esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 5 Time-Dependent Reliability Analysis of the PSC Box-Girder Bridge
### Definition of Limit State  
The serviceability and ultimate limit states are taken into account in the reliability analysis of this bridge. Under the serviceability limit state, the determination of whether the structure meets the requirements of serviceability is usually based on the fact of whether the maximum deflection of the main girder under load exceeds the limit value specified in the code. For the PSC bridges, the code for highway bridges in China [46] specifies that the maximum deflection of the bridge should not exceed \(L/600\) under general operating conditions, where \(L\) denotes the length of the main span of the bridge. For the Jinghang Canal Bridge, with a main span of 150 m, the allowable deflection is \([\mu]=0.25\) m. Under the ultimate limit state, the failure state is judged by the maximum compressive stress of the concrete at the bottom of the main pier section of the box girder exceeding its allowable value. The code [46] also specifies that the allowable compressive stress is 0.5 f\({}_{\text{ck}}\) (f\({}_{\text{ck}}\) is the standard value of concrete compressive strength), and the allowable value of compressive stress is \([\sigma]=17.8\) MPa for the C55 concrete used in this bridge. Thus, the well-known performance functions can be expressed as follows:  
\[G_{1}(\mathbf{X})=[\mu]-[\mu]_{\text{max}}(\mathbf{X}), \tag{20}\]  
\[G_{2}(\mathbf{X})=[\sigma]-[\sigma]_{\text{max}}(\mathbf{X}), \tag{21}\]  
where \(X\) is a vector consisting of 167 random variables that affect the structural reliability, including the random variables in Table 2 and the discrete variables of the random fields in Table 3; \(\mu_{\text{max}}(X)\) represents the midspan deflection of the box-girder, and \(\sigma_{\text{max}}(X)\) denotes the compressive stress of the concrete at the bottom flange of the main pier section. Both are implicit functions of X and obtained by finite element analysis.
## 5 Summary and Conclusions
### IR and MW Peculiarities  
Infrared and microwave observations exhibit numerous differences in spectroscopic properties, propagation through clouds, and surface emission characteristics, providing highly complementary information to each other. In general, from the perspective of measuring the microphysical cloud properties, the algorithms based on MW observations exploit frequencies at 23.8, 31.4, 90, and 183 GHz to obtain information on atmospheric water vapor and liquid water contained in a cloud, while the 88-90 and 150-205 GHz channels are mainly used in approaches to study ice clouds and precipitation. On the other hand, passive infrared observations show sensitivity to liquid and ice particles closer to the top of the clouds within the observed volume. The strategies in the case of infrared channels are mainly based on radiance measurements, which also allow the retrieval of the cloud optical thickness and effective cloud particle diameter. The results reviewed in this paper quantifiably show that, although microwaves are less sensitive to clouds than infrared, higher sensitivity to cloud microphysical properties is achievable using higher frequencies.
# Collaborative Use of a Shared System Interface:
## 2 Theoretical Development  
### Gaze Convergence  
A literature review was conducted to assess interest in the GC construct. The keywords \"gaze convergence\" or \"shared gaze\" or \"scanpath comparison\" were used. The search was performed mainly through some of the most prominent databases, including Web of Science, ABI/INFORM, Wiley Online Library, and ProQuest. We found no study investigating GC in the context of collaborative system use. In past research, the concept of GC has been mainly investigated in terms of mutual GC, often referring to people looking at each other (e.g., [PERSON] et al. [16]). These works are mostly focused on face-to-face communication between avatars (e.g., [PERSON] et al. [17]), between robots and humans (e.g., [PERSON] et al. [18]), and between humans (e.g., [PERSON] et al. [19];[PERSON] et al. [16]). Clearly, HCI research on GC during collaborative system use is still embryonic, and the question as to how to measure it remains unexplored.  
In the present research, we examine two types of GC: system-oriented GC (SOGC) and mutual GC (MGC). In this study, we define SOGC as the extent to which a dyad of users look around the same locations on a shared system interface during collaborative system use. This definition considers that the two users are exposed to the same system interface layout, through the same monitor or through separate display devices with or without same dimensions. This kind of setting can be achieved via single display groupware systems, which allow coworkers to collaborate using desktop computers and mobile devices displaying the same system interface [20]. Meanwhile, MGC is defined in this research as the extent to which a dyad of users look at each other while collaborating on a shared system interface. This construct has a distinct content domain from the SOGC construct, since it focuses on dyad gazes directed to locations completely out of the system interface and monitor.
## 3 Numerical Simulation
### Influence of Dynamic Unbalance on Dynamic Rotational Error  
As a typical rotary machine, dynamic unbalance force is a critical factor attributed to the error motions of the SRB system when its center of mass deviates from the axis of rotation. As a kind of dynamic excitation, the frequency of the dynamic unbalance force is the same as the rotation frequency of the spindle. In the traditional test of the spindle rotational error motion, the fundamental frequency components, including installation eccentricity error of the target, are filtered. The dynamic unbalance force is a function of the rotational speed. From dynamic point of view, the vibration caused by  
Figure 10: Compared with the results in the literatures.  
Figure 9: Responses and rotational errors with the amplitude (2 \(\upmu\)m) and different orders of the surface waviness.  
dynamic unbalance force directly affects the total indicator reading (TIR) of the SRB system. Assume that the spindle has an eccentric mass \(m_{\text{u}}=0.005\) kg on the pulley side, and the eccentricity \(e_{\text{c}}=0.054\) m. The spindle rotational speed ranges from 1000 to 10,000 rpm. The response displacements at the front end of the SRB system are shown in Figure 11.  
Figure 11: Effects of dynamic unbalanced force on total indicator reading (TIR) at different speeds.  
The trends of the overall effect of spindle unbalance on TIR, synchronous and asynchronous errors are shown in Figure 12. As can be seen in Figure 12, the TIR caused by the spindle unbalance increases from 0.07 \(\upmu\)m in 1000 rpm to 5.61 \(\upmu\)m in 10,000 rpm. Spindle unbalance has little effect on the synchronous error, and the overall synchronous error is less than 0.4 \(\upmu\)m. The asynchronous error increases from 0.03 to 2.27 \(\upmu\)m, as the speed increasing from 1000 to 10,000 rpm.  
The frequency domain analysis of the simulated displacement response at 10,000 rpm is shown in Figure 13.  
The frequency domain analysis shows that it mainly includes two peaks. The frequency of the first peak corresponds to the excitation frequency of the spindle dynamic unbalance force, that is, the fundamental frequency (spindle rotational frequency). The frequency of the second peak corresponds to the spindle resonance frequency (natural frequency of the SRB system). The asynchronous error motion presents large value due to the resonance frequency is non-integer multiples of the fundamental frequency.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 5 Evaluation
### Experiments
This is because the system resources can be fully utilized by the pipeline of ECDedup (pipeline) to continuously process data when processing large amounts of data (e.g., 16 GB). Thus, ECDedup (pipeline) can effectively improve the client throughput by 43.3% to 51.9% compared to the centralized scheme. Figure 8: Exp 3: Impact of \((n,k)\) for ECDedup. Figure 7: Comparison of different encryption schemes. Experiment #5 (Average indexing time per chunk): We evaluate the average indexing time per chunk (the average chunk size is set to 8 KB, as in Section 2.1) of GDH-Dedup and our ECDedup. Figure 10 shows the average indexing time per chunk of GDH-Dedup and ECDedup. We can see that the indexing time of ECDedup is much lower than the time of GDH-Dedup. This is because different key servers in GDH-Dedup encrypt the same plaintext chunk into different ciphertext chunks, such that it requires the complex cryptographic computation for indexing a ciphertext chunk. Table 2 shows the average indexing time per chunk of GDH-Dedup for varying average chunk sizes. We can see that the indexing time of GDH-Dedup decreases exponentially when the average chunk size increases, since GDH-Dedup can only be applied to file-level deduplication [8]. Experiment #6 (Uploads and downloads): We evaluate the upload and download speed of the centralized scheme and our ECDedup on the four real-world datasets in Table 1. Figure 11 shows the upload and download performance of different encryption schemes. We can see that ECDedup (pipeline) outperforms the centralized scheme in all cases. Compared to the centralized scheme, ECDedup (pipeline) can improve the upload and download speed by up to 15.2% and 34.6%, respectively. Experiment #7 (Blockchain integration): We evaluate the integration of blockchain into ECDedup. Recall that we only use the blockchain to verify the secret key possessed by the key servers (see Section 4). In this experiment, we only focus on the performance overhead incurred from the blockchain integration. Table 3 shows the upload and download performance of ECDedup with and without blockchain. We can see that the blockchain \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Average chunk size** & 8 KB & 16 KB & 32 KB & 64 KB \\ \hline
**Indexing time (s)** & 1726.10 & 813.92 & 312.96 & 137.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average indexing time of GDH-Dedup. Figure 10: Exp 5: Average indexing time per chunk. Figure 9: Exp 4: Comparison of client throughput for different encryption scheme. integration only introduces a small overhead to the system, which is less than 0.1% of the total system overhead.
## 6 Limitations and Future Research  
This study does include some limitations. First, all the respondents in this study are from South Korea, so the results of this study may not be generalizable to other geographical regions. It has been suggested that the characteristics of consumers in the East and West differ, particularly in technology-based product/service-related research [87; 88], so it would be meaningful to apply the research model that is presented in this study to Western consumers in the future research. Second, this study was conducted based on a robotic golf caddy, so there may be some difficulties in regard to applying the results of this study to other industries. Third, this study did not identify whether the acceptance behavior of robotic caddies is applicable to multi-robot scenarios. Future research could examine this applicability based on theories, such as flocking dynamics for cooperation-antagonism multi-agent networks subject to limited communication resources [89] or Cucker-Smale flocking behavior for multi-agent networks with coopetition interactions and communication delays [90]. Lastly, robotic golf caddies have not yet been commercialized in South Korea, so the sample in this study does not include golfers who have actually used a robotic golf caddy. It would be a meaningful study if data were collected from golfers who actually used a robotic golf caddy in future studies in order to supplement this limitation.  
Conceptualization, K.J. and J.H.; methodology, H.M.K. and J.H.; writing--original draft preparation, K.J. and H.M.K.; writing--review and editing, H.M.K. and J.H.; supervision, J.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
###### Abstract  
The on-plane rotations of the inclined axle-chain system on the functional swing plane (FSP) can represent the angular motions of the golfer-club system closely. The purpose of this study was to identify key performance factors in golf through a comprehensive investigation of the association between the angular motion characteristics of the axle-chain system and clubhead speed in skilled golfers. Sixty-six male golfers (handicap \(\leq 3\)) performed full-effort shots in three club conditions: driver, 5-iron, and pitching wedge. Swing trials were captured with an optical motion capture system, and the hip/shoulder lines, upper lever, club, and wrist angular positions/velocities were calculated. Time, angular position, range of rotation, and peak angular velocity parameters were extracted and their correlation coefficients (Pearson and Spearman) to actual and normalized clubhead speeds were computed (\(p<0.05\)). Higher clubhead speed was associated with shorter downswing phases, larger rotation ranges (hip/shoulder lines, and upper lever), larger hip-shoulder separation at impact, delayed transitions (hip line and upper lever), faster rotations (backswing, downswing, and impact), and larger angular velocity losses (hip line and upper lever) with additional club- and body-specific correlations. Clubhead speed was not well associated with wrist cock angles/ranges, X-factors/stretches, and timings of the downswing peak.  
double pendulum; kinematic sequences; delayed release; transition phase +
Footnote †: journal: Applied Sciences
## 5 Findings of the Study
### Intrinsic Evaluation
#### 5.2.1 Descriptive Statistics  
Statistically, SLDA generated _myPersonality_ multiclass distribution consisting of 10,893 tokens with 4288 unique tokens, whereas _Sentiment140_ was comprised of 67,658 unique tokens from a total of 591,262 tokens. Meanwhile, the one-vs-all corpuses consisted of 12,670 tokens with 3357 unique tokens and 7,131,433 tokens with 944,592 unique tokens for _myPersonality_ and _Sentiment140_, respectively. The analysis of the top 30 words aggregated for the three traits showed Extraversion terms leading in both _myPersonality_ and _Sentiment140_ multiclass distributions (Figure 2). This finding supports the Pollyanna Hypothesis that asserts, conceptually, that humans' natural language tends toward universal positivity biases [53] resembled in the Extraversion language which promotes positive emotions and sentiments. This domination effect was also seen in the one vs. all distribution as the not_Psychoticism classes also consisted of the Extraversion instances. Therefore, the probability distribution of the top 30 words for multiclass and one vs. all indicated the nature of the human language where there was more positivity than negativity in the data.  
Figure 2: Probability distribution of the top 30 words.
# Bamboo Garden Trimming Problem: Priority Schedulings +
## Notation  
Given is a collection \(\{b_{1},b_{2},\ldots,b_{n}\}\) of \(n\) bamboos (i.e., a _garden_) along with respective daily growth rates \(h_{1}\), \(h_{2}\), \(\ldots\), \(h_{n}\). It is assumed that each \(h_{i}\) is a positive integer, for any \(1\leq i\leq n\), and call a _configuration_\(C^{t}\) the sequence \((\ell_{1}^{t},\ell_{2}^{t},\ldots,\ell_{n}^{t})\) of the bamboos' heights at a given day \(t\). Any configuration \(C^{t}=(\ell_{1}^{t},\ell_{2}^{t},\ldots,\ell_{n}^{t})\) is determined by a _growth mechanism_ applied on its predecessor \(C^{t-1}=(\ell_{1}^{t-1},\ell_{2}^{t-1},\ldots,\ell_{n}^{t-1})\), i.e., for any \(1\leq i\leq n\), it holds \(\ell_{i}^{t}=\ell_{i}^{t-1}+h_{i}\). The only exception to this behavior is what is called a _trimming_ operation. In particular, a bamboo \(b_{i}\) is said to be _trimmed_ (equivalently _cut_) at a given day \(t\) if the height \(\ell_{i}^{t}\) is reduced to zero and hence, trivially, \(\ell_{i}^{t+1}=h_{i}\). For the sake of simplicity, in what follows the term \(t\) is omitted from all notations when the number of the day is clear from the context. Finally, given a configuration \(C\), let \(V(C)\) denote the _volume_ of configuration \(C\), which is the sum of all bamboos' heights in \(C\), i.e.,  
\[V(C)=\sum_{i=1}^{n}\ell_{i}.\]  
An input instance \(I\) to the BGT problem is a set of growth rates \(\{h_{i}\}_{1\leq i\leq n}\), complemented by the initial configuration \(C^{0}\). The interest is in the design of perpetual schedules of cuts, which allow keeping the tallest bamboo in the garden as low as possible. In particular, it is assumed that at most one trimming operation can take place every day. Therefore, the aim at designing an algorithm \(\mathcal{A}\) that, for a given input instance \(I\), computes a _perpetual schedule_\(\mathcal{A}(I)=(i_{1},i_{2},\ldots)\), i.e., a sequence of indices \(i_{j}\in\{0,1,2,\ldots,n\}\) that determines, for any day \(t>0\), the bamboo to be trimmed, unless \(i_{j}=0\) when no bamboo is cut. In other words, a schedule of this kind defines an ordered sequence of trimming operations on the bamboos. In what follows, an algorithm determining perpetual schedules is called a _perppetual scheduling_, or simply _scheduling_ algorithm.  
Given an input instance \(I\) and a perpetual scheduling \(\mathcal{S}\), an _execution_\(E(I,\mathcal{S})\) is the sequence \((C^{0},C^{1},\ldots)\) obtained by applying the schedule computed by \(\mathcal{S}\) on \(C^{0}\). Moreover, given an execution \(E\) and a configuration \(C\), let \(M(E)\) (\(M(C)\), respectively) denote the maximum height reached by a bamboo in \(E\) (\(C\), respectively). Finally, let \(H=\sum_{i=1}^{n}h_{i}\) denote the sum of the growth rates. It is known that no algorithm can compute a schedule that keeps the heights of all the bamboos below \(H\) indefinitely (i.e., such that \(M(E)<H\)) [1].
## 7 Conclusions  
This study investigated the enhancement of collaborative localization for air-ground swarms using belief propagation aided by 3D terrain modelling. The proposed method effectively addresses the challenges posed by complex terrain environments, particularly satellite signal blocking and NLOS effects. By incorporating heterogeneous ranging and angular information into the belief-propagation algorithm, the method demonstrates superior localization accuracy for air-ground swarms in complex terrains. The simulation results indicate that the proposed method effectively suppresses NLOS localization errors. The localization error curves show that the proposed method can still maintain a high localization accuracy under the condition of limited satellite visibility.  
Conceptualization, R.W.; methodology, R.W. and W.Z.; software, W.Z.; validation, R.W. and W.Z.; investigation, R.W. and W.Z.; writing--original draft preparation, W.Z. and X.C.; writing--review and editing, R.W. and X.C.; visualization, W.Z.; supervision, R.W. and Z.X.; project administration, R.W.; funding acquisition, R.W. and Z.X. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China (Grant No. 62073163, 62388101, 61703208, 61873125), the Qinglan Project of Jiangsu Province of China, the Science and Technology Innovation Project for the Selected Returned Overseas Chinese Scholars in Nanjing, the Foundation of Key Laboratory of Navigation, Guidance and Health-Management Technologies of Advanced Aircraft (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, the Foundation of Engineering Research Center of Autonomous Control Technology of Aircraft (Nanjing University of Aeronautics and Astronautics), Ministry of Education, Jiangsu Key Laboratory \"Internet of Things and Control Technologies\" and the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
The data presented in this study are available on request from the corresponding author.  
The author would like to thank the anonymous reviewers for their helpful comments and valuable remarks.  
The authors declare no conflict of interest.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results
### Accuracy Limit  
All the experiments in this paper were carried out over a public dataset which was collected by means of crowdsourcing. In contrast to professional deployments with a dense radio map (small grid and many training fingerprints), the density of training fingerprints in the selected database was not so high only 0.03 fingerprints/m\({}^{2}\). This low density might have contributed to degrading the positioning accuracy. An additional experiment was carried out to compare the accuracy of the proposed D2D method (best case) with respect to the maximum possible accuracy for the database. This maximum accuracy is computed as the physical distance of the test fingerprint location with respect the nearest training fingerprint's location in the real-world (not RSS-space). Figure 13 shows the comparison between the maximum accuracy limit of the database and the best-achieved accuracy through the proposed method.  
The best mean 3D positioning accuracy through the proposed method is 4.83 m while the maximum mean accuracy limit for the database is 1.34 m. Apart from the unavoidable error, our proposed method offers mean 3.50 m level accuracy for such a vast and complex indoor environment which has strong merit.
## 5 The 26S proteasome: Structure and Function  
The proteasome-ubiquitin system (UPS) is the main process responsible for the degradation of damaged or no longer necessary proteins involved in several cellular functions such as cell cycle, apoptosis, and DNA repair [39]. It comprises two steps: the attachment of multiple ubiquitin molecules to the target protein and the degradation of the polyubiquitated protein by the 26S proteasome complex. The 26S proteasome is an ATP-dependent complex of different subunits composed of a core catalytic complex called 20S proteasome and two 19S regulatory complexes, which recognize the ubiquitated proteins and are responsible for their translocation into the core complex [40; 41]. Specifically, each 19S regulatory complex has a lid and a base attached to the ends of the 20S core complex. While the base is involved in the deubiquitin process, the lid is responsible for the recruitment, unfolding, and translocation of target proteins into the 20S core complex [42].  
The 20S region is a cylinder-shaped protease composed of 28 subunits organized in four rings in a \(\alpha\beta\gamma\beta\gamma\alpha\gamma\) configuration. The two outer \(\alpha\)-rings act as a link for the 19S complexes, whereas the two inner \(\beta\)-rings are responsible for proteolysis. The first step of the proteasome-mediated proteolysis involves three different enzymes belonging to the ubiquitin system: ubiquitin-activating enzyme or UBA (E1), ubiquitin-conjugating enzyme or UBC (E2), and ubiquitin ligase (E3) enzyme. Briefly, E1 is responsible for the ATP-dependent activation of ubiquitin, which moves to E2 enzyme and then to a target protein. The specificity is conferred by E3 enzyme, which catalyzes the binding between ubiquitin and the target protein. Once the proteasome recognizes the polyubiquitated protein, deubiquitining enzymes (DUBs) remove the ubiquitin molecules and the target protein moves into the 20S proteolytic core. Three different catalytic activities, chymotrypsin-like (CT-L), trypsin-like (T-L), and caspase-like (C-L), each located in three different \(\beta\)-subunits (\(\beta\)5, \(\beta\)2, and \(\beta\)1, respectively), are responsible for the degradation of proteins into small fragments [40].