esa-sceva/satcom-chunk-collection huggingface dataset:
# Reputation Consensus Mechanism for Blockchain Based on Information-Centric Networking
## 4 Design
### Reputation Model
#### 4.1.1 Reputation Evaluation Dimensions and Parameter Selection
.\]  
The parameter weights must be normalized to balance the influence of each factor in the reputation calculation, satisfying:  
\[\alpha_{P}+\alpha_{B}+\beta_{T}+\beta_{E}+\beta_{C}+\beta_{F}=1.\]  
Note that this paper presents a design framework for the reputation model; specific parameter settings are adjustable based on application needs and are therefore not discussed. Reputation parameters such as the staked amount and block generation rate can be directly verified through public blockchain data, ensuring transparency and traceability. For ICN network QoS and performance indicators such as average online duration, failure rate, traffic forwarding efficiency, and cache hit rate, existing research [35; 36; 37] provides a foundation for QoS measurement techniques in ICN. These indicators can be monitored and evaluated using network monitoring and performance testing tools, with edge computing resources handling data processing and verification. A third-party trusted institution oversees the final data audit to ensure objectivity and reduce risks of collusion attacks stemming from subjective evaluations. Regarding historical reputation, nodes exhibiting improper behavior will lose all reputation values upon detection, and their identities will be blacklisted, prohibiting participation in subsequent consensus processes. This punitive mechanism helps prevent malicious behavior, ensuring network security and stability. Specific methods for detecting malicious nodes can be found in the existing literature [38; 39]; they are not detailed here.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 5 Performance Evaluation
### Evaluation Settings  
We developed a discrete event simulator using Java to evaluate our algorithms and ran all the experiments on a Windows 10 laptop with Intel Core i7-6820 HQ CPU and 24 GB RAM. The topology generation package \"Brite\" [31] was integrated with our simulator to generate the 5G core infrastructure topology and the 5G core slice requests based on the Waxman topology model [32].  
To compare our results with those of existing research, the simulation parameters were set according to the parameter settings widely used in previous research [19; 28; 33]. They are described as follows and summarized in Table 2.  
The physical network nodes are randomly deployed in a rectangular area of 500 by 500. The initial total available CPU capacities of the nodes are real numbers uniformly distributed between 50 and 100. Adjacent nodes are connected by a probability of 0.5 to form physical links, whose initial total available bandwidths are real numbers uniformly distributed between 50 and 100.  
The 5G core slice requests arrive following a Poisson process. The number of nodes in the slice request is a uniformly distributed integer between 2 and 10. For each slice request, the slice nodes allow the provisioned position to have a deviation of less than 80. The CPU demands of the slice nodes are real numbers uniformly distributed between 1 and 20. Slice nodes are connected by a probability to form slice links. The bandwidth requirement of each slice link takes a uniformly distributed real number in the range [1; 20]. The lifetime of the slice request follows the exponential distribution with a mean of 500 time units. We have 2000 slice requests in total in the experiments.
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 2 Methodology for Predicting Axial Strength
### Method of Calculation
The API's calculation of shear strength \(f_{\rm{bk}}\) of grouted connections considers the influence of grouted material strength, shear key height, and the space of the shear key. [PERSON] et al. considered the failure mechanism of the pressure bar near the shear key in the grouted connecting section, and through linear regression analysis of the collected experimental data, the formula of shear strength is as shown in Equation (3). In order to ensure a certain safety factor, the API adjusts Equation (3) and adopts Equation (4) for design  
\[f_{\rm{bk}}=1.15+1.72f_{\rm{cu}}\frac{h}{s} \tag{3}\]  
\[f_{\rm{bk}}=0.184+0.67f_{\rm{cu}}\frac{h}{s} \tag{4}\]  
In DNV, the friction coefficient between the steel and the grouted material is considered by using the semi-empirical Equation (5), and the contact pressure between them is transformed into interfacial shear friction force after the radial deformation of the grouted connection section. \[f_{\rm{bk}}=\frac{\mu E}{F}\!\left(\frac{h}{21s}f_{\rm{cu}}^{0.4}\sqrt{\frac{ t_{\rm{P}}}{R_{\rm{P}}}}\right)\!\!\frac{s}{L_{\rm{g}}}N \tag{5}\]  
\[F=\frac{R_{\rm{P}}}{t_{\rm{P}}}+\frac{Et_{\rm{g}}}{E_{\rm{g}}R_{\rm{P}}}+ \frac{R_{\rm{s}}}{t_{\rm{s}}} \tag{6}\]  
Here, \(L_{\rm{g}}\) is the length of the grouted connection section (mm); \(\mu\) is the friction coefficient between the steel tube and the grouted material (0.6); \(E_{\rm{g}}\) and \(E\) are the elastic modulus of the steel tube and the grouted material, respectively; \(F\) is the radial flexibility coefficient; and \(N\) is the number of shear keys. In DNV-GL, two failure modes were proposed to calculate the shear strength of the grouted connection: strength \(f_{\rm{bk}}\), corresponding to the frictional sliding failure mode, and strength \(f_{\rm{bkg}}\), determined by the failure of the grouted material itself, as expressed by Equations (7) and (8), respectively, in which \(f_{\rm{bk}}\) should not be greater than \(f_{\rm{bkg}}\),  
\[f_{\rm{bk}}=\left[\frac{800}{D_{\rm{P}}}+140\left(\frac{h}{s}\right)^{0.8} \right]^{0.6}f_{\rm{cu}}^{0.3} \tag{7}\] \begin{table}
\begin{tabular}{c c c c c} \hline \(L/D_{\rm{P}}\) & 2 & 4 & 8 & \(\geq\)12 \\ \hline \(C_{\rm{L}}\) & 1.0 & 0.9 & 0.8 & 0.7 \\ \hline \end{tabular}
\end{table} 
Table 2: Values of parameter \(C_{\rm{L}}\). \[f_{\rm bkg}=\left(0.75-1.4\frac{h}{s}\right)f_{\rm cu}^{0.5} \tag{8}\]
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 1 Introduction
### The Importance of Padise Abbey, Historical Background  
Padise Area was an important trade settlement, e.g., the location of the stronghold suggests connections with the sea and a harbor site. Padise Stronghold, or the Old Stronghold (Vanalinnamagi) as it is popularly known, is a site of the Middle Iron Age (7 th-8 th centuries) hill fort.  
There is the Padise Abbey (Estonian: Padise klooster) close to the Old Stronghold. The Abbey was a former Cisterican monastery in Padise in Harju County, Estonia, settled in 1310 by the dispossessed monks of Dunamunde Abbey in Latvia. It was converted into a fortress after its dissolution in 1559 and later used as a country house until 1766. The ruins are a museum now.  
The interest of monks from Dunamunde Abbey in the present Daugavgriva near Riga in Padise was first documented in 1283 in a letter from King [PERSON] of Denmark regarding the acquisition of land for the construction of a Cisterican monastery, but almost certainly they had had a presence there for several decades previously as a part of the Christianization of the territories of Estonia newly conquered by the Teutonic Knights. In 1305, Dunamunde Abbey was appropriated by the Teutonic Knights and the monks dispossessed. Then, [PERSON] [PERSON] of Denmark gave them permission to build a fortified monastery in Padise, where they moved in 1310, although construction of the stone buildings did not begin until 1317. The new monastery was made subordinate to Stolpe Abbey in Pomerania in 1319.  
By 1343, at the time of the St. George's Night Uprising, when it was still only partly built, the monastery was burnt down and rebuilding began after 1370.  
By about 1400, the monastery had acquired extensive estates in Estonia and also in southern Finland and throughout the 15 th century, it enjoyed a period of great prosperity and influence as one of the most important spiritual centers of Estonia.  
During the Livonian War, in 1559, the monks were ejected, and the buildings and estates were confiscated. The monastery became a fortress, which the Swedes duly took in 1561, Russians took in 1576, and Swedes took again in 1580.  
In 1622, King [PERSON] of Sweden gave the estates of the former Padise Abbey (Figure 1) to [PERSON] and it remained in the possession of his family until 1919 [13; 14; 15; 16; 17; 18; 19].
## 4 Numerical Illustration
### Modelling Results
From Figure 1, we observe that the residuals indicate that the NBI and PLN are better assumptions than the ZIP model since the residuals of the former two are close to the right tail of the claim frequency distribution. Furthermore, the PLN model seems to fit the claim count data slightly better than the NBI model, since, as was previously mentioned, the tail of mixed Poisson models is equivalent to the tail of their mixing distributions [7], and in this case the lognormal mixing density has a thicker right tail than the Gamma mixing density. Therefore, overall it is reasonable to suggest the employment of the PLN model for modelling claim counts in our data set. As we are going to observe in what follows, the PLN model also provides better fitting performances than the NBI and PIG models in terms of the DEV, AIC, and SBC values.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 3 Background of Middleware
### Publish/Subscribe Middleware  
A publish/subscribe middleware is a software that enables asynchronous and decoupled interactions between message producers (publishers) and consumers (subscribers) suitable for distributed systems [27; 28]. In this paradigm, publishers generate messages (events) and send them to all subscribers that express interest in them. This loose coupling between components enhances scalability, flexibility, and efficiency in communication, allowing systems to handle dynamic and high-throughput environments more effectively [28].  
Figure 5 shows a typical publish/subscribe-middleware-based network. The figure shows devices communicating with one another through the middleware. These devices can be vehicles, traffic lights, RSUs, environmental sensors, or the command and control center of the traffic department. It also shows that the devices use a publisher to send  
Figure 4: Top-level architecture for typical middleware.  
data and a subscriber to collect data from the middleware. This architecture avoids direct communication between the devices, which decouples them temporally and spatially.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 2 Model Development
### Attention Mechanism
#### 2.2.1 Temporal Attention Mechanism  
In the HTSA-LSTM model, at a given time step, the T-step historical trajectories of the target vehicle and its neighboring vehicles, denoted as \(X_{t-T+1}^{v},\ldots,X_{t}^{v}\), are used as the input to the model. Considering the hidden states of the LSTM units in HTSA-LSTM, \(S_{t}^{v}=h_{t-T+1}^{v},\ldots,h_{j}^{v},\ldots,h_{t}^{v},S_{t}^{v}\in\mathbb{R} ^{d\times T},h_{j}^{v}\in\mathbb{R}^{d\times 1}\), with \(d\) representing the length of the hidden state. After generating these hidden states, the temporal attention weights associated with \(v\), denoted as \(A_{t}^{v}=\alpha_{t-T+1}^{v},\ldots,\alpha_{j}^{v},\ldots,\alpha_{t}^{v}\), are computed as follows:  
\[A_{t}^{v}=softmax(tanh(W_{\alpha}S_{t}^{v})),A_{t}^{v}\in\mathbb{R}^{1\times T },W_{\alpha}\in\mathbb{R}^{1\times d} \tag{11}\]  
where \(W_{\alpha}\) represents the learnable weights.  
Next, by combining the hidden states \(S_{t}^{v}\) with the temporal attention weights \(A_{t}^{v}\), the tensor cell value associated with \(v\) is derived as follows:  
\[H_{t}^{v}=S_{t}^{v}(A_{t}^{v})^{T}=\sum\limits_{t}\alpha_{j}^{v}h_{j}^{v},H_{ t}^{v}\in\mathbb{R}^{d\times 1} \tag{12}\]  
Equation (12) computes the tensor cell value \(H_{t}^{v}\) through temporal attention-weighted aggregation of the LSTM-hidden states \(S_{t}^{v}\in\mathbb{R}^{d\times T}\), yielding a compact latent representation \(H_{t}^{v}\in\mathbb{R}^{d\times 1}\) that encapsulates discriminative temporal patterns within the window \([t-T+1,t]\). Specifically, given \(A_{t}^{v}\in\mathbb{R}^{1\times T}\)--the softmax-normalized attention weights derived from \(\tanh(W_{\alpha}S_{t}^{v})\) in Equation (11)--this operation aggregates the hidden states via Equation (12), dynamically emphasizing salient historical moments (e.g., abrupt maneuvers) while compressing contextual information into a single feature vector. By integrating LSTM's sequential modeling capacity with attention-driven dynamic weight allocation, this mechanism efficiently balances temporal dependencies and spatially aware interactions in trajectory prediction, serving as a critical bridge between temporal context extraction and spatial relation learning.  
Overall, all tensor cell values are utilized to compute the spatial-level attention weights and to predict the trajectory of the target vehicle.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 3 Numerical Study of Blasting Seismic Energy Generation and Attenuation under In Situ Stress
### Seismic Energy Generation and Attenuation under Hydrostatic In Situ Stress  
#### 3.3.1 Seismic Energy on the Elastic Boundary  
The rock fracture caused by the blasting of the stoping holes in the fourth round under hydrostatic in situ stress is shown in Figure 6. This figure presents the fractured zones in the remaining rock mass under \(\sigma_{x}=\sigma_{y}=0,10,30\), and 60 MPa. As expected, the rock mass next to the blastholes is more severely fractured than that between the adjacent blastholes. The major cracks grow radially from the blasthole walls into the surrounding rock mass. Under \(\sigma_{x}=\sigma_{y}=0\) MPa, the major cracks extend 1.8 m into the remaining rock mass. Under \(\sigma_{x}=\sigma_{y}=10\), 30 and 60 MPa, the longest cracks are 1.1, 0.9 and 0.6 m, respectively. The fractured zone produced by blasting decreases significantly with the increase in the in situ stress level.  
The rock mass beyond the crack tip is elastic. Therefore, the circle boundary, which takes the tunnel center as the center and takes the distance from the tunnel center to the tip of the longest crack as the radius, is treated as the outer boundary of the fractured zone, namely the elastic boundary. Extracting the velocity histories on the elastic boundary and substituting them into Equation (6) yields the blasting seismic energy on the elastic boundary. According to Equation (7), the blasting seismic efficiency \(\eta\) can be obtained. Figure 7 shows the blasting seismic efficiency variation with the in situ stress levels. Under \(\sigma_{x}=\sigma_{y}=0\) MPa, the seismic blasting efficiency is 4.6%, which means that only 4.6% of the explosive energy is converted into blasting seismic energy. This percentage agrees well with the average of the measurements by [PERSON] et al. [33]. Under \(\sigma_{x}=\sigma_{y}=60\) MPa, a seismic efficiency of 17.8% is attained. The initial blasting seismic energy at this in situ stress level is about four times that of the case without in situ stress. Clearly, the initial blasting seismic energy increases significantly with increasing in situ stress levels. This indicates that compared to blasting in shallow rock masses, more seismic energy is generated in deep rock mass blasting. This is because with the increase in the in situ stress level, the extent of the fractured zone is reduced, and the explosive energy consumed in the rock fracture is accordingly reduced. Consequently, more explosive energy is converted into blasting seismic energy.  
Figure 5: Numerical model of the multiple-hole blasting in the circular tunnel excavation (unit: m).
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 2 Related Work
### Quantum Gates and Algorithm  
#### 2.3.1 Quantum Gates  
To perform the work of classical gates, quantum gates should be implemented. CNOT gate and Toffoli gate are the most commonly used in quantum circuits. The CNOT gate receives two qubits and XOR the first qubit to the second qubit (i.e., CNOT \((a,b)\to a=a,\ b=a\oplus b\)). This gate stores the XOR result of the two input qubits in the second qubit. The quantum circuit of CNOT gate is shown in Figure 3 (left). The Toffoli gate is more expensive and complex than the CNOT gate. Three qubits are input to the Toffoli gate, and the AND result of the first and second qubits is XORed to the third qubit (i.e., Toffoli \((a,b,c)\)\(\to a=a,\ b=b,\ c=c\oplus(a\cdot b)\). The quantum circuit of Toffoli gate is shown in Figure 3 (right). The notation (\(\cdot\)) indicates AND operation.  
The logical-OR quantum gate is composed of a combination of of Toffoli gate and \(X\) gate, as shown in Figure 4. The \(X\) gate operates on a single qubit and performs a NOT operation. In the logical OR quantum gate, \(a\) and \(b\) (input qubits) are changed (0 to 1, 1 to 0) by the \(X\) gate (i.e., Quantum OR \((a,b,c)\to a=\)\(\sim a,\ b=\)\(\sim b,\ c=c\oplus(a\lor b)\). To return to the original \(a\) or \(b\), reversible gate must be performed by executing the \(X\) gate once more. The OR quantum gate (reversible) is shown in Figure 4. The notation \(\vee\) represents logical-OR operation.  
Figure 3: CNOT gate (**left**) and Toffoli gate (**right**).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Rounds** & & & & & & & & & & & & & & & & & & \\ \hline
1 to 16 & 01 & 03 & 07 & 0F & 1F & 3E & 3D & 3B & 37 & 2F & 1E & 3C & 39 & 33 & 27 & 0E \\
17 to 32 & 1D & 3A & 35 & 2B & 16 & 2C & 18 & 30 & 21 & 02 & 05 & 0B & 17 & 2E & 1C & 38 \\
33 to 48 & 31 & 23 & 06 & 0D & 1B & 36 & 2D & 1A & 34 & 29 & 12 & 24 & 08 & 11 & 22 & 04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Round constants \(C\).
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 3 Characterizations/Properties of DLC Coatings
### Tribological Properties of DLC Coatings  
Numerous investigations have been carried out in the field of tribology on DLC coatings since the last decade, due to its low friction coefficient and low wear resistance. One of the foremost studies published recently regarded the impact of plasma pretreatment on the wear behavior of DLC coatings on PDMS (poly-dimethyl-siloxane) substrates [83]. A technology-supported low-pressure plasma and dielectric barrier discharge (DBD) plasma was used for the very first modification procedure. The essential properties of the DLC coatings delivered on unmodified substrates were contrasted with those of the coatings influenced to plasma pretreatment. The tests exhibited a cross-linking of the polymer substrate during plasma pretreatment.  
With 0.7%, 5.8%, and 23.3% Ti in DLC, coatings were applied via pulsed cathode arc deposition and magnetron sputtering on AISI 316L chrome steel substrates [84]. The variable value of Ti content was managed by setting the Ti target current at 3, 5, and 7 A. The tribological properties of Ti-doped DLC (Ti-DLC) coatings were studied using X-ray photoelectron spectroscopy, Raman spectroscopy, nano-indentation, and a ball-on-disc tribometer. The main output of this investigation regarded TiC, which formed when Ti content within the coating was above 5.8%. Ti-DLC with 0.7% Ti had the very best \(\frac{H}{E}\) and \(\frac{H3}{E2}\) ratios, and exhibited optimal tribological properties under lubrication.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multirow{2}{*}{Kevlar} & 304 Stainless steel \\  & 440 Stainless steel \\  & Iron \\  & Tantalum \\  & Aluminum (Al) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of DLC-compatible substratesIn another study, soft DLC films were deposited on Si (100), iron, and chrome steel substrates with a Cr adhesive interlayer using PVD magnetron sputtering technology [85]. The major output was to connect the coating chemical and mechanical behaviors to the various deposition parameters, such as discharge power and substrate-target distance. Stronger sp\({}^{3}\) dependence was found on the discharge power for DLC deposited closer to the target. These tribological results were not dependent on the chosen substrate-target distance, but rather on the hardness of the substrate.
## 6 Real-Life Case Study  
We conducted a case study using the Speed-Extract EMR dataset to demonstrate that our framework can be used in real-life healthcare event logs. The Speed-Extract dataset comprises retrospective data from a historical dataset extracted between 2013 and 2018 from a single Cerner Millennium EMR domain in Sydney, Australia [18]. The Speed-Extract dataset comprises anonymised patients that presented with suspected acute coronary syndrome (ACS) to facilities in Northern Sydney LHD and Central Coast LHD [18]. We aimed to study the treatment process of ST-elevation myocardial infarction (STEMI) patients, which is a type of heart attack that mainly affects the patient's heart's lower chambers [18]. In this paper, we demonstrated how we extract the event log from the Speed-Extract dataset and applied the proposed framework to improve the quality of the event log. We verified and substituted the detected redundant activity labels by using the domain knowledge to obtain a more representative event log. In the end, we applied the existing tool to mine the process models from the two event logs. Comparisons between these two models demonstrate that our proposed framework can be used as a preprocessing method for the event log to obtain a more structured and easier-to-understand process model. The pipeline for the case study is shown in Figure 9.  
The data we used in the Speed-Extract dataset includes the following tables: the _Patient-prepr_ table includes patients' demographics, such as age and gender; the _Medications-mapped_ table records prescription orders for each patient; the _Diagnosis-prepr_ table contains the diagnosis for each patient using the International Classification of Diseases (ICD)-10 codes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Event Log**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**F-Score (the Original**} & \multicolumn{2}{c}{**Average F-Score (the**} \\  & **Redundant Activity** & & **Log** & **Log with Redundant Activity Labels)** & **Average F-Score (the** \\ \hline \multirow{6}{*}{Hospital Billing} & 5254 (1\%) & 0.75 & 0.69 & 0.73 \\  & 21,693 (5\%) & 0.75 & 0.64 & 0.72 \\  & 44,890 (10\%) & 0.75 & 0.62 & 0.70 \\  & 66,368 (15\%) & 0.75 & 0.59 & 0.69 \\  & 90,273 (20\%) & 0.75 & 0.55 & 0.67 \\  & 112,840 (25\%) & 0.75 & 0.53 & 0.67 \\  & 135,480 (30\%) & 0.75 & 0.51 & 0.66 \\ \hline \multirow{6}{*}{Sepsis} & 180 (1\%) & 0.77 & 0.74 & 0.75 \\  & 745 (5\%) & 0.77 & 0.72 & 0.74 \\  & 1569 (10\%) & 0.77 & 0.66 & 0.72 \\ \cline{1-1}  & 2327 (15\%) & 0.77 & 0.64 & 0.70 \\ \cline{1-1}  & 3086 (20\%) & 0.77 & 0.61 & 0.70 \\ \cline{1-1}  & 3844 (25\%) & 0.77 & 0.60 & 0.68 \\ \cline{1-1}  & 4605 (30\%) & 0.77 & 0.57 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: F-score comparisons between process models discovered from the original, problematic, and repaired event logs.  
Figure 9: Overview of the case study pipeline.