared energy storage (CSES) optimization framework for AC/DC distribution systems with dual-time-scale coordination to address this issue. Firstly, optimal scheduling models for AC/DC distribution systems are formulated. Secondly, a novel CSES optimization framework is established where a large-scale CSES directly connects to multiple subnetworks. This framework maximizes RES utilization by coordinating CSES operation, leveraging complementary RES potential. Thirdly, based on dual-time-scale coordination, intraday stage adjustments are made based on the day-ahead scheduling to accommodate and coordinate with source-load changes. Day-ahead SOC trajectory is processed using linear interpolation to obtain intraday SOC trajectory, ensuring that the state of charge (SOC) constraints are satisfied. An alternating direction multiplication method (ADMM) algorithm is used to coordinate the intraday optimization. Finally, case studies on an AC/DC distribution system comprising three IEEE 33-node AC subnetworks show that the proposed strategy can increase the RES utilization rate to 99.31%, 88.10%, and 99.91%, and reduce the operational cost by 16.51%.  
AC/DC distribution system; shared energy storage; dual-time-scale framework; optimal scheduling; alternating direction method of multipliers +
Footnote †: journal: Applied sciences  
Article