esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Ring Deformation and Contact Characteristics of Thin-Walled Bearing for RV Reducer
## 3 Results and Discussion
### The Effect of Radial Force  
Figure 16 shows the influence of the radial force on the outer ring deformation under constant axial force. Under pure axial force, the ring deformation tends to be a standard circle. The reason is that all balls are loaded and exert almost the same force on the outer ring. As the radial force increases, the horizontal diameter of the ring continuously decreases while the vertical diameter increases. This is because the increase in radial force causes the concentration of the ball-raceway contact load in the vertical areas, resulting in an increase in the ring diameter in that direction, while the contact load in the horizontal direction decreases and eventually disappears, leading to a reduction in the ring diameter. Figure 17 illustrates the variation trend in the loaded zones under radial force.  
Figure 18a demonstrates the effect of radial force on the bearing contact state. As the radial force increases, the radial clearance increases and the number of loaded balls decreases gradually. The decreasing trend and mutation position of the number of loaded balls under the rigid and flexible models are different, proving that the rigid ring model inadequately describes the contact state inside the thin-walled bearings. Figure 18b shows that the degree of load optimization becomes more pronounced under high radial force conditions. This phenomenon is because the increase in radial force intensifies the difference between ball-raceway contact loads, consequently enhancing ring deformation and optimizing load distribution. However, the increase in radial force results in a rise in the contact stress. After accounting for ring deformation, the ball-raceway contact angle increases, bringing the ball  
Figure 16: Effect of radial force on ring deformation: (**a**) ring deformation; (**b**) diameter augment.  
Figure 17: Effect of radial force on variation trend in loaded zones.  
closer to the raceway rib and making the bearing more susceptible to elliptical truncation. Figure 18c shows that the radial displacement of the flexible inner ring is greater than that of the rigid inner ring. The cause behind this phenomenon mirrors that of the phenomenon depicted in Figure 15c, which is not explained here. However, it should be noted that the variance in displacement between the rigid and flexible rings increases alongside the increase in radial force. Compared with the flexible ring, the rigid ring only produces rigid displacement to balance the external load, but large rigid displacement leads to excessive ball-raceway contact load, which increases the error of accurately evaluating the bearing performance [19]. Figure 18d shows that the increase in radial force can decrease the contact angles near 90\({}^{\circ}\) azimuth and increase the contact angles near 270\({}^{\circ}\) azimuth. The effect of ring deformation on the contact angles parallels the discussion in Section 3.3.
## 1 Introduction  
Electrochemical energy storage systems (EESS) are power sources for many devices, e.g., cell phones, laptops, medical devices, electric vehicles (EVs), smart grid systems, etc. [1]. With a high demand of superior EESS, lithium-ion batteries (LIBs) have gained increasing popularity over other existing typical electrochemical batteries due to their favorable performances in high energy density, lightweight, long cyclic lifetime, low self-discharge rate, and almost zero memory effect [2; 3]. However, LIB is a nonlinear dynamic system with a very narrow operating range, and some incorrect operations could lead to irreversible damage and shortened life [4; 5; 6]. Therefore, a reliable and effective battery management system (BMS) is required to optimize performance, improve safety and prolong life of LIBs. The critical state estimation, such as state of charge (SOC), state of health, and state of power, is fundamental in a BMS. Specifically, the SOC is an essential indicator used to regulate the operating decisions and to avoid overcharge or overdischarge [7]. However, the SOC cannot be directly measured by sensors, and the battery itself is highly nonlinear, which makes accurate SOC estimation very difficult [8].
## 4 Experimental Study and Discussion
### Experimental Setup and Data Processing
The robust and stable energy kernel method was applied to process the EMG signals and extract the muscle activation when it integrated both the merits of RMS and MPF and reflected muscle recruitment level and firing rate at the same time [42]. The extracted feature of EMG signals was indicated as EK in the following. As only the EMG signal from RF was included, the dimension of the feature vector was 1.
# Multi-Branch Deep Residual Network for Single Image Super-Resolution
## 3 Proposed Methods
### Model Architecture  
In this section, the proposed model structure will be introduced, outlined in Figure 2. Our model conceptually consists of three parts: Feature extraction, mapping, and reconstruction. The feature extraction operation gets the features from the input LR image and represents them as a set of feature maps which are ready for the next mapping operation. In order to deliver more information to the next operation, in addition to using multiple filters and residual blocks to operate on the input, we also use a sliding connection to send the input directly to the mapping network. Then, non-linear mapping from LR to HR is worked by the mapping operation, which is the main component that solves the SR task. Obviously, the quality of SR mainly depends on the performance of the mapping network. In proposed model, the mapping network is composed of five branches. Each branch applies residual blocks and filters to achieve the effective mapping from LR to HR feature maps with different parameters. Moreover, the convolution layers are inserted between every two branches, which will result in different sizes of each branch network. This design provides the feasibility to implement multi-scale network and takes advantage of inter-scale correlation. Finally, the reconstruction networks undertake the task of rebuilding the super-resolution image. Since the output of each branch not only differs in size but also in number, every branch has its own independent reconstruction subnetwork. Every reconstruction network combines the output of the corresponding branch network with the original input to restore the SR image. Furthermore, we apply the sub-pixel convolution layers to every reconstruction network to upscale the LR image. Compared with the deconvolution layer or other implementations, which use various forms of upscaling before convolution, the sub-pixel convolution layer is faster in training. According to the structure of MRSR, the final SR image is derived from each reconstruction network.  
In the proposed architecture, the feature extraction modules consist of ten residual blocks and filters with 3 \(\times\) 3 kernels that allow more detailed texture information and hidden states to be passed. As the most important component of the model, the mapping network combines the advantages of multi-branch networks and residual networks where kernels are set to 5 \(\times\) 5. By adopting larger kernels, the larger receptive field can be covered in the mapping network. Our model has approximately 50 times more receptive field than DRCN.  
Figure 1: The comparison of different residual networks structure among the Original, SRResNet, EDSR, and our proposed.
# An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers
## 2 Related Works  
### Cdm  
With the widespread adoption of electronic health records (EHRs) in healthcare systems, clinical data are entering the digital era [16]. Large-scale EHR data analysis has produced influential discoveries, which have enabled the practice of precision healthcare [17]. However, there are many barriers that limit the usefulness of EHR data, primarily revolving around available expertise. Since EHR data are large and typically stored in relational databases, many clinical experts and scientists have no experience, lack sufficient time to spare, and need knowledge of Structured Query Language (SQL) programming. Moreover, the structure and data components of an EHR system are complex and require strong familiarity to be utilized most effectively. Many people solve this problem by building effective collaboration across multiple disciplines (e.g., doctors working with data science teams) but enabling more researchers to directly work with data is important. Thus, CDM facilitates the interoperability of EHR data for research, and it requires strong familiarity to enable many researchers to handle the data directly [18].  
CDM is a typical database model of medical information standardization for clinical data-based research [19]. Simultaneous multi-center analysis can be performed in the form of standardized schemas and vocabulary systems and has been continuously updated considering existing limitations. This allows us to transform different data structures and meanings from an institution to have the same structure and meaning, thus solving the difficulties due to difference in data structures for each institute [20].  
There are various CDMs such as Observational Medical Outcomes Partnership (OMOP)-CDM, Sentinel-CDM, and national-scale clinical research network (PCORnet) CDM [21]. In particular, the OMOP CDM is a common data model developed and operated by the Observational Health Data Sciences and Informatics (OHDISI) international consortium, with more than 200 organizations from 14 countries participating in the transition to CDM [22]. OMOP-CDM uses a common medical terminology system called the OMOP code as well as the same data structure, enabling an integrated analysis of clinical healthcare databases across multiple institutions. A CDM database built in each institution has the advantage of being able to perform more efficient and systematic analysis using the already developed CDM-based open-source standard analysis methods and analysis programs from libraries and web bases [23].
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
## 3 Method
### Target Localization Algorithm
If the transmitted path and the received path are the same, the radar geometry is equivalent to a monostatic radar geometry from virtual radar to the target directly. If the two paths are different, the radar geometry is equivalent to a bistatic radar geometry where the signal is transmitted from virtual radar \(\mathbf{r_{1}}\) to the target and received by virtual radar \(\mathbf{r_{2}}\). Therefore, the length of the received round-trip path is derived as  
\[h_{1}=\sqrt{(x_{r_{1}}-x_{d})^{2}+(y_{r_{1}}-y_{d})^{2}}+\sqrt{(x_{r_{2}}-x_{ d})^{2}+(y_{r_{2}}-y_{d})^{2}}, \tag{48}\]  
where \(\mathbf{x}=[x_{d},y_{d}]^{T}\) is the target location, and the received DOA is given as  
\[h_{2}=\arcsin\left(\frac{y_{r_{2}}-y_{d}}{\sqrt{(x_{r_{2}}-x_{d})^{2}+(y_{r_{ 2}}-y_{d})^{2}}}\right). \tag{49}\]The path parameter is expressed as a non-linear function of the target location by  
\[\mathbf{h}(\mathbf{x})=[h_{1},h_{2}]^{T}. \tag{50}\]  
The conditional probability function of the measurement \(\mathbf{z}\) with respect to the associated path is given as  
\[p(\mathbf{z}|\mathbf{h}(\mathbf{x}))=\frac{1}{2\pi\det^{\frac{1}{2}}(\Sigma)} \exp\biggl{[}-\frac{1}{2}(\mathbf{z}-\mathbf{h}(\mathbf{x}))^{T}\Sigma^{-1}( \mathbf{z}-\mathbf{h}(\mathbf{x}))\biggr{]}, \tag{51}\]  
where \(\mathbf{h}=[h_{1},h_{2}]^{T}\). Therefore, the probability of the assignment \(\hat{\theta}\) is derived as  
\[p(\mathbf{Z}|\mathbf{x})=p_{c}(\mathbf{C}|\hat{\theta})\prod_{\hat{\theta}_{k} =0}(1-P_{d})\prod_{\hat{\theta}_{k}>0}P_{d}\cdot p(\mathbf{z}^{\hat{\theta}_{k }}|\mathbf{h}^{k}(\mathbf{x})), \tag{52}\]  
where \(\hat{\theta}\) is the most significant association hypothesis selected from all valid hypotheses by  
\[\hat{\theta}=\operatorname*{arg\,max}_{\boldsymbol{\theta}}p(\mathbf{Z}| \mathbf{H}_{\mathbf{x}},\boldsymbol{\theta}). \tag{53}\]  
By maximizing the logarithmic likelihood function, the target localization problem is converted into an NLS optimization problem as  
\[\mathbf{\hat{x}}=\operatorname*{arg\,min}\sum_{\hat{\theta}_{k}>0}\left( \mathbf{z}^{\hat{\theta}_{k}}-\mathbf{h}^{k}(\mathbf{x})\right)^{T}\Sigma^{-1 }\left(\mathbf{z}^{\hat{\theta}_{k}}-\mathbf{h}^{k}(\mathbf{x})\right). \tag{54}\]  
The NLS optimization problem can be solved by numerical methods initialized with the target location estimated in the first step.
## 4 Complexity Analysis  
Complexity is highly affected by the number of multiplications, divisions, and number of iterations. An inappropriate number of iterations increases or decreases the number of multiplications and divisions. Therefore, the computational complexity and the performance are greatly affected by the initial estimation. In order to compute \(\mathbf{D}^{-1}\) and \(\mathbf{S}^{-1}\), \(K\) real number of divisions is required. However, the computation of \(\mathbf{S}^{-1}\) requires an additional \(3(K-1)\) real number of multiplications. However, this increment is a small. For instance, in the GS based \(32\times 256\) MIMO and \(n=2\), 8192 and 8285 number of multiplications are required if \(\mathbf{D}\) and \(\mathbf{S}\) are exploited, respectively. In order to obtain a good performance, a detector based on \(\mathbf{S}\) requires a small number of iterations in comparison with a detector based on \(\mathbf{D}\) matrix. The required number of multiplications are listed in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Stair Matrix (\(\mathbf{S}\))** & **Diagonal Matrix (\(\mathbf{D}\))** \\ \hline NI & \(2(n-1)K^{3}+NK^{2}+K(N+3)-3\) & \(2(n-1)K^{3}+NK^{2}+NK\) \\ \hline RI & \(4 nK^{2}+K(2n+3)-3\) & \(4 nK^{2}+2 nK\) \\ \hline SOR & \(4 nK^{2}+K(n+3)-3\) & \(4 nK^{2}+4 nK\) \\ \hline GS & \(4 nK^{2}+3(K-1)\) & \(4 nK^{2}\) \\ \hline JA & \(2 nK(2K-1)\) & \(n(4K^{2}-2K)\) \\ \hline NS & \((n-2)K^{3}+NK^{2}+K(N+3)-3\) & \((n-2)K^{3}+NK^{2}+NK\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complexity comparison among initialized detectors based on Diagonal and Stair Matrices.
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 3 The Proposed Schemes
### The S-Assist D2D Anonymous Authentication
#### 3.3.2 The S-Assist-Once-A-While D2S2D Anonymous Authentication and Direct D2D Authentication
\(D_{l}\to\ D_{\overline{i}}\): \(Res5\)  
\(D_{i}\) decrypts \(E_{3}\), gets \(N_{\overline{i},i},K_{tmp,i,\overline{j}}\), computes its own \(K_{tmp,i,\overline{j}}\), and verifies \(Res3\), \(Res4\), and the received \(K_{tmp,i,\overline{j}}\). If the verifications succeed, it uses \(N_{\overline{i},i}\) to activate \(D_{\overline{i}}\)\(s\) level2 seed \(-\) pseudonym vectors, and activates its own level-2 seed \(N_{\overline{i},\overline{j}}\). It accepts the session and computes the session key \(K_{sens}\). It further sends the response \(Res5\)\(=Mac\Big{(}K_{tmp,i,\overline{j}},Y\Big{)}\). Step 5. \(D_{\overline{i}}\): \(D_{\overline{i}}\) verifies the received \(Res5\). If it succeeds, then it accepts the session key \(K_{sens}\). Once two devices successfully complete one instance of the S-assist-once-a-while D2S2D anonymous authentication, they can run \(w2\) instances of the direct D2D anonymous authentication without the involvement of the server. Figure 6 depicts the flow. The top of Figure 7 shows the internal state of \(D_{l}^{\prime}s\) and \(D_{l}^{\prime}s\) level-2 seed and pseudonym vectors, and the rest shows the flow. The details are described as follows. Figure 6: Direct D2D anonymous authentication. Step 1. \(D_{i}\to D_{i}^{*}\), \(PN,\overline{PN},X\)  
\(D_{i}\) executes PN_L2 Seed_Update (\(N_{i;j}\)) to generates its level-2 pseudonym \(PN=h_{2}\Big{(}N_{i;\overline{i}}\Big{)}\) and updates its level-2 seed \(N_{i;\overline{i}}=h_{1}\Big{(}N_{i;\overline{i}}\Big{)}\). It chooses a random challenge \(x\in_{R}Z_{q}^{*}\), computes \(X=xP\), and sends the request \(\{PN,\overline{PN},X\}\), where \(\overline{PN}\) is \(D_{i}^{\prime}\)s active level-2 pseudonym. Step 2. \(D_{\overline{i}}\to D_{i}\): \(PN,\overline{PN},Y\),_Res2_  
\(D_{\overline{i}}^{*}\) runs PV_L2 Update(\(PN\)) to identify the device and to derive the shared key \(K_{tmp;\underline{i},\overline{i}}\). It updates \(D_{i}^{\prime}\)s level-2 pseudonym vectors. It chooses \(y\in_{R}Z_{q}^{*}\), and computes \(Y=yP\) and \(Res2=Mac\Big{(}PN,K_{tmp,\underline{i},\overline{i}},X,Y\Big{)}\). Step 3.\(D_{i}\to D_{\overline{i}}\): \(PN,\overline{PN}\),Res1  
It verifies \(Res_{2}\). If it succeeds, then it calculates \(Res1=Mac\Big{(}\overline{PN},K_{tmp,\underline{i},\overline{i}},Y,X\Big{)}\) and the session key \(K_{sens}=h_{1}\Big{(}^{*}D2D^{*},PN,\overline{PN},K_{tmp,\underline{i}, \overline{i}},xyP\Big{)}\). It updates \(D_{\overline{i}}\)s level-2 seed and PV. Step 4. \(D_{\overline{i}}\):  
\(D_{\overline{i}}\) verifies \(Res1\). If it succeeds, then it computes the session key \(K_{sens}\), and updates its level-2 seed and PVs.
## 4 Effects of Cannabinoids on Gastrointestinal Motility
### Effects of Phytocannabinoids on Gastrointestinalal Motility
#### 4.2.4 Summary of Phytocannabinoid Influence on Gastrointestinal Motility  
Summing up, phytocannabinoids show a strong ability to inhibit spontaneous and induced GIT contractions, which appears to be mediated via CB1; however, it is uncertain whether this occurs directly, through negative allosteric modulation, or indirectly, by inhibiting anandamide reuptake. A summary of phytocannabinoid influence on gastrointestinal motility is given in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Substance** & **Species** & **Experiment Type** & **GIT Segment** & **Effect** & **Mechanism** & **References** \\ \hline THC & Mouse & In vivo & Small intestine & Inhibition of GI transit & n/d & [72; 73; 77] \\ \hline THC & Guinea Pig & Ex vivo & & & Inhibition of contractile & & \\  & & & & response to electrical and & n/d & [74] \\  & & & & & 5-HT stimulation & & \\ \hline THC & Guinea Pig & Ex vivo & & Small intestine myenteric plexus longitudinal muscle strips & Inhibition of contractile & & \\  & & & & response to electrical & CB1 & [75; 76] \\ \hline THC & Rat & Ex vivo & & Small intestine myenteric plexus longitudinal muscle strips & Inhibition of contractile & & \\  & & & & pressure to electrical & response to electrical & CB1 & [78] \\ \hline CBD & Mouse & In vivo & Small intestine & & & & \\  & & & & inflammatory & & CB1 & [79] \\ \hline CBD & Mouse & Ex vivo & & & & Inhibition of ACh-induced contractions & CB1 & [79] \\ \hline CBD & Rat & Ex vivo & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline CBD & Rat & Ex vivo, in vivo & & Upper GIT, colon & & & & \\  & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline CBD, CBDA & Asian house & & & Proximal and distal intestinal segments & & & \\  & & & & intestinal & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline CBC & Mouse & In vivo & Small intestine & & & & \\  & & & & & hypercomtility & \\ \hline CBC & Mouse & Ex vivo & & & & Inhibition of contractile & & \\  & & & & response to electrical & & N and L-type & \\  & & & & stimulation and ACh & Ca\({}^{2+}\) channels & [61] \\ \hline CBN & Guinea Pig & Ex vivo & & Small intestine myenteric plexus longitudinal muscle strips & Inhibition of contractile & & \\  & & & & pressure to electrical & CB1 & [76] \\ \hline CBN & Mouse & In vivo & Colon & & Inhibition of colonic propulsion & CB1 & [83] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of phytocannabinoid influence on gastrointestinal motility.
## 3 Results  
### Larval Mortality  
Significant differences appeared among treatments; days of the experiment and used concentrations were proven to have a significant effect on larval mortality. The factors' interactions showed a considerable effect; this suggests that experimental factors affected the insects' survival time in various ways (Table 1).  
The mortality percentage is contingent on the concentration of the used treatment. The final mortality percentages of _T. pityocampa_ larvae after 6 days were 26.7 to 76.6% with HearNPV, 16.7 to 70% with CpG, and 30 to 73.3% with POG. Control larvae, which were treated only with ddH\({}_{2}\)O, recorded minor mortality (0.6%) until the end of the experiment (Figure 1). Similarly, the final mortality of _H. armigera_ larvae was 36.7 to 83.3% with HearNPV, 40 to 76.6% with CpG, and 46.7 to 70% with POG, while the control mortality was also very low (1.7%) (Figure 1).  
The survival time of _T. pityocampa_ larvae treated with the viruses was significantly reduced in comparison to that of the control larvae. More specifically, after exposure to HearNPV the lethal time of the larvae ranged from 5.5 to 2.1 days, after exposure to CpGG this was 5.6-2.2 days, and, finally, after exposure to PoG this was 5.5-2.7 days. For the control larvae, the survival time was 5.9 days (Table 2). As far as _H. armigena_ is concerned, the survival duration of treated larvae was likewise markedly shortened. More precisely, the survival time varied from 5.0 to 2.0 days following exposure to HearNPV, 5.1 to 2.3 days following exposure to CpGG, and 4.4 to 2.8 days following exposure to PoG. The respective period for untreated larvae was 5.9 days.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Insect** & **Virus Species** & **Survival Time (Days \(\pm\) sd)** \\ \hline \multirow{8}{*}{PogG} & & 5.5 \(\pm\) 0.1 a \\  & & 5.4 \(\pm\) 0.2 a \\  & & HearNPV & 5.4 \(\pm\) 0.3 a \\  & & 3.4\(\pm\) 0.3 b \\  & & 2.1 \(\pm\) 0.2 c \\ \cline{2-3}  & & 5.6 \(\pm\) 0.1 a \\  & & 5.5 \(\pm\) 0.2 a \\  & & CpGV & 4.0 \(\pm\) 0.4 b \\  & & 3.8 \(\pm\) 0.2 b \\  & & 2.2 \(\pm\) 0.4 c \\ \cline{2-3}  & & 5.5 \(\pm\) 0.3 a \\  & & 5.3 \(\pm\) 0.1 a \\  & & 4.8 \(\pm\) 0.3 a \\  & & 3.6 \(\pm\) 0.1 b \\  & & 2.7 \(\pm\) 0.4 c \\ \cline{2-3}  & & Control & 5.9 \(\pm\) 0.1 d \\ \hline \hline \end{tabular}
\end{table}
Table 2: Median survival time of _T. pityocampa_ and _H. armigena larvae_ (Kaplan–Meier method, F: 26.096; df: 29; \(p=0.000\)). Columns with the same letter did not differ significantly.  
Figure 1: Mean larval mortality of _T. pityocampa_ (**up**) and _H. armigena_ (**down**) treated with an entomopathogenic virus for a period of 6 days. Bars represent the standard error. Columns with the same letter did not differ significantly.
## 4 The WBANs Communication Stack
### Medium Access Control Layer
#### 4.2.4 Comparative Study of MAC Protocols  
At the end, a comparative study of the different categories of the MAC protocols is presented in this section. Key design characteristics and performance metrics are highlighted as shown in Table 9.  
On the one hand duty-cycling is necessary to reduce the energy consumption but it has to meet the dynamic traffic variations of WBANs, which becomes difficult under TDMA mechanisms that is widely used in duty cycling, however, work such as [4] could be very effective in this context. IEEE 802.15.6 MAC protocol provides higher throughput and security, greater flexibility and coexistence mechanism for applications specific designs. However, it is still to be effectively utilized and explored to have the best performance.